From 2d09b6de120c03e814efd09ba22a1b598e1631c7 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Fri, 10 Apr 2020 12:16:29 +0100 Subject: [PATCH 001/188] Negative Binomial and Gamma loss functions added --- notebooks/Loss function Calculations.ipynb | 658 +++++++ ...s functions fitted to simulated data.ipynb | 1613 +++++++++++++++++ pygom/loss/loss_type.py | 269 ++- pygom/loss/ode_loss.py | 40 +- pygom/utilR/distn.py | 100 + 5 files changed, 2675 insertions(+), 5 deletions(-) create mode 100644 notebooks/Loss function Calculations.ipynb create mode 100644 notebooks/Testing loss functions fitted to simulated data.ipynb diff --git a/notebooks/Loss function Calculations.ipynb b/notebooks/Loss function Calculations.ipynb new file mode 100644 index 00000000..3f46b706 --- /dev/null +++ b/notebooks/Loss function Calculations.ipynb @@ -0,0 +1,658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deriving derivative of loss class and testing markdown conversion for equation.#" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sympy as sym\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Negative binomial loss class ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Logliklihood function" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + " x, mu , k = sym.symbols('x mu k')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probability mass function (PMF) of negative binomial ${p}(x; \\mu,k) = \\frac{\\Gamma \\left(k+x\\right)}{\\Gamma \\left(k\\right)x!}(\\frac{k}{k+\\mu})^{k}(\\frac{\\mu}{k+\\mu})^{x}$ \n", + "This definition of the negative binomial distribution is often refered to as negative binomial 2. This parameterisation takes the mean (usually refered as $\\mu$, but in pygom $\\hat{y}$ as we are looking at a prediction) and $k$ (an overdispersion parameter). The variance = $\\mu+\\frac{\\mu^2}{k}$, some notation uses $\\alpha$, ($k=\\alpha^{-1}$). \n", + "See Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\left(\\frac{k}{k + \\mu}\\right)^{k} \\left(\\frac{\\mu}{k + \\mu}\\right)^{x} \\Gamma\\left(k + x\\right)}{x! \\Gamma\\left(k\\right)}$" + ], + "text/plain": [ + "(k/(k + mu))**k*(mu/(k + mu))**x*gamma(k + x)/(factorial(x)*gamma(k))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbpmf = (sym.gamma(k+x)/(sym.gamma(k)*sym.factorial(x)))*(k/(k+mu))**k*(mu/(k+mu))**x\n", + "nbpmf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due to the this PMF containing gamma functions and a factorial it is easier to calculate the sum of it's logged terms than to log it as one object (you end up with infinities otherwise). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((k/(k + mu))**k, (mu/(k + mu))**x, 1/factorial(x), 1/gamma(k), gamma(k + x))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbpmf.args" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle k \\left(\\log{\\left(k \\right)} - \\log{\\left(k + \\mu \\right)}\\right) + x \\left(\\log{\\left(\\mu \\right)} - \\log{\\left(k + \\mu \\right)}\\right) - \\log{\\left(x! \\right)} - \\log{\\left(\\Gamma\\left(k\\right) \\right)} + \\Gamma\\left(k + x\\right)$" + ], + "text/plain": [ + "k*(log(k) - log(k + mu)) + x*(log(mu) - log(k + mu)) - log(factorial(x)) - log(gamma(k)) + gamma(k + x)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logpmf_p1= k*(sym.ln(k)-sym.ln(k+mu))\n", + "logpmf_p2= x*(sym.ln(mu)-sym.ln(k+mu))\n", + "logpmf_p3= -sym.ln(sym.factorial(x))\n", + "logpmf_p4= -sym.ln(sym.gamma(k))\n", + "logpmf_p5= sym.gamma(k+x)\n", + "logpmf = logpmf_p1+logpmf_p2+logpmf_p3+logpmf_p4+logpmf_p5\n", + "logpmf" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-log(factorial(x)),\n", + " -log(gamma(k)),\n", + " k*(log(k) - log(k + mu)),\n", + " x*(log(mu) - log(k + mu)),\n", + " gamma(k + x))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logpmf.args" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.special import gammaln\n", + "def nb2logpmf(x, mu,k):\n", + " '''\n", + " The log probability mass function (pmf) of Negative Binomial 2 distribution. \n", + "\n", + " Parameters\n", + " ----------\n", + " x: array like observation.\n", + " mu: mean or prediction.\n", + " k: overdispersion parameter (variance = mean(1+mean/k)). Note some notation uses $\\alpha$, ($k=\\alpha^{-1}$).\n", + " See Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press.\n", + "\n", + " Returns\n", + " -------\n", + " log pmf:\n", + " math:`\\\\mathcal\\\\ln({p}(x; \\\\mu,k)) = \\\\ln(\\\\frac{\\\\Gamma \\\\left(k+x\\\\right)}{\\\\Gamma \\\\left(k\\\\right)x!}(\\\\frac{k}{k+\\\\mu})^{k}(\\\\frac{\\\\mu}{k+\\\\mu})^{x})`\n", + "\n", + " '''\n", + " # note that we input k the overdispersion parameter here\n", + "\n", + "\n", + " logpmf_p1= -gammaln(x+1) \n", + " logpmf_p2= -gammaln(k)\n", + " logpmf_p3= k*(np.log(k) - np.log(k + mu)) \n", + " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n", + " logpmf_p5= gammaln(k+x)\n", + " logpmf = logpmf_p1+logpmf_p2+logpmf_p3+logpmf_p4+logpmf_p5\n", + " return logpmf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our loss function is the negative of the logliklihood above." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "negloglikli=-logpmf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1st derivative of -Loglikelihood of negative binomial loss with respect to $\\mu$." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{k \\left(\\mu - x\\right)}{\\mu \\left(k + \\mu\\right)}$" + ], + "text/plain": [ + "k*(mu - x)/(mu*(k + mu))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbfirstderv= sym.diff(negloglikli,mu).simplify()\n", + "nbfirstderv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1st derivative of -Loglikelihood of negative binomial loss with respect to yhat: \n", + "$\\frac{k(\\mu-y)}{\\mu(k + \\mu)} $" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{k \\left(- \\mu \\left(k + \\mu\\right) + \\mu \\left(\\mu - x\\right) + \\left(k + \\mu\\right) \\left(\\mu - x\\right)\\right)}{\\mu^{2} \\left(k + \\mu\\right)^{2}}$" + ], + "text/plain": [ + "-k*(-mu*(k + mu) + mu*(mu - x) + (k + mu)*(mu - x))/(mu**2*(k + mu)**2)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbsecderv = sym.diff(nbfirstderv,mu).simplify()\n", + "nbsecderv.simplify()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2nd derivative of -Loglikelihood of negative binomial loss with respect to yhat: \n", + "$\\frac{k(\\mu(k + \\mu) + \\mu(y -\\mu) + (k + \\mu)(y - \\mu)}{\\mu^{2}(k + \\mu)^{2}} $" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(k, mu**(-2), (k + mu)**(-2), mu*(k + mu) - mu*(mu - x) - (k + mu)*(mu - x))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbsecderv.args" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Gamma loss class in terms of mean and shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + " a, s, x, mu= sym.symbols('a s x mu')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probability density function (PDF) of the gamma distribution is $\\frac{1}{s^a\\Gamma(a)}x^{a-1}e^{-x/s}$. However we need this in terms of mean (here $\\mu$), luckily we can subistitute in $s=\\frac{\\mu}{a}$ to get our likelihood function. But lets start with a log tranformation of the pdf.\n", + "\n", + "See Bolker, B. M. (2008). Gamma. In Ecological Models in R (pp. 131–133). Princeton University Press." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{s^{- a} x^{a - 1} e^{- \\frac{x}{s}}}{\\Gamma\\left(a\\right)}$" + ], + "text/plain": [ + "s**(-a)*x**(a - 1)*exp(-x/s)/gamma(a)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdf_gamma = 1/(s**a*sym.gamma(a))*(x**(a-1)*sym.E**(-x/s))\n", + "pdf_gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(s**(-a), x**(a - 1), 1/gamma(a), exp(-x/s))" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdf_gamma.args" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - a \\log{\\left(s \\right)} + \\left(a - 1\\right) \\log{\\left(x \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)} - \\frac{x}{s}$" + ], + "text/plain": [ + "-a*log(s) + (a - 1)*log(x) - log(gamma(a)) - x/s" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "log_pdf_gamma_p1 = -a*sym.ln(s)\n", + "log_pdf_gamma_p2 = (a-1)*sym.ln(x)\n", + "log_pdf_gamma_p3 = -sym.ln(sym.gamma(a))\n", + "log_pdf_gamma_p4 = -x/s\n", + "log_pdf_gamma= log_pdf_gamma_p1+log_pdf_gamma_p2+log_pdf_gamma_p3+log_pdf_gamma_p4\n", + "log_pdf_gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - a \\log{\\left(\\frac{\\mu}{a} \\right)} - \\frac{a x}{\\mu} + \\left(a - 1\\right) \\log{\\left(x \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)}$" + ], + "text/plain": [ + "-a*log(mu/a) - a*x/mu + (a - 1)*log(x) - log(gamma(a))" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_in_terms_mu_a = mu/a\n", + "log_pdf_mu_a_gamma = log_pdf_gamma.subs(s,s_in_terms_mu_a) \n", + "log_pdf_mu_a_gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-log(gamma(a)), (a - 1)*log(x), -a*log(mu/a), -a*x/mu)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "log_pdf_mu_a_gamma.args" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- a \\log{\\left(\\frac{\\mu}{a} \\right)} - \\frac{a x}{\\mu} + \\left(a - 1\\right) \\log{\\left(x \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)}\n" + ] + } + ], + "source": [ + "sym.print_latex(log_pdf_mu_a_gamma)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.special import gamma, factorial, gammaln\n", + "def gamma_mu_shape_logpdf(x, mu,shape):\n", + " '''\n", + " The log probability density function (pdf) of gamma distrbution in terms of mean and shape. \n", + "\n", + " Parameters\n", + " ----------\n", + " x: array like observation.\n", + " mu: mean or prediction.\n", + " v: variance.\n", + " See Bolker, B. M. (2008). Gamma. In Ecological Models in R (pp. 131–133). Princeton University Press.\n", + "\n", + "\n", + " Returns\n", + " -------\n", + " log pdf, :math:`\\\\mathcal\\\\ln({p}(x; \\\\mu,a)) = - a \\log{\\left(\\frac{\\mu}{a} \\right)} - \\frac{a x}{\\mu} + \\left(a - 1\\right) \\log{\\left(x \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)}`\n", + "`\n", + "\n", + " '''\n", + "\n", + " logpdf_p1= -gammaln(shape)\n", + " logpdf_p2= (shape - 1)*np.log(x)\n", + " logpdf_p3= -shape*np.log(mu/shape)\n", + " logpdf_p4= -shape*x/mu\n", + " logpdf = logpdf_p1+logpdf_p2+logpdf_p3+logpdf_p4\n", + " return logpdf" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle a \\log{\\left(\\frac{\\mu}{a} \\right)} + \\frac{a x}{\\mu} - \\left(a - 1\\right) \\log{\\left(x \\right)} + \\log{\\left(\\Gamma\\left(a\\right) \\right)}$" + ], + "text/plain": [ + "a*log(mu/a) + a*x/mu - (a - 1)*log(x) + log(gamma(a))" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "negloglikli_gamma_mu_a = -log_pdf_mu_a_gamma\n", + "negloglikli_gamma_mu_a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1st derivative of -Loglikelihood (gamma loss) with respect to $\\mu$." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{a}{\\mu} - \\frac{a x}{\\mu^{2}}$" + ], + "text/plain": [ + "a/mu - a*x/mu**2" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{a \\left(\\mu - x\\right)}{\\mu^{2}}$" + ], + "text/plain": [ + "a*(mu - x)/mu**2" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gammafirstderv= sym.diff(negloglikli_gamma_mu_a,mu)\n", + "display(gammafirstderv,gammafirstderv.simplify())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\frac{a \\left(\\mu - x\\right)}{\\mu^{2}}\n" + ] + } + ], + "source": [ + "sym.print_latex(gammafirstderv.simplify())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2nd derivative of -Loglikelihood (gamma loss) with respect to $\\mu$.: " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{a \\left(- \\mu + 2 x\\right)}{\\mu^{3}}$" + ], + "text/plain": [ + "a*(-mu + 2*x)/mu**3" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{a \\left(- \\mu + 2 x\\right)}{\\mu^{3}}$" + ], + "text/plain": [ + "a*(-mu + 2*x)/mu**3" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gammasecderv = sym.diff(gammafirstderv,mu).simplify()\n", + "display(gammasecderv,gammasecderv.simplify())" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\frac{a \\left(- \\mu + 2 x\\right)}{\\mu^{3}}\n" + ] + } + ], + "source": [ + "sym.print_latex(gammasecderv)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/Testing loss functions fitted to simulated data.ipynb b/notebooks/Testing loss functions fitted to simulated data.ipynb new file mode 100644 index 00000000..c73591c1 --- /dev/null +++ b/notebooks/Testing loss functions fitted to simulated data.ipynb @@ -0,0 +1,1613 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing Negativ Binomail and Gamma loss Data fitting and parameter estimation within PyGOM\n", + "\n", + "This is an example of parameter fitting with an SIR model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import Transition, TransitionType, SimulateOde, SquareLoss, PoissonLoss, NormalLoss, NegBinomLoss, GammaLoss\n", + "import numpy as np\n", + "import scipy.integrate\n", + "import matplotlib.pyplot as plt\n", + "import copy\n", + "\n", + "from scipy.optimize import minimize" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate some data for fitting\n", + "\n", + "# Standard SIR model with 2 parameters\n", + "# construct model \n", + "states = ['S', 'I', 'R']\n", + "params = ['beta', 'gamma', 'N']\n", + "transitions = [Transition(origin='S', destination='I', equation='beta*S*I/N', \n", + " transition_type=TransitionType.T),\n", + " Transition(origin='I', destination='R', equation='gamma*I', \n", + " transition_type=TransitionType.T)]\n", + "model = SimulateOde(states, params, transition=transitions)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# initial conditions \n", + "N = 1e6\n", + "in_inf = 2\n", + "init_state = [N - in_inf, in_inf, 0.0]\n", + "# time \n", + "t = np.arange (0 , 51 , 0.25)\n", + "# deterministic parameter values\n", + "param_evals = [('beta', 3.6), ('gamma', 0.2), ('N', N)]\n", + "model.parameters = param_evals\n", + "model.initial_values = (init_state, t[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# run 1 simulations\n", + "data = model.integrate(t[1:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data over 100 days, with observations from every day for infected and removed populations." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(t,data[:,0], 'bo') # infected observations\n", + "plt.plot(t,data[:,1], 'ro') # infected observations\n", + "plt.plot(t,data[:,2], 'go') # removed observations (recoverd/died)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding random noise to produce Continuos Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our data needs to be have some noise so lets put it through a function for randomisation using the gamma distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def runif_noise(x,noise_prop):\n", + " '''\n", + " Takes x and adds noise on the uniform distribution.\n", + " '''\n", + " i_len,j_len = x.shape\n", + " ans = copy.deepcopy(x)\n", + " for i in range(i_len):\n", + " for j in range(j_len):\n", + " ans[i,j]=x[i,j] + x[i,j]*np.random.uniform(low=-noise_prop,high=noise_prop,size=1)\n", + " \n", + " return ans" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9.99998e+05, 2.00000e+00, 0.00000e+00])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[0,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: RuntimeWarning: invalid value encountered in true_divide\n", + " (noised_data-data)/data\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 0. , 0. , nan],\n", + " [-0.26594371, 0.0481614 , 0.14354852],\n", + " [ 0.18044689, -0.21459589, -0.23607263],\n", + " [-0.22631835, -0.25986512, 0.00977265],\n", + " [ 0.08317535, -0.24033984, -0.13717155],\n", + " [ 0.01893378, -0.30765339, 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0.08092944],\n", + " [-0.14557532, -0.16221617, 0.20846123],\n", + " [-0.1530618 , 0.00607467, 0.1679275 ],\n", + " [ 0.2915459 , 0.03303514, 0.01665417],\n", + " [ 0.25416671, -0.13892646, -0.32628175],\n", + " [ 0.18898644, 0.21485992, -0.16411458],\n", + " [-0.31186503, -0.23293792, 0.24676476],\n", + " [ 0.01727794, -0.16295204, 0.29225053],\n", + " [-0.30057741, -0.19557768, 0.28403295],\n", + " [-0.08805621, -0.21801336, -0.14952844],\n", + " [ 0.17080778, -0.19999893, 0.10857179],\n", + " [-0.01016595, 0.03945573, -0.23341481],\n", + " [ 0.0889063 , -0.22661675, 0.27247754],\n", + " [-0.10284494, -0.21721198, -0.01215395],\n", + " [ 0.04750522, -0.05609498, -0.11760032],\n", + " [-0.12049032, 0.16369776, 0.22946754],\n", + " [-0.3259247 , 0.14942894, 0.01034819],\n", + " [ 0.30482666, 0.10876203, 0.18606517],\n", + " [-0.22421853, -0.1229635 , 0.31979197],\n", + " [-0.31666275, 0.18239926, -0.31941235],\n", + " [-0.15058556, 0.2803648 , 0.29741955],\n", + " [ 0.27943625, -0.31952391, -0.11504683],\n", + " [ 0.02835105, -0.15542857, -0.12654592],\n", + " [ 0.25274379, 0.06784749, -0.02814774],\n", + " [ 0.18619024, -0.15448604, -0.27449163],\n", + " [-0.19728779, 0.02537731, -0.00569949],\n", + " [-0.21443474, -0.01553967, 0.091896 ],\n", + " [-0.26827219, -0.05445831, -0.02413247],\n", + " [ 0.00827749, 0.1737784 , -0.02126288],\n", + " [ 0.23937747, 0.25076279, -0.00298444],\n", + " [-0.12216854, 0.29956893, 0.2265178 ],\n", + " [ 0.15886145, 0.1952358 , -0.10230103],\n", + " [-0.16707728, -0.11592906, 0.23063125],\n", + " [ 0.08048473, -0.28616788, 0.27649409],\n", + " [-0.2399056 , 0.17466809, 0.2670509 ],\n", + " [ 0.01100893, -0.22544906, 0.03502344],\n", + " [-0.24534992, -0.0282792 , 0.04012958],\n", + " [-0.32417314, 0.30503388, 0.07024417],\n", + " [-0.30165476, -0.03131702, -0.11390332],\n", + " [ 0.11602546, 0.17258092, 0.27320442],\n", + " [-0.01679539, 0.17988321, 0.07326227],\n", + " [ 0.32585914, -0.31318578, 0.21703063],\n", + " [-0.25118099, 0.08755176, 0.1418994 ],\n", + " [ 0.25308867, 0.27702219, -0.26432973],\n", + " [ 0.1694537 , 0.15167072, -0.24991087],\n", + " [ 0.32780948, -0.30710299, -0.16902237],\n", + " [ 0.25018489, -0.24231413, 0.03174931],\n", + " [ 0.05986629, 0.03359792, -0.30558626],\n", + " [-0.30815473, -0.11973154, 0.01174757],\n", + " [-0.13523555, -0.02621417, 0.18494329],\n", + " [ 0.19637621, -0.06536179, 0.16160518],\n", + " [ 0.16124011, 0.12639132, -0.02431527],\n", + " [ 0.19221785, 0.24196546, 0.00068597],\n", + " [ 0.08134284, 0.31422214, -0.20704816],\n", + " [ 0.1727692 , 0.02115635, -0.13793911],\n", + " [-0.28248369, 0.05719562, -0.09881328],\n", + " [-0.15519892, 0.30149671, 0.30585306],\n", + " [ 0.18343869, -0.18022277, 0.23596179],\n", + " [-0.09597099, 0.31572377, -0.02601189],\n", + " [-0.23722386, -0.20593368, -0.01834659],\n", + " [ 0.21902069, 0.24623962, -0.19736678],\n", + " [ 0.02226348, 0.21272945, -0.30751679],\n", + " [-0.24202336, -0.2567437 , 0.14965649],\n", + " [-0.25945811, 0.28880295, 0.33275319],\n", + " [ 0.28975049, 0.04492393, 0.24388337],\n", + " [-0.09991367, 0.11365879, -0.30462007],\n", + " [-0.33098441, -0.20249934, -0.32534145],\n", + " [-0.00702099, -0.2097019 , -0.11255923],\n", + " [ 0.23478388, 0.14543586, 0.32737759],\n", + " [-0.32148961, 0.03430904, -0.23725884],\n", + " [-0.15295436, -0.23766421, 0.16057446],\n", + " [ 0.24874516, 0.02814334, 0.05567249],\n", + " [ 0.18373544, 0.05456167, -0.00065203],\n", + " [ 0.29179694, -0.02776448, -0.27429118],\n", + " [ 0.19373542, -0.10868194, 0.10012632],\n", + " [-0.05908267, -0.20556755, -0.06223371],\n", + " [-0.08392523, -0.13577942, -0.14284998],\n", + " [ 0.10779361, 0.11329162, 0.17968936],\n", + " [-0.04555323, 0.22342669, 0.302655 ],\n", + " [ 0.31417606, 0.32366651, -0.24461707],\n", + " [ 0.25052832, 0.04730799, 0.0135891 ],\n", + " [ 0.24008863, 0.11260373, 0.10348191],\n", + " [ 0.26464085, -0.16812188, -0.23981266],\n", + " [-0.23031692, 0.25901614, 0.25404338],\n", + " [-0.08783901, 0.21082491, 0.09234899],\n", + " [-0.12828791, 0.23402825, -0.13942016],\n", + " [ 0.17317795, 0.06544127, -0.32470743],\n", + " [-0.01350832, -0.20846687, 0.06141013],\n", + " [ 0.08516377, -0.27381567, 0.19523863],\n", + " [-0.2885919 , 0.04913673, 0.27292814],\n", + " [-0.14942594, -0.11490962, -0.27067257],\n", + " [ 0.0428627 , 0.22205546, 0.16590721],\n", + " [ 0.16898513, -0.03668338, -0.06368085],\n", + " [ 0.30915912, -0.13559047, -0.05790722],\n", + " [ 0.28353642, -0.20041566, -0.00223925]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noised_data = runif_noise(data,1/3)\n", + "#we stil want the first row from data\n", + "noised_data[0,:] = data[0,:]\n", + "(noised_data-data)/data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(t,noised_data[:,0], 'bo') # infected observations\n", + "plt.plot(t,noised_data[:,1], 'ro') # infected observations\n", + "plt.plot(t,noised_data[:,2], 'go') # removed observations (recoverd/died)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We provide a guess for $\\beta$ and $\\gamma$. \n", + "\n", + "This example assumes we have information about the infected and removed population, up to 50 days of the epidemic.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.90464096e+00, 1.80229297e-01],\n", + " [8.59843745e+00, 4.02088747e-01],\n", + " [1.89574734e+01, 1.40262775e+00],\n", + " [4.55221314e+01, 2.94002911e+00],\n", + " [9.70595088e+01, 6.42741549e+00],\n", + " [2.60715165e+02, 2.20294695e+01],\n", + " 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1.22403191e+06],\n", + " [4.83390522e+03, 1.00609917e+06],\n", + " [4.43546984e+03, 1.18132044e+06],\n", + " [3.33736908e+03, 1.31476977e+06],\n", + " [4.27992342e+03, 6.78124116e+05],\n", + " [4.40850014e+03, 1.29295231e+06],\n", + " [2.22871884e+03, 8.82054729e+05],\n", + " [2.63126192e+03, 8.70732818e+05],\n", + " [3.16462468e+03, 9.68972110e+05],\n", + " [2.38352132e+03, 7.23463131e+05],\n", + " [2.74958560e+03, 9.91634243e+05],\n", + " [2.51111756e+03, 1.08911082e+06],\n", + " [2.29421855e+03, 9.73499719e+05],\n", + " [2.70910290e+03, 9.76478163e+05],\n", + " [2.74599424e+03, 9.94826643e+05],\n", + " [2.71399644e+03, 1.22395635e+06],\n", + " [2.37437235e+03, 8.95915646e+05],\n", + " [1.67058137e+03, 1.22830577e+06],\n", + " [1.28310408e+03, 1.27419959e+06],\n", + " [2.00847418e+03, 1.26488445e+06],\n", + " [1.25975575e+03, 1.03334003e+06],\n", + " [1.50336053e+03, 1.03852036e+06],\n", + " [1.92056369e+03, 1.06866912e+06],\n", + " [1.35604430e+03, 8.84856232e+05],\n", + " 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1.14917174e+06],\n", + " [5.16905055e+02, 1.33221864e+06],\n", + " [3.98652245e+02, 1.24340879e+06],\n", + " [4.04154080e+02, 6.95127565e+05],\n", + " [2.75303132e+02, 6.74425641e+05],\n", + " [2.59511329e+02, 8.87149341e+05],\n", + " [3.57784433e+02, 1.32696295e+06],\n", + " [3.07316774e+02, 7.62514516e+05],\n", + " [2.15460438e+02, 1.16024642e+06],\n", + " [2.76414090e+02, 1.05538866e+06],\n", + " [2.69689336e+02, 9.99092387e+05],\n", + " [2.36509482e+02, 7.25532274e+05],\n", + " [2.06250526e+02, 1.09987173e+06],\n", + " [1.74865694e+02, 9.37559865e+05],\n", + " [1.80949557e+02, 8.56970535e+05],\n", + " [2.21731370e+02, 1.17945438e+06],\n", + " [2.31782922e+02, 1.30240818e+06],\n", + " [2.38543368e+02, 7.55246791e+05],\n", + " [1.79534727e+02, 1.01341533e+06],\n", + " [1.81426132e+02, 1.10330196e+06],\n", + " [1.29034045e+02, 7.60069418e+05],\n", + " [1.85763844e+02, 1.25385833e+06],\n", + " [1.69940356e+02, 1.09219566e+06],\n", + " [1.64750052e+02, 8.60464933e+05],\n", + " [1.35305439e+02, 6.75206798e+05],\n", + " [9.56181015e+01, 1.06128189e+06],\n", + " [8.34455489e+01, 1.19510127e+06],\n", + " [1.14676301e+02, 1.27278898e+06],\n", + " [9.20268352e+01, 7.29251586e+05],\n", + " [1.20865697e+02, 1.16579188e+06],\n", + " [9.06288530e+01, 9.36231050e+05],\n", + " [7.73574703e+01, 9.42008457e+05],\n", + " [6.80663206e+01, 9.97675798e+05]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_to_fit = noised_data[:,1:3]\n", + "data_to_fit = data_to_fit[1::,:]\n", + "data_to_fit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting Normal loss" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_evals" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "theta = [3, 0.15,1e6]\n", + "boxBounds = [(2,5),(0.0,1.0),(1e6,1e6)]\n", + "\n", + "objSIR = NormalLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4209072260954.626" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.cost()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\scipy\\optimize\\_minimize.py:521: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", + " warn('Method %s cannot handle constraints nor bounds.' % method,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 3149715468391.5137\n", + " hess_inv: array([[ 1.02285673e-11, -3.28483610e-14, 3.23263121e-06],\n", + " [-3.28483423e-14, 5.00246087e-15, -3.92052034e-09],\n", + " [ 3.23263111e-06, -3.92052576e-09, 1.06428103e+00]])\n", + " jac: array([-1.89198473e+03, 2.05662437e+05, 6.51297284e-03])\n", + " message: 'Desired error not necessarily achieved due to precision loss.'\n", + " nfev: 74\n", + " nit: 10\n", + " njev: 62\n", + " status: 2\n", + " success: False\n", + " x: array([3.84976144e+00, 2.02859211e-01, 1.11835931e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='BFGS')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 3149715468391.6045\n", + " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", + " jac: array([ 2.80002429e+03, 1.18458645e+05, -9.63846812e-03])\n", + " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", + " nfev: 12\n", + " nit: 9\n", + " status: 0\n", + " success: True\n", + " x: array([3.44232967e+00, 2.02859211e-01, 1.00000000e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='L-BFGS-B')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 0.15, 1000000.0]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[(2, 5), (0.0, 1.0), (1000000.0, 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(theta,boxBounds,param_evals)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting Gamma loss" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = GammaLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1.17798986e+03, -1.40835509e+04, 3.53396958e-03])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.sensitivity()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5065.797364214306" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.cost()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\scipy\\optimize\\_minimize.py:521: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", + " warn('Method %s cannot handle constraints nor bounds.' % method,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 4716.804547726841\n", + " hess_inv: array([[ 1.93830562e-03, -1.55181618e-05, 1.36514445e-06],\n", + " [-1.55181618e-05, 1.96280251e-06, 1.90886878e-08],\n", + " [ 1.36514445e-06, 1.90886878e-08, 1.00000000e+00]])\n", + " jac: array([ 8.67860428e-08, -3.11402124e-06, -3.11981179e-13])\n", + " message: 'Optimization terminated successfully.'\n", + " nfev: 15\n", + " nit: 10\n", + " njev: 15\n", + " status: 0\n", + " success: True\n", + " x: array([3.59483138e+00, 1.99847237e-01, 1.00000000e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='BFGS')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\pygom-0.1.7.dev1+gbbd58f3.d20200410-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:108: RuntimeWarning: invalid value encountered in log\n", + " logpdf_p3= -shape*np.log(mu/shape)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 4716.804547726802\n", + " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", + " jac: array([-9.16923207e-05, -1.70890676e-03, 3.29618418e-10])\n", + " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", + " nfev: 15\n", + " nit: 10\n", + " status: 0\n", + " success: True\n", + " x: array([3.59483123e+00, 1.99847235e-01, 1.00000000e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='L-BFGS-B')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 0.15, 1000000.0]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[(2, 5), (0.0, 1.0), (1000000.0, 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(theta,boxBounds,param_evals)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding random noise to produce Count Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our data needs to a be counts:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5.000000e+00, 0.000000e+00],\n", + " [9.000000e+00, 0.000000e+00],\n", + " [1.900000e+01, 1.000000e+00],\n", + " [4.600000e+01, 3.000000e+00],\n", + " [9.700000e+01, 6.000000e+00],\n", + " [2.610000e+02, 2.200000e+01],\n", + " [9.900000e+02, 5.400000e+01],\n", + " [1.515000e+03, 1.210000e+02],\n", + " [3.991000e+03, 3.070000e+02],\n", + " [7.166000e+03, 4.150000e+02],\n", + " [1.973600e+04, 1.230000e+03],\n", + " [5.781700e+04, 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9.200930e+05],\n", + " [2.525600e+04, 1.049130e+06],\n", + " [1.843600e+04, 8.923940e+05],\n", + " [2.129100e+04, 9.476230e+05],\n", + " [1.524000e+04, 8.937350e+05],\n", + " [1.398800e+04, 1.030004e+06],\n", + " [1.943000e+04, 6.623530e+05],\n", + " [1.169100e+04, 6.970030e+05],\n", + " [1.667000e+04, 1.033941e+06],\n", + " [1.727100e+04, 1.278454e+06],\n", + " [1.212700e+04, 1.047077e+06],\n", + " [1.357500e+04, 6.871120e+05],\n", + " [1.420000e+04, 1.236557e+06],\n", + " [1.596000e+04, 1.142587e+06],\n", + " [1.496600e+04, 9.449100e+05],\n", + " [8.814000e+03, 1.262525e+06],\n", + " [1.033100e+04, 1.301655e+06],\n", + " [8.508000e+03, 8.369960e+05],\n", + " [8.702000e+03, 6.648640e+05],\n", + " [7.821000e+03, 1.070812e+06],\n", + " [7.459000e+03, 1.197702e+06],\n", + " [8.520000e+03, 1.158037e+06],\n", + " [8.322000e+03, 1.008464e+06],\n", + " [6.598000e+03, 6.685560e+05],\n", + " [8.855000e+03, 8.297920e+05],\n", + " [5.319000e+03, 1.238120e+06],\n", + " [5.521000e+03, 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1.274200e+06],\n", + " [2.008000e+03, 1.264884e+06],\n", + " [1.260000e+03, 1.033340e+06],\n", + " [1.503000e+03, 1.038520e+06],\n", + " [1.921000e+03, 1.068669e+06],\n", + " [1.356000e+03, 8.848560e+05],\n", + " [1.561000e+03, 1.271509e+06],\n", + " [1.495000e+03, 1.071903e+06],\n", + " [8.280000e+02, 1.215564e+06],\n", + " [1.246000e+03, 1.140591e+06],\n", + " [1.392000e+03, 7.348680e+05],\n", + " [1.194000e+03, 7.493110e+05],\n", + " [6.840000e+02, 8.301580e+05],\n", + " [7.110000e+02, 1.030781e+06],\n", + " [9.230000e+02, 6.937940e+05],\n", + " [7.470000e+02, 1.010889e+06],\n", + " [7.860000e+02, 1.183986e+06],\n", + " [7.180000e+02, 1.160713e+06],\n", + " [8.230000e+02, 9.749720e+05],\n", + " [8.630000e+02, 9.999900e+05],\n", + " [8.690000e+02, 7.924270e+05],\n", + " [6.420000e+02, 8.615190e+05],\n", + " [6.330000e+02, 9.006470e+05],\n", + " [7.410000e+02, 1.305110e+06],\n", + " [4.440000e+02, 1.235293e+06],\n", + " [6.780000e+02, 9.734870e+05],\n", + " [3.890000e+02, 9.811730e+05],\n", + " [5.810000e+02, 8.022590e+05],\n", + " [5.380000e+02, 6.921760e+05],\n", + " [3.130000e+02, 1.149172e+06],\n", + " [5.170000e+02, 1.332219e+06],\n", + " [3.990000e+02, 1.243409e+06],\n", + " [4.040000e+02, 6.951280e+05],\n", + " [2.750000e+02, 6.744260e+05],\n", + " [2.600000e+02, 8.871490e+05],\n", + " [3.580000e+02, 1.326963e+06],\n", + " [3.070000e+02, 7.625150e+05],\n", + " [2.150000e+02, 1.160246e+06],\n", + " [2.760000e+02, 1.055389e+06],\n", + " [2.700000e+02, 9.990920e+05],\n", + " [2.370000e+02, 7.255320e+05],\n", + " [2.060000e+02, 1.099872e+06],\n", + " [1.750000e+02, 9.375600e+05],\n", + " [1.810000e+02, 8.569710e+05],\n", + " [2.220000e+02, 1.179454e+06],\n", + " [2.320000e+02, 1.302408e+06],\n", + " [2.390000e+02, 7.552470e+05],\n", + " [1.800000e+02, 1.013415e+06],\n", + " [1.810000e+02, 1.103302e+06],\n", + " [1.290000e+02, 7.600690e+05],\n", + " [1.860000e+02, 1.253858e+06],\n", + " [1.700000e+02, 1.092196e+06],\n", + " [1.650000e+02, 8.604650e+05],\n", + " [1.350000e+02, 6.752070e+05],\n", + " [9.600000e+01, 1.061282e+06],\n", + " [8.300000e+01, 1.195101e+06],\n", + " [1.150000e+02, 1.272789e+06],\n", + " [9.200000e+01, 7.292520e+05],\n", + " [1.210000e+02, 1.165792e+06],\n", + " [9.100000e+01, 9.362310e+05],\n", + " [7.700000e+01, 9.420080e+05],\n", + " [6.800000e+01, 9.976760e+05]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "int_data_to_fit = np.around(data_to_fit)\n", + "int_data_to_fit " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Poisson loss" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = PoissonLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=int_data_to_fit, state_name=['I','R'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8329023.1484972015" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.cost()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 3557908.6464113127\n", + " hess_inv: array([[ 1.27472646e-07, -3.23818317e-09, 2.26214178e-06],\n", + " [-3.23818317e-09, 9.56376557e-10, 2.13419691e-08],\n", + " [ 2.26214178e-06, 2.13419691e-08, 1.00001208e+00]])\n", + " jac: array([-6.34498993e-04, 1.86534335e-03, 2.25490671e-09])\n", + " message: 'Desired error not necessarily achieved due to precision loss.'\n", + " nfev: 109\n", + " nit: 11\n", + " njev: 97\n", + " status: 2\n", + " success: False\n", + " x: array([3.55378376e+00, 1.99169650e-01, 1.00000147e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='BFGS')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 3557908.646411574\n", + " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", + " jac: array([-2.83986623e-01, -4.04398552e-01, 1.00922556e-06])\n", + " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", + " nfev: 15\n", + " nit: 11\n", + " status: 0\n", + " success: True\n", + " x: array([3.55377851e+00, 1.99169650e-01, 1.00000000e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='L-BFGS-B')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 0.15, 1000000.0]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[(2, 5), (0.0, 1.0), (1000000.0, 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(theta,boxBounds,param_evals)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NegBinom loss" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\pygom-0.1.7.dev1+gbbd58f3.d20200410-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", + " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n", + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\pygom-0.1.7.dev1+gbbd58f3.d20200410-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", + " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" + ] + } + ], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = NegBinomLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=int_data_to_fit, state_name=['I','R'])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2.92555910e+02, -3.50195352e+03, 8.77667731e-04])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.sensitivity()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5041.425763395273" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.cost()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\scipy\\optimize\\_minimize.py:521: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", + " warn('Method %s cannot handle constraints nor bounds.' % method,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 4867.899797539529\n", + " hess_inv: array([[ 7.67776936e-03, -5.97617430e-05, 1.74660277e-06],\n", + " [-5.97617430e-05, 7.85112146e-06, 1.25323010e-08],\n", + " [ 1.74660277e-06, 1.25323010e-08, 1.00000000e+00]])\n", + " jac: array([ 8.13167572e-09, -3.92696007e-06, -2.92247481e-14])\n", + " message: 'Optimization terminated successfully.'\n", + " nfev: 15\n", + " nit: 10\n", + " njev: 15\n", + " status: 0\n", + " success: True\n", + " x: array([3.59393939e+00, 1.99830691e-01, 1.00000000e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='BFGS')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\pygom-0.1.7.dev1+gbbd58f3.d20200410-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", + " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 4867.899797547801\n", + " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", + " jac: array([ 4.12746856e-04, 1.84890228e-02, -1.48338804e-09])\n", + " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", + " nfev: 17\n", + " nit: 12\n", + " status: 0\n", + " success: True\n", + " x: array([3.59394146e+00, 1.99830811e-01, 1.00000000e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='L-BFGS-B')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 0.15, 1000000.0]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[(2, 5), (0.0, 1.0), (1000000.0, 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(theta,boxBounds,param_evals)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index 0263e163..457371c1 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -9,14 +9,16 @@ __all__ = [ 'Square', 'Normal', - 'Poisson' + 'Poisson', + 'Gamma', + 'NegBinom' ] import numpy as np from pygom.model._model_errors import InitializeError from pygom.model.ode_utils import check_array_type -from pygom.utilR.distn import dnorm, dpois +from pygom.utilR.distn import dnorm, dpois, gamma_mu_shape, dnbinom class InputError(Exception): ''' @@ -253,6 +255,133 @@ def residual(self, yhat): resid = self._y - yhat return resid + +class Gamma(object): + ''' + Gamma distribution loss object + + Parameters + ---------- + y: array like + observation + Shape: float + shape (a in latex equations) + ''' + + def __init__(self, y, shape=2.0): + err_str = "Shape is not of the correct " + self._y = check_array_type(y) + if isinstance(shape, np.ndarray): + if len(shape.shape) > 1: + if 1 in shape.shape: + shape = shape.flatten() + + if y.shape == shape.shape: + self._shape = shape + else: + raise InitializeError(err_str + "size") + else: + if y.shape == shape.shape: + self._shape = shape + else: + raise InitializeError(err_str + "size") + elif shape is None or shape == 2.0: + self._shape = 2*np.ones(self._y.shape) + else: + raise InitializeError(err_str + "type") + + self.loss(self._y) + + def loss(self, yhat): + ''' + The loss under a gamma distribution. Defined as the negative + log-likelihood of the gamma distirbution in terms of mean and shape. + See: Bolker, B. M. (2008). Gamma. In Ecological Models in R (pp. 131–133). Princeton University Press. + File "Loss function Calculations.ipnyb" + + Parameters + ---------- + yhat: array like + prediction + + + Returns + ------- + negative log-likelihood, :math:`\\mathcal{L}(\\hat{y}; y,a)` + + ''' + if len(yhat.shape) > 1: + if 1 in yhat.shape: + yhat = yhat.ravel() + + return -gamma_mu_shape(x=self._y, mu=yhat,shape=self._shape,log=True).sum() + + def diff_loss(self, yhat): + ''' + Derivative of the loss function with respect to yhat which is + See: + File "Loss function Calculations.ipnyb" + + Parameters + ---------- + yhat: array like + prediction + + + Returns + ------- + first_deriv_yhat: array like + :math:`\\mathcal\\frac{a \\left(\\hat{y} - y\\right)}{\\hat{y}^{2}}` + + ''' + if len(yhat.shape) > 1: + if 1 in yhat.shape: + yhat = yhat.ravel() + + return self._shape*(yhat-self._y)/yhat**2 + + def diff2Loss(self, yhat): + ''' + Twice derivative of the loss function with respect to yhat. + See: + Jupiter notebook "Loss function Calculations.ipnyb" + + Parameters + ---------- + yhat: array like + observation + + + Returns + ------- + scnd_deriv_yhat: array like + :math:`\\mathcal\\frac{a \\left(- \\hat{y} + 2 y\\right)}{\\hat{y}^{3}}` + + ''' + y = self._y + shape = self._shape + + return shape*(-yhat+2*y)/yhat**3 + + def residual(self, yhat): + ''' + Raw residuals + + Parameters + ---------- + yhat: array like + observation + + Returns + ------- + r: array like + residuals + + ''' + if len(yhat.shape) > 1: + if 1 in yhat.shape: + yhat = yhat.ravel() + return self._y - yhat class Poisson(object): ''' @@ -344,3 +473,139 @@ def residual(self, yhat): yhat = yhat.ravel() return self._y - yhat +class NegBinom(object): + ''' + Negative Binomial distribution loss object + + Parameters + ---------- + y: array like + observation + k: float + Overdispersion parameter (k=mean+mean(mean/variance)) + ''' + + def __init__(self, y, k=1.0): + err_str = "k (the overdispersion parameter) is not of the correct " + self._y = check_array_type(y) + if isinstance(k, np.ndarray): + if len(k.shape) > 1: + if 1 in k.shape: + k = k.flatten() + + if y.shape == k.shape: + self._k = k + else: + raise InitializeError(err_str + "size") + else: + if y.shape == k.shape: + self._k = k + else: + raise InitializeError(err_str + "size") + elif k is None or k == 1.0: + self._k = np.ones(self._y.shape) + else: + raise InitializeError(err_str + "type") + + self.loss(self._y) + + def loss(self, yhat): + ''' + The loss under a Negative Binomial distribution. Defined as the + negative log-likelihood of the Negative Binomial 2 distribution. + + Parameters + ---------- + yhat: array like + observation + + Returns + ------- + negative log-likelihood, :math:`\\mathcal{L}(\\hat{y}; y,k)` + + ''' + if len(yhat.shape) > 1: + if 1 in yhat.shape: + yhat = yhat.ravel() + + return (-dnbinom(self._y, mu=yhat,size=self._k,log=True)).sum() + + def diff_loss(self, yhat): + ''' + Derivative of the loss function with respect to yhat which is + See: + Jupiter notebook "Loss function Calculations.ipnyb" + Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press + + Parameters + ---------- + yhat: array like + observation + + k: array like + observation + + Returns + ------- + first_deriv_yhat: array like + :math:`\\frac{k(\\hat{y}-y)}{\\hat{y}(k + \\hat{y})}` + + ''' + if len(yhat.shape) > 1: + if 1 in yhat.shape: + yhat = yhat.ravel() + + y = self._y + k = self._k + first_derivs_yhat = (k*(yhat-y))/(yhat*(k+yhat)) + return first_derivs_yhat + + def diff2Loss(self, yhat): + ''' + Twice derivative of the loss function with respect to yhat. + See: + Jupiter notebook "Loss function Calculations.ipnyb" + Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press + + Parameters + ---------- + yhat: array like + observation + + k: array like + observation + + Returns + ------- + scnd_deriv_yhat: array like + :math:`\\frac{k(\\hat{y}(k + \\hat{y}) + \\hat{y}(y -\\hat{y}) + (k + \\hat{y})(y - \\hat{y})}{\\hat{y}^{2}(k + \\hat{y})^{2}}` + + ''' + y = self._y + k = self._k + scnd_derivs_yhat_p1= k + scnd_derivs_yhat_p2= yhat**(-2) + scnd_derivs_yhat_p3= (k + yhat)**(-2) + scnd_derivs_yhat_p4= yhat*(k + yhat) - yhat*(yhat - x) - (k + yhat)*(yhat - x) + scnd_derivs_yhat= scnd_derivs_yhat_p1*scnd_derivs_yhat_p2*scnd_derivs_yhat_p3*scnd_derivs_yhat_p4 + return scnd_derivs_yhat + + def residual(self, yhat): + ''' + Raw residuals + + Parameters + ---------- + yhat: array like + observation + + Returns + ------- + r: array like + residuals + + ''' + if len(yhat.shape) > 1: + if 1 in yhat.shape: + yhat = yhat.ravel() + return self._y - yhat diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index cdc19dfb..af461878 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -10,11 +10,13 @@ __all__ = [ 'SquareLoss', 'NormalLoss', - 'PoissonLoss' + 'PoissonLoss', + 'GammaLoss', + 'NegBinomLoss' ] from pygom.loss.base_loss import BaseLoss -from pygom.loss.loss_type import Normal, Square, Poisson +from pygom.loss.loss_type import Normal, Square, Poisson, Gamma, NegBinom from pygom.model.ode_utils import check_array_type class SquareLoss(BaseLoss): @@ -64,6 +66,22 @@ def _setLossType(self): return Normal(self._y, 1.0/self._stateWeight) else: return Normal(self._y, 1.0/self._stateWeight) + +class GammaLoss(BaseLoss): + ''' + Realizations from a Gamma distribution taking parameters mean and shape. + ''' + def __init__(self, theta, ode, x0, t0, t, y, state_name, + shape=2, target_param=None, target_state=None): + super(GammaLoss, self).__init__(theta, ode, x0, t0, t, y, + state_name, shape, + target_param, target_state) + + def __repr__(self): + return "GammaLoss" + self._get_model_str() + + def _setLossType(self): + return Gamma(self._y) class PoissonLoss(BaseLoss): ''' @@ -77,4 +95,20 @@ def __repr__(self): return "PoissonLoss" + self._get_model_str() def _setLossType(self): - return Poisson(self._y) \ No newline at end of file + return Poisson(self._y) + +class NegBinomLoss(BaseLoss): + ''' + Realizations from a Negative Binomial distribution + ''' + def __init__(self, theta, ode, x0, t0, t, y, state_name, + k=1, target_param=None, target_state=None): + super(NegBinomLoss, self).__init__(theta, ode, x0, t0, t, y, + state_name, k, + target_param, target_state) + + def __repr__(self): + return "NegBinomLoss" + self._get_model_str() + + def _setLossType(self): + return NegBinom(self._y) diff --git a/pygom/utilR/distn.py b/pygom/utilR/distn.py index 71d9ad55..8ebdca96 100644 --- a/pygom/utilR/distn.py +++ b/pygom/utilR/distn.py @@ -11,6 +11,7 @@ import scipy.stats as st import numpy as np +from scipy.special import gammaln ############################################################### # @@ -80,6 +81,38 @@ def dgamma(x, shape, rate=1.0, log=False): return st.gamma.logpdf(x, a=shape, scale=1.0/rate) else: return st.gamma.pdf(x, a=shape, scale=1.0/rate) + +def gamma_mu_shape(x, mu,shape,log=False): + ''' + The probability density function (pdf) of gamma distrbution in terms of mean and shape. + + Parameters + ---------- + x: array like observation. + mu: mean or prediction. + shape: shape (a in latex equation below). + log: if True the natural log of the pmf is given. + See: Bolker, B. M. (2008). Gamma. In Ecological Models in R (pp. 131–133). Princeton University Press. + Jupyter notebook titled "Loss function Calculations.ipnyb" + + + Returns + ------- + pdf, :math:`\\mathcal\\{p}(x; \\mu,a) = \\exp(- a \\ln{\\left(\\frac{\\mu}{a} \\right)} - \\frac{a x}{\\mu} + \\left(a - 1\\right) \\ln{\\left(x \\right)} - \\ln{\\left(\\Gamma\\left(a\\right) \\right)})` +` + + ''' + + logpdf_p1= -gammaln(shape) + logpdf_p2= (shape - 1)*np.log(x) + logpdf_p3= -shape*np.log(mu/shape) + logpdf_p4= -shape*x/mu + logpdf = logpdf_p1+logpdf_p2+logpdf_p3+logpdf_p4 + if log: + ans = logpdf + else: + ans = np.exp(logpdf) + return ans def pgamma(q, shape, rate=1.0, log=False): ''' @@ -381,6 +414,73 @@ def rbinom(n, size, prob, seed=None): else: return rvs(n=size, p=prob, size=n)[0] +##### Negative Binomial distribution +def nb2pmf(x, mu,k,log=False): + ''' + The probability mass function (pmf) of Negative Binomial 2 distribution. + This definition of the negative binomial distribution is often refered to as + negative binomial 2, or the ecological parameterisation. This parameterisation + takes the mean and k (an overdispersion parameter). The variance = mean(1+mean/k), + some notation uses alpha (k=1/alpha). + See: Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press. + Jupyter notebook titled "Loss function Calculations.ipnyb" + + Parameters + ---------- + x: array like observation. + mu: mean or prediction. + k: overdispersion parameter (variance = mean(1+mean/k)). Note some notation uses $\alpha$, ($k=\alpha^{-1}$). + log: if True the natural log of the pmf is given. + See: + Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press. + File "Loss function Calculations.ipnyb" + + Returns + ------- + log pmf: + math:`\\mathcal\\p(x; \\mu,k)) = \\exp(\\ln(\\frac{\\Gamma \\left(k+x\\right)}{\\Gamma \\left(k\\right)x!}(\\frac{k}{k+\\mu})^{k}(\\frac{\\mu}{k+\\mu})^{x}))` + + ''' + # note that we input k the overdispersion parameter here + + + logpmf_p1= -gammaln(x+1) + logpmf_p2= -gammaln(k) + logpmf_p3= k*(np.log(k) - np.log(k + mu)) + logpmf_p4= x*(np.log(mu) - np.log(k + mu)) + logpmf_p5= gammaln(k+x) + logpmf = logpmf_p1+logpmf_p2+logpmf_p3+logpmf_p4+logpmf_p5 + if log: + ans= logpmf + else: + ans= np.exp(logpmf) + return ans + +def dnbinom(x, size, prob=None, mu=None, log =False): + ''' + See + https://stat.ethz.ch/R-manual/R-devel/library/stats/html/NegBinomial.html + ''' + if mu is None and prob is None: + raise Exception("Neither 'prob' or 'mu' were specified") + + if mu is not None: + if prob is not None: + raise Exception("'prob' and 'mu' both specified") + + if log: + ans =nb2pmf(x=x, mu=mu,k=size,log=True) + else: + ans =nb2pmf(x=x, mu=mu,k=size,log=False) + + else: + if log: + ans= st.nbinom.logpmf(x, n=size, p=prob) + else: + ans= st.nbinom.pmf(x, n=size, p=prob) + + return ans + def test_seed(seed): ''' Test the input type of `seed` and return a new random generator if From 1abe96f16c323419e28958ad4a8b991dff215fd4 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Thu, 18 Jun 2020 11:04:20 +0100 Subject: [PATCH 002/188] Fixed confusion with disrpersion and weighting. --- notebooks/Loss function Calculations.ipynb | 2 +- pygom/loss/loss_type.py | 4 ++-- pygom/loss/ode_loss.py | 12 ++++++------ 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/notebooks/Loss function Calculations.ipynb b/notebooks/Loss function Calculations.ipynb index 3f46b706..b870605a 100644 --- a/notebooks/Loss function Calculations.ipynb +++ b/notebooks/Loss function Calculations.ipynb @@ -650,7 +650,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index 457371c1..b13c3602 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -485,7 +485,7 @@ class NegBinom(object): Overdispersion parameter (k=mean+mean(mean/variance)) ''' - def __init__(self, y, k=1.0): + def __init__(self, y, k=1.0,): err_str = "k (the overdispersion parameter) is not of the correct " self._y = check_array_type(y) if isinstance(k, np.ndarray): @@ -586,7 +586,7 @@ def diff2Loss(self, yhat): scnd_derivs_yhat_p1= k scnd_derivs_yhat_p2= yhat**(-2) scnd_derivs_yhat_p3= (k + yhat)**(-2) - scnd_derivs_yhat_p4= yhat*(k + yhat) - yhat*(yhat - x) - (k + yhat)*(yhat - x) + scnd_derivs_yhat_p4= yhat*(k + yhat) - yhat*(yhat - y) - (k + yhat)*(yhat - y) scnd_derivs_yhat= scnd_derivs_yhat_p1*scnd_derivs_yhat_p2*scnd_derivs_yhat_p3*scnd_derivs_yhat_p4 return scnd_derivs_yhat diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index af461878..7927e9f9 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -72,16 +72,16 @@ class GammaLoss(BaseLoss): Realizations from a Gamma distribution taking parameters mean and shape. ''' def __init__(self, theta, ode, x0, t0, t, y, state_name, - shape=2, target_param=None, target_state=None): + shape=2,state_weight=None, target_param=None, target_state=None): super(GammaLoss, self).__init__(theta, ode, x0, t0, t, y, - state_name, shape, + state_name, state_weight, target_param, target_state) def __repr__(self): return "GammaLoss" + self._get_model_str() def _setLossType(self): - return Gamma(self._y) + return Gamma(self._y,self._shape) class PoissonLoss(BaseLoss): ''' @@ -102,13 +102,13 @@ class NegBinomLoss(BaseLoss): Realizations from a Negative Binomial distribution ''' def __init__(self, theta, ode, x0, t0, t, y, state_name, - k=1, target_param=None, target_state=None): + k=1,state_weight=None, target_param=None, target_state=None): super(NegBinomLoss, self).__init__(theta, ode, x0, t0, t, y, - state_name, k, + state_name, state_weight, target_param, target_state) def __repr__(self): return "NegBinomLoss" + self._get_model_str() def _setLossType(self): - return NegBinom(self._y) + return NegBinom(self._y,self._k) From ffb938712f30a41ac0fc7d2309c55cc8315b1c97 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Thu, 18 Jun 2020 12:03:01 +0100 Subject: [PATCH 003/188] Allows for different over dispersion parameters than default in neg binom and gamma loss. --- pygom/loss/loss_type.py | 6 +++++- pygom/loss/ode_loss.py | 10 ++++++---- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index b13c3602..a9621668 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -287,6 +287,8 @@ def __init__(self, y, shape=2.0): raise InitializeError(err_str + "size") elif shape is None or shape == 2.0: self._shape = 2*np.ones(self._y.shape) + elif isinstance(shape, (int, float)) and shape !=2: + self._shape = shape*np.ones(self._y.shape) else: raise InitializeError(err_str + "type") @@ -485,7 +487,7 @@ class NegBinom(object): Overdispersion parameter (k=mean+mean(mean/variance)) ''' - def __init__(self, y, k=1.0,): + def __init__(self, y, k=1.0): err_str = "k (the overdispersion parameter) is not of the correct " self._y = check_array_type(y) if isinstance(k, np.ndarray): @@ -504,6 +506,8 @@ def __init__(self, y, k=1.0,): raise InitializeError(err_str + "size") elif k is None or k == 1.0: self._k = np.ones(self._y.shape) + elif isinstance(k, (int, float)) and k >1: + self._k = k*np.ones(self._y.shape) else: raise InitializeError(err_str + "type") diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index 7927e9f9..c371107c 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -71,8 +71,9 @@ class GammaLoss(BaseLoss): ''' Realizations from a Gamma distribution taking parameters mean and shape. ''' - def __init__(self, theta, ode, x0, t0, t, y, state_name, - shape=2,state_weight=None, target_param=None, target_state=None): + def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, + shape=2, target_param=None, target_state=None): + self._shape = shape super(GammaLoss, self).__init__(theta, ode, x0, t0, t, y, state_name, state_weight, target_param, target_state) @@ -101,8 +102,9 @@ class NegBinomLoss(BaseLoss): ''' Realizations from a Negative Binomial distribution ''' - def __init__(self, theta, ode, x0, t0, t, y, state_name, - k=1,state_weight=None, target_param=None, target_state=None): + def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, + k=1, target_param=None, target_state=None): + self._k = k super(NegBinomLoss, self).__init__(theta, ode, x0, t0, t, y, state_name, state_weight, target_param, target_state) From 4de2bf01f7707addae93b6e1fff6c74025880254 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Thu, 18 Jun 2020 15:21:08 +0100 Subject: [PATCH 004/188] Weighted residuals can now be applied in neg binom, Poisson and gamma loss functions. --- pygom/loss/loss_type.py | 133 ++++++++++++++++++++++++++++++++++++---- pygom/loss/ode_loss.py | 8 +-- 2 files changed, 124 insertions(+), 17 deletions(-) diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index a9621668..d0ec6a95 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -154,7 +154,9 @@ def __init__(self, y, sigma=1.0): err_str = "Standard deviation not of the correct " self._y = check_array_type(y) if isinstance(sigma, np.ndarray): - if len(sigma.shape) > 1: + if np.any(sigma<0): + raise InitializeError('No elements in numpy array of sigma values should be negative') + elif len(sigma.shape) > 1: if 1 in sigma.shape: sigma = sigma.flatten() @@ -169,6 +171,11 @@ def __init__(self, y, sigma=1.0): raise InitializeError(err_str + "size") elif sigma is None or sigma == 1.0: self._sigma = np.ones(self._y.shape) + elif isinstance(sigma, (int, float)): + if sigma <0: + raise InitializeError('Sigma should not be negative') + else: + self._sigma = sigma*np.ones(self._y.shape) else: raise InitializeError(err_str + "type") @@ -268,11 +275,13 @@ class Gamma(object): shape (a in latex equations) ''' - def __init__(self, y, shape=2.0): + def __init__(self, y, shape=2.0, weights=None): err_str = "Shape is not of the correct " self._y = check_array_type(y) if isinstance(shape, np.ndarray): - if len(shape.shape) > 1: + if np.any(shape<0): + raise InitializeError('No elements in numpy array of shape values should be negative') + elif len(shape.shape) > 1: if 1 in shape.shape: shape = shape.flatten() @@ -287,11 +296,27 @@ def __init__(self, y, shape=2.0): raise InitializeError(err_str + "size") elif shape is None or shape == 2.0: self._shape = 2*np.ones(self._y.shape) - elif isinstance(shape, (int, float)) and shape !=2: - self._shape = shape*np.ones(self._y.shape) + elif isinstance(shape, (int, float)): + if shape <0: + raise InitializeError('Shape should not be negative') + else: + self._shape = shape*np.ones(self._y.shape) else: raise InitializeError(err_str + "type") + + if weights is None: + self._numVar = 0 + self._w = np.ones(self._y.shape) + else: + self._w = check_array_type(weights) + + if len(self._w.shape) > 1: + if self._w.shape[1] == 1: + self._w = self._w.flatten() + assert self._y.shape == self._w.shape, \ + "Input weight not of the same size as y" + self.loss(self._y) def loss(self, yhat): @@ -383,7 +408,24 @@ def residual(self, yhat): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - return self._y - yhat + return self._weightedResidual(yhat) + + def _weightedResidual(self, yhat): + ''' + Find the weighted residuals. + ''' + # resid = self._y - yhat + # print "In weighted resid" + # print self._y.shape + if len(yhat.shape) > 1: + if 1 in yhat.shape: + resid = self._y - yhat.ravel() + else: + resid = self._y - yhat + else: + resid = self._y - yhat + + return resid * self._w class Poisson(object): ''' @@ -395,8 +437,21 @@ class Poisson(object): observation ''' - def __init__(self, y): + def __init__(self, y, weights=None): self._y = check_array_type(y) + if weights is None: + self._numVar = 0 + self._w = np.ones(self._y.shape) + else: + self._w = check_array_type(weights) + + if len(self._w.shape) > 1: + if self._w.shape[1] == 1: + self._w = self._w.flatten() + + assert self._y.shape == self._w.shape, \ + "Input weight not of the same size as y" + self.loss(self._y) def loss(self, yhat): @@ -473,7 +528,24 @@ def residual(self, yhat): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - return self._y - yhat + return self._weightedResidual(yhat) + + def _weightedResidual(self, yhat): + ''' + Find the weighted residuals. + ''' + # resid = self._y - yhat + # print "In weighted resid" + # print self._y.shape + if len(yhat.shape) > 1: + if 1 in yhat.shape: + resid = self._y - yhat.ravel() + else: + resid = self._y - yhat + else: + resid = self._y - yhat + + return resid * self._w class NegBinom(object): ''' @@ -487,11 +559,13 @@ class NegBinom(object): Overdispersion parameter (k=mean+mean(mean/variance)) ''' - def __init__(self, y, k=1.0): + def __init__(self, y, k=1.0, weights=None): err_str = "k (the overdispersion parameter) is not of the correct " self._y = check_array_type(y) if isinstance(k, np.ndarray): - if len(k.shape) > 1: + if np.any(k<0): + raise InitializeError('No elements in numpy array of shape values should be negative') + elif len(k.shape) > 1: if 1 in k.shape: k = k.flatten() @@ -506,10 +580,26 @@ def __init__(self, y, k=1.0): raise InitializeError(err_str + "size") elif k is None or k == 1.0: self._k = np.ones(self._y.shape) - elif isinstance(k, (int, float)) and k >1: - self._k = k*np.ones(self._y.shape) + elif isinstance(k, (int, float)): + if k <0: + raise InitializeError('k should not be negative') + else: + self._k = k*np.ones(self._y.shape) else: raise InitializeError(err_str + "type") + + if weights is None: + self._numVar = 0 + self._w = np.ones(self._y.shape) + else: + self._w = check_array_type(weights) + + if len(self._w.shape) > 1: + if self._w.shape[1] == 1: + self._w = self._w.flatten() + + assert self._y.shape == self._w.shape, \ + "Input weight not of the same size as y" self.loss(self._y) @@ -612,4 +702,21 @@ def residual(self, yhat): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - return self._y - yhat + return self._weightedResidual(yhat) + + def _weightedResidual(self, yhat): + ''' + Find the weighted residuals. + ''' + # resid = self._y - yhat + # print "In weighted resid" + # print self._y.shape + if len(yhat.shape) > 1: + if 1 in yhat.shape: + resid = self._y - yhat.ravel() + else: + resid = self._y - yhat + else: + resid = self._y - yhat + + return resid * self._w diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index c371107c..cf590fee 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -82,13 +82,13 @@ def __repr__(self): return "GammaLoss" + self._get_model_str() def _setLossType(self): - return Gamma(self._y,self._shape) + return Gamma(self._y,self._shape,self._state_weight)) class PoissonLoss(BaseLoss): ''' Realizations from a Poisson distribution ''' - def __init__(self, theta, ode, x0, t0, t, y, state_name, + def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, target_param=None, target_state=None): super(PoissonLoss, self).__init__(theta, ode, x0, t0, t, y, state_name, None, target_param, target_state) @@ -96,7 +96,7 @@ def __repr__(self): return "PoissonLoss" + self._get_model_str() def _setLossType(self): - return Poisson(self._y) + return Poisson(self._y,self._state_weight)) class NegBinomLoss(BaseLoss): ''' @@ -113,4 +113,4 @@ def __repr__(self): return "NegBinomLoss" + self._get_model_str() def _setLossType(self): - return NegBinom(self._y,self._k) + return NegBinom(self._y,self._k,self._state_weight) From 793219dae06757ecc01af278934f9d5d67ab5d21 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Mon, 22 Jun 2020 14:26:40 +0100 Subject: [PATCH 005/188] Weighted residuals taken account of in gamma, nomral, Posson, nb loss functions. --- notebooks/Loss function Calculations.ipynb | 1055 ++++++++++++++++---- pygom/loss/loss_type.py | 410 +++----- pygom/loss/ode_loss.py | 58 +- 3 files changed, 1050 insertions(+), 473 deletions(-) diff --git a/notebooks/Loss function Calculations.ipynb b/notebooks/Loss function Calculations.ipynb index b870605a..ab5c2eb3 100644 --- a/notebooks/Loss function Calculations.ipynb +++ b/notebooks/Loss function Calculations.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -17,299 +17,441 @@ "import numpy as np" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Negative binomial loss class ##" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Logliklihood function" - ] - }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - " x, mu , k = sym.symbols('x mu k')" + "# Setting up residual for replacement, so that weighted residuals can be applied in code.\n", + "r ,y, yhat = sym.symbols('r,y yhat')\n", + "r_eq = y-yhat\n", + "minus_r_eq = yhat-y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Probability mass function (PMF) of negative binomial ${p}(x; \\mu,k) = \\frac{\\Gamma \\left(k+x\\right)}{\\Gamma \\left(k\\right)x!}(\\frac{k}{k+\\mu})^{k}(\\frac{\\mu}{k+\\mu})^{x}$ \n", - "This definition of the negative binomial distribution is often refered to as negative binomial 2. This parameterisation takes the mean (usually refered as $\\mu$, but in pygom $\\hat{y}$ as we are looking at a prediction) and $k$ (an overdispersion parameter). The variance = $\\mu+\\frac{\\mu^2}{k}$, some notation uses $\\alpha$, ($k=\\alpha^{-1}$). \n", - "See Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press." + "# 1. Square Loss class" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle \\frac{\\left(\\frac{k}{k + \\mu}\\right)^{k} \\left(\\frac{\\mu}{k + \\mu}\\right)^{x} \\Gamma\\left(k + x\\right)}{x! \\Gamma\\left(k\\right)}$" + "$\\displaystyle \\left(y - \\hat{y}\\right)^{2}$" ], "text/plain": [ - "(k/(k + mu))**k*(mu/(k + mu))**x*gamma(k + x)/(factorial(x)*gamma(k))" + "(y - yhat)**2" ] }, - "execution_count": 3, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nbpmf = (sym.gamma(k+x)/(sym.gamma(k)*sym.factorial(x)))*(k/(k+mu))**k*(mu/(k+mu))**x\n", - "nbpmf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Due to the this PMF containing gamma functions and a factorial it is easier to calculate the sum of it's logged terms than to log it as one object (you end up with infinities otherwise). " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "output_type": "display_data" + }, { "data": { + "text/latex": [ + "$\\displaystyle r^{2}$" + ], "text/plain": [ - "((k/(k + mu))**k, (mu/(k + mu))**x, 1/factorial(x), 1/gamma(k), gamma(k + x))" + "r**2" ] }, - "execution_count": 4, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle r^{2}$" + ], + "text/plain": [ + "r**2" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "nbpmf.args" + "square_loss=(y-yhat)**2\n", + "display(square_loss,square_loss.subs(r_eq,r),square_loss.subs(minus_r_eq,-r))" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle k \\left(\\log{\\left(k \\right)} - \\log{\\left(k + \\mu \\right)}\\right) + x \\left(\\log{\\left(\\mu \\right)} - \\log{\\left(k + \\mu \\right)}\\right) - \\log{\\left(x! \\right)} - \\log{\\left(\\Gamma\\left(k\\right) \\right)} + \\Gamma\\left(k + x\\right)$" + "$\\displaystyle - 2 y + 2 \\hat{y}$" ], "text/plain": [ - "k*(log(k) - log(k + mu)) + x*(log(mu) - log(k + mu)) - log(factorial(x)) - log(gamma(k)) + gamma(k + x)" + "-2*y + 2*yhat" ] }, - "execution_count": 5, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle - 2 y + 2 \\hat{y}$" + ], + "text/plain": [ + "-2*y + 2*yhat" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle - 2 y + 2 \\hat{y}$" + ], + "text/plain": [ + "-2*y + 2*yhat" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "logpmf_p1= k*(sym.ln(k)-sym.ln(k+mu))\n", - "logpmf_p2= x*(sym.ln(mu)-sym.ln(k+mu))\n", - "logpmf_p3= -sym.ln(sym.factorial(x))\n", - "logpmf_p4= -sym.ln(sym.gamma(k))\n", - "logpmf_p5= sym.gamma(k+x)\n", - "logpmf = logpmf_p1+logpmf_p2+logpmf_p3+logpmf_p4+logpmf_p5\n", - "logpmf" + "first_derv = sym.diff(square_loss,yhat).simplify()\n", + "display(first_derv,first_derv.simplify().subs(r_eq,r),first_derv.subs(minus_r_eq,-r))" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { + "text/latex": [ + "$\\displaystyle 2$" + ], "text/plain": [ - "(-log(factorial(x)),\n", - " -log(gamma(k)),\n", - " k*(log(k) - log(k + mu)),\n", - " x*(log(mu) - log(k + mu)),\n", - " gamma(k + x))" + "2" ] }, - "execution_count": 6, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "logpmf.args" + "scnd_derv= sym.diff(first_derv,yhat).simplify()\n", + "scnd_derv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Normal Loss\n", + "## 2.1 Logliklihood function" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "from scipy.special import gammaln\n", - "def nb2logpmf(x, mu,k):\n", - " '''\n", - " The log probability mass function (pmf) of Negative Binomial 2 distribution. \n", - "\n", - " Parameters\n", - " ----------\n", - " x: array like observation.\n", - " mu: mean or prediction.\n", - " k: overdispersion parameter (variance = mean(1+mean/k)). Note some notation uses $\\alpha$, ($k=\\alpha^{-1}$).\n", - " See Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press.\n", - "\n", - " Returns\n", - " -------\n", - " log pmf:\n", - " math:`\\\\mathcal\\\\ln({p}(x; \\\\mu,k)) = \\\\ln(\\\\frac{\\\\Gamma \\\\left(k+x\\\\right)}{\\\\Gamma \\\\left(k\\\\right)x!}(\\\\frac{k}{k+\\\\mu})^{k}(\\\\frac{\\\\mu}{k+\\\\mu})^{x})`\n", - "\n", - " '''\n", - " # note that we input k the overdispersion parameter here\n", - "\n", - "\n", - " logpmf_p1= -gammaln(x+1) \n", - " logpmf_p2= -gammaln(k)\n", - " logpmf_p3= k*(np.log(k) - np.log(k + mu)) \n", - " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n", - " logpmf_p5= gammaln(k+x)\n", - " logpmf = logpmf_p1+logpmf_p2+logpmf_p3+logpmf_p4+logpmf_p5\n", - " return logpmf" + "sigma, pi = sym.symbols('sigma pi')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Our loss function is the negative of the logliklihood above." + "Probability density function (PDF) of the normal distribution ${p}(y; \\hat{y},k) = \\frac{1}{\\sqrt {2 \\pi} \\sigma}e^{-\\frac {(y-\\hat{y}) ^{2}}{2 \\sigma ^{2}}$.\n", + "See Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 129–130). Princeton University Press." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\sqrt{2} e^{- \\frac{\\left(y - \\hat{y}\\right)^{2}}{2 \\sigma^{2}}}}{2 \\sqrt{\\pi} \\sigma}$" + ], + "text/plain": [ + "sqrt(2)*exp(-(y - yhat)**2/(2*sigma**2))/(2*sqrt(pi)*sigma)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\sqrt{2} e^{- \\frac{r^{2}}{2 \\sigma^{2}}}}{2 \\sqrt{\\pi} \\sigma}$" + ], + "text/plain": [ + "sqrt(2)*exp(-r**2/(2*sigma**2))/(2*sqrt(pi)*sigma)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\sqrt{2} e^{- \\frac{r^{2}}{2 \\sigma^{2}}}}{2 \\sqrt{\\pi} \\sigma}$" + ], + "text/plain": [ + "sqrt(2)*exp(-r**2/(2*sigma**2))/(2*sqrt(pi)*sigma)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "negloglikli=-logpmf" + "normpdf=1/(sym.sqrt(2*pi)*sigma) * sym.E**(-((y-yhat)**2/(2*sigma**2)))\n", + "display(normpdf,normpdf.subs(r_eq,r),normpdf.subs(minus_r_eq,-r))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 41, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1/2, sqrt(2), 1/sqrt(pi), 1/sigma, exp(-(y - yhat)**2/(2*sigma**2)))" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "1st derivative of -Loglikelihood of negative binomial loss with respect to $\\mu$." + "normpdf.args" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[-log(2),\n", + " log(2)/2,\n", + " -log(pi)/2,\n", + " log(1/sigma),\n", + " log(exp(-(y - yhat)**2/(2*sigma**2)))]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Our loss function is the negative of the logliklihood.\n", + "loglike_args= []\n", + "for arg in normpdf.args:\n", + " loglike_args.append(sym.ln(arg).simplify())\n", + " \n", + "loglike_args" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle \\frac{k \\left(\\mu - x\\right)}{\\mu \\left(k + \\mu\\right)}$" + "$\\displaystyle - \\frac{r^{2}}{2 \\sigma^{2}}$" ], "text/plain": [ - "k*(mu - x)/(mu*(k + mu))" + "-r**2/(2*sigma**2)" ] }, - "execution_count": 9, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nbfirstderv= sym.diff(negloglikli,mu).simplify()\n", - "nbfirstderv" + "logpdf_p1= loglike_args[0]\n", + "logpdf_p2= loglike_args[1]\n", + "logpdf_p3= loglike_args[2]\n", + "logpdf_p4= loglike_args[3]\n", + "logpdf_p5= -(y - yhat)**2/(2*sigma**2)\n", + "# logpdf_p5 has residual in:\n", + "logpdf_p5_alt= -(r)**2/(2*sigma**2)\n", + "logpdf_p5_alt" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 50, "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{\\log{\\left(\\pi \\right)}}{2} + \\log{\\left(\\frac{1}{\\sigma} \\right)} - \\frac{\\log{\\left(2 \\right)}}{2} - \\frac{\\left(y - \\hat{y}\\right)^{2}}{2 \\sigma^{2}}$" + ], + "text/plain": [ + "-log(pi)/2 + log(1/sigma) - log(2)/2 - (y - yhat)**2/(2*sigma**2)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{r^{2}}{2 \\sigma^{2}} - \\frac{\\log{\\left(\\pi \\right)}}{2} + \\log{\\left(\\frac{1}{\\sigma} \\right)} - \\frac{\\log{\\left(2 \\right)}}{2}$" + ], + "text/plain": [ + "-r**2/(2*sigma**2) - log(pi)/2 + log(1/sigma) - log(2)/2" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "1st derivative of -Loglikelihood of negative binomial loss with respect to yhat: \n", - "$\\frac{k(\\mu-y)}{\\mu(k + \\mu)} $" + "logpdf = logpdf_p1+logpdf_p2+logpdf_p3+logpdf_p4+logpdf_p5\n", + "logpdf_alt = logpdf_p1+logpdf_p2+logpdf_p3+logpdf_p4+logpdf_p5_alt\n", + "display(logpdf,logpdf_alt)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle - \\frac{k \\left(- \\mu \\left(k + \\mu\\right) + \\mu \\left(\\mu - x\\right) + \\left(k + \\mu\\right) \\left(\\mu - x\\right)\\right)}{\\mu^{2} \\left(k + \\mu\\right)^{2}}$" + "$\\displaystyle \\frac{\\log{\\left(\\pi \\right)}}{2} - \\log{\\left(\\frac{1}{\\sigma} \\right)} + \\frac{\\log{\\left(2 \\right)}}{2} + \\frac{\\left(y - \\hat{y}\\right)^{2}}{2 \\sigma^{2}}$" ], "text/plain": [ - "-k*(-mu*(k + mu) + mu*(mu - x) + (k + mu)*(mu - x))/(mu**2*(k + mu)**2)" + "log(pi)/2 - log(1/sigma) + log(2)/2 + (y - yhat)**2/(2*sigma**2)" ] }, - "execution_count": 10, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nbsecderv = sym.diff(nbfirstderv,mu).simplify()\n", - "nbsecderv.simplify()" + "normloss=-logpdf\n", + "normloss" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 54, "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{- y + \\hat{y}}{\\sigma^{2}}$" + ], + "text/plain": [ + "(-y + yhat)/sigma**2" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{r}{\\sigma^{2}}$" + ], + "text/plain": [ + "-r/sigma**2" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{r}{\\sigma^{2}}$" + ], + "text/plain": [ + "-r/sigma**2" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "2nd derivative of -Loglikelihood of negative binomial loss with respect to yhat: \n", - "$\\frac{k(\\mu(k + \\mu) + \\mu(y -\\mu) + (k + \\mu)(y - \\mu)}{\\mu^{2}(k + \\mu)^{2}} $" + "first_derv = sym.diff(normloss,yhat).simplify()\n", + "display(first_derv,first_derv.subs(r_eq,r),first_derv.subs(minus_r_eq,-r))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { + "text/latex": [ + "$\\displaystyle \\frac{1}{\\sigma^{2}}$" + ], "text/plain": [ - "(k, mu**(-2), (k + mu)**(-2), mu*(k + mu) - mu*(mu - x) - (k + mu)*(mu - x))" + "sigma**(-2)" ] }, - "execution_count": 11, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nbsecderv.args" + "scnd_derv = sym.diff(first_derv,yhat).simplify()\n", + "scnd_derv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# 2. Gamma loss class in terms of mean and shape" + "# 3. Gamma loss class in terms of mean and shape" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ - " a, s, x, mu= sym.symbols('a s x mu')" + " a, s, y, yhat= sym.symbols('a s y yhat')" ] }, { @@ -323,40 +465,40 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle \\frac{s^{- a} x^{a - 1} e^{- \\frac{x}{s}}}{\\Gamma\\left(a\\right)}$" + "$\\displaystyle \\frac{s^{- a} y^{a - 1} e^{- \\frac{y}{s}}}{\\Gamma\\left(a\\right)}$" ], "text/plain": [ - "s**(-a)*x**(a - 1)*exp(-x/s)/gamma(a)" + "s**(-a)*y**(a - 1)*exp(-y/s)/gamma(a)" ] }, - "execution_count": 13, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pdf_gamma = 1/(s**a*sym.gamma(a))*(x**(a-1)*sym.E**(-x/s))\n", + "pdf_gamma = 1/(s**a*sym.gamma(a))*(y**(a-1)*sym.E**(-y/s))\n", "pdf_gamma" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(s**(-a), x**(a - 1), 1/gamma(a), exp(-x/s))" + "(s**(-a), y**(a - 1), 1/gamma(a), exp(-y/s))" ] }, - "execution_count": 14, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -367,69 +509,69 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle - a \\log{\\left(s \\right)} + \\left(a - 1\\right) \\log{\\left(x \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)} - \\frac{x}{s}$" + "$\\displaystyle - a \\log{\\left(s \\right)} + \\left(a - 1\\right) \\log{\\left(y \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)} - \\frac{y}{s}$" ], "text/plain": [ - "-a*log(s) + (a - 1)*log(x) - log(gamma(a)) - x/s" + "-a*log(s) + (a - 1)*log(y) - log(gamma(a)) - y/s" ] }, - "execution_count": 15, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_pdf_gamma_p1 = -a*sym.ln(s)\n", - "log_pdf_gamma_p2 = (a-1)*sym.ln(x)\n", + "log_pdf_gamma_p2 = (a-1)*sym.ln(y)\n", "log_pdf_gamma_p3 = -sym.ln(sym.gamma(a))\n", - "log_pdf_gamma_p4 = -x/s\n", + "log_pdf_gamma_p4 = -y/s\n", "log_pdf_gamma= log_pdf_gamma_p1+log_pdf_gamma_p2+log_pdf_gamma_p3+log_pdf_gamma_p4\n", "log_pdf_gamma" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle - a \\log{\\left(\\frac{\\mu}{a} \\right)} - \\frac{a x}{\\mu} + \\left(a - 1\\right) \\log{\\left(x \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)}$" + "$\\displaystyle - \\frac{a y}{\\hat{y}} - a \\log{\\left(\\frac{\\hat{y}}{a} \\right)} + \\left(a - 1\\right) \\log{\\left(y \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)}$" ], "text/plain": [ - "-a*log(mu/a) - a*x/mu + (a - 1)*log(x) - log(gamma(a))" + "-a*y/yhat - a*log(yhat/a) + (a - 1)*log(y) - log(gamma(a))" ] }, - "execution_count": 16, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s_in_terms_mu_a = mu/a\n", + "s_in_terms_mu_a = yhat/a\n", "log_pdf_mu_a_gamma = log_pdf_gamma.subs(s,s_in_terms_mu_a) \n", "log_pdf_mu_a_gamma" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(-log(gamma(a)), (a - 1)*log(x), -a*log(mu/a), -a*x/mu)" + "(-log(gamma(a)), (a - 1)*log(y), -a*log(yhat/a), -a*y/yhat)" ] }, - "execution_count": 17, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -440,14 +582,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "- a \\log{\\left(\\frac{\\mu}{a} \\right)} - \\frac{a x}{\\mu} + \\left(a - 1\\right) \\log{\\left(x \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)}\n" + "- \\frac{a y}{\\hat{y}} - a \\log{\\left(\\frac{\\hat{y}}{a} \\right)} + \\left(a - 1\\right) \\log{\\left(y \\right)} - \\log{\\left(\\Gamma\\left(a\\right) \\right)}\n" ] } ], @@ -457,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -491,19 +633,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle a \\log{\\left(\\frac{\\mu}{a} \\right)} + \\frac{a x}{\\mu} - \\left(a - 1\\right) \\log{\\left(x \\right)} + \\log{\\left(\\Gamma\\left(a\\right) \\right)}$" + "$\\displaystyle \\frac{a y}{\\hat{y}} + a \\log{\\left(\\frac{\\hat{y}}{a} \\right)} - \\left(a - 1\\right) \\log{\\left(y \\right)} + \\log{\\left(\\Gamma\\left(a\\right) \\right)}$" ], "text/plain": [ - "a*log(mu/a) + a*x/mu - (a - 1)*log(x) + log(gamma(a))" + "a*y/yhat + a*log(yhat/a) - (a - 1)*log(y) + log(gamma(a))" ] }, - "execution_count": 20, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -522,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 67, "metadata": { "scrolled": true }, @@ -530,10 +672,10 @@ { "data": { "text/latex": [ - "$\\displaystyle \\frac{a}{\\mu} - \\frac{a x}{\\mu^{2}}$" + "$\\displaystyle - \\frac{a y}{\\hat{y}^{2}} + \\frac{a}{\\hat{y}}$" ], "text/plain": [ - "a/mu - a*x/mu**2" + "-a*y/yhat**2 + a/yhat" ] }, "metadata": {}, @@ -542,10 +684,10 @@ { "data": { "text/latex": [ - "$\\displaystyle \\frac{a \\left(\\mu - x\\right)}{\\mu^{2}}$" + "$\\displaystyle \\frac{a \\left(- y + \\hat{y}\\right)}{\\hat{y}^{2}}$" ], "text/plain": [ - "a*(mu - x)/mu**2" + "a*(-y + yhat)/yhat**2" ] }, "metadata": {}, @@ -553,19 +695,43 @@ } ], "source": [ - "gammafirstderv= sym.diff(negloglikli_gamma_mu_a,mu)\n", + "gammafirstderv= sym.diff(negloglikli_gamma_mu_a,yhat)\n", "display(gammafirstderv,gammafirstderv.simplify())" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 69, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{a r}{\\hat{y}^{2}}$" + ], + "text/plain": [ + "-a*r/yhat**2" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gammafirstderv=gammafirstderv.simplify()\n", + "gammafirstderv.subs(r_eq,r)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ "\\frac{a \\left(\\mu - x\\right)}{\\mu^{2}}\n" ] } @@ -583,16 +749,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle \\frac{a \\left(- \\mu + 2 x\\right)}{\\mu^{3}}$" + "$\\displaystyle \\frac{a \\left(2 y - \\hat{y}\\right)}{\\hat{y}^{3}}$" ], "text/plain": [ - "a*(-mu + 2*x)/mu**3" + "a*(2*y - yhat)/yhat**3" ] }, "metadata": {}, @@ -601,10 +767,10 @@ { "data": { "text/latex": [ - "$\\displaystyle \\frac{a \\left(- \\mu + 2 x\\right)}{\\mu^{3}}$" + "$\\displaystyle \\frac{a \\left(2 y - \\hat{y}\\right)}{\\hat{y}^{3}}$" ], "text/plain": [ - "a*(-mu + 2*x)/mu**3" + "a*(2*y - yhat)/yhat**3" ] }, "metadata": {}, @@ -612,10 +778,33 @@ } ], "source": [ - "gammasecderv = sym.diff(gammafirstderv,mu).simplify()\n", + "gammasecderv = sym.diff(gammafirstderv,yhat).simplify()\n", "display(gammasecderv,gammasecderv.simplify())" ] }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{a \\left(2 y - \\hat{y}\\right)}{\\hat{y}^{3}}$" + ], + "text/plain": [ + "a*(2*y - yhat)/yhat**3" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gammasecderv.subs(r_eq,r)" + ] + }, { "cell_type": "code", "execution_count": 24, @@ -632,6 +821,524 @@ "source": [ "sym.print_latex(gammasecderv)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Poisson" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probability density function (PDF) of the normal distribution ${p}(n;\\lambda) = \\frac{e^{-\\lambda}\\lambda^n}{n!}$.\n", + "See Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 1222-123). Princeton University Press." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\hat{y}^{y} e^{- \\hat{y}}}{y!}$" + ], + "text/plain": [ + "yhat**y*exp(-yhat)/factorial(y)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "poissonpmf = (sym.E**-yhat*yhat**y)/sym.factorial(y)\n", + "poissonpmf " + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(yhat**y, 1/factorial(y), exp(-yhat))" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "poissonpmf.args" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\hat{y} + \\log{\\left(\\hat{y}^{y} \\right)} - y!$" + ], + "text/plain": [ + "-yhat + log(yhat**y) - factorial(y)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logpmf_p1 = sym.ln(yhat**y)\n", + "logpmf_p2 = -yhat\n", + "logpmf_p3 = -sym.factorial(y)\n", + "logpmf = logpmf_p1+logpmf_p2+logpmf_p3\n", + "logpmf" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "possonloss=-logpmf" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{y}{\\hat{y}} + 1$" + ], + "text/plain": [ + "-y/yhat + 1" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{- y + \\hat{y}}{\\hat{y}}$" + ], + "text/plain": [ + "(-y + yhat)/yhat" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "possonfirstderv= sym.diff(possonloss,yhat)\n", + "display(possonfirstderv,possonfirstderv.simplify())" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{r}{\\hat{y}}$" + ], + "text/plain": [ + "-r/yhat" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "possonfirstderv.simplify().subs(r_eq,r)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{y}{\\hat{y}^{2}}$" + ], + "text/plain": [ + "y/yhat**2" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{y}{\\hat{y}^{2}}$" + ], + "text/plain": [ + "y/yhat**2" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "possonsecderv= sym.diff(possonfirstderv,yhat)\n", + "display(possonsecderv,possonsecderv.simplify())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Negative binomial loss class ##" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "k = sym.symbols('k')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Probability mass function (PMF) of negative binomial distribution ${p}(x; \\mu,k) = \\frac{\\Gamma \\left(k+x\\right)}{\\Gamma \\left(k\\right)x!}(\\frac{k}{k+\\mu})^{k}(\\frac{\\mu}{k+\\mu})^{x}$ \n", + "This definition of the negative binomial distribution is often refered to as negative binomial 2. This parameterisation takes the mean (usually refered as $\\mu$, but in pygom $\\hat{y}$ as we are looking at a prediction) and $k$ (an overdispersion parameter). The variance = $\\mu+\\frac{\\mu^2}{k}$, some notation uses $\\alpha$, ($k=\\alpha^{-1}$). \n", + "See Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\left(\\frac{k}{k + \\hat{y}}\\right)^{k} \\left(\\frac{\\hat{y}}{k + \\hat{y}}\\right)^{y} \\Gamma\\left(k + y\\right)}{y! \\Gamma\\left(k\\right)}$" + ], + "text/plain": [ + "(k/(k + yhat))**k*(yhat/(k + yhat))**y*gamma(k + y)/(factorial(y)*gamma(k))" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbpmf = (sym.gamma(k+y)/(sym.gamma(k)*sym.factorial(y)))*(k/(k+yhat))**k*(yhat/(k+yhat))**y\n", + "nbpmf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due to the this PMF containing gamma functions and a factorial it is easier to calculate the sum of it's logged terms than to log it as one object (you end up with infinities otherwise). " + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((k/(k + yhat))**k,\n", + " (yhat/(k + yhat))**y,\n", + " 1/factorial(y),\n", + " 1/gamma(k),\n", + " gamma(k + y))" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbpmf.args" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle k \\left(\\log{\\left(k \\right)} - \\log{\\left(k + \\hat{y} \\right)}\\right) + y \\left(\\log{\\left(\\hat{y} \\right)} - \\log{\\left(k + \\hat{y} \\right)}\\right) - \\log{\\left(y! \\right)} - \\log{\\left(\\Gamma\\left(k\\right) \\right)} + \\Gamma\\left(k + y\\right)$" + ], + "text/plain": [ + "k*(log(k) - log(k + yhat)) + y*(log(yhat) - log(k + yhat)) - log(factorial(y)) - log(gamma(k)) + gamma(k + y)" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logpmf_p1= k*(sym.ln(k)-sym.ln(k+yhat))\n", + "logpmf_p2= y*(sym.ln(yhat)-sym.ln(k+yhat))\n", + "logpmf_p3= -sym.ln(sym.factorial(y))\n", + "logpmf_p4= -sym.ln(sym.gamma(k))\n", + "logpmf_p5= sym.gamma(k+y)\n", + "logpmf = logpmf_p1+logpmf_p2+logpmf_p3+logpmf_p4+logpmf_p5\n", + "logpmf" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-log(factorial(y)),\n", + " -log(gamma(k)),\n", + " k*(log(k) - log(k + yhat)),\n", + " y*(log(yhat) - log(k + yhat)),\n", + " gamma(k + y))" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logpmf.args" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.special import gammaln\n", + "def nb2logpmf(x, mu,k):\n", + " '''\n", + " The log probability mass function (pmf) of Negative Binomial 2 distribution. \n", + "\n", + " Parameters\n", + " ----------\n", + " x: array like observation.\n", + " mu: mean or prediction.\n", + " k: overdispersion parameter (variance = mean(1+mean/k)). Note some notation uses $\\alpha$, ($k=\\alpha^{-1}$).\n", + " See Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press.\n", + "\n", + " Returns\n", + " -------\n", + " log pmf:\n", + " math:`\\\\mathcal\\\\ln({p}(x; \\\\mu,k)) = \\\\ln(\\\\frac{\\\\Gamma \\\\left(k+x\\\\right)}{\\\\Gamma \\\\left(k\\\\right)x!}(\\\\frac{k}{k+\\\\mu})^{k}(\\\\frac{\\\\mu}{k+\\\\mu})^{x})`\n", + "\n", + " '''\n", + " # note that we input k the overdispersion parameter here\n", + "\n", + "\n", + " logpmf_p1= -gammaln(x+1) \n", + " logpmf_p2= -gammaln(k)\n", + " logpmf_p3= k*(np.log(k) - np.log(k + mu)) \n", + " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n", + " logpmf_p5= gammaln(k+x)\n", + " logpmf = logpmf_p1+logpmf_p2+logpmf_p3+logpmf_p4+logpmf_p5\n", + " return logpmf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our loss function is the negative of the logliklihood above." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "negloglikli=-logpmf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1st derivative of -Loglikelihood of negative binomial loss with respect to $\\mu$." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{k \\left(- y + \\hat{y}\\right)}{\\hat{y} \\left(k + \\hat{y}\\right)}$" + ], + "text/plain": [ + "k*(-y + yhat)/(yhat*(k + yhat))" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbfirstderv= sym.diff(negloglikli,yhat).simplify()\n", + "nbfirstderv" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{k r}{\\hat{y} \\left(k + \\hat{y}\\right)}$" + ], + "text/plain": [ + "-k*r/(yhat*(k + yhat))" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbfirstderv.subs(r_eq,r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1st derivative of -Loglikelihood of negative binomial loss with respect to yhat: \n", + "$\\frac{k(\\mu-y)}{\\mu(k + \\mu)} $" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{k \\left(\\hat{y} \\left(k + \\hat{y}\\right) + \\hat{y} \\left(y - \\hat{y}\\right) + \\left(k + \\hat{y}\\right) \\left(y - \\hat{y}\\right)\\right)}{\\hat{y}^{2} \\left(k + \\hat{y}\\right)^{2}}$" + ], + "text/plain": [ + "k*(yhat*(k + yhat) + yhat*(y - yhat) + (k + yhat)*(y - yhat))/(yhat**2*(k + yhat)**2)" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbsecderv = sym.diff(nbfirstderv,yhat).simplify()\n", + "nbsecderv.simplify()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2nd derivative of -Loglikelihood of negative binomial loss with respect to yhat: \n", + "$\\frac{k(\\mu(k + \\mu) + \\mu(y -\\mu) + (k + \\mu)(y - \\mu)}{\\mu^{2}(k + \\mu)^{2}} $" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{k \\left(r \\hat{y} + r \\left(k + \\hat{y}\\right) + \\hat{y} \\left(k + \\hat{y}\\right)\\right)}{\\hat{y}^{2} \\left(k + \\hat{y}\\right)^{2}}$" + ], + "text/plain": [ + "k*(r*yhat + r*(k + yhat) + yhat*(k + yhat))/(yhat**2*(k + yhat)**2)" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbsecderv_alt=nbsecderv.simplify().subs(r_eq,r)\n", + "nbsecderv_alt" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(k, yhat**(-2), (k + yhat)**(-2), r*yhat + r*(k + yhat) + yhat*(k + yhat))" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbsecderv_alt.args" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -650,7 +1357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.1" } }, "nbformat": 4, diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index d0ec6a95..214a6662 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -26,36 +26,88 @@ class InputError(Exception): ''' pass -class Square(object): +class baseloss_type(object): ''' - Square loss object + This baseloss_type class provides common feature to be inherited by the + loss type objects, such as Square, Normal , etc. Parameters ---------- y: array like observations + state_weight: array like + weight for the observations ''' - def __init__(self, y, weights=None): + # There may be some some overlapp with these checks on weights and y within + # the base loss class object in base_loss.py, thus these checks maybe redundent (unless this + # module is being used without base_loss.py. + #Checks o n y: self._y = check_array_type(y) - self._numObv = len(self._y) - + #Checks on weights. if weights is None: self._numVar = 0 self._w = np.ones(self._y.shape) else: self._w = check_array_type(weights) - + if np.any(weights<0): + raise ValueError('No elements in numpy array of weights should be negative') if len(self._w.shape) > 1: - if self._w.shape[1] == 1: + if 1 in self._w.shape: self._w = self._w.flatten() - assert self._y.shape == self._w.shape, \ "Input weight not of the same size as y" + + def residual(self, yhat, weighting_applied = True): + ''' + Raw residuals returned if weighting_applied = False, else + the weighted residuals. + + Parameters + ---------- + yhat: array like + observation + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals returned. + + Returns + ------- + :math:`y_{i} - \\hat{y}_{i}` + + ''' + if isinstance(weighting_applied,bool)=False: + raise TypeError('weighting_applied should be boolean') + + if len(yhat.shape) > 1: + if 1 in yhat.shape: + resid = self._y - yhat.ravel() + else: + resid = self._y - yhat + else: + resid = self._y - yhat + if weighting_applied = True: + resid *= self._w + + return resid + + + +class Square(baseloss_type): + ''' + Square loss object + Parameters + ---------- + y: array like + observations + ''' + + def __init__(self, y, weights=None): + super().__init__(y, weights=None) self.loss(self._y) - def loss(self, yhat): + def loss(self, yhat, weighting_applied = True): ''' Loss under square loss. Not really saying much here @@ -63,14 +115,18 @@ def loss(self, yhat): ---------- yhat: array like observation + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. + Returns ------- - :math:`\\sum_{i=1}^{n} (\\hat{y} - y)^{2}` + :math:`\\sum_{i=1}^{n} (y-\\hat{y})^{2}` ''' - return (self.residual(yhat)**2).sum() + return (self.residual(yhat,weighting_applied)**2).sum() - def diff_loss(self, yhat): + def diff_loss(self, yhat,weighting_applied=True): ''' Derivative under square loss. Assuming that we are solving the minimization problem i.e. our objective function is the @@ -80,12 +136,15 @@ def diff_loss(self, yhat): ---------- yhat: array like observation + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns ------- :math:`-2(y_{i} - \\hat{y}_{i})` ''' - return -2*self.residual(yhat) + return -2*self.residual(yhat,weighting_applied)) def diff2Loss(self, yhat): ''' @@ -102,43 +161,12 @@ def diff2Loss(self, yhat): either a scalar, vector or matrix depending on the shape of of the input yhat ''' - return self._weightedResidual(2*np.ones(yhat.shape)) - - def residual(self, yhat): - ''' - Raw residuals if no weights was initialized, else - the weighted residuals - - Parameters - ---------- - yhat: array like - observation - - Returns - ------- - :math:`y_{i} - \\hat{y}_{i}` - - ''' - return self._weightedResidual(yhat) - - def _weightedResidual(self, yhat): - ''' - Find the weighted residuals. - ''' - # resid = self._y - yhat - # print "In weighted resid" - # print self._y.shape if len(yhat.shape) > 1: if 1 in yhat.shape: - resid = self._y - yhat.ravel() - else: - resid = self._y - yhat - else: - resid = self._y - yhat - - return resid * self._w + yhat = yhat.ravel() + return 2*np.ones(yhat.shape) -class Normal(object): +class Normal(baseloss_type): ''' Normal distribution loss object @@ -148,18 +176,16 @@ class Normal(object): observation sigma: float standard deviation - ''' - - def __init__(self, y, sigma=1.0): + ''' + def __init__(self, y, sigma=1.0,weight=None): + super().__init__(y, weights=None) err_str = "Standard deviation not of the correct " - self._y = check_array_type(y) if isinstance(sigma, np.ndarray): if np.any(sigma<0): - raise InitializeError('No elements in numpy array of sigma values should be negative') + raise ValueError('No elements in numpy array of sigma values should be negative') elif len(sigma.shape) > 1: if 1 in sigma.shape: sigma = sigma.flatten() - if y.shape == sigma.shape: self._sigma = sigma else: @@ -173,16 +199,16 @@ def __init__(self, y, sigma=1.0): self._sigma = np.ones(self._y.shape) elif isinstance(sigma, (int, float)): if sigma <0: - raise InitializeError('Sigma should not be negative') + raise ValueError('Sigma should not be negative') else: self._sigma = sigma*np.ones(self._y.shape) else: - raise InitializeError(err_str + "type") + raise TypeError(err_str + "type") self._sigma2 = self._sigma**2 self.loss(self._y) - def loss(self, yhat): + def loss(self, yhat,weighting_applied=True): ''' The loss under a normal distribution. Defined as the negative log-likelihood here. @@ -191,19 +217,28 @@ def loss(self, yhat): ---------- yhat: array like observation + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns ------- - negative log-likelihood, :math:`\\mathcal{L}(\\hat{y},y)` + negative log-likelihood, :math:`\\mathcal\\frac{1}{\\sqrt{2\\pi}\\sigma}e^{-\\frac{(y-\\hat{y})^{2}}{2\\sigma^{2}}` ''' - # note that we input the standard deviation here if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - return (-dnorm(self._y, yhat, self._sigma, True)).sum() + + # Calculate negative likelihood (depending on weighting of residuals). + logpdf_p1= -np.log(2) + logpdf_p2= np.log(2)/2 + logpdf_p3= -np.log(pi)/2 + logpdf_p4= np.log(1/self._sigma) + logpdf_p5_alt=-self.residual(yhat,weighting_applied)**2/(2*sigma**2) + return (-(logpdf_p1+logpdf_p2+logpdf_p3+logpdf_p4+logpdf_p5_alt)).sum() - def diff_loss(self, yhat): + def diff_loss(self, yhat,weighting_applied=True): ''' Derivative of the loss function which is :math:`\\sigma^{-1}(y - \\hat{y})` @@ -212,6 +247,9 @@ def diff_loss(self, yhat): ---------- yhat: array like observation + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns ------- @@ -219,7 +257,7 @@ def diff_loss(self, yhat): :math:`\\nabla \\mathcal{L}(\\hat{y}, y)` ''' - r = self.residual(yhat) + r = self.residual(yhat,weighting_applied) return -r/self._sigma2 def diff2Loss(self, yhat): @@ -236,34 +274,12 @@ def diff2Loss(self, yhat): s: array like inverse of the variance with shape = yhat.shape ''' - return np.ones(yhat.shape)/self._sigma2 - - def residual(self, yhat): - ''' - Residuals under a normal loss - - Parameters - ---------- - yhat: array like - observation - - Returns - ------- - r: array like - residuals - - ''' if len(yhat.shape) > 1: if 1 in yhat.shape: - resid = self._y - yhat.ravel() - else: - resid = self._y - yhat - else: - resid = self._y - yhat - - return resid + yhat = yhat.ravel() + return np.ones(yhat.shape)/self._sigma2 -class Gamma(object): +class Gamma(baseloss_type): ''' Gamma distribution loss object @@ -276,15 +292,14 @@ class Gamma(object): ''' def __init__(self, y, shape=2.0, weights=None): + super().__init__(y, weights=None) err_str = "Shape is not of the correct " - self._y = check_array_type(y) if isinstance(shape, np.ndarray): if np.any(shape<0): - raise InitializeError('No elements in numpy array of shape values should be negative') + raise ValueError('No elements in numpy array of shape values should be negative') elif len(shape.shape) > 1: if 1 in shape.shape: shape = shape.flatten() - if y.shape == shape.shape: self._shape = shape else: @@ -298,25 +313,11 @@ def __init__(self, y, shape=2.0, weights=None): self._shape = 2*np.ones(self._y.shape) elif isinstance(shape, (int, float)): if shape <0: - raise InitializeError('Shape should not be negative') + raise ValueError('Shape should not be negative') else: self._shape = shape*np.ones(self._y.shape) else: - raise InitializeError(err_str + "type") - - if weights is None: - self._numVar = 0 - self._w = np.ones(self._y.shape) - else: - self._w = check_array_type(weights) - - if len(self._w.shape) > 1: - if self._w.shape[1] == 1: - self._w = self._w.flatten() - - assert self._y.shape == self._w.shape, \ - "Input weight not of the same size as y" - + raise TypeError(err_str + "type") self.loss(self._y) def loss(self, yhat): @@ -343,7 +344,7 @@ def loss(self, yhat): return -gamma_mu_shape(x=self._y, mu=yhat,shape=self._shape,log=True).sum() - def diff_loss(self, yhat): + def diff_loss(self, yhat,weighting_applied=True): ''' Derivative of the loss function with respect to yhat which is See: @@ -353,19 +354,17 @@ def diff_loss(self, yhat): ---------- yhat: array like prediction + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. - Returns ------- first_deriv_yhat: array like :math:`\\mathcal\\frac{a \\left(\\hat{y} - y\\right)}{\\hat{y}^{2}}` ''' - if len(yhat.shape) > 1: - if 1 in yhat.shape: - yhat = yhat.ravel() - - return self._shape*(yhat-self._y)/yhat**2 + return self._shape*-self.residual(yhat,weighting_applied)/yhat**2 def diff2Loss(self, yhat): ''' @@ -385,49 +384,15 @@ def diff2Loss(self, yhat): :math:`\\mathcal\\frac{a \\left(- \\hat{y} + 2 y\\right)}{\\hat{y}^{3}}` ''' + if len(yhat.shape) > 1: + if 1 in yhat.shape: + yhat = yhat.ravel() y = self._y shape = self._shape return shape*(-yhat+2*y)/yhat**3 - - def residual(self, yhat): - ''' - Raw residuals - - Parameters - ---------- - yhat: array like - observation - - Returns - ------- - r: array like - residuals - - ''' - if len(yhat.shape) > 1: - if 1 in yhat.shape: - yhat = yhat.ravel() - return self._weightedResidual(yhat) - - def _weightedResidual(self, yhat): - ''' - Find the weighted residuals. - ''' - # resid = self._y - yhat - # print "In weighted resid" - # print self._y.shape - if len(yhat.shape) > 1: - if 1 in yhat.shape: - resid = self._y - yhat.ravel() - else: - resid = self._y - yhat - else: - resid = self._y - yhat - - return resid * self._w -class Poisson(object): +class Poisson(baseloss_type): ''' Poisson distribution loss object @@ -438,20 +403,7 @@ class Poisson(object): ''' def __init__(self, y, weights=None): - self._y = check_array_type(y) - if weights is None: - self._numVar = 0 - self._w = np.ones(self._y.shape) - else: - self._w = check_array_type(weights) - - if len(self._w.shape) > 1: - if self._w.shape[1] == 1: - self._w = self._w.flatten() - - assert self._y.shape == self._w.shape, \ - "Input weight not of the same size as y" - + super().__init__(y, weights=None) self.loss(self._y) def loss(self, yhat): @@ -475,7 +427,7 @@ def loss(self, yhat): # note that we input the standard deviation here return (-dpois(self._y, yhat, True)).sum() - def diff_loss(self, yhat): + def diff_loss(self, yhat,weighting_applied=True): ''' Derivative of the loss function, :math:`1 - y\\hat{y}^{-1}` @@ -483,16 +435,22 @@ def diff_loss(self, yhat): ---------- yhat: array like observation + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns ------- :math:`\\nabla \\mathcal{L}(\\hat{y},y)` ''' + if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - return 1 - self._y/yhat + + r = self.residual(yhat,weighting_applied) + return -r/yhat def diff2Loss(self, yhat): ''' @@ -508,46 +466,12 @@ def diff2Loss(self, yhat): s: array like :math:`\\frac{y}{\\hat{y}^{2}}` with shape = yhat.shape ''' - return self.y/(yhat**2) - - def residual(self, yhat): - ''' - Raw residuals - - Parameters - ---------- - yhat: array like - observation - - Returns - ------- - r: array like - residuals - - ''' if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - return self._weightedResidual(yhat) + return self._y/(yhat**2) - def _weightedResidual(self, yhat): - ''' - Find the weighted residuals. - ''' - # resid = self._y - yhat - # print "In weighted resid" - # print self._y.shape - if len(yhat.shape) > 1: - if 1 in yhat.shape: - resid = self._y - yhat.ravel() - else: - resid = self._y - yhat - else: - resid = self._y - yhat - - return resid * self._w - -class NegBinom(object): +class NegBinom(baseloss_type): ''' Negative Binomial distribution loss object @@ -560,15 +484,14 @@ class NegBinom(object): ''' def __init__(self, y, k=1.0, weights=None): + super().__init__(y, weights=None) err_str = "k (the overdispersion parameter) is not of the correct " - self._y = check_array_type(y) if isinstance(k, np.ndarray): if np.any(k<0): - raise InitializeError('No elements in numpy array of shape values should be negative') + raise ValueError('No elements in numpy array of shape values should be negative') elif len(k.shape) > 1: if 1 in k.shape: k = k.flatten() - if y.shape == k.shape: self._k = k else: @@ -582,25 +505,12 @@ def __init__(self, y, k=1.0, weights=None): self._k = np.ones(self._y.shape) elif isinstance(k, (int, float)): if k <0: - raise InitializeError('k should not be negative') + raise ValueError('k should not be negative') else: self._k = k*np.ones(self._y.shape) else: - raise InitializeError(err_str + "type") + raise TypeError(err_str + "type") - if weights is None: - self._numVar = 0 - self._w = np.ones(self._y.shape) - else: - self._w = check_array_type(weights) - - if len(self._w.shape) > 1: - if self._w.shape[1] == 1: - self._w = self._w.flatten() - - assert self._y.shape == self._w.shape, \ - "Input weight not of the same size as y" - self.loss(self._y) def loss(self, yhat): @@ -624,7 +534,7 @@ def loss(self, yhat): return (-dnbinom(self._y, mu=yhat,size=self._k,log=True)).sum() - def diff_loss(self, yhat): + def diff_loss(self, yhat,weighting_applied=True): ''' Derivative of the loss function with respect to yhat which is See: @@ -638,6 +548,10 @@ def diff_loss(self, yhat): k: array like observation + + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns ------- @@ -651,10 +565,11 @@ def diff_loss(self, yhat): y = self._y k = self._k - first_derivs_yhat = (k*(yhat-y))/(yhat*(k+yhat)) + r = self.residual(yhat,weighting_applied) + first_derivs_yhat = k*-r/(yhat*(k+yhat)) return first_derivs_yhat - def diff2Loss(self, yhat): + def diff2Loss(self, yhat,weighting_applied=True): ''' Twice derivative of the loss function with respect to yhat. See: @@ -668,6 +583,10 @@ def diff2Loss(self, yhat): k: array like observation + + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns ------- @@ -675,48 +594,15 @@ def diff2Loss(self, yhat): :math:`\\frac{k(\\hat{y}(k + \\hat{y}) + \\hat{y}(y -\\hat{y}) + (k + \\hat{y})(y - \\hat{y})}{\\hat{y}^{2}(k + \\hat{y})^{2}}` ''' + if len(yhat.shape) > 1: + if 1 in yhat.shape: + yhat = yhat.ravel() y = self._y k = self._k + r = self.residual(yhat,weighting_applied) scnd_derivs_yhat_p1= k scnd_derivs_yhat_p2= yhat**(-2) scnd_derivs_yhat_p3= (k + yhat)**(-2) - scnd_derivs_yhat_p4= yhat*(k + yhat) - yhat*(yhat - y) - (k + yhat)*(yhat - y) + scnd_derivs_yhat_p4= r*yhat + r*(k + yhat) + yhat*(k + yhat) scnd_derivs_yhat= scnd_derivs_yhat_p1*scnd_derivs_yhat_p2*scnd_derivs_yhat_p3*scnd_derivs_yhat_p4 - return scnd_derivs_yhat - - def residual(self, yhat): - ''' - Raw residuals - - Parameters - ---------- - yhat: array like - observation - - Returns - ------- - r: array like - residuals - - ''' - if len(yhat.shape) > 1: - if 1 in yhat.shape: - yhat = yhat.ravel() - return self._weightedResidual(yhat) - - def _weightedResidual(self, yhat): - ''' - Find the weighted residuals. - ''' - # resid = self._y - yhat - # print "In weighted resid" - # print self._y.shape - if len(yhat.shape) > 1: - if 1 in yhat.shape: - resid = self._y - yhat.ravel() - else: - resid = self._y - yhat - else: - resid = self._y - yhat - - return resid * self._w + return scnd_derivs_yhat \ No newline at end of file diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index cf590fee..3f70d32c 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -25,9 +25,8 @@ class SquareLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name, state_weight=None, target_param=None, target_state=None): - super(SquareLoss, self).__init__(theta, ode, x0, t0, t, y, - state_name, state_weight, - target_param, target_state) + super().__init__(theta, ode, x0, t0, t, y, + state_name, state_weight,target_param, target_state) def __repr__(self): return "SquareLoss" + self._get_model_str() @@ -40,32 +39,18 @@ class NormalLoss(BaseLoss): ''' Realizations from a Normal distribution ''' - def __init__(self, theta, ode, x0, t0, t, y, state_name, - sigma=None, target_param=None, target_state=None): - if sigma is None: - super(NormalLoss, self).__init__(theta, ode, x0, t0, t, y, - state_name, sigma, - target_param, target_state) - else: - sigma = check_array_type(sigma) - super(NormalLoss, self).__init__(theta, ode, x0, t0, t, y, - state_name, 1.0/sigma, - target_param, target_state) + def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, + sigma=1.0, target_param=None, target_state=None): + self._sigma=sigma + super().__init__(theta, ode, x0, t0, t, y, + state_name, state_weight,target_param, target_state) def __repr__(self): return "NormalLoss" + self._get_model_str() def _setLossType(self): - if self._stateWeight is None: - return Normal(self._y, 1.0) - else: - if len(self._stateWeight.shape) > 1: - if 1 in self._stateWeight.shape: - return Normal(self._y, 1.0/self._stateWeight.flatten()) - else: - return Normal(self._y, 1.0/self._stateWeight) - else: - return Normal(self._y, 1.0/self._stateWeight) + self._lossObj = Normal(self._y,self._sigma, self._stateWeight) + return self._lossObj class GammaLoss(BaseLoss): ''' @@ -73,16 +58,15 @@ class GammaLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, shape=2, target_param=None, target_state=None): - self._shape = shape - super(GammaLoss, self).__init__(theta, ode, x0, t0, t, y, - state_name, state_weight, - target_param, target_state) + self._shape=shape + super().__init__(theta, ode, x0, t0, t, y, + state_name, state_weight,target_param, target_state) def __repr__(self): return "GammaLoss" + self._get_model_str() def _setLossType(self): - return Gamma(self._y,self._shape,self._state_weight)) + return Gamma(self._y,self._shape,self._stateWeight)) class PoissonLoss(BaseLoss): ''' @@ -90,13 +74,14 @@ class PoissonLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, target_param=None, target_state=None): - super(PoissonLoss, self).__init__(theta, ode, x0, t0, t, y, state_name, - None, target_param, target_state) + super().__init__(theta, ode, x0, t0, t, y, + state_name, state_weight,target_param, target_state) + def __repr__(self): return "PoissonLoss" + self._get_model_str() def _setLossType(self): - return Poisson(self._y,self._state_weight)) + return Poisson(self._y,self._stateWeight)) class NegBinomLoss(BaseLoss): ''' @@ -104,13 +89,12 @@ class NegBinomLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, k=1, target_param=None, target_state=None): - self._k = k - super(NegBinomLoss, self).__init__(theta, ode, x0, t0, t, y, - state_name, state_weight, - target_param, target_state) + self._k=k + super().__init__(theta, ode, x0, t0, t, y, + state_name, state_weight,target_param, target_state) def __repr__(self): return "NegBinomLoss" + self._get_model_str() def _setLossType(self): - return NegBinom(self._y,self._k,self._state_weight) + return NegBinom(self._y,self._k,self._stateWeight) From d82e8a5418ba0cf584d2de96806314b13f4ccb97 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Tue, 23 Jun 2020 10:02:24 +0100 Subject: [PATCH 006/188] Gama loss functions 2nd derivative takes weighted residuals --- notebooks/Loss function Calculations.ipynb | 245 ++++++++++----------- pygom/loss/loss_type.py | 9 +- tests/test_loss_types.py | 91 +++++++- 3 files changed, 213 insertions(+), 132 deletions(-) diff --git a/notebooks/Loss function Calculations.ipynb b/notebooks/Loss function Calculations.ipynb index ab5c2eb3..8af4da95 100644 --- a/notebooks/Loss function Calculations.ipynb +++ b/notebooks/Loss function Calculations.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -19,14 +19,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Setting up residual for replacement, so that weighted residuals can be applied in code.\n", "r ,y, yhat = sym.symbols('r,y yhat')\n", - "r_eq = y-yhat\n", - "minus_r_eq = yhat-y" + "r_eq = y-yhat" ] }, { @@ -38,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -53,18 +52,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "data": { - "text/latex": [ - "$\\displaystyle r^{2}$" - ], - "text/plain": [ - "r**2" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/latex": [ @@ -80,12 +67,12 @@ ], "source": [ "square_loss=(y-yhat)**2\n", - "display(square_loss,square_loss.subs(r_eq,r),square_loss.subs(minus_r_eq,-r))" + "display(square_loss,square_loss.subs(r_eq,r))" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -111,28 +98,39 @@ }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "first_derv = sym.diff(square_loss,yhat).simplify()\n", + "display(first_derv,first_derv.simplify().subs(r_eq,r))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle - 2 y + 2 \\hat{y}$" + "$\\displaystyle - 2 r$" ], "text/plain": [ - "-2*y + 2*yhat" + "-2*r" ] }, + "execution_count": 5, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "first_derv = sym.diff(square_loss,yhat).simplify()\n", - "display(first_derv,first_derv.simplify().subs(r_eq,r),first_derv.subs(minus_r_eq,-r))" + "-2*r" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -144,7 +142,7 @@ "2" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -181,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -196,18 +194,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\sqrt{2} e^{- \\frac{r^{2}}{2 \\sigma^{2}}}}{2 \\sqrt{\\pi} \\sigma}$" - ], - "text/plain": [ - "sqrt(2)*exp(-r**2/(2*sigma**2))/(2*sqrt(pi)*sigma)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/latex": [ @@ -223,12 +209,12 @@ ], "source": [ "normpdf=1/(sym.sqrt(2*pi)*sigma) * sym.E**(-((y-yhat)**2/(2*sigma**2)))\n", - "display(normpdf,normpdf.subs(r_eq,r),normpdf.subs(minus_r_eq,-r))" + "display(normpdf,normpdf.subs(r_eq,r))" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -237,7 +223,7 @@ "(1/2, sqrt(2), 1/sqrt(pi), 1/sigma, exp(-(y - yhat)**2/(2*sigma**2)))" ] }, - "execution_count": 41, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -248,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 11, "metadata": { "scrolled": true }, @@ -263,7 +249,7 @@ " log(exp(-(y - yhat)**2/(2*sigma**2)))]" ] }, - "execution_count": 47, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -279,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -291,7 +277,7 @@ "-r**2/(2*sigma**2)" ] }, - "execution_count": 48, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -309,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -345,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -357,7 +343,7 @@ "log(pi)/2 - log(1/sigma) + log(2)/2 + (y - yhat)**2/(2*sigma**2)" ] }, - "execution_count": 51, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -384,18 +370,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "data": { - "text/latex": [ - "$\\displaystyle - \\frac{r}{\\sigma^{2}}$" - ], - "text/plain": [ - "-r/sigma**2" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/latex": [ @@ -411,12 +385,12 @@ ], "source": [ "first_derv = sym.diff(normloss,yhat).simplify()\n", - "display(first_derv,first_derv.subs(r_eq,r),first_derv.subs(minus_r_eq,-r))" + "display(first_derv,first_derv.subs(r_eq,r))" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -428,7 +402,7 @@ "sigma**(-2)" ] }, - "execution_count": 56, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -447,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -465,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -477,7 +451,7 @@ "s**(-a)*y**(a - 1)*exp(-y/s)/gamma(a)" ] }, - "execution_count": 58, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -489,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -498,7 +472,7 @@ "(s**(-a), y**(a - 1), 1/gamma(a), exp(-y/s))" ] }, - "execution_count": 59, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -509,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -521,7 +495,7 @@ "-a*log(s) + (a - 1)*log(y) - log(gamma(a)) - y/s" ] }, - "execution_count": 61, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -537,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -549,7 +523,7 @@ "-a*y/yhat - a*log(yhat/a) + (a - 1)*log(y) - log(gamma(a))" ] }, - "execution_count": 62, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -562,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -571,7 +545,7 @@ "(-log(gamma(a)), (a - 1)*log(y), -a*log(yhat/a), -a*y/yhat)" ] }, - "execution_count": 63, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -582,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -599,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -633,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -645,7 +619,7 @@ "a*y/yhat + a*log(yhat/a) - (a - 1)*log(y) + log(gamma(a))" ] }, - "execution_count": 66, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -664,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 26, "metadata": { "scrolled": true }, @@ -701,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -713,7 +687,7 @@ "-a*r/yhat**2" ] }, - "execution_count": 69, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -725,14 +699,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\\frac{a \\left(\\mu - x\\right)}{\\mu^{2}}\n" + "- \\frac{a \\left(y - \\hat{y}\\right)}{\\hat{y}^{2}}\n" ] } ], @@ -749,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -784,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -796,7 +770,7 @@ "a*(2*y - yhat)/yhat**3" ] }, - "execution_count": 72, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -807,14 +781,37 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{a \\left(r + y\\right)}{y^{3}}$" + ], + "text/plain": [ + "a*(r + y)/y**3" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a*(y+r)/y**3" + ] + }, + { + "cell_type": "code", + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\\frac{a \\left(- \\mu + 2 x\\right)}{\\mu^{3}}\n" + "\\frac{a \\left(2 y - \\hat{y}\\right)}{\\hat{y}^{3}}\n" ] } ], @@ -839,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -851,7 +848,7 @@ "yhat**y*exp(-yhat)/factorial(y)" ] }, - "execution_count": 74, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -863,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -872,7 +869,7 @@ "(yhat**y, 1/factorial(y), exp(-yhat))" ] }, - "execution_count": 76, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -883,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -895,7 +892,7 @@ "-yhat + log(yhat**y) - factorial(y)" ] }, - "execution_count": 78, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -910,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -919,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -954,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -966,7 +963,7 @@ "-r/yhat" ] }, - "execution_count": 81, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -977,7 +974,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1019,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1037,7 +1034,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1049,7 +1046,7 @@ "(k/(k + yhat))**k*(yhat/(k + yhat))**y*gamma(k + y)/(factorial(y)*gamma(k))" ] }, - "execution_count": 86, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1068,7 +1065,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1081,7 +1078,7 @@ " gamma(k + y))" ] }, - "execution_count": 87, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1092,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1104,7 +1101,7 @@ "k*(log(k) - log(k + yhat)) + y*(log(yhat) - log(k + yhat)) - log(factorial(y)) - log(gamma(k)) + gamma(k + y)" ] }, - "execution_count": 89, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1121,7 +1118,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1134,7 +1131,7 @@ " gamma(k + y))" ] }, - "execution_count": 90, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1145,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1188,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1204,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1216,7 +1213,7 @@ "k*(-y + yhat)/(yhat*(k + yhat))" ] }, - "execution_count": 94, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1228,7 +1225,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1240,7 +1237,7 @@ "-k*r/(yhat*(k + yhat))" ] }, - "execution_count": 95, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1259,7 +1256,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1271,7 +1268,7 @@ "k*(yhat*(k + yhat) + yhat*(y - yhat) + (k + yhat)*(y - yhat))/(yhat**2*(k + yhat)**2)" ] }, - "execution_count": 97, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1291,7 +1288,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1303,7 +1300,7 @@ "k*(r*yhat + r*(k + yhat) + yhat*(k + yhat))/(yhat**2*(k + yhat)**2)" ] }, - "execution_count": 98, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1315,7 +1312,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1324,7 +1321,7 @@ "(k, yhat**(-2), (k + yhat)**(-2), r*yhat + r*(k + yhat) + yhat*(k + yhat))" ] }, - "execution_count": 100, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index 214a6662..5e0e6445 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -52,6 +52,8 @@ def __init__(self, y, weights=None): self._w = check_array_type(weights) if np.any(weights<0): raise ValueError('No elements in numpy array of weights should be negative') + if np.all(weights==0.0): + raise ValueError('All elements in numpy array of weights should not be 0.0') if len(self._w.shape) > 1: if 1 in self._w.shape: self._w = self._w.flatten() @@ -366,7 +368,7 @@ def diff_loss(self, yhat,weighting_applied=True): ''' return self._shape*-self.residual(yhat,weighting_applied)/yhat**2 - def diff2Loss(self, yhat): + def diff2Loss(self, yhat,weighting_applied=True): ''' Twice derivative of the loss function with respect to yhat. See: @@ -376,6 +378,9 @@ def diff2Loss(self, yhat): ---------- yhat: array like observation + weighting_applied: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns @@ -390,7 +395,7 @@ def diff2Loss(self, yhat): y = self._y shape = self._shape - return shape*(-yhat+2*y)/yhat**3 + return shape*(self.residual(yhat,weighting_applied)+y)/yhat**3 class Poisson(baseloss_type): ''' diff --git a/tests/test_loss_types.py b/tests/test_loss_types.py index 0d9bb29d..c0345fa3 100644 --- a/tests/test_loss_types.py +++ b/tests/test_loss_types.py @@ -2,10 +2,10 @@ import numpy as np -from pygom import SquareLoss, NormalLoss +from pygom import SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss from pygom.model import common_models -class TestLossTypes(TestCase): +class Test_Square_loss_class(TestCase): def setUp(self): # initial values @@ -49,6 +49,85 @@ def test_FH_Square_vector_weight(self): self.assertTrue(np.allclose(obj.cost(), s)) + + def test_FH_Square_1State_Fail(self): + ## totalFail = 0 + ## expectedFail = 4 + w_list = list() + + w_list.append([-1.]) + w_list.append([0]) + w_list.append([2.0, 3.0]) + w_list.append(np.random.rand(30)) + + for w in w_list: + self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, + self.x0, self.t[0], self.t[1::], self.solution[1::,:], + 'R', w) + + def test_FH_Square_2State_Fail(self): + ## totalFail = 0 + ## expectedFail = 8 + w_list = list() + + w_list.append([-2.0]) + w_list.append([2.0, 3.0, 4.0]) + w_list.append([0.0, 0.0]) + w_list.append([1.0, -1.0]) + w_list.append(np.random.rand(30)) + w_list.append([np.random.rand(30), np.random.rand(31)]) + w_list.append([np.random.rand(31), np.random.rand(31)]) + w_list.append([np.random.rand(30), np.random.rand(30), np.random.rand(30)]) + + for w in w_list: + self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, + self.x0, self.t[0], self.t[1::], self.solution[1::,:], + 'R', w) + +class Test_Normal_loss_class(TestCase): + + def setUp(self): + # initial values + self.x0 = [-1.0, 1.0] + # params + self.param_eval = [('a', 0.2), ('b', 0.2),('c', 3.0)] + # the time points for our observations + self.t = np.linspace(0, 20, 30).astype('float64') + self.ode = common_models.FitzHugh(self.param_eval) + self.ode.initial_values = (self.x0, self.t[0]) + + # Standard. Find the solution which we will be used as + # "observations later" + self.solution = self.ode.integrate(self.t[1::]) + # initial guess + self.theta = [0.5, 0.5, 0.5] + + obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], + self.t[1::], self.solution[1::,:], ['V', 'R']) + self.r = obj.residual() + + def test_FH_Normal_scalar_weight(self): + # weight for each component + w = [2.0, 3.0] + + s = 0 + for i in range(2): s += ((self.r[:,i]*w[i])**2).sum() + + obj = NormalLoss(self.theta, self.ode, self.x0, self.t[0], + self.t[1::], self.solution[1::,:], ['V', 'R'], w) + + self.assertTrue(np.allclose(obj.cost(), s)) + + def test_FH_Normal_vector_weight(self): + # now the weight is a vector + w = np.random.rand(29, 2) + obj = NormalLoss(self.theta, self.ode, self.x0, self.t[0], + self.t[1::], self.solution[1::,:], ['V', 'R'], w) + + s = ((self.r * np.array(w))**2).sum() + + self.assertTrue(np.allclose(obj.cost(), s)) + def test_FH_Normal(self): objFH = NormalLoss(self.theta, self.ode, self.x0, self.t[0], self.t[1::], self.solution[1::,:], ['V', 'R']) @@ -65,7 +144,7 @@ def test_FH_Normal(self): self.assertFalse(np.allclose(objFH.cost(), objFH1.cost())) self.assertFalse(np.allclose(objFH1.cost(), objFH2.cost())) - def test_FH_Square_1State_Fail(self): + def test_FH_Normal_1State_Fail(self): ## totalFail = 0 ## expectedFail = 4 w_list = list() @@ -76,11 +155,11 @@ def test_FH_Square_1State_Fail(self): w_list.append(np.random.rand(30)) for w in w_list: - self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, + self.assertRaises(AssertionError, NormalLoss, self.theta, self.ode, self.x0, self.t[0], self.t[1::], self.solution[1::,:], 'R', w) - def test_FH_Square_2State_Fail(self): + def test_FH_Normal_2State_Fail(self): ## totalFail = 0 ## expectedFail = 8 w_list = list() @@ -95,7 +174,7 @@ def test_FH_Square_2State_Fail(self): w_list.append([np.random.rand(30), np.random.rand(30), np.random.rand(30)]) for w in w_list: - self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, + self.assertRaises(AssertionError, NormalLoss, self.theta, self.ode, self.x0, self.t[0], self.t[1::], self.solution[1::,:], 'R', w) From 126fc99b4fdd3c90e42e22e1c88e00d1b1688588 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Wed, 24 Jun 2020 10:53:12 +0100 Subject: [PATCH 007/188] Corrected typo --- pygom/loss/ode_loss.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index 3f70d32c..d8882249 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -66,8 +66,9 @@ def __repr__(self): return "GammaLoss" + self._get_model_str() def _setLossType(self): - return Gamma(self._y,self._shape,self._stateWeight)) - + self._lossObj = Gamma(self._y,self._shape,self._stateWeight) + return self._lossObj + class PoissonLoss(BaseLoss): ''' Realizations from a Poisson distribution @@ -81,7 +82,8 @@ def __repr__(self): return "PoissonLoss" + self._get_model_str() def _setLossType(self): - return Poisson(self._y,self._stateWeight)) + self._lossObj = Poisson(self._y,self._stateWeight) + return self._lossObj class NegBinomLoss(BaseLoss): ''' @@ -97,4 +99,5 @@ def __repr__(self): return "NegBinomLoss" + self._get_model_str() def _setLossType(self): - return NegBinom(self._y,self._k,self._stateWeight) + self._lossObj = NegBinom(self._y,self._k,self._stateWeight) + return self._lossObj \ No newline at end of file From 3f90a1da510de787ba1f01a0081cfdacd8558058 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Wed, 24 Jun 2020 12:14:06 +0100 Subject: [PATCH 008/188] Bugs in base_loss_type's handling of weights fixed --- ...s functions fitted to simulated data.ipynb | 1448 +++++++++-------- pygom/loss/base_loss.py | 2 +- pygom/loss/loss_type.py | 43 +- pygom/loss/ode_loss.py | 7 +- 4 files changed, 769 insertions(+), 731 deletions(-) diff --git a/notebooks/Testing loss functions fitted to simulated data.ipynb b/notebooks/Testing loss functions fitted to simulated data.ipynb index c73591c1..776f5847 100644 --- a/notebooks/Testing loss functions fitted to simulated data.ipynb +++ b/notebooks/Testing loss functions fitted to simulated data.ipynb @@ -87,7 +87,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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[-9.57521372e-02, 1.46961351e-01, 3.27614130e-01],\n", + " [ 3.20980053e-01, -1.54030722e-01, -3.70355927e-03],\n", + " [ 1.10411322e-01, -1.66169988e-01, -2.91024643e-01],\n", + " [ 2.03023170e-01, 2.57521968e-01, -1.37497649e-01]])" ] }, "execution_count": 8, @@ -400,7 +400,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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"execution_count": 13, @@ -711,19 +711,39 @@ } ], "source": [ - "objSIR.cost()" + "objSIR" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4909533605860.806" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.cost()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\scipy\\optimize\\_minimize.py:521: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", " warn('Method %s cannot handle constraints nor bounds.' % method,\n" ] }, @@ -731,18 +751,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 3149715468391.5137\n", - " hess_inv: array([[ 1.02285673e-11, -3.28483610e-14, 3.23263121e-06],\n", - " [-3.28483423e-14, 5.00246087e-15, -3.92052034e-09],\n", - " [ 3.23263111e-06, -3.92052576e-09, 1.06428103e+00]])\n", - " jac: array([-1.89198473e+03, 2.05662437e+05, 6.51297284e-03])\n", + " fun: 3257054048571.9863\n", + " hess_inv: array([[ 5.18410500e-11, -2.54658445e-12, 1.27206329e-05],\n", + " [-2.54658442e-12, 1.49261473e-13, -6.12083782e-07],\n", + " [ 1.27206329e-05, -6.12083790e-07, 3.12906462e+00]])\n", + " jac: array([-1.10600741e+00, -4.43260288e+00, 4.08760083e-06])\n", " message: 'Desired error not necessarily achieved due to precision loss.'\n", - " nfev: 74\n", - " nit: 10\n", - " njev: 62\n", + " nfev: 43\n", + " nit: 12\n", + " njev: 38\n", " status: 2\n", " success: False\n", - " x: array([3.84976144e+00, 2.02859211e-01, 1.11835931e+06])\n" + " x: array([5.52375636e+00, 2.03086137e-01, 1.49373372e+06])\n" ] } ], @@ -758,22 +778,23 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " fun: 3149715468391.6045\n", + " fun: 3257054048571.9043\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([ 2.80002429e+03, 1.18458645e+05, -9.63846812e-03])\n", + " jac: array([-8.79695180e+01, -1.47147132e+03, 3.25305614e-04])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 12\n", + " nfev: 14\n", " nit: 9\n", + " njev: 14\n", " status: 0\n", " success: True\n", - " x: array([3.44232967e+00, 2.02859211e-01, 1.00000000e+06])\n" + " x: array([3.69795251e+00, 2.03086137e-01, 1.00000000e+06])\n" ] } ], @@ -789,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -833,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": false }, @@ -845,16 +866,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-1.17798986e+03, -1.40835509e+04, 3.53396958e-03])" + "array([-5.77968064e+02, -6.98915605e+03, 1.73390419e-03])" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -865,16 +886,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "5065.797364214306" + "5062.059868394187" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -885,14 +906,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\scipy\\optimize\\_minimize.py:521: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", " warn('Method %s cannot handle constraints nor bounds.' % method,\n" ] }, @@ -900,18 +921,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4716.804547726841\n", - " hess_inv: array([[ 1.93830562e-03, -1.55181618e-05, 1.36514445e-06],\n", - " [-1.55181618e-05, 1.96280251e-06, 1.90886878e-08],\n", - " [ 1.36514445e-06, 1.90886878e-08, 1.00000000e+00]])\n", - " jac: array([ 8.67860428e-08, -3.11402124e-06, -3.11981179e-13])\n", - " message: 'Optimization terminated successfully.'\n", - " nfev: 15\n", + " fun: 4717.565272474467\n", + " hess_inv: array([[ 3.84423633e-03, -3.01340148e-05, 1.56049513e-06],\n", + " [-3.01340148e-05, 3.82956903e-06, 1.49977083e-08],\n", + " [ 1.56049513e-06, 1.49977083e-08, 1.00000000e+00]])\n", + " jac: array([ 8.12454287e-07, -3.28990725e-04, -2.91902226e-12])\n", + " message: 'Desired error not necessarily achieved due to precision loss.'\n", + " nfev: 57\n", " nit: 10\n", - " njev: 15\n", - " status: 0\n", - " success: True\n", - " x: array([3.59483138e+00, 1.99847237e-01, 1.00000000e+06])\n" + " njev: 46\n", + " status: 2\n", + " success: False\n", + " x: array([3.59284491e+00, 1.99665537e-01, 1.00000000e+06])\n" ] } ], @@ -927,14 +948,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - 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0\n", " success: True\n", - " x: array([3.59483123e+00, 1.99847235e-01, 1.00000000e+06])\n" + " x: array([3.59285115e+00, 1.99665226e-01, 1.00000000e+06])\n" ] } ], @@ -966,7 +988,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1017,218 +1039,218 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[5.000000e+00, 0.000000e+00],\n", - " [9.000000e+00, 0.000000e+00],\n", - " [1.900000e+01, 1.000000e+00],\n", - " [4.600000e+01, 3.000000e+00],\n", - " [9.700000e+01, 6.000000e+00],\n", - " [2.610000e+02, 2.200000e+01],\n", - " [9.900000e+02, 5.400000e+01],\n", - " [1.515000e+03, 1.210000e+02],\n", - " [3.991000e+03, 3.070000e+02],\n", - " [7.166000e+03, 4.150000e+02],\n", - " [1.973600e+04, 1.230000e+03],\n", - " [5.781700e+04, 3.684000e+03],\n", - " [1.468620e+05, 6.722000e+03],\n", - " [2.832220e+05, 1.723800e+04],\n", - " 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[7.500000e+01, 7.089120e+05],\n", + " [1.070000e+02, 8.624290e+05]])" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1247,7 +1269,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "scrolled": false }, @@ -1266,16 +1288,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "8329023.1484972015" + "9745677.652607694" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1286,25 +1308,33 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", + " warn('Method %s cannot handle constraints nor bounds.' % method,\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - " fun: 3557908.6464113127\n", - " hess_inv: array([[ 1.27472646e-07, -3.23818317e-09, 2.26214178e-06],\n", - " [-3.23818317e-09, 9.56376557e-10, 2.13419691e-08],\n", - " [ 2.26214178e-06, 2.13419691e-08, 1.00001208e+00]])\n", - " jac: array([-6.34498993e-04, 1.86534335e-03, 2.25490671e-09])\n", + " fun: 3692407.814497117\n", + " hess_inv: array([[ 1.13964289e-07, -2.49061166e-09, 2.20264892e-06],\n", + " [-2.49061166e-09, 9.27756704e-10, 9.40250405e-09],\n", + " [ 2.20264892e-06, 9.40250405e-09, 1.00000987e+00]])\n", + " jac: array([ 1.33293688e-02, -2.07506277e-02, -4.87274588e-08])\n", " message: 'Desired error not necessarily achieved due to precision loss.'\n", - " nfev: 109\n", - " nit: 11\n", - " njev: 97\n", + " nfev: 54\n", + " nit: 10\n", + " njev: 42\n", " status: 2\n", " success: False\n", - " x: array([3.55378376e+00, 1.99169650e-01, 1.00000147e+06])\n" + " x: array([3.65564294e+00, 2.00162197e-01, 9.99999090e+05])\n" ] } ], @@ -1320,22 +1350,23 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " fun: 3557908.646411574\n", + " fun: 3692407.8145229938\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([-2.83986623e-01, -4.04398552e-01, 1.00922556e-06])\n", + " jac: array([ 9.92225896e-02, -2.34421856e+02, -3.62722747e-07])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 15\n", - " nit: 11\n", + " nfev: 14\n", + " nit: 10\n", + " njev: 14\n", " status: 0\n", " success: True\n", - " x: array([3.55377851e+00, 1.99169650e-01, 1.00000000e+06])\n" + " x: array([3.65564687e+00, 2.00161978e-01, 1.00000000e+06])\n" ] } ], @@ -1351,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1395,7 +1426,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": { "scrolled": false }, @@ -1404,9 +1435,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\pygom-0.1.7.dev1+gbbd58f3.d20200410-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev7+g126fc99.d20200624-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n", - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\pygom-0.1.7.dev1+gbbd58f3.d20200410-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev7+g126fc99.d20200624-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" ] } @@ -1418,16 +1449,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-2.92555910e+02, -3.50195352e+03, 8.77667731e-04])" + "array([-2.87176322e+02, -3.47833060e+03, 8.61528965e-04])" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1438,16 +1469,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "5041.425763395273" + "5041.270821570561" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1458,14 +1489,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\scipy\\optimize\\_minimize.py:521: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", " warn('Method %s cannot handle constraints nor bounds.' % method,\n" ] }, @@ -1473,18 +1504,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4867.899797539529\n", - " hess_inv: array([[ 7.67776936e-03, -5.97617430e-05, 1.74660277e-06],\n", - " [-5.97617430e-05, 7.85112146e-06, 1.25323010e-08],\n", - " [ 1.74660277e-06, 1.25323010e-08, 1.00000000e+00]])\n", - " jac: array([ 8.13167572e-09, -3.92696007e-06, -2.92247481e-14])\n", + " fun: 4869.743686474076\n", + " hess_inv: array([[ 7.82577440e-03, -6.12363068e-05, 1.76151491e-06],\n", + " [-6.12363068e-05, 7.85949196e-06, 1.45278667e-08],\n", + " [ 1.76151491e-06, 1.45278667e-08, 1.00000000e+00]])\n", + " jac: array([-2.72469402e-08, 1.25576726e-07, 9.79041863e-14])\n", " message: 'Optimization terminated successfully.'\n", - " nfev: 15\n", - " nit: 10\n", - " njev: 15\n", + " nfev: 16\n", + " nit: 11\n", + " njev: 16\n", " status: 0\n", " success: True\n", - " x: array([3.59393939e+00, 1.99830691e-01, 1.00000000e+06])\n" + " x: array([3.59321800e+00, 1.99662852e-01, 1.00000000e+06])\n" ] } ], @@ -1500,14 +1531,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygom_loss_20200410\\lib\\site-packages\\pygom-0.1.7.dev1+gbbd58f3.d20200410-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev7+g126fc99.d20200624-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" ] }, @@ -1515,15 +1546,16 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4867.899797547801\n", + " fun: 4869.743686476613\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([ 4.12746856e-04, 1.84890228e-02, -1.48338804e-09])\n", + " jac: array([-1.47841000e-04, -9.54029439e-03, 5.31224858e-10])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 17\n", + " nfev: 18\n", " nit: 12\n", + " njev: 18\n", " status: 0\n", " success: True\n", - " x: array([3.59394146e+00, 1.99830811e-01, 1.00000000e+06])\n" + " x: array([3.59321743e+00, 1.99662786e-01, 1.00000000e+06])\n" ] } ], @@ -1539,7 +1571,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1605,7 +1637,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.8.3" } }, "nbformat": 4, diff --git a/pygom/loss/base_loss.py b/pygom/loss/base_loss.py index cca68359..68c582da 100644 --- a/pygom/loss/base_loss.py +++ b/pygom/loss/base_loss.py @@ -65,7 +65,7 @@ def __init__(self, theta, ode, y = ode_utils.check_array_type(y) if state_weight is None: - state_weight = 1.0 + state_weight = np.ones(y.shape) if len(y) == y.size: y = y.flatten() diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index 5e0e6445..b326c7bb 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -18,7 +18,7 @@ from pygom.model._model_errors import InitializeError from pygom.model.ode_utils import check_array_type -from pygom.utilR.distn import dnorm, dpois, gamma_mu_shape, dnbinom +from pygom.utilR.distn import dpois, gamma_mu_shape, dnbinom class InputError(Exception): ''' @@ -50,13 +50,15 @@ def __init__(self, y, weights=None): self._w = np.ones(self._y.shape) else: self._w = check_array_type(weights) - if np.any(weights<0): - raise ValueError('No elements in numpy array of weights should be negative') - if np.all(weights==0.0): - raise ValueError('All elements in numpy array of weights should not be 0.0') if len(self._w.shape) > 1: if 1 in self._w.shape: self._w = self._w.flatten() + + if np.any(self._w<0.0): + raise ValueError('No elements in numpy array of weights should be negative') + if np.all(self._w==0.0): + raise ValueError('All elements in numpy array of weights should not be 0.0') + assert self._y.shape == self._w.shape, \ "Input weight not of the same size as y" @@ -78,7 +80,7 @@ def residual(self, yhat, weighting_applied = True): :math:`y_{i} - \\hat{y}_{i}` ''' - if isinstance(weighting_applied,bool)=False: + if isinstance(weighting_applied,bool)==False: raise TypeError('weighting_applied should be boolean') if len(yhat.shape) > 1: @@ -88,7 +90,7 @@ def residual(self, yhat, weighting_applied = True): resid = self._y - yhat else: resid = self._y - yhat - if weighting_applied = True: + if weighting_applied == True: resid *= self._w return resid @@ -146,7 +148,7 @@ def diff_loss(self, yhat,weighting_applied=True): ------- :math:`-2(y_{i} - \\hat{y}_{i})` ''' - return -2*self.residual(yhat,weighting_applied)) + return -2*self.residual(yhat,weighting_applied) def diff2Loss(self, yhat): ''' @@ -231,13 +233,16 @@ def loss(self, yhat,weighting_applied=True): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - + + r=self.residual(yhat,weighting_applied) + sigma=self._sigma + # Calculate negative likelihood (depending on weighting of residuals). logpdf_p1= -np.log(2) logpdf_p2= np.log(2)/2 - logpdf_p3= -np.log(pi)/2 - logpdf_p4= np.log(1/self._sigma) - logpdf_p5_alt=-self.residual(yhat,weighting_applied)**2/(2*sigma**2) + logpdf_p3= -np.log(np.pi)/2 + logpdf_p4= np.log(1/sigma) + logpdf_p5_alt= -r**2 / (2*sigma**2) return (-(logpdf_p1+logpdf_p2+logpdf_p3+logpdf_p4+logpdf_p5_alt)).sum() def diff_loss(self, yhat,weighting_applied=True): @@ -366,7 +371,9 @@ def diff_loss(self, yhat,weighting_applied=True): :math:`\\mathcal\\frac{a \\left(\\hat{y} - y\\right)}{\\hat{y}^{2}}` ''' - return self._shape*-self.residual(yhat,weighting_applied)/yhat**2 + shape = self._shape + r = self.residual(yhat,weighting_applied) + return shape*-r/yhat**2 def diff2Loss(self, yhat,weighting_applied=True): ''' @@ -394,8 +401,8 @@ def diff2Loss(self, yhat,weighting_applied=True): yhat = yhat.ravel() y = self._y shape = self._shape - - return shape*(self.residual(yhat,weighting_applied)+y)/yhat**3 + r = self.residual(yhat,weighting_applied) + return shape*(r+y)/yhat**3 class Poisson(baseloss_type): ''' @@ -474,7 +481,8 @@ def diff2Loss(self, yhat): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - return self._y/(yhat**2) + y=self._y + return y/(yhat**2) class NegBinom(baseloss_type): ''' @@ -568,7 +576,6 @@ def diff_loss(self, yhat,weighting_applied=True): if 1 in yhat.shape: yhat = yhat.ravel() - y = self._y k = self._k r = self.residual(yhat,weighting_applied) first_derivs_yhat = k*-r/(yhat*(k+yhat)) @@ -602,7 +609,7 @@ def diff2Loss(self, yhat,weighting_applied=True): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - y = self._y + k = self._k r = self.residual(yhat,weighting_applied) scnd_derivs_yhat_p1= k diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index d8882249..e4182f54 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -17,7 +17,6 @@ from pygom.loss.base_loss import BaseLoss from pygom.loss.loss_type import Normal, Square, Poisson, Gamma, NegBinom -from pygom.model.ode_utils import check_array_type class SquareLoss(BaseLoss): ''' @@ -66,7 +65,7 @@ def __repr__(self): return "GammaLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = Gamma(self._y,self._shape,self._stateWeight) + self._lossObj = Gamma(self._y,self._shape,self._stateWeight) return self._lossObj class PoissonLoss(BaseLoss): @@ -82,7 +81,7 @@ def __repr__(self): return "PoissonLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = Poisson(self._y,self._stateWeight) + self._lossObj = Poisson(self._y,self._stateWeight) return self._lossObj class NegBinomLoss(BaseLoss): @@ -99,5 +98,5 @@ def __repr__(self): return "NegBinomLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = NegBinom(self._y,self._k,self._stateWeight) + self._lossObj = NegBinom(self._y,self._k,self._stateWeight) return self._lossObj \ No newline at end of file From 9b40b86f275e61aa53c9cc88d301555c8c074d46 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Tue, 30 Jun 2020 13:34:28 +0100 Subject: [PATCH 009/188] Check_array excludes boolean values by default --- ...s functions fitted to simulated data.ipynb | 1610 +++++++++-------- pygom/loss/base_loss.py | 60 +- pygom/loss/loss_type.py | 149 +- .../model/ode_utils/checks_and_conversions.py | 40 +- tests/test_loss_types.py | 273 +-- 5 files changed, 1220 insertions(+), 912 deletions(-) diff --git a/notebooks/Testing loss functions fitted to simulated data.ipynb b/notebooks/Testing loss functions fitted to simulated data.ipynb index 776f5847..573ea491 100644 --- a/notebooks/Testing loss functions fitted to simulated data.ipynb +++ b/notebooks/Testing loss functions fitted to simulated data.ipynb @@ -176,209 +176,209 @@ "data": { "text/plain": [ "array([[ 0.00000000e+00, 0.00000000e+00, nan],\n", - " [-3.21943456e-01, 1.85865527e-01, -1.13217774e-01],\n", - " [-2.10940831e-01, -1.40000086e-01, 3.21150841e-01],\n", - " [ 1.81602995e-01, 1.28076820e-02, 2.49787871e-01],\n", - " 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\n", 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-722,7 +722,7 @@ { "data": { "text/plain": [ - "4909533605860.806" + "9032988605419.916" ] }, "execution_count": 14, @@ -751,18 +751,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 3257054048571.9863\n", - " hess_inv: array([[ 5.18410500e-11, -2.54658445e-12, 1.27206329e-05],\n", - " [-2.54658442e-12, 1.49261473e-13, -6.12083782e-07],\n", - " [ 1.27206329e-05, -6.12083790e-07, 3.12906462e+00]])\n", - " jac: array([-1.10600741e+00, -4.43260288e+00, 4.08760083e-06])\n", + " fun: 6195089026101.063\n", + " hess_inv: array([[ 8.74144835e-12, -2.58712672e-14, 2.68445022e-06],\n", + " [-2.58712712e-14, 2.36956010e-15, -5.05342211e-09],\n", + " [ 2.68444976e-06, -5.05342099e-09, 8.41021723e-01]])\n", + " jac: array([ 4.61669282e+00, -8.58643370e+01, -1.67724298e-05])\n", " message: 'Desired error not necessarily achieved due to precision loss.'\n", - " nfev: 43\n", - " nit: 12\n", - " njev: 38\n", + " nfev: 57\n", + " nit: 11\n", + " njev: 46\n", " status: 2\n", " success: False\n", - " x: array([5.52375636e+00, 2.03086137e-01, 1.49373372e+06])\n" + " x: array([4.97711288e+00, 1.97932062e-01, 1.36982257e+06])\n" ] } ], @@ -785,16 +785,16 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 3257054048571.9043\n", + " fun: 6195089026101.077\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([-8.79695180e+01, -1.47147132e+03, 3.25305614e-04])\n", + " jac: array([ 7.75580656e+04, 3.96915387e+06, -2.81799456e-01])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 14\n", - " nit: 9\n", - " njev: 14\n", + " nfev: 16\n", + " nit: 10\n", + " njev: 16\n", " status: 0\n", " success: True\n", - " x: array([3.69795251e+00, 2.03086137e-01, 1.00000000e+06])\n" + " x: array([3.63339968e+00, 1.97932071e-01, 1.00000000e+06])\n" ] } ], @@ -849,53 +849,76 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Fitting Gamma loss" + "## Fitting Normal loss" ] }, { "cell_type": "code", "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_evals" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Initial guess of parameters, and bounding constraints\n", - "objSIR = GammaLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" + "theta = [3, 0.15,1e6]\n", + "boxBounds = [(2,5),(0.0,1.0),(1e6,1e6)]\n", + "\n", + "objSIR = NormalLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-5.77968064e+02, -6.98915605e+03, 1.73390419e-03])" + "NormalLoss([3.0, 0.15, 1000000.0], 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@@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -921,18 +944,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4717.565272474467\n", - " hess_inv: array([[ 3.84423633e-03, -3.01340148e-05, 1.56049513e-06],\n", - " [-3.01340148e-05, 3.82956903e-06, 1.49977083e-08],\n", - " [ 1.56049513e-06, 1.49977083e-08, 1.00000000e+00]])\n", - " jac: array([ 8.12454287e-07, -3.28990725e-04, -2.91902226e-12])\n", + " fun: 3097544513423.658\n", + " hess_inv: array([[ 1.03645359e-11, -4.61156610e-14, 3.16936003e-06],\n", + " [-4.61156587e-14, 4.75500055e-15, -8.98088545e-09],\n", + " [ 3.16936002e-06, -8.98088607e-09, 1.00244138e+00]])\n", + " jac: array([ 9.35430284e+04, -3.55363991e+05, -3.39879219e-01])\n", " message: 'Desired error not necessarily achieved due to precision loss.'\n", - " nfev: 57\n", - " nit: 10\n", + " nfev: 55\n", + " nit: 9\n", " njev: 46\n", " status: 2\n", " success: False\n", - " x: array([3.59284491e+00, 1.99665537e-01, 1.00000000e+06])\n" + " x: array([4.54710725e+00, 1.97932059e-01, 1.25147454e+06])\n" ] } ], @@ -948,14 +971,184 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 3097544513423.661\n", + " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", + " jac: array([ 3.87782251e+04, 1.98457720e+06, -1.40896750e-01])\n", + " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", + " nfev: 16\n", + " nit: 10\n", + " njev: 16\n", + " status: 0\n", + " success: True\n", + " x: array([3.63339968e+00, 1.97932071e-01, 1.00000000e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='L-BFGS-B')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 0.15, 1000000.0]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[(2, 5), (0.0, 1.0), (1000000.0, 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(theta,boxBounds,param_evals)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting Gamma loss" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = GammaLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-5.77039500e+02, -6.96845671e+03, 1.73111850e-03])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.sensitivity()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5058.726988342257" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objSIR.cost()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev7+g126fc99.d20200624-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:108: RuntimeWarning: invalid value encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", + " warn('Method %s cannot handle constraints nor bounds.' % method,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fun: 4717.132458517435\n", + " hess_inv: array([[ 3.86858865e-03, -3.00734373e-05, 3.30190074e-06],\n", + " [-3.00734373e-05, 3.91747625e-06, 2.36516472e-08],\n", + " [ 3.30190074e-06, 2.36516472e-08, 1.00000000e+00]])\n", + " jac: array([ 2.13941642e-10, 3.42940432e-08, -7.68280694e-16])\n", + " message: 'Optimization terminated successfully.'\n", + " nfev: 28\n", + " nit: 12\n", + " njev: 27\n", + " status: 0\n", + " success: True\n", + " x: array([3.59089103e+00, 1.99441546e-01, 1.00000000e+06])\n" + ] + } + ], + "source": [ + "# perform optimization\n", + "res = minimize(fun=objSIR.cost,\n", + " jac=objSIR.sensitivity,\n", + " x0=theta,\n", + " bounds=boxBounds,\n", + " method='BFGS')\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev8+g3f90a1d.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:108: RuntimeWarning: invalid value encountered in log\n", " logpdf_p3= -shape*np.log(mu/shape)\n" ] }, @@ -963,16 +1156,16 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4717.565272489127\n", + " fun: 4717.132458530734\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([ 1.06976387e-03, -7.18401978e-02, -3.84350237e-09])\n", + " jac: array([ 1.00361678e-03, -6.86270665e-02, -3.60388445e-09])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", " nfev: 17\n", " nit: 10\n", " njev: 17\n", " status: 0\n", " success: True\n", - " x: array([3.59285115e+00, 1.99665226e-01, 1.00000000e+06])\n" + " x: array([3.59089697e+00, 1.99441247e-01, 1.00000000e+06])\n" ] } ], @@ -988,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1039,218 +1232,218 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[6.000000e+00, 0.000000e+00],\n", - " [9.000000e+00, 1.000000e+00],\n", - " [2.600000e+01, 2.000000e+00],\n", - " [4.100000e+01, 4.000000e+00],\n", - " [1.080000e+02, 1.000000e+01],\n", - " [2.660000e+02, 2.000000e+01],\n", - " [8.140000e+02, 3.400000e+01],\n", - " [1.610000e+03, 8.400000e+01],\n", - " [4.034000e+03, 2.880000e+02],\n", - " [8.244000e+03, 6.960000e+02],\n", - " [2.944100e+04, 1.278000e+03],\n", - " [3.632600e+04, 3.083000e+03],\n", - " [1.343010e+05, 7.316000e+03],\n", - " [2.511210e+05, 1.222800e+04],\n", - " [5.123970e+05, 2.154300e+04],\n", - " 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[6.500000e+01, 7.000530e+05]])" ] }, - "execution_count": 24, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1269,7 +1462,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 32, "metadata": { "scrolled": false }, @@ -1281,23 +1474,16 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "9745677.652607694" + "8999599.052695096" ] }, - "execution_count": 26, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1308,7 +1494,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1323,18 +1509,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 3692407.814497117\n", - " hess_inv: array([[ 1.13964289e-07, -2.49061166e-09, 2.20264892e-06],\n", - " [-2.49061166e-09, 9.27756704e-10, 9.40250405e-09],\n", - " [ 2.20264892e-06, 9.40250405e-09, 1.00000987e+00]])\n", - " jac: array([ 1.33293688e-02, -2.07506277e-02, -4.87274588e-08])\n", + " fun: 3555725.9211099287\n", + " hess_inv: array([[ 1.12854102e-07, -2.48584571e-09, 2.27949584e-06],\n", + " [-2.48584571e-09, 9.25888041e-10, 9.47168312e-09],\n", + " [ 2.27949584e-06, 9.47168312e-09, 1.00000832e+00]])\n", + " jac: array([ 1.48618754e-02, -2.24162553e-02, -5.37171184e-08])\n", " message: 'Desired error not necessarily achieved due to precision loss.'\n", - " nfev: 54\n", - " nit: 10\n", - " njev: 42\n", + " nfev: 71\n", + " nit: 11\n", + " njev: 60\n", " status: 2\n", " success: False\n", - " x: array([3.65564294e+00, 2.00162197e-01, 9.99999090e+05])\n" + " x: array([3.61442328e+00, 1.98453976e-01, 9.99999326e+05])\n" ] } ], @@ -1350,23 +1536,23 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " fun: 3692407.8145229938\n", + " fun: 3555725.9211287573\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([ 9.92225896e-02, -2.34421856e+02, -3.62722747e-07])\n", + " jac: array([-7.98496027e-01, -2.01665131e+02, 2.88610490e-06])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", " nfev: 14\n", " nit: 10\n", " njev: 14\n", " status: 0\n", " success: True\n", - " x: array([3.65564687e+00, 2.00161978e-01, 1.00000000e+06])\n" + " x: array([3.61442613e+00, 1.98453791e-01, 1.00000000e+06])\n" ] } ], @@ -1382,7 +1568,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1426,7 +1612,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 37, "metadata": { "scrolled": false }, @@ -1435,9 +1621,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev7+g126fc99.d20200624-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev8+g3f90a1d.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n", - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev7+g126fc99.d20200624-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev8+g3f90a1d.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" ] } @@ -1449,16 +1635,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-2.87176322e+02, -3.47833060e+03, 8.61528965e-04])" + "array([-2.86825346e+02, -3.46781844e+03, 8.60476039e-04])" ] }, - "execution_count": 31, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1469,16 +1655,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "5041.270821570561" + "5038.2958154688895" ] }, - "execution_count": 32, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1489,7 +1675,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1504,18 +1690,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4869.743686474076\n", - " hess_inv: array([[ 7.82577440e-03, -6.12363068e-05, 1.76151491e-06],\n", - " [-6.12363068e-05, 7.85949196e-06, 1.45278667e-08],\n", - " [ 1.76151491e-06, 1.45278667e-08, 1.00000000e+00]])\n", - " jac: array([-2.72469402e-08, 1.25576726e-07, 9.79041863e-14])\n", + " fun: 4868.230711182974\n", + " hess_inv: array([[ 7.84349281e-03, -6.15336988e-05, 1.77314305e-06],\n", + " [-6.15336988e-05, 7.88477962e-06, 1.39267526e-08],\n", + " [ 1.77314305e-06, 1.39267526e-08, 1.00000000e+00]])\n", + " jac: array([-1.03570668e-08, 1.32455900e-07, 3.71928550e-14])\n", " message: 'Optimization terminated successfully.'\n", " nfev: 16\n", " nit: 11\n", " njev: 16\n", " status: 0\n", " success: True\n", - " x: array([3.59321800e+00, 1.99662852e-01, 1.00000000e+06])\n" + " x: array([3.59106407e+00, 1.99435913e-01, 1.00000000e+06])\n" ] } ], @@ -1531,14 +1717,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev7+g126fc99.d20200624-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev8+g3f90a1d.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" ] }, @@ -1546,16 +1732,16 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4869.743686476613\n", + " fun: 4868.230711171033\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([-1.47841000e-04, -9.54029439e-03, 5.31224858e-10])\n", + " jac: array([-8.31389989e-04, -6.80463204e-03, 2.98556969e-09])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 18\n", + " nfev: 26\n", " nit: 12\n", - " njev: 18\n", + " njev: 26\n", " status: 0\n", " success: True\n", - " x: array([3.59321743e+00, 1.99662786e-01, 1.00000000e+06])\n" + " x: array([3.59105802e+00, 1.99435910e-01, 1.00000000e+06])\n" ] } ], @@ -1571,7 +1757,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1613,6 +1799,20 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/pygom/loss/base_loss.py b/pygom/loss/base_loss.py index 68c582da..9f6df6d3 100644 --- a/pygom/loss/base_loss.py +++ b/pygom/loss/base_loss.py @@ -65,7 +65,7 @@ def __init__(self, theta, ode, y = ode_utils.check_array_type(y) if state_weight is None: - state_weight = np.ones(y.shape) + state_weight = 1.0 if len(y) == y.size: y = y.flatten() @@ -149,9 +149,6 @@ def __init__(self, theta, ode, else: raise InputError("State name should be str or of type list/tuple") - # if self._stateWeight is not None: - if np.any(self._stateWeight <= 0): - raise InputError("Weights should be strictly positive") # finish ordering information # now get the index of target states @@ -918,7 +915,7 @@ def fisher_information(self, theta=None, full_output=False, method=None): # ############################################################ - def cost(self, theta=None): + def cost(self, theta=None, apply_weighting = True): """ Find the cost/loss given time points and the corresponding observations. @@ -927,6 +924,9 @@ def cost(self, theta=None): ---------- theta: array like input value of the parameters + apply_weighting: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns ------- @@ -943,11 +943,11 @@ def cost(self, theta=None): """ yhat = self._getSolution(theta) - c = self._lossObj.loss(yhat) + c = self._lossObj.loss(yhat,apply_weighting = apply_weighting) return np.nan_to_num(c) if c == np.inf else c - def diff_loss(self, theta=None): + def diff_loss(self, theta=None, apply_weighting = True): """ Find the derivative of the loss function given time points and the corresponding observations, with initial conditions @@ -956,6 +956,9 @@ def diff_loss(self, theta=None): ---------- theta: array like input value of the parameters + apply_weighting: boolean + If True multiplies array of residuals by weightings, else raw + residuals are used. Returns ------- @@ -969,13 +972,13 @@ def diff_loss(self, theta=None): try: # the solution does not include the origin solution = self._getSolution(theta) - return self._lossObj.diff_loss(solution) + return self._lossObj.diff_loss(solution,apply_weighting = apply_weighting) except Exception as e: # print(e) # print("parameters = " +str(theta)) return np.nan_to_num((np.ones(self._y.shape)*np.inf)) - def residual(self, theta=None): + def residual(self, theta=None, apply_weighting = True): """ Find the residuals given time points and the corresponding observations, with initial conditions @@ -984,6 +987,9 @@ def residual(self, theta=None): ---------- theta: array like input value of the parameters + apply_weighting: boolean + If True multiplies array of residuals by weightings, else raw + residuals returned. Returns ------- @@ -1003,7 +1009,7 @@ def residual(self, theta=None): try: # the solution does not include the origin solution = self._getSolution(theta) - return self._lossObj.residual(solution) + return self._lossObj.residual(solution, apply_weighting = apply_weighting) except Exception as e: # print(e) return np.nan_to_num((np.ones(self._y.shape)*np.inf)) @@ -1014,7 +1020,7 @@ def residual(self, theta=None): # ############################################################ - def costIV(self, theta=None): + def costIV(self, theta=None, apply_weighting = True): """ Find the cost/loss given the parameters. The input theta here is assumed to include both the parameters as well as the @@ -1024,6 +1030,10 @@ def costIV(self, theta=None): ---------- theta: array like parameters and guess of initial values of the states + apply_weighting: boolean + If True multiplies array of residuals by weightings, else raw + residuals returned. + Returns ------- @@ -1039,9 +1049,9 @@ def costIV(self, theta=None): self._setParamStateInput(theta) solution = self._getSolution() - return self._lossObj.loss(solution) + return self._lossObj.loss(solution, apply_weighting = apply_weighting) - def diff_lossIV(self, theta=None): + def diff_lossIV(self, theta=None, apply_weighting = True): """ Find the derivative of the loss function w.r.t. the parameters given time points and the corresponding observations, with @@ -1051,6 +1061,9 @@ def diff_lossIV(self, theta=None): ---------- theta: array like parameters and initial values of the states + apply_weighting: boolean + If True multiplies array of residuals by weightings, else raw + residuals returned. Returns ------- @@ -1068,13 +1081,13 @@ def diff_lossIV(self, theta=None): try: # the solution does not include the origin solution = self._getSolution() - return self._lossObj.diff_loss(solution) + return self._lossObj.diff_loss(solution, apply_weighting = apply_weighting) except Exception as e: # print(e) # print("parameters = " + str(theta)) return np.nan_to_num((np.ones(self._y.shape)*np.inf)) - def residualIV(self, theta=None): + def residualIV(self, theta=None, apply_weighting = True): """ Find the residuals given time points and the corresponding observations, with initial conditions. @@ -1083,6 +1096,9 @@ def residualIV(self, theta=None): ---------- theta: array like parameters and initial values of the states + apply_weighting: boolean + If True multiplies array of residuals by weightings, else raw + residuals returned. Returns ------- @@ -1105,7 +1121,7 @@ def residualIV(self, theta=None): try: # the solution does not include the origin solution = self._getSolution() - return self._lossObj.residual(solution) + return self._lossObj.residual(solution, apply_weighting = apply_weighting) except Exception as e: # print(e) return np.nan_to_num((np.ones(self._y.shape)*np.inf)) @@ -1571,20 +1587,20 @@ def _setWeight(self, n, p, w): elif m == 1: self._stateWeight = np.ones((n, p))*w else: - raise InputError("Number of input weights is not equal " + - "to the number of observations") + raise AssertionError("Number of input weights is not equal " + + "to the number of observations") elif p == m: if q == 1: self._stateWeight = np.ones((n, p))*w else: - raise InputError("Number of input weights is not equal " + - "to number of states") + raise AssertionError("Number of input weights is not equal " + + "to number of states") else: if q == 1 and m == 1: self._stateWeight = np.ones((n, p))*w else: - raise InputError("Number of input weights differs from " + - "the number of observations") + raise AssertionError("Number of input weights differs from " + + "the number of observations") def _setX0(self, x0): """ diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index b326c7bb..584fe228 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -49,29 +49,28 @@ def __init__(self, y, weights=None): self._numVar = 0 self._w = np.ones(self._y.shape) else: - self._w = check_array_type(weights) + self._w = check_array_type(weights,accept_booleans=True) + if np.any(self._w<0.0): + raise ValueError('No elements in numpy array of weights should be negative') + if np.all(self._w==0.0): + raise ValueError('All elements in numpy array of weights should not be 0.0') if len(self._w.shape) > 1: if 1 in self._w.shape: self._w = self._w.flatten() - if np.any(self._w<0.0): - raise ValueError('No elements in numpy array of weights should be negative') - if np.all(self._w==0.0): - raise ValueError('All elements in numpy array of weights should not be 0.0') - assert self._y.shape == self._w.shape, \ "Input weight not of the same size as y" - def residual(self, yhat, weighting_applied = True): + def residual(self, yhat, apply_weighting = True): ''' - Raw residuals returned if weighting_applied = False, else + Raw residuals returned if apply_weighting = False, else the weighted residuals. Parameters ---------- yhat: array like observation - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals returned. @@ -80,8 +79,8 @@ def residual(self, yhat, weighting_applied = True): :math:`y_{i} - \\hat{y}_{i}` ''' - if isinstance(weighting_applied,bool)==False: - raise TypeError('weighting_applied should be boolean') + if isinstance(apply_weighting,bool)==False: + raise TypeError('apply_weighting should be boolean') if len(yhat.shape) > 1: if 1 in yhat.shape: @@ -90,7 +89,7 @@ def residual(self, yhat, weighting_applied = True): resid = self._y - yhat else: resid = self._y - yhat - if weighting_applied == True: + if apply_weighting == True: resid *= self._w return resid @@ -108,10 +107,10 @@ class Square(baseloss_type): ''' def __init__(self, y, weights=None): - super().__init__(y, weights=None) + super().__init__(y, weights) self.loss(self._y) - def loss(self, yhat, weighting_applied = True): + def loss(self, yhat, apply_weighting = True): ''' Loss under square loss. Not really saying much here @@ -119,18 +118,17 @@ def loss(self, yhat, weighting_applied = True): ---------- yhat: array like observation - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. - Returns ------- :math:`\\sum_{i=1}^{n} (y-\\hat{y})^{2}` ''' - return (self.residual(yhat,weighting_applied)**2).sum() + return (self.residual(yhat, apply_weighting)**2).sum() - def diff_loss(self, yhat,weighting_applied=True): + def diff_loss(self, yhat, apply_weighting=True): ''' Derivative under square loss. Assuming that we are solving the minimization problem i.e. our objective function is the @@ -140,7 +138,7 @@ def diff_loss(self, yhat,weighting_applied=True): ---------- yhat: array like observation - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. @@ -148,9 +146,9 @@ def diff_loss(self, yhat,weighting_applied=True): ------- :math:`-2(y_{i} - \\hat{y}_{i})` ''' - return -2*self.residual(yhat,weighting_applied) + return -2*self.residual(yhat, apply_weighting) - def diff2Loss(self, yhat): + def diff2Loss(self, yhat, apply_weighting=True): ''' Twice derivative of the square loss. Which is simply 2. @@ -158,6 +156,11 @@ def diff2Loss(self, yhat): ---------- yhat: array like observations + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. Returns ------- @@ -181,8 +184,8 @@ class Normal(baseloss_type): sigma: float standard deviation ''' - def __init__(self, y, sigma=1.0,weight=None): - super().__init__(y, weights=None) + def __init__(self, y, sigma=1.0, weights=None): + super().__init__(y, weights) err_str = "Standard deviation not of the correct " if isinstance(sigma, np.ndarray): if np.any(sigma<0): @@ -212,7 +215,7 @@ def __init__(self, y, sigma=1.0,weight=None): self._sigma2 = self._sigma**2 self.loss(self._y) - def loss(self, yhat,weighting_applied=True): + def loss(self, yhat, apply_weighting=True): ''' The loss under a normal distribution. Defined as the negative log-likelihood here. @@ -221,7 +224,7 @@ def loss(self, yhat,weighting_applied=True): ---------- yhat: array like observation - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. @@ -234,7 +237,7 @@ def loss(self, yhat,weighting_applied=True): if 1 in yhat.shape: yhat = yhat.ravel() - r=self.residual(yhat,weighting_applied) + r=self.residual(yhat, apply_weighting) sigma=self._sigma # Calculate negative likelihood (depending on weighting of residuals). @@ -245,7 +248,7 @@ def loss(self, yhat,weighting_applied=True): logpdf_p5_alt= -r**2 / (2*sigma**2) return (-(logpdf_p1+logpdf_p2+logpdf_p3+logpdf_p4+logpdf_p5_alt)).sum() - def diff_loss(self, yhat,weighting_applied=True): + def diff_loss(self, yhat, apply_weighting=True): ''' Derivative of the loss function which is :math:`\\sigma^{-1}(y - \\hat{y})` @@ -254,7 +257,7 @@ def diff_loss(self, yhat,weighting_applied=True): ---------- yhat: array like observation - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. @@ -264,10 +267,10 @@ def diff_loss(self, yhat,weighting_applied=True): :math:`\\nabla \\mathcal{L}(\\hat{y}, y)` ''' - r = self.residual(yhat,weighting_applied) + r = self.residual(yhat, apply_weighting) return -r/self._sigma2 - def diff2Loss(self, yhat): + def diff2Loss(self, yhat, apply_weighting=True): ''' Twice derivative of the normal loss. @@ -275,6 +278,11 @@ def diff2Loss(self, yhat): ---------- yhat: array like observations + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. Returns ------- @@ -299,7 +307,7 @@ class Gamma(baseloss_type): ''' def __init__(self, y, shape=2.0, weights=None): - super().__init__(y, weights=None) + super().__init__(y, weights) err_str = "Shape is not of the correct " if isinstance(shape, np.ndarray): if np.any(shape<0): @@ -327,7 +335,7 @@ def __init__(self, y, shape=2.0, weights=None): raise TypeError(err_str + "type") self.loss(self._y) - def loss(self, yhat): + def loss(self, yhat, apply_weighting=True): ''' The loss under a gamma distribution. Defined as the negative log-likelihood of the gamma distirbution in terms of mean and shape. @@ -338,7 +346,11 @@ def loss(self, yhat): ---------- yhat: array like prediction - + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. Returns ------- @@ -351,7 +363,7 @@ def loss(self, yhat): return -gamma_mu_shape(x=self._y, mu=yhat,shape=self._shape,log=True).sum() - def diff_loss(self, yhat,weighting_applied=True): + def diff_loss(self, yhat, apply_weighting=True): ''' Derivative of the loss function with respect to yhat which is See: @@ -361,7 +373,7 @@ def diff_loss(self, yhat,weighting_applied=True): ---------- yhat: array like prediction - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. @@ -372,10 +384,10 @@ def diff_loss(self, yhat,weighting_applied=True): ''' shape = self._shape - r = self.residual(yhat,weighting_applied) + r = self.residual(yhat, apply_weighting) return shape*-r/yhat**2 - def diff2Loss(self, yhat,weighting_applied=True): + def diff2Loss(self, yhat, apply_weighting=True): ''' Twice derivative of the loss function with respect to yhat. See: @@ -385,7 +397,7 @@ def diff2Loss(self, yhat,weighting_applied=True): ---------- yhat: array like observation - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. @@ -401,7 +413,7 @@ def diff2Loss(self, yhat,weighting_applied=True): yhat = yhat.ravel() y = self._y shape = self._shape - r = self.residual(yhat,weighting_applied) + r = self.residual(yhat, apply_weighting) return shape*(r+y)/yhat**3 class Poisson(baseloss_type): @@ -415,10 +427,10 @@ class Poisson(baseloss_type): ''' def __init__(self, y, weights=None): - super().__init__(y, weights=None) + super().__init__(y, weights) self.loss(self._y) - def loss(self, yhat): + def loss(self, yhat,apply_weighting=True): ''' The loss under a normal distribution. Defined as the negative log-likelihood here. @@ -427,6 +439,11 @@ def loss(self, yhat): ---------- yhat: array like observation + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. Returns ------- @@ -439,7 +456,7 @@ def loss(self, yhat): # note that we input the standard deviation here return (-dpois(self._y, yhat, True)).sum() - def diff_loss(self, yhat,weighting_applied=True): + def diff_loss(self, yhat, apply_weighting=True): ''' Derivative of the loss function, :math:`1 - y\\hat{y}^{-1}` @@ -447,7 +464,7 @@ def diff_loss(self, yhat,weighting_applied=True): ---------- yhat: array like observation - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. @@ -461,10 +478,10 @@ def diff_loss(self, yhat,weighting_applied=True): if 1 in yhat.shape: yhat = yhat.ravel() - r = self.residual(yhat,weighting_applied) + r = self.residual(yhat, apply_weighting) return -r/yhat - def diff2Loss(self, yhat): + def diff2Loss(self, yhat, apply_weighting=True): ''' Twice derivative of the Poisson loss. @@ -472,6 +489,11 @@ def diff2Loss(self, yhat): ---------- yhat: array like observations + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. Returns ------- @@ -497,7 +519,7 @@ class NegBinom(baseloss_type): ''' def __init__(self, y, k=1.0, weights=None): - super().__init__(y, weights=None) + super().__init__(y, weights) err_str = "k (the overdispersion parameter) is not of the correct " if isinstance(k, np.ndarray): if np.any(k<0): @@ -526,7 +548,7 @@ def __init__(self, y, k=1.0, weights=None): self.loss(self._y) - def loss(self, yhat): + def loss(self, yhat, apply_weighting=True): ''' The loss under a Negative Binomial distribution. Defined as the negative log-likelihood of the Negative Binomial 2 distribution. @@ -535,6 +557,11 @@ def loss(self, yhat): ---------- yhat: array like observation + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. Returns ------- @@ -547,7 +574,7 @@ def loss(self, yhat): return (-dnbinom(self._y, mu=yhat,size=self._k,log=True)).sum() - def diff_loss(self, yhat,weighting_applied=True): + def diff_loss(self, yhat, apply_weighting=True): ''' Derivative of the loss function with respect to yhat which is See: @@ -558,11 +585,13 @@ def diff_loss(self, yhat,weighting_applied=True): ---------- yhat: array like observation + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. - k: array like - observation - - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. @@ -577,11 +606,11 @@ def diff_loss(self, yhat,weighting_applied=True): yhat = yhat.ravel() k = self._k - r = self.residual(yhat,weighting_applied) + r = self.residual(yhat, apply_weighting) first_derivs_yhat = k*-r/(yhat*(k+yhat)) return first_derivs_yhat - def diff2Loss(self, yhat,weighting_applied=True): + def diff2Loss(self, yhat, apply_weighting=True): ''' Twice derivative of the loss function with respect to yhat. See: @@ -592,11 +621,13 @@ def diff2Loss(self, yhat,weighting_applied=True): ---------- yhat: array like observation - - k: array like - observation + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. - weighting_applied: boolean + apply_weighting: boolean If True multiplies array of residuals by weightings, else raw residuals are used. @@ -611,7 +642,7 @@ def diff2Loss(self, yhat,weighting_applied=True): yhat = yhat.ravel() k = self._k - r = self.residual(yhat,weighting_applied) + r = self.residual(yhat, apply_weighting) scnd_derivs_yhat_p1= k scnd_derivs_yhat_p2= yhat**(-2) scnd_derivs_yhat_p3= (k + yhat)**(-2) diff --git a/pygom/model/ode_utils/checks_and_conversions.py b/pygom/model/ode_utils/checks_and_conversions.py index 9a2a06db..b9817d85 100644 --- a/pygom/model/ode_utils/checks_and_conversions.py +++ b/pygom/model/ode_utils/checks_and_conversions.py @@ -4,12 +4,10 @@ @author: edwin.tye ''' import numpy as np -from numbers import Number -from pygom.model._model_errors import InputError, ArrayError -def check_array_type(x): +def check_array_type(x,accept_booleans=False): ''' Check to see if the type of input is suitable. Only operate on one or two dimension arrays @@ -18,32 +16,52 @@ def check_array_type(x): ---------- x: array like which can be either a :class:`numpy.ndarray` or list or tuple + accept_boolean: boolean + If true boolean elements are accepted, else they are not. Returns ------- x: :class:`numpy.ndarray` checked and converted array ''' - + accepted_types = (int,float,complex) + if accept_booleans==True: + type_error_message = 'Expecting elements/sub-elements to be of type float, int, complex or boolean' + if accept_booleans==False: + type_error_message = 'Expecting elements/sub-elements to be of type float, int or complex' + + if isinstance(x, np.ndarray): pass elif isinstance(x, (list, tuple)): - if isinstance(x[0], Number): - x = np.array(x) + if all(isinstance(item, accepted_types) for item in x): + if accept_booleans==True: + x = np.array(x) + elif accept_booleans==False and all(not isinstance(item, bool) for item in x): + x = np.array(x) + else: + TypeError(type_error_message) elif isinstance(x[0], (list, tuple, np.ndarray)): - if isinstance(x[0][0], Number): + sub_items_accepted = [] + for item in x: + if accept_booleans==False: + sub_items_accepted.append(all(isinstance(sub_item, accepted_types) for sub_item in item)*all(not isinstance(sub_item, bool) for sub_item in item)) + if accept_booleans==True: + sub_items_accepted.append(all(isinstance(sub_item, accepted_types) for sub_item in item)) + if all(sub_items_accepted): x = np.array(x) else: - raise ArrayError("Expecting elements of float or int") + raise TypeError(type_error_message) else: - raise ArrayError("Expecting elements of float or int") - elif isinstance(x, Number): + raise TypeError(type_error_message) + elif isinstance(x, accepted_types): x = np.array([x]) else: - raise ArrayError("Expecting an array like object, got %s" % type(x)) + raise TypeError("Expecting an array like object, got %s" % type(x)) return x + def check_dimension(x, y): ''' Compare the length of two array like objects. Converting both to a numpy diff --git a/tests/test_loss_types.py b/tests/test_loss_types.py index c0345fa3..c4d7fc88 100644 --- a/tests/test_loss_types.py +++ b/tests/test_loss_types.py @@ -27,7 +27,7 @@ def setUp(self): self.t[1::], self.solution[1::,:], ['V', 'R']) self.r = obj.residual() - def test_FH_Square_scalar_weight(self): + def test_FH_scalar_weights_for_two_states(self): # weight for each component w = [2.0, 3.0] @@ -39,10 +39,10 @@ def test_FH_Square_scalar_weight(self): self.assertTrue(np.allclose(obj.cost(), s)) - def test_FH_Square_vector_weight(self): + def test_FH_vector_weights_for_two_states(self): # now the weight is a vector w = np.random.rand(29, 2) - obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], + obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], self.t[1::], self.solution[1::,:], ['V', 'R'], w) s = ((self.r * np.array(w))**2).sum() @@ -50,133 +50,176 @@ def test_FH_Square_vector_weight(self): self.assertTrue(np.allclose(obj.cost(), s)) - def test_FH_Square_1State_Fail(self): - ## totalFail = 0 - ## expectedFail = 4 + def test_FH_1State_weights_Failures_TypeErrors(self): w_list = list() - - w_list.append([-1.]) - w_list.append([0]) - w_list.append([2.0, 3.0]) - w_list.append(np.random.rand(30)) - + w_list.append(['a']) + w_list.append([True]) + test_weight=np.ones(len(self.solution[1::,-1])) + test_weight[-1]=False + w_list.append(test_weight) for w in w_list: - self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,:], - 'R', w) - - def test_FH_Square_2State_Fail(self): - ## totalFail = 0 - ## expectedFail = 8 + with self.subTest('Weightings',w=w): + self.assertRaises(TypeError, SquareLoss, self.theta, self.ode, + self.x0, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', w) + + def test_FH_1State_weights_Failures_ValueErrors(self): w_list = list() - - w_list.append([-2.0]) - w_list.append([2.0, 3.0, 4.0]) - w_list.append([0.0, 0.0]) - w_list.append([1.0, -1.0]) - w_list.append(np.random.rand(30)) - w_list.append([np.random.rand(30), np.random.rand(31)]) - w_list.append([np.random.rand(31), np.random.rand(31)]) - w_list.append([np.random.rand(30), np.random.rand(30), np.random.rand(30)]) - + test_weight=np.ones(len(self.solution[1::,-1])) + w_list.append(-test_weight) + test_weight[-1]=-1 + w_list.append(test_weight) + w_list.append(np.zeros(len(self.solution[1::,-1]))) for w in w_list: - self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,:], - 'R', w) - -class Test_Normal_loss_class(TestCase): - - def setUp(self): - # initial values - self.x0 = [-1.0, 1.0] - # params - self.param_eval = [('a', 0.2), ('b', 0.2),('c', 3.0)] - # the time points for our observations - self.t = np.linspace(0, 20, 30).astype('float64') - self.ode = common_models.FitzHugh(self.param_eval) - self.ode.initial_values = (self.x0, self.t[0]) - - # Standard. Find the solution which we will be used as - # "observations later" - self.solution = self.ode.integrate(self.t[1::]) - # initial guess - self.theta = [0.5, 0.5, 0.5] - - obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R']) - self.r = obj.residual() - - def test_FH_Normal_scalar_weight(self): - # weight for each component - w = [2.0, 3.0] - - s = 0 - for i in range(2): s += ((self.r[:,i]*w[i])**2).sum() - - obj = NormalLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R'], w) - - self.assertTrue(np.allclose(obj.cost(), s)) - - def test_FH_Normal_vector_weight(self): - # now the weight is a vector - w = np.random.rand(29, 2) - obj = NormalLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R'], w) - - s = ((self.r * np.array(w))**2).sum() - - self.assertTrue(np.allclose(obj.cost(), s)) - - def test_FH_Normal(self): - objFH = NormalLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R']) - - w = [2.0, 3.0] - objFH1 = NormalLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R'], w) - - # now the weight is a vector - w = np.random.rand(29, 2) - objFH2 = NormalLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R'], w) - - self.assertFalse(np.allclose(objFH.cost(), objFH1.cost())) - self.assertFalse(np.allclose(objFH1.cost(), objFH2.cost())) - - def test_FH_Normal_1State_Fail(self): - ## totalFail = 0 - ## expectedFail = 4 - w_list = list() - - w_list.append([-1.]) - w_list.append([0]) + with self.subTest('Weightings',w=w): + self.assertRaises(ValueError, SquareLoss, self.theta, self.ode, + self.x0, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', w) + + def test_FH_1State_weights_Failures_ShapeErrors(self): + w_list = list() w_list.append([2.0, 3.0]) w_list.append(np.random.rand(30)) - for w in w_list: - self.assertRaises(AssertionError, NormalLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,:], - 'R', w) + with self.subTest('Weightings',w=w): + self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, + self.x0, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', w) - def test_FH_Normal_2State_Fail(self): - ## totalFail = 0 - ## expectedFail = 8 + def test_FH_2State_weights_Failures_TypeErrors(self): w_list = list() - - w_list.append([-2.0]) - w_list.append([2.0, 3.0, 4.0]) - w_list.append([0.0, 0.0]) + w_list.append(['a','b']) + w_list.append([True,False]) + test_weight=np.ones(self.solution[1::,:].shape) + test_weight[-1,-1]=False + w_list.append(test_weight) + for w in w_list: + with self.subTest('Weightings',w=w): + self.assertRaises(TypeError, SquareLoss, self.theta, self.ode, + self.x0, self.t[0], self.t[1::], self.solution[1::,:], + ['V', 'R'], w) + + def test_FH_2State_weights_Failures_ValueErrors(self): + w_list = list() + test_weight=np.ones(self.solution[1::,:].shape) + w_list.append(-test_weight) + test_weight[-1,-1]=-1 + w_list.append(test_weight) + w_list.append(np.zeros(self.solution[1::,:].shape)) w_list.append([1.0, -1.0]) + w_list.append([0, 0]) + for w in w_list: + with self.subTest('Weightings',w=w): + self.assertRaises(ValueError, SquareLoss, self.theta, self.ode, + self.x0, self.t[0], self.t[1::], self.solution[1::,:], + ['V', 'R'], w) + + def test_FH_2State_weights_Failures_ShapeErrors(self): + w_list = list() + w_list.append([2.0, 3.0, 4.0]) w_list.append(np.random.rand(30)) w_list.append([np.random.rand(30), np.random.rand(31)]) w_list.append([np.random.rand(31), np.random.rand(31)]) w_list.append([np.random.rand(30), np.random.rand(30), np.random.rand(30)]) - for w in w_list: - self.assertRaises(AssertionError, NormalLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,:], - 'R', w) + with self.subTest('Weightings',w=w): + self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, + self.x0, self.t[0], self.t[1::], self.solution[1::,:], + ['V', 'R'], w) + +# class Test_Normal_loss_class(TestCase): + +# def setUp(self): +# # initial values +# self.x0 = [-1.0, 1.0] +# # params +# self.param_eval = [('a', 0.2), ('b', 0.2),('c', 3.0)] +# # the time points for our observations +# self.t = np.linspace(0, 20, 30).astype('float64') +# self.ode = common_models.FitzHugh(self.param_eval) +# self.ode.initial_values = (self.x0, self.t[0]) + +# # Standard. Find the solution which we will be used as +# # "observations later" +# self.solution = self.ode.integrate(self.t[1::]) +# # initial guess +# self.theta = [0.5, 0.5, 0.5] + +# obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], +# self.t[1::], self.solution[1::,:], ['V', 'R']) +# self.r = obj.residual() + +# def test_FH_Normal_scalar_weight(self): +# # weight for each component +# w = [2.0, 3.0] + +# s = 0 +# for i in range(2): s += ((self.r[:,i]*w[i])**2).sum() + +# obj = NormalLoss(self.theta, self.ode, self.x0, self.t[0], +# self.t[1::], self.solution[1::,:], ['V', 'R'], w) + +# self.assertTrue(np.allclose(obj.cost(), s)) + +# def test_FH_Normal_vector_weight(self): +# # now the weight is a vector +# w = np.random.rand(29, 2) +# obj = NormalLoss(self.theta, self.ode, self.x0, self.t[0], +# self.t[1::], self.solution[1::,:], ['V', 'R'], w) + +# s = ((self.r * np.array(w))**2).sum() + +# self.assertTrue(np.allclose(obj.cost(), s)) + +# def test_FH_Normal(self): +# objFH = NormalLoss(self.theta, self.ode, self.x0, self.t[0], +# self.t[1::], self.solution[1::,:], ['V', 'R']) + +# w = [2.0, 3.0] +# objFH1 = NormalLoss(self.theta, self.ode, self.x0, self.t[0], +# self.t[1::], self.solution[1::,:], ['V', 'R'], w) + +# # now the weight is a vector +# w = np.random.rand(29, 2) +# objFH2 = NormalLoss(self.theta, self.ode, self.x0, self.t[0], +# self.t[1::], self.solution[1::,:], ['V', 'R'], w) + +# self.assertFalse(np.allclose(objFH.cost(), objFH1.cost())) +# self.assertFalse(np.allclose(objFH1.cost(), objFH2.cost())) + +# def test_FH_Normal_1State_Fail(self): +# ## totalFail = 0 +# ## expectedFail = 4 +# w_list = list() + +# w_list.append([-1.]) +# w_list.append([0]) +# w_list.append([2.0, 3.0]) +# w_list.append(np.random.rand(30)) + +# for w in w_list: +# self.assertRaises(AssertionError, NormalLoss, self.theta, self.ode, +# self.x0, self.t[0], self.t[1::], self.solution[1::,:], +# 'R', w) + +# def test_FH_Normal_2State_Fail(self): +# ## totalFail = 0 +# ## expectedFail = 8 +# w_list = list() + +# w_list.append([-2.0]) +# w_list.append([2.0, 3.0, 4.0]) +# w_list.append([0.0, 0.0]) +# w_list.append([1.0, -1.0]) +# w_list.append(np.random.rand(30)) +# w_list.append([np.random.rand(30), np.random.rand(31)]) +# w_list.append([np.random.rand(31), np.random.rand(31)]) +# w_list.append([np.random.rand(30), np.random.rand(30), np.random.rand(30)]) + +# for w in w_list: +# self.assertRaises(AssertionError, NormalLoss, self.theta, self.ode, +# self.x0, self.t[0], self.t[1::], self.solution[1::,:], +# 'R', w) if __name__ == '__main__': From 9708ace03726b7e6e9007ff71bf2f771976c87d9 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Tue, 30 Jun 2020 17:11:26 +0100 Subject: [PATCH 010/188] Square loss and residuals Weights Unit tested ok --- ...s functions fitted to simulated data.ipynb | 1707 +++++++---------- pygom/loss/loss_type.py | 31 +- tests/test_loss_types.py | 171 +- 3 files changed, 778 insertions(+), 1131 deletions(-) diff --git a/notebooks/Testing loss functions fitted to simulated data.ipynb b/notebooks/Testing loss functions fitted to simulated data.ipynb index 573ea491..150ab517 100644 --- a/notebooks/Testing loss functions fitted to simulated data.ipynb +++ b/notebooks/Testing loss functions fitted to simulated data.ipynb @@ -51,7 +51,7 @@ "source": [ "# initial conditions \n", "N = 1e6\n", - "in_inf = 2\n", + "in_inf = 1\n", "init_state = [N - in_inf, in_inf, 0.0]\n", "# time \n", "t = np.arange (0 , 51 , 0.25)\n", @@ -87,7 +87,7 @@ "outputs": [ { "data": { - "image/png": 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dBiCudq6HiUSkFxTovbK8zNTJ6LdUOxeRXlCg98rUFLueBmtoVyy8bKqdi0hPKNB7JPjELg7+ffC6h0PNYfeZ16vcIiI9oUDvkfLPDrG6ZeMxNzh09qk2NywVEUlGgd4jcQ8NLb/S29jbTkQkOQV6j8SurniS+E2lRUS6oEDvgWAh4NSZUzTul1E4A3OHaW+NdRGRhBToKattNbdyeoVzq+U6TL4ElcegtEDk5tEiIt1SoKesfLjM6tmNqy1icNHZapjD+c2jFeoikiIFespib4Y2PjW62sYG1CIiCSjQU9b0Zmgj3RwVkRQp0FM2d90chS0b9xUtvGzhzdBGF1/cn0GJSC4o0Htg2wXbzr2e3DZJ5TV7KH1vy+YTf/pT1dFFJDUK9BRt6HCpOv3yaXj7NfDKV27+DWfOqI4uIqlRoKcoqsNl9ewq5cNleOGF6N+kOrqIpESBnqLYDpeTy/EPE42NqewiIqlQoKcotsNlYmrD1nQbrK2pJ11EUqFAT1Fkh0tt79BSCSoVGI/YYFQ96SKSAgV6SoKF4FwNfdzC0C5OFDfuHVoqwfp69Aeoli4iXUoU6GZ2vZk9bWZHzezOJuf9AzNbM7PfTm+Iw6/W3bJ0cgmANV87NzPftJlFXC1dPeki0qWWgW5m48DdwA3AFcAtZnZFzHn/Bng87UEOu6bdLY3m5mCLetJFJH1JZuhvBY66+zPufgZ4CLgp4ryPAX8KPJfi+PonCGB6Ouw6mZ5uK1ybdrc0KpXUky4iPZEk0C8Bnq37/lj12DlmdgnwXuBAsw8ys1kzmzez+RMnTrQ71t4JgrDTZGkJ3MOvbXSeNO1uiaKedBHpgSSBbhHHGrZu4C7gE+6+1uyD3L3i7jPuPrNjx46kY+y9cjnsNKm3usqpfclmzE27W6KoJ11EeiBJoB8DLq37fidwvOGcGeAhM1sEfhu4x8x+K5UR9kPMzLiwspw4Xzet31Lf3dJIPeki0gNJAv0J4HIzu8zMtgLvBx6tP8HdL3P3aXefBv4E2OvuX0x9tL0SM2NeZqplWTt2/ZZm1JMuIj3QMtDd/WXgo4TdK08BD7v7k2a2x8z29HqAfTE3x0tsnDG/RIFPMdeyrN1Wh0s99aSLSMoS9aG7+yF3f527v9bd56rHDrj7ppug7v5Bd/+TtAfaU6USn5yssEiRdYxFivwOFR6k1HI/57Y6XBqpJ11EUqQnRauu3l/iDYVFxlnnMhZ5kBKFQljubqbtDpd66kkXkRQp0KtqZe1iEczCr5VKeLyZtjtcGn+oetJFJCUK9DqlEiwuhqXtxcXWYZ5o/ZZW4nrSl5Y0SxeRtijQO9TW+i3NNCvSq4VRRNqgQO9Qx90tjeJ60kEtjCLSlgsGPYBR1VV3S71aXefWW2N+kFoYRSQZzdA71FV3S6NSKbwLG0UtjCKSkAK9Q111t0R+oFoYRaQ7CvQOla4sUbmxQnGiiGHtd7ds+kC1MIpIdxToHaq1LC6fXGZqYqr97pYoamEUkS4o0DtQ37LoOEsnl5h9bJZgocvQVQujiHRBgd6B1FoWG6mFUUS6oLbFDqTWstioVQvj0lJ3ny8imaYZegdSbVls1KyF0UxlFxGJpUDvQOoti5t+wFwY3o3cYfduhbqIRFKgtymVBblaKZXC8I6ibepEJIYCvQ2pLciVRFzZBXSDVEQiKdDb0LPulijNOl5AN0hFZBMFeht61t0SpdlG0qAbpCKyiQK9DT3tbolSKsHBg/E3SFV2EZE6CvQ29Ly7JUqzG6RaEkBE6ijQ25D6glxJNbtBqo4XEakyj5v99djMzIzPz88P5GePnCAIg3t1Nfr9yUl4/vn+jklEBsLMjrj7TNR7mqG3IVgImL5rmrHPjDF913T3i3ElVbtBGmdlRbN0EVGgJ9WzFRaTarYkAOgGqYgo0JPqaw96nLkmN191g1Qk9xToQQDT0zA2Fn6NCcW+9qDHKZXCenkc3SAVybV8B3rtZuPSUtgauLQUG4p970GPs39/8zXT9+3r73hEZGjkO9DL5c2dI6urnNpX3jRpH0gPehTdIBWRGPkO9OXocklhZXnTpJ1vD6gHPUqrG6RaYlcklxIFupldb2ZPm9lRM7sz4v2SmX27+uuvzeyq9IfaAzF7eC6z8fjqawN2f3Oa2x65DYD7b76fxTsWBxPmNc1ukGqJXZFcahnoZjYO3A3cAFwB3GJmVzSc9gPgH7n7m4DfA5rUBIZIxIqGL1HgU9SF5ZUB3DjL2kUDaleM0+oGqerpIrmTZIb+VuCouz/j7meAh4Cb6k9w97929x9Xv/06sDPdYfZIrR5dLIYLYBWLfHKywoPUzbyvK8PWAbcrxml2gxRUTxfJmSSBfgnwbN33x6rH4nwY+FLUG2Y2a2bzZjZ/4sSJ5KPspVIJFhdhfR0WF7l6f2ljRk4MQbtinFZL7ILq6SI5kiTQI9ZuJXIBGDN7B2GgfyLqfXevuPuMu8/s2LEj+Sj7qHHSPv7SkLQrxqktsRtH9XSR3EgS6MeAS+u+3wkcbzzJzN4E3Afc5O4r6QxvMOon7Qc/MCTtis2oni4iJAv0J4DLzewyM9sKvB94tP4EM5sCHgFuc/e/S3+YgzOwJXPblaSevn27ZuoiGZZo+Vwz2wXcBYwDn3f3OTPbA+DuB8zsPuCfALWNLl+OW96xRsvn9kAQhDXztbX4cwqFsKZUGrL/QxKRRJotn6v10FsIFgLKh8ssn1xmamKKuevmhm92Xi8I4NZbm5+j9dNFRpbWQ+/QwJfM7USrejqo/CKSUQr0JoZiydxOtKqnQxjq6n4RyRQFehNDsWRuJ2q9l61m6up+EckUBXoTQ7NkbidKpbBOrvKLSG4o0JsYmiVzu6Hyi0huKNCbGJke9GZUfhHJDbUt5sn27eFsvJnJyXBWrz51kaGktsUOBAsB03dNM/aZMabvmh7uVsWkkpZfbrsN9u7tz5hEJDUK9Agj2X+eRNLyizvce69uloqMmHwHehCwafNQRrj/PImk3S+g2brIiMlvoAdB2NnRuHloELAU02ced3wkJSm/QPhnc+CAZuoiIyC/gV4uh50d9VZXoVxm/FR0n3nc8ZGUtPwCYahrowyRoZffQF+OmW0vL7P2+BycaZi9nimEx7OkVn65/fZwN49m1tbCRb/MNpSnRGR45DfQp2Jm21NTFF8swWMV+EkR3MKvj1XC41l0zz1w//3JZusQlqdUWxcZOvkN9Lm5zTXkQgHm5tj1iQD7jXK4n+jJKTg8R+H/lpjL2AR9g3Zm66BOGJEhlN9Ab9w8tFiESoXgTXDwx7P4xBKYw6uWsJtm2f3vgnw8a1ObrTfbeLqeOmFEhoaeFG0wfdc0SyeXNh0vThRZvGOx/wMalCAIg7qd/z70lKlIz+lJ0TaM7JK5aSuVYM+eZOWXmpUV3TgVGSAFeoORXjI3bbXyS7HY/u/VjVORvlOgN8jEkrlpKpVgcTEsvTzwQPJOGDh/49RMN09F+kCBXqe2IfTq2VXGLbwpOHlBkW1frnDbVSVVEdrthKlXX45RuIv0RD4DPWINl/oFuQDWfI2tVuDFL8yx8pVS4+oA+dZu33ojhbtIT+Svy6W2hkv9Y/+FAtOf2sbSyxFrhf+kCHctbjhULIZVCCGskR840F43TBx1yYi0pC6XejFruCyfjdn4YWJzd0vcqgG51M2N00b1M/fxcXXLiLQpf4Eek8ZTJ2POP7m5uyVu1YDc6ubGaZz19fDr0tL5kFeJRqSp/AV6TBrvOn4hxsYbfVutwJavbexuqa4OIHFqN07TCvZGmsWLxMpfoO/atalDI3jLFg6+4SzO+TqwYXz4Lbv5z/+8tCGXtm3r10BHXC3Y05y1N4qbxSvoJafyFehBAAcPbryBZ0Z518+x6mc2nOo4h75/CIDTp88fX1lRp0vb+hHu9eKCvv6XSjeSQfkK9IgbosEbnaWxU5GnL59cjt0HQ/s9dKjf4R6nvnQT90szfRkx+Qn0IAhnbPWHroTZG6GhdH7O1MRUbEfL2ppm6l0blnCPk2Smn+SX/jUgfZKPQN+7N1xXpCq4EqbvgFtvhtWt0b+l9rh/s46W1dXw77kmcCmoD/dawNdaIdt9KnXYJPnXQD9/6f9gMivRg0Vmdj2wHxgH7nP3329436rv7wJWgQ+6+zeafWYnDxYF9+6l/EyFpQvXMA83EwLAOT/LTvKahtcRHrj5AUpXliKfQ5LBuIWA/exjO+efGRjxqB8Yr/va6V8jve7s9TpjGOscHy+yODvH2+9p70G6rh4sMrNx4G7gBuAK4BYzu6LhtBuAy6u/ZoF72xphAsG9e5n9f/eydNEaGPgY4Z+QEV5FO69bpEBxokjpyvAPubYPRtL9HqR3HqTEq3meMZwxnBIPsEiRdWANw+HcL2mu0786et3963HWGQN2ri3xq/fO8j/3pvevpSQll7cCR939GXc/AzwE3NRwzk3AH3no68CrzOwXUxslUH6mwuqWND8xXuPKiqVS2Bwjw+VBSlzGIuM4F7DeMujXUeDLcLmQVaYr5dQ+L0mgXwI8W/f9seqxds/BzGbNbN7M5k+cONHWQJcvXGvr/E5Nbps8NzuvVyoN3z07iRYX9ON1gX+Cycigr3+t4Jd+eM1aemuJJAn0qAJF43/rSc7B3SvuPuPuMzt27EgyvnOmXup9zaOwpcD+G/bHvr9//+Z9pWX0NJZuakHf+DrJTD/Ja5Fmjo+nt5ZIkkA/Blxa9/1O4HgH53Rl7pdmKZxN8xM3Kk4UqdxYiZyd19TvKy3Z12qmn+R10n8N9PO1DI+XKLA4m95aIkkC/QngcjO7zMy2Au8HHm0451HgAxb6NeCku/8wtVECpdvvoXLJ7RRPjYODdfJfdNWYhZddnCjywM0P4J92Fu9YbBrm58bRsA5VVjrrpDeS/mugX6+H8f9g8vZ6jTHWgWPjRf729krbXS7NJG1b3AXcRdi2+Hl3nzOzPQDufqDatvg54HrCtsUPuXvTnsSBrYcuIjLCmrUtXpDkA9z9EHCo4diButcOfKSbQYqISHfy8aSoiEgOKNBFRDJCgS4ikhEKdBGRjEjU5dKTH2x2Aljq8LdvB55PcTjDTtebbbrebEv7eovuHvlk5sACvRtmNh/XtpNFut5s0/VmWz+vVyUXEZGMUKCLiGTEqAZ6ZdAD6DNdb7bperOtb9c7kjV0ERHZbFRn6CIi0kCBLiKSESMX6GZ2vZk9bWZHzezOQY8nbWb2eTN7zsy+U3fsYjP7spl9v/r15wc5xjSZ2aVm9ldm9pSZPWlm+6rHM3nNZvb3zOxvzOxb1ev9TPV4Jq8Xwn2JzexvzezPq99n9loBzGzRzBbM7JtmNl891pdrHqlAT7hh9aj7L4TLENe7Ezjs7pcDh6vfZ8XLwL9w99cDvwZ8pPq/aVav+WfAO939KuDNwPXVPQSyer0A+4Cn6r7P8rXWvMPd31zXf96Xax6pQCfZhtUjzd2/CrzQcPgmoLZN9UHgt/o6qB5y9x+6+zeqr39K+Bf/EjJ6zdWN1E9Vv91S/eVk9HrNbCfwLuC+usOZvNYW+nLNoxboiTajzqBfqO0AVf366gGPpyfMbBr4VeD/kOFrrpYgvgk8B3zZ3bN8vXcBH2fDnmGZvdYaB/6bmR0xs9nqsb5cc6INLoZIos2oZfSY2UXAnwJ3uPuLluE9/dx9DXizmb0K+IKZvXHQY+oFM3s38Jy7HzGzawc9nj66xt2Pm9mrgS+b2ff69YNHbYbe882oh9SPzOwXAapfnxvweFJlZlsIwzxw90eqhzN9zQDu/hPgK4T3TLJ4vdcA7zGzRcLy6DvN7AGyea3nuPvx6tfngC8Qlor7cs2jFuhJNqzOokeB3dXXu4E/G+BYUlXdj/Y/AU+5+2fr3srkNZvZjurMHDPbBvwG8D0yeL3u/kl33+nu04R/V/+7u99KBq+1xswuNLNX1F4D/xj4Dn265pF7UjRqw+oBDylVZvYgcC3hkps/Aj4NfBF4GJgCloH3uXvjjdORZGZvB74GLHC+zvopwjp65q7ZzN5EeFNsnHBC9bC7/2szmySD11tTLbn8rru/O8vXama/RDgrh7Ck/cfuPtevax65QBcRkWijVnIREZEYCvAZgFIAAAAlSURBVHQRkYxQoIuIZIQCXUQkIxToIiIZoUAXEckIBbqISEb8f0BpYTJc1lfEAAAAAElFTkSuQmCC\n", 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"text/plain": [ "
" ] @@ -125,6 +125,15 @@ "execution_count": 6, "metadata": {}, "outputs": [], + "source": [ + "np.random.seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], "source": [ "def runif_noise(x,noise_prop):\n", " '''\n", @@ -141,16 +150,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([9.99998e+05, 2.00000e+00, 0.00000e+00])" + "array([9.99999e+05, 1.00000e+00, 0.00000e+00])" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -161,227 +170,227 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - ":4: RuntimeWarning: invalid value encountered in true_divide\n", + ":4: RuntimeWarning: invalid value encountered in true_divide\n", " (noised_data-data)/data\n" ] }, { "data": { 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-0.21532634],\n", + " [-0.27419831, -0.25290942, -0.02614749],\n", + " [-0.19577752, -0.09048676, 0.00227818],\n", + " [ 0.12692989, -0.30712524, 0.19960693]])" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -395,12 +404,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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/ZTPKmEN+ckMRpPE4w9o2FClJDfJdD+Ldb9+8fdUk63k9oWThod8MvJtLQdHfVdUb+m0zaVA0aXAzaH2AVgv27q1vQDSPYpl+AT8vrlcR16vKIyAWVSwTNeY8iRM4dYl6mig7WBqHtB56mbP8DEra6y7s6Xf31t3su3lfqrEM5KGLyP3AXwM/JCInROR2EdktIrt7qxwBngaeAv4A2JNqlH2IOyG0m6K4Y8dqYw6wfn19jTnkUywTRpRXEVd/zSMglvR/iygqifMZXq8QqGSwNA5pNPGw8zYsK6pqhUBpvfuwp98jTx7JZZy1LP0PI8wr99JPc686WXm8/TzK8bXj7LpuF0eePDKQB1mkhl6mt9fvePrHUNcS9aA4QXtDu++5UcWnqiLI4wm1caX/YQRlwvipe4fFrDzeKA/I9T723bxv4MZJUZ5N2mKLsG3uvWlvadkUUamNQWOoY4l6UJzAO0tUFGHnZ2usVYkMmLwoOmWzUR56WCaMSxMKirLyeMv2EPMqbCmziCeJ91r28U/DIGNOmplUh1hCHPI4z6M89EYZ9LAURXA096Z0WMzCaBVZKRhEHQ1alpR9/NMwqHxQxYrZILLsrOnfXhb7PTQGvcll/nlQ5gVWdDl40cQ5tnUxcC7DcBPu1y6iCjfdodHQ42bCxKKpHb08xMlUyaupUNHaYpHEza12j/+hWw8B2VbS5kFVKj5d8jg3s+6sWTSNMuiQUd/ztI1jKk7SCyDPoo+qGYcsSZJamvYYF9W9z/s500en2XXdrkK6RMYZVx7nZtadNYumUZJLZjSwX0AazTbvR+y6SQ5xSSInZVmkk7VxrbLOn3X7gX7bjfsZRTA0kktSQlWVBnb0SlOQlHdqXVVnTh+UJHJSmmOcZTuFKE+/yo3Fkh63uB79oJ01y548Y2gNeqSqEtU4pqbaehrDUQWdO4+LJO8LL4mclOYYZ3Wj7Wfkqpwrn9d0hG6NQ2ustWobgrDrul2hsaYqVP4OrUGPbMc7M+Okx3gZH4ft22urracxHGXr3HlcJEVceEnKxNMc46xutP2MXBVu6GFkNR1h0PLOlg7r161ftTyqZL8qTzNDa9AjVZWwdJkjR2o7W0Yaw5HHVGlJvOMiOzVmfeHFlZPSHON+06y5x3fjhzay8UMbQ491PyNX9g09iqymIwxbnvTppCpPM0MbFE0V90zblL0ilB2ETBpky6OQZecDOxuR/x60b7C6t7sX/7GOE1gs+5zJiqTnXtKga5E5+kNTWJSEVEVIFct+qdvFVuRFUsUGXnmTNEOjylkseZDkekl6bIo8lpblEkCqIqQwbb2E2TKqEoRJQtLH0kEe+ZPMe1oVGWFQ4jzez5+Zvyi/5CGpVZkkWVVJj01VjuXQeuip6XYdzXxhwcl6KalBTJg3NiqjLOtyJT32vHKHg4iSaw7deqhWTzYu/Y5Fmkk26rDfcajb0+ogmIfuY6DMQ7cU9ZBTrs3OnaWkL4Z5Y0u6VFmPPW1gNk2uelQQbJD897JyjeM8kfXLofZSlXzyLKjj02peDJ1Bz6SqvwKtAeKkjlXtos3rsTTIyOaRoVGm4YiTneM/vq2xVmA+tUsV8smzoCopg1Vg6CSXTOKaFQiO9usK51K37I2kFNlnu8xug1Wadb5qNL1zpx+TXDxkUtUftnJYM/Yc8HtjozIauF4VikDyJMo7y7q1QJhH6w005sUgRT5VzifPgioXQBXN0Bn0sKr+K65IoKuHbUSkUNnFa7AOvulgLS/aQTXpIgs6ogxE3vLLIEa5KhkYedHv2FShx0pRDJ1BD8o8XLsWnn8+gSQ+M+MYbz+qpVWNFnHRZn1hZKFJF+md9Qs65qnbDvr9NrURGvSft3aYAqaxNHQRuRHYC4wC96nqB3zvbwAOAxPAGuC/qupHorZZZtqiP/Pw7FlYXF1rEi2JBxl0d3kNqkaTkkfhRBbabtHFMd55Q4Noqm5bV5oYPxhIQxeRUeDDwE3AtcBtInKtb7VfAf5OVa8DXgf8NxFZN9Coc8Q/Ccbp08HrRerq7Xbw8jA5JiZVfTwcNJMgaL+ykEuKlhNcT7e9Ifj7H0bdtspUpcdKUcSRXG4AnlLVp1X1AvAx4BbfOgpcLiICrAdOAy9lOtIYpM0vj+qWG0oOVaNVfjwc5MII2y93Al4/SY1iGXJC1oHGqt7I686wBUzjGPQrgW96Xp/oLfPye8CPAM8Ax4E7VLXQ585BUsNT2eZMJzB1qHI+7SAXRhPL8LN8MqjyjbzulJnhU8ZNuq+GLiJvBv6Vqr6j93oncIOqvsezzi8BrwX+PfCDwOeB61T1Od+2poApgImJievnM0zzGzQ1vAoV/XJ3sC5fBV12EK26iWX4WdJEnbdKlNEWIM/YzqB56CeAqz2vr8LxxL28DXhAHZ4CvgH8sH9DqjqrqltVdeumTZvijT4mg+aXr9DVZ7p0picLnZWoe7yLEGzQq/B4mNQj9XonIxJ8mg1ahl8UWXlaYdsZNp23aMo4x8p62l4TY51HgM0icg3wLeAtwFt96ywA24Avicj3Aj8EPJ3lQPsxMRHsoSeOUfr76rraDYS77Bm499NHp0O92KpIEJ0tnVgXg987WdKlVevURVrx74srhwCJe8CEbWdiw0Sgh16FG7mRjrJu0n09dFV9CXg38BDwBPBxVX1cRHaLyO7ear8F/KSIHAeOAu9T1VN5DTqIIB1cxLHHiZzsyLnpAsior0vYF61oJb3WKIK8E3A6QabRm8sMGGblaUVtp+mVnE0n6PwsKxgbq7BIVY+o6itU9QdVdaa3bL+q7u/9/Yyq/pyqblHVV6rq4TwHHYQ3RgmOMXfDA4lsbFLtJukNIISwLzosPa7KhN2clnU59LE3zGiXHTDMytOK2k7TKznLJG9nYM9n97DzgZ2rzs/tm7eXcpNuVKWoq4O326tniottY5PmMKYU7/0nWlknQB4k9U6ijHbZmT9ZeVr9thOm81o6Y3rydga6x7vsP7Z/lVR67sVzHHnySCk36UYZdJeBAqRJcxhTJLEHnWgHHzvIrut2NcJLSyohRBntsgOGWckhabZT9tNJ3cnbGQiLe8GlJ6+ig7GNNOipCoVckuaXp0hiDzvRjjx5pPIZH3FIKiFEGe2yC0OykkPSbKfsp5O6k7czELWdsgLajeyHnmoC6EE/MEGWy7D1b+5HVB72zLaZoZrI2Ete50lYXrZ3uVvFe/r86YvrQLb95fMm7/z+sO279RV5HZuh64eeQxFn/w/0Nofp80Fle515kVbvjZIjhjlgmMd5Eibj7PnsnhXLF88vsnh+8eI6b/vk23j7p96eWP5Jck5kHS/IO3soaPuCsHvr7tLOz0YadEhsYwslrA3r2QtnS9FHs7iQBtF7+xntPLXIqOyasoKR7mfPn5lfVWw2qEEKk3FmH52NnP3qxeUXubB0YdX/Rck/Sc6JPOIFeTsDQds/dOsh9t28L5Ptp6G2kksVSvUHoXu8yx2fu4PF8yv79hYtJ2RVolzH8vWwfd913S4OPnawFJknaEyCoOhFCWqQMYTJOIMgSKAEk+ScqOP5UxaNk1wqMEfzwHS2dFi/bv2q5a7XU5SHmFXgrexslDQk8VaLCkYGjck15lk8nYTJNWFTGMYhzKNOck7U8fyJoqwnvFoa9IxqeUonao7KotLVsrqQ6hgXCNvHoFYFUetDdtJN3oYtTFeeun4qcjamtSNrWTcaPcWB/6aX5Jyo4/kTRpnpprU06JlM9FwBorylpB6i13Bs/NBGNn5o48W/1//2euRuQe4WNn5o44oTK6sLqY7l60m91aSFUf5AoxtcdL+bIAOft2EL05X33bxvxfLWWIvWWOviOh/5xY9w4JYDF98Pw3vjSXJOxF03D883622WmW5aSw190Fa5VSFMw40KTgXpqEHbiWLd6Dpu/9HbOfLkkYuBN6+umlQv9k7LNiqjLOlSJnpv1vjT9bZv3h6olSfV0MP0X/dYROHf7p7P7gmsPmyNtdh7097KHM9+qaZR6Y9RHTqj0iLzaEmbR8wi77TkxmnoOUwWlA99plAK85ai+rcEPb6FNcMK48LSBfYf23/xglT0otfVGmsxtmaMnQ/sjOWteL1TcOQKb8phVUhSnev3VqOyI7rHu6Hzi/Yz5rDSc+se73LwsYOBxmDx/GKix/a8Ndwwj3r75u2r0h/Pv3SeQ7ce6hsD6JfNlIfnGxazgOBrLQ5lyke19NCh4CyXNB82QHVTHI/bG/3PKnOhNdbi/EvnE3lAdclOyGOc/b6nOB46XPLcwsboJc54i5o4O8ijDptAO4vzIQ/PN861k3TseR//xnnoUGCeeVBKzY4dsHFjdFrNAJFbr+cehlerzOrOv3h+MbEHVJfshDzGGfVkFCfQ6OJ+f3HGEmedojTcII86z/MhD883zv8mHXuZxXC1NeiFEWSYARYXo3MlB4zcJpldPqxQKSvS9KyoWnZCHuOMOi5hgUZ/pog38BdnLIMYoCJusnmeD3kE3uNcO2nGXtZMXGbQ+xFlgKM87oE6hF0izkns9wi8GQqtsRaXrb3s4rqtsRbv2vquwG22xlrBQ444oeuS3ZLHOKN62AdVuZ5676kVmSJ+z62fcYk73jJvsnmeD3l4vv6n4awrcwtHVUv5uf7667UWtNuqjtgS/CMS/H+HD6uOj69cd3zcWZ6Qw185rO3faav8pmj7d9p6+CvJtxFnm4e/cljHZ8aV3+Tiz/jMeN/Py2N8eZD1ONMer7hjbH2wpa0PthKPN49xJaEu50MQdRg7cExD7KoZ9H4EGWbvT7sd/b/ttmP02+1Uxrxo6nBCV4mqHq+qjssYnCiDXtssl0LpduGOOxzd3EuuPXkHo19Or2EY9aSRWS6F0unAqVNw+HAmPXnzzhGOKj22Kc0Mo7mYh14wSXJU03rZYfnMafLMDcOoFgN76CJyo4h8TUSeEpH3h6zzOhH5sog8LiJ/MciAm0zcHOGo/iD9POyw9LQ0eeaGYdSHvgZdREaBDwM3AdcCt4nItb51XgbsA35BVf858OYcxpoLfarzMydujnCY4XdL9qO6uCVNT6taEZBhGOmI46HfADylqk+r6gXgY8AtvnXeCjygqgsAqvpstsPMhzL6qsfNEQ4zsv4y5SAPOywXOE2euWEY9SGOQb8S+Kbn9YneMi+vAL5HRP5cRB4VkV8O2pCITInIMRE5dvLkyXQjzpAi+qr7g5DbN2+PVXiRxMj6jX9YAcbem/bWogjIMIx0rImxTlDzY38kdQ1wPbANGAP+WkQeVtWvr/gn1VlgFpygaPLhZkvefdW7x7tMfeLtnFNnLsb5M/McfPQP2XW907o2KtgZNNu9v82tS5Dx72zphAY6LZ3RMJpJHIN+Arja8/oq4JmAdU6p6gvACyLyReA64OtUmImJ4L7qCavzQ5l+8I6LxtzlnF7gyBd+n7kf+yjcGd1KFIjVvzuJhx1l6A3DqDdxJJdHgM0ico2IrAPeAjzoW+dTwE+LyBoRGQdeAzyR7VCzJ6ivuohj5LMIkC68uBi8/PLlWGK9v8FPkj7dhmEMH309dFV9SUTeDTwEjAIHVPVxEdnde3+/qj4hIn8KfAVYBu5T1a/mOfAscGuCpqcdIy7iBEfhUoDUu15SJs7A/MuCl18U6xNu3DxswzDCsMKiHnlMa9d9/UamfnKRc56OqeMXYPbT0DmOcwdZHnxKKsMwhgcr/Y9BWCD0tfPpE9U779jL7ENraX8HRKH9HY8xh+zEesMwDOIFRYeCoADpbXT5A5mC+V4QMqkO0+nQATphjb16k6BaIy3DMLLAPPQeQQHSD8g04zpgonqfxl5RjbQMwzCSYAa9R6fj2Fivzb1aUySqh/USCJkEtaj5Hw3DaD5m0D34ba60E04jF9FLIKxtbV0mWTYMo/qYhh7FzIxjkL39ATza9yp6vQS6W2B6GyxsgIkz59j+4L/j4Lxe9MRdWQWcKs+gVrfWX8UwjKSYhx5FkA4TNanFwgLdLTD1Rif/XMX5vf9HXgiVVeoyybJhGNXHPPR+dDrxi38mJpjeNr8i7xwcwx7EwpmFwBJ/y3IxDCMNZtCzZGaGhSd3xF7dlVWs+tMwjCwwySVLOh0mng8+pOIryJNzqroAABIDSURBVE0qq9hcoIZh9MMMeka4Bnf+8uXVxvsC7P4bUjfVslx1wzDiYL1cMiBo4mdRp2l8+wzMHIXOc+mbwoRN+tze0GbuznTbNAyjnlgvl5wJKg5SgVZv0c5bYfKdZ/t71CFFSZarbhhGHCwomgELAd4zwOI4LPYyXOZfWryYex4otbhFSedW942xXHXDMOJgHnoGTJwdDX7Dl64YWdIfMcGp5aobhhEHM+gZMPPQEuMXfAtDQhOhMknEBKdhkz5bqqNhGF5McsmAznNt+PS8p9wfzq6FxctWrztxdsTRyCcmnBYCbtFSnwlOLVfdMIx+mIeeBTMzdP7vOHP3wPLdMHcP7P3CWsZlZcno+IuON+9v3OVuY1X/3qi+MYZhGD7MoGdBQM+Xzq9+hNk3Hbgkk5wdZfZBz2xFsLK3uruNVuvS+2Njhe6GYRj1xvLQi2Jk5NIM1H7a7UueeFB3x6iGYIZhDBWWh56QsDkqBiJq/lBXfrnjjuBMl127MhqEYRhNJpZBF5EbReRrIvKUiLw/Yr1/ISJLIvJL2Q2xWCLmqBiMII3cy7lzq+cddVlaymgQhmE0mb4GXURGgQ8DNwHXAreJyLUh630QeCjrQRZJRDr4YHh19jRkMgjDMJpMHA/9BuApVX1aVS8AHwNuCVjvPcCfAM9mOL7CiUgHHxx3jrswo95qRXvxQWmNhmEYPeIY9CuBb3pen+gtu4iIXAm8CdgftSERmRKRYyJy7OTJk0nHWghhUneUBJ6YsBTFvXsdL340rPJUTHYxDCOUOAY9aL4df7rGPcD7VHUpakOqOquqW1V166ZNm+KOsVAKSQePmtqu04GDB53lflRNdjEMI5Q4Bv0EcLXn9VXAM751tgIfE5E54JeAfSLyi5mMsGCSTiOahBWTVJycpvvpGTh0yHlz585LKTWdTniKYybaj2EYTSRO6f8jwGYRuQb4FvAW4K3eFVT1GvdvEfkj4DOq+skMx1kocacR7R7vxp4L1N8zff7MPFOfeDs8qHTmX3RW8nRYpN2ObAVgGIbhp6+HrqovAe/GyV55Avi4qj4uIrtFZHfeA6wqSWcRCuqZfk4vMP3TL65c0c1msVYAhmEkxCpFU5J0FqGRu0fQgBaMok7/l5ULBZaXHflletqRWa64wnnv9OnVjb0MwxgarFI0B5LOIhQ2GcXEmaCFvXXdNMdDh+D8eafwKNNqJ8MwmoQZdC8Jav5DDXTI8sBJKmQdM19au3LFIFklt2onwzCahBl0l4Q1/0lnEQqcpOJNB+j86kf6p9TkWu1kGEZTMA3dZXIyOKuk3XZkjwCSZLkUPTbDMJpJlIZuBt0lrL2tG6AsE/8E0mBtdQ1jSLGgaBwKqflPSZ7VToZhNAYz6C5F5n2nabjuZrwsLzu/XWOeS/N2wzDqiBl0l4y84BXl/fdMri40yrLhem7N2w3DqCOmoWeIv7wfnMyX2TfOXgqWZhngtGCpYQwdpqEXRGB5/4vnmD7qyRfPMgXR0hkNw/BgBj1DYlWPZhl8rXIg1zCMwjGDniGxqkeDgq9r18LZs8kDm9bAyzAMD2bQMyRW9ag/+NpqOb/T9GmxdEbDMDxYUDRjElePWmDTMIwEWFA0Z7ypitNHp5nZNsPyXcvM3TnXvxVA0sCm5Z0bhhGCGfQBSTrRxSriBDZdIy7iTFXnzTvfudNZbsbdMIYeM+gDEitVMYp+gU1v8RCs7jfjvg7T3s2jN4yhwQx6SlyZJWjWIghPYVxFv8BmUC/0MPw90q2S1DCGCguKpiCoItRP2FR0iQnrAhmGtzukBVwNo3FYUDQOMaQJ1yvf8cCOSGMeNdFFYpIWCaleGr9VkhrGUGEGHfpKE93jXTZ+aCM7HtgRKrG4tDe0V/ZuGZQgjb0f7vjdiaX9WCWpYTSSNXFWEpEbgb3AKHCfqn7A934HeF/v5VngXar6WJYDzZWQOTtP7Jrm6g+C3DKFrumvY2cms3jxaukLC84TxNJS//87dw7GxpybgXffRGD79mzHaBhGJejroYvIKPBh4CbgWuA2EbnWt9o3gJ9V1VcBvwXMZj3QXAmRIL5/aQG2Tccy5pnKLH68vdAPHozvsZ8+Dbt2OUbcRdXZhgVGDaNxxJFcbgCeUtWnVfUC8DHgFu8KqvpXqvrt3suHgauyHWbOhEgQC0zAhv56c+YySxRBWTGtVvC6ExNw5MjqoKo/G8YwjEYQx6BfCXzT8/pEb1kYtwOfC3pDRKZE5JiIHDt58mT8UeZNgE79AuP8BjNwJlxvHl87zuFbD8erCM0S/+xFe/eG57JbYNQwhoY4Bl0ClgXm0YnI63EM+vuC3lfVWVXdqqpbN23aFH+UeePzek+Mtnkns9xPB47OwIVgiWNszVjBAw0hKpfdWuwaxtAQx6CfAK72vL4KeMa/koi8CrgPuEVVF7MZXoF4vN6/ODjHp8Z7HvfxDnx6FjnTBkA897fF84vJyvzzJGzOUWuxaxhDQxyD/giwWUSuEZF1wFuAB70riMgE8ACwU1W/nv0wcyQg/3yVw/tch0PXz9He0EZ9DyeJyvzLwFrsGsbQ0Negq+pLwLuBh4AngI+r6uMisltEdvdW+09AC9gnIl8WkXqUgEbknwc5vLFmJKoK3hvV9LTjkS8vO7+np8MLqKz3i2HUluEu/U9YGh/WuyWX/PNBcG9U3vzz8XH4iZ+AL3xhZdaLiPO63Xby0w8eXP1/5tEbRmWw0v8wEmaAxJqRqExc73rHjsBCKY4eje7WuH9/8P9ZiqNh1ILhNugJM0A6WzrMvnGW9oY2ghSbf94Pf5vdNIQ9rVmKo2HUguGWXMKkiTpKDGHyURZYd0bDqAwmuYTRpAyQvLxoS3E0jNow3AYdwvO3e3jnC528Z7IaOedB9CsUuuyy5F0b63yDM4whxAx6BAPPF1okUW12x8fh93//0tNIHLwyi6UxGkYtMIMewcDzhRaJVz4CGB11fnu9bPdppJ9Rd1vsBuXpu5NSj4w4v0Vg40Yz9IZRAYY7KNqHkbtHVlWGglP+v3zXcgkjyoigYLCf8XHHaJ89G2+b69bBgQMmzxhGzlhQNCUTG4J16bDltcEfDHa9eS/nzsU35gAXLli+umGUjBn0CGa2zbBOVurS66RChUSD4A0GL2f0tLGwYK0DDKNEzKAHcGky6J1ceGEMXmiBCnynjT44C19pmKyQVSvdK65Yrbnv2LFSZzeDbxi5YRq6DzezZUUwVAX+Zjd8bh/QwDqbOJp6P9atg8svh8WEnZPrWshlGCVhGnoCgjJbEIUb9sMWx5tsXCW8P0MmKSMjjoae1JiD9YoxjAwxg+4jtBWuKGxzDE8jJ/txNfXDh5MVII2PD67Bz887ssz69Y40Y3KMYaRiuA26T8/t3ruHEYk4JBsWLqZoN5Ykk1CPjg4m0/h54QXHy/fnvJtxN4xYDK9B9xXNdP/ZPFPfupclXQr/nzMTqDotwxttX+JOQr0UcayywNvad2oK9uyxgKphRDC8Bn16eoV3Ob0Nzq2NWP/CuDNhNEMo+4Y1MUuruafh3Dm4996VGTRve9sliWbjRpNrjKFneLNcRkZW9P8euctJZlmFAmfajjE/fikTQyS79O3a0u06skjUOSTipDOmCZhmwciI80W1206/G8umMWqOZbn46XadC93DFSFS8OgLbbhnboUxB+ffh94J7HRg927HaIcxMREs2RSFe9f15sT7f0ZHL+XKe718k3iMmjF8Bn3PHser9Oi/3S3w3HetXnXd6DqmfmAm0BYtLV2cT3q42bcPDh0KDpy6vdT9kk2rdWn9qJtBUbhGf3FxZVDWL/Hs2OFk4qxff+lm4M3MMdnHKJnhMejdrnOx3XvvRYmguwU2/jrsuBV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"execution_count": 11, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "param_evals" + "np.prod(data_to_fit.shape)" ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": false - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "# Initial guess of parameters, and bounding constraints\n", - "theta = [3, 0.15,1e6]\n", - "boxBounds = [(2,5),(0.0,1.0),(1e6,1e6)]\n", - "\n", - "objSIR = SquareLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" + "## Fitting Square l loss" ] }, { @@ -702,7 +695,7 @@ { "data": { "text/plain": [ - "SquareLoss([3.0, 0.15, 1000000.0], SimulateOde([ODEVariable('S', 'S', None, True), ODEVariable('I', 'I', None, True), ODEVariable('R', 'R', None, True)], [ODEVariable('beta', 'beta', None, True), ODEVariable('gamma', 'gamma', None, True), ODEVariable('N', 'N', None, True)], [], [Transition('S', 'beta*S*I/N', 'T', 'I', None, None), Transition('I', 'gamma*I', 'T', 'R', None, None)], [], []).setParameters({'beta': 3.6, 'gamma': 0.2, 'N': 1000000.0}), [999998.0, 2.0, 0.0], 0.0, [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0, 16.25, 16.5, 16.75, 17.0, 17.25, 17.5, 17.75, 18.0, 18.25, 18.5, 18.75, 19.0, 19.25, 19.5, 19.75, 20.0, 20.25, 20.5, 20.75, 21.0, 21.25, 21.5, 21.75, 22.0, 22.25, 22.5, 22.75, 23.0, 23.25, 23.5, 23.75, 24.0, 24.25, 24.5, 24.75, 25.0, 25.25, 25.5, 25.75, 26.0, 26.25, 26.5, 26.75, 27.0, 27.25, 27.5, 27.75, 28.0, 28.25, 28.5, 28.75, 29.0, 29.25, 29.5, 29.75, 30.0, 30.25, 30.5, 30.75, 31.0, 31.25, 31.5, 31.75, 32.0, 32.25, 32.5, 32.75, 33.0, 33.25, 33.5, 33.75, 34.0, 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1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])" + "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" ] }, "execution_count": 13, @@ -711,21 +704,36 @@ } ], "source": [ - "objSIR" + "param_evals" ] }, { "cell_type": "code", "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "theta = [3, 0.15,N]\n", + "boxBounds = [(2,5),(0.0,1.0),(N,N)]\n", + "\n", + "objSIR = SquareLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "9032988605419.916" + "9494886226076.777" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -734,48 +742,6 @@ "objSIR.cost()" ] }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", - " warn('Method %s cannot handle constraints nor bounds.' % method,\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " fun: 6195089026101.063\n", - " hess_inv: array([[ 8.74144835e-12, -2.58712672e-14, 2.68445022e-06],\n", - " [-2.58712712e-14, 2.36956010e-15, -5.05342211e-09],\n", - " [ 2.68444976e-06, -5.05342099e-09, 8.41021723e-01]])\n", - " jac: array([ 4.61669282e+00, -8.58643370e+01, -1.67724298e-05])\n", - " message: 'Desired error not necessarily achieved due to precision loss.'\n", - " nfev: 57\n", - " nit: 11\n", - " njev: 46\n", - " status: 2\n", - " success: False\n", - " x: array([4.97711288e+00, 1.97932062e-01, 1.36982257e+06])\n" - ] - } - ], - "source": [ - "# perform optimization\n", - "res = minimize(fun=objSIR.cost,\n", - " jac=objSIR.sensitivity,\n", - " x0=theta,\n", - " bounds=boxBounds,\n", - " method='BFGS')\n", - "print(res)" - ] - }, { "cell_type": "code", "execution_count": 16, @@ -785,16 +751,16 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 6195089026101.077\n", + " fun: 6793822783898.12\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([ 7.75580656e+04, 3.96915387e+06, -2.81799456e-01])\n", + " jac: array([ 3.71447419e+06, 1.99495082e+07, -1.34416872e+01])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 16\n", - " nit: 10\n", - " njev: 16\n", + " nfev: 13\n", + " nit: 9\n", + " njev: 13\n", " status: 0\n", " success: True\n", - " x: array([3.63339968e+00, 1.97932071e-01, 1.00000000e+06])\n" + " x: array([3.61873217e+00, 1.95448280e-01, 1.00000000e+06])\n" ] } ], @@ -855,70 +821,27 @@ { "cell_type": "code", "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_evals" - ] - }, - { - "cell_type": "code", - "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Initial guess of parameters, and bounding constraints\n", - "theta = [3, 0.15,1e6]\n", - "boxBounds = [(2,5),(0.0,1.0),(1e6,1e6)]\n", - "\n", "objSIR = NormalLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "NormalLoss([3.0, 0.15, 1000000.0], SimulateOde([ODEVariable('S', 'S', None, True), ODEVariable('I', 'I', None, True), ODEVariable('R', 'R', None, 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"C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", - " warn('Method %s cannot handle constraints nor bounds.' % method,\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " fun: 3097544513423.658\n", - " hess_inv: array([[ 1.03645359e-11, -4.61156610e-14, 3.16936003e-06],\n", - " [-4.61156587e-14, 4.75500055e-15, -8.98088545e-09],\n", - " [ 3.16936002e-06, -8.98088607e-09, 1.00244138e+00]])\n", - " jac: array([ 9.35430284e+04, -3.55363991e+05, -3.39879219e-01])\n", - " message: 'Desired error not necessarily achieved due to precision loss.'\n", - " nfev: 55\n", - " nit: 9\n", - " njev: 46\n", - " status: 2\n", - " success: False\n", - " x: array([4.54710725e+00, 1.97932059e-01, 1.25147454e+06])\n" - ] - } - ], - "source": [ - "# perform optimization\n", - "res = minimize(fun=objSIR.cost,\n", - " jac=objSIR.sensitivity,\n", - " x0=theta,\n", - " bounds=boxBounds,\n", - " method='BFGS')\n", - "print(res)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " fun: 3097544513423.661\n", + " fun: 3396911392322.0205\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([ 3.87782251e+04, 1.98457720e+06, -1.40896750e-01])\n", + " jac: array([ 1.85722536e+06, 9.97472077e+06, -6.72080114e+00])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 16\n", - " nit: 10\n", - " njev: 16\n", + " nfev: 13\n", + " nit: 9\n", + " njev: 13\n", " status: 0\n", " success: True\n", - " x: array([3.63339968e+00, 1.97932071e-01, 1.00000000e+06])\n" + " x: array([3.61873217e+00, 1.95448280e-01, 1.00000000e+06])\n" ] } ], @@ -1003,7 +884,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1047,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": { "scrolled": false }, @@ -1059,36 +940,16 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-5.77039500e+02, -6.96845671e+03, 1.73111850e-03])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "objSIR.sensitivity()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "5058.726988342257" + "5066.230498539779" ] }, - "execution_count": 27, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1099,56 +960,14 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", - " warn('Method %s cannot handle constraints nor bounds.' % method,\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " fun: 4717.132458517435\n", - " hess_inv: array([[ 3.86858865e-03, -3.00734373e-05, 3.30190074e-06],\n", - " [-3.00734373e-05, 3.91747625e-06, 2.36516472e-08],\n", - " [ 3.30190074e-06, 2.36516472e-08, 1.00000000e+00]])\n", - " jac: array([ 2.13941642e-10, 3.42940432e-08, -7.68280694e-16])\n", - " message: 'Optimization terminated successfully.'\n", - " nfev: 28\n", - " nit: 12\n", - " njev: 27\n", - " status: 0\n", - " success: True\n", - " x: array([3.59089103e+00, 1.99441546e-01, 1.00000000e+06])\n" - ] - } - ], - "source": [ - "# perform optimization\n", - "res = minimize(fun=objSIR.cost,\n", - " jac=objSIR.sensitivity,\n", - " x0=theta,\n", - " bounds=boxBounds,\n", - " method='BFGS')\n", - "print(res)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev8+g3f90a1d.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:108: RuntimeWarning: invalid value encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev9+g9b40b86.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:108: RuntimeWarning: invalid value encountered in log\n", " logpdf_p3= -shape*np.log(mu/shape)\n" ] }, @@ -1156,16 +975,16 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4717.132458530734\n", + " fun: 4699.518465235284\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([ 1.00361678e-03, -6.86270665e-02, -3.60388445e-09])\n", + " jac: array([-2.55035382e-03, 1.18874416e-01, 9.14788362e-09])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", " nfev: 17\n", " nit: 10\n", " njev: 17\n", " status: 0\n", " success: True\n", - " x: array([3.59089697e+00, 1.99441247e-01, 1.00000000e+06])\n" + " x: array([3.58690764e+00, 1.99822701e-01, 1.00000000e+06])\n" ] } ], @@ -1181,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1232,218 +1051,218 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[4.000000e+00, 0.000000e+00],\n", - " [9.000000e+00, 0.000000e+00],\n", - " [2.300000e+01, 2.000000e+00],\n", - " [4.800000e+01, 3.000000e+00],\n", - " [1.650000e+02, 1.000000e+01],\n", - " [4.100000e+02, 1.900000e+01],\n", - " [5.290000e+02, 4.800000e+01],\n", - " [1.447000e+03, 9.100000e+01],\n", - " [4.994000e+03, 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1.287891e+06],\n", + " [3.060000e+02, 6.839440e+05],\n", + " [5.040000e+02, 1.324297e+06],\n", + " [4.220000e+02, 9.202280e+05],\n", + " [4.640000e+02, 1.225089e+06],\n", + " [3.390000e+02, 8.486330e+05],\n", + " [4.250000e+02, 1.208187e+06],\n", + " [4.330000e+02, 1.036617e+06],\n", + " [4.010000e+02, 1.232717e+06],\n", + " [2.850000e+02, 7.525510e+05],\n", + " [3.000000e+02, 8.188660e+05],\n", + " [2.870000e+02, 9.052010e+05],\n", + " [2.820000e+02, 1.013282e+06],\n", + " [2.440000e+02, 1.234490e+06],\n", + " [2.390000e+02, 1.250816e+06],\n", + " [1.650000e+02, 6.857060e+05],\n", + " [2.240000e+02, 1.135817e+06],\n", + " [1.490000e+02, 6.762300e+05],\n", + " [1.990000e+02, 9.279880e+05],\n", + " [2.270000e+02, 8.986760e+05],\n", + " [2.020000e+02, 9.308700e+05],\n", + " [2.010000e+02, 1.299470e+06],\n", + " [1.970000e+02, 9.945910e+05],\n", + " [1.420000e+02, 1.319829e+06],\n", + " [1.230000e+02, 1.088782e+06],\n", + " [9.500000e+01, 7.524870e+05],\n", + " [9.700000e+01, 7.591230e+05],\n", + " [9.400000e+01, 8.970040e+05],\n", + " [1.120000e+02, 1.111579e+06],\n", + " [8.600000e+01, 6.938370e+05],\n", + " [8.800000e+01, 7.845930e+05],\n", + " [7.300000e+01, 9.737570e+05],\n", + " [8.500000e+01, 1.002185e+06],\n", + " [6.100000e+01, 1.199501e+06]])" ] }, - "execution_count": 31, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1462,7 +1281,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 27, "metadata": { "scrolled": false }, @@ -1474,16 +1293,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "8999599.052695096" + "9531545.662791345" ] }, - "execution_count": 33, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1494,65 +1313,23 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", - " warn('Method %s cannot handle constraints nor bounds.' % method,\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " fun: 3555725.9211099287\n", - " hess_inv: array([[ 1.12854102e-07, -2.48584571e-09, 2.27949584e-06],\n", - " [-2.48584571e-09, 9.25888041e-10, 9.47168312e-09],\n", - " [ 2.27949584e-06, 9.47168312e-09, 1.00000832e+00]])\n", - " jac: array([ 1.48618754e-02, -2.24162553e-02, -5.37171184e-08])\n", - " message: 'Desired error not necessarily achieved due to precision loss.'\n", - " nfev: 71\n", - " nit: 11\n", - " njev: 60\n", - " status: 2\n", - " success: False\n", - " x: array([3.61442328e+00, 1.98453976e-01, 9.99999326e+05])\n" - ] - } - ], - "source": [ - "# perform optimization\n", - "res = minimize(fun=objSIR.cost,\n", - " jac=objSIR.sensitivity,\n", - " x0=theta,\n", - " bounds=boxBounds,\n", - " method='BFGS')\n", - "print(res)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " fun: 3555725.9211287573\n", + " fun: 3907922.0850938573\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([-7.98496027e-01, -2.01665131e+02, 2.88610490e-06])\n", + " jac: array([-1.00647944e-01, -9.22317346e+01, 3.62277449e-07])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", " nfev: 14\n", " nit: 10\n", " njev: 14\n", " status: 0\n", " success: True\n", - " x: array([3.61442613e+00, 1.98453791e-01, 1.00000000e+06])\n" + " x: array([3.59945214e+00, 1.98189223e-01, 1.00000000e+06])\n" ] } ], @@ -1568,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1612,7 +1389,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 31, "metadata": { "scrolled": false }, @@ -1621,9 +1398,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev8+g3f90a1d.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev9+g9b40b86.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n", - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev8+g3f90a1d.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev9+g9b40b86.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" ] } @@ -1635,36 +1412,16 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-2.86825346e+02, -3.46781844e+03, 8.60476039e-04])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "objSIR.sensitivity()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "5038.2958154688895" + "5033.73795866175" ] }, - "execution_count": 39, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1675,56 +1432,14 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Local\\Continuum\\anaconda3\\envs\\pygomloss20200624\\lib\\site-packages\\scipy\\optimize\\_minimize.py:533: RuntimeWarning: Method BFGS cannot handle constraints nor bounds.\n", - " warn('Method %s cannot handle constraints nor bounds.' % method,\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " fun: 4868.230711182974\n", - " hess_inv: array([[ 7.84349281e-03, -6.15336988e-05, 1.77314305e-06],\n", - " [-6.15336988e-05, 7.88477962e-06, 1.39267526e-08],\n", - " [ 1.77314305e-06, 1.39267526e-08, 1.00000000e+00]])\n", - " jac: array([-1.03570668e-08, 1.32455900e-07, 3.71928550e-14])\n", - " message: 'Optimization terminated successfully.'\n", - " nfev: 16\n", - " nit: 11\n", - " njev: 16\n", - " status: 0\n", - " success: True\n", - " x: array([3.59106407e+00, 1.99435913e-01, 1.00000000e+06])\n" - ] - } - ], - "source": [ - "# perform optimization\n", - "res = minimize(fun=objSIR.cost,\n", - " jac=objSIR.sensitivity,\n", - " x0=theta,\n", - " bounds=boxBounds,\n", - " method='BFGS')\n", - "print(res)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev8+g3f90a1d.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev9+g9b40b86.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" ] }, @@ -1732,16 +1447,16 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: 4868.230711171033\n", + " fun: 4851.585921020361\n", " hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([-8.31389989e-04, -6.80463204e-03, 2.98556969e-09])\n", + " jac: array([-1.51180907e-03, -1.39068296e-02, 5.42136740e-09])\n", " message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 26\n", - " nit: 12\n", - " njev: 26\n", + " nfev: 17\n", + " nit: 11\n", + " njev: 17\n", " status: 0\n", " success: True\n", - " x: array([3.59105802e+00, 1.99435910e-01, 1.00000000e+06])\n" + " x: array([3.58601328e+00, 1.99815114e-01, 1.00000000e+06])\n" ] } ], @@ -1757,7 +1472,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 34, "metadata": {}, "outputs": [ { diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index 584fe228..9363e735 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -16,7 +16,6 @@ import numpy as np -from pygom.model._model_errors import InitializeError from pygom.model.ode_utils import check_array_type from pygom.utilR.distn import dpois, gamma_mu_shape, dnbinom @@ -50,16 +49,14 @@ def __init__(self, y, weights=None): self._w = np.ones(self._y.shape) else: self._w = check_array_type(weights,accept_booleans=True) - if np.any(self._w<0.0): + if (self._w<0).any(): raise ValueError('No elements in numpy array of weights should be negative') - if np.all(self._w==0.0): + if (self._w==0).all(): raise ValueError('All elements in numpy array of weights should not be 0.0') if len(self._w.shape) > 1: if 1 in self._w.shape: - self._w = self._w.flatten() - - assert self._y.shape == self._w.shape, \ - "Input weight not of the same size as y" + self._w = self._w.flatten() + assert self._y.shape == self._w.shape, "Input weight not of the same size as y" def residual(self, yhat, apply_weighting = True): ''' @@ -188,7 +185,7 @@ def __init__(self, y, sigma=1.0, weights=None): super().__init__(y, weights) err_str = "Standard deviation not of the correct " if isinstance(sigma, np.ndarray): - if np.any(sigma<0): + if (sigma<0).any(): raise ValueError('No elements in numpy array of sigma values should be negative') elif len(sigma.shape) > 1: if 1 in sigma.shape: @@ -196,12 +193,12 @@ def __init__(self, y, sigma=1.0, weights=None): if y.shape == sigma.shape: self._sigma = sigma else: - raise InitializeError(err_str + "size") + raise AssertionError(err_str + "size") else: if y.shape == sigma.shape: self._sigma = sigma else: - raise InitializeError(err_str + "size") + raise AssertionError(err_str + "size") elif sigma is None or sigma == 1.0: self._sigma = np.ones(self._y.shape) elif isinstance(sigma, (int, float)): @@ -310,7 +307,7 @@ def __init__(self, y, shape=2.0, weights=None): super().__init__(y, weights) err_str = "Shape is not of the correct " if isinstance(shape, np.ndarray): - if np.any(shape<0): + if (shape<0).any(): raise ValueError('No elements in numpy array of shape values should be negative') elif len(shape.shape) > 1: if 1 in shape.shape: @@ -318,12 +315,12 @@ def __init__(self, y, shape=2.0, weights=None): if y.shape == shape.shape: self._shape = shape else: - raise InitializeError(err_str + "size") + raise AssertionError(err_str + "size") else: if y.shape == shape.shape: self._shape = shape else: - raise InitializeError(err_str + "size") + raise AssertionError(err_str + "size") elif shape is None or shape == 2.0: self._shape = 2*np.ones(self._y.shape) elif isinstance(shape, (int, float)): @@ -522,20 +519,20 @@ def __init__(self, y, k=1.0, weights=None): super().__init__(y, weights) err_str = "k (the overdispersion parameter) is not of the correct " if isinstance(k, np.ndarray): - if np.any(k<0): - raise ValueError('No elements in numpy array of shape values should be negative') + if (k<0).any(): + raise ValueError('No elements in numpy array of k values should be negative') elif len(k.shape) > 1: if 1 in k.shape: k = k.flatten() if y.shape == k.shape: self._k = k else: - raise InitializeError(err_str + "size") + raise AssertionError(err_str + "size") else: if y.shape == k.shape: self._k = k else: - raise InitializeError(err_str + "size") + raise AssertionError(err_str + "size") elif k is None or k == 1.0: self._k = np.ones(self._y.shape) elif isinstance(k, (int, float)): diff --git a/tests/test_loss_types.py b/tests/test_loss_types.py index c4d7fc88..6b073328 100644 --- a/tests/test_loss_types.py +++ b/tests/test_loss_types.py @@ -2,48 +2,58 @@ import numpy as np -from pygom import SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss + +from pygom import Transition, TransitionType, SimulateOde, SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss from pygom.model import common_models class Test_Square_loss_class(TestCase): def setUp(self): # initial values - self.x0 = [-1.0, 1.0] + N = 1e6 + in_inf = 1 + self.init_state = [N - in_inf, in_inf, 0.0] # params - self.param_eval = [('a', 0.2), ('b', 0.2),('c', 3.0)] + self.param_eval = [('beta', 3.6), ('gamma', 0.2), ('N', N)] # the time points for our observations - self.t = np.linspace(0, 20, 30).astype('float64') - self.ode = common_models.FitzHugh(self.param_eval) - self.ode.initial_values = (self.x0, self.t[0]) + self.t = np.arange (0 , 51 , 0.25) + states = ['S', 'I', 'R'] + params = ['beta', 'gamma', 'N'] + transitions = [Transition(origin='S', destination='I', equation='beta*S*I/N', + transition_type=TransitionType.T), + Transition(origin='I', destination='R', equation='gamma*I', + transition_type=TransitionType.T)] + self.ode = SimulateOde(states, params, transition=transitions) + self.ode.parameters = self.param_eval + self.ode.initial_values = (self.init_state, self.t[0]) # Standard. Find the solution which we will be used as # "observations later" - self.solution = self.ode.integrate(self.t[1::]) + self.solution = self.ode.integrate(self.t[1:]) # initial guess - self.theta = [0.5, 0.5, 0.5] + self.theta = [3, 0.15,N] - obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R']) + obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R']) self.r = obj.residual() def test_FH_scalar_weights_for_two_states(self): # weight for each component w = [2.0, 3.0] - + s = 0 for i in range(2): s += ((self.r[:,i]*w[i])**2).sum() - obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R'], w) + obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) self.assertTrue(np.allclose(obj.cost(), s)) def test_FH_vector_weights_for_two_states(self): # now the weight is a vector - w = np.random.rand(29, 2) - obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], - self.t[1::], self.solution[1::,:], ['V', 'R'], w) + w = np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]) + obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) s = ((self.r * np.array(w))**2).sum() @@ -52,15 +62,14 @@ def test_FH_vector_weights_for_two_states(self): def test_FH_1State_weights_Failures_TypeErrors(self): w_list = list() - w_list.append(['a']) - w_list.append([True]) - test_weight=np.ones(len(self.solution[1::,-1])) - test_weight[-1]=False + w_list.append('a') + test_weight=np.ones(len(self.solution[1::,-1])).tolist() + test_weight[-1]='b' w_list.append(test_weight) for w in w_list: with self.subTest('Weightings',w=w): self.assertRaises(TypeError, SquareLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,-1], + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], 'R', w) def test_FH_1State_weights_Failures_ValueErrors(self): @@ -73,153 +82,79 @@ def test_FH_1State_weights_Failures_ValueErrors(self): for w in w_list: with self.subTest('Weightings',w=w): self.assertRaises(ValueError, SquareLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,-1], + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], 'R', w) def test_FH_1State_weights_Failures_ShapeErrors(self): w_list = list() w_list.append([2.0, 3.0]) - w_list.append(np.random.rand(30)) + w_list.append(np.random.rand(self.solution[1::,-1].shape[0]+1).tolist()) for w in w_list: with self.subTest('Weightings',w=w): self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,-1], + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], 'R', w) def test_FH_2State_weights_Failures_TypeErrors(self): w_list = list() w_list.append(['a','b']) - w_list.append([True,False]) - test_weight=np.ones(self.solution[1::,:].shape) - test_weight[-1,-1]=False + test_weight=np.ones(self.solution[1::,1:3].shape).tolist() + test_weight[-1][-1]='c' w_list.append(test_weight) for w in w_list: with self.subTest('Weightings',w=w): self.assertRaises(TypeError, SquareLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,:], - ['V', 'R'], w) + self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], + ['I', 'R'], w) def test_FH_2State_weights_Failures_ValueErrors(self): w_list = list() - test_weight=np.ones(self.solution[1::,:].shape) + test_weight=np.ones(self.solution[1::,1:3].shape) w_list.append(-test_weight) test_weight[-1,-1]=-1 w_list.append(test_weight) - w_list.append(np.zeros(self.solution[1::,:].shape)) + w_list.append(np.zeros(self.solution[1::,1:3].shape)) w_list.append([1.0, -1.0]) w_list.append([0, 0]) for w in w_list: with self.subTest('Weightings',w=w): self.assertRaises(ValueError, SquareLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,:], - ['V', 'R'], w) + self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], + ['I', 'R'], w) def test_FH_2State_weights_Failures_ShapeErrors(self): w_list = list() w_list.append([2.0, 3.0, 4.0]) - w_list.append(np.random.rand(30)) - w_list.append([np.random.rand(30), np.random.rand(31)]) - w_list.append([np.random.rand(31), np.random.rand(31)]) - w_list.append([np.random.rand(30), np.random.rand(30), np.random.rand(30)]) + w_list.append(np.random.rand(self.solution[1::,1:3].shape[0])) + w_list.append(np.random.rand(self.solution[1::,1:3].shape[0]+1,self.solution[1::,1:3].shape[1]).tolist()) + w_list.append(np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]+1).tolist()) for w in w_list: with self.subTest('Weightings',w=w): self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, - self.x0, self.t[0], self.t[1::], self.solution[1::,:], - ['V', 'R'], w) + self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], + ['I', 'R'], w) # class Test_Normal_loss_class(TestCase): -# def setUp(self): -# # initial values -# self.x0 = [-1.0, 1.0] -# # params -# self.param_eval = [('a', 0.2), ('b', 0.2),('c', 3.0)] -# # the time points for our observations -# self.t = np.linspace(0, 20, 30).astype('float64') -# self.ode = common_models.FitzHugh(self.param_eval) -# self.ode.initial_values = (self.x0, self.t[0]) - -# # Standard. Find the solution which we will be used as -# # "observations later" -# self.solution = self.ode.integrate(self.t[1::]) -# # initial guess -# self.theta = [0.5, 0.5, 0.5] - -# obj = SquareLoss(self.theta, self.ode, self.x0, self.t[0], -# self.t[1::], self.solution[1::,:], ['V', 'R']) -# self.r = obj.residual() - -# def test_FH_Normal_scalar_weight(self): -# # weight for each component -# w = [2.0, 3.0] - -# s = 0 -# for i in range(2): s += ((self.r[:,i]*w[i])**2).sum() - -# obj = NormalLoss(self.theta, self.ode, self.x0, self.t[0], -# self.t[1::], self.solution[1::,:], ['V', 'R'], w) -# self.assertTrue(np.allclose(obj.cost(), s)) - -# def test_FH_Normal_vector_weight(self): -# # now the weight is a vector -# w = np.random.rand(29, 2) -# obj = NormalLoss(self.theta, self.ode, self.x0, self.t[0], -# self.t[1::], self.solution[1::,:], ['V', 'R'], w) - -# s = ((self.r * np.array(w))**2).sum() - -# self.assertTrue(np.allclose(obj.cost(), s)) # def test_FH_Normal(self): -# objFH = NormalLoss(self.theta, self.ode, self.x0, self.t[0], -# self.t[1::], self.solution[1::,:], ['V', 'R']) +# objFH = NormalLoss(self.theta, self.ode, self.init_state, self.t[0], +# self.t[1::], self.solution[1::,1:3], ['I', 'R']) # w = [2.0, 3.0] -# objFH1 = NormalLoss(self.theta, self.ode, self.x0, self.t[0], -# self.t[1::], self.solution[1::,:], ['V', 'R'], w) +# objFH1 = NormalLoss(self.theta, self.ode, self.init_state, self.t[0], +# self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) # # now the weight is a vector # w = np.random.rand(29, 2) -# objFH2 = NormalLoss(self.theta, self.ode, self.x0, self.t[0], -# self.t[1::], self.solution[1::,:], ['V', 'R'], w) +# objFH2 = NormalLoss(self.theta, self.ode, self.init_state, self.t[0], +# self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) # self.assertFalse(np.allclose(objFH.cost(), objFH1.cost())) # self.assertFalse(np.allclose(objFH1.cost(), objFH2.cost())) -# def test_FH_Normal_1State_Fail(self): -# ## totalFail = 0 -# ## expectedFail = 4 -# w_list = list() - -# w_list.append([-1.]) -# w_list.append([0]) -# w_list.append([2.0, 3.0]) -# w_list.append(np.random.rand(30)) - -# for w in w_list: -# self.assertRaises(AssertionError, NormalLoss, self.theta, self.ode, -# self.x0, self.t[0], self.t[1::], self.solution[1::,:], -# 'R', w) - -# def test_FH_Normal_2State_Fail(self): -# ## totalFail = 0 -# ## expectedFail = 8 -# w_list = list() - -# w_list.append([-2.0]) -# w_list.append([2.0, 3.0, 4.0]) -# w_list.append([0.0, 0.0]) -# w_list.append([1.0, -1.0]) -# w_list.append(np.random.rand(30)) -# w_list.append([np.random.rand(30), np.random.rand(31)]) -# w_list.append([np.random.rand(31), np.random.rand(31)]) -# w_list.append([np.random.rand(30), np.random.rand(30), np.random.rand(30)]) - -# for w in w_list: -# self.assertRaises(AssertionError, NormalLoss, self.theta, self.ode, -# self.x0, self.t[0], self.t[1::], self.solution[1::,:], -# 'R', w) + if __name__ == '__main__': From 990f8769fb802566f0bd3218e7c36fada5830813 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Wed, 1 Jul 2020 17:12:37 +0100 Subject: [PATCH 011/188] Unittests for weightings and comparison of costs done --- pygom/loss/base_loss.py | 51 ++- pygom/loss/loss_type.py | 6 +- pygom/loss/ode_loss.py | 27 +- .../model/ode_utils/checks_and_conversions.py | 7 +- tests/test_loss_types.py | 337 ++++++++++++++---- 5 files changed, 314 insertions(+), 114 deletions(-) diff --git a/pygom/loss/base_loss.py b/pygom/loss/base_loss.py index 9f6df6d3..e908c33d 100644 --- a/pygom/loss/base_loss.py +++ b/pygom/loss/base_loss.py @@ -44,8 +44,12 @@ class BaseLoss(object): observations state_name: str the state which the observations came from - state_weight: array like + state_weight: array like or none weight for the observations + spread_param: array like or none + spead parameter for obsevations + (e.g. normal, negative binomial and gamma distributions sigma, k and + shape, respectivly). target_param: str or array like parameters that are not fixed target_state: str or array like @@ -55,7 +59,7 @@ class BaseLoss(object): def __init__(self, theta, ode, x0, t0, t, y, - state_name, state_weight=None, + state_name, state_weight=None,spread_param=None, target_param=None, target_state=None): ### Execute all the checks first @@ -136,7 +140,9 @@ def __init__(self, theta, ode, # then if if solution.shape[1] == p: state_name = [str(i) for i in self._ode._iterStateList()] - self._setWeight(n, p, state_weight) + self._weight = self._setWeight_or_spread(n, p, state_weight,is_weights= True) + if spread_param is not None: + self._spread_param = self._setWeight_or_spread(n, p, spread_param,is_weights= False) else: raise InputError("Expecting the name of states " + "for the observations") @@ -145,7 +151,9 @@ def __init__(self, theta, ode, state_name = [state_name] assert p == len(state_name), "len(state_name) and len(y[0]) not equal" - self._setWeight(n, p, state_weight) + self._weight = self._setWeight_or_spread(n, p, state_weight,is_weights= True) + if spread_param is not None: + self._spread_param = self._setWeight_or_spread(n, p, spread_param,is_weights= False) else: raise InputError("State name should be str or of type list/tuple") @@ -1570,37 +1578,46 @@ def _setParam(self, theta): theta = ode_utils.check_array_type(theta) self._theta = np.copy(theta) - def _setWeight(self, n, p, w): + def _setWeight_or_spread(self, n, p, x,is_weights): # note that we NEVER scale the weights # also note that we can use the weights as a control # with normalized input - - w = ode_utils.check_array_type(w) - if len(w) == w.size: - m, q = len(w), 1 + x = ode_utils.check_array_type(x,accept_booleans=is_weights) + + if is_weights== True: + object_type='weights' + else: + object_type='spread parameter values' + + if len(x) == x.size: + m, q = len(x), 1 else: - m, q = w.shape + m, q = x.shape if p == q: if n == m: - self._stateWeight = w + x = x elif m == 1: - self._stateWeight = np.ones((n, p))*w + x = np.ones((n, p))*x else: - raise AssertionError("Number of input weights is not equal " + + raise AssertionError("Number of input " + object_type + + " is not equal " + "to the number of observations") elif p == m: if q == 1: - self._stateWeight = np.ones((n, p))*w + x = np.ones((n, p))*x else: - raise AssertionError("Number of input weights is not equal " + + raise AssertionError("Number of input " + object_type + + " is not equal " + "to number of states") else: if q == 1 and m == 1: - self._stateWeight = np.ones((n, p))*w + x = np.ones((n, p))*x else: - raise AssertionError("Number of input weights differs from " + + raise AssertionError("Number of input " + object_type + + " differs from " + "the number of observations") + return x def _setX0(self, x0): """ diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index 9363e735..745fd4ea 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -181,7 +181,7 @@ class Normal(baseloss_type): sigma: float standard deviation ''' - def __init__(self, y, sigma=1.0, weights=None): + def __init__(self, y, weights=None, sigma=1.0): super().__init__(y, weights) err_str = "Standard deviation not of the correct " if isinstance(sigma, np.ndarray): @@ -303,7 +303,7 @@ class Gamma(baseloss_type): shape (a in latex equations) ''' - def __init__(self, y, shape=2.0, weights=None): + def __init__(self, y, weights=None, shape=2.0): super().__init__(y, weights) err_str = "Shape is not of the correct " if isinstance(shape, np.ndarray): @@ -515,7 +515,7 @@ class NegBinom(baseloss_type): Overdispersion parameter (k=mean+mean(mean/variance)) ''' - def __init__(self, y, k=1.0, weights=None): + def __init__(self, y, weights=None, k=1.0): super().__init__(y, weights) err_str = "k (the overdispersion parameter) is not of the correct " if isinstance(k, np.ndarray): diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index e4182f54..ae0992ff 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -24,14 +24,14 @@ class SquareLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name, state_weight=None, target_param=None, target_state=None): - super().__init__(theta, ode, x0, t0, t, y, - state_name, state_weight,target_param, target_state) + super().__init__(theta, ode, x0, t0, t, y, state_name, state_weight, + None, target_param, target_state) def __repr__(self): return "SquareLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = Square(self._y, self._stateWeight) + self._lossObj = Square(self._y, self._weight) return self._lossObj class NormalLoss(BaseLoss): @@ -40,15 +40,14 @@ class NormalLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, sigma=1.0, target_param=None, target_state=None): - self._sigma=sigma super().__init__(theta, ode, x0, t0, t, y, - state_name, state_weight,target_param, target_state) + state_name, state_weight, sigma, target_param, target_state) def __repr__(self): return "NormalLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = Normal(self._y,self._sigma, self._stateWeight) + self._lossObj = Normal(self._y, self._weight, self._spread_param) return self._lossObj class GammaLoss(BaseLoss): @@ -57,15 +56,14 @@ class GammaLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, shape=2, target_param=None, target_state=None): - self._shape=shape super().__init__(theta, ode, x0, t0, t, y, - state_name, state_weight,target_param, target_state) + state_name, state_weight, shape,target_param, target_state) def __repr__(self): return "GammaLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = Gamma(self._y,self._shape,self._stateWeight) + self._lossObj = Gamma(self._y, self._weight, self._spread_param) return self._lossObj class PoissonLoss(BaseLoss): @@ -74,14 +72,14 @@ class PoissonLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, target_param=None, target_state=None): - super().__init__(theta, ode, x0, t0, t, y, - state_name, state_weight,target_param, target_state) + super().__init__(theta, ode, x0, t0, t, y, state_name, state_weight, + None,target_param, target_state) def __repr__(self): return "PoissonLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = Poisson(self._y,self._stateWeight) + self._lossObj = Poisson(self._y,self._weight) return self._lossObj class NegBinomLoss(BaseLoss): @@ -90,13 +88,12 @@ class NegBinomLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, k=1, target_param=None, target_state=None): - self._k=k super().__init__(theta, ode, x0, t0, t, y, - state_name, state_weight,target_param, target_state) + state_name, state_weight, k, target_param, target_state) def __repr__(self): return "NegBinomLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = NegBinom(self._y,self._k,self._stateWeight) + self._lossObj = NegBinom(self._y, self._weight,self._spread_param) return self._lossObj \ No newline at end of file diff --git a/pygom/model/ode_utils/checks_and_conversions.py b/pygom/model/ode_utils/checks_and_conversions.py index b9817d85..9d24c5a3 100644 --- a/pygom/model/ode_utils/checks_and_conversions.py +++ b/pygom/model/ode_utils/checks_and_conversions.py @@ -55,7 +55,12 @@ def check_array_type(x,accept_booleans=False): else: raise TypeError(type_error_message) elif isinstance(x, accepted_types): - x = np.array([x]) + if accept_booleans==True: + x = np.array(x) + elif accept_booleans==False and not isinstance(x, bool): + x = np.array(x) + else: + TypeError("Not expecting Boolean value") else: raise TypeError("Expecting an array like object, got %s" % type(x)) diff --git a/tests/test_loss_types.py b/tests/test_loss_types.py index 6b073328..0a923a9a 100644 --- a/tests/test_loss_types.py +++ b/tests/test_loss_types.py @@ -4,9 +4,8 @@ from pygom import Transition, TransitionType, SimulateOde, SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss -from pygom.model import common_models -class Test_Square_loss_class(TestCase): +class Test_loss_classes(TestCase): def setUp(self): # initial values @@ -32,83 +31,170 @@ def setUp(self): self.solution = self.ode.integrate(self.t[1:]) # initial guess self.theta = [3, 0.15,N] + + def test_all_Loss_functions_produce_different_costs(self): + Square_obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R']) + Normal_obj = NormalLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R']) + Gamma_obj = GammaLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R']) + Poisson_obj = PoissonLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R']) + NegBinom_obj = NegBinomLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R']) + + comparisons = [[Square_obj.cost(),Normal_obj.cost(),'SquareLoss compared to NormalLoss'], + [Square_obj.cost(),Gamma_obj.cost(),'SquareLoss compared to GammaLoss'], + [Square_obj.cost(),Poisson_obj.cost(),'SquareLoss compared to PossionLoss'], + [Square_obj.cost(),NegBinom_obj.cost(),'SquareLoss compared to NegBinomLoss'], + [Normal_obj.cost(),Gamma_obj.cost(),'NormalLoss compared to GammaLoss'], + [Normal_obj.cost(),Poisson_obj.cost(),'NormalLoss compared to PossionLoss'], + [Normal_obj.cost(),NegBinom_obj.cost(),'NormalLoss compared to NegBinomLoss'], + [Gamma_obj.cost(),Poisson_obj.cost(),'GammaLoss compared to PossionLoss'], + [Gamma_obj.cost(),NegBinom_obj.cost(),'GammaLoss compared to NegBinomLoss'], + [Poisson_obj.cost(),NegBinom_obj.cost(),'PoissonLoss compared to NegBinomLoss'] + ] + + for comparison in comparisons: + message = comparison[-1] + with self.subTest(message): + self.assertNotAlmostEqual(comparison[0],comparison[1],places=2) - obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], - self.t[1::], self.solution[1::,1:3], ['I', 'R']) - self.r = obj.residual() - def test_FH_scalar_weights_for_two_states(self): + def test_Square_and_Normal_Loss_cost_scalar_weights_for_two_states(self): + loss_functions = [SquareLoss, NormalLoss] # weight for each component w = [2.0, 3.0] - s = 0 - for i in range(2): s += ((self.r[:,i]*w[i])**2).sum() - obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], - self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - - self.assertTrue(np.allclose(obj.cost(), s)) + self.t[1::], self.solution[1::,1:3], ['I', 'R']) + self.r = obj.residual() + residual = [] + for i in range(2): + residual.append(self.r[:,i]*w[i]) + + residual = np.array(residual) + + square_cost = (residual**2).sum() + sigma= 1 + norm_logpdf_p1= -np.log(2) + norm_logpdf_p2= np.log(2)/2 + norm_logpdf_p3= -np.log(np.pi)/2 + norm_logpdf_p4= np.log(1/sigma) + norm_logpdf_p5_alt= -residual**2 / (2*sigma**2) + norm_cost = (-(norm_logpdf_p1+norm_logpdf_p2+norm_logpdf_p3+ + norm_logpdf_p4+norm_logpdf_p5_alt)).sum() + test_answers=[square_cost,norm_cost] + + for loss_function_index in range(len(loss_functions)): + loss_function= loss_functions[loss_function_index] + message = str(loss_function) + with self.subTest(message): + obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=2) - def test_FH_vector_weights_for_two_states(self): + def test_Square_and_Normal_Loss_cost_vector_weights_for_two_states(self): + loss_functions = [SquareLoss, NormalLoss] + obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R']) + self.r = obj.residual() # now the weight is a vector w = np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]) - obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], - self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - - s = ((self.r * np.array(w))**2).sum() - - self.assertTrue(np.allclose(obj.cost(), s)) - + + residual = self.r* np.array(w) + square_cost = (residual**2).sum() + sigma= 1 + norm_logpdf_p1= -np.log(2) + norm_logpdf_p2= np.log(2)/2 + norm_logpdf_p3= -np.log(np.pi)/2 + norm_logpdf_p4= np.log(1/sigma) + norm_logpdf_p5_alt= -residual**2 / (2*sigma**2) + norm_cost = (-(norm_logpdf_p1+norm_logpdf_p2+norm_logpdf_p3+ + norm_logpdf_p4+norm_logpdf_p5_alt)).sum() + + test_answers=[square_cost,norm_cost] - def test_FH_1State_weights_Failures_TypeErrors(self): + for loss_function_index in range(len(loss_functions)): + loss_function= loss_functions[loss_function_index] + message = str(loss_function) + with self.subTest(message): + obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=2) + + + def test_All_Loss_functions_1State_weights_Failures_TypeErrors(self): + loss_functions = [SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss] w_list = list() w_list.append('a') test_weight=np.ones(len(self.solution[1::,-1])).tolist() test_weight[-1]='b' w_list.append(test_weight) - for w in w_list: - with self.subTest('Weightings',w=w): - self.assertRaises(TypeError, SquareLoss, self.theta, self.ode, - self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], - 'R', w) + for loss_function in loss_functions: + for weight_index in range(len(w_list)): + w = w_list[weight_index] + message = str(loss_function)+' with weighting '+str(weight_index) + with self.subTest(message): + self.assertRaises(TypeError, loss_function, self.theta, self.ode, + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', w) - def test_FH_1State_weights_Failures_ValueErrors(self): + def test_AllLoss_1State_weights_Failures_ValueErrors(self): + loss_functions = [SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss] w_list = list() + w_list.append(-1) + w_list.append(0) test_weight=np.ones(len(self.solution[1::,-1])) w_list.append(-test_weight) test_weight[-1]=-1 w_list.append(test_weight) w_list.append(np.zeros(len(self.solution[1::,-1]))) - for w in w_list: - with self.subTest('Weightings',w=w): - self.assertRaises(ValueError, SquareLoss, self.theta, self.ode, - self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], - 'R', w) + for loss_function in loss_functions: + for weight_index in range(len(w_list)): + w = w_list[weight_index] + message = str(loss_function)+' with weighting '+str(weight_index) + with self.subTest(message): + self.assertRaises(ValueError, loss_function, self.theta, self.ode, + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', w) - def test_FH_1State_weights_Failures_ShapeErrors(self): + def test_AllLoss_1State_weights_Failures_ShapeErrors(self): + loss_functions = [SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss] w_list = list() w_list.append([2.0, 3.0]) w_list.append(np.random.rand(self.solution[1::,-1].shape[0]+1).tolist()) - for w in w_list: - with self.subTest('Weightings',w=w): - self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, - self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], - 'R', w) + for loss_function in loss_functions: + for weight_index in range(len(w_list)): + w = w_list[weight_index] + message = str(loss_function)+' with weighting '+str(weight_index) + with self.subTest(message): + self.assertRaises(AssertionError, loss_function, self.theta, self.ode, + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', w) - def test_FH_2State_weights_Failures_TypeErrors(self): + def test_AllLoss_2State_weights_Failures_TypeErrors(self): + loss_functions = [SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss] w_list = list() w_list.append(['a','b']) test_weight=np.ones(self.solution[1::,1:3].shape).tolist() test_weight[-1][-1]='c' w_list.append(test_weight) - for w in w_list: - with self.subTest('Weightings',w=w): - self.assertRaises(TypeError, SquareLoss, self.theta, self.ode, - self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], - ['I', 'R'], w) + for loss_function in loss_functions: + for weight_index in range(len(w_list)): + w = w_list[weight_index] + message = str(loss_function)+' with weighting '+str(weight_index) + with self.subTest(message): + self.assertRaises(TypeError, loss_function, self.theta, self.ode, + self.init_state, self.t[0], self.t[1::], + self.solution[1::,1:3],['I', 'R'], w) - def test_FH_2State_weights_Failures_ValueErrors(self): + def test_AllLoss_2State_weights_Failures_ValueErrors(self): + loss_functions = [SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss] w_list = list() + w_list.append(-1) + w_list.append(0) test_weight=np.ones(self.solution[1::,1:3].shape) w_list.append(-test_weight) test_weight[-1,-1]=-1 @@ -116,46 +202,141 @@ def test_FH_2State_weights_Failures_ValueErrors(self): w_list.append(np.zeros(self.solution[1::,1:3].shape)) w_list.append([1.0, -1.0]) w_list.append([0, 0]) - for w in w_list: - with self.subTest('Weightings',w=w): - self.assertRaises(ValueError, SquareLoss, self.theta, self.ode, - self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], - ['I', 'R'], w) + for loss_function in loss_functions: + for weight_index in range(len(w_list)): + w = w_list[weight_index] + message = str(loss_function)+' with weighting '+str(weight_index) + with self.subTest(message): + self.assertRaises(ValueError, loss_function, self.theta, + self.ode,self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], + ['I', 'R'], w) - def test_FH_2State_weights_Failures_ShapeErrors(self): + def test_AllLoss_2State_weights_Failures_ShapeErrors(self): + loss_functions = [SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss] w_list = list() w_list.append([2.0, 3.0, 4.0]) w_list.append(np.random.rand(self.solution[1::,1:3].shape[0])) w_list.append(np.random.rand(self.solution[1::,1:3].shape[0]+1,self.solution[1::,1:3].shape[1]).tolist()) w_list.append(np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]+1).tolist()) - for w in w_list: - with self.subTest('Weightings',w=w): - self.assertRaises(AssertionError, SquareLoss, self.theta, self.ode, - self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], - ['I', 'R'], w) - -# class Test_Normal_loss_class(TestCase): - - - -# def test_FH_Normal(self): -# objFH = NormalLoss(self.theta, self.ode, self.init_state, self.t[0], -# self.t[1::], self.solution[1::,1:3], ['I', 'R']) - -# w = [2.0, 3.0] -# objFH1 = NormalLoss(self.theta, self.ode, self.init_state, self.t[0], -# self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - -# # now the weight is a vector -# w = np.random.rand(29, 2) -# objFH2 = NormalLoss(self.theta, self.ode, self.init_state, self.t[0], -# self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - -# self.assertFalse(np.allclose(objFH.cost(), objFH1.cost())) -# self.assertFalse(np.allclose(objFH1.cost(), objFH2.cost())) - - + for loss_function in loss_functions: + for weight_index in range(len(w_list)): + w = w_list[weight_index] + message = str(loss_function)+' with weighting '+str(weight_index) + with self.subTest(message): + self.assertRaises(AssertionError, loss_function, self.theta, + self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], + ['I', 'R'], w) + + + def test_Applicable_Loss_functions_1State_spread_param_Failures_TypeErrors(self): + loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] + spread_param_list = list() + spread_param_list.append('a') + spread_param_list.append(True) + test_spread_param=np.ones(len(self.solution[1::,-1])).tolist() + test_spread_param[-1]='b' + spread_param_list.append(test_spread_param) + test_spread_param[-1]=False + spread_param_list.append(test_spread_param) + for loss_function in loss_functions: + for spread_param_index in range(len(spread_param_list)): + spread_param = spread_param_list[spread_param_index] + message = str(loss_function)+' with spread params '+str(spread_param_index) + with self.subTest(message): + self.assertRaises(TypeError, loss_function, self.theta, self.ode, + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', None,spread_param) + + def test_Applicable_Loss_functionss_1State_spread_param_Failures_ValueErrors(self): + loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] + spread_param_list = list() + spread_param_list.append(-1) + test_spread_param=np.ones(len(self.solution[1::,-1])) + spread_param_list.append(-test_spread_param) + test_spread_param[-1]=-1 + spread_param_list.append(test_spread_param) + for loss_function in loss_functions: + for spread_param_index in range(len(spread_param_list)): + spread_param = spread_param_list[spread_param_index] + message = str(loss_function)+' with spread params'+str(spread_param_index) + with self.subTest(message): + self.assertRaises(ValueError, loss_function, self.theta, self.ode, + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', None,spread_param) + + def test_Applicable_Loss_functions_1State_spread_param_Failures_ShapeErrors(self): + loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] + spread_param_list = list() + spread_param_list.append([2.0, 3.0]) + spread_param_list.append(np.random.rand(self.solution[1::,-1].shape[0]+1).tolist()) + for loss_function in loss_functions: + for spread_param_index in range(len(spread_param_list)): + spread_param = spread_param_list[spread_param_index] + message = str(loss_function)+' with spread params'+str(spread_param_index) + with self.subTest(message): + self.assertRaises(AssertionError, loss_function, self.theta, self.ode, + self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], + 'R', None,spread_param) + def test_Applicable_Loss_functions_2State_spread_param_Failures_TypeErrors(self): + loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] + spread_param_list = list() + spread_param_list.append('a') + spread_param_list.append(True) + spread_param_list.append(['a','b']) + spread_param_list.append([True,False]) + test_spread_param=np.ones(self.solution[1::,1:3].shape).tolist() + test_spread_param[-1][-1]='c' + spread_param_list.append(test_spread_param) + test_spread_param[-1][-1]=False + spread_param_list.append(test_spread_param) + for loss_function in loss_functions: + for spread_param_index in range(len(spread_param_list)): + spread_param = spread_param_list[spread_param_index] + message = str(loss_function)+' with spread params'+str(spread_param_index) + with self.subTest(message): + self.assertRaises(TypeError, loss_function, self.theta, self.ode, + self.init_state, self.t[0], self.t[1::], + self.solution[1::,1:3],['I', 'R'], None,spread_param) + + def test_Applicable_Loss_functions_2State_spread_param_Failures_ValueErrors(self): + loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] + spread_param_list = list() + spread_param_list.append(-1) + test_spread_param=np.ones(self.solution[1::,1:3].shape) + spread_param_list.append(-test_spread_param) + test_spread_param[-1,-1]=-1 + spread_param_list.append(test_spread_param) + spread_param_list.append([1.0, -1.0]) + spread_param_list.append([-1.0, -1.0]) + for loss_function in loss_functions: + for spread_param_index in range(len(spread_param_list)): + spread_param = spread_param_list[spread_param_index] + message = str(loss_function)+' with spread params'+str(spread_param_index) + with self.subTest(message): + self.assertRaises(ValueError, loss_function, self.theta, + self.ode,self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], + ['I', 'R'], None,spread_param) + + def test_Applicable_Loss_functions_2State_spread_param_Failures_ShapeErrors(self): + loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] + spread_param_list = list() + spread_param_list.append([2.0, 3.0, 4.0]) + spread_param_list.append(np.random.rand(self.solution[1::,1:3].shape[0])) + spread_param_list.append(np.random.rand(self.solution[1::,1:3].shape[0]+1,self.solution[1::,1:3].shape[1]).tolist()) + spread_param_list.append(np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]+1).tolist()) + for loss_function in loss_functions: + for spread_param_index in range(len(spread_param_list)): + spread_param = spread_param_list[spread_param_index] + message = str(loss_function)+' with spread params'+str(spread_param_index) + with self.subTest(message): + self.assertRaises(AssertionError, loss_function, self.theta, + self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], + ['I', 'R'], None,spread_param) if __name__ == '__main__': main() From 2395c5544640d4de1fa9513c1a405b1ba40f1414 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Thu, 2 Jul 2020 13:28:11 +0100 Subject: [PATCH 012/188] Finished unittesting new loss functions, leading to several bug fixes and improvements --- ...s functions fitted to simulated data.ipynb | 294 ++++++++++-------- pygom/loss/base_loss.py | 23 +- .../model/ode_utils/checks_and_conversions.py | 28 +- tests/test_loss_types.py | 171 ++++++---- 4 files changed, 302 insertions(+), 214 deletions(-) diff --git a/notebooks/Testing loss functions fitted to simulated data.ipynb b/notebooks/Testing loss functions fitted to simulated data.ipynb index 150ab517..dc0c813b 100644 --- a/notebooks/Testing loss functions fitted to simulated data.ipynb +++ b/notebooks/Testing loss functions fitted to simulated data.ipynb @@ -660,26 +660,6 @@ "data_to_fit" ] }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "406" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.prod(data_to_fit.shape)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -689,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -698,7 +678,7 @@ "[('beta', 3.6), ('gamma', 0.2), ('N', 1000000.0)]" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -709,22 +689,7 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# Initial guess of parameters, and bounding constraints\n", - "theta = [3, 0.15,N]\n", - "boxBounds = [(2,5),(0.0,1.0),(N,N)]\n", - "\n", - "objSIR = SquareLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -733,18 +698,24 @@ "9494886226076.777" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "theta = [3, 0.15,N]\n", + "boxBounds = [(2,5),(0.0,1.0),(N,N)]\n", + "\n", + "objSIR = SquareLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])\n", + "\n", "objSIR.cost()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -776,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -820,19 +791,7 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# Initial guess of parameters, and bounding constraints\n", - "objSIR = NormalLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -841,18 +800,21 @@ "4747443113411.479" ] }, - "execution_count": 19, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = NormalLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])\n", + "\n", "objSIR.cost()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -884,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -920,27 +882,61 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 20, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18989772452245.23" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Fitting Gamma loss" + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = NormalLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'],sigma=0.5)\n", + "\n", + "objSIR.cost()" ] }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": false - }, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5804367986465.104" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Initial guess of parameters, and bounding constraints\n", - "objSIR = GammaLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])" + "objSIR = NormalLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'],sigma=[0.5,1.5])\n", + "\n", + "objSIR.cost()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting Gamma loss" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -949,25 +945,28 @@ "5066.230498539779" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = GammaLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'])\n", + "\n", "objSIR.cost()" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev9+g9b40b86.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:108: RuntimeWarning: invalid value encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev11+g990f876.d20200702-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:108: RuntimeWarning: invalid value encountered in log\n", " logpdf_p3= -shape*np.log(mu/shape)\n" ] }, @@ -1000,7 +999,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1035,6 +1034,52 @@ "display(theta,boxBounds,param_evals)" ] }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5105.389439344021" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = GammaLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'],shape=0.5)\n", + "\n", + "objSIR.cost()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5044.423929863387" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = GammaLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=data_to_fit, state_name=['I','R'],shape=[0.5,1])\n", + "\n", + "objSIR.cost()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1051,7 +1096,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1262,7 +1307,7 @@ " [6.100000e+01, 1.199501e+06]])" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1279,18 +1324,6 @@ "## Poisson loss" ] }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# Initial guess of parameters, and bounding constraints\n", - "objSIR = PoissonLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=int_data_to_fit, state_name=['I','R'])" - ] - }, { "cell_type": "code", "execution_count": 28, @@ -1308,6 +1341,9 @@ } ], "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = PoissonLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=int_data_to_fit, state_name=['I','R'])\n", + "\n", "objSIR.cost()" ] }, @@ -1390,56 +1426,46 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev9+g9b40b86.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev11+g990f876.d20200702-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: divide by zero encountered in log\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n", - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev9+g9b40b86.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev11+g990f876.d20200702-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in multiply\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" ] - } - ], - "source": [ - "# Initial guess of parameters, and bounding constraints\n", - "objSIR = NegBinomLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=int_data_to_fit, state_name=['I','R'])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ + }, { "data": { "text/plain": [ "5033.73795866175" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = NegBinomLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=int_data_to_fit, state_name=['I','R'])\n", + "\n", "objSIR.cost()" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev9+g9b40b86.d20200630-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", + "C:\\Users\\martin.grunnill\\AppData\\Roaming\\Python\\Python38\\site-packages\\pygom-0.1.7.dev11+g990f876.d20200702-py3.8-win-amd64.egg\\pygom\\utilR\\distn.py:450: RuntimeWarning: invalid value encountered in log\n", " logpmf_p4= x*(np.log(mu) - np.log(k + mu))\n" ] }, @@ -1472,7 +1498,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1509,31 +1535,49 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "5107.155188410919" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = NegBinomLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=int_data_to_fit, state_name=['I','R'],k=0.5)\n", + "\n", + "objSIR.cost()" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "5024.382571986069" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initial guess of parameters, and bounding constraints\n", + "objSIR = NegBinomLoss(theta, model, x0=data[0,:], t0=t[0], t=t[1::], y=int_data_to_fit, state_name=['I','R'],k=[0.5,1.5])\n", + "\n", + "objSIR.cost()" + ] } ], "metadata": { diff --git a/pygom/loss/base_loss.py b/pygom/loss/base_loss.py index e908c33d..826ad453 100644 --- a/pygom/loss/base_loss.py +++ b/pygom/loss/base_loss.py @@ -207,8 +207,8 @@ def _get_model_str(self): self._observeT.tolist(), self._y.tolist(), self._stateName) - if self._stateWeight is not None: - model_str += ", %s" % self._stateWeight.tolist() + if self._weight is not None: + model_str += ", %s" % self._weight.tolist() if self._targetParam is not None: model_str += ", %s" % self._targetParam if self._targetState is not None: @@ -1169,7 +1169,7 @@ def sens_to_grad(self, sens, diff_loss): sens = np.reshape(sens, (n, num_s, num_out), 'F') for j in range(num_out): - sens[:, :, j] *= self._stateWeight + sens[:, :, j] *= self._weight grad = functools.reduce(np.add,map(np.dot, diff_loss, sens)).ravel() @@ -1209,7 +1209,7 @@ def sens_to_jtj(self, sens, resid=None): sens = np.reshape(sens, (n, num_s, num_out), 'F') for j in range(num_out): - sens[:,:,j] *= self._stateWeight + sens[:,:,j] *= self._weight for i, s in enumerate(sens): if resid is None: @@ -1581,13 +1581,12 @@ def _setParam(self, theta): def _setWeight_or_spread(self, n, p, x,is_weights): # note that we NEVER scale the weights # also note that we can use the weights as a control - # with normalized input + # with normalized input x = ode_utils.check_array_type(x,accept_booleans=is_weights) - if is_weights== True: - object_type='weights' + object_contents='weights' else: - object_type='spread parameter values' + object_contents='spread parameter values' if len(x) == x.size: m, q = len(x), 1 @@ -1600,21 +1599,21 @@ def _setWeight_or_spread(self, n, p, x,is_weights): elif m == 1: x = np.ones((n, p))*x else: - raise AssertionError("Number of input " + object_type + + raise AssertionError("Number of input " + object_contents + " is not equal " + "to the number of observations") elif p == m: if q == 1: x = np.ones((n, p))*x else: - raise AssertionError("Number of input " + object_type + + raise AssertionError("Number of input " + object_contents + " is not equal " + "to number of states") else: if q == 1 and m == 1: x = np.ones((n, p))*x else: - raise AssertionError("Number of input " + object_type + + raise AssertionError("Number of input " + object_contents + " differs from " + "the number of observations") return x @@ -1633,7 +1632,7 @@ def _setLossType(self): be override in the module odeLoss. Basically, all other operations remains but this will change. """ - self._lossObj = Square(self._y, self._stateWeight) + self._lossObj = Square(self._y, self._weight) return self._lossObj def _unrollParam(self, theta): diff --git a/pygom/model/ode_utils/checks_and_conversions.py b/pygom/model/ode_utils/checks_and_conversions.py index 9d24c5a3..8ca427fc 100644 --- a/pygom/model/ode_utils/checks_and_conversions.py +++ b/pygom/model/ode_utils/checks_and_conversions.py @@ -37,28 +37,26 @@ def check_array_type(x,accept_booleans=False): if all(isinstance(item, accepted_types) for item in x): if accept_booleans==True: x = np.array(x) - elif accept_booleans==False and all(not isinstance(item, bool) for item in x): + elif accept_booleans==False: + if any(isinstance(item, bool) for item in x): + raise TypeError('No elements of array type object should be Boolean values') x = np.array(x) else: TypeError(type_error_message) elif isinstance(x[0], (list, tuple, np.ndarray)): - sub_items_accepted = [] for item in x: - if accept_booleans==False: - sub_items_accepted.append(all(isinstance(sub_item, accepted_types) for sub_item in item)*all(not isinstance(sub_item, bool) for sub_item in item)) - if accept_booleans==True: - sub_items_accepted.append(all(isinstance(sub_item, accepted_types) for sub_item in item)) - if all(sub_items_accepted): - x = np.array(x) - else: - raise TypeError(type_error_message) + if any(not isinstance(sub_item, accepted_types) for sub_item in item): + raise TypeError(type_error_message) + if accept_booleans==False and any(isinstance(sub_item, bool) for sub_item in item): + raise TypeError('No elements of array type object should be Boolean values') + x = np.array(x) else: raise TypeError(type_error_message) elif isinstance(x, accepted_types): if accept_booleans==True: - x = np.array(x) + x = np.array([x]) elif accept_booleans==False and not isinstance(x, bool): - x = np.array(x) + x = np.array([x]) else: TypeError("Not expecting Boolean value") else: @@ -91,8 +89,8 @@ def check_dimension(x, y): x = check_array_type(x) if len(y) != len(x): - raise InputError("The number of observations and time points " + - "should have the same length") + raise AssertionError("The number of observations and time points " + + "should have the same length") return (x, y) @@ -137,7 +135,7 @@ def str_or_list(x): elif isinstance(x, str): return [x] else: - raise InputError("Expecting a string or list") + raise TypeError("Expecting a string or list") def none_or_empty_list(x): diff --git a/tests/test_loss_types.py b/tests/test_loss_types.py index 0a923a9a..8de89766 100644 --- a/tests/test_loss_types.py +++ b/tests/test_loss_types.py @@ -4,6 +4,8 @@ from pygom import Transition, TransitionType, SimulateOde, SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss +from pygom.utilR.distn import gamma_mu_shape, dnbinom +import copy class Test_loss_classes(TestCase): @@ -30,7 +32,10 @@ def setUp(self): # "observations later" self.solution = self.ode.integrate(self.t[1:]) # initial guess - self.theta = [3, 0.15,N] + self.theta = [3.6, 0.2,N] + self.yhat_ode=copy.deepcopy(self.ode) + self.yhat_ode.parameters = [('beta', self.theta[0]), ('gamma', self.theta[1]), ('N', N)] + self.yhat= self.yhat_ode.integrate(self.t[1:]) def test_all_Loss_functions_produce_different_costs(self): Square_obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], @@ -45,34 +50,31 @@ def test_all_Loss_functions_produce_different_costs(self): self.t[1::], self.solution[1::,1:3], ['I', 'R']) comparisons = [[Square_obj.cost(),Normal_obj.cost(),'SquareLoss compared to NormalLoss'], - [Square_obj.cost(),Gamma_obj.cost(),'SquareLoss compared to GammaLoss'], - [Square_obj.cost(),Poisson_obj.cost(),'SquareLoss compared to PossionLoss'], - [Square_obj.cost(),NegBinom_obj.cost(),'SquareLoss compared to NegBinomLoss'], - [Normal_obj.cost(),Gamma_obj.cost(),'NormalLoss compared to GammaLoss'], - [Normal_obj.cost(),Poisson_obj.cost(),'NormalLoss compared to PossionLoss'], - [Normal_obj.cost(),NegBinom_obj.cost(),'NormalLoss compared to NegBinomLoss'], - [Gamma_obj.cost(),Poisson_obj.cost(),'GammaLoss compared to PossionLoss'], - [Gamma_obj.cost(),NegBinom_obj.cost(),'GammaLoss compared to NegBinomLoss'], - [Poisson_obj.cost(),NegBinom_obj.cost(),'PoissonLoss compared to NegBinomLoss'] - ] + [Square_obj.cost(),Gamma_obj.cost(),'SquareLoss compared to GammaLoss'], + [Square_obj.cost(),Poisson_obj.cost(),'SquareLoss compared to PossionLoss'], + [Square_obj.cost(),NegBinom_obj.cost(),'SquareLoss compared to NegBinomLoss'], + [Normal_obj.cost(),Gamma_obj.cost(),'NormalLoss compared to GammaLoss'], + [Normal_obj.cost(),Poisson_obj.cost(),'NormalLoss compared to PossionLoss'], + [Normal_obj.cost(),NegBinom_obj.cost(),'NormalLoss compared to NegBinomLoss'], + [Gamma_obj.cost(),Poisson_obj.cost(),'GammaLoss compared to PossionLoss'], + [Gamma_obj.cost(),NegBinom_obj.cost(),'GammaLoss compared to NegBinomLoss'], + [Poisson_obj.cost(),NegBinom_obj.cost(),'PoissonLoss compared to NegBinomLoss'] + ] for comparison in comparisons: message = comparison[-1] with self.subTest(message): - self.assertNotAlmostEqual(comparison[0],comparison[1],places=2) + self.assertNotAlmostEqual(comparison[0],comparison[1],places=0) def test_Square_and_Normal_Loss_cost_scalar_weights_for_two_states(self): loss_functions = [SquareLoss, NormalLoss] # weight for each component w = [2.0, 3.0] - - obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], - self.t[1::], self.solution[1::,1:3], ['I', 'R']) - self.r = obj.residual() + r = self.solution[1::,1:3]-self.yhat[1::,1:3] residual = [] for i in range(2): - residual.append(self.r[:,i]*w[i]) + residual.append(r[:,i]*w[i]) residual = np.array(residual) @@ -84,7 +86,7 @@ def test_Square_and_Normal_Loss_cost_scalar_weights_for_two_states(self): norm_logpdf_p4= np.log(1/sigma) norm_logpdf_p5_alt= -residual**2 / (2*sigma**2) norm_cost = (-(norm_logpdf_p1+norm_logpdf_p2+norm_logpdf_p3+ - norm_logpdf_p4+norm_logpdf_p5_alt)).sum() + norm_logpdf_p4+norm_logpdf_p5_alt)).sum() test_answers=[square_cost,norm_cost] for loss_function_index in range(len(loss_functions)): @@ -93,17 +95,15 @@ def test_Square_and_Normal_Loss_cost_scalar_weights_for_two_states(self): with self.subTest(message): obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=2) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=0) def test_Square_and_Normal_Loss_cost_vector_weights_for_two_states(self): loss_functions = [SquareLoss, NormalLoss] - obj = SquareLoss(self.theta, self.ode, self.init_state, self.t[0], - self.t[1::], self.solution[1::,1:3], ['I', 'R']) - self.r = obj.residual() + r = self.solution[1::,1:3]-self.yhat[1::,1:3] # now the weight is a vector w = np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]) - residual = self.r* np.array(w) + residual = r* w square_cost = (residual**2).sum() sigma= 1 norm_logpdf_p1= -np.log(2) @@ -112,7 +112,7 @@ def test_Square_and_Normal_Loss_cost_vector_weights_for_two_states(self): norm_logpdf_p4= np.log(1/sigma) norm_logpdf_p5_alt= -residual**2 / (2*sigma**2) norm_cost = (-(norm_logpdf_p1+norm_logpdf_p2+norm_logpdf_p3+ - norm_logpdf_p4+norm_logpdf_p5_alt)).sum() + norm_logpdf_p4+norm_logpdf_p5_alt)).sum() test_answers=[square_cost,norm_cost] @@ -122,7 +122,7 @@ def test_Square_and_Normal_Loss_cost_vector_weights_for_two_states(self): with self.subTest(message): obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=2) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=0) def test_All_Loss_functions_1State_weights_Failures_TypeErrors(self): @@ -133,9 +133,8 @@ def test_All_Loss_functions_1State_weights_Failures_TypeErrors(self): test_weight[-1]='b' w_list.append(test_weight) for loss_function in loss_functions: - for weight_index in range(len(w_list)): - w = w_list[weight_index] - message = str(loss_function)+' with weighting '+str(weight_index) + for w in w_list: + message = str(loss_function)+' with weighting '+str(w) with self.subTest(message): self.assertRaises(TypeError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], @@ -152,9 +151,8 @@ def test_AllLoss_1State_weights_Failures_ValueErrors(self): w_list.append(test_weight) w_list.append(np.zeros(len(self.solution[1::,-1]))) for loss_function in loss_functions: - for weight_index in range(len(w_list)): - w = w_list[weight_index] - message = str(loss_function)+' with weighting '+str(weight_index) + for w in w_list: + message = str(loss_function)+' with weighting '+str(w) with self.subTest(message): self.assertRaises(ValueError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], @@ -166,9 +164,8 @@ def test_AllLoss_1State_weights_Failures_ShapeErrors(self): w_list.append([2.0, 3.0]) w_list.append(np.random.rand(self.solution[1::,-1].shape[0]+1).tolist()) for loss_function in loss_functions: - for weight_index in range(len(w_list)): - w = w_list[weight_index] - message = str(loss_function)+' with weighting '+str(weight_index) + for w in w_list: + message = str(loss_function)+' with weighting '+str(w) with self.subTest(message): self.assertRaises(AssertionError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], @@ -182,9 +179,8 @@ def test_AllLoss_2State_weights_Failures_TypeErrors(self): test_weight[-1][-1]='c' w_list.append(test_weight) for loss_function in loss_functions: - for weight_index in range(len(w_list)): - w = w_list[weight_index] - message = str(loss_function)+' with weighting '+str(weight_index) + for w in w_list: + message = str(loss_function)+' with weighting '+str(w) with self.subTest(message): self.assertRaises(TypeError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], @@ -203,9 +199,8 @@ def test_AllLoss_2State_weights_Failures_ValueErrors(self): w_list.append([1.0, -1.0]) w_list.append([0, 0]) for loss_function in loss_functions: - for weight_index in range(len(w_list)): - w = w_list[weight_index] - message = str(loss_function)+' with weighting '+str(weight_index) + for w in w_list: + message = str(loss_function)+' with weighting '+str(w) with self.subTest(message): self.assertRaises(ValueError, loss_function, self.theta, self.ode,self.init_state, self.t[0], @@ -220,15 +215,73 @@ def test_AllLoss_2State_weights_Failures_ShapeErrors(self): w_list.append(np.random.rand(self.solution[1::,1:3].shape[0]+1,self.solution[1::,1:3].shape[1]).tolist()) w_list.append(np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]+1).tolist()) for loss_function in loss_functions: - for weight_index in range(len(w_list)): - w = w_list[weight_index] - message = str(loss_function)+' with weighting '+str(weight_index) + for w in w_list: + message = str(loss_function)+' with weighting '+str(w) with self.subTest(message): self.assertRaises(AssertionError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - + + def test_Applicable_Loss_functions_cost_scalar_spread_params_for_two_states(self): + loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] + # weight for each component + spread_params = [0.5, 1.5] + spread_params_unraveled = np.array([spread_params[0]* + np.ones(self.solution[1::,1:3].shape[0]), + spread_params[1]* + np.ones(self.solution[1::,1:3].shape[0])]).transpose().flatten() + y=self.solution[1::,1:3].flatten() + yhat = self.yhat[1::,1:3].flatten() + residual = y-yhat + norm_logpdf_p1= -np.log(2) + norm_logpdf_p2= np.log(2)/2 + norm_logpdf_p3= -np.log(np.pi)/2 + norm_logpdf_p4= np.log(1/spread_params_unraveled) + norm_logpdf_p5_alt= -residual**2 / (2*spread_params_unraveled**2) + norm_cost = (-(norm_logpdf_p1+norm_logpdf_p2+norm_logpdf_p3+ + norm_logpdf_p4+norm_logpdf_p5_alt)).sum() + + gamma_cost=(-gamma_mu_shape(x=y, mu=yhat,shape=spread_params_unraveled,log=True)).sum() + NegBinom_cost=(-dnbinom(x=y, mu=yhat,size=spread_params_unraveled,log=True)).sum() + + test_answers=[norm_cost,gamma_cost,NegBinom_cost] + + for loss_function_index in range(len(loss_functions)): + loss_function= loss_functions[loss_function_index] + message = str(loss_function) + with self.subTest(message): + obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R'], None,spread_params) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=0) + + def test_Applicable_Loss_functions_cost_vector_spread_params_for_two_states(self): + loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] + y=self.solution[1::,1:3].flatten() + yhat = self.yhat[1::,1:3].flatten() + residual = y-yhat + # now the weight is a vector + spread_params = np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]) + spread_params_unraveled=spread_params.flatten() + norm_logpdf_p1= -np.log(2) + norm_logpdf_p2= np.log(2)/2 + norm_logpdf_p3= -np.log(np.pi)/2 + norm_logpdf_p4= np.log(1/spread_params_unraveled) + norm_logpdf_p5_alt= -residual**2 / (2*spread_params_unraveled**2) + norm_cost = (-(norm_logpdf_p1+norm_logpdf_p2+norm_logpdf_p3+ + norm_logpdf_p4+norm_logpdf_p5_alt)).sum() + gamma_cost=(-gamma_mu_shape(x=y, mu=yhat,shape=spread_params_unraveled,log=True)).sum() + NegBinom_cost=(-dnbinom(x=y, mu=yhat,size=spread_params_unraveled,log=True)).sum() + + test_answers=[norm_cost,gamma_cost,NegBinom_cost] + + for loss_function_index in range(len(loss_functions)): + loss_function= loss_functions[loss_function_index] + message = str(loss_function) + with self.subTest(message): + obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], + self.t[1::], self.solution[1::,1:3], ['I', 'R'], None,spread_params) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=0) def test_Applicable_Loss_functions_1State_spread_param_Failures_TypeErrors(self): loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] @@ -241,9 +294,8 @@ def test_Applicable_Loss_functions_1State_spread_param_Failures_TypeErrors(self) test_spread_param[-1]=False spread_param_list.append(test_spread_param) for loss_function in loss_functions: - for spread_param_index in range(len(spread_param_list)): - spread_param = spread_param_list[spread_param_index] - message = str(loss_function)+' with spread params '+str(spread_param_index) + for spread_param in spread_param_list: + message = str(loss_function)+' with spread params '+str(spread_param) with self.subTest(message): self.assertRaises(TypeError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], @@ -258,9 +310,8 @@ def test_Applicable_Loss_functionss_1State_spread_param_Failures_ValueErrors(sel test_spread_param[-1]=-1 spread_param_list.append(test_spread_param) for loss_function in loss_functions: - for spread_param_index in range(len(spread_param_list)): - spread_param = spread_param_list[spread_param_index] - message = str(loss_function)+' with spread params'+str(spread_param_index) + for spread_param in spread_param_list: + message = str(loss_function)+' with spread params '+str(spread_param) with self.subTest(message): self.assertRaises(ValueError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], @@ -272,9 +323,8 @@ def test_Applicable_Loss_functions_1State_spread_param_Failures_ShapeErrors(self spread_param_list.append([2.0, 3.0]) spread_param_list.append(np.random.rand(self.solution[1::,-1].shape[0]+1).tolist()) for loss_function in loss_functions: - for spread_param_index in range(len(spread_param_list)): - spread_param = spread_param_list[spread_param_index] - message = str(loss_function)+' with spread params'+str(spread_param_index) + for spread_param in spread_param_list: + message = str(loss_function)+' with spread params '+str(spread_param) with self.subTest(message): self.assertRaises(AssertionError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], @@ -293,9 +343,8 @@ def test_Applicable_Loss_functions_2State_spread_param_Failures_TypeErrors(self) test_spread_param[-1][-1]=False spread_param_list.append(test_spread_param) for loss_function in loss_functions: - for spread_param_index in range(len(spread_param_list)): - spread_param = spread_param_list[spread_param_index] - message = str(loss_function)+' with spread params'+str(spread_param_index) + for spread_param in spread_param_list: + message = str(loss_function)+' with spread params '+str(spread_param) with self.subTest(message): self.assertRaises(TypeError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], @@ -312,9 +361,8 @@ def test_Applicable_Loss_functions_2State_spread_param_Failures_ValueErrors(self spread_param_list.append([1.0, -1.0]) spread_param_list.append([-1.0, -1.0]) for loss_function in loss_functions: - for spread_param_index in range(len(spread_param_list)): - spread_param = spread_param_list[spread_param_index] - message = str(loss_function)+' with spread params'+str(spread_param_index) + for spread_param in spread_param_list: + message = str(loss_function)+' with spread params '+str(spread_param) with self.subTest(message): self.assertRaises(ValueError, loss_function, self.theta, self.ode,self.init_state, self.t[0], @@ -329,9 +377,8 @@ def test_Applicable_Loss_functions_2State_spread_param_Failures_ShapeErrors(self spread_param_list.append(np.random.rand(self.solution[1::,1:3].shape[0]+1,self.solution[1::,1:3].shape[1]).tolist()) spread_param_list.append(np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]+1).tolist()) for loss_function in loss_functions: - for spread_param_index in range(len(spread_param_list)): - spread_param = spread_param_list[spread_param_index] - message = str(loss_function)+' with spread params'+str(spread_param_index) + for spread_param in spread_param_list: + message = str(loss_function)+' with spread params '+str(spread_param) with self.subTest(message): self.assertRaises(AssertionError, loss_function, self.theta, self.ode, self.init_state, self.t[0], From 9844fd34e1189790df5373d6612d9c1e6e13a274 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Thu, 2 Jul 2020 14:55:42 +0100 Subject: [PATCH 013/188] Unittest hopefully finished --- tests/test_loss_types.py | 64 +++++++++++++++++++++------------------- 1 file changed, 34 insertions(+), 30 deletions(-) diff --git a/tests/test_loss_types.py b/tests/test_loss_types.py index 8de89766..32657b6c 100644 --- a/tests/test_loss_types.py +++ b/tests/test_loss_types.py @@ -69,15 +69,13 @@ def test_all_Loss_functions_produce_different_costs(self): def test_Square_and_Normal_Loss_cost_scalar_weights_for_two_states(self): loss_functions = [SquareLoss, NormalLoss] - # weight for each component + y=self.solution[1::,1:3].flatten() + yhat = self.yhat[1::,1:3].flatten() + residual = y-yhat w = [2.0, 3.0] - r = self.solution[1::,1:3]-self.yhat[1::,1:3] - residual = [] - for i in range(2): - residual.append(r[:,i]*w[i]) - - residual = np.array(residual) - + w_unraveled = np.array([w[0]*np.ones(self.solution[1::,1:3].shape[0]), + w[1]*np.ones(self.solution[1::,1:3].shape[0])]).transpose().flatten() + residual =residual*w_unraveled square_cost = (residual**2).sum() sigma= 1 norm_logpdf_p1= -np.log(2) @@ -95,15 +93,17 @@ def test_Square_and_Normal_Loss_cost_scalar_weights_for_two_states(self): with self.subTest(message): obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=0) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=1) def test_Square_and_Normal_Loss_cost_vector_weights_for_two_states(self): loss_functions = [SquareLoss, NormalLoss] - r = self.solution[1::,1:3]-self.yhat[1::,1:3] - # now the weight is a vector - w = np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]) - - residual = r* w + y=self.solution[1::,1:3].flatten() + yhat = self.yhat[1::,1:3].flatten() + residual = y-yhat + w = [2.0, 3.0] + w = np.array([w[0]*np.ones(self.solution[1::,1:3].shape[0]), + w[1]*np.ones(self.solution[1::,1:3].shape[0])]).transpose() + residual = residual*w.flatten() square_cost = (residual**2).sum() sigma= 1 norm_logpdf_p1= -np.log(2) @@ -122,7 +122,7 @@ def test_Square_and_Normal_Loss_cost_vector_weights_for_two_states(self): with self.subTest(message): obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=0) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=1) def test_All_Loss_functions_1State_weights_Failures_TypeErrors(self): @@ -170,7 +170,7 @@ def test_AllLoss_1State_weights_Failures_ShapeErrors(self): self.assertRaises(AssertionError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], 'R', w) - + def test_AllLoss_2State_weights_Failures_TypeErrors(self): loss_functions = [SquareLoss, NormalLoss, GammaLoss, PoissonLoss, NegBinomLoss] w_list = list() @@ -222,25 +222,25 @@ def test_AllLoss_2State_weights_Failures_ShapeErrors(self): self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], ['I', 'R'], w) - + def test_Applicable_Loss_functions_cost_scalar_spread_params_for_two_states(self): loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] - # weight for each component + y=self.solution[1::,1:3].flatten() + yhat = self.yhat[1::,1:3].flatten() + residual = y-yhat + # spread parameter for each component spread_params = [0.5, 1.5] spread_params_unraveled = np.array([spread_params[0]* np.ones(self.solution[1::,1:3].shape[0]), spread_params[1]* np.ones(self.solution[1::,1:3].shape[0])]).transpose().flatten() - y=self.solution[1::,1:3].flatten() - yhat = self.yhat[1::,1:3].flatten() - residual = y-yhat norm_logpdf_p1= -np.log(2) norm_logpdf_p2= np.log(2)/2 norm_logpdf_p3= -np.log(np.pi)/2 norm_logpdf_p4= np.log(1/spread_params_unraveled) norm_logpdf_p5_alt= -residual**2 / (2*spread_params_unraveled**2) norm_cost = (-(norm_logpdf_p1+norm_logpdf_p2+norm_logpdf_p3+ - norm_logpdf_p4+norm_logpdf_p5_alt)).sum() + norm_logpdf_p4+norm_logpdf_p5_alt)).sum() gamma_cost=(-gamma_mu_shape(x=y, mu=yhat,shape=spread_params_unraveled,log=True)).sum() NegBinom_cost=(-dnbinom(x=y, mu=yhat,size=spread_params_unraveled,log=True)).sum() @@ -253,15 +253,19 @@ def test_Applicable_Loss_functions_cost_scalar_spread_params_for_two_states(self with self.subTest(message): obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], ['I', 'R'], None,spread_params) - self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=0) - + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=1) + def test_Applicable_Loss_functions_cost_vector_spread_params_for_two_states(self): loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] y=self.solution[1::,1:3].flatten() yhat = self.yhat[1::,1:3].flatten() residual = y-yhat - # now the weight is a vector - spread_params = np.random.rand(self.solution[1::,1:3].shape[0],self.solution[1::,1:3].shape[1]) + # now the spread parameter is a vector + spread_params = [0.5, 1.5] + spread_params = np.array([spread_params[0]* + np.ones(self.solution[1::,1:3].shape[0]), + spread_params[1]* + np.ones(self.solution[1::,1:3].shape[0])]).transpose() spread_params_unraveled=spread_params.flatten() norm_logpdf_p1= -np.log(2) norm_logpdf_p2= np.log(2)/2 @@ -269,19 +273,19 @@ def test_Applicable_Loss_functions_cost_vector_spread_params_for_two_states(self norm_logpdf_p4= np.log(1/spread_params_unraveled) norm_logpdf_p5_alt= -residual**2 / (2*spread_params_unraveled**2) norm_cost = (-(norm_logpdf_p1+norm_logpdf_p2+norm_logpdf_p3+ - norm_logpdf_p4+norm_logpdf_p5_alt)).sum() + norm_logpdf_p4+norm_logpdf_p5_alt)).sum() gamma_cost=(-gamma_mu_shape(x=y, mu=yhat,shape=spread_params_unraveled,log=True)).sum() NegBinom_cost=(-dnbinom(x=y, mu=yhat,size=spread_params_unraveled,log=True)).sum() test_answers=[norm_cost,gamma_cost,NegBinom_cost] - + for loss_function_index in range(len(loss_functions)): loss_function= loss_functions[loss_function_index] message = str(loss_function) with self.subTest(message): obj = loss_function(self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,1:3], ['I', 'R'], None,spread_params) - self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=0) + self.assertAlmostEqual(obj.cost(), test_answers[loss_function_index],places=1) def test_Applicable_Loss_functions_1State_spread_param_Failures_TypeErrors(self): loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] @@ -329,7 +333,7 @@ def test_Applicable_Loss_functions_1State_spread_param_Failures_ShapeErrors(self self.assertRaises(AssertionError, loss_function, self.theta, self.ode, self.init_state, self.t[0], self.t[1::], self.solution[1::,-1], 'R', None,spread_param) - + def test_Applicable_Loss_functions_2State_spread_param_Failures_TypeErrors(self): loss_functions = [NormalLoss, GammaLoss, NegBinomLoss] spread_param_list = list() From a2f01fe3cbdb1da5d1f94e93b049136ac107a472 Mon Sep 17 00:00:00 2001 From: MGrunnill Date: Thu, 2 Jul 2020 16:41:02 +0100 Subject: [PATCH 014/188] odeloss.py and lossype.py files checked with pylint --- pygom/loss/loss_type.py | 280 +++++++++++++++++++--------------------- pygom/loss/ode_loss.py | 35 ++--- 2 files changed, 153 insertions(+), 162 deletions(-) diff --git a/pygom/loss/loss_type.py b/pygom/loss/loss_type.py index 745fd4ea..f1385e65 100644 --- a/pygom/loss/loss_type.py +++ b/pygom/loss/loss_type.py @@ -19,16 +19,10 @@ from pygom.model.ode_utils import check_array_type from pygom.utilR.distn import dpois, gamma_mu_shape, dnbinom -class InputError(Exception): +class Baseloss_Type(object): ''' - As the name suggest. - ''' - pass - -class baseloss_type(object): - ''' - This baseloss_type class provides common feature to be inherited by the - loss type objects, such as Square, Normal , etc. + This Baseloss_Type class provides common feature to be inherited by the + loss type objects, such as Square, Normal , etc. Parameters ---------- @@ -43,22 +37,22 @@ def __init__(self, y, weights=None): # module is being used without base_loss.py. #Checks o n y: self._y = check_array_type(y) - #Checks on weights. + #Checks on weights. if weights is None: self._numVar = 0 self._w = np.ones(self._y.shape) else: - self._w = check_array_type(weights,accept_booleans=True) - if (self._w<0).any(): + self._w = check_array_type(weights, accept_booleans=True) + if (self._w < 0).any(): raise ValueError('No elements in numpy array of weights should be negative') - if (self._w==0).all(): + if (self._w == 0).all(): raise ValueError('All elements in numpy array of weights should not be 0.0') if len(self._w.shape) > 1: if 1 in self._w.shape: - self._w = self._w.flatten() + self._w = self._w.flatten() assert self._y.shape == self._w.shape, "Input weight not of the same size as y" - - def residual(self, yhat, apply_weighting = True): + + def residual(self, yhat, apply_weighting=True): ''' Raw residuals returned if apply_weighting = False, else the weighted residuals. @@ -68,7 +62,7 @@ def residual(self, yhat, apply_weighting = True): yhat: array like observation apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals returned. Returns @@ -76,9 +70,9 @@ def residual(self, yhat, apply_weighting = True): :math:`y_{i} - \\hat{y}_{i}` ''' - if isinstance(apply_weighting,bool)==False: + if not isinstance(apply_weighting, bool): raise TypeError('apply_weighting should be boolean') - + if len(yhat.shape) > 1: if 1 in yhat.shape: resid = self._y - yhat.ravel() @@ -86,14 +80,12 @@ def residual(self, yhat, apply_weighting = True): resid = self._y - yhat else: resid = self._y - yhat - if apply_weighting == True: + if apply_weighting: resid *= self._w - + return resid - - -class Square(baseloss_type): +class Square(Baseloss_Type): ''' Square loss object @@ -107,7 +99,7 @@ def __init__(self, y, weights=None): super().__init__(y, weights) self.loss(self._y) - def loss(self, yhat, apply_weighting = True): + def loss(self, yhat, apply_weighting=True): ''' Loss under square loss. Not really saying much here @@ -116,7 +108,7 @@ def loss(self, yhat, apply_weighting = True): yhat: array like observation apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. Returns @@ -136,7 +128,7 @@ def diff_loss(self, yhat, apply_weighting=True): yhat: array like observation apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. Returns @@ -154,9 +146,9 @@ def diff2Loss(self, yhat, apply_weighting=True): yhat: array like observations apply_weighting: boolean - Residuals are not used in calculation, as such True or False - argument makes no difference. Argument has been kept as the equivalent - calculation is made with residuals for other loss functions, so + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so without it missing arguments errors could occur. Returns @@ -170,7 +162,7 @@ def diff2Loss(self, yhat, apply_weighting=True): yhat = yhat.ravel() return 2*np.ones(yhat.shape) -class Normal(baseloss_type): +class Normal(Baseloss_Type): ''' Normal distribution loss object @@ -180,14 +172,14 @@ class Normal(baseloss_type): observation sigma: float standard deviation - ''' + ''' def __init__(self, y, weights=None, sigma=1.0): super().__init__(y, weights) err_str = "Standard deviation not of the correct " if isinstance(sigma, np.ndarray): - if (sigma<0).any(): + if (sigma < 0).any(): raise ValueError('No elements in numpy array of sigma values should be negative') - elif len(sigma.shape) > 1: + if len(sigma.shape) > 1: if 1 in sigma.shape: sigma = sigma.flatten() if y.shape == sigma.shape: @@ -202,10 +194,9 @@ def __init__(self, y, weights=None, sigma=1.0): elif sigma is None or sigma == 1.0: self._sigma = np.ones(self._y.shape) elif isinstance(sigma, (int, float)): - if sigma <0: + if sigma < 0: raise ValueError('Sigma should not be negative') - else: - self._sigma = sigma*np.ones(self._y.shape) + self._sigma = sigma*np.ones(self._y.shape) else: raise TypeError(err_str + "type") @@ -222,27 +213,28 @@ def loss(self, yhat, apply_weighting=True): yhat: array like observation apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. Returns ------- - negative log-likelihood, :math:`\\mathcal\\frac{1}{\\sqrt{2\\pi}\\sigma}e^{-\\frac{(y-\\hat{y})^{2}}{2\\sigma^{2}}` + negative log-likelihood, :math: + `\\mathcal\\frac{1}{\\sqrt{2\\pi}\\sigma}e^{-\\frac{(y-\\hat{y})^{2}}{2\\sigma^{2}}` ''' if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - - r=self.residual(yhat, apply_weighting) - sigma=self._sigma - + + residual = self.residual(yhat, apply_weighting) + sigma = self._sigma + # Calculate negative likelihood (depending on weighting of residuals). - logpdf_p1= -np.log(2) - logpdf_p2= np.log(2)/2 - logpdf_p3= -np.log(np.pi)/2 - logpdf_p4= np.log(1/sigma) - logpdf_p5_alt= -r**2 / (2*sigma**2) + logpdf_p1 = -np.log(2) + logpdf_p2 = np.log(2)/2 + logpdf_p3 = -np.log(np.pi)/2 + logpdf_p4 = np.log(1/sigma) + logpdf_p5_alt = -residual**2 / (2*sigma**2) return (-(logpdf_p1+logpdf_p2+logpdf_p3+logpdf_p4+logpdf_p5_alt)).sum() def diff_loss(self, yhat, apply_weighting=True): @@ -255,7 +247,7 @@ def diff_loss(self, yhat, apply_weighting=True): yhat: array like observation apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. Returns @@ -264,8 +256,8 @@ def diff_loss(self, yhat, apply_weighting=True): :math:`\\nabla \\mathcal{L}(\\hat{y}, y)` ''' - r = self.residual(yhat, apply_weighting) - return -r/self._sigma2 + residual = self.residual(yhat, apply_weighting) + return -residual/self._sigma2 def diff2Loss(self, yhat, apply_weighting=True): ''' @@ -276,9 +268,9 @@ def diff2Loss(self, yhat, apply_weighting=True): yhat: array like observations apply_weighting: boolean - Residuals are not used in calculation, as such True or False - argument makes no difference. Argument has been kept as the equivalent - calculation is made with residuals for other loss functions, so + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so without it missing arguments errors could occur. Returns @@ -290,8 +282,8 @@ def diff2Loss(self, yhat, apply_weighting=True): if 1 in yhat.shape: yhat = yhat.ravel() return np.ones(yhat.shape)/self._sigma2 - -class Gamma(baseloss_type): + +class Gamma(Baseloss_Type): ''' Gamma distribution loss object @@ -299,17 +291,17 @@ class Gamma(baseloss_type): ---------- y: array like observation - Shape: float + Shape: float shape (a in latex equations) ''' - + def __init__(self, y, weights=None, shape=2.0): super().__init__(y, weights) err_str = "Shape is not of the correct " if isinstance(shape, np.ndarray): - if (shape<0).any(): + if (shape < 0).any(): raise ValueError('No elements in numpy array of shape values should be negative') - elif len(shape.shape) > 1: + if len(shape.shape) > 1: if 1 in shape.shape: shape = shape.flatten() if y.shape == shape.shape: @@ -324,19 +316,19 @@ def __init__(self, y, weights=None, shape=2.0): elif shape is None or shape == 2.0: self._shape = 2*np.ones(self._y.shape) elif isinstance(shape, (int, float)): - if shape <0: + if shape < 0: raise ValueError('Shape should not be negative') - else: - self._shape = shape*np.ones(self._y.shape) + self._shape = shape*np.ones(self._y.shape) else: raise TypeError(err_str + "type") self.loss(self._y) - + def loss(self, yhat, apply_weighting=True): ''' - The loss under a gamma distribution. Defined as the negative + The loss under a gamma distribution. Defined as the negative log-likelihood of the gamma distirbution in terms of mean and shape. - See: Bolker, B. M. (2008). Gamma. In Ecological Models in R (pp. 131–133). Princeton University Press. + See: Bolker, B. M. (2008). Gamma. In Ecological Models in R (pp. 131–133). + Princeton University Press. File "Loss function Calculations.ipnyb" Parameters @@ -344,10 +336,10 @@ def loss(self, yhat, apply_weighting=True): yhat: array like prediction apply_weighting: boolean - Residuals are not used in calculation, as such True or False - argument makes no difference. Argument has been kept as the equivalent - calculation is made with residuals for other loss functions, so - without it missing arguments errors could occur. + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur. Returns ------- @@ -357,23 +349,23 @@ def loss(self, yhat, apply_weighting=True): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - - return -gamma_mu_shape(x=self._y, mu=yhat,shape=self._shape,log=True).sum() - + + return -gamma_mu_shape(x=self._y, mu=yhat, shape=self._shape, log=True).sum() + def diff_loss(self, yhat, apply_weighting=True): ''' Derivative of the loss function with respect to yhat which is - See: + See: File "Loss function Calculations.ipnyb" - + Parameters ---------- yhat: array like prediction apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. - + Returns ------- first_deriv_yhat: array like @@ -381,23 +373,22 @@ def diff_loss(self, yhat, apply_weighting=True): ''' shape = self._shape - r = self.residual(yhat, apply_weighting) - return shape*-r/yhat**2 + residual = self.residual(yhat, apply_weighting) + return shape*-residual/yhat**2 def diff2Loss(self, yhat, apply_weighting=True): ''' Twice derivative of the loss function with respect to yhat. - See: + See: Jupiter notebook "Loss function Calculations.ipnyb" - + Parameters ---------- yhat: array like observation apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. - Returns ------- @@ -408,12 +399,11 @@ def diff2Loss(self, yhat, apply_weighting=True): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - y = self._y shape = self._shape - r = self.residual(yhat, apply_weighting) - return shape*(r+y)/yhat**3 + residual = self.residual(yhat, apply_weighting) + return shape*(residual+self._y)/yhat**3 -class Poisson(baseloss_type): +class Poisson(Baseloss_Type): ''' Poisson distribution loss object @@ -427,7 +417,7 @@ def __init__(self, y, weights=None): super().__init__(y, weights) self.loss(self._y) - def loss(self, yhat,apply_weighting=True): + def loss(self, yhat, apply_weighting=True): ''' The loss under a normal distribution. Defined as the negative log-likelihood here. @@ -437,9 +427,9 @@ def loss(self, yhat,apply_weighting=True): yhat: array like observation apply_weighting: boolean - Residuals are not used in calculation, as such True or False - argument makes no difference. Argument has been kept as the equivalent - calculation is made with residuals for other loss functions, so + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so without it missing arguments errors could occur. Returns @@ -462,7 +452,7 @@ def diff_loss(self, yhat, apply_weighting=True): yhat: array like observation apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. Returns @@ -470,13 +460,12 @@ def diff_loss(self, yhat, apply_weighting=True): :math:`\\nabla \\mathcal{L}(\\hat{y},y)` ''' - if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - - r = self.residual(yhat, apply_weighting) - return -r/yhat + + residual = self.residual(yhat, apply_weighting) + return -residual/yhat def diff2Loss(self, yhat, apply_weighting=True): ''' @@ -487,9 +476,9 @@ def diff2Loss(self, yhat, apply_weighting=True): yhat: array like observations apply_weighting: boolean - Residuals are not used in calculation, as such True or False - argument makes no difference. Argument has been kept as the equivalent - calculation is made with residuals for other loss functions, so + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so without it missing arguments errors could occur. Returns @@ -500,10 +489,9 @@ def diff2Loss(self, yhat, apply_weighting=True): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - y=self._y - return y/(yhat**2) + return self._y/(yhat**2) -class NegBinom(baseloss_type): +class NegBinom(Baseloss_Type): ''' Negative Binomial distribution loss object @@ -511,17 +499,16 @@ class NegBinom(baseloss_type): ---------- y: array like observation - k: float + k: float Overdispersion parameter (k=mean+mean(mean/variance)) ''' - - def __init__(self, y, weights=None, k=1.0): + def __init__(self, y, weights=None, k=1.0): super().__init__(y, weights) err_str = "k (the overdispersion parameter) is not of the correct " if isinstance(k, np.ndarray): - if (k<0).any(): + if (k < 0).any(): raise ValueError('No elements in numpy array of k values should be negative') - elif len(k.shape) > 1: + if len(k.shape) > 1: if 1 in k.shape: k = k.flatten() if y.shape == k.shape: @@ -536,15 +523,14 @@ def __init__(self, y, weights=None, k=1.0): elif k is None or k == 1.0: self._k = np.ones(self._y.shape) elif isinstance(k, (int, float)): - if k <0: + if k < 0: raise ValueError('k should not be negative') - else: - self._k = k*np.ones(self._y.shape) + self._k = k*np.ones(self._y.shape) else: raise TypeError(err_str + "type") - + self.loss(self._y) - + def loss(self, yhat, apply_weighting=True): ''' The loss under a Negative Binomial distribution. Defined as the @@ -555,9 +541,9 @@ def loss(self, yhat, apply_weighting=True): yhat: array like observation apply_weighting: boolean - Residuals are not used in calculation, as such True or False - argument makes no difference. Argument has been kept as the equivalent - calculation is made with residuals for other loss functions, so + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so without it missing arguments errors could occur. Returns @@ -568,28 +554,29 @@ def loss(self, yhat, apply_weighting=True): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - - return (-dnbinom(self._y, mu=yhat,size=self._k,log=True)).sum() - + + return (-dnbinom(self._y, mu=yhat, size=self._k, log=True)).sum() + def diff_loss(self, yhat, apply_weighting=True): ''' Derivative of the loss function with respect to yhat which is - See: + See: Jupiter notebook "Loss function Calculations.ipnyb" - Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press + Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). + Princeton University Press Parameters ---------- yhat: array like observation + apply_weighting: boolean + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so + without it missing arguments errors could occur + apply_weighting: boolean - Residuals are not used in calculation, as such True or False - argument makes no difference. Argument has been kept as the equivalent - calculation is made with residuals for other loss functions, so - without it missing arguments errors could occur. - - apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. Returns @@ -601,37 +588,40 @@ def diff_loss(self, yhat, apply_weighting=True): if len(yhat.shape) > 1: if 1 in yhat.shape: yhat = yhat.ravel() - + k = self._k - r = self.residual(yhat, apply_weighting) - first_derivs_yhat = k*-r/(yhat*(k+yhat)) + residual = self.residual(yhat, apply_weighting) + first_derivs_yhat = k*-residual/(yhat*(k+yhat)) return first_derivs_yhat def diff2Loss(self, yhat, apply_weighting=True): ''' Twice derivative of the loss function with respect to yhat. - See: + See: Jupiter notebook "Loss function Calculations.ipnyb" - Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). Princeton University Press + Bolker, B. M. (2008). Negative Binomial. In Ecological Models in R (pp. 124–126). + Princeton University Press Parameters ---------- yhat: array like observation apply_weighting: boolean - Residuals are not used in calculation, as such True or False - argument makes no difference. Argument has been kept as the equivalent - calculation is made with residuals for other loss functions, so + Residuals are not used in calculation, as such True or False + argument makes no difference. Argument has been kept as the equivalent + calculation is made with residuals for other loss functions, so without it missing arguments errors could occur. - + apply_weighting: boolean - If True multiplies array of residuals by weightings, else raw + If True multiplies array of residuals by weightings, else raw residuals are used. Returns ------- scnd_deriv_yhat: array like - :math:`\\frac{k(\\hat{y}(k + \\hat{y}) + \\hat{y}(y -\\hat{y}) + (k + \\hat{y})(y - \\hat{y})}{\\hat{y}^{2}(k + \\hat{y})^{2}}` + :math: + `\\frac{k(\\hat{y}(k + \\hat{y}) + \\hat{y}(y -\\hat{y}) + (k + + \\hat{y})(y - \\hat{y})}{\\hat{y}^{2}(k + \\hat{y})^{2}}` ''' if len(yhat.shape) > 1: @@ -639,10 +629,10 @@ def diff2Loss(self, yhat, apply_weighting=True): yhat = yhat.ravel() k = self._k - r = self.residual(yhat, apply_weighting) - scnd_derivs_yhat_p1= k - scnd_derivs_yhat_p2= yhat**(-2) - scnd_derivs_yhat_p3= (k + yhat)**(-2) - scnd_derivs_yhat_p4= r*yhat + r*(k + yhat) + yhat*(k + yhat) - scnd_derivs_yhat= scnd_derivs_yhat_p1*scnd_derivs_yhat_p2*scnd_derivs_yhat_p3*scnd_derivs_yhat_p4 - return scnd_derivs_yhat \ No newline at end of file + residual = self.residual(yhat, apply_weighting) + scnd_derivs_yhat_p1 = k + scnd_derivs_yhat_p2 = yhat**(-2) + scnd_derivs_yhat_p3 = (k + yhat)**(-2) + scnd_derivs_yhat_p4 = residual*yhat + residual*(k + yhat) + yhat*(k + yhat) + scnd_derivs_yhat = scnd_derivs_yhat_p1*scnd_derivs_yhat_p2*scnd_derivs_yhat_p3*scnd_derivs_yhat_p4 + return scnd_derivs_yhat diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index ae0992ff..a4a950f0 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -24,7 +24,7 @@ class SquareLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name, state_weight=None, target_param=None, target_state=None): - super().__init__(theta, ode, x0, t0, t, y, state_name, state_weight, + super().__init__(theta, ode, x0, t0, t, y, state_name, state_weight, None, target_param, target_state) def __repr__(self): @@ -38,7 +38,7 @@ class NormalLoss(BaseLoss): ''' Realizations from a Normal distribution ''' - def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, + def __init__(self, theta, ode, x0, t0, t, y, state_name, state_weight=None, sigma=1.0, target_param=None, target_state=None): super().__init__(theta, ode, x0, t0, t, y, state_name, state_weight, sigma, target_param, target_state) @@ -49,51 +49,52 @@ def __repr__(self): def _setLossType(self): self._lossObj = Normal(self._y, self._weight, self._spread_param) return self._lossObj - + class GammaLoss(BaseLoss): ''' Realizations from a Gamma distribution taking parameters mean and shape. ''' - def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, + def __init__(self, theta, ode, x0, t0, t, y, state_name, state_weight=None, shape=2, target_param=None, target_state=None): super().__init__(theta, ode, x0, t0, t, y, - state_name, state_weight, shape,target_param, target_state) - + state_name, state_weight, shape, target_param, target_state) + def __repr__(self): return "GammaLoss" + self._get_model_str() def _setLossType(self): self._lossObj = Gamma(self._y, self._weight, self._spread_param) return self._lossObj - + class PoissonLoss(BaseLoss): ''' Realizations from a Poisson distribution ''' - def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, + def __init__(self, theta, ode, x0, t0, t, y, state_name, state_weight=None, target_param=None, target_state=None): super().__init__(theta, ode, x0, t0, t, y, state_name, state_weight, - None,target_param, target_state) - + None, target_param, target_state) + def __repr__(self): return "PoissonLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = Poisson(self._y,self._weight) - return self._lossObj - + self._lossObj = Poisson(self._y, self._weight) + return self._lossObj + class NegBinomLoss(BaseLoss): ''' Realizations from a Negative Binomial distribution ''' - def __init__(self, theta, ode, x0, t0, t, y, state_name,state_weight=None, + def __init__(self, theta, ode, x0, t0, t, y, state_name, state_weight=None, k=1, target_param=None, target_state=None): super().__init__(theta, ode, x0, t0, t, y, state_name, state_weight, k, target_param, target_state) - + def __repr__(self): return "NegBinomLoss" + self._get_model_str() def _setLossType(self): - self._lossObj = NegBinom(self._y, self._weight,self._spread_param) - return self._lossObj \ No newline at end of file + self._lossObj = NegBinom(self._y, self._weight, self._spread_param) + return self._lossObj + \ No newline at end of file From 01f5961befd49483d06a2a18234ec8a360da34a1 Mon Sep 17 00:00:00 2001 From: "Jonathan.Carruthers" Date: Tue, 7 Jul 2020 11:45:41 +0100 Subject: [PATCH 015/188] loss type to string --- .../approximate_bayesian_computation.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py index 1012fc2c..c756d68d 100644 --- a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py +++ b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py @@ -155,14 +155,16 @@ def create_loss(loss_type, parameters, ode, x0, t0, t, y, state_name, target_state = _get_target(parameters, ode.state_list) theta = [param.random_sample() for param in parameters if param.name in target_param] - if loss_type == SquareLoss: + if loss_type == "SquareLoss": return SquareLoss(theta, ode, x0, t0, t, y, state_name, state_weight, target_param, target_state) - elif loss_type == NormalLoss: + elif loss_type == "NormalLoss": return NormalLoss(theta, ode, x0, t0, t, y, state_name, sigma, target_param, target_state) - elif loss_type == PoissonLoss: + elif loss_type == "PoissonLoss": return PoissonLoss(theta, ode, x0, t0, t, y, state_name, target_param, target_state) + else: + raise ValueError("Please choose from the available loss functions") #%% """ ABC class and methods for obtaining an approximate posterior sample/plotting the results """ From cea7964d31bde57e5afff534d1c1e56490e2f8e7 Mon Sep 17 00:00:00 2001 From: "Jonathan.Carruthers" Date: Tue, 7 Jul 2020 11:48:44 +0100 Subject: [PATCH 016/188] update abc tests --- tests/test_abc.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_abc.py b/tests/test_abc.py index 9584ec53..b63f2fd5 100644 --- a/tests/test_abc.py +++ b/tests/test_abc.py @@ -32,7 +32,7 @@ def test_SIR_abc_SquareLoss(self): pgabc.Parameter('gamma', 'unif', 0, 3, logscale=False)] # creating the loss and abc objects - sir_obj = pgabc.create_loss(SquareLoss, parameters, self.ode, self.x0, self.t[0], + sir_obj = pgabc.create_loss("SquareLoss", parameters, self.ode, self.x0, self.t[0], self.t[1::], y, ['I', 'R']) sir_abc = pgabc.ABC(sir_obj, parameters) @@ -50,7 +50,7 @@ def test_SIR_abc_SquareLoss_MNN(self): y = self.solution[1::, 1:3] parameters = [pgabc.Parameter('beta', 'unif', 0, 3, logscale=False), pgabc.Parameter('gamma', 'unif', 0, 3, logscale=False)] - sir_obj = pgabc.create_loss(SquareLoss, parameters, self.ode, self.x0, self.t[0], + sir_obj = pgabc.create_loss("SquareLoss", parameters, self.ode, self.x0, self.t[0], self.t[1::], y, ['I', 'R']) sir_abc = pgabc.ABC(sir_obj, parameters) sir_abc.get_posterior_sample(N=100, tol=np.inf, G=10, q=0.5, M=50) @@ -63,7 +63,7 @@ def test_SIR_abc_NormalLoss(self): y = self.solution[1::, 1:3] parameters = [pgabc.Parameter('beta', 'unif', 0, 3, logscale=False), pgabc.Parameter('gamma', 'unif', 0, 3, logscale=False)] - sir_obj = pgabc.create_loss(NormalLoss, parameters, self.ode, self.x0, self.t[0], + sir_obj = pgabc.create_loss("NormalLoss", parameters, self.ode, self.x0, self.t[0], self.t[1::], y, ['I', 'R']) sir_abc = pgabc.ABC(sir_obj, parameters) sir_abc.get_posterior_sample(N=100, tol=np.inf, G=10, q=0.5) From 2c5e0bf5605833e94d2bb7d8f3462c2144de4d44 Mon Sep 17 00:00:00 2001 From: "Jonathan.Carruthers" Date: Tue, 7 Jul 2020 16:11:55 +0100 Subject: [PATCH 017/188] git test --- .../approximate_bayesian_computation.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py index c756d68d..d532332a 100644 --- a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py +++ b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py @@ -27,6 +27,7 @@ selected states and plot posterior histograms/pairs plots for specific parameters. """ +print("git test") #%% def _log_limits(par, logscale): From 9e24995688f8a4a39345951bc63faea72faecb00 Mon Sep 17 00:00:00 2001 From: "Jonathan.Carruthers" Date: Tue, 7 Jul 2020 16:13:02 +0100 Subject: [PATCH 018/188] undo git test --- .../approximate_bayesian_computation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py index d532332a..c756d68d 100644 --- a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py +++ b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py @@ -27,7 +27,6 @@ selected states and plot posterior histograms/pairs plots for specific parameters. """ -print("git test") #%% def _log_limits(par, logscale): From e1a8fc9179538d46188c41fa14a3d52f23c9b506 Mon Sep 17 00:00:00 2001 From: "Jonathan.Carruthers" Date: Fri, 31 Jul 2020 15:35:41 +0100 Subject: [PATCH 019/188] corrected issue with parameter order --- .../approximate_bayesian_computation.py | 29 ++++++++++++++----- 1 file changed, 21 insertions(+), 8 deletions(-) diff --git a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py index c756d68d..3f38a2b0 100644 --- a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py +++ b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py @@ -41,15 +41,24 @@ def get_length(attr): return len(attr) else: return 0 - -def _get_target(parameters,target): - # used to separate target_param and target_state from a single list of parameters + +def _get_target_parameters(parameters,target): + # used to separate target_param from a single list of parameters target_list = [param.name for param in parameters if param.name in target] if len(target_list) == 0: return None else: return target_list +def _get_target_states(parameters,target): + # used to separate and re-order target_state from a single list of parameters + parameter_names = [parameter.name for parameter in parameters] + target_list = [tar.name for tar in target if tar in parameter_names] + if len(target_list) == 0: + return None + else: + return target_list + def get_function(str): # gets a function from a string for a distribution - is there a better way to write this? """ @@ -151,8 +160,8 @@ def create_loss(loss_type, parameters, ode, x0, t0, t, y, state_name, assert t0 != t[0], "Make sure that the times, t, do not include t0" assert all(param.name in (ode.param_list+ode.state_list) for param in parameters), "Parameters have been provided that are not in the model" - target_param = _get_target(parameters, ode.param_list) - target_state = _get_target(parameters, ode.state_list) + target_param = _get_target_parameters(parameters, ode.param_list) + target_state = _get_target_states(parameters, ode.state_list) theta = [param.random_sample() for param in parameters if param.name in target_param] if loss_type == "SquareLoss": @@ -192,6 +201,10 @@ def __init__(self, loss_object, parameters, constraint=None): self.log = np.array([param.logscale for param in self.parameters]) self.prior_range = np.array([(param.prior_high-param.prior_low) for param in self.parameters]) + + ordered_parameters = (_get_target_parameters(parameters,self.obj._ode.param_list) or []) + (_get_target_states(parameters,self.obj._ode.state_list) or []) + parameter_names = [par.name for par in parameters] + self.par_order = [parameter_names.index(par) for par in ordered_parameters] if constraint is not None: self.pop_size = constraint[0] @@ -282,7 +295,7 @@ def get_posterior_sample_original(self, N, tol, G=1, q=None, M=None, progress=Fa if w1: # converting from log-scale and ensuring total population size is conserved model_params = self._log_parameters(trial_params.copy()) - par_update(model_params) + par_update(model_params[self.par_order]) if hasattr(self,"con_state"): self.obj._x0[self.con_state] = self.pop_size - self.obj._x0[self.con_state_indices].sum() @@ -432,7 +445,7 @@ def _perform_generation(self, if w1: # converting from log-scale and ensuring total population size is conserved model_params = self._log_parameters(trial_params.copy()) - par_update(model_params) + par_update(model_params[self.par_order]) if hasattr(self,"con_state"): self.obj._x0[self.con_state] = self.pop_size - self.obj._x0[self.con_state_indices].sum() @@ -580,7 +593,7 @@ def plot_pointwise_predictions(self,plot_states=None,new_time=None,max_ncol=3): for i in range(self.N): params = self.res[i].copy() params = self._log_parameters(params) - par_update(params) + par_update(params[self.par_order]) if hasattr(self,"con_state"): self.obj._x0[self.con_state] = self.pop_size - self.obj._x0[self.con_state_indices].sum() self.obj._ode.parameters = self.obj._theta From 4be295e858b285ecd2aa3e4d3dd063022e0faca9 Mon Sep 17 00:00:00 2001 From: Thomas Finnie Date: Thu, 26 Jan 2023 14:14:10 +0000 Subject: [PATCH 020/188] Update the supported and tested versions of Python in CI --- .travis.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index 45ba3248..5eec188c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,10 +1,11 @@ # specify the language and which version language: python python: - - 3.5 - - 3.6 - 3.7 - 3.8 + - 3.9 + - 3.10 + - 3.11 before_install: - pip install -U pip @@ -33,4 +34,4 @@ deploy: secure: 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 on: tags: true - python: 3.7 \ No newline at end of file + python: 3.7 From 0341400bd798652ee0babd9bce20dfee20d1ade4 Mon Sep 17 00:00:00 2001 From: Thomas Finnie Date: Thu, 26 Jan 2023 15:15:25 +0000 Subject: [PATCH 021/188] An updated version of the .c file that should work with newer versions of gcc --- pygom/model/_tau_leap.c | 8174 +++++++++++++++++---------------------- 1 file changed, 3651 insertions(+), 4523 deletions(-) diff --git a/pygom/model/_tau_leap.c b/pygom/model/_tau_leap.c index 8267fe60..b6af6e0a 100644 --- a/pygom/model/_tau_leap.c +++ b/pygom/model/_tau_leap.c @@ -1,14 +1,16 @@ -/* Generated by Cython 0.29.15 */ +/* Generated by Cython 0.29.33 */ +#ifndef PY_SSIZE_T_CLEAN #define PY_SSIZE_T_CLEAN +#endif /* PY_SSIZE_T_CLEAN */ #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) #error Cython requires Python 2.6+ or Python 3.3+. #else -#define CYTHON_ABI "0_29_15" -#define CYTHON_HEX_VERSION 0x001D0FF0 +#define CYTHON_ABI "0_29_33" +#define CYTHON_HEX_VERSION 0x001D21F0 #define CYTHON_FUTURE_DIVISION 0 #include #ifndef offsetof @@ -47,6 +49,7 @@ #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_NOGIL 0 #undef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 0 #undef CYTHON_USE_PYTYPE_LOOKUP @@ -83,10 +86,14 @@ #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif #elif defined(PYSTON_VERSION) #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 1 #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_NOGIL 0 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif @@ -124,10 +131,59 @@ #define CYTHON_USE_DICT_VERSIONS 0 #undef CYTHON_USE_EXC_INFO_STACK #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(PY_NOGIL) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_NOGIL 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #ifndef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_PYSTON 0 #define CYTHON_COMPILING_IN_CPYTHON 1 + #define CYTHON_COMPILING_IN_NOGIL 0 #ifndef CYTHON_USE_TYPE_SLOTS #define CYTHON_USE_TYPE_SLOTS 1 #endif @@ -155,7 +211,7 @@ #ifndef CYTHON_USE_UNICODE_INTERNALS #define CYTHON_USE_UNICODE_INTERNALS 1 #endif - #if PY_VERSION_HEX < 0x030300F0 + #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2 #undef CYTHON_USE_UNICODE_WRITER #define CYTHON_USE_UNICODE_WRITER 0 #elif !defined(CYTHON_USE_UNICODE_WRITER) @@ -170,11 +226,14 @@ #ifndef CYTHON_UNPACK_METHODS #define CYTHON_UNPACK_METHODS 1 #endif - #ifndef CYTHON_FAST_THREAD_STATE + #if PY_VERSION_HEX >= 0x030B00A4 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #elif !defined(CYTHON_FAST_THREAD_STATE) #define CYTHON_FAST_THREAD_STATE 1 #endif #ifndef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 1 + #define CYTHON_FAST_PYCALL (PY_VERSION_HEX < 0x030A0000) #endif #ifndef CYTHON_PEP489_MULTI_PHASE_INIT #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) @@ -185,15 +244,23 @@ #ifndef CYTHON_USE_DICT_VERSIONS #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) #endif - #ifndef CYTHON_USE_EXC_INFO_STACK + #if PY_VERSION_HEX >= 0x030B00A4 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #elif !defined(CYTHON_USE_EXC_INFO_STACK) #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) #endif + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 + #endif #endif #if !defined(CYTHON_FAST_PYCCALL) #define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) #endif #if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" + #if PY_MAJOR_VERSION < 3 + #include "longintrepr.h" + #endif #undef SHIFT #undef BASE #undef MASK @@ -310,9 +377,68 @@ #define __Pyx_DefaultClassType PyClass_Type #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" -#if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 - #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#if PY_VERSION_HEX >= 0x030B00A1 + static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL; + PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL; + const char *fn_cstr=NULL; + const char *name_cstr=NULL; + PyCodeObject* co=NULL; + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + if (!(kwds=PyDict_New())) goto end; + if (!(argcount=PyLong_FromLong(a))) goto end; + if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end; + if (!(posonlyargcount=PyLong_FromLong(0))) goto end; + if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end; + if (!(kwonlyargcount=PyLong_FromLong(k))) goto end; + if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end; + if (!(nlocals=PyLong_FromLong(l))) goto end; + if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end; + if (!(stacksize=PyLong_FromLong(s))) goto end; + if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end; + if (!(flags=PyLong_FromLong(f))) goto end; + if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end; + if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end; + if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end; + if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end; + if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end; + if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto cleanup_code_too; + if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here + if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too; + Py_XDECREF((PyObject*)co); + co = (PyCodeObject*)call_result; + call_result = NULL; + if (0) { + cleanup_code_too: + Py_XDECREF((PyObject*)co); + co = NULL; + } + end: + Py_XDECREF(kwds); + Py_XDECREF(argcount); + Py_XDECREF(posonlyargcount); + Py_XDECREF(kwonlyargcount); + Py_XDECREF(nlocals); + Py_XDECREF(stacksize); + Py_XDECREF(replace); + Py_XDECREF(call_result); + Py_XDECREF(empty); + if (type) { + PyErr_Restore(type, value, traceback); + } + return co; + } #else #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) @@ -426,8 +552,12 @@ static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 - #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ - 0 : _PyUnicode_Ready((PyObject *)(op))) + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_READY(op) (0) + #else + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #endif #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) @@ -435,7 +565,15 @@ static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) + #else + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #endif + #endif #else #define CYTHON_PEP393_ENABLED 0 #define PyUnicode_1BYTE_KIND 1 @@ -484,8 +622,10 @@ static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact +#ifndef PyObject_Unicode #define PyObject_Unicode PyObject_Str #endif +#endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) @@ -496,6 +636,13 @@ static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif #if CYTHON_ASSUME_SAFE_MACROS #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) #else @@ -529,13 +676,13 @@ static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { #if PY_VERSION_HEX < 0x030200A4 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong - #define __Pyx_PyInt_AsHash_t PyInt_AsLong + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsHash_t #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t - #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsSsize_t #endif #if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func)) + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func)) #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif @@ -557,8 +704,10 @@ static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { } __Pyx_PyAsyncMethodsStruct; #endif -#if defined(WIN32) || defined(MS_WINDOWS) - #define _USE_MATH_DEFINES +#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS) + #if !defined(_USE_MATH_DEFINES) + #define _USE_MATH_DEFINES + #endif #endif #include #ifdef NAN @@ -576,11 +725,10 @@ static CYTHON_INLINE float __PYX_NAN() { #define __Pyx_truncl truncl #endif - +#define __PYX_MARK_ERR_POS(f_index, lineno) \ + { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } #define __PYX_ERR(f_index, lineno, Ln_error) \ -{ \ - __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ -} + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } #ifndef __PYX_EXTERN_C #ifdef __cplusplus @@ -596,7 +744,13 @@ static CYTHON_INLINE float __PYX_NAN() { #include #include #include "numpy/arrayobject.h" +#include "numpy/ndarrayobject.h" +#include "numpy/ndarraytypes.h" +#include "numpy/arrayscalars.h" #include "numpy/ufuncobject.h" + + /* NumPy API declarations from "numpy/__init__.pxd" */ + #include #include "pythread.h" #include @@ -697,6 +851,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); #if CYTHON_ASSUME_SAFE_MACROS #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else @@ -888,30 +1043,26 @@ typedef struct { #ifndef CYTHON_ATOMICS #define CYTHON_ATOMICS 1 #endif +#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS #define __pyx_atomic_int_type int -#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ - (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ - !defined(__i386__) - #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) - #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) +#if CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\ + (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2)))) + #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1) #ifdef __PYX_DEBUG_ATOMICS #warning "Using GNU atomics" #endif -#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 - #include +#elif CYTHON_ATOMICS && defined(_MSC_VER) && CYTHON_COMPILING_IN_NOGIL + #include #undef __pyx_atomic_int_type - #define __pyx_atomic_int_type LONG - #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) - #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) + #define __pyx_atomic_int_type long + #pragma intrinsic (_InterlockedExchangeAdd) + #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1) + #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1) #ifdef __PYX_DEBUG_ATOMICS #pragma message ("Using MSVC atomics") #endif -#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 - #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) - #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) - #ifdef __PYX_DEBUG_ATOMICS - #warning "Using Intel atomics" - #endif #else #undef CYTHON_ATOMICS #define CYTHON_ATOMICS 0 @@ -922,9 +1073,9 @@ typedef struct { typedef volatile __pyx_atomic_int_type __pyx_atomic_int; #if CYTHON_ATOMICS #define __pyx_add_acquisition_count(memview)\ - __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview)) #define __pyx_sub_acquisition_count(memview)\ - __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview)) #else #define __pyx_add_acquisition_count(memview)\ __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) @@ -945,7 +1096,7 @@ typedef volatile __pyx_atomic_int_type __pyx_atomic_int; #define __Pyx_FastGilFuncInit() -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":776 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":689 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< @@ -954,7 +1105,7 @@ typedef volatile __pyx_atomic_int_type __pyx_atomic_int; */ typedef npy_int8 __pyx_t_5numpy_int8_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":777 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":690 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< @@ -963,7 +1114,7 @@ typedef npy_int8 __pyx_t_5numpy_int8_t; */ typedef npy_int16 __pyx_t_5numpy_int16_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":778 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":691 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< @@ -972,7 +1123,7 @@ typedef npy_int16 __pyx_t_5numpy_int16_t; */ typedef npy_int32 __pyx_t_5numpy_int32_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":779 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":692 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< @@ -981,7 +1132,7 @@ typedef npy_int32 __pyx_t_5numpy_int32_t; */ typedef npy_int64 __pyx_t_5numpy_int64_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":783 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":696 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< @@ -990,7 +1141,7 @@ typedef npy_int64 __pyx_t_5numpy_int64_t; */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":784 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":697 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< @@ -999,7 +1150,7 @@ typedef npy_uint8 __pyx_t_5numpy_uint8_t; */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":785 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":698 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< @@ -1008,7 +1159,7 @@ typedef npy_uint16 __pyx_t_5numpy_uint16_t; */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":786 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":699 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< @@ -1017,7 +1168,7 @@ typedef npy_uint32 __pyx_t_5numpy_uint32_t; */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":790 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":703 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< @@ -1026,7 +1177,7 @@ typedef npy_uint64 __pyx_t_5numpy_uint64_t; */ typedef npy_float32 __pyx_t_5numpy_float32_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":791 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":704 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< @@ -1035,7 +1186,7 @@ typedef npy_float32 __pyx_t_5numpy_float32_t; */ typedef npy_float64 __pyx_t_5numpy_float64_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":800 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":713 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< @@ -1044,7 +1195,7 @@ typedef npy_float64 __pyx_t_5numpy_float64_t; */ typedef npy_long __pyx_t_5numpy_int_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":801 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":714 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< @@ -1053,7 +1204,7 @@ typedef npy_long __pyx_t_5numpy_int_t; */ typedef npy_longlong __pyx_t_5numpy_long_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":802 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":715 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< @@ -1062,7 +1213,7 @@ typedef npy_longlong __pyx_t_5numpy_long_t; */ typedef npy_longlong __pyx_t_5numpy_longlong_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":804 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":717 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< @@ -1071,7 +1222,7 @@ typedef npy_longlong __pyx_t_5numpy_longlong_t; */ typedef npy_ulong __pyx_t_5numpy_uint_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":805 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":718 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< @@ -1080,7 +1231,7 @@ typedef npy_ulong __pyx_t_5numpy_uint_t; */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":806 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":719 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< @@ -1089,7 +1240,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":808 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":721 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< @@ -1098,7 +1249,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; */ typedef npy_intp __pyx_t_5numpy_intp_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":809 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":722 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< @@ -1107,7 +1258,7 @@ typedef npy_intp __pyx_t_5numpy_intp_t; */ typedef npy_uintp __pyx_t_5numpy_uintp_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":811 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":724 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< @@ -1116,7 +1267,7 @@ typedef npy_uintp __pyx_t_5numpy_uintp_t; */ typedef npy_double __pyx_t_5numpy_float_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":812 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":725 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< @@ -1125,7 +1276,7 @@ typedef npy_double __pyx_t_5numpy_float_t; */ typedef npy_double __pyx_t_5numpy_double_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":813 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":726 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< @@ -1164,7 +1315,7 @@ struct __pyx_MemviewEnum_obj; struct __pyx_memoryview_obj; struct __pyx_memoryviewslice_obj; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":815 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":728 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< @@ -1173,7 +1324,7 @@ struct __pyx_memoryviewslice_obj; */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":816 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":729 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< @@ -1182,7 +1333,7 @@ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":817 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":730 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< @@ -1191,7 +1342,7 @@ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":819 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":732 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< @@ -1199,8 +1350,80 @@ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; * cdef inline object PyArray_MultiIterNew1(a): */ typedef npy_cdouble __pyx_t_5numpy_complex_t; +struct __pyx_fuse_0__pyx_opt_args_5scipy_7special_14cython_special_spherical_jn; +struct __pyx_fuse_1__pyx_opt_args_5scipy_7special_14cython_special_spherical_jn; +struct __pyx_fuse_0__pyx_opt_args_5scipy_7special_14cython_special_spherical_yn; +struct __pyx_fuse_1__pyx_opt_args_5scipy_7special_14cython_special_spherical_yn; +struct __pyx_fuse_0__pyx_opt_args_5scipy_7special_14cython_special_spherical_in; +struct __pyx_fuse_1__pyx_opt_args_5scipy_7special_14cython_special_spherical_in; +struct __pyx_fuse_0__pyx_opt_args_5scipy_7special_14cython_special_spherical_kn; +struct __pyx_fuse_1__pyx_opt_args_5scipy_7special_14cython_special_spherical_kn; + +/* "scipy/special/cython_special.pxd":8 + * double + * + * cpdef number_t spherical_jn(long n, number_t z, bint derivative=*) nogil # <<<<<<<<<<<<<< + * cpdef number_t spherical_yn(long n, number_t z, bint derivative=*) nogil + * cpdef number_t spherical_in(long n, number_t z, bint derivative=*) nogil + */ +struct __pyx_fuse_0__pyx_opt_args_5scipy_7special_14cython_special_spherical_jn { + int __pyx_n; + int derivative; +}; +struct __pyx_fuse_1__pyx_opt_args_5scipy_7special_14cython_special_spherical_jn { + int __pyx_n; + int derivative; +}; + +/* "scipy/special/cython_special.pxd":9 + * + * cpdef number_t spherical_jn(long n, number_t z, bint derivative=*) nogil + * cpdef number_t spherical_yn(long n, number_t z, bint derivative=*) nogil # <<<<<<<<<<<<<< + * cpdef number_t spherical_in(long n, number_t z, bint derivative=*) nogil + * cpdef number_t spherical_kn(long n, number_t z, bint derivative=*) nogil + */ +struct __pyx_fuse_0__pyx_opt_args_5scipy_7special_14cython_special_spherical_yn { + int __pyx_n; + int derivative; +}; +struct __pyx_fuse_1__pyx_opt_args_5scipy_7special_14cython_special_spherical_yn { + int __pyx_n; + int derivative; +}; + +/* "scipy/special/cython_special.pxd":10 + * cpdef number_t spherical_jn(long n, number_t z, bint derivative=*) nogil + * cpdef number_t spherical_yn(long n, number_t z, bint derivative=*) nogil + * cpdef number_t spherical_in(long n, number_t z, bint derivative=*) nogil # <<<<<<<<<<<<<< + * cpdef number_t spherical_kn(long n, number_t z, bint derivative=*) nogil + * + */ +struct __pyx_fuse_0__pyx_opt_args_5scipy_7special_14cython_special_spherical_in { + int __pyx_n; + int derivative; +}; +struct __pyx_fuse_1__pyx_opt_args_5scipy_7special_14cython_special_spherical_in { + int __pyx_n; + int derivative; +}; + +/* "scipy/special/cython_special.pxd":11 + * cpdef number_t spherical_yn(long n, number_t z, bint derivative=*) nogil + * cpdef number_t spherical_in(long n, number_t z, bint derivative=*) nogil + * cpdef number_t spherical_kn(long n, number_t z, bint derivative=*) nogil # <<<<<<<<<<<<<< + * + * ctypedef fused Dd_number_t: + */ +struct __pyx_fuse_0__pyx_opt_args_5scipy_7special_14cython_special_spherical_kn { + int __pyx_n; + int derivative; +}; +struct __pyx_fuse_1__pyx_opt_args_5scipy_7special_14cython_special_spherical_kn { + int __pyx_n; + int derivative; +}; -/* "View.MemoryView":105 +/* "View.MemoryView":106 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< @@ -1225,7 +1448,7 @@ struct __pyx_array_obj { }; -/* "View.MemoryView":279 +/* "View.MemoryView":280 * * @cname('__pyx_MemviewEnum') * cdef class Enum(object): # <<<<<<<<<<<<<< @@ -1238,7 +1461,7 @@ struct __pyx_MemviewEnum_obj { }; -/* "View.MemoryView":330 +/* "View.MemoryView":331 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< @@ -1261,7 +1484,7 @@ struct __pyx_memoryview_obj { }; -/* "View.MemoryView":965 +/* "View.MemoryView":967 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< @@ -1278,7 +1501,7 @@ struct __pyx_memoryviewslice_obj { -/* "View.MemoryView":105 +/* "View.MemoryView":106 * * @cname("__pyx_array") * cdef class array: # <<<<<<<<<<<<<< @@ -1292,7 +1515,7 @@ struct __pyx_vtabstruct_array { static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; -/* "View.MemoryView":330 +/* "View.MemoryView":331 * * @cname('__pyx_memoryview') * cdef class memoryview(object): # <<<<<<<<<<<<<< @@ -1312,7 +1535,7 @@ struct __pyx_vtabstruct_memoryview { static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; -/* "View.MemoryView":965 +/* "View.MemoryView":967 * * @cname('__pyx_memoryviewslice') * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< @@ -1466,18 +1689,18 @@ static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UIN /* GetModuleGlobalName.proto */ #if CYTHON_USE_DICT_VERSIONS -#define __Pyx_GetModuleGlobalName(var, name) {\ +#define __Pyx_GetModuleGlobalName(var, name) do {\ static PY_UINT64_T __pyx_dict_version = 0;\ static PyObject *__pyx_dict_cached_value = NULL;\ (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ -} -#define __Pyx_GetModuleGlobalNameUncached(var, name) {\ +} while(0) +#define __Pyx_GetModuleGlobalNameUncached(var, name) do {\ PY_UINT64_T __pyx_dict_version;\ PyObject *__pyx_dict_cached_value;\ (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ -} +} while(0) static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); #else #define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) @@ -1506,13 +1729,21 @@ static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, #ifndef Py_MEMBER_SIZE #define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) #endif +#if CYTHON_FAST_PYCALL static size_t __pyx_pyframe_localsplus_offset = 0; #include "frameobject.h" +#if PY_VERSION_HEX >= 0x030b00a6 + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif #define __Pxy_PyFrame_Initialize_Offsets()\ ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) #define __Pyx_PyFrame_GetLocalsplus(frame)\ (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif // CYTHON_FAST_PYCALL #endif /* PyObjectCall.proto */ @@ -1595,32 +1826,6 @@ static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject #define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) #endif -/* RaiseException.proto */ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); - -/* DictGetItem.proto */ -#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY -static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key); -#define __Pyx_PyObject_Dict_GetItem(obj, name)\ - (likely(PyDict_CheckExact(obj)) ?\ - __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name)) -#else -#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) -#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name) -#endif - -/* RaiseTooManyValuesToUnpack.proto */ -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); - -/* RaiseNeedMoreValuesToUnpack.proto */ -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); - -/* RaiseNoneIterError.proto */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); - -/* ExtTypeTest.proto */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); - /* GetTopmostException.proto */ #if CYTHON_USE_EXC_INFO_STACK static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); @@ -1653,6 +1858,9 @@ static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); #endif +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + /* IncludeStringH.proto */ #include @@ -1669,7 +1877,7 @@ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int #define __Pyx_PyString_Equals __Pyx_PyBytes_Equals #endif -/* None.proto */ +/* DivInt[Py_ssize_t].proto */ static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); /* UnaryNegOverflows.proto */ @@ -1733,6 +1941,18 @@ static CYTHON_INLINE PyObject* __Pyx_decode_c_string( /* GetAttr3.proto */ static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + /* SwapException.proto */ #if CYTHON_FAST_THREAD_STATE #define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) @@ -1766,7 +1986,7 @@ static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { if (likely(L->allocated > len)) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); - Py_SIZE(list) = len+1; + __Pyx_SET_SIZE(list, len + 1); return 0; } return PyList_Append(list, x); @@ -1804,7 +2024,7 @@ static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { Py_INCREF(x); PyList_SET_ITEM(list, len, x); - Py_SIZE(list) = len+1; + __Pyx_SET_SIZE(list, len + 1); return 0; } return PyList_Append(list, x); @@ -1816,9 +2036,15 @@ static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { /* None.proto */ static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); -/* None.proto */ +/* DivInt[long].proto */ static CYTHON_INLINE long __Pyx_div_long(long, long); +/* PySequenceContains.proto */ +static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { + int result = PySequence_Contains(seq, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + /* ImportFrom.proto */ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); @@ -1842,6 +2068,9 @@ static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_nam /* SetVTable.proto */ static int __Pyx_SetVtable(PyObject *dict, void *vtable); +/* PyObjectGetAttrStrNoError.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); + /* SetupReduce.proto */ static int __Pyx_setup_reduce(PyObject* type_obj); @@ -1916,8 +2145,30 @@ static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, /* Capsule.proto */ static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value); +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_nn___pyx_t_5numpy_int64_t(PyObject *, int writable_flag); + +/* GCCDiagnostics.proto */ +#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) +#define __Pyx_HAS_GCC_DIAGNOSTIC +#endif /* RealImag.proto */ #if CYTHON_CCOMPLEX @@ -2017,12 +2268,6 @@ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value); #endif #endif -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); - /* MemviewSliceCopyTemplate.proto */ static __Pyx_memviewslice __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, @@ -2030,6 +2275,9 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, size_t sizeof_dtype, int contig_flag, int dtype_is_object); +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value); + /* CIntFromPy.proto */ static CYTHON_INLINE npy_int64 __Pyx_PyInt_As_npy_int64(PyObject *); @@ -2039,32 +2287,15 @@ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); /* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + /* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); /* CIntFromPy.proto */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); -/* TypeInfoCompare.proto */ -static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); - -/* MemviewSliceValidateAndInit.proto */ -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_nn___pyx_t_5numpy_int64_t(PyObject *, int writable_flag); - /* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); @@ -2111,8 +2342,17 @@ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_generic = 0; +static PyTypeObject *__pyx_ptype_5numpy_number = 0; +static PyTypeObject *__pyx_ptype_5numpy_integer = 0; +static PyTypeObject *__pyx_ptype_5numpy_signedinteger = 0; +static PyTypeObject *__pyx_ptype_5numpy_unsignedinteger = 0; +static PyTypeObject *__pyx_ptype_5numpy_inexact = 0; +static PyTypeObject *__pyx_ptype_5numpy_floating = 0; +static PyTypeObject *__pyx_ptype_5numpy_complexfloating = 0; +static PyTypeObject *__pyx_ptype_5numpy_flexible = 0; +static PyTypeObject *__pyx_ptype_5numpy_character = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; -static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ /* Module declarations from 'scipy.special.cython_special' */ static double (*__pyx_f_5scipy_7special_14cython_special_pdtr)(double, double, int __pyx_skip_dispatch); /*proto*/ @@ -2177,9 +2417,8 @@ int __pyx_module_is_main_pygom__model___tau_leap = 0; /* Implementation of 'pygom.model._tau_leap' */ static PyObject *__pyx_builtin_range; -static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_builtin_RuntimeError; static PyObject *__pyx_builtin_ImportError; +static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_enumerate; static PyObject *__pyx_builtin_TypeError; @@ -2256,7 +2495,6 @@ static const char __pyx_k_ImportError[] = "ImportError"; static const char __pyx_k_MemoryError[] = "MemoryError"; static const char __pyx_k_PickleError[] = "PickleError"; static const char __pyx_k_n_reactants[] = "n_reactants"; -static const char __pyx_k_RuntimeError[] = "RuntimeError"; static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; static const char __pyx_k_reactant_mat[] = "reactant_mat"; static const char __pyx_k_stringsource[] = "stringsource"; @@ -2282,29 +2520,23 @@ static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type ' static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; static const char __pyx_k_pygom_model__tau_leap_pyx[] = "pygom/model/_tau_leap.pyx"; static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; -static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; static const char __pyx_k_strided_and_direct_or_indirect[] = ""; static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; -static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; -static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; -static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; +static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name))"; static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; -static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; -static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; -static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; static PyObject *__pyx_n_s_ASCII; static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; @@ -2313,10 +2545,8 @@ static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; static PyObject *__pyx_kp_s_Cannot_index_with_type_s; static PyObject *__pyx_n_s_Ellipsis; static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; -static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; -static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; static PyObject *__pyx_n_s_ImportError; -static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; +static PyObject *__pyx_kp_s_Incompatible_checksums_0x_x_vs_0; static PyObject *__pyx_n_s_IndexError; static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; @@ -2324,11 +2554,9 @@ static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; static PyObject *__pyx_n_s_MemoryError; static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; static PyObject *__pyx_kp_s_MemoryView_of_r_object; -static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; static PyObject *__pyx_n_b_O; static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; static PyObject *__pyx_n_s_PickleError; -static PyObject *__pyx_n_s_RuntimeError; static PyObject *__pyx_n_s_TypeError; static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; static PyObject *__pyx_n_s_ValueError; @@ -2371,8 +2599,6 @@ static PyObject *__pyx_n_s_n_rates; static PyObject *__pyx_n_s_n_reactants; static PyObject *__pyx_n_s_name; static PyObject *__pyx_n_s_name_2; -static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; -static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; static PyObject *__pyx_n_s_ndim; static PyObject *__pyx_n_s_new; static PyObject *__pyx_n_s_new_cdf; @@ -2421,14 +2647,11 @@ static PyObject *__pyx_n_s_test; static PyObject *__pyx_n_s_total_rate; static PyObject *__pyx_kp_s_unable_to_allocate_array_data; static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; -static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd; static PyObject *__pyx_n_s_unpack; static PyObject *__pyx_n_s_update; static PyObject *__pyx_n_s_x; static PyObject *__pyx_n_s_x_view; static PyObject *__pyx_pf_5pygom_5model_9_tau_leap__cy_test_tau_leap_safety(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_reactant_mat, PyArrayObject *__pyx_v_rates, double __pyx_v_tau_scale, double __pyx_v_epsilon); /* proto */ -static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ @@ -2477,6 +2700,8 @@ static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); 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goto __pyx_L0; - /* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":1026 + /* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":932 * PyArray_SetBaseObject(arr, base) * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< @@ -5134,12 +3772,12 @@ static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__py return __pyx_r; } -/* "../../anaconda3/lib/python3.7/site-packages/Cython/Includes/numpy/__init__.pxd":1034 +/* "../../anaconda3/envs/pygom_test_3.10/lib/python3.10/site-packages/numpy/__init__.pxd":940 * # Versions of the import_* functions which are more suitable for * # Cython code. * cdef inline int import_array() except -1: # <<<<<<<<<<<<<< * try: - * _import_array() + * __pyx_import_array() */ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) { @@ -5153,13 +3791,16 @@ static CYTHON_INLINE int __pyx_f_5numpy_import_array(void) { PyObject *__pyx_t_6 = NULL; PyObject *__pyx_t_7 = NULL; 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__pyx_t_10 = 0; __pyx_v_self->dtype_is_object = __pyx_t_4; - /* "View.MemoryView":171 + /* "View.MemoryView":172 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< @@ -6122,7 +4952,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ __pyx_t_4 = (__pyx_v_allocate_buffer != 0); if (__pyx_t_4) { - /* "View.MemoryView":174 + /* "View.MemoryView":175 * * * self.data = malloc(self.len) # <<<<<<<<<<<<<< @@ -6131,7 +4961,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len)); - /* "View.MemoryView":175 + /* "View.MemoryView":176 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< @@ -6141,20 +4971,20 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0); if (unlikely(__pyx_t_4)) { - /* "View.MemoryView":176 + /* "View.MemoryView":177 * self.data = malloc(self.len) * if not self.data: * raise MemoryError("unable to allocate array data.") # <<<<<<<<<<<<<< * * if self.dtype_is_object: */ - __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 176, __pyx_L1_error) + __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_10)) __PYX_ERR(2, 177, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_10); __Pyx_Raise(__pyx_t_10, 0, 0, 0); __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0; - __PYX_ERR(2, 176, __pyx_L1_error) + __PYX_ERR(2, 177, __pyx_L1_error) - /* "View.MemoryView":175 + /* "View.MemoryView":176 * * self.data = malloc(self.len) * if not self.data: # <<<<<<<<<<<<<< @@ -6163,7 +4993,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ } - /* "View.MemoryView":178 + /* "View.MemoryView":179 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< @@ -6173,7 +5003,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_4) { - /* "View.MemoryView":179 + /* "View.MemoryView":180 * * if self.dtype_is_object: * p = self.data # <<<<<<<<<<<<<< @@ -6182,7 +5012,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ __pyx_v_p = ((PyObject **)__pyx_v_self->data); - /* "View.MemoryView":180 + /* "View.MemoryView":181 * if self.dtype_is_object: * p = self.data * for i in range(self.len / itemsize): # <<<<<<<<<<<<<< @@ -6191,18 +5021,18 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ if (unlikely(__pyx_v_itemsize == 0)) { PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); - __PYX_ERR(2, 180, __pyx_L1_error) + __PYX_ERR(2, 181, __pyx_L1_error) } else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1) && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_self->len))) { PyErr_SetString(PyExc_OverflowError, "value too large to perform division"); - __PYX_ERR(2, 180, __pyx_L1_error) + __PYX_ERR(2, 181, __pyx_L1_error) } __pyx_t_1 = __Pyx_div_Py_ssize_t(__pyx_v_self->len, __pyx_v_itemsize); __pyx_t_9 = __pyx_t_1; for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_9; __pyx_t_11+=1) { __pyx_v_i = __pyx_t_11; - /* "View.MemoryView":181 + /* "View.MemoryView":182 * p = self.data * for i in range(self.len / itemsize): * p[i] = Py_None # <<<<<<<<<<<<<< @@ -6211,7 +5041,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ (__pyx_v_p[__pyx_v_i]) = Py_None; - /* "View.MemoryView":182 + /* "View.MemoryView":183 * for i in range(self.len / itemsize): * p[i] = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< @@ -6221,7 +5051,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ Py_INCREF(Py_None); } - /* "View.MemoryView":178 + /* "View.MemoryView":179 * raise MemoryError("unable to allocate array data.") * * if self.dtype_is_object: # <<<<<<<<<<<<<< @@ -6230,7 +5060,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ } - /* "View.MemoryView":171 + /* "View.MemoryView":172 * self.free_data = allocate_buffer * self.dtype_is_object = format == b'O' * if allocate_buffer: # <<<<<<<<<<<<<< @@ -6239,7 +5069,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ */ } - /* "View.MemoryView":122 + /* "View.MemoryView":123 * cdef bint dtype_is_object * * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< @@ -6263,7 +5093,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __ return __pyx_r; } -/* "View.MemoryView":185 +/* "View.MemoryView":186 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< @@ -6295,6 +5125,9 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru Py_ssize_t __pyx_t_5; int __pyx_t_6; Py_ssize_t *__pyx_t_7; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; if (__pyx_v_info == NULL) { PyErr_SetString(PyExc_BufferError, "PyObject_GetBuffer: view==NULL argument is obsolete"); return -1; @@ -6303,7 +5136,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None); __Pyx_GIVEREF(__pyx_v_info->obj); - /* "View.MemoryView":186 + /* "View.MemoryView":187 * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 # <<<<<<<<<<<<<< @@ -6312,18 +5145,18 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ __pyx_v_bufmode = -1; - /* "View.MemoryView":187 + /* "View.MemoryView":188 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": */ - __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 187, __pyx_L1_error) + __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 188, __pyx_L1_error) __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":188 + /* "View.MemoryView":189 * cdef int bufmode = -1 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< @@ -6332,7 +5165,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); - /* "View.MemoryView":187 + /* "View.MemoryView":188 * def __getbuffer__(self, Py_buffer *info, int flags): * cdef int bufmode = -1 * if self.mode == u"c": # <<<<<<<<<<<<<< @@ -6342,18 +5175,18 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru goto __pyx_L3; } - /* "View.MemoryView":189 + /* "View.MemoryView":190 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): */ - __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 189, __pyx_L1_error) + __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) __PYX_ERR(2, 190, __pyx_L1_error) __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { - /* "View.MemoryView":190 + /* "View.MemoryView":191 * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS # <<<<<<<<<<<<<< @@ -6362,7 +5195,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS); - /* "View.MemoryView":189 + /* "View.MemoryView":190 * if self.mode == u"c": * bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * elif self.mode == u"fortran": # <<<<<<<<<<<<<< @@ -6372,7 +5205,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru } __pyx_L3:; - /* "View.MemoryView":191 + /* "View.MemoryView":192 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< @@ -6382,20 +5215,20 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0); if (unlikely(__pyx_t_1)) { - /* "View.MemoryView":192 + /* "View.MemoryView":193 * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") # <<<<<<<<<<<<<< * info.buf = self.data * info.len = self.len */ - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 192, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 193, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(2, 192, __pyx_L1_error) + __PYX_ERR(2, 193, __pyx_L1_error) - /* "View.MemoryView":191 + /* "View.MemoryView":192 * elif self.mode == u"fortran": * bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS * if not (flags & bufmode): # <<<<<<<<<<<<<< @@ -6404,7 +5237,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ } - /* "View.MemoryView":193 + /* "View.MemoryView":194 * if not (flags & bufmode): * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data # <<<<<<<<<<<<<< @@ -6414,7 +5247,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_4 = __pyx_v_self->data; __pyx_v_info->buf = __pyx_t_4; - /* "View.MemoryView":194 + /* "View.MemoryView":195 * raise ValueError("Can only create a buffer that is contiguous in memory.") * info.buf = self.data * info.len = self.len # <<<<<<<<<<<<<< @@ -6424,7 +5257,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_5 = __pyx_v_self->len; __pyx_v_info->len = __pyx_t_5; - /* "View.MemoryView":195 + /* "View.MemoryView":196 * info.buf = self.data * info.len = self.len * info.ndim = self.ndim # <<<<<<<<<<<<<< @@ -6434,7 +5267,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_6 = __pyx_v_self->ndim; __pyx_v_info->ndim = __pyx_t_6; - /* "View.MemoryView":196 + /* "View.MemoryView":197 * info.len = self.len * info.ndim = self.ndim * info.shape = self._shape # <<<<<<<<<<<<<< @@ -6444,7 +5277,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_7 = __pyx_v_self->_shape; __pyx_v_info->shape = __pyx_t_7; - /* "View.MemoryView":197 + /* "View.MemoryView":198 * info.ndim = self.ndim * info.shape = self._shape * info.strides = self._strides # <<<<<<<<<<<<<< @@ -6454,7 +5287,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_7 = __pyx_v_self->_strides; __pyx_v_info->strides = __pyx_t_7; - /* "View.MemoryView":198 + /* "View.MemoryView":199 * info.shape = self._shape * info.strides = self._strides * info.suboffsets = NULL # <<<<<<<<<<<<<< @@ -6463,7 +5296,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ __pyx_v_info->suboffsets = NULL; - /* "View.MemoryView":199 + /* "View.MemoryView":200 * info.strides = self._strides * info.suboffsets = NULL * info.itemsize = self.itemsize # <<<<<<<<<<<<<< @@ -6473,7 +5306,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_5 = __pyx_v_self->itemsize; __pyx_v_info->itemsize = __pyx_t_5; - /* "View.MemoryView":200 + /* "View.MemoryView":201 * info.suboffsets = NULL * info.itemsize = self.itemsize * info.readonly = 0 # <<<<<<<<<<<<<< @@ -6482,7 +5315,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru */ __pyx_v_info->readonly = 0; - /* "View.MemoryView":202 + /* "View.MemoryView":203 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< @@ -6492,7 +5325,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { - /* "View.MemoryView":203 + /* "View.MemoryView":204 * * if flags & PyBUF_FORMAT: * info.format = self.format # <<<<<<<<<<<<<< @@ -6502,7 +5335,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __pyx_t_4 = __pyx_v_self->format; __pyx_v_info->format = __pyx_t_4; - /* "View.MemoryView":202 + /* "View.MemoryView":203 * info.readonly = 0 * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< @@ -6512,7 +5345,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru goto __pyx_L5; } - /* "View.MemoryView":205 + /* "View.MemoryView":206 * info.format = self.format * else: * info.format = NULL # <<<<<<<<<<<<<< @@ -6524,7 +5357,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru } __pyx_L5:; - /* "View.MemoryView":207 + /* "View.MemoryView":208 * info.format = NULL * * info.obj = self # <<<<<<<<<<<<<< @@ -6537,7 +5370,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = ((PyObject *)__pyx_v_self); - /* "View.MemoryView":185 + /* "View.MemoryView":186 * * @cname('getbuffer') * def __getbuffer__(self, Py_buffer *info, int flags): # <<<<<<<<<<<<<< @@ -6567,7 +5400,7 @@ static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(stru return __pyx_r; } -/* "View.MemoryView":211 +/* "View.MemoryView":212 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< @@ -6591,7 +5424,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc int __pyx_t_1; __Pyx_RefNannySetupContext("__dealloc__", 0); - /* "View.MemoryView":212 + /* "View.MemoryView":213 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< @@ -6601,7 +5434,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0); if (__pyx_t_1) { - /* "View.MemoryView":213 + /* "View.MemoryView":214 * def __dealloc__(array self): * if self.callback_free_data != NULL: * self.callback_free_data(self.data) # <<<<<<<<<<<<<< @@ -6610,7 +5443,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ __pyx_v_self->callback_free_data(__pyx_v_self->data); - /* "View.MemoryView":212 + /* "View.MemoryView":213 * * def __dealloc__(array self): * if self.callback_free_data != NULL: # <<<<<<<<<<<<<< @@ -6620,7 +5453,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc goto __pyx_L3; } - /* "View.MemoryView":214 + /* "View.MemoryView":215 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< @@ -6630,7 +5463,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc __pyx_t_1 = (__pyx_v_self->free_data != 0); if (__pyx_t_1) { - /* "View.MemoryView":215 + /* "View.MemoryView":216 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< @@ -6640,7 +5473,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0); if (__pyx_t_1) { - /* "View.MemoryView":216 + /* "View.MemoryView":217 * elif self.free_data: * if self.dtype_is_object: * refcount_objects_in_slice(self.data, self._shape, # <<<<<<<<<<<<<< @@ -6649,7 +5482,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0); - /* "View.MemoryView":215 + /* "View.MemoryView":216 * self.callback_free_data(self.data) * elif self.free_data: * if self.dtype_is_object: # <<<<<<<<<<<<<< @@ -6658,7 +5491,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ } - /* "View.MemoryView":218 + /* "View.MemoryView":219 * refcount_objects_in_slice(self.data, self._shape, * self._strides, self.ndim, False) * free(self.data) # <<<<<<<<<<<<<< @@ -6667,7 +5500,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ free(__pyx_v_self->data); - /* "View.MemoryView":214 + /* "View.MemoryView":215 * if self.callback_free_data != NULL: * self.callback_free_data(self.data) * elif self.free_data: # <<<<<<<<<<<<<< @@ -6677,7 +5510,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc } __pyx_L3:; - /* "View.MemoryView":219 + /* "View.MemoryView":220 * self._strides, self.ndim, False) * free(self.data) * PyObject_Free(self._shape) # <<<<<<<<<<<<<< @@ -6686,7 +5519,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc */ PyObject_Free(__pyx_v_self->_shape); - /* "View.MemoryView":211 + /* "View.MemoryView":212 * __pyx_getbuffer = capsule( &__pyx_array_getbuffer, "getbuffer(obj, view, flags)") * * def __dealloc__(array self): # <<<<<<<<<<<<<< @@ -6698,7 +5531,7 @@ static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struc __Pyx_RefNannyFinishContext(); } -/* "View.MemoryView":222 +/* "View.MemoryView":223 * * @property * def memview(self): # <<<<<<<<<<<<<< @@ -6723,9 +5556,12 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct _ PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); - /* "View.MemoryView":223 + /* "View.MemoryView":224 * @property * def memview(self): * return self.get_memview() # <<<<<<<<<<<<<< @@ -6733,13 +5569,13 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct _ * @cname('get_memview') */ __Pyx_XDECREF(__pyx_r); 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} -/* "View.MemoryView":281 +/* "View.MemoryView":282 * cdef class Enum(object): * cdef object name * def __init__(self, name): # <<<<<<<<<<<<<< @@ -7365,6 +6222,9 @@ static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_name = 0; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__ (wrapper)", 0); @@ -7387,7 +6247,7 @@ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_ar else goto __pyx_L5_argtuple_error; } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) __PYX_ERR(2, 281, __pyx_L3_error) + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) __PYX_ERR(2, 282, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 1) { goto __pyx_L5_argtuple_error; @@ -7398,7 +6258,7 @@ static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_ar } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 281, __pyx_L3_error) + __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); __PYX_ERR(2, 282, __pyx_L3_error) __pyx_L3_error:; __Pyx_AddTraceback("View.MemoryView.Enum.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); @@ -7416,7 +6276,7 @@ static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struc __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__init__", 0); - /* "View.MemoryView":282 + /* "View.MemoryView":283 * cdef object name * def __init__(self, name): * self.name = name # <<<<<<<<<<<<<< @@ -7429,7 +6289,7 @@ static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struc __Pyx_DECREF(__pyx_v_self->name); __pyx_v_self->name = __pyx_v_name; 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PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__reduce_cython__", 0); /* "(tree fragment)":5 @@ -7751,6 +6614,9 @@ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_Me PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__setstate_cython__", 0); /* "(tree fragment)":17 @@ -7758,7 +6624,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_Me * def __setstate_cython__(self, __pyx_state): * __pyx_unpickle_Enum__set_state(self, __pyx_state) # <<<<<<<<<<<<<< */ - if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 17, __pyx_L1_error) + if (!(likely(PyTuple_CheckExact(__pyx_v___pyx_state))||((__pyx_v___pyx_state) == Py_None)||((void)PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_v___pyx_state)->tp_name), 0))) __PYX_ERR(2, 17, __pyx_L1_error) __pyx_t_1 = __pyx_unpickle_Enum__set_state(__pyx_v_self, ((PyObject*)__pyx_v___pyx_state)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 17, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; @@ -7783,7 +6649,7 @@ static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_Me return __pyx_r; } -/* "View.MemoryView":298 +/* "View.MemoryView":299 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< @@ -7797,7 +6663,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) void *__pyx_r; int __pyx_t_1; - /* "View.MemoryView":300 + /* "View.MemoryView":301 * cdef void *align_pointer(void *memory, size_t alignment) nogil: * "Align pointer memory on a given boundary" * cdef Py_intptr_t aligned_p = memory # <<<<<<<<<<<<<< @@ -7806,7 +6672,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) */ __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory); - /* "View.MemoryView":304 + /* "View.MemoryView":305 * * with cython.cdivision(True): * offset = aligned_p % alignment # <<<<<<<<<<<<<< @@ -7815,7 +6681,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) */ __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment); - /* "View.MemoryView":306 + /* "View.MemoryView":307 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< @@ -7825,7 +6691,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) __pyx_t_1 = ((__pyx_v_offset > 0) != 0); if (__pyx_t_1) { - /* "View.MemoryView":307 + /* "View.MemoryView":308 * * if offset > 0: * aligned_p += alignment - offset # <<<<<<<<<<<<<< @@ -7834,7 +6700,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) */ __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset)); - /* "View.MemoryView":306 + /* "View.MemoryView":307 * offset = aligned_p % alignment * * if offset > 0: # <<<<<<<<<<<<<< @@ -7843,7 +6709,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) */ } - /* "View.MemoryView":309 + /* "View.MemoryView":310 * aligned_p += alignment - offset * * return aligned_p # <<<<<<<<<<<<<< @@ -7853,7 +6719,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) __pyx_r = ((void *)__pyx_v_aligned_p); goto __pyx_L0; - /* "View.MemoryView":298 + /* "View.MemoryView":299 * * @cname('__pyx_align_pointer') * cdef void *align_pointer(void *memory, size_t alignment) nogil: # <<<<<<<<<<<<<< @@ -7866,7 +6732,7 @@ static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) return __pyx_r; } -/* "View.MemoryView":345 +/* "View.MemoryView":346 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< @@ -7880,6 +6746,9 @@ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_ar PyObject *__pyx_v_obj = 0; int __pyx_v_flags; int __pyx_v_dtype_is_object; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; int __pyx_r; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0); @@ -7908,7 +6777,7 @@ static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_ar case 1: if (likely((values[1] = __Pyx_PyDict_GetItemStr(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--; 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int __pyx_t_3; int __pyx_t_4; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__cinit__", 0); - /* "View.MemoryView":346 + /* "View.MemoryView":347 * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj # <<<<<<<<<<<<<< @@ -7975,7 +6847,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __Pyx_DECREF(__pyx_v_self->obj); __pyx_v_self->obj = __pyx_v_obj; - /* "View.MemoryView":347 + /* "View.MemoryView":348 * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): * self.obj = obj * self.flags = flags # <<<<<<<<<<<<<< @@ -7984,7 +6856,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_v_self->flags = __pyx_v_flags; - /* "View.MemoryView":348 + /* "View.MemoryView":349 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< @@ -8004,16 +6876,16 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_L4_bool_binop_done:; if (__pyx_t_1) { - /* "View.MemoryView":349 + /* "View.MemoryView":350 * self.flags = flags * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) # <<<<<<<<<<<<<< * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None */ - __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 349, __pyx_L1_error) + __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == ((int)-1))) __PYX_ERR(2, 350, __pyx_L1_error) - /* "View.MemoryView":350 + /* "View.MemoryView":351 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< @@ -8023,7 +6895,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_t_1 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0); if (__pyx_t_1) { - /* "View.MemoryView":351 + /* "View.MemoryView":352 * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None # <<<<<<<<<<<<<< @@ -8032,16 +6904,16 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None; - /* "View.MemoryView":352 + /* "View.MemoryView":353 * if self.view.obj == NULL: * (<__pyx_buffer *> &self.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< * - * global __pyx_memoryview_thread_locks_used + * if not __PYX_CYTHON_ATOMICS_ENABLED(): */ Py_INCREF(Py_None); - /* "View.MemoryView":350 + /* "View.MemoryView":351 * if type(self) is memoryview or obj is not None: * __Pyx_GetBuffer(obj, &self.view, flags) * if self.view.obj == NULL: # <<<<<<<<<<<<<< @@ -8050,7 +6922,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ } - /* "View.MemoryView":348 + /* "View.MemoryView":349 * self.obj = obj * self.flags = flags * if type(self) is memoryview or obj is not None: # <<<<<<<<<<<<<< @@ -8060,100 +6932,119 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ } /* "View.MemoryView":355 + * Py_INCREF(Py_None) * - * global __pyx_memoryview_thread_locks_used - * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< - * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] - * __pyx_memoryview_thread_locks_used += 1 + * if not __PYX_CYTHON_ATOMICS_ENABLED(): # <<<<<<<<<<<<<< + * global __pyx_memoryview_thread_locks_used + * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: */ - __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0); + __pyx_t_1 = ((!(__PYX_CYTHON_ATOMICS_ENABLED() != 0)) != 0); if (__pyx_t_1) { - /* "View.MemoryView":356 - * global __pyx_memoryview_thread_locks_used - * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: - * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< - * __pyx_memoryview_thread_locks_used += 1 - * if self.lock is NULL: - */ - __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); - /* "View.MemoryView":357 - * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: - * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] - * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< - * if self.lock is NULL: - * self.lock = PyThread_allocate_lock() + * if not __PYX_CYTHON_ATOMICS_ENABLED(): + * global __pyx_memoryview_thread_locks_used + * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] + * __pyx_memoryview_thread_locks_used += 1 */ - __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); + __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0); + if (__pyx_t_1) { - /* "View.MemoryView":355 - * - * global __pyx_memoryview_thread_locks_used - * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< - * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] - * __pyx_memoryview_thread_locks_used += 1 + /* "View.MemoryView":358 + * global __pyx_memoryview_thread_locks_used + * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< + * __pyx_memoryview_thread_locks_used += 1 + * if self.lock is NULL: */ - } + __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); - /* "View.MemoryView":358 - * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] - * __pyx_memoryview_thread_locks_used += 1 - * if self.lock is NULL: # <<<<<<<<<<<<<< - * self.lock = PyThread_allocate_lock() + /* "View.MemoryView":359 + * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] + * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< * if self.lock is NULL: + * self.lock = PyThread_allocate_lock() */ - __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); - if (__pyx_t_1) { + __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); - /* "View.MemoryView":359 - * __pyx_memoryview_thread_locks_used += 1 - * if self.lock is NULL: - * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< - * if self.lock is NULL: - * raise MemoryError + /* "View.MemoryView":357 + * if not __PYX_CYTHON_ATOMICS_ENABLED(): + * global __pyx_memoryview_thread_locks_used + * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: # <<<<<<<<<<<<<< + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] + * __pyx_memoryview_thread_locks_used += 1 */ - __pyx_v_self->lock = PyThread_allocate_lock(); + } /* "View.MemoryView":360 - * if self.lock is NULL: - * self.lock = PyThread_allocate_lock() + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] + * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< - * raise MemoryError - * + * self.lock = PyThread_allocate_lock() + * if self.lock is NULL: */ __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); - if (unlikely(__pyx_t_1)) { + if (__pyx_t_1) { /* "View.MemoryView":361 - * self.lock = PyThread_allocate_lock() + * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: - * raise MemoryError # <<<<<<<<<<<<<< + * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< + * if self.lock is NULL: + * raise MemoryError + */ + __pyx_v_self->lock = PyThread_allocate_lock(); + + /* "View.MemoryView":362 + * if self.lock is NULL: + * self.lock = PyThread_allocate_lock() + * if self.lock is NULL: # <<<<<<<<<<<<<< + * raise MemoryError + * + */ + __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0); + if (unlikely(__pyx_t_1)) { + + /* "View.MemoryView":363 + * self.lock = PyThread_allocate_lock() + * if self.lock is NULL: + * raise MemoryError # <<<<<<<<<<<<<< * * if flags & PyBUF_FORMAT: */ - PyErr_NoMemory(); __PYX_ERR(2, 361, __pyx_L1_error) + PyErr_NoMemory(); __PYX_ERR(2, 363, __pyx_L1_error) + + /* "View.MemoryView":362 + * if self.lock is NULL: + * self.lock = PyThread_allocate_lock() + * if self.lock is NULL: # <<<<<<<<<<<<<< + * raise MemoryError + * + */ + } /* "View.MemoryView":360 - * if self.lock is NULL: - * self.lock = PyThread_allocate_lock() + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] + * __pyx_memoryview_thread_locks_used += 1 * if self.lock is NULL: # <<<<<<<<<<<<<< - * raise MemoryError - * + * self.lock = PyThread_allocate_lock() + * if self.lock is NULL: */ } - /* "View.MemoryView":358 - * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] - * __pyx_memoryview_thread_locks_used += 1 - * if self.lock is NULL: # <<<<<<<<<<<<<< - * self.lock = PyThread_allocate_lock() - * if self.lock is NULL: + /* "View.MemoryView":355 + * Py_INCREF(Py_None) + * + * if not __PYX_CYTHON_ATOMICS_ENABLED(): # <<<<<<<<<<<<<< + * global __pyx_memoryview_thread_locks_used + * if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED: */ } - /* "View.MemoryView":363 - * raise MemoryError + /* "View.MemoryView":365 + * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') @@ -8162,7 +7053,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { - /* "View.MemoryView":364 + /* "View.MemoryView":366 * * if flags & PyBUF_FORMAT: * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') # <<<<<<<<<<<<<< @@ -8173,24 +7064,24 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ if (__pyx_t_2) { } else { __pyx_t_1 = __pyx_t_2; - goto __pyx_L11_bool_binop_done; + goto __pyx_L12_bool_binop_done; } __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\x00') != 0); __pyx_t_1 = __pyx_t_2; - __pyx_L11_bool_binop_done:; + __pyx_L12_bool_binop_done:; __pyx_v_self->dtype_is_object = __pyx_t_1; - /* "View.MemoryView":363 - * raise MemoryError + /* "View.MemoryView":365 + * raise MemoryError * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: */ - goto __pyx_L10; + goto __pyx_L11; } - /* "View.MemoryView":366 + /* "View.MemoryView":368 * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') * else: * self.dtype_is_object = dtype_is_object # <<<<<<<<<<<<<< @@ -8200,9 +7091,9 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ /*else*/ { __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object; } - __pyx_L10:; + __pyx_L11:; - /* "View.MemoryView":368 + /* "View.MemoryView":370 * self.dtype_is_object = dtype_is_object * * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( # <<<<<<<<<<<<<< @@ -8211,7 +7102,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int)))); - /* "View.MemoryView":370 + /* "View.MemoryView":372 * self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer( * &self.acquisition_count[0], sizeof(__pyx_atomic_int)) * self.typeinfo = NULL # <<<<<<<<<<<<<< @@ -8220,7 +7111,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ */ __pyx_v_self->typeinfo = NULL; - /* "View.MemoryView":345 + /* "View.MemoryView":346 * cdef __Pyx_TypeInfo *typeinfo * * def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False): # <<<<<<<<<<<<<< @@ -8239,7 +7130,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit_ return __pyx_r; } -/* "View.MemoryView":372 +/* "View.MemoryView":374 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< @@ -8270,7 +7161,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal PyThread_type_lock __pyx_t_7; __Pyx_RefNannySetupContext("__dealloc__", 0); - /* "View.MemoryView":373 + /* "View.MemoryView":375 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< @@ -8281,7 +7172,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":374 + /* "View.MemoryView":376 * def __dealloc__(memoryview self): * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) # <<<<<<<<<<<<<< @@ -8290,7 +7181,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ __Pyx_ReleaseBuffer((&__pyx_v_self->view)); - /* "View.MemoryView":373 + /* "View.MemoryView":375 * * def __dealloc__(memoryview self): * if self.obj is not None: # <<<<<<<<<<<<<< @@ -8300,7 +7191,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal goto __pyx_L3; } - /* "View.MemoryView":375 + /* "View.MemoryView":377 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< @@ -8310,7 +7201,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0); if (__pyx_t_2) { - /* "View.MemoryView":377 + /* "View.MemoryView":379 * elif (<__pyx_buffer *> &self.view).obj == Py_None: * * (<__pyx_buffer *> &self.view).obj = NULL # <<<<<<<<<<<<<< @@ -8319,7 +7210,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL; - /* "View.MemoryView":378 + /* "View.MemoryView":380 * * (<__pyx_buffer *> &self.view).obj = NULL * Py_DECREF(Py_None) # <<<<<<<<<<<<<< @@ -8328,7 +7219,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ Py_DECREF(Py_None); - /* "View.MemoryView":375 + /* "View.MemoryView":377 * if self.obj is not None: * __Pyx_ReleaseBuffer(&self.view) * elif (<__pyx_buffer *> &self.view).obj == Py_None: # <<<<<<<<<<<<<< @@ -8338,7 +7229,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal } __pyx_L3:; - /* "View.MemoryView":382 + /* "View.MemoryView":384 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< @@ -8348,7 +7239,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0); if (__pyx_t_2) { - /* "View.MemoryView":383 + /* "View.MemoryView":385 * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): # <<<<<<<<<<<<<< @@ -8360,7 +7251,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; - /* "View.MemoryView":384 + /* "View.MemoryView":386 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< @@ -8370,7 +7261,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0); if (__pyx_t_2) { - /* "View.MemoryView":385 + /* "View.MemoryView":387 * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 # <<<<<<<<<<<<<< @@ -8379,7 +7270,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1); - /* "View.MemoryView":386 + /* "View.MemoryView":388 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< @@ -8389,7 +7280,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0); if (__pyx_t_2) { - /* "View.MemoryView":388 + /* "View.MemoryView":390 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) # <<<<<<<<<<<<<< @@ -8399,7 +7290,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]); - /* "View.MemoryView":387 + /* "View.MemoryView":389 * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( # <<<<<<<<<<<<<< @@ -8409,7 +7300,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6; (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7; - /* "View.MemoryView":386 + /* "View.MemoryView":388 * if __pyx_memoryview_thread_locks[i] is self.lock: * __pyx_memoryview_thread_locks_used -= 1 * if i != __pyx_memoryview_thread_locks_used: # <<<<<<<<<<<<<< @@ -8418,7 +7309,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ } - /* "View.MemoryView":389 + /* "View.MemoryView":391 * __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = ( * __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i]) * break # <<<<<<<<<<<<<< @@ -8427,7 +7318,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ goto __pyx_L6_break; - /* "View.MemoryView":384 + /* "View.MemoryView":386 * if self.lock != NULL: * for i in range(__pyx_memoryview_thread_locks_used): * if __pyx_memoryview_thread_locks[i] is self.lock: # <<<<<<<<<<<<<< @@ -8438,7 +7329,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal } /*else*/ { - /* "View.MemoryView":391 + /* "View.MemoryView":393 * break * else: * PyThread_free_lock(self.lock) # <<<<<<<<<<<<<< @@ -8449,7 +7340,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal } __pyx_L6_break:; - /* "View.MemoryView":382 + /* "View.MemoryView":384 * cdef int i * global __pyx_memoryview_thread_locks_used * if self.lock != NULL: # <<<<<<<<<<<<<< @@ -8458,7 +7349,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal */ } - /* "View.MemoryView":372 + /* "View.MemoryView":374 * self.typeinfo = NULL * * def __dealloc__(memoryview self): # <<<<<<<<<<<<<< @@ -8470,7 +7361,7 @@ static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__deal __Pyx_RefNannyFinishContext(); } -/* "View.MemoryView":393 +/* "View.MemoryView":395 * PyThread_free_lock(self.lock) * * cdef char *get_item_pointer(memoryview self, object index) except NULL: # <<<<<<<<<<<<<< @@ -8491,9 +7382,12 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py PyObject *__pyx_t_5 = NULL; Py_ssize_t __pyx_t_6; char *__pyx_t_7; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_item_pointer", 0); - /* "View.MemoryView":395 + /* "View.MemoryView":397 * cdef char *get_item_pointer(memoryview self, object index) except NULL: * cdef Py_ssize_t dim * cdef char *itemp = self.view.buf # <<<<<<<<<<<<<< @@ -8502,7 +7396,7 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py */ __pyx_v_itemp = ((char *)__pyx_v_self->view.buf); - /* "View.MemoryView":397 + /* "View.MemoryView":399 * cdef char *itemp = self.view.buf * * for dim, idx in enumerate(index): # <<<<<<<<<<<<<< @@ -8514,26 +7408,26 @@ static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__py __pyx_t_2 = __pyx_v_index; 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if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 435, __pyx_L4_error) + __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 437, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_7); - /* "View.MemoryView":434 + /* "View.MemoryView":436 * if not isinstance(obj, memoryview): * try: * obj = memoryview(obj, self.flags & ~PyBUF_WRITABLE | PyBUF_ANY_CONTIGUOUS, # <<<<<<<<<<<<<< * self.dtype_is_object) * except TypeError: */ - __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 434, __pyx_L4_error) + __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) __PYX_ERR(2, 436, __pyx_L4_error) __Pyx_GOTREF(__pyx_t_8); __Pyx_INCREF(__pyx_v_obj); __Pyx_GIVEREF(__pyx_v_obj); @@ -9104,13 +8007,13 @@ static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_ PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7); __pyx_t_6 = 0; __pyx_t_7 = 0; - __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) __PYX_ERR(2, 434, __pyx_L4_error) + __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)__pyx_memoryview_type), __pyx_t_8, NULL); 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__pyx_v_info->shape = __pyx_t_4; - /* "View.MemoryView":522 + /* "View.MemoryView":524 * raise ValueError("Cannot create writable memory view from read-only memoryview") * * if flags & PyBUF_ND: # <<<<<<<<<<<<<< @@ -10275,7 +9196,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu goto __pyx_L6; } - /* "View.MemoryView":525 + /* "View.MemoryView":527 * info.shape = self.view.shape * else: * info.shape = NULL # <<<<<<<<<<<<<< @@ -10287,7 +9208,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu } __pyx_L6:; - /* "View.MemoryView":527 + /* "View.MemoryView":529 * info.shape = NULL * * if flags & PyBUF_STRIDES: # <<<<<<<<<<<<<< @@ -10297,7 +9218,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0); if (__pyx_t_1) { - /* "View.MemoryView":528 + /* "View.MemoryView":530 * * if flags & PyBUF_STRIDES: * info.strides = self.view.strides # <<<<<<<<<<<<<< @@ -10307,7 +9228,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu __pyx_t_4 = __pyx_v_self->view.strides; 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__pyx_v_info->suboffsets = __pyx_t_4; - /* "View.MemoryView":532 + /* "View.MemoryView":534 * info.strides = NULL * * if flags & PyBUF_INDIRECT: # <<<<<<<<<<<<<< @@ -10359,7 +9280,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu goto __pyx_L8; } - /* "View.MemoryView":535 + /* "View.MemoryView":537 * info.suboffsets = self.view.suboffsets * else: * info.suboffsets = NULL # <<<<<<<<<<<<<< @@ -10371,7 +9292,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu } __pyx_L8:; - /* "View.MemoryView":537 + /* "View.MemoryView":539 * info.suboffsets = NULL * * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< @@ -10381,7 +9302,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); if (__pyx_t_1) { - /* "View.MemoryView":538 + /* "View.MemoryView":540 * * if flags & PyBUF_FORMAT: * info.format = self.view.format # <<<<<<<<<<<<<< @@ -10391,7 +9312,7 @@ static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbu __pyx_t_5 = __pyx_v_self->view.format; 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if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 555, __pyx_L1_error) + __pyx_t_2 = __pyx_memslice_transpose((&__pyx_v_result->from_slice)); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 557, __pyx_L1_error) - /* "View.MemoryView":556 + /* "View.MemoryView":558 * cdef _memoryviewslice result = memoryview_copy(self) * transpose_memslice(&result.from_slice) * return result # <<<<<<<<<<<<<< @@ -10569,7 +9493,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct _ __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; - /* "View.MemoryView":553 + /* "View.MemoryView":555 * * @property * def T(self): # <<<<<<<<<<<<<< @@ -10589,7 +9513,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct _ return __pyx_r; } -/* "View.MemoryView":559 +/* "View.MemoryView":561 * * @property * def base(self): # <<<<<<<<<<<<<< @@ -10615,7 +9539,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struc __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); 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- /* "View.MemoryView":582 + /* "View.MemoryView":584 * * @property * def ndim(self): # <<<<<<<<<<<<<< @@ -11006,7 +9942,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struc return __pyx_r; } -/* "View.MemoryView":586 +/* "View.MemoryView":588 * * @property * def itemsize(self): # <<<<<<<<<<<<<< @@ -11031,9 +9967,12 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(s PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__get__", 0); - /* "View.MemoryView":587 + /* "View.MemoryView":589 * @property * def itemsize(self): * return self.view.itemsize # <<<<<<<<<<<<<< @@ -11041,13 +9980,13 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(s * @property */ __Pyx_XDECREF(__pyx_r); - __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 587, __pyx_L1_error) + __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); 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if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 748, __pyx_L1_error) + __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 750, __pyx_L1_error) - /* "View.MemoryView":747 + /* "View.MemoryView":749 * * for dim, index in enumerate(indices): * if PyIndex_Check(index): # <<<<<<<<<<<<<< @@ -12899,7 +11880,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ goto __pyx_L6; } - /* "View.MemoryView":754 + /* "View.MemoryView":756 * 0, 0, 0, # have_{start,stop,step} * False) * elif index is None: # <<<<<<<<<<<<<< @@ -12910,7 +11891,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ __pyx_t_1 = (__pyx_t_2 != 0); if (__pyx_t_1) { - /* "View.MemoryView":755 + /* "View.MemoryView":757 * False) * elif index is None: * p_dst.shape[new_ndim] = 1 # <<<<<<<<<<<<<< @@ -12919,7 +11900,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ */ (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1; 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if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 762, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L7_bool_binop_done; @@ -12979,20 +11960,20 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ __pyx_L7_bool_binop_done:; __pyx_v_start = __pyx_t_10; - /* "View.MemoryView":761 + /* "View.MemoryView":763 * else: * start = index.start or 0 * stop = index.stop or 0 # <<<<<<<<<<<<<< * step = index.step or 0 * */ - __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 761, __pyx_L1_error) + __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 763, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 761, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 763, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { - __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 761, __pyx_L1_error) + __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 763, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L9_bool_binop_done; @@ -13001,20 +11982,20 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ __pyx_L9_bool_binop_done:; __pyx_v_stop = __pyx_t_10; - /* "View.MemoryView":762 + /* "View.MemoryView":764 * start = index.start or 0 * stop = index.stop or 0 * step = index.step or 0 # <<<<<<<<<<<<<< * * have_start = index.start is not None */ - __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 762, __pyx_L1_error) + __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 764, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 762, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(2, 764, __pyx_L1_error) if (!__pyx_t_1) { __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; } else { - __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 762, __pyx_L1_error) + __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 764, __pyx_L1_error) __pyx_t_10 = __pyx_t_12; __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; goto __pyx_L11_bool_binop_done; @@ -13023,55 +12004,55 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ __pyx_L11_bool_binop_done:; __pyx_v_step = __pyx_t_10; - /* "View.MemoryView":764 + /* "View.MemoryView":766 * step = index.step or 0 * * have_start = index.start is not None # <<<<<<<<<<<<<< * have_stop = index.stop is not None * have_step = index.step is not None */ - __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 764, __pyx_L1_error) + __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 766, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_start = __pyx_t_1; - /* "View.MemoryView":765 + /* "View.MemoryView":767 * * have_start = index.start is not None * have_stop = index.stop is not None # <<<<<<<<<<<<<< * have_step = index.step is not None * */ - __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 765, __pyx_L1_error) + __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 767, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_stop = __pyx_t_1; - /* "View.MemoryView":766 + /* "View.MemoryView":768 * have_start = index.start is not None * have_stop = index.stop is not None * have_step = index.step is not None # <<<<<<<<<<<<<< * * slice_memviewslice( */ - __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 766, __pyx_L1_error) + __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 768, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_9); __pyx_t_1 = (__pyx_t_9 != Py_None); __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0; __pyx_v_have_step = __pyx_t_1; - /* "View.MemoryView":768 + /* "View.MemoryView":770 * have_step = index.step is not None * * slice_memviewslice( # <<<<<<<<<<<<<< * p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim], * dim, new_ndim, p_suboffset_dim, */ - __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 768, __pyx_L1_error) + __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 770, __pyx_L1_error) - /* "View.MemoryView":774 + /* "View.MemoryView":776 * have_start, have_stop, have_step, * True) * new_ndim += 1 # <<<<<<<<<<<<<< @@ -13082,7 +12063,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ } __pyx_L6:; - /* "View.MemoryView":746 + /* "View.MemoryView":748 * cdef bint have_start, have_stop, have_step * * for dim, index in enumerate(indices): # <<<<<<<<<<<<<< @@ -13092,7 +12073,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ } __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - /* "View.MemoryView":776 + /* "View.MemoryView":778 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -13103,7 +12084,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":777 + /* "View.MemoryView":779 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< @@ -13112,39 +12093,39 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ */ __Pyx_XDECREF(((PyObject *)__pyx_r)); - /* "View.MemoryView":778 + /* "View.MemoryView":780 * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, # <<<<<<<<<<<<<< * memviewsliceobj.to_dtype_func, * memview.dtype_is_object) */ - if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 778, __pyx_L1_error) } + if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 780, __pyx_L1_error) } - /* "View.MemoryView":779 + /* "View.MemoryView":781 * return memoryview_fromslice(dst, new_ndim, * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< * memview.dtype_is_object) * else: */ - if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 779, __pyx_L1_error) } + if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 781, __pyx_L1_error) } - /* "View.MemoryView":777 + /* "View.MemoryView":779 * * if isinstance(memview, _memoryviewslice): * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< * memviewsliceobj.to_object_func, * memviewsliceobj.to_dtype_func, */ - __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 777, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 779, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 777, __pyx_L1_error) + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 779, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; - /* "View.MemoryView":776 + /* "View.MemoryView":778 * new_ndim += 1 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -13153,7 +12134,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ */ } - /* "View.MemoryView":782 + /* "View.MemoryView":784 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< @@ -13163,30 +12144,30 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ /*else*/ { __Pyx_XDECREF(((PyObject *)__pyx_r)); - /* "View.MemoryView":783 + /* "View.MemoryView":785 * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ - __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 782, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 784, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - /* "View.MemoryView":782 + /* "View.MemoryView":784 * memview.dtype_is_object) * else: * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< * memview.dtype_is_object) * */ - if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 782, __pyx_L1_error) + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 784, __pyx_L1_error) __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); __pyx_t_3 = 0; goto __pyx_L0; } - /* "View.MemoryView":710 + /* "View.MemoryView":712 * * @cname('__pyx_memview_slice') * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< @@ -13208,7 +12189,7 @@ static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_ return __pyx_r; } -/* "View.MemoryView":807 +/* "View.MemoryView":809 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< @@ -13223,8 +12204,11 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; - /* "View.MemoryView":827 + /* "View.MemoryView":829 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< @@ -13234,7 +12218,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_1) { - /* "View.MemoryView":829 + /* "View.MemoryView":831 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< @@ -13244,7 +12228,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_1 = ((__pyx_v_start < 0) != 0); if (__pyx_t_1) { - /* "View.MemoryView":830 + /* "View.MemoryView":832 * * if start < 0: * start += shape # <<<<<<<<<<<<<< @@ -13253,7 +12237,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - /* "View.MemoryView":829 + /* "View.MemoryView":831 * if not is_slice: * * if start < 0: # <<<<<<<<<<<<<< @@ -13262,7 +12246,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":831 + /* "View.MemoryView":833 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< @@ -13276,16 +12260,16 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":832 + /* "View.MemoryView":834 * start += shape * if not 0 <= start < shape: * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< * else: * */ - __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 832, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 834, __pyx_L1_error) - /* "View.MemoryView":831 + /* "View.MemoryView":833 * if start < 0: * start += shape * if not 0 <= start < shape: # <<<<<<<<<<<<<< @@ -13294,7 +12278,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":827 + /* "View.MemoryView":829 * cdef bint negative_step * * if not is_slice: # <<<<<<<<<<<<<< @@ -13304,7 +12288,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L3; } - /* "View.MemoryView":835 + /* "View.MemoryView":837 * else: * * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< @@ -13323,7 +12307,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_L6_bool_binop_done:; __pyx_v_negative_step = __pyx_t_2; - /* "View.MemoryView":837 + /* "View.MemoryView":839 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< @@ -13341,16 +12325,16 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_L9_bool_binop_done:; if (__pyx_t_2) { - /* "View.MemoryView":838 + /* "View.MemoryView":840 * * if have_step and step == 0: * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< * * */ - __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 838, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 840, __pyx_L1_error) - /* "View.MemoryView":837 + /* "View.MemoryView":839 * negative_step = have_step != 0 and step < 0 * * if have_step and step == 0: # <<<<<<<<<<<<<< @@ -13359,7 +12343,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":841 + /* "View.MemoryView":843 * * * if have_start: # <<<<<<<<<<<<<< @@ -13369,7 +12353,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_have_start != 0); if (__pyx_t_2) { - /* "View.MemoryView":842 + /* "View.MemoryView":844 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< @@ -13379,7 +12363,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":843 + /* "View.MemoryView":845 * if have_start: * if start < 0: * start += shape # <<<<<<<<<<<<<< @@ -13388,7 +12372,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - /* "View.MemoryView":844 + /* "View.MemoryView":846 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< @@ -13398,7 +12382,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_start < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":845 + /* "View.MemoryView":847 * start += shape * if start < 0: * start = 0 # <<<<<<<<<<<<<< @@ -13407,7 +12391,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = 0; - /* "View.MemoryView":844 + /* "View.MemoryView":846 * if start < 0: * start += shape * if start < 0: # <<<<<<<<<<<<<< @@ -13416,7 +12400,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":842 + /* "View.MemoryView":844 * * if have_start: * if start < 0: # <<<<<<<<<<<<<< @@ -13426,7 +12410,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L12; } - /* "View.MemoryView":846 + /* "View.MemoryView":848 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< @@ -13436,7 +12420,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); if (__pyx_t_2) { - /* "View.MemoryView":847 + /* "View.MemoryView":849 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< @@ -13446,7 +12430,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { - /* "View.MemoryView":848 + /* "View.MemoryView":850 * elif start >= shape: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< @@ -13455,7 +12439,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = (__pyx_v_shape - 1); - /* "View.MemoryView":847 + /* "View.MemoryView":849 * start = 0 * elif start >= shape: * if negative_step: # <<<<<<<<<<<<<< @@ -13465,7 +12449,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L14; } - /* "View.MemoryView":850 + /* "View.MemoryView":852 * start = shape - 1 * else: * start = shape # <<<<<<<<<<<<<< @@ -13477,7 +12461,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L14:; - /* "View.MemoryView":846 + /* "View.MemoryView":848 * if start < 0: * start = 0 * elif start >= shape: # <<<<<<<<<<<<<< @@ -13487,7 +12471,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L12:; - /* "View.MemoryView":841 + /* "View.MemoryView":843 * * * if have_start: # <<<<<<<<<<<<<< @@ -13497,7 +12481,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L11; } - /* "View.MemoryView":852 + /* "View.MemoryView":854 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< @@ -13508,7 +12492,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { - /* "View.MemoryView":853 + /* "View.MemoryView":855 * else: * if negative_step: * start = shape - 1 # <<<<<<<<<<<<<< @@ -13517,7 +12501,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_start = (__pyx_v_shape - 1); - /* "View.MemoryView":852 + /* "View.MemoryView":854 * start = shape * else: * if negative_step: # <<<<<<<<<<<<<< @@ -13527,7 +12511,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L15; } - /* "View.MemoryView":855 + /* "View.MemoryView":857 * start = shape - 1 * else: * start = 0 # <<<<<<<<<<<<<< @@ -13541,7 +12525,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L11:; - /* "View.MemoryView":857 + /* "View.MemoryView":859 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< @@ -13551,7 +12535,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_have_stop != 0); if (__pyx_t_2) { - /* "View.MemoryView":858 + /* "View.MemoryView":860 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< @@ -13561,7 +12545,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":859 + /* "View.MemoryView":861 * if have_stop: * if stop < 0: * stop += shape # <<<<<<<<<<<<<< @@ -13570,7 +12554,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); - /* "View.MemoryView":860 + /* "View.MemoryView":862 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< @@ -13580,7 +12564,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_stop < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":861 + /* "View.MemoryView":863 * stop += shape * if stop < 0: * stop = 0 # <<<<<<<<<<<<<< @@ -13589,7 +12573,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_stop = 0; - /* "View.MemoryView":860 + /* "View.MemoryView":862 * if stop < 0: * stop += shape * if stop < 0: # <<<<<<<<<<<<<< @@ -13598,7 +12582,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":858 + /* "View.MemoryView":860 * * if have_stop: * if stop < 0: # <<<<<<<<<<<<<< @@ -13608,7 +12592,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L17; } - /* "View.MemoryView":862 + /* "View.MemoryView":864 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< @@ -13618,7 +12602,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); if (__pyx_t_2) { - /* "View.MemoryView":863 + /* "View.MemoryView":865 * stop = 0 * elif stop > shape: * stop = shape # <<<<<<<<<<<<<< @@ -13627,7 +12611,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_stop = __pyx_v_shape; - /* "View.MemoryView":862 + /* "View.MemoryView":864 * if stop < 0: * stop = 0 * elif stop > shape: # <<<<<<<<<<<<<< @@ -13637,7 +12621,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L17:; - /* "View.MemoryView":857 + /* "View.MemoryView":859 * start = 0 * * if have_stop: # <<<<<<<<<<<<<< @@ -13647,7 +12631,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L16; } - /* "View.MemoryView":865 + /* "View.MemoryView":867 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< @@ -13658,7 +12642,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (__pyx_v_negative_step != 0); if (__pyx_t_2) { - /* "View.MemoryView":866 + /* "View.MemoryView":868 * else: * if negative_step: * stop = -1 # <<<<<<<<<<<<<< @@ -13667,7 +12651,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_stop = -1L; - /* "View.MemoryView":865 + /* "View.MemoryView":867 * stop = shape * else: * if negative_step: # <<<<<<<<<<<<<< @@ -13677,7 +12661,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L19; } - /* "View.MemoryView":868 + /* "View.MemoryView":870 * stop = -1 * else: * stop = shape # <<<<<<<<<<<<<< @@ -13691,7 +12675,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L16:; - /* "View.MemoryView":870 + /* "View.MemoryView":872 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< @@ -13701,7 +12685,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":871 + /* "View.MemoryView":873 * * if not have_step: * step = 1 # <<<<<<<<<<<<<< @@ -13710,7 +12694,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_step = 1; - /* "View.MemoryView":870 + /* "View.MemoryView":872 * stop = shape * * if not have_step: # <<<<<<<<<<<<<< @@ -13719,7 +12703,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":875 + /* "View.MemoryView":877 * * with cython.cdivision(True): * new_shape = (stop - start) // step # <<<<<<<<<<<<<< @@ -13728,7 +12712,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); - /* "View.MemoryView":877 + /* "View.MemoryView":879 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< @@ -13738,7 +12722,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":878 + /* "View.MemoryView":880 * * if (stop - start) - step * new_shape: * new_shape += 1 # <<<<<<<<<<<<<< @@ -13747,7 +12731,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_new_shape = (__pyx_v_new_shape + 1); - /* "View.MemoryView":877 + /* "View.MemoryView":879 * new_shape = (stop - start) // step * * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< @@ -13756,7 +12740,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":880 + /* "View.MemoryView":882 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< @@ -13766,7 +12750,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":881 + /* "View.MemoryView":883 * * if new_shape < 0: * new_shape = 0 # <<<<<<<<<<<<<< @@ -13775,7 +12759,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_new_shape = 0; - /* "View.MemoryView":880 + /* "View.MemoryView":882 * new_shape += 1 * * if new_shape < 0: # <<<<<<<<<<<<<< @@ -13784,7 +12768,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":884 + /* "View.MemoryView":886 * * * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< @@ -13793,7 +12777,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); - /* "View.MemoryView":885 + /* "View.MemoryView":887 * * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< @@ -13802,7 +12786,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; - /* "View.MemoryView":886 + /* "View.MemoryView":888 * dst.strides[new_ndim] = stride * step * dst.shape[new_ndim] = new_shape * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< @@ -13813,7 +12797,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L3:; - /* "View.MemoryView":889 + /* "View.MemoryView":891 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< @@ -13823,7 +12807,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":890 + /* "View.MemoryView":892 * * if suboffset_dim[0] < 0: * dst.data += start * stride # <<<<<<<<<<<<<< @@ -13832,7 +12816,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); - /* "View.MemoryView":889 + /* "View.MemoryView":891 * * * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< @@ -13842,7 +12826,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L23; } - /* "View.MemoryView":892 + /* "View.MemoryView":894 * dst.data += start * stride * else: * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< @@ -13855,7 +12839,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L23:; - /* "View.MemoryView":894 + /* "View.MemoryView":896 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -13865,7 +12849,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":895 + /* "View.MemoryView":897 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< @@ -13875,7 +12859,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":896 + /* "View.MemoryView":898 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< @@ -13885,7 +12869,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":897 + /* "View.MemoryView":899 * if not is_slice: * if new_ndim == 0: * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< @@ -13894,7 +12878,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); - /* "View.MemoryView":896 + /* "View.MemoryView":898 * if suboffset >= 0: * if not is_slice: * if new_ndim == 0: # <<<<<<<<<<<<<< @@ -13904,7 +12888,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L26; } - /* "View.MemoryView":899 + /* "View.MemoryView":901 * dst.data = ( dst.data)[0] + suboffset * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< @@ -13913,18 +12897,18 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ /*else*/ { - /* "View.MemoryView":900 + /* "View.MemoryView":902 * else: * _err_dim(IndexError, "All dimensions preceding dimension %d " * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< * else: * suboffset_dim[0] = new_ndim */ - __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 899, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 901, __pyx_L1_error) } __pyx_L26:; - /* "View.MemoryView":895 + /* "View.MemoryView":897 * * if suboffset >= 0: * if not is_slice: # <<<<<<<<<<<<<< @@ -13934,7 +12918,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, goto __pyx_L25; } - /* "View.MemoryView":902 + /* "View.MemoryView":904 * "must be indexed and not sliced", dim) * else: * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< @@ -13946,7 +12930,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, } __pyx_L25:; - /* "View.MemoryView":894 + /* "View.MemoryView":896 * dst.suboffsets[suboffset_dim[0]] += start * stride * * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -13955,7 +12939,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, */ } - /* "View.MemoryView":904 + /* "View.MemoryView":906 * suboffset_dim[0] = new_ndim * * return 0 # <<<<<<<<<<<<<< @@ -13965,7 +12949,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, __pyx_r = 0; goto __pyx_L0; - /* "View.MemoryView":807 + /* "View.MemoryView":809 * * @cname('__pyx_memoryview_slice_memviewslice') * cdef int slice_memviewslice( # <<<<<<<<<<<<<< @@ -13989,7 +12973,7 @@ static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, return __pyx_r; } -/* "View.MemoryView":910 +/* "View.MemoryView":912 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< @@ -14009,9 +12993,12 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("pybuffer_index", 0); - /* "View.MemoryView":912 + /* "View.MemoryView":914 * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, * Py_ssize_t dim) except NULL: * cdef Py_ssize_t shape, stride, suboffset = -1 # <<<<<<<<<<<<<< @@ -14020,7 +13007,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_suboffset = -1L; 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} - /* "View.MemoryView":920 + /* "View.MemoryView":922 * stride = itemsize * else: * shape = view.shape[dim] # <<<<<<<<<<<<<< @@ -14086,7 +13073,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P /*else*/ { __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]); - /* "View.MemoryView":921 + /* "View.MemoryView":923 * else: * shape = view.shape[dim] * stride = view.strides[dim] # <<<<<<<<<<<<<< @@ -14095,7 +13082,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]); - /* "View.MemoryView":922 + /* "View.MemoryView":924 * shape = view.shape[dim] * stride = view.strides[dim] * if view.suboffsets != NULL: # <<<<<<<<<<<<<< @@ -14105,7 +13092,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0); if (__pyx_t_2) { - /* "View.MemoryView":923 + /* "View.MemoryView":925 * stride = view.strides[dim] * if view.suboffsets != NULL: * suboffset = view.suboffsets[dim] # <<<<<<<<<<<<<< @@ -14114,7 +13101,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]); 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if (unlikely(__pyx_t_2)) { - /* "View.MemoryView":928 + /* "View.MemoryView":930 * index += view.shape[dim] * if index < 0: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * if index >= shape: */ - __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) + __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 930, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 928, __pyx_L1_error) + __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 930, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 928, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 930, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(2, 928, __pyx_L1_error) + __PYX_ERR(2, 930, __pyx_L1_error) - /* "View.MemoryView":927 + /* "View.MemoryView":929 * if index < 0: * index += view.shape[dim] * if index < 0: # <<<<<<<<<<<<<< @@ -14182,7 +13169,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ } - /* "View.MemoryView":925 + /* "View.MemoryView":927 * suboffset = view.suboffsets[dim] * * if index < 0: # <<<<<<<<<<<<<< @@ -14191,7 +13178,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ } - /* "View.MemoryView":930 + /* "View.MemoryView":932 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< @@ -14201,26 +13188,26 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0); if (unlikely(__pyx_t_2)) { - /* "View.MemoryView":931 + /* "View.MemoryView":933 * * if index >= shape: * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) # <<<<<<<<<<<<<< * * resultp = bufp + index * stride */ - __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) + __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 933, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 931, __pyx_L1_error) + __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 933, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 931, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 933, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(2, 931, __pyx_L1_error) + __PYX_ERR(2, 933, __pyx_L1_error) - /* "View.MemoryView":930 + /* "View.MemoryView":932 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * if index >= shape: # <<<<<<<<<<<<<< @@ -14229,7 +13216,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ } - /* "View.MemoryView":933 + /* "View.MemoryView":935 * raise IndexError("Out of bounds on buffer access (axis %d)" % dim) * * resultp = bufp + index * stride # <<<<<<<<<<<<<< @@ -14238,7 +13225,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride)); - /* "View.MemoryView":934 + /* "View.MemoryView":936 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -14248,7 +13235,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":935 + /* "View.MemoryView":937 * resultp = bufp + index * stride * if suboffset >= 0: * resultp = ( resultp)[0] + suboffset # <<<<<<<<<<<<<< @@ -14257,7 +13244,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset); - /* "View.MemoryView":934 + /* "View.MemoryView":936 * * resultp = bufp + index * stride * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -14266,7 +13253,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P */ } - /* "View.MemoryView":937 + /* "View.MemoryView":939 * resultp = ( resultp)[0] + suboffset * * return resultp # <<<<<<<<<<<<<< @@ -14276,7 +13263,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P __pyx_r = __pyx_v_resultp; goto __pyx_L0; - /* "View.MemoryView":910 + /* "View.MemoryView":912 * * @cname('__pyx_pybuffer_index') * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, # <<<<<<<<<<<<<< @@ -14295,7 +13282,7 @@ static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, P return __pyx_r; } -/* "View.MemoryView":943 +/* "View.MemoryView":945 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< @@ -14319,8 +13306,11 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { int __pyx_t_7; int __pyx_t_8; int __pyx_t_9; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; - /* "View.MemoryView":944 + /* "View.MemoryView":946 * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: * cdef int ndim = memslice.memview.view.ndim # <<<<<<<<<<<<<< @@ -14330,7 +13320,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_t_1 = __pyx_v_memslice->memview->view.ndim; __pyx_v_ndim = __pyx_t_1; - /* "View.MemoryView":946 + /* "View.MemoryView":948 * cdef int ndim = memslice.memview.view.ndim * * cdef Py_ssize_t *shape = memslice.shape # <<<<<<<<<<<<<< @@ -14340,7 +13330,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_t_2 = __pyx_v_memslice->shape; __pyx_v_shape = __pyx_t_2; - /* "View.MemoryView":947 + /* "View.MemoryView":949 * * cdef Py_ssize_t *shape = memslice.shape * cdef Py_ssize_t *strides = memslice.strides # <<<<<<<<<<<<<< @@ -14350,7 +13340,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_t_2 = __pyx_v_memslice->strides; __pyx_v_strides = __pyx_t_2; - /* "View.MemoryView":951 + /* "View.MemoryView":953 * * cdef int i, j * for i in range(ndim / 2): # <<<<<<<<<<<<<< @@ -14362,7 +13352,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_4; __pyx_t_1+=1) { __pyx_v_i = __pyx_t_1; - /* "View.MemoryView":952 + /* "View.MemoryView":954 * cdef int i, j * for i in range(ndim / 2): * j = ndim - 1 - i # <<<<<<<<<<<<<< @@ -14371,7 +13361,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { */ __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i); - /* "View.MemoryView":953 + /* "View.MemoryView":955 * for i in range(ndim / 2): * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] # <<<<<<<<<<<<<< @@ -14383,7 +13373,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { (__pyx_v_strides[__pyx_v_i]) = __pyx_t_5; (__pyx_v_strides[__pyx_v_j]) = __pyx_t_6; - /* "View.MemoryView":954 + /* "View.MemoryView":956 * j = ndim - 1 - i * strides[i], strides[j] = strides[j], strides[i] * shape[i], shape[j] = shape[j], shape[i] # <<<<<<<<<<<<<< @@ -14395,7 +13385,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { (__pyx_v_shape[__pyx_v_i]) = __pyx_t_6; (__pyx_v_shape[__pyx_v_j]) = __pyx_t_5; - /* "View.MemoryView":956 + /* "View.MemoryView":958 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< @@ -14413,16 +13403,16 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_L6_bool_binop_done:; if (__pyx_t_7) { - /* "View.MemoryView":957 + /* "View.MemoryView":959 * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") # <<<<<<<<<<<<<< * * return 1 */ - __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 957, __pyx_L1_error) + __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, ((char *)"Cannot transpose memoryview with indirect dimensions")); if (unlikely(__pyx_t_9 == ((int)-1))) __PYX_ERR(2, 959, __pyx_L1_error) - /* "View.MemoryView":956 + /* "View.MemoryView":958 * shape[i], shape[j] = shape[j], shape[i] * * if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0: # <<<<<<<<<<<<<< @@ -14432,7 +13422,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { } } - /* "View.MemoryView":959 + /* "View.MemoryView":961 * _err(ValueError, "Cannot transpose memoryview with indirect dimensions") * * return 1 # <<<<<<<<<<<<<< @@ -14442,7 +13432,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { __pyx_r = 1; goto __pyx_L0; - /* "View.MemoryView":943 + /* "View.MemoryView":945 * * @cname('__pyx_memslice_transpose') * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0: # <<<<<<<<<<<<<< @@ -14466,7 +13456,7 @@ static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) { return __pyx_r; 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if (__pyx_t_1) { - /* "View.MemoryView":987 + /* "View.MemoryView":989 * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: * self.to_dtype_func(itemp, value) # <<<<<<<<<<<<<< * else: * memoryview.assign_item_from_object(self, itemp, value) */ - __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 987, __pyx_L1_error) + __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == ((int)0))) __PYX_ERR(2, 989, __pyx_L1_error) - /* "View.MemoryView":986 + /* "View.MemoryView":988 * * cdef assign_item_from_object(self, char *itemp, object value): * if self.to_dtype_func != NULL: # <<<<<<<<<<<<<< @@ -14638,7 +13634,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo goto __pyx_L3; } - /* "View.MemoryView":989 + /* "View.MemoryView":991 * self.to_dtype_func(itemp, value) * else: * memoryview.assign_item_from_object(self, itemp, value) # <<<<<<<<<<<<<< @@ -14646,13 +13642,13 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo * @property */ /*else*/ { - __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 989, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 991, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; } __pyx_L3:; - /* "View.MemoryView":985 + /* "View.MemoryView":987 * return memoryview.convert_item_to_object(self, itemp) * * cdef assign_item_from_object(self, char *itemp, object value): # <<<<<<<<<<<<<< @@ -14673,7 +13669,7 @@ static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memo return __pyx_r; } -/* "View.MemoryView":992 +/* "View.MemoryView":994 * * @property * def base(self): # <<<<<<<<<<<<<< @@ -14699,7 +13695,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__ __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__get__", 0); - /* "View.MemoryView":993 + /* "View.MemoryView":995 * @property * def base(self): * return self.from_object # <<<<<<<<<<<<<< @@ -14711,7 +13707,7 @@ static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__ __pyx_r = __pyx_v_self->from_object; 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@@ -14833,7 +13835,7 @@ static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUS return __pyx_r; } -/* "View.MemoryView":999 +/* "View.MemoryView":1001 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< @@ -14856,9 +13858,12 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl Py_ssize_t *__pyx_t_7; Py_ssize_t *__pyx_t_8; Py_ssize_t __pyx_t_9; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_fromslice", 0); - /* "View.MemoryView":1007 + /* "View.MemoryView":1009 * cdef _memoryviewslice result * * if memviewslice.memview == Py_None: # <<<<<<<<<<<<<< @@ -14868,7 +13873,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1008 + /* "View.MemoryView":1010 * * if memviewslice.memview == Py_None: * return None # <<<<<<<<<<<<<< @@ -14879,7 +13884,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_r = Py_None; 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- /* "View.MemoryView":1024 + /* "View.MemoryView":1026 * result.view.buf = memviewslice.data * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None # <<<<<<<<<<<<<< @@ -14994,7 +13999,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None; - /* "View.MemoryView":1025 + /* "View.MemoryView":1027 * result.view.ndim = ndim * (<__pyx_buffer *> &result.view).obj = Py_None * Py_INCREF(Py_None) # <<<<<<<<<<<<<< @@ -15003,7 +14008,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ Py_INCREF(Py_None); - /* "View.MemoryView":1027 + /* "View.MemoryView":1029 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< @@ -15013,7 +14018,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_1 = ((((struct __pyx_memoryview_obj *)__pyx_v_memviewslice.memview)->flags & PyBUF_WRITABLE) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1028 + /* "View.MemoryView":1030 * * if (memviewslice.memview).flags & PyBUF_WRITABLE: * result.flags = PyBUF_RECORDS # <<<<<<<<<<<<<< @@ -15022,7 +14027,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS; - /* "View.MemoryView":1027 + /* "View.MemoryView":1029 * Py_INCREF(Py_None) * * if (memviewslice.memview).flags & PyBUF_WRITABLE: # <<<<<<<<<<<<<< @@ -15032,7 +14037,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl goto __pyx_L4; } - /* "View.MemoryView":1030 + /* "View.MemoryView":1032 * result.flags = PyBUF_RECORDS * else: * result.flags = PyBUF_RECORDS_RO # <<<<<<<<<<<<<< @@ -15044,7 +14049,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl } __pyx_L4:; - /* "View.MemoryView":1032 + /* "View.MemoryView":1034 * result.flags = PyBUF_RECORDS_RO * * result.view.shape = result.from_slice.shape # <<<<<<<<<<<<<< @@ -15053,7 +14058,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape); - /* "View.MemoryView":1033 + /* "View.MemoryView":1035 * * result.view.shape = result.from_slice.shape * result.view.strides = result.from_slice.strides # <<<<<<<<<<<<<< @@ -15062,7 +14067,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides); - /* "View.MemoryView":1036 + /* "View.MemoryView":1038 * * * result.view.suboffsets = NULL # <<<<<<<<<<<<<< @@ -15071,7 +14076,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.suboffsets = NULL; - /* "View.MemoryView":1037 + /* "View.MemoryView":1039 * * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: # <<<<<<<<<<<<<< @@ -15083,7 +14088,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_6 = __pyx_t_8; __pyx_v_suboffset = (__pyx_t_6[0]); - /* "View.MemoryView":1038 + /* "View.MemoryView":1040 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -15093,7 +14098,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_1 = ((__pyx_v_suboffset >= 0) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1039 + /* "View.MemoryView":1041 * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets # <<<<<<<<<<<<<< @@ -15102,7 +14107,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets); - /* "View.MemoryView":1040 + /* "View.MemoryView":1042 * if suboffset >= 0: * result.view.suboffsets = result.from_slice.suboffsets * break # <<<<<<<<<<<<<< @@ -15111,7 +14116,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ goto __pyx_L6_break; - /* "View.MemoryView":1038 + /* "View.MemoryView":1040 * result.view.suboffsets = NULL * for suboffset in result.from_slice.suboffsets[:ndim]: * if suboffset >= 0: # <<<<<<<<<<<<<< @@ -15122,7 +14127,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl } __pyx_L6_break:; - /* "View.MemoryView":1042 + /* "View.MemoryView":1044 * break * * result.view.len = result.view.itemsize # <<<<<<<<<<<<<< @@ -15132,7 +14137,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_9 = __pyx_v_result->__pyx_base.view.itemsize; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; - /* "View.MemoryView":1043 + /* "View.MemoryView":1045 * * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: # <<<<<<<<<<<<<< @@ -15142,29 +14147,29 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_t_7 = (__pyx_v_result->__pyx_base.view.shape + __pyx_v_ndim); for (__pyx_t_8 = __pyx_v_result->__pyx_base.view.shape; __pyx_t_8 < __pyx_t_7; __pyx_t_8++) { __pyx_t_6 = __pyx_t_8; - __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1043, __pyx_L1_error) + __pyx_t_2 = PyInt_FromSsize_t((__pyx_t_6[0])); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1045, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_2); __pyx_t_2 = 0; - /* "View.MemoryView":1044 + /* "View.MemoryView":1046 * result.view.len = result.view.itemsize * for length in result.view.shape[:ndim]: * result.view.len *= length # <<<<<<<<<<<<<< * * result.to_object_func = to_object_func */ - __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1044, __pyx_L1_error) + __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_result->__pyx_base.view.len); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1046, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1044, __pyx_L1_error) + __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_t_2, __pyx_v_length); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1046, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 1044, __pyx_L1_error) + __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_t_3); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(2, 1046, __pyx_L1_error) __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_v_result->__pyx_base.view.len = __pyx_t_9; } - /* "View.MemoryView":1046 + /* "View.MemoryView":1048 * result.view.len *= length * * result.to_object_func = to_object_func # <<<<<<<<<<<<<< @@ -15173,7 +14178,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->to_object_func = __pyx_v_to_object_func; - /* "View.MemoryView":1047 + /* "View.MemoryView":1049 * * result.to_object_func = to_object_func * result.to_dtype_func = to_dtype_func # <<<<<<<<<<<<<< @@ -15182,7 +14187,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl */ __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func; - /* "View.MemoryView":1049 + /* "View.MemoryView":1051 * result.to_dtype_func = to_dtype_func * * return result # <<<<<<<<<<<<<< @@ -15194,7 +14199,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl __pyx_r = ((PyObject *)__pyx_v_result); goto __pyx_L0; - /* "View.MemoryView":999 + /* "View.MemoryView":1001 * * @cname('__pyx_memoryview_fromslice') * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice, # <<<<<<<<<<<<<< @@ -15216,7 +14221,7 @@ static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewsl return __pyx_r; } -/* "View.MemoryView":1052 +/* "View.MemoryView":1054 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< @@ -15231,9 +14236,12 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p int __pyx_t_1; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("get_slice_from_memview", 0); - /* "View.MemoryView":1055 + /* "View.MemoryView":1057 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -15244,20 +14252,20 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":1056 + /* "View.MemoryView":1058 * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): * obj = memview # <<<<<<<<<<<<<< * return &obj.from_slice * else: */ - if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 1056, __pyx_L1_error) + if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(2, 1058, __pyx_L1_error) __pyx_t_3 = ((PyObject *)__pyx_v_memview); __Pyx_INCREF(__pyx_t_3); __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3); __pyx_t_3 = 0; - /* "View.MemoryView":1057 + /* "View.MemoryView":1059 * if isinstance(memview, _memoryviewslice): * obj = memview * return &obj.from_slice # <<<<<<<<<<<<<< @@ -15267,7 +14275,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p __pyx_r = (&__pyx_v_obj->from_slice); goto __pyx_L0; - /* "View.MemoryView":1055 + /* "View.MemoryView":1057 * __Pyx_memviewslice *mslice) except NULL: * cdef _memoryviewslice obj * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -15276,7 +14284,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p */ } - /* "View.MemoryView":1059 + /* "View.MemoryView":1061 * return &obj.from_slice * else: * slice_copy(memview, mslice) # <<<<<<<<<<<<<< @@ -15286,7 +14294,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p /*else*/ { __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice); - /* "View.MemoryView":1060 + /* "View.MemoryView":1062 * else: * slice_copy(memview, mslice) * return mslice # <<<<<<<<<<<<<< @@ -15297,7 +14305,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p goto __pyx_L0; } - /* "View.MemoryView":1052 + /* "View.MemoryView":1054 * * @cname('__pyx_memoryview_get_slice_from_memoryview') * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview, # <<<<<<<<<<<<<< @@ -15316,7 +14324,7 @@ static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __p return __pyx_r; } -/* "View.MemoryView":1063 +/* "View.MemoryView":1065 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< @@ -15337,7 +14345,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem Py_ssize_t __pyx_t_5; __Pyx_RefNannySetupContext("slice_copy", 0); - /* "View.MemoryView":1067 + /* "View.MemoryView":1069 * cdef (Py_ssize_t*) shape, strides, suboffsets * * shape = memview.view.shape # <<<<<<<<<<<<<< @@ -15347,7 +14355,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem __pyx_t_1 = __pyx_v_memview->view.shape; __pyx_v_shape = __pyx_t_1; - /* "View.MemoryView":1068 + /* "View.MemoryView":1070 * * shape = memview.view.shape * strides = memview.view.strides # <<<<<<<<<<<<<< @@ -15357,7 +14365,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem __pyx_t_1 = __pyx_v_memview->view.strides; __pyx_v_strides = __pyx_t_1; - /* "View.MemoryView":1069 + /* "View.MemoryView":1071 * shape = memview.view.shape * strides = memview.view.strides * suboffsets = memview.view.suboffsets # <<<<<<<<<<<<<< @@ -15367,7 +14375,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem __pyx_t_1 = __pyx_v_memview->view.suboffsets; __pyx_v_suboffsets = __pyx_t_1; - /* "View.MemoryView":1071 + /* "View.MemoryView":1073 * suboffsets = memview.view.suboffsets * * dst.memview = <__pyx_memoryview *> memview # <<<<<<<<<<<<<< @@ -15376,7 +14384,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem */ __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview); - /* "View.MemoryView":1072 + /* "View.MemoryView":1074 * * dst.memview = <__pyx_memoryview *> memview * dst.data = memview.view.buf # <<<<<<<<<<<<<< @@ -15385,7 +14393,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem */ __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf); - /* "View.MemoryView":1074 + /* "View.MemoryView":1076 * dst.data = memview.view.buf * * for dim in range(memview.view.ndim): # <<<<<<<<<<<<<< @@ -15397,7 +14405,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_dim = __pyx_t_4; - /* "View.MemoryView":1075 + /* "View.MemoryView":1077 * * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] # <<<<<<<<<<<<<< @@ -15406,7 +14414,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem */ (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]); - /* "View.MemoryView":1076 + /* "View.MemoryView":1078 * for dim in range(memview.view.ndim): * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] # <<<<<<<<<<<<<< @@ -15415,7 +14423,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem */ (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]); - /* "View.MemoryView":1077 + /* "View.MemoryView":1079 * dst.shape[dim] = shape[dim] * dst.strides[dim] = strides[dim] * dst.suboffsets[dim] = suboffsets[dim] if suboffsets else -1 # <<<<<<<<<<<<<< @@ -15430,7 +14438,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem (__pyx_v_dst->suboffsets[__pyx_v_dim]) = __pyx_t_5; } - /* "View.MemoryView":1063 + /* "View.MemoryView":1065 * * @cname('__pyx_memoryview_slice_copy') * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst): # <<<<<<<<<<<<<< @@ -15442,7 +14450,7 @@ static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_mem __Pyx_RefNannyFinishContext(); } -/* "View.MemoryView":1080 +/* "View.MemoryView":1082 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< @@ -15455,9 +14463,12 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_copy", 0); - /* "View.MemoryView":1083 + /* "View.MemoryView":1085 * "Create a new memoryview object" * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) # <<<<<<<<<<<<<< @@ -15466,7 +14477,7 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx */ __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice)); - /* "View.MemoryView":1084 + /* "View.MemoryView":1086 * cdef __Pyx_memviewslice memviewslice * slice_copy(memview, &memviewslice) * return memoryview_copy_from_slice(memview, &memviewslice) # <<<<<<<<<<<<<< @@ -15474,13 +14485,13 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx * @cname('__pyx_memoryview_copy_object_from_slice') */ __Pyx_XDECREF(__pyx_r); - __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1084, __pyx_L1_error) + __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1086, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_r = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L0; - /* "View.MemoryView":1080 + /* "View.MemoryView":1082 * * @cname('__pyx_memoryview_copy_object') * cdef memoryview_copy(memoryview memview): # <<<<<<<<<<<<<< @@ -15499,7 +14510,7 @@ static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx return __pyx_r; } -/* "View.MemoryView":1087 +/* "View.MemoryView":1089 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< @@ -15517,9 +14528,12 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview PyObject *(*__pyx_t_3)(char *); int (*__pyx_t_4)(char *, PyObject *); PyObject *__pyx_t_5 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0); - /* "View.MemoryView":1094 + /* "View.MemoryView":1096 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -15530,7 +14544,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview __pyx_t_2 = (__pyx_t_1 != 0); if (__pyx_t_2) { - /* "View.MemoryView":1095 + /* "View.MemoryView":1097 * * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< @@ -15540,7 +14554,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; __pyx_v_to_object_func = __pyx_t_3; - /* "View.MemoryView":1096 + /* "View.MemoryView":1098 * if isinstance(memview, _memoryviewslice): * to_object_func = (<_memoryviewslice> memview).to_object_func * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< @@ -15550,7 +14564,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; __pyx_v_to_dtype_func = __pyx_t_4; - /* "View.MemoryView":1094 + /* "View.MemoryView":1096 * cdef int (*to_dtype_func)(char *, object) except 0 * * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< @@ -15560,7 +14574,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview goto __pyx_L3; } - /* "View.MemoryView":1098 + /* "View.MemoryView":1100 * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func * else: * to_object_func = NULL # <<<<<<<<<<<<<< @@ -15570,7 +14584,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview /*else*/ { __pyx_v_to_object_func = NULL; - /* "View.MemoryView":1099 + /* "View.MemoryView":1101 * else: * to_object_func = NULL * to_dtype_func = NULL # <<<<<<<<<<<<<< @@ -15581,7 +14595,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview } __pyx_L3:; - /* "View.MemoryView":1101 + /* "View.MemoryView":1103 * to_dtype_func = NULL * * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< @@ -15590,20 +14604,20 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview */ __Pyx_XDECREF(__pyx_r); - /* "View.MemoryView":1103 + /* "View.MemoryView":1105 * return memoryview_fromslice(memviewslice[0], memview.view.ndim, * to_object_func, to_dtype_func, * memview.dtype_is_object) # <<<<<<<<<<<<<< * * */ - __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1101, __pyx_L1_error) + __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1103, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_5); __pyx_r = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L0; - /* "View.MemoryView":1087 + /* "View.MemoryView":1089 * * @cname('__pyx_memoryview_copy_object_from_slice') * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< @@ -15622,7 +14636,7 @@ static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview return __pyx_r; } -/* "View.MemoryView":1109 +/* "View.MemoryView":1111 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< @@ -15634,7 +14648,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { Py_ssize_t __pyx_r; int __pyx_t_1; - /* "View.MemoryView":1110 + /* "View.MemoryView":1112 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< @@ -15644,7 +14658,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { __pyx_t_1 = ((__pyx_v_arg < 0) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1111 + /* "View.MemoryView":1113 * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: * return -arg # <<<<<<<<<<<<<< @@ -15654,7 +14668,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { __pyx_r = (-__pyx_v_arg); goto __pyx_L0; - /* "View.MemoryView":1110 + /* "View.MemoryView":1112 * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: * if arg < 0: # <<<<<<<<<<<<<< @@ -15663,7 +14677,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { */ } - /* "View.MemoryView":1113 + /* "View.MemoryView":1115 * return -arg * else: * return arg # <<<<<<<<<<<<<< @@ -15675,7 +14689,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { goto __pyx_L0; } - /* "View.MemoryView":1109 + /* "View.MemoryView":1111 * * * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< @@ -15688,7 +14702,7 @@ static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { return __pyx_r; } -/* "View.MemoryView":1116 +/* "View.MemoryView":1118 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< @@ -15706,7 +14720,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ int __pyx_t_3; int __pyx_t_4; - /* "View.MemoryView":1121 + /* "View.MemoryView":1123 * """ * cdef int i * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< @@ -15715,7 +14729,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ __pyx_v_c_stride = 0; - /* "View.MemoryView":1122 + /* "View.MemoryView":1124 * cdef int i * cdef Py_ssize_t c_stride = 0 * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< @@ -15724,7 +14738,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ __pyx_v_f_stride = 0; - /* "View.MemoryView":1124 + /* "View.MemoryView":1126 * cdef Py_ssize_t f_stride = 0 * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< @@ -15734,7 +14748,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; - /* "View.MemoryView":1125 + /* "View.MemoryView":1127 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< @@ -15744,7 +14758,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1126 + /* "View.MemoryView":1128 * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< @@ -15753,7 +14767,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); - /* "View.MemoryView":1127 + /* "View.MemoryView":1129 * if mslice.shape[i] > 1: * c_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< @@ -15762,7 +14776,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ goto __pyx_L4_break; - /* "View.MemoryView":1125 + /* "View.MemoryView":1127 * * for i in range(ndim - 1, -1, -1): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< @@ -15773,7 +14787,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ } __pyx_L4_break:; - /* "View.MemoryView":1129 + /* "View.MemoryView":1131 * break * * for i in range(ndim): # <<<<<<<<<<<<<< @@ -15785,7 +14799,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_i = __pyx_t_4; - /* "View.MemoryView":1130 + /* "View.MemoryView":1132 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< @@ -15795,7 +14809,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1131 + /* "View.MemoryView":1133 * for i in range(ndim): * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< @@ -15804,7 +14818,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); - /* "View.MemoryView":1132 + /* "View.MemoryView":1134 * if mslice.shape[i] > 1: * f_stride = mslice.strides[i] * break # <<<<<<<<<<<<<< @@ -15813,7 +14827,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ goto __pyx_L7_break; - /* "View.MemoryView":1130 + /* "View.MemoryView":1132 * * for i in range(ndim): * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< @@ -15824,7 +14838,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ } __pyx_L7_break:; - /* "View.MemoryView":1134 + /* "View.MemoryView":1136 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< @@ -15834,7 +14848,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1135 + /* "View.MemoryView":1137 * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): * return 'C' # <<<<<<<<<<<<<< @@ -15844,7 +14858,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ __pyx_r = 'C'; goto __pyx_L0; - /* "View.MemoryView":1134 + /* "View.MemoryView":1136 * break * * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< @@ -15853,7 +14867,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ */ } - /* "View.MemoryView":1137 + /* "View.MemoryView":1139 * return 'C' * else: * return 'F' # <<<<<<<<<<<<<< @@ -15865,7 +14879,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ goto __pyx_L0; } - /* "View.MemoryView":1116 + /* "View.MemoryView":1118 * * @cname('__pyx_get_best_slice_order') * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< @@ -15878,7 +14892,7 @@ static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int _ return __pyx_r; } -/* "View.MemoryView":1140 +/* "View.MemoryView":1142 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< @@ -15899,7 +14913,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v Py_ssize_t __pyx_t_5; Py_ssize_t __pyx_t_6; - /* "View.MemoryView":1147 + /* "View.MemoryView":1149 * * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< @@ -15908,7 +14922,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_src_extent = (__pyx_v_src_shape[0]); - /* "View.MemoryView":1148 + /* "View.MemoryView":1150 * cdef Py_ssize_t i * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< @@ -15917,7 +14931,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); - /* "View.MemoryView":1149 + /* "View.MemoryView":1151 * cdef Py_ssize_t src_extent = src_shape[0] * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< @@ -15926,7 +14940,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_src_stride = (__pyx_v_src_strides[0]); - /* "View.MemoryView":1150 + /* "View.MemoryView":1152 * cdef Py_ssize_t dst_extent = dst_shape[0] * cdef Py_ssize_t src_stride = src_strides[0] * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< @@ -15935,7 +14949,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); - /* "View.MemoryView":1152 + /* "View.MemoryView":1154 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< @@ -15945,7 +14959,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); if (__pyx_t_1) { - /* "View.MemoryView":1153 + /* "View.MemoryView":1155 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< @@ -15965,7 +14979,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v goto __pyx_L5_bool_binop_done; } - /* "View.MemoryView":1154 + /* "View.MemoryView":1156 * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< @@ -15980,7 +14994,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v __pyx_t_1 = __pyx_t_3; __pyx_L5_bool_binop_done:; - /* "View.MemoryView":1153 + /* "View.MemoryView":1155 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< @@ -15989,7 +15003,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ if (__pyx_t_1) { - /* "View.MemoryView":1155 + /* "View.MemoryView":1157 * if (src_stride > 0 and dst_stride > 0 and * src_stride == itemsize == dst_stride): * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< @@ -15998,7 +15012,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); - /* "View.MemoryView":1153 + /* "View.MemoryView":1155 * * if ndim == 1: * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< @@ -16008,7 +15022,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v goto __pyx_L4; } - /* "View.MemoryView":1157 + /* "View.MemoryView":1159 * memcpy(dst_data, src_data, itemsize * dst_extent) * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< @@ -16021,7 +15035,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; - /* "View.MemoryView":1158 + /* "View.MemoryView":1160 * else: * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< @@ -16030,7 +15044,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); - /* "View.MemoryView":1159 + /* "View.MemoryView":1161 * for i in range(dst_extent): * memcpy(dst_data, src_data, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< @@ -16039,7 +15053,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - /* "View.MemoryView":1160 + /* "View.MemoryView":1162 * memcpy(dst_data, src_data, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< @@ -16051,7 +15065,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v } __pyx_L4:; - /* "View.MemoryView":1152 + /* "View.MemoryView":1154 * cdef Py_ssize_t dst_stride = dst_strides[0] * * if ndim == 1: # <<<<<<<<<<<<<< @@ -16061,7 +15075,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v goto __pyx_L3; } - /* "View.MemoryView":1162 + /* "View.MemoryView":1164 * dst_data += dst_stride * else: * for i in range(dst_extent): # <<<<<<<<<<<<<< @@ -16074,7 +15088,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; - /* "View.MemoryView":1163 + /* "View.MemoryView":1165 * else: * for i in range(dst_extent): * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< @@ -16083,7 +15097,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); - /* "View.MemoryView":1167 + /* "View.MemoryView":1169 * src_shape + 1, dst_shape + 1, * ndim - 1, itemsize) * src_data += src_stride # <<<<<<<<<<<<<< @@ -16092,7 +15106,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v */ __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - /* "View.MemoryView":1168 + /* "View.MemoryView":1170 * ndim - 1, itemsize) * src_data += src_stride * dst_data += dst_stride # <<<<<<<<<<<<<< @@ -16104,7 +15118,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v } __pyx_L3:; - /* "View.MemoryView":1140 + /* "View.MemoryView":1142 * * @cython.cdivision(True) * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< @@ -16115,7 +15129,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v /* function exit code */ } -/* "View.MemoryView":1170 +/* "View.MemoryView":1172 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< @@ -16125,7 +15139,7 @@ static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - /* "View.MemoryView":1173 + /* "View.MemoryView":1175 * __Pyx_memviewslice *dst, * int ndim, size_t itemsize) nogil: * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< @@ -16134,7 +15148,7 @@ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memvi */ _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); - /* "View.MemoryView":1170 + /* "View.MemoryView":1172 * dst_data += dst_stride * * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< @@ -16145,7 +15159,7 @@ static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memvi /* function exit code */ } -/* "View.MemoryView":1177 +/* "View.MemoryView":1179 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< @@ -16162,7 +15176,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr Py_ssize_t *__pyx_t_3; Py_ssize_t *__pyx_t_4; - /* "View.MemoryView":1179 + /* "View.MemoryView":1181 * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: * "Return the size of the memory occupied by the slice in number of bytes" * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< @@ -16172,7 +15186,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_size = __pyx_t_1; - /* "View.MemoryView":1181 + /* "View.MemoryView":1183 * cdef Py_ssize_t shape, size = src.memview.view.itemsize * * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< @@ -16184,7 +15198,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr __pyx_t_2 = __pyx_t_4; __pyx_v_shape = (__pyx_t_2[0]); - /* "View.MemoryView":1182 + /* "View.MemoryView":1184 * * for shape in src.shape[:ndim]: * size *= shape # <<<<<<<<<<<<<< @@ -16194,7 +15208,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr __pyx_v_size = (__pyx_v_size * __pyx_v_shape); } - /* "View.MemoryView":1184 + /* "View.MemoryView":1186 * size *= shape * * return size # <<<<<<<<<<<<<< @@ -16204,7 +15218,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr __pyx_r = __pyx_v_size; goto __pyx_L0; - /* "View.MemoryView":1177 + /* "View.MemoryView":1179 * * @cname('__pyx_memoryview_slice_get_size') * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< @@ -16217,7 +15231,7 @@ static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_sr return __pyx_r; } -/* "View.MemoryView":1187 +/* "View.MemoryView":1189 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< @@ -16233,7 +15247,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ int __pyx_t_3; int __pyx_t_4; - /* "View.MemoryView":1196 + /* "View.MemoryView":1198 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< @@ -16243,7 +15257,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ __pyx_t_1 = ((__pyx_v_order == 'F') != 0); if (__pyx_t_1) { - /* "View.MemoryView":1197 + /* "View.MemoryView":1199 * * if order == 'F': * for idx in range(ndim): # <<<<<<<<<<<<<< @@ -16255,7 +15269,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { __pyx_v_idx = __pyx_t_4; - /* "View.MemoryView":1198 + /* "View.MemoryView":1200 * if order == 'F': * for idx in range(ndim): * strides[idx] = stride # <<<<<<<<<<<<<< @@ -16264,7 +15278,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - /* "View.MemoryView":1199 + /* "View.MemoryView":1201 * for idx in range(ndim): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< @@ -16274,7 +15288,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); } - /* "View.MemoryView":1196 + /* "View.MemoryView":1198 * cdef int idx * * if order == 'F': # <<<<<<<<<<<<<< @@ -16284,7 +15298,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ goto __pyx_L3; } - /* "View.MemoryView":1201 + /* "View.MemoryView":1203 * stride *= shape[idx] * else: * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< @@ -16295,7 +15309,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { __pyx_v_idx = __pyx_t_2; - /* "View.MemoryView":1202 + /* "View.MemoryView":1204 * else: * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride # <<<<<<<<<<<<<< @@ -16304,7 +15318,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ */ (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - /* "View.MemoryView":1203 + /* "View.MemoryView":1205 * for idx in range(ndim - 1, -1, -1): * strides[idx] = stride * stride *= shape[idx] # <<<<<<<<<<<<<< @@ -16316,7 +15330,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ } __pyx_L3:; - /* "View.MemoryView":1205 + /* "View.MemoryView":1207 * stride *= shape[idx] * * return stride # <<<<<<<<<<<<<< @@ -16326,7 +15340,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ __pyx_r = __pyx_v_stride; goto __pyx_L0; - /* "View.MemoryView":1187 + /* "View.MemoryView":1189 * * @cname('__pyx_fill_contig_strides_array') * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< @@ -16339,7 +15353,7 @@ static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ return __pyx_r; } -/* "View.MemoryView":1208 +/* "View.MemoryView":1210 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< @@ -16359,8 +15373,11 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, struct __pyx_memoryview_obj *__pyx_t_4; int __pyx_t_5; int __pyx_t_6; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; - /* "View.MemoryView":1219 + /* "View.MemoryView":1221 * cdef void *result * * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< @@ -16370,7 +15387,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_1 = __pyx_v_src->memview->view.itemsize; __pyx_v_itemsize = __pyx_t_1; - /* "View.MemoryView":1220 + /* "View.MemoryView":1222 * * cdef size_t itemsize = src.memview.view.itemsize * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< @@ -16379,7 +15396,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); - /* "View.MemoryView":1222 + /* "View.MemoryView":1224 * cdef size_t size = slice_get_size(src, ndim) * * result = malloc(size) # <<<<<<<<<<<<<< @@ -16388,7 +15405,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ __pyx_v_result = malloc(__pyx_v_size); - /* "View.MemoryView":1223 + /* "View.MemoryView":1225 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< @@ -16398,16 +15415,16 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1224 + /* "View.MemoryView":1226 * result = malloc(size) * if not result: * _err(MemoryError, NULL) # <<<<<<<<<<<<<< * * */ - __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1224, __pyx_L1_error) + __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1226, __pyx_L1_error) - /* "View.MemoryView":1223 + /* "View.MemoryView":1225 * * result = malloc(size) * if not result: # <<<<<<<<<<<<<< @@ -16416,7 +15433,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ } - /* "View.MemoryView":1227 + /* "View.MemoryView":1229 * * * tmpslice.data = result # <<<<<<<<<<<<<< @@ -16425,7 +15442,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ __pyx_v_tmpslice->data = ((char *)__pyx_v_result); - /* "View.MemoryView":1228 + /* "View.MemoryView":1230 * * tmpslice.data = result * tmpslice.memview = src.memview # <<<<<<<<<<<<<< @@ -16435,7 +15452,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_4 = __pyx_v_src->memview; __pyx_v_tmpslice->memview = __pyx_t_4; - /* "View.MemoryView":1229 + /* "View.MemoryView":1231 * tmpslice.data = result * tmpslice.memview = src.memview * for i in range(ndim): # <<<<<<<<<<<<<< @@ -16447,7 +15464,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; - /* "View.MemoryView":1230 + /* "View.MemoryView":1232 * tmpslice.memview = src.memview * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< @@ -16456,7 +15473,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); - /* "View.MemoryView":1231 + /* "View.MemoryView":1233 * for i in range(ndim): * tmpslice.shape[i] = src.shape[i] * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< @@ -16466,7 +15483,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; } - /* "View.MemoryView":1233 + /* "View.MemoryView":1235 * tmpslice.suboffsets[i] = -1 * * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< @@ -16475,7 +15492,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); - /* "View.MemoryView":1237 + /* "View.MemoryView":1239 * * * for i in range(ndim): # <<<<<<<<<<<<<< @@ -16487,7 +15504,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { __pyx_v_i = __pyx_t_6; - /* "View.MemoryView":1238 + /* "View.MemoryView":1240 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< @@ -16497,7 +15514,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1239 + /* "View.MemoryView":1241 * for i in range(ndim): * if tmpslice.shape[i] == 1: * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< @@ -16506,7 +15523,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; - /* "View.MemoryView":1238 + /* "View.MemoryView":1240 * * for i in range(ndim): * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< @@ -16516,7 +15533,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, } } - /* "View.MemoryView":1241 + /* "View.MemoryView":1243 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< @@ -16526,7 +15543,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1242 + /* "View.MemoryView":1244 * * if slice_is_contig(src[0], order, ndim): * memcpy(result, src.data, size) # <<<<<<<<<<<<<< @@ -16535,7 +15552,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, */ (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size)); - /* "View.MemoryView":1241 + /* "View.MemoryView":1243 * tmpslice.strides[i] = 0 * * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< @@ -16545,7 +15562,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, goto __pyx_L9; } - /* "View.MemoryView":1244 + /* "View.MemoryView":1246 * memcpy(result, src.data, size) * else: * copy_strided_to_strided(src, tmpslice, ndim, itemsize) # <<<<<<<<<<<<<< @@ -16557,7 +15574,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, } __pyx_L9:; - /* "View.MemoryView":1246 + /* "View.MemoryView":1248 * copy_strided_to_strided(src, tmpslice, ndim, itemsize) * * return result # <<<<<<<<<<<<<< @@ -16567,7 +15584,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __pyx_r = __pyx_v_result; goto __pyx_L0; - /* "View.MemoryView":1208 + /* "View.MemoryView":1210 * * @cname('__pyx_memoryview_copy_data_to_temp') * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< @@ -16591,7 +15608,7 @@ static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, return __pyx_r; } -/* "View.MemoryView":1251 +/* "View.MemoryView":1253 * * @cname('__pyx_memoryview_err_extents') * cdef int _err_extents(int i, Py_ssize_t extent1, # <<<<<<<<<<<<<< @@ -16606,25 +15623,28 @@ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; #ifdef WITH_THREAD PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); #endif __Pyx_RefNannySetupContext("_err_extents", 0); - /* "View.MemoryView":1254 + /* "View.MemoryView":1256 * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % * (i, extent1, extent2)) # <<<<<<<<<<<<<< * * @cname('__pyx_memoryview_err_dim') */ - __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1254, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 1256, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); - __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1254, __pyx_L1_error) + __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) __PYX_ERR(2, 1256, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1254, __pyx_L1_error) + __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 1256, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); - __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1254, __pyx_L1_error) + __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 1256, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_4); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1); @@ -16636,24 +15656,24 @@ static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent __pyx_t_2 = 0; __pyx_t_3 = 0; - /* "View.MemoryView":1253 + /* "View.MemoryView":1255 * cdef int _err_extents(int i, Py_ssize_t extent1, * Py_ssize_t extent2) except -1 with gil: * raise ValueError("got differing extents in dimension %d (got %d and %d)" % # <<<<<<<<<<<<<< * (i, extent1, extent2)) * */ - __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); 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if (__pyx_t_2) { - /* "View.MemoryView":1294 + /* "View.MemoryView":1296 * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: * broadcasting = True # <<<<<<<<<<<<<< @@ -17052,7 +16081,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_broadcasting = 1; - /* "View.MemoryView":1295 + /* "View.MemoryView":1297 * if src.shape[i] == 1: * broadcasting = True * src.strides[i] = 0 # <<<<<<<<<<<<<< @@ -17061,7 +16090,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ (__pyx_v_src.strides[__pyx_v_i]) = 0; - /* "View.MemoryView":1293 + /* "View.MemoryView":1295 * for i in range(ndim): * if src.shape[i] != dst.shape[i]: * if src.shape[i] == 1: # <<<<<<<<<<<<<< @@ -17071,7 +16100,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ goto __pyx_L7; } - /* "View.MemoryView":1297 + /* "View.MemoryView":1299 * src.strides[i] = 0 * else: * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< @@ -17079,11 +16108,11 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ * if src.suboffsets[i] >= 0: */ /*else*/ { - __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1297, __pyx_L1_error) + __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1299, __pyx_L1_error) } __pyx_L7:; - /* "View.MemoryView":1292 + /* "View.MemoryView":1294 * * for i in range(ndim): * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< @@ -17092,7 +16121,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1299 + /* "View.MemoryView":1301 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< @@ -17102,16 +16131,16 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1300 + /* "View.MemoryView":1302 * * if src.suboffsets[i] >= 0: * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< * * if slices_overlap(&src, &dst, ndim, itemsize): */ - __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1300, __pyx_L1_error) + __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1302, __pyx_L1_error) - /* "View.MemoryView":1299 + /* "View.MemoryView":1301 * _err_extents(i, dst.shape[i], src.shape[i]) * * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< @@ -17121,7 +16150,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ } } - /* "View.MemoryView":1302 + /* "View.MemoryView":1304 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< @@ -17131,7 +16160,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1304 + /* "View.MemoryView":1306 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< @@ -17141,7 +16170,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1305 + /* "View.MemoryView":1307 * * if not slice_is_contig(src, order, ndim): * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< @@ -17150,7 +16179,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); - /* "View.MemoryView":1304 + /* "View.MemoryView":1306 * if slices_overlap(&src, &dst, ndim, itemsize): * * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< @@ -17159,17 +16188,17 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1307 + /* "View.MemoryView":1309 * order = get_best_order(&dst, ndim) * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< * src = tmp * */ - __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1307, __pyx_L1_error) + __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1309, __pyx_L1_error) __pyx_v_tmpdata = __pyx_t_7; - /* "View.MemoryView":1308 + /* "View.MemoryView":1310 * * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) * src = tmp # <<<<<<<<<<<<<< @@ -17178,7 +16207,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_src = __pyx_v_tmp; - /* "View.MemoryView":1302 + /* "View.MemoryView":1304 * _err_dim(ValueError, "Dimension %d is not direct", i) * * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< @@ -17187,7 +16216,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1310 + /* "View.MemoryView":1312 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< @@ -17197,7 +16226,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1313 + /* "View.MemoryView":1315 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< @@ -17207,7 +16236,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1314 + /* "View.MemoryView":1316 * * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< @@ -17216,7 +16245,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); - /* "View.MemoryView":1313 + /* "View.MemoryView":1315 * * * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< @@ -17226,7 +16255,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ goto __pyx_L12; } - /* "View.MemoryView":1315 + /* "View.MemoryView":1317 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< @@ -17236,7 +16265,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); if (__pyx_t_2) { - /* "View.MemoryView":1316 + /* "View.MemoryView":1318 * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< @@ -17245,7 +16274,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); - /* "View.MemoryView":1315 + /* "View.MemoryView":1317 * if slice_is_contig(src, 'C', ndim): * direct_copy = slice_is_contig(dst, 'C', ndim) * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< @@ -17255,7 +16284,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ } __pyx_L12:; - /* "View.MemoryView":1318 + /* "View.MemoryView":1320 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< @@ -17265,7 +16294,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_2 = (__pyx_v_direct_copy != 0); if (__pyx_t_2) { - /* "View.MemoryView":1320 + /* "View.MemoryView":1322 * if direct_copy: * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< @@ -17274,7 +16303,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - /* "View.MemoryView":1321 + /* "View.MemoryView":1323 * * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< @@ -17283,7 +16312,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); - /* "View.MemoryView":1322 + /* "View.MemoryView":1324 * refcount_copying(&dst, dtype_is_object, ndim, False) * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< @@ -17292,7 +16321,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - /* "View.MemoryView":1323 + /* "View.MemoryView":1325 * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) # <<<<<<<<<<<<<< @@ -17301,7 +16330,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ free(__pyx_v_tmpdata); - /* "View.MemoryView":1324 + /* "View.MemoryView":1326 * refcount_copying(&dst, dtype_is_object, ndim, True) * free(tmpdata) * return 0 # <<<<<<<<<<<<<< @@ -17311,7 +16340,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_r = 0; goto __pyx_L0; - /* "View.MemoryView":1318 + /* "View.MemoryView":1320 * direct_copy = slice_is_contig(dst, 'F', ndim) * * if direct_copy: # <<<<<<<<<<<<<< @@ -17320,7 +16349,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1310 + /* "View.MemoryView":1312 * src = tmp * * if not broadcasting: # <<<<<<<<<<<<<< @@ -17329,7 +16358,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1326 + /* "View.MemoryView":1328 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< @@ -17343,25 +16372,25 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_t_8 = (__pyx_t_2 != 0); if (__pyx_t_8) { - /* "View.MemoryView":1329 + /* "View.MemoryView":1331 * * * transpose_memslice(&src) # <<<<<<<<<<<<<< * transpose_memslice(&dst) * */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1329, __pyx_L1_error) + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1331, __pyx_L1_error) - /* "View.MemoryView":1330 + /* "View.MemoryView":1332 * * transpose_memslice(&src) * transpose_memslice(&dst) # <<<<<<<<<<<<<< * * refcount_copying(&dst, dtype_is_object, ndim, False) */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1330, __pyx_L1_error) + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1332, __pyx_L1_error) - /* "View.MemoryView":1326 + /* "View.MemoryView":1328 * return 0 * * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< @@ -17370,7 +16399,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ } - /* "View.MemoryView":1332 + /* "View.MemoryView":1334 * transpose_memslice(&dst) * * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< @@ -17379,7 +16408,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - /* "View.MemoryView":1333 + /* "View.MemoryView":1335 * * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< @@ -17388,7 +16417,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); - /* "View.MemoryView":1334 + /* "View.MemoryView":1336 * refcount_copying(&dst, dtype_is_object, ndim, False) * copy_strided_to_strided(&src, &dst, ndim, itemsize) * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< @@ -17397,7 +16426,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - /* "View.MemoryView":1336 + /* "View.MemoryView":1338 * refcount_copying(&dst, dtype_is_object, ndim, True) * * free(tmpdata) # <<<<<<<<<<<<<< @@ -17406,7 +16435,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ */ free(__pyx_v_tmpdata); - /* "View.MemoryView":1337 + /* "View.MemoryView":1339 * * free(tmpdata) * return 0 # <<<<<<<<<<<<<< @@ -17416,7 +16445,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ __pyx_r = 0; goto __pyx_L0; - /* "View.MemoryView":1268 + /* "View.MemoryView":1270 * * @cname('__pyx_memoryview_copy_contents') * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< @@ -17440,7 +16469,7 @@ static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_ return __pyx_r; } -/* "View.MemoryView":1340 +/* "View.MemoryView":1342 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< @@ -17455,7 +16484,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic int __pyx_t_2; int __pyx_t_3; - /* "View.MemoryView":1344 + /* "View.MemoryView":1346 * int ndim_other) nogil: * cdef int i * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< @@ -17464,7 +16493,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); - /* "View.MemoryView":1346 + /* "View.MemoryView":1348 * cdef int offset = ndim_other - ndim * * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< @@ -17474,7 +16503,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { __pyx_v_i = __pyx_t_1; - /* "View.MemoryView":1347 + /* "View.MemoryView":1349 * * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< @@ -17483,7 +16512,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); - /* "View.MemoryView":1348 + /* "View.MemoryView":1350 * for i in range(ndim - 1, -1, -1): * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< @@ -17492,7 +16521,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); - /* "View.MemoryView":1349 + /* "View.MemoryView":1351 * mslice.shape[i + offset] = mslice.shape[i] * mslice.strides[i + offset] = mslice.strides[i] * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< @@ -17502,7 +16531,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); } - /* "View.MemoryView":1351 + /* "View.MemoryView":1353 * mslice.suboffsets[i + offset] = mslice.suboffsets[i] * * for i in range(offset): # <<<<<<<<<<<<<< @@ -17514,7 +16543,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { __pyx_v_i = __pyx_t_3; - /* "View.MemoryView":1352 + /* "View.MemoryView":1354 * * for i in range(offset): * mslice.shape[i] = 1 # <<<<<<<<<<<<<< @@ -17523,7 +16552,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ (__pyx_v_mslice->shape[__pyx_v_i]) = 1; - /* "View.MemoryView":1353 + /* "View.MemoryView":1355 * for i in range(offset): * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< @@ -17532,7 +16561,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic */ (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); - /* "View.MemoryView":1354 + /* "View.MemoryView":1356 * mslice.shape[i] = 1 * mslice.strides[i] = mslice.strides[0] * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< @@ -17542,7 +16571,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; } - /* "View.MemoryView":1340 + /* "View.MemoryView":1342 * * @cname('__pyx_memoryview_broadcast_leading') * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< @@ -17553,7 +16582,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic /* function exit code */ } -/* "View.MemoryView":1362 +/* "View.MemoryView":1364 * * @cname('__pyx_memoryview_refcount_copying') * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< @@ -17564,7 +16593,7 @@ static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslic static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { int __pyx_t_1; 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PyObject *__pyx_t_7 = NULL; PyObject *__pyx_t_8 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("__pyx_unpickle_Enum__set_state", 0); /* "(tree fragment)":12 @@ -18372,9 +17414,9 @@ static void __pyx_tp_dealloc_array(PyObject *o) { { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); - ++Py_REFCNT(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_array___dealloc__(o); - --Py_REFCNT(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->mode); @@ -18522,12 +17564,15 @@ static PyTypeObject __pyx_type___pyx_array = { #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif - #if PY_VERSION_HEX >= 0x030800b1 + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 + 0, /*tp_pypy_flags*/ + #endif }; static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { @@ -18641,12 +17686,15 @@ static PyTypeObject __pyx_type___pyx_MemviewEnum = { #if PY_VERSION_HEX >= 0x030400a1 0, /*tp_finalize*/ #endif - #if PY_VERSION_HEX >= 0x030800b1 + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) 0, /*tp_vectorcall*/ #endif #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 0, /*tp_print*/ #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 + 0, /*tp_pypy_flags*/ + #endif }; static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; @@ -18683,9 +17731,9 @@ static void __pyx_tp_dealloc_memoryview(PyObject *o) { { PyObject *etype, *eval, *etb; PyErr_Fetch(&etype, &eval, &etb); - ++Py_REFCNT(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); __pyx_memoryview___dealloc__(o); - --Py_REFCNT(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); PyErr_Restore(etype, eval, etb); } Py_CLEAR(p->obj); 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static PyMethodDef __pyx_methods[] = { @@ -19110,10 +18164,8 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0}, {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, - {&__pyx_kp_u_Format_string_allocated_too_shor, __pyx_k_Format_string_allocated_too_shor, sizeof(__pyx_k_Format_string_allocated_too_shor), 0, 1, 0, 0}, - {&__pyx_kp_u_Format_string_allocated_too_shor_2, __pyx_k_Format_string_allocated_too_shor_2, sizeof(__pyx_k_Format_string_allocated_too_shor_2), 0, 1, 0, 0}, {&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1}, - {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0}, + {&__pyx_kp_s_Incompatible_checksums_0x_x_vs_0, __pyx_k_Incompatible_checksums_0x_x_vs_0, sizeof(__pyx_k_Incompatible_checksums_0x_x_vs_0), 0, 0, 1, 0}, {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1}, {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0}, {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0}, @@ -19121,11 +18173,9 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1}, {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0}, {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0}, - {&__pyx_kp_u_Non_native_byte_order_not_suppor, __pyx_k_Non_native_byte_order_not_suppor, sizeof(__pyx_k_Non_native_byte_order_not_suppor), 0, 1, 0, 0}, {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1}, {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0}, {&__pyx_n_s_PickleError, __pyx_k_PickleError, sizeof(__pyx_k_PickleError), 0, 0, 1, 1}, - {&__pyx_n_s_RuntimeError, __pyx_k_RuntimeError, sizeof(__pyx_k_RuntimeError), 0, 0, 1, 1}, {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1}, {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0}, {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1}, @@ -19168,8 +18218,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_n_reactants, __pyx_k_n_reactants, sizeof(__pyx_k_n_reactants), 0, 0, 1, 1}, {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1}, {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1}, - {&__pyx_kp_u_ndarray_is_not_C_contiguous, __pyx_k_ndarray_is_not_C_contiguous, sizeof(__pyx_k_ndarray_is_not_C_contiguous), 0, 1, 0, 0}, - {&__pyx_kp_u_ndarray_is_not_Fortran_contiguou, __pyx_k_ndarray_is_not_Fortran_contiguou, sizeof(__pyx_k_ndarray_is_not_Fortran_contiguou), 0, 1, 0, 0}, {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1}, {&__pyx_n_s_new, __pyx_k_new, sizeof(__pyx_k_new), 0, 0, 1, 1}, {&__pyx_n_s_new_cdf, __pyx_k_new_cdf, sizeof(__pyx_k_new_cdf), 0, 0, 1, 1}, @@ -19218,7 +18266,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_total_rate, __pyx_k_total_rate, sizeof(__pyx_k_total_rate), 0, 0, 1, 1}, {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0}, {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0}, - {&__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_k_unknown_dtype_code_in_numpy_pxd, sizeof(__pyx_k_unknown_dtype_code_in_numpy_pxd), 0, 1, 0, 0}, {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1}, {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1}, {&__pyx_n_s_x, __pyx_k_x, sizeof(__pyx_k_x), 0, 0, 1, 1}, @@ -19227,15 +18274,14 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { }; 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if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 286, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__24, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 287, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(generic); __Pyx_DECREF_SET(generic, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; - /* "View.MemoryView":287 + /* "View.MemoryView":288 * * cdef generic = Enum("") * cdef strided = Enum("") # default # <<<<<<<<<<<<<< * cdef indirect = Enum("") * */ - __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__29, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 287, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__25, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 288, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(strided); __Pyx_DECREF_SET(strided, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; - /* "View.MemoryView":288 + /* "View.MemoryView":289 * cdef generic = Enum("") * cdef strided = Enum("") # default * cdef indirect = Enum("") # <<<<<<<<<<<<<< * * */ - __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__30, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 288, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__26, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 289, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(indirect); __Pyx_DECREF_SET(indirect, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; - /* "View.MemoryView":291 + /* "View.MemoryView":292 * * * cdef contiguous = Enum("") # <<<<<<<<<<<<<< * cdef indirect_contiguous = Enum("") * */ - __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__31, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 291, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__27, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 292, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(contiguous); __Pyx_DECREF_SET(contiguous, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; - /* "View.MemoryView":292 + /* "View.MemoryView":293 * * cdef contiguous = Enum("") * cdef indirect_contiguous = Enum("") # <<<<<<<<<<<<<< * * */ - __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__32, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 292, __pyx_L1_error) + __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)__pyx_MemviewEnum_type), __pyx_tuple__28, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 293, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __Pyx_XGOTREF(indirect_contiguous); __Pyx_DECREF_SET(indirect_contiguous, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); __pyx_t_1 = 0; - /* "View.MemoryView":316 + /* "View.MemoryView":317 * * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 # <<<<<<<<<<<<<< @@ -20090,7 +19119,7 @@ if (!__Pyx_RefNanny) { */ __pyx_memoryview_thread_locks_used = 0; - /* "View.MemoryView":317 + /* "View.MemoryView":318 * DEF THREAD_LOCKS_PREALLOCATED = 8 * cdef int __pyx_memoryview_thread_locks_used = 0 * cdef PyThread_type_lock[THREAD_LOCKS_PREALLOCATED] __pyx_memoryview_thread_locks = [ # <<<<<<<<<<<<<< @@ -20107,29 +19136,29 @@ if (!__Pyx_RefNanny) { __pyx_t_2[7] = PyThread_allocate_lock(); memcpy(&(__pyx_memoryview_thread_locks[0]), __pyx_t_2, sizeof(__pyx_memoryview_thread_locks[0]) * (8)); - /* "View.MemoryView":549 + /* "View.MemoryView":551 * info.obj = self * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ - __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 549, __pyx_L1_error) + __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 551, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem((PyObject *)__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 549, __pyx_L1_error) + if (PyDict_SetItem((PyObject *)__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 551, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_memoryview_type); - /* "View.MemoryView":995 + /* "View.MemoryView":997 * return self.from_object * * __pyx_getbuffer = capsule( &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)") # <<<<<<<<<<<<<< * * */ - __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 995, __pyx_L1_error) + __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), ((char *)"getbuffer(obj, view, flags)")); if (unlikely(!__pyx_t_1)) __PYX_ERR(2, 997, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem((PyObject *)__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 995, __pyx_L1_error) + if (PyDict_SetItem((PyObject *)__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) __PYX_ERR(2, 997, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; PyType_Modified(__pyx_memoryviewslice_type); @@ -20283,7 +19312,7 @@ static int __Pyx_ParseOptionalKeywords( } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 - if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + if (likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { @@ -20310,7 +19339,7 @@ static int __Pyx_ParseOptionalKeywords( while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; @@ -20326,7 +19355,7 @@ static int __Pyx_ParseOptionalKeywords( while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; @@ -20722,9 +19751,7 @@ static PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; + int i = 0, number, ndim; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, @@ -20732,6 +19759,7 @@ __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ndim = ctx->head->field->type->ndim; while (*ts && *ts != ')') { switch (*ts) { case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; @@ -20861,8 +19889,8 @@ static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const cha case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { ctx->enc_count += ctx->new_count; ctx->new_count = 1; got_Z = 0; @@ -21152,7 +20180,7 @@ static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; - ternaryfunc call = func->ob_type->tp_call; + ternaryfunc call = Py_TYPE(func)->tp_call; if (unlikely(!call)) return PyObject_Call(func, arg, kw); if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) @@ -21239,7 +20267,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObjec if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); #if CYTHON_FAST_PYCCALL - } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + } else if (__Pyx_PyFastCFunction_Check(func)) { return __Pyx_PyCFunction_FastCall(func, &arg, 1); #endif } @@ -21268,7 +20296,7 @@ __Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, int i, retval=-1; Py_buffer *buf = &memview->view; __Pyx_RefNannySetupContext("init_memviewslice", 0); - if (memviewslice->memview || memviewslice->data) { + if (unlikely(memviewslice->memview || memviewslice->data)) { PyErr_SetString(PyExc_ValueError, "memviewslice is already initialized!"); goto fail; @@ -21313,7 +20341,7 @@ __Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { va_list vargs; char msg[200]; -#ifdef HAVE_STDARG_PROTOTYPES +#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES) va_start(vargs, fmt); #else va_start(vargs); @@ -21338,296 +20366,80 @@ __pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, { int result; PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)--; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE void -__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) -{ - int first_time; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (!memview || (PyObject *) memview == Py_None) - return; - if (__pyx_get_slice_count(memview) < 0) - __pyx_fatalerror("Acquisition count is %d (line %d)", - __pyx_get_slice_count(memview), lineno); - first_time = __pyx_add_acquisition_count(memview) == 0; - if (first_time) { - if (have_gil) { - Py_INCREF((PyObject *) memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_INCREF((PyObject *) memview); - PyGILState_Release(_gilstate); - } - } -} -static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, - int have_gil, int lineno) { - int last_time; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (!memview ) { - return; - } else if ((PyObject *) memview == Py_None) { - memslice->memview = NULL; - return; - } - if (__pyx_get_slice_count(memview) <= 0) - __pyx_fatalerror("Acquisition count is %d (line %d)", - __pyx_get_slice_count(memview), lineno); - last_time = __pyx_sub_acquisition_count(memview) == 1; - memslice->data = NULL; - if (last_time) { - if (have_gil) { - Py_CLEAR(memslice->memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_CLEAR(memslice->memview); - PyGILState_Release(_gilstate); - } - } else { - memslice->memview = NULL; - } -} - -/* PyErrFetchRestore */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -} -#endif - -/* RaiseException */ - #if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + int first_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) + return; + if (unlikely(__pyx_get_slice_count(memview) < 0)) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + first_time = __pyx_add_acquisition_count(memview) == 0; + if (unlikely(first_time)) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); } -#endif } -bad: - Py_XDECREF(owned_instance); - return; } -#endif - -/* DictGetItem */ - #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY -static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { - PyObject *value; - value = PyDict_GetItemWithError(d, key); - if (unlikely(!value)) { - if (!PyErr_Occurred()) { - if (unlikely(PyTuple_Check(key))) { - PyObject* args = PyTuple_Pack(1, key); - if (likely(args)) { - PyErr_SetObject(PyExc_KeyError, args); - Py_DECREF(args); - } - } else { - PyErr_SetObject(PyExc_KeyError, key); - } +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + int last_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + memslice->memview = NULL; + return; + } + if (unlikely(__pyx_get_slice_count(memview) <= 0)) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + last_time = __pyx_sub_acquisition_count(memview) == 1; + memslice->data = NULL; + if (unlikely(last_time)) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); } - return NULL; + } else { + memslice->memview = NULL; } - Py_INCREF(value); - return value; -} -#endif - -/* RaiseTooManyValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } -/* RaiseNeedMoreValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ - static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +/* PyErrFetchRestore */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); } - -/* ExtTypeTest */ - static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(__Pyx_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; } +#endif /* GetTopmostException */ #if CYTHON_USE_EXC_INFO_STACK @@ -21784,6 +20596,165 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) return -1; } +/* RaiseException */ + #if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + /* BytesEquals */ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { #if CYTHON_COMPILING_IN_PYPY @@ -21804,7 +20775,7 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) return (equals == Py_EQ); } else { int result; -#if CYTHON_USE_UNICODE_INTERNALS +#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000) Py_hash_t hash1, hash2; hash1 = ((PyBytesObject*)s1)->ob_shash; hash2 = ((PyBytesObject*)s2)->ob_shash; @@ -21933,7 +20904,7 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) #endif } -/* None */ +/* DivInt[Py_ssize_t] */ static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { Py_ssize_t q = a / b; Py_ssize_t r = a - q*b; @@ -22044,7 +21015,7 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, /* ObjectGetItem */ #if CYTHON_USE_TYPE_SLOTS static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { - PyObject *runerr; + PyObject *runerr = NULL; Py_ssize_t key_value; PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; if (unlikely(!(m && m->sq_item))) { @@ -22092,9 +21063,9 @@ static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { if (stop < 0) stop += length; } + if (unlikely(stop <= start)) + return __Pyx_NewRef(__pyx_empty_unicode); length = stop - start; - if (unlikely(length <= 0)) - return PyUnicode_FromUnicode(NULL, 0); cstring += start; if (decode_func) { return decode_func(cstring, length, errors); @@ -22118,6 +21089,37 @@ static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject return (likely(r)) ? r : __Pyx_GetAttr3Default(d); } +/* RaiseTooManyValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ + static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ + static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + /* SwapException */ #if CYTHON_FAST_THREAD_STATE static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { @@ -22183,7 +21185,7 @@ static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, { #if PY_MAJOR_VERSION >= 3 if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { + if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { module = PyImport_ImportModuleLevelObject( name, global_dict, empty_dict, list, 1); if (!module) { @@ -22447,7 +21449,7 @@ static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); } -/* None */ +/* DivInt[long] */ static CYTHON_INLINE long __Pyx_div_long(long a, long b) { long q = a / b; long r = a - q*b; @@ -22555,6 +21557,28 @@ static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_nam return -1; } +/* PyObjectGetAttrStrNoError */ + static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + __Pyx_PyErr_Clear(); +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { + return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); + } +#endif + result = __Pyx_PyObject_GetAttrStr(obj, attr_name); + if (unlikely(!result)) { + __Pyx_PyObject_GetAttrStr_ClearAttributeError(); + } + return result; +} + /* SetupReduce */ static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { int ret; @@ -22575,17 +21599,35 @@ static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_nam static int __Pyx_setup_reduce(PyObject* type_obj) { int ret = 0; PyObject *object_reduce = NULL; + PyObject *object_getstate = NULL; PyObject *object_reduce_ex = NULL; PyObject *reduce = NULL; PyObject *reduce_ex = NULL; PyObject *reduce_cython = NULL; PyObject *setstate = NULL; PyObject *setstate_cython = NULL; + PyObject *getstate = NULL; #if CYTHON_USE_PYTYPE_LOOKUP - if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; + getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate); #else - if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; + getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate); + if (!getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (getstate) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate); +#else + object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate); + if (!object_getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } #endif + if (object_getstate != getstate) { + goto __PYX_GOOD; + } + } #if CYTHON_USE_PYTYPE_LOOKUP object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; #else @@ -22600,15 +21642,23 @@ static int __Pyx_setup_reduce(PyObject* type_obj) { #endif reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { - reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto __PYX_BAD; - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); + if (likely(reduce_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (reduce == object_reduce || PyErr_Occurred()) { + goto __PYX_BAD; + } setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); if (!setstate) PyErr_Clear(); if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { - setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto __PYX_BAD; - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); + if (likely(setstate_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (!setstate || PyErr_Occurred()) { + goto __PYX_BAD; + } } PyType_Modified((PyTypeObject*)type_obj); } @@ -22622,6 +21672,8 @@ static int __Pyx_setup_reduce(PyObject* type_obj) { #if !CYTHON_USE_PYTYPE_LOOKUP Py_XDECREF(object_reduce); Py_XDECREF(object_reduce_ex); + Py_XDECREF(object_getstate); + Py_XDECREF(getstate); #endif Py_XDECREF(reduce); Py_XDECREF(reduce_ex); @@ -22694,7 +21746,7 @@ static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, /* CLineInTraceback */ #ifndef CYTHON_CLINE_IN_TRACEBACK -static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { +static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) { PyObject *use_cline; PyObject *ptype, *pvalue, *ptraceback; #if CYTHON_COMPILING_IN_CPYTHON @@ -22724,7 +21776,7 @@ static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { } if (!use_cline) { c_line = 0; - PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); } else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { c_line = 0; @@ -22798,7 +21850,7 @@ static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } @@ -22818,33 +21870,40 @@ static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { #include "compile.h" #include "frameobject.h" #include "traceback.h" +#if PY_VERSION_HEX >= 0x030b00a6 + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; + PyCodeObject *py_code = NULL; + PyObject *py_funcname = NULL; #if PY_MAJOR_VERSION < 3 + PyObject *py_srcfile = NULL; py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif if (!py_srcfile) goto bad; + #endif if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + funcname = PyUnicode_AsUTF8(py_funcname); + if (!funcname) goto bad; #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); + if (!py_funcname) goto bad; #endif } - if (!py_funcname) goto bad; + #if PY_MAJOR_VERSION < 3 py_code = __Pyx_PyCode_New( 0, 0, @@ -22863,11 +21922,16 @@ static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); + #else + py_code = PyCode_NewEmpty(filename, funcname, py_line); + #endif + Py_XDECREF(py_funcname); // XDECREF since it's only set on Py3 if cline return py_code; bad: - Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_srcfile); + #endif return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, @@ -22875,14 +21939,24 @@ static void __Pyx_AddTraceback(const char *funcname, int c_line, PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject *ptype, *pvalue, *ptraceback; if (c_line) { c_line = __Pyx_CLineForTraceback(tstate, c_line); } py_code = __pyx_find_code_object(c_line ? -c_line : py_line); if (!py_code) { + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); - if (!py_code) goto bad; + if (!py_code) { + /* If the code object creation fails, then we should clear the + fetched exception references and propagate the new exception */ + Py_XDECREF(ptype); + Py_XDECREF(pvalue); + Py_XDECREF(ptraceback); + goto bad; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); } py_frame = PyFrame_New( @@ -22902,7 +21976,6 @@ static void __Pyx_AddTraceback(const char *funcname, int c_line, #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); @@ -22916,7 +21989,6 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { return; } if ((0)) {} - else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); view->obj = NULL; Py_DECREF(obj); } @@ -22944,85 +22016,322 @@ __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) } return 1; } - -/* OverlappingSlices */ - static void -__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, - void **out_start, void **out_end, - int ndim, size_t itemsize) + +/* OverlappingSlices */ + static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* Capsule */ + static CYTHON_INLINE PyObject * +__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) +{ + PyObject *cobj; +#if PY_VERSION_HEX >= 0x02070000 + cobj = PyCapsule_New(p, sig, NULL); +#else + cobj = PyCObject_FromVoidPtr(p, NULL); +#endif + return cobj; +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (unlikely(buf->strides[dim] != sizeof(void *))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (unlikely(buf->strides[dim] != buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (unlikely(stride < buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (unlikely(buf->suboffsets)) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) +{ + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) { - char *start, *end; - int i; - start = end = slice->data; - for (i = 0; i < ndim; i++) { - Py_ssize_t stride = slice->strides[i]; - Py_ssize_t extent = slice->shape[i]; - if (extent == 0) { - *out_start = *out_end = start; - return; - } else { - if (stride > 0) - end += stride * (extent - 1); - else - start += stride * (extent - 1); + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (unlikely(buf->ndim != ndim)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; + } + if (unlikely((unsigned) buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->len > 0) { + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) + goto fail; + if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) + goto fail; } + if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) + goto fail; } - *out_start = start; - *out_end = end + itemsize; -} -static int -__pyx_slices_overlap(__Pyx_memviewslice *slice1, - __Pyx_memviewslice *slice2, - int ndim, size_t itemsize) -{ - void *start1, *end1, *start2, *end2; - __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); - __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); - return (start1 < end2) && (start2 < end1); + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; } -/* Capsule */ - static CYTHON_INLINE PyObject * -__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) -{ - PyObject *cobj; -#if PY_VERSION_HEX >= 0x02070000 - cobj = PyCapsule_New(p, sig, NULL); -#else - cobj = PyCObject_FromVoidPtr(p, NULL); -#endif - return cobj; +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 1, + &__Pyx_TypeInfo_double, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; } -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value) { - const npy_int64 neg_one = (npy_int64) ((npy_int64) 0 - (npy_int64) 1), const_zero = (npy_int64) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(npy_int64) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(npy_int64) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(npy_int64) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(npy_int64) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(npy_int64) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(npy_int64), - little, !is_unsigned); +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_nn___pyx_t_5numpy_int64_t(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 2, + &__Pyx_TypeInfo_nn___pyx_t_5numpy_int64_t, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; } /* CIntFromPyVerify */ @@ -23176,7 +22485,7 @@ __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) if (a.imag == 0) { if (a.real == 0) { return a; - } else if (b.imag == 0) { + } else if ((b.imag == 0) && (a.real >= 0)) { z.real = powf(a.real, b.real); z.imag = 0; return z; @@ -23314,108 +22623,46 @@ __pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) case 0: z.real = 1; z.imag = 0; - return z; - case 1: - return a; - case 2: - return __Pyx_c_prod_double(a, a); - case 3: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, a); - case 4: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = pow(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2(0.0, -1.0); - } - } else { - r = __Pyx_c_abs_double(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) ((enum NPY_TYPES) 0 - (enum NPY_TYPES) 1), const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if ((b.imag == 0) && (a.real >= 0)) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); + #endif #endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} /* MemviewSliceCopyTemplate */ static __Pyx_memviewslice @@ -23435,7 +22682,7 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, struct __pyx_memoryview_obj *memview_obj = NULL; __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); for (i = 0; i < ndim; i++) { - if (from_mvs->suboffsets[i] >= 0) { + if (unlikely(from_mvs->suboffsets[i] >= 0)) { PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " "indirect dimensions (axis %d)", i); goto fail; @@ -23484,9 +22731,54 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, return new_mvs; } +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const npy_int64 neg_one = (npy_int64) -1, const_zero = (npy_int64) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(npy_int64) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(npy_int64) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(npy_int64) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(npy_int64) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(npy_int64) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(npy_int64), + little, !is_unsigned); + } +} + /* CIntFromPy */ static CYTHON_INLINE npy_int64 __Pyx_PyInt_As_npy_int64(PyObject *x) { - const npy_int64 neg_one = (npy_int64) ((npy_int64) 0 - (npy_int64) 1), const_zero = (npy_int64) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const npy_int64 neg_one = (npy_int64) -1, const_zero = (npy_int64) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { @@ -23675,7 +22967,14 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, /* CIntFromPy */ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { @@ -23864,7 +23163,14 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, /* CIntFromPy */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { @@ -24051,9 +23357,54 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, return (long) -1; } +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + /* CIntToPy */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif const int is_unsigned = neg_one > const_zero; if (is_unsigned) { if (sizeof(long) < sizeof(long)) { @@ -24084,7 +23435,14 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, /* CIntFromPy */ static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { - const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const char neg_one = (char) -1, const_zero = (char) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 if (likely(PyInt_Check(x))) { @@ -24197,356 +23555,109 @@ __pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); } } - break; - case -4: - if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { - return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(char) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { - return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - } -#endif - if (sizeof(char) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - char val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (char) -1; - } - } else { - char val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (char) -1; - val = __Pyx_PyInt_As_char(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to char"); - return (char) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to char"); - return (char) -1; -} - -/* TypeInfoCompare */ - static int -__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) -{ - int i; - if (!a || !b) - return 0; - if (a == b) - return 1; - if (a->size != b->size || a->typegroup != b->typegroup || - a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { - if (a->typegroup == 'H' || b->typegroup == 'H') { - return a->size == b->size; - } else { - return 0; - } - } - if (a->ndim) { - for (i = 0; i < a->ndim; i++) - if (a->arraysize[i] != b->arraysize[i]) - return 0; - } - if (a->typegroup == 'S') { - if (a->flags != b->flags) - return 0; - if (a->fields || b->fields) { - if (!(a->fields && b->fields)) - return 0; - for (i = 0; a->fields[i].type && b->fields[i].type; i++) { - __Pyx_StructField *field_a = a->fields + i; - __Pyx_StructField *field_b = b->fields + i; - if (field_a->offset != field_b->offset || - !__pyx_typeinfo_cmp(field_a->type, field_b->type)) - return 0; - } - return !a->fields[i].type && !b->fields[i].type; - } - } - return 1; -} - -/* MemviewSliceValidateAndInit */ - static int -__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) -{ - if (buf->shape[dim] <= 1) - return 1; - if (buf->strides) { - if (spec & __Pyx_MEMVIEW_CONTIG) { - if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { - if (buf->strides[dim] != sizeof(void *)) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly contiguous " - "in dimension %d.", dim); - goto fail; - } - } else if (buf->strides[dim] != buf->itemsize) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; + break; + case -4: + if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; } - } - if (spec & __Pyx_MEMVIEW_FOLLOW) { - Py_ssize_t stride = buf->strides[dim]; - if (stride < 0) - stride = -stride; - if (stride < buf->itemsize) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; +#endif + if (sizeof(char) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif } } - } else { - if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not contiguous in " - "dimension %d", dim); - goto fail; - } else if (spec & (__Pyx_MEMVIEW_PTR)) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not indirect in " - "dimension %d", dim); - goto fail; - } else if (buf->suboffsets) { - PyErr_SetString(PyExc_ValueError, - "Buffer exposes suboffsets but no strides"); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) -{ - if (spec & __Pyx_MEMVIEW_DIRECT) { - if (buf->suboffsets && buf->suboffsets[dim] >= 0) { - PyErr_Format(PyExc_ValueError, - "Buffer not compatible with direct access " - "in dimension %d.", dim); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_PTR) { - if (!buf->suboffsets || (buf->suboffsets[dim] < 0)) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly accessible " - "in dimension %d.", dim); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) -{ - int i; - if (c_or_f_flag & __Pyx_IS_F_CONTIG) { - Py_ssize_t stride = 1; - for (i = 0; i < ndim; i++) { - if (stride * buf->itemsize != buf->strides[i] && - buf->shape[i] > 1) - { - PyErr_SetString(PyExc_ValueError, - "Buffer not fortran contiguous."); - goto fail; + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); } - stride = stride * buf->shape[i]; - } - } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { - Py_ssize_t stride = 1; - for (i = ndim - 1; i >- 1; i--) { - if (stride * buf->itemsize != buf->strides[i] && - buf->shape[i] > 1) { - PyErr_SetString(PyExc_ValueError, - "Buffer not C contiguous."); - goto fail; + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; } - stride = stride * buf->shape[i]; +#endif + return (char) -1; } - } - return 1; -fail: - return 0; -} -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj) -{ - struct __pyx_memoryview_obj *memview, *new_memview; - __Pyx_RefNannyDeclarations - Py_buffer *buf; - int i, spec = 0, retval = -1; - __Pyx_BufFmt_Context ctx; - int from_memoryview = __pyx_memoryview_check(original_obj); - __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); - if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) - original_obj)->typeinfo)) { - memview = (struct __pyx_memoryview_obj *) original_obj; - new_memview = NULL; } else { - memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - original_obj, buf_flags, 0, dtype); - new_memview = memview; - if (unlikely(!memview)) - goto fail; - } - buf = &memview->view; - if (buf->ndim != ndim) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - ndim, buf->ndim); - goto fail; - } - if (new_memview) { - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned) buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " - "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", - buf->itemsize, - (buf->itemsize > 1) ? "s" : "", - dtype->name, - dtype->size, - (dtype->size > 1) ? "s" : ""); - goto fail; - } - for (i = 0; i < ndim; i++) { - spec = axes_specs[i]; - if (!__pyx_check_strides(buf, i, ndim, spec)) - goto fail; - if (!__pyx_check_suboffsets(buf, i, ndim, spec)) - goto fail; - } - if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, - new_memview != NULL) == -1)) { - goto fail; - } - retval = 0; - goto no_fail; -fail: - Py_XDECREF(new_memview); - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, - PyBUF_RECORDS_RO | writable_flag, 1, - &__Pyx_TypeInfo_double, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_nn___pyx_t_5numpy_int64_t(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, - PyBUF_RECORDS_RO | writable_flag, 2, - &__Pyx_TypeInfo_nn___pyx_t_5numpy_int64_t, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; } /* CheckBinaryVersion */ static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char ctversion[5]; + int same=1, i, found_dot; + const char* rt_from_call = Py_GetVersion(); + PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + found_dot = 0; + for (i = 0; i < 4; i++) { + if (!ctversion[i]) { + same = (rt_from_call[i] < '0' || rt_from_call[i] > '9'); + break; + } + if (rt_from_call[i] != ctversion[i]) { + same = 0; + break; + } + } + if (!same) { + char rtversion[5] = {'\0'}; char message[200]; + for (i=0; i<4; ++i) { + if (rt_from_call[i] == '.') { + if (found_dot) break; + found_dot = 1; + } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') { + break; + } + rtversion[i] = rt_from_call[i]; + } PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", @@ -24858,6 +23969,23 @@ static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_DECREF(x); return ival; } +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { + if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { + return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); +#if PY_MAJOR_VERSION < 3 + } else if (likely(PyInt_CheckExact(o))) { + return PyInt_AS_LONG(o); +#endif + } else { + Py_ssize_t ival; + PyObject *x; + x = PyNumber_Index(o); + if (!x) return -1; + ival = PyInt_AsLong(x); + Py_DECREF(x); + return ival; + } +} static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); } From 5fb761d11a67d39f2ee30510aecbf3f0dcccea96 Mon Sep 17 00:00:00 2001 From: Thomas Finnie Date: Thu, 26 Jan 2023 15:52:12 +0000 Subject: [PATCH 022/188] Fix calls removed from numpy --- tests/test_fh_estimate.py | 2 +- tests/test_ode_func.py | 2 +- tests/test_sir_discrete_estimate.py | 2 +- tests/test_sir_estimate.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_fh_estimate.py b/tests/test_fh_estimate.py index af24324d..c435b6dd 100644 --- a/tests/test_fh_estimate.py +++ b/tests/test_fh_estimate.py @@ -28,7 +28,7 @@ def setUp(self): g = self.obj.gradient() assert np.linalg.norm(g) > 0 - EPSILON = np.sqrt(np.finfo(np.float).eps) + EPSILON = np.sqrt(np.finfo(float).eps) self.box_bounds = [(EPSILON, 5.0)]*len(self.theta) diff --git a/tests/test_ode_func.py b/tests/test_ode_func.py index b39daa6f..22e660ef 100644 --- a/tests/test_ode_func.py +++ b/tests/test_ode_func.py @@ -11,7 +11,7 @@ class TestJacobians(TestCase): def setUp(self): - self.h = np.sqrt(np.finfo(np.float).eps) + self.h = np.sqrt(np.finfo(float).eps) # initial time self.t0 = 0 # the initial state, normalized to zero one diff --git a/tests/test_sir_discrete_estimate.py b/tests/test_sir_discrete_estimate.py index e991effe..ed904f49 100644 --- a/tests/test_sir_discrete_estimate.py +++ b/tests/test_sir_discrete_estimate.py @@ -38,7 +38,7 @@ def setUp(self): self.theta = np.array([0.4, 0.3]) # constraints - EPSILON = np.sqrt(np.finfo(np.float).eps) + EPSILON = np.sqrt(np.finfo(float).eps) self.box_bounds = [(EPSILON, 2), (EPSILON, 2)] self.target = np.array([0.5, 1.0/3.0]) diff --git a/tests/test_sir_estimate.py b/tests/test_sir_estimate.py index a3e9f060..629bcc99 100644 --- a/tests/test_sir_estimate.py +++ b/tests/test_sir_estimate.py @@ -27,7 +27,7 @@ def setUp(self): self.target = np.array([0.5, 1.0/3.0]) # constraints - EPSILON = np.sqrt(np.finfo(np.float).eps) + EPSILON = np.sqrt(np.finfo(float).eps) self.box_bounds = [(EPSILON, 5), (EPSILON, 5)] From cff95c7c0482971772c83590b24caa46f2b2406e Mon Sep 17 00:00:00 2001 From: Thomas Finnie Date: Thu, 26 Jan 2023 19:44:37 +0000 Subject: [PATCH 023/188] Give a bit more detail in the error message for incorrect types. --- pygom/model/ode_utils/checks_and_conversions.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pygom/model/ode_utils/checks_and_conversions.py b/pygom/model/ode_utils/checks_and_conversions.py index 8ca427fc..83d76719 100644 --- a/pygom/model/ode_utils/checks_and_conversions.py +++ b/pygom/model/ode_utils/checks_and_conversions.py @@ -51,7 +51,7 @@ def check_array_type(x,accept_booleans=False): raise TypeError('No elements of array type object should be Boolean values') x = np.array(x) else: - raise TypeError(type_error_message) + raise TypeError(type_error_message + ' got ' + str(type(x))) elif isinstance(x, accepted_types): if accept_booleans==True: x = np.array([x]) From 74f580627b185985e269667b0e0cdbd000645365 Mon Sep 17 00:00:00 2001 From: Thomas Finnie Date: Thu, 26 Jan 2023 20:15:01 +0000 Subject: [PATCH 024/188] Remove Travis CI and move to GitHub actions --- .github/workflows/main.yml | 2 +- .travis.yml | 37 ------------------------------------- README.rst | 2 -- 3 files changed, 1 insertion(+), 40 deletions(-) delete mode 100644 .travis.yml diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a59d3801..3ff9aca9 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -17,7 +17,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.5, 3.6, 3.7, 3.8] + python-version: [3.7, 3.8, 3.9, 3.10, 3.11] steps: - uses: actions/checkout@v2 diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 5eec188c..00000000 --- a/.travis.yml +++ /dev/null @@ -1,37 +0,0 @@ -# specify the language and which version -language: python -python: - - 3.7 - - 3.8 - - 3.9 - - 3.10 - - 3.11 - -before_install: - - pip install -U pip - - pip install codecov - -install: - - pip install -r requirements.txt - - python setup.py build - - python setup.py install - -# Runs test with coverage -script: - - coverage run setup.py test - -# Push the result to -after_success: - - codecov - -# deploy to PyPI -deploy: - provider: pypi - twine_version: 1.13.0 - skip_existing: true - user: twomagpi - password: - secure: 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 - on: - tags: true - python: 3.7 diff --git a/README.rst b/README.rst index 87ec1e3c..092aa624 100644 --- a/README.rst +++ b/README.rst @@ -6,8 +6,6 @@ pygom - ODE modelling in Python .. |pypi version| image:: https://img.shields.io/pypi/v/pygom.svg :target: https://pypi.python.org/pypi/pygom -.. |Build status| image:: https://travis-ci.org/PublicHealthEngland/pygom.svg?branch=master - :target: https://travis-ci.org/PublicHealthEngland/pygom .. |Documentation Status| image:: https://readthedocs.org/projects/pygom/badge/?version=master :target: https://pygom.readthedocs.io/en/master/?badge=master .. |licence| image:: https://img.shields.io/pypi/l/pygom?color=green :alt: PyPI - License From c884ab599c933c6784a7194e119f5d8ed9067b7a Mon Sep 17 00:00:00 2001 From: Thomas Finnie Date: Thu, 26 Jan 2023 20:28:31 +0000 Subject: [PATCH 025/188] In versioning 3.10 != 3.1 --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 3ff9aca9..fd6f95b9 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -17,7 +17,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.7, 3.8, 3.9, 3.10, 3.11] + python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] steps: - uses: actions/checkout@v2 From 30cbc1a6bdd86d8f45447a6a4ee86924727e2de8 Mon Sep 17 00:00:00 2001 From: Thomas Finnie Date: Thu, 26 Jan 2023 20:40:47 +0000 Subject: [PATCH 026/188] I don't like enabling insecure workflow commands but it seems (currently) that MS VS needs this (or we don't test windows). --- .github/workflows/main.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index fd6f95b9..41b80bc4 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -11,6 +11,9 @@ on: branches: - master +env: + ACTIONS_ALLOW_UNSECURE_COMMANDS: true + jobs: build: runs-on: ${{ matrix.os }} From ee11e09baf3167c5f040d0a54fbeed85183b75cb Mon Sep 17 00:00:00 2001 From: Thomas Finnie Date: Fri, 27 Jan 2023 21:00:13 +0000 Subject: [PATCH 027/188] Fix the test for ABC with normal loss by adding a value for sigma --- README.rst | 2 +- .../approximate_bayesian_computation.py | 13 +++++++++++-- pygom/loss/ode_loss.py | 15 ++++++++++++--- tests/test_abc.py | 2 +- 4 files changed, 25 insertions(+), 7 deletions(-) diff --git a/README.rst b/README.rst index 092aa624..60af8b40 100644 --- a/README.rst +++ b/README.rst @@ -2,7 +2,7 @@ pygom - ODE modelling in Python =============================== -|Build status| |Github actions| |Documentation Status| |pypi version| |licence| +|Github actions| |Documentation Status| |pypi version| |licence| .. |pypi version| image:: https://img.shields.io/pypi/v/pygom.svg :target: https://pypi.python.org/pypi/pygom diff --git a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py index 1012fc2c..4b125bff 100644 --- a/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py +++ b/pygom/approximate_bayesian_computation/approximate_bayesian_computation.py @@ -159,8 +159,17 @@ def create_loss(loss_type, parameters, ode, x0, t0, t, y, state_name, return SquareLoss(theta, ode, x0, t0, t, y, state_name, state_weight, target_param, target_state) elif loss_type == NormalLoss: - return NormalLoss(theta, ode, x0, t0, t, y, state_name, sigma, target_param, target_state) - + return NormalLoss(theta=theta, + ode=ode, + x0=x0, + t0=t0, + t=t, + y=y, + state_name=state_name, + sigma=sigma, + target_param=target_param, + target_state=target_state) + elif loss_type == PoissonLoss: return PoissonLoss(theta, ode, x0, t0, t, y, state_name, target_param, target_state) diff --git a/pygom/loss/ode_loss.py b/pygom/loss/ode_loss.py index a4a950f0..b7352422 100644 --- a/pygom/loss/ode_loss.py +++ b/pygom/loss/ode_loss.py @@ -40,9 +40,18 @@ class NormalLoss(BaseLoss): ''' def __init__(self, theta, ode, x0, t0, t, y, state_name, state_weight=None, sigma=1.0, target_param=None, target_state=None): - super().__init__(theta, ode, x0, t0, t, y, - state_name, state_weight, sigma, target_param, target_state) - + super().__init__(theta=theta, + ode=ode, + x0=x0, + t0=t0, + t=t, + y=y, + state_name=state_name, + state_weight=state_weight, + spread_param=sigma, + target_param=target_param, + target_state=target_state) + def __repr__(self): return "NormalLoss" + self._get_model_str() diff --git a/tests/test_abc.py b/tests/test_abc.py index 9584ec53..7985f0f6 100644 --- a/tests/test_abc.py +++ b/tests/test_abc.py @@ -64,7 +64,7 @@ def test_SIR_abc_NormalLoss(self): parameters = [pgabc.Parameter('beta', 'unif', 0, 3, logscale=False), pgabc.Parameter('gamma', 'unif', 0, 3, logscale=False)] sir_obj = pgabc.create_loss(NormalLoss, parameters, self.ode, self.x0, self.t[0], - self.t[1::], y, ['I', 'R']) + self.t[1::], y, ['I', 'R'], sigma=1.0) sir_abc = pgabc.ABC(sir_obj, parameters) sir_abc.get_posterior_sample(N=100, tol=np.inf, G=10, q=0.5) sir_abc.continue_posterior_sample(N=100, tol=sir_abc.next_tol, G=10, q=0.5) From a4078def27bdc7c73ddb22e18ad5819b24d454fc Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Mon, 30 Jan 2023 20:07:02 +0000 Subject: [PATCH 028/188] Add todo for readthedocs --- doc/source/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/source/conf.py b/doc/source/conf.py index 7b711798..2870ab90 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -136,6 +136,7 @@ else: # RTD will time out if we try to build the whole of the documentation so # ignore some of the longer bits and perhaps add them later + # // TODO: speed up runtime for longer examples for readthedocs exclude_patterns = ['common_models/*.rst', # 'bvpSimple.rst', 'epi.rst', From 22ff76d3935072f99f7aa46223bafcf83e0d1419 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Mon, 30 Jan 2023 20:07:34 +0000 Subject: [PATCH 029/188] Typos --- doc/source/getting_started.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/doc/source/getting_started.rst b/doc/source/getting_started.rst index e16f2fca..cf58249b 100644 --- a/doc/source/getting_started.rst +++ b/doc/source/getting_started.rst @@ -10,8 +10,8 @@ What this package does ====================== The purpose of this package is to allow the end user to easily define a set of -ordinary differential equations (ode) and obtain information about the ode by -simply invoking the the appropriate methods. Here, we define the set of ode's +ordinary differential equations (ODE) and obtain information about the ODE by +invoking the the appropriate methods. Here, we define the set of ODE's as .. math:: @@ -19,11 +19,11 @@ as where :math:`\mathbf{x} = \left(x_{1},x_{2},\ldots,x_{n}\right)` is the state vector with :math:`d` state and :math:`\boldsymbol{\theta}` the parameters of -:math:`p` dimension. Currently, this package allows the end user to find the -algebraic expression of the ode, Jacobian, gradient and forward sensitivity of -the ode. A numerical output is given when all the state and parameter values -are provided. Note that the only important class is :file:`DeterministicOde` -all the functionality described previously are exposed. +:math:`p` dimension. Currently, this package allows the user to find the +algebraic expression of the ODE, Jacobian, gradient and forward sensitivity of +the ODE. A numerical output is given when all the state and parameter values +are provided. Note that the only important class is :file:`DeterministicOde` +where all the functionality described previously are exposed. The current plan is to extend the functionality to include @@ -61,7 +61,7 @@ The package is currently as follows:: requirements.txt setup.py -with files in each of the three main folder not shown. You can install the +with files in each of the three main folders not shown. You can install the package via command line:: python setup.py install From 8b11d9b30edb2a7e256b64058aa5cf973a5ee3a2 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Mon, 30 Jan 2023 20:13:16 +0000 Subject: [PATCH 030/188] Comment out toctree for readding --- doc/source/index.rst | 36 +++++++++++++++++++----------------- 1 file changed, 19 insertions(+), 17 deletions(-) diff --git a/doc/source/index.rst b/doc/source/index.rst index 732af680..f2c08c9d 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -6,6 +6,8 @@ pygom is a package that aims to facilitate the application of ordinary different This is an open source project hosted on `Github `_. +Insert intro text + ################## User Documentation ################## @@ -14,20 +16,20 @@ User Documentation :maxdepth: 5 getting_started.rst - sir.rst - transition.rst - stochastic.rst - unrollOde.rst - epi.rst - epijson.rst - bvpSimple.rst - gradient.rst - fh.rst - estimate1.rst - estimate2.rst - initialGuess.rst - profile.rst - common_models.rst + #sir.rst + #transition.rst + #stochastic.rst + #unrollOde.rst + #epi.rst + #epijson.rst + #bvpSimple.rst + #gradient.rst + #fh.rst + #estimate1.rst + #estimate2.rst + #initialGuess.rst + #profile.rst + #common_models.rst ########################## Code Documentation and FAQ @@ -36,8 +38,8 @@ Code Documentation and FAQ .. toctree:: :maxdepth: 5 - faq.rst - mod/index.rst + #faq.rst + #mod/index.rst ########## References @@ -45,7 +47,7 @@ References .. toctree:: - ref.rst + #ref.rst ################## Indices and tables From 7eeb9a91408424b33410ea51298a1e1bb420db31 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Mon, 30 Jan 2023 20:22:42 +0000 Subject: [PATCH 031/188] include jupyter notebook extension --- doc/source/conf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index 2870ab90..d7ef04b0 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -45,7 +45,8 @@ 'matplotlib.sphinxext.plot_directive', 'numpydoc', 'IPython.sphinxext.ipython_console_highlighting', - 'IPython.sphinxext.ipython_directive' + 'IPython.sphinxext.ipython_directive', + 'nbsphinx' ] # the mapping for code in other packages From 12306678fcd196921ebfacb90b26bf3cd4198f6f Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 12:42:09 +0000 Subject: [PATCH 032/188] temporarily remove original rsts and paths for jupyter conversion --- doc/source/conf.py | 1 + doc/source/index.rst | 1 + 2 files changed, 2 insertions(+) diff --git a/doc/source/conf.py b/doc/source/conf.py index d7ef04b0..aedce28d 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -134,6 +134,7 @@ import sphinx_rtd_theme html_theme = 'sphinx_rtd_theme' html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] + exclude_patterns = ['doc_to_sort.*'] else: # RTD will time out if we try to build the whole of the documentation so # ignore some of the longer bits and perhaps add them later diff --git a/doc/source/index.rst b/doc/source/index.rst index f2c08c9d..e9560e51 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -17,6 +17,7 @@ User Documentation getting_started.rst #sir.rst + #notebooks/sir.ipynb #transition.rst #stochastic.rst #unrollOde.rst From 9508502652c8557b40724d9407d1f3ea8f324cc9 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 12:49:36 +0000 Subject: [PATCH 033/188] temporarily move rst documentation for converting to jupyter notebooks --- doc/{source => doc_to_sort}/bvpSimple.rst | 374 ++++++------ doc/{source => doc_to_sort}/common_models.rst | 0 .../common_models/FitzHugh.rst | 0 .../common_models/Legrand_Ebola_SEIHFR.rst | 0 .../common_models/Lorenz.rst | 0 .../common_models/Lotka_Volterra.rst | 0 .../common_models/Lotka_Volterra_4State.rst | 0 .../common_models/Robertson.rst | 0 .../common_models/SEIR.rst | 0 .../common_models/SEIR_Birth_Death.rst | 0 .../SEIR_Birth_Death_Periodic.rst | 0 .../common_models/SEIR_Multiple.rst | 0 .../common_models/SIR.rst | 0 .../common_models/SIR_Birth_Death.rst | 0 .../common_models/SIS.rst | 0 .../common_models/SIS_Periodic.rst | 0 .../common_models/vanDelPol.rst | 0 doc/{source => doc_to_sort}/epi.rst | 0 doc/{source => doc_to_sort}/epijson.rst | 0 doc/{source => doc_to_sort}/estimate1.rst | 0 doc/{source => doc_to_sort}/estimate2.rst | 570 +++++++++--------- doc/{source => doc_to_sort}/faq.rst | 120 ++-- doc/{source => doc_to_sort}/fh.rst | 0 doc/{source => doc_to_sort}/gradient.rst | 0 doc/{source => doc_to_sort}/initialGuess.rst | 0 .../mod/common_models.rst | 0 .../mod/confidence_interval.rst | 0 .../mod/deterministic.rst | 0 .../mod/epi_analysis.rst | 0 doc/{source => doc_to_sort}/mod/get_init.rst | 0 doc/{source => doc_to_sort}/mod/index.rst | 0 doc/{source => doc_to_sort}/mod/losstype.rst | 0 doc/{source => doc_to_sort}/mod/odeloss.rst | 0 doc/{source => doc_to_sort}/mod/odeutils.rst | 0 doc/{source => doc_to_sort}/mod/simulate.rst | 0 .../mod/transition.rst | 0 doc/{source => doc_to_sort}/profile.rst | 0 doc/{source => doc_to_sort}/ref.rst | 0 doc/{source => doc_to_sort}/stochastic.rst | 0 doc/{source => doc_to_sort}/transition.rst | 0 .../unroll/unrollBD.rst | 0 .../unroll/unrollHard.rst | 0 .../unroll/unrollSimple.rst | 0 doc/{source => doc_to_sort}/unrollOde.rst | 0 44 files changed, 532 insertions(+), 532 deletions(-) rename doc/{source => doc_to_sort}/bvpSimple.rst (97%) rename doc/{source => doc_to_sort}/common_models.rst (100%) rename doc/{source => doc_to_sort}/common_models/FitzHugh.rst (100%) rename doc/{source => doc_to_sort}/common_models/Legrand_Ebola_SEIHFR.rst (100%) rename doc/{source => doc_to_sort}/common_models/Lorenz.rst (100%) rename doc/{source => doc_to_sort}/common_models/Lotka_Volterra.rst (100%) rename doc/{source => doc_to_sort}/common_models/Lotka_Volterra_4State.rst (100%) rename doc/{source => doc_to_sort}/common_models/Robertson.rst (100%) rename 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Here, we are going to show how a BVP can be solved by treating it as a parameter estimation problem. Essentially, a shooting method where the first boundary condition defines the initial condition of an IVP and the second boundary condition is an observation. Two examples, both from MATLAB [1]_, will be shown here. - -Simple model 1 -============== - -We are trying to find the solution to the second order differential equation - -.. math:: - \nabla^{2} y + |y| = 0 - -subject to the boundary conditions :math:`y(0) = 0` and :math:`y(4) = -2`. Convert this into a set of first order ODE - -.. math:: - - \frac{d y_{0}}{dt} &= y_{1} \\ - \frac{d y_{1}}{dt} &= -|y_{0}| - -using an auxiliary variable :math:`y_{1} = \nabla y` and :math:`y_{0} = y`. Setting up the system below - -.. ipython:: - - In [1]: from pygom import Transition, TransitionType, DeterministicOde, SquareLoss - - In [1]: import matplotlib.pyplot as plt - - In [2]: stateList = ['y0', 'y1'] - - In [3]: paramList = [] - - In [4]: ode1 = Transition(origin='y0', - ...: equation='y1', - ...: transition_type=TransitionType.ODE) - - In [5]: ode2 = Transition(origin='y1', - ...: equation='-abs(y0)', - ...: transition_type=TransitionType.ODE) - - In [6]: model = DeterministicOde(stateList, - ...: paramList, - ...: ode=[ode1, ode2]) - - In [7]: model.get_ode_eqn() - -We check that the equations are correct before proceeding to set up our loss function. - -.. ipython:: - - In [1]: import numpy - - In [2]: from scipy.optimize import minimize - - In [3]: initialState = [0.0, 1.0] - - In [4]: t = numpy.linspace(0, 4, 100) - - In [5]: model.initial_values = (initialState, t[0]) - - In [6]: solution = model.integrate(t[1::]) - - In [7]: f = plt.figure() - - @savefig bvp1_random_guess_plot.png - In [8]: model.plot() - - In [9]: plt.close() - -Setting up the second boundary condition :math:`y(4) = -2` is easy, because that -is just a single observation attached to the state :math:`y_{1}`. Enforcing the -first boundary condition requires us to set it as the initial condition. -Because the condition only states that :math:`y(0) = 0`, the starting value of -the other state :math:`y_1` is free. We let our loss object know that it is -free through the targetState input argument. - -.. ipython:: - - In [10]: theta = [0.0] - - In [11]: obj = SquareLoss(theta=theta, - ....: ode=model, - ....: x0=initialState, - ....: t0=t[0], - ....: t=t[-1], - ....: y=[-2], - ....: state_name=['y0'], - ....: target_state=['y1']) - - In [12]: thetaHat = minimize(fun=obj.costIV, x0=[0.0]) - - In [13]: print(thetaHat) - - In [14]: model.initial_values = ([0.0] + thetaHat['x'].tolist(), t[0]) - - In [15]: solution = model.integrate(t[1::]) - - In [16]: f = plt.figure() - - @savefig bvp1_solution_plot.png - In [17]: model.plot() - - In [18]: plt.close() - -We are going to visualize the solution, and also check the boundary condition. The first became our initial condition, so it is always satisfied and only the latter is of concern, which is zero (subject to numerical error) from thetaHat. - -Simple model 2 -============== - -Our second example is different as it involves an actual parameter and also time. We have the Mathieu's Equation - -.. math:: - - \nabla^{2} y + \left(p - 2q \cos(2x)\right)y = 0 - -and the aim is to compute the fourth eigenvalue :math:`q=5`. There are three boundary conditions - -.. math:: - - \nabla y(0) = 0, \quad \nabla y(\pi) = 0, \quad y(0) = 1 - -and we aim to solve it by converting it to a first order ODE and tackle it as an IVP. As our model object does not allow the use of the time component in the equations, we introduce a anxiliary state :math:`\tau` that replaces time :math:`t`. Rewrite the equations using :math:`y_{0} = y, y_{1} = \nabla y` and define our model as - -.. ipython:: - - In [1]: stateList = ['y0', 'y1', 'tau'] - - In [2]: paramList = ['p'] - - In [3]: ode1 = Transition('y0', 'y1', TransitionType.ODE) - - In [4]: ode2 = Transition('y1', '-(p - 2*5*cos(2*tau))*y0', TransitionType.ODE) - - In [5]: ode3 = Transition('tau', '1', TransitionType.ODE) - - In [6]: model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3]) - - In [7]: theta = [1.0, 1.0, 0.0] - - In [8]: p = 15.0 - - In [9]: t = numpy.linspace(0, numpy.pi) - - In [10]: model.parameters = [('p',p)] - - In [11]: model.initial_values = (theta, t[0]) - - In [12]: solution = model.integrate(t[1::]) - - In [13]: f = plt.figure() - - @savefig bvp2_random_guess_plot.png - In [14]: model.plot() - - In [15]: plt.close() - -Now we are ready to setup the estimation. Like before, we setup the second boundary condition by pretending that it is an observation. We have all the initial conditions defined by the first boundary condition - -.. ipython:: - - In [1]: obj = SquareLoss(15.0, model, x0=[1.0, 0.0, 0.0], t0=0.0, t=numpy.pi, y=0.0, state_name='y1') - - In [2]: xhatObj = minimize(obj.cost,[15]) - - In [3]: print(xhatObj) - - In [4]: model.parameters = [('p', xhatObj['x'][0])] - - In [5]: model.initial_values = ([1.0, 0.0, 0.0], t[0]) - - In [5]: solution = model.integrate(t[1::]) - - In [6]: f = plt.figure() - - @savefig bvp2_solution_plot.png - In [7]: model.plot() - - In [8]: plt.close() - -The plot of the solution shows the path that satisfies all boundary condition. The last subplot is time which obvious is redundant here but the :meth:`DeterministicOde.plot` method is not yet able to recognize the time component. Possible speed up can be achieved through the use of derivative information or via root finding method that tackles the gradient directly, instead of the cost function. - -**Reference** - -.. [1] http://uk.mathworks.com/help/matlab/ref/bvp4c.html +.. _bvpSimple: + +******************************* +Solving Boundary Value Problems +******************************* + +In addition to finding solutions for an IVP and estimate the unknown parameters, this package also allows you to solve BVP with a little bit of imagination. Here, we are going to show how a BVP can be solved by treating it as a parameter estimation problem. Essentially, a shooting method where the first boundary condition defines the initial condition of an IVP and the second boundary condition is an observation. Two examples, both from MATLAB [1]_, will be shown here. + +Simple model 1 +============== + +We are trying to find the solution to the second order differential equation + +.. math:: + \nabla^{2} y + |y| = 0 + +subject to the boundary conditions :math:`y(0) = 0` and :math:`y(4) = -2`. Convert this into a set of first order ODE + +.. math:: + + \frac{d y_{0}}{dt} &= y_{1} \\ + \frac{d y_{1}}{dt} &= -|y_{0}| + +using an auxiliary variable :math:`y_{1} = \nabla y` and :math:`y_{0} = y`. Setting up the system below + +.. ipython:: + + In [1]: from pygom import Transition, TransitionType, DeterministicOde, SquareLoss + + In [1]: import matplotlib.pyplot as plt + + In [2]: stateList = ['y0', 'y1'] + + In [3]: paramList = [] + + In [4]: ode1 = Transition(origin='y0', + ...: equation='y1', + ...: transition_type=TransitionType.ODE) + + In [5]: ode2 = Transition(origin='y1', + ...: equation='-abs(y0)', + ...: transition_type=TransitionType.ODE) + + In [6]: model = DeterministicOde(stateList, + ...: paramList, + ...: ode=[ode1, ode2]) + + In [7]: model.get_ode_eqn() + +We check that the equations are correct before proceeding to set up our loss function. + +.. ipython:: + + In [1]: import numpy + + In [2]: from scipy.optimize import minimize + + In [3]: initialState = [0.0, 1.0] + + In [4]: t = numpy.linspace(0, 4, 100) + + In [5]: model.initial_values = (initialState, t[0]) + + In [6]: solution = model.integrate(t[1::]) + + In [7]: f = plt.figure() + + @savefig bvp1_random_guess_plot.png + In [8]: model.plot() + + In [9]: plt.close() + +Setting up the second boundary condition :math:`y(4) = -2` is easy, because that +is just a single observation attached to the state :math:`y_{1}`. Enforcing the +first boundary condition requires us to set it as the initial condition. +Because the condition only states that :math:`y(0) = 0`, the starting value of +the other state :math:`y_1` is free. We let our loss object know that it is +free through the targetState input argument. + +.. ipython:: + + In [10]: theta = [0.0] + + In [11]: obj = SquareLoss(theta=theta, + ....: ode=model, + ....: x0=initialState, + ....: t0=t[0], + ....: t=t[-1], + ....: y=[-2], + ....: state_name=['y0'], + ....: target_state=['y1']) + + In [12]: thetaHat = minimize(fun=obj.costIV, x0=[0.0]) + + In [13]: print(thetaHat) + + In [14]: model.initial_values = ([0.0] + thetaHat['x'].tolist(), t[0]) + + In [15]: solution = model.integrate(t[1::]) + + In [16]: f = plt.figure() + + @savefig bvp1_solution_plot.png + In [17]: model.plot() + + In [18]: plt.close() + +We are going to visualize the solution, and also check the boundary condition. The first became our initial condition, so it is always satisfied and only the latter is of concern, which is zero (subject to numerical error) from thetaHat. + +Simple model 2 +============== + +Our second example is different as it involves an actual parameter and also time. We have the Mathieu's Equation + +.. math:: + + \nabla^{2} y + \left(p - 2q \cos(2x)\right)y = 0 + +and the aim is to compute the fourth eigenvalue :math:`q=5`. There are three boundary conditions + +.. math:: + + \nabla y(0) = 0, \quad \nabla y(\pi) = 0, \quad y(0) = 1 + +and we aim to solve it by converting it to a first order ODE and tackle it as an IVP. As our model object does not allow the use of the time component in the equations, we introduce a anxiliary state :math:`\tau` that replaces time :math:`t`. Rewrite the equations using :math:`y_{0} = y, y_{1} = \nabla y` and define our model as + +.. ipython:: + + In [1]: stateList = ['y0', 'y1', 'tau'] + + In [2]: paramList = ['p'] + + In [3]: ode1 = Transition('y0', 'y1', TransitionType.ODE) + + In [4]: ode2 = Transition('y1', '-(p - 2*5*cos(2*tau))*y0', TransitionType.ODE) + + In [5]: ode3 = Transition('tau', '1', TransitionType.ODE) + + In [6]: model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3]) + + In [7]: theta = [1.0, 1.0, 0.0] + + In [8]: p = 15.0 + + In [9]: t = numpy.linspace(0, numpy.pi) + + In [10]: model.parameters = [('p',p)] + + In [11]: model.initial_values = (theta, t[0]) + + In [12]: solution = model.integrate(t[1::]) + + In [13]: f = plt.figure() + + @savefig bvp2_random_guess_plot.png + In [14]: model.plot() + + In [15]: plt.close() + +Now we are ready to setup the estimation. Like before, we setup the second boundary condition by pretending that it is an observation. We have all the initial conditions defined by the first boundary condition + +.. ipython:: + + In [1]: obj = SquareLoss(15.0, model, x0=[1.0, 0.0, 0.0], t0=0.0, t=numpy.pi, y=0.0, state_name='y1') + + In [2]: xhatObj = minimize(obj.cost,[15]) + + In [3]: print(xhatObj) + + In [4]: model.parameters = [('p', xhatObj['x'][0])] + + In [5]: model.initial_values = ([1.0, 0.0, 0.0], t[0]) + + In [5]: solution = model.integrate(t[1::]) + + In [6]: f = plt.figure() + + @savefig bvp2_solution_plot.png + In [7]: model.plot() + + In [8]: plt.close() + +The plot of the solution shows the path that satisfies all boundary condition. The last subplot is time which obvious is redundant here but the :meth:`DeterministicOde.plot` method is not yet able to recognize the time component. Possible speed up can be achieved through the use of derivative information or via root finding method that tackles the gradient directly, instead of the cost function. + +**Reference** + +.. [1] http://uk.mathworks.com/help/matlab/ref/bvp4c.html diff --git a/doc/source/common_models.rst b/doc/doc_to_sort/common_models.rst similarity index 100% rename from doc/source/common_models.rst rename to doc/doc_to_sort/common_models.rst diff --git a/doc/source/common_models/FitzHugh.rst b/doc/doc_to_sort/common_models/FitzHugh.rst similarity index 100% rename from doc/source/common_models/FitzHugh.rst rename to doc/doc_to_sort/common_models/FitzHugh.rst diff --git a/doc/source/common_models/Legrand_Ebola_SEIHFR.rst b/doc/doc_to_sort/common_models/Legrand_Ebola_SEIHFR.rst similarity index 100% rename from doc/source/common_models/Legrand_Ebola_SEIHFR.rst rename to doc/doc_to_sort/common_models/Legrand_Ebola_SEIHFR.rst diff --git a/doc/source/common_models/Lorenz.rst b/doc/doc_to_sort/common_models/Lorenz.rst similarity index 100% rename from doc/source/common_models/Lorenz.rst rename to doc/doc_to_sort/common_models/Lorenz.rst diff --git a/doc/source/common_models/Lotka_Volterra.rst b/doc/doc_to_sort/common_models/Lotka_Volterra.rst similarity index 100% rename from doc/source/common_models/Lotka_Volterra.rst rename to doc/doc_to_sort/common_models/Lotka_Volterra.rst diff --git a/doc/source/common_models/Lotka_Volterra_4State.rst b/doc/doc_to_sort/common_models/Lotka_Volterra_4State.rst similarity index 100% rename from doc/source/common_models/Lotka_Volterra_4State.rst rename to doc/doc_to_sort/common_models/Lotka_Volterra_4State.rst diff --git a/doc/source/common_models/Robertson.rst b/doc/doc_to_sort/common_models/Robertson.rst similarity index 100% rename from doc/source/common_models/Robertson.rst rename to doc/doc_to_sort/common_models/Robertson.rst diff --git a/doc/source/common_models/SEIR.rst b/doc/doc_to_sort/common_models/SEIR.rst similarity index 100% rename from doc/source/common_models/SEIR.rst rename to doc/doc_to_sort/common_models/SEIR.rst diff --git a/doc/source/common_models/SEIR_Birth_Death.rst b/doc/doc_to_sort/common_models/SEIR_Birth_Death.rst similarity index 100% rename from doc/source/common_models/SEIR_Birth_Death.rst rename to doc/doc_to_sort/common_models/SEIR_Birth_Death.rst diff --git a/doc/source/common_models/SEIR_Birth_Death_Periodic.rst b/doc/doc_to_sort/common_models/SEIR_Birth_Death_Periodic.rst similarity index 100% rename from doc/source/common_models/SEIR_Birth_Death_Periodic.rst rename to doc/doc_to_sort/common_models/SEIR_Birth_Death_Periodic.rst diff --git a/doc/source/common_models/SEIR_Multiple.rst b/doc/doc_to_sort/common_models/SEIR_Multiple.rst similarity index 100% rename from doc/source/common_models/SEIR_Multiple.rst rename to doc/doc_to_sort/common_models/SEIR_Multiple.rst diff --git a/doc/source/common_models/SIR.rst b/doc/doc_to_sort/common_models/SIR.rst similarity index 100% rename from doc/source/common_models/SIR.rst rename to doc/doc_to_sort/common_models/SIR.rst diff --git a/doc/source/common_models/SIR_Birth_Death.rst b/doc/doc_to_sort/common_models/SIR_Birth_Death.rst similarity index 100% rename from doc/source/common_models/SIR_Birth_Death.rst rename to doc/doc_to_sort/common_models/SIR_Birth_Death.rst diff --git a/doc/source/common_models/SIS.rst b/doc/doc_to_sort/common_models/SIS.rst similarity index 100% rename from doc/source/common_models/SIS.rst rename to doc/doc_to_sort/common_models/SIS.rst diff --git a/doc/source/common_models/SIS_Periodic.rst b/doc/doc_to_sort/common_models/SIS_Periodic.rst similarity index 100% rename from doc/source/common_models/SIS_Periodic.rst rename to doc/doc_to_sort/common_models/SIS_Periodic.rst diff --git a/doc/source/common_models/vanDelPol.rst b/doc/doc_to_sort/common_models/vanDelPol.rst similarity index 100% rename from doc/source/common_models/vanDelPol.rst rename to doc/doc_to_sort/common_models/vanDelPol.rst diff --git a/doc/source/epi.rst b/doc/doc_to_sort/epi.rst similarity index 100% rename from doc/source/epi.rst rename to doc/doc_to_sort/epi.rst diff --git a/doc/source/epijson.rst b/doc/doc_to_sort/epijson.rst similarity index 100% rename from doc/source/epijson.rst rename to doc/doc_to_sort/epijson.rst diff --git a/doc/source/estimate1.rst b/doc/doc_to_sort/estimate1.rst similarity index 100% rename from doc/source/estimate1.rst rename to doc/doc_to_sort/estimate1.rst diff --git a/doc/source/estimate2.rst b/doc/doc_to_sort/estimate2.rst similarity index 97% rename from doc/source/estimate2.rst rename to doc/doc_to_sort/estimate2.rst index ebd7d7c5..10d27b23 100644 --- a/doc/source/estimate2.rst +++ b/doc/doc_to_sort/estimate2.rst @@ -1,285 +1,285 @@ -.. _estimate2: - -******************************* -Example: Parameter Estimation 2 -******************************* - -Continuing from the :ref:`estimate1`, we show why estimating the parameters for ode's are hard. This is especially true if there is a lack of data or when there are too much flexibility in the model. Note that for reproducibility purposes, only deterministic models are used here and a fixed seed whenever a stochastic algorithm is needed. - -Standard SEIR model -=================== - -We demonstrate the estimation on the recent Ebola outbreak in West Africa. We use the number of deaths in Guinea and its corresponding time the data was recorded. These data are publicly available and they can be obtained easily on the internet such as https://github.com/cmrivers/ebola. It is stated out here for simplicity. - -.. ipython:: - - In [34]: # the number of deaths and cases in Guinea - - In [35]: yDeath = [29.0, 59.0, 60.0, 62.0, 66.0, 70.0, 70.0, 80.0, 83.0, 86.0, 95.0, 101.0, 106.0, 108.0, - ....: 122.0, 129.0, 136.0, 141.0, 143.0, 149.0, 155.0, 157.0, 158.0, 157.0, 171.0, 174.0, - ....: 186.0, 193.0, 208.0, 215.0, 226.0, 264.0, 267.0, 270.0, 303.0, 305.0, 307.0, 309.0, - ....: 304.0, 310.0, 310.0, 314.0, 319.0, 339.0, 346.0, 358.0, 363.0, 367.0, 373.0, 377.0, - ....: 380.0, 394.0, 396.0, 406.0, 430.0, 494.0, 517.0, 557.0, 568.0, 595.0, 601.0, 632.0, - ....: 635.0, 648.0, 710.0, 739.0, 768.0, 778.0, 843.0, 862.0, 904.0, 926.0, 997.0] - - In [35]: yCase = [49.0, 86.0, 86.0, 86.0, 103.0, 112.0, 112.0, 122.0, 127.0, 143.0, 151.0, 158.0, - ....: 159.0, 168.0, 197.0, 203.0, 208.0, 218.0, 224.0, 226.0, 231.0, 235.0, 236.0, - ....: 233.0, 248.0, 258.0, 281.0, 291.0, 328.0, 344.0, 351.0, 398.0, 390.0, 390.0, - ....: 413.0, 412.0, 408.0, 409.0, 406.0, 411.0, 410.0, 415.0, 427.0, 460.0, 472.0, - ....: 485.0, 495.0, 495.0, 506.0, 510.0, 519.0, 543.0, 579.0, 607.0, 648.0, 771.0, - ....: 812.0, 861.0, 899.0, 936.0, 942.0, 1008.0, 1022.0, 1074.0, 1157.0, 1199.0, 1298.0, - ....: 1350.0, 1472.0, 1519.0, 1540.0, 1553.0, 1906.0] - - In [36]: # the corresponding time - - In [37]: t = [0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 13.0, 16.0, 18.0, 20.0, 23.0, 25.0, 26.0, 29.0, - ....: 32.0, 35.0, 40.0, 42.0, 44.0, 46.0, 49.0, 51.0, 62.0, 66.0, 67.0, 71.0, 73.0, 80.0, 86.0, 88.0, - ....: 90.0, 100.0, 102.0, 106.0, 108.0, 112.0, 114.0, 117.0, 120.0, 123.0, 126.0, 129.0, 132.0, 135.0, - ....: 137.0, 140.0, 142.0, 144.0, 147.0, 149.0, 151.0, 157.0, 162.0, 167.0, 169.0, 172.0, 175.0, 176.0, - ....: 181.0, 183.0, 185.0, 190.0, 193.0, 197.0, 199.0, 204.0, 206.0, 211.0, 213.0, 218.0] - -Simple estimation ------------------ - -First ,we are going to fit a standard **SEIR** model to the data. Details of the models can be found in :mod:`common_models` Defining the model as usual with some random guess on what the parameters might be, here, we choose the values to be the mid point of our feasible region (defined by our box constraints later) - -.. ipython:: - - In [1]: from pygom import SquareLoss, common_models - - In [1]: import numpy, scipy.optimize - - In [1]: import matplotlib.pyplot as plt - - In [1]: theta = numpy.array([5.0, 5.0, 5.0]) - - In [2]: ode = common_models.SEIR(theta) - - In [3]: population = 1175e4 - - In [4]: y = numpy.reshape(numpy.append(numpy.array(yCase), numpy.array(yDeath)), (len(yCase),2), 'F')/population - - In [5]: x0 = [1., 0., 49.0/population, 29.0/population] - - In [6]: t0 = t[0] - - In [7]: objLegrand = SquareLoss(theta, ode, x0, t0, t[1::], y[1::,:], ['I','R'], numpy.sqrt([population]*2)) - -Then we optimize, first, assuming that the initial conditions are accurate. Some relatively large bounds are used for this particular problem. - -.. ipython:: - - In [8]: boxBounds = [ (0.0,10.0), (0.0,10.0), (0.0,10.0) ] - - In [9]: res = scipy.optimize.minimize(fun=objLegrand.cost, - ...: jac=objLegrand.sensitivity, - ...: x0=theta, - ...: bounds=boxBounds, - ...: method='l-bfgs-b') - - In [10]: print(res) - - In [11]: f = plt.figure() - - @savefig ebola_seir_straight.png - In [12]: objLegrand.plot() - - In [13]: plt.close() - -We can see from our visual confirmation that the estimated parameters are not exactly ideal. This is confirmed by the information returned from the :func:`scipy.optimize.minimize` routine, and probably caused by the poor starting point. An attempt to find a more suitable value can be done by some form of parameter space exploration. Given that the evaluation of the objective function is not expensive here, we have plenty of options to choose from. To reduce the number of packages required to build this documentation, routines from :mod:`scipy.optimize` remains our preferred option. - -Improved initial guess ----------------------- - -.. ipython:: - - In [8]: resDE = scipy.optimize.differential_evolution(objLegrand.cost, bounds=boxBounds, polish=False, seed=20921391) - - In [9]: print(objLegrand.sensitivity(resDE['x'])) - - In [10]: f = plt.figure() - - @savefig ebola_seir_de.png - In [11]: objLegrand.plot() - - In [12]: plt.close() - -Looking at the output of the estimates (below this paragraph), we can see our inference on Ebola is wrong when compared to the *known* values (from field observation) even though the graphs looks *``reasonable"*. Namely, :math:`\gamma^{-1}` the third element in the vector below, our time from infectious to death, is within the expected range but :math:`\alpha^{-1}` (second element), the incubation period, is a lot higher than expected. - -.. ipython:: - - In [1]: 1/resDE['x'] - -Multimodal surface ------------------- - -A reason for this type of behavior is that we simply lack the information/data to make proper inference. Without data on the state :math:`E`, the parameters :math:`\beta,\alpha` for the two states :math:`I` and :math:`E` are dependent only on observations on :math:`I`. Hence, some other random combination of :math:`\beta,\alpha` that is capable of generating realization close to observations in :math:`I` is feasible. In such cases, the only requirement is that there exist some :math:`\gamma` in the feasible region that can compensate for the ill suited :math:`\beta,\alpha`. For example, we know (obtained elsewhere and not shown here) that there is another set of parameters capable of generating a similar looking curves as before. Note the reversal of magnitude in :math:`\beta` and :math:`\alpha`. - -.. ipython:: - - In [11]: objLegrand.cost([3.26106524e+00, 2.24798702e-04, 1.23660721e-02]) - - In [12]: ## objLegrand.cost([ 0.02701867, 9.00004776, 0.01031861]) # similar graph - - @savefig ebola_seir_prior.png - In [13]: objLegrand.plot() - - In [14]: plt.close() - -With initial values as parameters ---------------------------------- - -Obviously, the assumption that the whole population being susceptible is an overestimate. We now try to estimate the initial conditions of the ode as well. Given previous estimates of the parameters :math:`\hat{\beta}, \hat{\alpha}, \hat{\gamma}` it is appropriate to start our initial guess there. - -Furthermore, given that we now estimate the initial values for all the states, we can use the first time point as our observation. So our time begins at :math:`t = -1` where our observations include the previous initial condition, i.e. 49 and 29 for the number of cases and death at :math:`t = 0` respectively. The following code block demonstrates how we would do that; feel free to try it out yourself to see the much improved result. - -.. ipython:: - :verbatim: - - In [1]: thetaIV = theta.tolist() + x0 - - In [2]: thetaIV[3] -= 1e-8 # to make sure that the initial guess satisfy the constraints - - In [3]: boxBoundsIV = boxBounds + [(0.,1.), (0.,1.), (0.,1.), (0.,1.)] - - In [4]: objLegrand = SquareLoss(theta, ode, x0, -1, t, y, ['I','R'], numpy.sqrt([population]*2)) - - In [5]: resDEIV = scipy.optimize.differential_evolution(objLegrand.costIV, bounds=boxBoundsIV, polish=False, seed=20921391) - - In [6]: print(resDEIV) - - In [7]: f = plt.figure() - - In [8]: objLegrand.plot() - - In [9]: plt.close() - - -Legrand Ebola SEIHFR Model -========================== - -Next, we demonstrate the estimation on a model that is widely used in the recent Ebola outbreak in west Africa. Again, the model has been defined in :mod:`.common_models` already. - -.. ipython:: - - In [1]: ode = common_models.Legrand_Ebola_SEIHFR() - - In [27]: # initial guess from the paper that studied the outbreak in Congo - - In [28]: theta = numpy.array([0.588,0.794,7.653, ### the beta - ....: 10.0,9.6,5.0,2.0, ### the omega - ....: 7.0,0.81,0.80, ### alpha, delta, theta - ....: 100.,1.0]) ### kappa,intervention time - - In [29]: # initial conditions, note that we have a 0.0 at the end because the model is a non-automonous ode which we have converted the time component out - - In [30]: x0 = numpy.array([population, 0.0, 49.0, 0.0, 0.0, 29.0, 0.0])/population - - In [30]: ode.parameters = theta - - In [31]: ode.initial_values = (x0, t[0]) - - In [32]: objLegrand = SquareLoss(theta, ode, x0, t[0], t[1::], y[1::,:], ['I','R'], numpy.sqrt([population]*2)) - -Now, it is important to set additional constraints accurately because a simply box constraint is much larger than the feasible set. Namely, :math:`\omega_{I}, \omega_{D}` are the time taken from onset until end of infectious/death, which has to be bigger than :math:`\omega_{H}`, onset to hospitalization given the nature of the disease. Therefore, we create extra inequality constraints in addition to the box constraints - -.. ipython:: - - In [549]: boxBounds = [ - .....: (0.001, 100.), # \beta_I - .....: (0.001, 100.), # \beta_H - .....: (0.001, 100.), # \beta_F - .....: (0.001, 100.), # \omega_I - .....: (0.001, 100.), # \omega_D - .....: (0.001, 100.), # \omega_H - .....: (0.001, 100.), # \omega_F - .....: (0.001, 100.), # \alpha^{-1} - .....: (0.0001, 1.), # \delta - .....: (0.0001, 1.), # \theta - .....: (0.001, 1000.), # \kappa - .....: (0.,218.) # intervention tine - .....: ] - - In [550]: cons = ({'type': 'ineq', 'fun' : lambda x: numpy.array([x[3]-x[5], x[4]-x[5]])}) - -We can now try to find the optimal values, but because this is a difficult problem that can take a very long time without guarantee on the quality of solution - -.. ipython:: - :okexcept: - :okwarning: - - In [213]: res = scipy.optimize.minimize(fun=objLegrand.cost, - .....: jac=objLegrand.sensitivity, - .....: x0=theta, - .....: constraints=cons, - .....: bounds=boxBounds, - .....: method='SLSQP') - - In [214]: print(res) - - In [215]: f = plt.figure() - - @savefig ebola_legrand_runtime.png - In [216]: objLegrand.plot() - - In [217]: plt.close() - -Evidently, the estimated parameters are very much unrealistic given that a lot of them are near the boundaries. It is also known from other sources that some of the epidemiology properties of Ebola, with incubation period of around 2 weeks and a mortality rate of around 80 percent. - -As the estimate does not appear to provide anything sensible, we also provide a set of values previously obtained (that looks semi-reasonable) here plot the epidemic curve with the observations layered on top - -.. ipython:: - :okexcept: - :okwarning: - - In [1]: theta = numpy.array([3.96915071e-02, 1.72302620e+01, 1.99749990e+01, - ...: 2.67759445e+01, 4.99999990e+01, 5.56122691e+00, - ...: 4.99999990e+01, 8.51599523e+00, 9.99999000e-01, - ...: 1.00000000e-06, 3.85807562e+00, 1.88385318e+00]) - - In [2]: print(objLegrand.cost(theta)) - - In [2]: solution = ode.integrate(t[1::]) - - In [3]: f, axarr = plt.subplots(2,3) - - In [4]: axarr[0,0].plot(t, solution[:,0]); - - In [5]: axarr[0,0].set_title('Susceptible'); - - In [6]: axarr[0,1].plot(t, solution[:,1]); - - In [7]: axarr[0,1].set_title('Exposed'); - - In [8]: axarr[0,2].plot(t, solution[:,2]); - - In [9]: axarr[0,2].plot(t, y[:,0], 'r'); - - In [10]: axarr[0,2].set_title('Infectious'); - - In [11]: axarr[1,0].plot(t, solution[:,3]); - - In [12]: axarr[1,0].set_title('Hospitalised'); - - In [13]: axarr[1,1].plot(t, solution[:,4]); - - In [14]: axarr[1,1].set_title('Awaiting Burial'); - - In [15]: axarr[1,2].plot(t, solution[:,5]); - - In [16]: axarr[1,2].plot(t, y[:,1], 'r'); - - In [17]: axarr[1,2].set_title('Removed'); - - In [18]: f.text(0.5, 0.04, 'Days from outbreak', ha='center'); - - In [19]: f.text(0.01, 0.5, 'Population', va='center', rotation='vertical'); - - In [20]: f.tight_layout(); - - @savefig ebola_seihfr_straight_prior.png - In [21]: plt.show() - - In [22]: plt.close() - - +.. _estimate2: + +******************************* +Example: Parameter Estimation 2 +******************************* + +Continuing from the :ref:`estimate1`, we show why estimating the parameters for ode's are hard. This is especially true if there is a lack of data or when there are too much flexibility in the model. Note that for reproducibility purposes, only deterministic models are used here and a fixed seed whenever a stochastic algorithm is needed. + +Standard SEIR model +=================== + +We demonstrate the estimation on the recent Ebola outbreak in West Africa. We use the number of deaths in Guinea and its corresponding time the data was recorded. These data are publicly available and they can be obtained easily on the internet such as https://github.com/cmrivers/ebola. It is stated out here for simplicity. + +.. ipython:: + + In [34]: # the number of deaths and cases in Guinea + + In [35]: yDeath = [29.0, 59.0, 60.0, 62.0, 66.0, 70.0, 70.0, 80.0, 83.0, 86.0, 95.0, 101.0, 106.0, 108.0, + ....: 122.0, 129.0, 136.0, 141.0, 143.0, 149.0, 155.0, 157.0, 158.0, 157.0, 171.0, 174.0, + ....: 186.0, 193.0, 208.0, 215.0, 226.0, 264.0, 267.0, 270.0, 303.0, 305.0, 307.0, 309.0, + ....: 304.0, 310.0, 310.0, 314.0, 319.0, 339.0, 346.0, 358.0, 363.0, 367.0, 373.0, 377.0, + ....: 380.0, 394.0, 396.0, 406.0, 430.0, 494.0, 517.0, 557.0, 568.0, 595.0, 601.0, 632.0, + ....: 635.0, 648.0, 710.0, 739.0, 768.0, 778.0, 843.0, 862.0, 904.0, 926.0, 997.0] + + In [35]: yCase = [49.0, 86.0, 86.0, 86.0, 103.0, 112.0, 112.0, 122.0, 127.0, 143.0, 151.0, 158.0, + ....: 159.0, 168.0, 197.0, 203.0, 208.0, 218.0, 224.0, 226.0, 231.0, 235.0, 236.0, + ....: 233.0, 248.0, 258.0, 281.0, 291.0, 328.0, 344.0, 351.0, 398.0, 390.0, 390.0, + ....: 413.0, 412.0, 408.0, 409.0, 406.0, 411.0, 410.0, 415.0, 427.0, 460.0, 472.0, + ....: 485.0, 495.0, 495.0, 506.0, 510.0, 519.0, 543.0, 579.0, 607.0, 648.0, 771.0, + ....: 812.0, 861.0, 899.0, 936.0, 942.0, 1008.0, 1022.0, 1074.0, 1157.0, 1199.0, 1298.0, + ....: 1350.0, 1472.0, 1519.0, 1540.0, 1553.0, 1906.0] + + In [36]: # the corresponding time + + In [37]: t = [0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 13.0, 16.0, 18.0, 20.0, 23.0, 25.0, 26.0, 29.0, + ....: 32.0, 35.0, 40.0, 42.0, 44.0, 46.0, 49.0, 51.0, 62.0, 66.0, 67.0, 71.0, 73.0, 80.0, 86.0, 88.0, + ....: 90.0, 100.0, 102.0, 106.0, 108.0, 112.0, 114.0, 117.0, 120.0, 123.0, 126.0, 129.0, 132.0, 135.0, + ....: 137.0, 140.0, 142.0, 144.0, 147.0, 149.0, 151.0, 157.0, 162.0, 167.0, 169.0, 172.0, 175.0, 176.0, + ....: 181.0, 183.0, 185.0, 190.0, 193.0, 197.0, 199.0, 204.0, 206.0, 211.0, 213.0, 218.0] + +Simple estimation +----------------- + +First ,we are going to fit a standard **SEIR** model to the data. Details of the models can be found in :mod:`common_models` Defining the model as usual with some random guess on what the parameters might be, here, we choose the values to be the mid point of our feasible region (defined by our box constraints later) + +.. ipython:: + + In [1]: from pygom import SquareLoss, common_models + + In [1]: import numpy, scipy.optimize + + In [1]: import matplotlib.pyplot as plt + + In [1]: theta = numpy.array([5.0, 5.0, 5.0]) + + In [2]: ode = common_models.SEIR(theta) + + In [3]: population = 1175e4 + + In [4]: y = numpy.reshape(numpy.append(numpy.array(yCase), numpy.array(yDeath)), (len(yCase),2), 'F')/population + + In [5]: x0 = [1., 0., 49.0/population, 29.0/population] + + In [6]: t0 = t[0] + + In [7]: objLegrand = SquareLoss(theta, ode, x0, t0, t[1::], y[1::,:], ['I','R'], numpy.sqrt([population]*2)) + +Then we optimize, first, assuming that the initial conditions are accurate. Some relatively large bounds are used for this particular problem. + +.. ipython:: + + In [8]: boxBounds = [ (0.0,10.0), (0.0,10.0), (0.0,10.0) ] + + In [9]: res = scipy.optimize.minimize(fun=objLegrand.cost, + ...: jac=objLegrand.sensitivity, + ...: x0=theta, + ...: bounds=boxBounds, + ...: method='l-bfgs-b') + + In [10]: print(res) + + In [11]: f = plt.figure() + + @savefig ebola_seir_straight.png + In [12]: objLegrand.plot() + + In [13]: plt.close() + +We can see from our visual confirmation that the estimated parameters are not exactly ideal. This is confirmed by the information returned from the :func:`scipy.optimize.minimize` routine, and probably caused by the poor starting point. An attempt to find a more suitable value can be done by some form of parameter space exploration. Given that the evaluation of the objective function is not expensive here, we have plenty of options to choose from. To reduce the number of packages required to build this documentation, routines from :mod:`scipy.optimize` remains our preferred option. + +Improved initial guess +---------------------- + +.. ipython:: + + In [8]: resDE = scipy.optimize.differential_evolution(objLegrand.cost, bounds=boxBounds, polish=False, seed=20921391) + + In [9]: print(objLegrand.sensitivity(resDE['x'])) + + In [10]: f = plt.figure() + + @savefig ebola_seir_de.png + In [11]: objLegrand.plot() + + In [12]: plt.close() + +Looking at the output of the estimates (below this paragraph), we can see our inference on Ebola is wrong when compared to the *known* values (from field observation) even though the graphs looks *``reasonable"*. Namely, :math:`\gamma^{-1}` the third element in the vector below, our time from infectious to death, is within the expected range but :math:`\alpha^{-1}` (second element), the incubation period, is a lot higher than expected. + +.. ipython:: + + In [1]: 1/resDE['x'] + +Multimodal surface +------------------ + +A reason for this type of behavior is that we simply lack the information/data to make proper inference. Without data on the state :math:`E`, the parameters :math:`\beta,\alpha` for the two states :math:`I` and :math:`E` are dependent only on observations on :math:`I`. Hence, some other random combination of :math:`\beta,\alpha` that is capable of generating realization close to observations in :math:`I` is feasible. In such cases, the only requirement is that there exist some :math:`\gamma` in the feasible region that can compensate for the ill suited :math:`\beta,\alpha`. For example, we know (obtained elsewhere and not shown here) that there is another set of parameters capable of generating a similar looking curves as before. Note the reversal of magnitude in :math:`\beta` and :math:`\alpha`. + +.. ipython:: + + In [11]: objLegrand.cost([3.26106524e+00, 2.24798702e-04, 1.23660721e-02]) + + In [12]: ## objLegrand.cost([ 0.02701867, 9.00004776, 0.01031861]) # similar graph + + @savefig ebola_seir_prior.png + In [13]: objLegrand.plot() + + In [14]: plt.close() + +With initial values as parameters +--------------------------------- + +Obviously, the assumption that the whole population being susceptible is an overestimate. We now try to estimate the initial conditions of the ode as well. Given previous estimates of the parameters :math:`\hat{\beta}, \hat{\alpha}, \hat{\gamma}` it is appropriate to start our initial guess there. + +Furthermore, given that we now estimate the initial values for all the states, we can use the first time point as our observation. So our time begins at :math:`t = -1` where our observations include the previous initial condition, i.e. 49 and 29 for the number of cases and death at :math:`t = 0` respectively. The following code block demonstrates how we would do that; feel free to try it out yourself to see the much improved result. + +.. ipython:: + :verbatim: + + In [1]: thetaIV = theta.tolist() + x0 + + In [2]: thetaIV[3] -= 1e-8 # to make sure that the initial guess satisfy the constraints + + In [3]: boxBoundsIV = boxBounds + [(0.,1.), (0.,1.), (0.,1.), (0.,1.)] + + In [4]: objLegrand = SquareLoss(theta, ode, x0, -1, t, y, ['I','R'], numpy.sqrt([population]*2)) + + In [5]: resDEIV = scipy.optimize.differential_evolution(objLegrand.costIV, bounds=boxBoundsIV, polish=False, seed=20921391) + + In [6]: print(resDEIV) + + In [7]: f = plt.figure() + + In [8]: objLegrand.plot() + + In [9]: plt.close() + + +Legrand Ebola SEIHFR Model +========================== + +Next, we demonstrate the estimation on a model that is widely used in the recent Ebola outbreak in west Africa. Again, the model has been defined in :mod:`.common_models` already. + +.. ipython:: + + In [1]: ode = common_models.Legrand_Ebola_SEIHFR() + + In [27]: # initial guess from the paper that studied the outbreak in Congo + + In [28]: theta = numpy.array([0.588,0.794,7.653, ### the beta + ....: 10.0,9.6,5.0,2.0, ### the omega + ....: 7.0,0.81,0.80, ### alpha, delta, theta + ....: 100.,1.0]) ### kappa,intervention time + + In [29]: # initial conditions, note that we have a 0.0 at the end because the model is a non-automonous ode which we have converted the time component out + + In [30]: x0 = numpy.array([population, 0.0, 49.0, 0.0, 0.0, 29.0, 0.0])/population + + In [30]: ode.parameters = theta + + In [31]: ode.initial_values = (x0, t[0]) + + In [32]: objLegrand = SquareLoss(theta, ode, x0, t[0], t[1::], y[1::,:], ['I','R'], numpy.sqrt([population]*2)) + +Now, it is important to set additional constraints accurately because a simply box constraint is much larger than the feasible set. Namely, :math:`\omega_{I}, \omega_{D}` are the time taken from onset until end of infectious/death, which has to be bigger than :math:`\omega_{H}`, onset to hospitalization given the nature of the disease. Therefore, we create extra inequality constraints in addition to the box constraints + +.. ipython:: + + In [549]: boxBounds = [ + .....: (0.001, 100.), # \beta_I + .....: (0.001, 100.), # \beta_H + .....: (0.001, 100.), # \beta_F + .....: (0.001, 100.), # \omega_I + .....: (0.001, 100.), # \omega_D + .....: (0.001, 100.), # \omega_H + .....: (0.001, 100.), # \omega_F + .....: (0.001, 100.), # \alpha^{-1} + .....: (0.0001, 1.), # \delta + .....: (0.0001, 1.), # \theta + .....: (0.001, 1000.), # \kappa + .....: (0.,218.) # intervention tine + .....: ] + + In [550]: cons = ({'type': 'ineq', 'fun' : lambda x: numpy.array([x[3]-x[5], x[4]-x[5]])}) + +We can now try to find the optimal values, but because this is a difficult problem that can take a very long time without guarantee on the quality of solution + +.. ipython:: + :okexcept: + :okwarning: + + In [213]: res = scipy.optimize.minimize(fun=objLegrand.cost, + .....: jac=objLegrand.sensitivity, + .....: x0=theta, + .....: constraints=cons, + .....: bounds=boxBounds, + .....: method='SLSQP') + + In [214]: print(res) + + In [215]: f = plt.figure() + + @savefig ebola_legrand_runtime.png + In [216]: objLegrand.plot() + + In [217]: plt.close() + +Evidently, the estimated parameters are very much unrealistic given that a lot of them are near the boundaries. It is also known from other sources that some of the epidemiology properties of Ebola, with incubation period of around 2 weeks and a mortality rate of around 80 percent. + +As the estimate does not appear to provide anything sensible, we also provide a set of values previously obtained (that looks semi-reasonable) here plot the epidemic curve with the observations layered on top + +.. ipython:: + :okexcept: + :okwarning: + + In [1]: theta = numpy.array([3.96915071e-02, 1.72302620e+01, 1.99749990e+01, + ...: 2.67759445e+01, 4.99999990e+01, 5.56122691e+00, + ...: 4.99999990e+01, 8.51599523e+00, 9.99999000e-01, + ...: 1.00000000e-06, 3.85807562e+00, 1.88385318e+00]) + + In [2]: print(objLegrand.cost(theta)) + + In [2]: solution = ode.integrate(t[1::]) + + In [3]: f, axarr = plt.subplots(2,3) + + In [4]: axarr[0,0].plot(t, solution[:,0]); + + In [5]: axarr[0,0].set_title('Susceptible'); + + In [6]: axarr[0,1].plot(t, solution[:,1]); + + In [7]: axarr[0,1].set_title('Exposed'); + + In [8]: axarr[0,2].plot(t, solution[:,2]); + + In [9]: axarr[0,2].plot(t, y[:,0], 'r'); + + In [10]: axarr[0,2].set_title('Infectious'); + + In [11]: axarr[1,0].plot(t, solution[:,3]); + + In [12]: axarr[1,0].set_title('Hospitalised'); + + In [13]: axarr[1,1].plot(t, solution[:,4]); + + In [14]: axarr[1,1].set_title('Awaiting Burial'); + + In [15]: axarr[1,2].plot(t, solution[:,5]); + + In [16]: axarr[1,2].plot(t, y[:,1], 'r'); + + In [17]: axarr[1,2].set_title('Removed'); + + In [18]: f.text(0.5, 0.04, 'Days from outbreak', ha='center'); + + In [19]: f.text(0.01, 0.5, 'Population', va='center', rotation='vertical'); + + In [20]: f.tight_layout(); + + @savefig ebola_seihfr_straight_prior.png + In [21]: plt.show() + + In [22]: plt.close() + + diff --git a/doc/source/faq.rst b/doc/doc_to_sort/faq.rst similarity index 98% rename from doc/source/faq.rst rename to doc/doc_to_sort/faq.rst index f14c71e4..a57c2675 100644 --- a/doc/source/faq.rst +++ b/doc/doc_to_sort/faq.rst @@ -1,60 +1,60 @@ -.. _faq: - -************************ -Frequent asked questions -************************ - -Code runs slowly -================ - -This is because the package is not optimized for speed. Although the some of the main functions are lambdified using :mod:`sympy` or compiled against :mod:`cython` when available, there are many more optimization that can be done. One example is the lines: - -.. python: - - J = self.Jacobian(state,t) - G = self.Grad(state,t) - A = numpy.dot(J,S) + G - -in :func:`.DeterministicOde.evalSensitivity`. The first two operations can be inlined into the third and the third line itself can be rewritten as: - -.. python: - - G += numpy.dot(J,S) - -and save the explicit copy operation by :mod:`numpy` when making A. If desired, we could have also made used of the :mod:`numexpr` package that provides further speed up on elementwise operations in place of numpy. - -Why not compile the numeric computation form sympy against Theano -================================================================= - -Setup of the package has been simplified as much as possible. If you look closely enough, you will realize that the current code generation only uses :mod:`cython` and not :mod:`f2py`. This is because we are not prepared to do all the system checks, i.e. does a fortran compiler exist, is gcc installed, was python built as a shared library etc. We are very much aware of the benefit, especially considering the possibility of GPU computation in :mod:`theano`. - -Why not use mpmath library throughout? -====================================== - -This is because we have a fair number of operations that depends on :mod:`scipy`. Obviously, we can solve ode using :mod:`mpmath` and do standard linear algebra. Unfortunately, optimization and statistics packages and routine are mostly based on :mod:`numpy`. - -Computing the gradient using :class:`.SquareLoss` is slow -========================================================= - -It will always be slow on the first operation. This is due to the design where the initialization of the class is fast and only find derivative information/compile function during runtime. After the first calculation, things should be significantly faster. - -**Why some of my code is not a fortran object?** - -When we detec either a :math:`\exp` or a :math:`\log` in the equations, we automatically force the compile to use mpmath to ensure that we obtain the highest precision. To turn this on/off will be considered as a feature in the future. - -Can you not convert a non-autonumous system to an autonomous system for me automatically -======================================================================================== - -Although we can do that, it is not, and will not be implemented. This is to ensure that the end user such as yourself are fully aware of the equations being defined. - -Getting the sensitivities from :class:`.SquareLoss` did not get a speed up when I used a restricted set of parameters -===================================================================================================================== - -This is because we currently evaluate the full set of sensitivities before extracting them out. Speeding this up for a restrictive set is being considered. A main reason that stopped us from implementing is that we find the symbolic gradient of the ode before compiling it. Which means that one function call to the compiled file will return the full set of sensitivities and we would only be extracting the appropriate elements from the matrix. This only amounts to a small speed up. The best method would be to compile only the necessary elements of the gradient matrix, but this would require much more work both within the code, and later on when variables are being added/deleted as all these compilation are perfromed in runtime. - -Why do not have the option to obtain gradient via complex differencing -====================================================================== - -It is currently not implemented. Feature under consideration. - - +.. _faq: + +************************ +Frequent asked questions +************************ + +Code runs slowly +================ + +This is because the package is not optimized for speed. Although the some of the main functions are lambdified using :mod:`sympy` or compiled against :mod:`cython` when available, there are many more optimization that can be done. One example is the lines: + +.. python: + + J = self.Jacobian(state,t) + G = self.Grad(state,t) + A = numpy.dot(J,S) + G + +in :func:`.DeterministicOde.evalSensitivity`. The first two operations can be inlined into the third and the third line itself can be rewritten as: + +.. python: + + G += numpy.dot(J,S) + +and save the explicit copy operation by :mod:`numpy` when making A. If desired, we could have also made used of the :mod:`numexpr` package that provides further speed up on elementwise operations in place of numpy. + +Why not compile the numeric computation form sympy against Theano +================================================================= + +Setup of the package has been simplified as much as possible. If you look closely enough, you will realize that the current code generation only uses :mod:`cython` and not :mod:`f2py`. This is because we are not prepared to do all the system checks, i.e. does a fortran compiler exist, is gcc installed, was python built as a shared library etc. We are very much aware of the benefit, especially considering the possibility of GPU computation in :mod:`theano`. + +Why not use mpmath library throughout? +====================================== + +This is because we have a fair number of operations that depends on :mod:`scipy`. Obviously, we can solve ode using :mod:`mpmath` and do standard linear algebra. Unfortunately, optimization and statistics packages and routine are mostly based on :mod:`numpy`. + +Computing the gradient using :class:`.SquareLoss` is slow +========================================================= + +It will always be slow on the first operation. This is due to the design where the initialization of the class is fast and only find derivative information/compile function during runtime. After the first calculation, things should be significantly faster. + +**Why some of my code is not a fortran object?** + +When we detec either a :math:`\exp` or a :math:`\log` in the equations, we automatically force the compile to use mpmath to ensure that we obtain the highest precision. To turn this on/off will be considered as a feature in the future. + +Can you not convert a non-autonumous system to an autonomous system for me automatically +======================================================================================== + +Although we can do that, it is not, and will not be implemented. This is to ensure that the end user such as yourself are fully aware of the equations being defined. + +Getting the sensitivities from :class:`.SquareLoss` did not get a speed up when I used a restricted set of parameters +===================================================================================================================== + +This is because we currently evaluate the full set of sensitivities before extracting them out. Speeding this up for a restrictive set is being considered. A main reason that stopped us from implementing is that we find the symbolic gradient of the ode before compiling it. Which means that one function call to the compiled file will return the full set of sensitivities and we would only be extracting the appropriate elements from the matrix. This only amounts to a small speed up. The best method would be to compile only the necessary elements of the gradient matrix, but this would require much more work both within the code, and later on when variables are being added/deleted as all these compilation are perfromed in runtime. + +Why do not have the option to obtain gradient via complex differencing +====================================================================== + +It is currently not implemented. Feature under consideration. + + diff --git a/doc/source/fh.rst b/doc/doc_to_sort/fh.rst similarity index 100% rename from doc/source/fh.rst rename to doc/doc_to_sort/fh.rst diff --git a/doc/source/gradient.rst b/doc/doc_to_sort/gradient.rst similarity index 100% rename from doc/source/gradient.rst rename to doc/doc_to_sort/gradient.rst diff --git a/doc/source/initialGuess.rst b/doc/doc_to_sort/initialGuess.rst similarity index 100% rename from doc/source/initialGuess.rst rename to doc/doc_to_sort/initialGuess.rst diff --git a/doc/source/mod/common_models.rst b/doc/doc_to_sort/mod/common_models.rst similarity index 100% rename from doc/source/mod/common_models.rst rename to doc/doc_to_sort/mod/common_models.rst diff --git a/doc/source/mod/confidence_interval.rst b/doc/doc_to_sort/mod/confidence_interval.rst similarity index 100% rename from doc/source/mod/confidence_interval.rst rename to doc/doc_to_sort/mod/confidence_interval.rst diff --git a/doc/source/mod/deterministic.rst b/doc/doc_to_sort/mod/deterministic.rst similarity index 100% rename from doc/source/mod/deterministic.rst rename to doc/doc_to_sort/mod/deterministic.rst diff --git a/doc/source/mod/epi_analysis.rst b/doc/doc_to_sort/mod/epi_analysis.rst similarity index 100% rename from doc/source/mod/epi_analysis.rst rename to doc/doc_to_sort/mod/epi_analysis.rst diff --git a/doc/source/mod/get_init.rst b/doc/doc_to_sort/mod/get_init.rst similarity index 100% rename from doc/source/mod/get_init.rst rename to doc/doc_to_sort/mod/get_init.rst diff --git a/doc/source/mod/index.rst b/doc/doc_to_sort/mod/index.rst similarity index 100% rename from doc/source/mod/index.rst rename to doc/doc_to_sort/mod/index.rst diff --git a/doc/source/mod/losstype.rst b/doc/doc_to_sort/mod/losstype.rst similarity index 100% rename from doc/source/mod/losstype.rst rename to doc/doc_to_sort/mod/losstype.rst diff --git a/doc/source/mod/odeloss.rst b/doc/doc_to_sort/mod/odeloss.rst similarity index 100% rename from doc/source/mod/odeloss.rst rename to doc/doc_to_sort/mod/odeloss.rst diff --git a/doc/source/mod/odeutils.rst b/doc/doc_to_sort/mod/odeutils.rst similarity index 100% rename from doc/source/mod/odeutils.rst rename to doc/doc_to_sort/mod/odeutils.rst diff --git a/doc/source/mod/simulate.rst b/doc/doc_to_sort/mod/simulate.rst similarity index 100% rename from doc/source/mod/simulate.rst rename to doc/doc_to_sort/mod/simulate.rst diff --git a/doc/source/mod/transition.rst b/doc/doc_to_sort/mod/transition.rst similarity index 100% rename from doc/source/mod/transition.rst rename to doc/doc_to_sort/mod/transition.rst diff --git a/doc/source/profile.rst b/doc/doc_to_sort/profile.rst similarity index 100% rename from doc/source/profile.rst rename to doc/doc_to_sort/profile.rst diff --git a/doc/source/ref.rst b/doc/doc_to_sort/ref.rst similarity index 100% rename from doc/source/ref.rst rename to doc/doc_to_sort/ref.rst diff --git a/doc/source/stochastic.rst b/doc/doc_to_sort/stochastic.rst similarity index 100% rename from doc/source/stochastic.rst rename to doc/doc_to_sort/stochastic.rst diff --git a/doc/source/transition.rst b/doc/doc_to_sort/transition.rst similarity index 100% rename from doc/source/transition.rst rename to doc/doc_to_sort/transition.rst diff --git a/doc/source/unroll/unrollBD.rst b/doc/doc_to_sort/unroll/unrollBD.rst similarity index 100% rename from doc/source/unroll/unrollBD.rst rename to doc/doc_to_sort/unroll/unrollBD.rst diff --git a/doc/source/unroll/unrollHard.rst b/doc/doc_to_sort/unroll/unrollHard.rst similarity index 100% rename from doc/source/unroll/unrollHard.rst rename to doc/doc_to_sort/unroll/unrollHard.rst diff --git a/doc/source/unroll/unrollSimple.rst b/doc/doc_to_sort/unroll/unrollSimple.rst similarity index 100% rename from doc/source/unroll/unrollSimple.rst rename to doc/doc_to_sort/unroll/unrollSimple.rst diff --git a/doc/source/unrollOde.rst b/doc/doc_to_sort/unrollOde.rst similarity index 100% rename from doc/source/unrollOde.rst rename to doc/doc_to_sort/unrollOde.rst From 272a31f63ff4839edd574d38a4f7291f34bc695c Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 13:09:06 +0000 Subject: [PATCH 034/188] Ammended intro text --- doc/source/index.rst | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/doc/source/index.rst b/doc/source/index.rst index e9560e51..e4625e13 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -2,11 +2,16 @@ Welcome to the documentation of pygom ##################################### -pygom is a package that aims to facilitate the application of ordinary differential equations (ODEs) in the real world, with a focus in epidemiology. This package helps the end user define their ODE system in an intuitive manner and provides convenience functions - making use of various algebraic and numerical libraries in the backend - that can be used in a straight forward fashion. +PyGOM (Python Generic ODE Model) is a Python package that aims to facilitate the application of ordinary differential equations (ODEs) in the real world, +with a focus in epidemiology. +This package helps the user define their ODE system in an intuitive manner and provides convenience functions - +making use of various algebraic and numerical libraries in the backend - that can be used in a straight forward fashion. This is an open source project hosted on `Github `_. -Insert intro text +A manuscript containing a shortened motivation and use is hosted on `arxXiv `_. + +# // TODO Insert intro text ################## User Documentation From b7b0324519b356827afa902422eff89176670299 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 13:15:49 +0000 Subject: [PATCH 035/188] ammend instructions to check documentation build, ignore tests --- .travis.yml | 25 +++++++------------------ 1 file changed, 7 insertions(+), 18 deletions(-) diff --git a/.travis.yml b/.travis.yml index 45ba3248..30f7704c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,5 +1,10 @@ # specify the language and which version language: python + +branches: + only: + - install-fix-docs + python: - 3.5 - 3.6 @@ -8,29 +13,13 @@ python: before_install: - pip install -U pip - - pip install codecov install: - pip install -r requirements.txt - python setup.py build - python setup.py install + - pip install doc/requirements.txt # Runs test with coverage script: - - coverage run setup.py test - -# Push the result to -after_success: - - codecov - -# deploy to PyPI -deploy: - provider: pypi - twine_version: 1.13.0 - skip_existing: true - user: twomagpi - password: - secure: 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 - on: - tags: true - python: 3.7 \ No newline at end of file + - cd doc; make html linkcheck \ No newline at end of file From fef6f1295d8f01ab762c2b6c37ef6b7320bd9d7f Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 13:26:44 +0000 Subject: [PATCH 036/188] CI for documentation build, removed tests - this branch only --- .github/workflows/main.yml | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a59d3801..e0f26a73 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,15 +1,11 @@ -name: pygom +name: pygom-docbuild -on: +on: push: branches: - - master - - dev - - feature/* - - bugfix/* - pull_request: - branches: - - master + only: + - install-fix-docs + jobs: build: @@ -72,15 +68,19 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt + pip install -r doc/requirements.txt - name: Build and install package for c files run: | python setup.py build python setup.py install + - name: Fix matplotlib backend for MacOS if: startsWith(runner.os, 'macOS') run: | mkdir ~/.matplotlib echo "backend: TkAgg" >> ~/.matplotlib/matplotlibrc - - name: Test and coverage - run: python setup.py test + - name: Build documentation + run: | + cd doc + make html linkcheck \ No newline at end of file From 524afd42c36a8f76d05cededc0a459864686c8af Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 13:29:09 +0000 Subject: [PATCH 037/188] remove branch command --- .github/workflows/main.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index e0f26a73..84645955 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -3,8 +3,7 @@ name: pygom-docbuild on: push: branches: - only: - - install-fix-docs + - install-fix-docs jobs: From f2db3e6d0cfb103bc5b0c2241cb2bdf5b9ab99ef Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 13:34:41 +0000 Subject: [PATCH 038/188] update python versions --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 84645955..a7f8ab80 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -12,7 +12,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.5, 3.6, 3.7, 3.8] + python-version: [3.6, 3.7, 3.8, 3.9, 3.10] steps: - uses: actions/checkout@v2 From b7cd1d4236f3ec79b9a7db757f33ac7fb36c311c Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 13:35:38 +0000 Subject: [PATCH 039/188] update python versions --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a7f8ab80..a1a9357a 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -12,7 +12,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.6, 3.7, 3.8, 3.9, 3.10] + python-version: [3.6, 3.7, 3.8, 3.9] steps: - uses: actions/checkout@v2 From 4e0021cc4f9d1216a6dc3773cc2ee71ab78b1cef Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 2 Feb 2023 13:41:45 +0000 Subject: [PATCH 040/188] update python versions --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a1a9357a..ead1bdbb 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -12,7 +12,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.6, 3.7, 3.8, 3.9] + python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] steps: - uses: actions/checkout@v2 From 135f5e9e45b86a25fa18914e92e3aeda8533a161 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 08:30:39 +0000 Subject: [PATCH 041/188] only test documentation build on windows - remove mac and linux --- .github/workflows/main.yml | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index ead1bdbb..b9937c92 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -15,22 +15,7 @@ jobs: python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] steps: - - uses: actions/checkout@v2 - - uses: actions/cache@v1 - if: startsWith(runner.os, 'Linux') - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }} - restore-keys: | - ${{ runner.os }}-pip- - - uses: actions/cache@v1 - if: startsWith(runner.os, 'macOS') - with: - path: ~/Library/Caches/pip - key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }} - restore-keys: | - ${{ runner.os }}-pip- - uses: actions/cache@v1 if: startsWith(runner.os, 'Windows') with: From c354bf762196304f11d78669913a89ccd88355a9 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 08:33:32 +0000 Subject: [PATCH 042/188] only test documentation build on windows - remove mac and linux from os specification --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index b9937c92..de870eae 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -11,7 +11,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, macos-latest, windows-latest] + os: [windows-latest] python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] steps: From 0ab7744a74992a5d1bcd560726a8fa0c5844f90a Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 08:40:33 +0000 Subject: [PATCH 043/188] pull updates to github action workflow from dev --- .github/workflows/main.yml | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index de870eae..467fe698 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,10 +1,15 @@ -name: pygom-docbuild +name: pygom -on: +on: push: branches: - install-fix-docs + pull_request: + branches: + - install-fix-docs +env: + ACTIONS_ALLOW_UNSECURE_COMMANDS: true jobs: build: @@ -14,8 +19,7 @@ jobs: os: [windows-latest] python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] - steps: - + steps: - uses: actions/cache@v1 if: startsWith(runner.os, 'Windows') with: @@ -52,19 +56,15 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt - pip install -r doc/requirements.txt - name: Build and install package for c files run: | python setup.py build python setup.py install - - name: Fix matplotlib backend for MacOS if: startsWith(runner.os, 'macOS') run: | mkdir ~/.matplotlib echo "backend: TkAgg" >> ~/.matplotlib/matplotlibrc - - name: Build documentation - run: | - cd doc - make html linkcheck \ No newline at end of file + - name: Test and coverage + run: python setup.py test From 286699833df31d9b02a26f5e1275d6816a870fae Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 08:50:42 +0000 Subject: [PATCH 044/188] remove model tests, point to requirements files --- .github/workflows/main.yml | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 467fe698..a17c93e1 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,4 +1,4 @@ -name: pygom +name: Test-docs on: push: @@ -17,7 +17,7 @@ jobs: strategy: matrix: os: [windows-latest] - python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] + python-version: ["3.7"]#, "3.8", "3.9", "3.10", "3.11"] steps: - uses: actions/cache@v1 @@ -55,16 +55,15 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -r requirements.txt + pip install -r '**/requirements.txt' - name: Build and install package for c files run: | python setup.py build python setup.py install - - name: Fix matplotlib backend for MacOS - if: startsWith(runner.os, 'macOS') + + - name: Test documentation run: | - mkdir ~/.matplotlib - echo "backend: TkAgg" >> ~/.matplotlib/matplotlibrc - - name: Test and coverage - run: python setup.py test + pip install doc/requirements.txt + make html + From 019f4c7d81ad53505fff87b8e739362342dec3a0 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 10:27:31 +0000 Subject: [PATCH 045/188] check working directory for github action --- .github/workflows/main.yml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a17c93e1..6a212056 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -52,10 +52,13 @@ jobs: Get-Command rc.exe | Format-Table -AutoSize echo "::add-path::$(Get-Command rc.exe | Split-Path)" + - name: check working directory + run: pwd + - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -r '**/requirements.txt' + pip install -r home/requirements.txt - name: Build and install package for c files run: | @@ -64,6 +67,6 @@ jobs: - name: Test documentation run: | - pip install doc/requirements.txt + pip install -r home/doc/requirements.txt make html From f89a334b9f0758887ea55f41953218fc43ed7b70 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 10:30:10 +0000 Subject: [PATCH 046/188] correct path --- .github/workflows/main.yml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6a212056..532a5679 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -53,12 +53,14 @@ jobs: echo "::add-path::$(Get-Command rc.exe | Split-Path)" - name: check working directory - run: pwd - + run: | + pwd + ls + - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -r home/requirements.txt + pip install -r requirements.txt - name: Build and install package for c files run: | From 5ffd91ccc2b5879117e65fd628657f807cc9db99 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 10:33:23 +0000 Subject: [PATCH 047/188] correct requirements paths --- .github/workflows/main.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 532a5679..e246d1e8 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,4 +1,4 @@ -name: Test-docs +name: test-docs on: push: @@ -55,12 +55,12 @@ jobs: - name: check working directory run: | pwd - ls + - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -r requirements.txt + pip install -r ../requirements.txt - name: Build and install package for c files run: | @@ -69,6 +69,6 @@ jobs: - name: Test documentation run: | - pip install -r home/doc/requirements.txt + pip install -r ../doc/requirements.txt make html From 968220221ccaa21f220721dcab77341d000e0bd2 Mon Sep 17 00:00:00 2001 From: Hannah Williams Date: Fri, 3 Feb 2023 10:42:09 +0000 Subject: [PATCH 048/188] trying to locate requirements file --- .github/workflows/main.yml | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index e246d1e8..aed6f525 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -55,20 +55,21 @@ jobs: - name: check working directory run: | pwd + find '**requirements.txt' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r ../requirements.txt - - - name: Build and install package for c files - run: | - python setup.py build - python setup.py install +# - name: Install dependencies +# run: | +# python -m pip install --upgrade pip +# pip install -r ../requirements.txt +# +# - name: Build and install package for c files +# run: | +# python setup.py build +# python setup.py install - - name: Test documentation - run: | - pip install -r ../doc/requirements.txt - make html +# - name: Test documentation +# run: | +# pip install -r ../doc/requirements.txt +# make html From 1dbacf9faaa2ab5f225296be76be5cc405b1ec63 Mon Sep 17 00:00:00 2001 From: Hannah Williams Date: Fri, 3 Feb 2023 10:50:53 +0000 Subject: [PATCH 049/188] add list files command --- .github/workflows/main.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index aed6f525..fd4423d8 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -20,6 +20,8 @@ jobs: python-version: ["3.7"]#, "3.8", "3.9", "3.10", "3.11"] steps: + - uses: actions/checkout@v2 + - uses: actions/cache@v1 if: startsWith(runner.os, 'Windows') with: @@ -55,7 +57,9 @@ jobs: - name: check working directory run: | pwd - find '**requirements.txt' + + - name: list files + run: ls # - name: Install dependencies From 46c07904a3f0be8e6903e92309e6378121589845 Mon Sep 17 00:00:00 2001 From: Hannah Williams Date: Fri, 3 Feb 2023 10:53:33 +0000 Subject: [PATCH 050/188] reinclude install dependencies --- .github/workflows/main.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index fd4423d8..739e34f4 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -62,10 +62,10 @@ jobs: run: ls -# - name: Install dependencies -# run: | -# python -m pip install --upgrade pip -# pip install -r ../requirements.txt + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt # # - name: Build and install package for c files # run: | From b2877255714cb1fcd1930c018744241f6a7a40d8 Mon Sep 17 00:00:00 2001 From: Hannah Williams Date: Fri, 3 Feb 2023 10:58:18 +0000 Subject: [PATCH 051/188] reinclude documentation requirements and build --- .github/workflows/main.yml | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 739e34f4..e0b119be 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -66,14 +66,14 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt -# -# - name: Build and install package for c files -# run: | -# python setup.py build -# python setup.py install + + - name: Build and install package for c files + run: | + python setup.py build + python setup.py install -# - name: Test documentation -# run: | -# pip install -r ../doc/requirements.txt -# make html + - name: Test documentation + run: | + pip install -r doc/requirements.txt + make html From c7b8f05ae6b343fae8619d41a733e7eca9ffee5b Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 11:32:43 +0000 Subject: [PATCH 052/188] include make in environment install --- .github/workflows/main.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index e0b119be..db4e1f69 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -74,6 +74,7 @@ jobs: - name: Test documentation run: | + conda install make pip install -r doc/requirements.txt make html From 02bf205e3f62ba3713156a60dbcac349fb19884f Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 11:49:10 +0000 Subject: [PATCH 053/188] include make in requirements --- .github/workflows/main.yml | 1 - requirements.txt | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index db4e1f69..e0b119be 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -74,7 +74,6 @@ jobs: - name: Test documentation run: | - conda install make pip install -r doc/requirements.txt make html diff --git a/requirements.txt b/requirements.txt index c4928941..e71335bd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,3 +10,4 @@ numpydoc>=0.6.0 sphinx>=1.4.1 sphinx_rtd_theme>=0.2.0 cython>=0.29 +make From d67229a48979a8ab08164344bbc75d5cd8c792e6 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 12:22:29 +0000 Subject: [PATCH 054/188] fix environment to use make --- .github/workflows/main.yml | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index e0b119be..109cd7be 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -21,6 +21,8 @@ jobs: steps: - uses: actions/checkout@v2 + with: + fetch-depth: 1 - uses: actions/cache@v1 if: startsWith(runner.os, 'Windows') @@ -53,14 +55,6 @@ jobs: Invoke-VSDevEnvironment Get-Command rc.exe | Format-Table -AutoSize echo "::add-path::$(Get-Command rc.exe | Split-Path)" - - - name: check working directory - run: | - pwd - - - name: list files - run: ls - - name: Install dependencies run: | @@ -72,8 +66,11 @@ jobs: python setup.py build python setup.py install - - name: Test documentation + - name: Install documentation dependencies run: | pip install -r doc/requirements.txt + + - name: make html documentation + run: | make html From 6717c9db363dd306ab09fec81cbd89f7f2cc9aa7 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 12:38:44 +0000 Subject: [PATCH 055/188] trying to fix make html --- .github/workflows/main.yml | 2 ++ requirements.txt | 3 +-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 109cd7be..6f207416 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -69,8 +69,10 @@ jobs: - name: Install documentation dependencies run: | pip install -r doc/requirements.txt + pip install make - name: make html documentation run: | + make clean make html diff --git a/requirements.txt b/requirements.txt index e71335bd..81cb7e2d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,5 +9,4 @@ sympy>=1.0.0 numpydoc>=0.6.0 sphinx>=1.4.1 sphinx_rtd_theme>=0.2.0 -cython>=0.29 -make +cython>=0.29 \ No newline at end of file From 7b9034f888152e8f18da3b45877cde7cc37a485b Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Feb 2023 12:48:44 +0000 Subject: [PATCH 056/188] navigate to doc location --- .github/workflows/main.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6f207416..fb240d57 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -73,6 +73,7 @@ jobs: - name: make html documentation run: | + cd doc make clean make html From 06b51ca1d6dc842d91b247a4ccb0d23f9e7af51a Mon Sep 17 00:00:00 2001 From: HWilliams-PHE <36919240+HWilliams-PHE@users.noreply.github.com> Date: Thu, 16 Feb 2023 14:36:33 +0000 Subject: [PATCH 057/188] Comment out graphviz hack to check if CI will run --- doc/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index aedce28d..dc804650 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -13,8 +13,8 @@ import warnings #slight hack for graphvis on windows to ensure conda path is correct -if sys.platform == 'win32': - os.environ['PATH'] += os.pathsep + os.environ['CONDA_PREFIX'] + r'\Library\bin\graphviz' +#if sys.platform == 'win32': +# os.environ['PATH'] += os.pathsep + os.environ['CONDA_PREFIX'] + r'\Library\bin\graphviz' import sphinx if sphinx.__version__ < '1.4.1': From 90603f8a64f2863ad16d114cdd1ca3d65b713d46 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 3 Mar 2023 15:58:34 +0000 Subject: [PATCH 058/188] add nbsphinx to requirements --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 81cb7e2d..36e653d5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,5 @@ sympy>=1.0.0 numpydoc>=0.6.0 sphinx>=1.4.1 sphinx_rtd_theme>=0.2.0 -cython>=0.29 \ No newline at end of file +cython>=0.29 +nbsphinx \ No newline at end of file From 69f51e3ecb04a1ce5cc9d748151b6c55d42a80da Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 1 Jun 2023 12:34:54 +0100 Subject: [PATCH 059/188] restart building jupyter book --- doc/doc/pygom-doc/_config.yml | 32 +++++++ doc/doc/pygom-doc/_toc.yml | 9 ++ doc/doc/pygom-doc/intro.md | 11 +++ doc/doc/pygom-doc/logo.png | Bin 0 -> 9854 bytes doc/doc/pygom-doc/markdown-notebooks.md | 53 ++++++++++ doc/doc/pygom-doc/markdown.md | 55 +++++++++++ doc/doc/pygom-doc/notebooks.ipynb | 122 ++++++++++++++++++++++++ doc/doc/pygom-doc/references.bib | 56 +++++++++++ doc/doc/pygom-doc/requirements.txt | 3 + 9 files changed, 341 insertions(+) create mode 100644 doc/doc/pygom-doc/_config.yml create mode 100644 doc/doc/pygom-doc/_toc.yml create mode 100644 doc/doc/pygom-doc/intro.md create mode 100644 doc/doc/pygom-doc/logo.png create mode 100644 doc/doc/pygom-doc/markdown-notebooks.md create mode 100644 doc/doc/pygom-doc/markdown.md create mode 100644 doc/doc/pygom-doc/notebooks.ipynb create mode 100644 doc/doc/pygom-doc/references.bib create mode 100644 doc/doc/pygom-doc/requirements.txt diff --git a/doc/doc/pygom-doc/_config.yml b/doc/doc/pygom-doc/_config.yml new file mode 100644 index 00000000..5f534f80 --- /dev/null +++ b/doc/doc/pygom-doc/_config.yml @@ -0,0 +1,32 @@ +# Book settings +# Learn more at https://jupyterbook.org/customize/config.html + +title: My sample book +author: The Jupyter Book Community +logo: logo.png + +# Force re-execution of notebooks on each build. +# See https://jupyterbook.org/content/execute.html +execute: + execute_notebooks: force + +# Define the name of the latex output file for PDF builds +latex: + latex_documents: + targetname: book.tex + +# Add a bibtex file so that we can create citations +bibtex_bibfiles: + - references.bib + +# Information about where the book exists on the web +repository: + url: https://github.com/executablebooks/jupyter-book # Online location of your book + path_to_book: docs # Optional path to your book, relative to the repository root + branch: master # Which branch of the repository should be used when creating links (optional) + +# Add GitHub buttons to your book +# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository +html: + use_issues_button: true + use_repository_button: true diff --git a/doc/doc/pygom-doc/_toc.yml b/doc/doc/pygom-doc/_toc.yml new file mode 100644 index 00000000..74d5c710 --- /dev/null +++ b/doc/doc/pygom-doc/_toc.yml @@ -0,0 +1,9 @@ +# Table of contents +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: intro +chapters: +- file: markdown +- file: notebooks +- file: markdown-notebooks diff --git a/doc/doc/pygom-doc/intro.md b/doc/doc/pygom-doc/intro.md new file mode 100644 index 00000000..f8cdc73c --- /dev/null +++ b/doc/doc/pygom-doc/intro.md @@ -0,0 +1,11 @@ +# Welcome to your Jupyter Book + +This is a small sample book to give you a feel for how book content is +structured. +It shows off a few of the major file types, as well as some sample content. +It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information. + +Check out the content pages bundled with this sample book to see more. + +```{tableofcontents} +``` diff --git a/doc/doc/pygom-doc/logo.png b/doc/doc/pygom-doc/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..06d56f40c838b64eb048a63e036125964a069a3a GIT binary patch literal 9854 zcmcJ#_ghoX7cGn*K?4W`P^xr9dX-2=s)7_L0g)Pd2}GoYu8}Goq=uqY=^(vJ3q7D9 zgx&ncihTY58>yQhyHVAq6+lGO~MVagOauq5m9v<`6Yyea8LU7g^33d5#6JIpIaLG z-1|gCJhU3BN``QY-K@=)`@JW9M^t~b7uLWQYei|mDOJQ5UUg0~vIqbGt~C8wn@$P% z5pl~zRjG%ihlB(HjNnDEe-dDz2JixSk(`6?==a`q;3sz5kB=vYkB^Usv)VI9k21rD zJiTz9qguh+nKE8m#+pE4C16PIb7Iqf7uN3q_3Quydk+ycREf|Kaf=g!AT$7Pt5%T^ 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formats: md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.11.5 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Notebooks with MyST Markdown + +Jupyter Book also lets you write text-based notebooks using MyST Markdown. +See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions. +This page shows off a notebook written in MyST Markdown. + +## An example cell + +With MyST Markdown, you can define code cells with a directive like so: + +```{code-cell} +print(2 + 2) +``` + +When your book is built, the contents of any `{code-cell}` blocks will be +executed with your default Jupyter kernel, and their outputs will be displayed +in-line with the rest of your content. + +```{seealso} +Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html). +``` + +## Create a notebook with MyST Markdown + +MyST Markdown notebooks are defined by two things: + +1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed). + See the YAML at the top of this page for example. +2. The presence of `{code-cell}` directives, which will be executed with your book. + +That's all that is needed to get started! + +## Quickly add YAML metadata for MyST Notebooks + +If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command: + +``` +jupyter-book myst init path/to/markdownfile.md +``` diff --git a/doc/doc/pygom-doc/markdown.md b/doc/doc/pygom-doc/markdown.md new file mode 100644 index 00000000..0ddaab3f --- /dev/null +++ b/doc/doc/pygom-doc/markdown.md @@ -0,0 +1,55 @@ +# Markdown Files + +Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or +in regular markdown files (`.md`), you'll write in the same flavor of markdown +called **MyST Markdown**. +This is a simple file to help you get started and show off some syntax. + +## What is MyST? + +MyST stands for "Markedly Structured Text". It +is a slight variation on a flavor of markdown called "CommonMark" markdown, +with small syntax extensions to allow you to write **roles** and **directives** +in the Sphinx ecosystem. + +For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html). + +## Sample Roles and Directives + +Roles and directives are two of the most powerful tools in Jupyter Book. They +are kind of like functions, but written in a markup language. They both +serve a similar purpose, but **roles are written in one line**, whereas +**directives span many lines**. They both accept different kinds of inputs, +and what they do with those inputs depends on the specific role or directive +that is being called. + +Here is a "note" directive: + +```{note} +Here is a note +``` + +It will be rendered in a special box when you build your book. + +Here is an inline directive to refer to a document: {doc}`markdown-notebooks`. + + +## Citations + +You can also cite references that are stored in a `bibtex` file. For example, +the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like +this: {cite}`holdgraf_evidence_2014`. + +Moreover, you can insert a bibliography into your page with this syntax: +The `{bibliography}` directive must be used for all the `{cite}` roles to +render properly. +For example, if the references for your book are stored in `references.bib`, +then the bibliography is inserted with: + +```{bibliography} +``` + +## Learn more + +This is just a simple starter to get you started. +You can learn a lot more at [jupyterbook.org](https://jupyterbook.org). diff --git a/doc/doc/pygom-doc/notebooks.ipynb b/doc/doc/pygom-doc/notebooks.ipynb new file mode 100644 index 00000000..fdb7176c --- /dev/null +++ b/doc/doc/pygom-doc/notebooks.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Content with notebooks\n", + "\n", + "You can also create content with Jupyter Notebooks. This means that you can include\n", + "code blocks and their outputs in your book.\n", + "\n", + "## Markdown + notebooks\n", + "\n", + "As it is markdown, you can embed images, HTML, etc into your posts!\n", + "\n", + "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n", + "\n", + "You can also $add_{math}$ and\n", + "\n", + "$$\n", + "math^{blocks}\n", + "$$\n", + "\n", + "or\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\mbox{mean} la_{tex} \\\\ \\\\\n", + "math blocks\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n", + "\n", + "## MyST markdown\n", + "\n", + "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n", + "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n", + "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n", + "\n", + "## Code blocks and outputs\n", + "\n", + "Jupyter Book will also embed your code blocks and output in your book.\n", + "For example, here's some sample Matplotlib code:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import rcParams, cycler\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.ion()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fixing random state for reproducibility\n", + "np.random.seed(19680801)\n", + "\n", + "N = 10\n", + "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n", + "data = np.array(data).T\n", + "cmap = plt.cm.coolwarm\n", + "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n", + "\n", + "\n", + "from matplotlib.lines import Line2D\n", + "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n", + " Line2D([0], [0], color=cmap(.5), lw=4),\n", + " Line2D([0], [0], color=cmap(1.), lw=4)]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "lines = ax.plot(data)\n", + "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a lot more that you can do with outputs (such as including interactive outputs)\n", + "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/doc/pygom-doc/references.bib b/doc/doc/pygom-doc/references.bib new file mode 100644 index 00000000..783ec6aa --- /dev/null +++ b/doc/doc/pygom-doc/references.bib @@ -0,0 +1,56 @@ +--- +--- + +@inproceedings{holdgraf_evidence_2014, + address = {Brisbane, Australia, Australia}, + title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}}, + booktitle = {International {Conference} on {Cognitive} {Neuroscience}}, + publisher = {Frontiers in Neuroscience}, + author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.}, + year = {2014} +} + +@article{holdgraf_rapid_2016, + title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility}, + volume = {7}, + issn = {2041-1723}, + url = {http://www.nature.com/doifinder/10.1038/ncomms13654}, + doi = {10.1038/ncomms13654}, + number = {May}, + journal = {Nature Communications}, + author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.}, + year = {2016}, + pages = {13654}, + file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf} +} + +@inproceedings{holdgraf_portable_2017, + title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science}, + volume = {Part F1287}, + isbn = {978-1-4503-5272-7}, + doi = {10.1145/3093338.3093370}, + abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.}, + booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}}, + author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.}, + year = {2017}, + keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy} +} + +@article{holdgraf_encoding_2017, + title = {Encoding and decoding models in cognitive electrophysiology}, + volume = {11}, + issn = {16625137}, + doi = {10.3389/fnsys.2017.00061}, + abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.}, + journal = {Frontiers in Systems Neuroscience}, + author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.}, + year = {2017}, + keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials} +} + +@book{ruby, + title = {The Ruby Programming Language}, + author = {Flanagan, David and Matsumoto, Yukihiro}, + year = {2008}, + publisher = {O'Reilly Media} +} diff --git a/doc/doc/pygom-doc/requirements.txt b/doc/doc/pygom-doc/requirements.txt new file mode 100644 index 00000000..7e821e45 --- /dev/null +++ b/doc/doc/pygom-doc/requirements.txt @@ -0,0 +1,3 @@ +jupyter-book +matplotlib +numpy From 1dd60c737344287965b5e9ed53ce1f77f5c1a156 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Fri, 2 Jun 2023 12:08:21 +0100 Subject: [PATCH 060/188] Edit default config file --- doc/doc/pygom-doc/_config.yml | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/doc/doc/pygom-doc/_config.yml b/doc/doc/pygom-doc/_config.yml index 5f534f80..fd486944 100644 --- a/doc/doc/pygom-doc/_config.yml +++ b/doc/doc/pygom-doc/_config.yml @@ -1,14 +1,20 @@ # Book settings # Learn more at https://jupyterbook.org/customize/config.html -title: My sample book -author: The Jupyter Book Community -logo: logo.png +title: PyGOM documentation +author: Public Health England / UK Health Security Agency +logo: ukhsa.png -# Force re-execution of notebooks on each build. +# build files in table of contents +# used as alternative to exclude patterns +only_build_toc_files: true + +# cache notebook outputs to save time +# TODO check what is required for readthedocs +# this could avoid the issue of execution timing out # See https://jupyterbook.org/content/execute.html execute: - execute_notebooks: force + execute_notebooks: cache # Define the name of the latex output file for PDF builds latex: @@ -16,12 +22,16 @@ latex: targetname: book.tex # Add a bibtex file so that we can create citations -bibtex_bibfiles: - - references.bib +# use sphinx to specify style +sphinx: + config: + bibtex_reference_style: author_year + # TODO change md to bibtex + bibtex_bibfiles: "ref.md" # Information about where the book exists on the web repository: - url: https://github.com/executablebooks/jupyter-book # Online location of your book + url: https://github.com/ukhsa-collaboration/pygom # Online location of your book path_to_book: docs # Optional path to your book, relative to the repository root branch: master # Which branch of the repository should be used when creating links (optional) From 91d27f6a6cd3be71e7b13c479f5687447d1f19b7 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 15 Jun 2023 09:58:17 +0100 Subject: [PATCH 061/188] convert rst files to md and ipynb - maintains subdirectories except ones without --- doc/doc/pygom-doc/rst_to_md.sh | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 doc/doc/pygom-doc/rst_to_md.sh diff --git a/doc/doc/pygom-doc/rst_to_md.sh b/doc/doc/pygom-doc/rst_to_md.sh new file mode 100644 index 00000000..290f6df2 --- /dev/null +++ b/doc/doc/pygom-doc/rst_to_md.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +# convert rst to md using pandoc + +INDIR=$(realpath -e "$1") +OUTDIR=$(realpath -e "$2") + + +for f in $(find "${INDIR}" -iname '*.rst') +do + # extract filename without extension + FILESTRIP=$(basename "${f%.*}") + # extract directory of file + FILEDIR="$(dirname "${f}")" + # extract subdirectory of file + # NB this takes the last directory + SUBDIR="$(basename "${FILEDIR}")" + NEWDIR="${OUTDIR}"/"${SUBDIR}" + mkdir -p "${NEWDIR}" + echo "converting ${f} to ${NEWDIR}/$FILESTRIP.md" + pandoc "${f}" -f rst -t markdown -o "${NEWDIR}/${FILESTRIP}.md" + pandoc "${NEWDIR}/${FILESTRIP}.md" -o "${NEWDIR}/${FILESTRIP}.ipynb" +done + + From dff5acf4b51e3c3724c401ce75866f170b027592 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 15 Jun 2023 10:29:55 +0100 Subject: [PATCH 062/188] Converted files (md and ipynb) to edit --- doc/doc/pygom-doc/mdconvert/common_models/.md | 35 ++ .../mdconvert/common_models/FitzHugh.ipynb | 54 ++ .../mdconvert/common_models/FitzHugh.md | 44 ++ .../common_models/Legrand_Ebola_SEIHFR.ipynb | 103 ++++ .../common_models/Legrand_Ebola_SEIHFR.md | 96 ++++ .../mdconvert/common_models/Lorenz.ipynb | 45 ++ .../mdconvert/common_models/Lorenz.md | 34 ++ .../common_models/Lotka_Volterra.ipynb | 107 ++++ .../mdconvert/common_models/Lotka_Volterra.md | 103 ++++ .../common_models/Lotka_Volterra_4State.ipynb | 67 +++ .../common_models/Lotka_Volterra_4State.md | 60 +++ .../mdconvert/common_models/Robertson.ipynb | 94 ++++ .../mdconvert/common_models/Robertson.md | 89 +++ 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b/doc/doc/pygom-doc/mdconvert/common_models/.md new file mode 100644 index 00000000..7aa8ca23 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/.md @@ -0,0 +1,35 @@ +# `.SIR_Birth_Death`{.interpreted-text role="func"} + +Next, we look at an SIR model with birth death + +$$\begin{aligned} +\frac{dS}{dt} &= B -\beta SI - \mu S \\ +\frac{dI}{dt} &= \beta SI - \gamma I - \mu I \\ +\frac{dR}{dt} &= \gamma I +\end{aligned}$$ + +Continuing from the example above, but now with a much longer time +frame. Note that the birth and death rate are the same. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import numpy + +In \[1\]: B = 126372.0/365.0 + +In \[1\]: N = 7781984.0 + +In \[1\]: ode = common_models.SIR_Birth_Death({\'beta\':3.6, +\'gamma\':0.2, \'B\':B/N, \'mu\':B/N}) + +In \[1\]: t = numpy.linspace(0, 35\*365, 10001) + +In \[1\]: x0 = \[0.065, 123.0\*(5.0/30.0)/N, 0.0\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_sir_bd.png In \[1\]: ode.plot() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/FitzHugh.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/FitzHugh.ipynb new file mode 100644 index 00000000..a94f5f0e --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/FitzHugh.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.FitzHugh`\n", + "\n", + "The FitzHugh model [\\[FitzHugh1961\\]]() without external external\n", + "stimulus. This is a commonly used model when developing new methodology\n", + "with regard to ode's, see [\\[Ramsay2007\\]]() and [\\[Girolami2011\\]]()\n", + "and reference therein.\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dV}{dt} &= c ( V - \\frac{V^{3}}{3} + R) \\\\\n", + "\\frac{dR}{dt} &= -\\frac{1}{c}(V - a + bR).\n", + "\\end{aligned}$$\n", + "\n", + "An example would be\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: ode = common_models.FitzHugh({'a':0.2, 'b':0.2, 'c':3.0})\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 20, 101)\n", + "\n", + "In \\[1\\]: x0 = \\[1.0, -1.0\\]\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_fh_1.png In \\[1\\]: ode.plot()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "In \\[1\\]: fig = plt.figure()\n", + "\n", + "In \\[1\\]: plt.plot(solution\\[:,0\\], solution\\[:,1\\], 'b')\n", + "\n", + "@savefig common_models_fh_2.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/FitzHugh.md b/doc/doc/pygom-doc/mdconvert/common_models/FitzHugh.md new file mode 100644 index 00000000..6b563e6b --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/FitzHugh.md @@ -0,0 +1,44 @@ +# `.FitzHugh`{.interpreted-text role="func"} + +The FitzHugh model [\[FitzHugh1961\]]() without external external +stimulus. This is a commonly used model when developing new methodology +with regard to ode\'s, see [\[Ramsay2007\]]() and [\[Girolami2011\]]() +and reference therein. + +$$\begin{aligned} +\frac{dV}{dt} &= c ( V - \frac{V^{3}}{3} + R) \\ +\frac{dR}{dt} &= -\frac{1}{c}(V - a + bR). +\end{aligned}$$ + +An example would be + +::: {.ipython} +In \[1\]: import numpy + +In \[1\]: from pygom import common_models + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: ode = common_models.FitzHugh({\'a\':0.2, \'b\':0.2, +\'c\':3.0}) + +In \[1\]: t = numpy.linspace(0, 20, 101) + +In \[1\]: x0 = \[1.0, -1.0\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_fh_1.png In \[1\]: ode.plot() + +In \[1\]: plt.close() + +In \[1\]: fig = plt.figure() + +In \[1\]: plt.plot(solution\[:,0\], solution\[:,1\], \'b\') + +\@savefig common_models_fh_2.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.ipynb new file mode 100644 index 00000000..cab8043e --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.ipynb @@ -0,0 +1,103 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.Legrand_Ebola_SEIHFR`\n", + "\n", + "A commonly used model in the literature to model Ebola outbreaks is the\n", + "SEIHFR model proposed by [\\[Legrand2007\\]](). There are two extra\n", + "compartments on top of the standard SEIR, $H$ for hospitialization and\n", + "$F$ for funeral. A total of ten parameters (with some describing the\n", + "inverse) are required for the model, they are:\n", + "\n", + "| Symbol | Process |\n", + "|:-------------|:--------------------------------------------|\n", + "| $\\beta_{I}$ | Transmission rate in community |\n", + "| $\\beta_{H}$ | Transmission rate in hospital |\n", + "| $\\beta_{F}$ | Transmission rate in funeral |\n", + "| $\\gamma_{I}$ | (inverse) Onset to end of infectious |\n", + "| $\\gamma_{D}$ | (inverse) Onset to death |\n", + "| $\\gamma_{H}$ | (inverse) Onset of hospitilization |\n", + "| $\\gamma_{F}$ | (inverse) Death to burial |\n", + "| $\\alpha$ | (inverse) Duration of the incubation period |\n", + "| $\\theta$ | Proportional of cases hospitalized |\n", + "| $\\delta$ | Case--ftality ratio |\n", + "\n", + "The **(inverse)** denotes the parameter should be inverted to make\n", + "epidemiological sense. We use the parameters in their more natural from\n", + "in `.Legrand_Ebola_SEIHFR` and replace all the $\\gamma$'s with\n", + "$\\omega$'s, i.e. $\\omega_{i} = \\gamma_{i}^{-1}$ for $i \\in \\{I,D,H,F\\}$.\n", + "We also used $\\alpha^{-1}$ in our model instead of $\\alpha$ so that\n", + "reading the parameters directly gives a more intuitive meaning. There\n", + "arw five additional parameters that is derived. The two derived case\n", + "fatality ratio as\n", + "\n", + "$$\\begin{aligned}\n", + "\\delta_{1} &= \\frac{\\delta \\gamma_{I}}{\\delta \\gamma_{I} + (1-\\delta)\\gamma_{D}} \\\\\n", + "\\delta_{2} &= \\frac{\\delta \\gamma_{IH}}{\\delta \\gamma_{IH} + (1-\\delta)\\gamma_{DH}},\n", + "\\end{aligned}$$\n", + "\n", + "with an adjusted hospitalization parameter\n", + "\n", + "$$\\theta_{1} = \\frac{\\theta(\\gamma_{I}(1-\\delta_{1}) + \\gamma_{D}\\delta_{1})}{\\theta(\\gamma_{I}(1-\\delta_{1}) + \\gamma_{D}\\delta_{1}) + (1-\\theta)\\gamma_{H}},$$\n", + "\n", + "and the derived infectious period\n", + "\n", + "$$\\begin{aligned}\n", + "\\gamma_{IH} &= (\\gamma_{I}^{-1} - \\gamma_{H}^{-1})^{-1} \\\\\n", + "\\gamma_{DH} &= (\\gamma_{D}^{-1} - \\gamma_{H}^{-1})^{-1}.\n", + "\\end{aligned}$$\n", + "\n", + "Now we are ready to state the full set of ode's,\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -N^{-1} (\\beta_{I}SI + \\beta_{H}SH + \\beta_{F}(t) SF) \\\\\n", + "\\frac{dE}{dt} &= N^{-1} (\\beta_{I}SI + \\beta_{H}SH + \\beta_{F}(t) SF) - \\alpha E \\\\\n", + "\\frac{dI}{dt} &= \\alpha E - (\\gamma_{H} \\theta_{1} + \\gamma_{I}(1-\\theta_{1})(1-\\delta_{1}) + \\gamma_{D}(1-\\theta_{1})\\delta_{1})I \\\\\n", + "\\frac{dH}{dt} &= \\gamma_{H}\\theta_{1}I - (\\gamma_{DH}\\delta_{2} + \\gamma_{IH}(1-\\delta_{2}))H \\\\\n", + "\\frac{dF}{dt} &= \\gamma_{D}(1-\\theta_{1})\\delta_{1}I + \\gamma_{DH}\\delta_{2}H - \\gamma_{F}F \\\\\n", + "\\frac{dR}{dt} &= \\gamma_{I}(1-\\theta_{1})(1-\\delta_{1})I + \\gamma_{IH}(1-\\delta_{2})H + \\gamma_{F}F.\n", + "\\end{aligned}$$\n", + "\n", + "with $\\beta_{F}(t) = \\beta_{F}$ if $t > c$ and $0$ otherwise. We use a\n", + "slightly modified version by replacing the delta function with a sigmoid\n", + "function namely, the logistic function\n", + "\n", + "$$\\beta_{F}(t) = \\beta_{F} \\left(1 - \\frac{1}{1 + \\exp(-\\kappa (t - c))} \\right)$$\n", + "\n", + "A brief example (from \\[3\\]) is given here with a slightly more in depth\n", + "example in `estimate2`.\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: x0 = \\[1.0, 3.0/200000.0, 0.0, 0.0, 0.0, 0.0, 0.0\\]\n", + "\n", + "In \\[1\\]: t = numpy.linspace(1, 25, 100)\n", + "\n", + "In \\[1\\]: ode = common_models.Legrand_Ebola_SEIHFR(\\[ \n", + "...: ('beta_I',0.588), ...: ('beta_H',0.794), ...: ('beta_F',7.653),\n", + "...: ('omega_I',10.0/7.0), ...: ('omega_D',9.6/7.0), ...:\n", + "('omega_H',5.0/7.0), ...: ('omega_F',2.0/7.0), ...:\n", + "('alphaInv',7.0/7.0), ...: ('delta',0.81), ...: ('theta',0.80), ...:\n", + "('kappa',300.0), ...: ('interventionTime',7.0) ...: \\])\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t)\n", + "\n", + "@savefig common_models_seihfr.png In \\[1\\]: ode.plot()\n", + "\n", + "Note also that we have again standardized so that the number of\n", + "susceptible is 1 and equal to the whole population, i.e. $N$ does not\n", + "exist in our set of ode's as defined in `.common_models`." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.md b/doc/doc/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.md new file mode 100644 index 00000000..52141c92 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.md @@ -0,0 +1,96 @@ +# `.Legrand_Ebola_SEIHFR`{.interpreted-text role="func"} + +A commonly used model in the literature to model Ebola outbreaks is the +SEIHFR model proposed by [\[Legrand2007\]](). There are two extra +compartments on top of the standard SEIR, $H$ for hospitialization and +$F$ for funeral. A total of ten parameters (with some describing the +inverse) are required for the model, they are: + + Symbol Process + -------------- --------------------------------------------- + $\beta_{I}$ Transmission rate in community + $\beta_{H}$ Transmission rate in hospital + $\beta_{F}$ Transmission rate in funeral + $\gamma_{I}$ (inverse) Onset to end of infectious + $\gamma_{D}$ (inverse) Onset to death + $\gamma_{H}$ (inverse) Onset of hospitilization + $\gamma_{F}$ (inverse) Death to burial + $\alpha$ (inverse) Duration of the incubation period + $\theta$ Proportional of cases hospitalized + $\delta$ Case\--ftality ratio + +The **(inverse)** denotes the parameter should be inverted to make +epidemiological sense. We use the parameters in their more natural from +in `.Legrand_Ebola_SEIHFR`{.interpreted-text role="func"} and replace +all the $\gamma$\'s with $\omega$\'s, i.e. +$\omega_{i} = \gamma_{i}^{-1}$ for $i \in \{I,D,H,F\}$. We also used +$\alpha^{-1}$ in our model instead of $\alpha$ so that reading the +parameters directly gives a more intuitive meaning. There arw five +additional parameters that is derived. The two derived case fatality +ratio as + +$$\begin{aligned} +\delta_{1} &= \frac{\delta \gamma_{I}}{\delta \gamma_{I} + (1-\delta)\gamma_{D}} \\ +\delta_{2} &= \frac{\delta \gamma_{IH}}{\delta \gamma_{IH} + (1-\delta)\gamma_{DH}}, +\end{aligned}$$ + +with an adjusted hospitalization parameter + +$$\theta_{1} = \frac{\theta(\gamma_{I}(1-\delta_{1}) + \gamma_{D}\delta_{1})}{\theta(\gamma_{I}(1-\delta_{1}) + \gamma_{D}\delta_{1}) + (1-\theta)\gamma_{H}},$$ + +and the derived infectious period + +$$\begin{aligned} +\gamma_{IH} &= (\gamma_{I}^{-1} - \gamma_{H}^{-1})^{-1} \\ +\gamma_{DH} &= (\gamma_{D}^{-1} - \gamma_{H}^{-1})^{-1}. +\end{aligned}$$ + +Now we are ready to state the full set of ode\'s, + +$$\begin{aligned} +\frac{dS}{dt} &= -N^{-1} (\beta_{I}SI + \beta_{H}SH + \beta_{F}(t) SF) \\ +\frac{dE}{dt} &= N^{-1} (\beta_{I}SI + \beta_{H}SH + \beta_{F}(t) SF) - \alpha E \\ +\frac{dI}{dt} &= \alpha E - (\gamma_{H} \theta_{1} + \gamma_{I}(1-\theta_{1})(1-\delta_{1}) + \gamma_{D}(1-\theta_{1})\delta_{1})I \\ +\frac{dH}{dt} &= \gamma_{H}\theta_{1}I - (\gamma_{DH}\delta_{2} + \gamma_{IH}(1-\delta_{2}))H \\ +\frac{dF}{dt} &= \gamma_{D}(1-\theta_{1})\delta_{1}I + \gamma_{DH}\delta_{2}H - \gamma_{F}F \\ +\frac{dR}{dt} &= \gamma_{I}(1-\theta_{1})(1-\delta_{1})I + \gamma_{IH}(1-\delta_{2})H + \gamma_{F}F. +\end{aligned}$$ + +with $\beta_{F}(t) = \beta_{F}$ if $t > c$ and $0$ otherwise. We use a +slightly modified version by replacing the delta function with a sigmoid +function namely, the logistic function + +$$\beta_{F}(t) = \beta_{F} \left(1 - \frac{1}{1 + \exp(-\kappa (t - c))} \right)$$ + +A brief example (from \[3\]) is given here with a slightly more in depth +example in `estimate2`{.interpreted-text role="ref"}. + +::: {.ipython} +In \[1\]: import numpy + +In \[1\]: from pygom import common_models + +In \[1\]: x0 = \[1.0, 3.0/200000.0, 0.0, 0.0, 0.0, 0.0, 0.0\] + +In \[1\]: t = numpy.linspace(1, 25, 100) + +In \[1\]: ode = common_models.Legrand_Ebola_SEIHFR(\[ + +: \...: (\'beta_I\',0.588), \...: (\'beta_H\',0.794), \...: + (\'beta_F\',7.653), \...: (\'omega_I\',10.0/7.0), \...: + (\'omega_D\',9.6/7.0), \...: (\'omega_H\',5.0/7.0), \...: + (\'omega_F\',2.0/7.0), \...: (\'alphaInv\',7.0/7.0), \...: + (\'delta\',0.81), \...: (\'theta\',0.80), \...: (\'kappa\',300.0), + \...: (\'interventionTime\',7.0) \...: \]) + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t) + +\@savefig common_models_seihfr.png In \[1\]: ode.plot() +::: + +Note also that we have again standardized so that the number of +susceptible is 1 and equal to the whole population, i.e. $N$ does not +exist in our set of ode\'s as defined in +`.common_models`{.interpreted-text role="mod"}. diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Lorenz.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/Lorenz.ipynb new file mode 100644 index 00000000..74b29ea3 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Lorenz.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.Lorenz`\n", + "\n", + "The Lorenz attractor [\\[Lorenz1963\\]]() defined by the equations\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= \\sigma (y-x) \\\\\n", + "\\frac{dy}{dt} &= x (\\rho - z) - y \\\\\n", + "\\frac{dz}{dt} &= xy - \\beta z\n", + "\\end{aligned}$$\n", + "\n", + "A classic example is\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 100, 20000)\n", + "\n", + "In \\[1\\]: ode = common_models.Lorenz({'beta':8.0/3.0, 'sigma':10.0,\n", + "'rho':28.0})\n", + "\n", + "In \\[1\\]: ode.initial_values = (\\[1., 1., 1.\\], t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "In \\[1\\]: f = plt.figure()\n", + "\n", + "In \\[1\\]: plt.plot(solution\\[:,0\\], solution\\[:,2\\]);\n", + "\n", + "@savefig common_models_Lorenz.png In \\[1\\]: plt.show()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Lorenz.md b/doc/doc/pygom-doc/mdconvert/common_models/Lorenz.md new file mode 100644 index 00000000..907311ce --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Lorenz.md @@ -0,0 +1,34 @@ +# `.Lorenz`{.interpreted-text role="func"} + +The Lorenz attractor [\[Lorenz1963\]]() defined by the equations + +$$\begin{aligned} +\frac{dx}{dt} &= \sigma (y-x) \\ +\frac{dy}{dt} &= x (\rho - z) - y \\ +\frac{dz}{dt} &= xy - \beta z +\end{aligned}$$ + +A classic example is + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import numpy + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: t = numpy.linspace(0, 100, 20000) + +In \[1\]: ode = common_models.Lorenz({\'beta\':8.0/3.0, \'sigma\':10.0, +\'rho\':28.0}) + +In \[1\]: ode.initial_values = (\[1., 1., 1.\], t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +In \[1\]: f = plt.figure() + +In \[1\]: plt.plot(solution\[:,0\], solution\[:,2\]); + +\@savefig common_models_Lorenz.png In \[1\]: plt.show() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra.ipynb new file mode 100644 index 00000000..8063348d --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra.ipynb @@ -0,0 +1,107 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.Lotka_Volterra`\n", + "\n", + "A standard Lotka-Volterra (preditor and prey) model with two states and\n", + "four parameters [\\[Lotka1920\\]]().\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= \\alpha x - cxy \\\\\n", + "\\frac{dy}{dt} &= -\\delta y + \\gamma xy\n", + "\\end{aligned}$$\n", + "\n", + "with both birth and death processes.\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: x0 = \\[2.0, 6.0\\]\n", + "\n", + "In \\[1\\]: ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3,\n", + "'c':2, 'gamma':6})\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, 0)\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0.1, 100, 10000)\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t)\n", + "\n", + "@savefig common_models_Lotka_Volterra.png In \\[1\\]: ode.plot()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "Then we generate the graph at [Wolfram\n", + "Alpha](http://www.wolframalpha.com/input/?i=lotka-volterra+equations)\n", + "with varying initial conditions.\n", + "\n", + "In \\[1\\]: x1List = numpy.linspace(0.2, 2.0, 5)\n", + "\n", + "In \\[1\\]: x2List = numpy.linspace(0.6, 6.0, 5)\n", + "\n", + "In \\[1\\]: fig = plt.figure()\n", + "\n", + "In \\[1\\]: solutionList = list()\n", + "\n", + "In \\[1\\]: ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3,\n", + "'c':2, 'gamma':6})\n", + "\n", + "In \\[1\\]: for i in range(len(x1List)): \n", + "...: ode.initial_values = (\\[x1List\\[i\\], x2List\\[i\\]\\], 0) ...:\n", + "solutionList += \\[ode.integrate(t)\\]\n", + "\n", + "In \\[1\\]: for i in range(len(x1List)):\n", + "plt.plot(solutionList\\[i\\]\\[100::,0\\], solutionList\\[i\\]\\[100::,1\\],\n", + "'b')\n", + "\n", + "In \\[1\\]: plt.xlabel('x')\n", + "\n", + "In \\[1\\]: plt.ylabel('y')\n", + "\n", + "@savefig common_models_Lotka_Volterra_initial_condition.png In \\[1\\]:\n", + "plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "We also know that the system has the critical points at\n", + "$x = \\delta / \\gamma$ and $y=\\alpha / c$. If we changes the parameters\n", + "in such a way that the ration between $x$ and $y$ remains the same, then\n", + "we get a figure as below\n", + "\n", + "In \\[1\\]: cList = numpy.linspace(0.1, 2.0, 5)\n", + "\n", + "In \\[1\\]: gammaList = numpy.linspace(0.6, 6.0, 5)\n", + "\n", + "In \\[1\\]: fig = plt.figure()\n", + "\n", + "In \\[1\\]: for i in range(len(x1List)): \n", + "...: ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3,\n", + "'c':cList\\[i\\], 'gamma':gammaList\\[i\\]}) ...: ode.initial_values =\n", + "(x0, 0) ...: solutionList += \\[ode.integrate(t)\\]\n", + "\n", + "In \\[1\\]: for i in range(len(cList)):\n", + "plt.plot(solutionList\\[i\\]\\[100::,0\\], solutionList\\[i\\]\\[100::,1\\])\n", + "\n", + "In \\[1\\]: plt.xlabel('x')\n", + "\n", + "In \\[1\\]: plt.ylabel('y')\n", + "\n", + "@savefig common_models_Lotka_Volterra_critical_point.png In \\[1\\]:\n", + "plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "where all the cycles goes through the same points." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra.md b/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra.md new file mode 100644 index 00000000..c98870b1 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra.md @@ -0,0 +1,103 @@ +# `.Lotka_Volterra`{.interpreted-text role="func"} + +A standard Lotka-Volterra (preditor and prey) model with two states and +four parameters [\[Lotka1920\]](). + +$$\begin{aligned} +\frac{dx}{dt} &= \alpha x - cxy \\ +\frac{dy}{dt} &= -\delta y + \gamma xy +\end{aligned}$$ + +with both birth and death processes. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import numpy + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: x0 = \[2.0, 6.0\] + +In \[1\]: ode = common_models.Lotka_Volterra({\'alpha\':1, \'delta\':3, +\'c\':2, \'gamma\':6}) + +In \[1\]: ode.initial_values = (x0, 0) + +In \[1\]: t = numpy.linspace(0.1, 100, 10000) + +In \[1\]: solution = ode.integrate(t) + +\@savefig common_models_Lotka_Volterra.png In \[1\]: ode.plot() + +In \[1\]: plt.close() +::: + +Then we generate the graph at [Wolfram +Alpha](http://www.wolframalpha.com/input/?i=lotka-volterra+equations) +with varying initial conditions. + +::: {.ipython} +In \[1\]: x1List = numpy.linspace(0.2, 2.0, 5) + +In \[1\]: x2List = numpy.linspace(0.6, 6.0, 5) + +In \[1\]: fig = plt.figure() + +In \[1\]: solutionList = list() + +In \[1\]: ode = common_models.Lotka_Volterra({\'alpha\':1, \'delta\':3, +\'c\':2, \'gamma\':6}) + +In \[1\]: for i in range(len(x1List)): + +: \...: ode.initial_values = (\[x1List\[i\], x2List\[i\]\], 0) \...: + solutionList += \[ode.integrate(t)\] + +In \[1\]: for i in range(len(x1List)): +plt.plot(solutionList\[i\]\[100::,0\], solutionList\[i\]\[100::,1\], +\'b\') + +In \[1\]: plt.xlabel(\'x\') + +In \[1\]: plt.ylabel(\'y\') + +\@savefig common_models_Lotka_Volterra_initial_condition.png In \[1\]: +plt.show() + +In \[1\]: plt.close() +::: + +We also know that the system has the critical points at +$x = \delta / \gamma$ and $y=\alpha / c$. If we changes the parameters +in such a way that the ration between $x$ and $y$ remains the same, then +we get a figure as below + +::: {.ipython} +In \[1\]: cList = numpy.linspace(0.1, 2.0, 5) + +In \[1\]: gammaList = numpy.linspace(0.6, 6.0, 5) + +In \[1\]: fig = plt.figure() + +In \[1\]: for i in range(len(x1List)): + +: \...: ode = common_models.Lotka_Volterra({\'alpha\':1, \'delta\':3, + \'c\':cList\[i\], \'gamma\':gammaList\[i\]}) \...: + ode.initial_values = (x0, 0) \...: solutionList += + \[ode.integrate(t)\] + +In \[1\]: for i in range(len(cList)): +plt.plot(solutionList\[i\]\[100::,0\], solutionList\[i\]\[100::,1\]) + +In \[1\]: plt.xlabel(\'x\') + +In \[1\]: plt.ylabel(\'y\') + +\@savefig common_models_Lotka_Volterra_critical_point.png In \[1\]: +plt.show() + +In \[1\]: plt.close() +::: + +where all the cycles goes through the same points. diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.ipynb new file mode 100644 index 00000000..34ec9f82 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.Lotka_Volterra_4State`\n", + "\n", + "The Lotka-Volterra model with four states and three parameters\n", + "[\\[Lotka1920\\]](), explained by the following three transitions\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{da}{dt} &= k_{0} a x \\\\\n", + "\\frac{dx}{dt} &= k_{0} a x - k_{1} x y \\\\\n", + "\\frac{dy}{dt} &= k_{1} x y - k_{2} y \\\\\n", + "\\frac{db}{dt} &= k_{2} y.\n", + "\\end{aligned}$$\n", + "\n", + "First, we show the deterministic approach. Then we also show the\n", + "different process path using the parameters from [\\[Press2007\\]](). Note\n", + "that although the model is defined in `common_models`, it is based on\n", + "outputting an `OperateOdeModel` rather than `SimulateOdeModel`.\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: from pygom import Transition, TransitionType, ode_utils,\n", + "SimulateOde\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: stateList = \\['a', 'x', 'y', 'b'\\]\n", + "\n", + "In \\[1\\]: paramList = \\['k0', 'k1', 'k2'\\]\n", + "\n", + "In \\[1\\]: transitionList = \\[ \n", + "...: Transition(origin='a', destination='x', equation='k0\\*a\\*x',\n", + "transition_type=TransitionType.T), ...: Transition(origin='x',\n", + "destination='y', equation='k1\\*x\\*y', transition_type=TransitionType.T),\n", + "...: Transition(origin='y', destination='b', equation='k2\\*y',\n", + "transition_type=TransitionType.T) ...: \\]\n", + "\n", + "In \\[1\\]: ode = SimulateOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: x0 = \\[150.0, 10.0, 10.0, 0.0\\]\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 15, 100)\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: ode.parameters = \\[0.01, 0.1, 1.0\\]\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_Lotka_Volterra_4State.png In \\[1\\]: ode.plot()\n", + "\n", + "In \\[1\\]: simX, simT = ode.simulate_jump(t\\[1::\\], 5, full_output=True)\n", + "\n", + "@savefig common_models_Lotka_Volterra_Sim.png In \\[1\\]: ode.plot(simX,\n", + "simT)" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.md b/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.md new file mode 100644 index 00000000..af9ce277 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.md @@ -0,0 +1,60 @@ +# `.Lotka_Volterra_4State`{.interpreted-text role="func"} + +The Lotka-Volterra model with four states and three parameters +[\[Lotka1920\]](), explained by the following three transitions + +$$\begin{aligned} +\frac{da}{dt} &= k_{0} a x \\ +\frac{dx}{dt} &= k_{0} a x - k_{1} x y \\ +\frac{dy}{dt} &= k_{1} x y - k_{2} y \\ +\frac{db}{dt} &= k_{2} y. +\end{aligned}$$ + +First, we show the deterministic approach. Then we also show the +different process path using the parameters from [\[Press2007\]](). Note +that although the model is defined in `common_models`{.interpreted-text +role="mod"}, it is based on outputting an +`OperateOdeModel`{.interpreted-text role="class"} rather than +`SimulateOdeModel`{.interpreted-text role="class"}. + +::: {.ipython} +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: from pygom import Transition, TransitionType, ode_utils, +SimulateOde + +In \[1\]: import numpy + +In \[1\]: stateList = \[\'a\', \'x\', \'y\', \'b\'\] + +In \[1\]: paramList = \[\'k0\', \'k1\', \'k2\'\] + +In \[1\]: transitionList = \[ + +: \...: Transition(origin=\'a\', destination=\'x\', + equation=\'k0\*a\*x\', transition_type=TransitionType.T), \...: + Transition(origin=\'x\', destination=\'y\', equation=\'k1\*x\*y\', + transition_type=TransitionType.T), \...: Transition(origin=\'y\', + destination=\'b\', equation=\'k2\*y\', + transition_type=TransitionType.T) \...: \] + +In \[1\]: ode = SimulateOde(stateList, paramList, +transition=transitionList) + +In \[1\]: x0 = \[150.0, 10.0, 10.0, 0.0\] + +In \[1\]: t = numpy.linspace(0, 15, 100) + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: ode.parameters = \[0.01, 0.1, 1.0\] + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_Lotka_Volterra_4State.png In \[1\]: ode.plot() + +In \[1\]: simX, simT = ode.simulate_jump(t\[1::\], 5, full_output=True) + +\@savefig common_models_Lotka_Volterra_Sim.png In \[1\]: ode.plot(simX, +simT) +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Robertson.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/Robertson.ipynb new file mode 100644 index 00000000..fc1eb714 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Robertson.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.Robertson`\n", + "\n", + "The Robertson problem [\\[Robertson1966\\]]()\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dy_{1}}{dt} &= -0.04 y_{1} + 1 \\cdot 10^{4} y_{2} y_{3} \\\\\n", + "\\frac{dy_{2}}{dt} &= 0.04 y_{1} - 1 \\cdot 10^{4} y_{2} y_{3} + 3 \\cdot 10^{7} y_{2}^{2} \\\\\n", + "\\frac{dy_{3}}{dt} &= 3 \\cdot 10^{7} y_{2}^{2}.\n", + "\\end{aligned}$$\n", + "\n", + "This is a problem that describes an autocatalytic reaction. One of those\n", + "commonly used to test stiff ode solvers. As the parameters in the\n", + "literature is fixed, we show here how to define the states in a slightly\n", + "more compact format\n", + "\n", + "In \\[1\\]: from pygom import DeterministicOde, Transition, TransitionType\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: t = numpy.append(0, 4\\*numpy.logspace(-6, 6, 1000))\n", + "\n", + "In \\[1\\]: \\# note how we define the states\n", + "\n", + "In \\[1\\]: stateList = \\['y1:4'\\]\n", + "\n", + "In \\[1\\]: paramList = \\[\\]\n", + "\n", + "In \\[1\\]: transitionList = \\[ \n", + "...: Transition(origin='y1', destination='y2', equation='0.04\\*y1',\n", + "transition_type=TransitionType.T), ...: Transition(origin='y2',\n", + "destination='y1', equation='1e4\\*y2\\*y3',\n", + "transition_type=TransitionType.T), ...: Transition(origin='y2',\n", + "destination='y3', equation='3e7\\*y2\\*y2',\n", + "transition_type=TransitionType.T) ...: \\]\n", + "\n", + "In \\[1\\]: ode = DeterministicOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: ode.initial_values = (\\[1.0, 0.0, 0.0\\], t\\[0\\])\n", + "\n", + "In \\[1\\]: solution, output = ode.integrate(t\\[1::\\], full_output=True)\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1, 3)\n", + "\n", + "In \\[1\\]: for i in range(3): \n", + "...: axarr\\[i\\].plot(t, solution\\[:,i\\]) ...:\n", + "axarr\\[i\\].set_xscale('log')\n", + "\n", + "In \\[1\\]: f.tight_layout();\n", + "\n", + "@savefig common_models_Robertson_1.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "To simplify even further, we can use y\n", + "with the corresponding subscript directly instead of y1,y2,y3. Again, we do not have any parameters\n", + "as they are hard coded into our models.\n", + "\n", + "In \\[1\\]: stateList = \\['y1:4'\\]\n", + "\n", + "In \\[1\\]: transitionList = \\[ \n", + "...: Transition(origin='y\\[0\\]', destination='y\\[1\\]',\n", + "equation='0.04\\*y\\[0\\]', transition_type=TransitionType.T), ...:\n", + "Transition(origin='y\\[1\\]', destination='y\\[0\\]',\n", + "equation='1e4\\*y\\[1\\]*y\\[2\\]', transition_type=TransitionType.T), ...:\n", + "Transition(origin='y\\[1\\]', destination='y\\[2\\]',\n", + "equation='3e7*y\\[1\\]\\*y\\[1\\]', transition_type=TransitionType.T) ...: \\]\n", + "\n", + "In \\[1\\]: ode = DeterministicOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: ode.initial_values =(\\[1.0, 0.0, 0.0\\], t\\[0\\])\n", + "\n", + "In \\[1\\]: solution2 = ode.integrate(t\\[1::\\])\n", + "\n", + "In \\[1\\]: numpy.max(solution - solution2)\n", + "\n", + "and we have the identical solution as shown in the last line above." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/Robertson.md b/doc/doc/pygom-doc/mdconvert/common_models/Robertson.md new file mode 100644 index 00000000..1b55174a --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/Robertson.md @@ -0,0 +1,89 @@ +# `.Robertson`{.interpreted-text role="func"} + +The Robertson problem [\[Robertson1966\]]() + +$$\begin{aligned} +\frac{dy_{1}}{dt} &= -0.04 y_{1} + 1 \cdot 10^{4} y_{2} y_{3} \\ +\frac{dy_{2}}{dt} &= 0.04 y_{1} - 1 \cdot 10^{4} y_{2} y_{3} + 3 \cdot 10^{7} y_{2}^{2} \\ +\frac{dy_{3}}{dt} &= 3 \cdot 10^{7} y_{2}^{2}. +\end{aligned}$$ + +This is a problem that describes an autocatalytic reaction. One of those +commonly used to test stiff ode solvers. As the parameters in the +literature is fixed, we show here how to define the states in a slightly +more compact format + +::: {.ipython} +In \[1\]: from pygom import DeterministicOde, Transition, TransitionType + +In \[1\]: import numpy + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: t = numpy.append(0, 4\*numpy.logspace(-6, 6, 1000)) + +In \[1\]: \# note how we define the states + +In \[1\]: stateList = \[\'y1:4\'\] + +In \[1\]: paramList = \[\] + +In \[1\]: transitionList = \[ + +: \...: Transition(origin=\'y1\', destination=\'y2\', + equation=\'0.04\*y1\', transition_type=TransitionType.T), \...: + Transition(origin=\'y2\', destination=\'y1\', + equation=\'1e4\*y2\*y3\', transition_type=TransitionType.T), \...: + Transition(origin=\'y2\', destination=\'y3\', + equation=\'3e7\*y2\*y2\', transition_type=TransitionType.T) \...: \] + +In \[1\]: ode = DeterministicOde(stateList, paramList, +transition=transitionList) + +In \[1\]: ode.initial_values = (\[1.0, 0.0, 0.0\], t\[0\]) + +In \[1\]: solution, output = ode.integrate(t\[1::\], full_output=True) + +In \[1\]: f, axarr = plt.subplots(1, 3) + +In \[1\]: for i in range(3): + +: \...: axarr\[i\].plot(t, solution\[:,i\]) \...: + axarr\[i\].set_xscale(\'log\') + +In \[1\]: f.tight_layout(); + +\@savefig common_models_Robertson_1.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: + +To simplify even further, we can use [y]{.title-ref} with the +corresponding subscript directly instead of [y1,y2,y3]{.title-ref}. +Again, we do not have any parameters as they are hard coded into our +models. + +::: {.ipython} +In \[1\]: stateList = \[\'y1:4\'\] + +In \[1\]: transitionList = \[ + +: \...: Transition(origin=\'y\[0\]\', destination=\'y\[1\]\', + equation=\'0.04\*y\[0\]\', transition_type=TransitionType.T), \...: + Transition(origin=\'y\[1\]\', destination=\'y\[0\]\', + equation=\'1e4\*y\[1\]*y\[2\]\', transition_type=TransitionType.T), + \...: Transition(origin=\'y\[1\]\', destination=\'y\[2\]\', + equation=\'3e7*y\[1\]\*y\[1\]\', transition_type=TransitionType.T) + \...: \] + +In \[1\]: ode = DeterministicOde(stateList, paramList, +transition=transitionList) + +In \[1\]: ode.initial_values =(\[1.0, 0.0, 0.0\], t\[0\]) + +In \[1\]: solution2 = ode.integrate(t\[1::\]) + +In \[1\]: numpy.max(solution - solution2) +::: + +and we have the identical solution as shown in the last line above. diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SEIR.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/SEIR.ipynb new file mode 100644 index 00000000..dea8d27b --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SEIR.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.SEIR`\n", + "\n", + "A natural extension to the SIR is the SEIR model. An extra parameter\n", + "$\\alpha$, which is the inverse of the incubation period is introduced.\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta SI \\\\\n", + "\\end{aligned}$$$$\\begin{aligned}\n", + "\\frac{dE}{dt} &= \\beta SI - \\alpha E \\\\\n", + "\\end{aligned}$$$$\\begin{aligned}\n", + "\\frac{dI}{dt} &= \\alpha E - \\gamma I \\\\\n", + "\\end{aligned}$$$$\\frac{dR}{dt} &= \\gamma I$$\n", + "\n", + "We use the parameters from \\[Aron1984\\] here to generate our plots,\n", + "which does not yield a *nice* and *sensible* epidemic curve as the birth\n", + "and death processes are missing.\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: ode = common_models.SEIR({'beta':1800, 'gamma':100,\n", + "'alpha':35.84})\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 50, 1001)\n", + "\n", + "In \\[1\\]: x0 = \\[0.0658, 0.0007, 0.0002, 0.0\\]\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_seir.png In \\[1\\]: ode.plot()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SEIR.md b/doc/doc/pygom-doc/mdconvert/common_models/SEIR.md new file mode 100644 index 00000000..57d3c813 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SEIR.md @@ -0,0 +1,35 @@ +# `.SEIR`{.interpreted-text role="func"} + +A natural extension to the SIR is the SEIR model. An extra parameter +$\alpha$, which is the inverse of the incubation period is introduced. + +$$\begin{aligned} +\frac{dS}{dt} &= -\beta SI \\ +\end{aligned}$$$$\begin{aligned} +\frac{dE}{dt} &= \beta SI - \alpha E \\ +\end{aligned}$$$$\begin{aligned} +\frac{dI}{dt} &= \alpha E - \gamma I \\ +\end{aligned}$$$$\frac{dR}{dt} &= \gamma I$$ + +We use the parameters from \[Aron1984\] here to generate our plots, +which does not yield a *nice* and *sensible* epidemic curve as the birth +and death processes are missing. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import numpy + +In \[1\]: ode = common_models.SEIR({\'beta\':1800, \'gamma\':100, +\'alpha\':35.84}) + +In \[1\]: t = numpy.linspace(0, 50, 1001) + +In \[1\]: x0 = \[0.0658, 0.0007, 0.0002, 0.0\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_seir.png In \[1\]: ode.plot() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.ipynb new file mode 100644 index 00000000..a886b59d --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.SEIR_Birth_Death`\n", + "\n", + "Extending it to also include birth death process with equal rate $\\mu$\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= \\mu - \\beta SI - \\mu S \\\\\n", + "\\frac{dE}{dt} &= \\beta SI - (\\mu + \\alpha) E \\\\\n", + "\\frac{dI}{dt} &= \\alpha E - (\\mu + \\gamma) I \\\\\n", + "\\frac{dR}{dt} &= \\gamma I\n", + "\\end{aligned}$$\n", + "\n", + "Same parameters value taken from [\\[Aron1984\\]]() as the SEIR example\n", + "above is used here. Observe how the introduction of a birth and a death\n", + "process changes the graph even though the rest of the parameters remains\n", + "the same.\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: ode = common_models.SEIR_Birth_Death({'beta':1800,\n", + "'gamma':100, 'alpha':35.84, 'mu':0.02})\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 50, 1001)\n", + "\n", + "In \\[1\\]: x0 = \\[0.0658, 0.0007, 0.0002, 0.0\\]\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\], full_output=True)\n", + "\n", + "@savefig common_models_seir_bd.png In \\[1\\]: ode.plot()\n", + "\n", + "In \\[1\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.md b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.md new file mode 100644 index 00000000..83623160 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.md @@ -0,0 +1,38 @@ +# `.SEIR_Birth_Death`{.interpreted-text role="func"} + +Extending it to also include birth death process with equal rate $\mu$ + +$$\begin{aligned} +\frac{dS}{dt} &= \mu - \beta SI - \mu S \\ +\frac{dE}{dt} &= \beta SI - (\mu + \alpha) E \\ +\frac{dI}{dt} &= \alpha E - (\mu + \gamma) I \\ +\frac{dR}{dt} &= \gamma I +\end{aligned}$$ + +Same parameters value taken from [\[Aron1984\]]() as the SEIR example +above is used here. Observe how the introduction of a birth and a death +process changes the graph even though the rest of the parameters remains +the same. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: import numpy + +In \[1\]: ode = common_models.SEIR_Birth_Death({\'beta\':1800, +\'gamma\':100, \'alpha\':35.84, \'mu\':0.02}) + +In \[1\]: t = numpy.linspace(0, 50, 1001) + +In \[1\]: x0 = \[0.0658, 0.0007, 0.0002, 0.0\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\], full_output=True) + +\@savefig common_models_seir_bd.png In \[1\]: ode.plot() + +In \[1\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.ipynb new file mode 100644 index 00000000..cd2a67d7 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.ipynb @@ -0,0 +1,76 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.SEIR_Birth_Death_Periodic`\n", + "\n", + "Now extending the SEIR to also have periodic contact, as in\n", + "[\\[Aron1984\\]]().\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= \\mu - \\beta(t)SI - \\mu S \\\\\n", + "\\frac{dE}{dt} &= \\beta(t)SI - (\\mu + \\alpha) E \\\\\n", + "\\frac{dI}{dt} &= \\alpha E - (\\mu + \\gamma) I \\\\\n", + "\\frac{dR}{dt} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: ode = common_models.SEIR_Birth_Death_Periodic({'beta_0':1800,\n", + "'beta_1':0.2, 'gamma':100, 'alpha':35.84, 'mu':0.02})\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 50, 1001)\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: x0 = \\[0.0658, 0.0007, 0.0002, 0.0\\]\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_seir_bd_periodic1.png In \\[1\\]: ode.plot()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "The periodicity is obvious when looking at the the plot between states\n", + "$S$ and $E$, in logarithmic scale.\n", + "\n", + "In \\[1\\]: fig = plt.figure();\n", + "\n", + "In \\[1\\]: plt.plot(numpy.log(solution\\[:,0\\]),\n", + "numpy.log(solution\\[:,1\\]));\n", + "\n", + "In \\[1\\]: plt.xlabel('log of S');\n", + "\n", + "In \\[1\\]: plt.ylabel('log of E');\n", + "\n", + "@savefig common_models_seir_bd_periodic2.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "Similarly, we can see the same thing between the states $E$ and $I$.\n", + "\n", + "In \\[1\\]: fig = plt.figure();\n", + "\n", + "In \\[1\\]: plt.plot(numpy.log(solution\\[:,1\\]),\n", + "numpy.log(solution\\[:,2\\]));\n", + "\n", + "In \\[1\\]: plt.xlabel('log of E');\n", + "\n", + "In \\[1\\]: plt.ylabel('log of I');\n", + "\n", + "@savefig common_models_seir_bd_periodic3.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.md b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.md new file mode 100644 index 00000000..736c1947 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.md @@ -0,0 +1,70 @@ +# `.SEIR_Birth_Death_Periodic`{.interpreted-text role="func"} + +Now extending the SEIR to also have periodic contact, as in +[\[Aron1984\]](). + +$$\begin{aligned} +\frac{dS}{dt} &= \mu - \beta(t)SI - \mu S \\ +\frac{dE}{dt} &= \beta(t)SI - (\mu + \alpha) E \\ +\frac{dI}{dt} &= \alpha E - (\mu + \gamma) I \\ +\frac{dR}{dt} &= \gamma I. +\end{aligned}$$ + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import numpy + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: ode = +common_models.SEIR_Birth_Death_Periodic({\'beta_0\':1800, +\'beta_1\':0.2, \'gamma\':100, \'alpha\':35.84, \'mu\':0.02}) + +In \[1\]: t = numpy.linspace(0, 50, 1001) + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: x0 = \[0.0658, 0.0007, 0.0002, 0.0\] + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_seir_bd_periodic1.png In \[1\]: ode.plot() + +In \[1\]: plt.close() +::: + +The periodicity is obvious when looking at the the plot between states +$S$ and $E$, in logarithmic scale. + +::: {.ipython} +In \[1\]: fig = plt.figure(); + +In \[1\]: plt.plot(numpy.log(solution\[:,0\]), +numpy.log(solution\[:,1\])); + +In \[1\]: plt.xlabel(\'log of S\'); + +In \[1\]: plt.ylabel(\'log of E\'); + +\@savefig common_models_seir_bd_periodic2.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: + +Similarly, we can see the same thing between the states $E$ and $I$. + +::: {.ipython} +In \[1\]: fig = plt.figure(); + +In \[1\]: plt.plot(numpy.log(solution\[:,1\]), +numpy.log(solution\[:,2\])); + +In \[1\]: plt.xlabel(\'log of E\'); + +In \[1\]: plt.ylabel(\'log of I\'); + +\@savefig common_models_seir_bd_periodic3.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Multiple.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Multiple.ipynb new file mode 100644 index 00000000..e47426be --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Multiple.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.SEIR_Multiple`\n", + "\n", + "Multiple SEIR coupled together, without any birth death process.\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS_{i}}{dt} &= dN_{i} - dS_{i} - \\lambda_{i}S_{i} \\\\\n", + "\\frac{dE_{i}}{dt} &= \\lambda_{i}S_{i} - (d+\\epsilon)E_{i} \\\\\n", + "\\frac{dI_{i}}{dt} &= \\epsilon E_{i} - (d+\\gamma) I_{i} \\\\\n", + "\\frac{dR_{i}}{dt} &= \\gamma I_{i} - dR_{i}\n", + "\\end{aligned}$$\n", + "\n", + "where\n", + "\n", + "$$\\lambda_{i} = \\sum_{j=1}^{n} \\beta_{i,j} I_{j} (1\\{i\\neq j\\} p)$$\n", + "\n", + "with $n$ being the number of patch and $p$ the coupled factor.\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[2\\]: import numpy\n", + "\n", + "In \\[3\\]: paramEval = {'beta_00':0.0010107,'beta_01':0.0010107,'beta_10':0.0010107, \n", + "...: 'beta_11':0.0010107,'d':0.02,'epsilon':45.6,'gamma':73.0, ...:\n", + "'N_0':10\\**6,'N_1':10*\\*6,'p':0.01}\n", + "\n", + "In \\[4\\]: x0 = \\[36139.3224081278, 422.560577637822, 263.883351688369,\n", + "963174.233662546\\]\n", + "\n", + "In \\[5\\]: ode = common_models.SEIR_Multiple(param=paramEval)\n", + "\n", + "In \\[6\\]: t = numpy.linspace(0, 40, 100)\n", + "\n", + "In \\[7\\]: x01 = \\[\\]\n", + "\n", + "In \\[8\\]: for s in x0: \n", + "...: x01 += 2\\*\\[s\\]\n", + "\n", + "In \\[9\\]: ode.initial_values = (numpy.array(x01, float),t\\[0\\])\n", + "\n", + "In \\[10\\]: solution, output = ode.integrate(t\\[1::\\], full_output=True)\n", + "\n", + "@savefig common_models_seir_multiple.png In \\[11\\]: ode.plot()\n", + "\n", + "The initial conditions are those derived by using the stability\n", + "condition as stated in [\\[Lloyd1996\\]]() while the notations is taken\n", + "from [\\[Brauer2008\\]]()." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Multiple.md b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Multiple.md new file mode 100644 index 00000000..c7048c67 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SEIR_Multiple.md @@ -0,0 +1,51 @@ +# `.SEIR_Multiple`{.interpreted-text role="func"} + +Multiple SEIR coupled together, without any birth death process. + +$$\begin{aligned} +\frac{dS_{i}}{dt} &= dN_{i} - dS_{i} - \lambda_{i}S_{i} \\ +\frac{dE_{i}}{dt} &= \lambda_{i}S_{i} - (d+\epsilon)E_{i} \\ +\frac{dI_{i}}{dt} &= \epsilon E_{i} - (d+\gamma) I_{i} \\ +\frac{dR_{i}}{dt} &= \gamma I_{i} - dR_{i} +\end{aligned}$$ + +where + +$$\lambda_{i} = \sum_{j=1}^{n} \beta_{i,j} I_{j} (1\{i\neq j\} p)$$ + +with $n$ being the number of patch and $p$ the coupled factor. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[2\]: import numpy + +In \[3\]: paramEval = {\'beta_00\':0.0010107,\'beta_01\':0.0010107,\'beta_10\':0.0010107, + +: \...: + \'beta_11\':0.0010107,\'d\':0.02,\'epsilon\':45.6,\'gamma\':73.0, + \...: \'N_0\':10\**6,\'N_1\':10*\*6,\'p\':0.01} + +In \[4\]: x0 = \[36139.3224081278, 422.560577637822, 263.883351688369, +963174.233662546\] + +In \[5\]: ode = common_models.SEIR_Multiple(param=paramEval) + +In \[6\]: t = numpy.linspace(0, 40, 100) + +In \[7\]: x01 = \[\] + +In \[8\]: for s in x0: + +: \...: x01 += 2\*\[s\] + +In \[9\]: ode.initial_values = (numpy.array(x01, float),t\[0\]) + +In \[10\]: solution, output = ode.integrate(t\[1::\], full_output=True) + +\@savefig common_models_seir_multiple.png In \[11\]: ode.plot() +::: + +The initial conditions are those derived by using the stability +condition as stated in [\[Lloyd1996\]]() while the notations is taken +from [\[Brauer2008\]](). diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SIR.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/SIR.ipynb new file mode 100644 index 00000000..fd293399 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SIR.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.SIR`\n", + "\n", + "A standard SIR model defined by the equations\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta SI \\\\\n", + "\\frac{dI}{dt} &= \\beta SI - \\gamma I \\\\\n", + "\\frac{dR}{dt} &= \\gamma I\n", + "\\end{aligned}$$\n", + "\n", + "Note that the examples and parameters are taken from [\\[Brauer2008\\]](),\n", + "namely Figure 1.4. Hence, the first one below may not appear to make\n", + "much sense.\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: ode = common_models.SIR({'beta':3.6, 'gamma':0.2})\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 730, 1001)\n", + "\n", + "In \\[1\\]: N = 7781984.0\n", + "\n", + "In \\[1\\]: x0 = \\[1.0, 10.0/N, 0.0\\]\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_sir.png In \\[1\\]: ode.plot()\n", + "\n", + "Now we have the more sensible plot, where the initial susceptibles is\n", + "only a fraction of 1.\n", + "\n", + "In \\[1\\]: x0 = \\[0.065, 123\\*(5.0/30.0)/N, 0.0\\]\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_sir_realistic.png In \\[1\\]: ode.plot()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SIR.md b/doc/doc/pygom-doc/mdconvert/common_models/SIR.md new file mode 100644 index 00000000..6b0ce30f --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SIR.md @@ -0,0 +1,46 @@ +# `.SIR`{.interpreted-text role="func"} + +A standard SIR model defined by the equations + +$$\begin{aligned} +\frac{dS}{dt} &= -\beta SI \\ +\frac{dI}{dt} &= \beta SI - \gamma I \\ +\frac{dR}{dt} &= \gamma I +\end{aligned}$$ + +Note that the examples and parameters are taken from [\[Brauer2008\]](), +namely Figure 1.4. Hence, the first one below may not appear to make +much sense. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import numpy + +In \[1\]: ode = common_models.SIR({\'beta\':3.6, \'gamma\':0.2}) + +In \[1\]: t = numpy.linspace(0, 730, 1001) + +In \[1\]: N = 7781984.0 + +In \[1\]: x0 = \[1.0, 10.0/N, 0.0\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_sir.png In \[1\]: ode.plot() +::: + +Now we have the more sensible plot, where the initial susceptibles is +only a fraction of 1. + +::: {.ipython} +In \[1\]: x0 = \[0.065, 123\*(5.0/30.0)/N, 0.0\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_sir_realistic.png In \[1\]: ode.plot() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SIR_Birth_Death.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/SIR_Birth_Death.ipynb new file mode 100644 index 00000000..f58c7552 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SIR_Birth_Death.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.SIR_Birth_Death`\n", + "\n", + "Next, we look at an SIR model with birth death\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= B -\\beta SI - \\mu S \\\\\n", + "\\frac{dI}{dt} &= \\beta SI - \\gamma I - \\mu I \\\\\n", + "\\frac{dR}{dt} &= \\gamma I\n", + "\\end{aligned}$$\n", + "\n", + "Continuing from the example above, but now with a much longer time\n", + "frame. Note that the birth and death rate are the same.\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: B = 126372.0/365.0\n", + "\n", + "In \\[1\\]: N = 7781984.0\n", + "\n", + "In \\[1\\]: ode = common_models.SIR_Birth_Death({'beta':3.6, 'gamma':0.2,\n", + "'B':B/N, 'mu':B/N})\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 35\\*365, 10001)\n", + "\n", + "In \\[1\\]: x0 = \\[0.065, 123.0\\*(5.0/30.0)/N, 0.0\\]\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_sir_bd.png In \\[1\\]: ode.plot()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SIR_Birth_Death.md b/doc/doc/pygom-doc/mdconvert/common_models/SIR_Birth_Death.md new file mode 100644 index 00000000..7aa8ca23 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SIR_Birth_Death.md @@ -0,0 +1,35 @@ +# `.SIR_Birth_Death`{.interpreted-text role="func"} + +Next, we look at an SIR model with birth death + +$$\begin{aligned} +\frac{dS}{dt} &= B -\beta SI - \mu S \\ +\frac{dI}{dt} &= \beta SI - \gamma I - \mu I \\ +\frac{dR}{dt} &= \gamma I +\end{aligned}$$ + +Continuing from the example above, but now with a much longer time +frame. Note that the birth and death rate are the same. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import numpy + +In \[1\]: B = 126372.0/365.0 + +In \[1\]: N = 7781984.0 + +In \[1\]: ode = common_models.SIR_Birth_Death({\'beta\':3.6, +\'gamma\':0.2, \'B\':B/N, \'mu\':B/N}) + +In \[1\]: t = numpy.linspace(0, 35\*365, 10001) + +In \[1\]: x0 = \[0.065, 123.0\*(5.0/30.0)/N, 0.0\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_sir_bd.png In \[1\]: ode.plot() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SIS.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/SIS.ipynb new file mode 100644 index 00000000..ba80a5c7 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SIS.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.SIS`\n", + "\n", + "A standard SIS model without the total population $N$. We assume here\n", + "that $S + I = N$ so we can always normalize to 1. Evidently, the state\n", + "$S$ is not required for understanding the model because it is a\n", + "deterministic function of state $I$.\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta S I + \\gamma I \\\\\n", + "\\frac{dI}{dt} &= \\beta S I - \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "An example would be\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: ode = common_models.SIS({'beta':0.5,'gamma':0.2})\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 20, 101)\n", + "\n", + "In \\[1\\]: x0 = \\[1.0, 0.1\\]\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_sis.png In \\[1\\]: ode.plot()\n", + "\n", + "In \\[1\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SIS.md b/doc/doc/pygom-doc/mdconvert/common_models/SIS.md new file mode 100644 index 00000000..e7d79d8e --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SIS.md @@ -0,0 +1,35 @@ +# `.SIS`{.interpreted-text role="func"} + +A standard SIS model without the total population $N$. We assume here +that $S + I = N$ so we can always normalize to 1. Evidently, the state +$S$ is not required for understanding the model because it is a +deterministic function of state $I$. + +$$\begin{aligned} +\frac{dS}{dt} &= -\beta S I + \gamma I \\ +\frac{dI}{dt} &= \beta S I - \gamma I. +\end{aligned}$$ + +An example would be + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: import numpy + +In \[1\]: ode = common_models.SIS({\'beta\':0.5,\'gamma\':0.2}) + +In \[1\]: t = numpy.linspace(0, 20, 101) + +In \[1\]: x0 = \[1.0, 0.1\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_sis.png In \[1\]: ode.plot() + +In \[1\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SIS_Periodic.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/SIS_Periodic.ipynb new file mode 100644 index 00000000..4c2d7066 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SIS_Periodic.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.SIS_Periodic`\n", + "\n", + "Now we look at an extension of the SIS model by incorporating periodic\n", + "contact rate. Note how our equation is defined by a single ode for state\n", + "**I**.\n", + "\n", + "$$\\frac{dI}{dt} = (\\beta(t)N - \\alpha) I - \\beta(t)I^{2}$$\n", + "\n", + "where $\\beta(t) = 2 - 1.8 \\cos(5t)$. As the name suggests, it achieves a\n", + "(stable) periodic solution. Note how the plots have two sub-graphs,\n", + "where $\\tau$ is in fact our time component which we have taken out of\n", + "the original equation when converting it to a automonous system.\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: ode = common_models.SIS_Periodic({'alpha':1.0})\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 10, 101)\n", + "\n", + "In \\[1\\]: x0 = \\[0.1,0.\\]\n", + "\n", + "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_sis_periodic.png In \\[1\\]: ode.plot()\n", + "\n", + "In \\[1\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/SIS_Periodic.md b/doc/doc/pygom-doc/mdconvert/common_models/SIS_Periodic.md new file mode 100644 index 00000000..1f46029d --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/SIS_Periodic.md @@ -0,0 +1,34 @@ +# `.SIS_Periodic`{.interpreted-text role="func"} + +Now we look at an extension of the SIS model by incorporating periodic +contact rate. Note how our equation is defined by a single ode for state +**I**. + +$$\frac{dI}{dt} = (\beta(t)N - \alpha) I - \beta(t)I^{2}$$ + +where $\beta(t) = 2 - 1.8 \cos(5t)$. As the name suggests, it achieves a +(stable) periodic solution. Note how the plots have two sub-graphs, +where $\tau$ is in fact our time component which we have taken out of +the original equation when converting it to a automonous system. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: import numpy + +In \[1\]: ode = common_models.SIS_Periodic({\'alpha\':1.0}) + +In \[1\]: t = numpy.linspace(0, 10, 101) + +In \[1\]: x0 = \[0.1,0.\] + +In \[1\]: ode.initial_values = (x0, t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_sis_periodic.png In \[1\]: ode.plot() + +In \[1\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/common_models/vanDelPol.ipynb b/doc/doc/pygom-doc/mdconvert/common_models/vanDelPol.ipynb new file mode 100644 index 00000000..eceaacda --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/vanDelPol.ipynb @@ -0,0 +1,83 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `.vanDelPol`\n", + "\n", + "The van Del Pol oscillator [\\[vanderpol1926\\]]()\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= \\sigma (y-x) \\\\\n", + "\\frac{dy}{dt} &= x (\\rho - z) - y \\\\\n", + "\\frac{dz}{dt} &= xy - \\beta z\n", + "\\end{aligned}$$\n", + "\n", + "A classic example is\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 20, 1000)\n", + "\n", + "In \\[1\\]: ode = common_models.vanDelPol({'mu':1.0})\n", + "\n", + "In \\[1\\]: ode.initial_values = (\\[2.0, 0.0\\], t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "@savefig common_models_vanDelPol.png In \\[1\\]: ode.plot()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "In \\[1\\]: f = plt.figure()\n", + "\n", + "In \\[1\\]: plt.plot(solution\\[:,0\\], solution\\[:,1\\]);\n", + "\n", + "@savefig common_models_vanDelPol_yprime_y\\_1.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "When we change the value, as per\n", + "[Wolfram](http://mathworld.wolfram.com/vanderPolEquation.html)\n", + "\n", + "In \\[1\\]: t = numpy.linspace(0, 100, 1000)\n", + "\n", + "In \\[1\\]: ode.parameters = {'mu':1.0}\n", + "\n", + "In \\[1\\]: ode.initial_values = (\\[0.0, 0.2\\], t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "In \\[1\\]: f = plt.figure()\n", + "\n", + "In \\[1\\]: plt.plot(solution\\[:,0\\],solution\\[:,1\\]);\n", + "\n", + "@savefig common_models_vanDelPol_yprime_y\\_2.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "In \\[1\\]: ode.parameters = {'mu':0.2}\n", + "\n", + "In \\[1\\]: ode.initial_values = (\\[0.0, 0.2\\], t\\[0\\])\n", + "\n", + "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "In \\[1\\]: f = plt.figure()\n", + "\n", + "In \\[1\\]: plt.plot(solution\\[:,0\\], solution\\[:,1\\]);\n", + "\n", + "@savefig common_models_vanDelPol_yprime_y\\_3.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/common_models/vanDelPol.md b/doc/doc/pygom-doc/mdconvert/common_models/vanDelPol.md new file mode 100644 index 00000000..a8c0879d --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/common_models/vanDelPol.md @@ -0,0 +1,74 @@ +# `.vanDelPol`{.interpreted-text role="func"} + +The van Del Pol oscillator [\[vanderpol1926\]]() + +$$\begin{aligned} +\frac{dx}{dt} &= \sigma (y-x) \\ +\frac{dy}{dt} &= x (\rho - z) - y \\ +\frac{dz}{dt} &= xy - \beta z +\end{aligned}$$ + +A classic example is + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[1\]: import numpy + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: t = numpy.linspace(0, 20, 1000) + +In \[1\]: ode = common_models.vanDelPol({\'mu\':1.0}) + +In \[1\]: ode.initial_values = (\[2.0, 0.0\], t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +\@savefig common_models_vanDelPol.png In \[1\]: ode.plot() + +In \[1\]: plt.close() + +In \[1\]: f = plt.figure() + +In \[1\]: plt.plot(solution\[:,0\], solution\[:,1\]); + +\@savefig common_models_vanDelPol_yprime_y\_1.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: + +When we change the value, as per +[Wolfram](http://mathworld.wolfram.com/vanderPolEquation.html) + +::: {.ipython} +In \[1\]: t = numpy.linspace(0, 100, 1000) + +In \[1\]: ode.parameters = {\'mu\':1.0} + +In \[1\]: ode.initial_values = (\[0.0, 0.2\], t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +In \[1\]: f = plt.figure() + +In \[1\]: plt.plot(solution\[:,0\],solution\[:,1\]); + +\@savefig common_models_vanDelPol_yprime_y\_2.png In \[1\]: plt.show() + +In \[1\]: plt.close() + +In \[1\]: ode.parameters = {\'mu\':0.2} + +In \[1\]: ode.initial_values = (\[0.0, 0.2\], t\[0\]) + +In \[1\]: solution = ode.integrate(t\[1::\]) + +In \[1\]: f = plt.figure() + +In \[1\]: plt.plot(solution\[:,0\], solution\[:,1\]); + +\@savefig common_models_vanDelPol_yprime_y\_3.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/.md new file mode 100644 index 00000000..07745c25 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/.md @@ -0,0 +1,201 @@ +# Solving Boundary Value Problems {#bvpSimple} + +In addition to finding solutions for an IVP and estimate the unknown +parameters, this package also allows you to solve BVP with a little bit +of imagination. Here, we are going to show how a BVP can be solved by +treating it as a parameter estimation problem. Essentially, a shooting +method where the first boundary condition defines the initial condition +of an IVP and the second boundary condition is an observation. Two +examples, both from MATLAB[^1], will be shown here. + +## Simple model 1 + +We are trying to find the solution to the second order differential +equation + +$$\nabla^{2} y + |y| = 0$$ + +subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert +this into a set of first order ODE + +$$\begin{aligned} +\frac{d y_{0}}{dt} &= y_{1} \\ +\frac{d y_{1}}{dt} &= -|y_{0}| +\end{aligned}$$ + +using an auxiliary variable $y_{1} = \nabla y$ and $y_{0} = y$. Setting +up the system below + +::: {.ipython} +In \[1\]: from pygom import Transition, TransitionType, +DeterministicOde, SquareLoss + +In \[1\]: import matplotlib.pyplot as plt + +In \[2\]: stateList = \[\'y0\', \'y1\'\] + +In \[3\]: paramList = \[\] + +In \[4\]: ode1 = Transition(origin=\'y0\', + +: \...: equation=\'y1\', \...: transition_type=TransitionType.ODE) + +In \[5\]: ode2 = Transition(origin=\'y1\', + +: \...: equation=\'-abs(y0)\', \...: + transition_type=TransitionType.ODE) + +In \[6\]: model = DeterministicOde(stateList, + +: \...: paramList, \...: ode=\[ode1, ode2\]) + +In \[7\]: model.get_ode_eqn() +::: + +We check that the equations are correct before proceeding to set up our +loss function. + +::: {.ipython} +In \[1\]: import numpy + +In \[2\]: from scipy.optimize import minimize + +In \[3\]: initialState = \[0.0, 1.0\] + +In \[4\]: t = numpy.linspace(0, 4, 100) + +In \[5\]: model.initial_values = (initialState, t\[0\]) + +In \[6\]: solution = model.integrate(t\[1::\]) + +In \[7\]: f = plt.figure() + +\@savefig bvp1_random_guess_plot.png In \[8\]: model.plot() + +In \[9\]: plt.close() +::: + +Setting up the second boundary condition $y(4) = -2$ is easy, because +that is just a single observation attached to the state $y_{1}$. +Enforcing the first boundary condition requires us to set it as the +initial condition. Because the condition only states that $y(0) = 0$, +the starting value of the other state $y_1$ is free. We let our loss +object know that it is free through the targetState input argument. + +::: {.ipython} +In \[10\]: theta = \[0.0\] + +In \[11\]: obj = SquareLoss(theta=theta, + +: \....: ode=model, \....: x0=initialState, \....: t0=t\[0\], \....: + t=t\[-1\], \....: y=\[-2\], \....: state_name=\[\'y0\'\], \....: + target_state=\[\'y1\'\]) + +In \[12\]: thetaHat = minimize(fun=obj.costIV, x0=\[0.0\]) + +In \[13\]: print(thetaHat) + +In \[14\]: model.initial_values = (\[0.0\] + thetaHat\[\'x\'\].tolist(), +t\[0\]) + +In \[15\]: solution = model.integrate(t\[1::\]) + +In \[16\]: f = plt.figure() + +\@savefig bvp1_solution_plot.png In \[17\]: model.plot() + +In \[18\]: plt.close() +::: + +We are going to visualize the solution, and also check the boundary +condition. The first became our initial condition, so it is always +satisfied and only the latter is of concern, which is zero (subject to +numerical error) from thetaHat. + +## Simple model 2 + +Our second example is different as it involves an actual parameter and +also time. We have the Mathieu\'s Equation + +$$\nabla^{2} y + \left(p - 2q \cos(2x)\right)y = 0$$ + +and the aim is to compute the fourth eigenvalue $q=5$. There are three +boundary conditions + +$$\nabla y(0) = 0, \quad \nabla y(\pi) = 0, \quad y(0) = 1$$ + +and we aim to solve it by converting it to a first order ODE and tackle +it as an IVP. As our model object does not allow the use of the time +component in the equations, we introduce a anxiliary state $\tau$ that +replaces time $t$. Rewrite the equations using +$y_{0} = y, y_{1} = \nabla y$ and define our model as + +::: {.ipython} +In \[1\]: stateList = \[\'y0\', \'y1\', \'tau\'\] + +In \[2\]: paramList = \[\'p\'\] + +In \[3\]: ode1 = Transition(\'y0\', \'y1\', TransitionType.ODE) + +In \[4\]: ode2 = Transition(\'y1\', \'-(p - 2\*5\*cos(2\*tau))\*y0\', +TransitionType.ODE) + +In \[5\]: ode3 = Transition(\'tau\', \'1\', TransitionType.ODE) + +In \[6\]: model = DeterministicOde(stateList, paramList, ode=\[ode1, +ode2, ode3\]) + +In \[7\]: theta = \[1.0, 1.0, 0.0\] + +In \[8\]: p = 15.0 + +In \[9\]: t = numpy.linspace(0, numpy.pi) + +In \[10\]: model.parameters = \[(\'p\',p)\] + +In \[11\]: model.initial_values = (theta, t\[0\]) + +In \[12\]: solution = model.integrate(t\[1::\]) + +In \[13\]: f = plt.figure() + +\@savefig bvp2_random_guess_plot.png In \[14\]: model.plot() + +In \[15\]: plt.close() +::: + +Now we are ready to setup the estimation. Like before, we setup the +second boundary condition by pretending that it is an observation. We +have all the initial conditions defined by the first boundary condition + +::: {.ipython} +In \[1\]: obj = SquareLoss(15.0, model, x0=\[1.0, 0.0, 0.0\], t0=0.0, +t=numpy.pi, y=0.0, state_name=\'y1\') + +In \[2\]: xhatObj = minimize(obj.cost,\[15\]) + +In \[3\]: print(xhatObj) + +In \[4\]: model.parameters = \[(\'p\', xhatObj\[\'x\'\]\[0\])\] + +In \[5\]: model.initial_values = (\[1.0, 0.0, 0.0\], t\[0\]) + +In \[5\]: solution = model.integrate(t\[1::\]) + +In \[6\]: f = plt.figure() + +\@savefig bvp2_solution_plot.png In \[7\]: model.plot() + +In \[8\]: plt.close() +::: + +The plot of the solution shows the path that satisfies all boundary +condition. The last subplot is time which obvious is redundant here but +the `DeterministicOde.plot`{.interpreted-text role="meth"} method is not +yet able to recognize the time component. Possible speed up can be +achieved through the use of derivative information or via root finding +method that tackles the gradient directly, instead of the cost function. + +**Reference** + +[^1]: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/bvpSimple.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/bvpSimple.ipynb new file mode 100644 index 00000000..6ca0ce57 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/bvpSimple.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solving Boundary Value Problems\n", + "\n", + "In addition to finding solutions for an IVP and estimate the unknown\n", + "parameters, this package also allows you to solve BVP with a little bit\n", + "of imagination. Here, we are going to show how a BVP can be solved by\n", + "treating it as a parameter estimation problem. Essentially, a shooting\n", + "method where the first boundary condition defines the initial condition\n", + "of an IVP and the second boundary condition is an observation. Two\n", + "examples, both from MATLAB[1], will be shown here.\n", + "\n", + "## Simple model 1\n", + "\n", + "We are trying to find the solution to the second order differential\n", + "equation\n", + "\n", + "$$\\nabla^{2} y + |y| = 0$$\n", + "\n", + "subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert\n", + "this into a set of first order ODE\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{d y_{0}}{dt} &= y_{1} \\\\\n", + "\\frac{d y_{1}}{dt} &= -|y_{0}|\n", + "\\end{aligned}$$\n", + "\n", + "using an auxiliary variable $y_{1} = \\nabla y$ and $y_{0} = y$. Setting\n", + "up the system below\n", + "\n", + "In \\[1\\]: from pygom import Transition, TransitionType,\n", + "DeterministicOde, SquareLoss\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[2\\]: stateList = \\['y0', 'y1'\\]\n", + "\n", + "In \\[3\\]: paramList = \\[\\]\n", + "\n", + "In \\[4\\]: ode1 = Transition(origin='y0', \n", + "...: equation='y1', ...: transition_type=TransitionType.ODE)\n", + "\n", + "In \\[5\\]: ode2 = Transition(origin='y1', \n", + "...: equation='-abs(y0)', ...: transition_type=TransitionType.ODE)\n", + "\n", + "In \\[6\\]: model = DeterministicOde(stateList, \n", + "...: paramList, ...: ode=\\[ode1, ode2\\])\n", + "\n", + "In \\[7\\]: model.get_ode_eqn()\n", + "\n", + "We check that the equations are correct before proceeding to set up our\n", + "loss function.\n", + "\n", + "In \\[1\\]: import numpy\n", + "\n", + "In \\[2\\]: from scipy.optimize import minimize\n", + "\n", + "In \\[3\\]: initialState = \\[0.0, 1.0\\]\n", + "\n", + "In \\[4\\]: t = numpy.linspace(0, 4, 100)\n", + "\n", + "In \\[5\\]: model.initial_values = (initialState, t\\[0\\])\n", + "\n", + "In \\[6\\]: solution = model.integrate(t\\[1::\\])\n", + "\n", + "In \\[7\\]: f = plt.figure()\n", + "\n", + "@savefig bvp1_random_guess_plot.png In \\[8\\]: model.plot()\n", + "\n", + "In \\[9\\]: plt.close()\n", + "\n", + "Setting up the second boundary condition $y(4) = -2$ is easy, because\n", + "that is just a single observation attached to the state $y_{1}$.\n", + "Enforcing the first boundary condition requires us to set it as the\n", + "initial condition. Because the condition only states that $y(0) = 0$,\n", + "the starting value of the other state $y_1$ is free. We let our loss\n", + "object know that it is free through the targetState input argument.\n", + "\n", + "In \\[10\\]: theta = \\[0.0\\]\n", + "\n", + "In \\[11\\]: obj = SquareLoss(theta=theta, \n", + ".…: ode=model, .…: x0=initialState, .…: t0=t\\[0\\], .…: t=t\\[-1\\], .…:\n", + "y=\\[-2\\], .…: state_name=\\['y0'\\], .…: target_state=\\['y1'\\])\n", + "\n", + "In \\[12\\]: thetaHat = minimize(fun=obj.costIV, x0=\\[0.0\\])\n", + "\n", + "In \\[13\\]: print(thetaHat)\n", + "\n", + "In \\[14\\]: model.initial_values = (\\[0.0\\] + thetaHat\\['x'\\].tolist(),\n", + "t\\[0\\])\n", + "\n", + "In \\[15\\]: solution = model.integrate(t\\[1::\\])\n", + "\n", + "In \\[16\\]: f = plt.figure()\n", + "\n", + "@savefig bvp1_solution_plot.png In \\[17\\]: model.plot()\n", + "\n", + "In \\[18\\]: plt.close()\n", + "\n", + "We are going to visualize the solution, and also check the boundary\n", + "condition. The first became our initial condition, so it is always\n", + "satisfied and only the latter is of concern, which is zero (subject to\n", + "numerical error) from thetaHat.\n", + "\n", + "## Simple model 2\n", + "\n", + "Our second example is different as it involves an actual parameter and\n", + "also time. We have the Mathieu's Equation\n", + "\n", + "$$\\nabla^{2} y + \\left(p - 2q \\cos(2x)\\right)y = 0$$\n", + "\n", + "and the aim is to compute the fourth eigenvalue $q=5$. There are three\n", + "boundary conditions\n", + "\n", + "$$\\nabla y(0) = 0, \\quad \\nabla y(\\pi) = 0, \\quad y(0) = 1$$\n", + "\n", + "and we aim to solve it by converting it to a first order ODE and tackle\n", + "it as an IVP. As our model object does not allow the use of the time\n", + "component in the equations, we introduce a anxiliary state $\\tau$ that\n", + "replaces time $t$. Rewrite the equations using\n", + "$y_{0} = y, y_{1} = \\nabla y$ and define our model as\n", + "\n", + "In \\[1\\]: stateList = \\['y0', 'y1', 'tau'\\]\n", + "\n", + "In \\[2\\]: paramList = \\['p'\\]\n", + "\n", + "In \\[3\\]: ode1 = Transition('y0', 'y1', TransitionType.ODE)\n", + "\n", + "In \\[4\\]: ode2 = Transition('y1', '-(p - 2\\*5\\*cos(2\\*tau))\\*y0',\n", + "TransitionType.ODE)\n", + "\n", + "In \\[5\\]: ode3 = Transition('tau', '1', TransitionType.ODE)\n", + "\n", + "In \\[6\\]: model = DeterministicOde(stateList, paramList, ode=\\[ode1,\n", + "ode2, ode3\\])\n", + "\n", + "In \\[7\\]: theta = \\[1.0, 1.0, 0.0\\]\n", + "\n", + "In \\[8\\]: p = 15.0\n", + "\n", + "In \\[9\\]: t = numpy.linspace(0, numpy.pi)\n", + "\n", + "In \\[10\\]: model.parameters = \\[('p',p)\\]\n", + "\n", + "In \\[11\\]: model.initial_values = (theta, t\\[0\\])\n", + "\n", + "In \\[12\\]: solution = model.integrate(t\\[1::\\])\n", + "\n", + "In \\[13\\]: f = plt.figure()\n", + "\n", + "@savefig bvp2_random_guess_plot.png In \\[14\\]: model.plot()\n", + "\n", + "In \\[15\\]: plt.close()\n", + "\n", + "Now we are ready to setup the estimation. Like before, we setup the\n", + "second boundary condition by pretending that it is an observation. We\n", + "have all the initial conditions defined by the first boundary condition\n", + "\n", + "In \\[1\\]: obj = SquareLoss(15.0, model, x0=\\[1.0, 0.0, 0.0\\], t0=0.0,\n", + "t=numpy.pi, y=0.0, state_name='y1')\n", + "\n", + "In \\[2\\]: xhatObj = minimize(obj.cost,\\[15\\])\n", + "\n", + "In \\[3\\]: print(xhatObj)\n", + "\n", + "In \\[4\\]: model.parameters = \\[('p', xhatObj\\['x'\\]\\[0\\])\\]\n", + "\n", + "In \\[5\\]: model.initial_values = (\\[1.0, 0.0, 0.0\\], t\\[0\\])\n", + "\n", + "In \\[5\\]: solution = model.integrate(t\\[1::\\])\n", + "\n", + "In \\[6\\]: f = plt.figure()\n", + "\n", + "@savefig bvp2_solution_plot.png In \\[7\\]: model.plot()\n", + "\n", + "In \\[8\\]: plt.close()\n", + "\n", + "The plot of the solution shows the path that satisfies all boundary\n", + "condition. The last subplot is time which obvious is redundant here but\n", + "the `DeterministicOde.plot` method is not yet able to recognize the time\n", + "component. Possible speed up can be achieved through the use of\n", + "derivative information or via root finding method that tackles the\n", + "gradient directly, instead of the cost function.\n", + "\n", + "**Reference**\n", + "\n", + "[1] " + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/bvpSimple.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/bvpSimple.md new file mode 100644 index 00000000..07745c25 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/bvpSimple.md @@ -0,0 +1,201 @@ +# Solving Boundary Value Problems {#bvpSimple} + +In addition to finding solutions for an IVP and estimate the unknown +parameters, this package also allows you to solve BVP with a little bit +of imagination. Here, we are going to show how a BVP can be solved by +treating it as a parameter estimation problem. Essentially, a shooting +method where the first boundary condition defines the initial condition +of an IVP and the second boundary condition is an observation. Two +examples, both from MATLAB[^1], will be shown here. + +## Simple model 1 + +We are trying to find the solution to the second order differential +equation + +$$\nabla^{2} y + |y| = 0$$ + +subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert +this into a set of first order ODE + +$$\begin{aligned} +\frac{d y_{0}}{dt} &= y_{1} \\ +\frac{d y_{1}}{dt} &= -|y_{0}| +\end{aligned}$$ + +using an auxiliary variable $y_{1} = \nabla y$ and $y_{0} = y$. Setting +up the system below + +::: {.ipython} +In \[1\]: from pygom import Transition, TransitionType, +DeterministicOde, SquareLoss + +In \[1\]: import matplotlib.pyplot as plt + +In \[2\]: stateList = \[\'y0\', \'y1\'\] + +In \[3\]: paramList = \[\] + +In \[4\]: ode1 = Transition(origin=\'y0\', + +: \...: equation=\'y1\', \...: transition_type=TransitionType.ODE) + +In \[5\]: ode2 = Transition(origin=\'y1\', + +: \...: equation=\'-abs(y0)\', \...: + transition_type=TransitionType.ODE) + +In \[6\]: model = DeterministicOde(stateList, + +: \...: paramList, \...: ode=\[ode1, ode2\]) + +In \[7\]: model.get_ode_eqn() +::: + +We check that the equations are correct before proceeding to set up our +loss function. + +::: {.ipython} +In \[1\]: import numpy + +In \[2\]: from scipy.optimize import minimize + +In \[3\]: initialState = \[0.0, 1.0\] + +In \[4\]: t = numpy.linspace(0, 4, 100) + +In \[5\]: model.initial_values = (initialState, t\[0\]) + +In \[6\]: solution = model.integrate(t\[1::\]) + +In \[7\]: f = plt.figure() + +\@savefig bvp1_random_guess_plot.png In \[8\]: model.plot() + +In \[9\]: plt.close() +::: + +Setting up the second boundary condition $y(4) = -2$ is easy, because +that is just a single observation attached to the state $y_{1}$. +Enforcing the first boundary condition requires us to set it as the +initial condition. Because the condition only states that $y(0) = 0$, +the starting value of the other state $y_1$ is free. We let our loss +object know that it is free through the targetState input argument. + +::: {.ipython} +In \[10\]: theta = \[0.0\] + +In \[11\]: obj = SquareLoss(theta=theta, + +: \....: ode=model, \....: x0=initialState, \....: t0=t\[0\], \....: + t=t\[-1\], \....: y=\[-2\], \....: state_name=\[\'y0\'\], \....: + target_state=\[\'y1\'\]) + +In \[12\]: thetaHat = minimize(fun=obj.costIV, x0=\[0.0\]) + +In \[13\]: print(thetaHat) + +In \[14\]: model.initial_values = (\[0.0\] + thetaHat\[\'x\'\].tolist(), +t\[0\]) + +In \[15\]: solution = model.integrate(t\[1::\]) + +In \[16\]: f = plt.figure() + +\@savefig bvp1_solution_plot.png In \[17\]: model.plot() + +In \[18\]: plt.close() +::: + +We are going to visualize the solution, and also check the boundary +condition. The first became our initial condition, so it is always +satisfied and only the latter is of concern, which is zero (subject to +numerical error) from thetaHat. + +## Simple model 2 + +Our second example is different as it involves an actual parameter and +also time. We have the Mathieu\'s Equation + +$$\nabla^{2} y + \left(p - 2q \cos(2x)\right)y = 0$$ + +and the aim is to compute the fourth eigenvalue $q=5$. There are three +boundary conditions + +$$\nabla y(0) = 0, \quad \nabla y(\pi) = 0, \quad y(0) = 1$$ + +and we aim to solve it by converting it to a first order ODE and tackle +it as an IVP. As our model object does not allow the use of the time +component in the equations, we introduce a anxiliary state $\tau$ that +replaces time $t$. Rewrite the equations using +$y_{0} = y, y_{1} = \nabla y$ and define our model as + +::: {.ipython} +In \[1\]: stateList = \[\'y0\', \'y1\', \'tau\'\] + +In \[2\]: paramList = \[\'p\'\] + +In \[3\]: ode1 = Transition(\'y0\', \'y1\', TransitionType.ODE) + +In \[4\]: ode2 = Transition(\'y1\', \'-(p - 2\*5\*cos(2\*tau))\*y0\', +TransitionType.ODE) + +In \[5\]: ode3 = Transition(\'tau\', \'1\', TransitionType.ODE) + +In \[6\]: model = DeterministicOde(stateList, paramList, ode=\[ode1, +ode2, ode3\]) + +In \[7\]: theta = \[1.0, 1.0, 0.0\] + +In \[8\]: p = 15.0 + +In \[9\]: t = numpy.linspace(0, numpy.pi) + +In \[10\]: model.parameters = \[(\'p\',p)\] + +In \[11\]: model.initial_values = (theta, t\[0\]) + +In \[12\]: solution = model.integrate(t\[1::\]) + +In \[13\]: f = plt.figure() + +\@savefig bvp2_random_guess_plot.png In \[14\]: model.plot() + +In \[15\]: plt.close() +::: + +Now we are ready to setup the estimation. Like before, we setup the +second boundary condition by pretending that it is an observation. We +have all the initial conditions defined by the first boundary condition + +::: {.ipython} +In \[1\]: obj = SquareLoss(15.0, model, x0=\[1.0, 0.0, 0.0\], t0=0.0, +t=numpy.pi, y=0.0, state_name=\'y1\') + +In \[2\]: xhatObj = minimize(obj.cost,\[15\]) + +In \[3\]: print(xhatObj) + +In \[4\]: model.parameters = \[(\'p\', xhatObj\[\'x\'\]\[0\])\] + +In \[5\]: model.initial_values = (\[1.0, 0.0, 0.0\], t\[0\]) + +In \[5\]: solution = model.integrate(t\[1::\]) + +In \[6\]: f = plt.figure() + +\@savefig bvp2_solution_plot.png In \[7\]: model.plot() + +In \[8\]: plt.close() +::: + +The plot of the solution shows the path that satisfies all boundary +condition. The last subplot is time which obvious is redundant here but +the `DeterministicOde.plot`{.interpreted-text role="meth"} method is not +yet able to recognize the time component. Possible speed up can be +achieved through the use of derivative information or via root finding +method that tackles the gradient directly, instead of the cost function. + +**Reference** + +[^1]: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/common_models.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/common_models.ipynb new file mode 100644 index 00000000..938fed10 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/common_models.ipynb @@ -0,0 +1,31 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pre-defined Example common_models\n", + "\n", + "We have defined a set of models `common_models`, most of them commonly\n", + "used in epidemiology. They are there as examples and also save time for\n", + "end users. Most of them are of the compartmental type, and we use\n", + "standard naming conventions i.e. **S** = Susceptible, **E** = Exposed,\n", + "**I** = Infectious, **R** = Recovered. Extra state symbol will be\n", + "introduced when required.\n", + "\n", + "common_models/SIS.rst common_models/SIS_Periodic.rst\n", + "common_models/SIR.rst common_models/SIR_Birth_Death.rst\n", + "common_models/SEIR.rst common_models/SEIR_Multiple.rst\n", + "common_models/SEIR_Birth_Death.rst\n", + "common_models/SEIR_Birth_Death_Periodic.rst\n", + "common_models/Legrand_Ebola_SEIHFR.rst common_models/Lotka_Volterra.rst\n", + "common_models/Lotka_Volterra_4State.rst common_models/FitzHugh.rst\n", + "common_models/Lorenz.rst common_models/vanDelPol.rst\n", + "common_models/Robertson.rst" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/common_models.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/common_models.md new file mode 100644 index 00000000..994a2546 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/common_models.md @@ -0,0 +1,20 @@ +# Pre-defined Example common_models {#common_models} + +We have defined a set of models `common_models`{.interpreted-text +role="mod"}, most of them commonly used in epidemiology. They are there +as examples and also save time for end users. Most of them are of the +compartmental type, and we use standard naming conventions i.e. **S** = +Susceptible, **E** = Exposed, **I** = Infectious, **R** = Recovered. +Extra state symbol will be introduced when required. + +::: {.toctree} +common_models/SIS.rst common_models/SIS_Periodic.rst +common_models/SIR.rst common_models/SIR_Birth_Death.rst +common_models/SEIR.rst common_models/SEIR_Multiple.rst +common_models/SEIR_Birth_Death.rst +common_models/SEIR_Birth_Death_Periodic.rst +common_models/Legrand_Ebola_SEIHFR.rst common_models/Lotka_Volterra.rst +common_models/Lotka_Volterra_4State.rst common_models/FitzHugh.rst +common_models/Lorenz.rst common_models/vanDelPol.rst +common_models/Robertson.rst +::: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/epi.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/epi.ipynb new file mode 100644 index 00000000..48711b33 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/epi.ipynb @@ -0,0 +1,70 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Epidemic Analysis\n", + "\n", + "A common application of ordinary differential equations is in the field\n", + "of epidemiology modeling. More concretely, compartmental models that is\n", + "used to describe disease progression. We demonstrate some of the simple\n", + "algebraic analysis one may wish to take when given a compartment model.\n", + "Our use one of the simplest model, an SIR model with birth and death\n", + "processes, which is an extension of the one in `sir`. First, we\n", + "initialize the model below.\n", + "\n", + "In \\[1\\]: from pygom import common_models\n", + "\n", + "In \\[2\\]: ode = common_models.SIR_Birth_Death()\n", + "\n", + "In \\[3\\]: print(ode.get_ode_eqn())\n", + "\n", + "## Obtaining the R0\n", + "\n", + "The reproduction number, also known as the $R_{0}$, is the single most\n", + "powerful piece and reduced piece of information available from a\n", + "compartmental model. In a nutshell, it provides a single number - if the\n", + "parameters are known - which can the intuitive interpretation where\n", + "$R_{0} = 1$ defines the tipping point of an outbreak. A $R_{0}$ value of\n", + "more than one signifies an potential outbreak where less than one\n", + "indicates that the disease will stop spreading naturally.\n", + "\n", + "To obtain the $R_{0}$, we simply have to tell the function which states\n", + "represent the *disease state*, which in this case is the state **I**.\n", + "\n", + "In \\[1\\]: from pygom.model.epi_analysis import \\*\n", + "\n", + "In \\[2\\]: print(R0(ode, 'I'))\n", + "\n", + "## Algebraic R0\n", + "\n", + "We may also wish to get the $R_{0}$ in pure algebraic term. This can be\n", + "achieved by the following few lines. Note that the result below is\n", + "slightly different from the one above. The difference is due to the\n", + "internal working of the functions, where `getR0` computes the\n", + "disease-free equilibrium value for the states and substitute them back\n", + "into the equation.\n", + "\n", + "In \\[1\\]: F, V = disease_progression_matrices(ode, 'I')\n", + "\n", + "In \\[2\\]: e = R0_from_matrix(F, V)\n", + "\n", + "In \\[3\\]: print(e)\n", + "\n", + "To replicate the output before, we have to find the values where the\n", + "disease-free equilibrium will be achieved. Substitution can then be\n", + "performed to retrieve $R_{0}$ in pure parameters.\n", + "\n", + "In \\[1\\]: dfe = DFE(ode, \\['I'\\])\n", + "\n", + "In \\[2\\]: print(dfe)\n", + "\n", + "In \\[3\\]: print(e\\[0\\].subs(dfe))" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/epi.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/epi.md new file mode 100644 index 00000000..d10988f1 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/epi.md @@ -0,0 +1,65 @@ +# Simple Epidemic Analysis {#epi} + +A common application of ordinary differential equations is in the field +of epidemiology modeling. More concretely, compartmental models that is +used to describe disease progression. We demonstrate some of the simple +algebraic analysis one may wish to take when given a compartment model. +Our use one of the simplest model, an SIR model with birth and death +processes, which is an extension of the one in `sir`{.interpreted-text +role="ref"}. First, we initialize the model below. + +::: {.ipython} +In \[1\]: from pygom import common_models + +In \[2\]: ode = common_models.SIR_Birth_Death() + +In \[3\]: print(ode.get_ode_eqn()) +::: + +## Obtaining the R0 + +The reproduction number, also known as the $R_{0}$, is the single most +powerful piece and reduced piece of information available from a +compartmental model. In a nutshell, it provides a single number - if the +parameters are known - which can the intuitive interpretation where +$R_{0} = 1$ defines the tipping point of an outbreak. A $R_{0}$ value of +more than one signifies an potential outbreak where less than one +indicates that the disease will stop spreading naturally. + +To obtain the $R_{0}$, we simply have to tell the function which states +represent the *disease state*, which in this case is the state **I**. + +::: {.ipython} +In \[1\]: from pygom.model.epi_analysis import \* + +In \[2\]: print(R0(ode, \'I\')) +::: + +## Algebraic R0 + +We may also wish to get the $R_{0}$ in pure algebraic term. This can be +achieved by the following few lines. Note that the result below is +slightly different from the one above. The difference is due to the +internal working of the functions, where `getR0`{.interpreted-text +role="func"} computes the disease-free equilibrium value for the states +and substitute them back into the equation. + +::: {.ipython} +In \[1\]: F, V = disease_progression_matrices(ode, \'I\') + +In \[2\]: e = R0_from_matrix(F, V) + +In \[3\]: print(e) +::: + +To replicate the output before, we have to find the values where the +disease-free equilibrium will be achieved. Substitution can then be +performed to retrieve $R_{0}$ in pure parameters. + +::: {.ipython} +In \[1\]: dfe = DFE(ode, \[\'I\'\]) + +In \[2\]: print(dfe) + +In \[3\]: print(e\[0\].subs(dfe)) +::: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/epijson.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/epijson.ipynb new file mode 100644 index 00000000..1df66d51 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/epijson.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reading and using EpiJSON data\n", + "\n", + "Epidemiology data is complicated due to the many different stages a\n", + "patient can go through and whether a modeling technique is applicable\n", + "depends heavily on the recording of data. EpiJSON is a framework whih\n", + "tries to captures all the information [\\[Finnie2016\\]](), in a JSON\n", + "format as the name suggests.\n", + "\n", + "This package provides the functionality to process EpiJSON data. Due to\n", + "the nature of this package, modeling of ode, it processes the data file\n", + "with this in mind. The output is therefore in the cumulative form as\n", + "default, shown below, in a `pandas.DataFrame` format. The input can be\n", + "in a string format, a file or already a `dict`.\n", + "\n", + "In \\[1\\]: from pygom.loss.read_epijson import epijson_to_data_frame\n", + "\n", + "In \\[2\\]: import pkgutil\n", + "\n", + "In \\[3\\]: data = pkgutil.get_data('pygom', 'data/eg1.json')\n", + "\n", + "In \\[3\\]: df = epijson_to_data_frame(data)\n", + "\n", + "In \\[4\\]: print(df)\n", + "\n", + "Given that the aim of loading the data is usually for model fitting, we\n", + "allow EpiJSON as input directly to the loss class\n", + "`pygom.loss.EpijsonLoss` which uses the Poisson loss under the hood.\n", + "\n", + "In \\[1\\]: from pygom.model import common_models\n", + "\n", + "In \\[2\\]: from pygom.loss.epijson_loss import EpijsonLoss\n", + "\n", + "In \\[3\\]: ode = common_models.SIR(\\[0.5, 0.3\\])\n", + "\n", + "In \\[4\\]: obj = EpijsonLoss(\\[0.005, 0.03\\], ode, data, 'Death', 'R',\n", + "\\[300, 2, 0\\])\n", + "\n", + "In \\[5\\]: print(obj.cost())\n", + "\n", + "In \\[6\\]: print(obj.\\_df)\n", + "\n", + "Given an initialized object, all the operations are inherited from\n", + "`pygom.loss.BaseLoss`. We demonstrated above how to calculate the cost\n", + "and the rest will not be shown for brevity. The data frame is stored\n", + "inside of the loss object and can be retrieved for inspection at any\n", + "time point.\n", + "\n", + "Rather unfortunately, initial values for the states is still required,\n", + "but the time is not. When the time is not supplied, then the first time\n", + "point in the data will be treated as $t0$. The input Death indicate which column of the data is used\n", + "and $R$ the corresponding state the data belongs to." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/epijson.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/epijson.md new file mode 100644 index 00000000..166bf3f7 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/epijson.md @@ -0,0 +1,58 @@ +# Reading and using EpiJSON data {#epijson} + +Epidemiology data is complicated due to the many different stages a +patient can go through and whether a modeling technique is applicable +depends heavily on the recording of data. EpiJSON is a framework whih +tries to captures all the information [\[Finnie2016\]](), in a JSON +format as the name suggests. + +This package provides the functionality to process EpiJSON data. Due to +the nature of this package, modeling of ode, it processes the data file +with this in mind. The output is therefore in the cumulative form as +default, shown below, in a `pandas.DataFrame`{.interpreted-text +role="class"} format. The input can be in a string format, a file or +already a `dict`{.interpreted-text role="class"}. + +::: {.ipython} +In \[1\]: from pygom.loss.read_epijson import epijson_to_data_frame + +In \[2\]: import pkgutil + +In \[3\]: data = pkgutil.get_data(\'pygom\', \'data/eg1.json\') + +In \[3\]: df = epijson_to_data_frame(data) + +In \[4\]: print(df) +::: + +Given that the aim of loading the data is usually for model fitting, we +allow EpiJSON as input directly to the loss class +`pygom.loss.EpijsonLoss`{.interpreted-text role="class"} which uses the +Poisson loss under the hood. + +::: {.ipython} +In \[1\]: from pygom.model import common_models + +In \[2\]: from pygom.loss.epijson_loss import EpijsonLoss + +In \[3\]: ode = common_models.SIR(\[0.5, 0.3\]) + +In \[4\]: obj = EpijsonLoss(\[0.005, 0.03\], ode, data, \'Death\', +\'R\', \[300, 2, 0\]) + +In \[5\]: print(obj.cost()) + +In \[6\]: print(obj.\_df) +::: + +Given an initialized object, all the operations are inherited from +`pygom.loss.BaseLoss`{.interpreted-text role="class"}. We demonstrated +above how to calculate the cost and the rest will not be shown for +brevity. The data frame is stored inside of the loss object and can be +retrieved for inspection at any time point. + +Rather unfortunately, initial values for the states is still required, +but the time is not. When the time is not supplied, then the first time +point in the data will be treated as $t0$. The input [Death]{.title-ref} +indicate which column of the data is used and $R$ the corresponding +state the data belongs to. diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate1.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate1.ipynb new file mode 100644 index 00000000..332c081f --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate1.ipynb @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example: Parameter Estimation 1\n", + "\n", + "## Estimation under square loss\n", + "\n", + "To ease the estimation process when given data, a separate module\n", + "`ode_loss` has been constructed for observations coming from a single\n", + "state. We demonstrate how to do it via two examples, first, a standard\n", + "SIR model, then the Legrand SEIHFR model from [\\[Legrand2007\\]]() used\n", + "for Ebola in `estimate2`.\n", + "\n", + "### SIR Model\n", + "\n", + "We set up an SIR model as seen previously in `sir`.\n", + "\n", + "In \\[176\\]: from pygom import SquareLoss, common_models\n", + "\n", + "In \\[179\\]: import numpy\n", + "\n", + "In \\[180\\]: import scipy.integrate\n", + "\n", + "In \\[184\\]: import matplotlib.pyplot\n", + "\n", + "In \\[185\\]: \\# Again, standard SIR model with 2 parameter. See the first\n", + "script!\n", + "\n", + "In \\[191\\]: \\# define the parameters\n", + "\n", + "In \\[192\\]: paramEval = \\[('beta',0.5), ('gamma',1.0/3.0)\\]\n", + "\n", + "In \\[189\\]: \\# initialize the model\n", + "\n", + "In \\[190\\]: ode = common_models.SIR(paramEval)\n", + "\n", + "and we assume that we have perfect information about the $R$\n", + "compartment.\n", + "\n", + "In \\[196\\]: x0 = \\[1, 1.27e-6, 0\\]\n", + "\n", + "In \\[197\\]: \\# Time, including the initial time t0 at t=0\n", + "\n", + "In \\[198\\]: t = numpy.linspace(0, 150, 1000)\n", + "\n", + "In \\[200\\]: \\# Standard. Find the solution.\n", + "\n", + "In \\[201\\]: solution = scipy.integrate.odeint(ode.ode, x0, t)\n", + "\n", + "In \\[202\\]: y = solution\\[:,1:3\\].copy()\n", + "\n", + "Initialize the class with some initial guess\n", + "\n", + "In \\[209\\]: \\# our initial guess\n", + "\n", + "In \\[210\\]: theta = \\[0.2, 0.2\\]\n", + "\n", + "In \\[176\\]: objSIR = SquareLoss(theta, ode, x0, t\\[0\\], t\\[1::\\],\n", + "y\\[1::,:\\], \\['I','R'\\])\n", + "\n", + "Note that we need to provide the initial values, $x_{0}$ and $t_{0}$\n", + "differently to the observations $y$ and the corresponding time $t$.\n", + "Additionally, the state which the observation lies needs to be\n", + "specified. Either a single state, or multiple states are allowed, as\n", + "seen above.\n", + "\n", + "### Difference in gradient\n", + "\n", + "We have provided two different ways of obtaining the gradient, these are\n", + "explained in `gradient` in a bit more detail. First, lets see how\n", + "similar the output of the two methods are\n", + "\n", + "In \\[22\\]: objSIR.sensitivity()\n", + "\n", + "In \\[25\\]: objSIR.adjoint()\n", + "\n", + "and the time required to obtain the gradient for the SIR model under\n", + "$\\theta = (0.2,0.2)$, previously entered.\n", + "\n", + "In \\[22\\]: %timeit objSIR.sensitivity()\n", + "\n", + "In \\[25\\]: %timeit objSIR.adjoint()\n", + "\n", + "Obviously, the amount of time taken for both method is dependent on the\n", + "number of observations as well as the number of states. The effect on\n", + "the adjoint method as the number of observations differs can be quite\n", + "evident. This is because the adjoint method is under a discretization\n", + "which loops in Python where as the forward sensitivity equations are\n", + "solved simply via an integration. As the number of observation gets\n", + "larger, the affect of the Python loop becomes more obvious.\n", + "\n", + "Difference in gradient is larger when there are less observations. This\n", + "is because the adjoint method use interpolations on the output of the\n", + "ode between each consecutive time points. Given solution over the same\n", + "length of time, fewer discretization naturally leads to a less accurate\n", + "interpolation. Note that the interpolation is currently performed using\n", + "univaraite spline, due to the limitation of python packages. Ideally,\n", + "one would prefer to use an (adaptive) Hermite or Chebyshev\n", + "interpolation. Note how we ran the two gradient functions once before\n", + "timing it, that is because we only find the properties (Jacobian,\n", + "gradient) of the ode during runtime.\n", + "\n", + "### Optimized result\n", + "\n", + "Then standard optimization procedures with some suitable initial guess\n", + "should yield the correct result. It is important to set the boundaries\n", + "for compartmental models as we know that all the parameters are strictly\n", + "positive. We put a less restrictive inequality here for demonstration\n", + "purpose.\n", + "\n", + "In \\[211\\]: \\# what we think the bounds are\n", + "\n", + "In \\[212\\]: boxBounds = \\[(0.0,2.0),(0.0,2.0)\\]\n", + "\n", + "Then using the optimization routines in `scipy.optimize`, for example,\n", + "the *SLSQP* method with the gradient obtained by forward sensitivity.\n", + "\n", + "In \\[208\\]: from scipy.optimize import minimize\n", + "\n", + "In \\[213\\]: res = minimize(fun=objSIR.cost, \n", + ".….: jac=objSIR.sensitivity, .….: x0=theta, .….: bounds=boxBounds, .….:\n", + "method='SLSQP')\n", + "\n", + "In \\[214\\]: print(res)\n", + "\n", + "Other methods available in `scipy.optimize.minimize` can also be used,\n", + "such as the *L-BFGS-B* and *TNC*. We can also use methods that accepts\n", + "the exact Hessian such as *trust-ncg* but that should not be necessary\n", + "most of the time." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate1.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate1.md new file mode 100644 index 00000000..05f1fee7 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate1.md @@ -0,0 +1,144 @@ +# Example: Parameter Estimation 1 {#estimate1} + +## Estimation under square loss + +To ease the estimation process when given data, a separate module +`ode_loss`{.interpreted-text role="mod"} has been constructed for +observations coming from a single state. We demonstrate how to do it via +two examples, first, a standard SIR model, then the Legrand SEIHFR model +from [\[Legrand2007\]]() used for Ebola in `estimate2`{.interpreted-text +role="ref"}. + +### SIR Model + +We set up an SIR model as seen previously in `sir`{.interpreted-text +role="ref"}. + +::: {.ipython} +In \[176\]: from pygom import SquareLoss, common_models + +In \[179\]: import numpy + +In \[180\]: import scipy.integrate + +In \[184\]: import matplotlib.pyplot + +In \[185\]: \# Again, standard SIR model with 2 parameter. See the first +script! + +In \[191\]: \# define the parameters + +In \[192\]: paramEval = \[(\'beta\',0.5), (\'gamma\',1.0/3.0)\] + +In \[189\]: \# initialize the model + +In \[190\]: ode = common_models.SIR(paramEval) +::: + +and we assume that we have perfect information about the $R$ +compartment. + +::: {.ipython} +In \[196\]: x0 = \[1, 1.27e-6, 0\] + +In \[197\]: \# Time, including the initial time t0 at t=0 + +In \[198\]: t = numpy.linspace(0, 150, 1000) + +In \[200\]: \# Standard. Find the solution. + +In \[201\]: solution = scipy.integrate.odeint(ode.ode, x0, t) + +In \[202\]: y = solution\[:,1:3\].copy() +::: + +Initialize the class with some initial guess + +::: {.ipython} +In \[209\]: \# our initial guess + +In \[210\]: theta = \[0.2, 0.2\] + +In \[176\]: objSIR = SquareLoss(theta, ode, x0, t\[0\], t\[1::\], +y\[1::,:\], \[\'I\',\'R\'\]) +::: + +Note that we need to provide the initial values, $x_{0}$ and $t_{0}$ +differently to the observations $y$ and the corresponding time $t$. +Additionally, the state which the observation lies needs to be +specified. Either a single state, or multiple states are allowed, as +seen above. + +### Difference in gradient + +We have provided two different ways of obtaining the gradient, these are +explained in `gradient`{.interpreted-text role="ref"} in a bit more +detail. First, lets see how similar the output of the two methods are + +::: {.ipython} +In \[22\]: objSIR.sensitivity() + +In \[25\]: objSIR.adjoint() +::: + +and the time required to obtain the gradient for the SIR model under +$\theta = (0.2,0.2)$, previously entered. + +::: {.ipython} +In \[22\]: %timeit objSIR.sensitivity() + +In \[25\]: %timeit objSIR.adjoint() +::: + +Obviously, the amount of time taken for both method is dependent on the +number of observations as well as the number of states. The effect on +the adjoint method as the number of observations differs can be quite +evident. This is because the adjoint method is under a discretization +which loops in Python where as the forward sensitivity equations are +solved simply via an integration. As the number of observation gets +larger, the affect of the Python loop becomes more obvious. + +Difference in gradient is larger when there are less observations. This +is because the adjoint method use interpolations on the output of the +ode between each consecutive time points. Given solution over the same +length of time, fewer discretization naturally leads to a less accurate +interpolation. Note that the interpolation is currently performed using +univaraite spline, due to the limitation of python packages. Ideally, +one would prefer to use an (adaptive) Hermite or Chebyshev +interpolation. Note how we ran the two gradient functions once before +timing it, that is because we only find the properties (Jacobian, +gradient) of the ode during runtime. + +### Optimized result + +Then standard optimization procedures with some suitable initial guess +should yield the correct result. It is important to set the boundaries +for compartmental models as we know that all the parameters are strictly +positive. We put a less restrictive inequality here for demonstration +purpose. + +::: {.ipython} +In \[211\]: \# what we think the bounds are + +In \[212\]: boxBounds = \[(0.0,2.0),(0.0,2.0)\] +::: + +Then using the optimization routines in +`scipy.optimize`{.interpreted-text role="mod"}, for example, the *SLSQP* +method with the gradient obtained by forward sensitivity. + +::: {.ipython} +In \[208\]: from scipy.optimize import minimize + +In \[213\]: res = minimize(fun=objSIR.cost, + +: \.....: jac=objSIR.sensitivity, \.....: x0=theta, \.....: + bounds=boxBounds, \.....: method=\'SLSQP\') + +In \[214\]: print(res) +::: + +Other methods available in `scipy.optimize.minimize`{.interpreted-text +role="func"} can also be used, such as the *L-BFGS-B* and *TNC*. We can +also use methods that accepts the exact Hessian such as *trust-ncg* but +that should not be necessary most of the time. diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate2.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate2.ipynb new file mode 100644 index 00000000..898eb686 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate2.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example: Parameter Estimation 2\n", + "\n", + "Continuing from the `estimate1`, we show why estimating the parameters\n", + "for ode's are hard. This is especially true if there is a lack of data\n", + "or when there are too much flexibility in the model. Note that for\n", + "reproducibility purposes, only deterministic models are used here and a\n", + "fixed seed whenever a stochastic algorithm is needed.\n", + "\n", + "## Standard SEIR model\n", + "\n", + "We demonstrate the estimation on the recent Ebola outbreak in West\n", + "Africa. We use the number of deaths in Guinea and its corresponding time\n", + "the data was recorded. These data are publicly available and they can be\n", + "obtained easily on the internet such as\n", + ". It is stated out here for\n", + "simplicity.\n", + "\n", + "In \\[34\\]: \\# the number of deaths and cases in Guinea\n", + "\n", + "In \\[35\\]: yDeath = \\[29.0, 59.0, 60.0, 62.0, 66.0, 70.0, 70.0, 80.0, 83.0, 86.0, 95.0, 101.0, 106.0, 108.0, \n", + ".…: 122.0, 129.0, 136.0, 141.0, 143.0, 149.0, 155.0, 157.0, 158.0,\n", + "157.0, 171.0, 174.0, .…: 186.0, 193.0, 208.0, 215.0, 226.0, 264.0,\n", + "267.0, 270.0, 303.0, 305.0, 307.0, 309.0, .…: 304.0, 310.0, 310.0,\n", + "314.0, 319.0, 339.0, 346.0, 358.0, 363.0, 367.0, 373.0, 377.0, .…:\n", + "380.0, 394.0, 396.0, 406.0, 430.0, 494.0, 517.0, 557.0, 568.0, 595.0,\n", + "601.0, 632.0, .…: 635.0, 648.0, 710.0, 739.0, 768.0, 778.0, 843.0,\n", + "862.0, 904.0, 926.0, 997.0\\]\n", + "\n", + "In \\[35\\]: yCase = \\[49.0, 86.0, 86.0, 86.0, 103.0, 112.0, 112.0, 122.0, 127.0, 143.0, 151.0, 158.0, \n", + ".…: 159.0, 168.0, 197.0, 203.0, 208.0, 218.0, 224.0, 226.0, 231.0,\n", + "235.0, 236.0, .…: 233.0, 248.0, 258.0, 281.0, 291.0, 328.0, 344.0,\n", + "351.0, 398.0, 390.0, 390.0, .…: 413.0, 412.0, 408.0, 409.0, 406.0,\n", + "411.0, 410.0, 415.0, 427.0, 460.0, 472.0, .…: 485.0, 495.0, 495.0,\n", + "506.0, 510.0, 519.0, 543.0, 579.0, 607.0, 648.0, 771.0, .…: 812.0,\n", + "861.0, 899.0, 936.0, 942.0, 1008.0, 1022.0, 1074.0, 1157.0, 1199.0,\n", + "1298.0, .…: 1350.0, 1472.0, 1519.0, 1540.0, 1553.0, 1906.0\\]\n", + "\n", + "In \\[36\\]: \\# the corresponding time\n", + "\n", + "In \\[37\\]: t = \\[0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 13.0, 16.0, 18.0, 20.0, 23.0, 25.0, 26.0, 29.0, \n", + ".…: 32.0, 35.0, 40.0, 42.0, 44.0, 46.0, 49.0, 51.0, 62.0, 66.0, 67.0,\n", + "71.0, 73.0, 80.0, 86.0, 88.0, .…: 90.0, 100.0, 102.0, 106.0, 108.0,\n", + "112.0, 114.0, 117.0, 120.0, 123.0, 126.0, 129.0, 132.0, 135.0, .…:\n", + "137.0, 140.0, 142.0, 144.0, 147.0, 149.0, 151.0, 157.0, 162.0, 167.0,\n", + "169.0, 172.0, 175.0, 176.0, .…: 181.0, 183.0, 185.0, 190.0, 193.0,\n", + "197.0, 199.0, 204.0, 206.0, 211.0, 213.0, 218.0\\]\n", + "\n", + "### Simple estimation\n", + "\n", + "First ,we are going to fit a standard **SEIR** model to the data.\n", + "Details of the models can be found in `common_models` Defining the model\n", + "as usual with some random guess on what the parameters might be, here,\n", + "we choose the values to be the mid point of our feasible region (defined\n", + "by our box constraints later)\n", + "\n", + "In \\[1\\]: from pygom import SquareLoss, common_models\n", + "\n", + "In \\[1\\]: import numpy, scipy.optimize\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: theta = numpy.array(\\[5.0, 5.0, 5.0\\])\n", + "\n", + "In \\[2\\]: ode = common_models.SEIR(theta)\n", + "\n", + "In \\[3\\]: population = 1175e4\n", + "\n", + "In \\[4\\]: y = numpy.reshape(numpy.append(numpy.array(yCase),\n", + "numpy.array(yDeath)), (len(yCase),2), 'F')/population\n", + "\n", + "In \\[5\\]: x0 = \\[1., 0., 49.0/population, 29.0/population\\]\n", + "\n", + "In \\[6\\]: t0 = t\\[0\\]\n", + "\n", + "In \\[7\\]: objLegrand = SquareLoss(theta, ode, x0, t0, t\\[1::\\],\n", + "y\\[1::,:\\], \\['I','R'\\], numpy.sqrt(\\[population\\]\\*2))\n", + "\n", + "Then we optimize, first, assuming that the initial conditions are\n", + "accurate. Some relatively large bounds are used for this particular\n", + "problem.\n", + "\n", + "In \\[8\\]: boxBounds = \\[ (0.0,10.0), (0.0,10.0), (0.0,10.0) \\]\n", + "\n", + "In \\[9\\]: res = scipy.optimize.minimize(fun=objLegrand.cost, \n", + "...: jac=objLegrand.sensitivity, ...: x0=theta, ...: bounds=boxBounds,\n", + "...: method='l-bfgs-b')\n", + "\n", + "In \\[10\\]: print(res)\n", + "\n", + "In \\[11\\]: f = plt.figure()\n", + "\n", + "@savefig ebola_seir_straight.png In \\[12\\]: objLegrand.plot()\n", + "\n", + "In \\[13\\]: plt.close()\n", + "\n", + "We can see from our visual confirmation that the estimated parameters\n", + "are not exactly ideal. This is confirmed by the information returned\n", + "from the `scipy.optimize.minimize` routine, and probably caused by the\n", + "poor starting point. An attempt to find a more suitable value can be\n", + "done by some form of parameter space exploration. Given that the\n", + "evaluation of the objective function is not expensive here, we have\n", + "plenty of options to choose from. To reduce the number of packages\n", + "required to build this documentation, routines from `scipy.optimize`\n", + "remains our preferred option.\n", + "\n", + "### Improved initial guess\n", + "\n", + "In \\[8\\]: resDE = scipy.optimize.differential_evolution(objLegrand.cost,\n", + "bounds=boxBounds, polish=False, seed=20921391)\n", + "\n", + "In \\[9\\]: print(objLegrand.sensitivity(resDE\\['x'\\]))\n", + "\n", + "In \\[10\\]: f = plt.figure()\n", + "\n", + "@savefig ebola_seir_de.png In \\[11\\]: objLegrand.plot()\n", + "\n", + "In \\[12\\]: plt.close()\n", + "\n", + "Looking at the output of the estimates (below this paragraph), we can\n", + "see our inference on Ebola is wrong when compared to the *known* values\n", + "(from field observation) even though the graphs looks *\\`\\`reasonable\"*.\n", + "Namely, $\\gamma^{-1}$ the third element in the vector below, our time\n", + "from infectious to death, is within the expected range but $\\alpha^{-1}$\n", + "(second element), the incubation period, is a lot higher than expected.\n", + "\n", + "In \\[1\\]: 1/resDE\\['x'\\]\n", + "\n", + "### Multimodal surface\n", + "\n", + "A reason for this type of behavior is that we simply lack the\n", + "information/data to make proper inference. Without data on the state\n", + "$E$, the parameters $\\beta,\\alpha$ for the two states $I$ and $E$ are\n", + "dependent only on observations on $I$. Hence, some other random\n", + "combination of $\\beta,\\alpha$ that is capable of generating realization\n", + "close to observations in $I$ is feasible. In such cases, the only\n", + "requirement is that there exist some $\\gamma$ in the feasible region\n", + "that can compensate for the ill suited $\\beta,\\alpha$. For example, we\n", + "know (obtained elsewhere and not shown here) that there is another set\n", + "of parameters capable of generating a similar looking curves as before.\n", + "Note the reversal of magnitude in $\\beta$ and $\\alpha$.\n", + "\n", + "In \\[11\\]: objLegrand.cost(\\[3.26106524e+00, 2.24798702e-04,\n", + "1.23660721e-02\\])\n", + "\n", + "In \\[12\\]: \\#\\# objLegrand.cost(\\[ 0.02701867, 9.00004776, 0.01031861\\])\n", + "\\# similar graph\n", + "\n", + "@savefig ebola_seir_prior.png In \\[13\\]: objLegrand.plot()\n", + "\n", + "In \\[14\\]: plt.close()\n", + "\n", + "### With initial values as parameters\n", + "\n", + "Obviously, the assumption that the whole population being susceptible is\n", + "an overestimate. We now try to estimate the initial conditions of the\n", + "ode as well. Given previous estimates of the parameters\n", + "$\\hat{\\beta}, \\hat{\\alpha}, \\hat{\\gamma}$ it is appropriate to start our\n", + "initial guess there.\n", + "\n", + "Furthermore, given that we now estimate the initial values for all the\n", + "states, we can use the first time point as our observation. So our time\n", + "begins at $t = -1$ where our observations include the previous initial\n", + "condition, i.e. 49 and 29 for the number of cases and death at $t = 0$\n", + "respectively. The following code block demonstrates how we would do\n", + "that; feel free to try it out yourself to see the much improved result.\n", + "\n", + "In \\[1\\]: thetaIV = theta.tolist() + x0\n", + "\n", + "In \\[2\\]: thetaIV\\[3\\] -= 1e-8 \\# to make sure that the initial guess\n", + "satisfy the constraints\n", + "\n", + "In \\[3\\]: boxBoundsIV = boxBounds + \\[(0.,1.), (0.,1.), (0.,1.),\n", + "(0.,1.)\\]\n", + "\n", + "In \\[4\\]: objLegrand = SquareLoss(theta, ode, x0, -1, t, y, \\['I','R'\\],\n", + "numpy.sqrt(\\[population\\]\\*2))\n", + "\n", + "In \\[5\\]: resDEIV =\n", + "scipy.optimize.differential_evolution(objLegrand.costIV,\n", + "bounds=boxBoundsIV, polish=False, seed=20921391)\n", + "\n", + "In \\[6\\]: print(resDEIV)\n", + "\n", + "In \\[7\\]: f = plt.figure()\n", + "\n", + "In \\[8\\]: objLegrand.plot()\n", + "\n", + "In \\[9\\]: plt.close()\n", + "\n", + "## Legrand Ebola SEIHFR Model\n", + "\n", + "Next, we demonstrate the estimation on a model that is widely used in\n", + "the recent Ebola outbreak in west Africa. Again, the model has been\n", + "defined in `.common_models` already.\n", + "\n", + "In \\[1\\]: ode = common_models.Legrand_Ebola_SEIHFR()\n", + "\n", + "In \\[27\\]: \\# initial guess from the paper that studied the outbreak in\n", + "Congo\n", + "\n", + "In \\[28\\]: theta = numpy.array(\\[0.588,0.794,7.653, \\#\\#\\# the beta \n", + ".…: 10.0,9.6,5.0,2.0, \\#\\#\\# the omega .…: 7.0,0.81,0.80, \\#\\#\\# alpha,\n", + "delta, theta .…: 100.,1.0\\]) \\#\\#\\# kappa,intervention time\n", + "\n", + "In \\[29\\]: \\# initial conditions, note that we have a 0.0 at the end\n", + "because the model is a non-automonous ode which we have converted the\n", + "time component out\n", + "\n", + "In \\[30\\]: x0 = numpy.array(\\[population, 0.0, 49.0, 0.0, 0.0, 29.0,\n", + "0.0\\])/population\n", + "\n", + "In \\[30\\]: ode.parameters = theta\n", + "\n", + "In \\[31\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[32\\]: objLegrand = SquareLoss(theta, ode, x0, t\\[0\\], t\\[1::\\],\n", + "y\\[1::,:\\], \\['I','R'\\], numpy.sqrt(\\[population\\]\\*2))\n", + "\n", + "Now, it is important to set additional constraints accurately because a\n", + "simply box constraint is much larger than the feasible set. Namely,\n", + "$\\omega_{I}, \\omega_{D}$ are the time taken from onset until end of\n", + "infectious/death, which has to be bigger than $\\omega_{H}$, onset to\n", + "hospitalization given the nature of the disease. Therefore, we create\n", + "extra inequality constraints in addition to the box constraints\n", + "\n", + "In \\[549\\]: boxBounds = \\[ \n", + ".….: (0.001, 100.), \\# beta_I .….: (0.001, 100.), \\# beta_H .….: (0.001,\n", + "100.), \\# beta_F .….: (0.001, 100.), \\# omega_I .….: (0.001, 100.), \\#\n", + "omega_D .….: (0.001, 100.), \\# omega_H .….: (0.001, 100.), \\# omega_F\n", + ".….: (0.001, 100.), \\# alpha^{-1} .….: (0.0001, 1.), \\# delta .….:\n", + "(0.0001, 1.), \\# theta .….: (0.001, 1000.), \\# kappa .….: (0.,218.) \\#\n", + "intervention tine .….: \\]\n", + "\n", + "In \\[550\\]: cons = ({'type': 'ineq', 'fun' : lambda x:\n", + "numpy.array(\\[x\\[3\\]-x\\[5\\], x\\[4\\]-x\\[5\\]\\])})\n", + "\n", + "We can now try to find the optimal values, but because this is a\n", + "difficult problem that can take a very long time without guarantee on\n", + "the quality of solution\n", + "\n", + "In \\[213\\]: res = scipy.optimize.minimize(fun=objLegrand.cost, \n", + ".….: jac=objLegrand.sensitivity, .….: x0=theta, .….: constraints=cons,\n", + ".….: bounds=boxBounds, .….: method='SLSQP')\n", + "\n", + "In \\[214\\]: print(res)\n", + "\n", + "In \\[215\\]: f = plt.figure()\n", + "\n", + "@savefig ebola_legrand_runtime.png In \\[216\\]: objLegrand.plot()\n", + "\n", + "In \\[217\\]: plt.close()\n", + "\n", + "Evidently, the estimated parameters are very much unrealistic given that\n", + "a lot of them are near the boundaries. It is also known from other\n", + "sources that some of the epidemiology properties of Ebola, with\n", + "incubation period of around 2 weeks and a mortality rate of around 80\n", + "percent.\n", + "\n", + "As the estimate does not appear to provide anything sensible, we also\n", + "provide a set of values previously obtained (that looks semi-reasonable)\n", + "here plot the epidemic curve with the observations layered on top\n", + "\n", + "In \\[1\\]: theta = numpy.array(\\[3.96915071e-02, 1.72302620e+01, 1.99749990e+01, \n", + "...: 2.67759445e+01, 4.99999990e+01, 5.56122691e+00, ...:\n", + "4.99999990e+01, 8.51599523e+00, 9.99999000e-01, ...: 1.00000000e-06,\n", + "3.85807562e+00, 1.88385318e+00\\])\n", + "\n", + "In \\[2\\]: print(objLegrand.cost(theta))\n", + "\n", + "In \\[2\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "In \\[3\\]: f, axarr = plt.subplots(2,3)\n", + "\n", + "In \\[4\\]: axarr\\[0,0\\].plot(t, solution\\[:,0\\]);\n", + "\n", + "In \\[5\\]: axarr\\[0,0\\].set_title('Susceptible');\n", + "\n", + "In \\[6\\]: axarr\\[0,1\\].plot(t, solution\\[:,1\\]);\n", + "\n", + "In \\[7\\]: axarr\\[0,1\\].set_title('Exposed');\n", + "\n", + "In \\[8\\]: axarr\\[0,2\\].plot(t, solution\\[:,2\\]);\n", + "\n", + "In \\[9\\]: axarr\\[0,2\\].plot(t, y\\[:,0\\], 'r');\n", + "\n", + "In \\[10\\]: axarr\\[0,2\\].set_title('Infectious');\n", + "\n", + "In \\[11\\]: axarr\\[1,0\\].plot(t, solution\\[:,3\\]);\n", + "\n", + "In \\[12\\]: axarr\\[1,0\\].set_title('Hospitalised');\n", + "\n", + "In \\[13\\]: axarr\\[1,1\\].plot(t, solution\\[:,4\\]);\n", + "\n", + "In \\[14\\]: axarr\\[1,1\\].set_title('Awaiting Burial');\n", + "\n", + "In \\[15\\]: axarr\\[1,2\\].plot(t, solution\\[:,5\\]);\n", + "\n", + "In \\[16\\]: axarr\\[1,2\\].plot(t, y\\[:,1\\], 'r');\n", + "\n", + "In \\[17\\]: axarr\\[1,2\\].set_title('Removed');\n", + "\n", + "In \\[18\\]: f.text(0.5, 0.04, 'Days from outbreak', ha='center');\n", + "\n", + "In \\[19\\]: f.text(0.01, 0.5, 'Population', va='center',\n", + "rotation='vertical');\n", + "\n", + "In \\[20\\]: f.tight_layout();\n", + "\n", + "@savefig ebola_seihfr_straight_prior.png In \\[21\\]: plt.show()\n", + "\n", + "In \\[22\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate2.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate2.md new file mode 100644 index 00000000..aae49819 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/estimate2.md @@ -0,0 +1,353 @@ +# Example: Parameter Estimation 2 {#estimate2} + +Continuing from the `estimate1`{.interpreted-text role="ref"}, we show +why estimating the parameters for ode\'s are hard. This is especially +true if there is a lack of data or when there are too much flexibility +in the model. Note that for reproducibility purposes, only deterministic +models are used here and a fixed seed whenever a stochastic algorithm is +needed. + +## Standard SEIR model + +We demonstrate the estimation on the recent Ebola outbreak in West +Africa. We use the number of deaths in Guinea and its corresponding time +the data was recorded. These data are publicly available and they can be +obtained easily on the internet such as +. It is stated out here for +simplicity. + +::: {.ipython} +In \[34\]: \# the number of deaths and cases in Guinea + +In \[35\]: yDeath = \[29.0, 59.0, 60.0, 62.0, 66.0, 70.0, 70.0, 80.0, 83.0, 86.0, 95.0, 101.0, 106.0, 108.0, + +: \....: 122.0, 129.0, 136.0, 141.0, 143.0, 149.0, 155.0, 157.0, + 158.0, 157.0, 171.0, 174.0, \....: 186.0, 193.0, 208.0, 215.0, + 226.0, 264.0, 267.0, 270.0, 303.0, 305.0, 307.0, 309.0, \....: + 304.0, 310.0, 310.0, 314.0, 319.0, 339.0, 346.0, 358.0, 363.0, + 367.0, 373.0, 377.0, \....: 380.0, 394.0, 396.0, 406.0, 430.0, + 494.0, 517.0, 557.0, 568.0, 595.0, 601.0, 632.0, \....: 635.0, + 648.0, 710.0, 739.0, 768.0, 778.0, 843.0, 862.0, 904.0, 926.0, + 997.0\] + +In \[35\]: yCase = \[49.0, 86.0, 86.0, 86.0, 103.0, 112.0, 112.0, 122.0, 127.0, 143.0, 151.0, 158.0, + +: \....: 159.0, 168.0, 197.0, 203.0, 208.0, 218.0, 224.0, 226.0, + 231.0, 235.0, 236.0, \....: 233.0, 248.0, 258.0, 281.0, 291.0, + 328.0, 344.0, 351.0, 398.0, 390.0, 390.0, \....: 413.0, 412.0, + 408.0, 409.0, 406.0, 411.0, 410.0, 415.0, 427.0, 460.0, 472.0, + \....: 485.0, 495.0, 495.0, 506.0, 510.0, 519.0, 543.0, 579.0, + 607.0, 648.0, 771.0, \....: 812.0, 861.0, 899.0, 936.0, 942.0, + 1008.0, 1022.0, 1074.0, 1157.0, 1199.0, 1298.0, \....: 1350.0, + 1472.0, 1519.0, 1540.0, 1553.0, 1906.0\] + +In \[36\]: \# the corresponding time + +In \[37\]: t = \[0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 13.0, 16.0, 18.0, 20.0, 23.0, 25.0, 26.0, 29.0, + +: \....: 32.0, 35.0, 40.0, 42.0, 44.0, 46.0, 49.0, 51.0, 62.0, 66.0, + 67.0, 71.0, 73.0, 80.0, 86.0, 88.0, \....: 90.0, 100.0, 102.0, + 106.0, 108.0, 112.0, 114.0, 117.0, 120.0, 123.0, 126.0, 129.0, + 132.0, 135.0, \....: 137.0, 140.0, 142.0, 144.0, 147.0, 149.0, + 151.0, 157.0, 162.0, 167.0, 169.0, 172.0, 175.0, 176.0, \....: + 181.0, 183.0, 185.0, 190.0, 193.0, 197.0, 199.0, 204.0, 206.0, + 211.0, 213.0, 218.0\] +::: + +### Simple estimation + +First ,we are going to fit a standard **SEIR** model to the data. +Details of the models can be found in `common_models`{.interpreted-text +role="mod"} Defining the model as usual with some random guess on what +the parameters might be, here, we choose the values to be the mid point +of our feasible region (defined by our box constraints later) + +::: {.ipython} +In \[1\]: from pygom import SquareLoss, common_models + +In \[1\]: import numpy, scipy.optimize + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: theta = numpy.array(\[5.0, 5.0, 5.0\]) + +In \[2\]: ode = common_models.SEIR(theta) + +In \[3\]: population = 1175e4 + +In \[4\]: y = numpy.reshape(numpy.append(numpy.array(yCase), +numpy.array(yDeath)), (len(yCase),2), \'F\')/population + +In \[5\]: x0 = \[1., 0., 49.0/population, 29.0/population\] + +In \[6\]: t0 = t\[0\] + +In \[7\]: objLegrand = SquareLoss(theta, ode, x0, t0, t\[1::\], +y\[1::,:\], \[\'I\',\'R\'\], numpy.sqrt(\[population\]\*2)) +::: + +Then we optimize, first, assuming that the initial conditions are +accurate. Some relatively large bounds are used for this particular +problem. + +::: {.ipython} +In \[8\]: boxBounds = \[ (0.0,10.0), (0.0,10.0), (0.0,10.0) \] + +In \[9\]: res = scipy.optimize.minimize(fun=objLegrand.cost, + +: \...: jac=objLegrand.sensitivity, \...: x0=theta, \...: + bounds=boxBounds, \...: method=\'l-bfgs-b\') + +In \[10\]: print(res) + +In \[11\]: f = plt.figure() + +\@savefig ebola_seir_straight.png In \[12\]: objLegrand.plot() + +In \[13\]: plt.close() +::: + +We can see from our visual confirmation that the estimated parameters +are not exactly ideal. This is confirmed by the information returned +from the `scipy.optimize.minimize`{.interpreted-text role="func"} +routine, and probably caused by the poor starting point. An attempt to +find a more suitable value can be done by some form of parameter space +exploration. Given that the evaluation of the objective function is not +expensive here, we have plenty of options to choose from. To reduce the +number of packages required to build this documentation, routines from +`scipy.optimize`{.interpreted-text role="mod"} remains our preferred +option. + +### Improved initial guess + +::: {.ipython} +In \[8\]: resDE = scipy.optimize.differential_evolution(objLegrand.cost, +bounds=boxBounds, polish=False, seed=20921391) + +In \[9\]: print(objLegrand.sensitivity(resDE\[\'x\'\])) + +In \[10\]: f = plt.figure() + +\@savefig ebola_seir_de.png In \[11\]: objLegrand.plot() + +In \[12\]: plt.close() +::: + +Looking at the output of the estimates (below this paragraph), we can +see our inference on Ebola is wrong when compared to the *known* values +(from field observation) even though the graphs looks +*\`\`reasonable\"*. Namely, $\gamma^{-1}$ the third element in the +vector below, our time from infectious to death, is within the expected +range but $\alpha^{-1}$ (second element), the incubation period, is a +lot higher than expected. + +::: {.ipython} +In \[1\]: 1/resDE\[\'x\'\] +::: + +### Multimodal surface + +A reason for this type of behavior is that we simply lack the +information/data to make proper inference. Without data on the state +$E$, the parameters $\beta,\alpha$ for the two states $I$ and $E$ are +dependent only on observations on $I$. Hence, some other random +combination of $\beta,\alpha$ that is capable of generating realization +close to observations in $I$ is feasible. In such cases, the only +requirement is that there exist some $\gamma$ in the feasible region +that can compensate for the ill suited $\beta,\alpha$. For example, we +know (obtained elsewhere and not shown here) that there is another set +of parameters capable of generating a similar looking curves as before. +Note the reversal of magnitude in $\beta$ and $\alpha$. + +::: {.ipython} +In \[11\]: objLegrand.cost(\[3.26106524e+00, 2.24798702e-04, +1.23660721e-02\]) + +In \[12\]: \#\# objLegrand.cost(\[ 0.02701867, 9.00004776, 0.01031861\]) +\# similar graph + +\@savefig ebola_seir_prior.png In \[13\]: objLegrand.plot() + +In \[14\]: plt.close() +::: + +### With initial values as parameters + +Obviously, the assumption that the whole population being susceptible is +an overestimate. We now try to estimate the initial conditions of the +ode as well. Given previous estimates of the parameters +$\hat{\beta}, \hat{\alpha}, \hat{\gamma}$ it is appropriate to start our +initial guess there. + +Furthermore, given that we now estimate the initial values for all the +states, we can use the first time point as our observation. So our time +begins at $t = -1$ where our observations include the previous initial +condition, i.e. 49 and 29 for the number of cases and death at $t = 0$ +respectively. The following code block demonstrates how we would do +that; feel free to try it out yourself to see the much improved result. + +::: {.ipython verbatim=""} +In \[1\]: thetaIV = theta.tolist() + x0 + +In \[2\]: thetaIV\[3\] -= 1e-8 \# to make sure that the initial guess +satisfy the constraints + +In \[3\]: boxBoundsIV = boxBounds + \[(0.,1.), (0.,1.), (0.,1.), +(0.,1.)\] + +In \[4\]: objLegrand = SquareLoss(theta, ode, x0, -1, t, y, +\[\'I\',\'R\'\], numpy.sqrt(\[population\]\*2)) + +In \[5\]: resDEIV = +scipy.optimize.differential_evolution(objLegrand.costIV, +bounds=boxBoundsIV, polish=False, seed=20921391) + +In \[6\]: print(resDEIV) + +In \[7\]: f = plt.figure() + +In \[8\]: objLegrand.plot() + +In \[9\]: plt.close() +::: + +## Legrand Ebola SEIHFR Model + +Next, we demonstrate the estimation on a model that is widely used in +the recent Ebola outbreak in west Africa. Again, the model has been +defined in `.common_models`{.interpreted-text role="mod"} already. + +::: {.ipython} +In \[1\]: ode = common_models.Legrand_Ebola_SEIHFR() + +In \[27\]: \# initial guess from the paper that studied the outbreak in +Congo + +In \[28\]: theta = numpy.array(\[0.588,0.794,7.653, \#\#\# the beta + +: \....: 10.0,9.6,5.0,2.0, \#\#\# the omega \....: 7.0,0.81,0.80, + \#\#\# alpha, delta, theta \....: 100.,1.0\]) \#\#\# + kappa,intervention time + +In \[29\]: \# initial conditions, note that we have a 0.0 at the end +because the model is a non-automonous ode which we have converted the +time component out + +In \[30\]: x0 = numpy.array(\[population, 0.0, 49.0, 0.0, 0.0, 29.0, +0.0\])/population + +In \[30\]: ode.parameters = theta + +In \[31\]: ode.initial_values = (x0, t\[0\]) + +In \[32\]: objLegrand = SquareLoss(theta, ode, x0, t\[0\], t\[1::\], +y\[1::,:\], \[\'I\',\'R\'\], numpy.sqrt(\[population\]\*2)) +::: + +Now, it is important to set additional constraints accurately because a +simply box constraint is much larger than the feasible set. Namely, +$\omega_{I}, \omega_{D}$ are the time taken from onset until end of +infectious/death, which has to be bigger than $\omega_{H}$, onset to +hospitalization given the nature of the disease. Therefore, we create +extra inequality constraints in addition to the box constraints + +::: {.ipython} + +In \[549\]: boxBounds = \[ + +: \.....: (0.001, 100.), \# beta_I \.....: (0.001, 100.), \# beta_H + \.....: (0.001, 100.), \# beta_F \.....: (0.001, 100.), \# omega_I + \.....: (0.001, 100.), \# omega_D \.....: (0.001, 100.), \# omega_H + \.....: (0.001, 100.), \# omega_F \.....: (0.001, 100.), \# + alpha\^{-1} \.....: (0.0001, 1.), \# delta \.....: (0.0001, 1.), \# + theta \.....: (0.001, 1000.), \# kappa \.....: (0.,218.) \# + intervention tine \.....: \] + +In \[550\]: cons = ({\'type\': \'ineq\', \'fun\' : lambda x: +numpy.array(\[x\[3\]-x\[5\], x\[4\]-x\[5\]\])}) +::: + +We can now try to find the optimal values, but because this is a +difficult problem that can take a very long time without guarantee on +the quality of solution + +::: {.ipython okexcept="" okwarning=""} + +In \[213\]: res = scipy.optimize.minimize(fun=objLegrand.cost, + +: \.....: jac=objLegrand.sensitivity, \.....: x0=theta, \.....: + constraints=cons, \.....: bounds=boxBounds, \.....: + method=\'SLSQP\') + +In \[214\]: print(res) + +In \[215\]: f = plt.figure() + +\@savefig ebola_legrand_runtime.png In \[216\]: objLegrand.plot() + +In \[217\]: plt.close() +::: + +Evidently, the estimated parameters are very much unrealistic given that +a lot of them are near the boundaries. It is also known from other +sources that some of the epidemiology properties of Ebola, with +incubation period of around 2 weeks and a mortality rate of around 80 +percent. + +As the estimate does not appear to provide anything sensible, we also +provide a set of values previously obtained (that looks semi-reasonable) +here plot the epidemic curve with the observations layered on top + +::: {.ipython okexcept="" okwarning=""} + +In \[1\]: theta = numpy.array(\[3.96915071e-02, 1.72302620e+01, 1.99749990e+01, + +: \...: 2.67759445e+01, 4.99999990e+01, 5.56122691e+00, \...: + 4.99999990e+01, 8.51599523e+00, 9.99999000e-01, \...: + 1.00000000e-06, 3.85807562e+00, 1.88385318e+00\]) + +In \[2\]: print(objLegrand.cost(theta)) + +In \[2\]: solution = ode.integrate(t\[1::\]) + +In \[3\]: f, axarr = plt.subplots(2,3) + +In \[4\]: axarr\[0,0\].plot(t, solution\[:,0\]); + +In \[5\]: axarr\[0,0\].set_title(\'Susceptible\'); + +In \[6\]: axarr\[0,1\].plot(t, solution\[:,1\]); + +In \[7\]: axarr\[0,1\].set_title(\'Exposed\'); + +In \[8\]: axarr\[0,2\].plot(t, solution\[:,2\]); + +In \[9\]: axarr\[0,2\].plot(t, y\[:,0\], \'r\'); + +In \[10\]: axarr\[0,2\].set_title(\'Infectious\'); + +In \[11\]: axarr\[1,0\].plot(t, solution\[:,3\]); + +In \[12\]: axarr\[1,0\].set_title(\'Hospitalised\'); + +In \[13\]: axarr\[1,1\].plot(t, solution\[:,4\]); + +In \[14\]: axarr\[1,1\].set_title(\'Awaiting Burial\'); + +In \[15\]: axarr\[1,2\].plot(t, solution\[:,5\]); + +In \[16\]: axarr\[1,2\].plot(t, y\[:,1\], \'r\'); + +In \[17\]: axarr\[1,2\].set_title(\'Removed\'); + +In \[18\]: f.text(0.5, 0.04, \'Days from outbreak\', ha=\'center\'); + +In \[19\]: f.text(0.01, 0.5, \'Population\', va=\'center\', +rotation=\'vertical\'); + +In \[20\]: f.tight_layout(); + +\@savefig ebola_seihfr_straight_prior.png In \[21\]: plt.show() + +In \[22\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/faq.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/faq.ipynb new file mode 100644 index 00000000..016ed7b5 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/faq.ipynb @@ -0,0 +1,83 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Frequent asked questions\n", + "\n", + "## Code runs slowly\n", + "\n", + "This is because the package is not optimized for speed. Although the\n", + "some of the main functions are lambdified using `sympy` or compiled\n", + "against `cython` when available, there are many more optimization that\n", + "can be done. One example is the lines:\n", + "\n", + "in `.DeterministicOde.evalSensitivity`. The first two operations can be\n", + "inlined into the third and the third line itself can be rewritten as:\n", + "\n", + "and save the explicit copy operation by `numpy` when making A. If\n", + "desired, we could have also made used of the `numexpr` package that\n", + "provides further speed up on elementwise operations in place of numpy.\n", + "\n", + "## Why not compile the numeric computation form sympy against Theano\n", + "\n", + "Setup of the package has been simplified as much as possible. If you\n", + "look closely enough, you will realize that the current code generation\n", + "only uses `cython` and not `f2py`. This is because we are not prepared\n", + "to do all the system checks, i.e. does a fortran compiler exist, is gcc\n", + "installed, was python built as a shared library etc. We are very much\n", + "aware of the benefit, especially considering the possibility of GPU\n", + "computation in `theano`.\n", + "\n", + "## Why not use mpmath library throughout?\n", + "\n", + "This is because we have a fair number of operations that depends on\n", + "`scipy`. Obviously, we can solve ode using `mpmath` and do standard\n", + "linear algebra. Unfortunately, optimization and statistics packages and\n", + "routine are mostly based on `numpy`.\n", + "\n", + "## Computing the gradient using `.SquareLoss` is slow\n", + "\n", + "It will always be slow on the first operation. This is due to the design\n", + "where the initialization of the class is fast and only find derivative\n", + "information/compile function during runtime. After the first\n", + "calculation, things should be significantly faster.\n", + "\n", + "**Why some of my code is not a fortran object?**\n", + "\n", + "When we detec either a $\\exp$ or a $\\log$ in the equations, we\n", + "automatically force the compile to use mpmath to ensure that we obtain\n", + "the highest precision. To turn this on/off will be considered as a\n", + "feature in the future.\n", + "\n", + "## Can you not convert a non-autonumous system to an autonomous system for me automatically\n", + "\n", + "Although we can do that, it is not, and will not be implemented. This is\n", + "to ensure that the end user such as yourself are fully aware of the\n", + "equations being defined.\n", + "\n", + "## Getting the sensitivities from `.SquareLoss` did not get a speed up when I used a restricted set of parameters\n", + "\n", + "This is because we currently evaluate the full set of sensitivities\n", + "before extracting them out. Speeding this up for a restrictive set is\n", + "being considered. A main reason that stopped us from implementing is\n", + "that we find the symbolic gradient of the ode before compiling it. Which\n", + "means that one function call to the compiled file will return the full\n", + "set of sensitivities and we would only be extracting the appropriate\n", + "elements from the matrix. This only amounts to a small speed up. The\n", + "best method would be to compile only the necessary elements of the\n", + "gradient matrix, but this would require much more work both within the\n", + "code, and later on when variables are being added/deleted as all these\n", + "compilation are perfromed in runtime.\n", + "\n", + "## Why do not have the option to obtain gradient via complex differencing\n", + "\n", + "It is currently not implemented. Feature under consideration." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/faq.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/faq.md new file mode 100644 index 00000000..a22e19d8 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/faq.md @@ -0,0 +1,76 @@ +# Frequent asked questions {#faq} + +## Code runs slowly + +This is because the package is not optimized for speed. Although the +some of the main functions are lambdified using +`sympy`{.interpreted-text role="mod"} or compiled against +`cython`{.interpreted-text role="mod"} when available, there are many +more optimization that can be done. One example is the lines: + +in `.DeterministicOde.evalSensitivity`{.interpreted-text role="func"}. +The first two operations can be inlined into the third and the third +line itself can be rewritten as: + +and save the explicit copy operation by `numpy`{.interpreted-text +role="mod"} when making A. If desired, we could have also made used of +the `numexpr`{.interpreted-text role="mod"} package that provides +further speed up on elementwise operations in place of numpy. + +## Why not compile the numeric computation form sympy against Theano + +Setup of the package has been simplified as much as possible. If you +look closely enough, you will realize that the current code generation +only uses `cython`{.interpreted-text role="mod"} and not +`f2py`{.interpreted-text role="mod"}. This is because we are not +prepared to do all the system checks, i.e. does a fortran compiler +exist, is gcc installed, was python built as a shared library etc. We +are very much aware of the benefit, especially considering the +possibility of GPU computation in `theano`{.interpreted-text +role="mod"}. + +## Why not use mpmath library throughout? + +This is because we have a fair number of operations that depends on +`scipy`{.interpreted-text role="mod"}. Obviously, we can solve ode using +`mpmath`{.interpreted-text role="mod"} and do standard linear algebra. +Unfortunately, optimization and statistics packages and routine are +mostly based on `numpy`{.interpreted-text role="mod"}. + +## Computing the gradient using `.SquareLoss`{.interpreted-text role="class"} is slow + +It will always be slow on the first operation. This is due to the design +where the initialization of the class is fast and only find derivative +information/compile function during runtime. After the first +calculation, things should be significantly faster. + +**Why some of my code is not a fortran object?** + +When we detec either a $\exp$ or a $\log$ in the equations, we +automatically force the compile to use mpmath to ensure that we obtain +the highest precision. To turn this on/off will be considered as a +feature in the future. + +## Can you not convert a non-autonumous system to an autonomous system for me automatically + +Although we can do that, it is not, and will not be implemented. This is +to ensure that the end user such as yourself are fully aware of the +equations being defined. + +## Getting the sensitivities from `.SquareLoss`{.interpreted-text role="class"} did not get a speed up when I used a restricted set of parameters + +This is because we currently evaluate the full set of sensitivities +before extracting them out. Speeding this up for a restrictive set is +being considered. A main reason that stopped us from implementing is +that we find the symbolic gradient of the ode before compiling it. Which +means that one function call to the compiled file will return the full +set of sensitivities and we would only be extracting the appropriate +elements from the matrix. This only amounts to a small speed up. The +best method would be to compile only the necessary elements of the +gradient matrix, but this would require much more work both within the +code, and later on when variables are being added/deleted as all these +compilation are perfromed in runtime. + +## Why do not have the option to obtain gradient via complex differencing + +It is currently not implemented. Feature under consideration. diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/fh.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/fh.ipynb new file mode 100644 index 00000000..24e06f9a --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/fh.ipynb @@ -0,0 +1,135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example: Fitz Hugh\n", + "\n", + "## Defining the model\n", + "\n", + "We are going to investigate another classic model here, the\n", + "FitzHugh-Nagumo, or simply FitzHugh here. The model has already been\n", + "defined in `common_models` so we can load it easily\n", + "\n", + "In \\[1\\]: from pygom import SquareLoss, common_models\n", + "\n", + "In \\[2\\]: import numpy\n", + "\n", + "In \\[3\\]: import scipy.integrate, scipy.optimize\n", + "\n", + "In \\[4\\]: import math,time,copy\n", + "\n", + "In \\[5\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: x0 = \\[-1.0, 1.0\\]\n", + "\n", + "In \\[2\\]: t0 = 0\n", + "\n", + "In \\[3\\]: \\# params\n", + "\n", + "In \\[4\\]: paramEval = \\[('a',0.2), ('b',0.2), ('c',3.0)\\]\n", + "\n", + "In \\[5\\]: ode = common_models.FitzHugh(paramEval)\n", + "\n", + "In \\[5\\]: ode.initial_values = (x0, t0)\n", + "\n", + "Define a set of time points and lets see how the two states $V$ and $R$\n", + "are suppose to behave.\n", + "\n", + "In \\[6\\]: t = numpy.linspace(1, 20, 30).astype('float64')\n", + "\n", + "In \\[7\\]: solution = ode.integrate(t)\n", + "\n", + "@savefig fh_plot.png In \\[8\\]: ode.plot()\n", + "\n", + "## Estimate the parameters\n", + "\n", + "Obtaining the correct parameters for the FitzHugh model is well known to\n", + "be difficult, this is because the surface is multimodal. Although this\n", + "has been shown many times in the literature, so we will omit the\n", + "details. Regardless, we give it a go with some initial guess. with some\n", + "luck, we will be able to recover the original parameters. First, we try\n", + "it out with only one target state\n", + "\n", + "In \\[26\\]: theta = \\[0.5, 0.5, 0.5\\]\n", + "\n", + "In \\[27\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n", + "'R')\n", + "\n", + "In \\[28\\]: boxBounds = \\[ \n", + ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0) .…: \\]\n", + "\n", + "In \\[29\\]: res = scipy.optimize.minimize(fun=objFH.cost, \n", + ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n", + "method='L-BFGS-B')\n", + "\n", + "In \\[30\\]: print(res)\n", + "\n", + "Then we try the same again but with both state as our target. Now we\n", + "won't look at the iterations because they are pretty pointless.\n", + "\n", + "In \\[30\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n", + "\\['V','R'\\])\n", + "\n", + "In \\[31\\]: res = scipy.optimize.minimize(fun=objFH.cost, \n", + ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n", + "method='L-BFGS-B')\n", + "\n", + "In \\[32\\]: print(res)\n", + "\n", + "Note how the estimates are the same, unlike other models.\n", + "\n", + "## Estimate initial value\n", + "\n", + "We can further assume that we have no idea about the initial values for\n", + "$V$ and $R$ as well. We also provide guesstimate to set off the\n", + "optimization. The input vector $\\theta$ must have the parameters first,\n", + "then the initial values, along with the corresponding bounds.\n", + "\n", + "First, only a single target state, i.e. we only have observations for\n", + "one of states which is $R$ in this case\n", + "\n", + "In \\[35\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n", + "'R')\n", + "\n", + "In \\[35\\]: boxBounds = \\[ \n", + ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0), .…: (None,None), .…:\n", + "(None,None) .…: \\]\n", + "\n", + "In \\[36\\]: res = scipy.optimize.minimize(fun=objFH.costIV, \n", + ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5,0.5\\], .…:\n", + "bounds=boxBounds, .…: method='L-BFGS-B')\n", + "\n", + "In \\[37\\]: print(res)\n", + "\n", + "then both state as target at the same time\n", + "\n", + "In \\[38\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n", + "\\['V','R'\\])\n", + "\n", + "In \\[38\\]: res = scipy.optimize.minimize(fun=objFH.costIV, \n", + ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5, 0.5\\], .…:\n", + "bounds=boxBounds, .…: method='L-BFGS-B')\n", + "\n", + "In \\[39\\]: print(res)\n", + "\n", + "See the difference between the two estimate with the latter, both state\n", + "were used, yielding superior estimates. Note that only the forward\n", + "sensitivity method is implemented when estimating the initial value, and\n", + "it is assumed that the starting condition for all the states are\n", + "unknown.\n", + "\n", + "The choice of algorithm here is the **L-BFGS-B** which is a better\n", + "choice because the parameter space of the FitzHugh is rough (i.e. large\n", + "second derivative) as well as being multimodal. This means that the\n", + "Hessian is not guaranteed to be positive definite and approximation\n", + "using $J^{\\top}J$ is poor, with $J$ being the Jacobian of the objective\n", + "function." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/fh.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/fh.md new file mode 100644 index 00000000..a7b26a14 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/fh.md @@ -0,0 +1,141 @@ +# Example: Fitz Hugh {#fh} + +## Defining the model + +We are going to investigate another classic model here, the +FitzHugh-Nagumo, or simply FitzHugh here. The model has already been +defined in `common_models`{.interpreted-text role="mod"} so we can load +it easily + +::: {.ipython} +In \[1\]: from pygom import SquareLoss, common_models + +In \[2\]: import numpy + +In \[3\]: import scipy.integrate, scipy.optimize + +In \[4\]: import math,time,copy + +In \[5\]: import matplotlib.pyplot as plt + +In \[1\]: x0 = \[-1.0, 1.0\] + +In \[2\]: t0 = 0 + +In \[3\]: \# params + +In \[4\]: paramEval = \[(\'a\',0.2), (\'b\',0.2), (\'c\',3.0)\] + +In \[5\]: ode = common_models.FitzHugh(paramEval) + +In \[5\]: ode.initial_values = (x0, t0) +::: + +Define a set of time points and lets see how the two states $V$ and $R$ +are suppose to behave. + +::: {.ipython} +In \[6\]: t = numpy.linspace(1, 20, 30).astype(\'float64\') + +In \[7\]: solution = ode.integrate(t) + +\@savefig fh_plot.png In \[8\]: ode.plot() +::: + +## Estimate the parameters + +Obtaining the correct parameters for the FitzHugh model is well known to +be difficult, this is because the surface is multimodal. Although this +has been shown many times in the literature, so we will omit the +details. Regardless, we give it a go with some initial guess. with some +luck, we will be able to recover the original parameters. First, we try +it out with only one target state + +::: {.ipython} +In \[26\]: theta = \[0.5, 0.5, 0.5\] + +In \[27\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\[1::,1\], +\'R\') + +In \[28\]: boxBounds = \[ + +: \....: (0.0,5.0), \....: (0.0,5.0), \....: (0.0,5.0) \....: \] + +In \[29\]: res = scipy.optimize.minimize(fun=objFH.cost, + +: \....: jac=objFH.sensitivity, \....: x0=theta, \....: + bounds=boxBounds, \....: method=\'L-BFGS-B\') + +In \[30\]: print(res) +::: + +Then we try the same again but with both state as our target. Now we +won\'t look at the iterations because they are pretty pointless. + +::: {.ipython} +In \[30\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\[1::,:\], +\[\'V\',\'R\'\]) + +In \[31\]: res = scipy.optimize.minimize(fun=objFH.cost, + +: \....: jac=objFH.sensitivity, \....: x0=theta, \....: + bounds=boxBounds, \....: method=\'L-BFGS-B\') + +In \[32\]: print(res) +::: + +Note how the estimates are the same, unlike other models. + +## Estimate initial value + +We can further assume that we have no idea about the initial values for +$V$ and $R$ as well. We also provide guesstimate to set off the +optimization. The input vector $\theta$ must have the parameters first, +then the initial values, along with the corresponding bounds. + +First, only a single target state, i.e. we only have observations for +one of states which is $R$ in this case + +::: {.ipython} +In \[35\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\[1::,1\], +\'R\') + +In \[35\]: boxBounds = \[ + +: \....: (0.0,5.0), \....: (0.0,5.0), \....: (0.0,5.0), \....: + (None,None), \....: (None,None) \....: \] + +In \[36\]: res = scipy.optimize.minimize(fun=objFH.costIV, + +: \....: jac=objFH.sensitivityIV, \....: x0=theta + \[-0.5,0.5\], + \....: bounds=boxBounds, \....: method=\'L-BFGS-B\') + +In \[37\]: print(res) +::: + +then both state as target at the same time + +::: {.ipython} +In \[38\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\[1::,:\], +\[\'V\',\'R\'\]) + +In \[38\]: res = scipy.optimize.minimize(fun=objFH.costIV, + +: \....: jac=objFH.sensitivityIV, \....: x0=theta + \[-0.5, 0.5\], + \....: bounds=boxBounds, \....: method=\'L-BFGS-B\') + +In \[39\]: print(res) +::: + +See the difference between the two estimate with the latter, both state +were used, yielding superior estimates. Note that only the forward +sensitivity method is implemented when estimating the initial value, and +it is assumed that the starting condition for all the states are +unknown. + +The choice of algorithm here is the **L-BFGS-B** which is a better +choice because the parameter space of the FitzHugh is rough (i.e. large +second derivative) as well as being multimodal. This means that the +Hessian is not guaranteed to be positive definite and approximation +using $J^{\top}J$ is poor, with $J$ being the Jacobian of the objective +function. diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/gradient.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/gradient.ipynb new file mode 100644 index 00000000..e67c71b3 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/gradient.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient estimation under square loss\n", + "\n", + "Assuming that we have a set of $N$ observations $y_{i}$ at specific time\n", + "points $t_{i}$, $i = 1,\\ldots,N$, we may wish to test out a set of ode\n", + "to see whether it fits to the data. The most natural way to test such\n", + "*fit* is to minimize the sum of squares between our observations $y$ and\n", + "see whether the resulting solution of the ode and the estimationed\n", + "parameters makes sense.\n", + "\n", + "We assume that this estimation process will be tackled through a\n", + "non-linear optimization point of view. However, it should be noted that\n", + "such estimates can also be performed via MCMC or from a global\n", + "optimization perspective. A key element in non-linear optimization is\n", + "the gradient, which is the focus of this page.\n", + "\n", + "Multiple ways of obtaining the gradient have been implemented. All of\n", + "them serve a certain purpose and may not be a viable/appropriate options\n", + "depending on the type of ode. More generally, let $d,p$ be the number of\n", + "states and paramters respectively. Then finite difference methods have a\n", + "run order of $O(p+1)$ of the original ode, forward sensitivity require\n", + "an integration of an ode of size $(d+1)p$ rather than $d$. The adjoint\n", + "method require two run of size $d$ in principle, but actual run time is\n", + "dependent on the number of observations.\n", + "\n", + "For the details of the classes and methods, please refer to `mod`.\n", + "\n", + "## Notation\n", + "\n", + "We introduce the notations that will be used in the rest of the page,\n", + "some of which may be slightly unconventional but necessary due to the\n", + "complexity of the problem. Let $x \\in \\mathbb{R}^{d}$ and\n", + "$\\theta \\in \\mathbb{R}^{p}$ be the states and parameters respectively.\n", + "The term *state* or *simulation* are used interchangeably, even though\n", + "strictly speaking a state is $x$ whereas $x(t)$ is the simulation. An\n", + "ode is defined as\n", + "\n", + "$$f(x,\\theta) = \\dot{x} = \\frac{\\partial x}{\\partial t}$$\n", + "\n", + "and usually comes with a set of initial conditions $(x_0,t_0)$ where\n", + "$t_0 \\le t_{i} \\forall i$. Let $g(x,\\theta)$ be a function that maps the\n", + "set of states to the observations,\n", + "$g : \\mathbb{R}^{d} \\rightarrow \\mathbb{R}^{m}$. For compartmental\n", + "problems, which is our focus, $\\nabla_{\\theta}g(x,\\theta)$ is usually\n", + "zero and $\\nabla_{x}g(x,\\theta)$ is an identity function for some or all\n", + "of the states $x$. Denote $l(x_{0},\\theta,x)$ as our cost function\n", + "$l : \\mathbb{R}^{m} \\rightarrow \\mathbb{R}$ and $L(x_{0},\\theta,x)$ be\n", + "the sum of $l(\\cdot)$. Both $x$ and $x_{0}$ are usually dropped for\n", + "simplicity. We will be dealing exclusively with square loss here, which\n", + "means that\n", + "\n", + "$$L(\\theta) = \\sum_{i=1}^{N} \\left\\| y_{i} - g(x(t_{i})) \\right\\|^{2} = \\mathbf{e}^{\\top} \\mathbf{e}$$\n", + "\n", + "where $\\mathbf{e}$ is the residual vector, with elements\n", + "\n", + "$$e_{i} = y_{i} - x(t_{i}).$$\n", + "\n", + "## Model setup\n", + "\n", + "Again, we demonstrate the functionalities of our classes using an SIR\n", + "model.\n", + "\n", + "In \\[1\\]: from pygom import SquareLoss, common_models\n", + "\n", + "In \\[2\\]: import copy,time,numpy\n", + "\n", + "In \\[2\\]: ode = common_models.SIR()\n", + "\n", + "In \\[3\\]: paramEval = \\[('beta',0.5), ('gamma',1.0/3.0) \\]\n", + "\n", + "In \\[7\\]: \\# the initial state, normalized to zero one\n", + "\n", + "In \\[8\\]: x0 = \\[1., 1.27e-6, 0.\\]\n", + "\n", + "In \\[5\\]: \\# initial time\n", + "\n", + "In \\[6\\]: t0 = 0\n", + "\n", + "In \\[5\\]: ode.parameters = paramEval\n", + "\n", + "In \\[6\\]: ode.initial_values = (x0, t0)\n", + "\n", + "In \\[9\\]: \\# set the time sequence that we would like to observe\n", + "\n", + "In \\[10\\]: t = numpy.linspace(1, 150, 100)\n", + "\n", + "In \\[11\\]: numStep = len(t)\n", + "\n", + "In \\[11\\]: solution = ode.integrate(t)\n", + "\n", + "In \\[12\\]: y = solution\\[1::,2\\].copy()\n", + "\n", + "In \\[13\\]: y += numpy.random.normal(0, 0.1, y.shape)\n", + "\n", + "Now we have set up the model along with some observations, obtaining the\n", + "gradient only requires the end user to put the appropriate information\n", + "it into the class `SquareLoss`. Given the initial guess $\\theta$\n", + "\n", + "In \\[210\\]: theta = \\[0.2, 0.2\\]\n", + "\n", + "We initialize the `SquareLoss` simply as\n", + "\n", + "In \\[20\\]: objSIR = SquareLoss(theta, ode, x0, t0, t, y, 'R')\n", + "\n", + "where the we also have to specify the state our observations are from.\n", + "Now, we demonstrate the different methods in obtaining the gradient and\n", + "mathematics behind it.\n", + "\n", + "## Forward sensitivity\n", + "\n", + "The forward sensitivity equations are derived by differentiating the\n", + "states implicitly, which yields\n", + "\n", + "$$\\frac{d\\dot{x}}{d\\theta} = \\frac{\\partial f}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial f}{\\partial \\theta}.$$\n", + "\n", + "So finding the sensitivies $\\frac{dx}{d\\theta}$ simply require another\n", + "integration of a $p$ coupled ode of $d$ dimension, each with the same\n", + "Jacobian as the original ode. This integration is performed along with\n", + "the original ode because of possible non-linearity.\n", + "\n", + "A direct call to the method `sensitivity `\n", + "computed the gradient\n", + "\n", + "In \\[33\\]: gradSens = objSIR.sensitivity()\n", + "\n", + "whereas `.jac` will allow the end user to obtain the Jacobian (of the\n", + "objective function) and the residuals, the information required to get\n", + "the gradient as we see next.\n", + "\n", + "In \\[33\\]: objJac, output = objSIR.jac(full_output=True)\n", + "\n", + "## Gradient\n", + "\n", + "Just the sensitivities alone are not enough to obtain the gradient, but\n", + "we are $90\\%$ there. Differentiating the loss function\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dL}{d\\theta} &= \\nabla_{\\theta} \\sum_{i=1}^{N}\\frac{dl}{dg} \\\\\n", + " &= \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial l}{\\partial \\theta} \\\\\n", + " &= \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial \\theta}\n", + "\\end{aligned}$$\n", + "\n", + "via chain rule. When $\\frac{\\partial g}{\\partial \\theta} = 0$, the total\n", + "gradient simplifies to\n", + "\n", + "$$\\frac{dL}{d\\theta} = \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial x}\\frac{dx}{d\\theta}$$\n", + "\n", + "Obviously, the time indicies are dropped above but all the terms above\n", + "are evaluated only at the observed time points. More concretely, this\n", + "means that\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{\\partial l(x(j),\\theta)}{\\partial g} = \\left\\{ \\begin{array}{ll} -2(y_{i} - x(j)) & , \\; j = t_{i} \\\\ 0 & \\; \\text{otherwise} \\end{array} \\right.\n", + "\\end{aligned}$$\n", + "\n", + "When $g(\\cdot)$ is an identity function (which is assumed to be the case\n", + "in `SquareLoss`)\n", + "\n", + "$$\\frac{\\partial g(x(t_{i}),\\theta)}{\\partial x} = I_{d}$$\n", + "\n", + "then the gradient simplifies even further as it is simply\n", + "\n", + "$$\\frac{dL}{d\\theta} = -2\\mathbf{e}^{\\top}\\mathbf{S}$$\n", + "\n", + "where $\\mathbf{e}$ is the vector of residuals and\n", + "$\\mathbf{S} = \\left[\\mathbf{s}_{1},\\mathbf{s}_{2},\\ldots,\\mathbf{s}_{n}\\right]$\n", + "with elements\n", + "\n", + "$$\\mathbf{s}_{i} = \\frac{dx}{d\\theta}(t_{i}),$$\n", + "\n", + "the solution of the forward sensitivies at time $t_{i}$, obtained from\n", + "solving the coupled ode as mentioned previously.\n", + "\n", + "## Jacobian\n", + "\n", + "Now note how the gradient simplifies to $-2\\mathbf{e}^{\\top}\\mathbf{S}$.\n", + "Recall that a standard result in non-linear programming states that the\n", + "gradient of a sum of sqaures objective function $L(\\theta,y,x)$ is\n", + "\n", + "$$\\nabla_{\\theta} L(\\theta,y,x) = -2(\\mathbf{J}^{T} \\left[\\mathbf{y} - \\mathbf{f}(x,\\boldsymbol{\\theta}) \\right] )^{\\top}$$\n", + "\n", + "with $f(x,\\theta)$ our non-linear function and $J$ our Jacobian with\n", + "elements\n", + "\n", + "$$J_{i} = \\frac{\\partial f(x_{i},\\boldsymbol{\\theta})}{\\partial \\boldsymbol{\\theta}}.$$\n", + "\n", + "This is exactly what we have seen previously, substituting in reveals\n", + "that $J = \\mathbf{S}$. Hence, the Jacobian is (a necessary)by product\n", + "when we wish to obtain the gradient. In fact, this is exactly how we\n", + "proceed in `sensitivity ` where it makes\n", + "an internal call to `jac ` to obtain the Jacobian\n", + "first. This allows the end user to have more options when choosing which\n", + "type of algorithms to use, i.e. Gauss-Newton or Levenberg-Marquardt.\n", + "\n", + "To check that the output is in fact the same\n", + "\n", + "In \\[1\\]: objJac.transpose().dot(-2\\*output\\['resid'\\]) - gradSens\n", + "\n", + "## Adjoint\n", + "\n", + "When the number of parameters increases, the number of sensitivies also\n", + "increases. The time required scales directly with the number of\n", + "parameters. We describe another method which does not depend on the\n", + "number of parameters, but rather, the number of states and observations.\n", + "\n", + "The full derivations will not be shown here, but we aim to provide\n", + "enough information to work out the steps performed in the our code. Let\n", + "write our optimization problem as\n", + "\n", + "$$\\begin{aligned}\n", + "min_{\\theta} \\quad & \\int_{t_{0}}^{T} l(x_{0},\\theta,x(t)) dt \\\\\n", + "s.t. \\quad & \\dot{x} = f(x,\\theta)\n", + "\\end{aligned}$$\n", + "\n", + "which is identical to the original problem but in a continuous setting.\n", + "Now write the constrained problem in the Lagrangian form\n", + "\n", + "$$min_{\\theta} \\; L(\\theta) + \\int_{t_{0}}^{T} \\lambda^{\\top}(\\dot{x} - f(x,\\theta))$$\n", + "\n", + "with Lagrangian multiplier $\\lambda \\ge 0$. After some algebraic\n", + "manipulation, it can be shown that the total derivative of the\n", + "Lagrangian function is\n", + "\n", + "$$\\frac{dL}{d\\theta} = \\int_{t_{0}}^{T} \\left(\\frac{\\partial l}{\\partial \\theta} - \\lambda^{\\top}\\frac{\\partial f}{\\partial \\theta} \\right) dt.$$\n", + "\n", + "Using previously defined loss functions (the identity), the first term\n", + "is zero and evaluating $\\frac{\\partial f}{\\partial \\theta}$ is trivial.\n", + "What remains is the calculation of $\\lambda(t)$ for\n", + "$t \\in \\left[t_{0},T\\right]$.\n", + "\n", + "Although this still seem to be ill-posed problem when Looking at the\n", + "Lagrangian function, one can actually obtain the *adjoint equation*,\n", + "after certain assumptions and\n", + "\n", + "$$\\frac{d\\lambda^{\\top}}{dt} = \\frac{\\partial l}{\\partial x} - \\lambda^{\\top}\\frac{\\partial f}{\\partial \\theta}.$$\n", + "\n", + "which is again an integration. An unfortunate situation arise here for\n", + "non-linear systems because we use the minus Jacobian in the adjoint\n", + "equation. So if the eigenvalues of the Jacobian indicate that our\n", + "original ode is stable, such as -1, the minus eigenvalues (now 1)\n", + "implies that the adjoint equation is not stable. Therefore, one must\n", + "integrate backward in time to solve the adjoint equation and it cannot\n", + "be solved simultaneously as the ode, unlike the forward sensitivity\n", + "equations.\n", + "\n", + "Given a non-linearity ode, we must store information about the states\n", + "between $t_{0}$ and $T$ in order to perform the integration. There are\n", + "two options, both require storing many evaluated $x(j)$ within the\n", + "interval $\\left[t_{0},T\\right]$. Unfortunately, only one is available;\n", + "interpolation over all states and integrate using the interpolating\n", + "functions. The alternative of using observed $x(j)'s$ at fixed points is\n", + "not competitive because we are unable to use fortran routines for the\n", + "integration\n", + "\n", + "The method of choice here to perform the adjoint calcuation is to run a\n", + "forward integration, then perform an interpolation using splines with\n", + "explicit knots at the observed time points.\n", + "\n", + "In \\[326\\]: odeSIRAdjoint, outputAdjoint =\n", + "objSIR.adjoint(full_output=True)\n", + "\n", + "This is because evaluating the Jacobian may be expensive and Runge-kutta\n", + "method suffers as the complexity increases. In non-linear model such as\n", + "those found in epidemiology, each element of the Jacobian may be the\n", + "result of a complicated equation where linear step method will shine as\n", + "it makes as little function evaluation as possible. Note that\n", + "derivations in the literature, the initial condition when evaluating the\n", + "adjoint equation is $\\lambda(T)=0$. But in our code we used\n", + "$\\lambda(T) = -2(y(T)-x(T))$. Recall that we have observation $y(T)$ and\n", + "simulation $x(T)$, so that the adjoint equation evaluated at time $T$\n", + "\n", + "$$\\frac{\\partial \\lambda^{\\top}}{\\partial t} \\Big|_{T} = -2(y-f(x,\\theta))\\Big|_{T} - \\lambda(T)\\frac{\\partial f}{\\partial \\theta}\\Big|_{T}$$\n", + "\n", + "with the second term equal to zero. Integration under step size $h$\n", + "implies that\n", + "$\\lambda(T) \\approx \\lim_{h \\to 0} \\lambda(T-h) = -2(y(T)-x(T))$.\n", + "\n", + "## Time Comparison\n", + "\n", + "A simple time comparison between the different methods reveals that the\n", + "forward sensitivity method dominates the others by a wide margin. It\n", + "will be tempting to conclude that it is the best and should be the\n", + "default at all times but that is not true, due to the complexity of each\n", + "method mentioned previously. We leave it to the end user to find out the\n", + "best method for their specific problem.\n", + "\n", + "In \\[319\\]: %timeit gradSens = objSIR.sensitivity()\n", + "\n", + "In \\[326\\]: %timeit odeSIRAdjoint,outputAdjoint =\n", + "objSIR.adjoint(full_output=True)\n", + "\n", + "## Hessian\n", + "\n", + "The Hessian is defined by\n", + "\n", + "$$\\frac{\\partial^{2} l}{\\partial \\theta^{2}} = \\left( \\frac{\\partial l}{\\partial x} \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + \\frac{\\partial x}{\\partial \\theta}^{\\top}\\frac{\\partial^{2} l}{\\partial x^{2}}\\frac{\\partial x}{\\partial \\theta}$$\n", + "\n", + "where $\\otimes$ is the Kronecker product. Note that $\\nabla_{\\theta} x$\n", + "is the sensitivity and the second order sensitivities can be found again\n", + "via the forward method, which involve another set of ode's, namely the\n", + "forward-forward sensitivities\n", + "\n", + "$$\\frac{\\partial}{\\partial t}\\left(\\frac{\\partial^{2} x}{\\partial \\theta^{2}}\\right) = \\left( \\frac{\\partial f}{\\partial x} \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + \\left( I_{d} \\otimes \\frac{\\partial x}{\\partial \\theta}^{\\top} \\right) \\frac{\\partial^{2} f}{\\partial x^{2}} \\frac{\\partial x}{\\partial \\theta}.$$\n", + "\n", + "From before, we know that\n", + "\n", + "$$\\frac{\\partial l}{\\partial x} = (-2y+2x) \\quad and \\quad \\frac{\\partial^{2} l}{\\partial x^{2}} = 2I_{d}$$\n", + "\n", + "so our Hessian reduces to\n", + "\n", + "$$\\frac{\\partial^{2} l}{\\partial \\theta^{2}} = \\left( \\left(-2y+2x\\right) \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + 2S^{\\top}S,$$\n", + "\n", + "where the second term is a good approximation to the Hessian as\n", + "mentioned previously. This is the only implementation in place so far\n", + "even though obtaining the estimate this way is relatively slow.\n", + "\n", + "Just to demonstate how it works, lets look at the Hessian at the optimal\n", + "point. First, we obtain the optimal value\n", + "\n", + "In \\[211\\]: import scipy.linalg,scipy.optimize\n", + "\n", + "In \\[212\\]: boxBounds = \\[(0.0, 2.0), (0.0, 2.0)\\]\n", + "\n", + "In \\[213\\]: res = scipy.optimize.minimize(fun=objSIR.cost, \n", + ".….: jac=objSIR.sensitivity, .….: x0=theta, .….: bounds=boxBounds, .….:\n", + "method='L-BFGS-B')\n", + "\n", + "Then compare again the least square estimate of the covariance matrix\n", + "against our version\n", + "\n", + "In \\[211\\]: resLS, cov_x, infodict, mesg, ier =\n", + "scipy.optimize.leastsq(func=objSIR.residual, x0=res\\['x'\\],\n", + "full_output=True)\n", + "\n", + "In \\[212\\]: HJTJ, outputHJTJ = objSIR.hessian(full_output=True)\n", + "\n", + "In \\[311\\]: print(scipy.linalg.inv(HJTJ))\n", + "\n", + "In \\[312\\]: print(cov_x)\n", + "\n", + "also note the difference between the Hessian and the approximation using\n", + "the Jacobian, which is in fact what the least squares routine uses.\n", + "\n", + "In \\[313\\]: print(scipy.linalg.inv(outputHJTJ\\['JTJ'\\]))" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/gradient.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/gradient.md new file mode 100644 index 00000000..d0755502 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/gradient.md @@ -0,0 +1,372 @@ +# Gradient estimation under square loss {#gradient} + +Assuming that we have a set of $N$ observations $y_{i}$ at specific time +points $t_{i}$, $i = 1,\ldots,N$, we may wish to test out a set of ode +to see whether it fits to the data. The most natural way to test such +*fit* is to minimize the sum of squares between our observations $y$ and +see whether the resulting solution of the ode and the estimationed +parameters makes sense. + +We assume that this estimation process will be tackled through a +non-linear optimization point of view. However, it should be noted that +such estimates can also be performed via MCMC or from a global +optimization perspective. A key element in non-linear optimization is +the gradient, which is the focus of this page. + +Multiple ways of obtaining the gradient have been implemented. All of +them serve a certain purpose and may not be a viable/appropriate options +depending on the type of ode. More generally, let $d,p$ be the number of +states and paramters respectively. Then finite difference methods have a +run order of $O(p+1)$ of the original ode, forward sensitivity require +an integration of an ode of size $(d+1)p$ rather than $d$. The adjoint +method require two run of size $d$ in principle, but actual run time is +dependent on the number of observations. + +For the details of the classes and methods, please refer to +`mod`{.interpreted-text role="ref"}. + +## Notation + +We introduce the notations that will be used in the rest of the page, +some of which may be slightly unconventional but necessary due to the +complexity of the problem. Let $x \in \mathbb{R}^{d}$ and +$\theta \in \mathbb{R}^{p}$ be the states and parameters respectively. +The term *state* or *simulation* are used interchangeably, even though +strictly speaking a state is $x$ whereas $x(t)$ is the simulation. An +ode is defined as + +$$f(x,\theta) = \dot{x} = \frac{\partial x}{\partial t}$$ + +and usually comes with a set of initial conditions $(x_0,t_0)$ where +$t_0 \le t_{i} \forall i$. Let $g(x,\theta)$ be a function that maps the +set of states to the observations, +$g : \mathbb{R}^{d} \rightarrow \mathbb{R}^{m}$. For compartmental +problems, which is our focus, $\nabla_{\theta}g(x,\theta)$ is usually +zero and $\nabla_{x}g(x,\theta)$ is an identity function for some or all +of the states $x$. Denote $l(x_{0},\theta,x)$ as our cost function +$l : \mathbb{R}^{m} \rightarrow \mathbb{R}$ and $L(x_{0},\theta,x)$ be +the sum of $l(\cdot)$. Both $x$ and $x_{0}$ are usually dropped for +simplicity. We will be dealing exclusively with square loss here, which +means that + +$$L(\theta) = \sum_{i=1}^{N} \left\| y_{i} - g(x(t_{i})) \right\|^{2} = \mathbf{e}^{\top} \mathbf{e}$$ + +where $\mathbf{e}$ is the residual vector, with elements + +$$e_{i} = y_{i} - x(t_{i}).$$ + +## Model setup + +Again, we demonstrate the functionalities of our classes using an SIR +model. + +::: {.ipython} +In \[1\]: from pygom import SquareLoss, common_models + +In \[2\]: import copy,time,numpy + +In \[2\]: ode = common_models.SIR() + +In \[3\]: paramEval = \[(\'beta\',0.5), (\'gamma\',1.0/3.0) \] + +In \[7\]: \# the initial state, normalized to zero one + +In \[8\]: x0 = \[1., 1.27e-6, 0.\] + +In \[5\]: \# initial time + +In \[6\]: t0 = 0 + +In \[5\]: ode.parameters = paramEval + +In \[6\]: ode.initial_values = (x0, t0) + +In \[9\]: \# set the time sequence that we would like to observe + +In \[10\]: t = numpy.linspace(1, 150, 100) + +In \[11\]: numStep = len(t) + +In \[11\]: solution = ode.integrate(t) + +In \[12\]: y = solution\[1::,2\].copy() + +In \[13\]: y += numpy.random.normal(0, 0.1, y.shape) +::: + +Now we have set up the model along with some observations, obtaining the +gradient only requires the end user to put the appropriate information +it into the class `SquareLoss`{.interpreted-text role="class"}. Given +the initial guess $\theta$ + +::: {.ipython} +In \[210\]: theta = \[0.2, 0.2\] +::: + +We initialize the `SquareLoss`{.interpreted-text role="class"} simply as + +::: {.ipython} +In \[20\]: objSIR = SquareLoss(theta, ode, x0, t0, t, y, \'R\') +::: + +where the we also have to specify the state our observations are from. +Now, we demonstrate the different methods in obtaining the gradient and +mathematics behind it. + +## Forward sensitivity + +The forward sensitivity equations are derived by differentiating the +states implicitly, which yields + +$$\frac{d\dot{x}}{d\theta} = \frac{\partial f}{\partial x}\frac{dx}{d\theta} + \frac{\partial f}{\partial \theta}.$$ + +So finding the sensitivies $\frac{dx}{d\theta}$ simply require another +integration of a $p$ coupled ode of $d$ dimension, each with the same +Jacobian as the original ode. This integration is performed along with +the original ode because of possible non-linearity. + +A direct call to the method +`sensitivity `{.interpreted-text +role="meth"} computed the gradient + +::: {.ipython} +In \[33\]: gradSens = objSIR.sensitivity() +::: + +whereas `.jac`{.interpreted-text role="meth"} will allow the end user to +obtain the Jacobian (of the objective function) and the residuals, the +information required to get the gradient as we see next. + +::: {.ipython} +In \[33\]: objJac, output = objSIR.jac(full_output=True) +::: + +## Gradient + +Just the sensitivities alone are not enough to obtain the gradient, but +we are $90\%$ there. Differentiating the loss function + +$$\begin{aligned} +\frac{dL}{d\theta} &= \nabla_{\theta} \sum_{i=1}^{N}\frac{dl}{dg} \\ + &= \sum_{i=1}^{N} \frac{\partial l}{\partial x}\frac{dx}{d\theta} + \frac{\partial l}{\partial \theta} \\ + &= \sum_{i=1}^{N} \frac{\partial l}{\partial g}\frac{\partial g}{\partial x}\frac{dx}{d\theta} + \frac{\partial l}{\partial g}\frac{\partial g}{\partial \theta} +\end{aligned}$$ + +via chain rule. When $\frac{\partial g}{\partial \theta} = 0$, the total +gradient simplifies to + +$$\frac{dL}{d\theta} = \sum_{i=1}^{N} \frac{\partial l}{\partial g}\frac{\partial g}{\partial x}\frac{dx}{d\theta}$$ + +Obviously, the time indicies are dropped above but all the terms above +are evaluated only at the observed time points. More concretely, this +means that + +$$\begin{aligned} +\frac{\partial l(x(j),\theta)}{\partial g} = \left\{ \begin{array}{ll} -2(y_{i} - x(j)) & , \; j = t_{i} \\ 0 & \; \text{otherwise} \end{array} \right. +\end{aligned}$$ + +When $g(\cdot)$ is an identity function (which is assumed to be the case +in `SquareLoss`{.interpreted-text role="class"}) + +$$\frac{\partial g(x(t_{i}),\theta)}{\partial x} = I_{d}$$ + +then the gradient simplifies even further as it is simply + +$$\frac{dL}{d\theta} = -2\mathbf{e}^{\top}\mathbf{S}$$ + +where $\mathbf{e}$ is the vector of residuals and +$\mathbf{S} = \left[\mathbf{s}_{1},\mathbf{s}_{2},\ldots,\mathbf{s}_{n}\right]$ +with elements + +$$\mathbf{s}_{i} = \frac{dx}{d\theta}(t_{i}),$$ + +the solution of the forward sensitivies at time $t_{i}$, obtained from +solving the coupled ode as mentioned previously. + +## Jacobian + +Now note how the gradient simplifies to $-2\mathbf{e}^{\top}\mathbf{S}$. +Recall that a standard result in non-linear programming states that the +gradient of a sum of sqaures objective function $L(\theta,y,x)$ is + +$$\nabla_{\theta} L(\theta,y,x) = -2(\mathbf{J}^{T} \left[\mathbf{y} - \mathbf{f}(x,\boldsymbol{\theta}) \right] )^{\top}$$ + +with $f(x,\theta)$ our non-linear function and $J$ our Jacobian with +elements + +$$J_{i} = \frac{\partial f(x_{i},\boldsymbol{\theta})}{\partial \boldsymbol{\theta}}.$$ + +This is exactly what we have seen previously, substituting in reveals +that $J = \mathbf{S}$. Hence, the Jacobian is (a necessary)by product +when we wish to obtain the gradient. In fact, this is exactly how we +proceed in +`sensitivity `{.interpreted-text +role="func"} where it makes an internal call to +`jac `{.interpreted-text role="func"} to obtain +the Jacobian first. This allows the end user to have more options when +choosing which type of algorithms to use, i.e. Gauss-Newton or +Levenberg-Marquardt. + +To check that the output is in fact the same + +::: {.ipython} +In \[1\]: objJac.transpose().dot(-2\*output\[\'resid\'\]) - gradSens +::: + +## Adjoint + +When the number of parameters increases, the number of sensitivies also +increases. The time required scales directly with the number of +parameters. We describe another method which does not depend on the +number of parameters, but rather, the number of states and observations. + +The full derivations will not be shown here, but we aim to provide +enough information to work out the steps performed in the our code. Let +write our optimization problem as + +$$\begin{aligned} +min_{\theta} \quad & \int_{t_{0}}^{T} l(x_{0},\theta,x(t)) dt \\ +s.t. \quad & \dot{x} = f(x,\theta) +\end{aligned}$$ + +which is identical to the original problem but in a continuous setting. +Now write the constrained problem in the Lagrangian form + +$$min_{\theta} \; L(\theta) + \int_{t_{0}}^{T} \lambda^{\top}(\dot{x} - f(x,\theta))$$ + +with Lagrangian multiplier $\lambda \ge 0$. After some algebraic +manipulation, it can be shown that the total derivative of the +Lagrangian function is + +$$\frac{dL}{d\theta} = \int_{t_{0}}^{T} \left(\frac{\partial l}{\partial \theta} - \lambda^{\top}\frac{\partial f}{\partial \theta} \right) dt.$$ + +Using previously defined loss functions (the identity), the first term +is zero and evaluating $\frac{\partial f}{\partial \theta}$ is trivial. +What remains is the calculation of $\lambda(t)$ for +$t \in \left[t_{0},T\right]$. + +Although this still seem to be ill-posed problem when Looking at the +Lagrangian function, one can actually obtain the *adjoint equation*, +after certain assumptions and + +$$\frac{d\lambda^{\top}}{dt} = \frac{\partial l}{\partial x} - \lambda^{\top}\frac{\partial f}{\partial \theta}.$$ + +which is again an integration. An unfortunate situation arise here for +non-linear systems because we use the minus Jacobian in the adjoint +equation. So if the eigenvalues of the Jacobian indicate that our +original ode is stable, such as -1, the minus eigenvalues (now 1) +implies that the adjoint equation is not stable. Therefore, one must +integrate backward in time to solve the adjoint equation and it cannot +be solved simultaneously as the ode, unlike the forward sensitivity +equations. + +Given a non-linearity ode, we must store information about the states +between $t_{0}$ and $T$ in order to perform the integration. There are +two options, both require storing many evaluated $x(j)$ within the +interval $\left[t_{0},T\right]$. Unfortunately, only one is available; +interpolation over all states and integrate using the interpolating +functions. The alternative of using observed $x(j)'s$ at fixed points is +not competitive because we are unable to use fortran routines for the +integration + +The method of choice here to perform the adjoint calcuation is to run a +forward integration, then perform an interpolation using splines with +explicit knots at the observed time points. + +::: {.ipython} +In \[326\]: odeSIRAdjoint, outputAdjoint = +objSIR.adjoint(full_output=True) +::: + +This is because evaluating the Jacobian may be expensive and Runge-kutta +method suffers as the complexity increases. In non-linear model such as +those found in epidemiology, each element of the Jacobian may be the +result of a complicated equation where linear step method will shine as +it makes as little function evaluation as possible. Note that +derivations in the literature, the initial condition when evaluating the +adjoint equation is $\lambda(T)=0$. But in our code we used +$\lambda(T) = -2(y(T)-x(T))$. Recall that we have observation $y(T)$ and +simulation $x(T)$, so that the adjoint equation evaluated at time $T$ + +$$\frac{\partial \lambda^{\top}}{\partial t} \Big|_{T} = -2(y-f(x,\theta))\Big|_{T} - \lambda(T)\frac{\partial f}{\partial \theta}\Big|_{T}$$ + +with the second term equal to zero. Integration under step size $h$ +implies that +$\lambda(T) \approx \lim_{h \to 0} \lambda(T-h) = -2(y(T)-x(T))$. + +## Time Comparison + +A simple time comparison between the different methods reveals that the +forward sensitivity method dominates the others by a wide margin. It +will be tempting to conclude that it is the best and should be the +default at all times but that is not true, due to the complexity of each +method mentioned previously. We leave it to the end user to find out the +best method for their specific problem. + +::: {.ipython} +In \[319\]: %timeit gradSens = objSIR.sensitivity() + +In \[326\]: %timeit odeSIRAdjoint,outputAdjoint = +objSIR.adjoint(full_output=True) +::: + +## Hessian + +The Hessian is defined by + +$$\frac{\partial^{2} l}{\partial \theta^{2}} = \left( \frac{\partial l}{\partial x} \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + \frac{\partial x}{\partial \theta}^{\top}\frac{\partial^{2} l}{\partial x^{2}}\frac{\partial x}{\partial \theta}$$ + +where $\otimes$ is the Kronecker product. Note that $\nabla_{\theta} x$ +is the sensitivity and the second order sensitivities can be found again +via the forward method, which involve another set of ode\'s, namely the +forward-forward sensitivities + +$$\frac{\partial}{\partial t}\left(\frac{\partial^{2} x}{\partial \theta^{2}}\right) = \left( \frac{\partial f}{\partial x} \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + \left( I_{d} \otimes \frac{\partial x}{\partial \theta}^{\top} \right) \frac{\partial^{2} f}{\partial x^{2}} \frac{\partial x}{\partial \theta}.$$ + +From before, we know that + +$$\frac{\partial l}{\partial x} = (-2y+2x) \quad and \quad \frac{\partial^{2} l}{\partial x^{2}} = 2I_{d}$$ + +so our Hessian reduces to + +$$\frac{\partial^{2} l}{\partial \theta^{2}} = \left( \left(-2y+2x\right) \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + 2S^{\top}S,$$ + +where the second term is a good approximation to the Hessian as +mentioned previously. This is the only implementation in place so far +even though obtaining the estimate this way is relatively slow. + +Just to demonstate how it works, lets look at the Hessian at the optimal +point. First, we obtain the optimal value + +::: {.ipython} +In \[211\]: import scipy.linalg,scipy.optimize + +In \[212\]: boxBounds = \[(0.0, 2.0), (0.0, 2.0)\] + +In \[213\]: res = scipy.optimize.minimize(fun=objSIR.cost, + +: \.....: jac=objSIR.sensitivity, \.....: x0=theta, \.....: + bounds=boxBounds, \.....: method=\'L-BFGS-B\') +::: + +Then compare again the least square estimate of the covariance matrix +against our version + +::: {.ipython} +In \[211\]: resLS, cov_x, infodict, mesg, ier = +scipy.optimize.leastsq(func=objSIR.residual, x0=res\[\'x\'\], +full_output=True) + +In \[212\]: HJTJ, outputHJTJ = objSIR.hessian(full_output=True) + +In \[311\]: print(scipy.linalg.inv(HJTJ)) + +In \[312\]: print(cov_x) +::: + +also note the difference between the Hessian and the approximation using +the Jacobian, which is in fact what the least squares routine uses. + +::: {.ipython} +In \[313\]: print(scipy.linalg.inv(outputHJTJ\[\'JTJ\'\])) +::: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/initialGuess.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/initialGuess.ipynb new file mode 100644 index 00000000..a00cab66 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/initialGuess.ipynb @@ -0,0 +1,68 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Obtaining good initial value for parameters\n", + "\n", + "## Function Interpolation\n", + "\n", + "When we want to fit the model to data, one of the necessary steps is to\n", + "supply the optimization procedure a good set of initial guess for the\n", + "parameters $\\theta$. This may be a challenge when we do have a good\n", + "understanding of the process we are trying to model i.e. infectious\n", + "disease may all follow the same SIR process but with vastly different\n", + "incubation period.\n", + "\n", + "A method to obtain such initial guess based on the collocation is\n", + "available in this package. A restriction is that data must be present\n", + "for all states. We demonstrate this using the FitzHugh-Nagumo model.\n", + "\n", + "In \\[1\\]: from pygom import SquareLoss, common_models, get_init\n", + "\n", + "In \\[2\\]: import numpy\n", + "\n", + "In \\[3\\]: x0 = \\[-1.0, 1.0\\]\n", + "\n", + "In \\[4\\]: t0 = 0\n", + "\n", + "In \\[5\\]: \\# params\n", + "\n", + "In \\[6\\]: paramEval = \\[('a',0.2), ('b',0.2), ('c',3.0)\\]\n", + "\n", + "In \\[7\\]: ode = common_models.FitzHugh(paramEval)\n", + "\n", + "In \\[8\\]: ode.initial_values = (x0, t0)\n", + "\n", + "In \\[8\\]: t = numpy.linspace(1, 20, 30).astype('float64')\n", + "\n", + "In \\[9\\]: solution = ode.integrate(t)\n", + "\n", + "Below, we try to find the initial guess without supplying any further\n", + "information. The underlying method fits a cubic spline against the\n", + "observation and tries to minimize the difference between the first\n", + "derivative of the spline and the function of the ode. Varying degree of\n", + "smoothness penalty is applied to the spline and the best set of\n", + "parameters is the ones that yields the smallest total error, combining\n", + "both the fit of the spline against data and the spline against the ode.\n", + "\n", + "In \\[10\\]: theta, sInfo = get_init(solution\\[1::,:\\], t, ode,\n", + "theta=None, full_output=True)\n", + "\n", + "In \\[11\\]: print(theta)\n", + "\n", + "In \\[12\\]: print(sInfo)\n", + "\n", + "As seen above, we have obtained a very good guess of the parameters, in\n", + "fact almost the same as the generating process. The information\n", + "regarding the smoothing factor shows that the amount of penalty used is\n", + "small, which is expected given that we use the solution of the ode as\n", + "observations." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/initialGuess.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/initialGuess.md new file mode 100644 index 00000000..358fd49f --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/initialGuess.md @@ -0,0 +1,59 @@ +# Obtaining good initial value for parameters {#initialGuess} + +## Function Interpolation + +When we want to fit the model to data, one of the necessary steps is to +supply the optimization procedure a good set of initial guess for the +parameters $\theta$. This may be a challenge when we do have a good +understanding of the process we are trying to model i.e. infectious +disease may all follow the same SIR process but with vastly different +incubation period. + +A method to obtain such initial guess based on the collocation is +available in this package. A restriction is that data must be present +for all states. We demonstrate this using the FitzHugh-Nagumo model. + +::: {.ipython} +In \[1\]: from pygom import SquareLoss, common_models, get_init + +In \[2\]: import numpy + +In \[3\]: x0 = \[-1.0, 1.0\] + +In \[4\]: t0 = 0 + +In \[5\]: \# params + +In \[6\]: paramEval = \[(\'a\',0.2), (\'b\',0.2), (\'c\',3.0)\] + +In \[7\]: ode = common_models.FitzHugh(paramEval) + +In \[8\]: ode.initial_values = (x0, t0) + +In \[8\]: t = numpy.linspace(1, 20, 30).astype(\'float64\') + +In \[9\]: solution = ode.integrate(t) +::: + +Below, we try to find the initial guess without supplying any further +information. The underlying method fits a cubic spline against the +observation and tries to minimize the difference between the first +derivative of the spline and the function of the ode. Varying degree of +smoothness penalty is applied to the spline and the best set of +parameters is the ones that yields the smallest total error, combining +both the fit of the spline against data and the spline against the ode. + +::: {.ipython} +In \[10\]: theta, sInfo = get_init(solution\[1::,:\], t, ode, +theta=None, full_output=True) + +In \[11\]: print(theta) + +In \[12\]: print(sInfo) +::: + +As seen above, we have obtained a very good guess of the parameters, in +fact almost the same as the generating process. The information +regarding the smoothing factor shows that the amount of penalty used is +small, which is expected given that we use the solution of the ode as +observations. diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/profile.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/profile.ipynb new file mode 100644 index 00000000..debb0e4c --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/profile.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Confidence Interval of Estimated Parameters\n", + "\n", + "After obtaining the *best* fit, it is natural to report both the point\n", + "estimate and the confidence level at the $\\alpha$ level. The easiest way\n", + "to do this is by invoking the normality argument and use Fisher\n", + "information of the likelihood. As explained previously at the bottom of\n", + "`gradient`, we can find the Hessian, $\\mathbf{H}$, or the approximated\n", + "Hessian for the estimated parameters. The Cramer--Rao inequality, we\n", + "know that\n", + "\n", + "$$Var(\\hat{\\theta}) \\ge \\frac{1}{I(\\theta)},$$\n", + "\n", + "where $I(\\theta)$ is the Fisher information, which is the Hessian\n", + "subject to regularity condition. Given the Hessian, computing the\n", + "confidence intervals is trivial. Note that this is also known as the\n", + "asymptotic confidence interval where the normality comes from invoking\n", + "the CLT. There are other ways of obtaining a confidence intervals, we\n", + "will the ones implemented in the package. First, we will set up a SIR\n", + "model as seen in `sir` which will be used throughout this page.\n", + "\n", + "In \\[1\\]: from pygom import NormalLoss, common_models\n", + "\n", + "In \\[2\\]: from pygom.utilR import qchisq\n", + "\n", + "In \\[3\\]: import numpy\n", + "\n", + "In \\[4\\]: import scipy.integrate\n", + "\n", + "In \\[5\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[6\\]: import copy\n", + "\n", + "In \\[7\\]: ode = common_models.SIR(\\[('beta', 0.5), ('gamma', 1.0/3.0)\\])\n", + "\n", + "and we assume that we only have observed realization from the $R$\n", + "compartment\n", + "\n", + "In \\[1\\]: x0 = \\[1, 1.27e-6, 0\\]\n", + "\n", + "In \\[2\\]: t = numpy.linspace(0, 150, 100).astype('float64')\n", + "\n", + "In \\[3\\]: ode.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[4\\]: solution = ode.integrate(t\\[1::\\])\n", + "\n", + "In \\[5\\]: theta = \\[0.2, 0.2\\]\n", + "\n", + "In \\[6\\]: targetState = \\['R'\\]\n", + "\n", + "In \\[7\\]: targetStateIndex =\n", + "numpy.array(ode.get_state_index(targetState))\n", + "\n", + "In \\[8\\]: y = solution\\[1::,targetStateIndex\\] + numpy.random.normal(0,\n", + "0.01, (len(solution\\[1::,targetStateIndex\\]), 1))\n", + "\n", + "In \\[9\\]: yObv = y.copy()\n", + "\n", + "In \\[10\\]: objSIR = NormalLoss(theta, ode, x0, t\\[0\\], t\\[1::\\], y,\n", + "targetState)\n", + "\n", + "In \\[11\\]: boxBounds = \\[(1e-8, 2.0), (1e-8, 2.0)\\]\n", + "\n", + "In \\[12\\]: boxBoundsArray = numpy.array(boxBounds)\n", + "\n", + "In \\[13\\]: xhat = objSIR.fit(theta, lb=boxBoundsArray\\[:,0\\],\n", + "ub=boxBoundsArray\\[:,1\\])\n", + "\n", + "## Asymptotic\n", + "\n", + "When the estimate is obtained say, under a square loss or a normal\n", + "assumption, the corresponding likelihood can be written down easily. In\n", + "such a case, likelihood ratio test under a Chi--squared distribution is\n", + "\n", + "$$2 (\\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) - \\mathcal{L}(\\boldsymbol{\\theta})) \\le \\chi_{1 - \\alpha}^{2}(k)$$\n", + "\n", + "where $1-\\alpha$ is the size of the confidence region and $k$ is the\n", + "degree of freedom. The corresponding asymptotic confidence interval for\n", + "parameter $j$ can be derived as\n", + "\n", + "$$\\hat{\\theta}_{j} \\pm \\sqrt{\\chi_{1 - \\alpha}^{2}(k) H_{i,i}}.$$\n", + "\n", + "A pointwise confidence interval is obtained when $k = 1$. We assume in\n", + "our package that a pointwise confidence interval is desired. This can be\n", + "obtained simply by\n", + "\n", + "In \\[1\\]: from pygom import confidence_interval as ci\n", + "\n", + "In \\[2\\]: alpha = 0.05\n", + "\n", + "In \\[3\\]: xL, xU = ci.asymptotic(objSIR, alpha, xhat,\n", + "lb=boxBoundsArray\\[:,0\\], ub=boxBoundsArray\\[:,1\\])\n", + "\n", + "In \\[4\\]: print(xL)\n", + "\n", + "In \\[5\\]: print(xU)\n", + "\n", + "Note that the set of bounds here is only used for check the validity of\n", + "$\\hat{\\mathbf{x}}$ and not used in the calculation of the confidence\n", + "intervals. Therefore the resulting output can be outside of the box\n", + "constraints.\n", + "\n", + "## Profile Likelihood\n", + "\n", + "Another approach to calculate the confidence interval is to tackle one\n", + "parameter at a time, treating the rest of them as nuisance parameters,\n", + "hence the term *profile*. Let $\\mathcal{L}(\\boldsymbol{\\theta})$ be our\n", + "log--likelihood with parameter $\\boldsymbol{\\theta}$. Element\n", + "$\\theta_{j}$ is our parameter of interest and $\\boldsymbol{\\theta}_{-j}$\n", + "represents the complement such that\n", + "$\\boldsymbol{\\theta} = \\theta_{j} \\cup \\boldsymbol{\\theta}_{-j}$. For\n", + "simply models such as linear regression with only regression\n", + "coefficients $\\boldsymbol{\\beta}$, then\n", + "$\\boldsymbol{\\theta} = \\boldsymbol{\\beta}$.\n", + "\n", + "To shorten the notation, let\n", + "\n", + "$$\\mathcal{L}(\\boldsymbol{\\theta}_{-j} \\mid \\theta_{j}) = \\max \\mathcal{L}(\\boldsymbol{\\theta}_{-j} \\mid \\theta_{j})$$\n", + "\n", + "which is the maxima of $\\boldsymbol{\\theta}_{-j}$ given $\\theta_{j}$.\n", + "$\\hat{\\boldsymbol{\\theta}}$ denotes the MLE of the parameters as usual.\n", + "The profile--likelihood based confidence interval for $\\theta_{j}$ is\n", + "defined as\n", + "\n", + "$$\\begin{aligned}\n", + "\\theta_{j}^{U} &= \\sup \\left\\{ \\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) - \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j}) \\le \\frac{1}{2} \\chi_{1 - \\alpha}^{2}(1) \\right\\} \\\\\n", + "\\theta_{j}^{L} &= \\inf \\left\\{ \\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) - \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j}) \\le \\frac{1}{2} \\chi_{1 - \\alpha}^{2}(1) \\right\\}\n", + "\\end{aligned}$$\n", + "\n", + "where again we have made use of the normal approximation, but without\n", + "imposing symmetry. The set of equations above automatically implies that\n", + "the interval width is $\\theta_{j}^{U} - \\theta_{j}^{L}$ and\n", + "\n", + "$$\\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) - \\frac{1}{2} \\chi_{1-\\alpha}^{2}(1) - \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j}) = 0.$$\n", + "\n", + "As mentioned previously, $\\boldsymbol{\\theta}_{-j}$ is the maximizer of\n", + "the nuisance parameters, which has a gradient of zero. Combining this\n", + "with the equation above yields a non--linear system of equations of size\n", + "$p$,\n", + "\n", + "$$\\begin{aligned}\n", + "g(\\boldsymbol{\\theta}) = \\left[ \\begin{array}{c} \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j}) - c \\\\ \\frac{\\partial \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j})}{\\partial \\boldsymbol{\\theta}_{-j}} \\end{array} \\right] = 0\n", + "\\end{aligned}$$\n", + "\n", + "where\n", + "$c = \\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) + \\frac{1}{2} \\chi_{1-\\alpha}^{2}(1)$.\n", + "Solving this set of system of equations only need simple Newton like\n", + "steps, possibly with correction terms as per [\\[Venzon1988\\]](). We\n", + "provide a function to obtain such estimate\n", + "\n", + "In \\[1\\]: xLProfile, xUProfile, xLProfileList, xUProfileList =\n", + "ci.profile(objSIR, alpha, xhat, lb=boxBoundsArray\\[:,0\\],\n", + "ub=boxBoundsArray\\[:,1\\], full_output=True)\n", + "\n", + "but unfortunately this is not accurate most of the time due to the\n", + "complicated surface at locations not around $\\hat{\\theta}$. This is a\n", + "common scenario for non--linear least square problems because the\n", + "Hessian is not guaranteed to be a PSD everywhere. Therefore, a safeguard\n", + "is in place to obtain the $\\theta_{j}^{U},\\theta_{j}^{L}$ by iteratively\n", + "by updating $\\theta_{j}$ and find the solution to `nuisanceOptim`.\n", + "\n", + "Furthermore, we also provide the functions necessary to obtain the\n", + "estimates such as the four below.\n", + "\n", + "In \\[1\\]: i = 0\n", + "\n", + "In \\[1\\]: funcF = ci.\\_profileF(xhat, i, 0.05, objSIR)\n", + "\n", + "In \\[2\\]: funcG = ci.\\_profileG(xhat, i, 0.05, objSIR)\n", + "\n", + "In \\[3\\]: funcGC = ci.\\_profileGSecondOrderCorrection(xhat, i, alpha,\n", + "objSIR)\n", + "\n", + "In \\[4\\]: funcH = ci.\\_profileH(xhat, i, 0.05, objSIR)\n", + "\n", + "Where $i$ is the index of the parameter of interest. `_profileF` is the\n", + "squared norm of `obj`, which easy the optimization process for solvers\n", + "which requires a converted form from system of equations to non-linear\n", + "least squares. `_profileG` is the systems of equations `obj`,\n", + "`_profileH` is the derivative of `obj`\n", + "\n", + "$$\\begin{aligned}\n", + "\\nabla g(\\boldsymbol{\\theta}) = \\left[ \\begin{array}{c} \\frac{\\partial \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j})}{\\partial \\theta_{j}} \\\\ \\frac{\\partial^{2} \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j})}{\\partial \\boldsymbol{\\beta}_{-j} \\partial \\theta_{j}} \\end{array} \\right]\n", + "\\end{aligned}$$\n", + "\n", + "and `_profileGSecondOrderCorrection` has the second order correction\n", + "[\\[Venzon1988\\]]().\n", + "\n", + "## Geometric profile likelihood\n", + "\n", + "Due to the difficulty in obtain a profile likelihood via the standard\n", + "Newton like steps, we also provide a way to generate a similar result\n", + "using the geometric structure of the likelihood surface. We follow the\n", + "method in [\\[Moolgavkar1987\\]](), which involves solving a set of\n", + "differential equations\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{d\\beta_{j}}{dt} &= k g^{-1/2} \\\\\n", + "\\frac{d\\boldsymbol{\\beta}_{-j}}{dt} &= \\frac{d\\boldsymbol{\\beta}_{-j}}{d\\beta_{j}} \\frac{d\\beta_{j}}{dt},\n", + "\\end{aligned}$$\n", + "\n", + "where $k = \\Phi(1-\\alpha)$ is the quantile we want to obtain under a\n", + "normal distribution, and\n", + "\n", + "$$\\begin{aligned}\n", + "g = J_{\\beta_{j}}^{\\top} I^{\\boldsymbol{\\beta}} J_{\\beta_{j}}, \\quad J_{\\beta_{j}} = \\left( \\begin{array}{c} 1 \\\\ \\frac{d\\boldsymbol{\\beta}_{-j}}{d\\beta_{j}} \\end{array} \\right).\n", + "\\end{aligned}$$\n", + "\n", + "Here, $J_{\\beta_{j}}$ is the Jacobian between $\\beta_{j}$ and\n", + "$\\boldsymbol{\\beta}_{-j}$ with the term\n", + "\n", + "$$\\frac{d\\boldsymbol{\\beta}_{-j}}{d\\beta_{j}} = -\\left( \\frac{\\partial^{2} \\mathcal{L}}{\\partial \\boldsymbol{\\beta}_{-j}\\partial \\boldsymbol{\\beta}_{-j}^{\\top} } \\right)^{-1} \\frac{\\partial^{2} \\mathcal{L}}{\\partial \\beta_{j} \\partial \\beta_{-j}^{\\top}}$$\n", + "\n", + "and hence the first element is $1$ (identity transformation).\n", + "$I^{\\boldsymbol{\\beta}}$ is the Fisher information of\n", + "$\\boldsymbol{\\beta}$, which is\n", + "\n", + "$$I^{\\boldsymbol{\\beta}} = \\frac{\\partial \\boldsymbol{\\theta}}{\\partial \\boldsymbol{\\beta}^{\\top}} \\Sigma^{\\boldsymbol{\\theta}(\\boldsymbol{\\beta})} \\frac{\\partial \\boldsymbol{\\theta}}{\\partial \\boldsymbol{\\beta}}.$$\n", + "\n", + "It is simply $\\Sigma^{\\boldsymbol{\\beta}}$ if\n", + "$\\boldsymbol{\\theta} = \\boldsymbol{\\beta}$. Different Fisher information\n", + "can be used for $\\Sigma^{\\boldsymbol{\\beta}}$ such as the expected or\n", + "observed, at $\\hat{\\boldsymbol{\\beta}}$ or $\\boldsymbol{\\beta}$. After\n", + "some trivial algebraic manipulation, we can show that our ode boils\n", + "downs to\n", + "\n", + "$$\\begin{aligned}\n", + "\\left[ \\begin{array}{c} \\frac{d\\beta_{j}}{dt} \\\\ \\frac{d\\boldsymbol{\\beta_{-j}}}{dt} \\end{array} \\right] = k \\left[ \\begin{array}{c} 1 \\\\ -A^{-1}w \\end{array} \\right] \\left( v - w^{\\top}A^{-1}w \\right)^{-1/2}\n", + "\\end{aligned}$$\n", + "\n", + "where the symbols on the RHS above correspond to partitions in the\n", + "Fisher information\n", + "\n", + "$$\\begin{aligned}\n", + "I^{\\boldsymbol{\\beta}} = \\left[ \\begin{array}{cc} v & w^{\\top} \\\\ w & A \\end{array} \\right].\n", + "\\end{aligned}$$\n", + "\n", + "The integration is perform from $t = 0$ to $1$ and is all handled\n", + "internally via `geometric`\n", + "\n", + "In \\[1\\]: xLGeometric, xUGeometric, xLList, xUList =\n", + "ci.geometric(objSIR, alpha, xhat, full_output=True)\n", + "\n", + "In \\[2\\]: print(xLGeometric)\n", + "\n", + "In \\[3\\]: print(xUGeometric)\n", + "\n", + "## Bootstrap\n", + "\n", + "This is perhaps the favorite method to estimate confidence interval for\n", + "a lot of people. Although there are many ways to implement bootstrap,\n", + "semi-parametric is the only logical choice (even though the underlying\n", + "assumptions may be violated at times). As we have only implemented OLS\n", + "type loss functions in this package, the parametric approach seem to be\n", + "inappropriate when there is no self--efficiency guarantee.\n", + "Non-parametric approach requires at least a conditional independence\n", + "assumption, something easily violated by our **ode**. Block bootstrap is\n", + "an option but we are also aware that the errors of an **ode** can be\n", + "rather rigid, and consistently over/under estimate at certain periods of\n", + "time.\n", + "\n", + "When we say semi-parametric, we mean the exchange of errors between the\n", + "observations. Let our raw error be\n", + "\n", + "$$\\varepsilon_{i} = y_{i} - \\hat{y}_{i}$$\n", + "\n", + "where $\\hat{y}_{i}$ will be the prediction under\n", + "$\\hat{\\boldsymbol{\\theta}}$ under our model. Then we construct a new set\n", + "of observations via\n", + "\n", + "$$y_{i}^{\\ast} = \\hat{y}_{i} + \\varepsilon^{\\ast}, \\quad \\varepsilon^{\\ast} \\sim \\mathcal{F}$$\n", + "\n", + "with $\\mathcal{F}$ being the empirical distribution of the raw errors. A\n", + "new set of parameters $\\theta^{\\ast}$ are then found for the\n", + "bootstrapped samples, and we obtain the $\\alpha$ confidence interval by\n", + "taking the $\\alpha/2$ quantiles. Invoke the correspond python function\n", + "yields our bootstrap estimates. Unlike `asymptotic`, the bounds here are\n", + "used when estimating the parameters of each bootstrap samples. An error\n", + "may be returned if estimation failed for any of the bootstrap samples.\n", + "\n", + "In \\[1\\]: xLBootstrap, xUBootstrap, setX = ci.bootstrap(objSIR, alpha,\n", + "xhat, iteration=10, lb=boxBoundsArray\\[:,0\\], ub=boxBoundsArray\\[:,1\\],\n", + "full_output=True)\n", + "\n", + "In \\[2\\]: print(xLBootstrap)\n", + "\n", + "In \\[3\\]: print(xUBootstrap)\n", + "\n", + "The additional information here can be used to compute the bias, tail\n", + "effects and test against the normality assumption. If desired, a\n", + "simultaneous confidence interval can also be approximated empirically.\n", + "Note however that because we are using a semi--parameter method here, if\n", + "the model specification is wrong then the resulting estimates for the\n", + "bias is also wrong. The confidence interval still has the normal\n", + "approximation guarantee if number of sample is large.\n", + "\n", + "In this case, because the error in the observation is extremely small,\n", + "the confidence interval is narrow.\n", + "\n", + "In \\[1\\]: import pylab as P\n", + "\n", + "In \\[2\\]: f = plt.figure()\n", + "\n", + "In \\[3\\]: n, bins, patches = P.hist(setX\\[:,0\\], 50)\n", + "\n", + "In \\[4\\]: P.xlabel(r'Estimates of \\$beta\\$');\n", + "\n", + "In \\[5\\]: P.ylabel('Frequency');\n", + "\n", + "In \\[6\\]: P.title('Estimates under a semi-parametric bootstrap scheme');\n", + "\n", + "@savefig bootstrapCIHist.png In \\[7\\]: P.show()\n", + "\n", + "In \\[8\\]: P.close()\n", + "\n", + "## Comparison Between Methods\n", + "\n", + "Although we have shown the numerical values for the confidence interval\n", + "obtained using different method, it may be hard to comprehend how they\n", + "vary. As they say, a picture says a million word, and given that this\n", + "particular model only has two parameters, we can obtain inspect and\n", + "compare the methods visually via a contour plot. The code to perform\n", + "this is shown below but the code block will not be run to save time and\n", + "space.\n", + "\n", + "In the plot above, the bootstrap confidence interval were so close to\n", + "the MLE, it is impossible to distinguish the two on such a coarse scale.\n", + "\n", + "Furthermore, because the geometric confidence interval is the result of\n", + "an integration, we can trace the path that lead to the final output that\n", + "was shown previously. Again, we are space conscious (and time\n", + "constrained) so the code block below will not be run.\n", + "\n", + "In \\[1\\]: fig = plt.figure()\n", + "\n", + "In \\[2\\]: CS = plt.contour(xi, yi, zi, linewidth=0.5)\n", + "\n", + "In \\[3\\]: plt.clabel(CS, fontsize=10, inline=1)\n", + "\n", + "In \\[4\\]: l1 = plt.scatter(xLList\\[0\\]\\[:,0\\], xLList\\[0\\]\\[:,1\\],\n", + "marker='o', c='m', s=10);\n", + "\n", + "In \\[5\\]: l2 = plt.scatter(xUList\\[0\\]\\[:,0\\], xUList\\[0\\]\\[:,1\\],\n", + "marker='x', c='m', s=10);\n", + "\n", + "In \\[6\\]: plt.legend((l1, l2), ('Lower CI path', 'Upper CI path'),\n", + "loc='upper left');\n", + "\n", + "In \\[7\\]: plt.ylabel(r'Estimates of \\$gamma\\$');\n", + "\n", + "In \\[8\\]: plt.xlabel(r'Estimates of \\$beta\\$');\n", + "\n", + "In \\[9\\]: plt.title('Integration path of the geometric confidence\n", + "intervals on the likelihood surface');\n", + "\n", + "In \\[10\\]: plt.tight_layout();\n", + "\n", + "In \\[11\\]: plt.show()\n", + "\n", + "In \\[12\\]: plt.close()\n", + "\n", + "## Profile Likelihood Surface\n", + "\n", + "To investigate why it was hard to find the profile likelihood confidence\n", + "interval, we can simply look at the surface (which is simply a line as\n", + "we are profiling). We find solution of `nuisanceOptim` for each\n", + "$\\boldsymbol{\\theta}_{-j}$ at various points of $\\boldsymbol{\\theta}$.\n", + "Equivalently, we can minimize the original loss function as defined\n", + "previously, and this is the approach below. We focus out attention to\n", + "the parameter $\\beta$ of our SIR model. The results are not shown here\n", + "but the existence of a solution to `obj` is evident by simply\n", + "*eyeballing* the plots.\n", + "\n", + "In \\[1\\]: numIter = 100\n", + "\n", + "In \\[2\\]: x2 = numpy.linspace(0.0, 2.0, numIter)\n", + "\n", + "In \\[3\\]: funcOut = numpy.linspace(0.0, 2.0, numIter)\n", + "\n", + "In \\[4\\]: ode.parameters = \\[('beta',0.5), ('gamma',1.0/3.0)\\]\n", + "\n", + "In \\[5\\]: for i in range(numIter): \n", + "...: paramEval = \\[('beta',x2\\[i\\]), ('gamma',x2\\[i\\])\\] ...: ode2 =\n", + "copy.deepcopy(ode) ...: ode2.parameters = paramEval ...:\n", + "ode2.initial_values = (x0, t\\[0\\]) ...: objSIR2 = NormalLoss(x2\\[i\\],\n", + "ode2, x0, t\\[0\\], t\\[1::\\], yObv.copy(), targetState,\n", + "target_param='gamma') ...: res =\n", + "scipy.optimize.minimize(fun=objSIR2.cost, ...: jac=objSIR2.gradient,\n", + "...: x0=x2\\[i\\], ...: bounds=\\[(0,2)\\], ...: method='L-BFGS-B') ...:\n", + "funcOut\\[i\\] = res\\['fun'\\]\n", + "\n", + "In \\[10\\]: fig = plt.figure()\n", + "\n", + "In \\[10\\]: plt.plot(x2, objSIR.cost(xhat) - funcOut)\n", + "\n", + "In \\[11\\]: l1 = plt.axhline(-0.5\\*qchisq(1 - alpha, df=1), 0, 2,\n", + "color='r')\n", + "\n", + "In \\[12\\]: plt.ylabel(r'\\$mathcal{L}(hat{theta}) - mathcal{L}(theta mid\n", + "beta)\\$');\n", + "\n", + "In \\[13\\]: plt.xlabel(r'Fixed value of \\$beta\\$');\n", + "\n", + "In \\[14\\]: plt.title('Difference in objective function between MLEn and\n", + "the maximization of the nuisance parameters given then parameter of\n", + "interest, beta in this case');\n", + "\n", + "In \\[15\\]: plt.tight_layout();\n", + "\n", + "In \\[16\\]: plt.legend((l1,), (r'\\$-0.5mathcal{X}\\_{1 -\n", + "alpha}^{2}(1)\\$',), loc='lower right');\n", + "\n", + "@savefig profileLLMaximizerGivenBeta.png In \\[17\\]: plt.show() \\#\n", + "@savefig profileLLMaximizerGivenBeta.png\n", + "\n", + "In \\[18\\]: plt.close()\n", + "\n", + "Both the upper and lower confidence interval can be found in the\n", + "profiling procedure, but the part between of\n", + "$\\beta \\in \\left[0,\\hat{\\beta}\\right]$ is not convex, with $\\hat{\\beta}$\n", + "being the MLE. This non--quadratic profile likelihood is due to the\n", + "non-identifiability of the model given data [\\[Raue2009\\]](). For this\n", + "particular case, we can fix it simply by introducing additional\n", + "observations in the form of the $I$ state. We encourage the users to try\n", + "it out for themselves to confirm.\n", + "\n", + "In \\[1\\]: targetState = \\['I', 'R'\\]\n", + "\n", + "In \\[2\\]: targetStateIndex =\n", + "numpy.array(ode.get_state_index(targetState))\n", + "\n", + "In \\[3\\]: y = solution\\[1::,targetStateIndex\\] + numpy.random.normal(0,\n", + "0.01, (len(solution\\[1::,targetStateIndex\\]), 1))\n", + "\n", + "In \\[4\\]: objSIR = NormalLoss(theta, ode, x0, t\\[0\\], t\\[1::\\],\n", + "y.copy(), targetState)\n", + "\n", + "In \\[5\\]: xhat = objSIR.fit(theta, lb=boxBoundsArray\\[:,0\\],\n", + "ub=boxBoundsArray\\[:,1\\])\n", + "\n", + "In \\[6\\]: for i in range(numIter): \n", + "...: paramEval = \\[('beta', x2\\[i\\]), ('gamma', x2\\[i\\])\\] ...: ode2 =\n", + "copy.deepcopy(ode) ...: ode2.parameters = paramEval ...:\n", + "ode2.initial_values = (x0, t\\[0\\]) ...: objSIR2 = NormalLoss(x2\\[i\\],\n", + "ode2, x0, t\\[0\\], t\\[1::\\], y.copy(), targetState, target_param='gamma')\n", + "...: res = scipy.optimize.minimize(fun=objSIR2.cost, ...:\n", + "jac=objSIR2.gradient, ...: x0=x2\\[i\\], ...: bounds=\\[(0,2)\\], ...:\n", + "method='L-BFGS-B') ...: funcOut\\[i\\] = res\\['fun'\\]\n", + "\n", + "In \\[10\\]: fig = plt.figure()\n", + "\n", + "In \\[10\\]: plt.plot(x2, objSIR.cost(xhat) - funcOut);\n", + "\n", + "In \\[11\\]: l1 = plt.axhline(-0.5\\*qchisq(1 - alpha, df=1), 0, 2,\n", + "color='r')\n", + "\n", + "In \\[12\\]: plt.ylabel(r'\\$mathcal{L}(hat{theta}) - mathcal{L}(theta mid\n", + "beta)\\$');\n", + "\n", + "In \\[13\\]: plt.xlabel(r'Fixed value of \\$beta\\$');\n", + "\n", + "In \\[14\\]: plt.title('Profile likelihood curve for the parameter ofn\n", + "interest with more observation');\n", + "\n", + "In \\[15\\]: plt.tight_layout();\n", + "\n", + "In \\[16\\]: plt.legend((l1,), (r'\\$-0.5mathcal{X}\\_{1 -\n", + "alpha}^{2}(1)\\$',), loc='lower right');\n", + "\n", + "@savefig profileLLMaximizerGivenBetaMoreObs.png In \\[17\\]: plt.show() \\#\n", + "@savefig profileLLMaximizerGivenBetaMoreObs.png\n", + "\n", + "In \\[18\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/profile.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/profile.md new file mode 100644 index 00000000..aa50fb5a --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/profile.md @@ -0,0 +1,506 @@ +# Confidence Interval of Estimated Parameters {#profile} + +After obtaining the *best* fit, it is natural to report both the point +estimate and the confidence level at the $\alpha$ level. The easiest way +to do this is by invoking the normality argument and use Fisher +information of the likelihood. As explained previously at the bottom of +`gradient`{.interpreted-text role="ref"}, we can find the Hessian, +$\mathbf{H}$, or the approximated Hessian for the estimated parameters. +The Cramer\--Rao inequality, we know that + +$$Var(\hat{\theta}) \ge \frac{1}{I(\theta)},$$ + +where $I(\theta)$ is the Fisher information, which is the Hessian +subject to regularity condition. Given the Hessian, computing the +confidence intervals is trivial. Note that this is also known as the +asymptotic confidence interval where the normality comes from invoking +the CLT. There are other ways of obtaining a confidence intervals, we +will the ones implemented in the package. First, we will set up a SIR +model as seen in `sir`{.interpreted-text role="ref"} which will be used +throughout this page. + +::: {.ipython} +In \[1\]: from pygom import NormalLoss, common_models + +In \[2\]: from pygom.utilR import qchisq + +In \[3\]: import numpy + +In \[4\]: import scipy.integrate + +In \[5\]: import matplotlib.pyplot as plt + +In \[6\]: import copy + +In \[7\]: ode = common_models.SIR(\[(\'beta\', 0.5), (\'gamma\', +1.0/3.0)\]) +::: + +and we assume that we only have observed realization from the $R$ +compartment + +::: {.ipython} +In \[1\]: x0 = \[1, 1.27e-6, 0\] + +In \[2\]: t = numpy.linspace(0, 150, 100).astype(\'float64\') + +In \[3\]: ode.initial_values = (x0, t\[0\]) + +In \[4\]: solution = ode.integrate(t\[1::\]) + +In \[5\]: theta = \[0.2, 0.2\] + +In \[6\]: targetState = \[\'R\'\] + +In \[7\]: targetStateIndex = +numpy.array(ode.get_state_index(targetState)) + +In \[8\]: y = solution\[1::,targetStateIndex\] + numpy.random.normal(0, +0.01, (len(solution\[1::,targetStateIndex\]), 1)) + +In \[9\]: yObv = y.copy() + +In \[10\]: objSIR = NormalLoss(theta, ode, x0, t\[0\], t\[1::\], y, +targetState) + +In \[11\]: boxBounds = \[(1e-8, 2.0), (1e-8, 2.0)\] + +In \[12\]: boxBoundsArray = numpy.array(boxBounds) + +In \[13\]: xhat = objSIR.fit(theta, lb=boxBoundsArray\[:,0\], +ub=boxBoundsArray\[:,1\]) +::: + +## Asymptotic + +When the estimate is obtained say, under a square loss or a normal +assumption, the corresponding likelihood can be written down easily. In +such a case, likelihood ratio test under a Chi\--squared distribution is + +$$2 (\mathcal{L}(\hat{\boldsymbol{\theta}}) - \mathcal{L}(\boldsymbol{\theta})) \le \chi_{1 - \alpha}^{2}(k)$$ + +where $1-\alpha$ is the size of the confidence region and $k$ is the +degree of freedom. The corresponding asymptotic confidence interval for +parameter $j$ can be derived as + +$$\hat{\theta}_{j} \pm \sqrt{\chi_{1 - \alpha}^{2}(k) H_{i,i}}.$$ + +A pointwise confidence interval is obtained when $k = 1$. We assume in +our package that a pointwise confidence interval is desired. This can be +obtained simply by + +::: {.ipython} +In \[1\]: from pygom import confidence_interval as ci + +In \[2\]: alpha = 0.05 + +In \[3\]: xL, xU = ci.asymptotic(objSIR, alpha, xhat, +lb=boxBoundsArray\[:,0\], ub=boxBoundsArray\[:,1\]) + +In \[4\]: print(xL) + +In \[5\]: print(xU) +::: + +Note that the set of bounds here is only used for check the validity of +$\hat{\mathbf{x}}$ and not used in the calculation of the confidence +intervals. Therefore the resulting output can be outside of the box +constraints. + +## Profile Likelihood + +Another approach to calculate the confidence interval is to tackle one +parameter at a time, treating the rest of them as nuisance parameters, +hence the term *profile*. Let $\mathcal{L}(\boldsymbol{\theta})$ be our +log\--likelihood with parameter $\boldsymbol{\theta}$. Element +$\theta_{j}$ is our parameter of interest and $\boldsymbol{\theta}_{-j}$ +represents the complement such that +$\boldsymbol{\theta} = \theta_{j} \cup \boldsymbol{\theta}_{-j}$. For +simply models such as linear regression with only regression +coefficients $\boldsymbol{\beta}$, then +$\boldsymbol{\theta} = \boldsymbol{\beta}$. + +To shorten the notation, let + +$$\mathcal{L}(\boldsymbol{\theta}_{-j} \mid \theta_{j}) = \max \mathcal{L}(\boldsymbol{\theta}_{-j} \mid \theta_{j})$$ + +which is the maxima of $\boldsymbol{\theta}_{-j}$ given $\theta_{j}$. +$\hat{\boldsymbol{\theta}}$ denotes the MLE of the parameters as usual. +The profile\--likelihood based confidence interval for $\theta_{j}$ is +defined as + +$$\begin{aligned} +\theta_{j}^{U} &= \sup \left\{ \mathcal{L}(\hat{\boldsymbol{\theta}}) - \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j}) \le \frac{1}{2} \chi_{1 - \alpha}^{2}(1) \right\} \\ +\theta_{j}^{L} &= \inf \left\{ \mathcal{L}(\hat{\boldsymbol{\theta}}) - \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j}) \le \frac{1}{2} \chi_{1 - \alpha}^{2}(1) \right\} +\end{aligned}$$ + +where again we have made use of the normal approximation, but without +imposing symmetry. The set of equations above automatically implies that +the interval width is $\theta_{j}^{U} - \theta_{j}^{L}$ and + +$$\mathcal{L}(\hat{\boldsymbol{\theta}}) - \frac{1}{2} \chi_{1-\alpha}^{2}(1) - \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j}) = 0.$$ + +As mentioned previously, $\boldsymbol{\theta}_{-j}$ is the maximizer of +the nuisance parameters, which has a gradient of zero. Combining this +with the equation above yields a non\--linear system of equations of +size $p$, + +$$\begin{aligned} +g(\boldsymbol{\theta}) = \left[ \begin{array}{c} \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j}) - c \\ \frac{\partial \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j})}{\partial \boldsymbol{\theta}_{-j}} \end{array} \right] = 0 +\end{aligned}$$ + +where +$c = \mathcal{L}(\hat{\boldsymbol{\theta}}) + \frac{1}{2} \chi_{1-\alpha}^{2}(1)$. +Solving this set of system of equations only need simple Newton like +steps, possibly with correction terms as per [\[Venzon1988\]](). We +provide a function to obtain such estimate + +::: {.ipython verbatim=""} +In \[1\]: xLProfile, xUProfile, xLProfileList, xUProfileList = +ci.profile(objSIR, alpha, xhat, lb=boxBoundsArray\[:,0\], +ub=boxBoundsArray\[:,1\], full_output=True) +::: + +but unfortunately this is not accurate most of the time due to the +complicated surface at locations not around $\hat{\theta}$. This is a +common scenario for non\--linear least square problems because the +Hessian is not guaranteed to be a PSD everywhere. Therefore, a safeguard +is in place to obtain the $\theta_{j}^{U},\theta_{j}^{L}$ by iteratively +by updating $\theta_{j}$ and find the solution to +`nuisanceOptim`{.interpreted-text role="eq"}. + +Furthermore, we also provide the functions necessary to obtain the +estimates such as the four below. + +::: {.ipython} +In \[1\]: i = 0 + +In \[1\]: funcF = ci.\_profileF(xhat, i, 0.05, objSIR) + +In \[2\]: funcG = ci.\_profileG(xhat, i, 0.05, objSIR) + +In \[3\]: funcGC = ci.\_profileGSecondOrderCorrection(xhat, i, alpha, +objSIR) + +In \[4\]: funcH = ci.\_profileH(xhat, i, 0.05, objSIR) +::: + +Where $i$ is the index of the parameter of interest. +`_profileF`{.interpreted-text role="func"} is the squared norm of +`obj`{.interpreted-text role="eq"}, which easy the optimization process +for solvers which requires a converted form from system of equations to +non-linear least squares. `_profileG`{.interpreted-text role="func"} is +the systems of equations `obj`{.interpreted-text role="eq"}, +`_profileH`{.interpreted-text role="func"} is the derivative of +`obj`{.interpreted-text role="eq"} + +$$\begin{aligned} +\nabla g(\boldsymbol{\theta}) = \left[ \begin{array}{c} \frac{\partial \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j})}{\partial \theta_{j}} \\ \frac{\partial^{2} \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j})}{\partial \boldsymbol{\beta}_{-j} \partial \theta_{j}} \end{array} \right] +\end{aligned}$$ + +and `_profileGSecondOrderCorrection`{.interpreted-text role="func"} has +the second order correction [\[Venzon1988\]](). + +## Geometric profile likelihood + +Due to the difficulty in obtain a profile likelihood via the standard +Newton like steps, we also provide a way to generate a similar result +using the geometric structure of the likelihood surface. We follow the +method in [\[Moolgavkar1987\]](), which involves solving a set of +differential equations + +$$\begin{aligned} +\frac{d\beta_{j}}{dt} &= k g^{-1/2} \\ +\frac{d\boldsymbol{\beta}_{-j}}{dt} &= \frac{d\boldsymbol{\beta}_{-j}}{d\beta_{j}} \frac{d\beta_{j}}{dt}, +\end{aligned}$$ + +where $k = \Phi(1-\alpha)$ is the quantile we want to obtain under a +normal distribution, and + +$$\begin{aligned} +g = J_{\beta_{j}}^{\top} I^{\boldsymbol{\beta}} J_{\beta_{j}}, \quad J_{\beta_{j}} = \left( \begin{array}{c} 1 \\ \frac{d\boldsymbol{\beta}_{-j}}{d\beta_{j}} \end{array} \right). +\end{aligned}$$ + +Here, $J_{\beta_{j}}$ is the Jacobian between $\beta_{j}$ and +$\boldsymbol{\beta}_{-j}$ with the term + +$$\frac{d\boldsymbol{\beta}_{-j}}{d\beta_{j}} = -\left( \frac{\partial^{2} \mathcal{L}}{\partial \boldsymbol{\beta}_{-j}\partial \boldsymbol{\beta}_{-j}^{\top} } \right)^{-1} \frac{\partial^{2} \mathcal{L}}{\partial \beta_{j} \partial \beta_{-j}^{\top}}$$ + +and hence the first element is $1$ (identity transformation). +$I^{\boldsymbol{\beta}}$ is the Fisher information of +$\boldsymbol{\beta}$, which is + +$$I^{\boldsymbol{\beta}} = \frac{\partial \boldsymbol{\theta}}{\partial \boldsymbol{\beta}^{\top}} \Sigma^{\boldsymbol{\theta}(\boldsymbol{\beta})} \frac{\partial \boldsymbol{\theta}}{\partial \boldsymbol{\beta}}.$$ + +It is simply $\Sigma^{\boldsymbol{\beta}}$ if +$\boldsymbol{\theta} = \boldsymbol{\beta}$. Different Fisher information +can be used for $\Sigma^{\boldsymbol{\beta}}$ such as the expected or +observed, at $\hat{\boldsymbol{\beta}}$ or $\boldsymbol{\beta}$. After +some trivial algebraic manipulation, we can show that our ode boils +downs to + +$$\begin{aligned} +\left[ \begin{array}{c} \frac{d\beta_{j}}{dt} \\ \frac{d\boldsymbol{\beta_{-j}}}{dt} \end{array} \right] = k \left[ \begin{array}{c} 1 \\ -A^{-1}w \end{array} \right] \left( v - w^{\top}A^{-1}w \right)^{-1/2} +\end{aligned}$$ + +where the symbols on the RHS above correspond to partitions in the +Fisher information + +$$\begin{aligned} +I^{\boldsymbol{\beta}} = \left[ \begin{array}{cc} v & w^{\top} \\ w & A \end{array} \right]. +\end{aligned}$$ + +The integration is perform from $t = 0$ to $1$ and is all handled +internally via `geometric`{.interpreted-text role="func"} + +::: {.ipython} +In \[1\]: xLGeometric, xUGeometric, xLList, xUList = +ci.geometric(objSIR, alpha, xhat, full_output=True) + +In \[2\]: print(xLGeometric) + +In \[3\]: print(xUGeometric) +::: + +## Bootstrap + +This is perhaps the favorite method to estimate confidence interval for +a lot of people. Although there are many ways to implement bootstrap, +semi-parametric is the only logical choice (even though the underlying +assumptions may be violated at times). As we have only implemented OLS +type loss functions in this package, the parametric approach seem to be +inappropriate when there is no self\--efficiency guarantee. +Non-parametric approach requires at least a conditional independence +assumption, something easily violated by our **ode**. Block bootstrap is +an option but we are also aware that the errors of an **ode** can be +rather rigid, and consistently over/under estimate at certain periods of +time. + +When we say semi-parametric, we mean the exchange of errors between the +observations. Let our raw error be + +$$\varepsilon_{i} = y_{i} - \hat{y}_{i}$$ + +where $\hat{y}_{i}$ will be the prediction under +$\hat{\boldsymbol{\theta}}$ under our model. Then we construct a new set +of observations via + +$$y_{i}^{\ast} = \hat{y}_{i} + \varepsilon^{\ast}, \quad \varepsilon^{\ast} \sim \mathcal{F}$$ + +with $\mathcal{F}$ being the empirical distribution of the raw errors. A +new set of parameters $\theta^{\ast}$ are then found for the +bootstrapped samples, and we obtain the $\alpha$ confidence interval by +taking the $\alpha/2$ quantiles. Invoke the correspond python function +yields our bootstrap estimates. Unlike `asymptotic`{.interpreted-text +role="func"}, the bounds here are used when estimating the parameters of +each bootstrap samples. An error may be returned if estimation failed +for any of the bootstrap samples. + +::: {.ipython} +In \[1\]: xLBootstrap, xUBootstrap, setX = ci.bootstrap(objSIR, alpha, +xhat, iteration=10, lb=boxBoundsArray\[:,0\], ub=boxBoundsArray\[:,1\], +full_output=True) + +In \[2\]: print(xLBootstrap) + +In \[3\]: print(xUBootstrap) +::: + +The additional information here can be used to compute the bias, tail +effects and test against the normality assumption. If desired, a +simultaneous confidence interval can also be approximated empirically. +Note however that because we are using a semi\--parameter method here, +if the model specification is wrong then the resulting estimates for the +bias is also wrong. The confidence interval still has the normal +approximation guarantee if number of sample is large. + +In this case, because the error in the observation is extremely small, +the confidence interval is narrow. + +::: {.ipython} +In \[1\]: import pylab as P + +In \[2\]: f = plt.figure() + +In \[3\]: n, bins, patches = P.hist(setX\[:,0\], 50) + +In \[4\]: P.xlabel(r\'Estimates of \$beta\$\'); + +In \[5\]: P.ylabel(\'Frequency\'); + +In \[6\]: P.title(\'Estimates under a semi-parametric bootstrap +scheme\'); + +\@savefig bootstrapCIHist.png In \[7\]: P.show() + +In \[8\]: P.close() +::: + +## Comparison Between Methods + +Although we have shown the numerical values for the confidence interval +obtained using different method, it may be hard to comprehend how they +vary. As they say, a picture says a million word, and given that this +particular model only has two parameters, we can obtain inspect and +compare the methods visually via a contour plot. The code to perform +this is shown below but the code block will not be run to save time and +space. + +In the plot above, the bootstrap confidence interval were so close to +the MLE, it is impossible to distinguish the two on such a coarse scale. + +Furthermore, because the geometric confidence interval is the result of +an integration, we can trace the path that lead to the final output that +was shown previously. Again, we are space conscious (and time +constrained) so the code block below will not be run. + +::: {.ipython verbatim=""} +In \[1\]: fig = plt.figure() + +In \[2\]: CS = plt.contour(xi, yi, zi, linewidth=0.5) + +In \[3\]: plt.clabel(CS, fontsize=10, inline=1) + +In \[4\]: l1 = plt.scatter(xLList\[0\]\[:,0\], xLList\[0\]\[:,1\], +marker=\'o\', c=\'m\', s=10); + +In \[5\]: l2 = plt.scatter(xUList\[0\]\[:,0\], xUList\[0\]\[:,1\], +marker=\'x\', c=\'m\', s=10); + +In \[6\]: plt.legend((l1, l2), (\'Lower CI path\', \'Upper CI path\'), +loc=\'upper left\'); + +In \[7\]: plt.ylabel(r\'Estimates of \$gamma\$\'); + +In \[8\]: plt.xlabel(r\'Estimates of \$beta\$\'); + +In \[9\]: plt.title(\'Integration path of the geometric confidence +intervals on the likelihood surface\'); + +In \[10\]: plt.tight_layout(); + +In \[11\]: plt.show() + +In \[12\]: plt.close() +::: + +## Profile Likelihood Surface + +To investigate why it was hard to find the profile likelihood confidence +interval, we can simply look at the surface (which is simply a line as +we are profiling). We find solution of `nuisanceOptim`{.interpreted-text +role="eq"} for each $\boldsymbol{\theta}_{-j}$ at various points of +$\boldsymbol{\theta}$. Equivalently, we can minimize the original loss +function as defined previously, and this is the approach below. We focus +out attention to the parameter $\beta$ of our SIR model. The results are +not shown here but the existence of a solution to +`obj`{.interpreted-text role="eq"} is evident by simply *eyeballing* the +plots. + +::: {.ipython verbatim=""} +In \[1\]: numIter = 100 + +In \[2\]: x2 = numpy.linspace(0.0, 2.0, numIter) + +In \[3\]: funcOut = numpy.linspace(0.0, 2.0, numIter) + +In \[4\]: ode.parameters = \[(\'beta\',0.5), (\'gamma\',1.0/3.0)\] + +In \[5\]: for i in range(numIter): + +: \...: paramEval = \[(\'beta\',x2\[i\]), (\'gamma\',x2\[i\])\] \...: + ode2 = copy.deepcopy(ode) \...: ode2.parameters = paramEval \...: + ode2.initial_values = (x0, t\[0\]) \...: objSIR2 = + NormalLoss(x2\[i\], ode2, x0, t\[0\], t\[1::\], yObv.copy(), + targetState, target_param=\'gamma\') \...: res = + scipy.optimize.minimize(fun=objSIR2.cost, \...: + jac=objSIR2.gradient, \...: x0=x2\[i\], \...: bounds=\[(0,2)\], + \...: method=\'L-BFGS-B\') \...: funcOut\[i\] = res\[\'fun\'\] + +In \[10\]: fig = plt.figure() + +In \[10\]: plt.plot(x2, objSIR.cost(xhat) - funcOut) + +In \[11\]: l1 = plt.axhline(-0.5\*qchisq(1 - alpha, df=1), 0, 2, +color=\'r\') + +In \[12\]: plt.ylabel(r\'\$mathcal{L}(hat{theta}) - mathcal{L}(theta mid +beta)\$\'); + +In \[13\]: plt.xlabel(r\'Fixed value of \$beta\$\'); + +In \[14\]: plt.title(\'Difference in objective function between MLEn and +the maximization of the nuisance parameters given then parameter of +interest, beta in this case\'); + +In \[15\]: plt.tight_layout(); + +In \[16\]: plt.legend((l1,), (r\'\$-0.5mathcal{X}\_{1 - +alpha}\^{2}(1)\$\',), loc=\'lower right\'); + +\@savefig profileLLMaximizerGivenBeta.png In \[17\]: plt.show() \# +\@savefig profileLLMaximizerGivenBeta.png + +In \[18\]: plt.close() +::: + +Both the upper and lower confidence interval can be found in the +profiling procedure, but the part between of +$\beta \in \left[0,\hat{\beta}\right]$ is not convex, with $\hat{\beta}$ +being the MLE. This non\--quadratic profile likelihood is due to the +non-identifiability of the model given data [\[Raue2009\]](). For this +particular case, we can fix it simply by introducing additional +observations in the form of the $I$ state. We encourage the users to try +it out for themselves to confirm. + +::: {.ipython verbatim=""} +In \[1\]: targetState = \[\'I\', \'R\'\] + +In \[2\]: targetStateIndex = +numpy.array(ode.get_state_index(targetState)) + +In \[3\]: y = solution\[1::,targetStateIndex\] + numpy.random.normal(0, +0.01, (len(solution\[1::,targetStateIndex\]), 1)) + +In \[4\]: objSIR = NormalLoss(theta, ode, x0, t\[0\], t\[1::\], +y.copy(), targetState) + +In \[5\]: xhat = objSIR.fit(theta, lb=boxBoundsArray\[:,0\], +ub=boxBoundsArray\[:,1\]) + +In \[6\]: for i in range(numIter): + +: \...: paramEval = \[(\'beta\', x2\[i\]), (\'gamma\', x2\[i\])\] + \...: ode2 = copy.deepcopy(ode) \...: ode2.parameters = paramEval + \...: ode2.initial_values = (x0, t\[0\]) \...: objSIR2 = + NormalLoss(x2\[i\], ode2, x0, t\[0\], t\[1::\], y.copy(), + targetState, target_param=\'gamma\') \...: res = + scipy.optimize.minimize(fun=objSIR2.cost, \...: + jac=objSIR2.gradient, \...: x0=x2\[i\], \...: bounds=\[(0,2)\], + \...: method=\'L-BFGS-B\') \...: funcOut\[i\] = res\[\'fun\'\] + +In \[10\]: fig = plt.figure() + +In \[10\]: plt.plot(x2, objSIR.cost(xhat) - funcOut); + +In \[11\]: l1 = plt.axhline(-0.5\*qchisq(1 - alpha, df=1), 0, 2, +color=\'r\') + +In \[12\]: plt.ylabel(r\'\$mathcal{L}(hat{theta}) - mathcal{L}(theta mid +beta)\$\'); + +In \[13\]: plt.xlabel(r\'Fixed value of \$beta\$\'); + +In \[14\]: plt.title(\'Profile likelihood curve for the parameter ofn +interest with more observation\'); + +In \[15\]: plt.tight_layout(); + +In \[16\]: plt.legend((l1,), (r\'\$-0.5mathcal{X}\_{1 - +alpha}\^{2}(1)\$\',), loc=\'lower right\'); + +\@savefig profileLLMaximizerGivenBetaMoreObs.png In \[17\]: plt.show() +\# \@savefig profileLLMaximizerGivenBetaMoreObs.png + +In \[18\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/ref.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/ref.ipynb new file mode 100644 index 00000000..41409918 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/ref.ipynb @@ -0,0 +1,103 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "Aron1984 \n", + "Seasonality and period-doubling bifurcations in an epidemic model,\n", + "Joan L. Aron and Ira B. Schwartz, Journal of Theoretical Biology, Volume\n", + "110, Issue 4, page 665-679, 1984\n", + "\n", + "Brauer2008 \n", + "Mathematical Epidemiology, Lecture Notes in Mathematics, Fred Brauer,\n", + "Springer 2008\n", + "\n", + "Cao2006 \n", + "Efficient step size selection for the tau-leaping simulation method,\n", + "Yang Cao et el., The Journal of Chemical Physics, Volume 124, Issue 4,\n", + "page 044109, 2006\n", + "\n", + "Finnie2016 \n", + "EpiJSON: A unified data-format for epidemiology, Thomas Finnie et al.,\n", + "Epidemics, Volume 15, page 20-26, 2016\n", + "\n", + "FitzHugh1961 \n", + "Impulses and Physiological States in Theoretical Models of Nerve\n", + "Membrane, Richard FitzHugh, Biophysical Journal, Volume 1, Issue 6, page\n", + "445-466, 1961\n", + "\n", + "Gillespie1977 \n", + "Exact stochastic simulation of coupled chemical reactions, Danial T.\n", + "Gillespie, The Journal of Physical Chemistry, Volume 81, Issue 25, page\n", + "2340-2361, 1977\n", + "\n", + "Girolami2011 \n", + "Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Mark\n", + "Girolami and Ben Calderhead, Journal of the Royal Statistical Society\n", + "Series B, Volume 73, Issue 2, page 123-214, 2011.\n", + "\n", + "Hethcote1973 \n", + "Asymptotic behavior in a deterministic epidemic model, Herbert W.\n", + "Hethcote, Bulletin of Mathematical Biology, Volume 35, page 607-614,\n", + "1973\n", + "\n", + "Legrand2007 \n", + "Understanding the dynamics of Ebola epidemics, J. Legrand et al.\n", + "Epidemiology and Infection, Volume 135, Issue 4, page 610-621, 2007\n", + "\n", + "Lloyd1996 \n", + "Spatial Heterogeneity in Epidemic Models, A.L. Lloyd and R.M. May,\n", + "Journal of Theoretical Biology, Volume 179, Issue 1, page 1-11, 1996\n", + "\n", + "Lorenz1963 \n", + "Deterministic Nonperiodic Flow, Edward N. Lorenz, Journal of the\n", + "Atmospheric Sciences, Volume 20, Issue 2, page 130-141, 1963\n", + "\n", + "Lotka1920 \n", + "Analytical Note on Certain Rhythmic Relations in Organic Systems,\n", + "Alfred J. Lotka, Proceedings of the National Academy of Sciences of the\n", + "United States of America, Volume 7, Issue 7, page 410-415, 1920\n", + "\n", + "Moolgavkar1987 \n", + "Confidence Regions for Parameters of the Proportional Hazards Model: A\n", + "Simulation Study, S.H. Moolgavkar and D.J. Venzon, Scandianvia Journal\n", + "of Statistics, Volume 14, page 43-56, 1987\n", + "\n", + "Press2007 \n", + "Numerical Recipes 3rd Edition: The Art of Scientific Computing, W.H.\n", + "Press et al., Cambridge University Press, 2007\n", + "\n", + "Ramsay2007 \n", + "Parameter estimation for differential equations: a generalized smoothing\n", + "approach, Journal of the Royal Statistical Society Series B, James O.\n", + "Ramsay et al., Volume 69, Issue 5, page 741-796, 2007\n", + "\n", + "Raue2009 \n", + "Structural and Practical Identifiability Analysis of Partially Observed\n", + "Dynamical Models by Exploiting the Profile Likelihood, A. Raue et al.,\n", + "Bioinformatics, Volume 25, Issue 15, page 1923-1929, 2009\n", + "\n", + "Robertson1966 \n", + "The solution of a set of reaction rate equations, H.H. Robertson,\n", + "Academic Press, page 178-182, 1966\n", + "\n", + "Venzon1988 \n", + "A Method for Computing Profile-Likelihood-Based Confidence Intervals,\n", + "D.J. Venzon and S.H. Moolgavkar, Journal of the Royal Statistical\n", + "Society Series C (Applied Statistics), Volume 37, Issue 1, page 87-94,\n", + "1988\n", + "\n", + "vanderpol1926 \n", + "On Relaxed Oscillations, Balthasar van der Pol, The London, Edinburgh,\n", + "and Dublin Philosophical Magazine and Journal of Science, Volume 2,\n", + "Issue 11, page 978-992, 1926" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/ref.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/ref.md new file mode 100644 index 00000000..ce3d7538 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/ref.md @@ -0,0 +1,113 @@ +# References {#ref} + +::: {#citations} + +[Aron1984]{#Aron1984 .citation-label} + +: Seasonality and period-doubling bifurcations in an epidemic model, + Joan L. Aron and Ira B. Schwartz, Journal of Theoretical Biology, + Volume 110, Issue 4, page 665-679, 1984 + +[Brauer2008]{#Brauer2008 .citation-label} + +: Mathematical Epidemiology, Lecture Notes in Mathematics, Fred + Brauer, Springer 2008 + +[Cao2006]{#Cao2006 .citation-label} + +: Efficient step size selection for the tau-leaping simulation method, + Yang Cao et el., The Journal of Chemical Physics, Volume 124, Issue + 4, page 044109, 2006 + +[Finnie2016]{#Finnie2016 .citation-label} + +: EpiJSON: A unified data-format for epidemiology, Thomas Finnie et + al., Epidemics, Volume 15, page 20-26, 2016 + +[FitzHugh1961]{#FitzHugh1961 .citation-label} + +: Impulses and Physiological States in Theoretical Models of Nerve + Membrane, Richard FitzHugh, Biophysical Journal, Volume 1, Issue 6, + page 445-466, 1961 + +[Gillespie1977]{#Gillespie1977 .citation-label} + +: Exact stochastic simulation of coupled chemical reactions, Danial T. + Gillespie, The Journal of Physical Chemistry, Volume 81, Issue 25, + page 2340-2361, 1977 + +[Girolami2011]{#Girolami2011 .citation-label} + +: Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Mark + Girolami and Ben Calderhead, Journal of the Royal Statistical + Society Series B, Volume 73, Issue 2, page 123-214, 2011. + +[Hethcote1973]{#Hethcote1973 .citation-label} + +: Asymptotic behavior in a deterministic epidemic model, Herbert W. + Hethcote, Bulletin of Mathematical Biology, Volume 35, page 607-614, + 1973 + +[Legrand2007]{#Legrand2007 .citation-label} + +: Understanding the dynamics of Ebola epidemics, J. Legrand et al. + Epidemiology and Infection, Volume 135, Issue 4, page 610-621, 2007 + +[Lloyd1996]{#Lloyd1996 .citation-label} + +: Spatial Heterogeneity in Epidemic Models, A.L. Lloyd and R.M. May, + Journal of Theoretical Biology, Volume 179, Issue 1, page 1-11, 1996 + +[Lorenz1963]{#Lorenz1963 .citation-label} + +: Deterministic Nonperiodic Flow, Edward N. Lorenz, Journal of the + Atmospheric Sciences, Volume 20, Issue 2, page 130-141, 1963 + +[Lotka1920]{#Lotka1920 .citation-label} + +: Analytical Note on Certain Rhythmic Relations in Organic Systems, + Alfred J. Lotka, Proceedings of the National Academy of Sciences of + the United States of America, Volume 7, Issue 7, page 410-415, 1920 + +[Moolgavkar1987]{#Moolgavkar1987 .citation-label} + +: Confidence Regions for Parameters of the Proportional Hazards Model: + A Simulation Study, S.H. Moolgavkar and D.J. Venzon, Scandianvia + Journal of Statistics, Volume 14, page 43-56, 1987 + +[Press2007]{#Press2007 .citation-label} + +: Numerical Recipes 3rd Edition: The Art of Scientific Computing, W.H. + Press et al., Cambridge University Press, 2007 + +[Ramsay2007]{#Ramsay2007 .citation-label} + +: Parameter estimation for differential equations: a generalized + smoothing approach, Journal of the Royal Statistical Society Series + B, James O. Ramsay et al., Volume 69, Issue 5, page 741-796, 2007 + +[Raue2009]{#Raue2009 .citation-label} + +: Structural and Practical Identifiability Analysis of Partially + Observed Dynamical Models by Exploiting the Profile Likelihood, A. + Raue et al., Bioinformatics, Volume 25, Issue 15, page 1923-1929, + 2009 + +[Robertson1966]{#Robertson1966 .citation-label} + +: The solution of a set of reaction rate equations, H.H. Robertson, + Academic Press, page 178-182, 1966 + +[Venzon1988]{#Venzon1988 .citation-label} + +: A Method for Computing Profile-Likelihood-Based Confidence + Intervals, D.J. Venzon and S.H. Moolgavkar, Journal of the Royal + Statistical Society Series C (Applied Statistics), Volume 37, Issue + 1, page 87-94, 1988 + +[vanderpol1926]{#vanderpol1926 .citation-label} + +: On Relaxed Oscillations, Balthasar van der Pol, The London, + Edinburgh, and Dublin Philosophical Magazine and Journal of Science, + Volume 2, Issue 11, page 978-992, 1926 +::: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.ipynb new file mode 100644 index 00000000..fd83edb7 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stochastic representation of ode\n", + "\n", + "There are multiple interpretation of stochasticity of a deterministic\n", + "ode. We have implemented two of the most common interpretation; when the\n", + "parameters are realizations of some underlying distribution, and when we\n", + "have a so called chemical master equation where each transition\n", + "represent a jump. Again, we use the standard SIR example as previously\n", + "seen in ref:sir.\n", + "\n", + "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: import numpy as np\n", + "\n", + "In \\[1\\]: x0 = \\[1, 1.27e-6, 0\\]\n", + "\n", + "In \\[1\\]: t = np.linspace(0, 150, 100)\n", + "\n", + "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n", + "\n", + "In \\[1\\]: paramList = \\['beta', 'gamma'\\]\n", + "\n", + "In \\[1\\]: transitionList = \\[ \n", + "...: Transition(origin='S', destination='I', equation='beta\\*S\\*I',\n", + "transition_type=TransitionType.T), ...: Transition(origin='I',\n", + "destination='R', equation='gamma\\*I', transition_type=TransitionType.T)\n", + "...: \\]\n", + "\n", + "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0\\]\n", + "\n", + "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solutionReference = odeS.integrate(t\\[1::\\],\n", + "full_output=False)\n", + "\n", + "## Stochastic Parameter\n", + "\n", + "In our first scenario, we assume that the parameters follow some\n", + "underlying distribution. Given that both $\\beta$ and $\\gamma$ in our SIR\n", + "model has to be non-negative, it seemed natural to use a Gamma\n", + "distribution. We make use of the familiar syntax from\n", + "[R](http://www.r-project.org/) to define our distribution.\n", + "Unfortunately, we have to define it via a tuple, where the first is the\n", + "function handle (name) while the second the parameters. Note that the\n", + "parameters can be defined as either a dictionary or as the same sequence\n", + "as [R](http://www.r-project.org/), which is the shape then the rate in\n", + "the Gamma case.\n", + "\n", + "In \\[1\\]: from pygom.utilR import rgamma\n", + "\n", + "In \\[1\\]: d = dict()\n", + "\n", + "In \\[1\\]: d\\['beta'\\] = (rgamma,{'shape':100.0, 'rate':200.0})\n", + "\n", + "In \\[1\\]: d\\['gamma'\\] = (rgamma,(100.0, 300.0))\n", + "\n", + "In \\[1\\]: odeS.parameters = d\n", + "\n", + "In \\[1\\]: Ymean, Yall = odeS.simulate_param(t\\[1::\\], 10,\n", + "full_output=True)\n", + "\n", + "Note that a message is printed above where it is trying to connect to an\n", + "mpi backend, as our module has the capability to compute in parallel\n", + "using the IPython. We have simulated a total of 10 different solutions\n", + "using different parameters, the plots can be seen below\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", + "\n", + "In \\[1\\]: for solution in Yall: \n", + "...: axarr\\[0\\].plot(t, solution\\[:,0\\]) ...: axarr\\[1\\].plot(t,\n", + "solution\\[:,1\\]) ...: axarr\\[2\\].plot(t, solution\\[:,2\\])\n", + "\n", + "@savefig stochastic_param_all.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "We then see how the expected results, using the sample average of the\n", + "simulations\n", + "\n", + "$$\\tilde{x}(T) = \\mathbb{E}\\left[ \\int_{t_{0}}^{T} f(\\theta,x,t) dt \\right]$$\n", + "\n", + "differs from the reference solution\n", + "\n", + "$$\\hat{x}(T) = \\int_{t_{0}}^{T} f(\\mathbb{E}\\left[ \\theta \\right],x,t) dt$$\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", + "\n", + "In \\[1\\]: for i in range(3): axarr\\[i\\].plot(t, Ymean\\[:,i\\] -\n", + "solutionReference\\[:,i\\])\n", + "\n", + "@savefig stochastic_param_compare.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "The difference is relatively large especially for the $S$ state. We can\n", + "decrease this difference as we increase the number of simulation, and\n", + "more sophisticated sampling method for the generation of random\n", + "variables can also decrease the difference.\n", + "\n", + "In addition to using the built-in functions to represent stochasticity,\n", + "we can also use standard frozen distributions from scipy. Note that it\n", + "must be a frozen distribution as that is the only for the parameters of\n", + "the distributions to propagate through the model.\n", + "\n", + "In \\[1\\]: import scipy.stats as st\n", + "\n", + "In \\[1\\]: d = dict()\n", + "\n", + "In \\[1\\]: d\\['beta'\\] = st.gamma(a=100.0, scale=1.0/200.0)\n", + "\n", + "In \\[1\\]: d\\['gamma'\\] = st.gamma(a=100.0, scale=1.0/300.0)\n", + "\n", + "In \\[1\\]: odeS.parameters = d\n", + "\n", + "Obviously, there may be scenarios where only some of the parameters are\n", + "stochastic. Let's say that the $\\gamma$ parameter is fixed at $1/3$,\n", + "then simply replace the distribution information with a scalar. A quick\n", + "visual inspection at the resulting plot suggests that the system of ODE\n", + "potentially has less variation when compared to the case where both\n", + "parameters are stochastic.\n", + "\n", + "In \\[1\\]: d\\['gamma'\\] = 1.0/3.0\n", + "\n", + "In \\[1\\]: odeS.parameters = d\n", + "\n", + "In \\[1\\]: YmeanSingle, YallSingle = odeS.simulate_param(t\\[1::\\], 5,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", + "\n", + "In \\[1\\]: for solution in YallSingle: \n", + "...: axarr\\[0\\].plot(t,solution\\[:,0\\]) ...:\n", + "axarr\\[1\\].plot(t,solution\\[:,1\\]) ...:\n", + "axarr\\[2\\].plot(t,solution\\[:,2\\])\n", + "\n", + "@savefig stochastic_param_single.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "## Continuous Markov Representation\n", + "\n", + "Another common method of introducing stochasticity into a set of ode is\n", + "by assuming each movement in the system is a result of a jump process.\n", + "More concretely, the probabilty of a move for transition $j$ is governed\n", + "by an exponential distribution such that\n", + "\n", + "$$\\Pr(\\text{process $j$ jump within time } \\tau) = \\lambda_{j} e^{-\\lambda_{j} \\tau},$$\n", + "\n", + "where $\\lambda_{j}$ is the rate of transition for process $j$ and $\\tau$\n", + "the time elapsed after current time $t$.\n", + "\n", + "A couple of the commmon implementation for the jump process have been\n", + "implemented where two of them are used during a normal simulation; the\n", + "first reaction method [\\[Gillespie1977\\]]() and the $\\tau$-Leap method\n", + "[\\[Cao2006\\]](). The two changes interactively depending on the size of\n", + "the states.\n", + "\n", + "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", + "\n", + "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n", + "\n", + "In \\[1\\]: paramList = \\['beta', 'gamma', 'N'\\]\n", + "\n", + "In \\[1\\]: transitionList = \\[ \n", + "...: Transition(origin='S', destination='I', equation='beta\\*S\\*I/N',\n", + "transition_type=TransitionType.T), ...: Transition(origin='I',\n", + "destination='R', equation='gamma\\*I', transition_type=TransitionType.T)\n", + "...: \\]\n", + "\n", + "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0, x0\\[0\\]\\]\n", + "\n", + "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solutionReference = odeS.integrate(t\\[1::\\])\n", + "\n", + "In \\[1\\]: simX, simT = odeS.simulate_jump(t\\[1:10\\], 10,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1, 3)\n", + "\n", + "In \\[1\\]: for solution in simX: \n", + "...: axarr\\[0\\].plot(t\\[:9\\], solution\\[:,0\\]) ...:\n", + "axarr\\[1\\].plot(t\\[:9\\], solution\\[:,1\\]) ...: axarr\\[2\\].plot(t\\[:9\\],\n", + "solution\\[:,2\\])\n", + "\n", + "@savefig stochastic_process.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "Above, we see ten different simulation, again using the SIR model but\n", + "without standardization of the initial conditions. We restrict our time\n", + "frame to be only the first 10 time points so that the individual changes\n", + "can be seen more clearly above. If we use the same time frame as the one\n", + "used previously for the deterministic system (as shown below), the\n", + "trajectories are smoothed out and we no longer observe the *jumps*.\n", + "Looking at the raw trajectories of the ODE below, it is obvious that the\n", + "mean from a jump process is very different to the deterministic\n", + "solution. The reason behind this is that the jump process above was able\n", + "to fully remove all the initial infected individuals before any new\n", + "ones.\n", + "\n", + "In \\[1\\]: simX,simT = odeS.simulate_jump(t, 5, full_output=True)\n", + "\n", + "In \\[1\\]: simMean = np.mean(simX, axis=0)\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", + "\n", + "In \\[1\\]: for solution in simX: \n", + "...: axarr\\[0\\].plot(t, solution\\[:,0\\]) ...: axarr\\[1\\].plot(t,\n", + "solution\\[:,1\\]) ...: axarr\\[2\\].plot(t, solution\\[:,2\\])\n", + "\n", + "@savefig stochastic_process_compare_large_n\\_curves.png In \\[1\\]:\n", + "plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "## Repeatable Simulation\n", + "\n", + "One of the possible use of compartmental models is to generate\n", + "forecasts. Although most of the time the requirement would be to have\n", + "(at least point-wise) convergence in the limit, reproducibility is also\n", + "important. For both types of interpretation explained above, we have\n", + "given the package the capability to repeat the simulations by setting a\n", + "seed. When the assumption is that the parameters follows some sort of\n", + "distribution, we simply set the seed which governs the global state of\n", + "the random number generator.\n", + "\n", + "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", + "\n", + "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: d = {'beta': st.gamma(a=100.0, scale=1.0/200.0), 'gamma':\n", + "st.gamma(a=100.0, scale=1.0/300.0), 'N': x0\\[0\\]}\n", + "\n", + "In \\[1\\]: odeS.parameters = d\n", + "\n", + "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: Ymean, Yall = odeS.simulate_param(t\\[1::\\], 10,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: np.random.seed(1)\n", + "\n", + "In \\[1\\]: Ymean1, Yall1 = odeS.simulate_param(t\\[1::\\], 10,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: np.random.seed(1)\n", + "\n", + "In \\[1\\]: Ymean2, Yall2 = odeS.simulate_param(t\\[1::\\], 10,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: sim_diff = \\[np.linalg.norm(Yall\\[i\\] - yi) for i, yi in\n", + "enumerate(Yall1)\\]\n", + "\n", + "In \\[1\\]: sim_diff12 = \\[np.linalg.norm(Yall2\\[i\\] - yi) for i, yi in\n", + "enumerate(Yall1)\\]\n", + "\n", + "In \\[1\\]: print(\"Different in the simulations and the mean: (%s, %s) \" %\n", + "(np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))\n", + "\n", + "In \\[1\\]: print(\"Different in the simulations and the mean using same\n", + "seed: (%s, %s) \" % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 -\n", + "Ymean1))))\n", + "\n", + "In the alternative interpretation, setting the global seed is\n", + "insufficient. Unlike simulation based on the parameters, where we can\n", + "pre-generate all the parameter values and send them off to individual\n", + "processes in the parallel backend, this is prohibitive here. In a\n", + "nutshell, the seed does not propagate when using a parallel backend\n", + "because each *integration* requires an unknown number of random samples.\n", + "Therefore, we have an additional flag **parallel** in the function\n", + "signature. By ensuring that the computation runs in serial, we can make\n", + "use of the global seed and generate identical runs.\n", + "\n", + "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", + "\n", + "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0, x0\\[0\\]\\]\n", + "\n", + "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: simX, simT = odeS.simulate_jump(t\\[1:10\\], 10, parallel=False,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: np.random.seed(1)\n", + "\n", + "In \\[1\\]: simX1, simT1 = odeS.simulate_jump(t\\[1:10\\], 10,\n", + "parallel=False, full_output=True)\n", + "\n", + "In \\[1\\]: np.random.seed(1)\n", + "\n", + "In \\[1\\]: simX2, simT2 = odeS.simulate_jump(t\\[1:10\\], 10,\n", + "parallel=False, full_output=True)\n", + "\n", + "In \\[1\\]: sim_diff = \\[np.linalg.norm(simX\\[i\\] - x1) for i, x1 in\n", + "enumerate(simX1)\\]\n", + "\n", + "In \\[1\\]: sim_diff12 = \\[np.linalg.norm(simX2\\[i\\] - x1) for i, x1 in\n", + "enumerate(simX1)\\]\n", + "\n", + "In \\[1\\]: print(\"Difference in simulation: %s\" %\n", + "np.sum(np.abs(sim_diff)))\n", + "\n", + "In \\[1\\]: print(\"Difference in simulation using same seed: %s\" %\n", + "np.sum(np.abs(sim_diff12)))" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.md new file mode 100644 index 00000000..58bf60b2 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.md @@ -0,0 +1,341 @@ +# Stochastic representation of ode {#stochastic} + +There are multiple interpretation of stochasticity of a deterministic +ode. We have implemented two of the most common interpretation; when the +parameters are realizations of some underlying distribution, and when we +have a so called chemical master equation where each transition +represent a jump. Again, we use the standard SIR example as previously +seen in ref:[sir]{.title-ref}. + +::: {.ipython} +In \[1\]: from pygom import SimulateOde, Transition, TransitionType + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: import numpy as np + +In \[1\]: x0 = \[1, 1.27e-6, 0\] + +In \[1\]: t = np.linspace(0, 150, 100) + +In \[1\]: stateList = \[\'S\', \'I\', \'R\'\] + +In \[1\]: paramList = \[\'beta\', \'gamma\'\] + +In \[1\]: transitionList = \[ + +: \...: Transition(origin=\'S\', destination=\'I\', + equation=\'beta\*S\*I\', transition_type=TransitionType.T), \...: + Transition(origin=\'I\', destination=\'R\', equation=\'gamma\*I\', + transition_type=TransitionType.T) \...: \] + +In \[1\]: odeS = SimulateOde(stateList, paramList, +transition=transitionList) + +In \[1\]: odeS.parameters = \[0.5, 1.0/3.0\] + +In \[1\]: odeS.initial_values = (x0, t\[0\]) + +In \[1\]: solutionReference = odeS.integrate(t\[1::\], +full_output=False) +::: + +## Stochastic Parameter + +In our first scenario, we assume that the parameters follow some +underlying distribution. Given that both $\beta$ and $\gamma$ in our SIR +model has to be non-negative, it seemed natural to use a Gamma +distribution. We make use of the familiar syntax from +[R](http://www.r-project.org/) to define our distribution. +Unfortunately, we have to define it via a tuple, where the first is the +function handle (name) while the second the parameters. Note that the +parameters can be defined as either a dictionary or as the same sequence +as [R](http://www.r-project.org/), which is the shape then the rate in +the Gamma case. + +::: {.ipython} +In \[1\]: from pygom.utilR import rgamma + +In \[1\]: d = dict() + +In \[1\]: d\[\'beta\'\] = (rgamma,{\'shape\':100.0, \'rate\':200.0}) + +In \[1\]: d\[\'gamma\'\] = (rgamma,(100.0, 300.0)) + +In \[1\]: odeS.parameters = d + +In \[1\]: Ymean, Yall = odeS.simulate_param(t\[1::\], 10, +full_output=True) +::: + +Note that a message is printed above where it is trying to connect to an +mpi backend, as our module has the capability to compute in parallel +using the IPython. We have simulated a total of 10 different solutions +using different parameters, the plots can be seen below + +::: {.ipython} +In \[1\]: f, axarr = plt.subplots(1,3) + +In \[1\]: for solution in Yall: + +: \...: axarr\[0\].plot(t, solution\[:,0\]) \...: axarr\[1\].plot(t, + solution\[:,1\]) \...: axarr\[2\].plot(t, solution\[:,2\]) + +\@savefig stochastic_param_all.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: + +We then see how the expected results, using the sample average of the +simulations + +$$\tilde{x}(T) = \mathbb{E}\left[ \int_{t_{0}}^{T} f(\theta,x,t) dt \right]$$ + +differs from the reference solution + +$$\hat{x}(T) = \int_{t_{0}}^{T} f(\mathbb{E}\left[ \theta \right],x,t) dt$$ + +::: {.ipython} +In \[1\]: f, axarr = plt.subplots(1,3) + +In \[1\]: for i in range(3): axarr\[i\].plot(t, Ymean\[:,i\] - +solutionReference\[:,i\]) + +\@savefig stochastic_param_compare.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: + +The difference is relatively large especially for the $S$ state. We can +decrease this difference as we increase the number of simulation, and +more sophisticated sampling method for the generation of random +variables can also decrease the difference. + +In addition to using the built-in functions to represent stochasticity, +we can also use standard frozen distributions from scipy. Note that it +must be a frozen distribution as that is the only for the parameters of +the distributions to propagate through the model. + +::: {.ipython} +In \[1\]: import scipy.stats as st + +In \[1\]: d = dict() + +In \[1\]: d\[\'beta\'\] = st.gamma(a=100.0, scale=1.0/200.0) + +In \[1\]: d\[\'gamma\'\] = st.gamma(a=100.0, scale=1.0/300.0) + +In \[1\]: odeS.parameters = d +::: + +Obviously, there may be scenarios where only some of the parameters are +stochastic. Let\'s say that the $\gamma$ parameter is fixed at $1/3$, +then simply replace the distribution information with a scalar. A quick +visual inspection at the resulting plot suggests that the system of ODE +potentially has less variation when compared to the case where both +parameters are stochastic. + +::: {.ipython} +In \[1\]: d\[\'gamma\'\] = 1.0/3.0 + +In \[1\]: odeS.parameters = d + +In \[1\]: YmeanSingle, YallSingle = odeS.simulate_param(t\[1::\], 5, +full_output=True) + +In \[1\]: f, axarr = plt.subplots(1,3) + +In \[1\]: for solution in YallSingle: + +: \...: axarr\[0\].plot(t,solution\[:,0\]) \...: + axarr\[1\].plot(t,solution\[:,1\]) \...: + axarr\[2\].plot(t,solution\[:,2\]) + +\@savefig stochastic_param_single.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: + +## Continuous Markov Representation + +Another common method of introducing stochasticity into a set of ode is +by assuming each movement in the system is a result of a jump process. +More concretely, the probabilty of a move for transition $j$ is governed +by an exponential distribution such that + +$$\Pr(\text{process $j$ jump within time } \tau) = \lambda_{j} e^{-\lambda_{j} \tau},$$ + +where $\lambda_{j}$ is the rate of transition for process $j$ and $\tau$ +the time elapsed after current time $t$. + +A couple of the commmon implementation for the jump process have been +implemented where two of them are used during a normal simulation; the +first reaction method [\[Gillespie1977\]]() and the $\tau$-Leap method +[\[Cao2006\]](). The two changes interactively depending on the size of +the states. + +::: {.ipython} +In \[1\]: x0 = \[2362206.0, 3.0, 0.0\] + +In \[1\]: stateList = \[\'S\', \'I\', \'R\'\] + +In \[1\]: paramList = \[\'beta\', \'gamma\', \'N\'\] + +In \[1\]: transitionList = \[ + +: \...: Transition(origin=\'S\', destination=\'I\', + equation=\'beta\*S\*I/N\', transition_type=TransitionType.T), \...: + Transition(origin=\'I\', destination=\'R\', equation=\'gamma\*I\', + transition_type=TransitionType.T) \...: \] + +In \[1\]: odeS = SimulateOde(stateList, paramList, +transition=transitionList) + +In \[1\]: odeS.parameters = \[0.5, 1.0/3.0, x0\[0\]\] + +In \[1\]: odeS.initial_values = (x0, t\[0\]) + +In \[1\]: solutionReference = odeS.integrate(t\[1::\]) + +In \[1\]: simX, simT = odeS.simulate_jump(t\[1:10\], 10, +full_output=True) + +In \[1\]: f, axarr = plt.subplots(1, 3) + +In \[1\]: for solution in simX: + +: \...: axarr\[0\].plot(t\[:9\], solution\[:,0\]) \...: + axarr\[1\].plot(t\[:9\], solution\[:,1\]) \...: + axarr\[2\].plot(t\[:9\], solution\[:,2\]) + +\@savefig stochastic_process.png In \[1\]: plt.show() + +In \[1\]: plt.close() +::: + +Above, we see ten different simulation, again using the SIR model but +without standardization of the initial conditions. We restrict our time +frame to be only the first 10 time points so that the individual changes +can be seen more clearly above. If we use the same time frame as the one +used previously for the deterministic system (as shown below), the +trajectories are smoothed out and we no longer observe the *jumps*. +Looking at the raw trajectories of the ODE below, it is obvious that the +mean from a jump process is very different to the deterministic +solution. The reason behind this is that the jump process above was able +to fully remove all the initial infected individuals before any new +ones. + +::: {.ipython} +In \[1\]: simX,simT = odeS.simulate_jump(t, 5, full_output=True) + +In \[1\]: simMean = np.mean(simX, axis=0) + +In \[1\]: f, axarr = plt.subplots(1,3) + +In \[1\]: for solution in simX: + +: \...: axarr\[0\].plot(t, solution\[:,0\]) \...: axarr\[1\].plot(t, + solution\[:,1\]) \...: axarr\[2\].plot(t, solution\[:,2\]) + +\@savefig stochastic_process_compare_large_n\_curves.png In \[1\]: +plt.show() + +In \[1\]: plt.close() +::: + +## Repeatable Simulation + +One of the possible use of compartmental models is to generate +forecasts. Although most of the time the requirement would be to have +(at least point-wise) convergence in the limit, reproducibility is also +important. For both types of interpretation explained above, we have +given the package the capability to repeat the simulations by setting a +seed. When the assumption is that the parameters follows some sort of +distribution, we simply set the seed which governs the global state of +the random number generator. + +::: {.ipython} +In \[1\]: x0 = \[2362206.0, 3.0, 0.0\] + +In \[1\]: odeS = SimulateOde(stateList, paramList, +transition=transitionList) + +In \[1\]: d = {\'beta\': st.gamma(a=100.0, scale=1.0/200.0), \'gamma\': +st.gamma(a=100.0, scale=1.0/300.0), \'N\': x0\[0\]} + +In \[1\]: odeS.parameters = d + +In \[1\]: odeS.initial_values = (x0, t\[0\]) + +In \[1\]: Ymean, Yall = odeS.simulate_param(t\[1::\], 10, +full_output=True) + +In \[1\]: np.random.seed(1) + +In \[1\]: Ymean1, Yall1 = odeS.simulate_param(t\[1::\], 10, +full_output=True) + +In \[1\]: np.random.seed(1) + +In \[1\]: Ymean2, Yall2 = odeS.simulate_param(t\[1::\], 10, +full_output=True) + +In \[1\]: sim_diff = \[np.linalg.norm(Yall\[i\] - yi) for i, yi in +enumerate(Yall1)\] + +In \[1\]: sim_diff12 = \[np.linalg.norm(Yall2\[i\] - yi) for i, yi in +enumerate(Yall1)\] + +In \[1\]: print(\"Different in the simulations and the mean: (%s, %s) \" +% (np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean)))) + +In \[1\]: print(\"Different in the simulations and the mean using same +seed: (%s, %s) \" % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 - +Ymean1)))) +::: + +In the alternative interpretation, setting the global seed is +insufficient. Unlike simulation based on the parameters, where we can +pre-generate all the parameter values and send them off to individual +processes in the parallel backend, this is prohibitive here. In a +nutshell, the seed does not propagate when using a parallel backend +because each *integration* requires an unknown number of random samples. +Therefore, we have an additional flag **parallel** in the function +signature. By ensuring that the computation runs in serial, we can make +use of the global seed and generate identical runs. + +::: {.ipython} +In \[1\]: x0 = \[2362206.0, 3.0, 0.0\] + +In \[1\]: odeS = SimulateOde(stateList, paramList, +transition=transitionList) + +In \[1\]: odeS.parameters = \[0.5, 1.0/3.0, x0\[0\]\] + +In \[1\]: odeS.initial_values = (x0, t\[0\]) + +In \[1\]: simX, simT = odeS.simulate_jump(t\[1:10\], 10, parallel=False, +full_output=True) + +In \[1\]: np.random.seed(1) + +In \[1\]: simX1, simT1 = odeS.simulate_jump(t\[1:10\], 10, +parallel=False, full_output=True) + +In \[1\]: np.random.seed(1) + +In \[1\]: simX2, simT2 = odeS.simulate_jump(t\[1:10\], 10, +parallel=False, full_output=True) + +In \[1\]: sim_diff = \[np.linalg.norm(simX\[i\] - x1) for i, x1 in +enumerate(simX1)\] + +In \[1\]: sim_diff12 = \[np.linalg.norm(simX2\[i\] - x1) for i, x1 in +enumerate(simX1)\] + +In \[1\]: print(\"Difference in simulation: %s\" % +np.sum(np.abs(sim_diff))) + +In \[1\]: print(\"Difference in simulation using same seed: %s\" % +np.sum(np.abs(sim_diff12))) +::: diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/transition.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/transition.ipynb new file mode 100644 index 00000000..a80e94a4 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/transition.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transition Object\n", + "\n", + "The most important part of setting up the model is to correctly define\n", + "the set odes, which is based solely on the classes defined in\n", + "`transition`. All transitions that gets fed into the ode system needs to\n", + "be defined as a transition object, `Transition`. It takes a total of\n", + "four input arguments\n", + "\n", + "1. The origin state\n", + "2. Equation that describe the process\n", + "3. The type of transition\n", + "4. The destination state\n", + "\n", + "where the first three are mandatory. To demonstrate, we go back to the\n", + "SIR model defined previously in the section `sir`. Recall that the set\n", + "of odes are\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{\\partial S}{\\partial t} &= -\\beta SI \\\\\n", + "\\frac{\\partial I}{\\partial t} &= \\beta SI - \\gamma I \\\\\n", + "\\frac{\\partial R}{\\partial t} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "We can simply define the set of ode, as seen previously, via\n", + "\n", + "In \\[1\\]: from pygom import Transition, TransitionType, common_models\n", + "\n", + "In \\[2\\]: ode1 = Transition(origin='S', equation='-beta\\*S\\*I',\n", + "transition_type=TransitionType.ODE)\n", + "\n", + "In \\[3\\]: ode2 = Transition(origin='I', equation='beta\\*S\\*I -\n", + "gamma\\*I', transition_type=TransitionType.ODE)\n", + "\n", + "In \\[4\\]: ode3 = Transition(origin='R', equation='gamma\\*I',\n", + "transition_type=TransitionType.ODE)\n", + "\n", + "Note that we need to state explicitly the type of equation we are\n", + "inputting, which is simply of type **ODE** in this case. We can confirm\n", + "this has been entered correctly by putting it into `DeterministicOde`\n", + "\n", + "In \\[1\\]: from pygom import DeterministicOde\n", + "\n", + "In \\[2\\]: stateList = \\['S', 'I', 'R'\\]\n", + "\n", + "In \\[3\\]: paramList = \\['beta', 'gamma'\\]\n", + "\n", + "In \\[4\\]: model = DeterministicOde(stateList, \n", + "...: paramList, ...: ode=\\[ode1, ode2, ode3\\])\n", + "\n", + "and check it\n", + "\n", + "In \\[1\\]: model.get_ode_eqn()\n", + "\n", + "An alternative print function `print_ode` is also available which may be\n", + "more suitable in other situation. The default prints the formula in a\n", + "rendered format and another which prints out the latex format which can\n", + "be used directly in a latex document. The latter is useful as it saves\n", + "typing out the formula twice, once in the code and another in documents.\n", + "\n", + "In \\[1\\]: model.print_ode(False)\n", + "\n", + "In \\[2\\]: model.print_ode(True)\n", + "\n", + "Now we are going to show the different ways of defining the same set of\n", + "odes.\n", + "\n", + "## Defining the equations\n", + "\n", + "Recognizing that the set of odes defining the SIR model is the result of\n", + "two transitions,\n", + "\n", + "$$\\begin{aligned}\n", + "S \\rightarrow I &= \\beta SI \\\\\n", + "I \\rightarrow R &= \\gamma I\n", + "\\end{aligned}$$\n", + "\n", + "where $S \\rightarrow I$ denotes a transition from state $S$ to state\n", + "$I$. Therefore, we can simply define our model by these two transition,\n", + "but now these two transition needs to be inputted via the `transition`\n", + "argument instead of the `ode` argument. Note that we are initializing\n", + "the model using a different class, because the stochastic implementation\n", + "has more operation on transitions.\n", + "\n", + "In \\[600\\]: from pygom import SimulateOde\n", + "\n", + "In \\[601\\]: t1 = Transition(origin='S', destination='I',\n", + "equation='beta\\*S\\*I', transition_type=TransitionType.T)\n", + "\n", + "In \\[602\\]: t2 = Transition(origin='I', destination='R',\n", + "equation='gamma\\*I', transition_type=TransitionType.T)\n", + "\n", + "In \\[603\\]: modelTrans = SimulateOde(stateList, \n", + ".….: paramList, .….: transition=\\[t1, t2\\])\n", + "\n", + "In \\[604\\]: modelTrans.get_ode_eqn()\n", + "\n", + "We can see that the resulting ode is exactly the same, as expected. The\n", + "transition matrix that defines this process can easily be visualized\n", + "using graphviz. Because only certain renderer permit the use of sub and\n", + "superscript, operators such as $**$ are left as they are in the\n", + "equation.\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[2\\]: f = plt.figure()\n", + "\n", + "In \\[3\\]: modelTrans.get_transition_matrix()\n", + "\n", + "@savefig sir_transition_graph.png In \\[4\\]: dot =\n", + "modelTrans.get_transition_graph()\n", + "\n", + "If we put in via the wrong argument like below (not run), then an error\n", + "will appear.\n", + "\n", + "In \\[1\\]: \\# modelTrans = DeterministicOde(stateList, paramList,\n", + "ode=\\[t1, t2\\])\n", + "\n", + "because `TranstionType` was defined explicitly as a transition instead\n", + "of an ode. The same can be observed when the wrong `TransitionType` is\n", + "used for any of the input argument.\n", + "\n", + "This though, only encourages us to define the transitions carefully. We\n", + "can also pretend that the set of odes are in fact just a set of birth\n", + "process\n", + "\n", + "In \\[619\\]: birth1 = Transition(origin='S', equation='-beta\\*S\\*I',\n", + "transition_type=TransitionType.B)\n", + "\n", + "In \\[620\\]: birth2 = Transition(origin='I', equation='beta\\*S\\*I -\n", + "gamma\\*I', transition_type=TransitionType.B)\n", + "\n", + "In \\[621\\]: birth3 = Transition(origin='R', equation='gamma\\*I',\n", + "transition_type=TransitionType.B)\n", + "\n", + "In \\[622\\]: modelBirth = DeterministicOde(stateList, \n", + ".….: paramList, .….: birth_death=\\[birth1, birth2, birth3\\])\n", + "\n", + "In \\[623\\]: modelBirth.get_ode_eqn()\n", + "\n", + "which will yield the same set result. Alternatively, we can use the\n", + "negative of the equation but set it to be a death process. For example,\n", + "we multiply the equations for state $S$ and $R$ with a negative sign and\n", + "set the transition type to be a death process instead.\n", + "\n", + "In \\[624\\]: death1 = Transition(origin='S', equation='beta\\*S\\*I',\n", + "transition_type=TransitionType.D)\n", + "\n", + "In \\[625\\]: birth2 = Transition(origin='I', equation='beta\\*S\\*I -\n", + "gamma\\*I', transition_type=TransitionType.B)\n", + "\n", + "In \\[626\\]: death3 = Transition(origin='R', equation='-gamma\\*I',\n", + "transition_type=TransitionType.D)\n", + "\n", + "In \\[627\\]: modelBD = DeterministicOde(stateList, \n", + ".….: paramList, .….: birth_death=\\[death1, birth2, death3\\])\n", + "\n", + "In \\[628\\]: modelBD.get_ode_eqn()\n", + "\n", + "We can see that all the above ways yield the same set of ode at the end.\n", + "\n", + "## Model Addition\n", + "\n", + "Because we allow the separation of transitions between states and\n", + "birth/death processes, the birth/death processes can be added later on.\n", + "\n", + "In \\[1\\]: modelBD2 = modelTrans\n", + "\n", + "In \\[1\\]: modelBD2.param_list = paramList + \\['mu', 'B'\\]\n", + "\n", + "In \\[1\\]: birthDeathList = \\[Transition(origin='S', equation='B', transition_type=TransitionType.B), \n", + "...: Transition(origin='S', equation='mu\\*S',\n", + "transition_type=TransitionType.D), ...: Transition(origin='I',\n", + "equation='mu\\*I', transition_type=TransitionType.D)\\]\n", + "\n", + "In \\[1\\]: modelBD2.birth_death_list = birthDeathList\n", + "\n", + "In \\[1\\]: modelBD2.get_ode_eqn()\n", + "\n", + "So modeling can be done in stages. Start with a standard closed system\n", + "and extend it with additional flows that interact with the environment.\n", + "\n", + "## Transition type\n", + "\n", + "There are currently four different type of transitions allowed, which is\n", + "defined in an enum class also located in `transition`. The four types\n", + "are B, D, ODE and T, where they represent different type of process with\n", + "explanation in their corresponding value.\n", + "\n", + "In \\[1\\]: from pygom import transition\n", + "\n", + "In \\[2\\]: for i in transition.TransitionType: \n", + "...: print(str(i) + \" = \" + i.value)\n", + "\n", + "Each birth process are added to the origin state while each death\n", + "process are deducted from the state, i.e. added to the state after\n", + "multiplying with a negative sign. An ode type is also added to the state\n", + "and we forbid the number of input ode to be greater than the number of\n", + "state inputted." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/transition.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/transition.md new file mode 100644 index 00000000..b0be6933 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/transition.md @@ -0,0 +1,230 @@ +# Transition Object {#transition} + +The most important part of setting up the model is to correctly define +the set odes, which is based solely on the classes defined in +`transition`{.interpreted-text role="mod"}. All transitions that gets +fed into the ode system needs to be defined as a transition object, +`Transition`{.interpreted-text role="class"}. It takes a total of four +input arguments + +1. The origin state +2. Equation that describe the process +3. The type of transition +4. The destination state + +where the first three are mandatory. To demonstrate, we go back to the +SIR model defined previously in the section `sir`{.interpreted-text +role="ref"}. Recall that the set of odes are + +$$\begin{aligned} +\frac{\partial S}{\partial t} &= -\beta SI \\ +\frac{\partial I}{\partial t} &= \beta SI - \gamma I \\ +\frac{\partial R}{\partial t} &= \gamma I. +\end{aligned}$$ + +We can simply define the set of ode, as seen previously, via + +::: {.ipython} +In \[1\]: from pygom import Transition, TransitionType, common_models + +In \[2\]: ode1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\', +transition_type=TransitionType.ODE) + +In \[3\]: ode2 = Transition(origin=\'I\', equation=\'beta\*S\*I - +gamma\*I\', transition_type=TransitionType.ODE) + +In \[4\]: ode3 = Transition(origin=\'R\', equation=\'gamma\*I\', +transition_type=TransitionType.ODE) +::: + +Note that we need to state explicitly the type of equation we are +inputting, which is simply of type **ODE** in this case. We can confirm +this has been entered correctly by putting it into +`DeterministicOde`{.interpreted-text role="class"} + +::: {.ipython} +In \[1\]: from pygom import DeterministicOde + +In \[2\]: stateList = \[\'S\', \'I\', \'R\'\] + +In \[3\]: paramList = \[\'beta\', \'gamma\'\] + +In \[4\]: model = DeterministicOde(stateList, + +: \...: paramList, \...: ode=\[ode1, ode2, ode3\]) +::: + +and check it + +::: {.ipython} +In \[1\]: model.get_ode_eqn() +::: + +An alternative print function `print_ode`{.interpreted-text role="func"} +is also available which may be more suitable in other situation. The +default prints the formula in a rendered format and another which prints +out the latex format which can be used directly in a latex document. The +latter is useful as it saves typing out the formula twice, once in the +code and another in documents. + +::: {.ipython} +In \[1\]: model.print_ode(False) + +In \[2\]: model.print_ode(True) +::: + +Now we are going to show the different ways of defining the same set of +odes. + +## Defining the equations {#defining-eqn} + +Recognizing that the set of odes defining the SIR model is the result of +two transitions, + +$$\begin{aligned} +S \rightarrow I &= \beta SI \\ +I \rightarrow R &= \gamma I +\end{aligned}$$ + +where $S \rightarrow I$ denotes a transition from state $S$ to state +$I$. Therefore, we can simply define our model by these two transition, +but now these two transition needs to be inputted via the `transition` +argument instead of the `ode` argument. Note that we are initializing +the model using a different class, because the stochastic implementation +has more operation on transitions. + +::: {.ipython} +In \[600\]: from pygom import SimulateOde + +In \[601\]: t1 = Transition(origin=\'S\', destination=\'I\', +equation=\'beta\*S\*I\', transition_type=TransitionType.T) + +In \[602\]: t2 = Transition(origin=\'I\', destination=\'R\', +equation=\'gamma\*I\', transition_type=TransitionType.T) + +In \[603\]: modelTrans = SimulateOde(stateList, + +: \.....: paramList, \.....: transition=\[t1, t2\]) + +In \[604\]: modelTrans.get_ode_eqn() +::: + +We can see that the resulting ode is exactly the same, as expected. The +transition matrix that defines this process can easily be visualized +using graphviz. Because only certain renderer permit the use of sub and +superscript, operators such as $**$ are left as they are in the +equation. + +::: {.ipython} +In \[1\]: import matplotlib.pyplot as plt + +In \[2\]: f = plt.figure() + +In \[3\]: modelTrans.get_transition_matrix() + +\@savefig sir_transition_graph.png In \[4\]: dot = +modelTrans.get_transition_graph() +::: + +If we put in via the wrong argument like below (not run), then an error +will appear. + +::: {.ipython} +In \[1\]: \# modelTrans = DeterministicOde(stateList, paramList, +ode=\[t1, t2\]) +::: + +because `TranstionType`{.interpreted-text role="class"} was defined +explicitly as a transition instead of an ode. The same can be observed +when the wrong `TransitionType`{.interpreted-text role="class"} is used +for any of the input argument. + +This though, only encourages us to define the transitions carefully. We +can also pretend that the set of odes are in fact just a set of birth +process + +::: {.ipython} +In \[619\]: birth1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\', +transition_type=TransitionType.B) + +In \[620\]: birth2 = Transition(origin=\'I\', equation=\'beta\*S\*I - +gamma\*I\', transition_type=TransitionType.B) + +In \[621\]: birth3 = Transition(origin=\'R\', equation=\'gamma\*I\', +transition_type=TransitionType.B) + +In \[622\]: modelBirth = DeterministicOde(stateList, + +: \.....: paramList, \.....: birth_death=\[birth1, birth2, birth3\]) + +In \[623\]: modelBirth.get_ode_eqn() +::: + +which will yield the same set result. Alternatively, we can use the +negative of the equation but set it to be a death process. For example, +we multiply the equations for state $S$ and $R$ with a negative sign and +set the transition type to be a death process instead. + +::: {.ipython} +In \[624\]: death1 = Transition(origin=\'S\', equation=\'beta\*S\*I\', +transition_type=TransitionType.D) + +In \[625\]: birth2 = Transition(origin=\'I\', equation=\'beta\*S\*I - +gamma\*I\', transition_type=TransitionType.B) + +In \[626\]: death3 = Transition(origin=\'R\', equation=\'-gamma\*I\', +transition_type=TransitionType.D) + +In \[627\]: modelBD = DeterministicOde(stateList, + +: \.....: paramList, \.....: birth_death=\[death1, birth2, death3\]) + +In \[628\]: modelBD.get_ode_eqn() +::: + +We can see that all the above ways yield the same set of ode at the end. + +## Model Addition + +Because we allow the separation of transitions between states and +birth/death processes, the birth/death processes can be added later on. + +::: {.ipython} +In \[1\]: modelBD2 = modelTrans + +In \[1\]: modelBD2.param_list = paramList + \[\'mu\', \'B\'\] + +In \[1\]: birthDeathList = \[Transition(origin=\'S\', equation=\'B\', transition_type=TransitionType.B), + +: \...: Transition(origin=\'S\', equation=\'mu\*S\', + transition_type=TransitionType.D), \...: Transition(origin=\'I\', + equation=\'mu\*I\', transition_type=TransitionType.D)\] + +In \[1\]: modelBD2.birth_death_list = birthDeathList + +In \[1\]: modelBD2.get_ode_eqn() +::: + +So modeling can be done in stages. Start with a standard closed system +and extend it with additional flows that interact with the environment. + +## Transition type + +There are currently four different type of transitions allowed, which is +defined in an enum class also located in `transition`{.interpreted-text +role="mod"}. The four types are B, D, ODE and T, where they represent +different type of process with explanation in their corresponding value. + +::: {.ipython} +In \[1\]: from pygom import transition + +In \[2\]: for i in transition.TransitionType: + +: \...: print(str(i) + \" = \" + i.value) +::: + +Each birth process are added to the origin state while each death +process are deducted from the state, i.e. added to the state after +multiplying with a negative sign. An ode type is also added to the state +and we forbid the number of input ode to be greater than the number of +state inputted. diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/unrollOde.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/unrollOde.ipynb new file mode 100644 index 00000000..644be889 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/unrollOde.ipynb @@ -0,0 +1,23 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convert ODE into transitions\n", + "\n", + "As seen previously in `transition`, we can define the model via the\n", + "transitions or explicitly as ODEs. There are times when we all just want\n", + "to test out some model in a paper and the only available information are\n", + "the ODEs themselves. Even though we know that the ODEs come from some\n", + "underlying transitions, breaking them down can be a time consuming\n", + "process. We provide the functionalities to do this automatically.\n", + "\n", + "unroll/unrollSimple.rst unroll/unrollBD.rst unroll/unrollHard.rst" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/unrollOde.md b/doc/doc/pygom-doc/mdconvert/doc_to_sort/unrollOde.md new file mode 100644 index 00000000..92e3cf5c --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/doc_to_sort/unrollOde.md @@ -0,0 +1,13 @@ +# Convert ODE into transitions {#unrollOde} + +As seen previously in `transition`{.interpreted-text role="ref"}, we can +define the model via the transitions or explicitly as ODEs. There are +times when we all just want to test out some model in a paper and the +only available information are the ODEs themselves. Even though we know +that the ODEs come from some underlying transitions, breaking them down +can be a time consuming process. We provide the functionalities to do +this automatically. + +::: {.toctree} +unroll/unrollSimple.rst unroll/unrollBD.rst unroll/unrollHard.rst +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/.md b/doc/doc/pygom-doc/mdconvert/mod/.md new file mode 100644 index 00000000..8c6b1b35 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/.md @@ -0,0 +1,5 @@ +# stochastic + +::: {.automodule members="" noindex=""} +pygom.model.simulate +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/common_models.ipynb b/doc/doc/pygom-doc/mdconvert/mod/common_models.ipynb new file mode 100644 index 00000000..c16f1f3e --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/common_models.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# common_models\n", + "\n", + "pygom.model.common_models" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/common_models.md b/doc/doc/pygom-doc/mdconvert/mod/common_models.md new file mode 100644 index 00000000..b65e53b2 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/common_models.md @@ -0,0 +1,5 @@ +# common_models + +::: {.automodule members="" noindex=""} +pygom.model.common_models +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/confidence_interval.ipynb b/doc/doc/pygom-doc/mdconvert/mod/confidence_interval.ipynb new file mode 100644 index 00000000..ca61c381 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/confidence_interval.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# loss_type\n", + "\n", + "pygom.loss.loss_type" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/confidence_interval.md b/doc/doc/pygom-doc/mdconvert/mod/confidence_interval.md new file mode 100644 index 00000000..577e3dd7 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/confidence_interval.md @@ -0,0 +1,5 @@ +# loss_type + +::: {.automodule members="" noindex=""} +pygom.loss.loss_type +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/deterministic.ipynb b/doc/doc/pygom-doc/mdconvert/mod/deterministic.ipynb new file mode 100644 index 00000000..6ea2c788 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/deterministic.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# deterministic\n", + "\n", + "pygom.model.deterministic" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/deterministic.md b/doc/doc/pygom-doc/mdconvert/mod/deterministic.md new file mode 100644 index 00000000..a8e585c4 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/deterministic.md @@ -0,0 +1,5 @@ +# deterministic + +::: {.automodule members="" noindex=""} +pygom.model.deterministic +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/epi_analysis.ipynb b/doc/doc/pygom-doc/mdconvert/mod/epi_analysis.ipynb new file mode 100644 index 00000000..265113af --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/epi_analysis.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# epi_analysis\n", + "\n", + "pygom.model.epi_analysis" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/epi_analysis.md b/doc/doc/pygom-doc/mdconvert/mod/epi_analysis.md new file mode 100644 index 00000000..ef6bd06b --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/epi_analysis.md @@ -0,0 +1,5 @@ +# epi_analysis + +::: {.automodule members="" noindex=""} +pygom.model.epi_analysis +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/get_init.ipynb b/doc/doc/pygom-doc/mdconvert/mod/get_init.ipynb new file mode 100644 index 00000000..80dd8a60 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/get_init.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# get_init\n", + "\n", + "pygom.loss.get_init" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/get_init.md b/doc/doc/pygom-doc/mdconvert/mod/get_init.md new file mode 100644 index 00000000..87c54755 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/get_init.md @@ -0,0 +1,5 @@ +# get_init + +::: {.automodule members="" noindex=""} +pygom.loss.get_init +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/index.ipynb b/doc/doc/pygom-doc/mdconvert/mod/index.ipynb new file mode 100644 index 00000000..156566f3 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/index.ipynb @@ -0,0 +1,22 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Code documentations\n", + "\n", + "## model\n", + "\n", + "common_models transition deterministic simulate epi_analysis odeutils\n", + "\n", + "## loss\n", + "\n", + "odeloss losstype confidence_interval get_init" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/index.md b/doc/doc/pygom-doc/mdconvert/mod/index.md new file mode 100644 index 00000000..58e690a2 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/index.md @@ -0,0 +1,13 @@ +# Code documentations {#mod} + +## model + +::: {.toctree} +common_models transition deterministic simulate epi_analysis odeutils +::: + +## loss + +::: {.toctree} +odeloss losstype confidence_interval get_init +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/losstype.ipynb b/doc/doc/pygom-doc/mdconvert/mod/losstype.ipynb new file mode 100644 index 00000000..90930726 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/losstype.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# confidence_interval\n", + "\n", + "pygom.loss.confidence_interval" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/losstype.md b/doc/doc/pygom-doc/mdconvert/mod/losstype.md new file mode 100644 index 00000000..11277a2c --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/losstype.md @@ -0,0 +1,5 @@ +# confidence_interval + +::: {.automodule members="" noindex=""} +pygom.loss.confidence_interval +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/odeloss.ipynb b/doc/doc/pygom-doc/mdconvert/mod/odeloss.ipynb new file mode 100644 index 00000000..ad8b8e73 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/odeloss.ipynb @@ -0,0 +1,24 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ode_loss\n", + "\n", + "These are basically the interfaces for `pygom.loss.BaseLoss`\n", + "\n", + "pygom.loss.ode_loss\n", + "\n", + "# calculations\n", + "\n", + "The base class which contains has all the calculation implemented\n", + "\n", + "pygom.loss.base_loss" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/odeloss.md b/doc/doc/pygom-doc/mdconvert/mod/odeloss.md new file mode 100644 index 00000000..82ac8080 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/odeloss.md @@ -0,0 +1,16 @@ +# ode_loss + +These are basically the interfaces for +`pygom.loss.BaseLoss`{.interpreted-text role="class"} + +::: {.automodule members="" noindex=""} +pygom.loss.ode_loss +::: + +# calculations + +The base class which contains has all the calculation implemented + +::: {.automodule members="" noindex=""} +pygom.loss.base_loss +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/odeutils.ipynb b/doc/doc/pygom-doc/mdconvert/mod/odeutils.ipynb new file mode 100644 index 00000000..4e591188 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/odeutils.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ode_utils\n", + "\n", + "pygom.model.ode_utils" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/odeutils.md b/doc/doc/pygom-doc/mdconvert/mod/odeutils.md new file mode 100644 index 00000000..192c4502 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/odeutils.md @@ -0,0 +1,5 @@ +# ode_utils + +::: {.automodule members="" noindex=""} +pygom.model.ode_utils +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/simulate.ipynb b/doc/doc/pygom-doc/mdconvert/mod/simulate.ipynb new file mode 100644 index 00000000..c56782d0 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/simulate.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# stochastic\n", + "\n", + "pygom.model.simulate" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/simulate.md b/doc/doc/pygom-doc/mdconvert/mod/simulate.md new file mode 100644 index 00000000..8c6b1b35 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/simulate.md @@ -0,0 +1,5 @@ +# stochastic + +::: {.automodule members="" noindex=""} +pygom.model.simulate +::: diff --git a/doc/doc/pygom-doc/mdconvert/mod/transition.ipynb b/doc/doc/pygom-doc/mdconvert/mod/transition.ipynb new file mode 100644 index 00000000..0708eba4 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/transition.ipynb @@ -0,0 +1,16 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# transition\n", + "\n", + "pygom.model.transition" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/mod/transition.md b/doc/doc/pygom-doc/mdconvert/mod/transition.md new file mode 100644 index 00000000..46920265 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/mod/transition.md @@ -0,0 +1,5 @@ +# transition + +::: {.automodule members="" noindex=""} +pygom.model.transition +::: diff --git a/doc/doc/pygom-doc/mdconvert/unroll/.md b/doc/doc/pygom-doc/mdconvert/unroll/.md new file mode 100644 index 00000000..07b17497 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/unroll/.md @@ -0,0 +1,57 @@ +# Simple Problem {#unrollSimple} + +For a simple problem, we consider the SIR model defined by + +$$\begin{aligned} +\frac{dS}{dt} &= -\beta SI \\ +\frac{dI}{dt} &= \beta SI - \gamma I \\ +\frac{dR}{dt} &= \gamma I. +\end{aligned}$$ + +which consists of two transitions + +::: {.graphviz} + +digraph SIR_Model { + +: rankdir=LR; size=\"8\" node \[shape = circle\]; S -\> I \[ label = + \"βSI\" \]; I -\> R \[ label = \"γI\" \]; + +} +::: + +Let\'s define this using the code block below + +::: {.ipython} +In \[1\]: from pygom import SimulateOde, Transition, TransitionType + +In \[2\]: ode1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\', +transition_type=TransitionType.ODE) + +In \[3\]: ode2 = Transition(origin=\'I\', equation=\'beta\*S\*I - +gamma\*I\', transition_type=TransitionType.ODE) + +In \[4\]: ode3 = Transition(origin=\'R\', equation=\'gamma\*I\', +transition_type=TransitionType.ODE) + +In \[6\]: stateList = \[\'S\', \'I\', \'R\'\] + +In \[7\]: paramList = \[\'beta\', \'gamma\'\] + +In \[8\]: ode = SimulateOde(stateList, + +: \...: paramList, \...: ode=\[ode1, ode2, ode3\]) + +In \[9\]: ode.get_transition_matrix() +::: + +and the last line shows that the transition matrix is empty. This is the +expected result because `SimulateOdeModel`{.interpreted-text +role="class"} was not initialized using transitions. We populate the +transition matrix below and demonstrate the difference. + +::: {.ipython} +In \[1\]: ode = ode.get_unrolled_obj() + +In \[2\]: ode.get_transition_matrix() +::: diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollBD.ipynb b/doc/doc/pygom-doc/mdconvert/unroll/unrollBD.ipynb new file mode 100644 index 00000000..fadb2e43 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/unroll/unrollBD.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ODE With Birth and Death Process\n", + "\n", + "We follow on from the SIR model of `unrollSimple` but with additional\n", + "birth and death processes.\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta SI + B - \\mu S\\\\\n", + "\\frac{dI}{dt} &= \\beta SI - \\gamma I - \\mu I\\\\\n", + "\\frac{dR}{dt} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "which consists of two transitions and three birth and death process\n", + "\n", + "digraph SIR_Model { \n", + "rankdir=LR; size=\"8\" node \\[shape = circle\\]; S -\\> I \\[ label = \"βSI\"\n", + "\\]; I -\\> R \\[ label = \"γI\" \\]; B \\[height=0 margin=0 shape=plaintext\n", + "width=0\\]; B -\\> S; \"S\\**2*μ\" \\[height=0 margin=0 shape=plaintext\n", + "width=0\\]; S -\\> \"S\\**2*μ\"; \"I\\*μ\" \\[height=0 margin=0 shape=plaintext\n", + "width=0\\]; I -\\> \"I\\*μ\";\n", + "\n", + "}\n", + "\n", + "Let's define this in terms of ODEs, and unroll it back to the individual\n", + "processes.\n", + "\n", + "In \\[1\\]: from pygom import Transition, TransitionType, SimulateOde,\n", + "common_models\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n", + "\n", + "In \\[1\\]: paramList = \\['beta', 'gamma', 'B', 'mu'\\]\n", + "\n", + "In \\[1\\]: odeList = \\[ \n", + "...: Transition(origin='S', ...: equation='-beta\\*S\\*I + B - mu\\*S',\n", + "...: transition_type=TransitionType.ODE), ...: Transition(origin='I',\n", + "...: equation='beta\\*S\\*I - gamma\\*I - mu\\*I', ...:\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='R', ...:\n", + "equation='gamma\\*I', ...: transition_type=TransitionType.ODE) ...: \\]\n", + "\n", + "In \\[1\\]: ode = SimulateOde(stateList, paramList, ode=odeList)\n", + "\n", + "In \\[1\\]: ode2 = ode.get_unrolled_obj()\n", + "\n", + "In \\[1\\]: f = plt.figure()\n", + "\n", + "@savefig sir_unrolled_transition_graph.png In \\[1\\]:\n", + "ode2.get_transition_graph()\n", + "\n", + "In \\[1\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollBD.md b/doc/doc/pygom-doc/mdconvert/unroll/unrollBD.md new file mode 100644 index 00000000..bc808719 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/unroll/unrollBD.md @@ -0,0 +1,60 @@ +# ODE With Birth and Death Process {#unrollBD} + +We follow on from the SIR model of `unrollSimple`{.interpreted-text +role="ref"} but with additional birth and death processes. + +$$\begin{aligned} +\frac{dS}{dt} &= -\beta SI + B - \mu S\\ +\frac{dI}{dt} &= \beta SI - \gamma I - \mu I\\ +\frac{dR}{dt} &= \gamma I. +\end{aligned}$$ + +which consists of two transitions and three birth and death process + +::: {.graphviz} + +digraph SIR_Model { + +: rankdir=LR; size=\"8\" node \[shape = circle\]; S -\> I \[ label = + \"βSI\" \]; I -\> R \[ label = \"γI\" \]; B \[height=0 + margin=0 shape=plaintext width=0\]; B -\> S; \"S\**2*μ\" + \[height=0 margin=0 shape=plaintext width=0\]; S -\> \"S\**2*μ\"; + \"I\*μ\" \[height=0 margin=0 shape=plaintext width=0\]; I -\> + \"I\*μ\"; + +} +::: + +Let\'s define this in terms of ODEs, and unroll it back to the +individual processes. + +::: {.ipython} +In \[1\]: from pygom import Transition, TransitionType, SimulateOde, +common_models + +In \[1\]: import matplotlib.pyplot as plt + +In \[1\]: stateList = \[\'S\', \'I\', \'R\'\] + +In \[1\]: paramList = \[\'beta\', \'gamma\', \'B\', \'mu\'\] + +In \[1\]: odeList = \[ + +: \...: Transition(origin=\'S\', \...: equation=\'-beta\*S\*I + B - + mu\*S\', \...: transition_type=TransitionType.ODE), \...: + Transition(origin=\'I\', \...: equation=\'beta\*S\*I - gamma\*I - + mu\*I\', \...: transition_type=TransitionType.ODE), \...: + Transition(origin=\'R\', \...: equation=\'gamma\*I\', \...: + transition_type=TransitionType.ODE) \...: \] + +In \[1\]: ode = SimulateOde(stateList, paramList, ode=odeList) + +In \[1\]: ode2 = ode.get_unrolled_obj() + +In \[1\]: f = plt.figure() + +\@savefig sir_unrolled_transition_graph.png In \[1\]: +ode2.get_transition_graph() + +In \[1\]: plt.close() +::: diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollHard.ipynb b/doc/doc/pygom-doc/mdconvert/unroll/unrollHard.ipynb new file mode 100644 index 00000000..64e14bd0 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/unroll/unrollHard.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hard Problem\n", + "\n", + "Now we turn to a harder problem that does not have a one to one mapping\n", + "between all the transitions and the terms in the ODEs. We use the model\n", + "in `Influenza_SLIARN`, defined by\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -S \\beta (I + \\delta A) \\\\ \n", + "\\frac{dL}{dt} &= S \\beta (I + \\delta A) - \\kappa L \\\\ \n", + "\\frac{dI}{dt} &= p \\kappa L - \\alpha I \\\\\n", + "\\frac{dA}{dt} &= (1 - p) \\kappa L - \\eta A \\\\\n", + "\\frac{dR}{dt} &= f \\alpha I + \\eta A \\\\\n", + "\\frac{dN}{dt} &= -(1 - f) \\alpha I.\n", + "\\end{aligned}$$\n", + "\n", + "The outflow of state **L**, $\\kappa L$, is composed of two transitions,\n", + "one to **I** and the other to **A** but the ode of **L** only reflects\n", + "the total flow going out of the state. Same can be said for state **I**,\n", + "where the flow $\\alpha I$ goes to both **R** and **N**. Graphically, it\n", + "is a rather simple process as shown below.\n", + "\n", + "digraph SLIARD_Model { \n", + "labelloc = \"t\"; label = \"Original transitions\"; rankdir=LR; size=\"8\"\n", + "node \\[shape = circle\\]; S -\\> L \\[ label = \"-Sβ(I + δA)/N\" \\]; L -\\> I\n", + "\\[ label = \"κLp\" \\]; L -\\> A \\[ label = \"κL(1-p)\" \\]; I -\\> R \\[ label =\n", + "\"αIf\" \\]; I -\\> D \\[ label = \"αI(1-f)\" \\]; A -\\> R \\[ label = \"ηA\" \\];\n", + "\n", + "}\n", + "\n", + "We slightly change the model by introducing a new state **D** to convert\n", + "it into a closed system. The combination of state **D** and **N** is a\n", + "constant, the total population. So we can remove **N** and this new\n", + "system consist of six transitions. We define them explicitly as ODEs and\n", + "unroll them into transitions.\n", + "\n", + "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n", + "\n", + "In \\[1\\]: stateList = \\['S', 'L', 'I', 'A', 'R', 'D'\\]\n", + "\n", + "In \\[2\\]: paramList = \\['beta', 'p', 'kappa', 'alpha', 'f', 'delta',\n", + "'epsilon', 'N'\\]\n", + "\n", + "In \\[3\\]: odeList = \\[ \n", + "...: Transition(origin='S', equation='- beta\\*S/N\\*(I + delta\\*A)',\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='L',\n", + "equation='beta\\*S/N\\*(I + delta\\*A) - kappa\\*L',\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='I',\n", + "equation='p\\*kappa\\*L - alpha\\*I', transition_type=TransitionType.ODE),\n", + "...: Transition(origin='A', equation='(1 - p)*kappa* L - epsilon\\*A',\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='R',\n", + "equation='f\\*alpha\\*I + epsilon\\*A',\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='D',\n", + "equation='(1 - f)*alpha*I', transition_type=TransitionType.ODE) \\]\n", + "\n", + "In \\[4\\]: ode = SimulateOde(stateList, paramList, ode=odeList)\n", + "\n", + "In \\[5\\]: ode.get_transition_matrix()\n", + "\n", + "In \\[6\\]: ode2 = ode.get_unrolled_obj()\n", + "\n", + "In \\[7\\]: ode2.get_transition_matrix()\n", + "\n", + "In \\[8\\]: ode2.get_ode_eqn()\n", + "\n", + "After unrolling the odes, we have the following transition graph\n", + "\n", + "@savefig sir_unrolled_transition_graph_hard.png In \\[1\\]:\n", + "ode2.get_transition_graph()\n", + "\n", + "In \\[2\\]: plt.close()\n", + "\n", + "In \\[3\\]: print(sum(ode.get_ode_eqn() - ode2.get_ode_eqn()).simplify())\n", + "\\# difference\n", + "\n", + "which is exactly the same apart from slightly weird arrangement of\n", + "symbols in some of the equations. The last line with a value of zero\n", + "also reaffirms the result." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollHard.md b/doc/doc/pygom-doc/mdconvert/unroll/unrollHard.md new file mode 100644 index 00000000..e02cd29d --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/unroll/unrollHard.md @@ -0,0 +1,89 @@ +# Hard Problem {#unrollHard} + +Now we turn to a harder problem that does not have a one to one mapping +between all the transitions and the terms in the ODEs. We use the model +in `Influenza_SLIARN`{.interpreted-text role="func"}, defined by + +$$\begin{aligned} +\frac{dS}{dt} &= -S \beta (I + \delta A) \\ +\frac{dL}{dt} &= S \beta (I + \delta A) - \kappa L \\ +\frac{dI}{dt} &= p \kappa L - \alpha I \\ +\frac{dA}{dt} &= (1 - p) \kappa L - \eta A \\ +\frac{dR}{dt} &= f \alpha I + \eta A \\ +\frac{dN}{dt} &= -(1 - f) \alpha I. +\end{aligned}$$ + +The outflow of state **L**, $\kappa L$, is composed of two transitions, +one to **I** and the other to **A** but the ode of **L** only reflects +the total flow going out of the state. Same can be said for state **I**, +where the flow $\alpha I$ goes to both **R** and **N**. Graphically, it +is a rather simple process as shown below. + +::: {.graphviz} + +digraph SLIARD_Model { + +: labelloc = \"t\"; label = \"Original transitions\"; rankdir=LR; + size=\"8\" node \[shape = circle\]; S -\> L \[ label = + \"-Sβ(I + δA)/N\" \]; L -\> I \[ label = \"κLp\" + \]; L -\> A \[ label = \"κL(1-p)\" \]; I -\> R \[ label = + \"αIf\" \]; I -\> D \[ label = \"αI(1-f)\" \]; A -\> R + \[ label = \"ηA\" \]; + +} +::: + +We slightly change the model by introducing a new state **D** to convert +it into a closed system. The combination of state **D** and **N** is a +constant, the total population. So we can remove **N** and this new +system consist of six transitions. We define them explicitly as ODEs and +unroll them into transitions. + +::: {.ipython} +In \[1\]: from pygom import SimulateOde, Transition, TransitionType + +In \[1\]: stateList = \[\'S\', \'L\', \'I\', \'A\', \'R\', \'D\'\] + +In \[2\]: paramList = \[\'beta\', \'p\', \'kappa\', \'alpha\', \'f\', +\'delta\', \'epsilon\', \'N\'\] + +In \[3\]: odeList = \[ + +: \...: Transition(origin=\'S\', equation=\'- beta\*S/N\*(I + + delta\*A)\', transition_type=TransitionType.ODE), \...: + Transition(origin=\'L\', equation=\'beta\*S/N\*(I + delta\*A) - + kappa\*L\', transition_type=TransitionType.ODE), \...: + Transition(origin=\'I\', equation=\'p\*kappa\*L - alpha\*I\', + transition_type=TransitionType.ODE), \...: Transition(origin=\'A\', + equation=\'(1 - p)*kappa* L - epsilon\*A\', + transition_type=TransitionType.ODE), \...: Transition(origin=\'R\', + equation=\'f\*alpha\*I + epsilon\*A\', + transition_type=TransitionType.ODE), \...: Transition(origin=\'D\', + equation=\'(1 - f)*alpha*I\', transition_type=TransitionType.ODE) \] + +In \[4\]: ode = SimulateOde(stateList, paramList, ode=odeList) + +In \[5\]: ode.get_transition_matrix() + +In \[6\]: ode2 = ode.get_unrolled_obj() + +In \[7\]: ode2.get_transition_matrix() + +In \[8\]: ode2.get_ode_eqn() +::: + +After unrolling the odes, we have the following transition graph + +::: {.ipython} +\@savefig sir_unrolled_transition_graph_hard.png In \[1\]: +ode2.get_transition_graph() + +In \[2\]: plt.close() + +In \[3\]: print(sum(ode.get_ode_eqn() - ode2.get_ode_eqn()).simplify()) +\# difference +::: + +which is exactly the same apart from slightly weird arrangement of +symbols in some of the equations. The last line with a value of zero +also reaffirms the result. diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollSimple.ipynb b/doc/doc/pygom-doc/mdconvert/unroll/unrollSimple.ipynb new file mode 100644 index 00000000..88bbd592 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/unroll/unrollSimple.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Problem\n", + "\n", + "For a simple problem, we consider the SIR model defined by\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta SI \\\\\n", + "\\frac{dI}{dt} &= \\beta SI - \\gamma I \\\\\n", + "\\frac{dR}{dt} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "which consists of two transitions\n", + "\n", + "digraph SIR_Model { \n", + "rankdir=LR; size=\"8\" node \\[shape = circle\\]; S -\\> I \\[ label = \"βSI\"\n", + "\\]; I -\\> R \\[ label = \"γI\" \\];\n", + "\n", + "}\n", + "\n", + "Let's define this using the code block below\n", + "\n", + "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n", + "\n", + "In \\[2\\]: ode1 = Transition(origin='S', equation='-beta\\*S\\*I',\n", + "transition_type=TransitionType.ODE)\n", + "\n", + "In \\[3\\]: ode2 = Transition(origin='I', equation='beta\\*S\\*I -\n", + "gamma\\*I', transition_type=TransitionType.ODE)\n", + "\n", + "In \\[4\\]: ode3 = Transition(origin='R', equation='gamma\\*I',\n", + "transition_type=TransitionType.ODE)\n", + "\n", + "In \\[6\\]: stateList = \\['S', 'I', 'R'\\]\n", + "\n", + "In \\[7\\]: paramList = \\['beta', 'gamma'\\]\n", + "\n", + "In \\[8\\]: ode = SimulateOde(stateList, \n", + "...: paramList, ...: ode=\\[ode1, ode2, ode3\\])\n", + "\n", + "In \\[9\\]: ode.get_transition_matrix()\n", + "\n", + "and the last line shows that the transition matrix is empty. This is the\n", + "expected result because `SimulateOdeModel` was not initialized using\n", + "transitions. We populate the transition matrix below and demonstrate the\n", + "difference.\n", + "\n", + "In \\[1\\]: ode = ode.get_unrolled_obj()\n", + "\n", + "In \\[2\\]: ode.get_transition_matrix()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollSimple.md b/doc/doc/pygom-doc/mdconvert/unroll/unrollSimple.md new file mode 100644 index 00000000..07b17497 --- /dev/null +++ b/doc/doc/pygom-doc/mdconvert/unroll/unrollSimple.md @@ -0,0 +1,57 @@ +# Simple Problem {#unrollSimple} + +For a simple problem, we consider the SIR model defined by + +$$\begin{aligned} +\frac{dS}{dt} &= -\beta SI \\ +\frac{dI}{dt} &= \beta SI - \gamma I \\ +\frac{dR}{dt} &= \gamma I. +\end{aligned}$$ + +which consists of two transitions + +::: {.graphviz} + +digraph SIR_Model { + +: rankdir=LR; size=\"8\" node \[shape = circle\]; S -\> I \[ label = + \"βSI\" \]; I -\> R \[ label = \"γI\" \]; + +} +::: + +Let\'s define this using the code block below + +::: {.ipython} +In \[1\]: from pygom import SimulateOde, Transition, TransitionType + +In \[2\]: ode1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\', +transition_type=TransitionType.ODE) + +In \[3\]: ode2 = Transition(origin=\'I\', equation=\'beta\*S\*I - +gamma\*I\', transition_type=TransitionType.ODE) + +In \[4\]: ode3 = Transition(origin=\'R\', equation=\'gamma\*I\', +transition_type=TransitionType.ODE) + +In \[6\]: stateList = \[\'S\', \'I\', \'R\'\] + +In \[7\]: paramList = \[\'beta\', \'gamma\'\] + +In \[8\]: ode = SimulateOde(stateList, + +: \...: paramList, \...: ode=\[ode1, ode2, ode3\]) + +In \[9\]: ode.get_transition_matrix() +::: + +and the last line shows that the transition matrix is empty. This is the +expected result because `SimulateOdeModel`{.interpreted-text +role="class"} was not initialized using transitions. We populate the +transition matrix below and demonstrate the difference. + +::: {.ipython} +In \[1\]: ode = ode.get_unrolled_obj() + +In \[2\]: ode.get_transition_matrix() +::: From 22eaacaef79a623a31854dd0df1aece15f09870a Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 20 Jun 2023 12:51:39 +0100 Subject: [PATCH 063/188] edit introduction --- doc/doc/pygom-doc/intro.md | 25 ++++++++++++++----- .../transition.ipynb | 0 .../{mdconvert/doc_to_sort => }/ref.md | 0 3 files changed, 19 insertions(+), 6 deletions(-) rename doc/doc/pygom-doc/{mdconvert/doc_to_sort => notebooks}/transition.ipynb (100%) rename doc/doc/pygom-doc/{mdconvert/doc_to_sort => }/ref.md (100%) diff --git a/doc/doc/pygom-doc/intro.md b/doc/doc/pygom-doc/intro.md index f8cdc73c..02905b2c 100644 --- a/doc/doc/pygom-doc/intro.md +++ b/doc/doc/pygom-doc/intro.md @@ -1,11 +1,24 @@ -# Welcome to your Jupyter Book +# Welcome to the documentation for PyGOM + +PyGOM (Python Generic ODE Model) is a Python package that aims to facilitate the application of ordinary differential equations (ODEs) in the real world, +with a focus in epidemiology. +This package helps the user define their ODE system in an intuitive manner and provides convenience functions - +making use of various algebraic and numerical libraries in the backend - that can be used in a straight forward fashion. + +This is an open source project hosted on [Github](https://github.com/PublicHealthEngland/pygom). + +A manuscript containing a shortened motivation and use is hosted on [arxXiv](https://arxiv.org/abs/1803.06934). + +#TODO insert intro text -This is a small sample book to give you a feel for how book content is -structured. -It shows off a few of the major file types, as well as some sample content. -It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information. -Check out the content pages bundled with this sample book to see more. ```{tableofcontents} ``` + +# Code Documentation and FAQ +#TODO from index.rst +# References +#TODO +# Indices and tables +#TODO \ No newline at end of file diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/transition.ipynb b/doc/doc/pygom-doc/notebooks/transition.ipynb similarity index 100% rename from doc/doc/pygom-doc/mdconvert/doc_to_sort/transition.ipynb rename to doc/doc/pygom-doc/notebooks/transition.ipynb diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/ref.md b/doc/doc/pygom-doc/ref.md similarity index 100% rename from doc/doc/pygom-doc/mdconvert/doc_to_sort/ref.md rename to doc/doc/pygom-doc/ref.md From 144e57be662ea347204253191caabd153e3205f7 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 20 Jun 2023 12:52:42 +0100 Subject: [PATCH 064/188] updated table of contents and config files --- doc/doc/pygom-doc/_config.yml | 5 +++-- doc/doc/pygom-doc/_toc.yml | 29 +++++++++++++++++++++++++---- 2 files changed, 28 insertions(+), 6 deletions(-) diff --git a/doc/doc/pygom-doc/_config.yml b/doc/doc/pygom-doc/_config.yml index fd486944..ddf3ee23 100644 --- a/doc/doc/pygom-doc/_config.yml +++ b/doc/doc/pygom-doc/_config.yml @@ -3,7 +3,8 @@ title: PyGOM documentation author: Public Health England / UK Health Security Agency -logo: ukhsa.png +#TODO change logo +logo: logo.png # build files in table of contents # used as alternative to exclude patterns @@ -14,7 +15,7 @@ only_build_toc_files: true # this could avoid the issue of execution timing out # See https://jupyterbook.org/content/execute.html execute: - execute_notebooks: cache + execute_notebooks: auto # Define the name of the latex output file for PDF builds latex: diff --git a/doc/doc/pygom-doc/_toc.yml b/doc/doc/pygom-doc/_toc.yml index 74d5c710..29b83e52 100644 --- a/doc/doc/pygom-doc/_toc.yml +++ b/doc/doc/pygom-doc/_toc.yml @@ -3,7 +3,28 @@ format: jb-book root: intro -chapters: -- file: markdown -- file: notebooks -- file: markdown-notebooks +parts: + - caption: User documentation + chapters: + - file: markdown.md + - file: md/getting_started.md + - file: notebooks/sir.ipynb + - file: notebooks/transition.ipynb + #notebooks/sir.ipynb + #transition.rst + #stochastic.rst + #unrollOde.rst + #epi.rst + #epijson.rst + #bvpSimple.rst + #gradient.rst + #fh.rst + #estimate1.rst + #estimate2.rst + #initialGuess.rst + #profile.rst + #common_models.rst +# - caption: Frequently asked questions +# - caption: Code documentation +# - caption: References +# - caption: Indices and tables From fc06c3c76643c523d7f3d9791ce130b837faf56a Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 20 Jun 2023 12:53:28 +0100 Subject: [PATCH 065/188] reformatted with edits --- doc/doc/pygom-doc/notebooks/sir.ipynb | 874 ++++++++++++++++++++++++++ 1 file changed, 874 insertions(+) create mode 100644 doc/doc/pygom-doc/notebooks/sir.ipynb diff --git a/doc/doc/pygom-doc/notebooks/sir.ipynb b/doc/doc/pygom-doc/notebooks/sir.ipynb new file mode 100644 index 00000000..5f64e3d6 --- /dev/null +++ b/doc/doc/pygom-doc/notebooks/sir.ipynb @@ -0,0 +1,874 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Motivating Example: SIR Model\n", + "{download}`this notebook <./sir.ipynb>`\n", + "\n", + "## Defining the model\n", + "\n", + "First, we are going to go through an SIR model to show the functionality\n", + "of the package. The SIR model is defined by the following equations\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta SI \\\\\n", + "\\frac{dI}{dt} &= \\beta SI- \\gamma I \\\\\n", + "\\frac{dR}{dt} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "We can set this up as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "586834ab", + "metadata": {}, + "source": [ + "1. import the classes required to define the transitions" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "4c80a36a", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import Transition, TransitionType" + ] + }, + { + "cell_type": "markdown", + "id": "0ec4c9d0", + "metadata": {}, + "source": [ + "2. define our states, in this case the population states of **S**usceptible, **I**nfected and **R**emoved" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "3ce016f2", + "metadata": {}, + "outputs": [], + "source": [ + "stateList = ['S', 'I', 'R']" + ] + }, + { + "cell_type": "markdown", + "id": "021a1927", + "metadata": {}, + "source": [ + "3. define the set of parameters for our model, here there are only two" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "441e2287", + "metadata": {}, + "outputs": [], + "source": [ + "paramList = ['beta', 'gamma']" + ] + }, + { + "cell_type": "markdown", + "id": "dfd736b2", + "metadata": {}, + "source": [ + "4. specify the transitions of the modelled states; this will form our ODE system" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "b5f80ab0", + "metadata": {}, + "outputs": [], + "source": [ + "odeList = [\n", + " Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE),\n", + " Transition(origin='I',equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE),\n", + " Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE) \n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "21629e7b", + "metadata": {}, + "source": [ + "```{note}\n", + "Here, we have invoked a class from `Transition` to define the transition object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See [defining the equations](./transition.ipynb) for an example when the input is wrong.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "69bfb7c3", + "metadata": {}, + "source": [ + "\n", + "5. import the ode module" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "f78c33d4", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import DeterministicOde" + ] + }, + { + "cell_type": "markdown", + "id": "477d6f84", + "metadata": {}, + "source": [ + "6. initialize the model, which constructs our ODE system from all the information we have provided" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c9fbcce0", + "metadata": {}, + "outputs": [], + "source": [ + "model = DeterministicOde(stateList, paramList, ode=odeList)" + ] + }, + { + "cell_type": "markdown", + "id": "4bf43064", + "metadata": {}, + "source": [ + "That is all the information required to define a simple SIR model. We can verify the model using" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "6ad54e09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ -I*S*beta],\n", + "[I*S*beta - I*gamma],\n", + "[ I*gamma]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "143a6871", + "metadata": {}, + "source": [ + "where we can see the equations corresponding to their respective $S,I$ and $R$ state. " + ] + }, + { + "cell_type": "markdown", + "id": "08207474", + "metadata": {}, + "source": [ + "```{note}\n", + "The ordering of the equations is in the standard $S,I,R$ sequence because of how we defined the states initially. \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "7054e58a", + "metadata": {}, + "source": [ + "We can rearrange the state list," + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "07f81fd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}I \\gamma\\\\- I S \\beta\\\\I S \\beta - I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ I*gamma],\n", + "[ -I*S*beta],\n", + "[I*S*beta - I*gamma]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now we are going to define in a different order. note that the output ode changed with the input state\n", + "stateList = ['R', 'S', 'I']\n", + "\n", + "model = DeterministicOde(stateList, paramList, ode=odeList)\n", + "\n", + "model.get_ode_eqn()\n" + ] + }, + { + "cell_type": "markdown", + "id": "d3f587d4", + "metadata": {}, + "source": [ + "and find that the set of ODEs comes out in the order that we specified. " + ] + }, + { + "cell_type": "markdown", + "id": "1175c832", + "metadata": {}, + "source": [ + "```{tip}\n", + "In addition to showing the equation form of the ODEs, we can also display them as either symbols or latex code, which can save some extra typing when porting the equations to another document.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "960fdc0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "⎡dR/dt= I⋅γ ⎤\n", + "⎢ ⎥\n", + "⎢dS/dt= -I⋅S⋅β ⎥\n", + "⎢ ⎥\n", + "⎣dI/dt= I⋅S⋅β - I⋅γ⎦\n" + ] + } + ], + "source": [ + "model.print_ode()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "93e32c75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{array}{cc}dR/dt= & I \\gamma\\\\dS/dt= & - I S \\beta\\\\dI/dt= & I S \\beta - I \\gamma\\end{array}\n" + ] + } + ], + "source": [ + "model.print_ode(True)" + ] + }, + { + "cell_type": "markdown", + "id": "79c154ba", + "metadata": {}, + "source": [ + "#TODO links to unroll\n", + "Here the SIR model was provided to PyGOM as a set ODEs by using the `Transition` to define them. \n", + "We have also provided the capability to obtain a *best guess* transition matrix when only the ODEs are available. See the section `unrollOde` for more information, and in particular `unrollSimple` for the continuing demonstration of the SIR model." + ] + }, + { + "cell_type": "markdown", + "id": "51ed6fa2", + "metadata": {}, + "source": [ + "## Extracting model information\n", + "\n", + "We may wish to determine if the set of ODEs are linear. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "61684654", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.linear_ode()" + ] + }, + { + "cell_type": "markdown", + "id": "4cf58543", + "metadata": {}, + "source": [ + "Since we know that the SIR model is not linear, we may want to have a look at the Jacobian.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "1c8ff090", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}0 & 0 & \\gamma\\\\0 & - I \\beta & - S \\beta\\\\0 & I \\beta & S \\beta - \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[0, 0, gamma],\n", + "[0, -I*beta, -S*beta],\n", + "[0, I*beta, S*beta - gamma]])" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_jacobian_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "bab79c98", + "metadata": {}, + "source": [ + "Or maybe we want to know the gradient." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "abb02502", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}0 & I\\\\- I S & 0\\\\I S & - I\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ 0, I],\n", + "[-I*S, 0],\n", + "[ I*S, -I]])" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_grad_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "9d0052ec", + "metadata": {}, + "source": [ + "```{Warning}\n", + "Invoking the functions that compute the derivatives, $f(x)$, `model.ode()` or `model.jacobian()`, will return an error\n", + "\n", + "These functions are used to solve the ODEs numerically, and require values of initial state values, time, and parameter values.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "35abe902", + "metadata": {}, + "source": [ + "For setting initial conditions, the numeric values of the states **must** be set in the same order that list of states appear. We can use the following to check the state ordering, as well as displaying all of the states that we have included." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "e703c888", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ODEVariable('R', 'R', None, True),\n", + " ODEVariable('S', 'S', None, True),\n", + " ODEVariable('I', 'I', None, True)]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.state_list" + ] + }, + { + "cell_type": "markdown", + "id": "54e681d0", + "metadata": {}, + "source": [ + "#TODO unsure if this is needed\n", + "There is currently no mechanism to set the numeric initial conditions the states when the states are defined. This is because of an implementation issue with external package, such as solving an initial value problem." + ] + }, + { + "cell_type": "markdown", + "id": "13bed7f9", + "metadata": {}, + "source": [ + "## Initial value problem\n", + "\n", + "By setting the state initial conditions, time, and parameters, we can evaluate our model." + ] + }, + { + "cell_type": "markdown", + "id": "04fb8dd9", + "metadata": {}, + "source": [ + "1. Define the model parameters. (We can call the parameters to check what we must provide.)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "46042ebf", + "metadata": {}, + "outputs": [], + "source": [ + "model.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "9ec016b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{beta: 0.5, gamma: 0.3333333333333333}" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paramEval = [('beta',0.5), ('gamma',1.0/3.0)]\n", + "\n", + "model.parameters = paramEval\n", + "\n", + "model.parameters" + ] + }, + { + "cell_type": "markdown", + "id": "efcdac90", + "metadata": {}, + "source": [ + "2. Provide initial conditions for the states." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "c43074c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4.23333333e-07, -6.35000000e-07, 2.11666667e-07])" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "initialState = [0, 1, 1.27e-6]\n", + " \n", + "model.ode(state=initialState, t=1)" + ] + }, + { + "cell_type": "markdown", + "id": "a8dc4681", + "metadata": {}, + "source": [ + "\n", + "3. Implement an ODE solver.\n", + "\n", + "We are well equipped to solve an initial value problem, using the standard numerical integrator such as `odeint ` from `scipy.integrate`. We also used `matplotlib.pyplot` for plotting and `linspace ` to create the time vector." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "a14b9901", + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.integrate\n", + "import numpy\n", + "\n", + "t = numpy.linspace(0, 150, 100)\n", + "\n", + "solution = scipy.integrate.odeint(model.ode, initialState, t)" + ] + }, + { + "cell_type": "markdown", + "id": "c4feebae", + "metadata": {}, + "source": [ + "We can plot our solution to observe a standard SIR shape." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "8303885c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(t, solution[:,0], label='R')\n", + "\n", + "plt.plot(t, solution[:,1], label='S')\n", + "\n", + "plt.plot(t, solution[:,2], label='I')\n", + "\n", + "plt.xlabel('Time')\n", + "\n", + "plt.ylabel('Population proportion')\n", + "\n", + "plt.title('Standard SIR model')\n", + "\n", + "plt.legend(loc=0)\n", + "\n", + "#@savefig sir_plot.png In \n", + "\n", + "plt.show()\n", + "\n", + "#plt.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "ba849579", + "metadata": {}, + "source": [ + "Alternatively, we can integrate and plot via the **ode** object which we initialized earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "efddd3a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.initial_values = (initialState, t[0])\n", + "\n", + "model.parameters = paramEval\n", + "\n", + "solution = model.integrate(t[1::])\n", + "\n", + "model.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "4dd6e250", + "metadata": {}, + "source": [ + "We could solve the ODEs above using the Jacobian as well. Unfortunately, it does not help because the number of times the Jacobian was evaluated was zero, as expected given that our set of equations are not stiff." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "7a7a568f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.58 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "#TODO what does this show?\n", + "%timeit solution1, output1 = scipy.integrate.odeint(model.ode, initialState, t, full_output=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "91b7f9d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.84 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "\n", + "%timeit solution2, output2 = scipy.integrate.odeint(model.ode, initialState, t, Dfun=model.jacobian, mu=None, ml=None, full_output=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "3403884c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.68 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "\n", + "%timeit solution3, output3 = model.integrate(t, full_output=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b6b9ee47", + "metadata": {}, + "source": [ + "It is important to note that we return our Jacobian as a dense square matrix. Hence, the two argument (mu,ml) for the ODE solver was set to `None` to let it know the output explicitly." + ] + }, + { + "cell_type": "markdown", + "id": "5ca18fa3", + "metadata": {}, + "source": [ + "## Solving the forward sensitivity equation\n", + "\n", + "The sensitivity equations are also solved as an initial value problem. Let us redefine the model in the standard SIR order and we solve it with the sensitivity all set at zero, i.e. we do not wish to infer the initial value of the states." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "87173626", + "metadata": {}, + "outputs": [], + "source": [ + "stateList = ['S', 'I', 'R']\n", + "\n", + "model = DeterministicOde(stateList, paramList, ode=odeList)\n", + "\n", + "initialState = [1, 1.27e-6, 0]\n", + "\n", + "paramEval = [('beta', 0.5), ('gamma', 1.0/3.0)]\n", + "\n", + "model.parameters = paramEval\n", + "\n", + "solution = scipy.integrate.odeint(model.ode_and_sensitivity, numpy.append(initialState, numpy.zeros(6)), t)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "8b63ba62", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "{\n", + " \"tags\": [\n", + " \"hide-input\",\n", + " ]\n", + "}\n", + "f,axarr = plt.subplots(3,3);\n", + "\n", + "f.text(0.5,0.975,'SIR with forward sensitivity solved via ode',fontsize=16,horizontalalignment='center',verticalalignment='top')\n", + "\n", + "axarr[0,0].plot(t, solution[:,0])\n", + "\n", + "axarr[0,0].set_title('S')\n", + "\n", + "axarr[0,1].plot(t, solution[:,1])\n", + "\n", + "axarr[0,1].set_title('I')\n", + "\n", + "axarr[0,2].plot(t, solution[:,2]);\n", + "\n", + "axarr[0,2].set_title('R')\n", + "\n", + "axarr[1,0].plot(t, solution[:,3])\n", + "\n", + "axarr[1,0].set_title(r'state S parameter $beta$')\n", + "\n", + "axarr[2,0].plot(t, solution[:,4])\n", + "\n", + "axarr[2,0].set_title(r'state S parameter $gamma$')\n", + "\n", + "axarr[1,1].plot(t, solution[:,5])\n", + "\n", + "axarr[1,1].set_title(r'state I parameter $beta$')\n", + "\n", + "axarr[2,1].plot(t, solution[:,6])\n", + "\n", + "axarr[2,1].set_title(r'state I parameter $gamma$')\n", + "\n", + "axarr[1,2].plot(t, solution[:,7])\n", + "\n", + "axarr[1,2].set_title(r'state R parameter $beta$')\n", + "\n", + "axarr[2,2].plot(t, solution[:,8])\n", + "\n", + "axarr[2,2].set_title(r'state R parameter $gamma$')\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2f64d869", + "metadata": {}, + "source": [ + "#TODO links\n", + "This concludes the introductory example and we will be moving on to look at parameter estimation next in `estimate1` and the most important part in terms of setting up the ODE object; defining the equations in various different ways in `transition`." + ] + }, + { + "cell_type": "markdown", + "id": "7fbd236c", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.15 ('sphinx-doc')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "vscode": { + "interpreter": { + "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From bf8197610640ebed29e6c9cefe2a73a57f99e15a Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 20 Jun 2023 12:53:56 +0100 Subject: [PATCH 066/188] reformatted with edits --- doc/doc/pygom-doc/md/getting_started.md | 73 +++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 doc/doc/pygom-doc/md/getting_started.md diff --git a/doc/doc/pygom-doc/md/getting_started.md b/doc/doc/pygom-doc/md/getting_started.md new file mode 100644 index 00000000..fd7094b7 --- /dev/null +++ b/doc/doc/pygom-doc/md/getting_started.md @@ -0,0 +1,73 @@ +# Getting started + +## What does this package do? + +The purpose of this package is to allow the end user to easily define a +set of ordinary differential equations (ODEs) and obtain information +about the ODEs by invoking the the appropriate methods. Here, we define +the set of ODEs as + +$$\frac{d \mathbf{x}}{d t} = f(\mathbf{x},\boldsymbol{\theta})$$ + +where $\mathbf{x} = \left(x_{1},x_{2},\ldots,x_{n}\right)$ is the state +vector with $d$ state and $\boldsymbol{\theta}$ the parameters of $p$ +dimension. Currently, this package allows the user to find the algebraic +expression of the ODE, Jacobian, gradient and forward sensitivity of the +ODE. A numerical output is given when all the state and parameter values +are provided. Note that the only important class is +`DeterministicOde`{.interpreted-text role="file"} where all the +functionality described previously are exposed. + +#TODO do we want this updating or referencing issue board? +The current plan is to extend the functionality to include + +- Solving the ode analytically when it is linear +- Analysis of the system via eigenvalues during the integration +- Detection of DAE + +## Obtaining the package + +The location of the package is current on GitHub and can be pulled via +https from: + + https://github.com/PublicHealthEngland/pygom.git + +#TODO required? +The package is currently as follows: + + pygom/ + bin/ + doc/ + pygom/ + loss/ + tests/ + model/ + tests/ + sbml_translate/ + utilR/ + LICENSE.txt + README.rst + requirements.txt + setup.py + +with files in each of the three main folders not shown. You can install +the package via command line: + + python setup.py install + +or locally on a user level: + + python setup.py install --user + +Please note that there are current redundant file are kept for +development purposes for the time being. + +## Testing the package + +Testing can be performed prior or after the installation. Some standard +test files can be found in their respective folder and they can be run +in the command line: + + python setup.py test + +which can be performed prior to installing the package if desired. From 3f24bfa2cebb22629f42a300d529af1974a9e87e Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 20 Jun 2023 15:00:37 +0100 Subject: [PATCH 067/188] corrected reference to transition subsection --- doc/doc/pygom-doc/notebooks/sir.ipynb | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/doc/doc/pygom-doc/notebooks/sir.ipynb b/doc/doc/pygom-doc/notebooks/sir.ipynb index 5f64e3d6..b429f9c5 100644 --- a/doc/doc/pygom-doc/notebooks/sir.ipynb +++ b/doc/doc/pygom-doc/notebooks/sir.ipynb @@ -103,7 +103,7 @@ "metadata": {}, "source": [ "```{note}\n", - "Here, we have invoked a class from `Transition` to define the transition object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See [defining the equations](./transition.ipynb) for an example when the input is wrong.\n", + "Here, we have invoked a class from `Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`defining-the-equations` for an example when the input is wrong.\n", "```" ] }, @@ -835,14 +835,8 @@ "metadata": {}, "source": [ "#TODO links\n", - "This concludes the introductory example and we will be moving on to look at parameter estimation next in `estimate1` and the most important part in terms of setting up the ODE object; defining the equations in various different ways in `transition`." + "This concludes the introductory example and we will be moving on to look at parameter estimation next in {doc}`estimate1` and the most important part in terms of setting up the ODE object; defining the equations in various different ways in {doc}`transition`." ] - }, - { - "cell_type": "markdown", - "id": "7fbd236c", - "metadata": {}, - "source": [] } ], "metadata": { From b1f8145e4114027337c2237e4dae432d0503625d Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 20 Jun 2023 16:33:14 +0100 Subject: [PATCH 068/188] finished transition updates --- doc/doc/pygom-doc/notebooks/sir.ipynb | 2 +- doc/doc/pygom-doc/notebooks/transition.ipynb | 516 ++++++++++++++----- 2 files changed, 379 insertions(+), 139 deletions(-) diff --git a/doc/doc/pygom-doc/notebooks/sir.ipynb b/doc/doc/pygom-doc/notebooks/sir.ipynb index b429f9c5..89e9fad2 100644 --- a/doc/doc/pygom-doc/notebooks/sir.ipynb +++ b/doc/doc/pygom-doc/notebooks/sir.ipynb @@ -103,7 +103,7 @@ "metadata": {}, "source": [ "```{note}\n", - "Here, we have invoked a class from `Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`defining-the-equations` for an example when the input is wrong.\n", + "Here, we have invoked a class from `Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`transition:defining-the-equations` for an example when the input is wrong.\n", "```" ] }, diff --git a/doc/doc/pygom-doc/notebooks/transition.ipynb b/doc/doc/pygom-doc/notebooks/transition.ipynb index a80e94a4..d37c4e10 100644 --- a/doc/doc/pygom-doc/notebooks/transition.ipynb +++ b/doc/doc/pygom-doc/notebooks/transition.ipynb @@ -6,73 +6,107 @@ "source": [ "# Transition Object\n", "\n", - "The most important part of setting up the model is to correctly define\n", - "the set odes, which is based solely on the classes defined in\n", - "`transition`. All transitions that gets fed into the ode system needs to\n", - "be defined as a transition object, `Transition`. It takes a total of\n", - "four input arguments\n", + "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n", + "\n", + "\n", + "All transitions that get fed into the ODE system need to be defined as a transition object, `Transition`. It takes a total of four input arguments:\n", "\n", "1. The origin state\n", "2. Equation that describe the process\n", "3. The type of transition\n", "4. The destination state\n", "\n", - "where the first three are mandatory. To demonstrate, we go back to the\n", - "SIR model defined previously in the section `sir`. Recall that the set\n", - "of odes are\n", + "where the first three are mandatory. To demonstrate, we go back to the SIR model defined previously in the section {doc}`sir`. Recall that the set of ODEs are\n", "\n", - "$$\\begin{aligned}\n", - "\\frac{\\partial S}{\\partial t} &= -\\beta SI \\\\\n", - "\\frac{\\partial I}{\\partial t} &= \\beta SI - \\gamma I \\\\\n", - "\\frac{\\partial R}{\\partial t} &= \\gamma I.\n", + "$$begin{aligned}\n", + " frac{d S}{d t} &= - beta SI \\\\\n", + "\\frac{d I}{d t} &= beta SI - \\gamma I \\\\\n", + "\\frac{d R}{d t} &= \\gamma I.\n", "\\end{aligned}$$\n", "\n", - "We can simply define the set of ode, as seen previously, via\n", - "\n", - "In \\[1\\]: from pygom import Transition, TransitionType, common_models\n", + "We can define the set of ODEs, as seen previously, via" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68d41d64", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import Transition, TransitionType, common_models\n", "\n", - "In \\[2\\]: ode1 = Transition(origin='S', equation='-beta\\*S\\*I',\n", - "transition_type=TransitionType.ODE)\n", + "ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)\n", "\n", - "In \\[3\\]: ode2 = Transition(origin='I', equation='beta\\*S\\*I -\n", - "gamma\\*I', transition_type=TransitionType.ODE)\n", + "ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)\n", "\n", - "In \\[4\\]: ode3 = Transition(origin='R', equation='gamma\\*I',\n", - "transition_type=TransitionType.ODE)\n", + "ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)" + ] + }, + { + "cell_type": "markdown", + "id": "d393a33a", + "metadata": {}, + "source": [ "\n", "Note that we need to state explicitly the type of equation we are\n", "inputting, which is simply of type **ODE** in this case. We can confirm\n", - "this has been entered correctly by putting it into `DeterministicOde`\n", - "\n", - "In \\[1\\]: from pygom import DeterministicOde\n", - "\n", - "In \\[2\\]: stateList = \\['S', 'I', 'R'\\]\n", - "\n", - "In \\[3\\]: paramList = \\['beta', 'gamma'\\]\n", - "\n", - "In \\[4\\]: model = DeterministicOde(stateList, \n", - "...: paramList, ...: ode=\\[ode1, ode2, ode3\\])\n", - "\n", - "and check it\n", - "\n", - "In \\[1\\]: model.get_ode_eqn()\n", + "this has been entered correctly by putting it into `DeterministicOde`," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b0801dd", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import DeterministicOde\n", "\n", - "An alternative print function `print_ode` is also available which may be\n", - "more suitable in other situation. The default prints the formula in a\n", - "rendered format and another which prints out the latex format which can\n", - "be used directly in a latex document. The latter is useful as it saves\n", - "typing out the formula twice, once in the code and another in documents.\n", + "stateList = ['S', 'I', 'R']\n", "\n", - "In \\[1\\]: model.print_ode(False)\n", + "paramList = ['beta', 'gamma']\n", "\n", - "In \\[2\\]: model.print_ode(True)\n", + "model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3])" + ] + }, + { + "cell_type": "markdown", + "id": "6e764826", + "metadata": {}, + "source": [ "\n", + "and then checking it.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5782a96f", + "metadata": {}, + "outputs": [], + "source": [ + "model.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "c693cbc7", + "metadata": {}, + "source": [ "Now we are going to show the different ways of defining the same set of\n", - "odes.\n", - "\n", + "ODEs.\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa631d9f", + "metadata": {}, + "source": [ + "(transition:defining-the-equations)=\n", "## Defining the equations\n", "\n", - "Recognizing that the set of odes defining the SIR model is the result of\n", + "We first recognize that the set of ODEs defining the SIR model are the result of\n", "two transitions,\n", "\n", "$$\\begin{aligned}\n", @@ -81,131 +115,337 @@ "\\end{aligned}$$\n", "\n", "where $S \\rightarrow I$ denotes a transition from state $S$ to state\n", - "$I$. Therefore, we can simply define our model by these two transition,\n", - "but now these two transition needs to be inputted via the `transition`\n", - "argument instead of the `ode` argument. Note that we are initializing\n", - "the model using a different class, because the stochastic implementation\n", - "has more operation on transitions.\n", + "$I$. Therefore, we can define our model by these two transitions,\n", + "but they need to be passed as the `transition`\n", + "argument instead of the `ode` argument of `DeterministicOde` or `SimulateOde`.\n", "\n", - "In \\[600\\]: from pygom import SimulateOde\n", + "```{note}\n", + "We are initializing the model using the `SimulateOde` class, rather than `DeterministicOde`, because the stochastic implementation has more available operations on transitions.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6966fc5e", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import SimulateOde\n", "\n", - "In \\[601\\]: t1 = Transition(origin='S', destination='I',\n", - "equation='beta\\*S\\*I', transition_type=TransitionType.T)\n", + "t1 = Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T)\n", "\n", - "In \\[602\\]: t2 = Transition(origin='I', destination='R',\n", - "equation='gamma\\*I', transition_type=TransitionType.T)\n", + "t2 = Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)\n", "\n", - "In \\[603\\]: modelTrans = SimulateOde(stateList, \n", - ".….: paramList, .….: transition=\\[t1, t2\\])\n", + "modelTrans = SimulateOde(stateList, paramList, transition=[t1, t2])\n", "\n", - "In \\[604\\]: modelTrans.get_ode_eqn()\n", + "modelTrans.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "b896d5c9", + "metadata": {}, + "source": [ "\n", - "We can see that the resulting ode is exactly the same, as expected. The\n", - "transition matrix that defines this process can easily be visualized\n", - "using graphviz. Because only certain renderer permit the use of sub and\n", + "We can see that the resulting ODE is exactly the same, as expected. The\n", + "transition matrix that defines this process can be visualized\n", + "using graphviz. Because only certain renderers permit the use of sub and\n", "superscript, operators such as $**$ are left as they are in the\n", - "equation.\n", - "\n", - "In \\[1\\]: import matplotlib.pyplot as plt\n", - "\n", - "In \\[2\\]: f = plt.figure()\n", - "\n", - "In \\[3\\]: modelTrans.get_transition_matrix()\n", - "\n", - "@savefig sir_transition_graph.png In \\[4\\]: dot =\n", - "modelTrans.get_transition_graph()\n", - "\n", - "If we put in via the wrong argument like below (not run), then an error\n", - "will appear.\n", - "\n", - "In \\[1\\]: \\# modelTrans = DeterministicOde(stateList, paramList,\n", - "ode=\\[t1, t2\\])\n", - "\n", - "because `TranstionType` was defined explicitly as a transition instead\n", - "of an ode. The same can be observed when the wrong `TransitionType` is\n", - "used for any of the input argument.\n", + "equation." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1e17eb7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}0 & I S \\beta & 0\\\\0 & 0 & I \\gamma\\\\0 & 0 & 0\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[0, I*S*beta, 0],\n", + "[0, 0, I*gamma],\n", + "[0, 0, 0]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "modelTrans.get_transition_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "570ceed5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n\n\n\nS\n\nS\n\n\n\nI\n\nI\n\n\n\nS->I\n\n\nβ*S*I\n\n\n\nR\n\nR\n\n\n\nI->R\n\n\nγ*I\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "# TODO why are two images produced? issue #75\n", + "modelTrans.get_transition_graph(show=False)" + ] + }, + { + "cell_type": "markdown", + "id": "5d982b87", + "metadata": {}, + "source": [ + "```{warning}\n", + "The execution will error if the incorrect `TransitionType` is used against the wrong argument.\n", "\n", - "This though, only encourages us to define the transitions carefully. We\n", - "can also pretend that the set of odes are in fact just a set of birth\n", - "process\n", + "`modelTrans = DeterministicOde(stateList, paramList, ode=[t1, t2])`\n", "\n", - "In \\[619\\]: birth1 = Transition(origin='S', equation='-beta\\*S\\*I',\n", - "transition_type=TransitionType.B)\n", + "Here the error occurs because `t1` and `t2` used `transition_type=TransitionType.T` argument, but `DeterministicOde is expecting a `TransitionType.ODE` argument.\n", "\n", - "In \\[620\\]: birth2 = Transition(origin='I', equation='beta\\*S\\*I -\n", - "gamma\\*I', transition_type=TransitionType.B)\n", + "Similarly `DeterministicOde(stateList, paramList, transition=[ode1, ode2, ode3])` would fail.\n", "\n", - "In \\[621\\]: birth3 = Transition(origin='R', equation='gamma\\*I',\n", - "transition_type=TransitionType.B)\n", + "This therefore forces us to construct our model carefully.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "d590f4d5", + "metadata": {}, + "source": [ + "The third option is to reframe the system as a set of birth processes, using `transition_type=TransitionType.B`. For this simple example, this formulation takes a similar form to defining using ODE equations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9f24cb0a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ -I*S*beta],\n", + "[I*S*beta - I*gamma],\n", + "[ I*gamma]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "birth1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.B)\n", "\n", - "In \\[622\\]: modelBirth = DeterministicOde(stateList, \n", - ".….: paramList, .….: birth_death=\\[birth1, birth2, birth3\\])\n", + "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n", "\n", - "In \\[623\\]: modelBirth.get_ode_eqn()\n", + "birth3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.B)\n", "\n", - "which will yield the same set result. Alternatively, we can use the\n", - "negative of the equation but set it to be a death process. For example,\n", - "we multiply the equations for state $S$ and $R$ with a negative sign and\n", - "set the transition type to be a death process instead.\n", + "modelBirth = DeterministicOde(stateList, paramList, birth_death=[birth1, birth2, birth3])\n", "\n", - "In \\[624\\]: death1 = Transition(origin='S', equation='beta\\*S\\*I',\n", - "transition_type=TransitionType.D)\n", + "modelBirth.get_ode_eqn()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1d61012", + "metadata": {}, + "source": [ + "Alternatively, we can use the negative of the equation to configure the ODEs to represent death processes. Since the death process is the removal of a flow, we take the negative of the birth process alongside using `transition_type=TransitionType.D`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9a3b3758", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ -I*S*beta],\n", + "[I*S*beta - I*gamma],\n", + "[ I*gamma]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "death1 = Transition(origin='S', equation='beta*S*I', transition_type=TransitionType.D)\n", "\n", - "In \\[625\\]: birth2 = Transition(origin='I', equation='beta\\*S\\*I -\n", - "gamma\\*I', transition_type=TransitionType.B)\n", + "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n", "\n", - "In \\[626\\]: death3 = Transition(origin='R', equation='-gamma\\*I',\n", - "transition_type=TransitionType.D)\n", + "death3 = Transition(origin='R', equation='-gamma*I', transition_type=TransitionType.D)\n", "\n", - "In \\[627\\]: modelBD = DeterministicOde(stateList, \n", - ".….: paramList, .….: birth_death=\\[death1, birth2, death3\\])\n", + "modelBD = DeterministicOde(stateList, paramList, birth_death=[death1, birth2, death3])\n", "\n", - "In \\[628\\]: modelBD.get_ode_eqn()\n", + "modelBD.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "1c8d48b8", + "metadata": {}, + "source": [ "\n", - "We can see that all the above ways yield the same set of ode at the end.\n", + "We can see that all four approaches have yielded the same set of ODEs at the end." + ] + }, + { + "cell_type": "markdown", + "id": "23a105af", + "metadata": {}, + "source": [ "\n", "## Model Addition\n", "\n", - "Because we allow the separation of transitions between states and\n", - "birth/death processes, the birth/death processes can be added later on.\n", - "\n", - "In \\[1\\]: modelBD2 = modelTrans\n", - "\n", - "In \\[1\\]: modelBD2.param_list = paramList + \\['mu', 'B'\\]\n", - "\n", - "In \\[1\\]: birthDeathList = \\[Transition(origin='S', equation='B', transition_type=TransitionType.B), \n", - "...: Transition(origin='S', equation='mu\\*S',\n", - "transition_type=TransitionType.D), ...: Transition(origin='I',\n", - "equation='mu\\*I', transition_type=TransitionType.D)\\]\n", - "\n", - "In \\[1\\]: modelBD2.birth_death_list = birthDeathList\n", + "Because we allow the separation of transitions between states and birth/death processes, the birth/death processes can be added later on. The following example takes the model that was defined using transitions (`modelTrans`) and includes a birth process to the $S$ state, and death processes to the $S$ and $I$ states." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3a6a15ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}B - I S \\beta - S \\mu\\\\I S \\beta - I \\gamma - I \\mu\\\\I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ B - I*S*beta - S*mu],\n", + "[I*S*beta - I*gamma - I*mu],\n", + "[ I*gamma]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "modelBD2 = modelTrans\n", "\n", - "In \\[1\\]: modelBD2.get_ode_eqn()\n", + "modelBD2.param_list = paramList + ['mu', 'B']\n", "\n", - "So modeling can be done in stages. Start with a standard closed system\n", - "and extend it with additional flows that interact with the environment.\n", + "birthDeathList = [Transition(origin='S', equation='B', transition_type=TransitionType.B), \n", + " Transition(origin='S', equation='mu*S', transition_type=TransitionType.D), \n", + " Transition(origin='I', equation='mu*I', transition_type=TransitionType.D)\n", + " ]\n", "\n", - "## Transition type\n", + "modelBD2.birth_death_list = birthDeathList\n", "\n", - "There are currently four different type of transitions allowed, which is\n", - "defined in an enum class also located in `transition`. The four types\n", - "are B, D, ODE and T, where they represent different type of process with\n", - "explanation in their corresponding value.\n", + "modelBD2.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "076bdc12", + "metadata": {}, + "source": [ "\n", - "In \\[1\\]: from pygom import transition\n", + "This demonstrates that we can approach our in stages. Start off with a standard closed system using `TransitionType.T`, and then extend it with additional flows that interact with the populations' surrounding environments using `TransitionType.B` or `TransitionType.D`." + ] + }, + { + "cell_type": "markdown", + "id": "ec63c1e1", + "metadata": {}, + "source": [ + "## Transition type summary\n", "\n", - "In \\[2\\]: for i in transition.TransitionType: \n", - "...: print(str(i) + \" = \" + i.value)\n", + "In summary, there are four different types of transitions allowed. These are B, D, ODE and T, which are defined in an enum class also located in `transition`." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ce5787f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TransitionType.B = Birth process\n", + "TransitionType.D = Death process\n", + "TransitionType.T = Between states\n", + "TransitionType.ODE = ODE _equation\n" + ] + } + ], + "source": [ + "from pygom import transition\n", "\n", - "Each birth process are added to the origin state while each death\n", - "process are deducted from the state, i.e. added to the state after\n", - "multiplying with a negative sign. An ode type is also added to the state\n", - "and we forbid the number of input ode to be greater than the number of\n", - "state inputted." + "for i in transition.TransitionType: \n", + " print(str(i) + \" = \" + i.value)\n" + ] + }, + { + "cell_type": "markdown", + "id": "89607b7d", + "metadata": {}, + "source": [ + "Each birth process is added to the origin state, while each death\n", + "process is deducted from the origin state (alternatively added to the state after\n", + "multiplying the flow with a negative sign). An ODE type is also added to the state, but we forbid the number of input ODEs to be greater than the number of states inputted. These strict definitions should help us to improve the bookeeping of states and flows when we have models of greater complexity." ] } ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.15 ('sphinx-doc')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "vscode": { + "interpreter": { + "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e" + } + } + }, "nbformat": 4, - "nbformat_minor": 5, - "metadata": {} + "nbformat_minor": 5 } From 6b53e39fe2241ba9afce9eb2287219b01e41d772 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 20 Jun 2023 16:48:02 +0100 Subject: [PATCH 069/188] add download button --- doc/doc/pygom-doc/notebooks/transition.ipynb | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/doc/pygom-doc/notebooks/transition.ipynb b/doc/doc/pygom-doc/notebooks/transition.ipynb index d37c4e10..086ee8dc 100644 --- a/doc/doc/pygom-doc/notebooks/transition.ipynb +++ b/doc/doc/pygom-doc/notebooks/transition.ipynb @@ -5,6 +5,8 @@ "metadata": {}, "source": [ "# Transition Object\n", + "{download}`this notebook <./sir.ipynb>`\n", + "\n", "\n", "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n", "\n", From e957687a6c06895b345876988e0144cb7fdfcd76 Mon Sep 17 00:00:00 2001 From: HWilliams-PHE <36919240+HWilliams-PHE@users.noreply.github.com> Date: Wed, 21 Jun 2023 11:49:42 +0100 Subject: [PATCH 070/188] Create pull_request_template.md proposed template for a pull request, including guidance for reviewer --- .github/pull_request_template.md | 68 ++++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 .github/pull_request_template.md diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 00000000..449cbaa8 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,68 @@ +## Description of the PR + + +Replace this text with a description of the pull request including the issue number. + +## Checks + +This is an itemised checklist for the QA process within UKHSA and represents the bare minimum a QA should be. + +Full instructions on reviewing work can be found at Confluence on the [ONS QA of code guidance](https://best-practice-and-impact.github.io/qa-of-code-guidance/intro.html) page. + +**To the reviewer:** Check the boxes once you have completed the checks below. + +- [ ] It runs + - Can you get the code run to completion in a new instance and from top to bottom in the case of notebooks, or in a new R session? + - Can original analysis results be accurately & easily reproduced from the code? +This is a basic form of Smoke Testing +- [ ] Data and security + - Use nbstripout to prevent Jupyter notebook output being committed to git repositories + - Files containing individual user's secret files and config files are not in repo, however examples of these files and setup instructions are included in the repo. + - Secrets include s3 bucket names, login credentials, and organisation information. These can be handled using secrets.yml + - If you are unsure whether an item should be secret please raise with qa.datascience@ukhsa.gov.uk + - The changes do not include unreleased policy or official information. +- [ ] Sensible + - Does the code execute the task accurately? This is a subjective challenge. + - Does the code do what the comments and readme say it does\*? + - Is the code robust enough to handle missing or challenging data? +- [ ] Documentation + - The purpose of the code is clearly defined, whether in a markdown chunk at the top of a notebook or in a README + - Assumptions of the analysis & input data are clearly displayed to the reader, whether in a markdown chunk at the top of a notebook or in a README + - Comments are included in the code so the reader can follow why the code behaves in the way it does + - Teams with high quality documentation are better able to implement technical practices more readily and perform better as a whole (DORA, 2021). + - Is the code written in a standard way? (In a hurry this may be a nice to have, but if at all possible, this standard from the beginning can cut long term costs dramatically) + - Code is modular, storing functions & classes in the src and being imported into a notebook or script + - Projects should be based on the UKHSA repo template developed to work with cookiecutter + - Variable, function & module names should be intuitive to the reader + - For example, intuitive names include df_geo_lookup & non-intuitive names include foobar + - Common and useful checks for coding we use broadly across UKHSA include: + - Rstyler + - lintr + - black + - flake8 +- [ ] Pair coding review completed (optional, but highly recommended for QA in a hurry) + - Pair programming is a way of working and reviewing that can result in the same standard of work being completed 40%-50% faster (Williams et al., 2000, Nosek, 1998) and is better than solo programming for tasks involving efficient knowledge transferring and for working on highly connected systems (Dawande et al., 2008). + - Have the assignee and reviewer been on a video call or in person together during the code development in a line by line writing and review process? + +\* If the comments or readme do not have enough information, this check fails. + +## How to QA this PR + +Before accepting the PR and merging to `main` or `master`, please follow these steps on a terminal in your development instance: + +- `git status` check what branch you are on and if you have any uncommitted changes. +- Handle the work on your current branch: + - `git commit` if you would like to keep the changes you have made on your current branch. + - `git stash` if you do not want to keep these changes, although you can recover these later. +- `git checkout main` or `git checkout master` to go to the branch that will be merged to. +- `git pull origin main` or `git pull origin master` fetches the most up to date git info on this branch. +- `git branch -a` lists all the available branches. +- `git checkout BRANCHNAME-FOR-PR` moves you into the branch to QA. +- `git pull origin BRANCHNAME-FOR-PR` ensures you have the most recent changes for the PR. +- Run the notebooks or code. +- Run `pre-commit`. This runs some automated checks to check the code is well formatted and does not contain data. + - Are there any small changes that can be made to get it to run? + - Yes: make annotations on github and notify code creator to correct them + - No, it runs: done! + - No, and it looks like it would need a lot of work: note major points in Github PR chat and discuss with author. +- Carefully read through the code or documentation and make sure it makes sense. From 72b0e18fda11f1eea975b70c3e46401564a076df Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Wed, 21 Jun 2023 14:22:21 +0100 Subject: [PATCH 071/188] ammend workflow to test build the jupyter book, and make it happen only on a push to the relevant directory --- .github/workflows/main.yml | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index fb240d57..1a1a2577 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -2,11 +2,11 @@ name: test-docs on: push: - branches: - - install-fix-docs + paths: + - doc/** pull_request: - branches: - - install-fix-docs + paths: + - doc/** env: ACTIONS_ALLOW_UNSECURE_COMMANDS: true @@ -17,7 +17,7 @@ jobs: strategy: matrix: os: [windows-latest] - python-version: ["3.7"]#, "3.8", "3.9", "3.10", "3.11"] + python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] steps: - uses: actions/checkout@v2 @@ -71,9 +71,8 @@ jobs: pip install -r doc/requirements.txt pip install make - - name: make html documentation + - name: Build the book run: | - cd doc - make clean - make html + cd doc/doc/pygom-doc + jupyter-book build . From 8fdf81a835c9db2ee359fcbc1b28f7ae52524dc9 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Wed, 21 Jun 2023 14:24:01 +0100 Subject: [PATCH 072/188] stochastic addition --- doc/doc/pygom-doc/_toc.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/doc/doc/pygom-doc/_toc.yml b/doc/doc/pygom-doc/_toc.yml index 29b83e52..3dc56a25 100644 --- a/doc/doc/pygom-doc/_toc.yml +++ b/doc/doc/pygom-doc/_toc.yml @@ -10,9 +10,7 @@ parts: - file: md/getting_started.md - file: notebooks/sir.ipynb - file: notebooks/transition.ipynb - #notebooks/sir.ipynb - #transition.rst - #stochastic.rst + - file: notebooks/stochastic.ipynb #unrollOde.rst #epi.rst #epijson.rst From b3b5f2630b70f23b16765dcb83230edfb2e9b0f9 Mon Sep 17 00:00:00 2001 From: HWilliams-PHE <36919240+HWilliams-PHE@users.noreply.github.com> Date: Wed, 21 Jun 2023 14:31:50 +0100 Subject: [PATCH 073/188] include jupyter book in requirements should fix CI error --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 36e653d5..3133a4b3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,4 +10,5 @@ numpydoc>=0.6.0 sphinx>=1.4.1 sphinx_rtd_theme>=0.2.0 cython>=0.29 -nbsphinx \ No newline at end of file +nbsphinx +jupyter-book From 09f8a840b4c320db5148db8eb053e38860d72d7b Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Wed, 21 Jun 2023 14:41:48 +0100 Subject: [PATCH 074/188] correct download link --- doc/doc/pygom-doc/notebooks/transition.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/doc/pygom-doc/notebooks/transition.ipynb b/doc/doc/pygom-doc/notebooks/transition.ipynb index 086ee8dc..89c6a5e2 100644 --- a/doc/doc/pygom-doc/notebooks/transition.ipynb +++ b/doc/doc/pygom-doc/notebooks/transition.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Transition Object\n", - "{download}`this notebook <./sir.ipynb>`\n", + "{download}`this notebook <./transition.ipynb>`\n", "\n", "\n", "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n", From d9f88c2bffe73d337c20084cf257cf3e2f28f6ab Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Thu, 22 Jun 2023 13:40:06 +0100 Subject: [PATCH 075/188] correct typos --- doc/doc/pygom-doc/notebooks/transition.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/doc/pygom-doc/notebooks/transition.ipynb b/doc/doc/pygom-doc/notebooks/transition.ipynb index 89c6a5e2..dd1568bf 100644 --- a/doc/doc/pygom-doc/notebooks/transition.ipynb +++ b/doc/doc/pygom-doc/notebooks/transition.ipynb @@ -20,8 +20,8 @@ "\n", "where the first three are mandatory. To demonstrate, we go back to the SIR model defined previously in the section {doc}`sir`. Recall that the set of ODEs are\n", "\n", - "$$begin{aligned}\n", - " frac{d S}{d t} &= - beta SI \\\\\n", + "$$\\begin{aligned}\n", + " \\frac{d S}{d t} &= - beta SI \\\\\n", "\\frac{d I}{d t} &= beta SI - \\gamma I \\\\\n", "\\frac{d R}{d t} &= \\gamma I.\n", "\\end{aligned}$$\n", @@ -218,7 +218,7 @@ "\n", "`modelTrans = DeterministicOde(stateList, paramList, ode=[t1, t2])`\n", "\n", - "Here the error occurs because `t1` and `t2` used `transition_type=TransitionType.T` argument, but `DeterministicOde is expecting a `TransitionType.ODE` argument.\n", + "Here the error occurs because `t1` and `t2` used `transition_type=TransitionType.T` argument, but `DeterministicOde` is expecting a `TransitionType.ODE` argument.\n", "\n", "Similarly `DeterministicOde(stateList, paramList, transition=[ode1, ode2, ode3])` would fail.\n", "\n", From cdee52c93fe8e3185cbb501ca3900a5ec2f56820 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Mon, 3 Jul 2023 17:02:44 +0100 Subject: [PATCH 076/188] github actions for building jupyterbook on github pages --- .github/workflows/book.yml | 42 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 .github/workflows/book.yml diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml new file mode 100644 index 00000000..f9d56606 --- /dev/null +++ b/.github/workflows/book.yml @@ -0,0 +1,42 @@ +name: deploy-book + +# only run when specific files change +# only run on install-fix-docs branch +# TODO change this to dev/main branch once integrated +# TODO edit filepath once filepath levels are corrected + +on: + push: + branches: + - install-fix-docs + paths: + - doc/** + +jobs: + deploy-book: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + # install python + - name: Set up Python 3.8 + uses: actions/setup-python@v2 + with: + python-version: 3.8 + + # install dependencies + - name: Install dependencies + run: | + pip install -r doc/doc/pygom-doc/requirements.txt + + # build the book + - name: Build the book + run: | + jupyter-book build doc/doc/pygom-doc + + # deploy book to github-pages + - name: GitHub Pages + uses: peaceiris/actions-gh-pages@v3.6.1 + with: + publish_dir: doc/doc/pygom-doc/_build/html + From 7915016249b75eb22d5e575eac0b9e8be9fe12c8 Mon Sep 17 00:00:00 2001 From: HWilliams-PHE <36919240+HWilliams-PHE@users.noreply.github.com> Date: Mon, 3 Jul 2023 17:09:03 +0100 Subject: [PATCH 077/188] correct instruction on when action should occur --- .github/workflows/book.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml index f9d56606..c2d3e573 100644 --- a/.github/workflows/book.yml +++ b/.github/workflows/book.yml @@ -9,8 +9,8 @@ on: push: branches: - install-fix-docs - paths: - - doc/** + paths: + - doc/** jobs: deploy-book: From cc39535ae59ac4046090e94b1c11d85bcc30ddfb Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Mon, 3 Jul 2023 17:58:17 +0100 Subject: [PATCH 078/188] add unroll pages --- doc/doc/pygom-doc/_toc.yml | 5 ++ doc/doc/pygom-doc/md/unrollOde.md | 15 ++++ .../pygom-doc/notebooks/unroll/unrollBD.ipynb | 64 +++++++++++++ .../notebooks/unroll/unrollHard.ipynb | 90 +++++++++++++++++++ .../notebooks/unroll/unrollSimple.ipynb | 61 +++++++++++++ 5 files changed, 235 insertions(+) create mode 100644 doc/doc/pygom-doc/md/unrollOde.md create mode 100644 doc/doc/pygom-doc/notebooks/unroll/unrollBD.ipynb create mode 100644 doc/doc/pygom-doc/notebooks/unroll/unrollHard.ipynb create mode 100644 doc/doc/pygom-doc/notebooks/unroll/unrollSimple.ipynb diff --git a/doc/doc/pygom-doc/_toc.yml b/doc/doc/pygom-doc/_toc.yml index 3dc56a25..af79456b 100644 --- a/doc/doc/pygom-doc/_toc.yml +++ b/doc/doc/pygom-doc/_toc.yml @@ -11,6 +11,11 @@ parts: - file: notebooks/sir.ipynb - file: notebooks/transition.ipynb - file: notebooks/stochastic.ipynb + - file: md/unrollOde.md + sections: + - file: notebooks/unroll/unrollSimple.ipynb + - file: notebooks/unroll/unrollBD.ipynb + - file: notebooks/unroll/unrollHard.ipynb #unrollOde.rst #epi.rst #epijson.rst diff --git a/doc/doc/pygom-doc/md/unrollOde.md b/doc/doc/pygom-doc/md/unrollOde.md new file mode 100644 index 00000000..9da3bd11 --- /dev/null +++ b/doc/doc/pygom-doc/md/unrollOde.md @@ -0,0 +1,15 @@ +# Convert ODE into transitions + +As seen previously in `transition`{.interpreted-text role="ref"}, we can +define the model via the transitions or explicitly as ODEs. There are +times when we all just want to test out some model in a paper and the +only available information are the ODEs themselves. Even though we know +that the ODEs come from some underlying transitions, breaking them down +can be a time consuming process. We provide the functionalities to do +this automatically. + +::: {.toctree} +unroll/unrollSimple.rst +unroll/unrollBD.rst +unroll/unrollHard.rst +::: diff --git a/doc/doc/pygom-doc/notebooks/unroll/unrollBD.ipynb b/doc/doc/pygom-doc/notebooks/unroll/unrollBD.ipynb new file mode 100644 index 00000000..fadb2e43 --- /dev/null +++ b/doc/doc/pygom-doc/notebooks/unroll/unrollBD.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ODE With Birth and Death Process\n", + "\n", + "We follow on from the SIR model of `unrollSimple` but with additional\n", + "birth and death processes.\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta SI + B - \\mu S\\\\\n", + "\\frac{dI}{dt} &= \\beta SI - \\gamma I - \\mu I\\\\\n", + "\\frac{dR}{dt} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "which consists of two transitions and three birth and death process\n", + "\n", + "digraph SIR_Model { \n", + "rankdir=LR; size=\"8\" node \\[shape = circle\\]; S -\\> I \\[ label = \"βSI\"\n", + "\\]; I -\\> R \\[ label = \"γI\" \\]; B \\[height=0 margin=0 shape=plaintext\n", + "width=0\\]; B -\\> S; \"S\\**2*μ\" \\[height=0 margin=0 shape=plaintext\n", + "width=0\\]; S -\\> \"S\\**2*μ\"; \"I\\*μ\" \\[height=0 margin=0 shape=plaintext\n", + "width=0\\]; I -\\> \"I\\*μ\";\n", + "\n", + "}\n", + "\n", + "Let's define this in terms of ODEs, and unroll it back to the individual\n", + "processes.\n", + "\n", + "In \\[1\\]: from pygom import Transition, TransitionType, SimulateOde,\n", + "common_models\n", + "\n", + "In \\[1\\]: import matplotlib.pyplot as plt\n", + "\n", + "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n", + "\n", + "In \\[1\\]: paramList = \\['beta', 'gamma', 'B', 'mu'\\]\n", + "\n", + "In \\[1\\]: odeList = \\[ \n", + "...: Transition(origin='S', ...: equation='-beta\\*S\\*I + B - mu\\*S',\n", + "...: transition_type=TransitionType.ODE), ...: Transition(origin='I',\n", + "...: equation='beta\\*S\\*I - gamma\\*I - mu\\*I', ...:\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='R', ...:\n", + "equation='gamma\\*I', ...: transition_type=TransitionType.ODE) ...: \\]\n", + "\n", + "In \\[1\\]: ode = SimulateOde(stateList, paramList, ode=odeList)\n", + "\n", + "In \\[1\\]: ode2 = ode.get_unrolled_obj()\n", + "\n", + "In \\[1\\]: f = plt.figure()\n", + "\n", + "@savefig sir_unrolled_transition_graph.png In \\[1\\]:\n", + "ode2.get_transition_graph()\n", + "\n", + "In \\[1\\]: plt.close()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/notebooks/unroll/unrollHard.ipynb b/doc/doc/pygom-doc/notebooks/unroll/unrollHard.ipynb new file mode 100644 index 00000000..64e14bd0 --- /dev/null +++ b/doc/doc/pygom-doc/notebooks/unroll/unrollHard.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hard Problem\n", + "\n", + "Now we turn to a harder problem that does not have a one to one mapping\n", + "between all the transitions and the terms in the ODEs. We use the model\n", + "in `Influenza_SLIARN`, defined by\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -S \\beta (I + \\delta A) \\\\ \n", + "\\frac{dL}{dt} &= S \\beta (I + \\delta A) - \\kappa L \\\\ \n", + "\\frac{dI}{dt} &= p \\kappa L - \\alpha I \\\\\n", + "\\frac{dA}{dt} &= (1 - p) \\kappa L - \\eta A \\\\\n", + "\\frac{dR}{dt} &= f \\alpha I + \\eta A \\\\\n", + "\\frac{dN}{dt} &= -(1 - f) \\alpha I.\n", + "\\end{aligned}$$\n", + "\n", + "The outflow of state **L**, $\\kappa L$, is composed of two transitions,\n", + "one to **I** and the other to **A** but the ode of **L** only reflects\n", + "the total flow going out of the state. Same can be said for state **I**,\n", + "where the flow $\\alpha I$ goes to both **R** and **N**. Graphically, it\n", + "is a rather simple process as shown below.\n", + "\n", + "digraph SLIARD_Model { \n", + "labelloc = \"t\"; label = \"Original transitions\"; rankdir=LR; size=\"8\"\n", + "node \\[shape = circle\\]; S -\\> L \\[ label = \"-Sβ(I + δA)/N\" \\]; L -\\> I\n", + "\\[ label = \"κLp\" \\]; L -\\> A \\[ label = \"κL(1-p)\" \\]; I -\\> R \\[ label =\n", + "\"αIf\" \\]; I -\\> D \\[ label = \"αI(1-f)\" \\]; A -\\> R \\[ label = \"ηA\" \\];\n", + "\n", + "}\n", + "\n", + "We slightly change the model by introducing a new state **D** to convert\n", + "it into a closed system. The combination of state **D** and **N** is a\n", + "constant, the total population. So we can remove **N** and this new\n", + "system consist of six transitions. We define them explicitly as ODEs and\n", + "unroll them into transitions.\n", + "\n", + "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n", + "\n", + "In \\[1\\]: stateList = \\['S', 'L', 'I', 'A', 'R', 'D'\\]\n", + "\n", + "In \\[2\\]: paramList = \\['beta', 'p', 'kappa', 'alpha', 'f', 'delta',\n", + "'epsilon', 'N'\\]\n", + "\n", + "In \\[3\\]: odeList = \\[ \n", + "...: Transition(origin='S', equation='- beta\\*S/N\\*(I + delta\\*A)',\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='L',\n", + "equation='beta\\*S/N\\*(I + delta\\*A) - kappa\\*L',\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='I',\n", + "equation='p\\*kappa\\*L - alpha\\*I', transition_type=TransitionType.ODE),\n", + "...: Transition(origin='A', equation='(1 - p)*kappa* L - epsilon\\*A',\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='R',\n", + "equation='f\\*alpha\\*I + epsilon\\*A',\n", + "transition_type=TransitionType.ODE), ...: Transition(origin='D',\n", + "equation='(1 - f)*alpha*I', transition_type=TransitionType.ODE) \\]\n", + "\n", + "In \\[4\\]: ode = SimulateOde(stateList, paramList, ode=odeList)\n", + "\n", + "In \\[5\\]: ode.get_transition_matrix()\n", + "\n", + "In \\[6\\]: ode2 = ode.get_unrolled_obj()\n", + "\n", + "In \\[7\\]: ode2.get_transition_matrix()\n", + "\n", + "In \\[8\\]: ode2.get_ode_eqn()\n", + "\n", + "After unrolling the odes, we have the following transition graph\n", + "\n", + "@savefig sir_unrolled_transition_graph_hard.png In \\[1\\]:\n", + "ode2.get_transition_graph()\n", + "\n", + "In \\[2\\]: plt.close()\n", + "\n", + "In \\[3\\]: print(sum(ode.get_ode_eqn() - ode2.get_ode_eqn()).simplify())\n", + "\\# difference\n", + "\n", + "which is exactly the same apart from slightly weird arrangement of\n", + "symbols in some of the equations. The last line with a value of zero\n", + "also reaffirms the result." + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} diff --git a/doc/doc/pygom-doc/notebooks/unroll/unrollSimple.ipynb b/doc/doc/pygom-doc/notebooks/unroll/unrollSimple.ipynb new file mode 100644 index 00000000..88bbd592 --- /dev/null +++ b/doc/doc/pygom-doc/notebooks/unroll/unrollSimple.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Problem\n", + "\n", + "For a simple problem, we consider the SIR model defined by\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta SI \\\\\n", + "\\frac{dI}{dt} &= \\beta SI - \\gamma I \\\\\n", + "\\frac{dR}{dt} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "which consists of two transitions\n", + "\n", + "digraph SIR_Model { \n", + "rankdir=LR; size=\"8\" node \\[shape = circle\\]; S -\\> I \\[ label = \"βSI\"\n", + "\\]; I -\\> R \\[ label = \"γI\" \\];\n", + "\n", + "}\n", + "\n", + "Let's define this using the code block below\n", + "\n", + "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n", + "\n", + "In \\[2\\]: ode1 = Transition(origin='S', equation='-beta\\*S\\*I',\n", + "transition_type=TransitionType.ODE)\n", + "\n", + "In \\[3\\]: ode2 = Transition(origin='I', equation='beta\\*S\\*I -\n", + "gamma\\*I', transition_type=TransitionType.ODE)\n", + "\n", + "In \\[4\\]: ode3 = Transition(origin='R', equation='gamma\\*I',\n", + "transition_type=TransitionType.ODE)\n", + "\n", + "In \\[6\\]: stateList = \\['S', 'I', 'R'\\]\n", + "\n", + "In \\[7\\]: paramList = \\['beta', 'gamma'\\]\n", + "\n", + "In \\[8\\]: ode = SimulateOde(stateList, \n", + "...: paramList, ...: ode=\\[ode1, ode2, ode3\\])\n", + "\n", + "In \\[9\\]: ode.get_transition_matrix()\n", + "\n", + "and the last line shows that the transition matrix is empty. This is the\n", + "expected result because `SimulateOdeModel` was not initialized using\n", + "transitions. We populate the transition matrix below and demonstrate the\n", + "difference.\n", + "\n", + "In \\[1\\]: ode = ode.get_unrolled_obj()\n", + "\n", + "In \\[2\\]: ode.get_transition_matrix()" + ] + } + ], + "nbformat": 4, + "nbformat_minor": 5, + "metadata": {} +} From 53ffaa0ad9ba66a827f1cc6f2038dc78fb83a4a2 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Mon, 3 Jul 2023 18:14:23 +0100 Subject: [PATCH 079/188] connect pat --- .github/workflows/book.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml index c2d3e573..04e75d87 100644 --- a/.github/workflows/book.yml +++ b/.github/workflows/book.yml @@ -38,5 +38,6 @@ jobs: - name: GitHub Pages uses: peaceiris/actions-gh-pages@v3.6.1 with: + github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: doc/doc/pygom-doc/_build/html From b7494a25f4c3e2729254b566456d18001cc6580d Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Mon, 3 Jul 2023 18:58:28 +0100 Subject: [PATCH 080/188] add to requirements --- doc/requirements.txt | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/doc/requirements.txt b/doc/requirements.txt index fe1dfe4f..d6e1e29a 100644 --- a/doc/requirements.txt +++ b/doc/requirements.txt @@ -1,11 +1,13 @@ -dask[complete]>=0.13.0 -graphviz>=0.4.9 -matplotlib>=1.0.0 -numpy>=1.6.0 -pandas>=0.15.0 -python-dateutil>=2.0.0 -scipy>=0.10.0 -sympy>=1.0.0 -numpydoc>=0.6.0 -sphinx>=1.4.1 sphinx_rtd_theme>=0.2.0 -ipython>=7.1.1 +dask[complete]>=0.13.0 +graphviz>=0.4.9 +matplotlib>=1.0.0 +numpy>=1.6.0 +pandas>=0.15.0 +python-dateutil>=2.0.0 +scipy>=0.10.0 +sympy>=1.0.0 +numpydoc>=0.6.0 +#sphinx>=1.4.1 +#sphinx_rtd_theme>=0.2.0 +#ipython>=7.1.1 +jupyter-book From 7a29d0732b1fd7d081994076c5f7be232908a42c Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 4 Jul 2023 10:22:22 +0100 Subject: [PATCH 081/188] correct token call --- .github/workflows/book.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml index 04e75d87..c66c3eb6 100644 --- a/.github/workflows/book.yml +++ b/.github/workflows/book.yml @@ -38,6 +38,6 @@ jobs: - name: GitHub Pages uses: peaceiris/actions-gh-pages@v3.6.1 with: - github_token: ${{ secrets.GITHUB_TOKEN }} + github_token: ${{ secrets.DEPLOY_BOOK }} publish_dir: doc/doc/pygom-doc/_build/html From 2ffe6fe04a58c517c2e68fe39b0f5d1ad2de4466 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 4 Jul 2023 10:25:36 +0100 Subject: [PATCH 082/188] remove download button --- doc/doc/pygom-doc/notebooks/sir.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/doc/doc/pygom-doc/notebooks/sir.ipynb b/doc/doc/pygom-doc/notebooks/sir.ipynb index 89e9fad2..0767ca96 100644 --- a/doc/doc/pygom-doc/notebooks/sir.ipynb +++ b/doc/doc/pygom-doc/notebooks/sir.ipynb @@ -5,7 +5,6 @@ "metadata": {}, "source": [ "# Motivating Example: SIR Model\n", - "{download}`this notebook <./sir.ipynb>`\n", "\n", "## Defining the model\n", "\n", @@ -763,7 +762,7 @@ "id": "8b63ba62", "metadata": { "tags": [ - "hide-input" + "hide-output" ] }, "outputs": [ From a76cb50b1414de9846f6119d4737699008dc928e Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 4 Jul 2023 10:44:10 +0100 Subject: [PATCH 083/188] remove download button --- doc/doc/pygom-doc/notebooks/transition.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/doc/doc/pygom-doc/notebooks/transition.ipynb b/doc/doc/pygom-doc/notebooks/transition.ipynb index dd1568bf..2ed607d2 100644 --- a/doc/doc/pygom-doc/notebooks/transition.ipynb +++ b/doc/doc/pygom-doc/notebooks/transition.ipynb @@ -5,7 +5,6 @@ "metadata": {}, "source": [ "# Transition Object\n", - "{download}`this notebook <./transition.ipynb>`\n", "\n", "\n", "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n", From 7e28f8a75826cd08af2e3503d184beb648bcf7a0 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 4 Jul 2023 11:50:16 +0100 Subject: [PATCH 084/188] hide plotting cells --- doc/doc/pygom-doc/notebooks/stochastic.ipynb | 472 +++++++++++++++++++ 1 file changed, 472 insertions(+) create mode 100644 doc/doc/pygom-doc/notebooks/stochastic.ipynb diff --git a/doc/doc/pygom-doc/notebooks/stochastic.ipynb b/doc/doc/pygom-doc/notebooks/stochastic.ipynb new file mode 100644 index 00000000..a69349bb --- /dev/null +++ b/doc/doc/pygom-doc/notebooks/stochastic.ipynb @@ -0,0 +1,472 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stochastic representation of ODEs\n", + "\n", + "There are multiple interpretations of the stochasticity of a deterministic\n", + "ODE. We have implemented two of the most common interpretations; when the\n", + "parameters are realizations of some underlying distribution, and when we\n", + "have a so called chemical master equation where each transition\n", + "represent a jump. Again, we use the standard SIR example as previously\n", + "seen in {doc}`sir`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3f44792d", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import SimulateOde, Transition, TransitionType\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import numpy as np\n", + "\n", + "# initial conditions\n", + "Ix0 = [1, 1.27e-6, 0]\n", + "\n", + "# time period\n", + "t = np.linspace(0, 150, 100)\n", + "\n", + "stateList = ['S', 'I', 'R']\n", + "\n", + "paramList = ['beta', 'gamma']\n", + "\n", + "transitionList = [Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T), \n", + " Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)]\n", + "\n", + "# create the ODEs\n", + "odeS = SimulateOde(stateList, paramList, transition=transitionList)\n", + "\n", + "# assign parameter values\n", + "odeS.parameters = [0.5, 1.0/3.0]\n", + "\n", + "# assign initial values\n", + "odeS.initial_values = (Ix0, t[0])\n", + "\n", + "# solve deterministic solution\n", + "solutionReference = odeS.integrate(t[1::], full_output=False)" + ] + }, + { + "cell_type": "markdown", + "id": "0e516f09", + "metadata": {}, + "source": [ + "\n", + "## Stochastic Parameters\n", + "\n", + "In our first implementation, we assume that the parameters follow some\n", + "underlying distribution. Given that both $\\beta$ and $\\gamma$ in our SIR\n", + "model has to be non-negative, it seems natural to use a Gamma\n", + "distribution. We make use of the familiar syntax from\n", + "[R](http://www.r-project.org/) to define our distribution.\n", + "Unfortunately, we have to define it via a tuple, where the first item is the\n", + "function handle (name) and the second the parameters. \n", + "\n", + "```{note}\n", + "The parameters can be defined as either a dictionary or as the same sequence\n", + "as [R](http://www.r-project.org/) (without specifying the arguments), which for the [Gamma distribution](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/GammaDist.html) is the shape followed by the rate.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a3e9bc81", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom.utilR import rgamma\n", + "\n", + "d = dict()\n", + "\n", + "# E(X) = 0.5 = shape * scale = shape / rate \n", + "d['beta'] = (rgamma,{'shape':100.0, 'rate':200.0})\n", + "\n", + "# E(X) = 1.0/3.0 = shape / rate\n", + "d['gamma'] = (rgamma,(100.0, 300.0))\n", + "\n", + "odeS.parameters = d\n", + "\n", + "Ymean, Yall = odeS.simulate_param(t[1::], 10, full_output=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2221479d", + "metadata": {}, + "source": [ + "```{note}\n", + "Note that a message may be printed above where PyGOM is trying to connect to an\n", + "mpi backend, as our module has the capability to compute in parallel\n", + "using the IPython. We have simulated a total of 10 different solutions\n", + "using different parameters, the plots can be seen below.\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "8849566b", + "metadata": { + "tags": [ + "hide-cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axarr = plt.subplots(1,3, layout='constrained')\n", + "\n", + "#TODO tidy up plots\n", + "for solution in Yall: \n", + " axarr[0].plot(t, solution[:,0]) \n", + " axarr[1].plot(t, solution[:,1]) \n", + " axarr[2].plot(t, solution[:,2])\n", + "\n", + "for idx, state in enumerate(stateList):\n", + " axarr[idx].set_title(state)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1fd28659", + "metadata": {}, + "source": [ + "\n", + "We then see how the expected results, using the sample average of the\n", + "simulations\n", + "\n", + "$$\\tilde{x}(T) = \\mathbb{E}\\left[ \\int_{t_{0}}^{T} f(\\theta,x,t) dt \\right]$$\n", + "\n", + "differs from the reference solution\n", + "\n", + "$$\\hat{x}(T) = \\int_{t_{0}}^{T} f(\\mathbb{E}\\left[ \\theta \\right],x,t) dt$$ " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4e6f4164", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axarr = plt.subplots(1,3)\n", + "\n", + "for i in range(3): \n", + " axarr[i].plot(t, Ymean[:,i] - solutionReference[:,i])\n", + "\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ee4d496d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axarr = plt.subplots(1,3, layout='constrained', sharey=True)\n", + "\n", + "for i in range(3): \n", + " axarr[i].plot(t, Ymean[:,i], label='sample average')\n", + " axarr[i].plot(t, solutionReference[:,i], label='reference')\n", + "\n", + "axarr[2].legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aa230d7f", + "metadata": {}, + "source": [ + "\n", + "The difference is relatively large especially for the $S$ state. We can\n", + "decrease this difference as we increase the number of simulation, and\n", + "more sophisticated sampling method for the generation of random\n", + "variables can also decrease the difference.\n", + "\n", + "In addition to using the built-in functions to represent stochasticity,\n", + "we can also use standard frozen distributions from scipy. Note that it\n", + "must be a frozen distribution as that is the only for the parameters of\n", + "the distributions to propagate through the model.\n", + "\n", + "In \\[1\\]: import scipy.stats as st\n", + "\n", + "In \\[1\\]: d = dict()\n", + "\n", + "In \\[1\\]: d\\['beta'\\] = st.gamma(a=100.0, scale=1.0/200.0)\n", + "\n", + "In \\[1\\]: d\\['gamma'\\] = st.gamma(a=100.0, scale=1.0/300.0)\n", + "\n", + "In \\[1\\]: odeS.parameters = d\n", + "\n", + "Obviously, there may be scenarios where only some of the parameters are\n", + "stochastic. Let's say that the $\\gamma$ parameter is fixed at $1/3$,\n", + "then simply replace the distribution information with a scalar. A quick\n", + "visual inspection at the resulting plot suggests that the system of ODE\n", + "potentially has less variation when compared to the case where both\n", + "parameters are stochastic.\n", + "\n", + "In \\[1\\]: d\\['gamma'\\] = 1.0/3.0\n", + "\n", + "In \\[1\\]: odeS.parameters = d\n", + "\n", + "In \\[1\\]: YmeanSingle, YallSingle = odeS.simulate_param(t\\[1::\\], 5,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", + "\n", + "In \\[1\\]: for solution in YallSingle: \n", + "...: axarr\\[0\\].plot(t,solution\\[:,0\\]) ...:\n", + "axarr\\[1\\].plot(t,solution\\[:,1\\]) ...:\n", + "axarr\\[2\\].plot(t,solution\\[:,2\\])\n", + "\n", + "@savefig stochastic_param_single.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "## Continuous Markov Representation\n", + "\n", + "Another common method of introducing stochasticity into a set of ode is\n", + "by assuming each movement in the system is a result of a jump process.\n", + "More concretely, the probabilty of a move for transition $j$ is governed\n", + "by an exponential distribution such that\n", + "\n", + "$$\\Pr(\\text{process $j$ jump within time } \\tau) = \\lambda_{j} e^{-\\lambda_{j} \\tau},$$\n", + "\n", + "where $\\lambda_{j}$ is the rate of transition for process $j$ and $\\tau$\n", + "the time elapsed after current time $t$.\n", + "\n", + "A couple of the commmon implementation for the jump process have been\n", + "implemented where two of them are used during a normal simulation; the\n", + "first reaction method [\\[Gillespie1977\\]]() and the $\\tau$-Leap method\n", + "[\\[Cao2006\\]](). The two changes interactively depending on the size of\n", + "the states.\n", + "\n", + "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", + "\n", + "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n", + "\n", + "In \\[1\\]: paramList = \\['beta', 'gamma', 'N'\\]\n", + "\n", + "In \\[1\\]: transitionList = \\[ \n", + "...: Transition(origin='S', destination='I', equation='beta\\*S\\*I/N',\n", + "transition_type=TransitionType.T), ...: Transition(origin='I',\n", + "destination='R', equation='gamma\\*I', transition_type=TransitionType.T)\n", + "...: \\]\n", + "\n", + "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0, x0\\[0\\]\\]\n", + "\n", + "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: solutionReference = odeS.integrate(t\\[1::\\])\n", + "\n", + "In \\[1\\]: simX, simT = odeS.simulate_jump(t\\[1:10\\], 10,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1, 3)\n", + "\n", + "In \\[1\\]: for solution in simX: \n", + "...: axarr\\[0\\].plot(t\\[:9\\], solution\\[:,0\\]) ...:\n", + "axarr\\[1\\].plot(t\\[:9\\], solution\\[:,1\\]) ...: axarr\\[2\\].plot(t\\[:9\\],\n", + "solution\\[:,2\\])\n", + "\n", + "@savefig stochastic_process.png In \\[1\\]: plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "Above, we see ten different simulation, again using the SIR model but\n", + "without standardization of the initial conditions. We restrict our time\n", + "frame to be only the first 10 time points so that the individual changes\n", + "can be seen more clearly above. If we use the same time frame as the one\n", + "used previously for the deterministic system (as shown below), the\n", + "trajectories are smoothed out and we no longer observe the *jumps*.\n", + "Looking at the raw trajectories of the ODE below, it is obvious that the\n", + "mean from a jump process is very different to the deterministic\n", + "solution. The reason behind this is that the jump process above was able\n", + "to fully remove all the initial infected individuals before any new\n", + "ones.\n", + "\n", + "In \\[1\\]: simX,simT = odeS.simulate_jump(t, 5, full_output=True)\n", + "\n", + "In \\[1\\]: simMean = np.mean(simX, axis=0)\n", + "\n", + "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", + "\n", + "In \\[1\\]: for solution in simX: \n", + "...: axarr\\[0\\].plot(t, solution\\[:,0\\]) ...: axarr\\[1\\].plot(t,\n", + "solution\\[:,1\\]) ...: axarr\\[2\\].plot(t, solution\\[:,2\\])\n", + "\n", + "@savefig stochastic_process_compare_large_n\\_curves.png In \\[1\\]:\n", + "plt.show()\n", + "\n", + "In \\[1\\]: plt.close()\n", + "\n", + "## Repeatable Simulation\n", + "\n", + "One of the possible use of compartmental models is to generate\n", + "forecasts. Although most of the time the requirement would be to have\n", + "(at least point-wise) convergence in the limit, reproducibility is also\n", + "important. For both types of interpretation explained above, we have\n", + "given the package the capability to repeat the simulations by setting a\n", + "seed. When the assumption is that the parameters follows some sort of\n", + "distribution, we simply set the seed which governs the global state of\n", + "the random number generator.\n", + "\n", + "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", + "\n", + "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: d = {'beta': st.gamma(a=100.0, scale=1.0/200.0), 'gamma':\n", + "st.gamma(a=100.0, scale=1.0/300.0), 'N': x0\\[0\\]}\n", + "\n", + "In \\[1\\]: odeS.parameters = d\n", + "\n", + "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: Ymean, Yall = odeS.simulate_param(t\\[1::\\], 10,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: np.random.seed(1)\n", + "\n", + "In \\[1\\]: Ymean1, Yall1 = odeS.simulate_param(t\\[1::\\], 10,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: np.random.seed(1)\n", + "\n", + "In \\[1\\]: Ymean2, Yall2 = odeS.simulate_param(t\\[1::\\], 10,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: sim_diff = \\[np.linalg.norm(Yall\\[i\\] - yi) for i, yi in\n", + "enumerate(Yall1)\\]\n", + "\n", + "In \\[1\\]: sim_diff12 = \\[np.linalg.norm(Yall2\\[i\\] - yi) for i, yi in\n", + "enumerate(Yall1)\\]\n", + "\n", + "In \\[1\\]: print(\"Different in the simulations and the mean: (%s, %s) \" %\n", + "(np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))\n", + "\n", + "In \\[1\\]: print(\"Different in the simulations and the mean using same\n", + "seed: (%s, %s) \" % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 -\n", + "Ymean1))))\n", + "\n", + "In the alternative interpretation, setting the global seed is\n", + "insufficient. Unlike simulation based on the parameters, where we can\n", + "pre-generate all the parameter values and send them off to individual\n", + "processes in the parallel backend, this is prohibitive here. In a\n", + "nutshell, the seed does not propagate when using a parallel backend\n", + "because each *integration* requires an unknown number of random samples.\n", + "Therefore, we have an additional flag **parallel** in the function\n", + "signature. By ensuring that the computation runs in serial, we can make\n", + "use of the global seed and generate identical runs.\n", + "\n", + "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", + "\n", + "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", + "transition=transitionList)\n", + "\n", + "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0, x0\\[0\\]\\]\n", + "\n", + "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", + "\n", + "In \\[1\\]: simX, simT = odeS.simulate_jump(t\\[1:10\\], 10, parallel=False,\n", + "full_output=True)\n", + "\n", + "In \\[1\\]: np.random.seed(1)\n", + "\n", + "In \\[1\\]: simX1, simT1 = odeS.simulate_jump(t\\[1:10\\], 10,\n", + "parallel=False, full_output=True)\n", + "\n", + "In \\[1\\]: np.random.seed(1)\n", + "\n", + "In \\[1\\]: simX2, simT2 = odeS.simulate_jump(t\\[1:10\\], 10,\n", + "parallel=False, full_output=True)\n", + "\n", + "In \\[1\\]: sim_diff = \\[np.linalg.norm(simX\\[i\\] - x1) for i, x1 in\n", + "enumerate(simX1)\\]\n", + "\n", + "In \\[1\\]: sim_diff12 = \\[np.linalg.norm(simX2\\[i\\] - x1) for i, x1 in\n", + "enumerate(simX1)\\]\n", + "\n", + "In \\[1\\]: print(\"Difference in simulation: %s\" %\n", + "np.sum(np.abs(sim_diff)))\n", + "\n", + "In \\[1\\]: print(\"Difference in simulation using same seed: %s\" %\n", + "np.sum(np.abs(sim_diff12)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.15 ('sphinx-doc')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "vscode": { + "interpreter": { + "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From bbf5ee725b9b774c8c7c9c5a6ae36d580e94b703 Mon Sep 17 00:00:00 2001 From: "Hannah.Williams" Date: Tue, 4 Jul 2023 11:51:25 +0100 Subject: [PATCH 085/188] include pygom logo --- doc/doc/pygom-doc/_config.yml | 2 +- doc/doc/pygom-doc/logo_pygom.jpg | Bin 0 -> 9931 bytes 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 doc/doc/pygom-doc/logo_pygom.jpg diff --git a/doc/doc/pygom-doc/_config.yml b/doc/doc/pygom-doc/_config.yml index ddf3ee23..951c4fd6 100644 --- a/doc/doc/pygom-doc/_config.yml +++ b/doc/doc/pygom-doc/_config.yml @@ -4,7 +4,7 @@ title: PyGOM documentation author: Public Health England / UK Health Security Agency #TODO change logo -logo: logo.png +logo: logo_pygom.jpg # build files in table of contents # used as alternative to exclude patterns diff --git a/doc/doc/pygom-doc/logo_pygom.jpg b/doc/doc/pygom-doc/logo_pygom.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e35317b423633a265e313cabfa38acfd255c4768 GIT binary patch literal 9931 zcmcI~2Urx_wq_A+at4(kIX6Kvf(S@xGEL4IBxev%Q6y)YoCTZE-fO3S{cH99s_OUb?{5IHk~~BnKtlrnG}Hn7UO}6M 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peaceiris/actions-gh-pages@v3.6.1 with: github_token: ${{ secrets.DEPLOY_BOOK }} - publish_dir: doc/doc/pygom-doc/_build/html + publish_dir: docs/pygom-doc/_build/html diff --git a/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.ipynb b/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.ipynb deleted file mode 100644 index fd83edb7..00000000 --- a/doc/doc/pygom-doc/mdconvert/doc_to_sort/stochastic.ipynb +++ /dev/null @@ -1,328 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Stochastic representation of ode\n", - "\n", - "There are multiple interpretation of stochasticity of a deterministic\n", - "ode. We have implemented two of the most common interpretation; when the\n", - "parameters are realizations of some underlying distribution, and when we\n", - "have a so called chemical master equation where each transition\n", - "represent a jump. Again, we use the standard SIR example as previously\n", - "seen in ref:sir.\n", - "\n", - "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n", - "\n", - "In \\[1\\]: import matplotlib.pyplot as plt\n", - "\n", - "In \\[1\\]: import numpy as np\n", - "\n", - "In \\[1\\]: x0 = \\[1, 1.27e-6, 0\\]\n", - "\n", - "In \\[1\\]: t = np.linspace(0, 150, 100)\n", - "\n", - "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n", - "\n", - "In \\[1\\]: paramList = \\['beta', 'gamma'\\]\n", - "\n", - "In \\[1\\]: transitionList = \\[ \n", - "...: Transition(origin='S', destination='I', equation='beta\\*S\\*I',\n", - "transition_type=TransitionType.T), ...: Transition(origin='I',\n", - "destination='R', equation='gamma\\*I', transition_type=TransitionType.T)\n", - "...: \\]\n", - "\n", - "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", - "transition=transitionList)\n", - "\n", - "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0\\]\n", - "\n", - "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", - "\n", - "In \\[1\\]: solutionReference = odeS.integrate(t\\[1::\\],\n", - "full_output=False)\n", - "\n", - "## Stochastic Parameter\n", - "\n", - "In our first scenario, we assume that the parameters follow some\n", - "underlying distribution. Given that both $\\beta$ and $\\gamma$ in our SIR\n", - "model has to be non-negative, it seemed natural to use a Gamma\n", - "distribution. We make use of the familiar syntax from\n", - "[R](http://www.r-project.org/) to define our distribution.\n", - "Unfortunately, we have to define it via a tuple, where the first is the\n", - "function handle (name) while the second the parameters. Note that the\n", - "parameters can be defined as either a dictionary or as the same sequence\n", - "as [R](http://www.r-project.org/), which is the shape then the rate in\n", - "the Gamma case.\n", - "\n", - "In \\[1\\]: from pygom.utilR import rgamma\n", - "\n", - "In \\[1\\]: d = dict()\n", - "\n", - "In \\[1\\]: d\\['beta'\\] = (rgamma,{'shape':100.0, 'rate':200.0})\n", - "\n", - "In \\[1\\]: d\\['gamma'\\] = (rgamma,(100.0, 300.0))\n", - "\n", - "In \\[1\\]: odeS.parameters = d\n", - "\n", - "In \\[1\\]: Ymean, Yall = odeS.simulate_param(t\\[1::\\], 10,\n", - "full_output=True)\n", - "\n", - "Note that a message is printed above where it is trying to connect to an\n", - "mpi backend, as our module has the capability to compute in parallel\n", - "using the IPython. We have simulated a total of 10 different solutions\n", - "using different parameters, the plots can be seen below\n", - "\n", - "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", - "\n", - "In \\[1\\]: for solution in Yall: \n", - "...: axarr\\[0\\].plot(t, solution\\[:,0\\]) ...: axarr\\[1\\].plot(t,\n", - "solution\\[:,1\\]) ...: axarr\\[2\\].plot(t, solution\\[:,2\\])\n", - "\n", - "@savefig stochastic_param_all.png In \\[1\\]: plt.show()\n", - "\n", - "In \\[1\\]: plt.close()\n", - "\n", - "We then see how the expected results, using the sample average of the\n", - "simulations\n", - "\n", - "$$\\tilde{x}(T) = \\mathbb{E}\\left[ \\int_{t_{0}}^{T} f(\\theta,x,t) dt \\right]$$\n", - "\n", - "differs from the reference solution\n", - "\n", - "$$\\hat{x}(T) = \\int_{t_{0}}^{T} f(\\mathbb{E}\\left[ \\theta \\right],x,t) dt$$\n", - "\n", - "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", - "\n", - "In \\[1\\]: for i in range(3): axarr\\[i\\].plot(t, Ymean\\[:,i\\] -\n", - "solutionReference\\[:,i\\])\n", - "\n", - "@savefig stochastic_param_compare.png In \\[1\\]: plt.show()\n", - "\n", - "In \\[1\\]: plt.close()\n", - "\n", - "The difference is relatively large especially for the $S$ state. We can\n", - "decrease this difference as we increase the number of simulation, and\n", - "more sophisticated sampling method for the generation of random\n", - "variables can also decrease the difference.\n", - "\n", - "In addition to using the built-in functions to represent stochasticity,\n", - "we can also use standard frozen distributions from scipy. Note that it\n", - "must be a frozen distribution as that is the only for the parameters of\n", - "the distributions to propagate through the model.\n", - "\n", - "In \\[1\\]: import scipy.stats as st\n", - "\n", - "In \\[1\\]: d = dict()\n", - "\n", - "In \\[1\\]: d\\['beta'\\] = st.gamma(a=100.0, scale=1.0/200.0)\n", - "\n", - "In \\[1\\]: d\\['gamma'\\] = st.gamma(a=100.0, scale=1.0/300.0)\n", - "\n", - "In \\[1\\]: odeS.parameters = d\n", - "\n", - "Obviously, there may be scenarios where only some of the parameters are\n", - "stochastic. Let's say that the $\\gamma$ parameter is fixed at $1/3$,\n", - "then simply replace the distribution information with a scalar. A quick\n", - "visual inspection at the resulting plot suggests that the system of ODE\n", - "potentially has less variation when compared to the case where both\n", - "parameters are stochastic.\n", - "\n", - "In \\[1\\]: d\\['gamma'\\] = 1.0/3.0\n", - "\n", - "In \\[1\\]: odeS.parameters = d\n", - "\n", - "In \\[1\\]: YmeanSingle, YallSingle = odeS.simulate_param(t\\[1::\\], 5,\n", - "full_output=True)\n", - "\n", - "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", - "\n", - "In \\[1\\]: for solution in YallSingle: \n", - "...: axarr\\[0\\].plot(t,solution\\[:,0\\]) ...:\n", - "axarr\\[1\\].plot(t,solution\\[:,1\\]) ...:\n", - "axarr\\[2\\].plot(t,solution\\[:,2\\])\n", - "\n", - "@savefig stochastic_param_single.png In \\[1\\]: plt.show()\n", - "\n", - "In \\[1\\]: plt.close()\n", - "\n", - "## Continuous Markov Representation\n", - "\n", - "Another common method of introducing stochasticity into a set of ode is\n", - "by assuming each movement in the system is a result of a jump process.\n", - "More concretely, the probabilty of a move for transition $j$ is governed\n", - "by an exponential distribution such that\n", - "\n", - "$$\\Pr(\\text{process $j$ jump within time } \\tau) = \\lambda_{j} e^{-\\lambda_{j} \\tau},$$\n", - "\n", - "where $\\lambda_{j}$ is the rate of transition for process $j$ and $\\tau$\n", - "the time elapsed after current time $t$.\n", - "\n", - "A couple of the commmon implementation for the jump process have been\n", - "implemented where two of them are used during a normal simulation; the\n", - "first reaction method [\\[Gillespie1977\\]]() and the $\\tau$-Leap method\n", - "[\\[Cao2006\\]](). The two changes interactively depending on the size of\n", - "the states.\n", - "\n", - "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", - "\n", - "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n", - "\n", - "In \\[1\\]: paramList = \\['beta', 'gamma', 'N'\\]\n", - "\n", - "In \\[1\\]: transitionList = \\[ \n", - "...: Transition(origin='S', destination='I', equation='beta\\*S\\*I/N',\n", - "transition_type=TransitionType.T), ...: Transition(origin='I',\n", - "destination='R', equation='gamma\\*I', transition_type=TransitionType.T)\n", - "...: \\]\n", - "\n", - "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", - "transition=transitionList)\n", - "\n", - "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0, x0\\[0\\]\\]\n", - "\n", - "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", - "\n", - "In \\[1\\]: solutionReference = odeS.integrate(t\\[1::\\])\n", - "\n", - "In \\[1\\]: simX, simT = odeS.simulate_jump(t\\[1:10\\], 10,\n", - "full_output=True)\n", - "\n", - "In \\[1\\]: f, axarr = plt.subplots(1, 3)\n", - "\n", - "In \\[1\\]: for solution in simX: \n", - "...: axarr\\[0\\].plot(t\\[:9\\], solution\\[:,0\\]) ...:\n", - "axarr\\[1\\].plot(t\\[:9\\], solution\\[:,1\\]) ...: axarr\\[2\\].plot(t\\[:9\\],\n", - "solution\\[:,2\\])\n", - "\n", - "@savefig stochastic_process.png In \\[1\\]: plt.show()\n", - "\n", - "In \\[1\\]: plt.close()\n", - "\n", - "Above, we see ten different simulation, again using the SIR model but\n", - "without standardization of the initial conditions. We restrict our time\n", - "frame to be only the first 10 time points so that the individual changes\n", - "can be seen more clearly above. If we use the same time frame as the one\n", - "used previously for the deterministic system (as shown below), the\n", - "trajectories are smoothed out and we no longer observe the *jumps*.\n", - "Looking at the raw trajectories of the ODE below, it is obvious that the\n", - "mean from a jump process is very different to the deterministic\n", - "solution. The reason behind this is that the jump process above was able\n", - "to fully remove all the initial infected individuals before any new\n", - "ones.\n", - "\n", - "In \\[1\\]: simX,simT = odeS.simulate_jump(t, 5, full_output=True)\n", - "\n", - "In \\[1\\]: simMean = np.mean(simX, axis=0)\n", - "\n", - "In \\[1\\]: f, axarr = plt.subplots(1,3)\n", - "\n", - "In \\[1\\]: for solution in simX: \n", - "...: axarr\\[0\\].plot(t, solution\\[:,0\\]) ...: axarr\\[1\\].plot(t,\n", - "solution\\[:,1\\]) ...: axarr\\[2\\].plot(t, solution\\[:,2\\])\n", - "\n", - "@savefig stochastic_process_compare_large_n\\_curves.png In \\[1\\]:\n", - "plt.show()\n", - "\n", - "In \\[1\\]: plt.close()\n", - "\n", - "## Repeatable Simulation\n", - "\n", - "One of the possible use of compartmental models is to generate\n", - "forecasts. Although most of the time the requirement would be to have\n", - "(at least point-wise) convergence in the limit, reproducibility is also\n", - "important. For both types of interpretation explained above, we have\n", - "given the package the capability to repeat the simulations by setting a\n", - "seed. When the assumption is that the parameters follows some sort of\n", - "distribution, we simply set the seed which governs the global state of\n", - "the random number generator.\n", - "\n", - "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", - "\n", - "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", - "transition=transitionList)\n", - "\n", - "In \\[1\\]: d = {'beta': st.gamma(a=100.0, scale=1.0/200.0), 'gamma':\n", - "st.gamma(a=100.0, scale=1.0/300.0), 'N': x0\\[0\\]}\n", - "\n", - "In \\[1\\]: odeS.parameters = d\n", - "\n", - "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", - "\n", - "In \\[1\\]: Ymean, Yall = odeS.simulate_param(t\\[1::\\], 10,\n", - "full_output=True)\n", - "\n", - "In \\[1\\]: np.random.seed(1)\n", - "\n", - "In \\[1\\]: Ymean1, Yall1 = odeS.simulate_param(t\\[1::\\], 10,\n", - "full_output=True)\n", - "\n", - "In \\[1\\]: np.random.seed(1)\n", - "\n", - "In \\[1\\]: Ymean2, Yall2 = odeS.simulate_param(t\\[1::\\], 10,\n", - "full_output=True)\n", - "\n", - "In \\[1\\]: sim_diff = \\[np.linalg.norm(Yall\\[i\\] - yi) for i, yi in\n", - "enumerate(Yall1)\\]\n", - "\n", - "In \\[1\\]: sim_diff12 = \\[np.linalg.norm(Yall2\\[i\\] - yi) for i, yi in\n", - "enumerate(Yall1)\\]\n", - "\n", - "In \\[1\\]: print(\"Different in the simulations and the mean: (%s, %s) \" %\n", - "(np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))\n", - "\n", - "In \\[1\\]: print(\"Different in the simulations and the mean using same\n", - "seed: (%s, %s) \" % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 -\n", - "Ymean1))))\n", - "\n", - "In the alternative interpretation, setting the global seed is\n", - "insufficient. Unlike simulation based on the parameters, where we can\n", - "pre-generate all the parameter values and send them off to individual\n", - "processes in the parallel backend, this is prohibitive here. In a\n", - "nutshell, the seed does not propagate when using a parallel backend\n", - "because each *integration* requires an unknown number of random samples.\n", - "Therefore, we have an additional flag **parallel** in the function\n", - "signature. By ensuring that the computation runs in serial, we can make\n", - "use of the global seed and generate identical runs.\n", - "\n", - "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n", - "\n", - "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n", - "transition=transitionList)\n", - "\n", - "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0, x0\\[0\\]\\]\n", - "\n", - "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n", - "\n", - "In \\[1\\]: simX, simT = odeS.simulate_jump(t\\[1:10\\], 10, parallel=False,\n", - "full_output=True)\n", - "\n", - "In \\[1\\]: np.random.seed(1)\n", - "\n", - "In \\[1\\]: simX1, simT1 = odeS.simulate_jump(t\\[1:10\\], 10,\n", - "parallel=False, full_output=True)\n", - "\n", - "In \\[1\\]: np.random.seed(1)\n", - "\n", - "In \\[1\\]: simX2, simT2 = odeS.simulate_jump(t\\[1:10\\], 10,\n", - "parallel=False, full_output=True)\n", - "\n", - "In \\[1\\]: sim_diff = \\[np.linalg.norm(simX\\[i\\] - x1) for i, x1 in\n", - "enumerate(simX1)\\]\n", - "\n", - "In \\[1\\]: sim_diff12 = \\[np.linalg.norm(simX2\\[i\\] - x1) for i, x1 in\n", - "enumerate(simX1)\\]\n", - "\n", - "In \\[1\\]: print(\"Difference in simulation: %s\" %\n", - "np.sum(np.abs(sim_diff)))\n", - "\n", - "In \\[1\\]: print(\"Difference in simulation using same seed: %s\" %\n", - "np.sum(np.abs(sim_diff12)))" - ] - } - ], - "nbformat": 4, - "nbformat_minor": 5, - "metadata": {} -} diff --git a/docs/pygom-doc/_build/.doctrees/environment.pickle b/docs/pygom-doc/_build/.doctrees/environment.pickle new file mode 100644 index 0000000000000000000000000000000000000000..675a2f8b92289b900dc8bd92563bec10a7fa9285 GIT binary patch literal 53145 zcmdUY4Rl;dR^IqmmTbwEWqa)T*?G2i#*#;BNv(g+dUrgQ$M(pUjpdoyosp;Kb@yw% z_tbyWuUodpcW5WXmXma3ifIxsHKo*ujSO_GW1P(d;gr5aAyJ54iY|bH^kVA6V z6ClZw@7}6b7i>IpcnMuc~g{s(b6+TeqrieeI6#{FdK(g#YvRdqvyH--=oJ z6{l3l=d66W>hF7@;$+k3v`^nr9r$SVPPO0PW)_RpdA}=NNR>;Lm3B&1|CO_+7GL(P zlDBxl%;!xz_NtT3I%dvWoGqj(1kzhva?142t|S+Wt4oF4A^`9|ejB2=g0>VF 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When it is not found, a full rebuild will be done. +config: 019113c917082bb23be372a31e705afc +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/pygom-doc/_build/html/_downloads/289ac28c9b7c69b5b79db17a12b844bf/sir.ipynb b/docs/pygom-doc/_build/html/_downloads/289ac28c9b7c69b5b79db17a12b844bf/sir.ipynb new file mode 100644 index 00000000..84796466 --- /dev/null +++ b/docs/pygom-doc/_build/html/_downloads/289ac28c9b7c69b5b79db17a12b844bf/sir.ipynb @@ -0,0 +1,868 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Motivating Example: SIR Model\n", + "{download}`this notebook <./sir.ipynb>`\n", + "\n", + "## Defining the model\n", + "\n", + "First, we are going to go through an SIR model to show the functionality\n", + "of the package. The SIR model is defined by the following equations\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dS}{dt} &= -\\beta SI \\\\\n", + "\\frac{dI}{dt} &= \\beta SI- \\gamma I \\\\\n", + "\\frac{dR}{dt} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "We can set this up as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "586834ab", + "metadata": {}, + "source": [ + "1. import the classes required to define the transitions" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "4c80a36a", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import Transition, TransitionType" + ] + }, + { + "cell_type": "markdown", + "id": "0ec4c9d0", + "metadata": {}, + "source": [ + "2. define our states, in this case the population states of **S**usceptible, **I**nfected and **R**emoved" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "3ce016f2", + "metadata": {}, + "outputs": [], + "source": [ + "stateList = ['S', 'I', 'R']" + ] + }, + { + "cell_type": "markdown", + "id": "021a1927", + "metadata": {}, + "source": [ + "3. define the set of parameters for our model, here there are only two" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "441e2287", + "metadata": {}, + "outputs": [], + "source": [ + "paramList = ['beta', 'gamma']" + ] + }, + { + "cell_type": "markdown", + "id": "dfd736b2", + "metadata": {}, + "source": [ + "4. specify the transitions of the modelled states; this will form our ODE system" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "b5f80ab0", + "metadata": {}, + "outputs": [], + "source": [ + "odeList = [\n", + " Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE),\n", + " Transition(origin='I',equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE),\n", + " Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE) \n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "21629e7b", + "metadata": {}, + "source": [ + "```{note}\n", + "Here, we have invoked a class from `Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`transition:defining-the-equations` for an example when the input is wrong.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "69bfb7c3", + "metadata": {}, + "source": [ + "\n", + "5. import the ode module" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "f78c33d4", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import DeterministicOde" + ] + }, + { + "cell_type": "markdown", + "id": "477d6f84", + "metadata": {}, + "source": [ + "6. initialize the model, which constructs our ODE system from all the information we have provided" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c9fbcce0", + "metadata": {}, + "outputs": [], + "source": [ + "model = DeterministicOde(stateList, paramList, ode=odeList)" + ] + }, + { + "cell_type": "markdown", + "id": "4bf43064", + "metadata": {}, + "source": [ + "That is all the information required to define a simple SIR model. We can verify the model using" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "6ad54e09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ -I*S*beta],\n", + "[I*S*beta - I*gamma],\n", + "[ I*gamma]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "143a6871", + "metadata": {}, + "source": [ + "where we can see the equations corresponding to their respective $S,I$ and $R$ state. " + ] + }, + { + "cell_type": "markdown", + "id": "08207474", + "metadata": {}, + "source": [ + "```{note}\n", + "The ordering of the equations is in the standard $S,I,R$ sequence because of how we defined the states initially. \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "7054e58a", + "metadata": {}, + "source": [ + "We can rearrange the state list," + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "07f81fd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}I \\gamma\\\\- I S \\beta\\\\I S \\beta - I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ I*gamma],\n", + "[ -I*S*beta],\n", + "[I*S*beta - I*gamma]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now we are going to define in a different order. note that the output ode changed with the input state\n", + "stateList = ['R', 'S', 'I']\n", + "\n", + "model = DeterministicOde(stateList, paramList, ode=odeList)\n", + "\n", + "model.get_ode_eqn()\n" + ] + }, + { + "cell_type": "markdown", + "id": "d3f587d4", + "metadata": {}, + "source": [ + "and find that the set of ODEs comes out in the order that we specified. " + ] + }, + { + "cell_type": "markdown", + "id": "1175c832", + "metadata": {}, + "source": [ + "```{tip}\n", + "In addition to showing the equation form of the ODEs, we can also display them as either symbols or latex code, which can save some extra typing when porting the equations to another document.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "960fdc0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "⎡dR/dt= I⋅γ ⎤\n", + "⎢ ⎥\n", + "⎢dS/dt= -I⋅S⋅β ⎥\n", + "⎢ ⎥\n", + "⎣dI/dt= I⋅S⋅β - I⋅γ⎦\n" + ] + } + ], + "source": [ + "model.print_ode()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "93e32c75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{array}{cc}dR/dt= & I \\gamma\\\\dS/dt= & - I S \\beta\\\\dI/dt= & I S \\beta - I \\gamma\\end{array}\n" + ] + } + ], + "source": [ + "model.print_ode(True)" + ] + }, + { + "cell_type": "markdown", + "id": "79c154ba", + "metadata": {}, + "source": [ + "#TODO links to unroll\n", + "Here the SIR model was provided to PyGOM as a set ODEs by using the `Transition` to define them. \n", + "We have also provided the capability to obtain a *best guess* transition matrix when only the ODEs are available. See the section `unrollOde` for more information, and in particular `unrollSimple` for the continuing demonstration of the SIR model." + ] + }, + { + "cell_type": "markdown", + "id": "51ed6fa2", + "metadata": {}, + "source": [ + "## Extracting model information\n", + "\n", + "We may wish to determine if the set of ODEs are linear. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "61684654", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.linear_ode()" + ] + }, + { + "cell_type": "markdown", + "id": "4cf58543", + "metadata": {}, + "source": [ + "Since we know that the SIR model is not linear, we may want to have a look at the Jacobian.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "1c8ff090", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}0 & 0 & \\gamma\\\\0 & - I \\beta & - S \\beta\\\\0 & I \\beta & S \\beta - \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[0, 0, gamma],\n", + "[0, -I*beta, -S*beta],\n", + "[0, I*beta, S*beta - gamma]])" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_jacobian_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "bab79c98", + "metadata": {}, + "source": [ + "Or maybe we want to know the gradient." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "abb02502", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}0 & I\\\\- I S & 0\\\\I S & - I\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ 0, I],\n", + "[-I*S, 0],\n", + "[ I*S, -I]])" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.get_grad_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "9d0052ec", + "metadata": {}, + "source": [ + "```{Warning}\n", + "Invoking the functions that compute the derivatives, $f(x)$, `model.ode()` or `model.jacobian()`, will return an error\n", + "\n", + "These functions are used to solve the ODEs numerically, and require values of initial state values, time, and parameter values.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "35abe902", + "metadata": {}, + "source": [ + "For setting initial conditions, the numeric values of the states **must** be set in the same order that list of states appear. We can use the following to check the state ordering, as well as displaying all of the states that we have included." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "e703c888", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ODEVariable('R', 'R', None, True),\n", + " ODEVariable('S', 'S', None, True),\n", + " ODEVariable('I', 'I', None, True)]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.state_list" + ] + }, + { + "cell_type": "markdown", + "id": "54e681d0", + "metadata": {}, + "source": [ + "#TODO unsure if this is needed\n", + "There is currently no mechanism to set the numeric initial conditions the states when the states are defined. This is because of an implementation issue with external package, such as solving an initial value problem." + ] + }, + { + "cell_type": "markdown", + "id": "13bed7f9", + "metadata": {}, + "source": [ + "## Initial value problem\n", + "\n", + "By setting the state initial conditions, time, and parameters, we can evaluate our model." + ] + }, + { + "cell_type": "markdown", + "id": "04fb8dd9", + "metadata": {}, + "source": [ + "1. Define the model parameters. (We can call the parameters to check what we must provide.)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "46042ebf", + "metadata": {}, + "outputs": [], + "source": [ + "model.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "9ec016b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{beta: 0.5, gamma: 0.3333333333333333}" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paramEval = [('beta',0.5), ('gamma',1.0/3.0)]\n", + "\n", + "model.parameters = paramEval\n", + "\n", + "model.parameters" + ] + }, + { + "cell_type": "markdown", + "id": "efcdac90", + "metadata": {}, + "source": [ + "2. Provide initial conditions for the states." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "c43074c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4.23333333e-07, -6.35000000e-07, 2.11666667e-07])" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "initialState = [0, 1, 1.27e-6]\n", + " \n", + "model.ode(state=initialState, t=1)" + ] + }, + { + "cell_type": "markdown", + "id": "a8dc4681", + "metadata": {}, + "source": [ + "\n", + "3. Implement an ODE solver.\n", + "\n", + "We are well equipped to solve an initial value problem, using the standard numerical integrator such as `odeint ` from `scipy.integrate`. We also used `matplotlib.pyplot` for plotting and `linspace ` to create the time vector." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "a14b9901", + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.integrate\n", + "import numpy\n", + "\n", + "t = numpy.linspace(0, 150, 100)\n", + "\n", + "solution = scipy.integrate.odeint(model.ode, initialState, t)" + ] + }, + { + "cell_type": "markdown", + "id": "c4feebae", + "metadata": {}, + "source": [ + "We can plot our solution to observe a standard SIR shape." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "8303885c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(t, solution[:,0], label='R')\n", + "\n", + "plt.plot(t, solution[:,1], label='S')\n", + "\n", + "plt.plot(t, solution[:,2], label='I')\n", + "\n", + "plt.xlabel('Time')\n", + "\n", + "plt.ylabel('Population proportion')\n", + "\n", + "plt.title('Standard SIR model')\n", + "\n", + "plt.legend(loc=0)\n", + "\n", + "#@savefig sir_plot.png In \n", + "\n", + "plt.show()\n", + "\n", + "#plt.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "ba849579", + "metadata": {}, + "source": [ + "Alternatively, we can integrate and plot via the **ode** object which we initialized earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "efddd3a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.initial_values = (initialState, t[0])\n", + "\n", + "model.parameters = paramEval\n", + "\n", + "solution = model.integrate(t[1::])\n", + "\n", + "model.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "4dd6e250", + "metadata": {}, + "source": [ + "We could solve the ODEs above using the Jacobian as well. Unfortunately, it does not help because the number of times the Jacobian was evaluated was zero, as expected given that our set of equations are not stiff." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "7a7a568f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.58 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "#TODO what does this show?\n", + "%timeit solution1, output1 = scipy.integrate.odeint(model.ode, initialState, t, full_output=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "91b7f9d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.84 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "\n", + "%timeit solution2, output2 = scipy.integrate.odeint(model.ode, initialState, t, Dfun=model.jacobian, mu=None, ml=None, full_output=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "3403884c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.68 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "\n", + "%timeit solution3, output3 = model.integrate(t, full_output=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b6b9ee47", + "metadata": {}, + "source": [ + "It is important to note that we return our Jacobian as a dense square matrix. Hence, the two argument (mu,ml) for the ODE solver was set to `None` to let it know the output explicitly." + ] + }, + { + "cell_type": "markdown", + "id": "5ca18fa3", + "metadata": {}, + "source": [ + "## Solving the forward sensitivity equation\n", + "\n", + "The sensitivity equations are also solved as an initial value problem. Let us redefine the model in the standard SIR order and we solve it with the sensitivity all set at zero, i.e. we do not wish to infer the initial value of the states." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "87173626", + "metadata": {}, + "outputs": [], + "source": [ + "stateList = ['S', 'I', 'R']\n", + "\n", + "model = DeterministicOde(stateList, paramList, ode=odeList)\n", + "\n", + "initialState = [1, 1.27e-6, 0]\n", + "\n", + "paramEval = [('beta', 0.5), ('gamma', 1.0/3.0)]\n", + "\n", + "model.parameters = paramEval\n", + "\n", + "solution = scipy.integrate.odeint(model.ode_and_sensitivity, numpy.append(initialState, numpy.zeros(6)), t)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "8b63ba62", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "{\n", + " \"tags\": [\n", + " \"hide-input\",\n", + " ]\n", + "}\n", + "f,axarr = plt.subplots(3,3);\n", + "\n", + "f.text(0.5,0.975,'SIR with forward sensitivity solved via ode',fontsize=16,horizontalalignment='center',verticalalignment='top')\n", + "\n", + "axarr[0,0].plot(t, solution[:,0])\n", + "\n", + "axarr[0,0].set_title('S')\n", + "\n", + "axarr[0,1].plot(t, solution[:,1])\n", + "\n", + "axarr[0,1].set_title('I')\n", + "\n", + "axarr[0,2].plot(t, solution[:,2]);\n", + "\n", + "axarr[0,2].set_title('R')\n", + "\n", + "axarr[1,0].plot(t, solution[:,3])\n", + "\n", + "axarr[1,0].set_title(r'state S parameter $beta$')\n", + "\n", + "axarr[2,0].plot(t, solution[:,4])\n", + "\n", + "axarr[2,0].set_title(r'state S parameter $gamma$')\n", + "\n", + "axarr[1,1].plot(t, solution[:,5])\n", + "\n", + "axarr[1,1].set_title(r'state I parameter $beta$')\n", + "\n", + "axarr[2,1].plot(t, solution[:,6])\n", + "\n", + "axarr[2,1].set_title(r'state I parameter $gamma$')\n", + "\n", + "axarr[1,2].plot(t, solution[:,7])\n", + "\n", + "axarr[1,2].set_title(r'state R parameter $beta$')\n", + "\n", + "axarr[2,2].plot(t, solution[:,8])\n", + "\n", + "axarr[2,2].set_title(r'state R parameter $gamma$')\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2f64d869", + "metadata": {}, + "source": [ + "#TODO links\n", + "This concludes the introductory example and we will be moving on to look at parameter estimation next in {doc}`estimate1` and the most important part in terms of setting up the ODE object; defining the equations in various different ways in {doc}`transition`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.15 ('sphinx-doc')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "vscode": { + "interpreter": { + "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/pygom-doc/_build/html/_downloads/c78f2e88b6a97c6b5b77ae96e28c2fa2/transition.ipynb b/docs/pygom-doc/_build/html/_downloads/c78f2e88b6a97c6b5b77ae96e28c2fa2/transition.ipynb new file mode 100644 index 00000000..dd1568bf --- /dev/null +++ b/docs/pygom-doc/_build/html/_downloads/c78f2e88b6a97c6b5b77ae96e28c2fa2/transition.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transition Object\n", + "{download}`this notebook <./transition.ipynb>`\n", + "\n", + "\n", + "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n", + "\n", + "\n", + "All transitions that get fed into the ODE system need to be defined as a transition object, `Transition`. It takes a total of four input arguments:\n", + "\n", + "1. The origin state\n", + "2. Equation that describe the process\n", + "3. The type of transition\n", + "4. The destination state\n", + "\n", + "where the first three are mandatory. To demonstrate, we go back to the SIR model defined previously in the section {doc}`sir`. Recall that the set of ODEs are\n", + "\n", + "$$\\begin{aligned}\n", + " \\frac{d S}{d t} &= - beta SI \\\\\n", + "\\frac{d I}{d t} &= beta SI - \\gamma I \\\\\n", + "\\frac{d R}{d t} &= \\gamma I.\n", + "\\end{aligned}$$\n", + "\n", + "We can define the set of ODEs, as seen previously, via" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68d41d64", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import Transition, TransitionType, common_models\n", + "\n", + "ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)\n", + "\n", + "ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)\n", + "\n", + "ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)" + ] + }, + { + "cell_type": "markdown", + "id": "d393a33a", + "metadata": {}, + "source": [ + "\n", + "Note that we need to state explicitly the type of equation we are\n", + "inputting, which is simply of type **ODE** in this case. We can confirm\n", + "this has been entered correctly by putting it into `DeterministicOde`," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b0801dd", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import DeterministicOde\n", + "\n", + "stateList = ['S', 'I', 'R']\n", + "\n", + "paramList = ['beta', 'gamma']\n", + "\n", + "model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3])" + ] + }, + { + "cell_type": "markdown", + "id": "6e764826", + "metadata": {}, + "source": [ + "\n", + "and then checking it.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5782a96f", + "metadata": {}, + "outputs": [], + "source": [ + "model.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "c693cbc7", + "metadata": {}, + "source": [ + "Now we are going to show the different ways of defining the same set of\n", + "ODEs.\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa631d9f", + "metadata": {}, + "source": [ + "(transition:defining-the-equations)=\n", + "## Defining the equations\n", + "\n", + "We first recognize that the set of ODEs defining the SIR model are the result of\n", + "two transitions,\n", + "\n", + "$$\\begin{aligned}\n", + "S \\rightarrow I &= \\beta SI \\\\\n", + "I \\rightarrow R &= \\gamma I\n", + "\\end{aligned}$$\n", + "\n", + "where $S \\rightarrow I$ denotes a transition from state $S$ to state\n", + "$I$. Therefore, we can define our model by these two transitions,\n", + "but they need to be passed as the `transition`\n", + "argument instead of the `ode` argument of `DeterministicOde` or `SimulateOde`.\n", + "\n", + "```{note}\n", + "We are initializing the model using the `SimulateOde` class, rather than `DeterministicOde`, because the stochastic implementation has more available operations on transitions.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6966fc5e", + "metadata": {}, + "outputs": [], + "source": [ + "from pygom import SimulateOde\n", + "\n", + "t1 = Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T)\n", + "\n", + "t2 = Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)\n", + "\n", + "modelTrans = SimulateOde(stateList, paramList, transition=[t1, t2])\n", + "\n", + "modelTrans.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "b896d5c9", + "metadata": {}, + "source": [ + "\n", + "We can see that the resulting ODE is exactly the same, as expected. The\n", + "transition matrix that defines this process can be visualized\n", + "using graphviz. Because only certain renderers permit the use of sub and\n", + "superscript, operators such as $**$ are left as they are in the\n", + "equation." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1e17eb7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}0 & I S \\beta & 0\\\\0 & 0 & I \\gamma\\\\0 & 0 & 0\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[0, I*S*beta, 0],\n", + "[0, 0, I*gamma],\n", + "[0, 0, 0]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "modelTrans.get_transition_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "570ceed5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n\n\n\n\n\n\n\nS\n\nS\n\n\n\nI\n\nI\n\n\n\nS->I\n\n\nβ*S*I\n\n\n\nR\n\nR\n\n\n\nI->R\n\n\nγ*I\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "# TODO why are two images produced? issue #75\n", + "modelTrans.get_transition_graph(show=False)" + ] + }, + { + "cell_type": "markdown", + "id": "5d982b87", + "metadata": {}, + "source": [ + "```{warning}\n", + "The execution will error if the incorrect `TransitionType` is used against the wrong argument.\n", + "\n", + "`modelTrans = DeterministicOde(stateList, paramList, ode=[t1, t2])`\n", + "\n", + "Here the error occurs because `t1` and `t2` used `transition_type=TransitionType.T` argument, but `DeterministicOde` is expecting a `TransitionType.ODE` argument.\n", + "\n", + "Similarly `DeterministicOde(stateList, paramList, transition=[ode1, ode2, ode3])` would fail.\n", + "\n", + "This therefore forces us to construct our model carefully.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "d590f4d5", + "metadata": {}, + "source": [ + "The third option is to reframe the system as a set of birth processes, using `transition_type=TransitionType.B`. For this simple example, this formulation takes a similar form to defining using ODE equations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9f24cb0a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ -I*S*beta],\n", + "[I*S*beta - I*gamma],\n", + "[ I*gamma]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "birth1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.B)\n", + "\n", + "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n", + "\n", + "birth3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.B)\n", + "\n", + "modelBirth = DeterministicOde(stateList, paramList, birth_death=[birth1, birth2, birth3])\n", + "\n", + "modelBirth.get_ode_eqn()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1d61012", + "metadata": {}, + "source": [ + "Alternatively, we can use the negative of the equation to configure the ODEs to represent death processes. Since the death process is the removal of a flow, we take the negative of the birth process alongside using `transition_type=TransitionType.D`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9a3b3758", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ -I*S*beta],\n", + "[I*S*beta - I*gamma],\n", + "[ I*gamma]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "death1 = Transition(origin='S', equation='beta*S*I', transition_type=TransitionType.D)\n", + "\n", + "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n", + "\n", + "death3 = Transition(origin='R', equation='-gamma*I', transition_type=TransitionType.D)\n", + "\n", + "modelBD = DeterministicOde(stateList, paramList, birth_death=[death1, birth2, death3])\n", + "\n", + "modelBD.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "1c8d48b8", + "metadata": {}, + "source": [ + "\n", + "We can see that all four approaches have yielded the same set of ODEs at the end." + ] + }, + { + "cell_type": "markdown", + "id": "23a105af", + "metadata": {}, + "source": [ + "\n", + "## Model Addition\n", + "\n", + "Because we allow the separation of transitions between states and birth/death processes, the birth/death processes can be added later on. The following example takes the model that was defined using transitions (`modelTrans`) and includes a birth process to the $S$ state, and death processes to the $S$ and $I$ states." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3a6a15ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}B - I S \\beta - S \\mu\\\\I S \\beta - I \\gamma - I \\mu\\\\I \\gamma\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ B - I*S*beta - S*mu],\n", + "[I*S*beta - I*gamma - I*mu],\n", + "[ I*gamma]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "modelBD2 = modelTrans\n", + "\n", + "modelBD2.param_list = paramList + ['mu', 'B']\n", + "\n", + "birthDeathList = [Transition(origin='S', equation='B', transition_type=TransitionType.B), \n", + " Transition(origin='S', equation='mu*S', transition_type=TransitionType.D), \n", + " Transition(origin='I', equation='mu*I', transition_type=TransitionType.D)\n", + " ]\n", + "\n", + "modelBD2.birth_death_list = birthDeathList\n", + "\n", + "modelBD2.get_ode_eqn()" + ] + }, + { + "cell_type": "markdown", + "id": "076bdc12", + "metadata": {}, + "source": [ + "\n", + "This demonstrates that we can approach our in stages. Start off with a standard closed system using `TransitionType.T`, and then extend it with additional flows that interact with the populations' surrounding environments using `TransitionType.B` or `TransitionType.D`." + ] + }, + { + "cell_type": "markdown", + "id": "ec63c1e1", + "metadata": {}, + "source": [ + "## Transition type summary\n", + "\n", + "In summary, there are four different types of transitions allowed. These are B, D, ODE and T, which are defined in an enum class also located in `transition`." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ce5787f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TransitionType.B = Birth process\n", + "TransitionType.D = Death process\n", + "TransitionType.T = Between states\n", + "TransitionType.ODE = ODE _equation\n" + ] + } + ], + "source": [ + "from pygom import transition\n", + "\n", + "for i in transition.TransitionType: \n", + " print(str(i) + \" = \" + i.value)\n" + ] + }, + { + "cell_type": "markdown", + "id": "89607b7d", + "metadata": {}, + "source": [ + "Each birth process is added to the origin state, while each death\n", + "process is deducted from the origin state (alternatively added to the state after\n", + "multiplying the flow with a negative sign). An ODE type is also added to the state, but we forbid the number of input ODEs to be greater than the number of states inputted. These strict definitions should help us to improve the bookeeping of states and flows when we have models of greater complexity." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.15 ('sphinx-doc')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "vscode": { + "interpreter": { + "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/pygom-doc/_build/html/_images/0bbd9ae219fda3e5af2c2faf5b8745cf0187f817a37553a042c31af498e6c31f.png b/docs/pygom-doc/_build/html/_images/0bbd9ae219fda3e5af2c2faf5b8745cf0187f817a37553a042c31af498e6c31f.png new file mode 100644 index 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HcmV?d00001

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diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/sir.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/sir.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Motivating Example: SIR Model\n",
+    "{download}`this notebook <./sir.ipynb>`\n",
+    "\n",
+    "## Defining the model\n",
+    "\n",
+    "First, we are going to go through an SIR model to show the functionality\n",
+    "of the package. The SIR model is defined by the following equations\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{dS}{dt} &= -\\beta SI \\\\\n",
+    "\\frac{dI}{dt} &= \\beta SI- \\gamma I \\\\\n",
+    "\\frac{dR}{dt} &= \\gamma I.\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "We can set this up as follows:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "586834ab",
+   "metadata": {},
+   "source": [
+    "1. import the classes required to define the transitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "4c80a36a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import Transition, TransitionType"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ec4c9d0",
+   "metadata": {},
+   "source": [
+    "2. define our states, in this case the population states of **S**usceptible, **I**nfected and **R**emoved"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "3ce016f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stateList = ['S', 'I', 'R']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "021a1927",
+   "metadata": {},
+   "source": [
+    "3. define the set of parameters for our model, here there are only two"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "441e2287",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paramList = ['beta', 'gamma']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfd736b2",
+   "metadata": {},
+   "source": [
+    "4. specify the transitions of the modelled states; this will form our ODE system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "b5f80ab0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "odeList = [\n",
+    "    Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE),\n",
+    "    Transition(origin='I',equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE),\n",
+    "    Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE) \n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21629e7b",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "Here, we have invoked a class from `Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`transition:defining-the-equations` for an example when the input is wrong.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69bfb7c3",
+   "metadata": {},
+   "source": [
+    "\n",
+    "5. import the ode module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "f78c33d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import DeterministicOde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "477d6f84",
+   "metadata": {},
+   "source": [
+    "6. initialize the model, which constructs our ODE system from all the information we have provided"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "c9fbcce0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = DeterministicOde(stateList, paramList, ode=odeList)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bf43064",
+   "metadata": {},
+   "source": [
+    "That is all the information required to define a simple SIR model. We can verify the model using"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "6ad54e09",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[         -I*S*beta],\n",
+       "[I*S*beta - I*gamma],\n",
+       "[           I*gamma]])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.get_ode_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "143a6871",
+   "metadata": {},
+   "source": [
+    "where we can see the equations corresponding to their respective $S,I$ and $R$ state. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08207474",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "The ordering of the equations is in the standard $S,I,R$ sequence because of how we defined the states initially. \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7054e58a",
+   "metadata": {},
+   "source": [
+    "We can rearrange the state list,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "07f81fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}I \\gamma\\\\- I S \\beta\\\\I S \\beta - I \\gamma\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[           I*gamma],\n",
+       "[         -I*S*beta],\n",
+       "[I*S*beta - I*gamma]])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# now we are going to define in a different order. note that the output ode changed with the input state\n",
+    "stateList = ['R', 'S', 'I']\n",
+    "\n",
+    "model = DeterministicOde(stateList, paramList, ode=odeList)\n",
+    "\n",
+    "model.get_ode_eqn()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3f587d4",
+   "metadata": {},
+   "source": [
+    "and find that the set of ODEs comes out in the order that we specified. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1175c832",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "In addition to showing the equation form of the ODEs, we can also display them as either symbols or latex code, which can save some extra typing when porting the equations to another document.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "960fdc0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "⎡dR/dt=      I⋅γ    ⎤\n",
+      "⎢                   ⎥\n",
+      "⎢dS/dt=    -I⋅S⋅β   ⎥\n",
+      "⎢                   ⎥\n",
+      "⎣dI/dt=  I⋅S⋅β - I⋅γ⎦\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.print_ode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "93e32c75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{array}{cc}dR/dt= & I \\gamma\\\\dS/dt= & - I S \\beta\\\\dI/dt= & I S \\beta - I \\gamma\\end{array}\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.print_ode(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79c154ba",
+   "metadata": {},
+   "source": [
+    "#TODO links to unroll\n",
+    "Here the SIR model was provided to PyGOM as a set ODEs by using the `Transition` to define them.  \n",
+    "We have also provided the capability to obtain a *best guess* transition matrix when only the ODEs are available. See the section `unrollOde` for more information, and in particular `unrollSimple` for the continuing demonstration of the SIR model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51ed6fa2",
+   "metadata": {},
+   "source": [
+    "## Extracting model information\n",
+    "\n",
+    "We may wish to determine if the set of ODEs are linear. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "61684654",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.linear_ode()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cf58543",
+   "metadata": {},
+   "source": [
+    "Since we know that the SIR model is not linear, we may want to have a look at the Jacobian.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "1c8ff090",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & \\gamma\\\\0 & - I \\beta & - S \\beta\\\\0 & I \\beta & S \\beta - \\gamma\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[0,       0,          gamma],\n",
+       "[0, -I*beta,        -S*beta],\n",
+       "[0,  I*beta, S*beta - gamma]])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.get_jacobian_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bab79c98",
+   "metadata": {},
+   "source": [
+    "Or maybe we want to know the gradient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "abb02502",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & I\\\\- I S & 0\\\\I S & - I\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[   0,  I],\n",
+       "[-I*S,  0],\n",
+       "[ I*S, -I]])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.get_grad_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d0052ec",
+   "metadata": {},
+   "source": [
+    "```{Warning}\n",
+    "Invoking the functions that compute the derivatives, $f(x)$, `model.ode()` or `model.jacobian()`, will return an error\n",
+    "\n",
+    "These functions are used to solve the ODEs numerically, and require values of initial state values, time, and parameter values.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35abe902",
+   "metadata": {},
+   "source": [
+    "For setting initial conditions, the numeric values of the states **must** be set in the same order that list of states appear. We can use the following to check the state ordering, as well as displaying all of the states that we have included."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "e703c888",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[ODEVariable('R', 'R', None, True),\n",
+       " ODEVariable('S', 'S', None, True),\n",
+       " ODEVariable('I', 'I', None, True)]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.state_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54e681d0",
+   "metadata": {},
+   "source": [
+    "#TODO unsure if this is needed\n",
+    "There is currently no mechanism to set the numeric initial conditions the states when the states are defined. This is because of an implementation issue with external package, such as solving an initial value problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13bed7f9",
+   "metadata": {},
+   "source": [
+    "## Initial value problem\n",
+    "\n",
+    "By setting the state initial conditions, time, and parameters, we can evaluate our model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04fb8dd9",
+   "metadata": {},
+   "source": [
+    "1. Define the model parameters. (We can call the parameters to check what we must provide.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "46042ebf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "9ec016b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{beta: 0.5, gamma: 0.3333333333333333}"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "paramEval = [('beta',0.5), ('gamma',1.0/3.0)]\n",
+    "\n",
+    "model.parameters = paramEval\n",
+    "\n",
+    "model.parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efcdac90",
+   "metadata": {},
+   "source": [
+    "2. Provide initial conditions for the states."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "c43074c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 4.23333333e-07, -6.35000000e-07,  2.11666667e-07])"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "initialState = [0, 1, 1.27e-6]\n",
+    "    \n",
+    "model.ode(state=initialState, t=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8dc4681",
+   "metadata": {},
+   "source": [
+    "\n",
+    "3. Implement an ODE solver.\n",
+    "\n",
+    "We are well equipped to solve an initial value problem, using the standard numerical integrator such as `odeint ` from `scipy.integrate`. We also used `matplotlib.pyplot` for plotting and `linspace ` to create the time vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "a14b9901",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.integrate\n",
+    "import numpy\n",
+    "\n",
+    "t = numpy.linspace(0, 150, 100)\n",
+    "\n",
+    "solution = scipy.integrate.odeint(model.ode, initialState, t)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4feebae",
+   "metadata": {},
+   "source": [
+    "We can plot our solution to observe a standard SIR shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "8303885c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure()\n",
+    "\n",
+    "plt.plot(t, solution[:,0], label='R')\n",
+    "\n",
+    "plt.plot(t, solution[:,1], label='S')\n",
+    "\n",
+    "plt.plot(t, solution[:,2], label='I')\n",
+    "\n",
+    "plt.xlabel('Time')\n",
+    "\n",
+    "plt.ylabel('Population proportion')\n",
+    "\n",
+    "plt.title('Standard SIR model')\n",
+    "\n",
+    "plt.legend(loc=0)\n",
+    "\n",
+    "#@savefig sir_plot.png In \n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "#plt.close()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba849579",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can integrate and plot via the **ode** object which we initialized earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "efddd3a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.initial_values = (initialState, t[0])\n",
+    "\n",
+    "model.parameters = paramEval\n",
+    "\n",
+    "solution = model.integrate(t[1::])\n",
+    "\n",
+    "model.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dd6e250",
+   "metadata": {},
+   "source": [
+    "We could solve the ODEs above using the Jacobian as well. Unfortunately, it does not help because the number of times the Jacobian was evaluated was zero, as expected given that our set of equations are not stiff."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "7a7a568f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.58 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#TODO what does this show?\n",
+    "%timeit solution1, output1 = scipy.integrate.odeint(model.ode, initialState, t, full_output=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "91b7f9d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.84 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "%timeit solution2, output2 = scipy.integrate.odeint(model.ode, initialState, t, Dfun=model.jacobian, mu=None, ml=None, full_output=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "3403884c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.68 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "%timeit solution3, output3 = model.integrate(t, full_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6b9ee47",
+   "metadata": {},
+   "source": [
+    "It is important to note that we return our Jacobian as a dense square matrix. Hence, the two argument (mu,ml) for the ODE solver was set to `None` to let it know the output explicitly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ca18fa3",
+   "metadata": {},
+   "source": [
+    "## Solving the forward sensitivity equation\n",
+    "\n",
+    "The sensitivity equations are also solved as an initial value problem. Let us redefine the model in the standard SIR order and we solve it with the sensitivity all set at zero, i.e. we do not wish to infer the initial value of the states."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "87173626",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stateList = ['S', 'I', 'R']\n",
+    "\n",
+    "model = DeterministicOde(stateList, paramList, ode=odeList)\n",
+    "\n",
+    "initialState = [1, 1.27e-6, 0]\n",
+    "\n",
+    "paramEval = [('beta', 0.5), ('gamma', 1.0/3.0)]\n",
+    "\n",
+    "model.parameters = paramEval\n",
+    "\n",
+    "solution = scipy.integrate.odeint(model.ode_and_sensitivity, numpy.append(initialState, numpy.zeros(6)), t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "8b63ba62",
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    \"tags\": [\n",
+    "        \"hide-input\",\n",
+    "    ]\n",
+    "}\n",
+    "f,axarr = plt.subplots(3,3);\n",
+    "\n",
+    "f.text(0.5,0.975,'SIR with forward sensitivity solved via ode',fontsize=16,horizontalalignment='center',verticalalignment='top')\n",
+    "\n",
+    "axarr[0,0].plot(t, solution[:,0])\n",
+    "\n",
+    "axarr[0,0].set_title('S')\n",
+    "\n",
+    "axarr[0,1].plot(t, solution[:,1])\n",
+    "\n",
+    "axarr[0,1].set_title('I')\n",
+    "\n",
+    "axarr[0,2].plot(t, solution[:,2]);\n",
+    "\n",
+    "axarr[0,2].set_title('R')\n",
+    "\n",
+    "axarr[1,0].plot(t, solution[:,3])\n",
+    "\n",
+    "axarr[1,0].set_title(r'state S parameter $beta$')\n",
+    "\n",
+    "axarr[2,0].plot(t, solution[:,4])\n",
+    "\n",
+    "axarr[2,0].set_title(r'state S parameter $gamma$')\n",
+    "\n",
+    "axarr[1,1].plot(t, solution[:,5])\n",
+    "\n",
+    "axarr[1,1].set_title(r'state I parameter $beta$')\n",
+    "\n",
+    "axarr[2,1].plot(t, solution[:,6])\n",
+    "\n",
+    "axarr[2,1].set_title(r'state I parameter $gamma$')\n",
+    "\n",
+    "axarr[1,2].plot(t, solution[:,7])\n",
+    "\n",
+    "axarr[1,2].set_title(r'state R parameter $beta$')\n",
+    "\n",
+    "axarr[2,2].plot(t, solution[:,8])\n",
+    "\n",
+    "axarr[2,2].set_title(r'state R parameter $gamma$')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f64d869",
+   "metadata": {},
+   "source": [
+    "#TODO links\n",
+    "This concludes the introductory example and we will be moving on to look at parameter estimation next in {doc}`estimate1` and the most important part in terms of setting up the ODE object; defining the equations in various different ways in {doc}`transition`."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/doc/pygom-doc/notebooks/stochastic.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/stochastic.ipynb
similarity index 100%
rename from doc/doc/pygom-doc/notebooks/stochastic.ipynb
rename to docs/pygom-doc/_build/html/_sources/notebooks/stochastic.ipynb
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/transition.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/transition.ipynb
new file mode 100644
index 00000000..dd1568bf
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_sources/notebooks/transition.ipynb
@@ -0,0 +1,453 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transition Object\n",
+    "{download}`this notebook <./transition.ipynb>`\n",
+    "\n",
+    "\n",
+    "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n",
+    "\n",
+    "\n",
+    "All transitions that get fed into the ODE system need to be defined as a transition object, `Transition`. It takes a total of four input arguments:\n",
+    "\n",
+    "1.  The origin state\n",
+    "2.  Equation that describe the process\n",
+    "3.  The type of transition\n",
+    "4.  The destination state\n",
+    "\n",
+    "where the first three are mandatory. To demonstrate, we go back to the SIR model defined previously in the section {doc}`sir`. Recall that the set of ODEs are\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    " \\frac{d S}{d t} &= - beta SI \\\\\n",
+    "\\frac{d I}{d t} &= beta SI - \\gamma I \\\\\n",
+    "\\frac{d R}{d t} &= \\gamma I.\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "We can define the set of ODEs, as seen previously, via"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68d41d64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import Transition, TransitionType, common_models\n",
+    "\n",
+    "ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)\n",
+    "\n",
+    "ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)\n",
+    "\n",
+    "ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d393a33a",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Note that we need to state explicitly the type of equation we are\n",
+    "inputting, which is simply of type **ODE** in this case. We can confirm\n",
+    "this has been entered correctly by putting it into `DeterministicOde`,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b0801dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import DeterministicOde\n",
+    "\n",
+    "stateList = ['S', 'I', 'R']\n",
+    "\n",
+    "paramList = ['beta', 'gamma']\n",
+    "\n",
+    "model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e764826",
+   "metadata": {},
+   "source": [
+    "\n",
+    "and then checking it.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5782a96f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.get_ode_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c693cbc7",
+   "metadata": {},
+   "source": [
+    "Now we are going to show the different ways of defining the same set of\n",
+    "ODEs.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa631d9f",
+   "metadata": {},
+   "source": [
+    "(transition:defining-the-equations)=\n",
+    "## Defining the equations\n",
+    "\n",
+    "We first recognize that the set of ODEs defining the SIR model are the result of\n",
+    "two transitions,\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "S \\rightarrow I &= \\beta SI \\\\\n",
+    "I \\rightarrow R &= \\gamma I\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "where $S \\rightarrow I$ denotes a transition from state $S$ to state\n",
+    "$I$. Therefore, we can define our model by these two transitions,\n",
+    "but they need to be passed as the `transition`\n",
+    "argument instead of the `ode` argument of `DeterministicOde` or `SimulateOde`.\n",
+    "\n",
+    "```{note}\n",
+    "We are initializing the model using the `SimulateOde` class, rather than `DeterministicOde`, because the stochastic implementation has more available operations on transitions.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6966fc5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import SimulateOde\n",
+    "\n",
+    "t1 = Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T)\n",
+    "\n",
+    "t2 = Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)\n",
+    "\n",
+    "modelTrans = SimulateOde(stateList, paramList, transition=[t1, t2])\n",
+    "\n",
+    "modelTrans.get_ode_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b896d5c9",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We can see that the resulting ODE is exactly the same, as expected. The\n",
+    "transition matrix that defines this process can be visualized\n",
+    "using graphviz. Because only certain renderers permit the use of sub and\n",
+    "superscript, operators such as $**$ are left as they are in the\n",
+    "equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1e17eb7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & I S \\beta & 0\\\\0 & 0 & I \\gamma\\\\0 & 0 & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[0, I*S*beta,       0],\n",
+       "[0,        0, I*gamma],\n",
+       "[0,        0,       0]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modelTrans.get_transition_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "570ceed5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": "\n\n\n\n\n\n\n\n\nS\n\nS\n\n\n\nI\n\nI\n\n\n\nS->I\n\n\nβ*S*I\n\n\n\nR\n\nR\n\n\n\nI->R\n\n\nγ*I\n\n\n\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "# TODO why are two images produced? issue #75\n",
+    "modelTrans.get_transition_graph(show=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d982b87",
+   "metadata": {},
+   "source": [
+    "```{warning}\n",
+    "The execution will error if the incorrect `TransitionType` is used against the wrong argument.\n",
+    "\n",
+    "`modelTrans = DeterministicOde(stateList, paramList, ode=[t1, t2])`\n",
+    "\n",
+    "Here the error occurs because `t1` and `t2` used `transition_type=TransitionType.T` argument, but `DeterministicOde` is expecting a `TransitionType.ODE` argument.\n",
+    "\n",
+    "Similarly `DeterministicOde(stateList, paramList, transition=[ode1, ode2, ode3])` would fail.\n",
+    "\n",
+    "This therefore forces us to construct our model carefully.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d590f4d5",
+   "metadata": {},
+   "source": [
+    "The third option is to reframe the system as a set of birth processes, using `transition_type=TransitionType.B`. For this simple example, this formulation takes a similar form to defining using ODE equations.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9f24cb0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[         -I*S*beta],\n",
+       "[I*S*beta - I*gamma],\n",
+       "[           I*gamma]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "birth1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.B)\n",
+    "\n",
+    "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n",
+    "\n",
+    "birth3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.B)\n",
+    "\n",
+    "modelBirth = DeterministicOde(stateList, paramList, birth_death=[birth1, birth2, birth3])\n",
+    "\n",
+    "modelBirth.get_ode_eqn()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1d61012",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can use the negative of the equation to configure the ODEs to represent death processes. Since the death process is the removal of a flow, we take the negative of the birth process alongside using `transition_type=TransitionType.D`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9a3b3758",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[         -I*S*beta],\n",
+       "[I*S*beta - I*gamma],\n",
+       "[           I*gamma]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "death1 = Transition(origin='S', equation='beta*S*I', transition_type=TransitionType.D)\n",
+    "\n",
+    "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n",
+    "\n",
+    "death3 = Transition(origin='R', equation='-gamma*I', transition_type=TransitionType.D)\n",
+    "\n",
+    "modelBD = DeterministicOde(stateList, paramList, birth_death=[death1, birth2, death3])\n",
+    "\n",
+    "modelBD.get_ode_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c8d48b8",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We can see that all four approaches have yielded the same set of ODEs at the end."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23a105af",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Model Addition\n",
+    "\n",
+    "Because we allow the separation of transitions between states and birth/death processes, the birth/death processes can be added later on. The following example takes the model that was defined using transitions (`modelTrans`) and includes a birth process to the $S$ state, and death processes to the $S$ and $I$ states."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "3a6a15ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}B - I S \\beta - S \\mu\\\\I S \\beta - I \\gamma - I \\mu\\\\I \\gamma\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[      B - I*S*beta - S*mu],\n",
+       "[I*S*beta - I*gamma - I*mu],\n",
+       "[                  I*gamma]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "modelBD2 = modelTrans\n",
+    "\n",
+    "modelBD2.param_list = paramList + ['mu', 'B']\n",
+    "\n",
+    "birthDeathList = [Transition(origin='S', equation='B', transition_type=TransitionType.B),  \n",
+    "                  Transition(origin='S', equation='mu*S', transition_type=TransitionType.D), \n",
+    "                  Transition(origin='I', equation='mu*I', transition_type=TransitionType.D)\n",
+    "                  ]\n",
+    "\n",
+    "modelBD2.birth_death_list = birthDeathList\n",
+    "\n",
+    "modelBD2.get_ode_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "076bdc12",
+   "metadata": {},
+   "source": [
+    "\n",
+    "This demonstrates that we can approach our in stages. Start off with a standard closed system using `TransitionType.T`, and then extend it with additional flows that interact with the populations' surrounding environments using `TransitionType.B` or `TransitionType.D`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec63c1e1",
+   "metadata": {},
+   "source": [
+    "## Transition type summary\n",
+    "\n",
+    "In summary, there are four different types of transitions allowed. These are B, D, ODE and T, which are defined in an enum class also located in `transition`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ce5787f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TransitionType.B = Birth process\n",
+      "TransitionType.D = Death process\n",
+      "TransitionType.T = Between states\n",
+      "TransitionType.ODE = ODE _equation\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pygom import transition\n",
+    "\n",
+    "for i in transition.TransitionType:  \n",
+    "    print(str(i) + \" = \" + i.value)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89607b7d",
+   "metadata": {},
+   "source": [
+    "Each birth process is added to the origin state, while each death\n",
+    "process is deducted from the origin state (alternatively added to the state after\n",
+    "multiplying the flow with a negative sign). An ODE type is also added to the state, but we forbid the number of input ODEs to be greater than the number of states inputted. These strict definitions should help us to improve the bookeeping of states and flows when we have models of greater complexity."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollBD.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollBD.ipynb
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diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollHard.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollHard.ipynb
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diff --git a/doc/doc/pygom-doc/mdconvert/unroll/unrollSimple.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollSimple.ipynb
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rename to docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollSimple.ipynb
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0;position:relative}.sd-tab-set>input{opacity:0;position:absolute}.sd-tab-set>input:checked+label{border-color:var(--sd-color-tabs-underline-active);color:var(--sd-color-tabs-label-active)}.sd-tab-set>input:checked+label+.sd-tab-content{display:block}.sd-tab-set>input:not(:checked)+label:hover{color:var(--sd-color-tabs-label-hover);border-color:var(--sd-color-tabs-underline-hover)}.sd-tab-set>input:focus+label{outline-style:auto}.sd-tab-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.sd-tab-set>label{border-bottom:.125rem solid transparent;margin-bottom:0;color:var(--sd-color-tabs-label-inactive);border-color:var(--sd-color-tabs-underline-inactive);cursor:pointer;font-size:var(--sd-fontsize-tabs-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .sd-tab-set>label:hover{color:var(--sd-color-tabs-label-active)}.sd-col>.sd-tab-set{width:100%}.sd-tab-content{box-shadow:0 -0.0625rem var(--sd-color-tabs-overline),0 .0625rem var(--sd-color-tabs-underline);display:none;order:99;padding-bottom:.75rem;padding-top:.75rem;width:100%}.sd-tab-content>:first-child{margin-top:0 !important}.sd-tab-content>:last-child{margin-bottom:0 !important}.sd-tab-content>.sd-tab-set{margin:0}.sd-sphinx-override,.sd-sphinx-override *{-moz-box-sizing:border-box;-webkit-box-sizing:border-box;box-sizing:border-box}.sd-sphinx-override p{margin-top:0}:root{--sd-color-primary: #007bff;--sd-color-secondary: #6c757d;--sd-color-success: #28a745;--sd-color-info: #17a2b8;--sd-color-warning: #f0b37e;--sd-color-danger: #dc3545;--sd-color-light: #f8f9fa;--sd-color-muted: #6c757d;--sd-color-dark: #212529;--sd-color-black: black;--sd-color-white: white;--sd-color-primary-highlight: #0069d9;--sd-color-secondary-highlight: #5c636a;--sd-color-success-highlight: #228e3b;--sd-color-info-highlight: #148a9c;--sd-color-warning-highlight: #cc986b;--sd-color-danger-highlight: #bb2d3b;--sd-color-light-highlight: #d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem}
diff --git a/docs/pygom-doc/_build/html/_sphinx_design_static/design-tabs.js b/docs/pygom-doc/_build/html/_sphinx_design_static/design-tabs.js
new file mode 100644
index 00000000..36b38cf0
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_sphinx_design_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
+  for (const label of li) {
+    syncId = label.getAttribute("data-sync-id");
+    if (syncId) {
+      label.onclick = onLabelClick;
+      if (!sd_labels_by_text[syncId]) {
+        sd_labels_by_text[syncId] = [];
+      }
+      sd_labels_by_text[syncId].push(label);
+    }
+  }
+}
+
+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
+    if (label === this) continue;
+    label.previousElementSibling.checked = true;
+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/docs/pygom-doc/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/pygom-doc/_build/html/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 00000000..8549469d
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,134 @@
+/*
+ * _sphinx_javascript_frameworks_compat.js
+ * ~~~~~~~~~~
+ *
+ * Compatability shim for jQuery and underscores.js.
+ *
+ * WILL BE REMOVED IN Sphinx 6.0
+ * xref RemovedInSphinx60Warning
+ *
+ */
+
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/docs/pygom-doc/_build/html/_static/basic.css b/docs/pygom-doc/_build/html/_static/basic.css
new file mode 100644
index 00000000..5685b52e
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/basic.css
@@ -0,0 +1,928 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 270px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+a.brackets:before,
+span.brackets > a:before{
+    content: "[";
+}
+
+a.brackets:after,
+span.brackets > a:after {
+    content: "]";
+}
+
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+/* Docutils 0.17 and older (footnotes & citations) */
+dl.footnote > dt,
+dl.citation > dt {
+    float: left;
+    margin-right: 0.5em;
+}
+
+dl.footnote > dd,
+dl.citation > dd {
+    margin-bottom: 0em;
+}
+
+dl.footnote > dd:after,
+dl.citation > dd:after {
+    content: "";
+    clear: both;
+}
+
+/* Docutils 0.18+ (footnotes & citations) */
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+/* Footnotes & citations ends */
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dt:after {
+    content: ":";
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/_static/check-solid.svg b/docs/pygom-doc/_build/html/_static/check-solid.svg
new file mode 100644
index 00000000..92fad4b5
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/check-solid.svg
@@ -0,0 +1,4 @@
+
+  
+  
+
diff --git a/docs/pygom-doc/_build/html/_static/clipboard.min.js b/docs/pygom-doc/_build/html/_static/clipboard.min.js
new file mode 100644
index 00000000..54b3c463
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1
+  
+  
+  
+
diff --git a/docs/pygom-doc/_build/html/_static/copybutton.css b/docs/pygom-doc/_build/html/_static/copybutton.css
new file mode 100644
index 00000000..f1916ec7
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * 
Short

+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/docs/pygom-doc/_build/html/_static/copybutton.js b/docs/pygom-doc/_build/html/_static/copybutton.js
new file mode 100644
index 00000000..2ea7ff3e
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `
+  ${messages[locale]['copy_success']}
+  
+  
+`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `
+  ${messages[locale]['copy_to_clipboard']}
+  
+  
+  
+`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    ``
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/_static/copybutton_funcs.js b/docs/pygom-doc/_build/html/_static/copybutton_funcs.js
new file mode 100644
index 00000000..dbe1aaad
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/docs/pygom-doc/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/docs/pygom-doc/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
new file mode 100644
index 00000000..3225661c
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
@@ -0,0 +1 @@
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diff --git a/docs/pygom-doc/_build/html/_static/design-tabs.js b/docs/pygom-doc/_build/html/_static/design-tabs.js
new file mode 100644
index 00000000..36b38cf0
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
+  for (const label of li) {
+    syncId = label.getAttribute("data-sync-id");
+    if (syncId) {
+      label.onclick = onLabelClick;
+      if (!sd_labels_by_text[syncId]) {
+        sd_labels_by_text[syncId] = [];
+      }
+      sd_labels_by_text[syncId].push(label);
+    }
+  }
+}
+
+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
+    if (label === this) continue;
+    label.previousElementSibling.checked = true;
+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/docs/pygom-doc/_build/html/_static/doctools.js b/docs/pygom-doc/_build/html/_static/doctools.js
new file mode 100644
index 00000000..c3db08d1
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/doctools.js
@@ -0,0 +1,264 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.highlightSearchWords();
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * highlight the search words provided in the url in the text
+   */
+  highlightSearchWords: () => {
+    const highlight =
+      new URLSearchParams(window.location.search).get("highlight") || "";
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '
' +
+            '' +
+            Documentation.gettext("Hide Search Matches") +
+            "
"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    const url = new URL(window.location);
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    const blacklistedElements = new Set([
+      "TEXTAREA",
+      "INPUT",
+      "SELECT",
+      "BUTTON",
+    ]);
+    document.addEventListener("keydown", (event) => {
+      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
+      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "Escape":
+            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+            Documentation.hideSearchWords();
+            event.preventDefault();
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/docs/pygom-doc/_build/html/_static/documentation_options.js b/docs/pygom-doc/_build/html/_static/documentation_options.js
new file mode 100644
index 00000000..30637825
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/documentation_options.js
@@ -0,0 +1,14 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: '',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '',
+    NAVIGATION_WITH_KEYS: true,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: false,
+};
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/_static/file.png b/docs/pygom-doc/_build/html/_static/file.png
new file mode 100644
index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3
GIT binary patch
literal 286
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literal 0
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diff --git a/docs/pygom-doc/_build/html/_static/images/logo_binder.svg b/docs/pygom-doc/_build/html/_static/images/logo_binder.svg
new file mode 100644
index 00000000..45fecf75
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/images/logo_binder.svg
@@ -0,0 +1,19 @@
+
+
+
+
+logo
+
+	
+	
+	
+	
+	
+
+
diff --git a/docs/pygom-doc/_build/html/_static/images/logo_colab.png b/docs/pygom-doc/_build/html/_static/images/logo_colab.png
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diff --git a/docs/pygom-doc/_build/html/_static/images/logo_jupyterhub.svg b/docs/pygom-doc/_build/html/_static/images/logo_jupyterhub.svg
new file mode 100644
index 00000000..60cfe9f2
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/images/logo_jupyterhub.svg
@@ -0,0 +1 @@
+logo_jupyterhubHub
diff --git a/docs/pygom-doc/_build/html/_static/jquery-3.6.0.js b/docs/pygom-doc/_build/html/_static/jquery-3.6.0.js
new file mode 100644
index 00000000..fc6c299b
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML  elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "" +
+				"";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "" +
+				"";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over  to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			//  elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces ";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting  or other required elements.
+	thead: [ 1, "", "
" ],
+	col: [ 2, "", "
" ],
+	tr: [ 2, "", "
" ],
+	td: [ 3, "", "
" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG  instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "
+{% endmacro %}
diff --git a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js
new file mode 100644
index 00000000..766173ab
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js
@@ -0,0 +1,3 @@
+/*! For license information please see bootstrap.js.LICENSE.txt */
+(()=>{"use strict";var t={d:(e,i)=>{for(var n in i)t.o(i,n)&&!t.o(e,n)&&Object.defineProperty(e,n,{enumerable:!0,get:i[n]})},o:(t,e)=>Object.prototype.hasOwnProperty.call(t,e),r:t=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})}},e={};t.r(e),t.d(e,{afterMain:()=>w,afterRead:()=>b,afterWrite:()=>C,applyStyles:()=>$,arrow:()=>G,auto:()=>r,basePlacements:()=>a,beforeMain:()=>v,beforeRead:()=>m,beforeWrite:()=>A,bottom:()=>n,clippingParents:()=>h,computeStyles:()=>et,createPopper:()=>Dt,createPopperBase:()=>Lt,createPopperLite:()=>$t,detectOverflow:()=>mt,end:()=>c,eventListeners:()=>nt,flip:()=>_t,hide:()=>yt,left:()=>o,main:()=>y,modifierPhases:()=>T,offset:()=>wt,placements:()=>g,popper:()=>d,popperGenerator:()=>kt,popperOffsets:()=>At,preventOverflow:()=>Et,read:()=>_,reference:()=>f,right:()=>s,start:()=>l,top:()=>i,variationPlacements:()=>p,viewport:()=>u,write:()=>E});var 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NAME(){return"carousel"}next(){this._slide(Fe)}nextWhenVisible(){!document.hidden&&Wt(this._element)&&this.next()}prev(){this._slide(Be)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?de.one(this._element,Ve,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void de.one(this._element,Ve,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?Fe:Be;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return 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l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?Be:Fe:t===ze?Fe:Be}_orderToDirection(t){return Kt()?t===Be?ze:qe:t===Be?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}de.on(document,Ge,"[data-bs-slide], 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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",de.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(de.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),de.trigger(this._element,ui)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}de.on(document,di,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=be.getDataAttributes(this._element);for(const t of Object.keys(e))es.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=ps.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(ps);const gs=".bs.scrollspy",ms=`activate${gs}`,_s=`click${gs}`,bs=`load${gs}.data-api`,vs="active",ys="[href]",ws=".nav-link",As=`${ws}, .nav-item > ${ws}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Cs={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ye{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return Es}static get DefaultType(){return Cs}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(de.off(this._config.target,_s),de.on(this._config.target,_s,ys,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ke.find(ys,this._config.target);for(const e of t){if(!e.hash||Ft(e))continue;const t=ke.findOne(e.hash,this._element);Wt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(vs),this._activateParents(t),de.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ke.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(vs);else for(const e of ke.parents(t,".nav, .list-group"))for(const t of ke.prev(e,As))t.classList.add(vs)}_clearActiveClass(t){t.classList.remove(vs);const e=ke.find(`${ys}.${vs}`,t);for(const t of e)t.classList.remove(vs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(window,bs,(()=>{for(const t of ke.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Os=".bs.tab",xs=`hide${Os}`,ks=`hidden${Os}`,Ls=`show${Os}`,Ds=`shown${Os}`,$s=`click${Os}`,Ss=`keydown${Os}`,Is=`load${Os}`,Ns="ArrowLeft",Ps="ArrowRight",js="ArrowUp",Ms="ArrowDown",Hs="active",Ws="fade",Fs="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,Rs=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Vs extends ye{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),de.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?de.trigger(e,xs,{relatedTarget:t}):null;de.trigger(t,Ls,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),de.trigger(t,Ds,{relatedTarget:e})):t.classList.add(Fs)}),t,t.classList.contains(Ws)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),de.trigger(t,ks,{relatedTarget:e})):t.classList.remove(Fs)}),t,t.classList.contains(Ws)))}_keydown(t){if(![Ns,Ps,js,Ms].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Ps,Ms].includes(t.key),i=Ut(this._getChildren().filter((t=>!Ft(t))),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return ke.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Pt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`#${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=ke.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Hs),n(".dropdown-menu",Fs),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Hs)}_getInnerElement(t){return t.matches(qs)?t:ke.findOne(qs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(document,$s,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this)||Vs.getOrCreateInstance(this).show()})),de.on(window,Is,(()=>{for(const t of ke.find(Rs))Vs.getOrCreateInstance(t)})),Qt(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends ye{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){de.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),qt(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),de.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(de.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),de.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){de.on(this._element,Qs,(t=>this._onInteraction(t,!0))),de.on(this._element,Xs,(t=>this._onInteraction(t,!1))),de.on(this._element,Ys,(t=>this._onInteraction(t,!0))),de.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}var ao;we(ro),Qt(ro),ao=function(){[].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')).map((function(t){return new us(t,{delay:{show:500,hide:100}})}))},"loading"!=document.readyState?ao():document.addEventListener("DOMContentLoaded",ao)})();
+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.LICENSE.txt b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.LICENSE.txt
new file mode 100644
index 00000000..91ad10aa
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.LICENSE.txt
@@ -0,0 +1,5 @@
+/*!
+  * Bootstrap v5.2.3 (https://getbootstrap.com/)
+  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
diff --git a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.map b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.map
new file mode 100644
index 00000000..d83e2f7c
--- /dev/null
+++ b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.map
@@ -0,0 +1 @@
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= 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (process.env.NODE_ENV !== \"production\") {\n    if (!isHTMLElement(arrowElement)) {\n      console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n    }\n\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n  if (process.env.NODE_ENV !== \"production\") {\n    var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n    if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n      return transitionProperty.indexOf(property) >= 0;\n    })) {\n      console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n    }\n  }\n\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If  has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on \n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n    }\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        }); // Validate the provided modifiers so that the consumer will get warned\n        // if one of the modifiers is invalid for any reason\n\n        if (process.env.NODE_ENV !== \"production\") {\n          var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n            var name = _ref.name;\n            return name;\n          });\n          validateModifiers(modifiers);\n\n          if (getBasePlacement(state.options.placement) === auto) {\n            var flipModifier = state.orderedModifiers.find(function (_ref2) {\n              var name = _ref2.name;\n              return name === 'flip';\n            });\n\n            if (!flipModifier) {\n              console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n            }\n          }\n\n          var _getComputedStyle = getComputedStyle(popper),\n              marginTop = _getComputedStyle.marginTop,\n              marginRight = _getComputedStyle.marginRight,\n              marginBottom = _getComputedStyle.marginBottom,\n              marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n          // cause bugs with positioning, so we'll warn the consumer\n\n\n          if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n            return parseFloat(margin);\n          })) {\n            console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n          }\n        }\n\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          if (process.env.NODE_ENV !== \"production\") {\n            console.error(INVALID_ELEMENT_ERROR);\n          }\n\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n        var __debug_loops__ = 0;\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (process.env.NODE_ENV !== \"production\") {\n            __debug_loops__ += 1;\n\n            if (__debug_loops__ > 100) {\n              console.error(INFINITE_LOOP_ERROR);\n              break;\n            }\n          }\n\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      if (process.env.NODE_ENV !== \"production\") {\n        console.error(INVALID_ELEMENT_ERROR);\n      }\n\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref3) {\n        var name = _ref3.name,\n            _ref3$options = _ref3.options,\n            options = _ref3$options === void 0 ? {} : _ref3$options,\n            effect = _ref3.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.2.3 (https://getbootstrap.com/)\n  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n\n  return prefix;\n};\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n  }\n\n  return selector;\n};\n\nconst getSelectorFromElement = element => {\n  const selector = getSelector(element);\n\n  if (selector) {\n    return document.querySelector(selector) ? selector : null;\n  }\n\n  return null;\n};\n\nconst getElementFromSelector = element => {\n  const selector = getSelector(element);\n  return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  } // Get transition-duration of the element\n\n\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  } // If multiple durations are defined, take the first\n\n\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n\n  return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(object);\n  }\n\n  return null;\n};\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n  const closedDetails = element.closest('details:not([open])');\n\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n\n    if (summary === null) {\n      return false;\n    }\n  }\n\n  return elementIsVisible;\n};\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  } // Can find the shadow root otherwise it'll return the document\n\n\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element;\n  } // when we don't find a shadow root\n\n\n  if (!element.parentNode) {\n    return null;\n  }\n\n  return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n\n  return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\n\nconst execute = callback => {\n  if (typeof callback === 'function') {\n    callback();\n  }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n\n  index += shouldGetNext ? 1 : -1;\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n\n    return fn.apply(element, [event]);\n  };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n\n  return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n\n    callable = wrapFunction(callable);\n  }\n\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n  if (!fn) {\n    return;\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n\n  for (const handlerKey of Object.keys(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      const event = storeElementEvent[handlerKey];\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n\n    for (const keyHandlers of Object.keys(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        const event = storeElementEvent[keyHandlers];\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n\n    let evt = new Event(event, {\n      bubbles,\n      cancelable: true\n    });\n    evt = hydrateObj(evt, args);\n\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n\n    return evt;\n  }\n\n};\n\nfunction hydrateObj(obj, meta) {\n  for (const [key, value] of Object.entries(meta || {})) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n\n        get() {\n          return value;\n        }\n\n      });\n    }\n  }\n\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n\n    const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n\n    instanceMap.set(key, instance);\n  },\n\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n\n    return null;\n  },\n\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n\n  if (value === 'false') {\n    return false;\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n\n  if (value === '' || value === 'null') {\n    return null;\n  }\n\n  if (typeof value !== 'string') {\n    return value;\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n\n    return attributes;\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n\n  static get DefaultType() {\n    return {};\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  }\n\n  _configAfterMerge(config) {\n    return config;\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return { ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const property of Object.keys(configTypes)) {\n      const expectedTypes = configTypes[property];\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n\n    if (!element) {\n      return;\n    }\n\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  } // Public\n\n\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  } // Static\n\n\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n\n  static get VERSION() {\n    return VERSION;\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n\n    if (isDisabled(this)) {\n      return;\n    }\n\n    const target = getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  } // Public\n\n\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  } // Private\n\n\n  _destroyElement() {\n    this._element.remove();\n\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](this);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  } // Public\n\n\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n\n    return parents;\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n\n      previous = previous.previousElementSibling;\n    }\n\n    return [];\n  },\n\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n\n      next = next.nextElementSibling;\n    }\n\n    return [];\n  },\n\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n\n    this._initEvents();\n  } // Getters\n\n\n  static get Default() {\n    return Default$c;\n  }\n\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n\n  static get NAME() {\n    return NAME$d;\n  } // Public\n\n\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  } // Private\n\n\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n\n    this._handleSwipe();\n\n    execute(this._config.endCallback);\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n\n    if (!direction) {\n      return;\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  } // Static\n\n\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n    this._addEventListeners();\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$b;\n  }\n\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n\n  static get NAME() {\n    return NAME$c;\n  } // Public\n\n\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n\n    this._clearInterval();\n  }\n\n  cycle() {\n    this._clearInterval();\n\n    this._updateInterval();\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n\n    this.cycle();\n  }\n\n  to(index) {\n    const items = this._getItems();\n\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive());\n\n    if (activeIndex === index) {\n      return;\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n    this._slide(order, items[index]);\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n\n    super.dispose();\n  } // Private\n\n\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      } // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n\n      this.pause();\n\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key];\n\n    if (direction) {\n      event.preventDefault();\n\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n\n    if (!element) {\n      return;\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n\n    const activeElement = this._getActive();\n\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n    if (nextElement === activeElement) {\n      return;\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement);\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // todo: change tests that use empty divs to avoid this check\n      return;\n    }\n\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n\n    this._setActiveIndicatorElement(nextElementIndex);\n\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n\n  if (slideIndex) {\n    carousel.to(slideIndex);\n\n    carousel._maybeEnableCycle();\n\n    return;\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n\n    carousel._maybeEnableCycle();\n\n    return;\n  }\n\n  carousel.prev();\n\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n    for (const elem of toggleList) {\n      const selector = getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n\n    this._initializeChildren();\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$a;\n  }\n\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n\n  static get NAME() {\n    return NAME$b;\n  } // Public\n\n\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n\n    let activeChildren = []; // find active children\n\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n\n    const dimension = this._getDimension();\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n    this._element.style[dimension] = 0;\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n    this._isTransitioning = true;\n\n    const complete = () => {\n      this._isTransitioning = false;\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n\n    this._queueCallback(complete, this._element, true);\n\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n\n    const dimension = this._getDimension();\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n    for (const trigger of this._triggerArray) {\n      const element = getElementFromSelector(trigger);\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n\n    this._isTransitioning = true;\n\n    const complete = () => {\n      this._isTransitioning = false;\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n\n    this._element.style[dimension] = '';\n\n    this._queueCallback(complete, this._element, true);\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  } // Private\n\n\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n\n    config.parent = getElement(config.parent);\n    return config;\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n    for (const element of children) {\n      const selected = getElementFromSelector(element);\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    const _config = {};\n\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for  elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n\n  const selector = getSelectorFromElement(this);\n  const selectorElements = SelectorEngine.find(selector);\n\n  for (const element of selectorElements) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  } // Getters\n\n\n  static get Default() {\n    return Default$9;\n  }\n\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n\n  static get NAME() {\n    return NAME$a;\n  } // Public\n\n\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._createPopper(); // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n\n    this._element.focus();\n\n    this._element.setAttribute('aria-expanded', true);\n\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n\n    this._completeHide(relatedTarget);\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n\n    super.dispose();\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar();\n\n    if (this._popper) {\n      this._popper.update();\n    }\n  } // Private\n\n\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    } // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy();\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n    this._element.setAttribute('aria-expanded', 'false');\n\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config);\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n\n    return config;\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n\n    let referenceElement = this._element;\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n\n    const popperConfig = this._getPopperConfig();\n\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent;\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    } // We need to trim the value because custom properties can also include spaces\n\n\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n\n    return offset;\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n\n    return { ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    };\n  }\n\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n    if (!items.length) {\n      return;\n    } // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n\n      context._completeHide(relatedTarget);\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n\n    event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n\n      instance._selectMenuItem(event);\n\n      return;\n    }\n\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  } // Public\n\n\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n\n  hide() {\n    const width = this.getWidth();\n\n    this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0;\n  } // Private\n\n\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n\n    this._element.style.overflow = 'hidden';\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n\n      this._saveInitialAttribute(element, styleProperty);\n\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  } // Getters\n\n\n  static get Default() {\n    return Default$8;\n  }\n\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n\n  static get NAME() {\n    return NAME$9;\n  } // Public\n\n\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n\n    this._append();\n\n    const element = this._getElement();\n\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n\n    element.classList.add(CLASS_NAME_SHOW$5);\n\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n    this._element.remove();\n\n    this._isAppended = false;\n  } // Private\n\n\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n\n      this._element = backdrop;\n    }\n\n    return this._element;\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n\n    const element = this._getElement();\n\n    this._config.rootElement.append(element);\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  } // Getters\n\n\n  static get Default() {\n    return Default$7;\n  }\n\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n\n  static get NAME() {\n    return NAME$8;\n  } // Public\n\n\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  } // Private\n\n\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement);\n\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n\n    this._addEventListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default$6;\n  }\n\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n\n  static get NAME() {\n    return NAME$7;\n  } // Public\n\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = true;\n    this._isTransitioning = true;\n\n    this._scrollBar.hide();\n\n    document.body.classList.add(CLASS_NAME_OPEN);\n\n    this._adjustDialog();\n\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = false;\n    this._isTransitioning = true;\n\n    this._focustrap.deactivate();\n\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n\n  dispose() {\n    for (const htmlElement of [window, this._dialog]) {\n      EventHandler.off(htmlElement, EVENT_KEY$4);\n    }\n\n    this._backdrop.dispose();\n\n    this._focustrap.deactivate();\n\n    super.dispose();\n  }\n\n  handleUpdate() {\n    this._adjustDialog();\n  } // Private\n\n\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n\n    this._element.style.display = 'block';\n\n    this._element.removeAttribute('aria-hidden');\n\n    this._element.setAttribute('aria-modal', true);\n\n    this._element.setAttribute('role', 'dialog');\n\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n\n      if (this._config.keyboard) {\n        event.preventDefault();\n        this.hide();\n        return;\n      }\n\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n\n          return;\n        }\n\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none';\n\n    this._element.setAttribute('aria-hidden', true);\n\n    this._element.removeAttribute('aria-modal');\n\n    this._element.removeAttribute('role');\n\n    this._isTransitioning = false;\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n\n      this._resetAdjustments();\n\n      this._scrollBar.reset();\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC);\n\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n\n    this._element.focus();\n  }\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n    const scrollbarWidth = this._scrollBar.getWidth();\n\n    const isBodyOverflowing = scrollbarWidth > 0;\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  } // Static\n\n\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](relatedTarget);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  }); // avoid conflict when clicking modal toggler while another one is open\n\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n\n    this._addEventListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default$5;\n  }\n\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n\n  static get NAME() {\n    return NAME$6;\n  } // Public\n\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = true;\n\n    this._backdrop.show();\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n\n    this._element.setAttribute('aria-modal', true);\n\n    this._element.setAttribute('role', 'dialog');\n\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._focustrap.deactivate();\n\n    this._element.blur();\n\n    this._isShown = false;\n\n    this._element.classList.add(CLASS_NAME_HIDING);\n\n    this._backdrop.hide();\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n      this._element.removeAttribute('aria-modal');\n\n      this._element.removeAttribute('role');\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n\n    this._queueCallback(completeCallback, this._element, true);\n  }\n\n  dispose() {\n    this._backdrop.dispose();\n\n    this._focustrap.deactivate();\n\n    super.dispose();\n  } // Private\n\n\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n\n      this.hide();\n    }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n\n      if (!this._config.keyboard) {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n\n      this.hide();\n    });\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](this);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  if (isDisabled(this)) {\n    return;\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n    }\n\n    return true;\n  } // Check if a regular expression validates the attribute.\n\n\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '
'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  } // Getters\n\n\n  static get Default() {\n    return Default$4;\n  }\n\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n\n  static get NAME() {\n    return NAME$5;\n  } // Public\n\n\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n\n  changeContent(content) {\n    this._checkContent(content);\n\n    this._config.content = { ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n\n    const template = templateWrapper.children[0];\n\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n\n    return template;\n  } // Private\n\n\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n\n    this._checkContent(config.content);\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n\n    if (!templateElement) {\n      return;\n    }\n\n    content = this._resolvePossibleFunction(content);\n\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n\n      return;\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n\n    templateElement.textContent = content;\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n\n  _resolvePossibleFunction(arg) {\n    return typeof arg === 'function' ? arg(this) : arg;\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n\n    templateElement.textContent = element.textContent;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 0],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '
' + '
' + '
' + '
',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n\n    super(element, config); // Private\n\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null; // Protected\n\n    this.tip = null;\n\n    this._setListeners();\n\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$3;\n  }\n\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n\n  static get NAME() {\n    return NAME$4;\n  } // Public\n\n\n  enable() {\n    this._isEnabled = true;\n  }\n\n  disable() {\n    this._isEnabled = false;\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click;\n\n    if (this._isShown()) {\n      this._leave();\n\n      return;\n    }\n\n    this._enter();\n  }\n\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n\n    this._disposePopper();\n\n    super.dispose();\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    } // todo v6 remove this OR make it optional\n\n\n    this._disposePopper();\n\n    const tip = this._getTipElement();\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n    const {\n      container\n    } = this._config;\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n      if (this._isHovered === false) {\n        this._leave();\n      }\n\n      this._isHovered = false;\n    };\n\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const tip = this._getTipElement();\n\n    tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n\n      this._element.removeAttribute('aria-describedby');\n\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  } // Protected\n\n\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n\n    return this.tip;\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n    if (!tip) {\n      return null;\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n\n    return tip;\n  }\n\n  setContent(content) {\n    this._newContent = content;\n\n    if (this._isShown()) {\n      this._disposePopper();\n\n      this.show();\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({ ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n\n    return this._templateFactory;\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  } // Private\n\n\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n\n  _createPopper(tip) {\n    const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n\n    return offset;\n  }\n\n  _resolvePossibleFunction(arg) {\n    return typeof arg === 'function' ? arg.call(this._element) : arg;\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return { ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    };\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n          context._leave();\n        });\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n\n    if (!title) {\n      return;\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n    this._element.removeAttribute('title');\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n\n    this._isHovered = true;\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n\n    this._isHovered = false;\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n\n    config = { ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n\n    return config;\n  }\n\n  _getDelegateConfig() {\n    const config = {};\n\n    for (const key in this._config) {\n      if (this.constructor.Default[key] !== this._config[key]) {\n        config[key] = this._config[key];\n      }\n    }\n\n    config.selector = false;\n    config.trigger = 'manual'; // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n\n    return config;\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n\n      this._popper = null;\n    }\n\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '
' + '
' + '
' + '
' + '
',\n  trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n\n  static get NAME() {\n    return NAME$3;\n  } // Overrides\n\n\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  } // Private\n\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  } // Getters\n\n\n  static get Default() {\n    return Default$1;\n  }\n\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n\n  static get NAME() {\n    return NAME$2;\n  } // Public\n\n\n  refresh() {\n    this._initializeTargetsAndObservables();\n\n    this._maybeEnableSmoothScroll();\n\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect();\n\n    super.dispose();\n  } // Private\n\n\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n\n    return config;\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    } // unregister any previous listeners\n\n\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        } // Chrome 60 doesn't support `scrollTo`\n\n\n        root.scrollTop = height;\n      }\n    });\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  } // The logic of selection\n\n\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n      this._process(targetElement(entry));\n    };\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n\n        this._clearActiveClass(targetElement(entry));\n\n        continue;\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n        if (!parentScrollTop) {\n          return;\n        }\n\n        continue;\n      } // if we are scrolling up, pick the smallest offsetTop\n\n\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n\n      const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(anchor.hash, anchor);\n\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n\n    this._clearActiveClass(this._config.target);\n\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n\n    this._activateParents(target);\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both 
 
     
diff --git a/docs/pygom-doc/_build/html/intro.html b/docs/pygom-doc/_build/html/intro.html
index 21a47ace..6f2b3597 100644
--- a/docs/pygom-doc/_build/html/intro.html
+++ b/docs/pygom-doc/_build/html/intro.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Welcome to the documentation for PyGOM — PyGOM documentation
   
@@ -168,6 +168,11 @@
 
  • Hard Problem
    • 
 
 
+
  • Simple Epidemic Analysis
    • 
+
  • Reading and using EpiJSON data
    • 
+
  • Solving Boundary Value Problems
    • 
+
  • Gradient estimation under square loss
    • 
+
  • Example: Fitz Hugh
    • 
 
 
     
@@ -395,6 +400,11 @@ 
    Welcome to the documentation for PyGOMTransition Object
 
  • Stochastic representation of ODEs
    • 
 
  • Convert ODE into transitions
    • 
+
  • Simple Epidemic Analysis
    • 
+
  • Reading and using EpiJSON data
    • 
+
  • Solving Boundary Value Problems
    • 
+
  • Gradient estimation under square loss
    • 
+
  • Example: Fitz Hugh
    • 
 
 
 
diff --git a/docs/pygom-doc/_build/html/markdown.html b/docs/pygom-doc/_build/html/markdown.html
index 05a09819..38e51878 100644
--- a/docs/pygom-doc/_build/html/markdown.html
+++ b/docs/pygom-doc/_build/html/markdown.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Markdown Files — PyGOM documentation
   
@@ -163,12 +163,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
diff --git a/docs/pygom-doc/_build/html/md/getting_started.html b/docs/pygom-doc/_build/html/md/getting_started.html
index 765c0be6..731460dc 100644
--- a/docs/pygom-doc/_build/html/md/getting_started.html
+++ b/docs/pygom-doc/_build/html/md/getting_started.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Getting started — PyGOM documentation
   
@@ -165,12 +165,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
diff --git a/docs/pygom-doc/_build/html/md/unrollOde.html b/docs/pygom-doc/_build/html/md/unrollOde.html
index 59b8a6eb..9c8bb3d7 100644
--- a/docs/pygom-doc/_build/html/md/unrollOde.html
+++ b/docs/pygom-doc/_build/html/md/unrollOde.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Convert ODE into transitions — PyGOM documentation
   
@@ -169,6 +169,11 @@
 
  • Hard Problem
    • 
 
 
+
  • Simple Epidemic Analysis
    • 
+
  • Reading and using EpiJSON data
    • 
+
  • Solving Boundary Value Problems
    • 
+
  • Gradient estimation under square loss
    • 
+
  • Example: Fitz Hugh
    • 
 
 
     
diff --git a/docs/pygom-doc/_build/html/mdconvert/common_models/.html b/docs/pygom-doc/_build/html/mdconvert/common_models/.html
index 4217db16..80e32cd3 100644
--- a/docs/pygom-doc/_build/html/mdconvert/common_models/.html
+++ b/docs/pygom-doc/_build/html/mdconvert/common_models/.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     .SIR_Birth_Death{.interpreted-text role=”func”} — PyGOM documentation
   
@@ -163,12 +163,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
diff --git a/docs/pygom-doc/_build/html/mdconvert/doc_to_sort/.html b/docs/pygom-doc/_build/html/mdconvert/doc_to_sort/.html
index 325aa637..4fb4d738 100644
--- a/docs/pygom-doc/_build/html/mdconvert/doc_to_sort/.html
+++ b/docs/pygom-doc/_build/html/mdconvert/doc_to_sort/.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Solving Boundary Value Problems {#bvpSimple} — PyGOM documentation
   
@@ -163,12 +163,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
@@ -383,7 +388,7 @@ 
    Solving Boundary Value Problems {#bvpSimple}1, will be shown here.
    
+examples, both from MATLAB[1], will be shown here.
    
 
    
 
    Simple model 1#
    
 
    We are trying to find the solution to the second order differential
@@ -438,11 +443,10 @@ 
    Simple model 2
-
    
-
    1
    
-
    http://uk.mathworks.com/help/matlab/ref/bvp4c.html
    
-
    
-
    
+
 
    
 
 
diff --git a/docs/pygom-doc/_build/html/mdconvert/mod/.html b/docs/pygom-doc/_build/html/mdconvert/mod/.html
index d321a8c1..c21b9e60 100644
--- a/docs/pygom-doc/_build/html/mdconvert/mod/.html
+++ b/docs/pygom-doc/_build/html/mdconvert/mod/.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     stochastic — PyGOM documentation
   
@@ -161,12 +161,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
diff --git a/docs/pygom-doc/_build/html/mdconvert/unroll/.html b/docs/pygom-doc/_build/html/mdconvert/unroll/.html
index d6121664..384f7963 100644
--- a/docs/pygom-doc/_build/html/mdconvert/unroll/.html
+++ b/docs/pygom-doc/_build/html/mdconvert/unroll/.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Simple Problem {#unrollSimple} — PyGOM documentation
   
@@ -163,12 +163,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
diff --git a/docs/pygom-doc/_build/html/notebooks/bvpSimple.html b/docs/pygom-doc/_build/html/notebooks/bvpSimple.html
new file mode 100644
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+
+
+
+
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+  
+    
+    
+
+    Solving Boundary Value Problems — PyGOM documentation
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+              
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+
    
+    
    Solving Boundary Value Problems
    
+    
+    
    
+        
    
+            
+            
    
+                
     Contents 
    
+            
    
+            
+        
    
+    
    
+
    
+
+              
+                
+
+                
    
+                  
+  
    
+
    Solving Boundary Value Problems#
    
+
    In addition to finding solutions for an IVP and estimate the unknown
+parameters, this package also allows you to solve BVP with a little bit
+of imagination. Here, we are going to show how a BVP can be solved by
+treating it as a parameter estimation problem. Essentially, a shooting
+method where the first boundary condition defines the initial condition
+of an IVP and the second boundary condition is an observation. Two
+examples, both from MATLAB[1], will be shown here.
    
+
    
+
    Simple model 1#
    
+
    We are trying to find the solution to the second order differential
+equation
    
+
    
+\[\nabla^{2} y + |y| = 0\]
    
+
    subject to the boundary conditions \(y(0) = 0\) and \(y(4) = -2\). Convert
+this into a set of first order ODE
    
+
    
+\[\begin{split}\begin{aligned}
+\frac{d y_{0}}{dt} &= y_{1} \\
+\frac{d y_{1}}{dt} &= -|y_{0}|
+\end{aligned}\end{split}\]
    
+
    using an auxiliary variable \(y_{1} = \nabla y\) and \(y_{0} = y\). Setting
+up the system below
    
+
    In [1]: from pygom import Transition, TransitionType,
+DeterministicOde, SquareLoss
    
+
    In [1]: import matplotlib.pyplot as plt
    
+
    In [2]: stateList = [‘y0’, ‘y1’]
    
+
    In [3]: paramList = []
    
+
    In [4]: ode1 = Transition(origin=’y0’,
    
+…: equation=’y1’, …: transition_type=TransitionType.ODE)
    
+
    In [5]: ode2 = Transition(origin=’y1’,
    
+…: equation=’-abs(y0)’, …: transition_type=TransitionType.ODE)
    
+
    In [6]: model = DeterministicOde(stateList,
    
+…: paramList, …: ode=[ode1, ode2])
    
+
    In [7]: model.get_ode_eqn()
    
+
    We check that the equations are correct before proceeding to set up our
+loss function.
    
+
    In [1]: import numpy
    
+
    In [2]: from scipy.optimize import minimize
    
+
    In [3]: initialState = [0.0, 1.0]
    
+
    In [4]: t = numpy.linspace(0, 4, 100)
    
+
    In [5]: model.initial_values = (initialState, t[0])
    
+
    In [6]: solution = model.integrate(t[1::])
    
+
    In [7]: f = plt.figure()
    
+
    @savefig bvp1_random_guess_plot.png In [8]: model.plot()
    
+
    In [9]: plt.close()
    
+
    Setting up the second boundary condition \(y(4) = -2\) is easy, because
+that is just a single observation attached to the state \(y_{1}\).
+Enforcing the first boundary condition requires us to set it as the
+initial condition. Because the condition only states that \(y(0) = 0\),
+the starting value of the other state \(y_1\) is free. We let our loss
+object know that it is free through the targetState input argument.
    
+
    In [10]: theta = [0.0]
    
+
    In [11]: obj = SquareLoss(theta=theta,
    
+.…: ode=model, .…: x0=initialState, .…: t0=t[0], .…: t=t[-1], .…:
+y=[-2], .…: state_name=[‘y0’], .…: target_state=[‘y1’])
    
+
    In [12]: thetaHat = minimize(fun=obj.costIV, x0=[0.0])
    
+
    In [13]: print(thetaHat)
    
+
    In [14]: model.initial_values = ([0.0] + thetaHat[‘x’].tolist(),
+t[0])
    
+
    In [15]: solution = model.integrate(t[1::])
    
+
    In [16]: f = plt.figure()
    
+
    @savefig bvp1_solution_plot.png In [17]: model.plot()
    
+
    In [18]: plt.close()
    
+
    We are going to visualize the solution, and also check the boundary
+condition. The first became our initial condition, so it is always
+satisfied and only the latter is of concern, which is zero (subject to
+numerical error) from thetaHat.
    
+
    
+
    
+
    Simple model 2#
    
+
    Our second example is different as it involves an actual parameter and
+also time. We have the Mathieu’s Equation
    
+
    
+\[\nabla^{2} y + \left(p - 2q \cos(2x)\right)y = 0\]
    
+
    and the aim is to compute the fourth eigenvalue \(q=5\). There are three
+boundary conditions
    
+
    
+\[\nabla y(0) = 0, \quad \nabla y(\pi) = 0, \quad y(0) = 1\]
    
+
    and we aim to solve it by converting it to a first order ODE and tackle
+it as an IVP. As our model object does not allow the use of the time
+component in the equations, we introduce a anxiliary state \(\tau\) that
+replaces time \(t\). Rewrite the equations using
+\(y_{0} = y, y_{1} = \nabla y\) and define our model as
    
+
    In [1]: stateList = [‘y0’, ‘y1’, ‘tau’]
    
+
    In [2]: paramList = [‘p’]
    
+
    In [3]: ode1 = Transition(‘y0’, ‘y1’, TransitionType.ODE)
    
+
    In [4]: ode2 = Transition(‘y1’, ‘-(p - 2*5*cos(2*tau))*y0’,
+TransitionType.ODE)
    
+
    In [5]: ode3 = Transition(‘tau’, ‘1’, TransitionType.ODE)
    
+
    In [6]: model = DeterministicOde(stateList, paramList, ode=[ode1,
+ode2, ode3])
    
+
    In [7]: theta = [1.0, 1.0, 0.0]
    
+
    In [8]: p = 15.0
    
+
    In [9]: t = numpy.linspace(0, numpy.pi)
    
+
    In [10]: model.parameters = [(‘p’,p)]
    
+
    In [11]: model.initial_values = (theta, t[0])
    
+
    In [12]: solution = model.integrate(t[1::])
    
+
    In [13]: f = plt.figure()
    
+
    @savefig bvp2_random_guess_plot.png In [14]: model.plot()
    
+
    In [15]: plt.close()
    
+
    Now we are ready to setup the estimation. Like before, we setup the
+second boundary condition by pretending that it is an observation. We
+have all the initial conditions defined by the first boundary condition
    
+
    In [1]: obj = SquareLoss(15.0, model, x0=[1.0, 0.0, 0.0], t0=0.0,
+t=numpy.pi, y=0.0, state_name=’y1’)
    
+
    In [2]: xhatObj = minimize(obj.cost,[15])
    
+
    In [3]: print(xhatObj)
    
+
    In [4]: model.parameters = [(‘p’, xhatObj[‘x’][0])]
    
+
    In [5]: model.initial_values = ([1.0, 0.0, 0.0], t[0])
    
+
    In [5]: solution = model.integrate(t[1::])
    
+
    In [6]: f = plt.figure()
    
+
    @savefig bvp2_solution_plot.png In [7]: model.plot()
    
+
    In [8]: plt.close()
    
+
    The plot of the solution shows the path that satisfies all boundary
+condition. The last subplot is time which obvious is redundant here but
+the DeterministicOde.plot method is not yet able to recognize the time
+component. Possible speed up can be achieved through the use of
+derivative information or via root finding method that tackles the
+gradient directly, instead of the cost function.
    
+
    Reference
    
+
    [1] http://uk.mathworks.com/help/matlab/ref/bvp4c.html
    
+
    
+
    
+
+    
+    
+
+                
    
+              
+
+              
+              
+                
+              
+            
    
+            
+            
+              
+                
    
+
+  
    
+  
    
+     Contents
+  
    
+  
    
+
+
    
+              
+            
+          
    
+          
    
+            
+
    
+  
+  
    
+    
+
    
+By Public Health England / UK Health Security Agency
+
    
+
+  
    
+  
+  
    
+    
+  
    
+    
+      © Copyright 2022.
+      
    
+    
+  
    
+
+  
    
+  
+  
    
+    
+  
    
+  
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+    
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+        
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+    
    
+  
    
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+
+  
    
+  
    
+  
+
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/notebooks/epi.html b/docs/pygom-doc/_build/html/notebooks/epi.html
new file mode 100644
index 00000000..3bc258be
--- /dev/null
+++ b/docs/pygom-doc/_build/html/notebooks/epi.html
@@ -0,0 +1,553 @@
+
+
+
+
+
+
+
+  
+    
+    
+
+    Simple Epidemic Analysis — PyGOM documentation
+  
+  
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+
    
+      
+    
    
+  
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+
    
+              
+              
+
+
    
+    
    Simple Epidemic Analysis
    
+    
+    
    
+        
    
+            
+            
    
+                
     Contents 
    
+            
    
+            
+        
    
+    
    
+
    
+
+              
+                
+
+                
    
+                  
+  
    
+
    Simple Epidemic Analysis#
    
+
    A common application of ordinary differential equations is in the field
+of epidemiology modeling. More concretely, compartmental models that is
+used to describe disease progression. We demonstrate some of the simple
+algebraic analysis one may wish to take when given a compartment model.
+Our use one of the simplest model, an SIR model with birth and death
+processes, which is an extension of the one in sir. First, we
+initialize the model below.
    
+
    In [1]: from pygom import common_models
    
+
    In [2]: ode = common_models.SIR_Birth_Death()
    
+
    In [3]: print(ode.get_ode_eqn())
    
+
    
+
    Obtaining the R0#
    
+
    The reproduction number, also known as the \(R_{0}\), is the single most
+powerful piece and reduced piece of information available from a
+compartmental model. In a nutshell, it provides a single number - if the
+parameters are known - which can the intuitive interpretation where
+\(R_{0} = 1\) defines the tipping point of an outbreak. A \(R_{0}\) value of
+more than one signifies an potential outbreak where less than one
+indicates that the disease will stop spreading naturally.
    
+
    To obtain the \(R_{0}\), we simply have to tell the function which states
+represent the disease state, which in this case is the state I.
    
+
    In [1]: from pygom.model.epi_analysis import *
    
+
    In [2]: print(R0(ode, ‘I’))
    
+
    
+
    
+
    Algebraic R0#
    
+
    We may also wish to get the \(R_{0}\) in pure algebraic term. This can be
+achieved by the following few lines. Note that the result below is
+slightly different from the one above. The difference is due to the
+internal working of the functions, where getR0 computes the
+disease-free equilibrium value for the states and substitute them back
+into the equation.
    
+
    In [1]: F, V = disease_progression_matrices(ode, ‘I’)
    
+
    In [2]: e = R0_from_matrix(F, V)
    
+
    In [3]: print(e)
    
+
    To replicate the output before, we have to find the values where the
+disease-free equilibrium will be achieved. Substitution can then be
+performed to retrieve \(R_{0}\) in pure parameters.
    
+
    In [1]: dfe = DFE(ode, [‘I’])
    
+
    In [2]: print(dfe)
    
+
    In [3]: print(e[0].subs(dfe))
    
+
    
+
    
+
+    
+    
+
+                
    
+              
+
+              
+              
+                
+              
+            
    
+            
+            
+              
+                
    
+
+  
    
+  
    
+     Contents
+  
    
+  
    
+
+
    
+              
+            
+          
    
+          
    
+            
+
    
+  
+  
    
+    
+
    
+By Public Health England / UK Health Security Agency
+
    
+
+  
    
+  
+  
    
+    
+  
    
+    
+      © Copyright 2022.
+      
    
+    
+  
    
+
+  
    
+  
+  
    
+    
+  
    
+  
+  
    
+    
+  
    
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+        
+
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+    
    
+  
    
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+  
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+  
    
+  
    
+  
+
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/notebooks/epijson.html b/docs/pygom-doc/_build/html/notebooks/epijson.html
new file mode 100644
index 00000000..88de1cca
--- /dev/null
+++ b/docs/pygom-doc/_build/html/notebooks/epijson.html
@@ -0,0 +1,520 @@
+
+
+
+
+
+
+
+  
+    
+    
+
+    Reading and using EpiJSON data — PyGOM documentation
+  
+  
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+              
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+
    
+    
    Reading and using EpiJSON data
    
+    
+    
    
+        
    
+            
+        
    
+    
    
+
    
+
+              
+                
+
+                
    
+                  
+  
    
+
    Reading and using EpiJSON data#
    
+
    Epidemiology data is complicated due to the many different stages a
+patient can go through and whether a modeling technique is applicable
+depends heavily on the recording of data. EpiJSON is a framework whih
+tries to captures all the information [Finnie2016], in a JSON
+format as the name suggests.
    
+
    This package provides the functionality to process EpiJSON data. Due to
+the nature of this package, modeling of ode, it processes the data file
+with this in mind. The output is therefore in the cumulative form as
+default, shown below, in a pandas.DataFrame format. The input can be
+in a string format, a file or already a dict.
    
+
    In [1]: from pygom.loss.read_epijson import epijson_to_data_frame
    
+
    In [2]: import pkgutil
    
+
    In [3]: data = pkgutil.get_data(‘pygom’, ‘data/eg1.json’)
    
+
    In [3]: df = epijson_to_data_frame(data)
    
+
    In [4]: print(df)
    
+
    Given that the aim of loading the data is usually for model fitting, we
+allow EpiJSON as input directly to the loss class
+pygom.loss.EpijsonLoss which uses the Poisson loss under the hood.
    
+
    In [1]: from pygom.model import common_models
    
+
    In [2]: from pygom.loss.epijson_loss import EpijsonLoss
    
+
    In [3]: ode = common_models.SIR([0.5, 0.3])
    
+
    In [4]: obj = EpijsonLoss([0.005, 0.03], ode, data, ‘Death’, ‘R’,
+[300, 2, 0])
    
+
    In [5]: print(obj.cost())
    
+
    In [6]: print(obj._df)
    
+
    Given an initialized object, all the operations are inherited from
+pygom.loss.BaseLoss. We demonstrated above how to calculate the cost
+and the rest will not be shown for brevity. The data frame is stored
+inside of the loss object and can be retrieved for inspection at any
+time point.
    
+
    Rather unfortunately, initial values for the states is still required,
+but the time is not. When the time is not supplied, then the first time
+point in the data will be treated as \(t0\). The input Death indicate which column of the data is used
+and \(R\) the corresponding state the data belongs to.
    
+
    
+
+    
+    
+
+                
    
+              
+
+              
+              
+                
+              
+            
    
+            
+            
+              
+            
+          
    
+          
    
+            
+
    
+  
+  
    
+    
+
    
+By Public Health England / UK Health Security Agency
+
    
+
+  
    
+  
+  
    
+    
+  
    
+    
+      © Copyright 2022.
+      
    
+    
+  
    
+
+  
    
+  
+  
    
+    
+  
    
+  
+  
    
+    
+  
    
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+    Example: Fitz Hugh — PyGOM documentation
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+              
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+
    
+    
    Example: Fitz Hugh
    
+    
+    
    
+        
+    
    
+
    
+
+              
+                
+
+                
    
+                  
+  
    
+
    Example: Fitz Hugh#
    
+
    
+
    Defining the model#
    
+
    We are going to investigate another classic model here, the
+FitzHugh-Nagumo, or simply FitzHugh here. The model has already been
+defined in common_models so we can load it easily
    
+
    In [1]: from pygom import SquareLoss, common_models
    
+
    In [2]: import numpy
    
+
    In [3]: import scipy.integrate, scipy.optimize
    
+
    In [4]: import math,time,copy
    
+
    In [5]: import matplotlib.pyplot as plt
    
+
    In [1]: x0 = [-1.0, 1.0]
    
+
    In [2]: t0 = 0
    
+
    In [3]: # params
    
+
    In [4]: paramEval = [(‘a’,0.2), (‘b’,0.2), (‘c’,3.0)]
    
+
    In [5]: ode = common_models.FitzHugh(paramEval)
    
+
    In [5]: ode.initial_values = (x0, t0)
    
+
    Define a set of time points and lets see how the two states \(V\) and \(R\)
+are suppose to behave.
    
+
    In [6]: t = numpy.linspace(1, 20, 30).astype(‘float64’)
    
+
    In [7]: solution = ode.integrate(t)
    
+
    @savefig fh_plot.png In [8]: ode.plot()
    
+
    
+
    
+
    Estimate the parameters#
    
+
    Obtaining the correct parameters for the FitzHugh model is well known to
+be difficult, this is because the surface is multimodal. Although this
+has been shown many times in the literature, so we will omit the
+details. Regardless, we give it a go with some initial guess. with some
+luck, we will be able to recover the original parameters. First, we try
+it out with only one target state
    
+
    In [26]: theta = [0.5, 0.5, 0.5]
    
+
    In [27]: objFH = SquareLoss(theta, ode, x0, t0, t, solution[1::,1],
+‘R’)
    
+
    In [28]: boxBounds = [
    
+.…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0) .…: ]
    
+
    In [29]: res = scipy.optimize.minimize(fun=objFH.cost,
    
+.…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:
+method=’L-BFGS-B’)
    
+
    In [30]: print(res)
    
+
    Then we try the same again but with both state as our target. Now we
+won’t look at the iterations because they are pretty pointless.
    
+
    In [30]: objFH = SquareLoss(theta, ode, x0, t0, t, solution[1::,:],
+[‘V’,’R’])
    
+
    In [31]: res = scipy.optimize.minimize(fun=objFH.cost,
    
+.…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:
+method=’L-BFGS-B’)
    
+
    In [32]: print(res)
    
+
    Note how the estimates are the same, unlike other models.
    
+
    
+
    
+
    Estimate initial value#
    
+
    We can further assume that we have no idea about the initial values for
+\(V\) and \(R\) as well. We also provide guesstimate to set off the
+optimization. The input vector \(\theta\) must have the parameters first,
+then the initial values, along with the corresponding bounds.
    
+
    First, only a single target state, i.e. we only have observations for
+one of states which is \(R\) in this case
    
+
    In [35]: objFH = SquareLoss(theta, ode, x0, t0, t, solution[1::,1],
+‘R’)
    
+
    In [35]: boxBounds = [
    
+.…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0), .…: (None,None), .…:
+(None,None) .…: ]
    
+
    In [36]: res = scipy.optimize.minimize(fun=objFH.costIV,
    
+.…: jac=objFH.sensitivityIV, .…: x0=theta + [-0.5,0.5], .…:
+bounds=boxBounds, .…: method=’L-BFGS-B’)
    
+
    In [37]: print(res)
    
+
    then both state as target at the same time
    
+
    In [38]: objFH = SquareLoss(theta, ode, x0, t0, t, solution[1::,:],
+[‘V’,’R’])
    
+
    In [38]: res = scipy.optimize.minimize(fun=objFH.costIV,
    
+.…: jac=objFH.sensitivityIV, .…: x0=theta + [-0.5, 0.5], .…:
+bounds=boxBounds, .…: method=’L-BFGS-B’)
    
+
    In [39]: print(res)
    
+
    See the difference between the two estimate with the latter, both state
+were used, yielding superior estimates. Note that only the forward
+sensitivity method is implemented when estimating the initial value, and
+it is assumed that the starting condition for all the states are
+unknown.
    
+
    The choice of algorithm here is the L-BFGS-B which is a better
+choice because the parameter space of the FitzHugh is rough (i.e. large
+second derivative) as well as being multimodal. This means that the
+Hessian is not guaranteed to be positive definite and approximation
+using \(J^{\top}J\) is poor, with \(J\) being the Jacobian of the objective
+function.
    
+
    
+
    
+
+    
+    
+
+                
    
+              
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+     Contents
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+              
+            
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+          
    
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+    
+
    
+By Public Health England / UK Health Security Agency
+
    
+
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+      © Copyright 2022.
+      
    
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+
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+    Gradient estimation under square loss — PyGOM documentation
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+    
    Gradient estimation under square loss
    
+    
+    
    
+        
+    
    
+
    
+
+              
+                
+
+                
    
+                  
+  
    
+
    Gradient estimation under square loss#
    
+
    Assuming that we have a set of \(N\) observations \(y_{i}\) at specific time
+points \(t_{i}\), \(i = 1,\ldots,N\), we may wish to test out a set of ode
+to see whether it fits to the data. The most natural way to test such
+fit is to minimize the sum of squares between our observations \(y\) and
+see whether the resulting solution of the ode and the estimationed
+parameters makes sense.
    
+
    We assume that this estimation process will be tackled through a
+non-linear optimization point of view. However, it should be noted that
+such estimates can also be performed via MCMC or from a global
+optimization perspective. A key element in non-linear optimization is
+the gradient, which is the focus of this page.
    
+
    Multiple ways of obtaining the gradient have been implemented. All of
+them serve a certain purpose and may not be a viable/appropriate options
+depending on the type of ode. More generally, let \(d,p\) be the number of
+states and paramters respectively. Then finite difference methods have a
+run order of \(O(p+1)\) of the original ode, forward sensitivity require
+an integration of an ode of size \((d+1)p\) rather than \(d\). The adjoint
+method require two run of size \(d\) in principle, but actual run time is
+dependent on the number of observations.
    
+
    For the details of the classes and methods, please refer to mod.
    
+
    
+
    Notation#
    
+
    We introduce the notations that will be used in the rest of the page,
+some of which may be slightly unconventional but necessary due to the
+complexity of the problem. Let \(x \in \mathbb{R}^{d}\) and
+\(\theta \in \mathbb{R}^{p}\) be the states and parameters respectively.
+The term state or simulation are used interchangeably, even though
+strictly speaking a state is \(x\) whereas \(x(t)\) is the simulation. An
+ode is defined as
    
+
    
+\[f(x,\theta) = \dot{x} = \frac{\partial x}{\partial t}\]
    
+
    and usually comes with a set of initial conditions \((x_0,t_0)\) where
+\(t_0 \le t_{i} \forall i\). Let \(g(x,\theta)\) be a function that maps the
+set of states to the observations,
+\(g : \mathbb{R}^{d} \rightarrow \mathbb{R}^{m}\). For compartmental
+problems, which is our focus, \(\nabla_{\theta}g(x,\theta)\) is usually
+zero and \(\nabla_{x}g(x,\theta)\) is an identity function for some or all
+of the states \(x\). Denote \(l(x_{0},\theta,x)\) as our cost function
+\(l : \mathbb{R}^{m} \rightarrow \mathbb{R}\) and \(L(x_{0},\theta,x)\) be
+the sum of \(l(\cdot)\). Both \(x\) and \(x_{0}\) are usually dropped for
+simplicity. We will be dealing exclusively with square loss here, which
+means that
    
+
    
+\[L(\theta) = \sum_{i=1}^{N} \left\| y_{i} - g(x(t_{i})) \right\|^{2} = \mathbf{e}^{\top} \mathbf{e}\]
    
+
    where \(\mathbf{e}\) is the residual vector, with elements
    
+
    
+\[e_{i} = y_{i} - x(t_{i}).\]
    
+
    
+
    
+
    Model setup#
    
+
    Again, we demonstrate the functionalities of our classes using an SIR
+model.
    
+
    In [1]: from pygom import SquareLoss, common_models
    
+
    In [2]: import copy,time,numpy
    
+
    In [2]: ode = common_models.SIR()
    
+
    In [3]: paramEval = [(‘beta’,0.5), (‘gamma’,1.0/3.0) ]
    
+
    In [7]: # the initial state, normalized to zero one
    
+
    In [8]: x0 = [1., 1.27e-6, 0.]
    
+
    In [5]: # initial time
    
+
    In [6]: t0 = 0
    
+
    In [5]: ode.parameters = paramEval
    
+
    In [6]: ode.initial_values = (x0, t0)
    
+
    In [9]: # set the time sequence that we would like to observe
    
+
    In [10]: t = numpy.linspace(1, 150, 100)
    
+
    In [11]: numStep = len(t)
    
+
    In [11]: solution = ode.integrate(t)
    
+
    In [12]: y = solution[1::,2].copy()
    
+
    In [13]: y += numpy.random.normal(0, 0.1, y.shape)
    
+
    Now we have set up the model along with some observations, obtaining the
+gradient only requires the end user to put the appropriate information
+it into the class SquareLoss. Given the initial guess \(\theta\)
    
+
    In [210]: theta = [0.2, 0.2]
    
+
    We initialize the SquareLoss simply as
    
+
    In [20]: objSIR = SquareLoss(theta, ode, x0, t0, t, y, ‘R’)
    
+
    where the we also have to specify the state our observations are from.
+Now, we demonstrate the different methods in obtaining the gradient and
+mathematics behind it.
    
+
    
+
    
+
    Forward sensitivity#
    
+
    The forward sensitivity equations are derived by differentiating the
+states implicitly, which yields
    
+
    
+\[\frac{d\dot{x}}{d\theta} = \frac{\partial f}{\partial x}\frac{dx}{d\theta} + \frac{\partial f}{\partial \theta}.\]
    
+
    So finding the sensitivies \(\frac{dx}{d\theta}\) simply require another
+integration of a \(p\) coupled ode of \(d\) dimension, each with the same
+Jacobian as the original ode. This integration is performed along with
+the original ode because of possible non-linearity.
    
+
    A direct call to the method sensitivity <pygom.SquareLoss.sensitivity>
+computed the gradient
    
+
    In [33]: gradSens = objSIR.sensitivity()
    
+
    whereas .jac will allow the end user to obtain the Jacobian (of the
+objective function) and the residuals, the information required to get
+the gradient as we see next.
    
+
    In [33]: objJac, output = objSIR.jac(full_output=True)
    
+
    
+
    
+
    Gradient#
    
+
    Just the sensitivities alone are not enough to obtain the gradient, but
+we are \(90\%\) there. Differentiating the loss function
    
+
    
+\[\begin{split}\begin{aligned}
+\frac{dL}{d\theta} &= \nabla_{\theta} \sum_{i=1}^{N}\frac{dl}{dg} \\
+                   &= \sum_{i=1}^{N} \frac{\partial l}{\partial x}\frac{dx}{d\theta} + \frac{\partial l}{\partial \theta} \\
+                   &= \sum_{i=1}^{N} \frac{\partial l}{\partial g}\frac{\partial g}{\partial x}\frac{dx}{d\theta} + \frac{\partial l}{\partial g}\frac{\partial g}{\partial \theta}
+\end{aligned}\end{split}\]
    
+
    via chain rule. When \(\frac{\partial g}{\partial \theta} = 0\), the total
+gradient simplifies to
    
+
    
+\[\frac{dL}{d\theta} = \sum_{i=1}^{N} \frac{\partial l}{\partial g}\frac{\partial g}{\partial x}\frac{dx}{d\theta}\]
    
+
    Obviously, the time indicies are dropped above but all the terms above
+are evaluated only at the observed time points. More concretely, this
+means that
    
+
    
+\[\begin{split}\begin{aligned}
+\frac{\partial l(x(j),\theta)}{\partial g} = \left\{ \begin{array}{ll} -2(y_{i} - x(j)) & , \; j = t_{i} \\ 0 & \; \text{otherwise} \end{array} \right.
+\end{aligned}\end{split}\]
    
+
    When \(g(\cdot)\) is an identity function (which is assumed to be the case
+in SquareLoss)
    
+
    
+\[\frac{\partial g(x(t_{i}),\theta)}{\partial x} = I_{d}\]
    
+
    then the gradient simplifies even further as it is simply
    
+
    
+\[\frac{dL}{d\theta} = -2\mathbf{e}^{\top}\mathbf{S}\]
    
+
    where \(\mathbf{e}\) is the vector of residuals and
+\(\mathbf{S} = \left[\mathbf{s}_{1},\mathbf{s}_{2},\ldots,\mathbf{s}_{n}\right]\)
+with elements
    
+
    
+\[\mathbf{s}_{i} = \frac{dx}{d\theta}(t_{i}),\]
    
+
    the solution of the forward sensitivies at time \(t_{i}\), obtained from
+solving the coupled ode as mentioned previously.
    
+
    
+
    
+
    Jacobian#
    
+
    Now note how the gradient simplifies to \(-2\mathbf{e}^{\top}\mathbf{S}\).
+Recall that a standard result in non-linear programming states that the
+gradient of a sum of sqaures objective function \(L(\theta,y,x)\) is
    
+
    
+\[\nabla_{\theta} L(\theta,y,x) = -2(\mathbf{J}^{T} \left[\mathbf{y} - \mathbf{f}(x,\boldsymbol{\theta}) \right] )^{\top}\]
    
+
    with \(f(x,\theta)\) our non-linear function and \(J\) our Jacobian with
+elements
    
+
    
+\[J_{i} = \frac{\partial f(x_{i},\boldsymbol{\theta})}{\partial \boldsymbol{\theta}}.\]
    
+
    This is exactly what we have seen previously, substituting in reveals
+that \(J = \mathbf{S}\). Hence, the Jacobian is (a necessary)by product
+when we wish to obtain the gradient. In fact, this is exactly how we
+proceed in sensitivity <pygom.SquareLoss.sensitivity> where it makes
+an internal call to jac <pygom.SqaureLoss.jac> to obtain the Jacobian
+first. This allows the end user to have more options when choosing which
+type of algorithms to use, i.e. Gauss-Newton or Levenberg-Marquardt.
    
+
    To check that the output is in fact the same
    
+
    In [1]: objJac.transpose().dot(-2*output[‘resid’]) - gradSens
    
+
    
+
    
+
    Adjoint#
    
+
    When the number of parameters increases, the number of sensitivies also
+increases. The time required scales directly with the number of
+parameters. We describe another method which does not depend on the
+number of parameters, but rather, the number of states and observations.
    
+
    The full derivations will not be shown here, but we aim to provide
+enough information to work out the steps performed in the our code. Let
+write our optimization problem as
    
+
    
+\[\begin{split}\begin{aligned}
+min_{\theta} \quad & \int_{t_{0}}^{T} l(x_{0},\theta,x(t)) dt \\
+s.t. \quad & \dot{x} = f(x,\theta)
+\end{aligned}\end{split}\]
    
+
    which is identical to the original problem but in a continuous setting.
+Now write the constrained problem in the Lagrangian form
    
+
    
+\[min_{\theta} \; L(\theta) + \int_{t_{0}}^{T} \lambda^{\top}(\dot{x} - f(x,\theta))\]
    
+
    with Lagrangian multiplier \(\lambda \ge 0\). After some algebraic
+manipulation, it can be shown that the total derivative of the
+Lagrangian function is
    
+
    
+\[\frac{dL}{d\theta} = \int_{t_{0}}^{T} \left(\frac{\partial l}{\partial \theta} - \lambda^{\top}\frac{\partial f}{\partial \theta} \right) dt.\]
    
+
    Using previously defined loss functions (the identity), the first term
+is zero and evaluating \(\frac{\partial f}{\partial \theta}\) is trivial.
+What remains is the calculation of \(\lambda(t)\) for
+\(t \in \left[t_{0},T\right]\).
    
+
    Although this still seem to be ill-posed problem when Looking at the
+Lagrangian function, one can actually obtain the adjoint equation,
+after certain assumptions and
    
+
    
+\[\frac{d\lambda^{\top}}{dt} = \frac{\partial l}{\partial x} - \lambda^{\top}\frac{\partial f}{\partial \theta}.\]
    
+
    which is again an integration. An unfortunate situation arise here for
+non-linear systems because we use the minus Jacobian in the adjoint
+equation. So if the eigenvalues of the Jacobian indicate that our
+original ode is stable, such as -1, the minus eigenvalues (now 1)
+implies that the adjoint equation is not stable. Therefore, one must
+integrate backward in time to solve the adjoint equation and it cannot
+be solved simultaneously as the ode, unlike the forward sensitivity
+equations.
    
+
    Given a non-linearity ode, we must store information about the states
+between \(t_{0}\) and \(T\) in order to perform the integration. There are
+two options, both require storing many evaluated \(x(j)\) within the
+interval \(\left[t_{0},T\right]\). Unfortunately, only one is available;
+interpolation over all states and integrate using the interpolating
+functions. The alternative of using observed \(x(j)'s\) at fixed points is
+not competitive because we are unable to use fortran routines for the
+integration
    
+
    The method of choice here to perform the adjoint calcuation is to run a
+forward integration, then perform an interpolation using splines with
+explicit knots at the observed time points.
    
+
    In [326]: odeSIRAdjoint, outputAdjoint =
+objSIR.adjoint(full_output=True)
    
+
    This is because evaluating the Jacobian may be expensive and Runge-kutta
+method suffers as the complexity increases. In non-linear model such as
+those found in epidemiology, each element of the Jacobian may be the
+result of a complicated equation where linear step method will shine as
+it makes as little function evaluation as possible. Note that
+derivations in the literature, the initial condition when evaluating the
+adjoint equation is \(\lambda(T)=0\). But in our code we used
+\(\lambda(T) = -2(y(T)-x(T))\). Recall that we have observation \(y(T)\) and
+simulation \(x(T)\), so that the adjoint equation evaluated at time \(T\)
    
+
    
+\[\frac{\partial \lambda^{\top}}{\partial t} \Big|_{T} = -2(y-f(x,\theta))\Big|_{T}  - \lambda(T)\frac{\partial f}{\partial \theta}\Big|_{T}\]
    
+
    with the second term equal to zero. Integration under step size \(h\)
+implies that
+\(\lambda(T) \approx \lim_{h \to 0} \lambda(T-h) = -2(y(T)-x(T))\).
    
+
    
+
    
+
    Time Comparison#
    
+
    A simple time comparison between the different methods reveals that the
+forward sensitivity method dominates the others by a wide margin. It
+will be tempting to conclude that it is the best and should be the
+default at all times but that is not true, due to the complexity of each
+method mentioned previously. We leave it to the end user to find out the
+best method for their specific problem.
    
+
    In [319]: %timeit gradSens = objSIR.sensitivity()
    
+
    In [326]: %timeit odeSIRAdjoint,outputAdjoint =
+objSIR.adjoint(full_output=True)
    
+
    
+
    
+
    Hessian#
    
+
    The Hessian is defined by
    
+
    
+\[\frac{\partial^{2} l}{\partial \theta^{2}} = \left( \frac{\partial l}{\partial x} \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + \frac{\partial x}{\partial \theta}^{\top}\frac{\partial^{2} l}{\partial x^{2}}\frac{\partial x}{\partial \theta}\]
    
+
    where \(\otimes\) is the Kronecker product. Note that \(\nabla_{\theta} x\)
+is the sensitivity and the second order sensitivities can be found again
+via the forward method, which involve another set of ode’s, namely the
+forward-forward sensitivities
    
+
    
+\[\frac{\partial}{\partial t}\left(\frac{\partial^{2} x}{\partial \theta^{2}}\right) = \left( \frac{\partial f}{\partial x} \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + \left( I_{d} \otimes \frac{\partial x}{\partial \theta}^{\top} \right) \frac{\partial^{2} f}{\partial x^{2}} \frac{\partial x}{\partial \theta}.\]
    
+
    From before, we know that
    
+
    
+\[\frac{\partial l}{\partial x} = (-2y+2x)  \quad and \quad \frac{\partial^{2} l}{\partial x^{2}} = 2I_{d}\]
    
+
    so our Hessian reduces to
    
+
    
+\[\frac{\partial^{2} l}{\partial \theta^{2}} = \left( \left(-2y+2x\right) \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + 2S^{\top}S,\]
    
+
    where the second term is a good approximation to the Hessian as
+mentioned previously. This is the only implementation in place so far
+even though obtaining the estimate this way is relatively slow.
    
+
    Just to demonstate how it works, lets look at the Hessian at the optimal
+point. First, we obtain the optimal value
    
+
    In [211]: import scipy.linalg,scipy.optimize
    
+
    In [212]: boxBounds = [(0.0, 2.0), (0.0, 2.0)]
    
+
    In [213]: res = scipy.optimize.minimize(fun=objSIR.cost,
    
+.….: jac=objSIR.sensitivity, .….: x0=theta, .….: bounds=boxBounds, .….:
+method=’L-BFGS-B’)
    
+
    Then compare again the least square estimate of the covariance matrix
+against our version
    
+
    In [211]: resLS, cov_x, infodict, mesg, ier =
+scipy.optimize.leastsq(func=objSIR.residual, x0=res[‘x’],
+full_output=True)
    
+
    In [212]: HJTJ, outputHJTJ = objSIR.hessian(full_output=True)
    
+
    In [311]: print(scipy.linalg.inv(HJTJ))
    
+
    In [312]: print(cov_x)
    
+
    also note the difference between the Hessian and the approximation using
+the Jacobian, which is in fact what the least squares routine uses.
    
+
    In [313]: print(scipy.linalg.inv(outputHJTJ[‘JTJ’]))
    
+
    
+
    
+
+    
+    
+
+                
    
+              
+
+              
+              
+                
+              
+            
    
+            
+            
+              
+                
    
+
+  
    
+  
    
+     Contents
+  
    
+  
    
+
+
    
+              
+            
+          
    
+          
    
+            
+
    
+  
+  
    
+    
+
    
+By Public Health England / UK Health Security Agency
+
    
+
+  
    
+  
+  
    
+    
+  
    
+    
+      © Copyright 2022.
+      
    
+    
+  
    
+
+  
    
+  
+  
    
+    
+  
    
+  
+  
    
+    
+  
    
+  
+
    
+          
    
+        
+
+      
    
+    
    
+  
    
+  
+  
+  
+
+
+  
    
+  
    
+  
+
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/notebooks/sir.html b/docs/pygom-doc/_build/html/notebooks/sir.html
index df37f3e5..e44e6e21 100644
--- a/docs/pygom-doc/_build/html/notebooks/sir.html
+++ b/docs/pygom-doc/_build/html/notebooks/sir.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Motivating Example: SIR Model — PyGOM documentation
   
@@ -165,12 +165,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
@@ -381,7 +386,6 @@ 
     Contents 
    
                   
   
    
 
    Motivating Example: SIR Model#
    
-
    this notebook
    
 
    
 
    Defining the model#
    
 
    First, we are going to go through an SIR model to show the functionality
@@ -398,7 +402,17 @@ 
    Defining the model
 
    
-
    from pygom import Transition, TransitionType
+
    from pygom import Transition, TransitionType
+
    
+
    
+
    
+
    
+
    ---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 1
+----> 1 from pygom import Transition, TransitionType
+
+ModuleNotFoundError: No module named 'pygom'
 
    
 
    
 
    
@@ -408,7 +422,7 @@ 
    Defining the model
 
    
-
    stateList = ['S', 'I', 'R']
+
    stateList = ['S', 'I', 'R']
 
    
 
    
 
    
@@ -418,7 +432,7 @@ 
    Defining the model
 
    
-
    paramList = ['beta', 'gamma']
+
    paramList = ['beta', 'gamma']
 
    
 
    
 
    
@@ -428,11 +442,11 @@ 
    Defining the model
 
    
-
    odeList = [
-    Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE),
-    Transition(origin='I',equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE),
-    Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE) 
-    ]
+
    odeList = [
+    Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE),
+    Transition(origin='I',equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE),
+    Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE) 
+    ]
 
    
 
    
 
    
@@ -446,7 +460,7 @@ 
    Defining the model
 
    
-
    from pygom import DeterministicOde
+
    from pygom import DeterministicOde
 
    
 
    
 
    
@@ -456,7 +470,7 @@ 
    Defining the model
 
    
-
    model = DeterministicOde(stateList, paramList, ode=odeList)
+
    model = DeterministicOde(stateList, paramList, ode=odeList)
 
    
 
    
 
    
@@ -464,7 +478,7 @@ 
    Defining the model
 
    
-
    model.get_ode_eqn()
+
    model.get_ode_eqn()
 
    
 
    
 
    
@@ -481,12 +495,12 @@ 
    Defining the model
 
    
-
    # now we are going to define in a different order. note that the output ode changed with the input state
-stateList = ['R', 'S', 'I']
+
    # now we are going to define in a different order. note that the output ode changed with the input state
+stateList = ['R', 'S', 'I']
 
-model = DeterministicOde(stateList, paramList, ode=odeList)
+model = DeterministicOde(stateList, paramList, ode=odeList)
 
-model.get_ode_eqn()
+model.get_ode_eqn()
 
    
 
    
 
    
@@ -502,7 +516,7 @@ 
    Defining the model
 
    
-
    model.print_ode()
+
    model.print_ode()
 
    
 
    
 
    
@@ -518,7 +532,7 @@ 
    Defining the model
 
    
-
    model.print_ode(True)
+
    model.print_ode(True)
 
    
 
    
 
    
@@ -537,7 +551,7 @@ 
    Extracting model information
 
    
-
    model.linear_ode()
+
    model.linear_ode()
 
    
 
    
 
    
@@ -550,7 +564,7 @@ 
    Extracting model information
 
    
-
    model.get_jacobian_eqn()
+
    model.get_jacobian_eqn()
 
    
 
    
 
    
@@ -562,7 +576,7 @@ 
    Extracting model information
 
    
-
    model.get_grad_eqn()
+
    model.get_grad_eqn()
 
    
 
    
 
    
@@ -579,7 +593,7 @@ 
    Extracting model information
 
    
-
    model.state_list
+
    model.state_list
 
    
 
    
 
    
@@ -602,18 +616,18 @@ 
    Initial value problem
 
    
 
    
-
    model.parameters
+
    model.parameters
 
    
 
    
 
    
 
    
 
    
 
    
-
    paramEval = [('beta',0.5), ('gamma',1.0/3.0)]
+
    paramEval = [('beta',0.5), ('gamma',1.0/3.0)]
 
-model.parameters = paramEval
+model.parameters = paramEval
 
-model.parameters
+model.parameters
 
    
 
    
 
    
@@ -628,9 +642,9 @@ 
    Initial value problem
 
    
 
    
-
    initialState = [0, 1, 1.27e-6]
+
    initialState = [0, 1, 1.27e-6]
     
-model.ode(state=initialState, t=1)
+model.ode(state=initialState, t=1)
 
    
 
    
 
    
@@ -646,12 +660,12 @@ 
    Initial value problemWe are well equipped to solve an initial value problem, using the standard numerical integrator such as odeint <scipy.integrate.odeint> from scipy.integrate. We also used matplotlib.pyplot for plotting and linspace <numpy.linspace> to create the time vector.
    
 
    
 
    
-
    import scipy.integrate
-import numpy
+
    import scipy.integrate
+import numpy
 
-t = numpy.linspace(0, 150, 100)
+t = numpy.linspace(0, 150, 100)
 
-solution = scipy.integrate.odeint(model.ode, initialState, t)
+solution = scipy.integrate.odeint(model.ode, initialState, t)
 
    
 
    
 
    
@@ -659,29 +673,29 @@ 
    Initial value problemWe can plot our solution to observe a standard SIR shape.
    
 
    
 
    
-
    import matplotlib.pyplot as plt
+
    import matplotlib.pyplot as plt
 
-plt.figure()
+plt.figure()
 
-plt.plot(t, solution[:,0], label='R')
+plt.plot(t, solution[:,0], label='R')
 
-plt.plot(t, solution[:,1], label='S')
+plt.plot(t, solution[:,1], label='S')
 
-plt.plot(t, solution[:,2], label='I')
+plt.plot(t, solution[:,2], label='I')
 
-plt.xlabel('Time')
+plt.xlabel('Time')
 
-plt.ylabel('Population proportion')
+plt.ylabel('Population proportion')
 
-plt.title('Standard SIR model')
+plt.title('Standard SIR model')
 
-plt.legend(loc=0)
+plt.legend(loc=0)
 
-#@savefig sir_plot.png In 
+#@savefig sir_plot.png In 
 
-plt.show()
+plt.show()
 
-#plt.close()
+#plt.close()
 
    
 
    
 
    
@@ -692,13 +706,13 @@ 
    Initial value problemAlternatively, we can integrate and plot via the ode object which we initialized earlier.
    
 
    
 
    
-
    model.initial_values = (initialState, t[0])
+
    model.initial_values = (initialState, t[0])
 
-model.parameters = paramEval
+model.parameters = paramEval
 
-solution = model.integrate(t[1::])
+solution = model.integrate(t[1::])
 
-model.plot()
+model.plot()
 
    
 
    
 
    
@@ -709,38 +723,39 @@ 
    Initial value problemWe could solve the ODEs above using the Jacobian as well. Unfortunately, it does not help because the number of times the Jacobian was evaluated was zero, as expected given that our set of equations are not stiff.
    
 
    
 
    
-
    #TODO what does this show?
-%timeit solution1, output1 = scipy.integrate.odeint(model.ode, initialState, t, full_output=True)
+
    #TODO what does this show?
+%timeit solution1, output1 = scipy.integrate.odeint(model.ode, initialState, t, full_output=True)
 
    
 
    
 
    
 
    
-
    1.11 ms ± 45.4 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
+
    2.58 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
    
 
    
 
    
 
    
 
    
 
    
-
    %timeit solution2, output2 = scipy.integrate.odeint(model.ode, initialState, t, Dfun=model.jacobian, mu=None, ml=None, full_output=True)
+
    
+%timeit solution2, output2 = scipy.integrate.odeint(model.ode, initialState, t, Dfun=model.jacobian, mu=None, ml=None, full_output=True)
 
    
 
    
 
    
 
    
-
    1.2 ms ± 89 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
+
    2.84 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
    
 
    
 
    
 
    
 
    
 
    
-
    %timeit solution3, output3 = model.integrate(t, full_output=True)
+
    
+%timeit solution3, output3 = model.integrate(t, full_output=True)
 
    
 
    
 
    
 
    
-
    The slowest run took 4.46 times longer than the fastest. This could mean that an intermediate result is being cached.
-2.2 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
    2.68 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
    
 
    
 
    
@@ -752,71 +767,71 @@ 
    Solving the forward sensitivity equation
 
    
-
    stateList = ['S', 'I', 'R']
+
    stateList = ['S', 'I', 'R']
 
-model = DeterministicOde(stateList, paramList, ode=odeList)
+model = DeterministicOde(stateList, paramList, ode=odeList)
 
-initialState = [1, 1.27e-6, 0]
+initialState = [1, 1.27e-6, 0]
 
-paramEval = [('beta', 0.5), ('gamma', 1.0/3.0)]
+paramEval = [('beta', 0.5), ('gamma', 1.0/3.0)]
 
-model.parameters = paramEval
+model.parameters = paramEval
 
-solution = scipy.integrate.odeint(model.ode_and_sensitivity, numpy.append(initialState, numpy.zeros(6)), t)
+solution = scipy.integrate.odeint(model.ode_and_sensitivity, numpy.append(initialState, numpy.zeros(6)), t)
 
    
 
    
 
    
 
    
 
    
 
    
-
    {
-    "tags": [
-        "hide-input",
-    ]
-}
-f,axarr = plt.subplots(3,3);
+
    {
+    "tags": [
+        "hide-input",
+    ]
+}
+f,axarr = plt.subplots(3,3);
 
-f.text(0.5,0.975,'SIR with forward sensitivity solved via ode',fontsize=16,horizontalalignment='center',verticalalignment='top')
+f.text(0.5,0.975,'SIR with forward sensitivity solved via ode',fontsize=16,horizontalalignment='center',verticalalignment='top')
 
-axarr[0,0].plot(t, solution[:,0])
+axarr[0,0].plot(t, solution[:,0])
 
-axarr[0,0].set_title('S')
+axarr[0,0].set_title('S')
 
-axarr[0,1].plot(t, solution[:,1])
+axarr[0,1].plot(t, solution[:,1])
 
-axarr[0,1].set_title('I')
+axarr[0,1].set_title('I')
 
-axarr[0,2].plot(t, solution[:,2]);
+axarr[0,2].plot(t, solution[:,2]);
 
-axarr[0,2].set_title('R')
+axarr[0,2].set_title('R')
 
-axarr[1,0].plot(t, solution[:,3])
+axarr[1,0].plot(t, solution[:,3])
 
-axarr[1,0].set_title(r'state S parameter $beta$')
+axarr[1,0].set_title(r'state S parameter $beta$')
 
-axarr[2,0].plot(t, solution[:,4])
+axarr[2,0].plot(t, solution[:,4])
 
-axarr[2,0].set_title(r'state S parameter $gamma$')
+axarr[2,0].set_title(r'state S parameter $gamma$')
 
-axarr[1,1].plot(t, solution[:,5])
+axarr[1,1].plot(t, solution[:,5])
 
-axarr[1,1].set_title(r'state I parameter $beta$')
+axarr[1,1].set_title(r'state I parameter $beta$')
 
-axarr[2,1].plot(t, solution[:,6])
+axarr[2,1].plot(t, solution[:,6])
 
-axarr[2,1].set_title(r'state I parameter $gamma$')
+axarr[2,1].set_title(r'state I parameter $gamma$')
 
-axarr[1,2].plot(t, solution[:,7])
+axarr[1,2].plot(t, solution[:,7])
 
-axarr[1,2].set_title(r'state R parameter $beta$')
+axarr[1,2].set_title(r'state R parameter $beta$')
 
-axarr[2,2].plot(t, solution[:,8])
+axarr[2,2].plot(t, solution[:,8])
 
-axarr[2,2].set_title(r'state R parameter $gamma$')
+axarr[2,2].set_title(r'state R parameter $gamma$')
 
-plt.tight_layout()
+plt.tight_layout()
 
-plt.show()
+plt.show()
 
    
 
    
 
    
diff --git a/docs/pygom-doc/_build/html/notebooks/stochastic.html b/docs/pygom-doc/_build/html/notebooks/stochastic.html
index e165133a..3898cdbd 100644
--- a/docs/pygom-doc/_build/html/notebooks/stochastic.html
+++ b/docs/pygom-doc/_build/html/notebooks/stochastic.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Stochastic representation of ODEs — PyGOM documentation
   
@@ -66,7 +66,7 @@
     
     
     
-    
+    
     
   
   
@@ -165,12 +165,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
    • 
@@ -386,38 +391,51 @@ 
    Stochastic representation of ODEsMotivating Example: SIR Model.
    
+
    #TODO add comment about using a density or frequency formulation (use of population size) - currently unexplained
    
 
    
 
    
-
    from pygom import SimulateOde, Transition, TransitionType
+
    from pygom import SimulateOde, Transition, TransitionType
 
-import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
 
-import numpy as np
+import numpy as np
 
-# initial conditions
-Ix0 = [1, 1.27e-6, 0]
+# initial conditions
+Ix0 = [1, 1.27e-6, 0]
 
-# time period
-t = np.linspace(0, 150, 100)
+# time period
+t = np.linspace(0, 150, 100)
 
-stateList = ['S', 'I', 'R']
+stateList = ['S', 'I', 'R']
 
-paramList = ['beta', 'gamma']
+paramList = ['beta', 'gamma']
 
-transitionList = [Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T), 
-                  Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)]
+transitionList = [Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T), 
+                  Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)]
 
-# create the ODEs
-odeS = SimulateOde(stateList, paramList, transition=transitionList)
+# create the ODEs
+odeS = SimulateOde(stateList, paramList, transition=transitionList)
 
-# assign parameter values
-odeS.parameters = [0.5, 1.0/3.0]
+# assign parameter values
+odeS.parameters = [0.5, 1.0/3.0]
 
-# assign initial values
-odeS.initial_values = (Ix0, t[0])
+# assign initial values
+odeS.initial_values = (Ix0, t[0])
 
-# solve deterministic solution
-solutionReference = odeS.integrate(t[1::], full_output=False)
+# solve deterministic solution
+solutionReference = odeS.integrate(t[1::], full_output=False)
+
    
+
    
+
    
+
    
+
    ---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 1
+----> 1 from pygom import SimulateOde, Transition, TransitionType
+      3 import matplotlib.pyplot as plt
+      5 import numpy as np
+
+ModuleNotFoundError: No module named 'pygom'
 
    
 
    
 
    
@@ -438,19 +456,19 @@ 
    Stochastic Parameters
 
    
 
    
-
    from pygom.utilR import rgamma
+
    from pygom.utilR import rgamma
 
-d = dict()
+d = dict()
 
-# E(X) = 0.5 = shape * scale = shape / rate 
-d['beta'] = (rgamma,{'shape':100.0, 'rate':200.0})
+# E(X) = 0.5 = shape * scale = shape / rate 
+d['beta'] = (rgamma,{'shape':100.0, 'rate':200.0})
 
-# E(X) = 1.0/3.0 = shape / rate
-d['gamma'] = (rgamma,(100.0, 300.0))
+# E(X) = 1.0/3.0 = shape / rate
+d['gamma'] = (rgamma,(100.0, 300.0))
 
-odeS.parameters = d
+odeS.parameters = d
 
-Ymean, Yall = odeS.simulate_param(t[1::], 10, full_output=True)
+Ymean, Yall = odeS.simulate_param(t[1::], 10, full_output=True)
 
    
 
    
 
    
@@ -462,32 +480,32 @@ 
    Stochastic Parameters
 
    
-
    
+
    
 
    
 
-
-Hide code cell content
+
+Hide code cell source
 
 
    
-
    f, axarr = plt.subplots(1,3, layout='constrained')
+
    f, axarr = plt.subplots(1,3, layout='constrained')
 
-#TODO tidy up plots
-for solution in Yall:  
-    axarr[0].plot(t, solution[:,0]) 
-    axarr[1].plot(t, solution[:,1]) 
-    axarr[2].plot(t, solution[:,2])
+#TODO tidy up plots
+for solution in Yall:  
+    axarr[0].plot(t, solution[:,0]) 
+    axarr[1].plot(t, solution[:,1]) 
+    axarr[2].plot(t, solution[:,2])
 
-for idx, state in enumerate(stateList):
-    axarr[idx].set_title(state)
+for idx, state in enumerate(stateList):
+    axarr[idx].set_title(state)
 
-plt.show()
+plt.show()
 
    
 
    
 
    
+
    
 
    
-../_images/a2b4c66c36cd941577d5e6e1b74bf026da21420ebaaa2c33c4960b32e1325dcf.png
+../_images/ab20c75bd7c6ee640cf34904982a63506772e03a04c4b1616eab4e0c779b74f2.png
 
    
-
 
    
 
    We then see how the expected results, using the sample average of the
 simulations
    
@@ -496,72 +514,122 @@ 
    Stochastic Parametersdiffers from the reference solution
    
 
    
 \[\hat{x}(T) = \int_{t_{0}}^{T} f(\mathbb{E}\left[ \theta \right],x,t) dt\]
    
-
    
+
    
+
    
+
+
+Hide code cell source
+
 
    
-
    f, axarr = plt.subplots(1,3)
+
    f, axarr = plt.subplots(1,3, layout='constrained')
+
+for i in range(3): 
+    axarr[i].plot(t, Ymean[:,i] - solutionReference[:,i])
+    axarr[i].set_title(stateList[i])
 
-for i in range(3): 
-    axarr[i].plot(t, Ymean[:,i] - solutionReference[:,i])
+plt.show()
 
-plt.show()
 
    
 
    
 
    
+
    
 
    
-../_images/e521435122381780396ee20a8582f3a46e43faa6c31200c2a2bef1fd514361f4.png
+../_images/edaf46c89c79b126b3212a4b236b512c0c1cc9a324dc2ba7c19d035ea3da00a0.png
 
    
 
    
-
    
+
    
+
    
+
+
+Hide code cell source
+
 
    
-
    f, axarr = plt.subplots(1,3, layout='constrained', sharey=True)
+
    f, axarr = plt.subplots(1,3, layout='constrained', sharey=False)
 
-for i in range(3): 
-    axarr[i].plot(t, Ymean[:,i], label='sample average')
-    axarr[i].plot(t, solutionReference[:,i], label='reference')
+for i in range(3): 
+    axarr[i].plot(t, Ymean[:,i], label='sample average')
+    axarr[i].plot(t, solutionReference[:,i], label='reference')
+    axarr[i].set_title(stateList[i])
 
-axarr[2].legend()
-plt.show()
+axarr[2].legend()
+plt.show()
 
    
 
    
 
    
+
    
 
    
-../_images/61cf086b003e645ccb1eaeebd0a201f4793ce35568e2df3fd9897fe93e8c4d57.png
+../_images/5bc73ee726871a07756efbfcc86834065460905c685c115099ea1ec6c422183c.png
 
    
 
    
-
    The difference is relatively large especially for the \(S\) state. We can
-decrease this difference as we increase the number of simulation, and
-more sophisticated sampling method for the generation of random
-variables can also decrease the difference.
    
+
    The difference between the deterministic reference and averaged solutions is relatively large, especially for the \(S\) state.
+We can decrease this difference as we increase the number of simulations, and also by using more sophisticated sampling methods for the generation of random
+variables.
    
+
    
+
    Tip
    
 
    In addition to using the built-in functions to represent stochasticity,
 we can also use standard frozen distributions from scipy. Note that it
 must be a frozen distribution as that is the only for the parameters of
 the distributions to propagate through the model.
    
-
    In [1]: import scipy.stats as st
    
-
    In [1]: d = dict()
    
-
    In [1]: d[‘beta’] = st.gamma(a=100.0, scale=1.0/200.0)
    
-
    In [1]: d[‘gamma’] = st.gamma(a=100.0, scale=1.0/300.0)
    
-
    In [1]: odeS.parameters = d
    
-
    Obviously, there may be scenarios where only some of the parameters are
+
    
+
    
+
    
+
    import scipy.stats as st
+
+d = dict()
+
+d['beta'] = st.gamma(a=100.0, scale=1.0/200.0)
+
+d['gamma'] = st.gamma(a=100.0, scale=1.0/300.0)
+
+odeS.parameters = d
+
    
+
    
+
    
+
    
+
    There may be scenarios where only some of the parameters are
 stochastic. Let’s say that the \(\gamma\) parameter is fixed at \(1/3\),
-then simply replace the distribution information with a scalar. A quick
-visual inspection at the resulting plot suggests that the system of ODE
+then we can replace the distribution information with a scalar. A quick
+visual inspection at the resulting plot suggests that the system of ODEs
 potentially has less variation when compared to the case where both
 parameters are stochastic.
    
-
    In [1]: d[‘gamma’] = 1.0/3.0
    
-
    In [1]: odeS.parameters = d
    
-
    In [1]: YmeanSingle, YallSingle = odeS.simulate_param(t[1::], 5,
-full_output=True)
    
-
    In [1]: f, axarr = plt.subplots(1,3)
    
-
    In [1]: for solution in YallSingle:
    
-…: axarr[0].plot(t,solution[:,0]) …:
-axarr[1].plot(t,solution[:,1]) …:
-axarr[2].plot(t,solution[:,2])
    
-
    @savefig stochastic_param_single.png In [1]: plt.show()
    
-
    In [1]: plt.close()
    
+
    
+
    
+
    d['gamma'] = 1.0/3.0
+
+odeS.parameters = d
+
+YmeanSingle, YallSingle = odeS.simulate_param(t[1::], 5, full_output=True)
+
    
+
    
+
    
+
    
+
    
+
    
+
+
+Hide code cell source
+
+
    
+
    f, axarr = plt.subplots(1,3)
+
+for solution in YallSingle:  
+    axarr[0].plot(t,solution[:,0]) 
+    axarr[1].plot(t,solution[:,1]) 
+    axarr[2].plot(t,solution[:,2])
+
+plt.show()
+
    
+
    
+
    
+
    
+
    
+../_images/c0aa142200428595aa44bb6588be6a7a4160105ee4702528ae9560e29d955782.png
+
    
+
    
 
    
 
    
 
    Continuous Markov Representation#
    
-
    Another common method of introducing stochasticity into a set of ode is
+
    Another common method of introducing stochasticity into a set of ODEs is
 by assuming each movement in the system is a result of a jump process.
 More concretely, the probabilty of a move for transition \(j\) is governed
 by an exponential distribution such that
    
@@ -569,88 +637,135 @@ 
    Continuous Markov Representation\(\lambda_{j}\) is the rate of transition for process \(j\) and \(\tau\)
 the time elapsed after current time \(t\).
    
-
    A couple of the commmon implementation for the jump process have been
-implemented where two of them are used during a normal simulation; the
-first reaction method [Gillespie1977] and the \(\tau\)-Leap method
-[Cao2006]. The two changes interactively depending on the size of
-the states.
    
-
    In [1]: x0 = [2362206.0, 3.0, 0.0]
    
-
    In [1]: stateList = [‘S’, ‘I’, ‘R’]
    
-
    In [1]: paramList = [‘beta’, ‘gamma’, ‘N’]
    
-
    In [1]: transitionList = [
    
-…: Transition(origin=’S’, destination=’I’, equation=’beta*S*I/N’,
-transition_type=TransitionType.T), …: Transition(origin=’I’,
-destination=’R’, equation=’gamma*I’, transition_type=TransitionType.T)
-…: ]
    
-
    In [1]: odeS = SimulateOde(stateList, paramList,
-transition=transitionList)
    
-
    In [1]: odeS.parameters = [0.5, 1.0/3.0, x0[0]]
    
-
    In [1]: odeS.initial_values = (x0, t[0])
    
-
    In [1]: solutionReference = odeS.integrate(t[1::])
    
-
    In [1]: simX, simT = odeS.simulate_jump(t[1:10], 10,
-full_output=True)
    
-
    In [1]: f, axarr = plt.subplots(1, 3)
    
-
    In [1]: for solution in simX:
    
-…: axarr[0].plot(t[:9], solution[:,0]) …:
-axarr[1].plot(t[:9], solution[:,1]) …: axarr[2].plot(t[:9],
-solution[:,2])
    
-
    @savefig stochastic_process.png In [1]: plt.show()
    
-
    In [1]: plt.close()
    
-
    Above, we see ten different simulation, again using the SIR model but
-without standardization of the initial conditions. We restrict our time
-frame to be only the first 10 time points so that the individual changes
-can be seen more clearly above. If we use the same time frame as the one
+
    Two of the commmon algorithms for the jump process have been
+implemented for use during simulation; the reaction method [Gillespie1977] and the \(\tau\)-Leap method
+[Cao2006]. The two change interactively depending on the size of the states.
    
+
    
+
    
+
    x0 = [2362206.0, 3.0, 0.0]
+
+stateList = ['S', 'I', 'R']
+
+paramList = ['beta', 'gamma', 'N']
+
+transitionList = [Transition(origin='S', destination='I', equation='beta*S*I/N', transition_type=TransitionType.T),
+                  Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)
+                  ]
+
+odeS = SimulateOde(stateList, paramList, transition=transitionList)
+
+odeS.parameters = [0.5, 1.0/3.0, x0[0]]
+
+odeS.initial_values = (x0, t[0])
+
+solutionReference = odeS.integrate(t[1::])
+
+simX, simT = odeS.simulate_jump(t[1:10], 10, full_output=True)
+
+f, axarr = plt.subplots(1, 3)
+
+for solution in simX:  
+    axarr[0].plot(t[:9], solution[:,0])
+    axarr[1].plot(t[:9], solution[:,1]) 
+    axarr[2].plot(t[:9], solution[:,2])
+
+plt.show()
+
    
+
    
+
    
+
    
+../_images/a41ee48b4c02f2ffb96d803d748a3fd307285265d95928863ec35ca238282231.png
+
    
+
    
+
    Above, we see 10 different simulations, again using the SIR model but
+without standardization of the initial conditions (we include \(N\) as a parameter in our model).
+For demonstration purposes, we restrict our time
+frame to be only the first 10 time points so that the individual changes or stochasticity
+can be seen more clearly. If we use the same time frame as the one
 used previously for the deterministic system (as shown below), the
 trajectories are smoothed out and we no longer observe the jumps.
-Looking at the raw trajectories of the ODE below, it is obvious that the
+Looking at the raw trajectories of the ODEs below, we see that the
 mean from a jump process is very different to the deterministic
-solution. The reason behind this is that the jump process above was able
+solution. This occurs because the jump process above was able
 to fully remove all the initial infected individuals before any new
-ones.
    
-
    In [1]: simX,simT = odeS.simulate_jump(t, 5, full_output=True)
    
-
    In [1]: simMean = np.mean(simX, axis=0)
    
-
    In [1]: f, axarr = plt.subplots(1,3)
    
-
    In [1]: for solution in simX:
    
-…: axarr[0].plot(t, solution[:,0]) …: axarr[1].plot(t,
-solution[:,1]) …: axarr[2].plot(t, solution[:,2])
    
-
    @savefig stochastic_process_compare_large_n_curves.png In [1]:
-plt.show()
    
-
    In [1]: plt.close()
    
+ones occured.
    
+
    
+
    
+
    simX,simT = odeS.simulate_jump(t, 5, full_output=True)
+
+simMean = np.mean(simX, axis=0)
+
+f, axarr = plt.subplots(1,3)
+
+for solution in simX:  
+    axarr[0].plot(t, solution[:,0]) 
+    axarr[1].plot(t, solution[:,1]) 
+    axarr[2].plot(t, solution[:,2])
+
+plt.show()
+
    
+
    
+
    
+
    
+../_images/77d7df2c54dad550496302d8c6230ce9bfb40c9f05977928aaf8c36881d21523.png
+
    
+
    
 
    
 
    
 
    Repeatable Simulation#
    
-
    One of the possible use of compartmental models is to generate
+
    One of the possible uses of compartmental models is to generate
 forecasts. Although most of the time the requirement would be to have
 (at least point-wise) convergence in the limit, reproducibility is also
 important. For both types of interpretation explained above, we have
 given the package the capability to repeat the simulations by setting a
 seed. When the assumption is that the parameters follows some sort of
-distribution, we simply set the seed which governs the global state of
+distribution, we can set the seed which governs the global state of
 the random number generator.
    
-
    In [1]: x0 = [2362206.0, 3.0, 0.0]
    
-
    In [1]: odeS = SimulateOde(stateList, paramList,
-transition=transitionList)
    
-
    In [1]: d = {‘beta’: st.gamma(a=100.0, scale=1.0/200.0), ‘gamma’:
-st.gamma(a=100.0, scale=1.0/300.0), ‘N’: x0[0]}
    
-
    In [1]: odeS.parameters = d
    
-
    In [1]: odeS.initial_values = (x0, t[0])
    
-
    In [1]: Ymean, Yall = odeS.simulate_param(t[1::], 10,
-full_output=True)
    
-
    In [1]: np.random.seed(1)
    
-
    In [1]: Ymean1, Yall1 = odeS.simulate_param(t[1::], 10,
-full_output=True)
    
-
    In [1]: np.random.seed(1)
    
-
    In [1]: Ymean2, Yall2 = odeS.simulate_param(t[1::], 10,
-full_output=True)
    
-
    In [1]: sim_diff = [np.linalg.norm(Yall[i] - yi) for i, yi in
-enumerate(Yall1)]
    
-
    In [1]: sim_diff12 = [np.linalg.norm(Yall2[i] - yi) for i, yi in
-enumerate(Yall1)]
    
-
    In [1]: print(“Different in the simulations and the mean: (%s, %s) ” %
-(np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))
    
-
    In [1]: print(“Different in the simulations and the mean using same
-seed: (%s, %s) ” % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 -
-Ymean1))))
    
+
    
+
    
+
    x0 = [2362206.0, 3.0, 0.0]
+
+odeS = SimulateOde(stateList, paramList, transition=transitionList)
+
+d = {'beta': st.gamma(a=100.0, scale=1.0/200.0), 'gamma':st.gamma(a=100.0, scale=1.0/300.0), 'N': x0[0]}
+
+odeS.parameters = d
+
+odeS.initial_values = (x0, t[0])
+
+Ymean, Yall = odeS.simulate_param(t[1::], 10, full_output=True)
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+np.random.seed(1)
+
+Ymean1, Yall1 = odeS.simulate_param(t[1::], 10, full_output=True)
+
+np.random.seed(1)
+
+Ymean2, Yall2 = odeS.simulate_param(t[1::], 10, full_output=True)
+
+sim_diff = [np.linalg.norm(Yall[i] - yi) for i, yi in enumerate(Yall1)]
+
+sim_diff12 = [np.linalg.norm(Yall2[i] - yi) for i, yi in enumerate(Yall1)]
+
+print("Different in the simulations and the mean: (%s, %s) " %(np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))
+
+print("Different in the simulations and the mean using same seed: (%s, %s) " % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 - Ymean1))))
+
    
+
    
+
    
+
    
+
    Different in the simulations and the mean: (81103473.8935847, 52437230.37979218) 
+Different in the simulations and the mean using same seed: (0.0, 0.0) 
+
    
+
    
+
    
+
    
 
    In the alternative interpretation, setting the global seed is
 insufficient. Unlike simulation based on the parameters, where we can
 pre-generate all the parameter values and send them off to individual
@@ -660,27 +775,43 @@ 
    Repeatable Simulationparallel in the function
 signature. By ensuring that the computation runs in serial, we can make
 use of the global seed and generate identical runs.
    
-
    In [1]: x0 = [2362206.0, 3.0, 0.0]
    
-
    In [1]: odeS = SimulateOde(stateList, paramList,
-transition=transitionList)
    
-
    In [1]: odeS.parameters = [0.5, 1.0/3.0, x0[0]]
    
-
    In [1]: odeS.initial_values = (x0, t[0])
    
-
    In [1]: simX, simT = odeS.simulate_jump(t[1:10], 10, parallel=False,
-full_output=True)
    
-
    In [1]: np.random.seed(1)
    
-
    In [1]: simX1, simT1 = odeS.simulate_jump(t[1:10], 10,
-parallel=False, full_output=True)
    
-
    In [1]: np.random.seed(1)
    
-
    In [1]: simX2, simT2 = odeS.simulate_jump(t[1:10], 10,
-parallel=False, full_output=True)
    
-
    In [1]: sim_diff = [np.linalg.norm(simX[i] - x1) for i, x1 in
-enumerate(simX1)]
    
-
    In [1]: sim_diff12 = [np.linalg.norm(simX2[i] - x1) for i, x1 in
-enumerate(simX1)]
    
-
    In [1]: print(“Difference in simulation: %s” %
-np.sum(np.abs(sim_diff)))
    
-
    In [1]: print(“Difference in simulation using same seed: %s” %
-np.sum(np.abs(sim_diff12)))
    
+
    
+
    
+
    x0 = [2362206.0, 3.0, 0.0]
+
+odeS = SimulateOde(stateList, paramList, transition=transitionList)
+
+odeS.parameters = [0.5, 1.0/3.0, x0[0]]
+
+odeS.initial_values = (x0, t[0])
+
+simX, simT = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)
+
+np.random.seed(1)
+
+simX1, simT1 = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)
+
+np.random.seed(1)
+
+simX2, simT2 = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)
+
+sim_diff = [np.linalg.norm(simX[i] - x1) for i, x1 in enumerate(simX1)]
+
+sim_diff12 = [np.linalg.norm(simX2[i] - x1) for i, x1 in enumerate(simX1)]
+
+print("Difference in simulation: %s" %np.sum(np.abs(sim_diff)))
+
+print("Difference in simulation using same seed: %s" %np.sum(np.abs(sim_diff12)))
+
    
+
    
+
    
+
    
+
    Difference in simulation: 2534.4614923250074
+Difference in simulation using same seed: 0.0
+
    
+
    
+
    
+
    
 
    
 
    
 
@@ -729,7 +860,7 @@ 
    Repeatable Simulation
       
    
         
    next
    
-        
    Convert ODE into transitions {#unrollOde}
    
+        
    Convert ODE into transitions
    
       
    
       
         
diff --git a/docs/pygom-doc/_build/html/notebooks/transition.html b/docs/pygom-doc/_build/html/notebooks/transition.html
index 2ee948a8..86112bc1 100644
--- a/docs/pygom-doc/_build/html/notebooks/transition.html
+++ b/docs/pygom-doc/_build/html/notebooks/transition.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Transition Object — PyGOM documentation
   
@@ -165,12 +165,17 @@
 
  • Motivating Example: SIR Model
    • 
 
  • Transition Object
    • 
 
  • Stochastic representation of ODEs
    • 
-
  • Convert ODE into transitions {#unrollOde}
 
     
@@ -380,7 +385,6 @@ 
     Contents 
    
                   
   
    
 
    Transition Object#
    
-
    this notebook
    
 
    The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in transition. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.)
    
 
    All transitions that get fed into the ODE system need to be defined as a transition object, Transition. It takes a total of four input arguments:
    
 
      
@@ -399,13 +403,25 @@ 
      Transition Object
 
      
-
      from pygom import Transition, TransitionType, common_models
+
      from pygom import Transition, TransitionType, common_models
 
-ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)
+ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)
 
-ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)
+ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)
 
-ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)
+ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)
+
      
+
      
+
      
+
      
+
      ---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 1
+----> 1 from pygom import Transition, TransitionType, common_models
+      3 ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)
+      5 ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)
+
+ModuleNotFoundError: No module named 'pygom'
 
      
 
      
 
      
@@ -415,13 +431,13 @@ 
      Transition ObjectDeterministicOde,
      
 
      
 
      
-
      from pygom import DeterministicOde
+
      from pygom import DeterministicOde
 
-stateList = ['S', 'I', 'R']
+stateList = ['S', 'I', 'R']
 
-paramList = ['beta', 'gamma']
+paramList = ['beta', 'gamma']
 
-model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3])
+model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3])
 
      
 
      
 
      
@@ -429,14 +445,10 @@ 
      Transition Object
 
      
-
      model.get_ode_eqn()
+
      model.get_ode_eqn()
 
      
 
      
 
      
-
      
-
      
-\[\begin{split}\displaystyle \left[\begin{matrix}- I S \beta\\I S \beta - I \gamma\\I \gamma\end{matrix}\right]\end{split}\]
      
-
      
 
      
 
      Now we are going to show the different ways of defining the same set of
 ODEs.
      
@@ -459,22 +471,18 @@ 
      Transition Object
 
      
-
      from pygom import SimulateOde
+
      from pygom import SimulateOde
 
-t1 = Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T)
+t1 = Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T)
 
-t2 = Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)
+t2 = Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)
 
-modelTrans = SimulateOde(stateList, paramList, transition=[t1, t2])
+modelTrans = SimulateOde(stateList, paramList, transition=[t1, t2])
 
-modelTrans.get_ode_eqn()
+modelTrans.get_ode_eqn()
 
      
 
      
 
      
-
      
-
      
-\[\begin{split}\displaystyle \left[\begin{matrix}- I S \beta\\I S \beta - I \gamma\\I \gamma\end{matrix}\right]\end{split}\]
      
-
      
 
      
 
      We can see that the resulting ODE is exactly the same, as expected. The
 transition matrix that defines this process can be visualized
@@ -483,7 +491,7 @@ 
      Transition Object
 
      
-
      modelTrans.get_transition_matrix()
+
      modelTrans.get_transition_matrix()
 
      
 
      
 
      
@@ -494,9 +502,9 @@ 
      Transition Object
 
      
-
      import matplotlib.pyplot as plt
-# TODO why are two images produced? issue #75
-modelTrans.get_transition_graph(show=False)
+
      import matplotlib.pyplot as plt
+# TODO why are two images produced? issue #75
+modelTrans.get_transition_graph(show=False)
 
      
 
      
 
      
@@ -514,15 +522,15 @@ 
      Transition Objecttransition_type=TransitionType.B. For this simple example, this formulation takes a similar form to defining using ODE equations.
      
 
      
 
      
-
      birth1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.B)
+
      birth1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.B)
 
-birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)
+birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)
 
-birth3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.B)
+birth3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.B)
 
-modelBirth = DeterministicOde(stateList, paramList, birth_death=[birth1, birth2, birth3])
+modelBirth = DeterministicOde(stateList, paramList, birth_death=[birth1, birth2, birth3])
 
-modelBirth.get_ode_eqn()
+modelBirth.get_ode_eqn()
 
      
 
      
 
      
@@ -534,15 +542,15 @@ 
      Transition Objecttransition_type=TransitionType.D.
      
 
      
 
      
-
      death1 = Transition(origin='S', equation='beta*S*I', transition_type=TransitionType.D)
+
      death1 = Transition(origin='S', equation='beta*S*I', transition_type=TransitionType.D)
 
-birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)
+birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)
 
-death3 = Transition(origin='R', equation='-gamma*I', transition_type=TransitionType.D)
+death3 = Transition(origin='R', equation='-gamma*I', transition_type=TransitionType.D)
 
-modelBD = DeterministicOde(stateList, paramList, birth_death=[death1, birth2, death3])
+modelBD = DeterministicOde(stateList, paramList, birth_death=[death1, birth2, death3])
 
-modelBD.get_ode_eqn()
+modelBD.get_ode_eqn()
 
      
 
      
 
      
@@ -558,18 +566,18 @@ 
      Model AdditionmodelTrans) and includes a birth process to the \(S\) state, and death processes to the \(S\) and \(I\) states.
      
 
      
 
      
-
      modelBD2 = modelTrans
+
      modelBD2 = modelTrans
 
-modelBD2.param_list = paramList + ['mu', 'B']
+modelBD2.param_list = paramList + ['mu', 'B']
 
-birthDeathList = [Transition(origin='S', equation='B', transition_type=TransitionType.B),  
-                  Transition(origin='S', equation='mu*S', transition_type=TransitionType.D), 
-                  Transition(origin='I', equation='mu*I', transition_type=TransitionType.D)
-                  ]
+birthDeathList = [Transition(origin='S', equation='B', transition_type=TransitionType.B),  
+                  Transition(origin='S', equation='mu*S', transition_type=TransitionType.D), 
+                  Transition(origin='I', equation='mu*I', transition_type=TransitionType.D)
+                  ]
 
-modelBD2.birth_death_list = birthDeathList
+modelBD2.birth_death_list = birthDeathList
 
-modelBD2.get_ode_eqn()
+modelBD2.get_ode_eqn()
 
      
 
      
 
      
@@ -585,10 +593,10 @@ 
      Transition type summaryIn summary, there are four different types of transitions allowed. These are B, D, ODE and T, which are defined in an enum class also located in transition.
      
 
      
 
      
-
      from pygom import transition
+
      from pygom import transition
 
-for i in transition.TransitionType:  
-    print(str(i) + " = " + i.value)
+for i in transition.TransitionType:  
+    print(str(i) + " = " + i.value)
 
      
 
      
 
      
diff --git a/docs/pygom-doc/_build/html/notebooks/unroll/unrollBD.html b/docs/pygom-doc/_build/html/notebooks/unroll/unrollBD.html
index f42b130a..c1d5ea3b 100644
--- a/docs/pygom-doc/_build/html/notebooks/unroll/unrollBD.html
+++ b/docs/pygom-doc/_build/html/notebooks/unroll/unrollBD.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     ODE With Birth and Death Process — PyGOM documentation
   
@@ -165,12 +165,17 @@
 
    1. Motivating Example: SIR Model
      2. 
 
    2. Transition Object
      3. 
 
    3. Stochastic representation of ODEs
      4. 
-
    4. Convert ODE into transitions {#unrollOde}
 
     
      5. 
diff --git a/docs/pygom-doc/_build/html/notebooks/unroll/unrollHard.html b/docs/pygom-doc/_build/html/notebooks/unroll/unrollHard.html
index b100d5aa..d35b5ca6 100644
--- a/docs/pygom-doc/_build/html/notebooks/unroll/unrollHard.html
+++ b/docs/pygom-doc/_build/html/notebooks/unroll/unrollHard.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Hard Problem — PyGOM documentation
   
@@ -66,6 +66,7 @@
     
     
     
+    
     
   
   
@@ -164,12 +165,17 @@
 
    5. Motivating Example: SIR Model
      6. 
 
    6. Transition Object
      7. 
 
    7. Stochastic representation of ODEs
      8. 
-
    8. Convert ODE into transitions {#unrollOde}
 
     
      9. 
@@ -466,6 +472,15 @@ 
      Hard ProblemODE With Birth and Death Process
      
       
      
     
+    
+      
      
+        
      next
      
+        
      Simple Epidemic Analysis
      
+      
      
+      
+          
 
      
   
 
      
diff --git a/docs/pygom-doc/_build/html/notebooks/unroll/unrollSimple.html b/docs/pygom-doc/_build/html/notebooks/unroll/unrollSimple.html
index 53c87b20..5e93c0e6 100644
--- a/docs/pygom-doc/_build/html/notebooks/unroll/unrollSimple.html
+++ b/docs/pygom-doc/_build/html/notebooks/unroll/unrollSimple.html
@@ -7,7 +7,7 @@
 
   
     
-    
+    
 
     Simple Problem — PyGOM documentation
   
@@ -67,7 +67,7 @@
     
     
     
-    
+    
   
   
   
@@ -165,12 +165,17 @@
 
    9. Motivating Example: SIR Model
      10. 
 
    10. Transition Object
      11. 
 
    11. Stochastic representation of ODEs
      12. 
-
    12. Convert ODE into transitions {#unrollOde}
 
     
      13. 
@@ -438,7 +443,7 @@ 
      Simple Problem
       
      
         
      previous
      
-        
      Convert ODE into transitions {#unrollOde}
      
+        
      Convert ODE into transitions
      
       
      
           
     K?Mjls7kOTNuw1zbYgY9THa1d=^hn+vwtb2l||5PYwM%|4tq~-=jT%Be7guhVr-?C#~0^jF3zg
z)ibWOM7t?bf$_6Ga@Z!~&(}A1n+xrz*epO&@qErf!0jDynp1)&5}Xda{r&UTuCf_F
zek4&j#D;L=k$>NZW2P1{;u_5%9K{7zR!MghU!9)lwLfaZ4djkF82n~P@MHh
zh@gpRPnZ?blg<*yc%CD^jk3wC4%%kIi9~Ie3#x&++#jc{qL~*|6;tAbS|r#aKKfZ#
zRGP?u`c;RNaAB{9iLB_Ab-C9HN}Y*!R+^*NFCY&d?SCjJC?o6+qzZ{it3>6)8*@Ld
zm!E`^BopMW7^dxt2)cZdA+0&D6@3@iRbC{{x#K9jt;2{`+pJb=3*7iJ?l&JAIN`rm
z(t$AO(^|cD!&p|d9VC5_^}cpl@|uLsf=Nl;xQzfiebb1Oy#wAb-`z*J3;UgBdD`+}
z_DLVvpMOvG%k2YXl(3p1kB^JRFz|&@q=rl9UQ;3J8=`J{eM^Kv%%FFyJ^yRz)GNXS
zvlys$Xr_A%`mgmFFdW+HNwTf~S>x4_3O>x(z
kpYo4r3Otnc<=w}OcJ}P_Jsklp8gc!j>%@~kVFM+vQC}`V3;+NC

delta 537
zcmV+!0_Oem1%m~Ub$?XLZrd;ryz46nJu1Z+s&OH&rM^vw$hK`n<+wi
z>1M4_XEh2?b~&b;#svDs{^3iumWHZq1DvYYg%bgDG~n7y3C_e^J8=8w*Y8843-|ar
zP<0X+VP?_2Pk(C;5kYZ}>g11Njg1!ET-Z0R8eltvxq{lV*PHxw+{^l?U?}Cfo{{jG
zKy3+&id&M!pfH^0Ol?fY&RWrHC$5o@?Rr5m;mhMCy(XexP&AGuI8-A+j`(bsR+?c8
zqr|n4OxngcRo)FZKshe_!u*=7>db|O*Sby9726*tcvIFkkT+`frK5&2S
z@!R}66g1$9XB!^t%{1W~P+;|5f09(O)|iZI!CG_r)f8auoXBTx#DB 1 from pygom import Transition, TransitionType
+
+ModuleNotFoundError: No module named 'pygom'
+ModuleNotFoundError: No module named 'pygom'
 
diff --git a/docs/pygom-doc/_build/html/reports/notebooks/stochastic.err.log b/docs/pygom-doc/_build/html/reports/notebooks/stochastic.err.log
new file mode 100644
index 00000000..71aae027
--- /dev/null
+++ b/docs/pygom-doc/_build/html/reports/notebooks/stochastic.err.log
@@ -0,0 +1,66 @@
+Traceback (most recent call last):
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\jupyter_cache\executors\utils.py", line 58, in single_nb_execution
+    executenb(
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 1265, in execute
+    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\jupyter_core\utils\__init__.py", line 160, in wrapped
+    return loop.run_until_complete(inner)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\asyncio\base_events.py", line 650, in run_until_complete
+    return future.result()
+           ^^^^^^^^^^^^^^^
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\contextlib.py", line 222, in __aexit__
+    await self.gen.athrow(typ, value, traceback)
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 648, in async_setup_kernel
+    yield
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 703, in async_execute
+    await self.async_execute_cell(
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 1021, in async_execute_cell
+    await self._check_raise_for_error(cell, cell_index, exec_reply)
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 915, in _check_raise_for_error
+    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
+nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
+------------------
+from pygom import SimulateOde, Transition, TransitionType
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+# initial conditions
+Ix0 = [1, 1.27e-6, 0]
+
+# time period
+t = np.linspace(0, 150, 100)
+
+stateList = ['S', 'I', 'R']
+
+paramList = ['beta', 'gamma']
+
+transitionList = [Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T), 
+                  Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)]
+
+# create the ODEs
+odeS = SimulateOde(stateList, paramList, transition=transitionList)
+
+# assign parameter values
+odeS.parameters = [0.5, 1.0/3.0]
+
+# assign initial values
+odeS.initial_values = (Ix0, t[0])
+
+# solve deterministic solution
+solutionReference = odeS.integrate(t[1::], full_output=False)
+------------------
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 1
+----> 1 from pygom import SimulateOde, Transition, TransitionType
+      3 import matplotlib.pyplot as plt
+      5 import numpy as np
+
+ModuleNotFoundError: No module named 'pygom'
+ModuleNotFoundError: No module named 'pygom'
+
diff --git a/docs/pygom-doc/_build/html/reports/notebooks/transition.err.log b/docs/pygom-doc/_build/html/reports/notebooks/transition.err.log
index de7de1b6..95faaa4b 100644
--- a/docs/pygom-doc/_build/html/reports/notebooks/transition.err.log
+++ b/docs/pygom-doc/_build/html/reports/notebooks/transition.err.log
@@ -1,43 +1,43 @@
 Traceback (most recent call last):
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\nbclient\client.py", line 776, in _async_poll_for_reply
-    msg = await ensure_async(self.kc.shell_channel.get_msg(timeout=new_timeout))
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\jupyter_core\utils\__init__.py", line 184, in ensure_async
-    result = await obj
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\jupyter_client\channels.py", line 230, in get_msg
-    raise Empty
-_queue.Empty
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\jupyter_cache\executors\utils.py", line 58, in single_nb_execution
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\jupyter_cache\executors\utils.py", line 58, in single_nb_execution
     executenb(
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\nbclient\client.py", line 1265, in execute
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 1265, in execute
     return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\jupyter_core\utils\__init__.py", line 168, in wrapped
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\jupyter_core\utils\__init__.py", line 160, in wrapped
     return loop.run_until_complete(inner)
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\asyncio\base_events.py", line 647, in run_until_complete
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\asyncio\base_events.py", line 650, in run_until_complete
     return future.result()
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\nbclient\client.py", line 703, in async_execute
+           ^^^^^^^^^^^^^^^
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\contextlib.py", line 222, in __aexit__
+    await self.gen.athrow(typ, value, traceback)
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 648, in async_setup_kernel
+    yield
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 703, in async_execute
     await self.async_execute_cell(
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\nbclient\client.py", line 1002, in async_execute_cell
-    exec_reply = await self.task_poll_for_reply
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\nbclient\client.py", line 800, in _async_poll_for_reply
-    error_on_timeout_execute_reply = await self._async_handle_timeout(timeout, cell)
-  File "C:\Users\Hannah.Williams\.conda\envs\sphinx-doc\lib\site-packages\nbclient\client.py", line 852, in _async_handle_timeout
-    raise CellTimeoutError.error_from_timeout_and_cell(
-nbclient.exceptions.CellTimeoutError: A cell timed out while it was being executed, after 30 seconds.
-The message was: Cell execution timed out.
-Here is a preview of the cell contents:
--------------------
-death1 = Transition(origin='S', equation='beta*S*I', transition_type=TransitionType.D)
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 1021, in async_execute_cell
+    await self._check_raise_for_error(cell, cell_index, exec_reply)
+  File "C:\Users\Hannah.Williams\.conda\envs\pygom-dev\Lib\site-packages\nbclient\client.py", line 915, in _check_raise_for_error
+    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
+nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
+------------------
+from pygom import Transition, TransitionType, common_models
+
+ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)
 
-birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)
+ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)
 
-death3 = Transition(origin='R', equation='-gamma*I', transition_type=TransitionType.D)
+ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)
+------------------
 
-modelBD = DeterministicOde(stateList, paramList, birth_death=[death1, birth2, death3])
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 1
+----> 1 from pygom import Transition, TransitionType, common_models
+      3 ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)
+      5 ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)
 
-modelBD.get_ode_eqn()
--------------------
+ModuleNotFoundError: No module named 'pygom'
+ModuleNotFoundError: No module named 'pygom'
 
diff --git a/docs/pygom-doc/_build/html/search.html b/docs/pygom-doc/_build/html/search.html
index 3dc6e509..6c826f04 100644
--- a/docs/pygom-doc/_build/html/search.html
+++ b/docs/pygom-doc/_build/html/search.html
@@ -168,6 +168,11 @@
 
    13. Hard Problem
      14. 
 
 
+
    14. Simple Epidemic Analysis
      15. 
+
    15. Reading and using EpiJSON data
      16. 
+
    16. Solving Boundary Value Problems
      17. 
+
    17. Gradient estimation under square loss
      18. 
+
    18. Example: Fitz Hugh
      19. 
 
 
     
      
diff --git a/docs/pygom-doc/_build/html/searchindex.js b/docs/pygom-doc/_build/html/searchindex.js
index 5ee53082..c275433c 100644
--- a/docs/pygom-doc/_build/html/searchindex.js
+++ b/docs/pygom-doc/_build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["intro", "markdown", "md/getting_started", "md/unrollOde", "mdconvert/common_models/", "mdconvert/doc_to_sort/", "mdconvert/mod/", "mdconvert/unroll/", "notebooks/sir", "notebooks/stochastic", "notebooks/transition", "notebooks/unroll/unrollBD", "notebooks/unroll/unrollHard", "notebooks/unroll/unrollSimple"], "filenames": ["intro.md", "markdown.md", "md\\getting_started.md", "md\\unrollOde.md", "mdconvert\\common_models\\.md", "mdconvert\\doc_to_sort\\.md", "mdconvert\\mod\\.md", "mdconvert\\unroll\\.md", "notebooks\\sir.ipynb", "notebooks\\stochastic.ipynb", "notebooks\\transition.ipynb", "notebooks\\unroll\\unrollBD.ipynb", "notebooks\\unroll\\unrollHard.ipynb", "notebooks\\unroll\\unrollSimple.ipynb"], "titles": ["Welcome to the documentation for PyGOM", "Markdown Files", "Getting started", "Convert ODE into transitions", ".SIR_Birth_Death{.interpreted-text role=\u201dfunc\u201d}", "Solving Boundary Value Problems {#bvpSimple}", "stochastic", "Simple Problem {#unrollSimple}", "Motivating Example: SIR Model", "Stochastic representation of ODEs", "Transition Object", "ODE With Birth and Death Process", "Hard Problem", "Simple Problem"], "terms": {"python": [0, 2], "gener": [0, 9], "od": [0, 2, 5, 8, 10, 12, 13], "model": [0, 2, 3, 4, 6, 7, 9, 11, 12, 13], "packag": [0, 5, 8, 9], "aim": [0, 5], "facilit": 0, "applic": 0, "ordinari": [0, 2], "differenti": [0, 2, 5], "equat": [0, 2, 5, 9, 11, 12, 13], "real": 0, "world": 0, "focu": 0, "epidemiolog": 0, "thi": [0, 1, 3, 5, 7, 8, 9, 10, 11, 12, 13], "help": [0, 1, 5, 8, 10], "defin": [0, 2, 3, 5, 7, 9, 11, 12, 13], "system": [0, 2, 5, 8, 9, 10, 12], "an": [0, 1, 4, 5, 8, 9, 10], "intuit": 0, "manner": 0, "provid": [0, 2, 3, 8], "conveni": 0, "function": [0, 1, 2, 3, 5, 8, 9], "make": [0, 9], "us": [0, 1, 5, 7, 8, 9, 10, 12, 13], "variou": [0, 8], "algebra": [0, 2], "numer": [0, 2, 5, 8], "librari": 0, "backend": [0, 9], "can": [0, 1, 2, 3, 5, 8, 9, 10, 12], "straight": 0, "forward": [0, 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diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/bvpSimple.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/bvpSimple.ipynb
new file mode 100644
index 00000000..08906320
--- /dev/null
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/bvpSimple.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8d35685a",
+   "metadata": {},
+   "source": [
+    "# Solving Boundary Value Problems\n",
+    "\n",
+    "In addition to finding solutions for an IVP and estimate the unknown\n",
+    "parameters, this package also allows you to solve BVP with a little bit\n",
+    "of imagination. Here, we are going to show how a BVP can be solved by\n",
+    "treating it as a parameter estimation problem. Essentially, a shooting\n",
+    "method where the first boundary condition defines the initial condition\n",
+    "of an IVP and the second boundary condition is an observation. Two\n",
+    "examples, both from MATLAB[1], will be shown here.\n",
+    "\n",
+    "## Simple model 1\n",
+    "\n",
+    "We are trying to find the solution to the second order differential\n",
+    "equation\n",
+    "\n",
+    "$$\\nabla^{2} y + |y| = 0$$\n",
+    "\n",
+    "subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert\n",
+    "this into a set of first order ODE\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{d y_{0}}{dt} &= y_{1} \\\\\n",
+    "\\frac{d y_{1}}{dt} &= -|y_{0}|\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "using an auxiliary variable $y_{1} = \\nabla y$ and $y_{0} = y$. Setting\n",
+    "up the system below\n",
+    "\n",
+    "In \\[1\\]: from pygom import Transition, TransitionType,\n",
+    "DeterministicOde, SquareLoss\n",
+    "\n",
+    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "\n",
+    "In \\[2\\]: stateList = \\['y0', 'y1'\\]\n",
+    "\n",
+    "In \\[3\\]: paramList = \\[\\]\n",
+    "\n",
+    "In \\[4\\]: ode1 = Transition(origin='y0',  \n",
+    "...: equation='y1', ...: transition_type=TransitionType.ODE)\n",
+    "\n",
+    "In \\[5\\]: ode2 = Transition(origin='y1',  \n",
+    "...: equation='-abs(y0)', ...: transition_type=TransitionType.ODE)\n",
+    "\n",
+    "In \\[6\\]: model = DeterministicOde(stateList,  \n",
+    "...: paramList, ...: ode=\\[ode1, ode2\\])\n",
+    "\n",
+    "In \\[7\\]: model.get_ode_eqn()\n",
+    "\n",
+    "We check that the equations are correct before proceeding to set up our\n",
+    "loss function.\n",
+    "\n",
+    "In \\[1\\]: import numpy\n",
+    "\n",
+    "In \\[2\\]: from scipy.optimize import minimize\n",
+    "\n",
+    "In \\[3\\]: initialState = \\[0.0, 1.0\\]\n",
+    "\n",
+    "In \\[4\\]: t = numpy.linspace(0, 4, 100)\n",
+    "\n",
+    "In \\[5\\]: model.initial_values = (initialState, t\\[0\\])\n",
+    "\n",
+    "In \\[6\\]: solution = model.integrate(t\\[1::\\])\n",
+    "\n",
+    "In \\[7\\]: f = plt.figure()\n",
+    "\n",
+    "@savefig bvp1_random_guess_plot.png In \\[8\\]: model.plot()\n",
+    "\n",
+    "In \\[9\\]: plt.close()\n",
+    "\n",
+    "Setting up the second boundary condition $y(4) = -2$ is easy, because\n",
+    "that is just a single observation attached to the state $y_{1}$.\n",
+    "Enforcing the first boundary condition requires us to set it as the\n",
+    "initial condition. Because the condition only states that $y(0) = 0$,\n",
+    "the starting value of the other state $y_1$ is free. We let our loss\n",
+    "object know that it is free through the targetState input argument.\n",
+    "\n",
+    "In \\[10\\]: theta = \\[0.0\\]\n",
+    "\n",
+    "In \\[11\\]: obj = SquareLoss(theta=theta,  \n",
+    ".…: ode=model, .…: x0=initialState, .…: t0=t\\[0\\], .…: t=t\\[-1\\], .…:\n",
+    "y=\\[-2\\], .…: state_name=\\['y0'\\], .…: target_state=\\['y1'\\])\n",
+    "\n",
+    "In \\[12\\]: thetaHat = minimize(fun=obj.costIV, x0=\\[0.0\\])\n",
+    "\n",
+    "In \\[13\\]: print(thetaHat)\n",
+    "\n",
+    "In \\[14\\]: model.initial_values = (\\[0.0\\] + thetaHat\\['x'\\].tolist(),\n",
+    "t\\[0\\])\n",
+    "\n",
+    "In \\[15\\]: solution = model.integrate(t\\[1::\\])\n",
+    "\n",
+    "In \\[16\\]: f = plt.figure()\n",
+    "\n",
+    "@savefig bvp1_solution_plot.png In \\[17\\]: model.plot()\n",
+    "\n",
+    "In \\[18\\]: plt.close()\n",
+    "\n",
+    "We are going to visualize the solution, and also check the boundary\n",
+    "condition. The first became our initial condition, so it is always\n",
+    "satisfied and only the latter is of concern, which is zero (subject to\n",
+    "numerical error) from thetaHat.\n",
+    "\n",
+    "## Simple model 2\n",
+    "\n",
+    "Our second example is different as it involves an actual parameter and\n",
+    "also time. We have the Mathieu's Equation\n",
+    "\n",
+    "$$\\nabla^{2} y + \\left(p - 2q \\cos(2x)\\right)y = 0$$\n",
+    "\n",
+    "and the aim is to compute the fourth eigenvalue $q=5$. There are three\n",
+    "boundary conditions\n",
+    "\n",
+    "$$\\nabla y(0) = 0, \\quad \\nabla y(\\pi) = 0, \\quad y(0) = 1$$\n",
+    "\n",
+    "and we aim to solve it by converting it to a first order ODE and tackle\n",
+    "it as an IVP. As our model object does not allow the use of the time\n",
+    "component in the equations, we introduce a anxiliary state $\\tau$ that\n",
+    "replaces time $t$. Rewrite the equations using\n",
+    "$y_{0} = y, y_{1} = \\nabla y$ and define our model as\n",
+    "\n",
+    "In \\[1\\]: stateList = \\['y0', 'y1', 'tau'\\]\n",
+    "\n",
+    "In \\[2\\]: paramList = \\['p'\\]\n",
+    "\n",
+    "In \\[3\\]: ode1 = Transition('y0', 'y1', TransitionType.ODE)\n",
+    "\n",
+    "In \\[4\\]: ode2 = Transition('y1', '-(p - 2\\*5\\*cos(2\\*tau))\\*y0',\n",
+    "TransitionType.ODE)\n",
+    "\n",
+    "In \\[5\\]: ode3 = Transition('tau', '1', TransitionType.ODE)\n",
+    "\n",
+    "In \\[6\\]: model = DeterministicOde(stateList, paramList, ode=\\[ode1,\n",
+    "ode2, ode3\\])\n",
+    "\n",
+    "In \\[7\\]: theta = \\[1.0, 1.0, 0.0\\]\n",
+    "\n",
+    "In \\[8\\]: p = 15.0\n",
+    "\n",
+    "In \\[9\\]: t = numpy.linspace(0, numpy.pi)\n",
+    "\n",
+    "In \\[10\\]: model.parameters = \\[('p',p)\\]\n",
+    "\n",
+    "In \\[11\\]: model.initial_values = (theta, t\\[0\\])\n",
+    "\n",
+    "In \\[12\\]: solution = model.integrate(t\\[1::\\])\n",
+    "\n",
+    "In \\[13\\]: f = plt.figure()\n",
+    "\n",
+    "@savefig bvp2_random_guess_plot.png In \\[14\\]: model.plot()\n",
+    "\n",
+    "In \\[15\\]: plt.close()\n",
+    "\n",
+    "Now we are ready to setup the estimation. Like before, we setup the\n",
+    "second boundary condition by pretending that it is an observation. We\n",
+    "have all the initial conditions defined by the first boundary condition\n",
+    "\n",
+    "In \\[1\\]: obj = SquareLoss(15.0, model, x0=\\[1.0, 0.0, 0.0\\], t0=0.0,\n",
+    "t=numpy.pi, y=0.0, state_name='y1')\n",
+    "\n",
+    "In \\[2\\]: xhatObj = minimize(obj.cost,\\[15\\])\n",
+    "\n",
+    "In \\[3\\]: print(xhatObj)\n",
+    "\n",
+    "In \\[4\\]: model.parameters = \\[('p', xhatObj\\['x'\\]\\[0\\])\\]\n",
+    "\n",
+    "In \\[5\\]: model.initial_values = (\\[1.0, 0.0, 0.0\\], t\\[0\\])\n",
+    "\n",
+    "In \\[5\\]: solution = model.integrate(t\\[1::\\])\n",
+    "\n",
+    "In \\[6\\]: f = plt.figure()\n",
+    "\n",
+    "@savefig bvp2_solution_plot.png In \\[7\\]: model.plot()\n",
+    "\n",
+    "In \\[8\\]: plt.close()\n",
+    "\n",
+    "The plot of the solution shows the path that satisfies all boundary\n",
+    "condition. The last subplot is time which obvious is redundant here but\n",
+    "the `DeterministicOde.plot` method is not yet able to recognize the time\n",
+    "component. Possible speed up can be achieved through the use of\n",
+    "derivative information or via root finding method that tackles the\n",
+    "gradient directly, instead of the cost function.\n",
+    "\n",
+    "**Reference**\n",
+    "\n",
+    "[1] "
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/epi.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/epi.ipynb
new file mode 100644
index 00000000..608e551c
--- /dev/null
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/epi.ipynb
@@ -0,0 +1,84 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aafa6e90",
+   "metadata": {},
+   "source": [
+    "# Simple Epidemic Analysis\n",
+    "\n",
+    "A common application of ordinary differential equations is in the field\n",
+    "of epidemiology modeling. More concretely, compartmental models that is\n",
+    "used to describe disease progression. We demonstrate some of the simple\n",
+    "algebraic analysis one may wish to take when given a compartment model.\n",
+    "Our use one of the simplest model, an SIR model with birth and death\n",
+    "processes, which is an extension of the one in `sir`. First, we\n",
+    "initialize the model below.\n",
+    "\n",
+    "In \\[1\\]: from pygom import common_models\n",
+    "\n",
+    "In \\[2\\]: ode = common_models.SIR_Birth_Death()\n",
+    "\n",
+    "In \\[3\\]: print(ode.get_ode_eqn())\n",
+    "\n",
+    "## Obtaining the R0\n",
+    "\n",
+    "The reproduction number, also known as the $R_{0}$, is the single most\n",
+    "powerful piece and reduced piece of information available from a\n",
+    "compartmental model. In a nutshell, it provides a single number - if the\n",
+    "parameters are known - which can the intuitive interpretation where\n",
+    "$R_{0} = 1$ defines the tipping point of an outbreak. A $R_{0}$ value of\n",
+    "more than one signifies an potential outbreak where less than one\n",
+    "indicates that the disease will stop spreading naturally.\n",
+    "\n",
+    "To obtain the $R_{0}$, we simply have to tell the function which states\n",
+    "represent the *disease state*, which in this case is the state **I**.\n",
+    "\n",
+    "In \\[1\\]: from pygom.model.epi_analysis import \\*\n",
+    "\n",
+    "In \\[2\\]: print(R0(ode, 'I'))\n",
+    "\n",
+    "## Algebraic R0\n",
+    "\n",
+    "We may also wish to get the $R_{0}$ in pure algebraic term. This can be\n",
+    "achieved by the following few lines. Note that the result below is\n",
+    "slightly different from the one above. The difference is due to the\n",
+    "internal working of the functions, where `getR0` computes the\n",
+    "disease-free equilibrium value for the states and substitute them back\n",
+    "into the equation.\n",
+    "\n",
+    "In \\[1\\]: F, V = disease_progression_matrices(ode, 'I')\n",
+    "\n",
+    "In \\[2\\]: e = R0_from_matrix(F, V)\n",
+    "\n",
+    "In \\[3\\]: print(e)\n",
+    "\n",
+    "To replicate the output before, we have to find the values where the\n",
+    "disease-free equilibrium will be achieved. Substitution can then be\n",
+    "performed to retrieve $R_{0}$ in pure parameters.\n",
+    "\n",
+    "In \\[1\\]: dfe = DFE(ode, \\['I'\\])\n",
+    "\n",
+    "In \\[2\\]: print(dfe)\n",
+    "\n",
+    "In \\[3\\]: print(e\\[0\\].subs(dfe))"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/epijson.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/epijson.ipynb
new file mode 100644
index 00000000..5a7dc99d
--- /dev/null
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/epijson.ipynb
@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "53730f4a",
+   "metadata": {},
+   "source": [
+    "# Reading and using EpiJSON data\n",
+    "\n",
+    "Epidemiology data is complicated due to the many different stages a\n",
+    "patient can go through and whether a modeling technique is applicable\n",
+    "depends heavily on the recording of data. EpiJSON is a framework whih\n",
+    "tries to captures all the information [\\[Finnie2016\\]](), in a JSON\n",
+    "format as the name suggests.\n",
+    "\n",
+    "This package provides the functionality to process EpiJSON data. Due to\n",
+    "the nature of this package, modeling of ode, it processes the data file\n",
+    "with this in mind. The output is therefore in the cumulative form as\n",
+    "default, shown below, in a `pandas.DataFrame` format. The input can be\n",
+    "in a string format, a file or already a `dict`.\n",
+    "\n",
+    "In \\[1\\]: from pygom.loss.read_epijson import epijson_to_data_frame\n",
+    "\n",
+    "In \\[2\\]: import pkgutil\n",
+    "\n",
+    "In \\[3\\]: data = pkgutil.get_data('pygom', 'data/eg1.json')\n",
+    "\n",
+    "In \\[3\\]: df = epijson_to_data_frame(data)\n",
+    "\n",
+    "In \\[4\\]: print(df)\n",
+    "\n",
+    "Given that the aim of loading the data is usually for model fitting, we\n",
+    "allow EpiJSON as input directly to the loss class\n",
+    "`pygom.loss.EpijsonLoss` which uses the Poisson loss under the hood.\n",
+    "\n",
+    "In \\[1\\]: from pygom.model import common_models\n",
+    "\n",
+    "In \\[2\\]: from pygom.loss.epijson_loss import EpijsonLoss\n",
+    "\n",
+    "In \\[3\\]: ode = common_models.SIR(\\[0.5, 0.3\\])\n",
+    "\n",
+    "In \\[4\\]: obj = EpijsonLoss(\\[0.005, 0.03\\], ode, data, 'Death', 'R',\n",
+    "\\[300, 2, 0\\])\n",
+    "\n",
+    "In \\[5\\]: print(obj.cost())\n",
+    "\n",
+    "In \\[6\\]: print(obj.\\_df)\n",
+    "\n",
+    "Given an initialized object, all the operations are inherited from\n",
+    "`pygom.loss.BaseLoss`. We demonstrated above how to calculate the cost\n",
+    "and the rest will not be shown for brevity. The data frame is stored\n",
+    "inside of the loss object and can be retrieved for inspection at any\n",
+    "time point.\n",
+    "\n",
+    "Rather unfortunately, initial values for the states is still required,\n",
+    "but the time is not. When the time is not supplied, then the first time\n",
+    "point in the data will be treated as $t0$. The input Death indicate which column of the data is used\n",
+    "and $R$ the corresponding state the data belongs to."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/fh.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/fh.ipynb
new file mode 100644
index 00000000..3626b0f1
--- /dev/null
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/fh.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "73506cd2",
+   "metadata": {},
+   "source": [
+    "# Example: Fitz Hugh\n",
+    "\n",
+    "## Defining the model\n",
+    "\n",
+    "We are going to investigate another classic model here, the\n",
+    "FitzHugh-Nagumo, or simply FitzHugh here. The model has already been\n",
+    "defined in `common_models` so we can load it easily\n",
+    "\n",
+    "In \\[1\\]: from pygom import SquareLoss, common_models\n",
+    "\n",
+    "In \\[2\\]: import numpy\n",
+    "\n",
+    "In \\[3\\]: import scipy.integrate, scipy.optimize\n",
+    "\n",
+    "In \\[4\\]: import math,time,copy\n",
+    "\n",
+    "In \\[5\\]: import matplotlib.pyplot as plt\n",
+    "\n",
+    "In \\[1\\]: x0 = \\[-1.0, 1.0\\]\n",
+    "\n",
+    "In \\[2\\]: t0 = 0\n",
+    "\n",
+    "In \\[3\\]: \\# params\n",
+    "\n",
+    "In \\[4\\]: paramEval = \\[('a',0.2), ('b',0.2), ('c',3.0)\\]\n",
+    "\n",
+    "In \\[5\\]: ode = common_models.FitzHugh(paramEval)\n",
+    "\n",
+    "In \\[5\\]: ode.initial_values = (x0, t0)\n",
+    "\n",
+    "Define a set of time points and lets see how the two states $V$ and $R$\n",
+    "are suppose to behave.\n",
+    "\n",
+    "In \\[6\\]: t = numpy.linspace(1, 20, 30).astype('float64')\n",
+    "\n",
+    "In \\[7\\]: solution = ode.integrate(t)\n",
+    "\n",
+    "@savefig fh_plot.png In \\[8\\]: ode.plot()\n",
+    "\n",
+    "## Estimate the parameters\n",
+    "\n",
+    "Obtaining the correct parameters for the FitzHugh model is well known to\n",
+    "be difficult, this is because the surface is multimodal. Although this\n",
+    "has been shown many times in the literature, so we will omit the\n",
+    "details. Regardless, we give it a go with some initial guess. with some\n",
+    "luck, we will be able to recover the original parameters. First, we try\n",
+    "it out with only one target state\n",
+    "\n",
+    "In \\[26\\]: theta = \\[0.5, 0.5, 0.5\\]\n",
+    "\n",
+    "In \\[27\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n",
+    "'R')\n",
+    "\n",
+    "In \\[28\\]: boxBounds = \\[  \n",
+    ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0) .…: \\]\n",
+    "\n",
+    "In \\[29\\]: res = scipy.optimize.minimize(fun=objFH.cost,  \n",
+    ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n",
+    "method='L-BFGS-B')\n",
+    "\n",
+    "In \\[30\\]: print(res)\n",
+    "\n",
+    "Then we try the same again but with both state as our target. Now we\n",
+    "won't look at the iterations because they are pretty pointless.\n",
+    "\n",
+    "In \\[30\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n",
+    "\\['V','R'\\])\n",
+    "\n",
+    "In \\[31\\]: res = scipy.optimize.minimize(fun=objFH.cost,  \n",
+    ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n",
+    "method='L-BFGS-B')\n",
+    "\n",
+    "In \\[32\\]: print(res)\n",
+    "\n",
+    "Note how the estimates are the same, unlike other models.\n",
+    "\n",
+    "## Estimate initial value\n",
+    "\n",
+    "We can further assume that we have no idea about the initial values for\n",
+    "$V$ and $R$ as well. We also provide guesstimate to set off the\n",
+    "optimization. The input vector $\\theta$ must have the parameters first,\n",
+    "then the initial values, along with the corresponding bounds.\n",
+    "\n",
+    "First, only a single target state, i.e. we only have observations for\n",
+    "one of states which is $R$ in this case\n",
+    "\n",
+    "In \\[35\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n",
+    "'R')\n",
+    "\n",
+    "In \\[35\\]: boxBounds = \\[  \n",
+    ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0), .…: (None,None), .…:\n",
+    "(None,None) .…: \\]\n",
+    "\n",
+    "In \\[36\\]: res = scipy.optimize.minimize(fun=objFH.costIV,  \n",
+    ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5,0.5\\], .…:\n",
+    "bounds=boxBounds, .…: method='L-BFGS-B')\n",
+    "\n",
+    "In \\[37\\]: print(res)\n",
+    "\n",
+    "then both state as target at the same time\n",
+    "\n",
+    "In \\[38\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n",
+    "\\['V','R'\\])\n",
+    "\n",
+    "In \\[38\\]: res = scipy.optimize.minimize(fun=objFH.costIV,  \n",
+    ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5, 0.5\\], .…:\n",
+    "bounds=boxBounds, .…: method='L-BFGS-B')\n",
+    "\n",
+    "In \\[39\\]: print(res)\n",
+    "\n",
+    "See the difference between the two estimate with the latter, both state\n",
+    "were used, yielding superior estimates. Note that only the forward\n",
+    "sensitivity method is implemented when estimating the initial value, and\n",
+    "it is assumed that the starting condition for all the states are\n",
+    "unknown.\n",
+    "\n",
+    "The choice of algorithm here is the **L-BFGS-B** which is a better\n",
+    "choice because the parameter space of the FitzHugh is rough (i.e. large\n",
+    "second derivative) as well as being multimodal. This means that the\n",
+    "Hessian is not guaranteed to be positive definite and approximation\n",
+    "using $J^{\\top}J$ is poor, with $J$ being the Jacobian of the objective\n",
+    "function."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
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@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ea266a4c",
+   "metadata": {},
+   "source": [
+    "# Gradient estimation under square loss\n",
+    "\n",
+    "Assuming that we have a set of $N$ observations $y_{i}$ at specific time\n",
+    "points $t_{i}$, $i = 1,\\ldots,N$, we may wish to test out a set of ode\n",
+    "to see whether it fits to the data. The most natural way to test such\n",
+    "*fit* is to minimize the sum of squares between our observations $y$ and\n",
+    "see whether the resulting solution of the ode and the estimationed\n",
+    "parameters makes sense.\n",
+    "\n",
+    "We assume that this estimation process will be tackled through a\n",
+    "non-linear optimization point of view. However, it should be noted that\n",
+    "such estimates can also be performed via MCMC or from a global\n",
+    "optimization perspective. A key element in non-linear optimization is\n",
+    "the gradient, which is the focus of this page.\n",
+    "\n",
+    "Multiple ways of obtaining the gradient have been implemented. All of\n",
+    "them serve a certain purpose and may not be a viable/appropriate options\n",
+    "depending on the type of ode. More generally, let $d,p$ be the number of\n",
+    "states and paramters respectively. Then finite difference methods have a\n",
+    "run order of $O(p+1)$ of the original ode, forward sensitivity require\n",
+    "an integration of an ode of size $(d+1)p$ rather than $d$. The adjoint\n",
+    "method require two run of size $d$ in principle, but actual run time is\n",
+    "dependent on the number of observations.\n",
+    "\n",
+    "For the details of the classes and methods, please refer to `mod`.\n",
+    "\n",
+    "## Notation\n",
+    "\n",
+    "We introduce the notations that will be used in the rest of the page,\n",
+    "some of which may be slightly unconventional but necessary due to the\n",
+    "complexity of the problem. Let $x \\in \\mathbb{R}^{d}$ and\n",
+    "$\\theta \\in \\mathbb{R}^{p}$ be the states and parameters respectively.\n",
+    "The term *state* or *simulation* are used interchangeably, even though\n",
+    "strictly speaking a state is $x$ whereas $x(t)$ is the simulation. An\n",
+    "ode is defined as\n",
+    "\n",
+    "$$f(x,\\theta) = \\dot{x} = \\frac{\\partial x}{\\partial t}$$\n",
+    "\n",
+    "and usually comes with a set of initial conditions $(x_0,t_0)$ where\n",
+    "$t_0 \\le t_{i} \\forall i$. Let $g(x,\\theta)$ be a function that maps the\n",
+    "set of states to the observations,\n",
+    "$g : \\mathbb{R}^{d} \\rightarrow \\mathbb{R}^{m}$. For compartmental\n",
+    "problems, which is our focus, $\\nabla_{\\theta}g(x,\\theta)$ is usually\n",
+    "zero and $\\nabla_{x}g(x,\\theta)$ is an identity function for some or all\n",
+    "of the states $x$. Denote $l(x_{0},\\theta,x)$ as our cost function\n",
+    "$l : \\mathbb{R}^{m} \\rightarrow \\mathbb{R}$ and $L(x_{0},\\theta,x)$ be\n",
+    "the sum of $l(\\cdot)$. Both $x$ and $x_{0}$ are usually dropped for\n",
+    "simplicity. We will be dealing exclusively with square loss here, which\n",
+    "means that\n",
+    "\n",
+    "$$L(\\theta) = \\sum_{i=1}^{N} \\left\\| y_{i} - g(x(t_{i})) \\right\\|^{2} = \\mathbf{e}^{\\top} \\mathbf{e}$$\n",
+    "\n",
+    "where $\\mathbf{e}$ is the residual vector, with elements\n",
+    "\n",
+    "$$e_{i} = y_{i} - x(t_{i}).$$\n",
+    "\n",
+    "## Model setup\n",
+    "\n",
+    "Again, we demonstrate the functionalities of our classes using an SIR\n",
+    "model.\n",
+    "\n",
+    "In \\[1\\]: from pygom import SquareLoss, common_models\n",
+    "\n",
+    "In \\[2\\]: import copy,time,numpy\n",
+    "\n",
+    "In \\[2\\]: ode = common_models.SIR()\n",
+    "\n",
+    "In \\[3\\]: paramEval = \\[('beta',0.5), ('gamma',1.0/3.0) \\]\n",
+    "\n",
+    "In \\[7\\]: \\# the initial state, normalized to zero one\n",
+    "\n",
+    "In \\[8\\]: x0 = \\[1., 1.27e-6, 0.\\]\n",
+    "\n",
+    "In \\[5\\]: \\# initial time\n",
+    "\n",
+    "In \\[6\\]: t0 = 0\n",
+    "\n",
+    "In \\[5\\]: ode.parameters = paramEval\n",
+    "\n",
+    "In \\[6\\]: ode.initial_values = (x0, t0)\n",
+    "\n",
+    "In \\[9\\]: \\# set the time sequence that we would like to observe\n",
+    "\n",
+    "In \\[10\\]: t = numpy.linspace(1, 150, 100)\n",
+    "\n",
+    "In \\[11\\]: numStep = len(t)\n",
+    "\n",
+    "In \\[11\\]: solution = ode.integrate(t)\n",
+    "\n",
+    "In \\[12\\]: y = solution\\[1::,2\\].copy()\n",
+    "\n",
+    "In \\[13\\]: y += numpy.random.normal(0, 0.1, y.shape)\n",
+    "\n",
+    "Now we have set up the model along with some observations, obtaining the\n",
+    "gradient only requires the end user to put the appropriate information\n",
+    "it into the class `SquareLoss`. Given the initial guess $\\theta$\n",
+    "\n",
+    "In \\[210\\]: theta = \\[0.2, 0.2\\]\n",
+    "\n",
+    "We initialize the `SquareLoss` simply as\n",
+    "\n",
+    "In \\[20\\]: objSIR = SquareLoss(theta, ode, x0, t0, t, y, 'R')\n",
+    "\n",
+    "where the we also have to specify the state our observations are from.\n",
+    "Now, we demonstrate the different methods in obtaining the gradient and\n",
+    "mathematics behind it.\n",
+    "\n",
+    "## Forward sensitivity\n",
+    "\n",
+    "The forward sensitivity equations are derived by differentiating the\n",
+    "states implicitly, which yields\n",
+    "\n",
+    "$$\\frac{d\\dot{x}}{d\\theta} = \\frac{\\partial f}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial f}{\\partial \\theta}.$$\n",
+    "\n",
+    "So finding the sensitivies $\\frac{dx}{d\\theta}$ simply require another\n",
+    "integration of a $p$ coupled ode of $d$ dimension, each with the same\n",
+    "Jacobian as the original ode. This integration is performed along with\n",
+    "the original ode because of possible non-linearity.\n",
+    "\n",
+    "A direct call to the method `sensitivity `\n",
+    "computed the gradient\n",
+    "\n",
+    "In \\[33\\]: gradSens = objSIR.sensitivity()\n",
+    "\n",
+    "whereas `.jac` will allow the end user to obtain the Jacobian (of the\n",
+    "objective function) and the residuals, the information required to get\n",
+    "the gradient as we see next.\n",
+    "\n",
+    "In \\[33\\]: objJac, output = objSIR.jac(full_output=True)\n",
+    "\n",
+    "## Gradient\n",
+    "\n",
+    "Just the sensitivities alone are not enough to obtain the gradient, but\n",
+    "we are $90\\%$ there. Differentiating the loss function\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{dL}{d\\theta} &= \\nabla_{\\theta} \\sum_{i=1}^{N}\\frac{dl}{dg} \\\\\n",
+    "                   &= \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial l}{\\partial \\theta} \\\\\n",
+    "                   &= \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial \\theta}\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "via chain rule. When $\\frac{\\partial g}{\\partial \\theta} = 0$, the total\n",
+    "gradient simplifies to\n",
+    "\n",
+    "$$\\frac{dL}{d\\theta} = \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial x}\\frac{dx}{d\\theta}$$\n",
+    "\n",
+    "Obviously, the time indicies are dropped above but all the terms above\n",
+    "are evaluated only at the observed time points. More concretely, this\n",
+    "means that\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{\\partial l(x(j),\\theta)}{\\partial g} = \\left\\{ \\begin{array}{ll} -2(y_{i} - x(j)) & , \\; j = t_{i} \\\\ 0 & \\; \\text{otherwise} \\end{array} \\right.\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "When $g(\\cdot)$ is an identity function (which is assumed to be the case\n",
+    "in `SquareLoss`)\n",
+    "\n",
+    "$$\\frac{\\partial g(x(t_{i}),\\theta)}{\\partial x} = I_{d}$$\n",
+    "\n",
+    "then the gradient simplifies even further as it is simply\n",
+    "\n",
+    "$$\\frac{dL}{d\\theta} = -2\\mathbf{e}^{\\top}\\mathbf{S}$$\n",
+    "\n",
+    "where $\\mathbf{e}$ is the vector of residuals and\n",
+    "$\\mathbf{S} = \\left[\\mathbf{s}_{1},\\mathbf{s}_{2},\\ldots,\\mathbf{s}_{n}\\right]$\n",
+    "with elements\n",
+    "\n",
+    "$$\\mathbf{s}_{i} = \\frac{dx}{d\\theta}(t_{i}),$$\n",
+    "\n",
+    "the solution of the forward sensitivies at time $t_{i}$, obtained from\n",
+    "solving the coupled ode as mentioned previously.\n",
+    "\n",
+    "## Jacobian\n",
+    "\n",
+    "Now note how the gradient simplifies to $-2\\mathbf{e}^{\\top}\\mathbf{S}$.\n",
+    "Recall that a standard result in non-linear programming states that the\n",
+    "gradient of a sum of sqaures objective function $L(\\theta,y,x)$ is\n",
+    "\n",
+    "$$\\nabla_{\\theta} L(\\theta,y,x) = -2(\\mathbf{J}^{T} \\left[\\mathbf{y} - \\mathbf{f}(x,\\boldsymbol{\\theta}) \\right] )^{\\top}$$\n",
+    "\n",
+    "with $f(x,\\theta)$ our non-linear function and $J$ our Jacobian with\n",
+    "elements\n",
+    "\n",
+    "$$J_{i} = \\frac{\\partial f(x_{i},\\boldsymbol{\\theta})}{\\partial \\boldsymbol{\\theta}}.$$\n",
+    "\n",
+    "This is exactly what we have seen previously, substituting in reveals\n",
+    "that $J = \\mathbf{S}$. Hence, the Jacobian is (a necessary)by product\n",
+    "when we wish to obtain the gradient. In fact, this is exactly how we\n",
+    "proceed in `sensitivity ` where it makes\n",
+    "an internal call to `jac ` to obtain the Jacobian\n",
+    "first. This allows the end user to have more options when choosing which\n",
+    "type of algorithms to use, i.e. Gauss-Newton or Levenberg-Marquardt.\n",
+    "\n",
+    "To check that the output is in fact the same\n",
+    "\n",
+    "In \\[1\\]: objJac.transpose().dot(-2\\*output\\['resid'\\]) - gradSens\n",
+    "\n",
+    "## Adjoint\n",
+    "\n",
+    "When the number of parameters increases, the number of sensitivies also\n",
+    "increases. The time required scales directly with the number of\n",
+    "parameters. We describe another method which does not depend on the\n",
+    "number of parameters, but rather, the number of states and observations.\n",
+    "\n",
+    "The full derivations will not be shown here, but we aim to provide\n",
+    "enough information to work out the steps performed in the our code. Let\n",
+    "write our optimization problem as\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "min_{\\theta} \\quad & \\int_{t_{0}}^{T} l(x_{0},\\theta,x(t)) dt \\\\\n",
+    "s.t. \\quad & \\dot{x} = f(x,\\theta)\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "which is identical to the original problem but in a continuous setting.\n",
+    "Now write the constrained problem in the Lagrangian form\n",
+    "\n",
+    "$$min_{\\theta} \\; L(\\theta) + \\int_{t_{0}}^{T} \\lambda^{\\top}(\\dot{x} - f(x,\\theta))$$\n",
+    "\n",
+    "with Lagrangian multiplier $\\lambda \\ge 0$. After some algebraic\n",
+    "manipulation, it can be shown that the total derivative of the\n",
+    "Lagrangian function is\n",
+    "\n",
+    "$$\\frac{dL}{d\\theta} = \\int_{t_{0}}^{T} \\left(\\frac{\\partial l}{\\partial \\theta} - \\lambda^{\\top}\\frac{\\partial f}{\\partial \\theta} \\right) dt.$$\n",
+    "\n",
+    "Using previously defined loss functions (the identity), the first term\n",
+    "is zero and evaluating $\\frac{\\partial f}{\\partial \\theta}$ is trivial.\n",
+    "What remains is the calculation of $\\lambda(t)$ for\n",
+    "$t \\in \\left[t_{0},T\\right]$.\n",
+    "\n",
+    "Although this still seem to be ill-posed problem when Looking at the\n",
+    "Lagrangian function, one can actually obtain the *adjoint equation*,\n",
+    "after certain assumptions and\n",
+    "\n",
+    "$$\\frac{d\\lambda^{\\top}}{dt} = \\frac{\\partial l}{\\partial x} - \\lambda^{\\top}\\frac{\\partial f}{\\partial \\theta}.$$\n",
+    "\n",
+    "which is again an integration. An unfortunate situation arise here for\n",
+    "non-linear systems because we use the minus Jacobian in the adjoint\n",
+    "equation. So if the eigenvalues of the Jacobian indicate that our\n",
+    "original ode is stable, such as -1, the minus eigenvalues (now 1)\n",
+    "implies that the adjoint equation is not stable. Therefore, one must\n",
+    "integrate backward in time to solve the adjoint equation and it cannot\n",
+    "be solved simultaneously as the ode, unlike the forward sensitivity\n",
+    "equations.\n",
+    "\n",
+    "Given a non-linearity ode, we must store information about the states\n",
+    "between $t_{0}$ and $T$ in order to perform the integration. There are\n",
+    "two options, both require storing many evaluated $x(j)$ within the\n",
+    "interval $\\left[t_{0},T\\right]$. Unfortunately, only one is available;\n",
+    "interpolation over all states and integrate using the interpolating\n",
+    "functions. The alternative of using observed $x(j)'s$ at fixed points is\n",
+    "not competitive because we are unable to use fortran routines for the\n",
+    "integration\n",
+    "\n",
+    "The method of choice here to perform the adjoint calcuation is to run a\n",
+    "forward integration, then perform an interpolation using splines with\n",
+    "explicit knots at the observed time points.\n",
+    "\n",
+    "In \\[326\\]: odeSIRAdjoint, outputAdjoint =\n",
+    "objSIR.adjoint(full_output=True)\n",
+    "\n",
+    "This is because evaluating the Jacobian may be expensive and Runge-kutta\n",
+    "method suffers as the complexity increases. In non-linear model such as\n",
+    "those found in epidemiology, each element of the Jacobian may be the\n",
+    "result of a complicated equation where linear step method will shine as\n",
+    "it makes as little function evaluation as possible. Note that\n",
+    "derivations in the literature, the initial condition when evaluating the\n",
+    "adjoint equation is $\\lambda(T)=0$. But in our code we used\n",
+    "$\\lambda(T) = -2(y(T)-x(T))$. Recall that we have observation $y(T)$ and\n",
+    "simulation $x(T)$, so that the adjoint equation evaluated at time $T$\n",
+    "\n",
+    "$$\\frac{\\partial \\lambda^{\\top}}{\\partial t} \\Big|_{T} = -2(y-f(x,\\theta))\\Big|_{T}  - \\lambda(T)\\frac{\\partial f}{\\partial \\theta}\\Big|_{T}$$\n",
+    "\n",
+    "with the second term equal to zero. Integration under step size $h$\n",
+    "implies that\n",
+    "$\\lambda(T) \\approx \\lim_{h \\to 0} \\lambda(T-h) = -2(y(T)-x(T))$.\n",
+    "\n",
+    "## Time Comparison\n",
+    "\n",
+    "A simple time comparison between the different methods reveals that the\n",
+    "forward sensitivity method dominates the others by a wide margin. It\n",
+    "will be tempting to conclude that it is the best and should be the\n",
+    "default at all times but that is not true, due to the complexity of each\n",
+    "method mentioned previously. We leave it to the end user to find out the\n",
+    "best method for their specific problem.\n",
+    "\n",
+    "In \\[319\\]: %timeit gradSens = objSIR.sensitivity()\n",
+    "\n",
+    "In \\[326\\]: %timeit odeSIRAdjoint,outputAdjoint =\n",
+    "objSIR.adjoint(full_output=True)\n",
+    "\n",
+    "## Hessian\n",
+    "\n",
+    "The Hessian is defined by\n",
+    "\n",
+    "$$\\frac{\\partial^{2} l}{\\partial \\theta^{2}} = \\left( \\frac{\\partial l}{\\partial x} \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + \\frac{\\partial x}{\\partial \\theta}^{\\top}\\frac{\\partial^{2} l}{\\partial x^{2}}\\frac{\\partial x}{\\partial \\theta}$$\n",
+    "\n",
+    "where $\\otimes$ is the Kronecker product. Note that $\\nabla_{\\theta} x$\n",
+    "is the sensitivity and the second order sensitivities can be found again\n",
+    "via the forward method, which involve another set of ode's, namely the\n",
+    "forward-forward sensitivities\n",
+    "\n",
+    "$$\\frac{\\partial}{\\partial t}\\left(\\frac{\\partial^{2} x}{\\partial \\theta^{2}}\\right) = \\left( \\frac{\\partial f}{\\partial x} \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + \\left( I_{d} \\otimes \\frac{\\partial x}{\\partial \\theta}^{\\top} \\right) \\frac{\\partial^{2} f}{\\partial x^{2}} \\frac{\\partial x}{\\partial \\theta}.$$\n",
+    "\n",
+    "From before, we know that\n",
+    "\n",
+    "$$\\frac{\\partial l}{\\partial x} = (-2y+2x)  \\quad and \\quad \\frac{\\partial^{2} l}{\\partial x^{2}} = 2I_{d}$$\n",
+    "\n",
+    "so our Hessian reduces to\n",
+    "\n",
+    "$$\\frac{\\partial^{2} l}{\\partial \\theta^{2}} = \\left( \\left(-2y+2x\\right) \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + 2S^{\\top}S,$$\n",
+    "\n",
+    "where the second term is a good approximation to the Hessian as\n",
+    "mentioned previously. This is the only implementation in place so far\n",
+    "even though obtaining the estimate this way is relatively slow.\n",
+    "\n",
+    "Just to demonstate how it works, lets look at the Hessian at the optimal\n",
+    "point. First, we obtain the optimal value\n",
+    "\n",
+    "In \\[211\\]: import scipy.linalg,scipy.optimize\n",
+    "\n",
+    "In \\[212\\]: boxBounds = \\[(0.0, 2.0), (0.0, 2.0)\\]\n",
+    "\n",
+    "In \\[213\\]: res = scipy.optimize.minimize(fun=objSIR.cost,  \n",
+    ".….: jac=objSIR.sensitivity, .….: x0=theta, .….: bounds=boxBounds, .….:\n",
+    "method='L-BFGS-B')\n",
+    "\n",
+    "Then compare again the least square estimate of the covariance matrix\n",
+    "against our version\n",
+    "\n",
+    "In \\[211\\]: resLS, cov_x, infodict, mesg, ier =\n",
+    "scipy.optimize.leastsq(func=objSIR.residual, x0=res\\['x'\\],\n",
+    "full_output=True)\n",
+    "\n",
+    "In \\[212\\]: HJTJ, outputHJTJ = objSIR.hessian(full_output=True)\n",
+    "\n",
+    "In \\[311\\]: print(scipy.linalg.inv(HJTJ))\n",
+    "\n",
+    "In \\[312\\]: print(cov_x)\n",
+    "\n",
+    "also note the difference between the Hessian and the approximation using\n",
+    "the Jacobian, which is in fact what the least squares routine uses.\n",
+    "\n",
+    "In \\[313\\]: print(scipy.linalg.inv(outputHJTJ\\['JTJ'\\]))"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/sir.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/sir.ipynb
index d90edec6..5a476736 100644
--- a/docs/pygom-doc/_build/jupyter_execute/notebooks/sir.ipynb
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/sir.ipynb
@@ -2,11 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6d53d797",
+   "id": "d2d9e2d3",
    "metadata": {},
    "source": [
     "# Motivating Example: SIR Model\n",
-    "{download}`this notebook <./sir.ipynb>`\n",
     "\n",
     "## Defining the model\n",
     "\n",
@@ -35,7 +34,19 @@
    "execution_count": 1,
    "id": "4c80a36a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pygom'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpygom\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Transition, TransitionType\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pygom'"
+     ]
+    }
+   ],
    "source": [
     "from pygom import Transition, TransitionType"
    ]
@@ -50,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 63,
    "id": "3ce016f2",
    "metadata": {},
    "outputs": [],
@@ -68,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 64,
    "id": "441e2287",
    "metadata": {},
    "outputs": [],
@@ -86,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 65,
    "id": "b5f80ab0",
    "metadata": {},
    "outputs": [],
@@ -119,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 66,
    "id": "f78c33d4",
    "metadata": {},
    "outputs": [],
@@ -137,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 67,
    "id": "c9fbcce0",
    "metadata": {},
    "outputs": [],
@@ -155,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 68,
    "id": "6ad54e09",
    "metadata": {},
    "outputs": [
@@ -171,7 +182,7 @@
        "[           I*gamma]])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -208,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 69,
    "id": "07f81fd1",
    "metadata": {},
    "outputs": [
@@ -224,7 +235,7 @@
        "[I*S*beta - I*gamma]])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -258,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 70,
    "id": "960fdc0c",
    "metadata": {},
    "outputs": [
@@ -280,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 71,
    "id": "93e32c75",
    "metadata": {},
    "outputs": [
@@ -318,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 72,
    "id": "61684654",
    "metadata": {},
    "outputs": [
@@ -328,7 +339,7 @@
        "False"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -347,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 73,
    "id": "1c8ff090",
    "metadata": {},
    "outputs": [
@@ -363,7 +374,7 @@
        "[0,  I*beta, S*beta - gamma]])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -382,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 74,
    "id": "abb02502",
    "metadata": {},
    "outputs": [
@@ -398,7 +409,7 @@
        "[ I*S, -I]])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 75,
    "id": "e703c888",
    "metadata": {},
    "outputs": [
@@ -441,7 +452,7 @@
        " ODEVariable('I', 'I', None, True)]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -479,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 76,
    "id": "46042ebf",
    "metadata": {},
    "outputs": [],
@@ -489,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 77,
    "id": "9ec016b5",
    "metadata": {},
    "outputs": [
@@ -499,7 +510,7 @@
        "{beta: 0.5, gamma: 0.3333333333333333}"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -522,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 78,
    "id": "c43074c6",
    "metadata": {},
    "outputs": [
@@ -532,7 +543,7 @@
        "array([ 4.23333333e-07, -6.35000000e-07,  2.11666667e-07])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 79,
    "id": "a14b9901",
    "metadata": {},
    "outputs": [],
@@ -579,13 +590,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 80,
    "id": "8303885c",
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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",
       "text/plain": [
        "
        "
       ]
@@ -630,13 +641,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 81,
    "id": "efddd3a5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
        "
       ]
@@ -665,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 82,
    "id": "7a7a568f",
    "metadata": {},
    "outputs": [
@@ -673,7 +684,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.11 ms ± 45.4 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "2.58 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -684,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 83,
    "id": "91b7f9d4",
    "metadata": {},
    "outputs": [
@@ -692,7 +703,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.2 ms ± 89 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "2.84 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -703,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 84,
    "id": "3403884c",
    "metadata": {},
    "outputs": [
@@ -711,8 +722,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The slowest run took 4.46 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "2.2 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "2.68 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -741,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 85,
    "id": "87173626",
    "metadata": {},
    "outputs": [],
@@ -857,7 +867,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.0"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/stochastic.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/stochastic.ipynb
index b6423673..336fc548 100644
--- a/docs/pygom-doc/_build/jupyter_execute/notebooks/stochastic.ipynb
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/stochastic.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "908538f3",
+   "id": "7ba7b302",
    "metadata": {},
    "source": [
     "# Stochastic representation of ODEs\n",
@@ -15,12 +15,32 @@
     "seen in {doc}`sir`."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d945f972",
+   "metadata": {},
+   "source": [
+    "#TODO add comment about using a density or frequency formulation (use of population size) - currently unexplained"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "3f44792d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pygom'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpygom\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SimulateOde, Transition, TransitionType\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pygom'"
+     ]
+    }
+   ],
    "source": [
     "from pygom import SimulateOde, Transition, TransitionType\n",
     "\n",
@@ -78,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 27,
    "id": "a3e9bc81",
    "metadata": {},
    "outputs": [],
@@ -113,17 +133,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 28,
    "id": "8849566b",
    "metadata": {
     "tags": [
-     "hide-cell"
+     "hide-input"
     ]
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "
        "
       ]
@@ -165,13 +185,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 29,
    "id": "4e6f4164",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
        "
       ]
@@ -181,10 +205,11 @@
     }
    ],
    "source": [
-    "f, axarr = plt.subplots(1,3)\n",
+    "f, axarr = plt.subplots(1,3, layout='constrained')\n",
     "\n",
     "for i in range(3): \n",
     "    axarr[i].plot(t, Ymean[:,i] - solutionReference[:,i])\n",
+    "    axarr[i].set_title(stateList[i])\n",
     "\n",
     "plt.show()\n",
     "\n"
@@ -192,13 +217,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 30,
    "id": "ee4d496d",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
        "
       ]
@@ -208,11 +237,12 @@
     }
    ],
    "source": [
-    "f, axarr = plt.subplots(1,3, layout='constrained', sharey=True)\n",
+    "f, axarr = plt.subplots(1,3, layout='constrained', sharey=False)\n",
     "\n",
     "for i in range(3): \n",
     "    axarr[i].plot(t, Ymean[:,i], label='sample average')\n",
     "    axarr[i].plot(t, solutionReference[:,i], label='reference')\n",
+    "    axarr[i].set_title(stateList[i])\n",
     "\n",
     "axarr[2].legend()\n",
     "plt.show()"
@@ -223,55 +253,109 @@
    "id": "aa230d7f",
    "metadata": {},
    "source": [
-    "\n",
-    "The difference is relatively large especially for the $S$ state. We can\n",
-    "decrease this difference as we increase the number of simulation, and\n",
-    "more sophisticated sampling method for the generation of random\n",
-    "variables can also decrease the difference.\n",
-    "\n",
+    "The difference between the deterministic reference and averaged solutions is relatively large, especially for the $S$ state. \n",
+    "We can decrease this difference as we increase the number of simulations, and also by using more sophisticated sampling methods for the generation of random\n",
+    "variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39d66f12",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
     "In addition to using the built-in functions to represent stochasticity,\n",
     "we can also use standard frozen distributions from scipy. Note that it\n",
     "must be a frozen distribution as that is the only for the parameters of\n",
     "the distributions to propagate through the model.\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "b8a806bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as st\n",
     "\n",
-    "In \\[1\\]: import scipy.stats as st\n",
-    "\n",
-    "In \\[1\\]: d = dict()\n",
-    "\n",
-    "In \\[1\\]: d\\['beta'\\] = st.gamma(a=100.0, scale=1.0/200.0)\n",
+    "d = dict()\n",
     "\n",
-    "In \\[1\\]: d\\['gamma'\\] = st.gamma(a=100.0, scale=1.0/300.0)\n",
+    "d['beta'] = st.gamma(a=100.0, scale=1.0/200.0)\n",
     "\n",
-    "In \\[1\\]: odeS.parameters = d\n",
+    "d['gamma'] = st.gamma(a=100.0, scale=1.0/300.0)\n",
     "\n",
-    "Obviously, there may be scenarios where only some of the parameters are\n",
+    "odeS.parameters = d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "030d6487",
+   "metadata": {},
+   "source": [
+    "There may be scenarios where only some of the parameters are\n",
     "stochastic. Let's say that the $\\gamma$ parameter is fixed at $1/3$,\n",
-    "then simply replace the distribution information with a scalar. A quick\n",
-    "visual inspection at the resulting plot suggests that the system of ODE\n",
+    "then we can replace the distribution information with a scalar. A quick\n",
+    "visual inspection at the resulting plot suggests that the system of ODEs\n",
     "potentially has less variation when compared to the case where both\n",
-    "parameters are stochastic.\n",
-    "\n",
-    "In \\[1\\]: d\\['gamma'\\] = 1.0/3.0\n",
-    "\n",
-    "In \\[1\\]: odeS.parameters = d\n",
-    "\n",
-    "In \\[1\\]: YmeanSingle, YallSingle = odeS.simulate_param(t\\[1::\\], 5,\n",
-    "full_output=True)\n",
-    "\n",
-    "In \\[1\\]: f, axarr = plt.subplots(1,3)\n",
+    "parameters are stochastic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "fa0d6ee2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d['gamma'] = 1.0/3.0\n",
     "\n",
-    "In \\[1\\]: for solution in YallSingle:  \n",
-    "...: axarr\\[0\\].plot(t,solution\\[:,0\\]) ...:\n",
-    "axarr\\[1\\].plot(t,solution\\[:,1\\]) ...:\n",
-    "axarr\\[2\\].plot(t,solution\\[:,2\\])\n",
+    "odeS.parameters = d\n",
     "\n",
-    "@savefig stochastic_param_single.png In \\[1\\]: plt.show()\n",
+    "YmeanSingle, YallSingle = odeS.simulate_param(t[1::], 5, full_output=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "eb4e969a",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, axarr = plt.subplots(1,3)\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "for solution in YallSingle:  \n",
+    "    axarr[0].plot(t,solution[:,0]) \n",
+    "    axarr[1].plot(t,solution[:,1]) \n",
+    "    axarr[2].plot(t,solution[:,2])\n",
     "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f01a3d9",
+   "metadata": {},
+   "source": [
     "## Continuous Markov Representation\n",
     "\n",
-    "Another common method of introducing stochasticity into a set of ode is\n",
+    "Another common method of introducing stochasticity into a set of ODEs is\n",
     "by assuming each movement in the system is a result of a jump process.\n",
     "More concretely, the probabilty of a move for transition $j$ is governed\n",
     "by an exponential distribution such that\n",
@@ -281,123 +365,187 @@
     "where $\\lambda_{j}$ is the rate of transition for process $j$ and $\\tau$\n",
     "the time elapsed after current time $t$.\n",
     "\n",
-    "A couple of the commmon implementation for the jump process have been\n",
-    "implemented where two of them are used during a normal simulation; the\n",
-    "first reaction method [\\[Gillespie1977\\]]() and the $\\tau$-Leap method\n",
-    "[\\[Cao2006\\]](). The two changes interactively depending on the size of\n",
-    "the states.\n",
-    "\n",
-    "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n",
-    "\n",
-    "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n",
-    "\n",
-    "In \\[1\\]: paramList = \\['beta', 'gamma', 'N'\\]\n",
+    "Two of the commmon algorithms for the jump process have been\n",
+    "implemented for use during simulation; the reaction method [\\[Gillespie1977\\]]() and the $\\tau$-Leap method\n",
+    "[\\[Cao2006\\]](). The two change interactively depending on the size of the states."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a0a0bcbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x0 = [2362206.0, 3.0, 0.0]\n",
     "\n",
-    "In \\[1\\]: transitionList = \\[  \n",
-    "...: Transition(origin='S', destination='I', equation='beta\\*S\\*I/N',\n",
-    "transition_type=TransitionType.T), ...: Transition(origin='I',\n",
-    "destination='R', equation='gamma\\*I', transition_type=TransitionType.T)\n",
-    "...: \\]\n",
+    "stateList = ['S', 'I', 'R']\n",
     "\n",
-    "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n",
-    "transition=transitionList)\n",
+    "paramList = ['beta', 'gamma', 'N']\n",
     "\n",
-    "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0, x0\\[0\\]\\]\n",
+    "transitionList = [Transition(origin='S', destination='I', equation='beta*S*I/N', transition_type=TransitionType.T),\n",
+    "                  Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)\n",
+    "                  ]\n",
     "\n",
-    "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n",
+    "odeS = SimulateOde(stateList, paramList, transition=transitionList)\n",
     "\n",
-    "In \\[1\\]: solutionReference = odeS.integrate(t\\[1::\\])\n",
+    "odeS.parameters = [0.5, 1.0/3.0, x0[0]]\n",
     "\n",
-    "In \\[1\\]: simX, simT = odeS.simulate_jump(t\\[1:10\\], 10,\n",
-    "full_output=True)\n",
+    "odeS.initial_values = (x0, t[0])\n",
     "\n",
-    "In \\[1\\]: f, axarr = plt.subplots(1, 3)\n",
+    "solutionReference = odeS.integrate(t[1::])\n",
     "\n",
-    "In \\[1\\]: for solution in simX:  \n",
-    "...: axarr\\[0\\].plot(t\\[:9\\], solution\\[:,0\\]) ...:\n",
-    "axarr\\[1\\].plot(t\\[:9\\], solution\\[:,1\\]) ...: axarr\\[2\\].plot(t\\[:9\\],\n",
-    "solution\\[:,2\\])\n",
+    "simX, simT = odeS.simulate_jump(t[1:10], 10, full_output=True)\n",
     "\n",
-    "@savefig stochastic_process.png In \\[1\\]: plt.show()\n",
+    "f, axarr = plt.subplots(1, 3)\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "for solution in simX:  \n",
+    "    axarr[0].plot(t[:9], solution[:,0])\n",
+    "    axarr[1].plot(t[:9], solution[:,1]) \n",
+    "    axarr[2].plot(t[:9], solution[:,2])\n",
     "\n",
-    "Above, we see ten different simulation, again using the SIR model but\n",
-    "without standardization of the initial conditions. We restrict our time\n",
-    "frame to be only the first 10 time points so that the individual changes\n",
-    "can be seen more clearly above. If we use the same time frame as the one\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e18e4fa7",
+   "metadata": {},
+   "source": [
+    "Above, we see 10 different simulations, again using the SIR model but\n",
+    "without standardization of the initial conditions (we include $N$ as a parameter in our model). \n",
+    "For demonstration purposes, we restrict our time\n",
+    "frame to be only the first 10 time points so that the individual changes or stochasticity\n",
+    "can be seen more clearly. If we use the same time frame as the one\n",
     "used previously for the deterministic system (as shown below), the\n",
     "trajectories are smoothed out and we no longer observe the *jumps*.\n",
-    "Looking at the raw trajectories of the ODE below, it is obvious that the\n",
+    "Looking at the raw trajectories of the ODEs below, we see that the\n",
     "mean from a jump process is very different to the deterministic\n",
-    "solution. The reason behind this is that the jump process above was able\n",
+    "solution. This occurs because the jump process above was able\n",
     "to fully remove all the initial infected individuals before any new\n",
-    "ones.\n",
-    "\n",
-    "In \\[1\\]: simX,simT = odeS.simulate_jump(t, 5, full_output=True)\n",
-    "\n",
-    "In \\[1\\]: simMean = np.mean(simX, axis=0)\n",
+    "ones occured."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "525a069b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "simX,simT = odeS.simulate_jump(t, 5, full_output=True)\n",
     "\n",
-    "In \\[1\\]: f, axarr = plt.subplots(1,3)\n",
+    "simMean = np.mean(simX, axis=0)\n",
     "\n",
-    "In \\[1\\]: for solution in simX:  \n",
-    "...: axarr\\[0\\].plot(t, solution\\[:,0\\]) ...: axarr\\[1\\].plot(t,\n",
-    "solution\\[:,1\\]) ...: axarr\\[2\\].plot(t, solution\\[:,2\\])\n",
+    "f, axarr = plt.subplots(1,3)\n",
     "\n",
-    "@savefig stochastic_process_compare_large_n\\_curves.png In \\[1\\]:\n",
-    "plt.show()\n",
+    "for solution in simX:  \n",
+    "    axarr[0].plot(t, solution[:,0]) \n",
+    "    axarr[1].plot(t, solution[:,1]) \n",
+    "    axarr[2].plot(t, solution[:,2])\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbef64f3",
+   "metadata": {},
+   "source": [
     "\n",
     "## Repeatable Simulation\n",
     "\n",
-    "One of the possible use of compartmental models is to generate\n",
+    "One of the possible uses of compartmental models is to generate\n",
     "forecasts. Although most of the time the requirement would be to have\n",
     "(at least point-wise) convergence in the limit, reproducibility is also\n",
     "important. For both types of interpretation explained above, we have\n",
     "given the package the capability to repeat the simulations by setting a\n",
     "seed. When the assumption is that the parameters follows some sort of\n",
-    "distribution, we simply set the seed which governs the global state of\n",
-    "the random number generator.\n",
-    "\n",
-    "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n",
-    "\n",
-    "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n",
-    "transition=transitionList)\n",
+    "distribution, we can set the seed which governs the global state of\n",
+    "the random number generator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "415cab81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x0 = [2362206.0, 3.0, 0.0]\n",
     "\n",
-    "In \\[1\\]: d = {'beta': st.gamma(a=100.0, scale=1.0/200.0), 'gamma':\n",
-    "st.gamma(a=100.0, scale=1.0/300.0), 'N': x0\\[0\\]}\n",
+    "odeS = SimulateOde(stateList, paramList, transition=transitionList)\n",
     "\n",
-    "In \\[1\\]: odeS.parameters = d\n",
+    "d = {'beta': st.gamma(a=100.0, scale=1.0/200.0), 'gamma':st.gamma(a=100.0, scale=1.0/300.0), 'N': x0[0]}\n",
     "\n",
-    "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n",
+    "odeS.parameters = d\n",
     "\n",
-    "In \\[1\\]: Ymean, Yall = odeS.simulate_param(t\\[1::\\], 10,\n",
-    "full_output=True)\n",
+    "odeS.initial_values = (x0, t[0])\n",
     "\n",
-    "In \\[1\\]: np.random.seed(1)\n",
+    "Ymean, Yall = odeS.simulate_param(t[1::], 10, full_output=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "7a51e644",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Different in the simulations and the mean: (81103473.8935847, 52437230.37979218) \n",
+      "Different in the simulations and the mean using same seed: (0.0, 0.0) \n"
+     ]
+    }
+   ],
+   "source": [
     "\n",
-    "In \\[1\\]: Ymean1, Yall1 = odeS.simulate_param(t\\[1::\\], 10,\n",
-    "full_output=True)\n",
+    "np.random.seed(1)\n",
     "\n",
-    "In \\[1\\]: np.random.seed(1)\n",
+    "Ymean1, Yall1 = odeS.simulate_param(t[1::], 10, full_output=True)\n",
     "\n",
-    "In \\[1\\]: Ymean2, Yall2 = odeS.simulate_param(t\\[1::\\], 10,\n",
-    "full_output=True)\n",
+    "np.random.seed(1)\n",
     "\n",
-    "In \\[1\\]: sim_diff = \\[np.linalg.norm(Yall\\[i\\] - yi) for i, yi in\n",
-    "enumerate(Yall1)\\]\n",
+    "Ymean2, Yall2 = odeS.simulate_param(t[1::], 10, full_output=True)\n",
     "\n",
-    "In \\[1\\]: sim_diff12 = \\[np.linalg.norm(Yall2\\[i\\] - yi) for i, yi in\n",
-    "enumerate(Yall1)\\]\n",
+    "sim_diff = [np.linalg.norm(Yall[i] - yi) for i, yi in enumerate(Yall1)]\n",
     "\n",
-    "In \\[1\\]: print(\"Different in the simulations and the mean: (%s, %s) \" %\n",
-    "(np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))\n",
+    "sim_diff12 = [np.linalg.norm(Yall2[i] - yi) for i, yi in enumerate(Yall1)]\n",
     "\n",
-    "In \\[1\\]: print(\"Different in the simulations and the mean using same\n",
-    "seed: (%s, %s) \" % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 -\n",
-    "Ymean1))))\n",
+    "print(\"Different in the simulations and the mean: (%s, %s) \" %(np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))\n",
     "\n",
+    "print(\"Different in the simulations and the mean using same seed: (%s, %s) \" % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 - Ymean1))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1fe54a8",
+   "metadata": {},
+   "source": [
     "In the alternative interpretation, setting the global seed is\n",
     "insufficient. Unlike simulation based on the parameters, where we can\n",
     "pre-generate all the parameter values and send them off to individual\n",
@@ -406,41 +554,50 @@
     "because each *integration* requires an unknown number of random samples.\n",
     "Therefore, we have an additional flag **parallel** in the function\n",
     "signature. By ensuring that the computation runs in serial, we can make\n",
-    "use of the global seed and generate identical runs.\n",
-    "\n",
-    "In \\[1\\]: x0 = \\[2362206.0, 3.0, 0.0\\]\n",
+    "use of the global seed and generate identical runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "d5a9e235",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Difference in simulation: 2534.4614923250074\n",
+      "Difference in simulation using same seed: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "x0 = [2362206.0, 3.0, 0.0]\n",
     "\n",
-    "In \\[1\\]: odeS = SimulateOde(stateList, paramList,\n",
-    "transition=transitionList)\n",
+    "odeS = SimulateOde(stateList, paramList, transition=transitionList)\n",
     "\n",
-    "In \\[1\\]: odeS.parameters = \\[0.5, 1.0/3.0, x0\\[0\\]\\]\n",
+    "odeS.parameters = [0.5, 1.0/3.0, x0[0]]\n",
     "\n",
-    "In \\[1\\]: odeS.initial_values = (x0, t\\[0\\])\n",
+    "odeS.initial_values = (x0, t[0])\n",
     "\n",
-    "In \\[1\\]: simX, simT = odeS.simulate_jump(t\\[1:10\\], 10, parallel=False,\n",
-    "full_output=True)\n",
+    "simX, simT = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)\n",
     "\n",
-    "In \\[1\\]: np.random.seed(1)\n",
+    "np.random.seed(1)\n",
     "\n",
-    "In \\[1\\]: simX1, simT1 = odeS.simulate_jump(t\\[1:10\\], 10,\n",
-    "parallel=False, full_output=True)\n",
+    "simX1, simT1 = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)\n",
     "\n",
-    "In \\[1\\]: np.random.seed(1)\n",
+    "np.random.seed(1)\n",
     "\n",
-    "In \\[1\\]: simX2, simT2 = odeS.simulate_jump(t\\[1:10\\], 10,\n",
-    "parallel=False, full_output=True)\n",
+    "simX2, simT2 = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)\n",
     "\n",
-    "In \\[1\\]: sim_diff = \\[np.linalg.norm(simX\\[i\\] - x1) for i, x1 in\n",
-    "enumerate(simX1)\\]\n",
+    "sim_diff = [np.linalg.norm(simX[i] - x1) for i, x1 in enumerate(simX1)]\n",
     "\n",
-    "In \\[1\\]: sim_diff12 = \\[np.linalg.norm(simX2\\[i\\] - x1) for i, x1 in\n",
-    "enumerate(simX1)\\]\n",
+    "sim_diff12 = [np.linalg.norm(simX2[i] - x1) for i, x1 in enumerate(simX1)]\n",
     "\n",
-    "In \\[1\\]: print(\"Difference in simulation: %s\" %\n",
-    "np.sum(np.abs(sim_diff)))\n",
+    "print(\"Difference in simulation: %s\" %np.sum(np.abs(sim_diff)))\n",
     "\n",
-    "In \\[1\\]: print(\"Difference in simulation using same seed: %s\" %\n",
-    "np.sum(np.abs(sim_diff12)))"
+    "print(\"Difference in simulation using same seed: %s\" %np.sum(np.abs(sim_diff12)))"
    ]
   }
  ],
@@ -460,7 +617,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.0"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/transition.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/transition.ipynb
index 4ed4374c..7f032919 100644
--- a/docs/pygom-doc/_build/jupyter_execute/notebooks/transition.ipynb
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/transition.ipynb
@@ -2,11 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e0ef4dc",
+   "id": "d79c1a27",
    "metadata": {},
    "source": [
     "# Transition Object\n",
-    "{download}`this notebook <./transition.ipynb>`\n",
     "\n",
     "\n",
     "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n",
@@ -35,7 +34,19 @@
    "execution_count": 1,
    "id": "68d41d64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pygom'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpygom\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Transition, TransitionType, common_models\n\u001b[0;32m      3\u001b[0m ode1 \u001b[38;5;241m=\u001b[39m Transition(origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mS\u001b[39m\u001b[38;5;124m'\u001b[39m, equation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-beta*S*I\u001b[39m\u001b[38;5;124m'\u001b[39m, transition_type\u001b[38;5;241m=\u001b[39mTransitionType\u001b[38;5;241m.\u001b[39mODE)\n\u001b[0;32m      5\u001b[0m ode2 \u001b[38;5;241m=\u001b[39m Transition(origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mI\u001b[39m\u001b[38;5;124m'\u001b[39m, equation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbeta*S*I - gamma*I\u001b[39m\u001b[38;5;124m'\u001b[39m, transition_type\u001b[38;5;241m=\u001b[39mTransitionType\u001b[38;5;241m.\u001b[39mODE)\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pygom'"
+     ]
+    }
+   ],
    "source": [
     "from pygom import Transition, TransitionType, common_models\n",
     "\n",
@@ -59,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "3b0801dd",
    "metadata": {},
    "outputs": [],
@@ -84,27 +95,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "5782a96f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.get_ode_eqn()"
    ]
@@ -146,27 +140,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "6966fc5e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pygom import SimulateOde\n",
     "\n",
@@ -194,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 23,
    "id": "1e17eb7c",
    "metadata": {},
    "outputs": [
@@ -210,7 +187,7 @@
        "[0,        0,       0]])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -221,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 22,
    "id": "570ceed5",
    "metadata": {},
    "outputs": [
@@ -274,10 +251,10 @@
        "\n"
       ],
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 6,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -316,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 24,
    "id": "9f24cb0a",
    "metadata": {},
    "outputs": [
@@ -332,7 +309,7 @@
        "[           I*gamma]])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -359,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 25,
    "id": "9a3b3758",
    "metadata": {},
    "outputs": [
@@ -375,7 +352,7 @@
        "[           I*gamma]])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -414,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 26,
    "id": "3a6a15ea",
    "metadata": {},
    "outputs": [
@@ -430,7 +407,7 @@
        "[                  I*gamma]])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -471,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "id": "ce5787f8",
    "metadata": {},
    "outputs": [
@@ -520,7 +497,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.0"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollBD.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollBD.ipynb
index 18527593..78cbb930 100644
--- a/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollBD.ipynb
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollBD.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cd7bbc96",
+   "id": "7f04f607",
    "metadata": {},
    "source": [
     "# ODE With Birth and Death Process\n",
@@ -59,7 +59,30 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.0 ('pygom-dev')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "fc17efc1b95c7b1781d7b7ce7a7c317a91a744529621a3902877be6d49554249"
+   }
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollHard.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollHard.ipynb
index ec7cbb42..585d8b82 100644
--- a/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollHard.ipynb
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollHard.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e58447d",
+   "id": "12e8474a",
    "metadata": {},
    "source": [
     "# Hard Problem\n",
@@ -85,7 +85,20 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollSimple.ipynb b/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollSimple.ipynb
index ed2b2689..552fdcdc 100644
--- a/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollSimple.ipynb
+++ b/docs/pygom-doc/_build/jupyter_execute/notebooks/unroll/unrollSimple.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9abb7b3b",
+   "id": "fe55e8d2",
    "metadata": {},
    "source": [
     "# Simple Problem\n",
@@ -56,7 +56,20 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }
\ No newline at end of file
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/common_models.md b/docs/pygom-doc/md/common_models.md
similarity index 100%
rename from docs/pygom-doc/mdconvert/doc_to_sort/common_models.md
rename to docs/pygom-doc/md/common_models.md
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/faq.md b/docs/pygom-doc/md/faq.md
similarity index 100%
rename from docs/pygom-doc/mdconvert/doc_to_sort/faq.md
rename to docs/pygom-doc/md/faq.md

From 48bbdf47de574628f4d2bc84e94d3a2cd76f3dad Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 5 Jul 2023 10:51:33 +0100
Subject: [PATCH 095/188] remove some parts of toc to check why the build is
 failing

---
 docs/pygom-doc/_toc.yml | 62 ++++++++++++++++++++---------------------
 1 file changed, 31 insertions(+), 31 deletions(-)

diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index 74961f14..185dc0f7 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -28,34 +28,34 @@ parts:
   - caption: Common biological compartmental models
     chapters:
     - file: md/common_models.md
-    - file: notebooks/common_models/SIS.ipynb
-    - file: notebooks/common_models/SIS_Periodic.ipynb
-    - file: notebooks/common_models/SIR.rst 
-    - file: notebooks/common_models/SIR_Birth_Death.rst
-    - file: notebooks/common_models/SEIR.rst 
-    - file: notebooks/common_models/SEIR_Multiple.rst
-    - file: notebooks/common_models/SEIR_Birth_Death.rst
-    - file: notebooks/common_models/SEIR_Birth_Death_Periodic.rst
-    - file: notebooks/common_models/Legrand_Ebola_SEIHFR.rst 
-    - file: notebooks/common_models/Lotka_Volterra.rst
-    - file: notebooks/common_models/Lotka_Volterra_4State.rst 
-    - file: notebooks/common_models/FitzHugh.rst
-    - file: notebooks/common_models/Lorenz.rst 
-    - file: notebooks/common_models/vanDelPol.rst
-    - file: notebooks/common_models/Robertson.rst
-  - caption: Frequently asked questions
-    chapters: 
-    - file: md/faq.md
-#  - caption: Code documentation
-#    chapters:
-#    - file:
-  - caption: References
-    chapters: 
-    - file: md/ref.md
-#  - caption: Indices and tables
-#    chapters:
-#    - file: 
-#  - caption: Glossary
-#    chapters:
-#    - file: 
-
+      sections:
+      - file: notebooks/common_models/SIS.ipynb
+      - file: notebooks/common_models/SIS_Periodic.ipynb
+      - file: notebooks/common_models/SIR.rst 
+      - file: notebooks/common_models/SIR_Birth_Death.rst
+      - file: notebooks/common_models/SEIR.rst 
+      - file: notebooks/common_models/SEIR_Multiple.rst
+      - file: notebooks/common_models/SEIR_Birth_Death.rst
+      - file: notebooks/common_models/SEIR_Birth_Death_Periodic.rst
+      - file: notebooks/common_models/Legrand_Ebola_SEIHFR.rst 
+      - file: notebooks/common_models/Lotka_Volterra.rst
+      - file: notebooks/common_models/Lotka_Volterra_4State.rst 
+      - file: notebooks/common_models/FitzHugh.rst
+      - file: notebooks/common_models/Lorenz.rst 
+      - file: notebooks/common_models/vanDelPol.rst
+      - file: notebooks/common_models/Robertson.rst
+#  - caption: Frequently asked questions
+#    chapters: 
+#    - file: md/faq.md
+##  - caption: Code documentation
+##    chapters:
+##    - file:
+#  - caption: References
+#    chapters: 
+#    - file: md/ref.md
+##  - caption: Indices and tables
+##    chapters:
+##    - file: 
+##  - caption: Glossary
+##    chapters:
+##    - file: 

From c4016ebee9d2264542a61de57a22f72675a133b6 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Thu, 6 Jul 2023 14:24:56 +0100
Subject: [PATCH 096/188] add flags to book build

---
 .github/workflows/book.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 13842248..859b59ec 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -32,7 +32,7 @@ jobs:
             # build the book
             - name: Build the book
               run: |
-                jupyter-book build docs/pygom-doc
+                jupyter-book build -W --all -v docs/pygom-doc
 
             # deploy book to github-pages
             - name: GitHub Pages 

From 62796b0133d3de103421894e3a7c6c549c5dffbd Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Thu, 6 Jul 2023 14:35:20 +0100
Subject: [PATCH 097/188] test to check build

---
 docs/pygom-doc/notebooks/sir.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pygom-doc/notebooks/sir.ipynb b/docs/pygom-doc/notebooks/sir.ipynb
index 0767ca96..86c30c65 100644
--- a/docs/pygom-doc/notebooks/sir.ipynb
+++ b/docs/pygom-doc/notebooks/sir.ipynb
@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pygom import Transition, TransitionType"
+    "#from pygom import Transition, TransitionType"
    ]
   },
   {

From c0954e91254987123b6133eddd1499a914cedfb0 Mon Sep 17 00:00:00 2001
From: HWilliams-PHE <36919240+HWilliams-PHE@users.noreply.github.com>
Date: Thu, 6 Jul 2023 14:37:55 +0100
Subject: [PATCH 098/188] Update book.yml

remove W flag
---
 .github/workflows/book.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 859b59ec..6af436e6 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -32,7 +32,7 @@ jobs:
             # build the book
             - name: Build the book
               run: |
-                jupyter-book build -W --all -v docs/pygom-doc
+                jupyter-book build --all -v docs/pygom-doc
 
             # deploy book to github-pages
             - name: GitHub Pages 

From 32379e74b4bf346a294385a3732b752cac392a53 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Thu, 6 Jul 2023 14:41:59 +0100
Subject: [PATCH 099/188] test build

---
 docs/pygom-doc/notebooks/transition.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/pygom-doc/notebooks/transition.ipynb b/docs/pygom-doc/notebooks/transition.ipynb
index 2ed607d2..4bb5f8ad 100644
--- a/docs/pygom-doc/notebooks/transition.ipynb
+++ b/docs/pygom-doc/notebooks/transition.ipynb
@@ -30,12 +30,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "68d41d64",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pygom import Transition, TransitionType, common_models\n",
+    "#from pygom import Transition, TransitionType, common_models\n",
     "\n",
     "ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)\n",
     "\n",
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "3b0801dd",
    "metadata": {},
    "outputs": [],

From 3ce3f92cd2d06bb5884d50d19fcc77ef4cbe7268 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 08:57:11 +0100
Subject: [PATCH 100/188] undo removal of imports (from testing CI)

---
 docs/pygom-doc/notebooks/sir.ipynb        | 289 ++++------------------
 docs/pygom-doc/notebooks/transition.ipynb |   2 +-
 2 files changed, 56 insertions(+), 235 deletions(-)

diff --git a/docs/pygom-doc/notebooks/sir.ipynb b/docs/pygom-doc/notebooks/sir.ipynb
index 86c30c65..7e6246c1 100644
--- a/docs/pygom-doc/notebooks/sir.ipynb
+++ b/docs/pygom-doc/notebooks/sir.ipynb
@@ -30,12 +30,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 1,
    "id": "4c80a36a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#from pygom import Transition, TransitionType"
+    "from pygom import Transition, TransitionType"
    ]
   },
   {
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 2,
    "id": "3ce016f2",
    "metadata": {},
    "outputs": [],
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 3,
    "id": "441e2287",
    "metadata": {},
    "outputs": [],
@@ -84,10 +84,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 4,
    "id": "b5f80ab0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Transition' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m odeList \u001b[39m=\u001b[39m [\n\u001b[1;32m----> 2\u001b[0m     Transition(origin\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mS\u001b[39m\u001b[39m'\u001b[39m, equation\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m-beta*S*I\u001b[39m\u001b[39m'\u001b[39m, transition_type\u001b[39m=\u001b[39mTransitionType\u001b[39m.\u001b[39mODE),\n\u001b[0;32m      3\u001b[0m     Transition(origin\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mI\u001b[39m\u001b[39m'\u001b[39m,equation\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbeta*S*I - gamma*I\u001b[39m\u001b[39m'\u001b[39m, transition_type\u001b[39m=\u001b[39mTransitionType\u001b[39m.\u001b[39mODE),\n\u001b[0;32m      4\u001b[0m     Transition(origin\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mR\u001b[39m\u001b[39m'\u001b[39m, equation\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mgamma*I\u001b[39m\u001b[39m'\u001b[39m, transition_type\u001b[39m=\u001b[39mTransitionType\u001b[39m.\u001b[39mODE) \n\u001b[0;32m      5\u001b[0m     ]\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'Transition' is not defined"
+     ]
+    }
+   ],
    "source": [
     "odeList = [\n",
     "    Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE),\n",
@@ -117,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "id": "f78c33d4",
    "metadata": {},
    "outputs": [],
@@ -135,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "c9fbcce0",
    "metadata": {},
    "outputs": [],
@@ -153,27 +165,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "id": "6ad54e09",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.get_ode_eqn()"
    ]
@@ -206,27 +201,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "id": "07f81fd1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}I \\gamma\\\\- I S \\beta\\\\I S \\beta - I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[           I*gamma],\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma]])"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# now we are going to define in a different order. note that the output ode changed with the input state\n",
     "stateList = ['R', 'S', 'I']\n",
@@ -256,40 +234,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "id": "960fdc0c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "⎡dR/dt=      I⋅γ    ⎤\n",
-      "⎢                   ⎥\n",
-      "⎢dS/dt=    -I⋅S⋅β   ⎥\n",
-      "⎢                   ⎥\n",
-      "⎣dI/dt=  I⋅S⋅β - I⋅γ⎦\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.print_ode()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "id": "93e32c75",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{array}{cc}dR/dt= & I \\gamma\\\\dS/dt= & - I S \\beta\\\\dI/dt= & I S \\beta - I \\gamma\\end{array}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.print_ode(True)"
    ]
@@ -316,21 +274,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "id": "61684654",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.linear_ode()"
    ]
@@ -345,27 +292,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "id": "1c8ff090",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & \\gamma\\\\0 & - I \\beta & - S \\beta\\\\0 & I \\beta & S \\beta - \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[0,       0,          gamma],\n",
-       "[0, -I*beta,        -S*beta],\n",
-       "[0,  I*beta, S*beta - gamma]])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.get_jacobian_eqn()"
    ]
@@ -380,27 +310,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "id": "abb02502",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & I\\\\- I S & 0\\\\I S & - I\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[   0,  I],\n",
-       "[-I*S,  0],\n",
-       "[ I*S, -I]])"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.get_grad_eqn()"
    ]
@@ -427,23 +340,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "id": "e703c888",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ODEVariable('R', 'R', None, True),\n",
-       " ODEVariable('S', 'S', None, True),\n",
-       " ODEVariable('I', 'I', None, True)]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.state_list"
    ]
@@ -477,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "id": "46042ebf",
    "metadata": {},
    "outputs": [],
@@ -487,21 +387,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
    "id": "9ec016b5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{beta: 0.5, gamma: 0.3333333333333333}"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "paramEval = [('beta',0.5), ('gamma',1.0/3.0)]\n",
     "\n",
@@ -520,21 +409,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "id": "c43074c6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4.23333333e-07, -6.35000000e-07,  2.11666667e-07])"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "initialState = [0, 1, 1.27e-6]\n",
     "    \n",
@@ -554,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
    "id": "a14b9901",
    "metadata": {},
    "outputs": [],
@@ -577,21 +455,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": null,
    "id": "8303885c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -628,21 +495,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": null,
    "id": "efddd3a5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.initial_values = (initialState, t[0])\n",
     "\n",
@@ -663,18 +519,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "id": "7a7a568f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.58 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#TODO what does this show?\n",
     "%timeit solution1, output1 = scipy.integrate.odeint(model.ode, initialState, t, full_output=True)\n"
@@ -682,18 +530,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
    "id": "91b7f9d4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.84 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "%timeit solution2, output2 = scipy.integrate.odeint(model.ode, initialState, t, Dfun=model.jacobian, mu=None, ml=None, full_output=True)\n"
@@ -701,18 +541,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
    "id": "3403884c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.68 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "%timeit solution3, output3 = model.integrate(t, full_output=True)"
@@ -738,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "id": "87173626",
    "metadata": {},
    "outputs": [],
@@ -758,25 +590,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "id": "8b63ba62",
    "metadata": {
     "tags": [
      "hide-output"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "{\n",
     "    \"tags\": [\n",
diff --git a/docs/pygom-doc/notebooks/transition.ipynb b/docs/pygom-doc/notebooks/transition.ipynb
index 4bb5f8ad..bea1277f 100644
--- a/docs/pygom-doc/notebooks/transition.ipynb
+++ b/docs/pygom-doc/notebooks/transition.ipynb
@@ -35,7 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#from pygom import Transition, TransitionType, common_models\n",
+    "from pygom import Transition, TransitionType, common_models\n",
     "\n",
     "ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)\n",
     "\n",

From b5b285f82daf79eb6e3e123d8835e6fc5dd1cd75 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 09:00:11 +0100
Subject: [PATCH 101/188] undo removals from config (used for testing CI)

---
 .github/workflows/book.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 6af436e6..32a45bac 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -32,7 +32,7 @@ jobs:
             # build the book
             - name: Build the book
               run: |
-                jupyter-book build --all -v docs/pygom-doc
+                jupyter-book build --all -v -W docs/pygom-doc
 
             # deploy book to github-pages
             - name: GitHub Pages 

From aead8cb7117a5ea9a1f551e8d330a9e925238f54 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 14:15:37 +0100
Subject: [PATCH 102/188] edited unroll pages

---
 .../notebooks/unroll/unrollSimple.ipynb       | 121 ++++++++++++++----
 1 file changed, 98 insertions(+), 23 deletions(-)

diff --git a/docs/pygom-doc/notebooks/unroll/unrollSimple.ipynb b/docs/pygom-doc/notebooks/unroll/unrollSimple.ipynb
index 88bbd592..55772cb3 100644
--- a/docs/pygom-doc/notebooks/unroll/unrollSimple.ipynb
+++ b/docs/pygom-doc/notebooks/unroll/unrollSimple.ipynb
@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Simple Problem\n",
+    "# A simple problem\n",
     "\n",
-    "For a simple problem, we consider the SIR model defined by\n",
+    "For a simple problem, where we can clearly distinguish the flows from one compartment to another, we consider the SIR model defined by\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dS}{dt} &= -\\beta SI \\\\\n",
@@ -22,40 +22,115 @@
     "\n",
     "}\n",
     "\n",
-    "Let's define this using the code block below\n",
-    "\n",
-    "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n",
+    "We can define this as an ODE, as seen in {}[sir.ipynb]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "52daed16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[0, 0, 0],\n",
+       "[0, 0, 0],\n",
+       "[0, 0, 0]])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pygom import SimulateOde, Transition, TransitionType\n",
     "\n",
-    "In \\[2\\]: ode1 = Transition(origin='S', equation='-beta\\*S\\*I',\n",
-    "transition_type=TransitionType.ODE)\n",
+    "ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)\n",
     "\n",
-    "In \\[3\\]: ode2 = Transition(origin='I', equation='beta\\*S\\*I -\n",
-    "gamma\\*I', transition_type=TransitionType.ODE)\n",
+    "ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)\n",
     "\n",
-    "In \\[4\\]: ode3 = Transition(origin='R', equation='gamma\\*I',\n",
-    "transition_type=TransitionType.ODE)\n",
+    "ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)\n",
     "\n",
-    "In \\[6\\]: stateList = \\['S', 'I', 'R'\\]\n",
+    "stateList = ['S', 'I', 'R']\n",
     "\n",
-    "In \\[7\\]: paramList = \\['beta', 'gamma'\\]\n",
+    "paramList = ['beta', 'gamma']\n",
     "\n",
-    "In \\[8\\]: ode = SimulateOde(stateList,  \n",
-    "...: paramList, ...: ode=\\[ode1, ode2, ode3\\])\n",
+    "ode = SimulateOde(stateList, paramList, ode=[ode1, ode2, ode3])\n",
     "\n",
-    "In \\[9\\]: ode.get_transition_matrix()\n",
+    "ode.get_transition_matrix()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d712e654",
+   "metadata": {},
+   "source": [
     "\n",
     "and the last line shows that the transition matrix is empty. This is the\n",
     "expected result because `SimulateOdeModel` was not initialized using\n",
-    "transitions. We populate the transition matrix below and demonstrate the\n",
-    "difference.\n",
-    "\n",
-    "In \\[1\\]: ode = ode.get_unrolled_obj()\n",
+    "transitions. We can populate the transition matrix by calling an algorithm to extract the flow information, and can see that the output matches that from {ref}`transition:defining-the-equations`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "768a4cd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & I S \\beta & 0\\\\0 & 0 & I \\gamma\\\\0 & 0 & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[0, I*S*beta,       0],\n",
+       "[0,        0, I*gamma],\n",
+       "[0,        0,       0]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ode = ode.get_unrolled_obj()\n",
     "\n",
-    "In \\[2\\]: ode.get_transition_matrix()"
+    "ode.get_transition_matrix()"
    ]
   }
  ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }

From a42addfb4c67e7ce02a9cd64ed90eba66645962f Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 14:17:19 +0100
Subject: [PATCH 103/188] inclusion of new documentation path

---
 .gitignore | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index 98aa5e82..eebbbe54 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,5 +17,6 @@ __pycache__
 .settings
 
 # For built documentation
-/doc/_build*
-/doc/source/savefig/*
+/docs/pygom-doc/_build*
+/doc/_build
+/docs/source/savefig/*

From 7da2f429b61091421c04419538c3ddd9e6e5510e Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 14:41:53 +0100
Subject: [PATCH 104/188] typo

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index eebbbe54..669de051 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,4 +19,4 @@ __pycache__
 # For built documentation
 /docs/pygom-doc/_build*
 /doc/_build
-/docs/source/savefig/*
+/doc/source/savefig/*

From a3f412ce4143e3a8c7b3f6443be4f0da561ceee2 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 14:58:20 +0100
Subject: [PATCH 105/188] change path for requirements to install pygom

---
 .github/workflows/book.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 32a45bac..4174495d 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -27,7 +27,7 @@ jobs:
             # install dependencies
             - name: Install dependencies
               run: |
-                pip install -r docs/pygom-doc/requirements.txt
+                pip install -r requirements.txt
 
             # build the book
             - name: Build the book

From d8c9304ef86f67dfe258e6c342ec45ca3db6ee03 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 15:01:14 +0100
Subject: [PATCH 106/188] resaved

---
 .../pygom-doc/notebooks/unroll/unrollBD.ipynb | 111 ++++++---
 .../notebooks/unroll/unrollHard.ipynb         | 235 +++++++++++++++---
 2 files changed, 277 insertions(+), 69 deletions(-)

diff --git a/docs/pygom-doc/notebooks/unroll/unrollBD.ipynb b/docs/pygom-doc/notebooks/unroll/unrollBD.ipynb
index ea56ca47..54b6f36d 100644
--- a/docs/pygom-doc/notebooks/unroll/unrollBD.ipynb
+++ b/docs/pygom-doc/notebooks/unroll/unrollBD.ipynb
@@ -6,16 +6,16 @@
    "source": [
     "# ODE With Birth and Death Process\n",
     "\n",
-    "We follow on from the SIR model of `unrollSimple` but with additional\n",
+    "We follow on from the SIR model of {doc}`unrollSimple` but now include additional\n",
     "birth and death processes.\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dS}{dt} &= -\\beta SI + B - \\mu S\\\\\n",
     "\\frac{dI}{dt} &= \\beta SI - \\gamma I - \\mu I\\\\\n",
-    "\\frac{dR}{dt} &= \\gamma I.\n",
+    "\\frac{dR}{dt} &= \\gamma I\n",
     "\\end{aligned}$$\n",
     "\n",
-    "which consists of two transitions and three birth and death process\n",
+    "which consists of two transitions and three birth and death process.\n",
     "\n",
     "digraph SIR_Model {  \n",
     "rankdir=LR; size=\"8\" node \\[shape = circle\\]; S -\\> I \\[ label = \"βSI\"\n",
@@ -27,50 +27,105 @@
     "}\n",
     "\n",
     "Let's define this in terms of ODEs, and unroll it back to the individual\n",
-    "processes.\n",
-    "\n",
-    "In \\[1\\]: from pygom import Transition, TransitionType, SimulateOde,\n",
-    "common_models\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
-    "\n",
-    "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n",
-    "\n",
-    "In \\[1\\]: paramList = \\['beta', 'gamma', 'B', 'mu'\\]\n",
+    "processes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eed075f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & I S \\beta & 0\\\\0 & 0 & I \\gamma\\\\0 & 0 & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[0, I*S*beta,       0],\n",
+       "[0,        0, I*gamma],\n",
+       "[0,        0,       0]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pygom import Transition, TransitionType, SimulateOde, common_models\n",
     "\n",
-    "In \\[1\\]: odeList = \\[  \n",
-    "...: Transition(origin='S', ...: equation='-beta\\*S\\*I + B - mu\\*S',\n",
-    "...: transition_type=TransitionType.ODE), ...: Transition(origin='I',\n",
-    "...: equation='beta\\*S\\*I - gamma\\*I - mu\\*I', ...:\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='R', ...:\n",
-    "equation='gamma\\*I', ...: transition_type=TransitionType.ODE) ...: \\]\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: ode = SimulateOde(stateList, paramList, ode=odeList)\n",
+    "stateList = ['S', 'I', 'R']\n",
     "\n",
-    "In \\[1\\]: ode2 = ode.get_unrolled_obj()\n",
+    "paramList = ['beta', 'gamma', 'B', 'mu']\n",
     "\n",
-    "In \\[1\\]: f = plt.figure()\n",
+    "odeList = [Transition(origin='S', equation='-beta*S*I + B - mu*S', transition_type=TransitionType.ODE),\n",
+    "           Transition(origin='I', equation='beta*S*I - gamma*I - mu*I', transition_type=TransitionType.ODE),\n",
+    "           Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)]\n",
     "\n",
-    "@savefig sir_unrolled_transition_graph.png In \\[1\\]:\n",
-    "ode2.get_transition_graph()\n",
+    "ode = SimulateOde(stateList, paramList, ode=odeList)\n",
     "\n",
-    "In \\[1\\]: plt.close()"
+    "ode2 = ode.get_unrolled_obj()\n",
+    "ode2.get_transition_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1759120d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/svg+xml": "\n\n\n\n\n\n\n\n\nS\n\nS\n\n\n\nI\n\nI\n\n\n\nS->I\n\n\nI*S*β\n\n\n\nS*μ\nS*μ\n\n\n\nS->S*μ\n\n\n\n\n\nR\n\nR\n\n\n\nI->R\n\n\nI*γ\n\n\n\nI*μ\nI*μ\n\n\n\nI->I*μ\n\n\n\n\n\nB\nB\n\n\n\nB->S\n\n\n\n\n\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ode2.get_transition_graph()\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.11.0 ('pygom-dev')",
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.11.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
   },
   "vscode": {
    "interpreter": {
-    "hash": "fc17efc1b95c7b1781d7b7ce7a7c317a91a744529621a3902877be6d49554249"
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
    }
   }
  },
diff --git a/docs/pygom-doc/notebooks/unroll/unrollHard.ipynb b/docs/pygom-doc/notebooks/unroll/unrollHard.ipynb
index 64e14bd0..d57d339e 100644
--- a/docs/pygom-doc/notebooks/unroll/unrollHard.ipynb
+++ b/docs/pygom-doc/notebooks/unroll/unrollHard.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "The outflow of state **L**, $\\kappa L$, is composed of two transitions,\n",
     "one to **I** and the other to **A** but the ode of **L** only reflects\n",
-    "the total flow going out of the state. Same can be said for state **I**,\n",
+    "the total flow going out of the state. The same can be said for state **I**,\n",
     "where the flow $\\alpha I$ goes to both **R** and **N**. Graphically, it\n",
     "is a rather simple process as shown below.\n",
     "\n",
@@ -37,54 +37,207 @@
     "it into a closed system. The combination of state **D** and **N** is a\n",
     "constant, the total population. So we can remove **N** and this new\n",
     "system consist of six transitions. We define them explicitly as ODEs and\n",
-    "unroll them into transitions.\n",
-    "\n",
-    "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n",
-    "\n",
-    "In \\[1\\]: stateList = \\['S', 'L', 'I', 'A', 'R', 'D'\\]\n",
-    "\n",
-    "In \\[2\\]: paramList = \\['beta', 'p', 'kappa', 'alpha', 'f', 'delta',\n",
-    "'epsilon', 'N'\\]\n",
-    "\n",
-    "In \\[3\\]: odeList = \\[  \n",
-    "...: Transition(origin='S', equation='- beta\\*S/N\\*(I + delta\\*A)',\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='L',\n",
-    "equation='beta\\*S/N\\*(I + delta\\*A) - kappa\\*L',\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='I',\n",
-    "equation='p\\*kappa\\*L - alpha\\*I', transition_type=TransitionType.ODE),\n",
-    "...: Transition(origin='A', equation='(1 - p)*kappa* L - epsilon\\*A',\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='R',\n",
-    "equation='f\\*alpha\\*I + epsilon\\*A',\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='D',\n",
-    "equation='(1 - f)*alpha*I', transition_type=TransitionType.ODE) \\]\n",
-    "\n",
-    "In \\[4\\]: ode = SimulateOde(stateList, paramList, ode=odeList)\n",
-    "\n",
-    "In \\[5\\]: ode.get_transition_matrix()\n",
-    "\n",
-    "In \\[6\\]: ode2 = ode.get_unrolled_obj()\n",
-    "\n",
-    "In \\[7\\]: ode2.get_transition_matrix()\n",
+    "unroll them into transitions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "df866435",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[0, 0, 0, 0, 0, 0],\n",
+       "[0, 0, 0, 0, 0, 0],\n",
+       "[0, 0, 0, 0, 0, 0],\n",
+       "[0, 0, 0, 0, 0, 0],\n",
+       "[0, 0, 0, 0, 0, 0],\n",
+       "[0, 0, 0, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pygom import SimulateOde, Transition, TransitionType\n",
     "\n",
-    "In \\[8\\]: ode2.get_ode_eqn()\n",
+    "stateList = ['S', 'L', 'I', 'A', 'R', 'D']\n",
     "\n",
-    "After unrolling the odes, we have the following transition graph\n",
+    "paramList = ['beta', 'p', 'kappa', 'alpha', 'f', 'delta', 'epsilon', 'N']\n",
     "\n",
-    "@savefig sir_unrolled_transition_graph_hard.png In \\[1\\]:\n",
-    "ode2.get_transition_graph()\n",
+    "odeList = [Transition(origin='S', equation='- beta*S/N*(I + delta*A)', transition_type=TransitionType.ODE), \n",
+    "           Transition(origin='L', equation='beta*S/N*(I + delta*A) - kappa*L', transition_type=TransitionType.ODE),\n",
+    "           Transition(origin='I', equation='p*kappa*L - alpha*I', transition_type=TransitionType.ODE),\n",
+    "           Transition(origin='A', equation='(1 - p)*kappa* L - epsilon*A', transition_type=TransitionType.ODE),\n",
+    "           Transition(origin='R', equation='f*alpha*I + epsilon*A', transition_type=TransitionType.ODE), \n",
+    "           Transition(origin='D', equation='(1 - f)*alpha*I', transition_type=TransitionType.ODE)]\n",
     "\n",
-    "In \\[2\\]: plt.close()\n",
+    "ode = SimulateOde(stateList, paramList, ode=odeList)\n",
     "\n",
-    "In \\[3\\]: print(sum(ode.get_ode_eqn() - ode2.get_ode_eqn()).simplify())\n",
-    "\\# difference\n",
+    "ode.get_transition_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "39beaaaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & \\frac{A S \\beta \\delta}{N} + \\frac{I S \\beta}{N} & 0 & 0 & 0 & 0\\\\0 & 0 & L \\kappa p & - L \\kappa p + L \\kappa & 0 & 0\\\\0 & 0 & 0 & 0 & I \\alpha f & - I \\alpha f + I \\alpha\\\\0 & 0 & 0 & 0 & A \\epsilon & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[0, A*S*beta*delta/N + I*S*beta/N,         0,                    0,         0,                    0],\n",
+       "[0,                             0, L*kappa*p, -L*kappa*p + L*kappa,         0,                    0],\n",
+       "[0,                             0,         0,                    0, I*alpha*f, -I*alpha*f + I*alpha],\n",
+       "[0,                             0,         0,                    0, A*epsilon,                    0],\n",
+       "[0,                             0,         0,                    0,         0,                    0],\n",
+       "[0,                             0,         0,                    0,         0,                    0]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ode2 = ode.get_unrolled_obj()\n",
     "\n",
-    "which is exactly the same apart from slightly weird arrangement of\n",
-    "symbols in some of the equations. The last line with a value of zero\n",
-    "also reaffirms the result."
+    "ode2.get_transition_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2bfa23c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}- \\frac{A S \\beta \\delta}{N} - \\frac{I S \\beta}{N}\\\\\\frac{A S \\beta \\delta}{N} + \\frac{I S \\beta}{N} - L \\kappa\\\\- I \\alpha + L \\kappa p\\\\- A \\epsilon - L \\kappa p + L \\kappa\\\\A \\epsilon + I \\alpha f\\\\- I \\alpha f + I \\alpha\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[         -A*S*beta*delta/N - I*S*beta/N],\n",
+       "[A*S*beta*delta/N + I*S*beta/N - L*kappa],\n",
+       "[                   -I*alpha + L*kappa*p],\n",
+       "[       -A*epsilon - L*kappa*p + L*kappa],\n",
+       "[                  A*epsilon + I*alpha*f],\n",
+       "[                   -I*alpha*f + I*alpha]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ode2.get_ode_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee801c33",
+   "metadata": {},
+   "source": [
+    "After unrolling the ODEs, we can extract the following transition graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2361a071",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/svg+xml": "\n\n\n\n\n\n\n\n\nS\n\nS\n\n\n\nL\n\nL\n\n\n\nS->L\n\n\nA*S*β*δ/N + I*S*β/N\n\n\n\nI\n\nI\n\n\n\nL->I\n\n\nL*κ*p\n\n\n\nA\n\nA\n\n\n\nL->A\n\n\n-L*κ*p + L*κ\n\n\n\nR\n\nR\n\n\n\nI->R\n\n\nI*α*f\n\n\n\nD\n\nD\n\n\n\nI->D\n\n\n-I*α*f + I*α\n\n\n\nA->R\n\n\nA*ε\n\n\n\n",
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ode2.get_transition_graph()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "599030a9",
+   "metadata": {},
+   "source": [
+    "There is a slight difference in the arrangement of terms, but this matches what we were expecting our transition graph to look like. We can reaffirm this by algebraically comparing the two ODE systms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "eeab1ca9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sum(ode.get_ode_eqn() - ode2.get_ode_eqn()).simplify())"
    ]
   }
  ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }

From 6eda8218e461211773684182f27af2e11885e1b0 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 15:06:52 +0100
Subject: [PATCH 107/188] add pygom build and install

---
 .github/workflows/book.yml | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 4174495d..228283bc 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -1,4 +1,4 @@
-name: deploy-book
+name: build-book
 
 # only run when specific files change
 # only run on install-fix-docs branch 
@@ -29,6 +29,12 @@ jobs:
               run: |
                 pip install -r requirements.txt
 
+            # set up pygom
+            - name: Build and install pygom
+              run: |
+                  python setup.py build
+                  python setup.py install
+
             # build the book
             - name: Build the book
               run: |

From 05cb7c1d7a5647c2cb444cafc901681cfef76f9a Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 15:16:02 +0100
Subject: [PATCH 108/188] correct filepath

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 669de051..416f2bf4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,6 @@ __pycache__
 .settings
 
 # For built documentation
-/docs/pygom-doc/_build*
+/docs/pygom-doc/_build
 /doc/_build
 /doc/source/savefig/*

From 3dc4a1ea5340fa3b4a1f5edb72eaf0e1a926df64 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 15:38:03 +0100
Subject: [PATCH 109/188] additional requirements for CI

---
 .github/workflows/book.yml | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 228283bc..b1b627da 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -13,11 +13,28 @@ on:
     - docs/**
 
 jobs:
-    deploy-book:
+    build-book:
         runs-on: ubuntu-latest
         steps:
             - uses: actions/checkout@v2
 
+            - uses: actions/cache@v1
+              with:
+                path: ~/.cache/pip
+                key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+                restore-keys: |
+                  ${{ runner.os }}-pip-
+        
+
+        
+            - name: Set up Python
+              uses: actions/setup-python@v1
+              with:
+                python-version: ${{ matrix.python-version }}
+        
+            - name: Check python version
+              run: python -c "import sys; print(sys.version)"
+
             # install python
             - name: Set up Python 3.8
               uses: actions/setup-python@v2
@@ -27,6 +44,7 @@ jobs:
             # install dependencies
             - name: Install dependencies
               run: |
+                python -m pip install --upgrade pip
                 pip install -r requirements.txt
 
             # set up pygom

From d3a17679cf59a68bd9fa21b0ab8e40628d140161 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 15:38:43 +0100
Subject: [PATCH 110/188] starting to edit file

---
 docs/pygom-doc/notebooks/epi.ipynb | 46 +++++++++++++++++++++---------
 1 file changed, 33 insertions(+), 13 deletions(-)

diff --git a/docs/pygom-doc/notebooks/epi.ipynb b/docs/pygom-doc/notebooks/epi.ipynb
index 48711b33..695f7678 100644
--- a/docs/pygom-doc/notebooks/epi.ipynb
+++ b/docs/pygom-doc/notebooks/epi.ipynb
@@ -4,21 +4,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Simple Epidemic Analysis\n",
+    "# Epidemic Analysis\n",
     "\n",
-    "A common application of ordinary differential equations is in the field\n",
-    "of epidemiology modeling. More concretely, compartmental models that is\n",
-    "used to describe disease progression. We demonstrate some of the simple\n",
-    "algebraic analysis one may wish to take when given a compartment model.\n",
-    "Our use one of the simplest model, an SIR model with birth and death\n",
-    "processes, which is an extension of the one in `sir`. First, we\n",
-    "initialize the model below.\n",
+    "A common application of ODEs is in the field\n",
+    "of epidemiology modeling, where compartmental models are\n",
+    "used to describe disease progression through a population. \n",
+    "We demonstrate some of the simpler algebraic analysis that you may wish to undertake on a compartmental model.\n",
     "\n",
-    "In \\[1\\]: from pygom import common_models\n",
+    "We revist the SIR model with birth and death\n",
+    "processes, which is an extension of the one in `sir`. \n",
     "\n",
-    "In \\[2\\]: ode = common_models.SIR_Birth_Death()\n",
+    "First, we initialize the model, this time by importing it from `common_models`, rather than constructing it ourselves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c84ea26",
+   "metadata": {},
+   "source": [
+    "\n",
+    "from pygom import common_models\n",
     "\n",
-    "In \\[3\\]: print(ode.get_ode_eqn())\n",
+    "ode = common_models.SIR_Birth_Death()\n",
+    "\n",
+    "print(ode.get_ode_eqn())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea8c4b15",
+   "metadata": {},
+   "source": [
     "\n",
     "## Obtaining the R0\n",
     "\n",
@@ -64,7 +80,11 @@
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }

From 83c910d2f5cf2f0c8557b556968e70f70ed2c20b Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 15:42:55 +0100
Subject: [PATCH 111/188] remove error code

---
 .github/workflows/book.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index b1b627da..089502da 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -54,9 +54,10 @@ jobs:
                   python setup.py install
 
             # build the book
+            # TODO check which flags are needed, -W
             - name: Build the book
               run: |
-                jupyter-book build --all -v -W docs/pygom-doc
+                jupyter-book build --all -v docs/pygom-doc
 
             # deploy book to github-pages
             - name: GitHub Pages 

From a300153941aa3db50030af8e4cf760c0d0461eec Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 15:48:07 +0100
Subject: [PATCH 112/188] typos

---
 docs/pygom-doc/_toc.yml        | 1 -
 docs/pygom-doc/md/unrollOde.md | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index 185dc0f7..3c466fcb 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -6,7 +6,6 @@ root: intro
 parts:
   - caption: User documentation
     chapters:
-    - file: markdown.md
     - file: md/getting_started.md
     - file: notebooks/sir.ipynb
     - file: notebooks/transition.ipynb
diff --git a/docs/pygom-doc/md/unrollOde.md b/docs/pygom-doc/md/unrollOde.md
index 9da3bd11..332f6e22 100644
--- a/docs/pygom-doc/md/unrollOde.md
+++ b/docs/pygom-doc/md/unrollOde.md
@@ -1,4 +1,4 @@
-# Convert ODE into transitions
+# Converting equations into transitions
 
 As seen previously in `transition`{.interpreted-text role="ref"}, we can
 define the model via the transitions or explicitly as ODEs. There are

From 890dc0f65b298a660821b073fdb35e2bd39a43ac Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 17:19:06 +0100
Subject: [PATCH 113/188] add graphviz

---
 .github/workflows/book.yml | 18 +++++++-----------
 1 file changed, 7 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 089502da..7ae76736 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -24,17 +24,13 @@ jobs:
                 key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
                 restore-keys: |
                   ${{ runner.os }}-pip-
-        
-
-        
-            - name: Set up Python
-              uses: actions/setup-python@v1
-              with:
-                python-version: ${{ matrix.python-version }}
-        
-            - name: Check python version
-              run: python -c "import sys; print(sys.version)"
-
+                    steps:
+            
+            - uses: actions/checkout@v3
+            
+            - name: Setup Graphviz
+              uses: ts-graphviz/setup-graphviz@v1
+      
             # install python
             - name: Set up Python 3.8
               uses: actions/setup-python@v2

From cbfe09d2b5aba3733f6109c474bf6f3f95a240ec Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 11 Jul 2023 17:33:40 +0100
Subject: [PATCH 114/188] attempt to add autodocstrings to documentation

---
 docs/pygom-doc/_config.yml | 2 ++
 docs/pygom-doc/autodoc.rst | 5 +++++
 2 files changed, 7 insertions(+)
 create mode 100644 docs/pygom-doc/autodoc.rst

diff --git a/docs/pygom-doc/_config.yml b/docs/pygom-doc/_config.yml
index 192865ab..8a49af3e 100644
--- a/docs/pygom-doc/_config.yml
+++ b/docs/pygom-doc/_config.yml
@@ -29,6 +29,8 @@ sphinx:
     bibtex_reference_style: author_year
     # TODO change md to bibtex
     bibtex_bibfiles: "ref.md"
+  extra_extensions:
+    - 'sphinx.ext.autodoc'
 
 # Information about where the book exists on the web
 repository:
diff --git a/docs/pygom-doc/autodoc.rst b/docs/pygom-doc/autodoc.rst
new file mode 100644
index 00000000..c3111c90
--- /dev/null
+++ b/docs/pygom-doc/autodoc.rst
@@ -0,0 +1,5 @@
+Code documentation
+=============
+
+.. automodule:: pygom.pygom
+    :members:
\ No newline at end of file

From ab117fcbbe0062f3fde67b208fb016369493d485 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Fri, 21 Jul 2023 15:18:01 +0100
Subject: [PATCH 115/188] edit docstring links

---
 docs/pygom-doc/notebooks/sir.ipynb | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/docs/pygom-doc/notebooks/sir.ipynb b/docs/pygom-doc/notebooks/sir.ipynb
index 7e6246c1..f90e3e31 100644
--- a/docs/pygom-doc/notebooks/sir.ipynb
+++ b/docs/pygom-doc/notebooks/sir.ipynb
@@ -114,7 +114,7 @@
    "metadata": {},
    "source": [
     "```{note}\n",
-    "Here, we have invoked a class from `Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`transition:defining-the-equations` for an example when the input is wrong.\n",
+    "Here, we have invoked a class from {class}`.Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`transition:defining-the-equations` for an example when the input is wrong.\n",
     "```"
    ]
   },
@@ -257,9 +257,8 @@
    "id": "79c154ba",
    "metadata": {},
    "source": [
-    "#TODO links to unroll\n",
-    "Here the SIR model was provided to PyGOM as a set ODEs by using the `Transition` to define them.  \n",
-    "We have also provided the capability to obtain a *best guess* transition matrix when only the ODEs are available. See the section `unrollOde` for more information, and in particular `unrollSimple` for the continuing demonstration of the SIR model."
+    "Here the SIR model was provided to PyGOM as a set ODEs by using the {class}`.Transition` to define them.  \n",
+    "We have also provided the capability to obtain a *best guess* transition matrix when only the ODEs are available. See the section {doc}`unrollOde` for more information, and in particular {doc}`unrollSimple` for the continuing demonstration of the SIR model."
    ]
   },
   {
@@ -354,6 +353,7 @@
    "metadata": {},
    "source": [
     "#TODO unsure if this is needed\n",
+    "\n",
     "There is currently no mechanism to set the numeric initial conditions the states when the states are defined. This is because of an implementation issue with external package, such as solving an initial value problem."
    ]
   },
@@ -654,7 +654,6 @@
    "id": "2f64d869",
    "metadata": {},
    "source": [
-    "#TODO links\n",
     "This concludes the introductory example and we will be moving on to look at parameter estimation next in {doc}`estimate1` and the most important part in terms of setting up the ODE object; defining the equations in various different ways in {doc}`transition`."
    ]
   }

From f102ba30e1cbab349857780adb7ec929712024bf Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Fri, 21 Jul 2023 15:22:55 +0100
Subject: [PATCH 116/188] update docstring creation

---
 docs/pygom-doc/autodoc-loss.rst  |  9 +++++++++
 docs/pygom-doc/autodoc-model.rst | 11 +++++++++++
 docs/pygom-doc/autodoc.rst       |  5 -----
 3 files changed, 20 insertions(+), 5 deletions(-)
 create mode 100644 docs/pygom-doc/autodoc-loss.rst
 create mode 100644 docs/pygom-doc/autodoc-model.rst
 delete mode 100644 docs/pygom-doc/autodoc.rst

diff --git a/docs/pygom-doc/autodoc-loss.rst b/docs/pygom-doc/autodoc-loss.rst
new file mode 100644
index 00000000..e5a8afc3
--- /dev/null
+++ b/docs/pygom-doc/autodoc-loss.rst
@@ -0,0 +1,9 @@
+Module: loss
+=============
+
+.. automodule:: pygom.loss
+    :members:
+
+.. autosummary::
+   :toctree: _autosummary
+   :recursive:
\ No newline at end of file
diff --git a/docs/pygom-doc/autodoc-model.rst b/docs/pygom-doc/autodoc-model.rst
new file mode 100644
index 00000000..1426e052
--- /dev/null
+++ b/docs/pygom-doc/autodoc-model.rst
@@ -0,0 +1,11 @@
+Module: model
+=============
+
+.. automodule:: pygom.model
+    :members:
+
+.. autosummary::
+   :toctree: _autosummary
+   :recursive:
+
+   
\ No newline at end of file
diff --git a/docs/pygom-doc/autodoc.rst b/docs/pygom-doc/autodoc.rst
deleted file mode 100644
index c3111c90..00000000
--- a/docs/pygom-doc/autodoc.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-Code documentation
-=============
-
-.. automodule:: pygom.pygom
-    :members:
\ No newline at end of file

From 20594df3a753bb5e4917194f47ac3135e3ca826f Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Sun, 27 Aug 2023 16:18:56 +0100
Subject: [PATCH 117/188] reformatted ipynb

---
 docs/pygom-doc/notebooks/epi.ipynb | 157 +++++++++++++++++++++++------
 1 file changed, 127 insertions(+), 30 deletions(-)

diff --git a/docs/pygom-doc/notebooks/epi.ipynb b/docs/pygom-doc/notebooks/epi.ipynb
index 695f7678..8b47d848 100644
--- a/docs/pygom-doc/notebooks/epi.ipynb
+++ b/docs/pygom-doc/notebooks/epi.ipynb
@@ -11,23 +11,32 @@
     "used to describe disease progression through a population. \n",
     "We demonstrate some of the simpler algebraic analysis that you may wish to undertake on a compartmental model.\n",
     "\n",
-    "We revist the SIR model with birth and death\n",
-    "processes, which is an extension of the one in `sir`. \n",
+    "We revisit the SIR model with birth and death\n",
+    "processes, which is an extension of the one in {doc}`sir`. \n",
     "\n",
-    "First, we initialize the model, this time by importing it from `common_models`, rather than constructing it ourselves."
+    "First, we initialize the model, this time by importing it from {mod}`.common_models`, rather than constructing it ourselves."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 1,
    "id": "8c84ea26",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix([[B - I*S*beta - S*mu], [I*S*beta - I*gamma - I*mu], [I*gamma]])\n"
+     ]
+    }
+   ],
    "source": [
-    "\n",
     "from pygom import common_models\n",
     "\n",
     "ode = common_models.SIR_Birth_Death()\n",
     "\n",
-    "print(ode.get_ode_eqn())"
+    "ode.get_ode_eqn()"
    ]
   },
   {
@@ -36,53 +45,141 @@
    "metadata": {},
    "source": [
     "\n",
-    "## Obtaining the R0\n",
+    "## Obtaining the reproduction number (R0)\n",
     "\n",
     "The reproduction number, also known as the $R_{0}$, is the single most\n",
-    "powerful piece and reduced piece of information available from a\n",
-    "compartmental model. In a nutshell, it provides a single number - if the\n",
-    "parameters are known - which can the intuitive interpretation where\n",
-    "$R_{0} = 1$ defines the tipping point of an outbreak. A $R_{0}$ value of\n",
-    "more than one signifies an potential outbreak where less than one\n",
+    "powerful piece and reduced piece of information available from an epidemiological\n",
+    "compartmental model. This value represents the number of the disease-naive population who can be infected by a single member of the infectious population. When the parameter values are known, $R_{0}$ provides a single number which can then lead to an interpretation of the system, where $R_{0} = 1$ defines the tipping point of an outbreak. An $R_{0}$ value of\n",
+    "more than one signifies growth of cases (a potential outbreak), and an $R_{0}$ of less than one\n",
     "indicates that the disease will stop spreading naturally.\n",
     "\n",
-    "To obtain the $R_{0}$, we simply have to tell the function which states\n",
+    "#TODO reference\n",
+    "\n",
+    "To obtain the $R_{0}$, we need have to tell the {func}`.R0` function which states\n",
     "represent the *disease state*, which in this case is the state **I**.\n",
     "\n",
-    "In \\[1\\]: from pygom.model.epi_analysis import \\*\n",
+    "#TODO is this the disease state, or the infectious state?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d66498eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{B \\beta}{\\mu \\left(\\gamma + \\mu\\right)}$"
+      ],
+      "text/plain": [
+       "B*beta/(mu*(gamma + mu))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pygom.model.epi_analysis import *\n",
     "\n",
-    "In \\[2\\]: print(R0(ode, 'I'))\n",
+    "R0(ode, 'I')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0130ca70",
+   "metadata": {},
+   "source": [
     "\n",
     "## Algebraic R0\n",
     "\n",
     "We may also wish to get the $R_{0}$ in pure algebraic term. This can be\n",
-    "achieved by the following few lines. Note that the result below is\n",
+    "achieved by the following few lines of code. Note that the result below is\n",
     "slightly different from the one above. The difference is due to the\n",
-    "internal working of the functions, where `getR0` computes the\n",
-    "disease-free equilibrium value for the states and substitute them back\n",
-    "into the equation.\n",
-    "\n",
-    "In \\[1\\]: F, V = disease_progression_matrices(ode, 'I')\n",
-    "\n",
-    "In \\[2\\]: e = R0_from_matrix(F, V)\n",
+    "internal working of the functions, where {func}`.getR0` computes the\n",
+    "disease-free equilibrium value for the states and substitutes them back\n",
+    "into the equation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "717c6868",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'disease_progression_matrices' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m F, V \u001b[39m=\u001b[39m disease_progression_matrices(ode, \u001b[39m'\u001b[39m\u001b[39mI\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      3\u001b[0m e \u001b[39m=\u001b[39m R0_from_matrix(F, V)\n\u001b[0;32m      5\u001b[0m \u001b[39mprint\u001b[39m(e)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'disease_progression_matrices' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "F, V = disease_progression_matrices(ode, 'I')\n",
     "\n",
-    "In \\[3\\]: print(e)\n",
+    "e = R0_from_matrix(F, V)\n",
     "\n",
+    "print(e)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8c270b9",
+   "metadata": {},
+   "source": [
     "To replicate the output before, we have to find the values where the\n",
     "disease-free equilibrium will be achieved. Substitution can then be\n",
-    "performed to retrieve $R_{0}$ in pure parameters.\n",
-    "\n",
-    "In \\[1\\]: dfe = DFE(ode, \\['I'\\])\n",
+    "performed to retrieve $R_{0}$ in pure parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa0cc36c",
+   "metadata": {
+    "vscode": {
+     "languageId": "javascript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dfe = DFE(ode, ['I'])\n",
     "\n",
-    "In \\[2\\]: print(dfe)\n",
+    "print(dfe)\n",
     "\n",
-    "In \\[3\\]: print(e\\[0\\].subs(dfe))"
+    "print(e[0].subs(dfe))"
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
   }
  },
  "nbformat": 4,

From c3c328e593bb9ee31dd0b824570c559886749740 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Sun, 27 Aug 2023 17:08:24 +0100
Subject: [PATCH 118/188] update unroll documents

---
 docs/pygom-doc/md/unrollOde.md                | 13 +++++----
 .../notebooks/unroll/unrollHard.ipynb         | 28 ++++++++++++++++---
 .../notebooks/unroll/unrollSimple.ipynb       | 24 ++++++++++++----
 3 files changed, 49 insertions(+), 16 deletions(-)

diff --git a/docs/pygom-doc/md/unrollOde.md b/docs/pygom-doc/md/unrollOde.md
index 332f6e22..5b53d60c 100644
--- a/docs/pygom-doc/md/unrollOde.md
+++ b/docs/pygom-doc/md/unrollOde.md
@@ -1,6 +1,6 @@
 # Converting equations into transitions
 
-As seen previously in `transition`{.interpreted-text role="ref"}, we can
+As seen previously in {doc}`transition`, we can
 define the model via the transitions or explicitly as ODEs. There are
 times when we all just want to test out some model in a paper and the
 only available information are the ODEs themselves. Even though we know
@@ -8,8 +8,9 @@ that the ODEs come from some underlying transitions, breaking them down
 can be a time consuming process. We provide the functionalities to do
 this automatically.
 
-::: {.toctree}
-unroll/unrollSimple.rst
-unroll/unrollBD.rst 
-unroll/unrollHard.rst
-:::
+
+{doc}`../notebooks/unroll/unrollSimple`
+
+{doc}`../notebooks/unroll/unrollBD`
+
+{doc}`../notebooks/unroll/unrollHard`
diff --git a/docs/pygom-doc/notebooks/unroll/unrollHard.ipynb b/docs/pygom-doc/notebooks/unroll/unrollHard.ipynb
index d57d339e..f178daa7 100644
--- a/docs/pygom-doc/notebooks/unroll/unrollHard.ipynb
+++ b/docs/pygom-doc/notebooks/unroll/unrollHard.ipynb
@@ -8,7 +8,14 @@
     "\n",
     "Now we turn to a harder problem that does not have a one to one mapping\n",
     "between all the transitions and the terms in the ODEs. We use the model\n",
-    "in `Influenza_SLIARN`, defined by\n",
+    "in {doc}`Influenza_SLIARN`, defined by"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f468e1e",
+   "metadata": {},
+   "source": [
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dS}{dt} &= -S \\beta (I + \\delta A) \\\\    \n",
@@ -23,15 +30,28 @@
     "one to **I** and the other to **A** but the ode of **L** only reflects\n",
     "the total flow going out of the state. The same can be said for state **I**,\n",
     "where the flow $\\alpha I$ goes to both **R** and **N**. Graphically, it\n",
-    "is a rather simple process as shown below.\n",
-    "\n",
+    "is a rather simple process as shown below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "709d6c69",
+   "metadata": {},
+   "source": [
     "digraph SLIARD_Model {  \n",
     "labelloc = \"t\"; label = \"Original transitions\"; rankdir=LR; size=\"8\"\n",
     "node \\[shape = circle\\]; S -\\> L \\[ label = \"-Sβ(I + δA)/N\" \\]; L -\\> I\n",
     "\\[ label = \"κLp\" \\]; L -\\> A \\[ label = \"κL(1-p)\" \\]; I -\\> R \\[ label =\n",
     "\"αIf\" \\]; I -\\> D \\[ label = \"αI(1-f)\" \\]; A -\\> R \\[ label = \"ηA\" \\];\n",
     "\n",
-    "}\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db8a91f0",
+   "metadata": {},
+   "source": [
     "\n",
     "We slightly change the model by introducing a new state **D** to convert\n",
     "it into a closed system. The combination of state **D** and **N** is a\n",
diff --git a/docs/pygom-doc/notebooks/unroll/unrollSimple.ipynb b/docs/pygom-doc/notebooks/unroll/unrollSimple.ipynb
index 55772cb3..1aa9646b 100644
--- a/docs/pygom-doc/notebooks/unroll/unrollSimple.ipynb
+++ b/docs/pygom-doc/notebooks/unroll/unrollSimple.ipynb
@@ -14,15 +14,27 @@
     "\\frac{dR}{dt} &= \\gamma I.\n",
     "\\end{aligned}$$\n",
     "\n",
-    "which consists of two transitions\n",
+    "which consists of two transitions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "336381fa",
+   "metadata": {},
+   "source": [
     "\n",
     "digraph SIR_Model {  \n",
     "rankdir=LR; size=\"8\" node \\[shape = circle\\]; S -\\> I \\[ label = \"βSI\"\n",
     "\\]; I -\\> R \\[ label = \"γI\" \\];\n",
-    "\n",
-    "}\n",
-    "\n",
-    "We can define this as an ODE, as seen in {}[sir.ipynb]"
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "160f9040",
+   "metadata": {},
+   "source": [
+    "We can define this as an ODE, as seen in {doc}`sir`."
    ]
   },
   {
@@ -73,7 +85,7 @@
    "source": [
     "\n",
     "and the last line shows that the transition matrix is empty. This is the\n",
-    "expected result because `SimulateOdeModel` was not initialized using\n",
+    "expected result because {class}`.SimulateOdeModel` was not initialized using\n",
     "transitions. We can populate the transition matrix by calling an algorithm to extract the flow information, and can see that the output matches that from {ref}`transition:defining-the-equations`."
    ]
   },

From e1d410d2653b951a1857fb8e5f42e77e9451e1fe Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Sun, 27 Aug 2023 17:48:23 +0100
Subject: [PATCH 119/188] reformatted ipynb

---
 docs/pygom-doc/notebooks/epijson.ipynb | 122 +++++++++++++++++++------
 1 file changed, 92 insertions(+), 30 deletions(-)

diff --git a/docs/pygom-doc/notebooks/epijson.ipynb b/docs/pygom-doc/notebooks/epijson.ipynb
index 1df66d51..8d7e282a 100644
--- a/docs/pygom-doc/notebooks/epijson.ipynb
+++ b/docs/pygom-doc/notebooks/epijson.ipynb
@@ -8,58 +8,120 @@
     "\n",
     "Epidemiology data is complicated due to the many different stages a\n",
     "patient can go through and whether a modeling technique is applicable\n",
-    "depends heavily on the recording of data. EpiJSON is a framework whih\n",
-    "tries to captures all the information [\\[Finnie2016\\]](), in a JSON\n",
-    "format as the name suggests.\n",
+    "depends heavily on the recording of data. [EpiJSON](https://github.com/Hackout2/EpiJSON) is a framework which\n",
+    "tries to captures all the information in a JSON format {cite:ts}`Finnie2016` [\\[Finnie2016\\]](),.\n",
     "\n",
-    "This package provides the functionality to process EpiJSON data. Due to\n",
-    "the nature of this package, modeling of ode, it processes the data file\n",
-    "with this in mind. The output is therefore in the cumulative form as\n",
-    "default, shown below, in a `pandas.DataFrame` format. The input can be\n",
-    "in a string format, a file or already a `dict`.\n",
+    "PyGOM provides the functionality to process EpiJSON data. Due to\n",
+    "the nature of this package, modeling of ODEs, data files are processed with this in mind. The output is therefore in the cumulative form as\n",
+    "default, shown below, in a `pandas.DataFrame` format. \n",
     "\n",
-    "In \\[1\\]: from pygom.loss.read_epijson import epijson_to_data_frame\n",
+    "#TODO unsure what this means\n",
     "\n",
-    "In \\[2\\]: import pkgutil\n",
     "\n",
-    "In \\[3\\]: data = pkgutil.get_data('pygom', 'data/eg1.json')\n",
-    "\n",
-    "In \\[3\\]: df = epijson_to_data_frame(data)\n",
+    "The input can be\n",
+    "in a string format, a file or already a `dict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cea94b62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom.loss.read_epijson import epijson_to_data_frame\n",
     "\n",
-    "In \\[4\\]: print(df)\n",
+    "import pkgutil\n",
     "\n",
-    "Given that the aim of loading the data is usually for model fitting, we\n",
-    "allow EpiJSON as input directly to the loss class\n",
-    "`pygom.loss.EpijsonLoss` which uses the Poisson loss under the hood.\n",
+    "data = pkgutil.get_data('pygom', 'data/eg1.json')\n",
     "\n",
-    "In \\[1\\]: from pygom.model import common_models\n",
+    "df = epijson_to_data_frame(data)\n",
     "\n",
-    "In \\[2\\]: from pygom.loss.epijson_loss import EpijsonLoss\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04004708",
+   "metadata": {},
+   "source": [
     "\n",
-    "In \\[3\\]: ode = common_models.SIR(\\[0.5, 0.3\\])\n",
+    "Given that the aim of loading the data is usually for model fitting, we\n",
+    "allow EpiJSON as an input directly to the loss class\n",
+    "{class}`.EpijsonLoss` which uses the Poisson loss under the hood."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a3cfb9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom.model import common_models\n",
     "\n",
-    "In \\[4\\]: obj = EpijsonLoss(\\[0.005, 0.03\\], ode, data, 'Death', 'R',\n",
-    "\\[300, 2, 0\\])\n",
+    "from pygom.loss.epijson_loss import EpijsonLoss\n",
     "\n",
-    "In \\[5\\]: print(obj.cost())\n",
+    "ode = common_models.SIR([0.5, 0.3])\n",
     "\n",
-    "In \\[6\\]: print(obj.\\_df)\n",
+    "obj = EpijsonLoss([0.005, 0.03], ode, data, 'Death', 'R', [300, 2, 0])\n",
     "\n",
+    "print(obj.cost())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09c0cfcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(obj._df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ac54c8",
+   "metadata": {},
+   "source": [
     "Given an initialized object, all the operations are inherited from\n",
-    "`pygom.loss.BaseLoss`. We demonstrated above how to calculate the cost\n",
+    "{class}`.BaseLoss`. We demonstrated above how to calculate the cost\n",
     "and the rest will not be shown for brevity. The data frame is stored\n",
     "inside of the loss object and can be retrieved for inspection at any\n",
     "time point.\n",
     "\n",
-    "Rather unfortunately, initial values for the states is still required,\n",
+    "```{note}\n",
+    "Initial values for the states are required,\n",
     "but the time is not. When the time is not supplied, then the first time\n",
     "point in the data will be treated as $t0$. The input Death indicate which column of the data is used\n",
-    "and $R$ the corresponding state the data belongs to."
+    "class=\"title-ref\">Death indicates which column of the data is used\n",
+    "and $R$ the corresponding state the data belongs to.\n",
+    "```"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43b2e082",
+   "metadata": {},
+   "source": []
   }
  ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }

From 295d637d846ba1e1d6f0707fb030ff2366ba9711 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Sun, 27 Aug 2023 22:11:48 +0100
Subject: [PATCH 120/188] change title to be more descriptive

---
 docs/pygom-doc/notebooks/transition.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/pygom-doc/notebooks/transition.ipynb b/docs/pygom-doc/notebooks/transition.ipynb
index bea1277f..fc65bc5a 100644
--- a/docs/pygom-doc/notebooks/transition.ipynb
+++ b/docs/pygom-doc/notebooks/transition.ipynb
@@ -4,10 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Transition Object\n",
+    "# Model set-up using the transition object\n",
     "\n",
     "\n",
-    "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n",
+    "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our book-keeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n",
     "\n",
     "\n",
     "All transitions that get fed into the ODE system need to be defined as a transition object, `Transition`. It takes a total of four input arguments:\n",

From 0498c466f22a7290bd3261499224e47d27bcce11 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Sun, 27 Aug 2023 22:17:01 +0100
Subject: [PATCH 121/188] add a parameter intro section file

---
 docs/pygom-doc/md/parameter_fitting.md | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)
 create mode 100644 docs/pygom-doc/md/parameter_fitting.md

diff --git a/docs/pygom-doc/md/parameter_fitting.md b/docs/pygom-doc/md/parameter_fitting.md
new file mode 100644
index 00000000..4b252c67
--- /dev/null
+++ b/docs/pygom-doc/md/parameter_fitting.md
@@ -0,0 +1,17 @@
+# Parameter fitting
+
+The following notebooks will demonstrate how to use the parameter fitting options within PyGOM.
+
+{doc}`../notebooks/paramfit/bvpSimple`
+
+{doc}`../notebooks/paramfit/gradient`
+
+{doc}`../notebooks/paramfit/fh`
+
+{doc}`../notebooks/paramfit/estimate1`
+
+{doc}`../notebooks/paramfit/estimate2`
+
+{doc}`../notebooks/paramfit/initialGuess`
+
+{doc}`../notebooks/paramfit/profile`
\ No newline at end of file

From d1d2753d616e45fea555bd0a91709abc5ff69104 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Sun, 27 Aug 2023 22:18:10 +0100
Subject: [PATCH 122/188] change parameter fitting pile faths

---
 docs/pygom-doc/_toc.yml | 24 ++++++++++++++----------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index 3c466fcb..96bb1ff9 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -17,13 +17,15 @@ parts:
       - file: notebooks/unroll/unrollHard.ipynb
     - file: notebooks/epi.ipynb
     - file: notebooks/epijson.ipynb
-    - file: notebooks/bvpSimple.ipynb
-    - file: notebooks/gradient.ipynb
-    - file: notebooks/fh.ipynb
-    - file: notebooks/estimate1.ipynb
-    - file: notebooks/estimate2.ipynb
-    - file: notebooks/initialGuess.ipynb
-    - file: notebooks/profile.ipynb
+    - file: md/parameter_fitting.md
+      sections:
+      - file: notebooks/paramfit/bvpSimple.ipynb
+      - file: notebooks/paramfit/gradient.ipynb
+      - file: notebooks/paramfit/fh.ipynb
+      - file: notebooks/paramfit/estimate1.ipynb
+      - file: notebooks/paramfit/estimate2.ipynb
+      - file: notebooks/paramfit/initialGuess.ipynb
+      - file: notebooks/paramfit/profile.ipynb
   - caption: Common biological compartmental models
     chapters:
     - file: md/common_models.md
@@ -46,10 +48,12 @@ parts:
 #  - caption: Frequently asked questions
 #    chapters: 
 #    - file: md/faq.md
-##  - caption: Code documentation
-##    chapters:
-##    - file:
+  - caption: Code documentation
+    chapters:
+    - file: autodoc-model.rst
+    - file: autodoc-loss.rst
 #  - caption: References
+# https://jupyterbook.org/en/stable/content/citations.html#id6
 #    chapters: 
 #    - file: md/ref.md
 ##  - caption: Indices and tables

From 37170c6bbde0df4d19f8d00f82ddcabd85cab6db Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 11:03:47 +0100
Subject: [PATCH 123/188] update docs requirements

---
 docs/pygom-doc/requirements.txt | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/docs/pygom-doc/requirements.txt b/docs/pygom-doc/requirements.txt
index 7e821e45..4e490b8a 100644
--- a/docs/pygom-doc/requirements.txt
+++ b/docs/pygom-doc/requirements.txt
@@ -1,3 +1,15 @@
+dask[complete]>=0.13.0
+graphviz>=0.4.9
+matplotlib>=1.0.0
+numpy>=1.6.0
+pandas>=0.15.0
+python-dateutil>=2.0.0
+scipy>=1.4.1
+sympy>=1.0.0
+numpydoc>=0.6.0
+sphinx>=1.4.1
+sphinx_rtd_theme>=0.2.0
+cython>=0.29
+nbsphinx
 jupyter-book
-matplotlib
-numpy
+docutils==0.17.1
\ No newline at end of file

From 705732d90acf4de5e89b2153258a703d13418425 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 11:05:11 +0100
Subject: [PATCH 124/188] start reformatting bibliography files

---
 docs/pygom-doc/md/bib.md | 1 +
 docs/pygom-doc/ref.bib   | 6 ++++++
 2 files changed, 7 insertions(+)
 create mode 100644 docs/pygom-doc/md/bib.md
 create mode 100644 docs/pygom-doc/ref.bib

diff --git a/docs/pygom-doc/md/bib.md b/docs/pygom-doc/md/bib.md
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/docs/pygom-doc/md/bib.md
@@ -0,0 +1 @@
+
diff --git a/docs/pygom-doc/ref.bib b/docs/pygom-doc/ref.bib
new file mode 100644
index 00000000..6e6ecea9
--- /dev/null
+++ b/docs/pygom-doc/ref.bib
@@ -0,0 +1,6 @@
+
+@article{Finnie2016
+, title = {piJSON: A unified data-format for epidemiology}
+, author = {Thomas Finnie et al.}
+, journal = {Epidemics}
+}
\ No newline at end of file

From 1f5d6c370af0ade18af7c98eee3426f34ecdabb6 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 11:13:43 +0100
Subject: [PATCH 125/188] reformatting ipynb, also addresses #89

---
 .../notebooks/paramfit/bvpSimple.ipynb        | 198 ++++++++++++++++++
 1 file changed, 198 insertions(+)
 create mode 100644 docs/pygom-doc/notebooks/paramfit/bvpSimple.ipynb

diff --git a/docs/pygom-doc/notebooks/paramfit/bvpSimple.ipynb b/docs/pygom-doc/notebooks/paramfit/bvpSimple.ipynb
new file mode 100644
index 00000000..6ca0ce57
--- /dev/null
+++ b/docs/pygom-doc/notebooks/paramfit/bvpSimple.ipynb
@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solving Boundary Value Problems\n",
+    "\n",
+    "In addition to finding solutions for an IVP and estimate the unknown\n",
+    "parameters, this package also allows you to solve BVP with a little bit\n",
+    "of imagination. Here, we are going to show how a BVP can be solved by\n",
+    "treating it as a parameter estimation problem. Essentially, a shooting\n",
+    "method where the first boundary condition defines the initial condition\n",
+    "of an IVP and the second boundary condition is an observation. Two\n",
+    "examples, both from MATLAB[1], will be shown here.\n",
+    "\n",
+    "## Simple model 1\n",
+    "\n",
+    "We are trying to find the solution to the second order differential\n",
+    "equation\n",
+    "\n",
+    "$$\\nabla^{2} y + |y| = 0$$\n",
+    "\n",
+    "subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert\n",
+    "this into a set of first order ODE\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{d y_{0}}{dt} &= y_{1} \\\\\n",
+    "\\frac{d y_{1}}{dt} &= -|y_{0}|\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "using an auxiliary variable $y_{1} = \\nabla y$ and $y_{0} = y$. Setting\n",
+    "up the system below\n",
+    "\n",
+    "In \\[1\\]: from pygom import Transition, TransitionType,\n",
+    "DeterministicOde, SquareLoss\n",
+    "\n",
+    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "\n",
+    "In \\[2\\]: stateList = \\['y0', 'y1'\\]\n",
+    "\n",
+    "In \\[3\\]: paramList = \\[\\]\n",
+    "\n",
+    "In \\[4\\]: ode1 = Transition(origin='y0',  \n",
+    "...: equation='y1', ...: transition_type=TransitionType.ODE)\n",
+    "\n",
+    "In \\[5\\]: ode2 = Transition(origin='y1',  \n",
+    "...: equation='-abs(y0)', ...: transition_type=TransitionType.ODE)\n",
+    "\n",
+    "In \\[6\\]: model = DeterministicOde(stateList,  \n",
+    "...: paramList, ...: ode=\\[ode1, ode2\\])\n",
+    "\n",
+    "In \\[7\\]: model.get_ode_eqn()\n",
+    "\n",
+    "We check that the equations are correct before proceeding to set up our\n",
+    "loss function.\n",
+    "\n",
+    "In \\[1\\]: import numpy\n",
+    "\n",
+    "In \\[2\\]: from scipy.optimize import minimize\n",
+    "\n",
+    "In \\[3\\]: initialState = \\[0.0, 1.0\\]\n",
+    "\n",
+    "In \\[4\\]: t = numpy.linspace(0, 4, 100)\n",
+    "\n",
+    "In \\[5\\]: model.initial_values = (initialState, t\\[0\\])\n",
+    "\n",
+    "In \\[6\\]: solution = model.integrate(t\\[1::\\])\n",
+    "\n",
+    "In \\[7\\]: f = plt.figure()\n",
+    "\n",
+    "@savefig bvp1_random_guess_plot.png In \\[8\\]: model.plot()\n",
+    "\n",
+    "In \\[9\\]: plt.close()\n",
+    "\n",
+    "Setting up the second boundary condition $y(4) = -2$ is easy, because\n",
+    "that is just a single observation attached to the state $y_{1}$.\n",
+    "Enforcing the first boundary condition requires us to set it as the\n",
+    "initial condition. Because the condition only states that $y(0) = 0$,\n",
+    "the starting value of the other state $y_1$ is free. We let our loss\n",
+    "object know that it is free through the targetState input argument.\n",
+    "\n",
+    "In \\[10\\]: theta = \\[0.0\\]\n",
+    "\n",
+    "In \\[11\\]: obj = SquareLoss(theta=theta,  \n",
+    ".…: ode=model, .…: x0=initialState, .…: t0=t\\[0\\], .…: t=t\\[-1\\], .…:\n",
+    "y=\\[-2\\], .…: state_name=\\['y0'\\], .…: target_state=\\['y1'\\])\n",
+    "\n",
+    "In \\[12\\]: thetaHat = minimize(fun=obj.costIV, x0=\\[0.0\\])\n",
+    "\n",
+    "In \\[13\\]: print(thetaHat)\n",
+    "\n",
+    "In \\[14\\]: model.initial_values = (\\[0.0\\] + thetaHat\\['x'\\].tolist(),\n",
+    "t\\[0\\])\n",
+    "\n",
+    "In \\[15\\]: solution = model.integrate(t\\[1::\\])\n",
+    "\n",
+    "In \\[16\\]: f = plt.figure()\n",
+    "\n",
+    "@savefig bvp1_solution_plot.png In \\[17\\]: model.plot()\n",
+    "\n",
+    "In \\[18\\]: plt.close()\n",
+    "\n",
+    "We are going to visualize the solution, and also check the boundary\n",
+    "condition. The first became our initial condition, so it is always\n",
+    "satisfied and only the latter is of concern, which is zero (subject to\n",
+    "numerical error) from thetaHat.\n",
+    "\n",
+    "## Simple model 2\n",
+    "\n",
+    "Our second example is different as it involves an actual parameter and\n",
+    "also time. We have the Mathieu's Equation\n",
+    "\n",
+    "$$\\nabla^{2} y + \\left(p - 2q \\cos(2x)\\right)y = 0$$\n",
+    "\n",
+    "and the aim is to compute the fourth eigenvalue $q=5$. There are three\n",
+    "boundary conditions\n",
+    "\n",
+    "$$\\nabla y(0) = 0, \\quad \\nabla y(\\pi) = 0, \\quad y(0) = 1$$\n",
+    "\n",
+    "and we aim to solve it by converting it to a first order ODE and tackle\n",
+    "it as an IVP. As our model object does not allow the use of the time\n",
+    "component in the equations, we introduce a anxiliary state $\\tau$ that\n",
+    "replaces time $t$. Rewrite the equations using\n",
+    "$y_{0} = y, y_{1} = \\nabla y$ and define our model as\n",
+    "\n",
+    "In \\[1\\]: stateList = \\['y0', 'y1', 'tau'\\]\n",
+    "\n",
+    "In \\[2\\]: paramList = \\['p'\\]\n",
+    "\n",
+    "In \\[3\\]: ode1 = Transition('y0', 'y1', TransitionType.ODE)\n",
+    "\n",
+    "In \\[4\\]: ode2 = Transition('y1', '-(p - 2\\*5\\*cos(2\\*tau))\\*y0',\n",
+    "TransitionType.ODE)\n",
+    "\n",
+    "In \\[5\\]: ode3 = Transition('tau', '1', TransitionType.ODE)\n",
+    "\n",
+    "In \\[6\\]: model = DeterministicOde(stateList, paramList, ode=\\[ode1,\n",
+    "ode2, ode3\\])\n",
+    "\n",
+    "In \\[7\\]: theta = \\[1.0, 1.0, 0.0\\]\n",
+    "\n",
+    "In \\[8\\]: p = 15.0\n",
+    "\n",
+    "In \\[9\\]: t = numpy.linspace(0, numpy.pi)\n",
+    "\n",
+    "In \\[10\\]: model.parameters = \\[('p',p)\\]\n",
+    "\n",
+    "In \\[11\\]: model.initial_values = (theta, t\\[0\\])\n",
+    "\n",
+    "In \\[12\\]: solution = model.integrate(t\\[1::\\])\n",
+    "\n",
+    "In \\[13\\]: f = plt.figure()\n",
+    "\n",
+    "@savefig bvp2_random_guess_plot.png In \\[14\\]: model.plot()\n",
+    "\n",
+    "In \\[15\\]: plt.close()\n",
+    "\n",
+    "Now we are ready to setup the estimation. Like before, we setup the\n",
+    "second boundary condition by pretending that it is an observation. We\n",
+    "have all the initial conditions defined by the first boundary condition\n",
+    "\n",
+    "In \\[1\\]: obj = SquareLoss(15.0, model, x0=\\[1.0, 0.0, 0.0\\], t0=0.0,\n",
+    "t=numpy.pi, y=0.0, state_name='y1')\n",
+    "\n",
+    "In \\[2\\]: xhatObj = minimize(obj.cost,\\[15\\])\n",
+    "\n",
+    "In \\[3\\]: print(xhatObj)\n",
+    "\n",
+    "In \\[4\\]: model.parameters = \\[('p', xhatObj\\['x'\\]\\[0\\])\\]\n",
+    "\n",
+    "In \\[5\\]: model.initial_values = (\\[1.0, 0.0, 0.0\\], t\\[0\\])\n",
+    "\n",
+    "In \\[5\\]: solution = model.integrate(t\\[1::\\])\n",
+    "\n",
+    "In \\[6\\]: f = plt.figure()\n",
+    "\n",
+    "@savefig bvp2_solution_plot.png In \\[7\\]: model.plot()\n",
+    "\n",
+    "In \\[8\\]: plt.close()\n",
+    "\n",
+    "The plot of the solution shows the path that satisfies all boundary\n",
+    "condition. The last subplot is time which obvious is redundant here but\n",
+    "the `DeterministicOde.plot` method is not yet able to recognize the time\n",
+    "component. Possible speed up can be achieved through the use of\n",
+    "derivative information or via root finding method that tackles the\n",
+    "gradient directly, instead of the cost function.\n",
+    "\n",
+    "**Reference**\n",
+    "\n",
+    "[1] "
+   ]
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}

From 49fe85363abdaa53b8199d209898e7a1ffd5b64e Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 12:12:06 +0100
Subject: [PATCH 126/188] reformatting

---
 .../notebooks/paramfit/bvpSimple.ipynb        | 335 +++++++++++++-----
 1 file changed, 244 insertions(+), 91 deletions(-)

diff --git a/docs/pygom-doc/notebooks/paramfit/bvpSimple.ipynb b/docs/pygom-doc/notebooks/paramfit/bvpSimple.ipynb
index 6ca0ce57..062c54f7 100644
--- a/docs/pygom-doc/notebooks/paramfit/bvpSimple.ipynb
+++ b/docs/pygom-doc/notebooks/paramfit/bvpSimple.ipynb
@@ -6,13 +6,25 @@
    "source": [
     "# Solving Boundary Value Problems\n",
     "\n",
-    "In addition to finding solutions for an IVP and estimate the unknown\n",
-    "parameters, this package also allows you to solve BVP with a little bit\n",
+    "In addition to finding solutions for an initial value problem (IVP) and estimating the unknown\n",
+    "parameters, this package also allows you to solve boundary value problems (BVP) with a little bit\n",
     "of imagination. Here, we are going to show how a BVP can be solved by\n",
     "treating it as a parameter estimation problem. Essentially, a shooting\n",
     "method where the first boundary condition defines the initial condition\n",
     "of an IVP and the second boundary condition is an observation. Two\n",
-    "examples, both from MATLAB[1], will be shown here.\n",
+    "examples, both from the [bvp4c implementation in MATLAB](https://uk.mathworks.com/help/matlab/ref/bvp4c.html), are demonstrated here.\n",
+    "\n",
+    "\n",
+    "```{note}\n",
+    "These examples are general and not specific to disease scenarios.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cfce26c",
+   "metadata": {},
+   "source": [
     "\n",
     "## Simple model 1\n",
     "\n",
@@ -22,95 +34,164 @@
     "$$\\nabla^{2} y + |y| = 0$$\n",
     "\n",
     "subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert\n",
-    "this into a set of first order ODE\n",
+    "this into a set of first order ODEs\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{d y_{0}}{dt} &= y_{1} \\\\\n",
     "\\frac{d y_{1}}{dt} &= -|y_{0}|\n",
     "\\end{aligned}$$\n",
     "\n",
-    "using an auxiliary variable $y_{1} = \\nabla y$ and $y_{0} = y$. Setting\n",
-    "up the system below\n",
-    "\n",
-    "In \\[1\\]: from pygom import Transition, TransitionType,\n",
-    "DeterministicOde, SquareLoss\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "using an auxiliary variable $y_{1} = \\nabla y$ and $y_{0} = y$. \n",
+    "Here we set up the system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "594a9de8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import Transition, TransitionType, DeterministicOde, SquareLoss\n",
     "\n",
-    "In \\[2\\]: stateList = \\['y0', 'y1'\\]\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[3\\]: paramList = \\[\\]\n",
+    "stateList = ['y0', 'y1']\n",
     "\n",
-    "In \\[4\\]: ode1 = Transition(origin='y0',  \n",
-    "...: equation='y1', ...: transition_type=TransitionType.ODE)\n",
+    "paramList = []\n",
     "\n",
-    "In \\[5\\]: ode2 = Transition(origin='y1',  \n",
-    "...: equation='-abs(y0)', ...: transition_type=TransitionType.ODE)\n",
+    "ode1 = Transition(origin='y0', equation='y1', transition_type=TransitionType.ODE)\n",
     "\n",
-    "In \\[6\\]: model = DeterministicOde(stateList,  \n",
-    "...: paramList, ...: ode=\\[ode1, ode2\\])\n",
+    "ode2 = Transition(origin='y1', equation='-abs(y0)', transition_type=TransitionType.ODE)\n",
     "\n",
-    "In \\[7\\]: model.get_ode_eqn()\n",
+    "model = DeterministicOde(stateList, paramList, ode=[ode1, ode2])\n",
     "\n",
+    "model.get_ode_eqn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9456e756",
+   "metadata": {},
+   "source": [
     "We check that the equations are correct before proceeding to set up our\n",
-    "loss function.\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
+    "loss function. The loss function enables us to optimize the parameter choice.\n",
     "\n",
-    "In \\[2\\]: from scipy.optimize import minimize\n",
-    "\n",
-    "In \\[3\\]: initialState = \\[0.0, 1.0\\]\n",
-    "\n",
-    "In \\[4\\]: t = numpy.linspace(0, 4, 100)\n",
+    "#TODO add some ref/notes for loss functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ae19c03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
     "\n",
-    "In \\[5\\]: model.initial_values = (initialState, t\\[0\\])\n",
+    "from scipy.optimize import minimize\n",
     "\n",
-    "In \\[6\\]: solution = model.integrate(t\\[1::\\])\n",
+    "initialState = [0.0, 1.0]\n",
     "\n",
-    "In \\[7\\]: f = plt.figure()\n",
+    "t = numpy.linspace(0, 4, 100)\n",
     "\n",
-    "@savefig bvp1_random_guess_plot.png In \\[8\\]: model.plot()\n",
+    "model.initial_values = (initialState, t[0])\n",
     "\n",
-    "In \\[9\\]: plt.close()\n",
+    "solution = model.integrate(t[1::])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3952c813",
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "f = plt.figure()\n",
+    "model.plot()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a6de472",
+   "metadata": {},
+   "source": [
     "\n",
     "Setting up the second boundary condition $y(4) = -2$ is easy, because\n",
-    "that is just a single observation attached to the state $y_{1}$.\n",
+    "that is only a single observation attached to the state $y_{1}$.\n",
     "Enforcing the first boundary condition requires us to set it as the\n",
     "initial condition. Because the condition only states that $y(0) = 0$,\n",
     "the starting value of the other state $y_1$ is free. We let our loss\n",
-    "object know that it is free through the targetState input argument.\n",
-    "\n",
-    "In \\[10\\]: theta = \\[0.0\\]\n",
+    "object know that it is free through the `targetState` input argument.\n",
     "\n",
-    "In \\[11\\]: obj = SquareLoss(theta=theta,  \n",
-    ".…: ode=model, .…: x0=initialState, .…: t0=t\\[0\\], .…: t=t\\[-1\\], .…:\n",
-    "y=\\[-2\\], .…: state_name=\\['y0'\\], .…: target_state=\\['y1'\\])\n",
-    "\n",
-    "In \\[12\\]: thetaHat = minimize(fun=obj.costIV, x0=\\[0.0\\])\n",
-    "\n",
-    "In \\[13\\]: print(thetaHat)\n",
+    "#TODO unsure what this means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaf5a503",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta = [0.0]\n",
     "\n",
-    "In \\[14\\]: model.initial_values = (\\[0.0\\] + thetaHat\\['x'\\].tolist(),\n",
-    "t\\[0\\])\n",
+    "obj = SquareLoss(theta=theta, ode=model, x0=initialState, t0=t[0], \n",
+    "                t=t[-1], y=[-2], state_name=['y0'], target_state=['y1'])\n",
     "\n",
-    "In \\[15\\]: solution = model.integrate(t\\[1::\\])\n",
+    "thetaHat = minimize(fun=obj.costIV, x0=[0.0])\n",
     "\n",
-    "In \\[16\\]: f = plt.figure()\n",
+    "print(thetaHat)\n",
     "\n",
-    "@savefig bvp1_solution_plot.png In \\[17\\]: model.plot()\n",
+    "model.initial_values = ([0.0] + thetaHat['x'].tolist(), t[0])\n",
     "\n",
-    "In \\[18\\]: plt.close()\n",
+    "solution = model.integrate(t[1::])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bffe9f2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = plt.figure()\n",
+    "model.plot()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "472ff583",
+   "metadata": {},
+   "source": [
     "\n",
     "We are going to visualize the solution, and also check the boundary\n",
     "condition. The first became our initial condition, so it is always\n",
     "satisfied and only the latter is of concern, which is zero (subject to\n",
-    "numerical error) from thetaHat.\n",
+    "numerical error) from thetahat.\n",
+    "\n",
+    "#TODO what is thetahat?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85032ca8",
+   "metadata": {},
+   "source": [
     "\n",
     "## Simple model 2\n",
     "\n",
     "Our second example is different as it involves an actual parameter and\n",
     "also time. We have the Mathieu's Equation\n",
     "\n",
+    "#TODO add ref\n",
+    "\n",
     "$$\\nabla^{2} y + \\left(p - 2q \\cos(2x)\\right)y = 0$$\n",
     "\n",
     "and the aim is to compute the fourth eigenvalue $q=5$. There are three\n",
@@ -122,77 +203,149 @@
     "it as an IVP. As our model object does not allow the use of the time\n",
     "component in the equations, we introduce a anxiliary state $\\tau$ that\n",
     "replaces time $t$. Rewrite the equations using\n",
-    "$y_{0} = y, y_{1} = \\nabla y$ and define our model as\n",
-    "\n",
-    "In \\[1\\]: stateList = \\['y0', 'y1', 'tau'\\]\n",
-    "\n",
-    "In \\[2\\]: paramList = \\['p'\\]\n",
-    "\n",
-    "In \\[3\\]: ode1 = Transition('y0', 'y1', TransitionType.ODE)\n",
-    "\n",
-    "In \\[4\\]: ode2 = Transition('y1', '-(p - 2\\*5\\*cos(2\\*tau))\\*y0',\n",
-    "TransitionType.ODE)\n",
+    "$y_{0} = y, y_{1} = \\nabla y$ and define our model as"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afcf4558",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stateList = ['y0', 'y1', 'tau']\n",
     "\n",
-    "In \\[5\\]: ode3 = Transition('tau', '1', TransitionType.ODE)\n",
+    "IparamList = ['p']\n",
     "\n",
-    "In \\[6\\]: model = DeterministicOde(stateList, paramList, ode=\\[ode1,\n",
-    "ode2, ode3\\])\n",
+    "ode1 = Transition('y0', 'y1', TransitionType.ODE)\n",
     "\n",
-    "In \\[7\\]: theta = \\[1.0, 1.0, 0.0\\]\n",
+    "ode2 = Transition('y1', '-(p - 2*5*cos(2*tau))*y0', TransitionType.ODE)\n",
     "\n",
-    "In \\[8\\]: p = 15.0\n",
+    "ode3 = Transition('tau', '1', TransitionType.ODE)\n",
     "\n",
-    "In \\[9\\]: t = numpy.linspace(0, numpy.pi)\n",
+    "model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3])\n",
     "\n",
-    "In \\[10\\]: model.parameters = \\[('p',p)\\]\n",
+    "theta = [1.0, 1.0, 0.0]\n",
     "\n",
-    "In \\[11\\]: model.initial_values = (theta, t\\[0\\])\n",
+    "p = 15.0\n",
     "\n",
-    "In \\[12\\]: solution = model.integrate(t\\[1::\\])\n",
+    "t = numpy.linspace(0, numpy.pi)\n",
     "\n",
-    "In \\[13\\]: f = plt.figure()\n",
+    "model.parameters = [('p',p)]\n",
     "\n",
-    "@savefig bvp2_random_guess_plot.png In \\[14\\]: model.plot()\n",
+    "model.initial_values = (theta, t[0])\n",
     "\n",
-    "In \\[15\\]: plt.close()\n",
+    "solution = model.integrate(t[1::])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c75eea5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = plt.figure()\n",
+    "model.plot()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e1407cf",
+   "metadata": {},
+   "source": [
     "\n",
-    "Now we are ready to setup the estimation. Like before, we setup the\n",
+    "Now we are ready to setup the estimation. Like before, we set up the\n",
     "second boundary condition by pretending that it is an observation. We\n",
-    "have all the initial conditions defined by the first boundary condition\n",
-    "\n",
-    "In \\[1\\]: obj = SquareLoss(15.0, model, x0=\\[1.0, 0.0, 0.0\\], t0=0.0,\n",
-    "t=numpy.pi, y=0.0, state_name='y1')\n",
-    "\n",
-    "In \\[2\\]: xhatObj = minimize(obj.cost,\\[15\\])\n",
+    "have all the initial conditions defined by the first boundary condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "110edf3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obj = SquareLoss(15.0, model, x0=[1.0, 0.0, 0.0], t0=0.0, \n",
+    "                 t=numpy.pi, y=0.0, state_name='y1')\n",
     "\n",
-    "In \\[3\\]: print(xhatObj)\n",
+    "xhatObj = minimize(obj.cost,[15])\n",
     "\n",
-    "In \\[4\\]: model.parameters = \\[('p', xhatObj\\['x'\\]\\[0\\])\\]\n",
+    "print(xhatObj)\n",
     "\n",
-    "In \\[5\\]: model.initial_values = (\\[1.0, 0.0, 0.0\\], t\\[0\\])\n",
+    "model.parameters = [('p', xhatObj['x'][0])]\n",
     "\n",
-    "In \\[5\\]: solution = model.integrate(t\\[1::\\])\n",
+    "model.initial_values = ([1.0, 0.0, 0.0], t[0])\n",
     "\n",
-    "In \\[6\\]: f = plt.figure()\n",
+    "solution = model.integrate(t[1::])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "653c998f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = plt.figure()\n",
     "\n",
-    "@savefig bvp2_solution_plot.png In \\[7\\]: model.plot()\n",
+    "model.plot()\n",
     "\n",
-    "In \\[8\\]: plt.close()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3719cf8",
+   "metadata": {},
+   "source": [
     "\n",
     "The plot of the solution shows the path that satisfies all boundary\n",
-    "condition. The last subplot is time which obvious is redundant here but\n",
+    "condition. The last subplot is time, which is redundant here but\n",
     "the `DeterministicOde.plot` method is not yet able to recognize the time\n",
     "component. Possible speed up can be achieved through the use of\n",
-    "derivative information or via root finding method that tackles the\n",
+    "derivative information or via the root finding method that tackles the\n",
     "gradient directly, instead of the cost function.\n",
+    "\n",
+    "#TODO add meth/fun/class refs for root finding method, derivative"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb79c4f6",
+   "metadata": {},
+   "source": [
     "\n",
     "**Reference**\n",
     "\n",
     "[1] "
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef9004bd",
+   "metadata": {},
+   "source": []
   }
  ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }

From 4cb80415cc99123b54c15c75f040d8d15f71e952 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 12:37:57 +0100
Subject: [PATCH 127/188] reformatted

---
 .../notebooks/paramfit/estimate1.ipynb        | 296 ++++++++++++++++++
 1 file changed, 296 insertions(+)
 create mode 100644 docs/pygom-doc/notebooks/paramfit/estimate1.ipynb

diff --git a/docs/pygom-doc/notebooks/paramfit/estimate1.ipynb b/docs/pygom-doc/notebooks/paramfit/estimate1.ipynb
new file mode 100644
index 00000000..415e8a30
--- /dev/null
+++ b/docs/pygom-doc/notebooks/paramfit/estimate1.ipynb
@@ -0,0 +1,296 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parameter Estimation: Example 1\n",
+    "\n",
+    "## Estimation under square loss\n",
+    "\n",
+    "To ease the estimation process when given data, a separate module\n",
+    "{mod}`ode_loss` has been constructed for observations coming from a single\n",
+    "state. We demonstrate how to do parametre fitting using two models; first, a the\n",
+    "SIR model, followed by the Legrand SEIHFR model from {citets}`Legrand2007` [\\[Legrand2007\\]]() used \n",
+    "for Ebola in the next page {doc}`.estimate2`.\n",
+    "\n",
+    "### SIR Model\n",
+    "\n",
+    "We set up an SIR model as seen previously in {doc}`.sir`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efea520f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import SquareLoss, common_models\n",
+    "\n",
+    "import numpy\n",
+    "\n",
+    "import scipy.integrate\n",
+    "\n",
+    "import matplotlib.pyplot\n",
+    "\n",
+    "# define the parameters\n",
+    "paramEval = [('beta',0.5), ('gamma',1.0/3.0)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbfac95a",
+   "metadata": {},
+   "source": [
+    "Initialize the model using the preloaded common model {obj}`.SIR`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f09342a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ode = common_models.SIR(paramEval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fb03842",
+   "metadata": {},
+   "source": [
+    "We assume that we have perfect information about the $R$\n",
+    "compartment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cb19871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x0 = [1, 1.27e-6, 0]\n",
+    "\n",
+    "# time, including the initial time t0 at t=0\n",
+    "\n",
+    "t = numpy.linspace(0, 150, 1000)\n",
+    "\n",
+    "# determine the solution.\n",
+    "\n",
+    "solution = scipy.integrate.odeint(ode.ode, x0, t)\n",
+    "\n",
+    "#TODO why use scipy?\n",
+    "\n",
+    "y = solution[:,1:3].copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a5c30e7",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Initialize the class with some initial guess\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "857371e4",
+   "metadata": {},
+   "source": [
+    "# our initial guess\n",
+    "\n",
+    "theta = [0.2, 0.2]\n",
+    "\n",
+    "objSIR = SquareLoss(theta, ode, x0, t[0], t[1::], y[1::,:], ['I','R'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cabcb9ba",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Note that we need to provide the initial values, $x_{0}$ and $t_{0}$\n",
+    "differently to the observations $y$ and the corresponding time $t$.\n",
+    "Additionally, the state which the observation lines needs to be\n",
+    "specified. Either a single state, or multiple states are allowed, as\n",
+    "seen above.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "140a5020",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Difference in gradient\n",
+    "\n",
+    "We have provided two different ways of obtaining the gradient, these are\n",
+    "explained in {doc}`.gradient` in a bit more detail. First, lets see how\n",
+    "similar the outputs of the two methods are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de043dc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "objSIR.sensitivity()\n",
+    "\n",
+    "objSIR.adjoint()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c47c6356",
+   "metadata": {},
+   "source": [
+    "\n",
+    "and the time required to obtain the gradient for the SIR model under\n",
+    "$\\theta = (0.2,0.2)$, previously entered.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c74c5449",
+   "metadata": {},
+   "source": [
+    "%timeit objSIR.sensitivity()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20856aa8",
+   "metadata": {},
+   "source": [
+    "%timeit objSIR.adjoint()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a86770d",
+   "metadata": {},
+   "source": [
+    "The amount of time taken for both method is dependent on the\n",
+    "number of observations as well as the number of states. The effect on\n",
+    "the adjoint method as the number of observations differs can be quite\n",
+    "evident. This is because the adjoint method is under a discretization\n",
+    "which loops in Python where as the forward sensitivity equations are\n",
+    "solved via an integration. As the number of observation gets\n",
+    "larger, the affect of the Python loop becomes more obvious.\n",
+    "\n",
+    "The difference in gradient is larger when there are less observations. This\n",
+    "is because the adjoint method use interpolations on the output of the\n",
+    "ode between each consecutive time points. Given solutions over the same\n",
+    "length of time, fewer discretizations leads to a less accurate\n",
+    "interpolation. Note that the interpolation is currently performed using a\n",
+    "univariate spline, due to the limitation of Python packages. Ideally,\n",
+    "one would prefer to use an (adaptive) Hermite or Chebyshev\n",
+    "interpolation. \n",
+    "\n",
+    "#TODO add refs\n",
+    "\n",
+    "Note how we ran the two gradient functions once before\n",
+    "timing it, that is because we only find the properties (Jacobian,\n",
+    "gradient) of the ODEs during runtime.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "543ec840",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Optimized result\n",
+    "\n",
+    "Then standard optimization procedures with some suitable initial guess\n",
+    "should yield the correct result. It is important to set the boundaries\n",
+    "for compartmental models as we know that all the parameters are strictly\n",
+    "positive. We put a less restrictive inequality here for demonstration\n",
+    "purpose.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff093f77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# what we think the bounds are\n",
+    "\n",
+    "boxBounds = [(0.0,2.0),(0.0,2.0)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7590328f",
+   "metadata": {},
+   "source": [
+    "Then using the optimization routines in `scipy.optimize`, for example,\n",
+    "the *SLSQP* method with the gradient obtained by forward sensitivity.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83eca0ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.optimize import minimize\n",
+    "\n",
+    "res = minimize(fun=objSIR.cost, jac=objSIR.sensitivity, x0=theta, \n",
+    "               bounds=boxBounds, method='SLSQP')\n",
+    "\n",
+    "print(res)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "404ddd1c",
+   "metadata": {},
+   "source": [
+    "Other methods available in `scipy.optimize.minimize` can also be used,\n",
+    "such as the *L-BFGS-B* and *TNC*. We can also use methods that accepts\n",
+    "the exact Hessian such as *trust-ncg* but that should not be necessary\n",
+    "most of the time.\n",
+    "\n",
+    "#TODO add doc refs for scipy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ce3d21c",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From d0b7666f9cec11ac75988bef85b5a514150ef83d Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 12:43:53 +0100
Subject: [PATCH 128/188] remove blank cell

---
 docs/pygom-doc/notebooks/paramfit/estimate1.ipynb | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/docs/pygom-doc/notebooks/paramfit/estimate1.ipynb b/docs/pygom-doc/notebooks/paramfit/estimate1.ipynb
index 415e8a30..68946810 100644
--- a/docs/pygom-doc/notebooks/paramfit/estimate1.ipynb
+++ b/docs/pygom-doc/notebooks/paramfit/estimate1.ipynb
@@ -267,12 +267,6 @@
     "\n",
     "#TODO add doc refs for scipy"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ce3d21c",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {

From ac21b5311bb666cd6025703b302aae38d4d9f1b7 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 15:31:46 +0100
Subject: [PATCH 129/188] reformatted

---
 .../notebooks/paramfit/estimate2.ipynb        | 544 ++++++++++++++++++
 1 file changed, 544 insertions(+)
 create mode 100644 docs/pygom-doc/notebooks/paramfit/estimate2.ipynb

diff --git a/docs/pygom-doc/notebooks/paramfit/estimate2.ipynb b/docs/pygom-doc/notebooks/paramfit/estimate2.ipynb
new file mode 100644
index 00000000..ce6d24f5
--- /dev/null
+++ b/docs/pygom-doc/notebooks/paramfit/estimate2.ipynb
@@ -0,0 +1,544 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parameter Estimation: Example 2\n",
+    "\n",
+    "Continuing from the example in {doc}`.estimate1`, we show why estimating the parameters\n",
+    "for ODE's can be difficult. This is especially true if there is a lack of data\n",
+    "or when there are too much flexibility in the model. \n",
+    "\n",
+    "```{note}\n",
+    "For reproducibility purposes, only deterministic models are used here. If a stochastic algorithm is required we use a fixed seed.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8897e924",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## SEIR model\n",
+    "\n",
+    "We demonstrate the estimation on data collected from an Ebola outbreak in West\n",
+    "Africa. We use the number of deaths in Guinea and its corresponding time\n",
+    "the data was recorded. These data are publicly available and they can be\n",
+    "obtained [here](https://github.com/cmrivers/ebola). \n",
+    "\n",
+    "We provide the data here for reproducibility and ease."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6826d12d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the number of deaths and cases in Guinea\n",
+    "\n",
+    "yDeath = [29.0, 59.0, 60.0, 62.0, 66.0, 70.0, 70.0, 80.0, 83.0, 86.0, 95.0, 101.0, 106.0, 108.0, 122.0, 129.0, 136.0, 141.0, 143.0, 149.0, 155.0, 157.0, 158.0,\n",
+    "157.0, 171.0, 174.0, 186.0, 193.0, 208.0, 215.0, 226.0, 264.0,\n",
+    "267.0, 270.0, 303.0, 305.0, 307.0, 309.0, 304.0, 310.0, 310.0,\n",
+    "314.0, 319.0, 339.0, 346.0, 358.0, 363.0, 367.0, 373.0, 377.0,\n",
+    "380.0, 394.0, 396.0, 406.0, 430.0, 494.0, 517.0, 557.0, 568.0, 595.0,\n",
+    "601.0, 632.0, 635.0, 648.0, 710.0, 739.0, 768.0, 778.0, 843.0,\n",
+    "862.0, 904.0, 926.0, 997.0]\n",
+    "\n",
+    "yCase = [49.0, 86.0, 86.0, 86.0, 103.0, 112.0, 112.0, 122.0, 127.0, 143.0, 151.0, 158.0, 159.0, 168.0, 197.0, 203.0, 208.0, 218.0, 224.0, 226.0, 231.0,\n",
+    "235.0, 236.0, 233.0, 248.0, 258.0, 281.0, 291.0, 328.0, 344.0,\n",
+    "351.0, 398.0, 390.0, 390.0, 413.0, 412.0, 408.0, 409.0, 406.0,\n",
+    "411.0, 410.0, 415.0, 427.0, 460.0, 472.0, 485.0, 495.0, 495.0,\n",
+    "506.0, 510.0, 519.0, 543.0, 579.0, 607.0, 648.0, 771.0, 812.0,\n",
+    "861.0, 899.0, 936.0, 942.0, 1008.0, 1022.0, 1074.0, 1157.0, 1199.0,\n",
+    "1298.0, 1350.0, 1472.0, 1519.0, 1540.0, 1553.0, 1906.0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d9b4bfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the corresponding time\n",
+    "\n",
+    "t = [0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 13.0, 16.0, 18.0, 20.0, 23.0, 25.0, 26.0, 29.0, 32.0, 35.0, 40.0, 42.0, 44.0, 46.0, 49.0, 51.0, 62.0, 66.0, 67.0,\n",
+    "71.0, 73.0, 80.0, 86.0, 88.0, 90.0, 100.0, 102.0, 106.0, 108.0,\n",
+    "112.0, 114.0, 117.0, 120.0, 123.0, 126.0, 129.0, 132.0, 135.0, 137.0, 140.0, 142.0, 144.0, 147.0, 149.0, 151.0, 157.0, 162.0, 167.0,\n",
+    "169.0, 172.0, 175.0, 176.0, 181.0, 183.0, 185.0, 190.0, 193.0,\n",
+    "197.0, 199.0, 204.0, 206.0, 211.0, 213.0, 218.0]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe8c48cf",
+   "metadata": {},
+   "source": [
+    "### Simple estimation\n",
+    "\n",
+    "First, we are going to fit a standard **SEIR** model to the data.\n",
+    "Details of the models can be found in {doc}`common_models` Defining the model\n",
+    "as usual with an approximation of what the parameters might be, here,\n",
+    "we choose the values to be the mid point of our feasible region (defined\n",
+    "by our box constraints later).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dfa5094",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import SquareLoss, common_models\n",
+    "\n",
+    "import numpy, scipy.optimize\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "theta = numpy.array([5.0, 5.0, 5.0])\n",
+    "\n",
+    "ode = common_models.SEIR(theta)\n",
+    "\n",
+    "population = 1175e4\n",
+    "\n",
+    "y = numpy.reshape(numpy.append(numpy.array(yCase), numpy.array(yDeath)), \n",
+    "                  (len(yCase),2), 'F')/population\n",
+    "\n",
+    "x0 = [1., 0., 49.0/population, 29.0/population]\n",
+    "\n",
+    "t0 = t[0]\n",
+    "\n",
+    "objLegrand = SquareLoss(theta, ode, x0, t0, t[1::], y[1::,:], ['I','R'], numpy.sqrt([population\\]*2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "288f8273",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Then we optimize, first, assuming that the initial conditions are\n",
+    "accurate. Some relatively large bounds are used for this particular\n",
+    "problem.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc31ee5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "boxBounds = [ (0.0,10.0), (0.0,10.0), (0.0,10.0)]\n",
+    "\n",
+    "res = scipy.optimize.minimize(fun=objLegrand.cost, jac=objLegrand.sensitivity, x0=theta, bounds=boxBounds, method='l-bfgs-b')\n",
+    "\n",
+    "print(res)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3e42fe2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = plt.figure()\n",
+    "\n",
+    "objLegrand.plot()\n",
+    "\n",
+    "plt.close()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef3374aa",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We can see from our visual confirmation that the estimated parameters\n",
+    "are not exactly ideal. This is confirmed by the information returned\n",
+    "from the `scipy.optimize.minimize` routine, and probably caused by the\n",
+    "poor starting point. An attempt to find a more suitable value can be\n",
+    "done by some form of parameter space exploration. Given that the\n",
+    "evaluation of the objective function is not expensive here, we have\n",
+    "plenty of options to choose from. To reduce the number of packages\n",
+    "required to build this documentation, routines from `scipy.optimize`\n",
+    "remain our preferred option."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be511db4",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Improved initial guess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38badab5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resDE = scipy.optimize.differential_evolution(objLegrand.cost,\n",
+    "bounds=boxBounds, polish=False, seed=20921391)\n",
+    "\n",
+    "print(objLegrand.sensitivity(resDE['x']))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f68ff44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = plt.figure()\n",
+    "\n",
+    "objLegrand.plot()\n",
+    "\n",
+    "plt.close()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d06e8bdf",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Looking at the output of the estimates (below this paragraph), we can\n",
+    "see our inference on Ebola is wrong when compared to the *known* values\n",
+    "(from field observations) even though the graphs looks *\\`\\`reasonable\"*.\n",
+    "Namely, $\\gamma^{-1}$, the third parameter in the vector below, our time\n",
+    "from infectiousness to death, is within the expected range but $\\alpha^{-1}$\n",
+    "(second parameter), the incubation period, is a lot higher than expected.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f01c604",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "1/resDE['x']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bc57a74",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Multimodal surface\n",
+    "\n",
+    "A reason for this type of behavior is that we simply lack the\n",
+    "information/data to make proper inference. Without data on the state\n",
+    "$E$, the parameters $\\beta,\\alpha$ for the two states $I$ and $E$ are\n",
+    "dependent only on observations on $I$. Hence, some other random\n",
+    "combination of $\\beta,\\alpha$ that is capable of generating realizations\n",
+    "close to observations in $I$ is feasible. In such cases, the only\n",
+    "requirement is that there exist some $\\gamma$ in the feasible region\n",
+    "that can compensate for the ill suited $\\beta,\\alpha$. For example, we\n",
+    "know (obtained elsewhere and not shown here) that there is another set\n",
+    "of parameters capable of generating a similar looking curves as before.\n",
+    "Note the reversal of magnitude in $\\beta$ and $\\alpha$.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4797fe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objLegrand.cost([3.26106524e+00, 2.24798702e-04, 1.23660721e-02])\n",
+    "\n",
+    "# objLegrand.cost([ 0.02701867, 9.00004776, 0.01031861])\n",
+    "# similar graph\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0573ccf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "objLegrand.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce36ae9e",
+   "metadata": {},
+   "source": [
+    "### With initial values as parameters\n",
+    "\n",
+    "The assumption that the whole population being susceptible is\n",
+    "an overestimate, therefore we want to estimate the initial conditions of the\n",
+    "ODEs as well. Given previous estimates of the parameters\n",
+    "$\\hat{\\beta}, \\hat{\\alpha}, \\hat{\\gamma}$ it is appropriate to start our\n",
+    "initial guess there.\n",
+    "\n",
+    "Furthermore, given that we now estimate the initial values for all the\n",
+    "states, we can use the first time point as our observation. So our time\n",
+    "begins at $t = -1$ where our observations include the previous initial\n",
+    "condition, i.e. 49 and 29 for the number of cases and death at $t = 0$\n",
+    "respectively. The following code block demonstrates how we would do\n",
+    "that; feel free to try it out yourself to see the much improved result.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe0f36b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thetaIV = theta.tolist() + x0\n",
+    "\n",
+    "thetaIV[3] -= 1e-8 # to make sure that the initial guess satisfies the constraints\n",
+    "\n",
+    "boxBoundsIV = boxBounds + [(0.,1.), (0.,1.), (0.,1.), (0.,1.)]\n",
+    "\n",
+    "objLegrand = SquareLoss(theta, ode, x0, -1, t, y, ['I','R'], numpy.sqrt([population]*2))\n",
+    "\n",
+    "resDEIV = scipy.optimize.differential_evolution(objLegrand.costIV, bounds=boxBoundsIV, polish=False, seed=20921391)\n",
+    "\n",
+    "print(resDEIV)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3331a258",
+   "metadata": {},
+   "source": [
+    "objLegrand.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2408c495",
+   "metadata": {},
+   "source": [
+    "## Legrand Ebola SEIHFR Model\n",
+    "\n",
+    "Next, we demonstrate the estimation on a model that has been widely used in\n",
+    "the 2014 Ebola outbreak in west Africa {citets}`Legrand`. This model has been\n",
+    "defined in {mod}`.common_models`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f726a4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ode = common_models.Legrand_Ebola_SEIHFR()\n",
+    "\n",
+    "# initial guess from the paper that studied the outbreak in Congo\n",
+    "\n",
+    "theta = numpy.array([0.588,0.794,7.653, # the beta  \n",
+    "                    10.0,9.6,5.0,2.0, # the omega\n",
+    "                    7.0,0.81,0.80, # alpha,\n",
+    "                    delta, \n",
+    "                    theta,\n",
+    "                    100.,1.0]) # kappa,intervention time\n",
+    "\n",
+    "# initial conditions, note that we have a 0.0 at the end because the model is a non-automonous ODE which we have converted the time component out of\n",
+    "\n",
+    "x0 = numpy.array([population, 0.0, 49.0, 0.0, 0.0, 29.0, 0.0])/population\n",
+    "\n",
+    "ode.parameters = theta\n",
+    "\n",
+    "ode.initial_values = (x0, t[0])\n",
+    "\n",
+    "objLegrand = SquareLoss(theta, ode, x0, t[0], t[1::], y[1::,:], \n",
+    "                        ['I','R'], numpy.sqrt([population]*2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71d2d4b6",
+   "metadata": {},
+   "source": [
+    "Now, it is important to set additional constraints accurately because a\n",
+    "simple box constraint is much larger than the feasible set. Namely,\n",
+    "$\\omega_{I}, \\omega_{D}$ are the time taken from onset until end of\n",
+    "infectious/death, which has to be bigger than $\\omega_{H}$, onset to\n",
+    "hospitalization given the nature of the disease. Therefore, we create\n",
+    "extra inequality constraints in addition to the box constraints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d928c542",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boxBounds = [(0.001, 100.), #beta_I \n",
+    "             (0.001, 100.), # beta_H\n",
+    "             (0.001, 100.), # beta_F \n",
+    "             (0.001, 100.), # omega_I \n",
+    "             (0.001, 100.), # omega_D \n",
+    "             (0.001, 100.), # omega_H \n",
+    "             (0.001, 100.), # omega_F\n",
+    "             (0.001, 100.), # alpha^{-1} \n",
+    "             (0.0001, 1.), # delta \n",
+    "             (0.0001, 1.), # theta .….: (0.001, 1000.), # kappa\n",
+    "             (0.,218.) # intervention time \n",
+    "             ]\n",
+    "\n",
+    "cons = ({'type': 'ineq', 'fun' : lambda x: numpy.array([x[3]-x[5], x[4]-x[5]])})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df3e4a0a",
+   "metadata": {},
+   "source": [
+    "We can now try to find the optimal values, but because this is a\n",
+    "difficult problem that can take a very long time without guarantee on\n",
+    "the quality of solution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e20c8987",
+   "metadata": {},
+   "source": [
+    "res = scipy.optimize.minimize(fun=objLegrand.cost, jac=objLegrand.sensitivity, \n",
+    "                              x0=theta, constraints=cons, bounds=boxBounds, method='SLSQP')\n",
+    "\n",
+    "print(res)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9e7a7d7",
+   "metadata": {},
+   "source": [
+    "objLegrand.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d0c6889",
+   "metadata": {},
+   "source": [
+    "The estimated parameters are very much unrealistic given that\n",
+    "a lot of them are near the boundaries. It is also known from other\n",
+    "sources that some of the epidemiology properties of Ebola, with\n",
+    "incubation period of around 2 weeks and a mortality rate of around 80\n",
+    "percent.\n",
+    "\n",
+    "As the estimate does not appear to provide anything sensible, we also\n",
+    "provide a set of values previously obtained (that looks semi-reasonable)\n",
+    "here plot the epidemic curve with the observations layered on top.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b383ece7",
+   "metadata": {},
+   "source": [
+    "theta = numpy.array([3.96915071e-02, 1.72302620e+01, 1.99749990e+01, 2.67759445e+01, \n",
+    "                     4.99999990e+01, 5.56122691e+00, 4.99999990e+01, 8.51599523e+00, \n",
+    "                     9.99999000e-01, 1.00000000e-06, 3.85807562e+00, 1.88385318e+00])\n",
+    "\n",
+    "print(objLegrand.cost(theta))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9232353",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ],
+    "vscode": {
+     "languageId": "javascript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "solution = ode.integrate(t[1::])\n",
+    "\n",
+    "f, axarr = plt.subplots(2,3)\n",
+    "\n",
+    "axarr[0,0].plot(t, solution[:,0]);\n",
+    "\n",
+    "axarr[0,0].set_title('Susceptible');\n",
+    "\n",
+    "axarr[0,1].plot(t, solution[:,1]);\n",
+    "\n",
+    "axarr[0,1].set_title('Exposed');\n",
+    "\n",
+    "axarr[0,2].plot(t, solution[:,2]);\n",
+    "\n",
+    " axarr[0,2].plot(t, y[:,0], 'r');\n",
+    "\n",
+    "axarr[0,2].set_title('Infectious');\n",
+    "\n",
+    "axarr[1,0].plot(t, solution[:,3]);\n",
+    "\n",
+    "axarr[1,0].set_title('Hospitalised');\n",
+    "\n",
+    "axarr[1,1].plot(t, solution[:,4]);\n",
+    "\n",
+    "axarr[1,1].set_title('Awaiting Burial');\n",
+    "\n",
+    "axarr[1,2].plot(t, solution[:,5]);\n",
+    "\n",
+    "axarr[1,2].plot(t, y[:,1], 'r');\n",
+    "\n",
+    "axarr[1,2].set_title('Removed');\n",
+    "\n",
+    "f.text(0.5, 0.04, 'Days from outbreak', ha='center');\n",
+    "\n",
+    "f.text(0.01, 0.5, 'Population', va='center', rotation='vertical');\n",
+    "\n",
+    "f.tight_layout();\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 9aa4ee5b1667b8d981e456161649ebf55994b938 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 15:51:56 +0100
Subject: [PATCH 130/188] reformatted

---
 docs/pygom-doc/notebooks/paramfit/fh.ipynb | 205 +++++++++++++++++++++
 1 file changed, 205 insertions(+)
 create mode 100644 docs/pygom-doc/notebooks/paramfit/fh.ipynb

diff --git a/docs/pygom-doc/notebooks/paramfit/fh.ipynb b/docs/pygom-doc/notebooks/paramfit/fh.ipynb
new file mode 100644
index 00000000..8d00a632
--- /dev/null
+++ b/docs/pygom-doc/notebooks/paramfit/fh.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parameter Estimation: FitzHugh Example\n",
+    "\n",
+    "## Defining the model\n",
+    "\n",
+    "We are going to investigate another classic model here, the\n",
+    "FitzHugh-Nagumo, or simply FitzHugh here. The model has already been\n",
+    "defined in {mod}`common_models` so we can load it rather than define by scratch.\n",
+    "\n",
+    "#TODO ref for FH-N"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a74b67ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import SquareLoss, common_models\n",
+    "\n",
+    "import numpy\n",
+    "\n",
+    "import scipy.integrate, scipy.optimize\n",
+    "\n",
+    "import math,time,copy\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x0 = \\[-1.0, 1.0\\]\n",
+    "\n",
+    "t0 = 0\n",
+    "\n",
+    "# params\n",
+    "paramEval = [('a',0.2), ('b',0.2), ('c',3.0)]\n",
+    "\n",
+    "ode = common_models.FitzHugh(paramEval)\n",
+    "\n",
+    "ode.initial_values = (x0, t0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "100b72c6",
+   "metadata": {},
+   "source": [
+    "Define a set of time points and we can see how the two states $V$ and $R$\n",
+    "are suppose to behave.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8291f05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = numpy.linspace(1, 20, 30).astype('float64')\n",
+    "\n",
+    "solution = ode.integrate(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2fccb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ode.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c177da58",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Estimate the parameters\n",
+    "\n",
+    "Obtaining the correct parameters for the FitzHugh model is well known to\n",
+    "be difficult, because of its multimodal surface. Although this\n",
+    "has been shown many times in the literature, so we will omit the\n",
+    "details. For further details see {citets}'FitzHugh`.\n",
+    "\n",
+    "#TODO ref?\n",
+    "\n",
+    "We will give the fitting a go with an initial guess which will enable us to recover the original parameters. First, we try the fitting process with only one target state.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97050c8b",
+   "metadata": {},
+   "source": [
+    "theta = \\[0.5, 0.5, 0.5\\]\n",
+    "\n",
+    "In \\[27\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n",
+    "'R')\n",
+    "\n",
+    "boxBounds = \\[  \n",
+    ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0) .…: \\]\n",
+    "\n",
+    "res = scipy.optimize.minimize(fun=objFH.cost,  \n",
+    ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n",
+    "method='L-BFGS-B')\n",
+    "\n",
+    "print(res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2cbc5b1",
+   "metadata": {},
+   "source": [
+    "Then we try the same again but with both state as our target. Now we\n",
+    "won't look at the iterations because they are pretty pointless.\n",
+    "\n",
+    "In \\[30\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n",
+    "\\['V','R'\\])\n",
+    "\n",
+    "In \\[31\\]: res = scipy.optimize.minimize(fun=objFH.cost,  \n",
+    ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n",
+    "method='L-BFGS-B')\n",
+    "\n",
+    "In \\[32\\]: print(res)\n",
+    "\n",
+    "Note how the estimates are the same, unlike other models.\n",
+    "\n",
+    "## Estimate initial value\n",
+    "\n",
+    "We can further assume that we have no idea about the initial values for\n",
+    "$V$ and $R$ as well. We also provide guesstimate to set off the\n",
+    "optimization. The input vector $\\theta$ must have the parameters first,\n",
+    "then the initial values, along with the corresponding bounds.\n",
+    "\n",
+    "First, only a single target state, i.e. we only have observations for\n",
+    "one of states which is $R$ in this case\n",
+    "\n",
+    "In \\[35\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n",
+    "'R')\n",
+    "\n",
+    "In \\[35\\]: boxBounds = \\[  \n",
+    ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0), .…: (None,None), .…:\n",
+    "(None,None) .…: \\]\n",
+    "\n",
+    "In \\[36\\]: res = scipy.optimize.minimize(fun=objFH.costIV,  \n",
+    ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5,0.5\\], .…:\n",
+    "bounds=boxBounds, .…: method='L-BFGS-B')\n",
+    "\n",
+    "In \\[37\\]: print(res)\n",
+    "\n",
+    "then both state as target at the same time\n",
+    "\n",
+    "In \\[38\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n",
+    "\\['V','R'\\])\n",
+    "\n",
+    "In \\[38\\]: res = scipy.optimize.minimize(fun=objFH.costIV,  \n",
+    ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5, 0.5\\], .…:\n",
+    "bounds=boxBounds, .…: method='L-BFGS-B')\n",
+    "\n",
+    "In \\[39\\]: print(res)\n",
+    "\n",
+    "See the difference between the two estimate with the latter, both state\n",
+    "were used, yielding superior estimates. Note that only the forward\n",
+    "sensitivity method is implemented when estimating the initial value, and\n",
+    "it is assumed that the starting condition for all the states are\n",
+    "unknown.\n",
+    "\n",
+    "The choice of algorithm here is the **L-BFGS-B** which is a better\n",
+    "choice because the parameter space of the FitzHugh is rough (i.e. large\n",
+    "second derivative) as well as being multimodal. This means that the\n",
+    "Hessian is not guaranteed to be positive definite and approximation\n",
+    "using $J^{\\top}J$ is poor, with $J$ being the Jacobian of the objective\n",
+    "function."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c65cd66bfb42a81d12ede40111d2dc694c51bdca Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 15:53:42 +0100
Subject: [PATCH 131/188] reformatted

---
 docs/pygom-doc/notebooks/paramfit/fh.ipynb | 134 ++++++++++++++-------
 1 file changed, 93 insertions(+), 41 deletions(-)

diff --git a/docs/pygom-doc/notebooks/paramfit/fh.ipynb b/docs/pygom-doc/notebooks/paramfit/fh.ipynb
index 8d00a632..3b30551b 100644
--- a/docs/pygom-doc/notebooks/paramfit/fh.ipynb
+++ b/docs/pygom-doc/notebooks/paramfit/fh.ipynb
@@ -97,21 +97,21 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "97050c8b",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "theta = \\[0.5, 0.5, 0.5\\]\n",
+    "theta = [0.5, 0.5, 0.5]\n",
     "\n",
-    "In \\[27\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n",
-    "'R')\n",
+    "objFH = SquareLoss(theta, ode, x0, t0, t, solution[1::,1], 'R')\n",
     "\n",
-    "boxBounds = \\[  \n",
-    ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0) .…: \\]\n",
+    "boxBounds = [(0.0,5.0), (0.0,5.0), (0.0,5.0)]\n",
     "\n",
-    "res = scipy.optimize.minimize(fun=objFH.cost,  \n",
-    ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n",
-    "method='L-BFGS-B')\n",
+    "res = scipy.optimize.minimize(fun=objFH.cost, jac=objFH.sensitivity, \n",
+    "                              x0=theta, bounds=boxBounds,\n",
+    "                              method='L-BFGS-B')\n",
     "\n",
     "print(res)"
    ]
@@ -121,55 +121,107 @@
    "id": "f2cbc5b1",
    "metadata": {},
    "source": [
-    "Then we try the same again but with both state as our target. Now we\n",
-    "won't look at the iterations because they are pretty pointless.\n",
-    "\n",
-    "In \\[30\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n",
-    "\\['V','R'\\])\n",
-    "\n",
-    "In \\[31\\]: res = scipy.optimize.minimize(fun=objFH.cost,  \n",
-    ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n",
-    "method='L-BFGS-B')\n",
+    "Then we try the same again but with both states as our target. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa53fd4a",
+   "metadata": {},
+   "source": [
+    "objFH = SquareLoss(theta, ode, x0, t0, t, solution[1::,:], ['V','R'])\n",
     "\n",
-    "In \\[32\\]: print(res)\n",
+    "res = scipy.optimize.minimize(fun=objFH.cost, jac=objFH.sensitivity, x0=theta,\n",
+    "                              bounds=boxBounds, method='L-BFGS-B')\n",
+    "print(res)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d94fcdab",
+   "metadata": {},
+   "source": [
     "\n",
     "Note how the estimates are the same, unlike other models.\n",
     "\n",
-    "## Estimate initial value\n",
+    "#TODO why is this?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "883a35e5",
+   "metadata": {},
+   "source": [
+    "## Estimating initial value\n",
     "\n",
     "We can further assume that we have no idea about the initial values for\n",
-    "$V$ and $R$ as well. We also provide guesstimate to set off the\n",
+    "$V$ and $R$ as well. We also provide a guesstimate to set off the\n",
     "optimization. The input vector $\\theta$ must have the parameters first,\n",
     "then the initial values, along with the corresponding bounds.\n",
     "\n",
     "First, only a single target state, i.e. we only have observations for\n",
-    "one of states which is $R$ in this case\n",
-    "\n",
-    "In \\[35\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n",
-    "'R')\n",
-    "\n",
-    "In \\[35\\]: boxBounds = \\[  \n",
-    ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0), .…: (None,None), .…:\n",
-    "(None,None) .…: \\]\n",
+    "one of states which is $R$ in this case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "138e3689",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objFH = SquareLoss(theta, ode, x0, t0, t, solution[1::,1], 'R')\n",
     "\n",
-    "In \\[36\\]: res = scipy.optimize.minimize(fun=objFH.costIV,  \n",
-    ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5,0.5\\], .…:\n",
-    "bounds=boxBounds, .…: method='L-BFGS-B')\n",
+    "boxBounds = [(0.0,5.0), \n",
+    "             (0.0,5.0),\n",
+    "             (0.0,5.0),\n",
+    "             (None,None),\n",
+    "             (None,None)]\n",
     "\n",
-    "In \\[37\\]: print(res)\n",
+    "res = scipy.optimize.minimize(fun=objFH.costIV,\n",
+    "                              jac=objFH.sensitivityIV,\n",
+    "                              x0=theta + [-0.5,0.5],\n",
+    "                              bounds=boxBounds, \n",
+    "                              method='L-BFGS-B')\n",
     "\n",
-    "then both state as target at the same time\n",
+    "print(res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af2b3afa",
+   "metadata": {},
+   "source": [
     "\n",
-    "In \\[38\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n",
-    "\\['V','R'\\])\n",
+    "Then we can find both states as target at the same time.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "869a5561",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objFH = SquareLoss(theta, ode, x0, t0, t, solution[1::,:], ['V','R'])\n",
     "\n",
-    "In \\[38\\]: res = scipy.optimize.minimize(fun=objFH.costIV,  \n",
-    ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5, 0.5\\], .…:\n",
-    "bounds=boxBounds, .…: method='L-BFGS-B')\n",
+    "res = scipy.optimize.minimize(fun=objFH.costIV, \n",
+    "                              jac=objFH.sensitivityIV, \n",
+    "                              x0=theta + [-0.5, 0.5],\n",
+    "                              bounds=boxBounds, \n",
+    "                              method='L-BFGS-B')\n",
     "\n",
-    "In \\[39\\]: print(res)\n",
+    "print(res)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c045544",
+   "metadata": {},
+   "source": [
     "\n",
-    "See the difference between the two estimate with the latter, both state\n",
+    "See the difference between the two estimates with the latter method; both states\n",
     "were used, yielding superior estimates. Note that only the forward\n",
     "sensitivity method is implemented when estimating the initial value, and\n",
     "it is assumed that the starting condition for all the states are\n",

From d76fe822356060e58d7874e50113fad8f380465c Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 15:58:01 +0100
Subject: [PATCH 132/188] added contents to chapter page

---
 docs/pygom-doc/md/common_models.md | 35 ++++++++++++++++++------------
 1 file changed, 21 insertions(+), 14 deletions(-)

diff --git a/docs/pygom-doc/md/common_models.md b/docs/pygom-doc/md/common_models.md
index 994a2546..9440c2ea 100644
--- a/docs/pygom-doc/md/common_models.md
+++ b/docs/pygom-doc/md/common_models.md
@@ -1,20 +1,27 @@
 # Pre-defined Example common_models {#common_models}
 
-We have defined a set of models `common_models`{.interpreted-text
-role="mod"}, most of them commonly used in epidemiology. They are there
-as examples and also save time for end users. Most of them are of the
+We have defined a set of models {mod}`common_models`, most of them commonly used in epidemiology. They are there
+as examples and also to save time for users. Most of them are of the
 compartmental type, and we use standard naming conventions i.e. **S** =
 Susceptible, **E** = Exposed, **I** = Infectious, **R** = Recovered.
+
+#TODO is R recovered, removed or dead? 
+
 Extra state symbol will be introduced when required.
 
-::: {.toctree}
-common_models/SIS.rst common_models/SIS_Periodic.rst
-common_models/SIR.rst common_models/SIR_Birth_Death.rst
-common_models/SEIR.rst common_models/SEIR_Multiple.rst
-common_models/SEIR_Birth_Death.rst
-common_models/SEIR_Birth_Death_Periodic.rst
-common_models/Legrand_Ebola_SEIHFR.rst common_models/Lotka_Volterra.rst
-common_models/Lotka_Volterra_4State.rst common_models/FitzHugh.rst
-common_models/Lorenz.rst common_models/vanDelPol.rst
-common_models/Robertson.rst
-:::
+{doc}`../notebooks/common_models/SIS`
+{doc}`../notebooks/common_models/SIS_Periodic`
+{doc}`../notebooks/common_models/SIR`
+{doc}`../notebooks/common_models/SIR_Birth_Death`
+{doc}`../notebooks/common_models/SEIR`
+{doc}`../notebooks/common_models/SEIR_Multiple`
+{doc}`../notebooks/common_models/SEIR_Birth_Death`
+{doc}`../notebooks/common_models/SEIR_Birth_Death_Periodic`
+{doc}`../notebooks/common_models/Legrand_Ebola_SEIHFR`
+{doc}`../notebooks/common_models/Lotka_Volterra`
+{doc}`../notebooks/common_models/Lotka_Volterra_4State`
+{doc}`../notebooks/common_models/FitzHugh`
+{doc}`../notebooks/common_models/Lorenz`
+{doc}`../notebooks/common_models/vanDelPol`
+{doc}`../notebooks/common_models/Robertson`
+

From 54e4c849ff13e02905d59bed19c27ec6133fdaba Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 16:01:23 +0100
Subject: [PATCH 133/188] correct referencing to common models

---
 docs/pygom-doc/_toc.yml            | 30 +++++++++++++++---------------
 docs/pygom-doc/md/common_models.md | 14 ++++++++++++++
 2 files changed, 29 insertions(+), 15 deletions(-)

diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index 96bb1ff9..261aa983 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -32,19 +32,19 @@ parts:
       sections:
       - file: notebooks/common_models/SIS.ipynb
       - file: notebooks/common_models/SIS_Periodic.ipynb
-      - file: notebooks/common_models/SIR.rst 
-      - file: notebooks/common_models/SIR_Birth_Death.rst
-      - file: notebooks/common_models/SEIR.rst 
-      - file: notebooks/common_models/SEIR_Multiple.rst
-      - file: notebooks/common_models/SEIR_Birth_Death.rst
-      - file: notebooks/common_models/SEIR_Birth_Death_Periodic.rst
-      - file: notebooks/common_models/Legrand_Ebola_SEIHFR.rst 
-      - file: notebooks/common_models/Lotka_Volterra.rst
-      - file: notebooks/common_models/Lotka_Volterra_4State.rst 
-      - file: notebooks/common_models/FitzHugh.rst
-      - file: notebooks/common_models/Lorenz.rst 
-      - file: notebooks/common_models/vanDelPol.rst
-      - file: notebooks/common_models/Robertson.rst
+      - file: notebooks/common_models/SIR.ipynb 
+      - file: notebooks/common_models/SIR_Birth_Death.ipynb 
+      - file: notebooks/common_models/SEIR.ipynb 
+      - file: notebooks/common_models/SEIR_Multiple.ipynb 
+      - file: notebooks/common_models/SEIR_Birth_Death.ipynb 
+      - file: notebooks/common_models/SEIR_Birth_Death_Periodic.ipynb 
+      - file: notebooks/common_models/Legrand_Ebola_SEIHFR.ipynb 
+      - file: notebooks/common_models/Lotka_Volterra.ipynb 
+      - file: notebooks/common_models/Lotka_Volterra_4State.ipynb  
+      - file: notebooks/common_models/FitzHugh.ipynb 
+      - file: notebooks/common_models/Lorenz.ipynb  
+      - file: notebooks/common_models/vanDelPol.ipynb 
+      - file: notebooks/common_models/Robertson.ipynb 
 #  - caption: Frequently asked questions
 #    chapters: 
 #    - file: md/faq.md
@@ -53,9 +53,9 @@ parts:
     - file: autodoc-model.rst
     - file: autodoc-loss.rst
 #  - caption: References
-# https://jupyterbook.org/en/stable/content/citations.html#id6
 #    chapters: 
-#    - file: md/ref.md
+#    - file: md/bib.md
+# https://jupyterbook.org/en/stable/content/citations.html#id6
 ##  - caption: Indices and tables
 ##    chapters:
 ##    - file: 
diff --git a/docs/pygom-doc/md/common_models.md b/docs/pygom-doc/md/common_models.md
index 9440c2ea..77048ef2 100644
--- a/docs/pygom-doc/md/common_models.md
+++ b/docs/pygom-doc/md/common_models.md
@@ -10,18 +10,32 @@ Susceptible, **E** = Exposed, **I** = Infectious, **R** = Recovered.
 Extra state symbol will be introduced when required.
 
 {doc}`../notebooks/common_models/SIS`
+
 {doc}`../notebooks/common_models/SIS_Periodic`
+
 {doc}`../notebooks/common_models/SIR`
+
 {doc}`../notebooks/common_models/SIR_Birth_Death`
+
 {doc}`../notebooks/common_models/SEIR`
+
 {doc}`../notebooks/common_models/SEIR_Multiple`
+
 {doc}`../notebooks/common_models/SEIR_Birth_Death`
+
 {doc}`../notebooks/common_models/SEIR_Birth_Death_Periodic`
+
 {doc}`../notebooks/common_models/Legrand_Ebola_SEIHFR`
+
 {doc}`../notebooks/common_models/Lotka_Volterra`
+
 {doc}`../notebooks/common_models/Lotka_Volterra_4State`
+
 {doc}`../notebooks/common_models/FitzHugh`
+
 {doc}`../notebooks/common_models/Lorenz`
+
 {doc}`../notebooks/common_models/vanDelPol`
+
 {doc}`../notebooks/common_models/Robertson`
 

From aa7b175631d4610f1e6ee740c68f1f5c36104350 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 21:29:59 +0100
Subject: [PATCH 134/188] correct bib citations

---
 docs/pygom-doc/_config.yml             |  36 ++-
 docs/pygom-doc/_toc.yml                |   6 +-
 docs/pygom-doc/md/bib.md               |   5 +
 docs/pygom-doc/notebooks/epijson.ipynb |  10 +-
 docs/pygom-doc/ref.bib                 | 298 ++++++++++++++++++++++++-
 5 files changed, 334 insertions(+), 21 deletions(-)

diff --git a/docs/pygom-doc/_config.yml b/docs/pygom-doc/_config.yml
index 8a49af3e..ecf87ec2 100644
--- a/docs/pygom-doc/_config.yml
+++ b/docs/pygom-doc/_config.yml
@@ -15,22 +15,50 @@ only_build_toc_files: true
 # this could avoid the issue of execution timing out
 # See https://jupyterbook.org/content/execute.html
 execute:
-  execute_notebooks: force
+  execute_notebooks: cache
 
 # Define the name of the latex output file for PDF builds
 latex:
   latex_documents:
-    targetname: book.tex
+    targetname: pygom-book.tex
 
 # Add a bibtex file so that we can create citations
 # use sphinx to specify style
+
+sphinx:
+  extra_extensions:
+  - 'sphinxcontrib.bibtex'
+
 sphinx:
   config:
     bibtex_reference_style: author_year
     # TODO change md to bibtex
-    bibtex_bibfiles: "ref.md"
+    bibtex_bibfiles: ref.bib
+
+#bibtex_bibfiles: 
+#  - ref.bib
+
+sphinx:  
   extra_extensions:
-    - 'sphinx.ext.autodoc'
+  #- 'sphinx.ext.doctest'
+  - 'sphinx.ext.autodoc'    
+  - 'sphinx.ext.napoleon'
+  - 'sphinx.ext.viewcode'
+  - 'sphinx.ext.autosummary'
+  config:
+    add_module_names: True 
+    autosummary_generate: True
+
+#sphinx:
+#  extra_extensions:
+#  - 'sphinx.ext.autosummary'
+#  config:
+#    autosummary_generate: True
+
+#sphinx:
+#  extra_extensions:
+#  - 'sphinx.ext.graphvizconfig'
+  
 
 # Information about where the book exists on the web
 repository:
diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index 261aa983..3d5df218 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -52,9 +52,9 @@ parts:
     chapters:
     - file: autodoc-model.rst
     - file: autodoc-loss.rst
-#  - caption: References
-#    chapters: 
-#    - file: md/bib.md
+  - caption: References
+    chapters: 
+    - file: md/bib.md
 # https://jupyterbook.org/en/stable/content/citations.html#id6
 ##  - caption: Indices and tables
 ##    chapters:
diff --git a/docs/pygom-doc/md/bib.md b/docs/pygom-doc/md/bib.md
index 8b137891..df56a0c2 100644
--- a/docs/pygom-doc/md/bib.md
+++ b/docs/pygom-doc/md/bib.md
@@ -1 +1,6 @@
+# References 
 
+```{bibliography}
+:style: unsrt
+#:all:
+```
\ No newline at end of file
diff --git a/docs/pygom-doc/notebooks/epijson.ipynb b/docs/pygom-doc/notebooks/epijson.ipynb
index 8d7e282a..f2e63cae 100644
--- a/docs/pygom-doc/notebooks/epijson.ipynb
+++ b/docs/pygom-doc/notebooks/epijson.ipynb
@@ -9,11 +9,11 @@
     "Epidemiology data is complicated due to the many different stages a\n",
     "patient can go through and whether a modeling technique is applicable\n",
     "depends heavily on the recording of data. [EpiJSON](https://github.com/Hackout2/EpiJSON) is a framework which\n",
-    "tries to captures all the information in a JSON format {cite:ts}`Finnie2016` [\\[Finnie2016\\]](),.\n",
+    "tries to captures all the information in a JSON format {cite:t}`Finnie2016`.\n",
     "\n",
     "PyGOM provides the functionality to process EpiJSON data. Due to\n",
     "the nature of this package, modeling of ODEs, data files are processed with this in mind. The output is therefore in the cumulative form as\n",
-    "default, shown below, in a `pandas.DataFrame` format. \n",
+    "default, shown below, in a [`pandas.DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) format. \n",
     "\n",
     "#TODO unsure what this means\n",
     "\n",
@@ -98,12 +98,6 @@
     "and $R$ the corresponding state the data belongs to.\n",
     "```"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43b2e082",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/pygom-doc/ref.bib b/docs/pygom-doc/ref.bib
index 6e6ecea9..72c66881 100644
--- a/docs/pygom-doc/ref.bib
+++ b/docs/pygom-doc/ref.bib
@@ -1,6 +1,292 @@
-
-@article{Finnie2016
-, title = {piJSON: A unified data-format for epidemiology}
-, author = {Thomas Finnie et al.}
-, journal = {Epidemics}
-}
\ No newline at end of file
+@article{Cao2006,
+   abstract = {The tau-leaping method of simulating the stochastic time evolution of a well-stirred chemically reacting system uses a Poisson approximation to take time steps that leap over many reaction events. Theory implies that tau leaping should be accurate so long as no propensity function changes its value "significantly" during any time step τ. Presented here is an improved procedure for estimating the largest value for τ that is consistent with this condition. This new τ -selection procedure is more accurate, easier to code, and faster to execute than the currently used procedure. The speedup in execution will be especially pronounced in systems that have many reaction channels. © 2006 American Institute of Physics.},
+   author = {Yang Cao and Daniel T. Gillespie and Linda R. Petzold},
+   doi = {10.1063/1.2159468},
+   issn = {00219606},
+   issue = {4},
+   journal = {Journal of Chemical Physics},
+   title = {Efficient step size selection for the tau-leaping simulation method},
+   volume = {124},
+   year = {2006},
+}
+@article{Finnie2016,
+   abstract = {Epidemiology relies on data but the divergent ways data are recorded and transferred, both within and between outbreaks, and the expanding range of data-types are creating an increasingly complex problem for the discipline. There is a need for a consistent, interpretable and precise way to transfer data while maintaining its fidelity. We introduce 'EpiJSON', a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. This format is designed to enable the widest range of epidemiological data to be unambiguously held and transferred between people, software and institutions. In this paper, we provide a full description of the format and a discussion of the design decisions made. We introduce a schema enabling automatic checks of the validity of data stored as EpiJSON, which can serve as a basis for the development of additional tools. In addition, we also present the R package 'repijson' which provides conversion tools between this format, line-list data and pre-existing analysis tools. An example is given to illustrate how EpiJSON can be used to store line list data. EpiJSON, designed around modern standards for interchange of information on the internet, is simple to implement, read and check. As such, it provides an ideal new standard for epidemiological, and other, data transfer to the fast-growing open-source platform for the analysis of disease outbreaks.},
+   author = {Thomas J.R. Finnie and Andy South and Ana Bento and Ellie Sherrard-Smith and Thibaut Jombart},
+   doi = {10.1016/j.epidem.2015.12.002},
+   issn = {18780067},
+   journal = {Epidemics},
+   title = {EpiJSON: A unified data-format for epidemiology},
+   volume = {15},
+   year = {2016},
+}
+@article{FitzHugh1961,
+   abstract = {Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting “BVP model” has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a “physiological state diagram,” with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable. © 1961, The Biophysical Society. All rights reserved.},
+   author = {Richard FitzHugh},
+   doi = {10.1016/S0006-3495(61)86902-6},
+   issn = {00063495},
+   issue = {6},
+   journal = {Biophysical Journal},
+   title = {Impulses and Physiological States in Theoretical Models of Nerve Membrane},
+   volume = {1},
+   year = {1961},
+}
+@inproceedings{Gillespie1977,
+   abstract = {There are two formalisms for mathematically describing the time behavior of a spatially homogeneous chemical system: The deterministic approach regards the time evolution as a continuous, wholly predictable process which is governed by a set of coupled, ordinary differential equations (the "reaction-rate equations"); the stochastic approach regards the time evolution as a kind of random-walk process which is governed by a single differential-difference equation (the "master equation"). Fairly simple kinetic theory arguments show that the stochastic formulation of chemical kinetics has a firmer physical basis than the deterministic formulation, but unfortunately the stochastic master equation is often mathematically intractable. There is, however, a way to make exact numerical calculations within the framework of the stochastic formulation without having to deal with the master equation directly. It is a relatively simple digital computer algorithm which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of the given chemical system. Like the master equation, this "stochastic simulation algorithm" correctly accounts for the inherent fluctuations and correlations that are necessarily ignored in the deterministic formulation. In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps Δt. The feasibility and utility of the simulation algorithm are demonstrated by applying it to several well-known model chemical systems, including the Lotka model, the Brusselator, and the Oregonator.},
+   author = {Daniel T. Gillespie},
+   doi = {10.1021/j100540a008},
+   issn = {00223654},
+   issue = {25},
+   journal = {Journal of Physical Chemistry},
+   title = {Exact stochastic simulation of coupled chemical reactions},
+   volume = {81},
+   year = {1977},
+}
+@article{Girolami2011,
+   abstract = {The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot runs that are required to tune proposal densities for Metropolis-Hastings or indeed Hamiltonian Monte Carlo and Metropolis adjusted Langevin algorithms. This allows for highly efficient sampling even in very high dimensions where different scalings may be required for the transient and stationary phases of the Markov chain. The methodology proposed exploits the Riemann geometry of the parameter space of statistical models and thus automatically adapts to the local structure when simulating paths across this manifold, providing highly efficient convergence and exploration of the target density. The performance of these Riemann manifold Monte Carlo methods is rigorously assessed by performing inference on logistic regression models, log-Gaussian Cox point processes, stochastic volatility models and Bayesian estimation of dynamic systems described by non-linear differential equations. Substantial improvements in the time-normalized effective sample size are reported when compared with alternative sampling approaches. MATLAB code that is available from allows replication of all the results reported. © 2011 Royal Statistical Society.},
+   author = {Mark Girolami and Ben Calderhead},
+   doi = {10.1111/j.1467-9868.2010.00765.x},
+   issn = {13697412},
+   issue = {2},
+   journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
+   title = {Riemann manifold Langevin and Hamiltonian Monte Carlo methods},
+   volume = {73},
+   year = {2011},
+}
+@article{Hethcote1973,
+   abstract = {The effects of a periodic contact rate and of carriers are considered for a generalization of Bailey's simple epidemic model. In this model it is assumed that individuals become susceptible again as soon as they recover from the infection so that a fixed population can be divided into a class of infectives and a class of susceptibles which vary with time. If the contact rate is periodic, then the number of infectives as time approaches infinity either tends to zero or is asymptotically periodic depending on whether the total population size is less than or greater than a threshold value. The behavior for large time of the number of infectives is determined for three modifications of the model which involve carriers. © 1973 Society for Mathematical Biology.},
+   author = {Herbert W. Hethcote},
+   doi = {10.1007/BF02458365},
+   issn = {00074985},
+   issue = {5-6},
+   journal = {Bulletin of Mathematical Biology},
+   title = {Asymptotic behavior in a deterministic epidemic model},
+   volume = {35},
+   year = {1973},
+}
+@article{Lloyd1996,
+   abstract = {Spatial heterogeneity is believed to play an important role in the persistence and dynamics of epidemics of childhood diseases because asynchrony between populations within different regions allows global persistence, even if the disease dies out locally. A simple multi-patch (metapopulation) model for spatial heterogeneity in epidemics is analysed and we examine conditions under which patches become synchronized. We show that the patches in non-seasonal deterministic models often oscillate in phase for all but the weakest between patch coupling. Synchronization is also seen for stochastic models, although slightly stronger coupling is needed to overcome the random effects. We demonstrate that the inclusion of seasonal forcing in deterministic models can lead to the maintenance of phase differences between patches. Complex dynamic behaviour is observed in the seasonally forced spatial model, along with the coexistence of many different behaviours. Compared to the non-spatial model, chaotic solutions are observed for weaker seasonal forcing; these solutions have a more realistic minimum number of infectives.},
+   author = {Alun L. Lloyd and Robert M. May},
+   doi = {10.1006/jtbi.1996.0042},
+   issn = {00225193},
+   issue = {1},
+   journal = {Journal of Theoretical Biology},
+   title = {Spatial heterogeneity in epidemic models},
+   volume = {179},
+   year = {1996},
+}
+@article{Lorenz1963,
+   abstract = {Finite systems of deterministic ordinary nonlinear differential equations may be designed to represent forced dissipative hydrodynamic flow. Solutions of these equations can be identified with trajectories in phase space. For those systems with bounded solutions, it is found that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states. Systems with bounded solutions are shown to possess bounded numerical solutions. A simple system representing cellular convection is solved numerically. All of the solutions are found to be unstable, and almost all of them are nonperiodic. The feasibility of very-long-range weather prediction is examined in the light of these results.},
+   author = {Edward N. Lorenz},
+   doi = {10.1175/1520-0469(1963)020<0130:dnf>2.0.co;2},
+   issn = {0022-4928},
+   issue = {2},
+   journal = {Journal of the Atmospheric Sciences},
+   title = {Deterministic Nonperiodic Flow},
+   volume = {20},
+   year = {1963},
+}
+@article{Lotka1920,
+   abstract = {Periodic phenomena play an important r6le in nature, both organic and inorganic. In chemical reactions rhythmic effects have been observed experimentally, and have also been shown, by the writer' and others,2 to follow, under certain conditions, from the laws of chemical dynamics. However, in the cases hitherto considered on the basis of chemical dynamics, the oscillations were found to be of the damped kind, and therefore, only transitory (unlike certain experimentally observed periodic reactions). Furthermore, in a much more general investigation by the writer, covering the kinetics not only of chemical but also of biological systems, it appeared, from the nature of the solution obtained, improbable3 that undamped, permanent oscillations would arise in the absence of geometrical, structural causes, in the very comprehensive class of systems considered. For it seemed that the occurrence of such permanent oscillations, the occurrence of purely imaginary exponents in the exponential series solution presented, would demand peculiar and very specific relations between the characteristic constants of the systems undergoing transformation; whereas in nature these constants would, presumably, stand in random relation.},
+   author = {Alfred J. Lotka},
+   doi = {10.1073/pnas.6.7.410},
+   issn = {0027-8424},
+   issue = {7},
+   journal = {Proceedings of the National Academy of Sciences},
+   title = {Analytical Note on Certain Rhythmic Relations in Organic Systems},
+   volume = {6},
+   year = {1920},
+}
+@book{Press2007,
+   abstract = {Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in color for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs; a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres; interior point methods for linear programming; MCMC; an expanded treatment of ODEs with completely new routines; and many new statistical distributions.},
+   author = {William H Press and Saul a Teukolsky and William T Vetterling and Brian P Flannery},
+   issn = {00361445},
+   journal = {Sample page from NUMBERICAL RECIPES IN C},
+   title = {Numerical Recipes 3rd Edition: The Art of Scientific Computing},
+   volume = {1},
+   year = {2007},
+}
+@article{Ramsay2007,
+   abstract = {We propose a new method for estimating parameters in models that are defined by a system of non-linear differential equations. Such equations represent changes in system outputs by linking the behaviour of derivatives of a process to the behaviour of the process itself. Current methods for estimating parameters in differential equations from noisy data are computationally intensive and often poorly suited to the realization of statistical objectives such as inference and interval estimation. The paper describes a new method that uses noisy measurements on a subset of variables to estimate the parameters defining a system of non-linear differential equations. The approach is based on a modification of data smoothing methods along with a generalization of profiled estimation. We derive estimates and confidence intervals, and show that these have low bias and good coverage properties respectively for data that are simulated from models in chemical engineering and neurobiology. The performance of the method is demonstrated by using real world data from chemistry and from the progress of the autoimmune disease lupus. © 2007 Royal Statistical Society.},
+   author = {J. O. Ramsay and G. Hooker and D. Campbell and J. Cao},
+   doi = {10.1111/j.1467-9868.2007.00610.x},
+   issn = {13697412},
+   issue = {5},
+   journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
+   title = {Parameter estimation for differential equations: A generalized smoothing approach},
+   volume = {69},
+   year = {2007},
+}
+@article{Raue2009,
+   abstract = {Motivation: Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. Results: We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction. © The Author 2009. Published by Oxford University Press. All rights reserved.},
+   author = {Andreas Raue and C. Kreutz and T. Maiwald and J. Bachmann and M. Schilling and U. Klingmüller and J. Timmer},
+   doi = {10.1093/bioinformatics/btp358},
+   issn = {13674803},
+   issue = {15},
+   journal = {Bioinformatics},
+   title = {Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood},
+   volume = {25},
+   year = {2009},
+}
+@article{Venzon1988,
+   abstract = {The method of constructing confidence regions based on the generalised likelihood ratio statistic is well known for parameter vectors. A similar construction of a confidence interval for a simple entry of a vector can be implemented by repeatedly maximising over the other parameters. We present an algorithm for finding these confidence interval endpoints that requires less computation. It employs a modified Newton-Raphson iteration to solve a system of equations that defines the endpoints.},
+   author = {D. J. Venzon and S. H. Moolgavkar},
+   doi = {10.2307/2347496},
+   issn = {00359254},
+   issue = {1},
+   journal = {Applied Statistics},
+   title = {A Method for Computing Profile-Likelihood-Based Confidence Intervals},
+   volume = {37},
+   year = {1988},
+}
+@article{,
+   abstract = {(1926). LXXXVIII. On “relaxation-oscillations”. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science: Vol. 2, No. 11, pp. 978-992.},
+   author = {Balth. van der Pol},
+   doi = {10.1080/14786442608564127},
+   issn = {1941-5982},
+   issue = {11},
+   journal = {The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science},
+   month = {11},
+   pages = {978-992},
+   publisher = {Informa UK Limited},
+   title = {On relaxation-oscillations},
+   volume = {2},
+   year = {1926},
+}
+@article{Robertson1966,
+   author = {H.H Robertson},
+   journal = {Academic Press},
+   pages = {178-182},
+   title = {The Solution of a Set of Reaction Rate Equations},
+   url = {https://www.scirp.org/%28S%28vtj3fa45qm1ean45vvffcz55%29%29/reference/referencespapers.aspx?referenceid=2485718},
+   year = {1966},
+}
+@article{Moolgavkar1987,
+   author = {Suresh H. Moolgavkar and David J. Venz},
+   issue = {1},
+   journal = {Journal of Statistics},
+   pages = {43-56},
+   title = {Confidence Regions for Parameters of the Proportional Hazards Model: A Simulation Study on JSTOR},
+   volume = {14},
+   url = {https://www.jstor.org/stable/4616047},
+   year = {1987},
+}
+@article{legrand2007utd,
+   abstract = {Ebola is a highly lethal virus, which has caused at least 14 confirmed outbreaks in Africa between 1976 and 2006. Using data from two epidemics [in Democratic Republic of Congo (DRC) in 1995 and in Uganda in 2000], we built a mathematical model for the spread of Ebola haemorrhagic fever epidemics taking into account transmission in different epidemiological settings. We estimated the basic reproduction number (R0) to be 2.7 (95% CI 1.9-2.8) for the 1995 epidemic in DRC, and 2.7 (95% CI 2.5-4.1) for the 2000 epidemic in Uganda. For each epidemic, we quantified transmission in different settings (illness in the community, hospitalization, and traditional burial) and simulated various epidemic scenarios to explore the impact of control interventions on a potential epidemic. A key parameter was the rapid institution of control measures. For both epidemic profiles identified, increasing hospitalization rate reduced the predicted epidemic size.},
+   author = {J. Legrand and R. F. Grais and P. Y. Boelle and A. J. Valleron and A. Flahault},
+   doi = {10.1017/S0950268806007217},
+   isbn = {0950-2688},
+   issn = {09502688},
+   issue = {4},
+   journal = {Epidemiology and Infection},
+   pages = {610-621},
+   pmid = {16999875},
+   title = {Understanding the dynamics of Ebola epidemics},
+   volume = {135},
+   year = {2007},
+}
+@article{Cao2006,
+   abstract = {The tau-leaping method of simulating the stochastic time evolution of a well-stirred chemically reacting system uses a Poisson approximation to take time steps that leap over many reaction events. Theory implies that tau leaping should be accurate so long as no propensity function changes its value "significantly" during any time step τ. Presented here is an improved procedure for estimating the largest value for τ that is consistent with this condition. This new τ -selection procedure is more accurate, easier to code, and faster to execute than the currently used procedure. The speedup in execution will be especially pronounced in systems that have many reaction channels. © 2006 American Institute of Physics.},
+   author = {Yang Cao and Daniel T. Gillespie and Linda R. Petzold},
+   doi = {10.1063/1.2159468},
+   issn = {00219606},
+   issue = {4},
+   journal = {Journal of Chemical Physics},
+   title = {Efficient step size selection for the tau-leaping simulation method},
+   volume = {124},
+   year = {2006},
+}
+@article{Finnie2016,
+   abstract = {Epidemiology relies on data but the divergent ways data are recorded and transferred, both within and between outbreaks, and the expanding range of data-types are creating an increasingly complex problem for the discipline. There is a need for a consistent, interpretable and precise way to transfer data while maintaining its fidelity. We introduce 'EpiJSON', a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. This format is designed to enable the widest range of epidemiological data to be unambiguously held and transferred between people, software and institutions. In this paper, we provide a full description of the format and a discussion of the design decisions made. We introduce a schema enabling automatic checks of the validity of data stored as EpiJSON, which can serve as a basis for the development of additional tools. In addition, we also present the R package 'repijson' which provides conversion tools between this format, line-list data and pre-existing analysis tools. An example is given to illustrate how EpiJSON can be used to store line list data. EpiJSON, designed around modern standards for interchange of information on the internet, is simple to implement, read and check. As such, it provides an ideal new standard for epidemiological, and other, data transfer to the fast-growing open-source platform for the analysis of disease outbreaks.},
+   author = {Thomas J.R. Finnie and Andy South and Ana Bento and Ellie Sherrard-Smith and Thibaut Jombart},
+   doi = {10.1016/j.epidem.2015.12.002},
+   issn = {18780067},
+   journal = {Epidemics},
+   title = {EpiJSON: A unified data-format for epidemiology},
+   volume = {15},
+   year = {2016},
+}
+@article{FitzHugh1961,
+   abstract = {Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting “BVP model” has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a “physiological state diagram,” with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable. © 1961, The Biophysical Society. All rights reserved.},
+   author = {Richard FitzHugh},
+   doi = {10.1016/S0006-3495(61)86902-6},
+   issn = {00063495},
+   issue = {6},
+   journal = {Biophysical Journal},
+   title = {Impulses and Physiological States in Theoretical Models of Nerve Membrane},
+   volume = {1},
+   year = {1961},
+}
+@inproceedings{Gillespie1977,
+   abstract = {There are two formalisms for mathematically describing the time behavior of a spatially homogeneous chemical system: The deterministic approach regards the time evolution as a continuous, wholly predictable process which is governed by a set of coupled, ordinary differential equations (the "reaction-rate equations"); the stochastic approach regards the time evolution as a kind of random-walk process which is governed by a single differential-difference equation (the "master equation"). Fairly simple kinetic theory arguments show that the stochastic formulation of chemical kinetics has a firmer physical basis than the deterministic formulation, but unfortunately the stochastic master equation is often mathematically intractable. There is, however, a way to make exact numerical calculations within the framework of the stochastic formulation without having to deal with the master equation directly. It is a relatively simple digital computer algorithm which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of the given chemical system. Like the master equation, this "stochastic simulation algorithm" correctly accounts for the inherent fluctuations and correlations that are necessarily ignored in the deterministic formulation. In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps Δt. The feasibility and utility of the simulation algorithm are demonstrated by applying it to several well-known model chemical systems, including the Lotka model, the Brusselator, and the Oregonator.},
+   author = {Daniel T. Gillespie},
+   doi = {10.1021/j100540a008},
+   issn = {00223654},
+   issue = {25},
+   journal = {Journal of Physical Chemistry},
+   title = {Exact stochastic simulation of coupled chemical reactions},
+   volume = {81},
+   year = {1977},
+}
+@article{Girolami2011,
+   abstract = {The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot runs that are required to tune proposal densities for Metropolis-Hastings or indeed Hamiltonian Monte Carlo and Metropolis adjusted Langevin algorithms. This allows for highly efficient sampling even in very high dimensions where different scalings may be required for the transient and stationary phases of the Markov chain. The methodology proposed exploits the Riemann geometry of the parameter space of statistical models and thus automatically adapts to the local structure when simulating paths across this manifold, providing highly efficient convergence and exploration of the target density. The performance of these Riemann manifold Monte Carlo methods is rigorously assessed by performing inference on logistic regression models, log-Gaussian Cox point processes, stochastic volatility models and Bayesian estimation of dynamic systems described by non-linear differential equations. Substantial improvements in the time-normalized effective sample size are reported when compared with alternative sampling approaches. MATLAB code that is available from allows replication of all the results reported. © 2011 Royal Statistical Society.},
+   author = {Mark Girolami and Ben Calderhead},
+   doi = {10.1111/j.1467-9868.2010.00765.x},
+   issn = {13697412},
+   issue = {2},
+   journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
+   title = {Riemann manifold Langevin and Hamiltonian Monte Carlo methods},
+   volume = {73},
+   year = {2011},
+}
+@article{Hethcote1973,
+   abstract = {The effects of a periodic contact rate and of carriers are considered for a generalization of Bailey's simple epidemic model. In this model it is assumed that individuals become susceptible again as soon as they recover from the infection so that a fixed population can be divided into a class of infectives and a class of susceptibles which vary with time. If the contact rate is periodic, then the number of infectives as time approaches infinity either tends to zero or is asymptotically periodic depending on whether the total population size is less than or greater than a threshold value. The behavior for large time of the number of infectives is determined for three modifications of the model which involve carriers. © 1973 Society for Mathematical Biology.},
+   author = {Herbert W. Hethcote},
+   doi = {10.1007/BF02458365},
+   issn = {00074985},
+   issue = {5-6},
+   journal = {Bulletin of Mathematical Biology},
+   title = {Asymptotic behavior in a deterministic epidemic model},
+   volume = {35},
+   year = {1973},
+}
+@inproceedings{Gillespie1977,
+   abstract = {There are two formalisms for mathematically describing the time behavior of a spatially homogeneous chemical system: The deterministic approach regards the time evolution as a continuous, wholly predictable process which is governed by a set of coupled, ordinary differential equations (the "reaction-rate equations"); the stochastic approach regards the time evolution as a kind of random-walk process which is governed by a single differential-difference equation (the "master equation"). Fairly simple kinetic theory arguments show that the stochastic formulation of chemical kinetics has a firmer physical basis than the deterministic formulation, but unfortunately the stochastic master equation is often mathematically intractable. There is, however, a way to make exact numerical calculations within the framework of the stochastic formulation without having to deal with the master equation directly. It is a relatively simple digital computer algorithm which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of the given chemical system. Like the master equation, this "stochastic simulation algorithm" correctly accounts for the inherent fluctuations and correlations that are necessarily ignored in the deterministic formulation. In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps Δt. The feasibility and utility of the simulation algorithm are demonstrated by applying it to several well-known model chemical systems, including the Lotka model, the Brusselator, and the Oregonator.},
+   author = {Daniel T. Gillespie},
+   doi = {10.1021/j100540a008},
+   issn = {00223654},
+   issue = {25},
+   journal = {Journal of Physical Chemistry},
+   title = {Exact stochastic simulation of coupled chemical reactions},
+   volume = {81},
+   year = {1977},
+}
+@book{Brauer2008,
+   author = {Fred Brauer},
+   city = {Berlin, Heidelberg},
+   doi = {10.1007/978-3-540-78911-6},
+   editor = {Fred Brauer and Pauline van den Driessche and Jianhong Wu},
+   isbn = {978-3-540-78910-9},
+   journal = {Sp},
+   publisher = {Springer Berlin Heidelberg},
+   title = {Mathematical Epidemiology},
+   volume = {1945},
+   url = {http://link.springer.com/10.1007/978-3-540-78911-6},
+   year = {2008},
+}
+@article{Aron1984,
+   abstract = {The annual incidence rates of some endemic infectious diseases are steady while others fluctuate dramatically, often in a regular cycle. In order to investigate the role of seasonality in driving cycles of recurrent epidemics, we analyze numerically the susceptible/exposed/infective/recovered (SEIR) epidemic model with seasonal transmission. We show that small-amplitude periodic solutions exhibit a sequence of period-doubling bifurcations as the amplitude of seasonal variation increases, predicting a transition to chaos of the kind studied in other biological contexts. The epidemiological implication is that the seasonal mechanism generating biennial epidemics may not be able to account for small-amplitude recurrent epidemics of arbitrary periodicity. © 1997 Elsevier Science Ltd. All rights reserved.},
+   author = {Joan L. Aron and Ira B. Schwartz},
+   doi = {10.1016/S0022-5193(84)80150-2},
+   issn = {0022-5193},
+   issue = {4},
+   journal = {Journal of theoretical biology},
+   keywords = {Biological*,Communicable Diseases / epidemiology*,Disease Outbreaks / epidemiology*,England,Humans,I B Schwartz,J L Aron,MEDLINE,Mathematics,Measles / epidemiology,Models,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,New York City,Periodicity*,PubMed Abstract,Seasons*,Time Factors,Wales,doi:10.1016/s0022-5193(84)80150-2,pmid:6521486},
+   month = {10},
+   pages = {665-679},
+   pmid = {6521486},
+   publisher = {J Theor Biol},
+   title = {Seasonality and period-doubling bifurcations in an epidemic model},
+   volume = {110},
+   url = {https://pubmed.ncbi.nlm.nih.gov/6521486/},
+   year = {1984},
+}

From 9c0096ec80c7481c7edd3798faff2919992033f5 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 22:42:41 +0100
Subject: [PATCH 135/188] reformatted

---
 .../notebooks/common_models/FitzHugh.ipynb    | 100 +++++++++++++-----
 1 file changed, 72 insertions(+), 28 deletions(-)

diff --git a/docs/pygom-doc/notebooks/common_models/FitzHugh.ipynb b/docs/pygom-doc/notebooks/common_models/FitzHugh.ipynb
index a94f5f0e..9d694ea3 100644
--- a/docs/pygom-doc/notebooks/common_models/FitzHugh.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/FitzHugh.ipynb
@@ -4,51 +4,95 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.FitzHugh`\n",
+    "# FitzHugh\n",
     "\n",
-    "The FitzHugh model [\\[FitzHugh1961\\]]() without external external\n",
-    "stimulus. This is a commonly used model when developing new methodology\n",
-    "with regard to ode's, see [\\[Ramsay2007\\]]() and [\\[Girolami2011\\]]()\n",
-    "and reference therein.\n",
+    "{func}`.FitzHugh` - the {cite:t}`FitzHugh1961` model without external stimulus\n",
+    "\n",
+    "This is a commonly used model when developing new methodology\n",
+    "with regards to ODEs, see {cite:p}`Ramsay2007` and {cite}`Girolami2011`.\n",
+    "\n",
+    "#TODO why common model?\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dV}{dt} &=  c ( V - \\frac{V^{3}}{3} + R) \\\\\n",
     "\\frac{dR}{dt} &= -\\frac{1}{c}(V - a + bR).\n",
     "\\end{aligned}$$\n",
     "\n",
-    "An example would be\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
-    "\n",
-    "In \\[1\\]: ode = common_models.FitzHugh({'a':0.2, 'b':0.2, 'c':3.0})\n",
-    "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 20, 101)\n",
-    "\n",
-    "In \\[1\\]: x0 = \\[1.0, -1.0\\]\n",
+    "An example of using this model follows.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c751b93a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "@savefig common_models_fh_1.png In \\[1\\]: ode.plot()\n",
+    "ode = common_models.FitzHugh({'a':0.2, 'b':0.2, 'c':3.0})\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "t = numpy.linspace(0, 20, 101)\n",
     "\n",
-    "In \\[1\\]: fig = plt.figure()\n",
+    "x0 = [1.0, -1.0]\n",
     "\n",
-    "In \\[1\\]: plt.plot(solution\\[:,0\\], solution\\[:,1\\], 'b')\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "@savefig common_models_fh_2.png In \\[1\\]: plt.show()\n",
+    "solution = ode.integrate(t[1::])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccee969d",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ode.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98a5e32e",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "fig = plt.figure()\n",
     "\n",
-    "In \\[1\\]: plt.close()"
+    "plt.plot(solution[:,0], solution[:,1], 'b')\n",
+    "plt.show()\n"
    ]
   }
  ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }

From e6f6944664872871d21663090c219f0ab7302165 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Mon, 28 Aug 2023 23:22:28 +0100
Subject: [PATCH 136/188] reformatted

---
 .../common_models/Legrand_Ebola_SEIHFR.ipynb  |  78 ++++++---
 .../notebooks/common_models/Lorenz.ipynb      |  92 ++++++++--
 .../common_models/Lotka_Volterra.ipynb        | 165 ++++++++++++------
 .../common_models/Lotka_Volterra_4State.ipynb |  79 +++++----
 .../notebooks/common_models/Robertson.ipynb   | 123 +++++++------
 5 files changed, 352 insertions(+), 185 deletions(-)

diff --git a/docs/pygom-doc/notebooks/common_models/Legrand_Ebola_SEIHFR.ipynb b/docs/pygom-doc/notebooks/common_models/Legrand_Ebola_SEIHFR.ipynb
index cab8043e..7affc240 100644
--- a/docs/pygom-doc/notebooks/common_models/Legrand_Ebola_SEIHFR.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/Legrand_Ebola_SEIHFR.ipynb
@@ -4,13 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.Legrand_Ebola_SEIHFR`\n",
+    "# `Legrand_Ebola_SEIHFR`\n",
     "\n",
-    "A commonly used model in the literature to model Ebola outbreaks is the\n",
-    "SEIHFR model proposed by [\\[Legrand2007\\]](). There are two extra\n",
-    "compartments on top of the standard SEIR, $H$ for hospitialization and\n",
-    "$F$ for funeral. A total of ten parameters (with some describing the\n",
-    "inverse) are required for the model, they are:\n",
+    "{func}`Legrand_Ebola_SEIHFR`\n",
+    "\n",
+    "A commonly used model in the literature to capture the dynamics of Ebola outbreaks is the\n",
+    "SEIHFR model proposed by {cite}`Legrand2007`. There are two extra\n",
+    "compartments on top of the SEIR: $H$ for hospitializations and\n",
+    "$F$ for funerals. A total of ten parameters (with some describing the\n",
+    "inverse) are required for the model.\n",
     "\n",
     "| Symbol       | Process                                     |\n",
     "|:-------------|:--------------------------------------------|\n",
@@ -27,7 +29,7 @@
     "\n",
     "The **(inverse)** denotes the parameter should be inverted to make\n",
     "epidemiological sense. We use the parameters in their more natural from\n",
-    "in `.Legrand_Ebola_SEIHFR` and replace all the $\\gamma$'s with\n",
+    "in {func}`Legrand_Ebola_SEIHFR` and replace all the $\\gamma$'s with\n",
     "$\\omega$'s, i.e. $\\omega_{i} = \\gamma_{i}^{-1}$ for $i \\in \\{I,D,H,F\\}$.\n",
     "We also used $\\alpha^{-1}$ in our model instead of $\\alpha$ so that\n",
     "reading the parameters directly gives a more intuitive meaning. There\n",
@@ -50,7 +52,7 @@
     "\\gamma_{DH} &= (\\gamma_{D}^{-1} - \\gamma_{H}^{-1})^{-1}.\n",
     "\\end{aligned}$$\n",
     "\n",
-    "Now we are ready to state the full set of ode's,\n",
+    "Now we are ready to state the full set of ODEs,\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dS}{dt} &= -N^{-1} (\\beta_{I}SI + \\beta_{H}SH + \\beta_{F}(t) SF) \\\\\n",
@@ -67,37 +69,55 @@
     "\n",
     "$$\\beta_{F}(t) = \\beta_{F} \\left(1 - \\frac{1}{1 + \\exp(-\\kappa (t - c))} \\right)$$\n",
     "\n",
-    "A brief example (from \\[3\\]) is given here with a slightly more in depth\n",
-    "example in `estimate2`.\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
+    "A brief example is given here with a slightly more in depth\n",
+    "example in {doc}`estimate2`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94abc37f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: x0 = \\[1.0, 3.0/200000.0, 0.0, 0.0, 0.0, 0.0, 0.0\\]\n",
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(1, 25, 100)\n",
+    "x0 = [1.0, 3.0/200000.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
     "\n",
-    "In \\[1\\]: ode = common_models.Legrand_Ebola_SEIHFR(\\[  \n",
-    "...: ('beta_I',0.588), ...: ('beta_H',0.794), ...: ('beta_F',7.653),\n",
-    "...: ('omega_I',10.0/7.0), ...: ('omega_D',9.6/7.0), ...:\n",
-    "('omega_H',5.0/7.0), ...: ('omega_F',2.0/7.0), ...:\n",
-    "('alphaInv',7.0/7.0), ...: ('delta',0.81), ...: ('theta',0.80), ...:\n",
-    "('kappa',300.0), ...: ('interventionTime',7.0) ...: \\])\n",
+    "t = numpy.linspace(1, 25, 100)\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "ode = common_models.Legrand_Ebola_SEIHFR([('beta_I',0.588), ('beta_H',0.794), ('beta_F',7.653), ('omega_I',10.0/7.0), ('omega_D',9.6/7.0),\n",
+    "('omega_H',5.0/7.0), ('omega_F',2.0/7.0), ('alphaInv',7.0/7.0), ('delta',0.81), ('theta',0.80), ('kappa',300.0), ('interventionTime',7.0)])\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t)\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "@savefig common_models_seihfr.png In \\[1\\]: ode.plot()\n",
+    "solution = ode.integrate(t)\n",
     "\n",
-    "Note also that we have again standardized so that the number of\n",
+    "ode.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4836c8f5",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "We have standardized the states so that the number of\n",
     "susceptible is 1 and equal to the whole population, i.e. $N$ does not\n",
-    "exist in our set of ode's as defined in `.common_models`."
+    "exist in our set of ODEs as defined in {mod}`common_models`.\n",
+    "```"
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/Lorenz.ipynb b/docs/pygom-doc/notebooks/common_models/Lorenz.ipynb
index 74b29ea3..2a95125e 100644
--- a/docs/pygom-doc/notebooks/common_models/Lorenz.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/Lorenz.ipynb
@@ -4,9 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.Lorenz`\n",
+    "# Lorenz\n",
     "\n",
-    "The Lorenz attractor [\\[Lorenz1963\\]]() defined by the equations\n",
+    "{func}`.Lorenz`\n",
+    "\n",
+    "The Lorenz attractor {cite}`Lorenz1963` is defined by these equations.\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dx}{dt} &= \\sigma (y-x) \\\\\n",
@@ -14,32 +16,86 @@
     "\\frac{dz}{dt} &= xy - \\beta z\n",
     "\\end{aligned}$$\n",
     "\n",
-    "A classic example is\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
+    "A classic example is given in the following code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e9d4dc8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 100, 20000)\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: ode = common_models.Lorenz({'beta':8.0/3.0, 'sigma':10.0,\n",
-    "'rho':28.0})\n",
+    "t = numpy.linspace(0, 100, 20000)\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (\\[1., 1., 1.\\], t\\[0\\])\n",
+    "ode = common_models.Lorenz({'beta':8.0/3.0, 'sigma':10.0, 'rho':28.0})\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "ode.initial_values = ([1., 1., 1.], t[0])\n",
     "\n",
-    "In \\[1\\]: f = plt.figure()\n",
+    "solution = ode.integrate(t[1::])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0244f462",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = plt.figure()\n",
     "\n",
-    "In \\[1\\]: plt.plot(solution\\[:,0\\], solution\\[:,2\\]);\n",
+    "plt.plot(solution[:,0], solution[:,2]);\n",
     "\n",
-    "@savefig common_models_Lorenz.png In \\[1\\]: plt.show()"
+    "plt.show()"
    ]
   }
  ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/Lotka_Volterra.ipynb b/docs/pygom-doc/notebooks/common_models/Lotka_Volterra.ipynb
index 8063348d..3b21bdb3 100644
--- a/docs/pygom-doc/notebooks/common_models/Lotka_Volterra.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/Lotka_Volterra.ipynb
@@ -4,104 +4,155 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.Lotka_Volterra`\n",
+    "# Lotka Volterra\n",
     "\n",
-    "A standard Lotka-Volterra (preditor and prey) model with two states and\n",
-    "four parameters [\\[Lotka1920\\]]().\n",
+    "{func}`.Lotka_Volterra` - the standard predator and prey model with two states and four parameters {cite}`Lotka1920`\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dx}{dt} &= \\alpha x - cxy \\\\\n",
     "\\frac{dy}{dt} &= -\\delta y + \\gamma xy\n",
     "\\end{aligned}$$\n",
     "\n",
-    "with both birth and death processes.\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "with both birth and death processes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "758e12e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: x0 = \\[2.0, 6.0\\]\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3,\n",
-    "'c':2, 'gamma':6})\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, 0)\n",
+    "x0 = [2.0, 6.0]\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0.1, 100, 10000)\n",
+    "ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3, 'c':2, 'gamma':6})\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t)\n",
+    "ode.initial_values = (x0, 0)\n",
     "\n",
-    "@savefig common_models_Lotka_Volterra.png In \\[1\\]: ode.plot()\n",
+    "t = numpy.linspace(0.1, 100, 10000)\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "solution = ode.integrate(t)\n",
     "\n",
-    "Then we generate the graph at [Wolfram\n",
+    "ode.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1943441d",
+   "metadata": {},
+   "source": [
+    "Then we can generate the graph at [Wolfram\n",
     "Alpha](http://www.wolframalpha.com/input/?i=lotka-volterra+equations)\n",
-    "with varying initial conditions.\n",
-    "\n",
-    "In \\[1\\]: x1List = numpy.linspace(0.2, 2.0, 5)\n",
-    "\n",
-    "In \\[1\\]: x2List = numpy.linspace(0.6, 6.0, 5)\n",
-    "\n",
-    "In \\[1\\]: fig = plt.figure()\n",
+    "with varying initial conditions.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5c82937",
+   "metadata": {},
+   "source": [
+    "x1List = numpy.linspace(0.2, 2.0, 5)\n",
     "\n",
-    "In \\[1\\]: solutionList = list()\n",
+    "x2List = numpy.linspace(0.6, 6.0, 5)\n",
     "\n",
-    "In \\[1\\]: ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3,\n",
-    "'c':2, 'gamma':6})\n",
+    "fig = plt.figure()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a492117",
+   "metadata": {},
+   "source": [
     "\n",
-    "In \\[1\\]: for i in range(len(x1List)):  \n",
-    "...: ode.initial_values = (\\[x1List\\[i\\], x2List\\[i\\]\\], 0) ...:\n",
-    "solutionList += \\[ode.integrate(t)\\]\n",
+    "solutionList = list()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2a1d859",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3, 'c':2, 'gamma':6})\n",
     "\n",
-    "In \\[1\\]: for i in range(len(x1List)):\n",
-    "plt.plot(solutionList\\[i\\]\\[100::,0\\], solutionList\\[i\\]\\[100::,1\\],\n",
-    "'b')\n",
+    "for i in range(len(x1List)): \n",
+    "    ode.initial_values = ([x1List[i], x2List[i]], 0)\n",
     "\n",
-    "In \\[1\\]: plt.xlabel('x')\n",
+    "solutionList += [ode.integrate(t)]\n",
     "\n",
-    "In \\[1\\]: plt.ylabel('y')\n",
+    "for i in range(len(x1List)):\n",
+    "    plt.plot(solutionList[i][100::,0], solutionList[i][100::,1], 'b')\n",
     "\n",
-    "@savefig common_models_Lotka_Volterra_initial_condition.png In \\[1\\]:\n",
-    "plt.show()\n",
+    "plt.xlabel('x')\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "plt.ylabel('y')\n",
     "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c628f283",
+   "metadata": {},
+   "source": [
     "We also know that the system has the critical points at\n",
     "$x = \\delta / \\gamma$ and $y=\\alpha / c$. If we changes the parameters\n",
     "in such a way that the ration between $x$ and $y$ remains the same, then\n",
-    "we get a figure as below\n",
+    "we get a figure as below.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "837940dd",
+   "metadata": {},
+   "source": [
+    "cList = numpy.linspace(0.1, 2.0, 5)\n",
     "\n",
-    "In \\[1\\]: cList = numpy.linspace(0.1, 2.0, 5)\n",
+    "gammaList = numpy.linspace(0.6, 6.0, 5)\n",
     "\n",
-    "In \\[1\\]: gammaList = numpy.linspace(0.6, 6.0, 5)\n",
+    "fig = plt.figure()\n",
     "\n",
-    "In \\[1\\]: fig = plt.figure()\n",
+    "for i in range(len(x1List)):  \n",
+    "    ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3, \n",
+    "    'c':cList[i], 'gamma':gammaList[i]})\n",
     "\n",
-    "In \\[1\\]: for i in range(len(x1List)):  \n",
-    "...: ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3,\n",
-    "'c':cList\\[i\\], 'gamma':gammaList\\[i\\]}) ...: ode.initial_values =\n",
-    "(x0, 0) ...: solutionList += \\[ode.integrate(t)\\]\n",
+    "ode.initial_values = (x0, 0) \n",
+    "solutionList += [ode.integrate(t)]\n",
     "\n",
-    "In \\[1\\]: for i in range(len(cList)):\n",
-    "plt.plot(solutionList\\[i\\]\\[100::,0\\], solutionList\\[i\\]\\[100::,1\\])\n",
+    "for i in range(len(cList)):\n",
+    "    plt.plot(solutionList[i][100::,0], solutionList[i][100::,1])\n",
     "\n",
-    "In \\[1\\]: plt.xlabel('x')\n",
+    "plt.xlabel('x')\n",
     "\n",
-    "In \\[1\\]: plt.ylabel('y')\n",
+    "plt.ylabel('y')\n",
     "\n",
-    "@savefig common_models_Lotka_Volterra_critical_point.png In \\[1\\]:\n",
-    "plt.show()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f05ce4a3",
+   "metadata": {},
+   "source": [
     "\n",
-    "In \\[1\\]: plt.close()\n",
     "\n",
     "where all the cycles goes through the same points."
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/Lotka_Volterra_4State.ipynb b/docs/pygom-doc/notebooks/common_models/Lotka_Volterra_4State.ipynb
index 34ec9f82..807863e0 100644
--- a/docs/pygom-doc/notebooks/common_models/Lotka_Volterra_4State.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/Lotka_Volterra_4State.ipynb
@@ -4,10 +4,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.Lotka_Volterra_4State`\n",
+    "# Lotka_Volterra (4 state)\n",
+    "\n",
+    "{func}`.Lotka_Volterra_4State`\n",
     "\n",
     "The Lotka-Volterra model with four states and three parameters\n",
-    "[\\[Lotka1920\\]](), explained by the following three transitions\n",
+    "{cite}`Lotka1920`, is explained by the following three equations.\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{da}{dt} &= k_{0} a x \\\\\n",
@@ -17,51 +19,66 @@
     "\\end{aligned}$$\n",
     "\n",
     "First, we show the deterministic approach. Then we also show the\n",
-    "different process path using the parameters from [\\[Press2007\\]](). Note\n",
-    "that although the model is defined in `common_models`, it is based on\n",
-    "outputting an `OperateOdeModel` rather than `SimulateOdeModel`.\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "different process path using the parameters from {cite}`Press2007`. Note\n",
+    "that although the model is defined in {class}`common_models`, it is based on\n",
+    "outputting an {func}`.OperateOdeModel` rather than {func}`.SimulateOdeModel`.\n",
     "\n",
-    "In \\[1\\]: from pygom import Transition, TransitionType, ode_utils,\n",
-    "SimulateOde\n",
+    "#TODO why is the predefined version not used?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c33dd44b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: import numpy\n",
+    "from pygom import Transition, TransitionType, ode_utils, SimulateOde\n",
     "\n",
-    "In \\[1\\]: stateList = \\['a', 'x', 'y', 'b'\\]\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: paramList = \\['k0', 'k1', 'k2'\\]\n",
+    "stateList = ['a', 'x', 'y', 'b']\n",
     "\n",
-    "In \\[1\\]: transitionList = \\[  \n",
-    "...: Transition(origin='a', destination='x', equation='k0\\*a\\*x',\n",
-    "transition_type=TransitionType.T), ...: Transition(origin='x',\n",
-    "destination='y', equation='k1\\*x\\*y', transition_type=TransitionType.T),\n",
-    "...: Transition(origin='y', destination='b', equation='k2\\*y',\n",
-    "transition_type=TransitionType.T) ...: \\]\n",
+    "paramList = ['k0', 'k1', 'k2']\n",
     "\n",
-    "In \\[1\\]: ode = SimulateOde(stateList, paramList,\n",
-    "transition=transitionList)\n",
+    "transitionList = [Transition(origin='a', destination='x', equation='k0*a*x', transition_type=TransitionType.T), \n",
+    "                  Transition(origin='x', destination='y', equation='k1*x*y', transition_type=TransitionType.T),Transition(origin='y', destination='b', equation='k2*y', transition_type=TransitionType.T)]\n",
     "\n",
-    "In \\[1\\]: x0 = \\[150.0, 10.0, 10.0, 0.0\\]\n",
+    "ode = SimulateOde(stateList, paramList, transition=transitionList)\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 15, 100)\n",
+    "x0 = [150.0, 10.0, 10.0, 0.0]\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "t = numpy.linspace(0, 15, 100)\n",
     "\n",
-    "In \\[1\\]: ode.parameters = \\[0.01, 0.1, 1.0\\]\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "ode.parameters = [0.01, 0.1, 1.0]\n",
     "\n",
-    "@savefig common_models_Lotka_Volterra_4State.png In \\[1\\]: ode.plot()\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "In \\[1\\]: simX, simT = ode.simulate_jump(t\\[1::\\], 5, full_output=True)\n",
+    "ode.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "478daeb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simX, simT = ode.simulate_jump(t[1::], 5, full_output=True)\n",
     "\n",
-    "@savefig common_models_Lotka_Volterra_Sim.png In \\[1\\]: ode.plot(simX,\n",
-    "simT)"
+    "ode.plot(simX, simT)"
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/Robertson.ipynb b/docs/pygom-doc/notebooks/common_models/Robertson.ipynb
index fc1eb714..19865a94 100644
--- a/docs/pygom-doc/notebooks/common_models/Robertson.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/Robertson.ipynb
@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.Robertson`\n",
+    "# Robertson\n",
     "\n",
-    "The Robertson problem [\\[Robertson1966\\]]()\n",
+    "{func}`.Robertson` - the Robertson problem {cite}`Robertson1966`\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dy_{1}}{dt} &= -0.04 y_{1} + 1 \\cdot 10^{4} y_{2} y_{3} \\\\\n",
@@ -15,80 +15,103 @@
     "\\end{aligned}$$\n",
     "\n",
     "This is a problem that describes an autocatalytic reaction. One of those\n",
-    "commonly used to test stiff ode solvers. As the parameters in the\n",
-    "literature is fixed, we show here how to define the states in a slightly\n",
-    "more compact format\n",
-    "\n",
-    "In \\[1\\]: from pygom import DeterministicOde, Transition, TransitionType\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
+    "commonly used to test stiff ODE solvers. As the parameters in the\n",
+    "literature are fixed, we show here how to define the states in a slightly more compact format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33e99698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import DeterministicOde, Transition, TransitionType\n",
     "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: t = numpy.append(0, 4\\*numpy.logspace(-6, 6, 1000))\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: \\# note how we define the states\n",
+    "t = numpy.append(0, 4*numpy.logspace(-6, 6, 1000))\n",
     "\n",
-    "In \\[1\\]: stateList = \\['y1:4'\\]\n",
+    "# note how we define the states\n",
     "\n",
-    "In \\[1\\]: paramList = \\[\\]\n",
+    "stateList = ['y1:4']\n",
     "\n",
-    "In \\[1\\]: transitionList = \\[  \n",
-    "...: Transition(origin='y1', destination='y2', equation='0.04\\*y1',\n",
-    "transition_type=TransitionType.T), ...: Transition(origin='y2',\n",
-    "destination='y1', equation='1e4\\*y2\\*y3',\n",
-    "transition_type=TransitionType.T), ...: Transition(origin='y2',\n",
-    "destination='y3', equation='3e7\\*y2\\*y2',\n",
-    "transition_type=TransitionType.T) ...: \\]\n",
+    "paramList = []\n",
     "\n",
-    "In \\[1\\]: ode = DeterministicOde(stateList, paramList,\n",
-    "transition=transitionList)\n",
+    "transitionList = [Transition(origin='y1', destination='y2', equation='0.04*y1', transition_type=TransitionType.T), \n",
+    "                  Transition(origin='y2', destination='y1', equation='1e4*y2*y3', transition_type=TransitionType.T), \n",
+    "                  Transition(origin='y2', destination='y3', equation='3e7*y2*y2', transition_type=TransitionType.T)\n",
+    "                  ]\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (\\[1.0, 0.0, 0.0\\], t\\[0\\])\n",
+    "ode = DeterministicOde(stateList, paramList, transition=transitionList)\n",
     "\n",
-    "In \\[1\\]: solution, output = ode.integrate(t\\[1::\\], full_output=True)\n",
+    "ode.initial_values = ([1.0, 0.0, 0.0], t[0])\n",
     "\n",
-    "In \\[1\\]: f, axarr = plt.subplots(1, 3)\n",
+    "solution, output = ode.integrate(t[1::], full_output=True)\n",
     "\n",
-    "In \\[1\\]: for i in range(3):  \n",
-    "...: axarr\\[i\\].plot(t, solution\\[:,i\\]) ...:\n",
-    "axarr\\[i\\].set_xscale('log')\n",
+    "f, axarr = plt.subplots(1, 3)\n",
     "\n",
-    "In \\[1\\]: f.tight_layout();\n",
+    "for i in range(3):\n",
+    "    axarr[i].plot(t, solution[:,i])\n",
+    "    axarr[i].set_xscale('log')\n",
     "\n",
-    "@savefig common_models_Robertson_1.png In \\[1\\]: plt.show()\n",
+    "f.tight_layout();\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea988ab7",
+   "metadata": {},
+   "source": [
     "\n",
     "To simplify even further, we can use y\n",
     "with the corresponding subscript directly instead of y1,y2,y3. Again, we do not have any parameters\n",
-    "as they are hard coded into our models.\n",
-    "\n",
-    "In \\[1\\]: stateList = \\['y1:4'\\]\n",
-    "\n",
-    "In \\[1\\]: transitionList = \\[  \n",
-    "...: Transition(origin='y\\[0\\]', destination='y\\[1\\]',\n",
-    "equation='0.04\\*y\\[0\\]', transition_type=TransitionType.T), ...:\n",
-    "Transition(origin='y\\[1\\]', destination='y\\[0\\]',\n",
-    "equation='1e4\\*y\\[1\\]*y\\[2\\]', transition_type=TransitionType.T), ...:\n",
-    "Transition(origin='y\\[1\\]', destination='y\\[2\\]',\n",
-    "equation='3e7*y\\[1\\]\\*y\\[1\\]', transition_type=TransitionType.T) ...: \\]\n",
+    "as they are hard coded into our models.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df7214d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stateList = ['y1:4']\n",
     "\n",
-    "In \\[1\\]: ode = DeterministicOde(stateList, paramList,\n",
-    "transition=transitionList)\n",
+    "transitionList = [Transition(origin='y[0]', destination='y[1]', equation='0.04*y[0]', transition_type=TransitionType.T),\n",
+    "                  Transition(origin='y[1]', destination='y[0]', equation='1e4*y[1]*y[2]', transition_type=TransitionType.T), \n",
+    "                  Transition(origin='y[1]', destination='y[2]', equation='3e7*y[1]*y[1]', transition_type=TransitionType.T)\n",
+    "                  ]\n",
     "\n",
-    "In \\[1\\]: ode.initial_values =(\\[1.0, 0.0, 0.0\\], t\\[0\\])\n",
+    "ode = DeterministicOde(stateList, paramList, transition=transitionList)\n",
     "\n",
-    "In \\[1\\]: solution2 = ode.integrate(t\\[1::\\])\n",
+    "ode.initial_values =([1.0, 0.0, 0.0], t[0])\n",
     "\n",
-    "In \\[1\\]: numpy.max(solution - solution2)\n",
+    "solution2 = ode.integrate(t[1::])\n",
     "\n",
+    "numpy.max(solution - solution2)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7604b73",
+   "metadata": {},
+   "source": [
     "and we have the identical solution as shown in the last line above."
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }

From 246b263fb9dcd7ebf7bdd429df56e162965c3e03 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 29 Aug 2023 13:37:10 +0100
Subject: [PATCH 137/188] fixed citations/referencing for bibliography - error
 was in config file

---
 docs/pygom-doc/_config.yml             |  19 +--
 docs/pygom-doc/_toc.yml                |  10 +-
 docs/pygom-doc/md/bib.md               |   9 +-
 docs/pygom-doc/notebooks/epijson.ipynb |  11 +-
 docs/pygom-doc/ref.bib                 | 218 ++++++++-----------------
 5 files changed, 102 insertions(+), 165 deletions(-)

diff --git a/docs/pygom-doc/_config.yml b/docs/pygom-doc/_config.yml
index ecf87ec2..9b3fdeb8 100644
--- a/docs/pygom-doc/_config.yml
+++ b/docs/pygom-doc/_config.yml
@@ -25,18 +25,16 @@ latex:
 # Add a bibtex file so that we can create citations
 # use sphinx to specify style
 
-sphinx:
-  extra_extensions:
-  - 'sphinxcontrib.bibtex'
+bibtex_bibfiles: 
+  - 'ref.bib'
+
+#sphinx:
+#  extra_extensions:
+#  - 'sphinxcontrib.bibtex'
+#  config:
+#    bibtex_reference_style: super
 
-sphinx:
-  config:
-    bibtex_reference_style: author_year
-    # TODO change md to bibtex
-    bibtex_bibfiles: ref.bib
 
-#bibtex_bibfiles: 
-#  - ref.bib
 
 sphinx:  
   extra_extensions:
@@ -48,6 +46,7 @@ sphinx:
   config:
     add_module_names: True 
     autosummary_generate: True
+    bibtex_reference_style: super
 
 #sphinx:
 #  extra_extensions:
diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index 3d5df218..f8260f5b 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -48,10 +48,12 @@ parts:
 #  - caption: Frequently asked questions
 #    chapters: 
 #    - file: md/faq.md
-  - caption: Code documentation
-    chapters:
-    - file: autodoc-model.rst
-    - file: autodoc-loss.rst
+#  - caption: Code documentation
+#    chapters:
+#    - file: autodoc-model.rst
+#    - file: autodoc-loss.rst
+    # add utils
+    # add abc
   - caption: References
     chapters: 
     - file: md/bib.md
diff --git a/docs/pygom-doc/md/bib.md b/docs/pygom-doc/md/bib.md
index df56a0c2..1b1b350d 100644
--- a/docs/pygom-doc/md/bib.md
+++ b/docs/pygom-doc/md/bib.md
@@ -1,6 +1,9 @@
 # References 
 
-```{bibliography}
-:style: unsrt
+```{bibliography} 
+:style: plain
+```
+
+#../zzzfinnie.bib
 #:all:
-```
\ No newline at end of file
+#:file: ../ref.bib
\ No newline at end of file
diff --git a/docs/pygom-doc/notebooks/epijson.ipynb b/docs/pygom-doc/notebooks/epijson.ipynb
index f2e63cae..882abe8b 100644
--- a/docs/pygom-doc/notebooks/epijson.ipynb
+++ b/docs/pygom-doc/notebooks/epijson.ipynb
@@ -9,7 +9,7 @@
     "Epidemiology data is complicated due to the many different stages a\n",
     "patient can go through and whether a modeling technique is applicable\n",
     "depends heavily on the recording of data. [EpiJSON](https://github.com/Hackout2/EpiJSON) is a framework which\n",
-    "tries to captures all the information in a JSON format {cite:t}`Finnie2016`.\n",
+    "tries to captures all the information in a JSON format {cite}`Finnie2016`.\n",
     "\n",
     "PyGOM provides the functionality to process EpiJSON data. Due to\n",
     "the nature of this package, modeling of ODEs, data files are processed with this in mind. The output is therefore in the cumulative form as\n",
@@ -98,6 +98,15 @@
     "and $R$ the corresponding state the data belongs to.\n",
     "```"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caa1d2f1",
+   "metadata": {},
+   "source": [
+    "```{footbibliography}\n",
+    "```"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/pygom-doc/ref.bib b/docs/pygom-doc/ref.bib
index 72c66881..89833a8e 100644
--- a/docs/pygom-doc/ref.bib
+++ b/docs/pygom-doc/ref.bib
@@ -1,3 +1,33 @@
+@article{Aron1984,
+   abstract = {The annual incidence rates of some endemic infectious diseases are steady while others fluctuate dramatically, often in a regular cycle. In order to investigate the role of seasonality in driving cycles of recurrent epidemics, we analyze numerically the susceptible/exposed/infective/recovered (SEIR) epidemic model with seasonal transmission. We show that small-amplitude periodic solutions exhibit a sequence of period-doubling bifurcations as the amplitude of seasonal variation increases, predicting a transition to chaos of the kind studied in other biological contexts. The epidemiological implication is that the seasonal mechanism generating biennial epidemics may not be able to account for small-amplitude recurrent epidemics of arbitrary periodicity. © 1997 Elsevier Science Ltd. All rights reserved.},
+   author = {Joan L. Aron and Ira B. Schwartz},
+   doi = {10.1016/S0022-5193(84)80150-2},
+   issn = {0022-5193},
+   issue = {4},
+   journal = {Journal of theoretical biology},
+   keywords = {Biological*,Communicable Diseases / epidemiology*,Disease Outbreaks / epidemiology*,England,Humans,I B Schwartz,J L Aron,MEDLINE,Mathematics,Measles / epidemiology,Models,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,New York City,Periodicity*,PubMed Abstract,Seasons*,Time Factors,Wales,doi:10.1016/s0022-5193(84)80150-2,pmid:6521486},
+   month = {10},
+   pages = {665-679},
+   pmid = {6521486},
+   publisher = {J Theor Biol},
+   title = {Seasonality and period-doubling bifurcations in an epidemic model},
+   volume = {110},
+   url = {https://pubmed.ncbi.nlm.nih.gov/6521486/},
+   year = {1984},
+}
+@book{Brauer2008,
+   author = {Fred Brauer},
+   city = {Berlin, Heidelberg},
+   doi = {10.1007/978-3-540-78911-6},
+   editor = {Fred Brauer and Pauline van den Driessche and Jianhong Wu},
+   isbn = {978-3-540-78910-9},
+   journal = {Sp},
+   publisher = {Springer Berlin Heidelberg},
+   title = {Mathematical Epidemiology},
+   volume = {1945},
+   url = {http://link.springer.com/10.1007/978-3-540-78911-6},
+   year = {2008},
+}
 @article{Cao2006,
    abstract = {The tau-leaping method of simulating the stochastic time evolution of a well-stirred chemically reacting system uses a Poisson approximation to take time steps that leap over many reaction events. Theory implies that tau leaping should be accurate so long as no propensity function changes its value "significantly" during any time step τ. Presented here is an improved procedure for estimating the largest value for τ that is consistent with this condition. This new τ -selection procedure is more accurate, easier to code, and faster to execute than the currently used procedure. The speedup in execution will be especially pronounced in systems that have many reaction channels. © 2006 American Institute of Physics.},
    author = {Yang Cao and Daniel T. Gillespie and Linda R. Petzold},
@@ -63,6 +93,20 @@ @article{Hethcote1973
    volume = {35},
    year = {1973},
 }
+@article{legrand2007utd,
+   abstract = {Ebola is a highly lethal virus, which has caused at least 14 confirmed outbreaks in Africa between 1976 and 2006. Using data from two epidemics [in Democratic Republic of Congo (DRC) in 1995 and in Uganda in 2000], we built a mathematical model for the spread of Ebola haemorrhagic fever epidemics taking into account transmission in different epidemiological settings. We estimated the basic reproduction number (R0) to be 2.7 (95% CI 1.9-2.8) for the 1995 epidemic in DRC, and 2.7 (95% CI 2.5-4.1) for the 2000 epidemic in Uganda. For each epidemic, we quantified transmission in different settings (illness in the community, hospitalization, and traditional burial) and simulated various epidemic scenarios to explore the impact of control interventions on a potential epidemic. A key parameter was the rapid institution of control measures. For both epidemic profiles identified, increasing hospitalization rate reduced the predicted epidemic size.},
+   author = {J. Legrand and R. F. Grais and P. Y. Boelle and A. J. Valleron and A. Flahault},
+   doi = {10.1017/S0950268806007217},
+   isbn = {0950-2688},
+   issn = {09502688},
+   issue = {4},
+   journal = {Epidemiology and Infection},
+   pages = {610-621},
+   pmid = {16999875},
+   title = {Understanding the dynamics of Ebola epidemics},
+   volume = {135},
+   year = {2007},
+}
 @article{Lloyd1996,
    abstract = {Spatial heterogeneity is believed to play an important role in the persistence and dynamics of epidemics of childhood diseases because asynchrony between populations within different regions allows global persistence, even if the disease dies out locally. A simple multi-patch (metapopulation) model for spatial heterogeneity in epidemics is analysed and we examine conditions under which patches become synchronized. We show that the patches in non-seasonal deterministic models often oscillate in phase for all but the weakest between patch coupling. Synchronization is also seen for stochastic models, although slightly stronger coupling is needed to overcome the random effects. We demonstrate that the inclusion of seasonal forcing in deterministic models can lead to the maintenance of phase differences between patches. Complex dynamic behaviour is observed in the seasonally forced spatial model, along with the coexistence of many different behaviours. Compared to the non-spatial model, chaotic solutions are observed for weaker seasonal forcing; these solutions have a more realistic minimum number of infectives.},
    author = {Alun L. Lloyd and Robert M. May},
@@ -96,6 +140,16 @@ @article{Lotka1920
    volume = {6},
    year = {1920},
 }
+@article{Moolgavkar1987,
+   author = {Suresh H. Moolgavkar and David J. Venz},
+   issue = {1},
+   journal = {Journal of Statistics},
+   pages = {43-56},
+   title = {Confidence Regions for Parameters of the Proportional Hazards Model: A Simulation Study on JSTOR},
+   volume = {14},
+   url = {https://www.jstor.org/stable/4616047},
+   year = {1987},
+}
 @book{Press2007,
    abstract = {Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in color for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs; a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres; interior point methods for linear programming; MCMC; an expanded treatment of ODEs with completely new routines; and many new statistical distributions.},
    author = {William H Press and Saul a Teukolsky and William T Vetterling and Brian P Flannery},
@@ -127,18 +181,15 @@ @article{Raue2009
    volume = {25},
    year = {2009},
 }
-@article{Venzon1988,
-   abstract = {The method of constructing confidence regions based on the generalised likelihood ratio statistic is well known for parameter vectors. A similar construction of a confidence interval for a simple entry of a vector can be implemented by repeatedly maximising over the other parameters. We present an algorithm for finding these confidence interval endpoints that requires less computation. It employs a modified Newton-Raphson iteration to solve a system of equations that defines the endpoints.},
-   author = {D. J. Venzon and S. H. Moolgavkar},
-   doi = {10.2307/2347496},
-   issn = {00359254},
-   issue = {1},
-   journal = {Applied Statistics},
-   title = {A Method for Computing Profile-Likelihood-Based Confidence Intervals},
-   volume = {37},
-   year = {1988},
+@article{Robertson1966,
+   author = {H.H Robertson},
+   journal = {Academic Press},
+   pages = {178-182},
+   title = {The Solution of a Set of Reaction Rate Equations},
+   url = {https://www.scirp.org/%28S%28vtj3fa45qm1ean45vvffcz55%29%29/reference/referencespapers.aspx?referenceid=2485718},
+   year = {1966},
 }
-@article{,
+@article{vanderPol1926,
    abstract = {(1926). LXXXVIII. On “relaxation-oscillations”. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science: Vol. 2, No. 11, pp. 978-992.},
    author = {Balth. van der Pol},
    doi = {10.1080/14786442608564127},
@@ -152,141 +203,14 @@ @article{
    volume = {2},
    year = {1926},
 }
-@article{Robertson1966,
-   author = {H.H Robertson},
-   journal = {Academic Press},
-   pages = {178-182},
-   title = {The Solution of a Set of Reaction Rate Equations},
-   url = {https://www.scirp.org/%28S%28vtj3fa45qm1ean45vvffcz55%29%29/reference/referencespapers.aspx?referenceid=2485718},
-   year = {1966},
-}
-@article{Moolgavkar1987,
-   author = {Suresh H. Moolgavkar and David J. Venz},
+@article{Venzon1988,
+   abstract = {The method of constructing confidence regions based on the generalised likelihood ratio statistic is well known for parameter vectors. A similar construction of a confidence interval for a simple entry of a vector can be implemented by repeatedly maximising over the other parameters. We present an algorithm for finding these confidence interval endpoints that requires less computation. It employs a modified Newton-Raphson iteration to solve a system of equations that defines the endpoints.},
+   author = {D. J. Venzon and S. H. Moolgavkar},
+   doi = {10.2307/2347496},
+   issn = {00359254},
    issue = {1},
-   journal = {Journal of Statistics},
-   pages = {43-56},
-   title = {Confidence Regions for Parameters of the Proportional Hazards Model: A Simulation Study on JSTOR},
-   volume = {14},
-   url = {https://www.jstor.org/stable/4616047},
-   year = {1987},
-}
-@article{legrand2007utd,
-   abstract = {Ebola is a highly lethal virus, which has caused at least 14 confirmed outbreaks in Africa between 1976 and 2006. Using data from two epidemics [in Democratic Republic of Congo (DRC) in 1995 and in Uganda in 2000], we built a mathematical model for the spread of Ebola haemorrhagic fever epidemics taking into account transmission in different epidemiological settings. We estimated the basic reproduction number (R0) to be 2.7 (95% CI 1.9-2.8) for the 1995 epidemic in DRC, and 2.7 (95% CI 2.5-4.1) for the 2000 epidemic in Uganda. For each epidemic, we quantified transmission in different settings (illness in the community, hospitalization, and traditional burial) and simulated various epidemic scenarios to explore the impact of control interventions on a potential epidemic. A key parameter was the rapid institution of control measures. For both epidemic profiles identified, increasing hospitalization rate reduced the predicted epidemic size.},
-   author = {J. Legrand and R. F. Grais and P. Y. Boelle and A. J. Valleron and A. Flahault},
-   doi = {10.1017/S0950268806007217},
-   isbn = {0950-2688},
-   issn = {09502688},
-   issue = {4},
-   journal = {Epidemiology and Infection},
-   pages = {610-621},
-   pmid = {16999875},
-   title = {Understanding the dynamics of Ebola epidemics},
-   volume = {135},
-   year = {2007},
-}
-@article{Cao2006,
-   abstract = {The tau-leaping method of simulating the stochastic time evolution of a well-stirred chemically reacting system uses a Poisson approximation to take time steps that leap over many reaction events. Theory implies that tau leaping should be accurate so long as no propensity function changes its value "significantly" during any time step τ. Presented here is an improved procedure for estimating the largest value for τ that is consistent with this condition. This new τ -selection procedure is more accurate, easier to code, and faster to execute than the currently used procedure. The speedup in execution will be especially pronounced in systems that have many reaction channels. © 2006 American Institute of Physics.},
-   author = {Yang Cao and Daniel T. Gillespie and Linda R. Petzold},
-   doi = {10.1063/1.2159468},
-   issn = {00219606},
-   issue = {4},
-   journal = {Journal of Chemical Physics},
-   title = {Efficient step size selection for the tau-leaping simulation method},
-   volume = {124},
-   year = {2006},
-}
-@article{Finnie2016,
-   abstract = {Epidemiology relies on data but the divergent ways data are recorded and transferred, both within and between outbreaks, and the expanding range of data-types are creating an increasingly complex problem for the discipline. There is a need for a consistent, interpretable and precise way to transfer data while maintaining its fidelity. We introduce 'EpiJSON', a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. This format is designed to enable the widest range of epidemiological data to be unambiguously held and transferred between people, software and institutions. In this paper, we provide a full description of the format and a discussion of the design decisions made. We introduce a schema enabling automatic checks of the validity of data stored as EpiJSON, which can serve as a basis for the development of additional tools. In addition, we also present the R package 'repijson' which provides conversion tools between this format, line-list data and pre-existing analysis tools. An example is given to illustrate how EpiJSON can be used to store line list data. EpiJSON, designed around modern standards for interchange of information on the internet, is simple to implement, read and check. As such, it provides an ideal new standard for epidemiological, and other, data transfer to the fast-growing open-source platform for the analysis of disease outbreaks.},
-   author = {Thomas J.R. Finnie and Andy South and Ana Bento and Ellie Sherrard-Smith and Thibaut Jombart},
-   doi = {10.1016/j.epidem.2015.12.002},
-   issn = {18780067},
-   journal = {Epidemics},
-   title = {EpiJSON: A unified data-format for epidemiology},
-   volume = {15},
-   year = {2016},
-}
-@article{FitzHugh1961,
-   abstract = {Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting “BVP model” has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a “physiological state diagram,” with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable. © 1961, The Biophysical Society. All rights reserved.},
-   author = {Richard FitzHugh},
-   doi = {10.1016/S0006-3495(61)86902-6},
-   issn = {00063495},
-   issue = {6},
-   journal = {Biophysical Journal},
-   title = {Impulses and Physiological States in Theoretical Models of Nerve Membrane},
-   volume = {1},
-   year = {1961},
-}
-@inproceedings{Gillespie1977,
-   abstract = {There are two formalisms for mathematically describing the time behavior of a spatially homogeneous chemical system: The deterministic approach regards the time evolution as a continuous, wholly predictable process which is governed by a set of coupled, ordinary differential equations (the "reaction-rate equations"); the stochastic approach regards the time evolution as a kind of random-walk process which is governed by a single differential-difference equation (the "master equation"). Fairly simple kinetic theory arguments show that the stochastic formulation of chemical kinetics has a firmer physical basis than the deterministic formulation, but unfortunately the stochastic master equation is often mathematically intractable. There is, however, a way to make exact numerical calculations within the framework of the stochastic formulation without having to deal with the master equation directly. It is a relatively simple digital computer algorithm which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of the given chemical system. Like the master equation, this "stochastic simulation algorithm" correctly accounts for the inherent fluctuations and correlations that are necessarily ignored in the deterministic formulation. In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps Δt. The feasibility and utility of the simulation algorithm are demonstrated by applying it to several well-known model chemical systems, including the Lotka model, the Brusselator, and the Oregonator.},
-   author = {Daniel T. Gillespie},
-   doi = {10.1021/j100540a008},
-   issn = {00223654},
-   issue = {25},
-   journal = {Journal of Physical Chemistry},
-   title = {Exact stochastic simulation of coupled chemical reactions},
-   volume = {81},
-   year = {1977},
-}
-@article{Girolami2011,
-   abstract = {The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot runs that are required to tune proposal densities for Metropolis-Hastings or indeed Hamiltonian Monte Carlo and Metropolis adjusted Langevin algorithms. This allows for highly efficient sampling even in very high dimensions where different scalings may be required for the transient and stationary phases of the Markov chain. The methodology proposed exploits the Riemann geometry of the parameter space of statistical models and thus automatically adapts to the local structure when simulating paths across this manifold, providing highly efficient convergence and exploration of the target density. The performance of these Riemann manifold Monte Carlo methods is rigorously assessed by performing inference on logistic regression models, log-Gaussian Cox point processes, stochastic volatility models and Bayesian estimation of dynamic systems described by non-linear differential equations. Substantial improvements in the time-normalized effective sample size are reported when compared with alternative sampling approaches. MATLAB code that is available from allows replication of all the results reported. © 2011 Royal Statistical Society.},
-   author = {Mark Girolami and Ben Calderhead},
-   doi = {10.1111/j.1467-9868.2010.00765.x},
-   issn = {13697412},
-   issue = {2},
-   journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
-   title = {Riemann manifold Langevin and Hamiltonian Monte Carlo methods},
-   volume = {73},
-   year = {2011},
-}
-@article{Hethcote1973,
-   abstract = {The effects of a periodic contact rate and of carriers are considered for a generalization of Bailey's simple epidemic model. In this model it is assumed that individuals become susceptible again as soon as they recover from the infection so that a fixed population can be divided into a class of infectives and a class of susceptibles which vary with time. If the contact rate is periodic, then the number of infectives as time approaches infinity either tends to zero or is asymptotically periodic depending on whether the total population size is less than or greater than a threshold value. The behavior for large time of the number of infectives is determined for three modifications of the model which involve carriers. © 1973 Society for Mathematical Biology.},
-   author = {Herbert W. Hethcote},
-   doi = {10.1007/BF02458365},
-   issn = {00074985},
-   issue = {5-6},
-   journal = {Bulletin of Mathematical Biology},
-   title = {Asymptotic behavior in a deterministic epidemic model},
-   volume = {35},
-   year = {1973},
-}
-@inproceedings{Gillespie1977,
-   abstract = {There are two formalisms for mathematically describing the time behavior of a spatially homogeneous chemical system: The deterministic approach regards the time evolution as a continuous, wholly predictable process which is governed by a set of coupled, ordinary differential equations (the "reaction-rate equations"); the stochastic approach regards the time evolution as a kind of random-walk process which is governed by a single differential-difference equation (the "master equation"). Fairly simple kinetic theory arguments show that the stochastic formulation of chemical kinetics has a firmer physical basis than the deterministic formulation, but unfortunately the stochastic master equation is often mathematically intractable. There is, however, a way to make exact numerical calculations within the framework of the stochastic formulation without having to deal with the master equation directly. It is a relatively simple digital computer algorithm which uses a rigorously derived Monte Carlo procedure to numerically simulate the time evolution of the given chemical system. Like the master equation, this "stochastic simulation algorithm" correctly accounts for the inherent fluctuations and correlations that are necessarily ignored in the deterministic formulation. In addition, unlike most procedures for numerically solving the deterministic reaction-rate equations, this algorithm never approximates infinitesimal time increments dt by finite time steps Δt. The feasibility and utility of the simulation algorithm are demonstrated by applying it to several well-known model chemical systems, including the Lotka model, the Brusselator, and the Oregonator.},
-   author = {Daniel T. Gillespie},
-   doi = {10.1021/j100540a008},
-   issn = {00223654},
-   issue = {25},
-   journal = {Journal of Physical Chemistry},
-   title = {Exact stochastic simulation of coupled chemical reactions},
-   volume = {81},
-   year = {1977},
-}
-@book{Brauer2008,
-   author = {Fred Brauer},
-   city = {Berlin, Heidelberg},
-   doi = {10.1007/978-3-540-78911-6},
-   editor = {Fred Brauer and Pauline van den Driessche and Jianhong Wu},
-   isbn = {978-3-540-78910-9},
-   journal = {Sp},
-   publisher = {Springer Berlin Heidelberg},
-   title = {Mathematical Epidemiology},
-   volume = {1945},
-   url = {http://link.springer.com/10.1007/978-3-540-78911-6},
-   year = {2008},
-}
-@article{Aron1984,
-   abstract = {The annual incidence rates of some endemic infectious diseases are steady while others fluctuate dramatically, often in a regular cycle. In order to investigate the role of seasonality in driving cycles of recurrent epidemics, we analyze numerically the susceptible/exposed/infective/recovered (SEIR) epidemic model with seasonal transmission. We show that small-amplitude periodic solutions exhibit a sequence of period-doubling bifurcations as the amplitude of seasonal variation increases, predicting a transition to chaos of the kind studied in other biological contexts. The epidemiological implication is that the seasonal mechanism generating biennial epidemics may not be able to account for small-amplitude recurrent epidemics of arbitrary periodicity. © 1997 Elsevier Science Ltd. All rights reserved.},
-   author = {Joan L. Aron and Ira B. Schwartz},
-   doi = {10.1016/S0022-5193(84)80150-2},
-   issn = {0022-5193},
-   issue = {4},
-   journal = {Journal of theoretical biology},
-   keywords = {Biological*,Communicable Diseases / epidemiology*,Disease Outbreaks / epidemiology*,England,Humans,I B Schwartz,J L Aron,MEDLINE,Mathematics,Measles / epidemiology,Models,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,New York City,Periodicity*,PubMed Abstract,Seasons*,Time Factors,Wales,doi:10.1016/s0022-5193(84)80150-2,pmid:6521486},
-   month = {10},
-   pages = {665-679},
-   pmid = {6521486},
-   publisher = {J Theor Biol},
-   title = {Seasonality and period-doubling bifurcations in an epidemic model},
-   volume = {110},
-   url = {https://pubmed.ncbi.nlm.nih.gov/6521486/},
-   year = {1984},
+   journal = {Applied Statistics},
+   title = {A Method for Computing Profile-Likelihood-Based Confidence Intervals},
+   volume = {37},
+   year = {1988},
 }

From 41f7da63239deefbeb0b69ddcd4c968d4be78fdf Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 29 Aug 2023 16:13:44 +0100
Subject: [PATCH 138/188] add code documentation for abc and utilR

---
 docs/pygom-doc/autodoc-abc.rst   | 9 +++++++++
 docs/pygom-doc/autodoc-utilR.rst | 9 +++++++++
 2 files changed, 18 insertions(+)
 create mode 100644 docs/pygom-doc/autodoc-abc.rst
 create mode 100644 docs/pygom-doc/autodoc-utilR.rst

diff --git a/docs/pygom-doc/autodoc-abc.rst b/docs/pygom-doc/autodoc-abc.rst
new file mode 100644
index 00000000..05b5d8ae
--- /dev/null
+++ b/docs/pygom-doc/autodoc-abc.rst
@@ -0,0 +1,9 @@
+Module: approximate_bayesian_computation
+=============
+
+.. automodule:: pygom.approximate_bayesian_computation
+    :members:
+
+.. autosummary::
+   :toctree: _autosummary
+   :recursive:
\ No newline at end of file
diff --git a/docs/pygom-doc/autodoc-utilR.rst b/docs/pygom-doc/autodoc-utilR.rst
new file mode 100644
index 00000000..8f242ad7
--- /dev/null
+++ b/docs/pygom-doc/autodoc-utilR.rst
@@ -0,0 +1,9 @@
+Module: utilR
+=============
+
+.. automodule:: pygom.utilR
+    :members:
+
+.. autosummary::
+   :toctree: _autosummary
+   :recursive:
\ No newline at end of file

From 5829d4863d64cb7905f13637a3dae9b8147d25ba Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 29 Aug 2023 18:58:29 +0100
Subject: [PATCH 139/188] autodocumentation reformatting and moving

---
 docs/pygom-doc/_config.yml                 | 17 ++---------------
 docs/pygom-doc/_toc.yml                    | 12 ++++++++----
 docs/pygom-doc/{ => rst}/autodoc-abc.rst   |  2 +-
 docs/pygom-doc/{ => rst}/autodoc-loss.rst  |  2 +-
 docs/pygom-doc/{ => rst}/autodoc-model.rst |  2 +-
 docs/pygom-doc/{ => rst}/autodoc-utilR.rst |  2 +-
 6 files changed, 14 insertions(+), 23 deletions(-)
 rename docs/pygom-doc/{ => rst}/autodoc-abc.rst (77%)
 rename docs/pygom-doc/{ => rst}/autodoc-loss.rst (89%)
 rename docs/pygom-doc/{ => rst}/autodoc-model.rst (89%)
 rename docs/pygom-doc/{ => rst}/autodoc-utilR.rst (89%)

diff --git a/docs/pygom-doc/_config.yml b/docs/pygom-doc/_config.yml
index 9b3fdeb8..7bd9b2d6 100644
--- a/docs/pygom-doc/_config.yml
+++ b/docs/pygom-doc/_config.yml
@@ -1,9 +1,8 @@
 # Book settings
-# Learn more at https://jupyterbook.org/customize/config.html
+# see https://jupyterbook.org/customize/config.html
 
 title: PyGOM documentation
 author: Public Health England / UK Health Security Agency
-#TODO change logo
 logo: logo_pygom.jpg
 
 # build files in table of contents
@@ -24,23 +23,16 @@ latex:
 
 # Add a bibtex file so that we can create citations
 # use sphinx to specify style
-
 bibtex_bibfiles: 
   - 'ref.bib'
 
-#sphinx:
-#  extra_extensions:
-#  - 'sphinxcontrib.bibtex'
-#  config:
-#    bibtex_reference_style: super
-
-
 
 sphinx:  
   extra_extensions:
   #- 'sphinx.ext.doctest'
   - 'sphinx.ext.autodoc'    
   - 'sphinx.ext.napoleon'
+#  - 'sphinx.ext.graphvizconfig'
   - 'sphinx.ext.viewcode'
   - 'sphinx.ext.autosummary'
   config:
@@ -48,11 +40,6 @@ sphinx:
     autosummary_generate: True
     bibtex_reference_style: super
 
-#sphinx:
-#  extra_extensions:
-#  - 'sphinx.ext.autosummary'
-#  config:
-#    autosummary_generate: True
 
 #sphinx:
 #  extra_extensions:
diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index f8260f5b..f7a533cf 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -48,10 +48,14 @@ parts:
 #  - caption: Frequently asked questions
 #    chapters: 
 #    - file: md/faq.md
-#  - caption: Code documentation
-#    chapters:
-#    - file: autodoc-model.rst
-#    - file: autodoc-loss.rst
+  - caption: Code documentation
+    chapters:
+      - file: module-documentation.md
+      sections:
+      - file: autodoc-model.rst
+      - file: autodoc-loss.rst
+      - file: autodoc-abc.rst
+      - file: autodoc-utilR.rst
     # add utils
     # add abc
   - caption: References
diff --git a/docs/pygom-doc/autodoc-abc.rst b/docs/pygom-doc/rst/autodoc-abc.rst
similarity index 77%
rename from docs/pygom-doc/autodoc-abc.rst
rename to docs/pygom-doc/rst/autodoc-abc.rst
index 05b5d8ae..1a3a8f3d 100644
--- a/docs/pygom-doc/autodoc-abc.rst
+++ b/docs/pygom-doc/rst/autodoc-abc.rst
@@ -1,4 +1,4 @@
-Module: approximate_bayesian_computation
+approximate_bayesian_computation
 =============
 
 .. automodule:: pygom.approximate_bayesian_computation
diff --git a/docs/pygom-doc/autodoc-loss.rst b/docs/pygom-doc/rst/autodoc-loss.rst
similarity index 89%
rename from docs/pygom-doc/autodoc-loss.rst
rename to docs/pygom-doc/rst/autodoc-loss.rst
index e5a8afc3..ec2b56da 100644
--- a/docs/pygom-doc/autodoc-loss.rst
+++ b/docs/pygom-doc/rst/autodoc-loss.rst
@@ -1,4 +1,4 @@
-Module: loss
+loss
 =============
 
 .. automodule:: pygom.loss
diff --git a/docs/pygom-doc/autodoc-model.rst b/docs/pygom-doc/rst/autodoc-model.rst
similarity index 89%
rename from docs/pygom-doc/autodoc-model.rst
rename to docs/pygom-doc/rst/autodoc-model.rst
index 1426e052..debfb420 100644
--- a/docs/pygom-doc/autodoc-model.rst
+++ b/docs/pygom-doc/rst/autodoc-model.rst
@@ -1,4 +1,4 @@
-Module: model
+model
 =============
 
 .. automodule:: pygom.model
diff --git a/docs/pygom-doc/autodoc-utilR.rst b/docs/pygom-doc/rst/autodoc-utilR.rst
similarity index 89%
rename from docs/pygom-doc/autodoc-utilR.rst
rename to docs/pygom-doc/rst/autodoc-utilR.rst
index 8f242ad7..40a5c130 100644
--- a/docs/pygom-doc/autodoc-utilR.rst
+++ b/docs/pygom-doc/rst/autodoc-utilR.rst
@@ -1,4 +1,4 @@
-Module: utilR
+utilR
 =============
 
 .. automodule:: pygom.utilR

From 77459126da5d5bf1c7954fcee2b644c4e0d3c649 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 29 Aug 2023 19:07:07 +0100
Subject: [PATCH 140/188] remove unused files

---
 docs/pygom-doc/markdown-notebooks.md |  53 ------------
 docs/pygom-doc/notebooks.ipynb       | 122 ---------------------------
 docs/pygom-doc/ref.md                | 113 -------------------------
 docs/pygom-doc/references.bib        |  56 ------------
 4 files changed, 344 deletions(-)
 delete mode 100644 docs/pygom-doc/markdown-notebooks.md
 delete mode 100644 docs/pygom-doc/notebooks.ipynb
 delete mode 100644 docs/pygom-doc/ref.md
 delete mode 100644 docs/pygom-doc/references.bib

diff --git a/docs/pygom-doc/markdown-notebooks.md b/docs/pygom-doc/markdown-notebooks.md
deleted file mode 100644
index a057a320..00000000
--- a/docs/pygom-doc/markdown-notebooks.md
+++ /dev/null
@@ -1,53 +0,0 @@
----
-jupytext:
-  formats: md:myst
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-    jupytext_version: 1.11.5
-kernelspec:
-  display_name: Python 3
-  language: python
-  name: python3
----
-
-# Notebooks with MyST Markdown
-
-Jupyter Book also lets you write text-based notebooks using MyST Markdown.
-See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions.
-This page shows off a notebook written in MyST Markdown.
-
-## An example cell
-
-With MyST Markdown, you can define code cells with a directive like so:
-
-```{code-cell}
-print(2 + 2)
-```
-
-When your book is built, the contents of any `{code-cell}` blocks will be
-executed with your default Jupyter kernel, and their outputs will be displayed
-in-line with the rest of your content.
-
-```{seealso}
-Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html).
-```
-
-## Create a notebook with MyST Markdown
-
-MyST Markdown notebooks are defined by two things:
-
-1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed).
-   See the YAML at the top of this page for example.
-2. The presence of `{code-cell}` directives, which will be executed with your book.
-
-That's all that is needed to get started!
-
-## Quickly add YAML metadata for MyST Notebooks
-
-If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command:
-
-```
-jupyter-book myst init path/to/markdownfile.md
-```
diff --git a/docs/pygom-doc/notebooks.ipynb b/docs/pygom-doc/notebooks.ipynb
deleted file mode 100644
index fdb7176c..00000000
--- a/docs/pygom-doc/notebooks.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Content with notebooks\n",
-    "\n",
-    "You can also create content with Jupyter Notebooks. This means that you can include\n",
-    "code blocks and their outputs in your book.\n",
-    "\n",
-    "## Markdown + notebooks\n",
-    "\n",
-    "As it is markdown, you can embed images, HTML, etc into your posts!\n",
-    "\n",
-    "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n",
-    "\n",
-    "You can also $add_{math}$ and\n",
-    "\n",
-    "$$\n",
-    "math^{blocks}\n",
-    "$$\n",
-    "\n",
-    "or\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mbox{mean} la_{tex} \\\\ \\\\\n",
-    "math blocks\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n",
-    "\n",
-    "## MyST markdown\n",
-    "\n",
-    "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n",
-    "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n",
-    "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n",
-    "\n",
-    "## Code blocks and outputs\n",
-    "\n",
-    "Jupyter Book will also embed your code blocks and output in your book.\n",
-    "For example, here's some sample Matplotlib code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams, cycler\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "plt.ion()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Fixing random state for reproducibility\n",
-    "np.random.seed(19680801)\n",
-    "\n",
-    "N = 10\n",
-    "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n",
-    "data = np.array(data).T\n",
-    "cmap = plt.cm.coolwarm\n",
-    "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n",
-    "\n",
-    "\n",
-    "from matplotlib.lines import Line2D\n",
-    "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n",
-    "                Line2D([0], [0], color=cmap(.5), lw=4),\n",
-    "                Line2D([0], [0], color=cmap(1.), lw=4)]\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 5))\n",
-    "lines = ax.plot(data)\n",
-    "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is a lot more that you can do with outputs (such as including interactive outputs)\n",
-    "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.0"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/pygom-doc/ref.md b/docs/pygom-doc/ref.md
deleted file mode 100644
index ce3d7538..00000000
--- a/docs/pygom-doc/ref.md
+++ /dev/null
@@ -1,113 +0,0 @@
-# References {#ref}
-
-::: {#citations}
-
-[Aron1984]{#Aron1984 .citation-label}
-
-:   Seasonality and period-doubling bifurcations in an epidemic model,
-    Joan L. Aron and Ira B. Schwartz, Journal of Theoretical Biology,
-    Volume 110, Issue 4, page 665-679, 1984
-
-[Brauer2008]{#Brauer2008 .citation-label}
-
-:   Mathematical Epidemiology, Lecture Notes in Mathematics, Fred
-    Brauer, Springer 2008
-
-[Cao2006]{#Cao2006 .citation-label}
-
-:   Efficient step size selection for the tau-leaping simulation method,
-    Yang Cao et el., The Journal of Chemical Physics, Volume 124, Issue
-    4, page 044109, 2006
-
-[Finnie2016]{#Finnie2016 .citation-label}
-
-:   EpiJSON: A unified data-format for epidemiology, Thomas Finnie et
-    al., Epidemics, Volume 15, page 20-26, 2016
-
-[FitzHugh1961]{#FitzHugh1961 .citation-label}
-
-:   Impulses and Physiological States in Theoretical Models of Nerve
-    Membrane, Richard FitzHugh, Biophysical Journal, Volume 1, Issue 6,
-    page 445-466, 1961
-
-[Gillespie1977]{#Gillespie1977 .citation-label}
-
-:   Exact stochastic simulation of coupled chemical reactions, Danial T.
-    Gillespie, The Journal of Physical Chemistry, Volume 81, Issue 25,
-    page 2340-2361, 1977
-
-[Girolami2011]{#Girolami2011 .citation-label}
-
-:   Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Mark
-    Girolami and Ben Calderhead, Journal of the Royal Statistical
-    Society Series B, Volume 73, Issue 2, page 123-214, 2011.
-
-[Hethcote1973]{#Hethcote1973 .citation-label}
-
-:   Asymptotic behavior in a deterministic epidemic model, Herbert W.
-    Hethcote, Bulletin of Mathematical Biology, Volume 35, page 607-614,
-    1973
-
-[Legrand2007]{#Legrand2007 .citation-label}
-
-:   Understanding the dynamics of Ebola epidemics, J. Legrand et al.
-    Epidemiology and Infection, Volume 135, Issue 4, page 610-621, 2007
-
-[Lloyd1996]{#Lloyd1996 .citation-label}
-
-:   Spatial Heterogeneity in Epidemic Models, A.L. Lloyd and R.M. May,
-    Journal of Theoretical Biology, Volume 179, Issue 1, page 1-11, 1996
-
-[Lorenz1963]{#Lorenz1963 .citation-label}
-
-:   Deterministic Nonperiodic Flow, Edward N. Lorenz, Journal of the
-    Atmospheric Sciences, Volume 20, Issue 2, page 130-141, 1963
-
-[Lotka1920]{#Lotka1920 .citation-label}
-
-:   Analytical Note on Certain Rhythmic Relations in Organic Systems,
-    Alfred J. Lotka, Proceedings of the National Academy of Sciences of
-    the United States of America, Volume 7, Issue 7, page 410-415, 1920
-
-[Moolgavkar1987]{#Moolgavkar1987 .citation-label}
-
-:   Confidence Regions for Parameters of the Proportional Hazards Model:
-    A Simulation Study, S.H. Moolgavkar and D.J. Venzon, Scandianvia
-    Journal of Statistics, Volume 14, page 43-56, 1987
-
-[Press2007]{#Press2007 .citation-label}
-
-:   Numerical Recipes 3rd Edition: The Art of Scientific Computing, W.H.
-    Press et al., Cambridge University Press, 2007
-
-[Ramsay2007]{#Ramsay2007 .citation-label}
-
-:   Parameter estimation for differential equations: a generalized
-    smoothing approach, Journal of the Royal Statistical Society Series
-    B, James O. Ramsay et al., Volume 69, Issue 5, page 741-796, 2007
-
-[Raue2009]{#Raue2009 .citation-label}
-
-:   Structural and Practical Identifiability Analysis of Partially
-    Observed Dynamical Models by Exploiting the Profile Likelihood, A.
-    Raue et al., Bioinformatics, Volume 25, Issue 15, page 1923-1929,
-    2009
-
-[Robertson1966]{#Robertson1966 .citation-label}
-
-:   The solution of a set of reaction rate equations, H.H. Robertson,
-    Academic Press, page 178-182, 1966
-
-[Venzon1988]{#Venzon1988 .citation-label}
-
-:   A Method for Computing Profile-Likelihood-Based Confidence
-    Intervals, D.J. Venzon and S.H. Moolgavkar, Journal of the Royal
-    Statistical Society Series C (Applied Statistics), Volume 37, Issue
-    1, page 87-94, 1988
-
-[vanderpol1926]{#vanderpol1926 .citation-label}
-
-:   On Relaxed Oscillations, Balthasar van der Pol, The London,
-    Edinburgh, and Dublin Philosophical Magazine and Journal of Science,
-    Volume 2, Issue 11, page 978-992, 1926
-:::
diff --git a/docs/pygom-doc/references.bib b/docs/pygom-doc/references.bib
deleted file mode 100644
index 783ec6aa..00000000
--- a/docs/pygom-doc/references.bib
+++ /dev/null
@@ -1,56 +0,0 @@
----
----
-
-@inproceedings{holdgraf_evidence_2014,
-	address = {Brisbane, Australia, Australia},
-	title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}},
-	booktitle = {International {Conference} on {Cognitive} {Neuroscience}},
-	publisher = {Frontiers in Neuroscience},
-	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.},
-	year = {2014}
-}
-
-@article{holdgraf_rapid_2016,
-	title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility},
-	volume = {7},
-	issn = {2041-1723},
-	url = {http://www.nature.com/doifinder/10.1038/ncomms13654},
-	doi = {10.1038/ncomms13654},
-	number = {May},
-	journal = {Nature Communications},
-	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.},
-	year = {2016},
-	pages = {13654},
-	file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf}
-}
-
-@inproceedings{holdgraf_portable_2017,
-	title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science},
-	volume = {Part F1287},
-	isbn = {978-1-4503-5272-7},
-	doi = {10.1145/3093338.3093370},
-	abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.},
-	booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}},
-	author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.},
-	year = {2017},
-	keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy}
-}
-
-@article{holdgraf_encoding_2017,
-	title = {Encoding and decoding models in cognitive electrophysiology},
-	volume = {11},
-	issn = {16625137},
-	doi = {10.3389/fnsys.2017.00061},
-	abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.},
-	journal = {Frontiers in Systems Neuroscience},
-	author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.},
-	year = {2017},
-	keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials}
-}
-
-@book{ruby,
-  title     = {The Ruby Programming Language},
-  author    = {Flanagan, David and Matsumoto, Yukihiro},
-  year      = {2008},
-  publisher = {O'Reilly Media}
-}

From f45c4a05ca6676c7db671cdb94d28ca24d46aaee Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 29 Aug 2023 22:22:26 +0100
Subject: [PATCH 141/188] tweaking autodoc generation

---
 docs/pygom-doc/_toc.yml                    |  10 ++++------
 docs/pygom-doc/{ => images}/logo_pygom.jpg | Bin
 docs/pygom-doc/md/module-documentation.md  |  11 +++++++++++
 docs/pygom-doc/rst/autodoc-model.rst       |   2 +-
 4 files changed, 16 insertions(+), 7 deletions(-)
 rename docs/pygom-doc/{ => images}/logo_pygom.jpg (100%)
 create mode 100644 docs/pygom-doc/md/module-documentation.md

diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index f7a533cf..7ad7ceca 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -50,12 +50,10 @@ parts:
 #    - file: md/faq.md
   - caption: Code documentation
     chapters:
-      - file: module-documentation.md
-      sections:
-      - file: autodoc-model.rst
-      - file: autodoc-loss.rst
-      - file: autodoc-abc.rst
-      - file: autodoc-utilR.rst
+    - file: rst/autodoc-model.rst 
+    - file: rst/autodoc-loss.rst
+    - file: rst/autodoc-abc.rst
+    - file: rst/autodoc-utilR.rst
     # add utils
     # add abc
   - caption: References
diff --git a/docs/pygom-doc/logo_pygom.jpg b/docs/pygom-doc/images/logo_pygom.jpg
similarity index 100%
rename from docs/pygom-doc/logo_pygom.jpg
rename to docs/pygom-doc/images/logo_pygom.jpg
diff --git a/docs/pygom-doc/md/module-documentation.md b/docs/pygom-doc/md/module-documentation.md
new file mode 100644
index 00000000..53f24ceb
--- /dev/null
+++ b/docs/pygom-doc/md/module-documentation.md
@@ -0,0 +1,11 @@
+# Modules
+
+Code documentation
+
+{doc}`../rst/autodoc-model.rst`
+
+{doc}`../rst/autodoc-loss.rst`
+
+{doc}`../rst/autodoc-abc.rst`
+
+{doc}`../rst/autodoc-utilR.rst`
\ No newline at end of file
diff --git a/docs/pygom-doc/rst/autodoc-model.rst b/docs/pygom-doc/rst/autodoc-model.rst
index debfb420..ae396cdf 100644
--- a/docs/pygom-doc/rst/autodoc-model.rst
+++ b/docs/pygom-doc/rst/autodoc-model.rst
@@ -1,4 +1,4 @@
-model
+# model
 =============
 
 .. automodule:: pygom.model

From 0a594dd754dcd5b3a00d1ddea6c926080a90e6e1 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Tue, 29 Aug 2023 23:45:35 +0100
Subject: [PATCH 142/188] reformatted common model notebooks

---
 .../common_models/Lotka_Volterra.ipynb        |  20 ++-
 .../common_models/Lotka_Volterra_4State.ipynb |  13 +-
 .../notebooks/common_models/Robertson.ipynb   |  13 +-
 .../notebooks/common_models/SEIR.ipynb        |  39 ++++--
 .../common_models/SEIR_Birth_Death.ipynb      |  46 ++++---
 .../SEIR_Birth_Death_Periodic.ipynb           | 119 ++++++++++++-----
 .../common_models/SEIR_Multiple.ipynb         |  62 +++++----
 .../notebooks/common_models/SIR.ipynb         |  71 +++++++---
 .../common_models/SIR_Birth_Death.ipynb       |  46 ++++---
 .../notebooks/common_models/SIS.ipynb         |  45 ++++---
 .../common_models/SIS_Periodic.ipynb          |  59 ++++++---
 .../notebooks/common_models/vanDelPol.ipynb   | 122 +++++++++++-------
 12 files changed, 444 insertions(+), 211 deletions(-)

diff --git a/docs/pygom-doc/notebooks/common_models/Lotka_Volterra.ipynb b/docs/pygom-doc/notebooks/common_models/Lotka_Volterra.ipynb
index 3b21bdb3..ddf146b7 100644
--- a/docs/pygom-doc/notebooks/common_models/Lotka_Volterra.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/Lotka_Volterra.ipynb
@@ -110,9 +110,11 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "837940dd",
    "metadata": {},
+   "outputs": [],
    "source": [
     "cList = numpy.linspace(0.1, 2.0, 5)\n",
     "\n",
@@ -121,8 +123,7 @@
     "fig = plt.figure()\n",
     "\n",
     "for i in range(len(x1List)):  \n",
-    "    ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3, \n",
-    "    'c':cList[i], 'gamma':gammaList[i]})\n",
+    "    ode = common_models.Lotka_Volterra({'alpha':1, 'delta':3, 'c':cList[i], 'gamma':gammaList[i]})\n",
     "\n",
     "ode.initial_values = (x0, 0) \n",
     "solutionList += [ode.integrate(t)]\n",
@@ -149,8 +150,19 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/pygom-doc/notebooks/common_models/Lotka_Volterra_4State.ipynb b/docs/pygom-doc/notebooks/common_models/Lotka_Volterra_4State.ipynb
index 807863e0..69d16ee7 100644
--- a/docs/pygom-doc/notebooks/common_models/Lotka_Volterra_4State.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/Lotka_Volterra_4State.ipynb
@@ -75,8 +75,19 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/pygom-doc/notebooks/common_models/Robertson.ipynb b/docs/pygom-doc/notebooks/common_models/Robertson.ipynb
index 19865a94..a057631c 100644
--- a/docs/pygom-doc/notebooks/common_models/Robertson.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/Robertson.ipynb
@@ -108,8 +108,19 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/pygom-doc/notebooks/common_models/SEIR.ipynb b/docs/pygom-doc/notebooks/common_models/SEIR.ipynb
index dea8d27b..3875aa0b 100644
--- a/docs/pygom-doc/notebooks/common_models/SEIR.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SEIR.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.SEIR`\n",
+    "# SEIR\n",
+    "{func}`.SEIR`\n",
     "\n",
     "A natural extension to the SIR is the SEIR model. An extra parameter\n",
     "$\\alpha$, which is the inverse of the incubation period is introduced.\n",
@@ -17,30 +18,40 @@
     "\\frac{dI}{dt} &= \\alpha E - \\gamma I \\\\\n",
     "\\end{aligned}$$$$\\frac{dR}{dt} &= \\gamma I$$\n",
     "\n",
-    "We use the parameters from \\[Aron1984\\] here to generate our plots,\n",
+    "We use the parameters from {cite:t}`Aron1984` here to generate our plots,\n",
     "which does not yield a *nice* and *sensible* epidemic curve as the birth\n",
     "and death processes are missing.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd15619d",
+   "metadata": {},
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: ode = common_models.SEIR({'beta':1800, 'gamma':100,\n",
-    "'alpha':35.84})\n",
+    "ode = common_models.SEIR({'beta':1800, 'gamma':100, 'alpha':35.84})\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 50, 1001)\n",
+    "t = numpy.linspace(0, 50, 1001)\n",
     "\n",
-    "In \\[1\\]: x0 = \\[0.0658, 0.0007, 0.0002, 0.0\\]\n",
+    "Ix0 = [0.0658, 0.0007, 0.0002, 0.0]\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "@savefig common_models_seir.png In \\[1\\]: ode.plot()"
+    "ode.plot()"
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/SEIR_Birth_Death.ipynb b/docs/pygom-doc/notebooks/common_models/SEIR_Birth_Death.ipynb
index a886b59d..8009c300 100644
--- a/docs/pygom-doc/notebooks/common_models/SEIR_Birth_Death.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SEIR_Birth_Death.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.SEIR_Birth_Death`\n",
+    "# SEIR - birth, death\n",
+    "{func}`.SEIR_Birth_Death`\n",
     "\n",
     "Extending it to also include birth death process with equal rate $\\mu$\n",
     "\n",
@@ -15,35 +16,44 @@
     "\\frac{dR}{dt} &= \\gamma I\n",
     "\\end{aligned}$$\n",
     "\n",
-    "Same parameters value taken from [\\[Aron1984\\]]() as the SEIR example\n",
+    "Same parameters value taken from {cite}`Aron1984``as the SEIR example\n",
     "above is used here. Observe how the introduction of a birth and a death\n",
     "process changes the graph even though the rest of the parameters remains\n",
-    "the same.\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "the same.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79c1b572",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: import numpy\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: ode = common_models.SEIR_Birth_Death({'beta':1800,\n",
-    "'gamma':100, 'alpha':35.84, 'mu':0.02})\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 50, 1001)\n",
+    "ode = common_models.SEIR_Birth_Death({'beta':1800, 'gamma':100, 'alpha':35.84, 'mu':0.02})\n",
     "\n",
-    "In \\[1\\]: x0 = \\[0.0658, 0.0007, 0.0002, 0.0\\]\n",
+    "t = numpy.linspace(0, 50, 1001)\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "x0 = [0.0658, 0.0007, 0.0002, 0.0]\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\], full_output=True)\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "@savefig common_models_seir_bd.png In \\[1\\]: ode.plot()\n",
+    "solution = ode.integrate(t[1::], full_output=True)\n",
     "\n",
-    "In \\[1\\]: plt.close()"
+    "ode.plot()"
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/SEIR_Birth_Death_Periodic.ipynb b/docs/pygom-doc/notebooks/common_models/SEIR_Birth_Death_Periodic.ipynb
index cd2a67d7..fdd70b67 100644
--- a/docs/pygom-doc/notebooks/common_models/SEIR_Birth_Death_Periodic.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SEIR_Birth_Death_Periodic.ipynb
@@ -4,73 +4,124 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.SEIR_Birth_Death_Periodic`\n",
+    "# SEIR - birth, death and periodic\n",
+    "\n",
+    "{func}`SEIR_Birth_Death_Periodic`\n",
     "\n",
     "Now extending the SEIR to also have periodic contact, as in\n",
-    "[\\[Aron1984\\]]().\n",
+    "{cite}`Aron1984`.\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dS}{dt} &= \\mu - \\beta(t)SI - \\mu S \\\\\n",
     "\\frac{dE}{dt} &= \\beta(t)SI - (\\mu + \\alpha) E \\\\\n",
     "\\frac{dI}{dt} &= \\alpha E - (\\mu + \\gamma) I \\\\\n",
     "\\frac{dR}{dt} &= \\gamma I.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
+    "\\end{aligned}$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc27b5b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: import numpy\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: ode = common_models.SEIR_Birth_Death_Periodic({'beta_0':1800,\n",
-    "'beta_1':0.2, 'gamma':100, 'alpha':35.84, 'mu':0.02})\n",
+    "ode = common_models.SEIR_Birth_Death_Periodic({'beta_0':1800, 'beta_1':0.2, 'gamma':100, 'alpha':35.84, 'mu':0.02})\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 50, 1001)\n",
+    "t = numpy.linspace(0, 50, 1001)\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "In \\[1\\]: x0 = \\[0.0658, 0.0007, 0.0002, 0.0\\]\n",
+    "x0 = [0.0658, 0.0007, 0.0002, 0.0]\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "@savefig common_models_seir_bd_periodic1.png In \\[1\\]: ode.plot()\n",
+    "ode.plot()\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "plt.close()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5614972a",
+   "metadata": {},
+   "source": [
     "\n",
     "The periodicity is obvious when looking at the the plot between states\n",
     "$S$ and $E$, in logarithmic scale.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29c5f471",
+   "metadata": {},
+   "source": [
+    "fig = plt.figure();\n",
     "\n",
-    "In \\[1\\]: fig = plt.figure();\n",
-    "\n",
-    "In \\[1\\]: plt.plot(numpy.log(solution\\[:,0\\]),\n",
-    "numpy.log(solution\\[:,1\\]));\n",
-    "\n",
-    "In \\[1\\]: plt.xlabel('log of S');\n",
+    "plt.plot(numpy.log(solution[:,0]), numpy.log(solution[:,1]));\n",
     "\n",
-    "In \\[1\\]: plt.ylabel('log of E');\n",
+    "plt.xlabel('log of S');\n",
     "\n",
-    "@savefig common_models_seir_bd_periodic2.png In \\[1\\]: plt.show()\n",
+    "plt.ylabel('log of E');\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "plt.show()\n",
     "\n",
-    "Similarly, we can see the same thing between the states $E$ and $I$.\n",
+    "plt.close()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d639850d",
+   "metadata": {},
+   "source": [
     "\n",
-    "In \\[1\\]: fig = plt.figure();\n",
+    "Similarly, we can see the same thing between the states $E$ and $I$.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f259172",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure();\n",
     "\n",
-    "In \\[1\\]: plt.plot(numpy.log(solution\\[:,1\\]),\n",
-    "numpy.log(solution\\[:,2\\]));\n",
+    "plt.plot(numpy.log(solution[:,1]), numpy.log(solution[:,2]));\n",
     "\n",
-    "In \\[1\\]: plt.xlabel('log of E');\n",
+    "plt.xlabel('log of E');\n",
     "\n",
-    "In \\[1\\]: plt.ylabel('log of I');\n",
+    "plt.ylabel('log of I');\n",
     "\n",
-    "@savefig common_models_seir_bd_periodic3.png In \\[1\\]: plt.show()\n",
+    "plt.show()\n",
     "\n",
-    "In \\[1\\]: plt.close()"
+    "plt.close()"
    ]
   }
  ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/SEIR_Multiple.ipynb b/docs/pygom-doc/notebooks/common_models/SEIR_Multiple.ipynb
index e47426be..b9ff28df 100644
--- a/docs/pygom-doc/notebooks/common_models/SEIR_Multiple.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SEIR_Multiple.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.SEIR_Multiple`\n",
+    "# SEIR, multiple\n",
+    "{func}`.SEIR_Multiple`\n",
     "\n",
     "Multiple SEIR coupled together, without any birth death process.\n",
     "\n",
@@ -19,41 +20,58 @@
     "\n",
     "$$\\lambda_{i} = \\sum_{j=1}^{n} \\beta_{i,j} I_{j} (1\\{i\\neq j\\} p)$$\n",
     "\n",
-    "with $n$ being the number of patch and $p$ the coupled factor.\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
+    "with $n$ being the number of patch and $p$ the coupled factor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "650477fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[2\\]: import numpy\n",
+    "import numpy\n",
     "\n",
-    "In \\[3\\]: paramEval = {'beta_00':0.0010107,'beta_01':0.0010107,'beta_10':0.0010107,  \n",
-    "...: 'beta_11':0.0010107,'d':0.02,'epsilon':45.6,'gamma':73.0, ...:\n",
-    "'N_0':10\\**6,'N_1':10*\\*6,'p':0.01}\n",
+    "paramEval = {'beta_00':0.0010107, 'beta_01':0.0010107, 'beta_10':0.0010107, 'beta_11':0.0010107,\n",
+    "             'd':0.02, 'epsilon':45.6, 'gamma':73.0, 'N_0':10**6,'N_1':10**6,'p':0.01}\n",
     "\n",
-    "In \\[4\\]: x0 = \\[36139.3224081278, 422.560577637822, 263.883351688369,\n",
-    "963174.233662546\\]\n",
+    "x0 = [36139.3224081278, 422.560577637822, 263.883351688369, 963174.233662546]\n",
     "\n",
-    "In \\[5\\]: ode = common_models.SEIR_Multiple(param=paramEval)\n",
+    "ode = common_models.SEIR_Multiple(param=paramEval)\n",
     "\n",
-    "In \\[6\\]: t = numpy.linspace(0, 40, 100)\n",
+    "t = numpy.linspace(0, 40, 100)\n",
     "\n",
-    "In \\[7\\]: x01 = \\[\\]\n",
+    "x01 = []\n",
     "\n",
-    "In \\[8\\]: for s in x0:  \n",
-    "...: x01 += 2\\*\\[s\\]\n",
+    "for s in x0:  \n",
+    "    x01 += 2*[s]\n",
     "\n",
-    "In \\[9\\]: ode.initial_values = (numpy.array(x01, float),t\\[0\\])\n",
+    "ode.initial_values = (numpy.array(x01, float),t[0])\n",
     "\n",
-    "In \\[10\\]: solution, output = ode.integrate(t\\[1::\\], full_output=True)\n",
+    "solution, output = ode.integrate(t[1::], full_output=True)\n",
     "\n",
-    "@savefig common_models_seir_multiple.png In \\[11\\]: ode.plot()\n",
+    "ode.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e12bde20",
+   "metadata": {},
+   "source": [
     "\n",
     "The initial conditions are those derived by using the stability\n",
-    "condition as stated in [\\[Lloyd1996\\]]() while the notations is taken\n",
-    "from [\\[Brauer2008\\]]()."
+    "condition as stated in {cite:t}`Lloyd1996` while the notations is taken\n",
+    "from {cite:t}`Brauer2008`."
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/SIR.ipynb b/docs/pygom-doc/notebooks/common_models/SIR.ipynb
index fd293399..ca73f476 100644
--- a/docs/pygom-doc/notebooks/common_models/SIR.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SIR.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# `.SIR`\n",
     "\n",
-    "A standard SIR model defined by the equations\n",
+    "A standard SIR model defined by the following equations.\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dS}{dt} &= -\\beta SI \\\\\n",
@@ -14,42 +14,71 @@
     "\\frac{dR}{dt} &= \\gamma I\n",
     "\\end{aligned}$$\n",
     "\n",
-    "Note that the examples and parameters are taken from [\\[Brauer2008\\]](),\n",
-    "namely Figure 1.4. Hence, the first one below may not appear to make\n",
+    "Note that the examples and parameters are taken from {cite:t}`Brauer2008`,\n",
+    "namely Figure 1.4. Hence, the first example  below may not appear to make\n",
     "much sense.\n",
     "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
+    "#TODO don't understand\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23fa5cee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: ode = common_models.SIR({'beta':3.6, 'gamma':0.2})\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 730, 1001)\n",
+    "ode = common_models.SIR({'beta':3.6, 'gamma':0.2})\n",
     "\n",
-    "In \\[1\\]: N = 7781984.0\n",
+    "t = numpy.linspace(0, 730, 1001)\n",
     "\n",
-    "In \\[1\\]: x0 = \\[1.0, 10.0/N, 0.0\\]\n",
+    "N = 7781984.0\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "x0 = [1.0, 10.0/N, 0.0]\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "@savefig common_models_sir.png In \\[1\\]: ode.plot()\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "Now we have the more sensible plot, where the initial susceptibles is\n",
-    "only a fraction of 1.\n",
+    "ode.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b5252ff",
+   "metadata": {},
+   "source": [
     "\n",
-    "In \\[1\\]: x0 = \\[0.065, 123\\*(5.0/30.0)/N, 0.0\\]\n",
+    "Now we have the more sensible plot, where the initial susceptible population is\n",
+    "only a fraction of 1.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd78eb6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x0 = [0.065, 123*(5.0/30.0)/N, 0.0]\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "@savefig common_models_sir_realistic.png In \\[1\\]: ode.plot()"
+    "ode.plot()"
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/SIR_Birth_Death.ipynb b/docs/pygom-doc/notebooks/common_models/SIR_Birth_Death.ipynb
index f58c7552..e93b3599 100644
--- a/docs/pygom-doc/notebooks/common_models/SIR_Birth_Death.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SIR_Birth_Death.ipynb
@@ -4,9 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.SIR_Birth_Death`\n",
+    "# SIR, birth and death \n",
+    "{func}`.SIR_Birth_Death`\n",
     "\n",
-    "Next, we look at an SIR model with birth death\n",
+    "Next, we look at an SIR model with birth and death processes, where populations are added (birth) or removed (death).\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dS}{dt} &= B -\\beta SI - \\mu S \\\\\n",
@@ -15,32 +16,43 @@
     "\\end{aligned}$$\n",
     "\n",
     "Continuing from the example above, but now with a much longer time\n",
-    "frame. Note that the birth and death rate are the same.\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
+    "frame. Note that the birth and death rate are the same to maintain a constant population.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e5a8099",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: import numpy\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: B = 126372.0/365.0\n",
+    "B = 126372.0/365.0\n",
     "\n",
-    "In \\[1\\]: N = 7781984.0\n",
+    "N = 7781984.0\n",
     "\n",
-    "In \\[1\\]: ode = common_models.SIR_Birth_Death({'beta':3.6, 'gamma':0.2,\n",
-    "'B':B/N, 'mu':B/N})\n",
+    "ode = common_models.SIR_Birth_Death({'beta':3.6, 'gamma':0.2, 'B':B/N, 'mu':B/N})\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 35\\*365, 10001)\n",
+    "t = numpy.linspace(0, 35*365, 10001)\n",
     "\n",
-    "In \\[1\\]: x0 = \\[0.065, 123.0\\*(5.0/30.0)/N, 0.0\\]\n",
+    "x0 = [0.065, 123.0*(5.0/30.0)/N, 0.0]\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "@savefig common_models_sir_bd.png In \\[1\\]: ode.plot()"
+    "ode.plot()"
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/SIS.ipynb b/docs/pygom-doc/notebooks/common_models/SIS.ipynb
index ba80a5c7..cb09e523 100644
--- a/docs/pygom-doc/notebooks/common_models/SIS.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SIS.ipynb
@@ -4,10 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.SIS`\n",
+    "# SIS\n",
+    "{func}`.SIS`\n",
     "\n",
     "A standard SIS model without the total population $N$. We assume here\n",
-    "that $S + I = N$ so we can always normalize to 1. Evidently, the state\n",
+    "that $S + I = N$ so we can always normalize to 1. The state\n",
     "$S$ is not required for understanding the model because it is a\n",
     "deterministic function of state $I$.\n",
     "\n",
@@ -16,31 +17,41 @@
     "\\frac{dI}{dt} &= \\beta S I - \\gamma I.\n",
     "\\end{aligned}$$\n",
     "\n",
-    "An example would be\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "An example of an implementation is given below.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "438844b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: import numpy\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: ode = common_models.SIS({'beta':0.5,'gamma':0.2})\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 20, 101)\n",
+    "ode = common_models.SIS({'beta':0.5,'gamma':0.2})\n",
     "\n",
-    "In \\[1\\]: x0 = \\[1.0, 0.1\\]\n",
+    "t = numpy.linspace(0, 20, 101)\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "x0 = [1.0, 0.1]\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "@savefig common_models_sis.png In \\[1\\]: ode.plot()\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "In \\[1\\]: plt.close()"
+    "ode.plot()\n"
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/SIS_Periodic.ipynb b/docs/pygom-doc/notebooks/common_models/SIS_Periodic.ipynb
index 4c2d7066..c19fe77d 100644
--- a/docs/pygom-doc/notebooks/common_models/SIS_Periodic.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SIS_Periodic.ipynb
@@ -4,10 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.SIS_Periodic`\n",
+    "# SIS, periodic\n",
+    "{func}`.SIS_Periodic`\n",
     "\n",
-    "Now we look at an extension of the SIS model by incorporating periodic\n",
-    "contact rate. Note how our equation is defined by a single ode for state\n",
+    "Now we look at an extension of the SIS model by incorporating a periodic\n",
+    "contact rate. Note how our equation is defined by a single ODE for state\n",
     "**I**.\n",
     "\n",
     "$$\\frac{dI}{dt} = (\\beta(t)N - \\alpha) I - \\beta(t)I^{2}$$\n",
@@ -15,31 +16,53 @@
     "where $\\beta(t) = 2 - 1.8 \\cos(5t)$. As the name suggests, it achieves a\n",
     "(stable) periodic solution. Note how the plots have two sub-graphs,\n",
     "where $\\tau$ is in fact our time component which we have taken out of\n",
-    "the original equation when converting it to a automonous system.\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
+    "the original equation when converting it to a autonomous system.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7321259",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: import numpy\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: ode = common_models.SIS_Periodic({'alpha':1.0})\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 10, 101)\n",
+    "ode = common_models.SIS_Periodic({'alpha':1.0})\n",
     "\n",
-    "In \\[1\\]: x0 = \\[0.1,0.\\]\n",
+    "t = numpy.linspace(0, 10, 101)\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (x0, t\\[0\\])\n",
+    "x0 = [0.1,0.]\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "ode.initial_values = (x0, t[0])\n",
     "\n",
-    "@savefig common_models_sis_periodic.png In \\[1\\]: ode.plot()\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "In \\[1\\]: plt.close()"
+    "ode.plot()"
    ]
   }
  ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }
diff --git a/docs/pygom-doc/notebooks/common_models/vanDelPol.ipynb b/docs/pygom-doc/notebooks/common_models/vanDelPol.ipynb
index eceaacda..7e26788d 100644
--- a/docs/pygom-doc/notebooks/common_models/vanDelPol.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/vanDelPol.ipynb
@@ -4,9 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.vanDelPol`\n",
-    "\n",
-    "The van Del Pol oscillator [\\[vanderpol1926\\]]()\n",
+    "# vanDelPol\n",
+    "{func}`.vanDelPol` - the van Del Pol oscillator {cite}`vanderpol1926`\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dx}{dt} &= \\sigma (y-x) \\\\\n",
@@ -14,70 +13,105 @@
     "\\frac{dz}{dt} &= xy - \\beta z\n",
     "\\end{aligned}$$\n",
     "\n",
-    "A classic example is\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
-    "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 20, 1000)\n",
-    "\n",
-    "In \\[1\\]: ode = common_models.vanDelPol({'mu':1.0})\n",
-    "\n",
-    "In \\[1\\]: ode.initial_values = (\\[2.0, 0.0\\], t\\[0\\])\n",
-    "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
-    "\n",
-    "@savefig common_models_vanDelPol.png In \\[1\\]: ode.plot()\n",
+    "A classic example is given.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca59c8d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import common_models\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "import numpy\n",
     "\n",
-    "In \\[1\\]: f = plt.figure()\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In \\[1\\]: plt.plot(solution\\[:,0\\], solution\\[:,1\\]);\n",
+    "t = numpy.linspace(0, 20, 1000)\n",
     "\n",
-    "@savefig common_models_vanDelPol_yprime_y\\_1.png In \\[1\\]: plt.show()\n",
+    "ode = common_models.vanDelPol({'mu':1.0})\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "ode.initial_values = ([2.0, 0.0], t[0])\n",
     "\n",
-    "When we change the value, as per\n",
-    "[Wolfram](http://mathworld.wolfram.com/vanderPolEquation.html)\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "In \\[1\\]: t = numpy.linspace(0, 100, 1000)\n",
+    "ode.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3e0bb15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = plt.figure()\n",
     "\n",
-    "In \\[1\\]: ode.parameters = {'mu':1.0}\n",
+    "plt.plot(solution[:,0], solution[:,1]);\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (\\[0.0, 0.2\\], t\\[0\\])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "060b0043",
+   "metadata": {},
+   "source": [
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "And another example using alternative parameters  from\n",
+    "[Wolfram](http://mathworld.wolfram.com/vanderPolEquation.html).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44bc2007",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = numpy.linspace(0, 100, 1000)\n",
     "\n",
-    "In \\[1\\]: f = plt.figure()\n",
+    "ode.parameters = {'mu':1.0}\n",
     "\n",
-    "In \\[1\\]: plt.plot(solution\\[:,0\\],solution\\[:,1\\]);\n",
+    "ode.initial_values = ([0.0, 0.2], t[0])\n",
     "\n",
-    "@savefig common_models_vanDelPol_yprime_y\\_2.png In \\[1\\]: plt.show()\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "In \\[1\\]: plt.close()\n",
+    "f = plt.figure()\n",
     "\n",
-    "In \\[1\\]: ode.parameters = {'mu':0.2}\n",
+    "plt.plot(solution[:,0],solution[:,1]);\n",
     "\n",
-    "In \\[1\\]: ode.initial_values = (\\[0.0, 0.2\\], t\\[0\\])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99020274",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ode.parameters = {'mu':0.2}\n",
     "\n",
-    "In \\[1\\]: solution = ode.integrate(t\\[1::\\])\n",
+    "ode.initial_values = ([0.0, 0.2], t[0])\n",
     "\n",
-    "In \\[1\\]: f = plt.figure()\n",
+    "solution = ode.integrate(t[1::])\n",
     "\n",
-    "In \\[1\\]: plt.plot(solution\\[:,0\\], solution\\[:,1\\]);\n",
+    "f = plt.figure()\n",
     "\n",
-    "@savefig common_models_vanDelPol_yprime_y\\_3.png In \\[1\\]: plt.show()\n",
+    "plt.plot(solution[:,0], solution[:,1]);\n",
     "\n",
-    "In \\[1\\]: plt.close()"
+    "plt.show()"
    ]
   }
  ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
+ "nbformat_minor": 5
 }

From 424aa7e27097442ba75007d83c74f7fdf10b25cc Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 09:39:53 +0100
Subject: [PATCH 143/188] update common models header page

---
 docs/pygom-doc/md/common_models.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pygom-doc/md/common_models.md b/docs/pygom-doc/md/common_models.md
index 77048ef2..08965ee7 100644
--- a/docs/pygom-doc/md/common_models.md
+++ b/docs/pygom-doc/md/common_models.md
@@ -1,4 +1,4 @@
-# Pre-defined Example common_models {#common_models}
+# Pre-defined examples - common epi models 
 
 We have defined a set of models {mod}`common_models`, most of them commonly used in epidemiology. They are there
 as examples and also to save time for users. Most of them are of the

From 1237af527507286f16cdb1247ddfb0c288d9f51f Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 11:12:01 +0100
Subject: [PATCH 144/188] remove whitelines

---
 docs/pygom-doc/ref.bib | 49 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)

diff --git a/docs/pygom-doc/ref.bib b/docs/pygom-doc/ref.bib
index 89833a8e..b7509dd4 100644
--- a/docs/pygom-doc/ref.bib
+++ b/docs/pygom-doc/ref.bib
@@ -214,3 +214,52 @@ @article{Venzon1988
    volume = {37},
    year = {1988},
 }
+@inproceedings{holdgraf_evidence_2014,
+	address = {Brisbane, Australia, Australia},
+	title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}},
+	booktitle = {International {Conference} on {Cognitive} {Neuroscience}},
+	publisher = {Frontiers in Neuroscience},
+	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.},
+	year = {2014}
+}
+@article{holdgraf_rapid_2016,
+	title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility},
+	volume = {7},
+	issn = {2041-1723},
+	url = {http://www.nature.com/doifinder/10.1038/ncomms13654},
+	doi = {10.1038/ncomms13654},
+	number = {May},
+	journal = {Nature Communications},
+	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.},
+	year = {2016},
+	pages = {13654},
+	file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf}
+}
+@inproceedings{holdgraf_portable_2017,
+	title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science},
+	volume = {Part F1287},
+	isbn = {978-1-4503-5272-7},
+	doi = {10.1145/3093338.3093370},
+	abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.},
+	booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}},
+	author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.},
+	year = {2017},
+	keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy}
+}
+@article{holdgraf_encoding_2017,
+	title = {Encoding and decoding models in cognitive electrophysiology},
+	volume = {11},
+	issn = {16625137},
+	doi = {10.3389/fnsys.2017.00061},
+	abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.},
+	journal = {Frontiers in Systems Neuroscience},
+	author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.},
+	year = {2017},
+	keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials}
+}
+@book{ruby,
+  title     = {The Ruby Programming Language},
+  author    = {Flanagan, David and Matsumoto, Yukihiro},
+  year      = {2008},
+  publisher = {O'Reilly Media}
+}
\ No newline at end of file

From 830579c1a555d00f1a0cf37c4013cdcc0aa6dee0 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 13:05:05 +0100
Subject: [PATCH 145/188] reformatted parameter fitting notebooks

---
 .../notebooks/paramfit/gradient.ipynb         | 549 +++++++++++++++
 .../notebooks/paramfit/initialGuess.ipynb     | 110 +++
 .../notebooks/paramfit/profile.ipynb          | 644 ++++++++++++++++++
 3 files changed, 1303 insertions(+)
 create mode 100644 docs/pygom-doc/notebooks/paramfit/gradient.ipynb
 create mode 100644 docs/pygom-doc/notebooks/paramfit/initialGuess.ipynb
 create mode 100644 docs/pygom-doc/notebooks/paramfit/profile.ipynb

diff --git a/docs/pygom-doc/notebooks/paramfit/gradient.ipynb b/docs/pygom-doc/notebooks/paramfit/gradient.ipynb
new file mode 100644
index 00000000..42345ef4
--- /dev/null
+++ b/docs/pygom-doc/notebooks/paramfit/gradient.ipynb
@@ -0,0 +1,549 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gradient estimation under square loss\n",
+    "\n",
+    "Assuming that we have a set of $N$ observations $y_{i}$ at specific time\n",
+    "points $t_{i}$, $i = 1,\\ldots,N$, we may wish to test out a set of ode\n",
+    "to see whether it fits to the data. The most natural way to test such\n",
+    "*fit* is to minimize the sum of squares between our observations $y$ and\n",
+    "see whether the resulting solution of the ode and the estimationed\n",
+    "parameters makes sense.\n",
+    "\n",
+    "We assume that this estimation process will be tackled through a\n",
+    "non-linear optimization point of view. However, it should be noted that\n",
+    "such estimates can also be performed via MCMC or from a global\n",
+    "optimization perspective. A key element in non-linear optimization is\n",
+    "the gradient, which is the focus of this page.\n",
+    "\n",
+    "Multiple ways of obtaining the gradient have been implemented. All of\n",
+    "them serve a certain purpose and may not be a viable/appropriate options\n",
+    "depending on the type of ode. More generally, let $d,p$ be the number of\n",
+    "states and paramters respectively. Then finite difference methods have a\n",
+    "run order of $O(p+1)$ of the original ode, forward sensitivity require\n",
+    "an integration of an ode of size $(d+1)p$ rather than $d$. The adjoint\n",
+    "method require two run of size $d$ in principle, but actual run time is\n",
+    "dependent on the number of observations.\n",
+    "\n",
+    "For the details of the classes and methods, please refer to {func}`mod`.\n",
+    "\n",
+    "## Notation\n",
+    "\n",
+    "We introduce the notations that will be used in the rest of the page,\n",
+    "some of which may be slightly unconventional but necessary due to the\n",
+    "complexity of the problem. Let $x \\in \\mathbb{R}^{d}$ and\n",
+    "$\\theta \\in \\mathbb{R}^{p}$ be the states and parameters respectively.\n",
+    "The term *state* or *simulation* are used interchangeably, even though\n",
+    "strictly speaking a state is $x$ whereas $x(t)$ is the simulation. An\n",
+    "ode is defined as\n",
+    "\n",
+    "$$f(x,\\theta) = \\dot{x} = \\frac{\\partial x}{\\partial t}$$\n",
+    "\n",
+    "and usually comes with a set of initial conditions $(x_0,t_0)$ where\n",
+    "$t_0 \\le t_{i} \\forall i$. Let $g(x,\\theta)$ be a function that maps the\n",
+    "set of states to the observations,\n",
+    "$g : \\mathbb{R}^{d} \\rightarrow \\mathbb{R}^{m}$. For compartmental\n",
+    "problems, which is our focus, $\\nabla_{\\theta}g(x,\\theta)$ is usually\n",
+    "zero and $\\nabla_{x}g(x,\\theta)$ is an identity function for some or all\n",
+    "of the states $x$. Denote $l(x_{0},\\theta,x)$ as our cost function\n",
+    "$l : \\mathbb{R}^{m} \\rightarrow \\mathbb{R}$ and $L(x_{0},\\theta,x)$ be\n",
+    "the sum of $l(\\cdot)$. Both $x$ and $x_{0}$ are usually dropped for\n",
+    "simplicity. We will be dealing exclusively with square loss here, which\n",
+    "means that\n",
+    "\n",
+    "$$L(\\theta) = \\sum_{i=1}^{N} \\left\\| y_{i} - g(x(t_{i})) \\right\\|^{2} = \\mathbf{e}^{\\top} \\mathbf{e}$$\n",
+    "\n",
+    "where $\\mathbf{e}$ is the residual vector, with elements\n",
+    "\n",
+    "$$e_{i} = y_{i} - x(t_{i}).$$\n",
+    "\n",
+    "## Model setup\n",
+    "\n",
+    "Again, we demonstrate the functionalities of our classes using an SIR\n",
+    "model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48daf3cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import SquareLoss, common_models\n",
+    "\n",
+    "import copy, time, numpy\n",
+    "\n",
+    "Iode = common_models.SIR()\n",
+    "\n",
+    "paramEval = [('beta',0.5), ('gamma',1.0/3.0) ]\n",
+    "\n",
+    "# the initial state, normalized to zero one\n",
+    "\n",
+    "x0 = [1., 1.27e-6, 0.]\n",
+    "\n",
+    "# initial time\n",
+    "\n",
+    "t0 = 0\n",
+    "\n",
+    "ode.parameters = paramEval\n",
+    "\n",
+    "ode.initial_values = (x0, t0)\n",
+    "\n",
+    "# set the time sequence that we would like to observe\n",
+    "\n",
+    "t = numpy.linspace(1, 150, 100)\n",
+    "\n",
+    "numStep = len(t)\n",
+    "\n",
+    "solution = ode.integrate(t)\n",
+    "\n",
+    "y = solution[1::,2].copy()\n",
+    "\n",
+    "y += numpy.random.normal(0, 0.1, y.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b98ce5a",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Now we have set up the model along with some observations, obtaining the\n",
+    "gradient only requires the end user to put the appropriate information\n",
+    "it into the {class}`SquareLoss`. Given the initial guess $\\theta$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "159bb572",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta = [0.2, 0.2]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a14885bb",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We initialize the {class}`SquareLoss` as\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b9d85b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objSIR = SquareLoss(theta, ode, x0, t0, t, y, 'R')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96b21f6a",
+   "metadata": {},
+   "source": [
+    "\n",
+    "where the we also have to specify the state our observations are from.\n",
+    "Now, we demonstrate the different methods in obtaining the gradient and\n",
+    "mathematics behind it.\n",
+    "\n",
+    "## Forward sensitivity\n",
+    "\n",
+    "The forward sensitivity equations are derived by differentiating the\n",
+    "states implicitly, which yields\n",
+    "\n",
+    "$$\\frac{d\\dot{x}}{d\\theta} = \\frac{\\partial f}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial f}{\\partial \\theta}.$$\n",
+    "\n",
+    "So finding the sensitivies $\\frac{dx}{d\\theta}$ require another\n",
+    "integration of a $p$ coupled ODE of $d$ dimension, each with the same\n",
+    "Jacobian as the original ode. This integration is performed along with\n",
+    "the original ODE because of possible non-linearity.\n",
+    "\n",
+    "A direct call to the method {meth}`sensitivity`\n",
+    "computes the gradient\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86c1113e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gradSens = objSIR.sensitivity()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e25fc403",
+   "metadata": {},
+   "source": [
+    "\n",
+    "whereas {meth}`jac` will allow the user to obtain the Jacobian (of the\n",
+    "objective function) and the residuals, the information required to get\n",
+    "the gradient as we see next.\n",
+    "\n",
+    "#TODO additional reading for Jacobian\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85611719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objJac, output = objSIR.jac(full_output=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ff3d25b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Gradient\n",
+    "\n",
+    "Just the sensitivities alone are not enough to obtain the gradient, but\n",
+    "we are $90\\%$ there. Differentiating the loss function\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{dL}{d\\theta} &= \\nabla_{\\theta} \\sum_{i=1}^{N}\\frac{dl}{dg} \\\\\n",
+    "                   &= \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial l}{\\partial \\theta} \\\\\n",
+    "                   &= \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial \\theta}\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "via chain rule. When $\\frac{\\partial g}{\\partial \\theta} = 0$, the total\n",
+    "gradient simplifies to\n",
+    "\n",
+    "$$\\frac{dL}{d\\theta} = \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial x}\\frac{dx}{d\\theta}$$\n",
+    "\n",
+    "The time indicies are dropped but all the terms above\n",
+    "are evaluated only at the observed time points. More concretely, this\n",
+    "means that\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{\\partial l(x(j),\\theta)}{\\partial g} = \\left\\{ \\begin{array}{ll} -2(y_{i} - x(j)) & , \\; j = t_{i} \\\\ 0 & \\; \\text{otherwise} \\end{array} \\right.\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "When $g(\\cdot)$ is an identity function (which is assumed to be the case\n",
+    "in {class}`SquareLoss`)\n",
+    "\n",
+    "$$\\frac{\\partial g(x(t_{i}),\\theta)}{\\partial x} = I_{d}$$\n",
+    "\n",
+    "then the gradient simplifies even further as it is simply\n",
+    "\n",
+    "$$\\frac{dL}{d\\theta} = -2\\mathbf{e}^{\\top}\\mathbf{S}$$\n",
+    "\n",
+    "where $\\mathbf{e}$ is the vector of residuals and\n",
+    "$\\mathbf{S} = \\left[\\mathbf{s}_{1},\\mathbf{s}_{2},\\ldots,\\mathbf{s}_{n}\\right]$\n",
+    "with elements\n",
+    "\n",
+    "$$\\mathbf{s}_{i} = \\frac{dx}{d\\theta}(t_{i}),$$\n",
+    "\n",
+    "the solution of the forward sensitivies at time $t_{i}$, obtained from\n",
+    "solving the coupled ode as mentioned previously.\n",
+    "\n",
+    "## Jacobian\n",
+    "\n",
+    "Now note how the gradient simplifies to $-2\\mathbf{e}^{\\top}\\mathbf{S}$.\n",
+    "Recall that a standard result in non-linear programming states that the\n",
+    "gradient of a sum of sqaures objective function $L(\\theta,y,x)$ is\n",
+    "\n",
+    "$$\\nabla_{\\theta} L(\\theta,y,x) = -2(\\mathbf{J}^{T} \\left[\\mathbf{y} - \\mathbf{f}(x,\\boldsymbol{\\theta}) \\right] )^{\\top}$$\n",
+    "\n",
+    "with $f(x,\\theta)$ our non-linear function and $J$ our Jacobian with\n",
+    "elements\n",
+    "\n",
+    "$$J_{i} = \\frac{\\partial f(x_{i},\\boldsymbol{\\theta})}{\\partial \\boldsymbol{\\theta}}.$$\n",
+    "\n",
+    "This is exactly what we have seen previously, substituting in reveals\n",
+    "that $J = \\mathbf{S}$. Hence, the Jacobian is (a necessary) by-product\n",
+    "when we wish to obtain the gradient. This is how we\n",
+    "proceed in {meth}`sensitivity` where it makes\n",
+    "an internal call to {func}`jac` to obtain the Jacobian\n",
+    "first. This allows the user to have more options when choosing which\n",
+    "type of algorithms to use, i.e. Gauss-Newton or Levenberg-Marquardt.\n",
+    "\n",
+    "#TODO ref for algorithms\n",
+    "\n",
+    "To check that the output is in fact the same we can calculate the difference.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a683b673",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objJac.transpose().dot(-2\\*output\\['resid'\\]) - gradSens\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37355764",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Adjoint\n",
+    "\n",
+    "When the number of parameters increases, the number of sensitivies also\n",
+    "increases. The time required scales directly with the number of\n",
+    "parameters. We describe another method which does not depend on the\n",
+    "number of parameters, but rather, the number of states and observations.\n",
+    "\n",
+    "The full derivations will not be shown here, \n",
+    "\n",
+    "#TODO reference\n",
+    "\n",
+    "but we aim to provide\n",
+    "enough information to work out the steps performed in the our code. We can\n",
+    "write our optimization problem as\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "min_{\\theta} \\quad & \\int_{t_{0}}^{T} l(x_{0},\\theta,x(t)) dt \\\\\n",
+    "s.t. \\quad & \\dot{x} = f(x,\\theta)\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "which is identical to the original problem but in a continuous setting.\n",
+    "Now write the constrained problem in the Lagrangian form\n",
+    "\n",
+    "$$min_{\\theta} \\; L(\\theta) + \\int_{t_{0}}^{T} \\lambda^{\\top}(\\dot{x} - f(x,\\theta))$$\n",
+    "\n",
+    "with Lagrangian multiplier $\\lambda \\ge 0$. After some algebraic\n",
+    "manipulation, it can be shown that the total derivative of the\n",
+    "Lagrangian function is\n",
+    "\n",
+    "$$\\frac{dL}{d\\theta} = \\int_{t_{0}}^{T} \\left(\\frac{\\partial l}{\\partial \\theta} - \\lambda^{\\top}\\frac{\\partial f}{\\partial \\theta} \\right) dt.$$\n",
+    "\n",
+    "Using previously defined loss functions (the identity), the first term\n",
+    "is zero and evaluating $\\frac{\\partial f}{\\partial \\theta}$ is trivial.\n",
+    "What remains is the calculation of $\\lambda(t)$ for\n",
+    "$t \\in \\left[t_{0},T\\right]$.\n",
+    "\n",
+    "Although this still seem to be ill-posed problem when Looking at the\n",
+    "Lagrangian function, one can actually obtain the *adjoint equation*,\n",
+    "after certain assumptions and\n",
+    "\n",
+    "$$\\frac{d\\lambda^{\\top}}{dt} = \\frac{\\partial l}{\\partial x} - \\lambda^{\\top}\\frac{\\partial f}{\\partial \\theta}.$$\n",
+    "\n",
+    "which is again an integration. An unfortunate situation arises here for\n",
+    "non-linear systems because we use the minus Jacobian in the adjoint\n",
+    "equation. So if the eigenvalues of the Jacobian indicate that our\n",
+    "original ODE is stable, such as -1, the minus eigenvalues (now 1)\n",
+    "implies that the adjoint equation is not stable. Therefore, one must\n",
+    "integrate backward in time to solve the adjoint equation and it cannot\n",
+    "be solved simultaneously as the ODE, unlike the forward sensitivity\n",
+    "equations.\n",
+    "\n",
+    "Given a non-linear ODE, we must store information about the states\n",
+    "between $t_{0}$ and $T$ in order to perform the integration. There are\n",
+    "two options, both require storing many evaluated $x(j)$ within the\n",
+    "interval $\\left[t_{0},T\\right]$. Unfortunately, only one is available;\n",
+    "interpolation over all states and integrate using the interpolating\n",
+    "functions. The alternative of using observed $x(j)'s$ at fixed points is\n",
+    "not competitive because we are unable to use fortran routines for the\n",
+    "integration\n",
+    "\n",
+    "The method of choice here to perform the adjoint calculation is to run a\n",
+    "forward integration, then perform an interpolation using splines with\n",
+    "explicit knots at the observed time points.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4dcdc898",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "odeSIRAdjoint, outputAdjoint = objSIR.adjoint(full_output=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df28857c",
+   "metadata": {},
+   "source": [
+    "\n",
+    "This is because evaluating the Jacobian may be expensive and Runge-kutta\n",
+    "method suffers as the complexity increases. In non-linear model such as\n",
+    "those found in epidemiology, each element of the Jacobian may be the\n",
+    "result of a complicated equation where linear step method will shine as\n",
+    "it makes as little function evaluation as possible. Note that\n",
+    "derivations in the literature, the initial condition when evaluating the\n",
+    "adjoint equation is $\\lambda(T)=0$. But in our code we used\n",
+    "$\\lambda(T) = -2(y(T)-x(T))$. Recall that we have observation $y(T)$ and\n",
+    "simulation $x(T)$, so that the adjoint equation evaluated at time $T$\n",
+    "\n",
+    "$$\\frac{\\partial \\lambda^{\\top}}{\\partial t} \\Big|_{T} = -2(y-f(x,\\theta))\\Big|_{T}  - \\lambda(T)\\frac{\\partial f}{\\partial \\theta}\\Big|_{T}$$\n",
+    "\n",
+    "with the second term equal to zero. Integration under step size $h$\n",
+    "implies that\n",
+    "$\\lambda(T) \\approx \\lim_{h \\to 0} \\lambda(T-h) = -2(y(T)-x(T))$.\n",
+    "\n",
+    "## Time Comparison\n",
+    "\n",
+    "A simple time comparison between the different methods reveals that the\n",
+    "forward sensitivity method dominates the others by a wide margin. It\n",
+    "will be tempting to conclude that it is the best and should be the\n",
+    "default at all times but that is not true, due to the complexity of each\n",
+    "method mentioned previously. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04636d5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%timeit gradSens = objSIR.sensitivity()\n",
+    "\n",
+    "%timeit odeSIRAdjoint,outputAdjoint = objSIR.adjoint(full_output=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15c000f6",
+   "metadata": {},
+   "source": [
+    "```{note} \n",
+    "We leave it to the user to find out the best method for their specific problem.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47256d1b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "## Hessian\n",
+    "\n",
+    "The Hessian is defined by\n",
+    "\n",
+    "$$\\frac{\\partial^{2} l}{\\partial \\theta^{2}} = \\left( \\frac{\\partial l}{\\partial x} \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + \\frac{\\partial x}{\\partial \\theta}^{\\top}\\frac{\\partial^{2} l}{\\partial x^{2}}\\frac{\\partial x}{\\partial \\theta}$$\n",
+    "\n",
+    "where $\\otimes$ is the Kronecker product. Note that $\\nabla_{\\theta} x$\n",
+    "is the sensitivity and the second order sensitivities can be found again\n",
+    "via the forward method, which involve another set of ode's, namely the\n",
+    "forward-forward sensitivities\n",
+    "\n",
+    "$$\\frac{\\partial}{\\partial t}\\left(\\frac{\\partial^{2} x}{\\partial \\theta^{2}}\\right) = \\left( \\frac{\\partial f}{\\partial x} \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + \\left( I_{d} \\otimes \\frac{\\partial x}{\\partial \\theta}^{\\top} \\right) \\frac{\\partial^{2} f}{\\partial x^{2}} \\frac{\\partial x}{\\partial \\theta}.$$\n",
+    "\n",
+    "From before, we know that\n",
+    "\n",
+    "$$\\frac{\\partial l}{\\partial x} = (-2y+2x)  \\quad and \\quad \\frac{\\partial^{2} l}{\\partial x^{2}} = 2I_{d}$$\n",
+    "\n",
+    "so our Hessian reduces to\n",
+    "\n",
+    "$$\\frac{\\partial^{2} l}{\\partial \\theta^{2}} = \\left( \\left(-2y+2x\\right) \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + 2S^{\\top}S,$$\n",
+    "\n",
+    "where the second term is a good approximation to the Hessian as\n",
+    "mentioned previously. This is the only implementation in place so far\n",
+    "even though obtaining the estimate this way is relatively slow.\n",
+    "\n",
+    "Just to demonstrate how it works, lets look at the Hessian at the optimal\n",
+    "point. First, we obtain the optimal value\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "678be374",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.linalg,scipy.optimize\n",
+    "\n",
+    "boxBounds = [(0.0, 2.0), (0.0, 2.0)]\n",
+    "\n",
+    "res = scipy.optimize.minimize(fun=objSIR.cost, jac=objSIR.sensitivity, x0=theta, bounds=boxBounds, method='L-BFGS-B')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91c87afe",
+   "metadata": {},
+   "source": [
+    "Then compare again the least square estimate of the covariance matrix\n",
+    "against our version\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87442b49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resLS, cov_x, infodict, mesg, ier = scipy.optimize.leastsq(func=objSIR.residual, x0=res['x'], full_output=True)\n",
+    "\n",
+    "HJTJ, outputHJTJ = objSIR.hessian(full_output=True)\n",
+    "\n",
+    "print(scipy.linalg.inv(HJTJ))\n",
+    "\n",
+    "print(cov_x)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78ef4147",
+   "metadata": {},
+   "source": [
+    "\n",
+    "also noting the difference between the Hessian and the approximation using\n",
+    "the Jacobian, which is what the least squares routine uses.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ed8a8f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "print(scipy.linalg.inv(outputHJTJ['JTJ']))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/pygom-doc/notebooks/paramfit/initialGuess.ipynb b/docs/pygom-doc/notebooks/paramfit/initialGuess.ipynb
new file mode 100644
index 00000000..f2223397
--- /dev/null
+++ b/docs/pygom-doc/notebooks/paramfit/initialGuess.ipynb
@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Obtaining good initial values for parameters\n",
+    "\n",
+    "## Function Interpolation\n",
+    "\n",
+    "When we want to fit the model to data, one of the necessary steps is to\n",
+    "supply the optimization procedure a good set of initial guess for the\n",
+    "parameters $\\theta$. This may be a challenge when we do have a good\n",
+    "understanding of the process we are trying to model i.e. infectious\n",
+    "diseases may all follow the same SIR process but with vastly different\n",
+    "incubation period.\n",
+    "\n",
+    "A method to obtain such initial guess based on the collocation is\n",
+    "available in this package. A restriction is that data must be present\n",
+    "for all states. We demonstrate this using the FitzHugh-Nagumo model ({func}`.FitzHugh`).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c0c4de8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pygom import SquareLoss, common_models, get_init\n",
+    "\n",
+    "import numpy\n",
+    "\n",
+    "x0 = [-1.0, 1.0]\n",
+    "\n",
+    "t0 = 0\n",
+    "\n",
+    "# params\n",
+    "\n",
+    "paramEval = [('a',0.2), ('b',0.2), ('c',3.0)]\n",
+    "\n",
+    "ode = common_models.FitzHugh(paramEval)\n",
+    "\n",
+    "ode.initial_values = (x0, t0)\n",
+    "\n",
+    "t = numpy.linspace(1, 20, 30).astype('float64')\n",
+    "\n",
+    "solution = ode.integrate(t)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08e8fe54",
+   "metadata": {},
+   "source": [
+    "Below, we try to find the initial guess without supplying any further\n",
+    "information. The underlying method fits a cubic spline against the\n",
+    "observation and tries to minimize the difference between the first\n",
+    "derivative of the spline and the function of the ode. Varying degree of\n",
+    "smoothness penalty is applied to the spline and the best set of\n",
+    "parameters is the ones that yields the smallest total error, combining\n",
+    "both the fit of the spline against data and the spline against the ode.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5766f56e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta, sInfo = get_init(solution[1::,:], t, ode, theta=None, full_output=True)\n",
+    "\n",
+    "print(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf832fff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(sInfo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "597d8480",
+   "metadata": {},
+   "source": [
+    "\n",
+    "As seen above, we have obtained a very good guess of the parameters, in\n",
+    "fact almost the same as the generating process. The information\n",
+    "regarding the smoothing factor shows that the amount of penalty used is\n",
+    "small, which is expected given that we use the solution of the ode as\n",
+    "observations."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/pygom-doc/notebooks/paramfit/profile.ipynb b/docs/pygom-doc/notebooks/paramfit/profile.ipynb
new file mode 100644
index 00000000..8e8d3def
--- /dev/null
+++ b/docs/pygom-doc/notebooks/paramfit/profile.ipynb
@@ -0,0 +1,644 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Confidence Interval of Estimated Parameters\n",
+    "\n",
+    "After obtaining the *best* fit, it is natural to report both the point\n",
+    "estimate and the confidence level at the $\\alpha$ level. The easiest way\n",
+    "to do this is by invoking the normality argument and use Fisher\n",
+    "information of the likelihood. As explained previously at the bottom of\n",
+    "{ref}`gradient:hessian`, we can find the Hessian, $\\mathbf{H}$, or the approximated\n",
+    "Hessian for the estimated parameters. From the Cramer--Rao \n",
+    "\n",
+    "#TODO add ref\n",
+    "\n",
+    "inequality, we\n",
+    "know that\n",
+    "\n",
+    "$$Var(\\hat{\\theta}) \\ge \\frac{1}{I(\\theta)},$$\n",
+    "\n",
+    "where $I(\\theta)$ is the Fisher information, which is the Hessian\n",
+    "subject to regularity condition. Given the Hessian, computing the\n",
+    "confidence intervals is trivial. Note that this is also known as the\n",
+    "asymptotic confidence interval where the normality comes from invoking\n",
+    "the CLT. There are other ways of obtaining a confidence intervals, we\n",
+    "will the ones implemented in the package. First, we will set up a SIR\n",
+    "model as seen in {doc}`sir` which will be used throughout this page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efed1abf",
+   "metadata": {},
+   "source": [
+    "from pygom import NormalLoss, common_models\n",
+    "\n",
+    "from pygom.utilR import qchisq\n",
+    "\n",
+    "import numpy\n",
+    "\n",
+    "import scipy.integrate\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import copy\n",
+    "\n",
+    "ode = common_models.SIR([('beta', 0.5), ('gamma', 1.0/3.0)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad8659d7",
+   "metadata": {},
+   "source": [
+    "\n",
+    "and we assume that we only have observed realization from the $R$\n",
+    "compartment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8425085",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x0 = [1, 1.27e-6, 0]\n",
+    "\n",
+    "t = numpy.linspace(0, 150, 100).astype('float64')\n",
+    "\n",
+    "ode.initial_values = (x0, t[0])\n",
+    "\n",
+    "solution = ode.integrate(t[1::])\n",
+    "\n",
+    "theta = [0.2, 0.2]\n",
+    "\n",
+    "targetState = ['R']\n",
+    "\n",
+    "targetStateIndex = numpy.array(ode.get_state_index(targetState))\n",
+    "\n",
+    "y = solution[1::,targetStateIndex] + numpy.random.normal(0,0.01, (len(solution[1::,targetStateIndex]), 1))\n",
+    "\n",
+    "yObv = y.copy()\n",
+    "\n",
+    "objSIR = NormalLoss(theta, ode, x0, t[0], t[1::], y, targetState)\n",
+    "\n",
+    "boxBounds = [(1e-8, 2.0), (1e-8, 2.0)]\n",
+    "\n",
+    "boxBoundsArray = numpy.array(boxBounds)\n",
+    "\n",
+    "xhat = objSIR.fit(theta, lb=boxBoundsArray[:,0], ub=boxBoundsArray[:,1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4d8f8c5",
+   "metadata": {},
+   "source": [
+    "## Asymptotic\n",
+    "\n",
+    "When the estimate is obtained say, under a square loss or a normal\n",
+    "assumption, the corresponding likelihood can be written down. In\n",
+    "such a case, the likelihood ratio test under a Chi--squared distribution is\n",
+    "\n",
+    "$$2 (\\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) - \\mathcal{L}(\\boldsymbol{\\theta})) \\le \\chi_{1 - \\alpha}^{2}(k)$$\n",
+    "\n",
+    "where $1-\\alpha$ is the size of the confidence region and $k$ is the\n",
+    "degree of freedom. The corresponding asymptotic confidence interval for\n",
+    "parameter $j$ can be derived as\n",
+    "\n",
+    "$$\\hat{\\theta}_{j} \\pm \\sqrt{\\chi_{1 - \\alpha}^{2}(k) H_{i,i}}.$$\n",
+    "\n",
+    "A point-wise confidence interval is obtained when $k = 1$. We assume in\n",
+    "our package that a point-wise confidence interval is desired. This can be\n",
+    "obtained with the following steps.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f93e2ba",
+   "metadata": {},
+   "source": [
+    "from pygom import confidence_interval as ci\n",
+    "\n",
+    "alpha = 0.05\n",
+    "\n",
+    "xL, xU = ci.asymptotic(objSIR, alpha, xhat, lb=boxBoundsArray[:,0], ub=boxBoundsArray[:,1])\n",
+    "\n",
+    "print(xL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eae33a2",
+   "metadata": {},
+   "source": [
+    "print(xU)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5482b63c",
+   "metadata": {},
+   "source": [
+    "'''{warning}\n",
+    "Note that the set of bounds here is only used for check the validity of\n",
+    "$\\hat{\\mathbf{x}}$ and not used in the calculation of the confidence\n",
+    "intervals. Therefore the resulting output can be outside of the box\n",
+    "constraints.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19e92acb",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Profile Likelihood\n",
+    "\n",
+    "Another approach to calculate the confidence interval is to tackle one\n",
+    "parameter at a time, treating the rest of them as nuisance parameters,\n",
+    "hence the term *profile*. Let $\\mathcal{L}(\\boldsymbol{\\theta})$ be our\n",
+    "log--likelihood with parameter $\\boldsymbol{\\theta}$. Element\n",
+    "$\\theta_{j}$ is our parameter of interest and $\\boldsymbol{\\theta}_{-j}$\n",
+    "represents the complement such that\n",
+    "$\\boldsymbol{\\theta} = \\theta_{j} \\cup \\boldsymbol{\\theta}_{-j}$. For\n",
+    "simply models such as linear regression with only regression\n",
+    "coefficients $\\boldsymbol{\\beta}$, then\n",
+    "$\\boldsymbol{\\theta} = \\boldsymbol{\\beta}$.\n",
+    "\n",
+    "To shorten the notation, let\n",
+    "\n",
+    "$$\\mathcal{L}(\\boldsymbol{\\theta}_{-j} \\mid \\theta_{j}) = \\max \\mathcal{L}(\\boldsymbol{\\theta}_{-j} \\mid \\theta_{j})$$\n",
+    "\n",
+    "which is the maxima of $\\boldsymbol{\\theta}_{-j}$ given $\\theta_{j}$.\n",
+    "$\\hat{\\boldsymbol{\\theta}}$ denotes the MLE of the parameters as usual.\n",
+    "The profile--likelihood based confidence interval for $\\theta_{j}$ is\n",
+    "defined as\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\theta_{j}^{U} &= \\sup \\left\\{ \\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) - \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j}) \\le \\frac{1}{2} \\chi_{1 - \\alpha}^{2}(1) \\right\\} \\\\\n",
+    "\\theta_{j}^{L} &= \\inf \\left\\{ \\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) - \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j}) \\le \\frac{1}{2} \\chi_{1 - \\alpha}^{2}(1) \\right\\}\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "where again we have made use of the normal approximation, but without\n",
+    "imposing symmetry. The set of equations above automatically implies that\n",
+    "the interval width is $\\theta_{j}^{U} - \\theta_{j}^{L}$ and\n",
+    "\n",
+    "$$\\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) - \\frac{1}{2} \\chi_{1-\\alpha}^{2}(1) - \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j}) = 0.$$\n",
+    "\n",
+    "As mentioned previously, $\\boldsymbol{\\theta}_{-j}$ is the maximizer of\n",
+    "the nuisance parameters, which has a gradient of zero. Combining this\n",
+    "with the equation above yields a non-linear system of equations of size\n",
+    "$p$,\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "g(\\boldsymbol{\\theta}) = \\left[ \\begin{array}{c} \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j}) - c \\\\ \\frac{\\partial \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j})}{\\partial \\boldsymbol{\\theta}_{-j}} \\end{array} \\right] = 0\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "where\n",
+    "$c = \\mathcal{L}(\\hat{\\boldsymbol{\\theta}}) + \\frac{1}{2} \\chi_{1-\\alpha}^{2}(1)$.\n",
+    "Solving this set of system of equations only need simple Newton like\n",
+    "steps, possibly with correction terms as per {cite:t}`Venzon1988`. We\n",
+    "provide a function to obtain such estimate, {func}`ci.profile`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dab2b66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xLProfile, xUProfile, xLProfileList, xUProfileList = ci.profile(objSIR, alpha, xhat, lb=boxBoundsArray[:,0], ub=boxBoundsArray[:,1], full_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecd49905",
+   "metadata": {},
+   "source": [
+    "but unfortunately this is not accurate most of the time due to the\n",
+    "complicated surface at locations not around $\\hat{\\theta}$. This is a\n",
+    "common scenario for non--linear least square problems because the\n",
+    "Hessian is not guaranteed to be a PSD everywhere. Therefore, a safeguard\n",
+    "is in place to obtain the $\\theta_{j}^{U},\\theta_{j}^{L}$ by iteratively updating $\\theta_{j}$ and find the solution to `nuisanceOptim`.\n",
+    "\n",
+    "#TODO what is nuisance optim?\n",
+    "\n",
+    "Furthermore, we also provide the functions necessary to obtain the\n",
+    "estimates such as the four below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e58e47ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = 0\n",
+    "\n",
+    "funcF = ci._profileF(xhat, i, 0.05, objSIR)\n",
+    "\n",
+    "funcG = ci._profileG(xhat, i, 0.05, objSIR)\n",
+    "\n",
+    "funcGC = ci._profileGSecondOrderCorrection(xhat, i, alpha, objSIR)\n",
+    "\n",
+    "funcH = ci._profileH(xhat, i, 0.05, objSIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2428994",
+   "metadata": {},
+   "source": [
+    "Where $i$ is the index of the parameter of interest. {func}`_profileF` is the\n",
+    "squared norm of {obj}`obj`, which easy the optimization process for solvers\n",
+    "which requires a converted form from system of equations to non-linear\n",
+    "least squares. {func}`_profileG` is the system of equations of {obj}`obj`,\n",
+    "and {func}`_profileH` is the derivative of {obj}`obj`.\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\nabla g(\\boldsymbol{\\theta}) = \\left[ \\begin{array}{c} \\frac{\\partial \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j})}{\\partial \\theta_{j}} \\\\ \\frac{\\partial^{2} \\mathcal{L}(\\boldsymbol{\\theta} \\mid \\theta_{j})}{\\partial \\boldsymbol{\\beta}_{-j} \\partial \\theta_{j}} \\end{array} \\right]\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "{func}`_profileGSecondOrderCorrection` is the second order correction {cite}Venzon1988.\n",
+    "\n",
+    "## Geometric profile likelihood\n",
+    "\n",
+    "Due to the difficulty in obtain a profile likelihood via the standard\n",
+    "Newton like steps, we also provide a way to generate a similar result\n",
+    "using the geometric structure of the likelihood surface. We follow the\n",
+    "method in {cite:t}`Moolgavkar1987`, which involves solving a set of\n",
+    "differential equations\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\frac{d\\beta_{j}}{dt} &= k g^{-1/2} \\\\\n",
+    "\\frac{d\\boldsymbol{\\beta}_{-j}}{dt} &= \\frac{d\\boldsymbol{\\beta}_{-j}}{d\\beta_{j}} \\frac{d\\beta_{j}}{dt},\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "where $k = \\Phi(1-\\alpha)$ is the quantile we want to obtain under a\n",
+    "normal distribution, and\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "g = J_{\\beta_{j}}^{\\top} I^{\\boldsymbol{\\beta}} J_{\\beta_{j}}, \\quad J_{\\beta_{j}} = \\left( \\begin{array}{c} 1 \\\\ \\frac{d\\boldsymbol{\\beta}_{-j}}{d\\beta_{j}} \\end{array} \\right).\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "Here, $J_{\\beta_{j}}$ is the Jacobian between $\\beta_{j}$ and\n",
+    "$\\boldsymbol{\\beta}_{-j}$ with the term\n",
+    "\n",
+    "$$\\frac{d\\boldsymbol{\\beta}_{-j}}{d\\beta_{j}} = -\\left( \\frac{\\partial^{2} \\mathcal{L}}{\\partial \\boldsymbol{\\beta}_{-j}\\partial \\boldsymbol{\\beta}_{-j}^{\\top} } \\right)^{-1} \\frac{\\partial^{2} \\mathcal{L}}{\\partial \\beta_{j} \\partial \\beta_{-j}^{\\top}}$$\n",
+    "\n",
+    "and hence the first element is $1$ (identity transformation).\n",
+    "$I^{\\boldsymbol{\\beta}}$ is the Fisher information of\n",
+    "$\\boldsymbol{\\beta}$, which is\n",
+    "\n",
+    "$$I^{\\boldsymbol{\\beta}} = \\frac{\\partial \\boldsymbol{\\theta}}{\\partial \\boldsymbol{\\beta}^{\\top}} \\Sigma^{\\boldsymbol{\\theta}(\\boldsymbol{\\beta})} \\frac{\\partial \\boldsymbol{\\theta}}{\\partial \\boldsymbol{\\beta}}.$$\n",
+    "\n",
+    "It is simply $\\Sigma^{\\boldsymbol{\\beta}}$ if\n",
+    "$\\boldsymbol{\\theta} = \\boldsymbol{\\beta}$. Different Fisher information\n",
+    "can be used for $\\Sigma^{\\boldsymbol{\\beta}}$ such as the expected or\n",
+    "observed, at $\\hat{\\boldsymbol{\\beta}}$ or $\\boldsymbol{\\beta}$. After\n",
+    "some trivial algebraic manipulation, we can show that our ODE boils\n",
+    "downs to\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\left[ \\begin{array}{c} \\frac{d\\beta_{j}}{dt} \\\\ \\frac{d\\boldsymbol{\\beta_{-j}}}{dt} \\end{array} \\right] = k \\left[ \\begin{array}{c} 1 \\\\ -A^{-1}w \\end{array} \\right] \\left( v - w^{\\top}A^{-1}w \\right)^{-1/2}\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "where the symbols on the RHS above correspond to partitions in the\n",
+    "Fisher information\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "I^{\\boldsymbol{\\beta}} = \\left[ \\begin{array}{cc} v & w^{\\top} \\\\ w & A \\end{array} \\right].\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "The integration is perform from $t = 0$ to $1$ and is all handled\n",
+    "internally via {class}`geometric`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb9240d7",
+   "metadata": {},
+   "source": [
+    "xLGeometric, xUGeometric, xLList, xUList = ci.geometric(objSIR, alpha, xhat, full_output=True)\n",
+    "\n",
+    "print(xLGeometric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09236cc",
+   "metadata": {},
+   "source": [
+    "print(xUGeometric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75d6f904",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Bootstrap\n",
+    "\n",
+    "This is perhaps the favorite method to estimate confidence intervals for\n",
+    "a lot of people. Although there are many ways to implement bootstrap,\n",
+    "semi-parametric is the only logical choice (even though the underlying\n",
+    "assumptions may be violated at times). As we have only implemented OLS\n",
+    "type loss functions in this package, the parametric approach seem to be\n",
+    "inappropriate when there is no self-efficiency guarantee.\n",
+    "Non-parametric approaches requires at least a conditional independence\n",
+    "assumption, something easily violated by our **ODE**. Block bootstrap is\n",
+    "an option but we are also aware that the errors of an **ODE** can be\n",
+    "rather rigid, and consistently over/under estimate at certain periods of\n",
+    "time.\n",
+    "\n",
+    "#TODO refs for bootstrap?\n",
+    "\n",
+    "When we say semi-parametric, we mean the exchange of errors between the\n",
+    "observations. Let our raw error be\n",
+    "\n",
+    "$$\\varepsilon_{i} = y_{i} - \\hat{y}_{i}$$\n",
+    "\n",
+    "where $\\hat{y}_{i}$ will be the prediction under\n",
+    "$\\hat{\\boldsymbol{\\theta}}$ under our model. Then we construct a new set\n",
+    "of observations via\n",
+    "\n",
+    "$$y_{i}^{\\ast} = \\hat{y}_{i} + \\varepsilon^{\\ast}, \\quad \\varepsilon^{\\ast} \\sim \\mathcal{F}$$\n",
+    "\n",
+    "with $\\mathcal{F}$ being the empirical distribution of the raw errors. A\n",
+    "new set of parameters $\\theta^{\\ast}$ are then found for the\n",
+    "bootstrapped samples, and we obtain the $\\alpha$ confidence interval by\n",
+    "taking the $\\alpha/2$ quantiles. Invoking the corresponding python function\n",
+    "yields our bootstrap estimates. Unlike {func}`asymptotic`, the bounds here are\n",
+    "used when estimating the parameters of each bootstrap samples. An error\n",
+    "may be returned if estimation failed for any of the bootstrap samples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d33c189c",
+   "metadata": {},
+   "source": [
+    "xLBootstrap, xUBootstrap, setX = ci.bootstrap(objSIR, alpha, xhat, iteration=10, lb=boxBoundsArray[:,0], ub=boxBoundsArray[:,1], full_output=True)\n",
+    "\n",
+    "print(xLBootstrap)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90e0585e",
+   "metadata": {},
+   "source": [
+    "print(xUBootstrap)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a8e31f0",
+   "metadata": {},
+   "source": [
+    "The additional information here can be used to compute the bias, tail\n",
+    "effects and test against the normality assumption. If desired, a\n",
+    "simultaneous confidence interval can also be approximated empirically.\n",
+    "Note however that because we are using a semi-parameter method here, if\n",
+    "the model specification is wrong then the resulting estimates for the\n",
+    "bias is also wrong. The confidence interval still has the normal\n",
+    "approximation guarantee if the number of samples is large.\n",
+    "\n",
+    "In this case, because the error in the observation is extremely small,\n",
+    "the confidence interval is narrow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c585db79",
+   "metadata": {
+    "vscode": {
+     "languageId": "javascript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pylab as P\n",
+    "\n",
+    "f = plt.figure()\n",
+    "\n",
+    "n, bins, patches = P.hist(setX[:,0], 50)\n",
+    "\n",
+    "P.xlabel(r'Estimates of $beta$');\n",
+    "\n",
+    "P.ylabel('Frequency');\n",
+    "\n",
+    "P.title('Estimates under a semi-parametric bootstrap scheme');\n",
+    "\n",
+    "P.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9edc8b6b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "## Comparison Between Methods\n",
+    "\n",
+    "Although we have shown the numerical values for the confidence interval\n",
+    "obtained using different methods, it can be hard to comprehend how they\n",
+    "vary. As they say, a picture says a million word, and given that this\n",
+    "particular model only has two parameters, we can obtain inspect and\n",
+    "compare the methods visually via a contour plot. The code to perform\n",
+    "this is shown below but the code block will not be run to save time and\n",
+    "space.\n",
+    "\n",
+    "In the plot above, the bootstrap confidence interval were so close to\n",
+    "the MLE, it is impossible to distinguish the two on such a coarse scale.\n",
+    "\n",
+    "Furthermore, because the geometric confidence interval is the result of\n",
+    "an integration, we can trace the path that lead to the final output that\n",
+    "was shown previously. Again, we are space conscious (and time\n",
+    "constrained) so the code block below will not be run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cef48a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "CS = plt.contour(xi, yi, zi, linewidth=0.5)\n",
+    "\n",
+    "plt.clabel(CS, fontsize=10, inline=1)\n",
+    "\n",
+    "l1 = plt.scatter(xLList[0][:,0], xLList[0][:,1], marker='o', c='m', s=10);\n",
+    "\n",
+    "l2 = plt.scatter(xUList[0][:,0], xUList[0][:,1], marker='x', c='m', s=10);\n",
+    "\n",
+    "plt.legend((l1, l2), ('Lower CI path', 'Upper CI path'), loc='upper left');\n",
+    "\n",
+    "plt.ylabel(r'Estimates of $gamma$');\n",
+    "\n",
+    "plt.xlabel(r'Estimates of $beta$');\n",
+    "\n",
+    "plt.title('Integration path of the geometric confidence intervals on the likelihood surface');\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b357c75",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Profile Likelihood Surface\n",
+    "\n",
+    "To investigate why it was hard to find the profile likelihood confidence\n",
+    "interval, we can look at the surface (which is a line as\n",
+    "we are profiling). We find the solution of {func}`nuisanceOptim` for each\n",
+    "$\\boldsymbol{\\theta}_{-j}$ at various points of $\\boldsymbol{\\theta}$.\n",
+    "Equivalently, we can minimize the original loss function as defined\n",
+    "previously, and this is the approach below. We focus out attention to\n",
+    "the parameter $\\beta$ of our SIR model. The results are not shown here\n",
+    "but the existence of a solution to {obj}`obj` is evident by *eyeballing* the plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc31f543",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numIter = 100\n",
+    "\n",
+    "x2 = numpy.linspace(0.0, 2.0, numIter)\n",
+    "\n",
+    "funcOut = numpy.linspace(0.0, 2.0, numIter)\n",
+    "\n",
+    "ode.parameters = [('beta',0.5), ('gamma',1.0/3.0)]\n",
+    "\n",
+    "for i in range(numIter):\n",
+    "    paramEval = [('beta',x2[i]), ('gamma',x2[i])]\n",
+    "    ode2 = copy.deepcopy(ode) \n",
+    "    ode2.parameters = paramEval\n",
+    "    ode2.initial_values = (x0, t[0])\n",
+    "    objSIR2 = NormalLoss(x2[i], ode2, x0, t[0], t[1::], yObv.copy(), targetState, target_param='gamma')\n",
+    "    res = scipy.optimize.minimize(fun=objSIR2.cost, jac=objSIR2.gradient, x0=x2[i], bounds=[(0,2)], method='L-BFGS-B')\n",
+    "    funcOut[i] = res['fun']\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "plt.plot(x2, objSIR.cost(xhat) - funcOut)\n",
+    "\n",
+    "l1 = plt.axhline(-0.5*qchisq(1 - alpha, df=1), 0, 2, color='r')\n",
+    "\n",
+    "plt.ylabel(r'\\$mathcal{L}(hat{theta}) - mathcal{L}(theta mid beta)$');\n",
+    "\n",
+    "plt.xlabel(r'Fixed value of $beta$');\n",
+    "\n",
+    "plt.title('Difference in objective function between MLEn and the maximization of the nuisance parameters given then parameter of interest, beta in this case');\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "\n",
+    "plt.legend((l1,), (r'$-0.5mathcal{X}_{1 - alpha}^{2}(1)$',), loc='lower right');\n",
+    "\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99c84d17",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Both the upper and lower confidence interval can be found in the\n",
+    "profiling procedure, but the part between of\n",
+    "$\\beta \\in \\left[0,\\hat{\\beta}\\right]$ is not convex, with $\\hat{\\beta}$\n",
+    "being the MLE. This non--quadratic profile likelihood is due to the\n",
+    "non-identifiability of the model given data {cite}`Raue2009`. For this\n",
+    "particular case, we can fix it simply by introducing additional\n",
+    "observations in the form of the $I$ state. We encourage the users to try\n",
+    "it out for themselves to confirm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ae8521",
+   "metadata": {},
+   "source": [
+    "targetState = \\['I', 'R'\\]\n",
+    "\n",
+    "targetStateIndex = numpy.array(ode.get_state_index(targetState))\n",
+    "\n",
+    "y = solution[1::,targetStateIndex] + numpy.random.normal(0, 0.01, (len(solution[1::,targetStateIndex]), 1))\n",
+    "\n",
+    "objSIR = NormalLoss(theta, ode, x0, t[0], t[1::], y.copy(), targetState)\n",
+    "\n",
+    "xhat = objSIR.fit(theta, lb=boxBoundsArray[:,0], ub=boxBoundsArray[:,1])\n",
+    "\n",
+    "for i in range(numIter):  \n",
+    "    paramEval = [('beta', x2[i]), ('gamma', x2[i])] \n",
+    "    ode2 = copy.deepcopy(ode)\n",
+    "    ode2.parameters = paramEval\n",
+    "    ode2.initial_values = (x0, t[0]) \n",
+    "    objSIR2 = NormalLoss(x2[i], ode2, x0, t[0], t[1::], y.copy(), targetState, target_param='gamma')\n",
+    "    res = scipy.optimize.minimize(fun=objSIR2.cost, jac=objSIR2.gradient, x0=x2[i], bounds=[(0,2)], method='L-BFGS-B')\n",
+    "    funcOut[i] = res['fun']\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "plt.plot(x2, objSIR.cost(xhat) - funcOut);\n",
+    "\n",
+    "l1 = plt.axhline(-0.5*qchisq(1 - alpha, df=1), 0, 2, color='r')\n",
+    "\n",
+    "plt.ylabel(r'$mathcal{L}(hat{theta}) - mathcal{L}(theta mid beta)$');\n",
+    "\n",
+    "plt.xlabel(r'Fixed value of $beta$');\n",
+    "\n",
+    "plt.title('Profile likelihood curve for the parameter of interest with more observations');\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "\n",
+    "plt.legend((l1,), (r'$-0.5mathcal{X}_{1 - alpha}^{2}(1)$',), loc='lower right');\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 6d9d86876dcf277ba4a62a49311409511a5134c6 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 14:20:41 +0100
Subject: [PATCH 146/188] add header

---
 docs/pygom-doc/rst/autodoc-abc.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pygom-doc/rst/autodoc-abc.rst b/docs/pygom-doc/rst/autodoc-abc.rst
index 1a3a8f3d..ad0084e7 100644
--- a/docs/pygom-doc/rst/autodoc-abc.rst
+++ b/docs/pygom-doc/rst/autodoc-abc.rst
@@ -1,4 +1,4 @@
-approximate_bayesian_computation
+# approximate_bayesian_computation
 =============
 
 .. automodule:: pygom.approximate_bayesian_computation

From 44b554732b41d57f5e14e8347f1cbb3852a6be2e Mon Sep 17 00:00:00 2001
From: hareball90 <36919240+hareball90@users.noreply.github.com>
Date: Wed, 30 Aug 2023 14:46:36 +0100
Subject: [PATCH 147/188] Update book.yml

changing action token
---
 .github/workflows/book.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml
index 7ae76736..edcf8d5c 100644
--- a/.github/workflows/book.yml
+++ b/.github/workflows/book.yml
@@ -59,6 +59,6 @@ jobs:
             - name: GitHub Pages 
               uses: peaceiris/actions-gh-pages@v3.6.1
               with:
-                github_token: ${{ secrets.DEPLOY_BOOK }}
+                github_token: ${{ secrets.GITHUB_TOKEN }}
                 publish_dir: docs/pygom-doc/_build/html
 

From 1c96e03cb937089a11048cdb4f4c9e28473a4f7f Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 14:47:42 +0100
Subject: [PATCH 148/188] add header

---
 docs/pygom-doc/rst/autodoc-loss.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pygom-doc/rst/autodoc-loss.rst b/docs/pygom-doc/rst/autodoc-loss.rst
index ec2b56da..c0919985 100644
--- a/docs/pygom-doc/rst/autodoc-loss.rst
+++ b/docs/pygom-doc/rst/autodoc-loss.rst
@@ -1,4 +1,4 @@
-loss
+# loss
 =============
 
 .. automodule:: pygom.loss

From 569f4fc46069ca17a24dcab6f7d1fc2421cef895 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 15:05:03 +0100
Subject: [PATCH 149/188] add header

---
 docs/pygom-doc/rst/autodoc-utilR.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pygom-doc/rst/autodoc-utilR.rst b/docs/pygom-doc/rst/autodoc-utilR.rst
index 40a5c130..e0480cb7 100644
--- a/docs/pygom-doc/rst/autodoc-utilR.rst
+++ b/docs/pygom-doc/rst/autodoc-utilR.rst
@@ -1,4 +1,4 @@
-utilR
+# utilR
 =============
 
 .. automodule:: pygom.utilR

From 746b39bbdb3afcf570771682cd86477cb8b1b442 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 15:14:39 +0100
Subject: [PATCH 150/188] edits

---
 docs/pygom-doc/notebooks/paramfit/gradient.ipynb    |  4 ++--
 .../pygom-doc/notebooks/paramfit/initialGuess.ipynb | 13 ++++++++++++-
 2 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/docs/pygom-doc/notebooks/paramfit/gradient.ipynb b/docs/pygom-doc/notebooks/paramfit/gradient.ipynb
index 42345ef4..90369963 100644
--- a/docs/pygom-doc/notebooks/paramfit/gradient.ipynb
+++ b/docs/pygom-doc/notebooks/paramfit/gradient.ipynb
@@ -10,7 +10,7 @@
     "points $t_{i}$, $i = 1,\\ldots,N$, we may wish to test out a set of ode\n",
     "to see whether it fits to the data. The most natural way to test such\n",
     "*fit* is to minimize the sum of squares between our observations $y$ and\n",
-    "see whether the resulting solution of the ode and the estimationed\n",
+    "see whether the resulting solution of the ODE and the estimated\n",
     "parameters makes sense.\n",
     "\n",
     "We assume that this estimation process will be tackled through a\n",
@@ -22,7 +22,7 @@
     "Multiple ways of obtaining the gradient have been implemented. All of\n",
     "them serve a certain purpose and may not be a viable/appropriate options\n",
     "depending on the type of ode. More generally, let $d,p$ be the number of\n",
-    "states and paramters respectively. Then finite difference methods have a\n",
+    "states and parameters respectively. Then finite difference methods have a\n",
     "run order of $O(p+1)$ of the original ode, forward sensitivity require\n",
     "an integration of an ode of size $(d+1)p$ rather than $d$. The adjoint\n",
     "method require two run of size $d$ in principle, but actual run time is\n",
diff --git a/docs/pygom-doc/notebooks/paramfit/initialGuess.ipynb b/docs/pygom-doc/notebooks/paramfit/initialGuess.ipynb
index f2223397..3f5c6e9c 100644
--- a/docs/pygom-doc/notebooks/paramfit/initialGuess.ipynb
+++ b/docs/pygom-doc/notebooks/paramfit/initialGuess.ipynb
@@ -101,8 +101,19 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
   }
  },
  "nbformat": 4,

From 25883ad8fa1fe7f1e060bc87cd2c5cfbd54c10d1 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 17:12:08 +0100
Subject: [PATCH 151/188] correct heading format

---
 docs/pygom-doc/rst/autodoc-abc.rst   | 5 +++--
 docs/pygom-doc/rst/autodoc-loss.rst  | 3 ++-
 docs/pygom-doc/rst/autodoc-model.rst | 3 ++-
 docs/pygom-doc/rst/autodoc-utilR.rst | 3 ++-
 4 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/docs/pygom-doc/rst/autodoc-abc.rst b/docs/pygom-doc/rst/autodoc-abc.rst
index ad0084e7..abfa38c8 100644
--- a/docs/pygom-doc/rst/autodoc-abc.rst
+++ b/docs/pygom-doc/rst/autodoc-abc.rst
@@ -1,5 +1,6 @@
-# approximate_bayesian_computation
-=============
+=================================
+approximate_bayesian_computation
+=================================
 
 .. automodule:: pygom.approximate_bayesian_computation
     :members:
diff --git a/docs/pygom-doc/rst/autodoc-loss.rst b/docs/pygom-doc/rst/autodoc-loss.rst
index c0919985..31bc8eff 100644
--- a/docs/pygom-doc/rst/autodoc-loss.rst
+++ b/docs/pygom-doc/rst/autodoc-loss.rst
@@ -1,4 +1,5 @@
-# loss
+=============
+loss
 =============
 
 .. automodule:: pygom.loss
diff --git a/docs/pygom-doc/rst/autodoc-model.rst b/docs/pygom-doc/rst/autodoc-model.rst
index ae396cdf..d6406ff2 100644
--- a/docs/pygom-doc/rst/autodoc-model.rst
+++ b/docs/pygom-doc/rst/autodoc-model.rst
@@ -1,4 +1,5 @@
-# model
+=============
+model
 =============
 
 .. automodule:: pygom.model
diff --git a/docs/pygom-doc/rst/autodoc-utilR.rst b/docs/pygom-doc/rst/autodoc-utilR.rst
index e0480cb7..670fb708 100644
--- a/docs/pygom-doc/rst/autodoc-utilR.rst
+++ b/docs/pygom-doc/rst/autodoc-utilR.rst
@@ -1,4 +1,5 @@
-# utilR
+=============
+utilR
 =============
 
 .. automodule:: pygom.utilR

From b6724f52212aed31d73561abda6a5efe69adb41a Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 17:12:56 +0100
Subject: [PATCH 152/188] clean up and ammend filepath for logo

---
 docs/pygom-doc/_config.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/pygom-doc/_config.yml b/docs/pygom-doc/_config.yml
index 7bd9b2d6..b7679bd7 100644
--- a/docs/pygom-doc/_config.yml
+++ b/docs/pygom-doc/_config.yml
@@ -3,7 +3,7 @@
 
 title: PyGOM documentation
 author: Public Health England / UK Health Security Agency
-logo: logo_pygom.jpg
+logo: images/logo_pygom.jpg
 
 # build files in table of contents
 # used as alternative to exclude patterns

From a3d5113b59cc4746f53044543121a0041cbe2c37 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 22:24:23 +0100
Subject: [PATCH 153/188] correct typos to common models

---
 .../notebooks/common_models/SIR.ipynb          | 17 +++++++++++++++--
 .../notebooks/common_models/vanDelPol.ipynb    | 18 +++++++++++++++---
 2 files changed, 30 insertions(+), 5 deletions(-)

diff --git a/docs/pygom-doc/notebooks/common_models/SIR.ipynb b/docs/pygom-doc/notebooks/common_models/SIR.ipynb
index ca73f476..73dceb7e 100644
--- a/docs/pygom-doc/notebooks/common_models/SIR.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/SIR.ipynb
@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `.SIR`\n",
+    "# SIR\n",
+    "\n",
+    "{func}`.SIR`\n",
     "\n",
     "A standard SIR model defined by the following equations.\n",
     "\n",
@@ -75,8 +77,19 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/pygom-doc/notebooks/common_models/vanDelPol.ipynb b/docs/pygom-doc/notebooks/common_models/vanDelPol.ipynb
index 7e26788d..7ab1e3b7 100644
--- a/docs/pygom-doc/notebooks/common_models/vanDelPol.ipynb
+++ b/docs/pygom-doc/notebooks/common_models/vanDelPol.ipynb
@@ -4,8 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# vanDelPol\n",
-    "{func}`.vanDelPol` - the van Del Pol oscillator {cite}`vanderpol1926`\n",
+    "# vanDerPol\n",
+    "\n",
+    "{func}`.vanDerPol` - the van Del Pol oscillator {cite}`vanderpol1926`\n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\frac{dx}{dt} &= \\sigma (y-x) \\\\\n",
@@ -108,8 +109,19 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.15 ('sphinx-doc')",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
+   }
   }
  },
  "nbformat": 4,

From 3e634d0d28a641911a1d815c28f81437d4f3f04d Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 22:56:40 +0100
Subject: [PATCH 154/188] draft contributing guidance

---
 CONTRIBUTING.md                          |  32 +++++++++++++++++++++++
 docs/pygom-doc/{ => images}/download.png | Bin
 2 files changed, 32 insertions(+)
 create mode 100644 CONTRIBUTING.md
 rename docs/pygom-doc/{ => images}/download.png (100%)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..36a38abf
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,32 @@
+# Contributor guidance 
+
+## suggested information for raising issues
+- is it possible to produce a template for a new issue?
+
+## requirements for amendments 
+- merge and pull requests should only be considered if the MR/PR explicitly states how the following has been addressed
+    - unit tests
+    - modified documentation
+    - justification
+    - successful execution of documentation (controlled by halting at errors in /docs/_config.yml, demonstrated by successful action)
+- #TODO is there a reasonable limit on the code quantity that is submitted per review? (e.g. 200 lines) to ensure a suitable review can be completed
+
+## workflow for incorporating merge and pull requests
+- dev and master are protected and require a code review by the project owner (or designated reviewer)
+- dev should contain all working amendments and collated before the next versioned merge request to master
+- only a merge request should be made to master from dev from the UKHSA repo
+- forked repo pull requests should be made into UKHSA dev branch (or others where appropriate)
+- master branch is for versioned releases, which should be tagged and a summary of alterations made to README.md
+- merge and pull requests to be made to dev, where builds and actions must run successfully before being incorporated
+- if closing a ticket, if possible, include the commit reference which addresses the issue
+- code reviewer should use the template to work through requirements (e.g. confirming tests have been added, documentation is appropriate, added to contributor file)
+
+## adding to the documentation how to add to the jupyterbook;
+
+## acknowledgements from contributors
+- what counts as a contribution?
+    - ticket? pr/mr?
+- how are contributors acknowledged?
+    - contributor md file?
+    - who adds the name to the contributor? suggest code reviewer on approval of MR/PR
+- provide citable repo link
\ No newline at end of file
diff --git a/docs/pygom-doc/download.png b/docs/pygom-doc/images/download.png
similarity index 100%
rename from docs/pygom-doc/download.png
rename to docs/pygom-doc/images/download.png

From 7bd80943bc2a3e065695501bac7665cadb7d2674 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 22:58:07 +0100
Subject: [PATCH 155/188] add links to download image and contributor guidance

---
 docs/pygom-doc/md/getting_started.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/docs/pygom-doc/md/getting_started.md b/docs/pygom-doc/md/getting_started.md
index fd7094b7..bf211df7 100644
--- a/docs/pygom-doc/md/getting_started.md
+++ b/docs/pygom-doc/md/getting_started.md
@@ -71,3 +71,15 @@ in the command line:
     python setup.py test
 
 which can be performed prior to installing the package if desired.
+
+## Using this documentation
+To use a notebook as a starting point, you can download any of the source code within this jupyterbook by using the download icon that is located at the top of each page of the documentation.
+![download file](../images/download.png)
+
+## Contributing to PyGOM
+
+Please see the (contribution guidance)[../../CONTRIBUTING.md] which outlines:
+- required information for raising issues;
+- the process by which code contributions should be incorporated;
+- what is required by additions to PyGOM, including how to add to the jupyterbook;
+- expectations for acknowledgements from contributions.

From 1f0643726af561e675dcf1e6e487ee053d834488 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 22:58:42 +0100
Subject: [PATCH 156/188] tidy up

---
 docs/pygom-doc/intro.md | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/docs/pygom-doc/intro.md b/docs/pygom-doc/intro.md
index 02905b2c..3f273853 100644
--- a/docs/pygom-doc/intro.md
+++ b/docs/pygom-doc/intro.md
@@ -15,10 +15,3 @@ A manuscript containing a shortened motivation and use is hosted on [arxXiv](htt
 
 ```{tableofcontents}
 ```
-
-# Code Documentation and FAQ
-#TODO from index.rst
-# References
-#TODO
-# Indices and tables
-#TODO
\ No newline at end of file

From 88394eec41e843d9bc2d5e15f6b5e17b8e26cf37 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Wed, 30 Aug 2023 23:00:26 +0100
Subject: [PATCH 157/188] move documentation root file

---
 docs/pygom-doc/_toc.yml          | 2 +-
 docs/pygom-doc/{ => md}/intro.md | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename docs/pygom-doc/{ => md}/intro.md (100%)

diff --git a/docs/pygom-doc/_toc.yml b/docs/pygom-doc/_toc.yml
index 7ad7ceca..c8c281ab 100644
--- a/docs/pygom-doc/_toc.yml
+++ b/docs/pygom-doc/_toc.yml
@@ -2,7 +2,7 @@
 # Learn more at https://jupyterbook.org/customize/toc.html
 
 format: jb-book
-root: intro
+root: md/intro
 parts:
   - caption: User documentation
     chapters:
diff --git a/docs/pygom-doc/intro.md b/docs/pygom-doc/md/intro.md
similarity index 100%
rename from docs/pygom-doc/intro.md
rename to docs/pygom-doc/md/intro.md

From ec14d130733ac16b601f42e5fd8cf5efb7820e27 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Thu, 31 Aug 2023 10:24:50 +0100
Subject: [PATCH 158/188] move bib file location

---
 docs/pygom-doc/_config.yml       | 16 ++++++----------
 docs/pygom-doc/{ => bib}/ref.bib |  0
 2 files changed, 6 insertions(+), 10 deletions(-)
 rename docs/pygom-doc/{ => bib}/ref.bib (100%)

diff --git a/docs/pygom-doc/_config.yml b/docs/pygom-doc/_config.yml
index b7679bd7..a0ca13ee 100644
--- a/docs/pygom-doc/_config.yml
+++ b/docs/pygom-doc/_config.yml
@@ -2,7 +2,7 @@
 # see https://jupyterbook.org/customize/config.html
 
 title: PyGOM documentation
-author: Public Health England / UK Health Security Agency
+author: UK Health Security Agency (previously Public Health England)
 logo: images/logo_pygom.jpg
 
 # build files in table of contents
@@ -14,7 +14,7 @@ only_build_toc_files: true
 # this could avoid the issue of execution timing out
 # See https://jupyterbook.org/content/execute.html
 execute:
-  execute_notebooks: cache
+  execute_notebooks: force
 
 # Define the name of the latex output file for PDF builds
 latex:
@@ -24,9 +24,9 @@ latex:
 # Add a bibtex file so that we can create citations
 # use sphinx to specify style
 bibtex_bibfiles: 
-  - 'ref.bib'
-
+  - 'bib/ref.bib'
 
+# additional sphinx libraries
 sphinx:  
   extra_extensions:
   #- 'sphinx.ext.doctest'
@@ -40,12 +40,6 @@ sphinx:
     autosummary_generate: True
     bibtex_reference_style: super
 
-
-#sphinx:
-#  extra_extensions:
-#  - 'sphinx.ext.graphvizconfig'
-  
-
 # Information about where the book exists on the web
 repository:
   url: https://github.com/ukhsa-collaboration/pygom  # Online location of your book
@@ -57,3 +51,5 @@ repository:
 html:
   use_issues_button: true
   use_repository_button: true
+
+# add a fail-on-warning: true
\ No newline at end of file
diff --git a/docs/pygom-doc/ref.bib b/docs/pygom-doc/bib/ref.bib
similarity index 100%
rename from docs/pygom-doc/ref.bib
rename to docs/pygom-doc/bib/ref.bib

From a55ddd1e8c3323a242f397d916402f070942699d Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Thu, 31 Aug 2023 10:27:48 +0100
Subject: [PATCH 159/188] remove redundant files

---
 docs/pygom-doc/mdconvert/common_models/.md    |  35 --
 .../mdconvert/common_models/FitzHugh.md       |  44 --
 .../common_models/Legrand_Ebola_SEIHFR.md     |  96 ----
 .../mdconvert/common_models/Lorenz.md         |  34 --
 .../mdconvert/common_models/Lotka_Volterra.md | 103 ----
 .../common_models/Lotka_Volterra_4State.md    |  60 ---
 .../mdconvert/common_models/Robertson.md      |  89 ---
 .../pygom-doc/mdconvert/common_models/SEIR.md |  35 --
 .../common_models/SEIR_Birth_Death.md         |  38 --
 .../SEIR_Birth_Death_Periodic.md              |  70 ---
 .../mdconvert/common_models/SEIR_Multiple.md  |  51 --
 docs/pygom-doc/mdconvert/common_models/SIR.md |  46 --
 .../common_models/SIR_Birth_Death.md          |  35 --
 docs/pygom-doc/mdconvert/common_models/SIS.md |  35 --
 .../mdconvert/common_models/SIS_Periodic.md   |  34 --
 .../mdconvert/common_models/vanDelPol.md      |  74 ---
 docs/pygom-doc/mdconvert/doc_to_sort/.md      | 201 -------
 .../mdconvert/doc_to_sort/bvpSimple.md        | 201 -------
 .../mdconvert/doc_to_sort/common_models.ipynb |  31 --
 docs/pygom-doc/mdconvert/doc_to_sort/epi.md   |  65 ---
 .../mdconvert/doc_to_sort/epijson.md          |  58 --
 .../mdconvert/doc_to_sort/estimate1.md        | 144 -----
 .../mdconvert/doc_to_sort/estimate2.md        | 353 ------------
 .../pygom-doc/mdconvert/doc_to_sort/faq.ipynb |  83 ---
 docs/pygom-doc/mdconvert/doc_to_sort/fh.md    | 141 -----
 .../mdconvert/doc_to_sort/gradient.md         | 372 -------------
 .../mdconvert/doc_to_sort/initialGuess.md     |  59 --
 .../mdconvert/doc_to_sort/profile.md          | 506 ------------------
 .../pygom-doc/mdconvert/doc_to_sort/ref.ipynb | 103 ----
 .../mdconvert/doc_to_sort/stochastic.md       | 341 ------------
 .../mdconvert/doc_to_sort/transition.md       | 230 --------
 .../mdconvert/doc_to_sort/unrollOde.ipynb     |  23 -
 docs/pygom-doc/mdconvert/mod/.md              |   5 -
 .../mdconvert/mod/common_models.ipynb         |  16 -
 docs/pygom-doc/mdconvert/mod/common_models.md |   5 -
 .../mdconvert/mod/confidence_interval.ipynb   |  16 -
 .../mdconvert/mod/confidence_interval.md      |   5 -
 .../mdconvert/mod/deterministic.ipynb         |  16 -
 docs/pygom-doc/mdconvert/mod/deterministic.md |   5 -
 .../mdconvert/mod/epi_analysis.ipynb          |  16 -
 docs/pygom-doc/mdconvert/mod/epi_analysis.md  |   5 -
 docs/pygom-doc/mdconvert/mod/get_init.ipynb   |  16 -
 docs/pygom-doc/mdconvert/mod/get_init.md      |   5 -
 docs/pygom-doc/mdconvert/mod/index.ipynb      |  22 -
 docs/pygom-doc/mdconvert/mod/index.md         |  13 -
 docs/pygom-doc/mdconvert/mod/losstype.ipynb   |  16 -
 docs/pygom-doc/mdconvert/mod/losstype.md      |   5 -
 docs/pygom-doc/mdconvert/mod/odeloss.ipynb    |  24 -
 docs/pygom-doc/mdconvert/mod/odeloss.md       |  16 -
 docs/pygom-doc/mdconvert/mod/odeutils.ipynb   |  16 -
 docs/pygom-doc/mdconvert/mod/odeutils.md      |   5 -
 docs/pygom-doc/mdconvert/mod/simulate.ipynb   |  16 -
 docs/pygom-doc/mdconvert/mod/simulate.md      |   5 -
 docs/pygom-doc/mdconvert/mod/transition.ipynb |  16 -
 docs/pygom-doc/mdconvert/mod/transition.md    |   5 -
 docs/pygom-doc/mdconvert/unroll/.md           |  57 --
 docs/pygom-doc/mdconvert/unroll/unrollBD.md   |  60 ---
 docs/pygom-doc/mdconvert/unroll/unrollHard.md |  89 ---
 .../mdconvert/unroll/unrollSimple.md          |  57 --
 59 files changed, 4322 deletions(-)
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/FitzHugh.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/Lorenz.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/Lotka_Volterra.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/Robertson.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/SEIR.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/SEIR_Multiple.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/SIR.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/SIR_Birth_Death.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/SIS.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/SIS_Periodic.md
 delete mode 100644 docs/pygom-doc/mdconvert/common_models/vanDelPol.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/bvpSimple.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/common_models.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/epi.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/epijson.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/estimate1.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/estimate2.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/faq.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/fh.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/gradient.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/initialGuess.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/profile.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/ref.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/stochastic.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/transition.md
 delete mode 100644 docs/pygom-doc/mdconvert/doc_to_sort/unrollOde.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/common_models.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/common_models.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/confidence_interval.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/confidence_interval.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/deterministic.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/deterministic.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/epi_analysis.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/epi_analysis.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/get_init.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/get_init.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/index.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/index.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/losstype.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/losstype.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/odeloss.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/odeloss.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/odeutils.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/odeutils.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/simulate.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/simulate.md
 delete mode 100644 docs/pygom-doc/mdconvert/mod/transition.ipynb
 delete mode 100644 docs/pygom-doc/mdconvert/mod/transition.md
 delete mode 100644 docs/pygom-doc/mdconvert/unroll/.md
 delete mode 100644 docs/pygom-doc/mdconvert/unroll/unrollBD.md
 delete mode 100644 docs/pygom-doc/mdconvert/unroll/unrollHard.md
 delete mode 100644 docs/pygom-doc/mdconvert/unroll/unrollSimple.md

diff --git a/docs/pygom-doc/mdconvert/common_models/.md b/docs/pygom-doc/mdconvert/common_models/.md
deleted file mode 100644
index 7aa8ca23..00000000
--- a/docs/pygom-doc/mdconvert/common_models/.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# `.SIR_Birth_Death`{.interpreted-text role="func"}
-
-Next, we look at an SIR model with birth death
-
-$$\begin{aligned}
-\frac{dS}{dt} &= B -\beta SI - \mu S \\
-\frac{dI}{dt} &= \beta SI - \gamma I - \mu I \\
-\frac{dR}{dt} &= \gamma I
-\end{aligned}$$
-
-Continuing from the example above, but now with a much longer time
-frame. Note that the birth and death rate are the same.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: B = 126372.0/365.0
-
-In \[1\]: N = 7781984.0
-
-In \[1\]: ode = common_models.SIR_Birth_Death({\'beta\':3.6,
-\'gamma\':0.2, \'B\':B/N, \'mu\':B/N})
-
-In \[1\]: t = numpy.linspace(0, 35\*365, 10001)
-
-In \[1\]: x0 = \[0.065, 123.0\*(5.0/30.0)/N, 0.0\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_sir_bd.png In \[1\]: ode.plot()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/FitzHugh.md b/docs/pygom-doc/mdconvert/common_models/FitzHugh.md
deleted file mode 100644
index 6b563e6b..00000000
--- a/docs/pygom-doc/mdconvert/common_models/FitzHugh.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# `.FitzHugh`{.interpreted-text role="func"}
-
-The FitzHugh model [\[FitzHugh1961\]]() without external external
-stimulus. This is a commonly used model when developing new methodology
-with regard to ode\'s, see [\[Ramsay2007\]]() and [\[Girolami2011\]]()
-and reference therein.
-
-$$\begin{aligned}
-\frac{dV}{dt} &=  c ( V - \frac{V^{3}}{3} + R) \\
-\frac{dR}{dt} &= -\frac{1}{c}(V - a + bR).
-\end{aligned}$$
-
-An example would be
-
-::: {.ipython}
-In \[1\]: import numpy
-
-In \[1\]: from pygom import common_models
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: ode = common_models.FitzHugh({\'a\':0.2, \'b\':0.2,
-\'c\':3.0})
-
-In \[1\]: t = numpy.linspace(0, 20, 101)
-
-In \[1\]: x0 = \[1.0, -1.0\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_fh_1.png In \[1\]: ode.plot()
-
-In \[1\]: plt.close()
-
-In \[1\]: fig = plt.figure()
-
-In \[1\]: plt.plot(solution\[:,0\], solution\[:,1\], \'b\')
-
-\@savefig common_models_fh_2.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.md b/docs/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.md
deleted file mode 100644
index 52141c92..00000000
--- a/docs/pygom-doc/mdconvert/common_models/Legrand_Ebola_SEIHFR.md
+++ /dev/null
@@ -1,96 +0,0 @@
-# `.Legrand_Ebola_SEIHFR`{.interpreted-text role="func"}
-
-A commonly used model in the literature to model Ebola outbreaks is the
-SEIHFR model proposed by [\[Legrand2007\]](). There are two extra
-compartments on top of the standard SEIR, $H$ for hospitialization and
-$F$ for funeral. A total of ten parameters (with some describing the
-inverse) are required for the model, they are:
-
-  Symbol         Process
-  -------------- ---------------------------------------------
-  $\beta_{I}$    Transmission rate in community
-  $\beta_{H}$    Transmission rate in hospital
-  $\beta_{F}$    Transmission rate in funeral
-  $\gamma_{I}$   (inverse) Onset to end of infectious
-  $\gamma_{D}$   (inverse) Onset to death
-  $\gamma_{H}$   (inverse) Onset of hospitilization
-  $\gamma_{F}$   (inverse) Death to burial
-  $\alpha$       (inverse) Duration of the incubation period
-  $\theta$       Proportional of cases hospitalized
-  $\delta$       Case\--ftality ratio
-
-The **(inverse)** denotes the parameter should be inverted to make
-epidemiological sense. We use the parameters in their more natural from
-in `.Legrand_Ebola_SEIHFR`{.interpreted-text role="func"} and replace
-all the $\gamma$\'s with $\omega$\'s, i.e.
-$\omega_{i} = \gamma_{i}^{-1}$ for $i \in \{I,D,H,F\}$. We also used
-$\alpha^{-1}$ in our model instead of $\alpha$ so that reading the
-parameters directly gives a more intuitive meaning. There arw five
-additional parameters that is derived. The two derived case fatality
-ratio as
-
-$$\begin{aligned}
-\delta_{1} &= \frac{\delta \gamma_{I}}{\delta \gamma_{I} + (1-\delta)\gamma_{D}} \\
-\delta_{2} &= \frac{\delta \gamma_{IH}}{\delta \gamma_{IH} + (1-\delta)\gamma_{DH}},
-\end{aligned}$$
-
-with an adjusted hospitalization parameter
-
-$$\theta_{1} = \frac{\theta(\gamma_{I}(1-\delta_{1}) + \gamma_{D}\delta_{1})}{\theta(\gamma_{I}(1-\delta_{1}) + \gamma_{D}\delta_{1}) + (1-\theta)\gamma_{H}},$$
-
-and the derived infectious period
-
-$$\begin{aligned}
-\gamma_{IH} &= (\gamma_{I}^{-1} - \gamma_{H}^{-1})^{-1} \\
-\gamma_{DH} &= (\gamma_{D}^{-1} - \gamma_{H}^{-1})^{-1}.
-\end{aligned}$$
-
-Now we are ready to state the full set of ode\'s,
-
-$$\begin{aligned}
-\frac{dS}{dt} &= -N^{-1} (\beta_{I}SI + \beta_{H}SH + \beta_{F}(t) SF) \\
-\frac{dE}{dt} &= N^{-1} (\beta_{I}SI + \beta_{H}SH + \beta_{F}(t) SF) - \alpha E \\
-\frac{dI}{dt} &= \alpha E - (\gamma_{H} \theta_{1} + \gamma_{I}(1-\theta_{1})(1-\delta_{1}) + \gamma_{D}(1-\theta_{1})\delta_{1})I \\
-\frac{dH}{dt} &= \gamma_{H}\theta_{1}I - (\gamma_{DH}\delta_{2} + \gamma_{IH}(1-\delta_{2}))H \\
-\frac{dF}{dt} &= \gamma_{D}(1-\theta_{1})\delta_{1}I + \gamma_{DH}\delta_{2}H - \gamma_{F}F \\
-\frac{dR}{dt} &= \gamma_{I}(1-\theta_{1})(1-\delta_{1})I + \gamma_{IH}(1-\delta_{2})H + \gamma_{F}F.
-\end{aligned}$$
-
-with $\beta_{F}(t) = \beta_{F}$ if $t > c$ and $0$ otherwise. We use a
-slightly modified version by replacing the delta function with a sigmoid
-function namely, the logistic function
-
-$$\beta_{F}(t) = \beta_{F} \left(1 - \frac{1}{1 + \exp(-\kappa (t - c))} \right)$$
-
-A brief example (from \[3\]) is given here with a slightly more in depth
-example in `estimate2`{.interpreted-text role="ref"}.
-
-::: {.ipython}
-In \[1\]: import numpy
-
-In \[1\]: from pygom import common_models
-
-In \[1\]: x0 = \[1.0, 3.0/200000.0, 0.0, 0.0, 0.0, 0.0, 0.0\]
-
-In \[1\]: t = numpy.linspace(1, 25, 100)
-
-In \[1\]: ode = common_models.Legrand_Ebola_SEIHFR(\[
-
-:   \...: (\'beta_I\',0.588), \...: (\'beta_H\',0.794), \...:
-    (\'beta_F\',7.653), \...: (\'omega_I\',10.0/7.0), \...:
-    (\'omega_D\',9.6/7.0), \...: (\'omega_H\',5.0/7.0), \...:
-    (\'omega_F\',2.0/7.0), \...: (\'alphaInv\',7.0/7.0), \...:
-    (\'delta\',0.81), \...: (\'theta\',0.80), \...: (\'kappa\',300.0),
-    \...: (\'interventionTime\',7.0) \...: \])
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t)
-
-\@savefig common_models_seihfr.png In \[1\]: ode.plot()
-:::
-
-Note also that we have again standardized so that the number of
-susceptible is 1 and equal to the whole population, i.e. $N$ does not
-exist in our set of ode\'s as defined in
-`.common_models`{.interpreted-text role="mod"}.
diff --git a/docs/pygom-doc/mdconvert/common_models/Lorenz.md b/docs/pygom-doc/mdconvert/common_models/Lorenz.md
deleted file mode 100644
index 907311ce..00000000
--- a/docs/pygom-doc/mdconvert/common_models/Lorenz.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# `.Lorenz`{.interpreted-text role="func"}
-
-The Lorenz attractor [\[Lorenz1963\]]() defined by the equations
-
-$$\begin{aligned}
-\frac{dx}{dt} &= \sigma (y-x) \\
-\frac{dy}{dt} &= x (\rho - z) - y \\
-\frac{dz}{dt} &= xy - \beta z
-\end{aligned}$$
-
-A classic example is
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: t = numpy.linspace(0, 100, 20000)
-
-In \[1\]: ode = common_models.Lorenz({\'beta\':8.0/3.0, \'sigma\':10.0,
-\'rho\':28.0})
-
-In \[1\]: ode.initial_values = (\[1., 1., 1.\], t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-In \[1\]: f = plt.figure()
-
-In \[1\]: plt.plot(solution\[:,0\], solution\[:,2\]);
-
-\@savefig common_models_Lorenz.png In \[1\]: plt.show()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/Lotka_Volterra.md b/docs/pygom-doc/mdconvert/common_models/Lotka_Volterra.md
deleted file mode 100644
index c98870b1..00000000
--- a/docs/pygom-doc/mdconvert/common_models/Lotka_Volterra.md
+++ /dev/null
@@ -1,103 +0,0 @@
-# `.Lotka_Volterra`{.interpreted-text role="func"}
-
-A standard Lotka-Volterra (preditor and prey) model with two states and
-four parameters [\[Lotka1920\]]().
-
-$$\begin{aligned}
-\frac{dx}{dt} &= \alpha x - cxy \\
-\frac{dy}{dt} &= -\delta y + \gamma xy
-\end{aligned}$$
-
-with both birth and death processes.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: x0 = \[2.0, 6.0\]
-
-In \[1\]: ode = common_models.Lotka_Volterra({\'alpha\':1, \'delta\':3,
-\'c\':2, \'gamma\':6})
-
-In \[1\]: ode.initial_values = (x0, 0)
-
-In \[1\]: t = numpy.linspace(0.1, 100, 10000)
-
-In \[1\]: solution = ode.integrate(t)
-
-\@savefig common_models_Lotka_Volterra.png In \[1\]: ode.plot()
-
-In \[1\]: plt.close()
-:::
-
-Then we generate the graph at [Wolfram
-Alpha](http://www.wolframalpha.com/input/?i=lotka-volterra+equations)
-with varying initial conditions.
-
-::: {.ipython}
-In \[1\]: x1List = numpy.linspace(0.2, 2.0, 5)
-
-In \[1\]: x2List = numpy.linspace(0.6, 6.0, 5)
-
-In \[1\]: fig = plt.figure()
-
-In \[1\]: solutionList = list()
-
-In \[1\]: ode = common_models.Lotka_Volterra({\'alpha\':1, \'delta\':3,
-\'c\':2, \'gamma\':6})
-
-In \[1\]: for i in range(len(x1List)):
-
-:   \...: ode.initial_values = (\[x1List\[i\], x2List\[i\]\], 0) \...:
-    solutionList += \[ode.integrate(t)\]
-
-In \[1\]: for i in range(len(x1List)):
-plt.plot(solutionList\[i\]\[100::,0\], solutionList\[i\]\[100::,1\],
-\'b\')
-
-In \[1\]: plt.xlabel(\'x\')
-
-In \[1\]: plt.ylabel(\'y\')
-
-\@savefig common_models_Lotka_Volterra_initial_condition.png In \[1\]:
-plt.show()
-
-In \[1\]: plt.close()
-:::
-
-We also know that the system has the critical points at
-$x = \delta / \gamma$ and $y=\alpha / c$. If we changes the parameters
-in such a way that the ration between $x$ and $y$ remains the same, then
-we get a figure as below
-
-::: {.ipython}
-In \[1\]: cList = numpy.linspace(0.1, 2.0, 5)
-
-In \[1\]: gammaList = numpy.linspace(0.6, 6.0, 5)
-
-In \[1\]: fig = plt.figure()
-
-In \[1\]: for i in range(len(x1List)):
-
-:   \...: ode = common_models.Lotka_Volterra({\'alpha\':1, \'delta\':3,
-    \'c\':cList\[i\], \'gamma\':gammaList\[i\]}) \...:
-    ode.initial_values = (x0, 0) \...: solutionList +=
-    \[ode.integrate(t)\]
-
-In \[1\]: for i in range(len(cList)):
-plt.plot(solutionList\[i\]\[100::,0\], solutionList\[i\]\[100::,1\])
-
-In \[1\]: plt.xlabel(\'x\')
-
-In \[1\]: plt.ylabel(\'y\')
-
-\@savefig common_models_Lotka_Volterra_critical_point.png In \[1\]:
-plt.show()
-
-In \[1\]: plt.close()
-:::
-
-where all the cycles goes through the same points.
diff --git a/docs/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.md b/docs/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.md
deleted file mode 100644
index af9ce277..00000000
--- a/docs/pygom-doc/mdconvert/common_models/Lotka_Volterra_4State.md
+++ /dev/null
@@ -1,60 +0,0 @@
-# `.Lotka_Volterra_4State`{.interpreted-text role="func"}
-
-The Lotka-Volterra model with four states and three parameters
-[\[Lotka1920\]](), explained by the following three transitions
-
-$$\begin{aligned}
-\frac{da}{dt} &= k_{0} a x \\
-\frac{dx}{dt} &= k_{0} a x - k_{1} x y \\
-\frac{dy}{dt} &= k_{1} x y - k_{2} y \\
-\frac{db}{dt} &= k_{2} y.
-\end{aligned}$$
-
-First, we show the deterministic approach. Then we also show the
-different process path using the parameters from [\[Press2007\]](). Note
-that although the model is defined in `common_models`{.interpreted-text
-role="mod"}, it is based on outputting an
-`OperateOdeModel`{.interpreted-text role="class"} rather than
-`SimulateOdeModel`{.interpreted-text role="class"}.
-
-::: {.ipython}
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: from pygom import Transition, TransitionType, ode_utils,
-SimulateOde
-
-In \[1\]: import numpy
-
-In \[1\]: stateList = \[\'a\', \'x\', \'y\', \'b\'\]
-
-In \[1\]: paramList = \[\'k0\', \'k1\', \'k2\'\]
-
-In \[1\]: transitionList = \[
-
-:   \...: Transition(origin=\'a\', destination=\'x\',
-    equation=\'k0\*a\*x\', transition_type=TransitionType.T), \...:
-    Transition(origin=\'x\', destination=\'y\', equation=\'k1\*x\*y\',
-    transition_type=TransitionType.T), \...: Transition(origin=\'y\',
-    destination=\'b\', equation=\'k2\*y\',
-    transition_type=TransitionType.T) \...: \]
-
-In \[1\]: ode = SimulateOde(stateList, paramList,
-transition=transitionList)
-
-In \[1\]: x0 = \[150.0, 10.0, 10.0, 0.0\]
-
-In \[1\]: t = numpy.linspace(0, 15, 100)
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: ode.parameters = \[0.01, 0.1, 1.0\]
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_Lotka_Volterra_4State.png In \[1\]: ode.plot()
-
-In \[1\]: simX, simT = ode.simulate_jump(t\[1::\], 5, full_output=True)
-
-\@savefig common_models_Lotka_Volterra_Sim.png In \[1\]: ode.plot(simX,
-simT)
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/Robertson.md b/docs/pygom-doc/mdconvert/common_models/Robertson.md
deleted file mode 100644
index 1b55174a..00000000
--- a/docs/pygom-doc/mdconvert/common_models/Robertson.md
+++ /dev/null
@@ -1,89 +0,0 @@
-# `.Robertson`{.interpreted-text role="func"}
-
-The Robertson problem [\[Robertson1966\]]()
-
-$$\begin{aligned}
-\frac{dy_{1}}{dt} &= -0.04 y_{1} + 1 \cdot 10^{4} y_{2} y_{3} \\
-\frac{dy_{2}}{dt} &= 0.04 y_{1} - 1 \cdot 10^{4} y_{2} y_{3} + 3 \cdot 10^{7} y_{2}^{2} \\
-\frac{dy_{3}}{dt} &= 3 \cdot 10^{7} y_{2}^{2}.
-\end{aligned}$$
-
-This is a problem that describes an autocatalytic reaction. One of those
-commonly used to test stiff ode solvers. As the parameters in the
-literature is fixed, we show here how to define the states in a slightly
-more compact format
-
-::: {.ipython}
-In \[1\]: from pygom import DeterministicOde, Transition, TransitionType
-
-In \[1\]: import numpy
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: t = numpy.append(0, 4\*numpy.logspace(-6, 6, 1000))
-
-In \[1\]: \# note how we define the states
-
-In \[1\]: stateList = \[\'y1:4\'\]
-
-In \[1\]: paramList = \[\]
-
-In \[1\]: transitionList = \[
-
-:   \...: Transition(origin=\'y1\', destination=\'y2\',
-    equation=\'0.04\*y1\', transition_type=TransitionType.T), \...:
-    Transition(origin=\'y2\', destination=\'y1\',
-    equation=\'1e4\*y2\*y3\', transition_type=TransitionType.T), \...:
-    Transition(origin=\'y2\', destination=\'y3\',
-    equation=\'3e7\*y2\*y2\', transition_type=TransitionType.T) \...: \]
-
-In \[1\]: ode = DeterministicOde(stateList, paramList,
-transition=transitionList)
-
-In \[1\]: ode.initial_values = (\[1.0, 0.0, 0.0\], t\[0\])
-
-In \[1\]: solution, output = ode.integrate(t\[1::\], full_output=True)
-
-In \[1\]: f, axarr = plt.subplots(1, 3)
-
-In \[1\]: for i in range(3):
-
-:   \...: axarr\[i\].plot(t, solution\[:,i\]) \...:
-    axarr\[i\].set_xscale(\'log\')
-
-In \[1\]: f.tight_layout();
-
-\@savefig common_models_Robertson_1.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
-
-To simplify even further, we can use [y]{.title-ref} with the
-corresponding subscript directly instead of [y1,y2,y3]{.title-ref}.
-Again, we do not have any parameters as they are hard coded into our
-models.
-
-::: {.ipython}
-In \[1\]: stateList = \[\'y1:4\'\]
-
-In \[1\]: transitionList = \[
-
-:   \...: Transition(origin=\'y\[0\]\', destination=\'y\[1\]\',
-    equation=\'0.04\*y\[0\]\', transition_type=TransitionType.T), \...:
-    Transition(origin=\'y\[1\]\', destination=\'y\[0\]\',
-    equation=\'1e4\*y\[1\]*y\[2\]\', transition_type=TransitionType.T),
-    \...: Transition(origin=\'y\[1\]\', destination=\'y\[2\]\',
-    equation=\'3e7*y\[1\]\*y\[1\]\', transition_type=TransitionType.T)
-    \...: \]
-
-In \[1\]: ode = DeterministicOde(stateList, paramList,
-transition=transitionList)
-
-In \[1\]: ode.initial_values =(\[1.0, 0.0, 0.0\], t\[0\])
-
-In \[1\]: solution2 = ode.integrate(t\[1::\])
-
-In \[1\]: numpy.max(solution - solution2)
-:::
-
-and we have the identical solution as shown in the last line above.
diff --git a/docs/pygom-doc/mdconvert/common_models/SEIR.md b/docs/pygom-doc/mdconvert/common_models/SEIR.md
deleted file mode 100644
index 57d3c813..00000000
--- a/docs/pygom-doc/mdconvert/common_models/SEIR.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# `.SEIR`{.interpreted-text role="func"}
-
-A natural extension to the SIR is the SEIR model. An extra parameter
-$\alpha$, which is the inverse of the incubation period is introduced.
-
-$$\begin{aligned}
-\frac{dS}{dt} &= -\beta SI \\
-\end{aligned}$$$$\begin{aligned}
-\frac{dE}{dt} &= \beta SI - \alpha E \\
-\end{aligned}$$$$\begin{aligned}
-\frac{dI}{dt} &= \alpha E - \gamma I \\
-\end{aligned}$$$$\frac{dR}{dt} &= \gamma I$$
-
-We use the parameters from \[Aron1984\] here to generate our plots,
-which does not yield a *nice* and *sensible* epidemic curve as the birth
-and death processes are missing.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: ode = common_models.SEIR({\'beta\':1800, \'gamma\':100,
-\'alpha\':35.84})
-
-In \[1\]: t = numpy.linspace(0, 50, 1001)
-
-In \[1\]: x0 = \[0.0658, 0.0007, 0.0002, 0.0\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_seir.png In \[1\]: ode.plot()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.md b/docs/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.md
deleted file mode 100644
index 83623160..00000000
--- a/docs/pygom-doc/mdconvert/common_models/SEIR_Birth_Death.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# `.SEIR_Birth_Death`{.interpreted-text role="func"}
-
-Extending it to also include birth death process with equal rate $\mu$
-
-$$\begin{aligned}
-\frac{dS}{dt} &= \mu - \beta SI - \mu S \\
-\frac{dE}{dt} &= \beta SI - (\mu + \alpha) E \\
-\frac{dI}{dt} &= \alpha E - (\mu + \gamma) I \\
-\frac{dR}{dt} &= \gamma I
-\end{aligned}$$
-
-Same parameters value taken from [\[Aron1984\]]() as the SEIR example
-above is used here. Observe how the introduction of a birth and a death
-process changes the graph even though the rest of the parameters remains
-the same.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: import numpy
-
-In \[1\]: ode = common_models.SEIR_Birth_Death({\'beta\':1800,
-\'gamma\':100, \'alpha\':35.84, \'mu\':0.02})
-
-In \[1\]: t = numpy.linspace(0, 50, 1001)
-
-In \[1\]: x0 = \[0.0658, 0.0007, 0.0002, 0.0\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\], full_output=True)
-
-\@savefig common_models_seir_bd.png In \[1\]: ode.plot()
-
-In \[1\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.md b/docs/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.md
deleted file mode 100644
index 736c1947..00000000
--- a/docs/pygom-doc/mdconvert/common_models/SEIR_Birth_Death_Periodic.md
+++ /dev/null
@@ -1,70 +0,0 @@
-# `.SEIR_Birth_Death_Periodic`{.interpreted-text role="func"}
-
-Now extending the SEIR to also have periodic contact, as in
-[\[Aron1984\]]().
-
-$$\begin{aligned}
-\frac{dS}{dt} &= \mu - \beta(t)SI - \mu S \\
-\frac{dE}{dt} &= \beta(t)SI - (\mu + \alpha) E \\
-\frac{dI}{dt} &= \alpha E - (\mu + \gamma) I \\
-\frac{dR}{dt} &= \gamma I.
-\end{aligned}$$
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: ode =
-common_models.SEIR_Birth_Death_Periodic({\'beta_0\':1800,
-\'beta_1\':0.2, \'gamma\':100, \'alpha\':35.84, \'mu\':0.02})
-
-In \[1\]: t = numpy.linspace(0, 50, 1001)
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: x0 = \[0.0658, 0.0007, 0.0002, 0.0\]
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_seir_bd_periodic1.png In \[1\]: ode.plot()
-
-In \[1\]: plt.close()
-:::
-
-The periodicity is obvious when looking at the the plot between states
-$S$ and $E$, in logarithmic scale.
-
-::: {.ipython}
-In \[1\]: fig = plt.figure();
-
-In \[1\]: plt.plot(numpy.log(solution\[:,0\]),
-numpy.log(solution\[:,1\]));
-
-In \[1\]: plt.xlabel(\'log of S\');
-
-In \[1\]: plt.ylabel(\'log of E\');
-
-\@savefig common_models_seir_bd_periodic2.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
-
-Similarly, we can see the same thing between the states $E$ and $I$.
-
-::: {.ipython}
-In \[1\]: fig = plt.figure();
-
-In \[1\]: plt.plot(numpy.log(solution\[:,1\]),
-numpy.log(solution\[:,2\]));
-
-In \[1\]: plt.xlabel(\'log of E\');
-
-In \[1\]: plt.ylabel(\'log of I\');
-
-\@savefig common_models_seir_bd_periodic3.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/SEIR_Multiple.md b/docs/pygom-doc/mdconvert/common_models/SEIR_Multiple.md
deleted file mode 100644
index c7048c67..00000000
--- a/docs/pygom-doc/mdconvert/common_models/SEIR_Multiple.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# `.SEIR_Multiple`{.interpreted-text role="func"}
-
-Multiple SEIR coupled together, without any birth death process.
-
-$$\begin{aligned}
-\frac{dS_{i}}{dt} &= dN_{i} - dS_{i} - \lambda_{i}S_{i} \\
-\frac{dE_{i}}{dt} &= \lambda_{i}S_{i} - (d+\epsilon)E_{i} \\
-\frac{dI_{i}}{dt} &= \epsilon E_{i} - (d+\gamma) I_{i} \\
-\frac{dR_{i}}{dt} &= \gamma I_{i} - dR_{i}
-\end{aligned}$$
-
-where
-
-$$\lambda_{i} = \sum_{j=1}^{n} \beta_{i,j} I_{j} (1\{i\neq j\} p)$$
-
-with $n$ being the number of patch and $p$ the coupled factor.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[2\]: import numpy
-
-In \[3\]: paramEval = {\'beta_00\':0.0010107,\'beta_01\':0.0010107,\'beta_10\':0.0010107,
-
-:   \...:
-    \'beta_11\':0.0010107,\'d\':0.02,\'epsilon\':45.6,\'gamma\':73.0,
-    \...: \'N_0\':10\**6,\'N_1\':10*\*6,\'p\':0.01}
-
-In \[4\]: x0 = \[36139.3224081278, 422.560577637822, 263.883351688369,
-963174.233662546\]
-
-In \[5\]: ode = common_models.SEIR_Multiple(param=paramEval)
-
-In \[6\]: t = numpy.linspace(0, 40, 100)
-
-In \[7\]: x01 = \[\]
-
-In \[8\]: for s in x0:
-
-:   \...: x01 += 2\*\[s\]
-
-In \[9\]: ode.initial_values = (numpy.array(x01, float),t\[0\])
-
-In \[10\]: solution, output = ode.integrate(t\[1::\], full_output=True)
-
-\@savefig common_models_seir_multiple.png In \[11\]: ode.plot()
-:::
-
-The initial conditions are those derived by using the stability
-condition as stated in [\[Lloyd1996\]]() while the notations is taken
-from [\[Brauer2008\]]().
diff --git a/docs/pygom-doc/mdconvert/common_models/SIR.md b/docs/pygom-doc/mdconvert/common_models/SIR.md
deleted file mode 100644
index 6b0ce30f..00000000
--- a/docs/pygom-doc/mdconvert/common_models/SIR.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# `.SIR`{.interpreted-text role="func"}
-
-A standard SIR model defined by the equations
-
-$$\begin{aligned}
-\frac{dS}{dt} &= -\beta SI \\
-\frac{dI}{dt} &= \beta SI - \gamma I \\
-\frac{dR}{dt} &= \gamma I
-\end{aligned}$$
-
-Note that the examples and parameters are taken from [\[Brauer2008\]](),
-namely Figure 1.4. Hence, the first one below may not appear to make
-much sense.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: ode = common_models.SIR({\'beta\':3.6, \'gamma\':0.2})
-
-In \[1\]: t = numpy.linspace(0, 730, 1001)
-
-In \[1\]: N = 7781984.0
-
-In \[1\]: x0 = \[1.0, 10.0/N, 0.0\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_sir.png In \[1\]: ode.plot()
-:::
-
-Now we have the more sensible plot, where the initial susceptibles is
-only a fraction of 1.
-
-::: {.ipython}
-In \[1\]: x0 = \[0.065, 123\*(5.0/30.0)/N, 0.0\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_sir_realistic.png In \[1\]: ode.plot()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/SIR_Birth_Death.md b/docs/pygom-doc/mdconvert/common_models/SIR_Birth_Death.md
deleted file mode 100644
index 7aa8ca23..00000000
--- a/docs/pygom-doc/mdconvert/common_models/SIR_Birth_Death.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# `.SIR_Birth_Death`{.interpreted-text role="func"}
-
-Next, we look at an SIR model with birth death
-
-$$\begin{aligned}
-\frac{dS}{dt} &= B -\beta SI - \mu S \\
-\frac{dI}{dt} &= \beta SI - \gamma I - \mu I \\
-\frac{dR}{dt} &= \gamma I
-\end{aligned}$$
-
-Continuing from the example above, but now with a much longer time
-frame. Note that the birth and death rate are the same.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: B = 126372.0/365.0
-
-In \[1\]: N = 7781984.0
-
-In \[1\]: ode = common_models.SIR_Birth_Death({\'beta\':3.6,
-\'gamma\':0.2, \'B\':B/N, \'mu\':B/N})
-
-In \[1\]: t = numpy.linspace(0, 35\*365, 10001)
-
-In \[1\]: x0 = \[0.065, 123.0\*(5.0/30.0)/N, 0.0\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_sir_bd.png In \[1\]: ode.plot()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/SIS.md b/docs/pygom-doc/mdconvert/common_models/SIS.md
deleted file mode 100644
index e7d79d8e..00000000
--- a/docs/pygom-doc/mdconvert/common_models/SIS.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# `.SIS`{.interpreted-text role="func"}
-
-A standard SIS model without the total population $N$. We assume here
-that $S + I = N$ so we can always normalize to 1. Evidently, the state
-$S$ is not required for understanding the model because it is a
-deterministic function of state $I$.
-
-$$\begin{aligned}
-\frac{dS}{dt} &=  -\beta S I + \gamma I \\
-\frac{dI}{dt} &= \beta S I - \gamma I.
-\end{aligned}$$
-
-An example would be
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: import numpy
-
-In \[1\]: ode = common_models.SIS({\'beta\':0.5,\'gamma\':0.2})
-
-In \[1\]: t = numpy.linspace(0, 20, 101)
-
-In \[1\]: x0 = \[1.0, 0.1\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_sis.png In \[1\]: ode.plot()
-
-In \[1\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/SIS_Periodic.md b/docs/pygom-doc/mdconvert/common_models/SIS_Periodic.md
deleted file mode 100644
index 1f46029d..00000000
--- a/docs/pygom-doc/mdconvert/common_models/SIS_Periodic.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# `.SIS_Periodic`{.interpreted-text role="func"}
-
-Now we look at an extension of the SIS model by incorporating periodic
-contact rate. Note how our equation is defined by a single ode for state
-**I**.
-
-$$\frac{dI}{dt} = (\beta(t)N - \alpha) I - \beta(t)I^{2}$$
-
-where $\beta(t) = 2 - 1.8 \cos(5t)$. As the name suggests, it achieves a
-(stable) periodic solution. Note how the plots have two sub-graphs,
-where $\tau$ is in fact our time component which we have taken out of
-the original equation when converting it to a automonous system.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: import numpy
-
-In \[1\]: ode = common_models.SIS_Periodic({\'alpha\':1.0})
-
-In \[1\]: t = numpy.linspace(0, 10, 101)
-
-In \[1\]: x0 = \[0.1,0.\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_sis_periodic.png In \[1\]: ode.plot()
-
-In \[1\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/common_models/vanDelPol.md b/docs/pygom-doc/mdconvert/common_models/vanDelPol.md
deleted file mode 100644
index a8c0879d..00000000
--- a/docs/pygom-doc/mdconvert/common_models/vanDelPol.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# `.vanDelPol`{.interpreted-text role="func"}
-
-The van Del Pol oscillator [\[vanderpol1926\]]()
-
-$$\begin{aligned}
-\frac{dx}{dt} &= \sigma (y-x) \\
-\frac{dy}{dt} &= x (\rho - z) - y \\
-\frac{dz}{dt} &= xy - \beta z
-\end{aligned}$$
-
-A classic example is
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: t = numpy.linspace(0, 20, 1000)
-
-In \[1\]: ode = common_models.vanDelPol({\'mu\':1.0})
-
-In \[1\]: ode.initial_values = (\[2.0, 0.0\], t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_vanDelPol.png In \[1\]: ode.plot()
-
-In \[1\]: plt.close()
-
-In \[1\]: f = plt.figure()
-
-In \[1\]: plt.plot(solution\[:,0\], solution\[:,1\]);
-
-\@savefig common_models_vanDelPol_yprime_y\_1.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
-
-When we change the value, as per
-[Wolfram](http://mathworld.wolfram.com/vanderPolEquation.html)
-
-::: {.ipython}
-In \[1\]: t = numpy.linspace(0, 100, 1000)
-
-In \[1\]: ode.parameters = {\'mu\':1.0}
-
-In \[1\]: ode.initial_values = (\[0.0, 0.2\], t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-In \[1\]: f = plt.figure()
-
-In \[1\]: plt.plot(solution\[:,0\],solution\[:,1\]);
-
-\@savefig common_models_vanDelPol_yprime_y\_2.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-
-In \[1\]: ode.parameters = {\'mu\':0.2}
-
-In \[1\]: ode.initial_values = (\[0.0, 0.2\], t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-In \[1\]: f = plt.figure()
-
-In \[1\]: plt.plot(solution\[:,0\], solution\[:,1\]);
-
-\@savefig common_models_vanDelPol_yprime_y\_3.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/.md b/docs/pygom-doc/mdconvert/doc_to_sort/.md
deleted file mode 100644
index 07745c25..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/.md
+++ /dev/null
@@ -1,201 +0,0 @@
-# Solving Boundary Value Problems {#bvpSimple}
-
-In addition to finding solutions for an IVP and estimate the unknown
-parameters, this package also allows you to solve BVP with a little bit
-of imagination. Here, we are going to show how a BVP can be solved by
-treating it as a parameter estimation problem. Essentially, a shooting
-method where the first boundary condition defines the initial condition
-of an IVP and the second boundary condition is an observation. Two
-examples, both from MATLAB[^1], will be shown here.
-
-## Simple model 1
-
-We are trying to find the solution to the second order differential
-equation
-
-$$\nabla^{2} y + |y| = 0$$
-
-subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert
-this into a set of first order ODE
-
-$$\begin{aligned}
-\frac{d y_{0}}{dt} &= y_{1} \\
-\frac{d y_{1}}{dt} &= -|y_{0}|
-\end{aligned}$$
-
-using an auxiliary variable $y_{1} = \nabla y$ and $y_{0} = y$. Setting
-up the system below
-
-::: {.ipython}
-In \[1\]: from pygom import Transition, TransitionType,
-DeterministicOde, SquareLoss
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[2\]: stateList = \[\'y0\', \'y1\'\]
-
-In \[3\]: paramList = \[\]
-
-In \[4\]: ode1 = Transition(origin=\'y0\',
-
-:   \...: equation=\'y1\', \...: transition_type=TransitionType.ODE)
-
-In \[5\]: ode2 = Transition(origin=\'y1\',
-
-:   \...: equation=\'-abs(y0)\', \...:
-    transition_type=TransitionType.ODE)
-
-In \[6\]: model = DeterministicOde(stateList,
-
-:   \...: paramList, \...: ode=\[ode1, ode2\])
-
-In \[7\]: model.get_ode_eqn()
-:::
-
-We check that the equations are correct before proceeding to set up our
-loss function.
-
-::: {.ipython}
-In \[1\]: import numpy
-
-In \[2\]: from scipy.optimize import minimize
-
-In \[3\]: initialState = \[0.0, 1.0\]
-
-In \[4\]: t = numpy.linspace(0, 4, 100)
-
-In \[5\]: model.initial_values = (initialState, t\[0\])
-
-In \[6\]: solution = model.integrate(t\[1::\])
-
-In \[7\]: f = plt.figure()
-
-\@savefig bvp1_random_guess_plot.png In \[8\]: model.plot()
-
-In \[9\]: plt.close()
-:::
-
-Setting up the second boundary condition $y(4) = -2$ is easy, because
-that is just a single observation attached to the state $y_{1}$.
-Enforcing the first boundary condition requires us to set it as the
-initial condition. Because the condition only states that $y(0) = 0$,
-the starting value of the other state $y_1$ is free. We let our loss
-object know that it is free through the targetState input argument.
-
-::: {.ipython}
-In \[10\]: theta = \[0.0\]
-
-In \[11\]: obj = SquareLoss(theta=theta,
-
-:   \....: ode=model, \....: x0=initialState, \....: t0=t\[0\], \....:
-    t=t\[-1\], \....: y=\[-2\], \....: state_name=\[\'y0\'\], \....:
-    target_state=\[\'y1\'\])
-
-In \[12\]: thetaHat = minimize(fun=obj.costIV, x0=\[0.0\])
-
-In \[13\]: print(thetaHat)
-
-In \[14\]: model.initial_values = (\[0.0\] + thetaHat\[\'x\'\].tolist(),
-t\[0\])
-
-In \[15\]: solution = model.integrate(t\[1::\])
-
-In \[16\]: f = plt.figure()
-
-\@savefig bvp1_solution_plot.png In \[17\]: model.plot()
-
-In \[18\]: plt.close()
-:::
-
-We are going to visualize the solution, and also check the boundary
-condition. The first became our initial condition, so it is always
-satisfied and only the latter is of concern, which is zero (subject to
-numerical error) from thetaHat.
-
-## Simple model 2
-
-Our second example is different as it involves an actual parameter and
-also time. We have the Mathieu\'s Equation
-
-$$\nabla^{2} y + \left(p - 2q \cos(2x)\right)y = 0$$
-
-and the aim is to compute the fourth eigenvalue $q=5$. There are three
-boundary conditions
-
-$$\nabla y(0) = 0, \quad \nabla y(\pi) = 0, \quad y(0) = 1$$
-
-and we aim to solve it by converting it to a first order ODE and tackle
-it as an IVP. As our model object does not allow the use of the time
-component in the equations, we introduce a anxiliary state $\tau$ that
-replaces time $t$. Rewrite the equations using
-$y_{0} = y, y_{1} = \nabla y$ and define our model as
-
-::: {.ipython}
-In \[1\]: stateList = \[\'y0\', \'y1\', \'tau\'\]
-
-In \[2\]: paramList = \[\'p\'\]
-
-In \[3\]: ode1 = Transition(\'y0\', \'y1\', TransitionType.ODE)
-
-In \[4\]: ode2 = Transition(\'y1\', \'-(p - 2\*5\*cos(2\*tau))\*y0\',
-TransitionType.ODE)
-
-In \[5\]: ode3 = Transition(\'tau\', \'1\', TransitionType.ODE)
-
-In \[6\]: model = DeterministicOde(stateList, paramList, ode=\[ode1,
-ode2, ode3\])
-
-In \[7\]: theta = \[1.0, 1.0, 0.0\]
-
-In \[8\]: p = 15.0
-
-In \[9\]: t = numpy.linspace(0, numpy.pi)
-
-In \[10\]: model.parameters = \[(\'p\',p)\]
-
-In \[11\]: model.initial_values = (theta, t\[0\])
-
-In \[12\]: solution = model.integrate(t\[1::\])
-
-In \[13\]: f = plt.figure()
-
-\@savefig bvp2_random_guess_plot.png In \[14\]: model.plot()
-
-In \[15\]: plt.close()
-:::
-
-Now we are ready to setup the estimation. Like before, we setup the
-second boundary condition by pretending that it is an observation. We
-have all the initial conditions defined by the first boundary condition
-
-::: {.ipython}
-In \[1\]: obj = SquareLoss(15.0, model, x0=\[1.0, 0.0, 0.0\], t0=0.0,
-t=numpy.pi, y=0.0, state_name=\'y1\')
-
-In \[2\]: xhatObj = minimize(obj.cost,\[15\])
-
-In \[3\]: print(xhatObj)
-
-In \[4\]: model.parameters = \[(\'p\', xhatObj\[\'x\'\]\[0\])\]
-
-In \[5\]: model.initial_values = (\[1.0, 0.0, 0.0\], t\[0\])
-
-In \[5\]: solution = model.integrate(t\[1::\])
-
-In \[6\]: f = plt.figure()
-
-\@savefig bvp2_solution_plot.png In \[7\]: model.plot()
-
-In \[8\]: plt.close()
-:::
-
-The plot of the solution shows the path that satisfies all boundary
-condition. The last subplot is time which obvious is redundant here but
-the `DeterministicOde.plot`{.interpreted-text role="meth"} method is not
-yet able to recognize the time component. Possible speed up can be
-achieved through the use of derivative information or via root finding
-method that tackles the gradient directly, instead of the cost function.
-
-**Reference**
-
-[^1]: 
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/bvpSimple.md b/docs/pygom-doc/mdconvert/doc_to_sort/bvpSimple.md
deleted file mode 100644
index 07745c25..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/bvpSimple.md
+++ /dev/null
@@ -1,201 +0,0 @@
-# Solving Boundary Value Problems {#bvpSimple}
-
-In addition to finding solutions for an IVP and estimate the unknown
-parameters, this package also allows you to solve BVP with a little bit
-of imagination. Here, we are going to show how a BVP can be solved by
-treating it as a parameter estimation problem. Essentially, a shooting
-method where the first boundary condition defines the initial condition
-of an IVP and the second boundary condition is an observation. Two
-examples, both from MATLAB[^1], will be shown here.
-
-## Simple model 1
-
-We are trying to find the solution to the second order differential
-equation
-
-$$\nabla^{2} y + |y| = 0$$
-
-subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert
-this into a set of first order ODE
-
-$$\begin{aligned}
-\frac{d y_{0}}{dt} &= y_{1} \\
-\frac{d y_{1}}{dt} &= -|y_{0}|
-\end{aligned}$$
-
-using an auxiliary variable $y_{1} = \nabla y$ and $y_{0} = y$. Setting
-up the system below
-
-::: {.ipython}
-In \[1\]: from pygom import Transition, TransitionType,
-DeterministicOde, SquareLoss
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[2\]: stateList = \[\'y0\', \'y1\'\]
-
-In \[3\]: paramList = \[\]
-
-In \[4\]: ode1 = Transition(origin=\'y0\',
-
-:   \...: equation=\'y1\', \...: transition_type=TransitionType.ODE)
-
-In \[5\]: ode2 = Transition(origin=\'y1\',
-
-:   \...: equation=\'-abs(y0)\', \...:
-    transition_type=TransitionType.ODE)
-
-In \[6\]: model = DeterministicOde(stateList,
-
-:   \...: paramList, \...: ode=\[ode1, ode2\])
-
-In \[7\]: model.get_ode_eqn()
-:::
-
-We check that the equations are correct before proceeding to set up our
-loss function.
-
-::: {.ipython}
-In \[1\]: import numpy
-
-In \[2\]: from scipy.optimize import minimize
-
-In \[3\]: initialState = \[0.0, 1.0\]
-
-In \[4\]: t = numpy.linspace(0, 4, 100)
-
-In \[5\]: model.initial_values = (initialState, t\[0\])
-
-In \[6\]: solution = model.integrate(t\[1::\])
-
-In \[7\]: f = plt.figure()
-
-\@savefig bvp1_random_guess_plot.png In \[8\]: model.plot()
-
-In \[9\]: plt.close()
-:::
-
-Setting up the second boundary condition $y(4) = -2$ is easy, because
-that is just a single observation attached to the state $y_{1}$.
-Enforcing the first boundary condition requires us to set it as the
-initial condition. Because the condition only states that $y(0) = 0$,
-the starting value of the other state $y_1$ is free. We let our loss
-object know that it is free through the targetState input argument.
-
-::: {.ipython}
-In \[10\]: theta = \[0.0\]
-
-In \[11\]: obj = SquareLoss(theta=theta,
-
-:   \....: ode=model, \....: x0=initialState, \....: t0=t\[0\], \....:
-    t=t\[-1\], \....: y=\[-2\], \....: state_name=\[\'y0\'\], \....:
-    target_state=\[\'y1\'\])
-
-In \[12\]: thetaHat = minimize(fun=obj.costIV, x0=\[0.0\])
-
-In \[13\]: print(thetaHat)
-
-In \[14\]: model.initial_values = (\[0.0\] + thetaHat\[\'x\'\].tolist(),
-t\[0\])
-
-In \[15\]: solution = model.integrate(t\[1::\])
-
-In \[16\]: f = plt.figure()
-
-\@savefig bvp1_solution_plot.png In \[17\]: model.plot()
-
-In \[18\]: plt.close()
-:::
-
-We are going to visualize the solution, and also check the boundary
-condition. The first became our initial condition, so it is always
-satisfied and only the latter is of concern, which is zero (subject to
-numerical error) from thetaHat.
-
-## Simple model 2
-
-Our second example is different as it involves an actual parameter and
-also time. We have the Mathieu\'s Equation
-
-$$\nabla^{2} y + \left(p - 2q \cos(2x)\right)y = 0$$
-
-and the aim is to compute the fourth eigenvalue $q=5$. There are three
-boundary conditions
-
-$$\nabla y(0) = 0, \quad \nabla y(\pi) = 0, \quad y(0) = 1$$
-
-and we aim to solve it by converting it to a first order ODE and tackle
-it as an IVP. As our model object does not allow the use of the time
-component in the equations, we introduce a anxiliary state $\tau$ that
-replaces time $t$. Rewrite the equations using
-$y_{0} = y, y_{1} = \nabla y$ and define our model as
-
-::: {.ipython}
-In \[1\]: stateList = \[\'y0\', \'y1\', \'tau\'\]
-
-In \[2\]: paramList = \[\'p\'\]
-
-In \[3\]: ode1 = Transition(\'y0\', \'y1\', TransitionType.ODE)
-
-In \[4\]: ode2 = Transition(\'y1\', \'-(p - 2\*5\*cos(2\*tau))\*y0\',
-TransitionType.ODE)
-
-In \[5\]: ode3 = Transition(\'tau\', \'1\', TransitionType.ODE)
-
-In \[6\]: model = DeterministicOde(stateList, paramList, ode=\[ode1,
-ode2, ode3\])
-
-In \[7\]: theta = \[1.0, 1.0, 0.0\]
-
-In \[8\]: p = 15.0
-
-In \[9\]: t = numpy.linspace(0, numpy.pi)
-
-In \[10\]: model.parameters = \[(\'p\',p)\]
-
-In \[11\]: model.initial_values = (theta, t\[0\])
-
-In \[12\]: solution = model.integrate(t\[1::\])
-
-In \[13\]: f = plt.figure()
-
-\@savefig bvp2_random_guess_plot.png In \[14\]: model.plot()
-
-In \[15\]: plt.close()
-:::
-
-Now we are ready to setup the estimation. Like before, we setup the
-second boundary condition by pretending that it is an observation. We
-have all the initial conditions defined by the first boundary condition
-
-::: {.ipython}
-In \[1\]: obj = SquareLoss(15.0, model, x0=\[1.0, 0.0, 0.0\], t0=0.0,
-t=numpy.pi, y=0.0, state_name=\'y1\')
-
-In \[2\]: xhatObj = minimize(obj.cost,\[15\])
-
-In \[3\]: print(xhatObj)
-
-In \[4\]: model.parameters = \[(\'p\', xhatObj\[\'x\'\]\[0\])\]
-
-In \[5\]: model.initial_values = (\[1.0, 0.0, 0.0\], t\[0\])
-
-In \[5\]: solution = model.integrate(t\[1::\])
-
-In \[6\]: f = plt.figure()
-
-\@savefig bvp2_solution_plot.png In \[7\]: model.plot()
-
-In \[8\]: plt.close()
-:::
-
-The plot of the solution shows the path that satisfies all boundary
-condition. The last subplot is time which obvious is redundant here but
-the `DeterministicOde.plot`{.interpreted-text role="meth"} method is not
-yet able to recognize the time component. Possible speed up can be
-achieved through the use of derivative information or via root finding
-method that tackles the gradient directly, instead of the cost function.
-
-**Reference**
-
-[^1]: 
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/common_models.ipynb b/docs/pygom-doc/mdconvert/doc_to_sort/common_models.ipynb
deleted file mode 100644
index 938fed10..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/common_models.ipynb
+++ /dev/null
@@ -1,31 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pre-defined Example common_models\n",
-    "\n",
-    "We have defined a set of models `common_models`, most of them commonly\n",
-    "used in epidemiology. They are there as examples and also save time for\n",
-    "end users. Most of them are of the compartmental type, and we use\n",
-    "standard naming conventions i.e. **S** = Susceptible, **E** = Exposed,\n",
-    "**I** = Infectious, **R** = Recovered. Extra state symbol will be\n",
-    "introduced when required.\n",
-    "\n",
-    "common_models/SIS.rst common_models/SIS_Periodic.rst\n",
-    "common_models/SIR.rst common_models/SIR_Birth_Death.rst\n",
-    "common_models/SEIR.rst common_models/SEIR_Multiple.rst\n",
-    "common_models/SEIR_Birth_Death.rst\n",
-    "common_models/SEIR_Birth_Death_Periodic.rst\n",
-    "common_models/Legrand_Ebola_SEIHFR.rst common_models/Lotka_Volterra.rst\n",
-    "common_models/Lotka_Volterra_4State.rst common_models/FitzHugh.rst\n",
-    "common_models/Lorenz.rst common_models/vanDelPol.rst\n",
-    "common_models/Robertson.rst"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/epi.md b/docs/pygom-doc/mdconvert/doc_to_sort/epi.md
deleted file mode 100644
index d10988f1..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/epi.md
+++ /dev/null
@@ -1,65 +0,0 @@
-# Simple Epidemic Analysis {#epi}
-
-A common application of ordinary differential equations is in the field
-of epidemiology modeling. More concretely, compartmental models that is
-used to describe disease progression. We demonstrate some of the simple
-algebraic analysis one may wish to take when given a compartment model.
-Our use one of the simplest model, an SIR model with birth and death
-processes, which is an extension of the one in `sir`{.interpreted-text
-role="ref"}. First, we initialize the model below.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[2\]: ode = common_models.SIR_Birth_Death()
-
-In \[3\]: print(ode.get_ode_eqn())
-:::
-
-## Obtaining the R0
-
-The reproduction number, also known as the $R_{0}$, is the single most
-powerful piece and reduced piece of information available from a
-compartmental model. In a nutshell, it provides a single number - if the
-parameters are known - which can the intuitive interpretation where
-$R_{0} = 1$ defines the tipping point of an outbreak. A $R_{0}$ value of
-more than one signifies an potential outbreak where less than one
-indicates that the disease will stop spreading naturally.
-
-To obtain the $R_{0}$, we simply have to tell the function which states
-represent the *disease state*, which in this case is the state **I**.
-
-::: {.ipython}
-In \[1\]: from pygom.model.epi_analysis import \*
-
-In \[2\]: print(R0(ode, \'I\'))
-:::
-
-## Algebraic R0
-
-We may also wish to get the $R_{0}$ in pure algebraic term. This can be
-achieved by the following few lines. Note that the result below is
-slightly different from the one above. The difference is due to the
-internal working of the functions, where `getR0`{.interpreted-text
-role="func"} computes the disease-free equilibrium value for the states
-and substitute them back into the equation.
-
-::: {.ipython}
-In \[1\]: F, V = disease_progression_matrices(ode, \'I\')
-
-In \[2\]: e = R0_from_matrix(F, V)
-
-In \[3\]: print(e)
-:::
-
-To replicate the output before, we have to find the values where the
-disease-free equilibrium will be achieved. Substitution can then be
-performed to retrieve $R_{0}$ in pure parameters.
-
-::: {.ipython}
-In \[1\]: dfe = DFE(ode, \[\'I\'\])
-
-In \[2\]: print(dfe)
-
-In \[3\]: print(e\[0\].subs(dfe))
-:::
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/epijson.md b/docs/pygom-doc/mdconvert/doc_to_sort/epijson.md
deleted file mode 100644
index 166bf3f7..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/epijson.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Reading and using EpiJSON data {#epijson}
-
-Epidemiology data is complicated due to the many different stages a
-patient can go through and whether a modeling technique is applicable
-depends heavily on the recording of data. EpiJSON is a framework whih
-tries to captures all the information [\[Finnie2016\]](), in a JSON
-format as the name suggests.
-
-This package provides the functionality to process EpiJSON data. Due to
-the nature of this package, modeling of ode, it processes the data file
-with this in mind. The output is therefore in the cumulative form as
-default, shown below, in a `pandas.DataFrame`{.interpreted-text
-role="class"} format. The input can be in a string format, a file or
-already a `dict`{.interpreted-text role="class"}.
-
-::: {.ipython}
-In \[1\]: from pygom.loss.read_epijson import epijson_to_data_frame
-
-In \[2\]: import pkgutil
-
-In \[3\]: data = pkgutil.get_data(\'pygom\', \'data/eg1.json\')
-
-In \[3\]: df = epijson_to_data_frame(data)
-
-In \[4\]: print(df)
-:::
-
-Given that the aim of loading the data is usually for model fitting, we
-allow EpiJSON as input directly to the loss class
-`pygom.loss.EpijsonLoss`{.interpreted-text role="class"} which uses the
-Poisson loss under the hood.
-
-::: {.ipython}
-In \[1\]: from pygom.model import common_models
-
-In \[2\]: from pygom.loss.epijson_loss import EpijsonLoss
-
-In \[3\]: ode = common_models.SIR(\[0.5, 0.3\])
-
-In \[4\]: obj = EpijsonLoss(\[0.005, 0.03\], ode, data, \'Death\',
-\'R\', \[300, 2, 0\])
-
-In \[5\]: print(obj.cost())
-
-In \[6\]: print(obj.\_df)
-:::
-
-Given an initialized object, all the operations are inherited from
-`pygom.loss.BaseLoss`{.interpreted-text role="class"}. We demonstrated
-above how to calculate the cost and the rest will not be shown for
-brevity. The data frame is stored inside of the loss object and can be
-retrieved for inspection at any time point.
-
-Rather unfortunately, initial values for the states is still required,
-but the time is not. When the time is not supplied, then the first time
-point in the data will be treated as $t0$. The input [Death]{.title-ref}
-indicate which column of the data is used and $R$ the corresponding
-state the data belongs to.
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/estimate1.md b/docs/pygom-doc/mdconvert/doc_to_sort/estimate1.md
deleted file mode 100644
index 05f1fee7..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/estimate1.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# Example: Parameter Estimation 1 {#estimate1}
-
-## Estimation under square loss
-
-To ease the estimation process when given data, a separate module
-`ode_loss`{.interpreted-text role="mod"} has been constructed for
-observations coming from a single state. We demonstrate how to do it via
-two examples, first, a standard SIR model, then the Legrand SEIHFR model
-from [\[Legrand2007\]]() used for Ebola in `estimate2`{.interpreted-text
-role="ref"}.
-
-### SIR Model
-
-We set up an SIR model as seen previously in `sir`{.interpreted-text
-role="ref"}.
-
-::: {.ipython}
-In \[176\]: from pygom import SquareLoss, common_models
-
-In \[179\]: import numpy
-
-In \[180\]: import scipy.integrate
-
-In \[184\]: import matplotlib.pyplot
-
-In \[185\]: \# Again, standard SIR model with 2 parameter. See the first
-script!
-
-In \[191\]: \# define the parameters
-
-In \[192\]: paramEval = \[(\'beta\',0.5), (\'gamma\',1.0/3.0)\]
-
-In \[189\]: \# initialize the model
-
-In \[190\]: ode = common_models.SIR(paramEval)
-:::
-
-and we assume that we have perfect information about the $R$
-compartment.
-
-::: {.ipython}
-In \[196\]: x0 = \[1, 1.27e-6, 0\]
-
-In \[197\]: \# Time, including the initial time t0 at t=0
-
-In \[198\]: t = numpy.linspace(0, 150, 1000)
-
-In \[200\]: \# Standard. Find the solution.
-
-In \[201\]: solution = scipy.integrate.odeint(ode.ode, x0, t)
-
-In \[202\]: y = solution\[:,1:3\].copy()
-:::
-
-Initialize the class with some initial guess
-
-::: {.ipython}
-In \[209\]: \# our initial guess
-
-In \[210\]: theta = \[0.2, 0.2\]
-
-In \[176\]: objSIR = SquareLoss(theta, ode, x0, t\[0\], t\[1::\],
-y\[1::,:\], \[\'I\',\'R\'\])
-:::
-
-Note that we need to provide the initial values, $x_{0}$ and $t_{0}$
-differently to the observations $y$ and the corresponding time $t$.
-Additionally, the state which the observation lies needs to be
-specified. Either a single state, or multiple states are allowed, as
-seen above.
-
-### Difference in gradient
-
-We have provided two different ways of obtaining the gradient, these are
-explained in `gradient`{.interpreted-text role="ref"} in a bit more
-detail. First, lets see how similar the output of the two methods are
-
-::: {.ipython}
-In \[22\]: objSIR.sensitivity()
-
-In \[25\]: objSIR.adjoint()
-:::
-
-and the time required to obtain the gradient for the SIR model under
-$\theta = (0.2,0.2)$, previously entered.
-
-::: {.ipython}
-In \[22\]: %timeit objSIR.sensitivity()
-
-In \[25\]: %timeit objSIR.adjoint()
-:::
-
-Obviously, the amount of time taken for both method is dependent on the
-number of observations as well as the number of states. The effect on
-the adjoint method as the number of observations differs can be quite
-evident. This is because the adjoint method is under a discretization
-which loops in Python where as the forward sensitivity equations are
-solved simply via an integration. As the number of observation gets
-larger, the affect of the Python loop becomes more obvious.
-
-Difference in gradient is larger when there are less observations. This
-is because the adjoint method use interpolations on the output of the
-ode between each consecutive time points. Given solution over the same
-length of time, fewer discretization naturally leads to a less accurate
-interpolation. Note that the interpolation is currently performed using
-univaraite spline, due to the limitation of python packages. Ideally,
-one would prefer to use an (adaptive) Hermite or Chebyshev
-interpolation. Note how we ran the two gradient functions once before
-timing it, that is because we only find the properties (Jacobian,
-gradient) of the ode during runtime.
-
-### Optimized result
-
-Then standard optimization procedures with some suitable initial guess
-should yield the correct result. It is important to set the boundaries
-for compartmental models as we know that all the parameters are strictly
-positive. We put a less restrictive inequality here for demonstration
-purpose.
-
-::: {.ipython}
-In \[211\]: \# what we think the bounds are
-
-In \[212\]: boxBounds = \[(0.0,2.0),(0.0,2.0)\]
-:::
-
-Then using the optimization routines in
-`scipy.optimize`{.interpreted-text role="mod"}, for example, the *SLSQP*
-method with the gradient obtained by forward sensitivity.
-
-::: {.ipython}
-In \[208\]: from scipy.optimize import minimize
-
-In \[213\]: res = minimize(fun=objSIR.cost,
-
-:   \.....: jac=objSIR.sensitivity, \.....: x0=theta, \.....:
-    bounds=boxBounds, \.....: method=\'SLSQP\')
-
-In \[214\]: print(res)
-:::
-
-Other methods available in `scipy.optimize.minimize`{.interpreted-text
-role="func"} can also be used, such as the *L-BFGS-B* and *TNC*. We can
-also use methods that accepts the exact Hessian such as *trust-ncg* but
-that should not be necessary most of the time.
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/estimate2.md b/docs/pygom-doc/mdconvert/doc_to_sort/estimate2.md
deleted file mode 100644
index aae49819..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/estimate2.md
+++ /dev/null
@@ -1,353 +0,0 @@
-# Example: Parameter Estimation 2 {#estimate2}
-
-Continuing from the `estimate1`{.interpreted-text role="ref"}, we show
-why estimating the parameters for ode\'s are hard. This is especially
-true if there is a lack of data or when there are too much flexibility
-in the model. Note that for reproducibility purposes, only deterministic
-models are used here and a fixed seed whenever a stochastic algorithm is
-needed.
-
-## Standard SEIR model
-
-We demonstrate the estimation on the recent Ebola outbreak in West
-Africa. We use the number of deaths in Guinea and its corresponding time
-the data was recorded. These data are publicly available and they can be
-obtained easily on the internet such as
-. It is stated out here for
-simplicity.
-
-::: {.ipython}
-In \[34\]: \# the number of deaths and cases in Guinea
-
-In \[35\]: yDeath = \[29.0, 59.0, 60.0, 62.0, 66.0, 70.0, 70.0, 80.0, 83.0, 86.0, 95.0, 101.0, 106.0, 108.0,
-
-:   \....: 122.0, 129.0, 136.0, 141.0, 143.0, 149.0, 155.0, 157.0,
-    158.0, 157.0, 171.0, 174.0, \....: 186.0, 193.0, 208.0, 215.0,
-    226.0, 264.0, 267.0, 270.0, 303.0, 305.0, 307.0, 309.0, \....:
-    304.0, 310.0, 310.0, 314.0, 319.0, 339.0, 346.0, 358.0, 363.0,
-    367.0, 373.0, 377.0, \....: 380.0, 394.0, 396.0, 406.0, 430.0,
-    494.0, 517.0, 557.0, 568.0, 595.0, 601.0, 632.0, \....: 635.0,
-    648.0, 710.0, 739.0, 768.0, 778.0, 843.0, 862.0, 904.0, 926.0,
-    997.0\]
-
-In \[35\]: yCase = \[49.0, 86.0, 86.0, 86.0, 103.0, 112.0, 112.0, 122.0, 127.0, 143.0, 151.0, 158.0,
-
-:   \....: 159.0, 168.0, 197.0, 203.0, 208.0, 218.0, 224.0, 226.0,
-    231.0, 235.0, 236.0, \....: 233.0, 248.0, 258.0, 281.0, 291.0,
-    328.0, 344.0, 351.0, 398.0, 390.0, 390.0, \....: 413.0, 412.0,
-    408.0, 409.0, 406.0, 411.0, 410.0, 415.0, 427.0, 460.0, 472.0,
-    \....: 485.0, 495.0, 495.0, 506.0, 510.0, 519.0, 543.0, 579.0,
-    607.0, 648.0, 771.0, \....: 812.0, 861.0, 899.0, 936.0, 942.0,
-    1008.0, 1022.0, 1074.0, 1157.0, 1199.0, 1298.0, \....: 1350.0,
-    1472.0, 1519.0, 1540.0, 1553.0, 1906.0\]
-
-In \[36\]: \# the corresponding time
-
-In \[37\]: t = \[0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 13.0, 16.0, 18.0, 20.0, 23.0, 25.0, 26.0, 29.0,
-
-:   \....: 32.0, 35.0, 40.0, 42.0, 44.0, 46.0, 49.0, 51.0, 62.0, 66.0,
-    67.0, 71.0, 73.0, 80.0, 86.0, 88.0, \....: 90.0, 100.0, 102.0,
-    106.0, 108.0, 112.0, 114.0, 117.0, 120.0, 123.0, 126.0, 129.0,
-    132.0, 135.0, \....: 137.0, 140.0, 142.0, 144.0, 147.0, 149.0,
-    151.0, 157.0, 162.0, 167.0, 169.0, 172.0, 175.0, 176.0, \....:
-    181.0, 183.0, 185.0, 190.0, 193.0, 197.0, 199.0, 204.0, 206.0,
-    211.0, 213.0, 218.0\]
-:::
-
-### Simple estimation
-
-First ,we are going to fit a standard **SEIR** model to the data.
-Details of the models can be found in `common_models`{.interpreted-text
-role="mod"} Defining the model as usual with some random guess on what
-the parameters might be, here, we choose the values to be the mid point
-of our feasible region (defined by our box constraints later)
-
-::: {.ipython}
-In \[1\]: from pygom import SquareLoss, common_models
-
-In \[1\]: import numpy, scipy.optimize
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: theta = numpy.array(\[5.0, 5.0, 5.0\])
-
-In \[2\]: ode = common_models.SEIR(theta)
-
-In \[3\]: population = 1175e4
-
-In \[4\]: y = numpy.reshape(numpy.append(numpy.array(yCase),
-numpy.array(yDeath)), (len(yCase),2), \'F\')/population
-
-In \[5\]: x0 = \[1., 0., 49.0/population, 29.0/population\]
-
-In \[6\]: t0 = t\[0\]
-
-In \[7\]: objLegrand = SquareLoss(theta, ode, x0, t0, t\[1::\],
-y\[1::,:\], \[\'I\',\'R\'\], numpy.sqrt(\[population\]\*2))
-:::
-
-Then we optimize, first, assuming that the initial conditions are
-accurate. Some relatively large bounds are used for this particular
-problem.
-
-::: {.ipython}
-In \[8\]: boxBounds = \[ (0.0,10.0), (0.0,10.0), (0.0,10.0) \]
-
-In \[9\]: res = scipy.optimize.minimize(fun=objLegrand.cost,
-
-:   \...: jac=objLegrand.sensitivity, \...: x0=theta, \...:
-    bounds=boxBounds, \...: method=\'l-bfgs-b\')
-
-In \[10\]: print(res)
-
-In \[11\]: f = plt.figure()
-
-\@savefig ebola_seir_straight.png In \[12\]: objLegrand.plot()
-
-In \[13\]: plt.close()
-:::
-
-We can see from our visual confirmation that the estimated parameters
-are not exactly ideal. This is confirmed by the information returned
-from the `scipy.optimize.minimize`{.interpreted-text role="func"}
-routine, and probably caused by the poor starting point. An attempt to
-find a more suitable value can be done by some form of parameter space
-exploration. Given that the evaluation of the objective function is not
-expensive here, we have plenty of options to choose from. To reduce the
-number of packages required to build this documentation, routines from
-`scipy.optimize`{.interpreted-text role="mod"} remains our preferred
-option.
-
-### Improved initial guess
-
-::: {.ipython}
-In \[8\]: resDE = scipy.optimize.differential_evolution(objLegrand.cost,
-bounds=boxBounds, polish=False, seed=20921391)
-
-In \[9\]: print(objLegrand.sensitivity(resDE\[\'x\'\]))
-
-In \[10\]: f = plt.figure()
-
-\@savefig ebola_seir_de.png In \[11\]: objLegrand.plot()
-
-In \[12\]: plt.close()
-:::
-
-Looking at the output of the estimates (below this paragraph), we can
-see our inference on Ebola is wrong when compared to the *known* values
-(from field observation) even though the graphs looks
-*\`\`reasonable\"*. Namely, $\gamma^{-1}$ the third element in the
-vector below, our time from infectious to death, is within the expected
-range but $\alpha^{-1}$ (second element), the incubation period, is a
-lot higher than expected.
-
-::: {.ipython}
-In \[1\]: 1/resDE\[\'x\'\]
-:::
-
-### Multimodal surface
-
-A reason for this type of behavior is that we simply lack the
-information/data to make proper inference. Without data on the state
-$E$, the parameters $\beta,\alpha$ for the two states $I$ and $E$ are
-dependent only on observations on $I$. Hence, some other random
-combination of $\beta,\alpha$ that is capable of generating realization
-close to observations in $I$ is feasible. In such cases, the only
-requirement is that there exist some $\gamma$ in the feasible region
-that can compensate for the ill suited $\beta,\alpha$. For example, we
-know (obtained elsewhere and not shown here) that there is another set
-of parameters capable of generating a similar looking curves as before.
-Note the reversal of magnitude in $\beta$ and $\alpha$.
-
-::: {.ipython}
-In \[11\]: objLegrand.cost(\[3.26106524e+00, 2.24798702e-04,
-1.23660721e-02\])
-
-In \[12\]: \#\# objLegrand.cost(\[ 0.02701867, 9.00004776, 0.01031861\])
-\# similar graph
-
-\@savefig ebola_seir_prior.png In \[13\]: objLegrand.plot()
-
-In \[14\]: plt.close()
-:::
-
-### With initial values as parameters
-
-Obviously, the assumption that the whole population being susceptible is
-an overestimate. We now try to estimate the initial conditions of the
-ode as well. Given previous estimates of the parameters
-$\hat{\beta}, \hat{\alpha}, \hat{\gamma}$ it is appropriate to start our
-initial guess there.
-
-Furthermore, given that we now estimate the initial values for all the
-states, we can use the first time point as our observation. So our time
-begins at $t = -1$ where our observations include the previous initial
-condition, i.e. 49 and 29 for the number of cases and death at $t = 0$
-respectively. The following code block demonstrates how we would do
-that; feel free to try it out yourself to see the much improved result.
-
-::: {.ipython verbatim=""}
-In \[1\]: thetaIV = theta.tolist() + x0
-
-In \[2\]: thetaIV\[3\] -= 1e-8 \# to make sure that the initial guess
-satisfy the constraints
-
-In \[3\]: boxBoundsIV = boxBounds + \[(0.,1.), (0.,1.), (0.,1.),
-(0.,1.)\]
-
-In \[4\]: objLegrand = SquareLoss(theta, ode, x0, -1, t, y,
-\[\'I\',\'R\'\], numpy.sqrt(\[population\]\*2))
-
-In \[5\]: resDEIV =
-scipy.optimize.differential_evolution(objLegrand.costIV,
-bounds=boxBoundsIV, polish=False, seed=20921391)
-
-In \[6\]: print(resDEIV)
-
-In \[7\]: f = plt.figure()
-
-In \[8\]: objLegrand.plot()
-
-In \[9\]: plt.close()
-:::
-
-## Legrand Ebola SEIHFR Model
-
-Next, we demonstrate the estimation on a model that is widely used in
-the recent Ebola outbreak in west Africa. Again, the model has been
-defined in `.common_models`{.interpreted-text role="mod"} already.
-
-::: {.ipython}
-In \[1\]: ode = common_models.Legrand_Ebola_SEIHFR()
-
-In \[27\]: \# initial guess from the paper that studied the outbreak in
-Congo
-
-In \[28\]: theta = numpy.array(\[0.588,0.794,7.653, \#\#\# the beta
-
-:   \....: 10.0,9.6,5.0,2.0, \#\#\# the omega \....: 7.0,0.81,0.80,
-    \#\#\# alpha, delta, theta \....: 100.,1.0\]) \#\#\#
-    kappa,intervention time
-
-In \[29\]: \# initial conditions, note that we have a 0.0 at the end
-because the model is a non-automonous ode which we have converted the
-time component out
-
-In \[30\]: x0 = numpy.array(\[population, 0.0, 49.0, 0.0, 0.0, 29.0,
-0.0\])/population
-
-In \[30\]: ode.parameters = theta
-
-In \[31\]: ode.initial_values = (x0, t\[0\])
-
-In \[32\]: objLegrand = SquareLoss(theta, ode, x0, t\[0\], t\[1::\],
-y\[1::,:\], \[\'I\',\'R\'\], numpy.sqrt(\[population\]\*2))
-:::
-
-Now, it is important to set additional constraints accurately because a
-simply box constraint is much larger than the feasible set. Namely,
-$\omega_{I}, \omega_{D}$ are the time taken from onset until end of
-infectious/death, which has to be bigger than $\omega_{H}$, onset to
-hospitalization given the nature of the disease. Therefore, we create
-extra inequality constraints in addition to the box constraints
-
-::: {.ipython}
-
-In \[549\]: boxBounds = \[
-
-:   \.....: (0.001, 100.), \# beta_I \.....: (0.001, 100.), \# beta_H
-    \.....: (0.001, 100.), \# beta_F \.....: (0.001, 100.), \# omega_I
-    \.....: (0.001, 100.), \# omega_D \.....: (0.001, 100.), \# omega_H
-    \.....: (0.001, 100.), \# omega_F \.....: (0.001, 100.), \#
-    alpha\^{-1} \.....: (0.0001, 1.), \# delta \.....: (0.0001, 1.), \#
-    theta \.....: (0.001, 1000.), \# kappa \.....: (0.,218.) \#
-    intervention tine \.....: \]
-
-In \[550\]: cons = ({\'type\': \'ineq\', \'fun\' : lambda x:
-numpy.array(\[x\[3\]-x\[5\], x\[4\]-x\[5\]\])})
-:::
-
-We can now try to find the optimal values, but because this is a
-difficult problem that can take a very long time without guarantee on
-the quality of solution
-
-::: {.ipython okexcept="" okwarning=""}
-
-In \[213\]: res = scipy.optimize.minimize(fun=objLegrand.cost,
-
-:   \.....: jac=objLegrand.sensitivity, \.....: x0=theta, \.....:
-    constraints=cons, \.....: bounds=boxBounds, \.....:
-    method=\'SLSQP\')
-
-In \[214\]: print(res)
-
-In \[215\]: f = plt.figure()
-
-\@savefig ebola_legrand_runtime.png In \[216\]: objLegrand.plot()
-
-In \[217\]: plt.close()
-:::
-
-Evidently, the estimated parameters are very much unrealistic given that
-a lot of them are near the boundaries. It is also known from other
-sources that some of the epidemiology properties of Ebola, with
-incubation period of around 2 weeks and a mortality rate of around 80
-percent.
-
-As the estimate does not appear to provide anything sensible, we also
-provide a set of values previously obtained (that looks semi-reasonable)
-here plot the epidemic curve with the observations layered on top
-
-::: {.ipython okexcept="" okwarning=""}
-
-In \[1\]: theta = numpy.array(\[3.96915071e-02, 1.72302620e+01, 1.99749990e+01,
-
-:   \...: 2.67759445e+01, 4.99999990e+01, 5.56122691e+00, \...:
-    4.99999990e+01, 8.51599523e+00, 9.99999000e-01, \...:
-    1.00000000e-06, 3.85807562e+00, 1.88385318e+00\])
-
-In \[2\]: print(objLegrand.cost(theta))
-
-In \[2\]: solution = ode.integrate(t\[1::\])
-
-In \[3\]: f, axarr = plt.subplots(2,3)
-
-In \[4\]: axarr\[0,0\].plot(t, solution\[:,0\]);
-
-In \[5\]: axarr\[0,0\].set_title(\'Susceptible\');
-
-In \[6\]: axarr\[0,1\].plot(t, solution\[:,1\]);
-
-In \[7\]: axarr\[0,1\].set_title(\'Exposed\');
-
-In \[8\]: axarr\[0,2\].plot(t, solution\[:,2\]);
-
-In \[9\]: axarr\[0,2\].plot(t, y\[:,0\], \'r\');
-
-In \[10\]: axarr\[0,2\].set_title(\'Infectious\');
-
-In \[11\]: axarr\[1,0\].plot(t, solution\[:,3\]);
-
-In \[12\]: axarr\[1,0\].set_title(\'Hospitalised\');
-
-In \[13\]: axarr\[1,1\].plot(t, solution\[:,4\]);
-
-In \[14\]: axarr\[1,1\].set_title(\'Awaiting Burial\');
-
-In \[15\]: axarr\[1,2\].plot(t, solution\[:,5\]);
-
-In \[16\]: axarr\[1,2\].plot(t, y\[:,1\], \'r\');
-
-In \[17\]: axarr\[1,2\].set_title(\'Removed\');
-
-In \[18\]: f.text(0.5, 0.04, \'Days from outbreak\', ha=\'center\');
-
-In \[19\]: f.text(0.01, 0.5, \'Population\', va=\'center\',
-rotation=\'vertical\');
-
-In \[20\]: f.tight_layout();
-
-\@savefig ebola_seihfr_straight_prior.png In \[21\]: plt.show()
-
-In \[22\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/faq.ipynb b/docs/pygom-doc/mdconvert/doc_to_sort/faq.ipynb
deleted file mode 100644
index 016ed7b5..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/faq.ipynb
+++ /dev/null
@@ -1,83 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Frequent asked questions\n",
-    "\n",
-    "## Code runs slowly\n",
-    "\n",
-    "This is because the package is not optimized for speed. Although the\n",
-    "some of the main functions are lambdified using `sympy` or compiled\n",
-    "against `cython` when available, there are many more optimization that\n",
-    "can be done. One example is the lines:\n",
-    "\n",
-    "in `.DeterministicOde.evalSensitivity`. The first two operations can be\n",
-    "inlined into the third and the third line itself can be rewritten as:\n",
-    "\n",
-    "and save the explicit copy operation by `numpy` when making A. If\n",
-    "desired, we could have also made used of the `numexpr` package that\n",
-    "provides further speed up on elementwise operations in place of numpy.\n",
-    "\n",
-    "## Why not compile the numeric computation form sympy against Theano\n",
-    "\n",
-    "Setup of the package has been simplified as much as possible. If you\n",
-    "look closely enough, you will realize that the current code generation\n",
-    "only uses `cython` and not `f2py`. This is because we are not prepared\n",
-    "to do all the system checks, i.e. does a fortran compiler exist, is gcc\n",
-    "installed, was python built as a shared library etc. We are very much\n",
-    "aware of the benefit, especially considering the possibility of GPU\n",
-    "computation in `theano`.\n",
-    "\n",
-    "## Why not use mpmath library throughout?\n",
-    "\n",
-    "This is because we have a fair number of operations that depends on\n",
-    "`scipy`. Obviously, we can solve ode using `mpmath` and do standard\n",
-    "linear algebra. Unfortunately, optimization and statistics packages and\n",
-    "routine are mostly based on `numpy`.\n",
-    "\n",
-    "## Computing the gradient using `.SquareLoss` is slow\n",
-    "\n",
-    "It will always be slow on the first operation. This is due to the design\n",
-    "where the initialization of the class is fast and only find derivative\n",
-    "information/compile function during runtime. After the first\n",
-    "calculation, things should be significantly faster.\n",
-    "\n",
-    "**Why some of my code is not a fortran object?**\n",
-    "\n",
-    "When we detec either a $\\exp$ or a $\\log$ in the equations, we\n",
-    "automatically force the compile to use mpmath to ensure that we obtain\n",
-    "the highest precision. To turn this on/off will be considered as a\n",
-    "feature in the future.\n",
-    "\n",
-    "## Can you not convert a non-autonumous system to an autonomous system for me automatically\n",
-    "\n",
-    "Although we can do that, it is not, and will not be implemented. This is\n",
-    "to ensure that the end user such as yourself are fully aware of the\n",
-    "equations being defined.\n",
-    "\n",
-    "## Getting the sensitivities from `.SquareLoss` did not get a speed up when I used a restricted set of parameters\n",
-    "\n",
-    "This is because we currently evaluate the full set of sensitivities\n",
-    "before extracting them out. Speeding this up for a restrictive set is\n",
-    "being considered. A main reason that stopped us from implementing is\n",
-    "that we find the symbolic gradient of the ode before compiling it. Which\n",
-    "means that one function call to the compiled file will return the full\n",
-    "set of sensitivities and we would only be extracting the appropriate\n",
-    "elements from the matrix. This only amounts to a small speed up. The\n",
-    "best method would be to compile only the necessary elements of the\n",
-    "gradient matrix, but this would require much more work both within the\n",
-    "code, and later on when variables are being added/deleted as all these\n",
-    "compilation are perfromed in runtime.\n",
-    "\n",
-    "## Why do not have the option to obtain gradient via complex differencing\n",
-    "\n",
-    "It is currently not implemented. Feature under consideration."
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/fh.md b/docs/pygom-doc/mdconvert/doc_to_sort/fh.md
deleted file mode 100644
index a7b26a14..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/fh.md
+++ /dev/null
@@ -1,141 +0,0 @@
-# Example: Fitz Hugh {#fh}
-
-## Defining the model
-
-We are going to investigate another classic model here, the
-FitzHugh-Nagumo, or simply FitzHugh here. The model has already been
-defined in `common_models`{.interpreted-text role="mod"} so we can load
-it easily
-
-::: {.ipython}
-In \[1\]: from pygom import SquareLoss, common_models
-
-In \[2\]: import numpy
-
-In \[3\]: import scipy.integrate, scipy.optimize
-
-In \[4\]: import math,time,copy
-
-In \[5\]: import matplotlib.pyplot as plt
-
-In \[1\]: x0 = \[-1.0, 1.0\]
-
-In \[2\]: t0 = 0
-
-In \[3\]: \# params
-
-In \[4\]: paramEval = \[(\'a\',0.2), (\'b\',0.2), (\'c\',3.0)\]
-
-In \[5\]: ode = common_models.FitzHugh(paramEval)
-
-In \[5\]: ode.initial_values = (x0, t0)
-:::
-
-Define a set of time points and lets see how the two states $V$ and $R$
-are suppose to behave.
-
-::: {.ipython}
-In \[6\]: t = numpy.linspace(1, 20, 30).astype(\'float64\')
-
-In \[7\]: solution = ode.integrate(t)
-
-\@savefig fh_plot.png In \[8\]: ode.plot()
-:::
-
-## Estimate the parameters
-
-Obtaining the correct parameters for the FitzHugh model is well known to
-be difficult, this is because the surface is multimodal. Although this
-has been shown many times in the literature, so we will omit the
-details. Regardless, we give it a go with some initial guess. with some
-luck, we will be able to recover the original parameters. First, we try
-it out with only one target state
-
-::: {.ipython}
-In \[26\]: theta = \[0.5, 0.5, 0.5\]
-
-In \[27\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\[1::,1\],
-\'R\')
-
-In \[28\]: boxBounds = \[
-
-:   \....: (0.0,5.0), \....: (0.0,5.0), \....: (0.0,5.0) \....: \]
-
-In \[29\]: res = scipy.optimize.minimize(fun=objFH.cost,
-
-:   \....: jac=objFH.sensitivity, \....: x0=theta, \....:
-    bounds=boxBounds, \....: method=\'L-BFGS-B\')
-
-In \[30\]: print(res)
-:::
-
-Then we try the same again but with both state as our target. Now we
-won\'t look at the iterations because they are pretty pointless.
-
-::: {.ipython}
-In \[30\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\[1::,:\],
-\[\'V\',\'R\'\])
-
-In \[31\]: res = scipy.optimize.minimize(fun=objFH.cost,
-
-:   \....: jac=objFH.sensitivity, \....: x0=theta, \....:
-    bounds=boxBounds, \....: method=\'L-BFGS-B\')
-
-In \[32\]: print(res)
-:::
-
-Note how the estimates are the same, unlike other models.
-
-## Estimate initial value
-
-We can further assume that we have no idea about the initial values for
-$V$ and $R$ as well. We also provide guesstimate to set off the
-optimization. The input vector $\theta$ must have the parameters first,
-then the initial values, along with the corresponding bounds.
-
-First, only a single target state, i.e. we only have observations for
-one of states which is $R$ in this case
-
-::: {.ipython}
-In \[35\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\[1::,1\],
-\'R\')
-
-In \[35\]: boxBounds = \[
-
-:   \....: (0.0,5.0), \....: (0.0,5.0), \....: (0.0,5.0), \....:
-    (None,None), \....: (None,None) \....: \]
-
-In \[36\]: res = scipy.optimize.minimize(fun=objFH.costIV,
-
-:   \....: jac=objFH.sensitivityIV, \....: x0=theta + \[-0.5,0.5\],
-    \....: bounds=boxBounds, \....: method=\'L-BFGS-B\')
-
-In \[37\]: print(res)
-:::
-
-then both state as target at the same time
-
-::: {.ipython}
-In \[38\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\[1::,:\],
-\[\'V\',\'R\'\])
-
-In \[38\]: res = scipy.optimize.minimize(fun=objFH.costIV,
-
-:   \....: jac=objFH.sensitivityIV, \....: x0=theta + \[-0.5, 0.5\],
-    \....: bounds=boxBounds, \....: method=\'L-BFGS-B\')
-
-In \[39\]: print(res)
-:::
-
-See the difference between the two estimate with the latter, both state
-were used, yielding superior estimates. Note that only the forward
-sensitivity method is implemented when estimating the initial value, and
-it is assumed that the starting condition for all the states are
-unknown.
-
-The choice of algorithm here is the **L-BFGS-B** which is a better
-choice because the parameter space of the FitzHugh is rough (i.e. large
-second derivative) as well as being multimodal. This means that the
-Hessian is not guaranteed to be positive definite and approximation
-using $J^{\top}J$ is poor, with $J$ being the Jacobian of the objective
-function.
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/gradient.md b/docs/pygom-doc/mdconvert/doc_to_sort/gradient.md
deleted file mode 100644
index d0755502..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/gradient.md
+++ /dev/null
@@ -1,372 +0,0 @@
-# Gradient estimation under square loss {#gradient}
-
-Assuming that we have a set of $N$ observations $y_{i}$ at specific time
-points $t_{i}$, $i = 1,\ldots,N$, we may wish to test out a set of ode
-to see whether it fits to the data. The most natural way to test such
-*fit* is to minimize the sum of squares between our observations $y$ and
-see whether the resulting solution of the ode and the estimationed
-parameters makes sense.
-
-We assume that this estimation process will be tackled through a
-non-linear optimization point of view. However, it should be noted that
-such estimates can also be performed via MCMC or from a global
-optimization perspective. A key element in non-linear optimization is
-the gradient, which is the focus of this page.
-
-Multiple ways of obtaining the gradient have been implemented. All of
-them serve a certain purpose and may not be a viable/appropriate options
-depending on the type of ode. More generally, let $d,p$ be the number of
-states and paramters respectively. Then finite difference methods have a
-run order of $O(p+1)$ of the original ode, forward sensitivity require
-an integration of an ode of size $(d+1)p$ rather than $d$. The adjoint
-method require two run of size $d$ in principle, but actual run time is
-dependent on the number of observations.
-
-For the details of the classes and methods, please refer to
-`mod`{.interpreted-text role="ref"}.
-
-## Notation
-
-We introduce the notations that will be used in the rest of the page,
-some of which may be slightly unconventional but necessary due to the
-complexity of the problem. Let $x \in \mathbb{R}^{d}$ and
-$\theta \in \mathbb{R}^{p}$ be the states and parameters respectively.
-The term *state* or *simulation* are used interchangeably, even though
-strictly speaking a state is $x$ whereas $x(t)$ is the simulation. An
-ode is defined as
-
-$$f(x,\theta) = \dot{x} = \frac{\partial x}{\partial t}$$
-
-and usually comes with a set of initial conditions $(x_0,t_0)$ where
-$t_0 \le t_{i} \forall i$. Let $g(x,\theta)$ be a function that maps the
-set of states to the observations,
-$g : \mathbb{R}^{d} \rightarrow \mathbb{R}^{m}$. For compartmental
-problems, which is our focus, $\nabla_{\theta}g(x,\theta)$ is usually
-zero and $\nabla_{x}g(x,\theta)$ is an identity function for some or all
-of the states $x$. Denote $l(x_{0},\theta,x)$ as our cost function
-$l : \mathbb{R}^{m} \rightarrow \mathbb{R}$ and $L(x_{0},\theta,x)$ be
-the sum of $l(\cdot)$. Both $x$ and $x_{0}$ are usually dropped for
-simplicity. We will be dealing exclusively with square loss here, which
-means that
-
-$$L(\theta) = \sum_{i=1}^{N} \left\| y_{i} - g(x(t_{i})) \right\|^{2} = \mathbf{e}^{\top} \mathbf{e}$$
-
-where $\mathbf{e}$ is the residual vector, with elements
-
-$$e_{i} = y_{i} - x(t_{i}).$$
-
-## Model setup
-
-Again, we demonstrate the functionalities of our classes using an SIR
-model.
-
-::: {.ipython}
-In \[1\]: from pygom import SquareLoss, common_models
-
-In \[2\]: import copy,time,numpy
-
-In \[2\]: ode = common_models.SIR()
-
-In \[3\]: paramEval = \[(\'beta\',0.5), (\'gamma\',1.0/3.0) \]
-
-In \[7\]: \# the initial state, normalized to zero one
-
-In \[8\]: x0 = \[1., 1.27e-6, 0.\]
-
-In \[5\]: \# initial time
-
-In \[6\]: t0 = 0
-
-In \[5\]: ode.parameters = paramEval
-
-In \[6\]: ode.initial_values = (x0, t0)
-
-In \[9\]: \# set the time sequence that we would like to observe
-
-In \[10\]: t = numpy.linspace(1, 150, 100)
-
-In \[11\]: numStep = len(t)
-
-In \[11\]: solution = ode.integrate(t)
-
-In \[12\]: y = solution\[1::,2\].copy()
-
-In \[13\]: y += numpy.random.normal(0, 0.1, y.shape)
-:::
-
-Now we have set up the model along with some observations, obtaining the
-gradient only requires the end user to put the appropriate information
-it into the class `SquareLoss`{.interpreted-text role="class"}. Given
-the initial guess $\theta$
-
-::: {.ipython}
-In \[210\]: theta = \[0.2, 0.2\]
-:::
-
-We initialize the `SquareLoss`{.interpreted-text role="class"} simply as
-
-::: {.ipython}
-In \[20\]: objSIR = SquareLoss(theta, ode, x0, t0, t, y, \'R\')
-:::
-
-where the we also have to specify the state our observations are from.
-Now, we demonstrate the different methods in obtaining the gradient and
-mathematics behind it.
-
-## Forward sensitivity
-
-The forward sensitivity equations are derived by differentiating the
-states implicitly, which yields
-
-$$\frac{d\dot{x}}{d\theta} = \frac{\partial f}{\partial x}\frac{dx}{d\theta} + \frac{\partial f}{\partial \theta}.$$
-
-So finding the sensitivies $\frac{dx}{d\theta}$ simply require another
-integration of a $p$ coupled ode of $d$ dimension, each with the same
-Jacobian as the original ode. This integration is performed along with
-the original ode because of possible non-linearity.
-
-A direct call to the method
-`sensitivity `{.interpreted-text
-role="meth"} computed the gradient
-
-::: {.ipython}
-In \[33\]: gradSens = objSIR.sensitivity()
-:::
-
-whereas `.jac`{.interpreted-text role="meth"} will allow the end user to
-obtain the Jacobian (of the objective function) and the residuals, the
-information required to get the gradient as we see next.
-
-::: {.ipython}
-In \[33\]: objJac, output = objSIR.jac(full_output=True)
-:::
-
-## Gradient
-
-Just the sensitivities alone are not enough to obtain the gradient, but
-we are $90\%$ there. Differentiating the loss function
-
-$$\begin{aligned}
-\frac{dL}{d\theta} &= \nabla_{\theta} \sum_{i=1}^{N}\frac{dl}{dg} \\
-                   &= \sum_{i=1}^{N} \frac{\partial l}{\partial x}\frac{dx}{d\theta} + \frac{\partial l}{\partial \theta} \\
-                   &= \sum_{i=1}^{N} \frac{\partial l}{\partial g}\frac{\partial g}{\partial x}\frac{dx}{d\theta} + \frac{\partial l}{\partial g}\frac{\partial g}{\partial \theta}
-\end{aligned}$$
-
-via chain rule. When $\frac{\partial g}{\partial \theta} = 0$, the total
-gradient simplifies to
-
-$$\frac{dL}{d\theta} = \sum_{i=1}^{N} \frac{\partial l}{\partial g}\frac{\partial g}{\partial x}\frac{dx}{d\theta}$$
-
-Obviously, the time indicies are dropped above but all the terms above
-are evaluated only at the observed time points. More concretely, this
-means that
-
-$$\begin{aligned}
-\frac{\partial l(x(j),\theta)}{\partial g} = \left\{ \begin{array}{ll} -2(y_{i} - x(j)) & , \; j = t_{i} \\ 0 & \; \text{otherwise} \end{array} \right.
-\end{aligned}$$
-
-When $g(\cdot)$ is an identity function (which is assumed to be the case
-in `SquareLoss`{.interpreted-text role="class"})
-
-$$\frac{\partial g(x(t_{i}),\theta)}{\partial x} = I_{d}$$
-
-then the gradient simplifies even further as it is simply
-
-$$\frac{dL}{d\theta} = -2\mathbf{e}^{\top}\mathbf{S}$$
-
-where $\mathbf{e}$ is the vector of residuals and
-$\mathbf{S} = \left[\mathbf{s}_{1},\mathbf{s}_{2},\ldots,\mathbf{s}_{n}\right]$
-with elements
-
-$$\mathbf{s}_{i} = \frac{dx}{d\theta}(t_{i}),$$
-
-the solution of the forward sensitivies at time $t_{i}$, obtained from
-solving the coupled ode as mentioned previously.
-
-## Jacobian
-
-Now note how the gradient simplifies to $-2\mathbf{e}^{\top}\mathbf{S}$.
-Recall that a standard result in non-linear programming states that the
-gradient of a sum of sqaures objective function $L(\theta,y,x)$ is
-
-$$\nabla_{\theta} L(\theta,y,x) = -2(\mathbf{J}^{T} \left[\mathbf{y} - \mathbf{f}(x,\boldsymbol{\theta}) \right] )^{\top}$$
-
-with $f(x,\theta)$ our non-linear function and $J$ our Jacobian with
-elements
-
-$$J_{i} = \frac{\partial f(x_{i},\boldsymbol{\theta})}{\partial \boldsymbol{\theta}}.$$
-
-This is exactly what we have seen previously, substituting in reveals
-that $J = \mathbf{S}$. Hence, the Jacobian is (a necessary)by product
-when we wish to obtain the gradient. In fact, this is exactly how we
-proceed in
-`sensitivity `{.interpreted-text
-role="func"} where it makes an internal call to
-`jac `{.interpreted-text role="func"} to obtain
-the Jacobian first. This allows the end user to have more options when
-choosing which type of algorithms to use, i.e. Gauss-Newton or
-Levenberg-Marquardt.
-
-To check that the output is in fact the same
-
-::: {.ipython}
-In \[1\]: objJac.transpose().dot(-2\*output\[\'resid\'\]) - gradSens
-:::
-
-## Adjoint
-
-When the number of parameters increases, the number of sensitivies also
-increases. The time required scales directly with the number of
-parameters. We describe another method which does not depend on the
-number of parameters, but rather, the number of states and observations.
-
-The full derivations will not be shown here, but we aim to provide
-enough information to work out the steps performed in the our code. Let
-write our optimization problem as
-
-$$\begin{aligned}
-min_{\theta} \quad & \int_{t_{0}}^{T} l(x_{0},\theta,x(t)) dt \\
-s.t. \quad & \dot{x} = f(x,\theta)
-\end{aligned}$$
-
-which is identical to the original problem but in a continuous setting.
-Now write the constrained problem in the Lagrangian form
-
-$$min_{\theta} \; L(\theta) + \int_{t_{0}}^{T} \lambda^{\top}(\dot{x} - f(x,\theta))$$
-
-with Lagrangian multiplier $\lambda \ge 0$. After some algebraic
-manipulation, it can be shown that the total derivative of the
-Lagrangian function is
-
-$$\frac{dL}{d\theta} = \int_{t_{0}}^{T} \left(\frac{\partial l}{\partial \theta} - \lambda^{\top}\frac{\partial f}{\partial \theta} \right) dt.$$
-
-Using previously defined loss functions (the identity), the first term
-is zero and evaluating $\frac{\partial f}{\partial \theta}$ is trivial.
-What remains is the calculation of $\lambda(t)$ for
-$t \in \left[t_{0},T\right]$.
-
-Although this still seem to be ill-posed problem when Looking at the
-Lagrangian function, one can actually obtain the *adjoint equation*,
-after certain assumptions and
-
-$$\frac{d\lambda^{\top}}{dt} = \frac{\partial l}{\partial x} - \lambda^{\top}\frac{\partial f}{\partial \theta}.$$
-
-which is again an integration. An unfortunate situation arise here for
-non-linear systems because we use the minus Jacobian in the adjoint
-equation. So if the eigenvalues of the Jacobian indicate that our
-original ode is stable, such as -1, the minus eigenvalues (now 1)
-implies that the adjoint equation is not stable. Therefore, one must
-integrate backward in time to solve the adjoint equation and it cannot
-be solved simultaneously as the ode, unlike the forward sensitivity
-equations.
-
-Given a non-linearity ode, we must store information about the states
-between $t_{0}$ and $T$ in order to perform the integration. There are
-two options, both require storing many evaluated $x(j)$ within the
-interval $\left[t_{0},T\right]$. Unfortunately, only one is available;
-interpolation over all states and integrate using the interpolating
-functions. The alternative of using observed $x(j)'s$ at fixed points is
-not competitive because we are unable to use fortran routines for the
-integration
-
-The method of choice here to perform the adjoint calcuation is to run a
-forward integration, then perform an interpolation using splines with
-explicit knots at the observed time points.
-
-::: {.ipython}
-In \[326\]: odeSIRAdjoint, outputAdjoint =
-objSIR.adjoint(full_output=True)
-:::
-
-This is because evaluating the Jacobian may be expensive and Runge-kutta
-method suffers as the complexity increases. In non-linear model such as
-those found in epidemiology, each element of the Jacobian may be the
-result of a complicated equation where linear step method will shine as
-it makes as little function evaluation as possible. Note that
-derivations in the literature, the initial condition when evaluating the
-adjoint equation is $\lambda(T)=0$. But in our code we used
-$\lambda(T) = -2(y(T)-x(T))$. Recall that we have observation $y(T)$ and
-simulation $x(T)$, so that the adjoint equation evaluated at time $T$
-
-$$\frac{\partial \lambda^{\top}}{\partial t} \Big|_{T} = -2(y-f(x,\theta))\Big|_{T}  - \lambda(T)\frac{\partial f}{\partial \theta}\Big|_{T}$$
-
-with the second term equal to zero. Integration under step size $h$
-implies that
-$\lambda(T) \approx \lim_{h \to 0} \lambda(T-h) = -2(y(T)-x(T))$.
-
-## Time Comparison
-
-A simple time comparison between the different methods reveals that the
-forward sensitivity method dominates the others by a wide margin. It
-will be tempting to conclude that it is the best and should be the
-default at all times but that is not true, due to the complexity of each
-method mentioned previously. We leave it to the end user to find out the
-best method for their specific problem.
-
-::: {.ipython}
-In \[319\]: %timeit gradSens = objSIR.sensitivity()
-
-In \[326\]: %timeit odeSIRAdjoint,outputAdjoint =
-objSIR.adjoint(full_output=True)
-:::
-
-## Hessian
-
-The Hessian is defined by
-
-$$\frac{\partial^{2} l}{\partial \theta^{2}} = \left( \frac{\partial l}{\partial x} \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + \frac{\partial x}{\partial \theta}^{\top}\frac{\partial^{2} l}{\partial x^{2}}\frac{\partial x}{\partial \theta}$$
-
-where $\otimes$ is the Kronecker product. Note that $\nabla_{\theta} x$
-is the sensitivity and the second order sensitivities can be found again
-via the forward method, which involve another set of ode\'s, namely the
-forward-forward sensitivities
-
-$$\frac{\partial}{\partial t}\left(\frac{\partial^{2} x}{\partial \theta^{2}}\right) = \left( \frac{\partial f}{\partial x} \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + \left( I_{d} \otimes \frac{\partial x}{\partial \theta}^{\top} \right) \frac{\partial^{2} f}{\partial x^{2}} \frac{\partial x}{\partial \theta}.$$
-
-From before, we know that
-
-$$\frac{\partial l}{\partial x} = (-2y+2x)  \quad and \quad \frac{\partial^{2} l}{\partial x^{2}} = 2I_{d}$$
-
-so our Hessian reduces to
-
-$$\frac{\partial^{2} l}{\partial \theta^{2}} = \left( \left(-2y+2x\right) \otimes I_{p} \right) \frac{\partial^{2} x}{\partial \theta^{2}} + 2S^{\top}S,$$
-
-where the second term is a good approximation to the Hessian as
-mentioned previously. This is the only implementation in place so far
-even though obtaining the estimate this way is relatively slow.
-
-Just to demonstate how it works, lets look at the Hessian at the optimal
-point. First, we obtain the optimal value
-
-::: {.ipython}
-In \[211\]: import scipy.linalg,scipy.optimize
-
-In \[212\]: boxBounds = \[(0.0, 2.0), (0.0, 2.0)\]
-
-In \[213\]: res = scipy.optimize.minimize(fun=objSIR.cost,
-
-:   \.....: jac=objSIR.sensitivity, \.....: x0=theta, \.....:
-    bounds=boxBounds, \.....: method=\'L-BFGS-B\')
-:::
-
-Then compare again the least square estimate of the covariance matrix
-against our version
-
-::: {.ipython}
-In \[211\]: resLS, cov_x, infodict, mesg, ier =
-scipy.optimize.leastsq(func=objSIR.residual, x0=res\[\'x\'\],
-full_output=True)
-
-In \[212\]: HJTJ, outputHJTJ = objSIR.hessian(full_output=True)
-
-In \[311\]: print(scipy.linalg.inv(HJTJ))
-
-In \[312\]: print(cov_x)
-:::
-
-also note the difference between the Hessian and the approximation using
-the Jacobian, which is in fact what the least squares routine uses.
-
-::: {.ipython}
-In \[313\]: print(scipy.linalg.inv(outputHJTJ\[\'JTJ\'\]))
-:::
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/initialGuess.md b/docs/pygom-doc/mdconvert/doc_to_sort/initialGuess.md
deleted file mode 100644
index 358fd49f..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/initialGuess.md
+++ /dev/null
@@ -1,59 +0,0 @@
-# Obtaining good initial value for parameters {#initialGuess}
-
-## Function Interpolation
-
-When we want to fit the model to data, one of the necessary steps is to
-supply the optimization procedure a good set of initial guess for the
-parameters $\theta$. This may be a challenge when we do have a good
-understanding of the process we are trying to model i.e. infectious
-disease may all follow the same SIR process but with vastly different
-incubation period.
-
-A method to obtain such initial guess based on the collocation is
-available in this package. A restriction is that data must be present
-for all states. We demonstrate this using the FitzHugh-Nagumo model.
-
-::: {.ipython}
-In \[1\]: from pygom import SquareLoss, common_models, get_init
-
-In \[2\]: import numpy
-
-In \[3\]: x0 = \[-1.0, 1.0\]
-
-In \[4\]: t0 = 0
-
-In \[5\]: \# params
-
-In \[6\]: paramEval = \[(\'a\',0.2), (\'b\',0.2), (\'c\',3.0)\]
-
-In \[7\]: ode = common_models.FitzHugh(paramEval)
-
-In \[8\]: ode.initial_values = (x0, t0)
-
-In \[8\]: t = numpy.linspace(1, 20, 30).astype(\'float64\')
-
-In \[9\]: solution = ode.integrate(t)
-:::
-
-Below, we try to find the initial guess without supplying any further
-information. The underlying method fits a cubic spline against the
-observation and tries to minimize the difference between the first
-derivative of the spline and the function of the ode. Varying degree of
-smoothness penalty is applied to the spline and the best set of
-parameters is the ones that yields the smallest total error, combining
-both the fit of the spline against data and the spline against the ode.
-
-::: {.ipython}
-In \[10\]: theta, sInfo = get_init(solution\[1::,:\], t, ode,
-theta=None, full_output=True)
-
-In \[11\]: print(theta)
-
-In \[12\]: print(sInfo)
-:::
-
-As seen above, we have obtained a very good guess of the parameters, in
-fact almost the same as the generating process. The information
-regarding the smoothing factor shows that the amount of penalty used is
-small, which is expected given that we use the solution of the ode as
-observations.
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/profile.md b/docs/pygom-doc/mdconvert/doc_to_sort/profile.md
deleted file mode 100644
index aa50fb5a..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/profile.md
+++ /dev/null
@@ -1,506 +0,0 @@
-# Confidence Interval of Estimated Parameters {#profile}
-
-After obtaining the *best* fit, it is natural to report both the point
-estimate and the confidence level at the $\alpha$ level. The easiest way
-to do this is by invoking the normality argument and use Fisher
-information of the likelihood. As explained previously at the bottom of
-`gradient`{.interpreted-text role="ref"}, we can find the Hessian,
-$\mathbf{H}$, or the approximated Hessian for the estimated parameters.
-The Cramer\--Rao inequality, we know that
-
-$$Var(\hat{\theta}) \ge \frac{1}{I(\theta)},$$
-
-where $I(\theta)$ is the Fisher information, which is the Hessian
-subject to regularity condition. Given the Hessian, computing the
-confidence intervals is trivial. Note that this is also known as the
-asymptotic confidence interval where the normality comes from invoking
-the CLT. There are other ways of obtaining a confidence intervals, we
-will the ones implemented in the package. First, we will set up a SIR
-model as seen in `sir`{.interpreted-text role="ref"} which will be used
-throughout this page.
-
-::: {.ipython}
-In \[1\]: from pygom import NormalLoss, common_models
-
-In \[2\]: from pygom.utilR import qchisq
-
-In \[3\]: import numpy
-
-In \[4\]: import scipy.integrate
-
-In \[5\]: import matplotlib.pyplot as plt
-
-In \[6\]: import copy
-
-In \[7\]: ode = common_models.SIR(\[(\'beta\', 0.5), (\'gamma\',
-1.0/3.0)\])
-:::
-
-and we assume that we only have observed realization from the $R$
-compartment
-
-::: {.ipython}
-In \[1\]: x0 = \[1, 1.27e-6, 0\]
-
-In \[2\]: t = numpy.linspace(0, 150, 100).astype(\'float64\')
-
-In \[3\]: ode.initial_values = (x0, t\[0\])
-
-In \[4\]: solution = ode.integrate(t\[1::\])
-
-In \[5\]: theta = \[0.2, 0.2\]
-
-In \[6\]: targetState = \[\'R\'\]
-
-In \[7\]: targetStateIndex =
-numpy.array(ode.get_state_index(targetState))
-
-In \[8\]: y = solution\[1::,targetStateIndex\] + numpy.random.normal(0,
-0.01, (len(solution\[1::,targetStateIndex\]), 1))
-
-In \[9\]: yObv = y.copy()
-
-In \[10\]: objSIR = NormalLoss(theta, ode, x0, t\[0\], t\[1::\], y,
-targetState)
-
-In \[11\]: boxBounds = \[(1e-8, 2.0), (1e-8, 2.0)\]
-
-In \[12\]: boxBoundsArray = numpy.array(boxBounds)
-
-In \[13\]: xhat = objSIR.fit(theta, lb=boxBoundsArray\[:,0\],
-ub=boxBoundsArray\[:,1\])
-:::
-
-## Asymptotic
-
-When the estimate is obtained say, under a square loss or a normal
-assumption, the corresponding likelihood can be written down easily. In
-such a case, likelihood ratio test under a Chi\--squared distribution is
-
-$$2 (\mathcal{L}(\hat{\boldsymbol{\theta}}) - \mathcal{L}(\boldsymbol{\theta})) \le \chi_{1 - \alpha}^{2}(k)$$
-
-where $1-\alpha$ is the size of the confidence region and $k$ is the
-degree of freedom. The corresponding asymptotic confidence interval for
-parameter $j$ can be derived as
-
-$$\hat{\theta}_{j} \pm \sqrt{\chi_{1 - \alpha}^{2}(k) H_{i,i}}.$$
-
-A pointwise confidence interval is obtained when $k = 1$. We assume in
-our package that a pointwise confidence interval is desired. This can be
-obtained simply by
-
-::: {.ipython}
-In \[1\]: from pygom import confidence_interval as ci
-
-In \[2\]: alpha = 0.05
-
-In \[3\]: xL, xU = ci.asymptotic(objSIR, alpha, xhat,
-lb=boxBoundsArray\[:,0\], ub=boxBoundsArray\[:,1\])
-
-In \[4\]: print(xL)
-
-In \[5\]: print(xU)
-:::
-
-Note that the set of bounds here is only used for check the validity of
-$\hat{\mathbf{x}}$ and not used in the calculation of the confidence
-intervals. Therefore the resulting output can be outside of the box
-constraints.
-
-## Profile Likelihood
-
-Another approach to calculate the confidence interval is to tackle one
-parameter at a time, treating the rest of them as nuisance parameters,
-hence the term *profile*. Let $\mathcal{L}(\boldsymbol{\theta})$ be our
-log\--likelihood with parameter $\boldsymbol{\theta}$. Element
-$\theta_{j}$ is our parameter of interest and $\boldsymbol{\theta}_{-j}$
-represents the complement such that
-$\boldsymbol{\theta} = \theta_{j} \cup \boldsymbol{\theta}_{-j}$. For
-simply models such as linear regression with only regression
-coefficients $\boldsymbol{\beta}$, then
-$\boldsymbol{\theta} = \boldsymbol{\beta}$.
-
-To shorten the notation, let
-
-$$\mathcal{L}(\boldsymbol{\theta}_{-j} \mid \theta_{j}) = \max \mathcal{L}(\boldsymbol{\theta}_{-j} \mid \theta_{j})$$
-
-which is the maxima of $\boldsymbol{\theta}_{-j}$ given $\theta_{j}$.
-$\hat{\boldsymbol{\theta}}$ denotes the MLE of the parameters as usual.
-The profile\--likelihood based confidence interval for $\theta_{j}$ is
-defined as
-
-$$\begin{aligned}
-\theta_{j}^{U} &= \sup \left\{ \mathcal{L}(\hat{\boldsymbol{\theta}}) - \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j}) \le \frac{1}{2} \chi_{1 - \alpha}^{2}(1) \right\} \\
-\theta_{j}^{L} &= \inf \left\{ \mathcal{L}(\hat{\boldsymbol{\theta}}) - \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j}) \le \frac{1}{2} \chi_{1 - \alpha}^{2}(1) \right\}
-\end{aligned}$$
-
-where again we have made use of the normal approximation, but without
-imposing symmetry. The set of equations above automatically implies that
-the interval width is $\theta_{j}^{U} - \theta_{j}^{L}$ and
-
-$$\mathcal{L}(\hat{\boldsymbol{\theta}}) - \frac{1}{2} \chi_{1-\alpha}^{2}(1) - \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j}) = 0.$$
-
-As mentioned previously, $\boldsymbol{\theta}_{-j}$ is the maximizer of
-the nuisance parameters, which has a gradient of zero. Combining this
-with the equation above yields a non\--linear system of equations of
-size $p$,
-
-$$\begin{aligned}
-g(\boldsymbol{\theta}) = \left[ \begin{array}{c} \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j}) - c \\ \frac{\partial \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j})}{\partial \boldsymbol{\theta}_{-j}} \end{array} \right] = 0
-\end{aligned}$$
-
-where
-$c = \mathcal{L}(\hat{\boldsymbol{\theta}}) + \frac{1}{2} \chi_{1-\alpha}^{2}(1)$.
-Solving this set of system of equations only need simple Newton like
-steps, possibly with correction terms as per [\[Venzon1988\]](). We
-provide a function to obtain such estimate
-
-::: {.ipython verbatim=""}
-In \[1\]: xLProfile, xUProfile, xLProfileList, xUProfileList =
-ci.profile(objSIR, alpha, xhat, lb=boxBoundsArray\[:,0\],
-ub=boxBoundsArray\[:,1\], full_output=True)
-:::
-
-but unfortunately this is not accurate most of the time due to the
-complicated surface at locations not around $\hat{\theta}$. This is a
-common scenario for non\--linear least square problems because the
-Hessian is not guaranteed to be a PSD everywhere. Therefore, a safeguard
-is in place to obtain the $\theta_{j}^{U},\theta_{j}^{L}$ by iteratively
-by updating $\theta_{j}$ and find the solution to
-`nuisanceOptim`{.interpreted-text role="eq"}.
-
-Furthermore, we also provide the functions necessary to obtain the
-estimates such as the four below.
-
-::: {.ipython}
-In \[1\]: i = 0
-
-In \[1\]: funcF = ci.\_profileF(xhat, i, 0.05, objSIR)
-
-In \[2\]: funcG = ci.\_profileG(xhat, i, 0.05, objSIR)
-
-In \[3\]: funcGC = ci.\_profileGSecondOrderCorrection(xhat, i, alpha,
-objSIR)
-
-In \[4\]: funcH = ci.\_profileH(xhat, i, 0.05, objSIR)
-:::
-
-Where $i$ is the index of the parameter of interest.
-`_profileF`{.interpreted-text role="func"} is the squared norm of
-`obj`{.interpreted-text role="eq"}, which easy the optimization process
-for solvers which requires a converted form from system of equations to
-non-linear least squares. `_profileG`{.interpreted-text role="func"} is
-the systems of equations `obj`{.interpreted-text role="eq"},
-`_profileH`{.interpreted-text role="func"} is the derivative of
-`obj`{.interpreted-text role="eq"}
-
-$$\begin{aligned}
-\nabla g(\boldsymbol{\theta}) = \left[ \begin{array}{c} \frac{\partial \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j})}{\partial \theta_{j}} \\ \frac{\partial^{2} \mathcal{L}(\boldsymbol{\theta} \mid \theta_{j})}{\partial \boldsymbol{\beta}_{-j} \partial \theta_{j}} \end{array} \right]
-\end{aligned}$$
-
-and `_profileGSecondOrderCorrection`{.interpreted-text role="func"} has
-the second order correction [\[Venzon1988\]]().
-
-## Geometric profile likelihood
-
-Due to the difficulty in obtain a profile likelihood via the standard
-Newton like steps, we also provide a way to generate a similar result
-using the geometric structure of the likelihood surface. We follow the
-method in [\[Moolgavkar1987\]](), which involves solving a set of
-differential equations
-
-$$\begin{aligned}
-\frac{d\beta_{j}}{dt} &= k g^{-1/2} \\
-\frac{d\boldsymbol{\beta}_{-j}}{dt} &= \frac{d\boldsymbol{\beta}_{-j}}{d\beta_{j}} \frac{d\beta_{j}}{dt},
-\end{aligned}$$
-
-where $k = \Phi(1-\alpha)$ is the quantile we want to obtain under a
-normal distribution, and
-
-$$\begin{aligned}
-g = J_{\beta_{j}}^{\top} I^{\boldsymbol{\beta}} J_{\beta_{j}}, \quad J_{\beta_{j}} = \left( \begin{array}{c} 1 \\ \frac{d\boldsymbol{\beta}_{-j}}{d\beta_{j}} \end{array} \right).
-\end{aligned}$$
-
-Here, $J_{\beta_{j}}$ is the Jacobian between $\beta_{j}$ and
-$\boldsymbol{\beta}_{-j}$ with the term
-
-$$\frac{d\boldsymbol{\beta}_{-j}}{d\beta_{j}} = -\left( \frac{\partial^{2} \mathcal{L}}{\partial \boldsymbol{\beta}_{-j}\partial \boldsymbol{\beta}_{-j}^{\top} } \right)^{-1} \frac{\partial^{2} \mathcal{L}}{\partial \beta_{j} \partial \beta_{-j}^{\top}}$$
-
-and hence the first element is $1$ (identity transformation).
-$I^{\boldsymbol{\beta}}$ is the Fisher information of
-$\boldsymbol{\beta}$, which is
-
-$$I^{\boldsymbol{\beta}} = \frac{\partial \boldsymbol{\theta}}{\partial \boldsymbol{\beta}^{\top}} \Sigma^{\boldsymbol{\theta}(\boldsymbol{\beta})} \frac{\partial \boldsymbol{\theta}}{\partial \boldsymbol{\beta}}.$$
-
-It is simply $\Sigma^{\boldsymbol{\beta}}$ if
-$\boldsymbol{\theta} = \boldsymbol{\beta}$. Different Fisher information
-can be used for $\Sigma^{\boldsymbol{\beta}}$ such as the expected or
-observed, at $\hat{\boldsymbol{\beta}}$ or $\boldsymbol{\beta}$. After
-some trivial algebraic manipulation, we can show that our ode boils
-downs to
-
-$$\begin{aligned}
-\left[ \begin{array}{c} \frac{d\beta_{j}}{dt} \\ \frac{d\boldsymbol{\beta_{-j}}}{dt} \end{array} \right] = k \left[ \begin{array}{c} 1 \\ -A^{-1}w \end{array} \right] \left( v - w^{\top}A^{-1}w \right)^{-1/2}
-\end{aligned}$$
-
-where the symbols on the RHS above correspond to partitions in the
-Fisher information
-
-$$\begin{aligned}
-I^{\boldsymbol{\beta}} = \left[ \begin{array}{cc} v & w^{\top} \\ w & A \end{array} \right].
-\end{aligned}$$
-
-The integration is perform from $t = 0$ to $1$ and is all handled
-internally via `geometric`{.interpreted-text role="func"}
-
-::: {.ipython}
-In \[1\]: xLGeometric, xUGeometric, xLList, xUList =
-ci.geometric(objSIR, alpha, xhat, full_output=True)
-
-In \[2\]: print(xLGeometric)
-
-In \[3\]: print(xUGeometric)
-:::
-
-## Bootstrap
-
-This is perhaps the favorite method to estimate confidence interval for
-a lot of people. Although there are many ways to implement bootstrap,
-semi-parametric is the only logical choice (even though the underlying
-assumptions may be violated at times). As we have only implemented OLS
-type loss functions in this package, the parametric approach seem to be
-inappropriate when there is no self\--efficiency guarantee.
-Non-parametric approach requires at least a conditional independence
-assumption, something easily violated by our **ode**. Block bootstrap is
-an option but we are also aware that the errors of an **ode** can be
-rather rigid, and consistently over/under estimate at certain periods of
-time.
-
-When we say semi-parametric, we mean the exchange of errors between the
-observations. Let our raw error be
-
-$$\varepsilon_{i} = y_{i} - \hat{y}_{i}$$
-
-where $\hat{y}_{i}$ will be the prediction under
-$\hat{\boldsymbol{\theta}}$ under our model. Then we construct a new set
-of observations via
-
-$$y_{i}^{\ast} = \hat{y}_{i} + \varepsilon^{\ast}, \quad \varepsilon^{\ast} \sim \mathcal{F}$$
-
-with $\mathcal{F}$ being the empirical distribution of the raw errors. A
-new set of parameters $\theta^{\ast}$ are then found for the
-bootstrapped samples, and we obtain the $\alpha$ confidence interval by
-taking the $\alpha/2$ quantiles. Invoke the correspond python function
-yields our bootstrap estimates. Unlike `asymptotic`{.interpreted-text
-role="func"}, the bounds here are used when estimating the parameters of
-each bootstrap samples. An error may be returned if estimation failed
-for any of the bootstrap samples.
-
-::: {.ipython}
-In \[1\]: xLBootstrap, xUBootstrap, setX = ci.bootstrap(objSIR, alpha,
-xhat, iteration=10, lb=boxBoundsArray\[:,0\], ub=boxBoundsArray\[:,1\],
-full_output=True)
-
-In \[2\]: print(xLBootstrap)
-
-In \[3\]: print(xUBootstrap)
-:::
-
-The additional information here can be used to compute the bias, tail
-effects and test against the normality assumption. If desired, a
-simultaneous confidence interval can also be approximated empirically.
-Note however that because we are using a semi\--parameter method here,
-if the model specification is wrong then the resulting estimates for the
-bias is also wrong. The confidence interval still has the normal
-approximation guarantee if number of sample is large.
-
-In this case, because the error in the observation is extremely small,
-the confidence interval is narrow.
-
-::: {.ipython}
-In \[1\]: import pylab as P
-
-In \[2\]: f = plt.figure()
-
-In \[3\]: n, bins, patches = P.hist(setX\[:,0\], 50)
-
-In \[4\]: P.xlabel(r\'Estimates of \$beta\$\');
-
-In \[5\]: P.ylabel(\'Frequency\');
-
-In \[6\]: P.title(\'Estimates under a semi-parametric bootstrap
-scheme\');
-
-\@savefig bootstrapCIHist.png In \[7\]: P.show()
-
-In \[8\]: P.close()
-:::
-
-## Comparison Between Methods
-
-Although we have shown the numerical values for the confidence interval
-obtained using different method, it may be hard to comprehend how they
-vary. As they say, a picture says a million word, and given that this
-particular model only has two parameters, we can obtain inspect and
-compare the methods visually via a contour plot. The code to perform
-this is shown below but the code block will not be run to save time and
-space.
-
-In the plot above, the bootstrap confidence interval were so close to
-the MLE, it is impossible to distinguish the two on such a coarse scale.
-
-Furthermore, because the geometric confidence interval is the result of
-an integration, we can trace the path that lead to the final output that
-was shown previously. Again, we are space conscious (and time
-constrained) so the code block below will not be run.
-
-::: {.ipython verbatim=""}
-In \[1\]: fig = plt.figure()
-
-In \[2\]: CS = plt.contour(xi, yi, zi, linewidth=0.5)
-
-In \[3\]: plt.clabel(CS, fontsize=10, inline=1)
-
-In \[4\]: l1 = plt.scatter(xLList\[0\]\[:,0\], xLList\[0\]\[:,1\],
-marker=\'o\', c=\'m\', s=10);
-
-In \[5\]: l2 = plt.scatter(xUList\[0\]\[:,0\], xUList\[0\]\[:,1\],
-marker=\'x\', c=\'m\', s=10);
-
-In \[6\]: plt.legend((l1, l2), (\'Lower CI path\', \'Upper CI path\'),
-loc=\'upper left\');
-
-In \[7\]: plt.ylabel(r\'Estimates of \$gamma\$\');
-
-In \[8\]: plt.xlabel(r\'Estimates of \$beta\$\');
-
-In \[9\]: plt.title(\'Integration path of the geometric confidence
-intervals on the likelihood surface\');
-
-In \[10\]: plt.tight_layout();
-
-In \[11\]: plt.show()
-
-In \[12\]: plt.close()
-:::
-
-## Profile Likelihood Surface
-
-To investigate why it was hard to find the profile likelihood confidence
-interval, we can simply look at the surface (which is simply a line as
-we are profiling). We find solution of `nuisanceOptim`{.interpreted-text
-role="eq"} for each $\boldsymbol{\theta}_{-j}$ at various points of
-$\boldsymbol{\theta}$. Equivalently, we can minimize the original loss
-function as defined previously, and this is the approach below. We focus
-out attention to the parameter $\beta$ of our SIR model. The results are
-not shown here but the existence of a solution to
-`obj`{.interpreted-text role="eq"} is evident by simply *eyeballing* the
-plots.
-
-::: {.ipython verbatim=""}
-In \[1\]: numIter = 100
-
-In \[2\]: x2 = numpy.linspace(0.0, 2.0, numIter)
-
-In \[3\]: funcOut = numpy.linspace(0.0, 2.0, numIter)
-
-In \[4\]: ode.parameters = \[(\'beta\',0.5), (\'gamma\',1.0/3.0)\]
-
-In \[5\]: for i in range(numIter):
-
-:   \...: paramEval = \[(\'beta\',x2\[i\]), (\'gamma\',x2\[i\])\] \...:
-    ode2 = copy.deepcopy(ode) \...: ode2.parameters = paramEval \...:
-    ode2.initial_values = (x0, t\[0\]) \...: objSIR2 =
-    NormalLoss(x2\[i\], ode2, x0, t\[0\], t\[1::\], yObv.copy(),
-    targetState, target_param=\'gamma\') \...: res =
-    scipy.optimize.minimize(fun=objSIR2.cost, \...:
-    jac=objSIR2.gradient, \...: x0=x2\[i\], \...: bounds=\[(0,2)\],
-    \...: method=\'L-BFGS-B\') \...: funcOut\[i\] = res\[\'fun\'\]
-
-In \[10\]: fig = plt.figure()
-
-In \[10\]: plt.plot(x2, objSIR.cost(xhat) - funcOut)
-
-In \[11\]: l1 = plt.axhline(-0.5\*qchisq(1 - alpha, df=1), 0, 2,
-color=\'r\')
-
-In \[12\]: plt.ylabel(r\'\$mathcal{L}(hat{theta}) - mathcal{L}(theta mid
-beta)\$\');
-
-In \[13\]: plt.xlabel(r\'Fixed value of \$beta\$\');
-
-In \[14\]: plt.title(\'Difference in objective function between MLEn and
-the maximization of the nuisance parameters given then parameter of
-interest, beta in this case\');
-
-In \[15\]: plt.tight_layout();
-
-In \[16\]: plt.legend((l1,), (r\'\$-0.5mathcal{X}\_{1 -
-alpha}\^{2}(1)\$\',), loc=\'lower right\');
-
-\@savefig profileLLMaximizerGivenBeta.png In \[17\]: plt.show() \#
-\@savefig profileLLMaximizerGivenBeta.png
-
-In \[18\]: plt.close()
-:::
-
-Both the upper and lower confidence interval can be found in the
-profiling procedure, but the part between of
-$\beta \in \left[0,\hat{\beta}\right]$ is not convex, with $\hat{\beta}$
-being the MLE. This non\--quadratic profile likelihood is due to the
-non-identifiability of the model given data [\[Raue2009\]](). For this
-particular case, we can fix it simply by introducing additional
-observations in the form of the $I$ state. We encourage the users to try
-it out for themselves to confirm.
-
-::: {.ipython verbatim=""}
-In \[1\]: targetState = \[\'I\', \'R\'\]
-
-In \[2\]: targetStateIndex =
-numpy.array(ode.get_state_index(targetState))
-
-In \[3\]: y = solution\[1::,targetStateIndex\] + numpy.random.normal(0,
-0.01, (len(solution\[1::,targetStateIndex\]), 1))
-
-In \[4\]: objSIR = NormalLoss(theta, ode, x0, t\[0\], t\[1::\],
-y.copy(), targetState)
-
-In \[5\]: xhat = objSIR.fit(theta, lb=boxBoundsArray\[:,0\],
-ub=boxBoundsArray\[:,1\])
-
-In \[6\]: for i in range(numIter):
-
-:   \...: paramEval = \[(\'beta\', x2\[i\]), (\'gamma\', x2\[i\])\]
-    \...: ode2 = copy.deepcopy(ode) \...: ode2.parameters = paramEval
-    \...: ode2.initial_values = (x0, t\[0\]) \...: objSIR2 =
-    NormalLoss(x2\[i\], ode2, x0, t\[0\], t\[1::\], y.copy(),
-    targetState, target_param=\'gamma\') \...: res =
-    scipy.optimize.minimize(fun=objSIR2.cost, \...:
-    jac=objSIR2.gradient, \...: x0=x2\[i\], \...: bounds=\[(0,2)\],
-    \...: method=\'L-BFGS-B\') \...: funcOut\[i\] = res\[\'fun\'\]
-
-In \[10\]: fig = plt.figure()
-
-In \[10\]: plt.plot(x2, objSIR.cost(xhat) - funcOut);
-
-In \[11\]: l1 = plt.axhline(-0.5\*qchisq(1 - alpha, df=1), 0, 2,
-color=\'r\')
-
-In \[12\]: plt.ylabel(r\'\$mathcal{L}(hat{theta}) - mathcal{L}(theta mid
-beta)\$\');
-
-In \[13\]: plt.xlabel(r\'Fixed value of \$beta\$\');
-
-In \[14\]: plt.title(\'Profile likelihood curve for the parameter ofn
-interest with more observation\');
-
-In \[15\]: plt.tight_layout();
-
-In \[16\]: plt.legend((l1,), (r\'\$-0.5mathcal{X}\_{1 -
-alpha}\^{2}(1)\$\',), loc=\'lower right\');
-
-\@savefig profileLLMaximizerGivenBetaMoreObs.png In \[17\]: plt.show()
-\# \@savefig profileLLMaximizerGivenBetaMoreObs.png
-
-In \[18\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/ref.ipynb b/docs/pygom-doc/mdconvert/doc_to_sort/ref.ipynb
deleted file mode 100644
index 41409918..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/ref.ipynb
+++ /dev/null
@@ -1,103 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# References\n",
-    "\n",
-    "Aron1984  \n",
-    "Seasonality and period-doubling bifurcations in an epidemic model,\n",
-    "Joan L. Aron and Ira B. Schwartz, Journal of Theoretical Biology, Volume\n",
-    "110, Issue 4, page 665-679, 1984\n",
-    "\n",
-    "Brauer2008  \n",
-    "Mathematical Epidemiology, Lecture Notes in Mathematics, Fred Brauer,\n",
-    "Springer 2008\n",
-    "\n",
-    "Cao2006  \n",
-    "Efficient step size selection for the tau-leaping simulation method,\n",
-    "Yang Cao et el., The Journal of Chemical Physics, Volume 124, Issue 4,\n",
-    "page 044109, 2006\n",
-    "\n",
-    "Finnie2016  \n",
-    "EpiJSON: A unified data-format for epidemiology, Thomas Finnie et al.,\n",
-    "Epidemics, Volume 15, page 20-26, 2016\n",
-    "\n",
-    "FitzHugh1961  \n",
-    "Impulses and Physiological States in Theoretical Models of Nerve\n",
-    "Membrane, Richard FitzHugh, Biophysical Journal, Volume 1, Issue 6, page\n",
-    "445-466, 1961\n",
-    "\n",
-    "Gillespie1977  \n",
-    "Exact stochastic simulation of coupled chemical reactions, Danial T.\n",
-    "Gillespie, The Journal of Physical Chemistry, Volume 81, Issue 25, page\n",
-    "2340-2361, 1977\n",
-    "\n",
-    "Girolami2011  \n",
-    "Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Mark\n",
-    "Girolami and Ben Calderhead, Journal of the Royal Statistical Society\n",
-    "Series B, Volume 73, Issue 2, page 123-214, 2011.\n",
-    "\n",
-    "Hethcote1973  \n",
-    "Asymptotic behavior in a deterministic epidemic model, Herbert W.\n",
-    "Hethcote, Bulletin of Mathematical Biology, Volume 35, page 607-614,\n",
-    "1973\n",
-    "\n",
-    "Legrand2007  \n",
-    "Understanding the dynamics of Ebola epidemics, J. Legrand et al.\n",
-    "Epidemiology and Infection, Volume 135, Issue 4, page 610-621, 2007\n",
-    "\n",
-    "Lloyd1996  \n",
-    "Spatial Heterogeneity in Epidemic Models, A.L. Lloyd and R.M. May,\n",
-    "Journal of Theoretical Biology, Volume 179, Issue 1, page 1-11, 1996\n",
-    "\n",
-    "Lorenz1963  \n",
-    "Deterministic Nonperiodic Flow, Edward N. Lorenz, Journal of the\n",
-    "Atmospheric Sciences, Volume 20, Issue 2, page 130-141, 1963\n",
-    "\n",
-    "Lotka1920  \n",
-    "Analytical Note on Certain Rhythmic Relations in Organic Systems,\n",
-    "Alfred J. Lotka, Proceedings of the National Academy of Sciences of the\n",
-    "United States of America, Volume 7, Issue 7, page 410-415, 1920\n",
-    "\n",
-    "Moolgavkar1987  \n",
-    "Confidence Regions for Parameters of the Proportional Hazards Model: A\n",
-    "Simulation Study, S.H. Moolgavkar and D.J. Venzon, Scandianvia Journal\n",
-    "of Statistics, Volume 14, page 43-56, 1987\n",
-    "\n",
-    "Press2007  \n",
-    "Numerical Recipes 3rd Edition: The Art of Scientific Computing, W.H.\n",
-    "Press et al., Cambridge University Press, 2007\n",
-    "\n",
-    "Ramsay2007  \n",
-    "Parameter estimation for differential equations: a generalized smoothing\n",
-    "approach, Journal of the Royal Statistical Society Series B, James O.\n",
-    "Ramsay et al., Volume 69, Issue 5, page 741-796, 2007\n",
-    "\n",
-    "Raue2009  \n",
-    "Structural and Practical Identifiability Analysis of Partially Observed\n",
-    "Dynamical Models by Exploiting the Profile Likelihood, A. Raue et al.,\n",
-    "Bioinformatics, Volume 25, Issue 15, page 1923-1929, 2009\n",
-    "\n",
-    "Robertson1966  \n",
-    "The solution of a set of reaction rate equations, H.H. Robertson,\n",
-    "Academic Press, page 178-182, 1966\n",
-    "\n",
-    "Venzon1988  \n",
-    "A Method for Computing Profile-Likelihood-Based Confidence Intervals,\n",
-    "D.J. Venzon and S.H. Moolgavkar, Journal of the Royal Statistical\n",
-    "Society Series C (Applied Statistics), Volume 37, Issue 1, page 87-94,\n",
-    "1988\n",
-    "\n",
-    "vanderpol1926  \n",
-    "On Relaxed Oscillations, Balthasar van der Pol, The London, Edinburgh,\n",
-    "and Dublin Philosophical Magazine and Journal of Science, Volume 2,\n",
-    "Issue 11, page 978-992, 1926"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/stochastic.md b/docs/pygom-doc/mdconvert/doc_to_sort/stochastic.md
deleted file mode 100644
index 58bf60b2..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/stochastic.md
+++ /dev/null
@@ -1,341 +0,0 @@
-# Stochastic representation of ode {#stochastic}
-
-There are multiple interpretation of stochasticity of a deterministic
-ode. We have implemented two of the most common interpretation; when the
-parameters are realizations of some underlying distribution, and when we
-have a so called chemical master equation where each transition
-represent a jump. Again, we use the standard SIR example as previously
-seen in ref:[sir]{.title-ref}.
-
-::: {.ipython}
-In \[1\]: from pygom import SimulateOde, Transition, TransitionType
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: import numpy as np
-
-In \[1\]: x0 = \[1, 1.27e-6, 0\]
-
-In \[1\]: t = np.linspace(0, 150, 100)
-
-In \[1\]: stateList = \[\'S\', \'I\', \'R\'\]
-
-In \[1\]: paramList = \[\'beta\', \'gamma\'\]
-
-In \[1\]: transitionList = \[
-
-:   \...: Transition(origin=\'S\', destination=\'I\',
-    equation=\'beta\*S\*I\', transition_type=TransitionType.T), \...:
-    Transition(origin=\'I\', destination=\'R\', equation=\'gamma\*I\',
-    transition_type=TransitionType.T) \...: \]
-
-In \[1\]: odeS = SimulateOde(stateList, paramList,
-transition=transitionList)
-
-In \[1\]: odeS.parameters = \[0.5, 1.0/3.0\]
-
-In \[1\]: odeS.initial_values = (x0, t\[0\])
-
-In \[1\]: solutionReference = odeS.integrate(t\[1::\],
-full_output=False)
-:::
-
-## Stochastic Parameter
-
-In our first scenario, we assume that the parameters follow some
-underlying distribution. Given that both $\beta$ and $\gamma$ in our SIR
-model has to be non-negative, it seemed natural to use a Gamma
-distribution. We make use of the familiar syntax from
-[R](http://www.r-project.org/) to define our distribution.
-Unfortunately, we have to define it via a tuple, where the first is the
-function handle (name) while the second the parameters. Note that the
-parameters can be defined as either a dictionary or as the same sequence
-as [R](http://www.r-project.org/), which is the shape then the rate in
-the Gamma case.
-
-::: {.ipython}
-In \[1\]: from pygom.utilR import rgamma
-
-In \[1\]: d = dict()
-
-In \[1\]: d\[\'beta\'\] = (rgamma,{\'shape\':100.0, \'rate\':200.0})
-
-In \[1\]: d\[\'gamma\'\] = (rgamma,(100.0, 300.0))
-
-In \[1\]: odeS.parameters = d
-
-In \[1\]: Ymean, Yall = odeS.simulate_param(t\[1::\], 10,
-full_output=True)
-:::
-
-Note that a message is printed above where it is trying to connect to an
-mpi backend, as our module has the capability to compute in parallel
-using the IPython. We have simulated a total of 10 different solutions
-using different parameters, the plots can be seen below
-
-::: {.ipython}
-In \[1\]: f, axarr = plt.subplots(1,3)
-
-In \[1\]: for solution in Yall:
-
-:   \...: axarr\[0\].plot(t, solution\[:,0\]) \...: axarr\[1\].plot(t,
-    solution\[:,1\]) \...: axarr\[2\].plot(t, solution\[:,2\])
-
-\@savefig stochastic_param_all.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
-
-We then see how the expected results, using the sample average of the
-simulations
-
-$$\tilde{x}(T) = \mathbb{E}\left[ \int_{t_{0}}^{T} f(\theta,x,t) dt \right]$$
-
-differs from the reference solution
-
-$$\hat{x}(T) = \int_{t_{0}}^{T} f(\mathbb{E}\left[ \theta \right],x,t) dt$$
-
-::: {.ipython}
-In \[1\]: f, axarr = plt.subplots(1,3)
-
-In \[1\]: for i in range(3): axarr\[i\].plot(t, Ymean\[:,i\] -
-solutionReference\[:,i\])
-
-\@savefig stochastic_param_compare.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
-
-The difference is relatively large especially for the $S$ state. We can
-decrease this difference as we increase the number of simulation, and
-more sophisticated sampling method for the generation of random
-variables can also decrease the difference.
-
-In addition to using the built-in functions to represent stochasticity,
-we can also use standard frozen distributions from scipy. Note that it
-must be a frozen distribution as that is the only for the parameters of
-the distributions to propagate through the model.
-
-::: {.ipython}
-In \[1\]: import scipy.stats as st
-
-In \[1\]: d = dict()
-
-In \[1\]: d\[\'beta\'\] = st.gamma(a=100.0, scale=1.0/200.0)
-
-In \[1\]: d\[\'gamma\'\] = st.gamma(a=100.0, scale=1.0/300.0)
-
-In \[1\]: odeS.parameters = d
-:::
-
-Obviously, there may be scenarios where only some of the parameters are
-stochastic. Let\'s say that the $\gamma$ parameter is fixed at $1/3$,
-then simply replace the distribution information with a scalar. A quick
-visual inspection at the resulting plot suggests that the system of ODE
-potentially has less variation when compared to the case where both
-parameters are stochastic.
-
-::: {.ipython}
-In \[1\]: d\[\'gamma\'\] = 1.0/3.0
-
-In \[1\]: odeS.parameters = d
-
-In \[1\]: YmeanSingle, YallSingle = odeS.simulate_param(t\[1::\], 5,
-full_output=True)
-
-In \[1\]: f, axarr = plt.subplots(1,3)
-
-In \[1\]: for solution in YallSingle:
-
-:   \...: axarr\[0\].plot(t,solution\[:,0\]) \...:
-    axarr\[1\].plot(t,solution\[:,1\]) \...:
-    axarr\[2\].plot(t,solution\[:,2\])
-
-\@savefig stochastic_param_single.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
-
-## Continuous Markov Representation
-
-Another common method of introducing stochasticity into a set of ode is
-by assuming each movement in the system is a result of a jump process.
-More concretely, the probabilty of a move for transition $j$ is governed
-by an exponential distribution such that
-
-$$\Pr(\text{process $j$ jump within time } \tau) = \lambda_{j} e^{-\lambda_{j} \tau},$$
-
-where $\lambda_{j}$ is the rate of transition for process $j$ and $\tau$
-the time elapsed after current time $t$.
-
-A couple of the commmon implementation for the jump process have been
-implemented where two of them are used during a normal simulation; the
-first reaction method [\[Gillespie1977\]]() and the $\tau$-Leap method
-[\[Cao2006\]](). The two changes interactively depending on the size of
-the states.
-
-::: {.ipython}
-In \[1\]: x0 = \[2362206.0, 3.0, 0.0\]
-
-In \[1\]: stateList = \[\'S\', \'I\', \'R\'\]
-
-In \[1\]: paramList = \[\'beta\', \'gamma\', \'N\'\]
-
-In \[1\]: transitionList = \[
-
-:   \...: Transition(origin=\'S\', destination=\'I\',
-    equation=\'beta\*S\*I/N\', transition_type=TransitionType.T), \...:
-    Transition(origin=\'I\', destination=\'R\', equation=\'gamma\*I\',
-    transition_type=TransitionType.T) \...: \]
-
-In \[1\]: odeS = SimulateOde(stateList, paramList,
-transition=transitionList)
-
-In \[1\]: odeS.parameters = \[0.5, 1.0/3.0, x0\[0\]\]
-
-In \[1\]: odeS.initial_values = (x0, t\[0\])
-
-In \[1\]: solutionReference = odeS.integrate(t\[1::\])
-
-In \[1\]: simX, simT = odeS.simulate_jump(t\[1:10\], 10,
-full_output=True)
-
-In \[1\]: f, axarr = plt.subplots(1, 3)
-
-In \[1\]: for solution in simX:
-
-:   \...: axarr\[0\].plot(t\[:9\], solution\[:,0\]) \...:
-    axarr\[1\].plot(t\[:9\], solution\[:,1\]) \...:
-    axarr\[2\].plot(t\[:9\], solution\[:,2\])
-
-\@savefig stochastic_process.png In \[1\]: plt.show()
-
-In \[1\]: plt.close()
-:::
-
-Above, we see ten different simulation, again using the SIR model but
-without standardization of the initial conditions. We restrict our time
-frame to be only the first 10 time points so that the individual changes
-can be seen more clearly above. If we use the same time frame as the one
-used previously for the deterministic system (as shown below), the
-trajectories are smoothed out and we no longer observe the *jumps*.
-Looking at the raw trajectories of the ODE below, it is obvious that the
-mean from a jump process is very different to the deterministic
-solution. The reason behind this is that the jump process above was able
-to fully remove all the initial infected individuals before any new
-ones.
-
-::: {.ipython}
-In \[1\]: simX,simT = odeS.simulate_jump(t, 5, full_output=True)
-
-In \[1\]: simMean = np.mean(simX, axis=0)
-
-In \[1\]: f, axarr = plt.subplots(1,3)
-
-In \[1\]: for solution in simX:
-
-:   \...: axarr\[0\].plot(t, solution\[:,0\]) \...: axarr\[1\].plot(t,
-    solution\[:,1\]) \...: axarr\[2\].plot(t, solution\[:,2\])
-
-\@savefig stochastic_process_compare_large_n\_curves.png In \[1\]:
-plt.show()
-
-In \[1\]: plt.close()
-:::
-
-## Repeatable Simulation
-
-One of the possible use of compartmental models is to generate
-forecasts. Although most of the time the requirement would be to have
-(at least point-wise) convergence in the limit, reproducibility is also
-important. For both types of interpretation explained above, we have
-given the package the capability to repeat the simulations by setting a
-seed. When the assumption is that the parameters follows some sort of
-distribution, we simply set the seed which governs the global state of
-the random number generator.
-
-::: {.ipython}
-In \[1\]: x0 = \[2362206.0, 3.0, 0.0\]
-
-In \[1\]: odeS = SimulateOde(stateList, paramList,
-transition=transitionList)
-
-In \[1\]: d = {\'beta\': st.gamma(a=100.0, scale=1.0/200.0), \'gamma\':
-st.gamma(a=100.0, scale=1.0/300.0), \'N\': x0\[0\]}
-
-In \[1\]: odeS.parameters = d
-
-In \[1\]: odeS.initial_values = (x0, t\[0\])
-
-In \[1\]: Ymean, Yall = odeS.simulate_param(t\[1::\], 10,
-full_output=True)
-
-In \[1\]: np.random.seed(1)
-
-In \[1\]: Ymean1, Yall1 = odeS.simulate_param(t\[1::\], 10,
-full_output=True)
-
-In \[1\]: np.random.seed(1)
-
-In \[1\]: Ymean2, Yall2 = odeS.simulate_param(t\[1::\], 10,
-full_output=True)
-
-In \[1\]: sim_diff = \[np.linalg.norm(Yall\[i\] - yi) for i, yi in
-enumerate(Yall1)\]
-
-In \[1\]: sim_diff12 = \[np.linalg.norm(Yall2\[i\] - yi) for i, yi in
-enumerate(Yall1)\]
-
-In \[1\]: print(\"Different in the simulations and the mean: (%s, %s) \"
-% (np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))
-
-In \[1\]: print(\"Different in the simulations and the mean using same
-seed: (%s, %s) \" % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 -
-Ymean1))))
-:::
-
-In the alternative interpretation, setting the global seed is
-insufficient. Unlike simulation based on the parameters, where we can
-pre-generate all the parameter values and send them off to individual
-processes in the parallel backend, this is prohibitive here. In a
-nutshell, the seed does not propagate when using a parallel backend
-because each *integration* requires an unknown number of random samples.
-Therefore, we have an additional flag **parallel** in the function
-signature. By ensuring that the computation runs in serial, we can make
-use of the global seed and generate identical runs.
-
-::: {.ipython}
-In \[1\]: x0 = \[2362206.0, 3.0, 0.0\]
-
-In \[1\]: odeS = SimulateOde(stateList, paramList,
-transition=transitionList)
-
-In \[1\]: odeS.parameters = \[0.5, 1.0/3.0, x0\[0\]\]
-
-In \[1\]: odeS.initial_values = (x0, t\[0\])
-
-In \[1\]: simX, simT = odeS.simulate_jump(t\[1:10\], 10, parallel=False,
-full_output=True)
-
-In \[1\]: np.random.seed(1)
-
-In \[1\]: simX1, simT1 = odeS.simulate_jump(t\[1:10\], 10,
-parallel=False, full_output=True)
-
-In \[1\]: np.random.seed(1)
-
-In \[1\]: simX2, simT2 = odeS.simulate_jump(t\[1:10\], 10,
-parallel=False, full_output=True)
-
-In \[1\]: sim_diff = \[np.linalg.norm(simX\[i\] - x1) for i, x1 in
-enumerate(simX1)\]
-
-In \[1\]: sim_diff12 = \[np.linalg.norm(simX2\[i\] - x1) for i, x1 in
-enumerate(simX1)\]
-
-In \[1\]: print(\"Difference in simulation: %s\" %
-np.sum(np.abs(sim_diff)))
-
-In \[1\]: print(\"Difference in simulation using same seed: %s\" %
-np.sum(np.abs(sim_diff12)))
-:::
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/transition.md b/docs/pygom-doc/mdconvert/doc_to_sort/transition.md
deleted file mode 100644
index b0be6933..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/transition.md
+++ /dev/null
@@ -1,230 +0,0 @@
-# Transition Object {#transition}
-
-The most important part of setting up the model is to correctly define
-the set odes, which is based solely on the classes defined in
-`transition`{.interpreted-text role="mod"}. All transitions that gets
-fed into the ode system needs to be defined as a transition object,
-`Transition`{.interpreted-text role="class"}. It takes a total of four
-input arguments
-
-1.  The origin state
-2.  Equation that describe the process
-3.  The type of transition
-4.  The destination state
-
-where the first three are mandatory. To demonstrate, we go back to the
-SIR model defined previously in the section `sir`{.interpreted-text
-role="ref"}. Recall that the set of odes are
-
-$$\begin{aligned}
-\frac{\partial S}{\partial t} &= -\beta SI \\
-\frac{\partial I}{\partial t} &= \beta SI - \gamma I \\
-\frac{\partial R}{\partial t} &= \gamma I.
-\end{aligned}$$
-
-We can simply define the set of ode, as seen previously, via
-
-::: {.ipython}
-In \[1\]: from pygom import Transition, TransitionType, common_models
-
-In \[2\]: ode1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\',
-transition_type=TransitionType.ODE)
-
-In \[3\]: ode2 = Transition(origin=\'I\', equation=\'beta\*S\*I -
-gamma\*I\', transition_type=TransitionType.ODE)
-
-In \[4\]: ode3 = Transition(origin=\'R\', equation=\'gamma\*I\',
-transition_type=TransitionType.ODE)
-:::
-
-Note that we need to state explicitly the type of equation we are
-inputting, which is simply of type **ODE** in this case. We can confirm
-this has been entered correctly by putting it into
-`DeterministicOde`{.interpreted-text role="class"}
-
-::: {.ipython}
-In \[1\]: from pygom import DeterministicOde
-
-In \[2\]: stateList = \[\'S\', \'I\', \'R\'\]
-
-In \[3\]: paramList = \[\'beta\', \'gamma\'\]
-
-In \[4\]: model = DeterministicOde(stateList,
-
-:   \...: paramList, \...: ode=\[ode1, ode2, ode3\])
-:::
-
-and check it
-
-::: {.ipython}
-In \[1\]: model.get_ode_eqn()
-:::
-
-An alternative print function `print_ode`{.interpreted-text role="func"}
-is also available which may be more suitable in other situation. The
-default prints the formula in a rendered format and another which prints
-out the latex format which can be used directly in a latex document. The
-latter is useful as it saves typing out the formula twice, once in the
-code and another in documents.
-
-::: {.ipython}
-In \[1\]: model.print_ode(False)
-
-In \[2\]: model.print_ode(True)
-:::
-
-Now we are going to show the different ways of defining the same set of
-odes.
-
-## Defining the equations {#defining-eqn}
-
-Recognizing that the set of odes defining the SIR model is the result of
-two transitions,
-
-$$\begin{aligned}
-S \rightarrow I &= \beta SI \\
-I \rightarrow R &= \gamma I
-\end{aligned}$$
-
-where $S \rightarrow I$ denotes a transition from state $S$ to state
-$I$. Therefore, we can simply define our model by these two transition,
-but now these two transition needs to be inputted via the `transition`
-argument instead of the `ode` argument. Note that we are initializing
-the model using a different class, because the stochastic implementation
-has more operation on transitions.
-
-::: {.ipython}
-In \[600\]: from pygom import SimulateOde
-
-In \[601\]: t1 = Transition(origin=\'S\', destination=\'I\',
-equation=\'beta\*S\*I\', transition_type=TransitionType.T)
-
-In \[602\]: t2 = Transition(origin=\'I\', destination=\'R\',
-equation=\'gamma\*I\', transition_type=TransitionType.T)
-
-In \[603\]: modelTrans = SimulateOde(stateList,
-
-:   \.....: paramList, \.....: transition=\[t1, t2\])
-
-In \[604\]: modelTrans.get_ode_eqn()
-:::
-
-We can see that the resulting ode is exactly the same, as expected. The
-transition matrix that defines this process can easily be visualized
-using graphviz. Because only certain renderer permit the use of sub and
-superscript, operators such as $**$ are left as they are in the
-equation.
-
-::: {.ipython}
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[2\]: f = plt.figure()
-
-In \[3\]: modelTrans.get_transition_matrix()
-
-\@savefig sir_transition_graph.png In \[4\]: dot =
-modelTrans.get_transition_graph()
-:::
-
-If we put in via the wrong argument like below (not run), then an error
-will appear.
-
-::: {.ipython}
-In \[1\]: \# modelTrans = DeterministicOde(stateList, paramList,
-ode=\[t1, t2\])
-:::
-
-because `TranstionType`{.interpreted-text role="class"} was defined
-explicitly as a transition instead of an ode. The same can be observed
-when the wrong `TransitionType`{.interpreted-text role="class"} is used
-for any of the input argument.
-
-This though, only encourages us to define the transitions carefully. We
-can also pretend that the set of odes are in fact just a set of birth
-process
-
-::: {.ipython}
-In \[619\]: birth1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\',
-transition_type=TransitionType.B)
-
-In \[620\]: birth2 = Transition(origin=\'I\', equation=\'beta\*S\*I -
-gamma\*I\', transition_type=TransitionType.B)
-
-In \[621\]: birth3 = Transition(origin=\'R\', equation=\'gamma\*I\',
-transition_type=TransitionType.B)
-
-In \[622\]: modelBirth = DeterministicOde(stateList,
-
-:   \.....: paramList, \.....: birth_death=\[birth1, birth2, birth3\])
-
-In \[623\]: modelBirth.get_ode_eqn()
-:::
-
-which will yield the same set result. Alternatively, we can use the
-negative of the equation but set it to be a death process. For example,
-we multiply the equations for state $S$ and $R$ with a negative sign and
-set the transition type to be a death process instead.
-
-::: {.ipython}
-In \[624\]: death1 = Transition(origin=\'S\', equation=\'beta\*S\*I\',
-transition_type=TransitionType.D)
-
-In \[625\]: birth2 = Transition(origin=\'I\', equation=\'beta\*S\*I -
-gamma\*I\', transition_type=TransitionType.B)
-
-In \[626\]: death3 = Transition(origin=\'R\', equation=\'-gamma\*I\',
-transition_type=TransitionType.D)
-
-In \[627\]: modelBD = DeterministicOde(stateList,
-
-:   \.....: paramList, \.....: birth_death=\[death1, birth2, death3\])
-
-In \[628\]: modelBD.get_ode_eqn()
-:::
-
-We can see that all the above ways yield the same set of ode at the end.
-
-## Model Addition
-
-Because we allow the separation of transitions between states and
-birth/death processes, the birth/death processes can be added later on.
-
-::: {.ipython}
-In \[1\]: modelBD2 = modelTrans
-
-In \[1\]: modelBD2.param_list = paramList + \[\'mu\', \'B\'\]
-
-In \[1\]: birthDeathList = \[Transition(origin=\'S\', equation=\'B\', transition_type=TransitionType.B),
-
-:   \...: Transition(origin=\'S\', equation=\'mu\*S\',
-    transition_type=TransitionType.D), \...: Transition(origin=\'I\',
-    equation=\'mu\*I\', transition_type=TransitionType.D)\]
-
-In \[1\]: modelBD2.birth_death_list = birthDeathList
-
-In \[1\]: modelBD2.get_ode_eqn()
-:::
-
-So modeling can be done in stages. Start with a standard closed system
-and extend it with additional flows that interact with the environment.
-
-## Transition type
-
-There are currently four different type of transitions allowed, which is
-defined in an enum class also located in `transition`{.interpreted-text
-role="mod"}. The four types are B, D, ODE and T, where they represent
-different type of process with explanation in their corresponding value.
-
-::: {.ipython}
-In \[1\]: from pygom import transition
-
-In \[2\]: for i in transition.TransitionType:
-
-:   \...: print(str(i) + \" = \" + i.value)
-:::
-
-Each birth process are added to the origin state while each death
-process are deducted from the state, i.e. added to the state after
-multiplying with a negative sign. An ode type is also added to the state
-and we forbid the number of input ode to be greater than the number of
-state inputted.
diff --git a/docs/pygom-doc/mdconvert/doc_to_sort/unrollOde.ipynb b/docs/pygom-doc/mdconvert/doc_to_sort/unrollOde.ipynb
deleted file mode 100644
index 644be889..00000000
--- a/docs/pygom-doc/mdconvert/doc_to_sort/unrollOde.ipynb
+++ /dev/null
@@ -1,23 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Convert ODE into transitions\n",
-    "\n",
-    "As seen previously in `transition`, we can define the model via the\n",
-    "transitions or explicitly as ODEs. There are times when we all just want\n",
-    "to test out some model in a paper and the only available information are\n",
-    "the ODEs themselves. Even though we know that the ODEs come from some\n",
-    "underlying transitions, breaking them down can be a time consuming\n",
-    "process. We provide the functionalities to do this automatically.\n",
-    "\n",
-    "unroll/unrollSimple.rst unroll/unrollBD.rst unroll/unrollHard.rst"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/.md b/docs/pygom-doc/mdconvert/mod/.md
deleted file mode 100644
index 8c6b1b35..00000000
--- a/docs/pygom-doc/mdconvert/mod/.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# stochastic
-
-::: {.automodule members="" noindex=""}
-pygom.model.simulate
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/common_models.ipynb b/docs/pygom-doc/mdconvert/mod/common_models.ipynb
deleted file mode 100644
index c16f1f3e..00000000
--- a/docs/pygom-doc/mdconvert/mod/common_models.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# common_models\n",
-    "\n",
-    "pygom.model.common_models"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/common_models.md b/docs/pygom-doc/mdconvert/mod/common_models.md
deleted file mode 100644
index b65e53b2..00000000
--- a/docs/pygom-doc/mdconvert/mod/common_models.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# common_models
-
-::: {.automodule members="" noindex=""}
-pygom.model.common_models
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/confidence_interval.ipynb b/docs/pygom-doc/mdconvert/mod/confidence_interval.ipynb
deleted file mode 100644
index ca61c381..00000000
--- a/docs/pygom-doc/mdconvert/mod/confidence_interval.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# loss_type\n",
-    "\n",
-    "pygom.loss.loss_type"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/confidence_interval.md b/docs/pygom-doc/mdconvert/mod/confidence_interval.md
deleted file mode 100644
index 577e3dd7..00000000
--- a/docs/pygom-doc/mdconvert/mod/confidence_interval.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# loss_type
-
-::: {.automodule members="" noindex=""}
-pygom.loss.loss_type
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/deterministic.ipynb b/docs/pygom-doc/mdconvert/mod/deterministic.ipynb
deleted file mode 100644
index 6ea2c788..00000000
--- a/docs/pygom-doc/mdconvert/mod/deterministic.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# deterministic\n",
-    "\n",
-    "pygom.model.deterministic"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/deterministic.md b/docs/pygom-doc/mdconvert/mod/deterministic.md
deleted file mode 100644
index a8e585c4..00000000
--- a/docs/pygom-doc/mdconvert/mod/deterministic.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# deterministic
-
-::: {.automodule members="" noindex=""}
-pygom.model.deterministic
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/epi_analysis.ipynb b/docs/pygom-doc/mdconvert/mod/epi_analysis.ipynb
deleted file mode 100644
index 265113af..00000000
--- a/docs/pygom-doc/mdconvert/mod/epi_analysis.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# epi_analysis\n",
-    "\n",
-    "pygom.model.epi_analysis"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/epi_analysis.md b/docs/pygom-doc/mdconvert/mod/epi_analysis.md
deleted file mode 100644
index ef6bd06b..00000000
--- a/docs/pygom-doc/mdconvert/mod/epi_analysis.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# epi_analysis
-
-::: {.automodule members="" noindex=""}
-pygom.model.epi_analysis
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/get_init.ipynb b/docs/pygom-doc/mdconvert/mod/get_init.ipynb
deleted file mode 100644
index 80dd8a60..00000000
--- a/docs/pygom-doc/mdconvert/mod/get_init.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# get_init\n",
-    "\n",
-    "pygom.loss.get_init"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/get_init.md b/docs/pygom-doc/mdconvert/mod/get_init.md
deleted file mode 100644
index 87c54755..00000000
--- a/docs/pygom-doc/mdconvert/mod/get_init.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# get_init
-
-::: {.automodule members="" noindex=""}
-pygom.loss.get_init
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/index.ipynb b/docs/pygom-doc/mdconvert/mod/index.ipynb
deleted file mode 100644
index 156566f3..00000000
--- a/docs/pygom-doc/mdconvert/mod/index.ipynb
+++ /dev/null
@@ -1,22 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Code documentations\n",
-    "\n",
-    "## model\n",
-    "\n",
-    "common_models transition deterministic simulate epi_analysis odeutils\n",
-    "\n",
-    "## loss\n",
-    "\n",
-    "odeloss losstype confidence_interval get_init"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/index.md b/docs/pygom-doc/mdconvert/mod/index.md
deleted file mode 100644
index 58e690a2..00000000
--- a/docs/pygom-doc/mdconvert/mod/index.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# Code documentations {#mod}
-
-## model
-
-::: {.toctree}
-common_models transition deterministic simulate epi_analysis odeutils
-:::
-
-## loss
-
-::: {.toctree}
-odeloss losstype confidence_interval get_init
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/losstype.ipynb b/docs/pygom-doc/mdconvert/mod/losstype.ipynb
deleted file mode 100644
index 90930726..00000000
--- a/docs/pygom-doc/mdconvert/mod/losstype.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# confidence_interval\n",
-    "\n",
-    "pygom.loss.confidence_interval"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/losstype.md b/docs/pygom-doc/mdconvert/mod/losstype.md
deleted file mode 100644
index 11277a2c..00000000
--- a/docs/pygom-doc/mdconvert/mod/losstype.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# confidence_interval
-
-::: {.automodule members="" noindex=""}
-pygom.loss.confidence_interval
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/odeloss.ipynb b/docs/pygom-doc/mdconvert/mod/odeloss.ipynb
deleted file mode 100644
index ad8b8e73..00000000
--- a/docs/pygom-doc/mdconvert/mod/odeloss.ipynb
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ode_loss\n",
-    "\n",
-    "These are basically the interfaces for `pygom.loss.BaseLoss`\n",
-    "\n",
-    "pygom.loss.ode_loss\n",
-    "\n",
-    "# calculations\n",
-    "\n",
-    "The base class which contains has all the calculation implemented\n",
-    "\n",
-    "pygom.loss.base_loss"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/odeloss.md b/docs/pygom-doc/mdconvert/mod/odeloss.md
deleted file mode 100644
index 82ac8080..00000000
--- a/docs/pygom-doc/mdconvert/mod/odeloss.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# ode_loss
-
-These are basically the interfaces for
-`pygom.loss.BaseLoss`{.interpreted-text role="class"}
-
-::: {.automodule members="" noindex=""}
-pygom.loss.ode_loss
-:::
-
-# calculations
-
-The base class which contains has all the calculation implemented
-
-::: {.automodule members="" noindex=""}
-pygom.loss.base_loss
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/odeutils.ipynb b/docs/pygom-doc/mdconvert/mod/odeutils.ipynb
deleted file mode 100644
index 4e591188..00000000
--- a/docs/pygom-doc/mdconvert/mod/odeutils.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ode_utils\n",
-    "\n",
-    "pygom.model.ode_utils"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/odeutils.md b/docs/pygom-doc/mdconvert/mod/odeutils.md
deleted file mode 100644
index 192c4502..00000000
--- a/docs/pygom-doc/mdconvert/mod/odeutils.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# ode_utils
-
-::: {.automodule members="" noindex=""}
-pygom.model.ode_utils
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/simulate.ipynb b/docs/pygom-doc/mdconvert/mod/simulate.ipynb
deleted file mode 100644
index c56782d0..00000000
--- a/docs/pygom-doc/mdconvert/mod/simulate.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# stochastic\n",
-    "\n",
-    "pygom.model.simulate"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/simulate.md b/docs/pygom-doc/mdconvert/mod/simulate.md
deleted file mode 100644
index 8c6b1b35..00000000
--- a/docs/pygom-doc/mdconvert/mod/simulate.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# stochastic
-
-::: {.automodule members="" noindex=""}
-pygom.model.simulate
-:::
diff --git a/docs/pygom-doc/mdconvert/mod/transition.ipynb b/docs/pygom-doc/mdconvert/mod/transition.ipynb
deleted file mode 100644
index 0708eba4..00000000
--- a/docs/pygom-doc/mdconvert/mod/transition.ipynb
+++ /dev/null
@@ -1,16 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# transition\n",
-    "\n",
-    "pygom.model.transition"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/mdconvert/mod/transition.md b/docs/pygom-doc/mdconvert/mod/transition.md
deleted file mode 100644
index 46920265..00000000
--- a/docs/pygom-doc/mdconvert/mod/transition.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# transition
-
-::: {.automodule members="" noindex=""}
-pygom.model.transition
-:::
diff --git a/docs/pygom-doc/mdconvert/unroll/.md b/docs/pygom-doc/mdconvert/unroll/.md
deleted file mode 100644
index 07b17497..00000000
--- a/docs/pygom-doc/mdconvert/unroll/.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Simple Problem {#unrollSimple}
-
-For a simple problem, we consider the SIR model defined by
-
-$$\begin{aligned}
-\frac{dS}{dt} &= -\beta SI \\
-\frac{dI}{dt} &= \beta SI - \gamma I \\
-\frac{dR}{dt} &= \gamma I.
-\end{aligned}$$
-
-which consists of two transitions
-
-::: {.graphviz}
-
-digraph SIR_Model {
-
-:   rankdir=LR; size=\"8\" node \[shape = circle\]; S -\> I \[ label =
-    \"βSI\" \]; I -\> R \[ label = \"γI\" \];
-
-}
-:::
-
-Let\'s define this using the code block below
-
-::: {.ipython}
-In \[1\]: from pygom import SimulateOde, Transition, TransitionType
-
-In \[2\]: ode1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\',
-transition_type=TransitionType.ODE)
-
-In \[3\]: ode2 = Transition(origin=\'I\', equation=\'beta\*S\*I -
-gamma\*I\', transition_type=TransitionType.ODE)
-
-In \[4\]: ode3 = Transition(origin=\'R\', equation=\'gamma\*I\',
-transition_type=TransitionType.ODE)
-
-In \[6\]: stateList = \[\'S\', \'I\', \'R\'\]
-
-In \[7\]: paramList = \[\'beta\', \'gamma\'\]
-
-In \[8\]: ode = SimulateOde(stateList,
-
-:   \...: paramList, \...: ode=\[ode1, ode2, ode3\])
-
-In \[9\]: ode.get_transition_matrix()
-:::
-
-and the last line shows that the transition matrix is empty. This is the
-expected result because `SimulateOdeModel`{.interpreted-text
-role="class"} was not initialized using transitions. We populate the
-transition matrix below and demonstrate the difference.
-
-::: {.ipython}
-In \[1\]: ode = ode.get_unrolled_obj()
-
-In \[2\]: ode.get_transition_matrix()
-:::
diff --git a/docs/pygom-doc/mdconvert/unroll/unrollBD.md b/docs/pygom-doc/mdconvert/unroll/unrollBD.md
deleted file mode 100644
index bc808719..00000000
--- a/docs/pygom-doc/mdconvert/unroll/unrollBD.md
+++ /dev/null
@@ -1,60 +0,0 @@
-# ODE With Birth and Death Process {#unrollBD}
-
-We follow on from the SIR model of `unrollSimple`{.interpreted-text
-role="ref"} but with additional birth and death processes.
-
-$$\begin{aligned}
-\frac{dS}{dt} &= -\beta SI + B - \mu S\\
-\frac{dI}{dt} &= \beta SI - \gamma I - \mu I\\
-\frac{dR}{dt} &= \gamma I.
-\end{aligned}$$
-
-which consists of two transitions and three birth and death process
-
-::: {.graphviz}
-
-digraph SIR_Model {
-
-:   rankdir=LR; size=\"8\" node \[shape = circle\]; S -\> I \[ label =
-    \"βSI\" \]; I -\> R \[ label = \"γI\" \]; B \[height=0
-    margin=0 shape=plaintext width=0\]; B -\> S; \"S\**2*μ\"
-    \[height=0 margin=0 shape=plaintext width=0\]; S -\> \"S\**2*μ\";
-    \"I\*μ\" \[height=0 margin=0 shape=plaintext width=0\]; I -\>
-    \"I\*μ\";
-
-}
-:::
-
-Let\'s define this in terms of ODEs, and unroll it back to the
-individual processes.
-
-::: {.ipython}
-In \[1\]: from pygom import Transition, TransitionType, SimulateOde,
-common_models
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[1\]: stateList = \[\'S\', \'I\', \'R\'\]
-
-In \[1\]: paramList = \[\'beta\', \'gamma\', \'B\', \'mu\'\]
-
-In \[1\]: odeList = \[
-
-:   \...: Transition(origin=\'S\', \...: equation=\'-beta\*S\*I + B -
-    mu\*S\', \...: transition_type=TransitionType.ODE), \...:
-    Transition(origin=\'I\', \...: equation=\'beta\*S\*I - gamma\*I -
-    mu\*I\', \...: transition_type=TransitionType.ODE), \...:
-    Transition(origin=\'R\', \...: equation=\'gamma\*I\', \...:
-    transition_type=TransitionType.ODE) \...: \]
-
-In \[1\]: ode = SimulateOde(stateList, paramList, ode=odeList)
-
-In \[1\]: ode2 = ode.get_unrolled_obj()
-
-In \[1\]: f = plt.figure()
-
-\@savefig sir_unrolled_transition_graph.png In \[1\]:
-ode2.get_transition_graph()
-
-In \[1\]: plt.close()
-:::
diff --git a/docs/pygom-doc/mdconvert/unroll/unrollHard.md b/docs/pygom-doc/mdconvert/unroll/unrollHard.md
deleted file mode 100644
index e02cd29d..00000000
--- a/docs/pygom-doc/mdconvert/unroll/unrollHard.md
+++ /dev/null
@@ -1,89 +0,0 @@
-# Hard Problem {#unrollHard}
-
-Now we turn to a harder problem that does not have a one to one mapping
-between all the transitions and the terms in the ODEs. We use the model
-in `Influenza_SLIARN`{.interpreted-text role="func"}, defined by
-
-$$\begin{aligned}
-\frac{dS}{dt} &= -S \beta (I + \delta A) \\    
-\frac{dL}{dt} &= S \beta (I + \delta A) - \kappa L \\  
-\frac{dI}{dt} &= p \kappa L - \alpha I \\
-\frac{dA}{dt} &= (1 - p) \kappa L - \eta A \\
-\frac{dR}{dt} &= f \alpha I + \eta A \\
-\frac{dN}{dt} &= -(1 - f) \alpha I.
-\end{aligned}$$
-
-The outflow of state **L**, $\kappa L$, is composed of two transitions,
-one to **I** and the other to **A** but the ode of **L** only reflects
-the total flow going out of the state. Same can be said for state **I**,
-where the flow $\alpha I$ goes to both **R** and **N**. Graphically, it
-is a rather simple process as shown below.
-
-::: {.graphviz}
-
-digraph SLIARD_Model {
-
-:   labelloc = \"t\"; label = \"Original transitions\"; rankdir=LR;
-    size=\"8\" node \[shape = circle\]; S -\> L \[ label =
-    \"-Sβ(I + δA)/N\" \]; L -\> I \[ label = \"κLp\"
-    \]; L -\> A \[ label = \"κL(1-p)\" \]; I -\> R \[ label =
-    \"αIf\" \]; I -\> D \[ label = \"αI(1-f)\" \]; A -\> R
-    \[ label = \"ηA\" \];
-
-}
-:::
-
-We slightly change the model by introducing a new state **D** to convert
-it into a closed system. The combination of state **D** and **N** is a
-constant, the total population. So we can remove **N** and this new
-system consist of six transitions. We define them explicitly as ODEs and
-unroll them into transitions.
-
-::: {.ipython}
-In \[1\]: from pygom import SimulateOde, Transition, TransitionType
-
-In \[1\]: stateList = \[\'S\', \'L\', \'I\', \'A\', \'R\', \'D\'\]
-
-In \[2\]: paramList = \[\'beta\', \'p\', \'kappa\', \'alpha\', \'f\',
-\'delta\', \'epsilon\', \'N\'\]
-
-In \[3\]: odeList = \[
-
-:   \...: Transition(origin=\'S\', equation=\'- beta\*S/N\*(I +
-    delta\*A)\', transition_type=TransitionType.ODE), \...:
-    Transition(origin=\'L\', equation=\'beta\*S/N\*(I + delta\*A) -
-    kappa\*L\', transition_type=TransitionType.ODE), \...:
-    Transition(origin=\'I\', equation=\'p\*kappa\*L - alpha\*I\',
-    transition_type=TransitionType.ODE), \...: Transition(origin=\'A\',
-    equation=\'(1 - p)*kappa* L - epsilon\*A\',
-    transition_type=TransitionType.ODE), \...: Transition(origin=\'R\',
-    equation=\'f\*alpha\*I + epsilon\*A\',
-    transition_type=TransitionType.ODE), \...: Transition(origin=\'D\',
-    equation=\'(1 - f)*alpha*I\', transition_type=TransitionType.ODE) \]
-
-In \[4\]: ode = SimulateOde(stateList, paramList, ode=odeList)
-
-In \[5\]: ode.get_transition_matrix()
-
-In \[6\]: ode2 = ode.get_unrolled_obj()
-
-In \[7\]: ode2.get_transition_matrix()
-
-In \[8\]: ode2.get_ode_eqn()
-:::
-
-After unrolling the odes, we have the following transition graph
-
-::: {.ipython}
-\@savefig sir_unrolled_transition_graph_hard.png In \[1\]:
-ode2.get_transition_graph()
-
-In \[2\]: plt.close()
-
-In \[3\]: print(sum(ode.get_ode_eqn() - ode2.get_ode_eqn()).simplify())
-\# difference
-:::
-
-which is exactly the same apart from slightly weird arrangement of
-symbols in some of the equations. The last line with a value of zero
-also reaffirms the result.
diff --git a/docs/pygom-doc/mdconvert/unroll/unrollSimple.md b/docs/pygom-doc/mdconvert/unroll/unrollSimple.md
deleted file mode 100644
index 07b17497..00000000
--- a/docs/pygom-doc/mdconvert/unroll/unrollSimple.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Simple Problem {#unrollSimple}
-
-For a simple problem, we consider the SIR model defined by
-
-$$\begin{aligned}
-\frac{dS}{dt} &= -\beta SI \\
-\frac{dI}{dt} &= \beta SI - \gamma I \\
-\frac{dR}{dt} &= \gamma I.
-\end{aligned}$$
-
-which consists of two transitions
-
-::: {.graphviz}
-
-digraph SIR_Model {
-
-:   rankdir=LR; size=\"8\" node \[shape = circle\]; S -\> I \[ label =
-    \"βSI\" \]; I -\> R \[ label = \"γI\" \];
-
-}
-:::
-
-Let\'s define this using the code block below
-
-::: {.ipython}
-In \[1\]: from pygom import SimulateOde, Transition, TransitionType
-
-In \[2\]: ode1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\',
-transition_type=TransitionType.ODE)
-
-In \[3\]: ode2 = Transition(origin=\'I\', equation=\'beta\*S\*I -
-gamma\*I\', transition_type=TransitionType.ODE)
-
-In \[4\]: ode3 = Transition(origin=\'R\', equation=\'gamma\*I\',
-transition_type=TransitionType.ODE)
-
-In \[6\]: stateList = \[\'S\', \'I\', \'R\'\]
-
-In \[7\]: paramList = \[\'beta\', \'gamma\'\]
-
-In \[8\]: ode = SimulateOde(stateList,
-
-:   \...: paramList, \...: ode=\[ode1, ode2, ode3\])
-
-In \[9\]: ode.get_transition_matrix()
-:::
-
-and the last line shows that the transition matrix is empty. This is the
-expected result because `SimulateOdeModel`{.interpreted-text
-role="class"} was not initialized using transitions. We populate the
-transition matrix below and demonstrate the difference.
-
-::: {.ipython}
-In \[1\]: ode = ode.get_unrolled_obj()
-
-In \[2\]: ode.get_transition_matrix()
-:::

From 913938b5022a6641efa3152b67413953f96eeb9c Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Thu, 31 Aug 2023 10:29:11 +0100
Subject: [PATCH 160/188] correct for documentation

---
 .gitignore | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index 416f2bf4..0c363370 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,6 @@ __pycache__
 .settings
 
 # For built documentation
-/docs/pygom-doc/_build
-/doc/_build
+/docs/pygom-doc/_build/*
+/doc/_build/
 /doc/source/savefig/*

From efa193b1feffe68f7a5524ac72ad3c339a20ae47 Mon Sep 17 00:00:00 2001
From: "Hannah.Williams" 
Date: Thu, 31 Aug 2023 10:31:23 +0100
Subject: [PATCH 161/188] remove build files

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diff --git a/docs/pygom-doc/_build/.jupyter_cache/__version__.txt b/docs/pygom-doc/_build/.jupyter_cache/__version__.txt
deleted file mode 100644
index 7ceb0404..00000000
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diff --git a/docs/pygom-doc/_build/.jupyter_cache/executed/241840d0abebeb4f04881fdc9ac5ec6b/base.ipynb b/docs/pygom-doc/_build/.jupyter_cache/executed/241840d0abebeb4f04881fdc9ac5ec6b/base.ipynb
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-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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- },
- "nbformat": 4,
- "nbformat_minor": 5
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\ No newline at end of file
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diff --git a/docs/pygom-doc/_build/html/.buildinfo b/docs/pygom-doc/_build/html/.buildinfo
deleted file mode 100644
index 62b8a4eb..00000000
--- a/docs/pygom-doc/_build/html/.buildinfo
+++ /dev/null
@@ -1,4 +0,0 @@
-# Sphinx build info version 1
-# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: a30965eb3508cc59ee9dc86536a23dfb
-tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/pygom-doc/_build/html/_downloads/289ac28c9b7c69b5b79db17a12b844bf/sir.ipynb b/docs/pygom-doc/_build/html/_downloads/289ac28c9b7c69b5b79db17a12b844bf/sir.ipynb
deleted file mode 100644
index 84796466..00000000
--- a/docs/pygom-doc/_build/html/_downloads/289ac28c9b7c69b5b79db17a12b844bf/sir.ipynb
+++ /dev/null
@@ -1,868 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Motivating Example: SIR Model\n",
-    "{download}`this notebook <./sir.ipynb>`\n",
-    "\n",
-    "## Defining the model\n",
-    "\n",
-    "First, we are going to go through an SIR model to show the functionality\n",
-    "of the package. The SIR model is defined by the following equations\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{dS}{dt} &= -\\beta SI \\\\\n",
-    "\\frac{dI}{dt} &= \\beta SI- \\gamma I \\\\\n",
-    "\\frac{dR}{dt} &= \\gamma I.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "We can set this up as follows:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "586834ab",
-   "metadata": {},
-   "source": [
-    "1. import the classes required to define the transitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "4c80a36a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import Transition, TransitionType"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ec4c9d0",
-   "metadata": {},
-   "source": [
-    "2. define our states, in this case the population states of **S**usceptible, **I**nfected and **R**emoved"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "3ce016f2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stateList = ['S', 'I', 'R']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "021a1927",
-   "metadata": {},
-   "source": [
-    "3. define the set of parameters for our model, here there are only two"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "441e2287",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "paramList = ['beta', 'gamma']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfd736b2",
-   "metadata": {},
-   "source": [
-    "4. specify the transitions of the modelled states; this will form our ODE system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "b5f80ab0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "odeList = [\n",
-    "    Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE),\n",
-    "    Transition(origin='I',equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE),\n",
-    "    Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE) \n",
-    "    ]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21629e7b",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "Here, we have invoked a class from `Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`transition:defining-the-equations` for an example when the input is wrong.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "69bfb7c3",
-   "metadata": {},
-   "source": [
-    "\n",
-    "5. import the ode module"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "f78c33d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import DeterministicOde"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "477d6f84",
-   "metadata": {},
-   "source": [
-    "6. initialize the model, which constructs our ODE system from all the information we have provided"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "c9fbcce0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = DeterministicOde(stateList, paramList, ode=odeList)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4bf43064",
-   "metadata": {},
-   "source": [
-    "That is all the information required to define a simple SIR model. We can verify the model using"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "6ad54e09",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "143a6871",
-   "metadata": {},
-   "source": [
-    "where we can see the equations corresponding to their respective $S,I$ and $R$ state. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08207474",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "The ordering of the equations is in the standard $S,I,R$ sequence because of how we defined the states initially. \n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7054e58a",
-   "metadata": {},
-   "source": [
-    "We can rearrange the state list,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "07f81fd1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}I \\gamma\\\\- I S \\beta\\\\I S \\beta - I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[           I*gamma],\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma]])"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# now we are going to define in a different order. note that the output ode changed with the input state\n",
-    "stateList = ['R', 'S', 'I']\n",
-    "\n",
-    "model = DeterministicOde(stateList, paramList, ode=odeList)\n",
-    "\n",
-    "model.get_ode_eqn()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3f587d4",
-   "metadata": {},
-   "source": [
-    "and find that the set of ODEs comes out in the order that we specified. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1175c832",
-   "metadata": {},
-   "source": [
-    "```{tip}\n",
-    "In addition to showing the equation form of the ODEs, we can also display them as either symbols or latex code, which can save some extra typing when porting the equations to another document.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "960fdc0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "⎡dR/dt=      I⋅γ    ⎤\n",
-      "⎢                   ⎥\n",
-      "⎢dS/dt=    -I⋅S⋅β   ⎥\n",
-      "⎢                   ⎥\n",
-      "⎣dI/dt=  I⋅S⋅β - I⋅γ⎦\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.print_ode()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "93e32c75",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{array}{cc}dR/dt= & I \\gamma\\\\dS/dt= & - I S \\beta\\\\dI/dt= & I S \\beta - I \\gamma\\end{array}\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.print_ode(True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79c154ba",
-   "metadata": {},
-   "source": [
-    "#TODO links to unroll\n",
-    "Here the SIR model was provided to PyGOM as a set ODEs by using the `Transition` to define them.  \n",
-    "We have also provided the capability to obtain a *best guess* transition matrix when only the ODEs are available. See the section `unrollOde` for more information, and in particular `unrollSimple` for the continuing demonstration of the SIR model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51ed6fa2",
-   "metadata": {},
-   "source": [
-    "## Extracting model information\n",
-    "\n",
-    "We may wish to determine if the set of ODEs are linear. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "61684654",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.linear_ode()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4cf58543",
-   "metadata": {},
-   "source": [
-    "Since we know that the SIR model is not linear, we may want to have a look at the Jacobian.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "1c8ff090",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & \\gamma\\\\0 & - I \\beta & - S \\beta\\\\0 & I \\beta & S \\beta - \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[0,       0,          gamma],\n",
-       "[0, -I*beta,        -S*beta],\n",
-       "[0,  I*beta, S*beta - gamma]])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_jacobian_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bab79c98",
-   "metadata": {},
-   "source": [
-    "Or maybe we want to know the gradient."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "abb02502",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & I\\\\- I S & 0\\\\I S & - I\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[   0,  I],\n",
-       "[-I*S,  0],\n",
-       "[ I*S, -I]])"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_grad_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d0052ec",
-   "metadata": {},
-   "source": [
-    "```{Warning}\n",
-    "Invoking the functions that compute the derivatives, $f(x)$, `model.ode()` or `model.jacobian()`, will return an error\n",
-    "\n",
-    "These functions are used to solve the ODEs numerically, and require values of initial state values, time, and parameter values.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35abe902",
-   "metadata": {},
-   "source": [
-    "For setting initial conditions, the numeric values of the states **must** be set in the same order that list of states appear. We can use the following to check the state ordering, as well as displaying all of the states that we have included."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "e703c888",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ODEVariable('R', 'R', None, True),\n",
-       " ODEVariable('S', 'S', None, True),\n",
-       " ODEVariable('I', 'I', None, True)]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.state_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54e681d0",
-   "metadata": {},
-   "source": [
-    "#TODO unsure if this is needed\n",
-    "There is currently no mechanism to set the numeric initial conditions the states when the states are defined. This is because of an implementation issue with external package, such as solving an initial value problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13bed7f9",
-   "metadata": {},
-   "source": [
-    "## Initial value problem\n",
-    "\n",
-    "By setting the state initial conditions, time, and parameters, we can evaluate our model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04fb8dd9",
-   "metadata": {},
-   "source": [
-    "1. Define the model parameters. (We can call the parameters to check what we must provide.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "46042ebf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "9ec016b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{beta: 0.5, gamma: 0.3333333333333333}"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "paramEval = [('beta',0.5), ('gamma',1.0/3.0)]\n",
-    "\n",
-    "model.parameters = paramEval\n",
-    "\n",
-    "model.parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "efcdac90",
-   "metadata": {},
-   "source": [
-    "2. Provide initial conditions for the states."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "c43074c6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4.23333333e-07, -6.35000000e-07,  2.11666667e-07])"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initialState = [0, 1, 1.27e-6]\n",
-    "    \n",
-    "model.ode(state=initialState, t=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a8dc4681",
-   "metadata": {},
-   "source": [
-    "\n",
-    "3. Implement an ODE solver.\n",
-    "\n",
-    "We are well equipped to solve an initial value problem, using the standard numerical integrator such as `odeint ` from `scipy.integrate`. We also used `matplotlib.pyplot` for plotting and `linspace ` to create the time vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "a14b9901",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.integrate\n",
-    "import numpy\n",
-    "\n",
-    "t = numpy.linspace(0, 150, 100)\n",
-    "\n",
-    "solution = scipy.integrate.odeint(model.ode, initialState, t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4feebae",
-   "metadata": {},
-   "source": [
-    "We can plot our solution to observe a standard SIR shape."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "8303885c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.figure()\n",
-    "\n",
-    "plt.plot(t, solution[:,0], label='R')\n",
-    "\n",
-    "plt.plot(t, solution[:,1], label='S')\n",
-    "\n",
-    "plt.plot(t, solution[:,2], label='I')\n",
-    "\n",
-    "plt.xlabel('Time')\n",
-    "\n",
-    "plt.ylabel('Population proportion')\n",
-    "\n",
-    "plt.title('Standard SIR model')\n",
-    "\n",
-    "plt.legend(loc=0)\n",
-    "\n",
-    "#@savefig sir_plot.png In \n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "#plt.close()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba849579",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can integrate and plot via the **ode** object which we initialized earlier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "efddd3a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model.initial_values = (initialState, t[0])\n",
-    "\n",
-    "model.parameters = paramEval\n",
-    "\n",
-    "solution = model.integrate(t[1::])\n",
-    "\n",
-    "model.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4dd6e250",
-   "metadata": {},
-   "source": [
-    "We could solve the ODEs above using the Jacobian as well. Unfortunately, it does not help because the number of times the Jacobian was evaluated was zero, as expected given that our set of equations are not stiff."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "7a7a568f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.58 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "#TODO what does this show?\n",
-    "%timeit solution1, output1 = scipy.integrate.odeint(model.ode, initialState, t, full_output=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "91b7f9d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.84 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "%timeit solution2, output2 = scipy.integrate.odeint(model.ode, initialState, t, Dfun=model.jacobian, mu=None, ml=None, full_output=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "3403884c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.68 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "%timeit solution3, output3 = model.integrate(t, full_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6b9ee47",
-   "metadata": {},
-   "source": [
-    "It is important to note that we return our Jacobian as a dense square matrix. Hence, the two argument (mu,ml) for the ODE solver was set to `None` to let it know the output explicitly."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ca18fa3",
-   "metadata": {},
-   "source": [
-    "## Solving the forward sensitivity equation\n",
-    "\n",
-    "The sensitivity equations are also solved as an initial value problem. Let us redefine the model in the standard SIR order and we solve it with the sensitivity all set at zero, i.e. we do not wish to infer the initial value of the states."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "87173626",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stateList = ['S', 'I', 'R']\n",
-    "\n",
-    "model = DeterministicOde(stateList, paramList, ode=odeList)\n",
-    "\n",
-    "initialState = [1, 1.27e-6, 0]\n",
-    "\n",
-    "paramEval = [('beta', 0.5), ('gamma', 1.0/3.0)]\n",
-    "\n",
-    "model.parameters = paramEval\n",
-    "\n",
-    "solution = scipy.integrate.odeint(model.ode_and_sensitivity, numpy.append(initialState, numpy.zeros(6)), t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "8b63ba62",
-   "metadata": {
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "{\n",
-    "    \"tags\": [\n",
-    "        \"hide-input\",\n",
-    "    ]\n",
-    "}\n",
-    "f,axarr = plt.subplots(3,3);\n",
-    "\n",
-    "f.text(0.5,0.975,'SIR with forward sensitivity solved via ode',fontsize=16,horizontalalignment='center',verticalalignment='top')\n",
-    "\n",
-    "axarr[0,0].plot(t, solution[:,0])\n",
-    "\n",
-    "axarr[0,0].set_title('S')\n",
-    "\n",
-    "axarr[0,1].plot(t, solution[:,1])\n",
-    "\n",
-    "axarr[0,1].set_title('I')\n",
-    "\n",
-    "axarr[0,2].plot(t, solution[:,2]);\n",
-    "\n",
-    "axarr[0,2].set_title('R')\n",
-    "\n",
-    "axarr[1,0].plot(t, solution[:,3])\n",
-    "\n",
-    "axarr[1,0].set_title(r'state S parameter $beta$')\n",
-    "\n",
-    "axarr[2,0].plot(t, solution[:,4])\n",
-    "\n",
-    "axarr[2,0].set_title(r'state S parameter $gamma$')\n",
-    "\n",
-    "axarr[1,1].plot(t, solution[:,5])\n",
-    "\n",
-    "axarr[1,1].set_title(r'state I parameter $beta$')\n",
-    "\n",
-    "axarr[2,1].plot(t, solution[:,6])\n",
-    "\n",
-    "axarr[2,1].set_title(r'state I parameter $gamma$')\n",
-    "\n",
-    "axarr[1,2].plot(t, solution[:,7])\n",
-    "\n",
-    "axarr[1,2].set_title(r'state R parameter $beta$')\n",
-    "\n",
-    "axarr[2,2].plot(t, solution[:,8])\n",
-    "\n",
-    "axarr[2,2].set_title(r'state R parameter $gamma$')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f64d869",
-   "metadata": {},
-   "source": [
-    "#TODO links\n",
-    "This concludes the introductory example and we will be moving on to look at parameter estimation next in {doc}`estimate1` and the most important part in terms of setting up the ODE object; defining the equations in various different ways in {doc}`transition`."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.15 ('sphinx-doc')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/pygom-doc/_build/html/_downloads/c78f2e88b6a97c6b5b77ae96e28c2fa2/transition.ipynb b/docs/pygom-doc/_build/html/_downloads/c78f2e88b6a97c6b5b77ae96e28c2fa2/transition.ipynb
deleted file mode 100644
index dd1568bf..00000000
--- a/docs/pygom-doc/_build/html/_downloads/c78f2e88b6a97c6b5b77ae96e28c2fa2/transition.ipynb
+++ /dev/null
@@ -1,453 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transition Object\n",
-    "{download}`this notebook <./transition.ipynb>`\n",
-    "\n",
-    "\n",
-    "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n",
-    "\n",
-    "\n",
-    "All transitions that get fed into the ODE system need to be defined as a transition object, `Transition`. It takes a total of four input arguments:\n",
-    "\n",
-    "1.  The origin state\n",
-    "2.  Equation that describe the process\n",
-    "3.  The type of transition\n",
-    "4.  The destination state\n",
-    "\n",
-    "where the first three are mandatory. To demonstrate, we go back to the SIR model defined previously in the section {doc}`sir`. Recall that the set of ODEs are\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    " \\frac{d S}{d t} &= - beta SI \\\\\n",
-    "\\frac{d I}{d t} &= beta SI - \\gamma I \\\\\n",
-    "\\frac{d R}{d t} &= \\gamma I.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "We can define the set of ODEs, as seen previously, via"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "68d41d64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import Transition, TransitionType, common_models\n",
-    "\n",
-    "ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)\n",
-    "\n",
-    "ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)\n",
-    "\n",
-    "ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d393a33a",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Note that we need to state explicitly the type of equation we are\n",
-    "inputting, which is simply of type **ODE** in this case. We can confirm\n",
-    "this has been entered correctly by putting it into `DeterministicOde`,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3b0801dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import DeterministicOde\n",
-    "\n",
-    "stateList = ['S', 'I', 'R']\n",
-    "\n",
-    "paramList = ['beta', 'gamma']\n",
-    "\n",
-    "model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e764826",
-   "metadata": {},
-   "source": [
-    "\n",
-    "and then checking it.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5782a96f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c693cbc7",
-   "metadata": {},
-   "source": [
-    "Now we are going to show the different ways of defining the same set of\n",
-    "ODEs.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa631d9f",
-   "metadata": {},
-   "source": [
-    "(transition:defining-the-equations)=\n",
-    "## Defining the equations\n",
-    "\n",
-    "We first recognize that the set of ODEs defining the SIR model are the result of\n",
-    "two transitions,\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "S \\rightarrow I &= \\beta SI \\\\\n",
-    "I \\rightarrow R &= \\gamma I\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "where $S \\rightarrow I$ denotes a transition from state $S$ to state\n",
-    "$I$. Therefore, we can define our model by these two transitions,\n",
-    "but they need to be passed as the `transition`\n",
-    "argument instead of the `ode` argument of `DeterministicOde` or `SimulateOde`.\n",
-    "\n",
-    "```{note}\n",
-    "We are initializing the model using the `SimulateOde` class, rather than `DeterministicOde`, because the stochastic implementation has more available operations on transitions.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6966fc5e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import SimulateOde\n",
-    "\n",
-    "t1 = Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T)\n",
-    "\n",
-    "t2 = Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)\n",
-    "\n",
-    "modelTrans = SimulateOde(stateList, paramList, transition=[t1, t2])\n",
-    "\n",
-    "modelTrans.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b896d5c9",
-   "metadata": {},
-   "source": [
-    "\n",
-    "We can see that the resulting ODE is exactly the same, as expected. The\n",
-    "transition matrix that defines this process can be visualized\n",
-    "using graphviz. Because only certain renderers permit the use of sub and\n",
-    "superscript, operators such as $**$ are left as they are in the\n",
-    "equation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "1e17eb7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & I S \\beta & 0\\\\0 & 0 & I \\gamma\\\\0 & 0 & 0\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[0, I*S*beta,       0],\n",
-       "[0,        0, I*gamma],\n",
-       "[0,        0,       0]])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "modelTrans.get_transition_matrix()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "570ceed5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": "\n\n\n\n\n\n\n\n\nS\n\nS\n\n\n\nI\n\nI\n\n\n\nS->I\n\n\nβ*S*I\n\n\n\nR\n\nR\n\n\n\nI->R\n\n\nγ*I\n\n\n\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "# TODO why are two images produced? issue #75\n",
-    "modelTrans.get_transition_graph(show=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d982b87",
-   "metadata": {},
-   "source": [
-    "```{warning}\n",
-    "The execution will error if the incorrect `TransitionType` is used against the wrong argument.\n",
-    "\n",
-    "`modelTrans = DeterministicOde(stateList, paramList, ode=[t1, t2])`\n",
-    "\n",
-    "Here the error occurs because `t1` and `t2` used `transition_type=TransitionType.T` argument, but `DeterministicOde` is expecting a `TransitionType.ODE` argument.\n",
-    "\n",
-    "Similarly `DeterministicOde(stateList, paramList, transition=[ode1, ode2, ode3])` would fail.\n",
-    "\n",
-    "This therefore forces us to construct our model carefully.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d590f4d5",
-   "metadata": {},
-   "source": [
-    "The third option is to reframe the system as a set of birth processes, using `transition_type=TransitionType.B`. For this simple example, this formulation takes a similar form to defining using ODE equations.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "9f24cb0a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "birth1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.B)\n",
-    "\n",
-    "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n",
-    "\n",
-    "birth3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.B)\n",
-    "\n",
-    "modelBirth = DeterministicOde(stateList, paramList, birth_death=[birth1, birth2, birth3])\n",
-    "\n",
-    "modelBirth.get_ode_eqn()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1d61012",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can use the negative of the equation to configure the ODEs to represent death processes. Since the death process is the removal of a flow, we take the negative of the birth process alongside using `transition_type=TransitionType.D`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "9a3b3758",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "death1 = Transition(origin='S', equation='beta*S*I', transition_type=TransitionType.D)\n",
-    "\n",
-    "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n",
-    "\n",
-    "death3 = Transition(origin='R', equation='-gamma*I', transition_type=TransitionType.D)\n",
-    "\n",
-    "modelBD = DeterministicOde(stateList, paramList, birth_death=[death1, birth2, death3])\n",
-    "\n",
-    "modelBD.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c8d48b8",
-   "metadata": {},
-   "source": [
-    "\n",
-    "We can see that all four approaches have yielded the same set of ODEs at the end."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23a105af",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Model Addition\n",
-    "\n",
-    "Because we allow the separation of transitions between states and birth/death processes, the birth/death processes can be added later on. The following example takes the model that was defined using transitions (`modelTrans`) and includes a birth process to the $S$ state, and death processes to the $S$ and $I$ states."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "3a6a15ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}B - I S \\beta - S \\mu\\\\I S \\beta - I \\gamma - I \\mu\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[      B - I*S*beta - S*mu],\n",
-       "[I*S*beta - I*gamma - I*mu],\n",
-       "[                  I*gamma]])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "modelBD2 = modelTrans\n",
-    "\n",
-    "modelBD2.param_list = paramList + ['mu', 'B']\n",
-    "\n",
-    "birthDeathList = [Transition(origin='S', equation='B', transition_type=TransitionType.B),  \n",
-    "                  Transition(origin='S', equation='mu*S', transition_type=TransitionType.D), \n",
-    "                  Transition(origin='I', equation='mu*I', transition_type=TransitionType.D)\n",
-    "                  ]\n",
-    "\n",
-    "modelBD2.birth_death_list = birthDeathList\n",
-    "\n",
-    "modelBD2.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "076bdc12",
-   "metadata": {},
-   "source": [
-    "\n",
-    "This demonstrates that we can approach our in stages. Start off with a standard closed system using `TransitionType.T`, and then extend it with additional flows that interact with the populations' surrounding environments using `TransitionType.B` or `TransitionType.D`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec63c1e1",
-   "metadata": {},
-   "source": [
-    "## Transition type summary\n",
-    "\n",
-    "In summary, there are four different types of transitions allowed. These are B, D, ODE and T, which are defined in an enum class also located in `transition`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "ce5787f8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TransitionType.B = Birth process\n",
-      "TransitionType.D = Death process\n",
-      "TransitionType.T = Between states\n",
-      "TransitionType.ODE = ODE _equation\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pygom import transition\n",
-    "\n",
-    "for i in transition.TransitionType:  \n",
-    "    print(str(i) + \" = \" + i.value)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89607b7d",
-   "metadata": {},
-   "source": [
-    "Each birth process is added to the origin state, while each death\n",
-    "process is deducted from the origin state (alternatively added to the state after\n",
-    "multiplying the flow with a negative sign). An ODE type is also added to the state, but we forbid the number of input ODEs to be greater than the number of states inputted. These strict definitions should help us to improve the bookeeping of states and flows when we have models of greater complexity."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.15 ('sphinx-doc')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/pygom-doc/_build/html/_images/0bbd9ae219fda3e5af2c2faf5b8745cf0187f817a37553a042c31af498e6c31f.png b/docs/pygom-doc/_build/html/_images/0bbd9ae219fda3e5af2c2faf5b8745cf0187f817a37553a042c31af498e6c31f.png
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diff --git a/docs/pygom-doc/_build/html/_sources/intro.md b/docs/pygom-doc/_build/html/_sources/intro.md
deleted file mode 100644
index 02905b2c..00000000
--- a/docs/pygom-doc/_build/html/_sources/intro.md
+++ /dev/null
@@ -1,24 +0,0 @@
-# Welcome to the documentation for PyGOM
-
-PyGOM (Python Generic ODE Model) is a Python package that aims to facilitate the application of ordinary differential equations (ODEs) in the real world, 
-with a focus in epidemiology.  
-This package helps the user define their ODE system in an intuitive manner and provides convenience functions - 
-making use of various algebraic and numerical libraries in the backend - that can be used in a straight forward fashion.
-
-This is an open source project hosted on [Github](https://github.com/PublicHealthEngland/pygom). 
-
-A manuscript containing a shortened motivation and use is hosted on [arxXiv](https://arxiv.org/abs/1803.06934).
-
-#TODO insert intro text
-
-
-
-```{tableofcontents}
-```
-
-# Code Documentation and FAQ
-#TODO from index.rst
-# References
-#TODO
-# Indices and tables
-#TODO
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/_sources/markdown.md b/docs/pygom-doc/_build/html/_sources/markdown.md
deleted file mode 100644
index 0ddaab3f..00000000
--- a/docs/pygom-doc/_build/html/_sources/markdown.md
+++ /dev/null
@@ -1,55 +0,0 @@
-# Markdown Files
-
-Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or
-in regular markdown files (`.md`), you'll write in the same flavor of markdown
-called **MyST Markdown**.
-This is a simple file to help you get started and show off some syntax.
-
-## What is MyST?
-
-MyST stands for "Markedly Structured Text". It
-is a slight variation on a flavor of markdown called "CommonMark" markdown,
-with small syntax extensions to allow you to write **roles** and **directives**
-in the Sphinx ecosystem.
-
-For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html).
-
-## Sample Roles and Directives
-
-Roles and directives are two of the most powerful tools in Jupyter Book. They
-are kind of like functions, but written in a markup language. They both
-serve a similar purpose, but **roles are written in one line**, whereas
-**directives span many lines**. They both accept different kinds of inputs,
-and what they do with those inputs depends on the specific role or directive
-that is being called.
-
-Here is a "note" directive:
-
-```{note}
-Here is a note
-```
-
-It will be rendered in a special box when you build your book.
-
-Here is an inline directive to refer to a document: {doc}`markdown-notebooks`.
-
-
-## Citations
-
-You can also cite references that are stored in a `bibtex` file. For example,
-the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like
-this: {cite}`holdgraf_evidence_2014`.
-
-Moreover, you can insert a bibliography into your page with this syntax:
-The `{bibliography}` directive must be used for all the `{cite}` roles to
-render properly.
-For example, if the references for your book are stored in `references.bib`,
-then the bibliography is inserted with:
-
-```{bibliography}
-```
-
-## Learn more
-
-This is just a simple starter to get you started.
-You can learn a lot more at [jupyterbook.org](https://jupyterbook.org).
diff --git a/docs/pygom-doc/_build/html/_sources/md/getting_started.md b/docs/pygom-doc/_build/html/_sources/md/getting_started.md
deleted file mode 100644
index fd7094b7..00000000
--- a/docs/pygom-doc/_build/html/_sources/md/getting_started.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# Getting started
-
-## What does this package do?
-
-The purpose of this package is to allow the end user to easily define a
-set of ordinary differential equations (ODEs) and obtain information
-about the ODEs by invoking the the appropriate methods. Here, we define
-the set of ODEs as
-
-$$\frac{d \mathbf{x}}{d t} = f(\mathbf{x},\boldsymbol{\theta})$$
-
-where $\mathbf{x} = \left(x_{1},x_{2},\ldots,x_{n}\right)$ is the state
-vector with $d$ state and $\boldsymbol{\theta}$ the parameters of $p$
-dimension. Currently, this package allows the user to find the algebraic
-expression of the ODE, Jacobian, gradient and forward sensitivity of the
-ODE. A numerical output is given when all the state and parameter values
-are provided. Note that the only important class is
-`DeterministicOde`{.interpreted-text role="file"} where all the
-functionality described previously are exposed.
-
-#TODO do we want this updating or referencing issue board?
-The current plan is to extend the functionality to include
-
--   Solving the ode analytically when it is linear
--   Analysis of the system via eigenvalues during the integration
--   Detection of DAE
-
-## Obtaining the package 
-
-The location of the package is current on GitHub and can be pulled via
-https from:
-
-    https://github.com/PublicHealthEngland/pygom.git
-
-#TODO required?
-The package is currently as follows:
-
-    pygom/
-        bin/
-        doc/
-        pygom/
-            loss/
-                tests/
-            model/
-                tests/
-            sbml_translate/
-            utilR/
-        LICENSE.txt
-        README.rst
-        requirements.txt
-        setup.py
-
-with files in each of the three main folders not shown. You can install
-the package via command line:
-
-    python setup.py install
-
-or locally on a user level:
-
-    python setup.py install --user
-
-Please note that there are current redundant file are kept for
-development purposes for the time being.
-
-## Testing the package
-
-Testing can be performed prior or after the installation. Some standard
-test files can be found in their respective folder and they can be run
-in the command line:
-
-    python setup.py test
-
-which can be performed prior to installing the package if desired.
diff --git a/docs/pygom-doc/_build/html/_sources/md/unrollOde.md b/docs/pygom-doc/_build/html/_sources/md/unrollOde.md
deleted file mode 100644
index 9da3bd11..00000000
--- a/docs/pygom-doc/_build/html/_sources/md/unrollOde.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# Convert ODE into transitions
-
-As seen previously in `transition`{.interpreted-text role="ref"}, we can
-define the model via the transitions or explicitly as ODEs. There are
-times when we all just want to test out some model in a paper and the
-only available information are the ODEs themselves. Even though we know
-that the ODEs come from some underlying transitions, breaking them down
-can be a time consuming process. We provide the functionalities to do
-this automatically.
-
-::: {.toctree}
-unroll/unrollSimple.rst
-unroll/unrollBD.rst 
-unroll/unrollHard.rst
-:::
diff --git a/docs/pygom-doc/_build/html/_sources/mdconvert/common_models/.md b/docs/pygom-doc/_build/html/_sources/mdconvert/common_models/.md
deleted file mode 100644
index 7aa8ca23..00000000
--- a/docs/pygom-doc/_build/html/_sources/mdconvert/common_models/.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# `.SIR_Birth_Death`{.interpreted-text role="func"}
-
-Next, we look at an SIR model with birth death
-
-$$\begin{aligned}
-\frac{dS}{dt} &= B -\beta SI - \mu S \\
-\frac{dI}{dt} &= \beta SI - \gamma I - \mu I \\
-\frac{dR}{dt} &= \gamma I
-\end{aligned}$$
-
-Continuing from the example above, but now with a much longer time
-frame. Note that the birth and death rate are the same.
-
-::: {.ipython}
-In \[1\]: from pygom import common_models
-
-In \[1\]: import numpy
-
-In \[1\]: B = 126372.0/365.0
-
-In \[1\]: N = 7781984.0
-
-In \[1\]: ode = common_models.SIR_Birth_Death({\'beta\':3.6,
-\'gamma\':0.2, \'B\':B/N, \'mu\':B/N})
-
-In \[1\]: t = numpy.linspace(0, 35\*365, 10001)
-
-In \[1\]: x0 = \[0.065, 123.0\*(5.0/30.0)/N, 0.0\]
-
-In \[1\]: ode.initial_values = (x0, t\[0\])
-
-In \[1\]: solution = ode.integrate(t\[1::\])
-
-\@savefig common_models_sir_bd.png In \[1\]: ode.plot()
-:::
diff --git a/docs/pygom-doc/_build/html/_sources/mdconvert/doc_to_sort/.md b/docs/pygom-doc/_build/html/_sources/mdconvert/doc_to_sort/.md
deleted file mode 100644
index 07745c25..00000000
--- a/docs/pygom-doc/_build/html/_sources/mdconvert/doc_to_sort/.md
+++ /dev/null
@@ -1,201 +0,0 @@
-# Solving Boundary Value Problems {#bvpSimple}
-
-In addition to finding solutions for an IVP and estimate the unknown
-parameters, this package also allows you to solve BVP with a little bit
-of imagination. Here, we are going to show how a BVP can be solved by
-treating it as a parameter estimation problem. Essentially, a shooting
-method where the first boundary condition defines the initial condition
-of an IVP and the second boundary condition is an observation. Two
-examples, both from MATLAB[^1], will be shown here.
-
-## Simple model 1
-
-We are trying to find the solution to the second order differential
-equation
-
-$$\nabla^{2} y + |y| = 0$$
-
-subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert
-this into a set of first order ODE
-
-$$\begin{aligned}
-\frac{d y_{0}}{dt} &= y_{1} \\
-\frac{d y_{1}}{dt} &= -|y_{0}|
-\end{aligned}$$
-
-using an auxiliary variable $y_{1} = \nabla y$ and $y_{0} = y$. Setting
-up the system below
-
-::: {.ipython}
-In \[1\]: from pygom import Transition, TransitionType,
-DeterministicOde, SquareLoss
-
-In \[1\]: import matplotlib.pyplot as plt
-
-In \[2\]: stateList = \[\'y0\', \'y1\'\]
-
-In \[3\]: paramList = \[\]
-
-In \[4\]: ode1 = Transition(origin=\'y0\',
-
-:   \...: equation=\'y1\', \...: transition_type=TransitionType.ODE)
-
-In \[5\]: ode2 = Transition(origin=\'y1\',
-
-:   \...: equation=\'-abs(y0)\', \...:
-    transition_type=TransitionType.ODE)
-
-In \[6\]: model = DeterministicOde(stateList,
-
-:   \...: paramList, \...: ode=\[ode1, ode2\])
-
-In \[7\]: model.get_ode_eqn()
-:::
-
-We check that the equations are correct before proceeding to set up our
-loss function.
-
-::: {.ipython}
-In \[1\]: import numpy
-
-In \[2\]: from scipy.optimize import minimize
-
-In \[3\]: initialState = \[0.0, 1.0\]
-
-In \[4\]: t = numpy.linspace(0, 4, 100)
-
-In \[5\]: model.initial_values = (initialState, t\[0\])
-
-In \[6\]: solution = model.integrate(t\[1::\])
-
-In \[7\]: f = plt.figure()
-
-\@savefig bvp1_random_guess_plot.png In \[8\]: model.plot()
-
-In \[9\]: plt.close()
-:::
-
-Setting up the second boundary condition $y(4) = -2$ is easy, because
-that is just a single observation attached to the state $y_{1}$.
-Enforcing the first boundary condition requires us to set it as the
-initial condition. Because the condition only states that $y(0) = 0$,
-the starting value of the other state $y_1$ is free. We let our loss
-object know that it is free through the targetState input argument.
-
-::: {.ipython}
-In \[10\]: theta = \[0.0\]
-
-In \[11\]: obj = SquareLoss(theta=theta,
-
-:   \....: ode=model, \....: x0=initialState, \....: t0=t\[0\], \....:
-    t=t\[-1\], \....: y=\[-2\], \....: state_name=\[\'y0\'\], \....:
-    target_state=\[\'y1\'\])
-
-In \[12\]: thetaHat = minimize(fun=obj.costIV, x0=\[0.0\])
-
-In \[13\]: print(thetaHat)
-
-In \[14\]: model.initial_values = (\[0.0\] + thetaHat\[\'x\'\].tolist(),
-t\[0\])
-
-In \[15\]: solution = model.integrate(t\[1::\])
-
-In \[16\]: f = plt.figure()
-
-\@savefig bvp1_solution_plot.png In \[17\]: model.plot()
-
-In \[18\]: plt.close()
-:::
-
-We are going to visualize the solution, and also check the boundary
-condition. The first became our initial condition, so it is always
-satisfied and only the latter is of concern, which is zero (subject to
-numerical error) from thetaHat.
-
-## Simple model 2
-
-Our second example is different as it involves an actual parameter and
-also time. We have the Mathieu\'s Equation
-
-$$\nabla^{2} y + \left(p - 2q \cos(2x)\right)y = 0$$
-
-and the aim is to compute the fourth eigenvalue $q=5$. There are three
-boundary conditions
-
-$$\nabla y(0) = 0, \quad \nabla y(\pi) = 0, \quad y(0) = 1$$
-
-and we aim to solve it by converting it to a first order ODE and tackle
-it as an IVP. As our model object does not allow the use of the time
-component in the equations, we introduce a anxiliary state $\tau$ that
-replaces time $t$. Rewrite the equations using
-$y_{0} = y, y_{1} = \nabla y$ and define our model as
-
-::: {.ipython}
-In \[1\]: stateList = \[\'y0\', \'y1\', \'tau\'\]
-
-In \[2\]: paramList = \[\'p\'\]
-
-In \[3\]: ode1 = Transition(\'y0\', \'y1\', TransitionType.ODE)
-
-In \[4\]: ode2 = Transition(\'y1\', \'-(p - 2\*5\*cos(2\*tau))\*y0\',
-TransitionType.ODE)
-
-In \[5\]: ode3 = Transition(\'tau\', \'1\', TransitionType.ODE)
-
-In \[6\]: model = DeterministicOde(stateList, paramList, ode=\[ode1,
-ode2, ode3\])
-
-In \[7\]: theta = \[1.0, 1.0, 0.0\]
-
-In \[8\]: p = 15.0
-
-In \[9\]: t = numpy.linspace(0, numpy.pi)
-
-In \[10\]: model.parameters = \[(\'p\',p)\]
-
-In \[11\]: model.initial_values = (theta, t\[0\])
-
-In \[12\]: solution = model.integrate(t\[1::\])
-
-In \[13\]: f = plt.figure()
-
-\@savefig bvp2_random_guess_plot.png In \[14\]: model.plot()
-
-In \[15\]: plt.close()
-:::
-
-Now we are ready to setup the estimation. Like before, we setup the
-second boundary condition by pretending that it is an observation. We
-have all the initial conditions defined by the first boundary condition
-
-::: {.ipython}
-In \[1\]: obj = SquareLoss(15.0, model, x0=\[1.0, 0.0, 0.0\], t0=0.0,
-t=numpy.pi, y=0.0, state_name=\'y1\')
-
-In \[2\]: xhatObj = minimize(obj.cost,\[15\])
-
-In \[3\]: print(xhatObj)
-
-In \[4\]: model.parameters = \[(\'p\', xhatObj\[\'x\'\]\[0\])\]
-
-In \[5\]: model.initial_values = (\[1.0, 0.0, 0.0\], t\[0\])
-
-In \[5\]: solution = model.integrate(t\[1::\])
-
-In \[6\]: f = plt.figure()
-
-\@savefig bvp2_solution_plot.png In \[7\]: model.plot()
-
-In \[8\]: plt.close()
-:::
-
-The plot of the solution shows the path that satisfies all boundary
-condition. The last subplot is time which obvious is redundant here but
-the `DeterministicOde.plot`{.interpreted-text role="meth"} method is not
-yet able to recognize the time component. Possible speed up can be
-achieved through the use of derivative information or via root finding
-method that tackles the gradient directly, instead of the cost function.
-
-**Reference**
-
-[^1]: 
diff --git a/docs/pygom-doc/_build/html/_sources/mdconvert/mod/.md b/docs/pygom-doc/_build/html/_sources/mdconvert/mod/.md
deleted file mode 100644
index 8c6b1b35..00000000
--- a/docs/pygom-doc/_build/html/_sources/mdconvert/mod/.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# stochastic
-
-::: {.automodule members="" noindex=""}
-pygom.model.simulate
-:::
diff --git a/docs/pygom-doc/_build/html/_sources/mdconvert/unroll/.md b/docs/pygom-doc/_build/html/_sources/mdconvert/unroll/.md
deleted file mode 100644
index 07b17497..00000000
--- a/docs/pygom-doc/_build/html/_sources/mdconvert/unroll/.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Simple Problem {#unrollSimple}
-
-For a simple problem, we consider the SIR model defined by
-
-$$\begin{aligned}
-\frac{dS}{dt} &= -\beta SI \\
-\frac{dI}{dt} &= \beta SI - \gamma I \\
-\frac{dR}{dt} &= \gamma I.
-\end{aligned}$$
-
-which consists of two transitions
-
-::: {.graphviz}
-
-digraph SIR_Model {
-
-:   rankdir=LR; size=\"8\" node \[shape = circle\]; S -\> I \[ label =
-    \"βSI\" \]; I -\> R \[ label = \"γI\" \];
-
-}
-:::
-
-Let\'s define this using the code block below
-
-::: {.ipython}
-In \[1\]: from pygom import SimulateOde, Transition, TransitionType
-
-In \[2\]: ode1 = Transition(origin=\'S\', equation=\'-beta\*S\*I\',
-transition_type=TransitionType.ODE)
-
-In \[3\]: ode2 = Transition(origin=\'I\', equation=\'beta\*S\*I -
-gamma\*I\', transition_type=TransitionType.ODE)
-
-In \[4\]: ode3 = Transition(origin=\'R\', equation=\'gamma\*I\',
-transition_type=TransitionType.ODE)
-
-In \[6\]: stateList = \[\'S\', \'I\', \'R\'\]
-
-In \[7\]: paramList = \[\'beta\', \'gamma\'\]
-
-In \[8\]: ode = SimulateOde(stateList,
-
-:   \...: paramList, \...: ode=\[ode1, ode2, ode3\])
-
-In \[9\]: ode.get_transition_matrix()
-:::
-
-and the last line shows that the transition matrix is empty. This is the
-expected result because `SimulateOdeModel`{.interpreted-text
-role="class"} was not initialized using transitions. We populate the
-transition matrix below and demonstrate the difference.
-
-::: {.ipython}
-In \[1\]: ode = ode.get_unrolled_obj()
-
-In \[2\]: ode.get_transition_matrix()
-:::
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/bvpSimple.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/bvpSimple.ipynb
deleted file mode 100644
index 6ca0ce57..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/bvpSimple.ipynb
+++ /dev/null
@@ -1,198 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Solving Boundary Value Problems\n",
-    "\n",
-    "In addition to finding solutions for an IVP and estimate the unknown\n",
-    "parameters, this package also allows you to solve BVP with a little bit\n",
-    "of imagination. Here, we are going to show how a BVP can be solved by\n",
-    "treating it as a parameter estimation problem. Essentially, a shooting\n",
-    "method where the first boundary condition defines the initial condition\n",
-    "of an IVP and the second boundary condition is an observation. Two\n",
-    "examples, both from MATLAB[1], will be shown here.\n",
-    "\n",
-    "## Simple model 1\n",
-    "\n",
-    "We are trying to find the solution to the second order differential\n",
-    "equation\n",
-    "\n",
-    "$$\\nabla^{2} y + |y| = 0$$\n",
-    "\n",
-    "subject to the boundary conditions $y(0) = 0$ and $y(4) = -2$. Convert\n",
-    "this into a set of first order ODE\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{d y_{0}}{dt} &= y_{1} \\\\\n",
-    "\\frac{d y_{1}}{dt} &= -|y_{0}|\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "using an auxiliary variable $y_{1} = \\nabla y$ and $y_{0} = y$. Setting\n",
-    "up the system below\n",
-    "\n",
-    "In \\[1\\]: from pygom import Transition, TransitionType,\n",
-    "DeterministicOde, SquareLoss\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
-    "\n",
-    "In \\[2\\]: stateList = \\['y0', 'y1'\\]\n",
-    "\n",
-    "In \\[3\\]: paramList = \\[\\]\n",
-    "\n",
-    "In \\[4\\]: ode1 = Transition(origin='y0',  \n",
-    "...: equation='y1', ...: transition_type=TransitionType.ODE)\n",
-    "\n",
-    "In \\[5\\]: ode2 = Transition(origin='y1',  \n",
-    "...: equation='-abs(y0)', ...: transition_type=TransitionType.ODE)\n",
-    "\n",
-    "In \\[6\\]: model = DeterministicOde(stateList,  \n",
-    "...: paramList, ...: ode=\\[ode1, ode2\\])\n",
-    "\n",
-    "In \\[7\\]: model.get_ode_eqn()\n",
-    "\n",
-    "We check that the equations are correct before proceeding to set up our\n",
-    "loss function.\n",
-    "\n",
-    "In \\[1\\]: import numpy\n",
-    "\n",
-    "In \\[2\\]: from scipy.optimize import minimize\n",
-    "\n",
-    "In \\[3\\]: initialState = \\[0.0, 1.0\\]\n",
-    "\n",
-    "In \\[4\\]: t = numpy.linspace(0, 4, 100)\n",
-    "\n",
-    "In \\[5\\]: model.initial_values = (initialState, t\\[0\\])\n",
-    "\n",
-    "In \\[6\\]: solution = model.integrate(t\\[1::\\])\n",
-    "\n",
-    "In \\[7\\]: f = plt.figure()\n",
-    "\n",
-    "@savefig bvp1_random_guess_plot.png In \\[8\\]: model.plot()\n",
-    "\n",
-    "In \\[9\\]: plt.close()\n",
-    "\n",
-    "Setting up the second boundary condition $y(4) = -2$ is easy, because\n",
-    "that is just a single observation attached to the state $y_{1}$.\n",
-    "Enforcing the first boundary condition requires us to set it as the\n",
-    "initial condition. Because the condition only states that $y(0) = 0$,\n",
-    "the starting value of the other state $y_1$ is free. We let our loss\n",
-    "object know that it is free through the targetState input argument.\n",
-    "\n",
-    "In \\[10\\]: theta = \\[0.0\\]\n",
-    "\n",
-    "In \\[11\\]: obj = SquareLoss(theta=theta,  \n",
-    ".…: ode=model, .…: x0=initialState, .…: t0=t\\[0\\], .…: t=t\\[-1\\], .…:\n",
-    "y=\\[-2\\], .…: state_name=\\['y0'\\], .…: target_state=\\['y1'\\])\n",
-    "\n",
-    "In \\[12\\]: thetaHat = minimize(fun=obj.costIV, x0=\\[0.0\\])\n",
-    "\n",
-    "In \\[13\\]: print(thetaHat)\n",
-    "\n",
-    "In \\[14\\]: model.initial_values = (\\[0.0\\] + thetaHat\\['x'\\].tolist(),\n",
-    "t\\[0\\])\n",
-    "\n",
-    "In \\[15\\]: solution = model.integrate(t\\[1::\\])\n",
-    "\n",
-    "In \\[16\\]: f = plt.figure()\n",
-    "\n",
-    "@savefig bvp1_solution_plot.png In \\[17\\]: model.plot()\n",
-    "\n",
-    "In \\[18\\]: plt.close()\n",
-    "\n",
-    "We are going to visualize the solution, and also check the boundary\n",
-    "condition. The first became our initial condition, so it is always\n",
-    "satisfied and only the latter is of concern, which is zero (subject to\n",
-    "numerical error) from thetaHat.\n",
-    "\n",
-    "## Simple model 2\n",
-    "\n",
-    "Our second example is different as it involves an actual parameter and\n",
-    "also time. We have the Mathieu's Equation\n",
-    "\n",
-    "$$\\nabla^{2} y + \\left(p - 2q \\cos(2x)\\right)y = 0$$\n",
-    "\n",
-    "and the aim is to compute the fourth eigenvalue $q=5$. There are three\n",
-    "boundary conditions\n",
-    "\n",
-    "$$\\nabla y(0) = 0, \\quad \\nabla y(\\pi) = 0, \\quad y(0) = 1$$\n",
-    "\n",
-    "and we aim to solve it by converting it to a first order ODE and tackle\n",
-    "it as an IVP. As our model object does not allow the use of the time\n",
-    "component in the equations, we introduce a anxiliary state $\\tau$ that\n",
-    "replaces time $t$. Rewrite the equations using\n",
-    "$y_{0} = y, y_{1} = \\nabla y$ and define our model as\n",
-    "\n",
-    "In \\[1\\]: stateList = \\['y0', 'y1', 'tau'\\]\n",
-    "\n",
-    "In \\[2\\]: paramList = \\['p'\\]\n",
-    "\n",
-    "In \\[3\\]: ode1 = Transition('y0', 'y1', TransitionType.ODE)\n",
-    "\n",
-    "In \\[4\\]: ode2 = Transition('y1', '-(p - 2\\*5\\*cos(2\\*tau))\\*y0',\n",
-    "TransitionType.ODE)\n",
-    "\n",
-    "In \\[5\\]: ode3 = Transition('tau', '1', TransitionType.ODE)\n",
-    "\n",
-    "In \\[6\\]: model = DeterministicOde(stateList, paramList, ode=\\[ode1,\n",
-    "ode2, ode3\\])\n",
-    "\n",
-    "In \\[7\\]: theta = \\[1.0, 1.0, 0.0\\]\n",
-    "\n",
-    "In \\[8\\]: p = 15.0\n",
-    "\n",
-    "In \\[9\\]: t = numpy.linspace(0, numpy.pi)\n",
-    "\n",
-    "In \\[10\\]: model.parameters = \\[('p',p)\\]\n",
-    "\n",
-    "In \\[11\\]: model.initial_values = (theta, t\\[0\\])\n",
-    "\n",
-    "In \\[12\\]: solution = model.integrate(t\\[1::\\])\n",
-    "\n",
-    "In \\[13\\]: f = plt.figure()\n",
-    "\n",
-    "@savefig bvp2_random_guess_plot.png In \\[14\\]: model.plot()\n",
-    "\n",
-    "In \\[15\\]: plt.close()\n",
-    "\n",
-    "Now we are ready to setup the estimation. Like before, we setup the\n",
-    "second boundary condition by pretending that it is an observation. We\n",
-    "have all the initial conditions defined by the first boundary condition\n",
-    "\n",
-    "In \\[1\\]: obj = SquareLoss(15.0, model, x0=\\[1.0, 0.0, 0.0\\], t0=0.0,\n",
-    "t=numpy.pi, y=0.0, state_name='y1')\n",
-    "\n",
-    "In \\[2\\]: xhatObj = minimize(obj.cost,\\[15\\])\n",
-    "\n",
-    "In \\[3\\]: print(xhatObj)\n",
-    "\n",
-    "In \\[4\\]: model.parameters = \\[('p', xhatObj\\['x'\\]\\[0\\])\\]\n",
-    "\n",
-    "In \\[5\\]: model.initial_values = (\\[1.0, 0.0, 0.0\\], t\\[0\\])\n",
-    "\n",
-    "In \\[5\\]: solution = model.integrate(t\\[1::\\])\n",
-    "\n",
-    "In \\[6\\]: f = plt.figure()\n",
-    "\n",
-    "@savefig bvp2_solution_plot.png In \\[7\\]: model.plot()\n",
-    "\n",
-    "In \\[8\\]: plt.close()\n",
-    "\n",
-    "The plot of the solution shows the path that satisfies all boundary\n",
-    "condition. The last subplot is time which obvious is redundant here but\n",
-    "the `DeterministicOde.plot` method is not yet able to recognize the time\n",
-    "component. Possible speed up can be achieved through the use of\n",
-    "derivative information or via root finding method that tackles the\n",
-    "gradient directly, instead of the cost function.\n",
-    "\n",
-    "**Reference**\n",
-    "\n",
-    "[1] "
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/epi.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/epi.ipynb
deleted file mode 100644
index 48711b33..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/epi.ipynb
+++ /dev/null
@@ -1,70 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Simple Epidemic Analysis\n",
-    "\n",
-    "A common application of ordinary differential equations is in the field\n",
-    "of epidemiology modeling. More concretely, compartmental models that is\n",
-    "used to describe disease progression. We demonstrate some of the simple\n",
-    "algebraic analysis one may wish to take when given a compartment model.\n",
-    "Our use one of the simplest model, an SIR model with birth and death\n",
-    "processes, which is an extension of the one in `sir`. First, we\n",
-    "initialize the model below.\n",
-    "\n",
-    "In \\[1\\]: from pygom import common_models\n",
-    "\n",
-    "In \\[2\\]: ode = common_models.SIR_Birth_Death()\n",
-    "\n",
-    "In \\[3\\]: print(ode.get_ode_eqn())\n",
-    "\n",
-    "## Obtaining the R0\n",
-    "\n",
-    "The reproduction number, also known as the $R_{0}$, is the single most\n",
-    "powerful piece and reduced piece of information available from a\n",
-    "compartmental model. In a nutshell, it provides a single number - if the\n",
-    "parameters are known - which can the intuitive interpretation where\n",
-    "$R_{0} = 1$ defines the tipping point of an outbreak. A $R_{0}$ value of\n",
-    "more than one signifies an potential outbreak where less than one\n",
-    "indicates that the disease will stop spreading naturally.\n",
-    "\n",
-    "To obtain the $R_{0}$, we simply have to tell the function which states\n",
-    "represent the *disease state*, which in this case is the state **I**.\n",
-    "\n",
-    "In \\[1\\]: from pygom.model.epi_analysis import \\*\n",
-    "\n",
-    "In \\[2\\]: print(R0(ode, 'I'))\n",
-    "\n",
-    "## Algebraic R0\n",
-    "\n",
-    "We may also wish to get the $R_{0}$ in pure algebraic term. This can be\n",
-    "achieved by the following few lines. Note that the result below is\n",
-    "slightly different from the one above. The difference is due to the\n",
-    "internal working of the functions, where `getR0` computes the\n",
-    "disease-free equilibrium value for the states and substitute them back\n",
-    "into the equation.\n",
-    "\n",
-    "In \\[1\\]: F, V = disease_progression_matrices(ode, 'I')\n",
-    "\n",
-    "In \\[2\\]: e = R0_from_matrix(F, V)\n",
-    "\n",
-    "In \\[3\\]: print(e)\n",
-    "\n",
-    "To replicate the output before, we have to find the values where the\n",
-    "disease-free equilibrium will be achieved. Substitution can then be\n",
-    "performed to retrieve $R_{0}$ in pure parameters.\n",
-    "\n",
-    "In \\[1\\]: dfe = DFE(ode, \\['I'\\])\n",
-    "\n",
-    "In \\[2\\]: print(dfe)\n",
-    "\n",
-    "In \\[3\\]: print(e\\[0\\].subs(dfe))"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/epijson.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/epijson.ipynb
deleted file mode 100644
index 1df66d51..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/epijson.ipynb
+++ /dev/null
@@ -1,65 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Reading and using EpiJSON data\n",
-    "\n",
-    "Epidemiology data is complicated due to the many different stages a\n",
-    "patient can go through and whether a modeling technique is applicable\n",
-    "depends heavily on the recording of data. EpiJSON is a framework whih\n",
-    "tries to captures all the information [\\[Finnie2016\\]](), in a JSON\n",
-    "format as the name suggests.\n",
-    "\n",
-    "This package provides the functionality to process EpiJSON data. Due to\n",
-    "the nature of this package, modeling of ode, it processes the data file\n",
-    "with this in mind. The output is therefore in the cumulative form as\n",
-    "default, shown below, in a `pandas.DataFrame` format. The input can be\n",
-    "in a string format, a file or already a `dict`.\n",
-    "\n",
-    "In \\[1\\]: from pygom.loss.read_epijson import epijson_to_data_frame\n",
-    "\n",
-    "In \\[2\\]: import pkgutil\n",
-    "\n",
-    "In \\[3\\]: data = pkgutil.get_data('pygom', 'data/eg1.json')\n",
-    "\n",
-    "In \\[3\\]: df = epijson_to_data_frame(data)\n",
-    "\n",
-    "In \\[4\\]: print(df)\n",
-    "\n",
-    "Given that the aim of loading the data is usually for model fitting, we\n",
-    "allow EpiJSON as input directly to the loss class\n",
-    "`pygom.loss.EpijsonLoss` which uses the Poisson loss under the hood.\n",
-    "\n",
-    "In \\[1\\]: from pygom.model import common_models\n",
-    "\n",
-    "In \\[2\\]: from pygom.loss.epijson_loss import EpijsonLoss\n",
-    "\n",
-    "In \\[3\\]: ode = common_models.SIR(\\[0.5, 0.3\\])\n",
-    "\n",
-    "In \\[4\\]: obj = EpijsonLoss(\\[0.005, 0.03\\], ode, data, 'Death', 'R',\n",
-    "\\[300, 2, 0\\])\n",
-    "\n",
-    "In \\[5\\]: print(obj.cost())\n",
-    "\n",
-    "In \\[6\\]: print(obj.\\_df)\n",
-    "\n",
-    "Given an initialized object, all the operations are inherited from\n",
-    "`pygom.loss.BaseLoss`. We demonstrated above how to calculate the cost\n",
-    "and the rest will not be shown for brevity. The data frame is stored\n",
-    "inside of the loss object and can be retrieved for inspection at any\n",
-    "time point.\n",
-    "\n",
-    "Rather unfortunately, initial values for the states is still required,\n",
-    "but the time is not. When the time is not supplied, then the first time\n",
-    "point in the data will be treated as $t0$. The input Death indicate which column of the data is used\n",
-    "and $R$ the corresponding state the data belongs to."
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/fh.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/fh.ipynb
deleted file mode 100644
index 24e06f9a..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/fh.ipynb
+++ /dev/null
@@ -1,135 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example: Fitz Hugh\n",
-    "\n",
-    "## Defining the model\n",
-    "\n",
-    "We are going to investigate another classic model here, the\n",
-    "FitzHugh-Nagumo, or simply FitzHugh here. The model has already been\n",
-    "defined in `common_models` so we can load it easily\n",
-    "\n",
-    "In \\[1\\]: from pygom import SquareLoss, common_models\n",
-    "\n",
-    "In \\[2\\]: import numpy\n",
-    "\n",
-    "In \\[3\\]: import scipy.integrate, scipy.optimize\n",
-    "\n",
-    "In \\[4\\]: import math,time,copy\n",
-    "\n",
-    "In \\[5\\]: import matplotlib.pyplot as plt\n",
-    "\n",
-    "In \\[1\\]: x0 = \\[-1.0, 1.0\\]\n",
-    "\n",
-    "In \\[2\\]: t0 = 0\n",
-    "\n",
-    "In \\[3\\]: \\# params\n",
-    "\n",
-    "In \\[4\\]: paramEval = \\[('a',0.2), ('b',0.2), ('c',3.0)\\]\n",
-    "\n",
-    "In \\[5\\]: ode = common_models.FitzHugh(paramEval)\n",
-    "\n",
-    "In \\[5\\]: ode.initial_values = (x0, t0)\n",
-    "\n",
-    "Define a set of time points and lets see how the two states $V$ and $R$\n",
-    "are suppose to behave.\n",
-    "\n",
-    "In \\[6\\]: t = numpy.linspace(1, 20, 30).astype('float64')\n",
-    "\n",
-    "In \\[7\\]: solution = ode.integrate(t)\n",
-    "\n",
-    "@savefig fh_plot.png In \\[8\\]: ode.plot()\n",
-    "\n",
-    "## Estimate the parameters\n",
-    "\n",
-    "Obtaining the correct parameters for the FitzHugh model is well known to\n",
-    "be difficult, this is because the surface is multimodal. Although this\n",
-    "has been shown many times in the literature, so we will omit the\n",
-    "details. Regardless, we give it a go with some initial guess. with some\n",
-    "luck, we will be able to recover the original parameters. First, we try\n",
-    "it out with only one target state\n",
-    "\n",
-    "In \\[26\\]: theta = \\[0.5, 0.5, 0.5\\]\n",
-    "\n",
-    "In \\[27\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n",
-    "'R')\n",
-    "\n",
-    "In \\[28\\]: boxBounds = \\[  \n",
-    ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0) .…: \\]\n",
-    "\n",
-    "In \\[29\\]: res = scipy.optimize.minimize(fun=objFH.cost,  \n",
-    ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n",
-    "method='L-BFGS-B')\n",
-    "\n",
-    "In \\[30\\]: print(res)\n",
-    "\n",
-    "Then we try the same again but with both state as our target. Now we\n",
-    "won't look at the iterations because they are pretty pointless.\n",
-    "\n",
-    "In \\[30\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n",
-    "\\['V','R'\\])\n",
-    "\n",
-    "In \\[31\\]: res = scipy.optimize.minimize(fun=objFH.cost,  \n",
-    ".…: jac=objFH.sensitivity, .…: x0=theta, .…: bounds=boxBounds, .…:\n",
-    "method='L-BFGS-B')\n",
-    "\n",
-    "In \\[32\\]: print(res)\n",
-    "\n",
-    "Note how the estimates are the same, unlike other models.\n",
-    "\n",
-    "## Estimate initial value\n",
-    "\n",
-    "We can further assume that we have no idea about the initial values for\n",
-    "$V$ and $R$ as well. We also provide guesstimate to set off the\n",
-    "optimization. The input vector $\\theta$ must have the parameters first,\n",
-    "then the initial values, along with the corresponding bounds.\n",
-    "\n",
-    "First, only a single target state, i.e. we only have observations for\n",
-    "one of states which is $R$ in this case\n",
-    "\n",
-    "In \\[35\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,1\\],\n",
-    "'R')\n",
-    "\n",
-    "In \\[35\\]: boxBounds = \\[  \n",
-    ".…: (0.0,5.0), .…: (0.0,5.0), .…: (0.0,5.0), .…: (None,None), .…:\n",
-    "(None,None) .…: \\]\n",
-    "\n",
-    "In \\[36\\]: res = scipy.optimize.minimize(fun=objFH.costIV,  \n",
-    ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5,0.5\\], .…:\n",
-    "bounds=boxBounds, .…: method='L-BFGS-B')\n",
-    "\n",
-    "In \\[37\\]: print(res)\n",
-    "\n",
-    "then both state as target at the same time\n",
-    "\n",
-    "In \\[38\\]: objFH = SquareLoss(theta, ode, x0, t0, t, solution\\[1::,:\\],\n",
-    "\\['V','R'\\])\n",
-    "\n",
-    "In \\[38\\]: res = scipy.optimize.minimize(fun=objFH.costIV,  \n",
-    ".…: jac=objFH.sensitivityIV, .…: x0=theta + \\[-0.5, 0.5\\], .…:\n",
-    "bounds=boxBounds, .…: method='L-BFGS-B')\n",
-    "\n",
-    "In \\[39\\]: print(res)\n",
-    "\n",
-    "See the difference between the two estimate with the latter, both state\n",
-    "were used, yielding superior estimates. Note that only the forward\n",
-    "sensitivity method is implemented when estimating the initial value, and\n",
-    "it is assumed that the starting condition for all the states are\n",
-    "unknown.\n",
-    "\n",
-    "The choice of algorithm here is the **L-BFGS-B** which is a better\n",
-    "choice because the parameter space of the FitzHugh is rough (i.e. large\n",
-    "second derivative) as well as being multimodal. This means that the\n",
-    "Hessian is not guaranteed to be positive definite and approximation\n",
-    "using $J^{\\top}J$ is poor, with $J$ being the Jacobian of the objective\n",
-    "function."
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/gradient.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/gradient.ipynb
deleted file mode 100644
index e67c71b3..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/gradient.ipynb
+++ /dev/null
@@ -1,356 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gradient estimation under square loss\n",
-    "\n",
-    "Assuming that we have a set of $N$ observations $y_{i}$ at specific time\n",
-    "points $t_{i}$, $i = 1,\\ldots,N$, we may wish to test out a set of ode\n",
-    "to see whether it fits to the data. The most natural way to test such\n",
-    "*fit* is to minimize the sum of squares between our observations $y$ and\n",
-    "see whether the resulting solution of the ode and the estimationed\n",
-    "parameters makes sense.\n",
-    "\n",
-    "We assume that this estimation process will be tackled through a\n",
-    "non-linear optimization point of view. However, it should be noted that\n",
-    "such estimates can also be performed via MCMC or from a global\n",
-    "optimization perspective. A key element in non-linear optimization is\n",
-    "the gradient, which is the focus of this page.\n",
-    "\n",
-    "Multiple ways of obtaining the gradient have been implemented. All of\n",
-    "them serve a certain purpose and may not be a viable/appropriate options\n",
-    "depending on the type of ode. More generally, let $d,p$ be the number of\n",
-    "states and paramters respectively. Then finite difference methods have a\n",
-    "run order of $O(p+1)$ of the original ode, forward sensitivity require\n",
-    "an integration of an ode of size $(d+1)p$ rather than $d$. The adjoint\n",
-    "method require two run of size $d$ in principle, but actual run time is\n",
-    "dependent on the number of observations.\n",
-    "\n",
-    "For the details of the classes and methods, please refer to `mod`.\n",
-    "\n",
-    "## Notation\n",
-    "\n",
-    "We introduce the notations that will be used in the rest of the page,\n",
-    "some of which may be slightly unconventional but necessary due to the\n",
-    "complexity of the problem. Let $x \\in \\mathbb{R}^{d}$ and\n",
-    "$\\theta \\in \\mathbb{R}^{p}$ be the states and parameters respectively.\n",
-    "The term *state* or *simulation* are used interchangeably, even though\n",
-    "strictly speaking a state is $x$ whereas $x(t)$ is the simulation. An\n",
-    "ode is defined as\n",
-    "\n",
-    "$$f(x,\\theta) = \\dot{x} = \\frac{\\partial x}{\\partial t}$$\n",
-    "\n",
-    "and usually comes with a set of initial conditions $(x_0,t_0)$ where\n",
-    "$t_0 \\le t_{i} \\forall i$. Let $g(x,\\theta)$ be a function that maps the\n",
-    "set of states to the observations,\n",
-    "$g : \\mathbb{R}^{d} \\rightarrow \\mathbb{R}^{m}$. For compartmental\n",
-    "problems, which is our focus, $\\nabla_{\\theta}g(x,\\theta)$ is usually\n",
-    "zero and $\\nabla_{x}g(x,\\theta)$ is an identity function for some or all\n",
-    "of the states $x$. Denote $l(x_{0},\\theta,x)$ as our cost function\n",
-    "$l : \\mathbb{R}^{m} \\rightarrow \\mathbb{R}$ and $L(x_{0},\\theta,x)$ be\n",
-    "the sum of $l(\\cdot)$. Both $x$ and $x_{0}$ are usually dropped for\n",
-    "simplicity. We will be dealing exclusively with square loss here, which\n",
-    "means that\n",
-    "\n",
-    "$$L(\\theta) = \\sum_{i=1}^{N} \\left\\| y_{i} - g(x(t_{i})) \\right\\|^{2} = \\mathbf{e}^{\\top} \\mathbf{e}$$\n",
-    "\n",
-    "where $\\mathbf{e}$ is the residual vector, with elements\n",
-    "\n",
-    "$$e_{i} = y_{i} - x(t_{i}).$$\n",
-    "\n",
-    "## Model setup\n",
-    "\n",
-    "Again, we demonstrate the functionalities of our classes using an SIR\n",
-    "model.\n",
-    "\n",
-    "In \\[1\\]: from pygom import SquareLoss, common_models\n",
-    "\n",
-    "In \\[2\\]: import copy,time,numpy\n",
-    "\n",
-    "In \\[2\\]: ode = common_models.SIR()\n",
-    "\n",
-    "In \\[3\\]: paramEval = \\[('beta',0.5), ('gamma',1.0/3.0) \\]\n",
-    "\n",
-    "In \\[7\\]: \\# the initial state, normalized to zero one\n",
-    "\n",
-    "In \\[8\\]: x0 = \\[1., 1.27e-6, 0.\\]\n",
-    "\n",
-    "In \\[5\\]: \\# initial time\n",
-    "\n",
-    "In \\[6\\]: t0 = 0\n",
-    "\n",
-    "In \\[5\\]: ode.parameters = paramEval\n",
-    "\n",
-    "In \\[6\\]: ode.initial_values = (x0, t0)\n",
-    "\n",
-    "In \\[9\\]: \\# set the time sequence that we would like to observe\n",
-    "\n",
-    "In \\[10\\]: t = numpy.linspace(1, 150, 100)\n",
-    "\n",
-    "In \\[11\\]: numStep = len(t)\n",
-    "\n",
-    "In \\[11\\]: solution = ode.integrate(t)\n",
-    "\n",
-    "In \\[12\\]: y = solution\\[1::,2\\].copy()\n",
-    "\n",
-    "In \\[13\\]: y += numpy.random.normal(0, 0.1, y.shape)\n",
-    "\n",
-    "Now we have set up the model along with some observations, obtaining the\n",
-    "gradient only requires the end user to put the appropriate information\n",
-    "it into the class `SquareLoss`. Given the initial guess $\\theta$\n",
-    "\n",
-    "In \\[210\\]: theta = \\[0.2, 0.2\\]\n",
-    "\n",
-    "We initialize the `SquareLoss` simply as\n",
-    "\n",
-    "In \\[20\\]: objSIR = SquareLoss(theta, ode, x0, t0, t, y, 'R')\n",
-    "\n",
-    "where the we also have to specify the state our observations are from.\n",
-    "Now, we demonstrate the different methods in obtaining the gradient and\n",
-    "mathematics behind it.\n",
-    "\n",
-    "## Forward sensitivity\n",
-    "\n",
-    "The forward sensitivity equations are derived by differentiating the\n",
-    "states implicitly, which yields\n",
-    "\n",
-    "$$\\frac{d\\dot{x}}{d\\theta} = \\frac{\\partial f}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial f}{\\partial \\theta}.$$\n",
-    "\n",
-    "So finding the sensitivies $\\frac{dx}{d\\theta}$ simply require another\n",
-    "integration of a $p$ coupled ode of $d$ dimension, each with the same\n",
-    "Jacobian as the original ode. This integration is performed along with\n",
-    "the original ode because of possible non-linearity.\n",
-    "\n",
-    "A direct call to the method `sensitivity `\n",
-    "computed the gradient\n",
-    "\n",
-    "In \\[33\\]: gradSens = objSIR.sensitivity()\n",
-    "\n",
-    "whereas `.jac` will allow the end user to obtain the Jacobian (of the\n",
-    "objective function) and the residuals, the information required to get\n",
-    "the gradient as we see next.\n",
-    "\n",
-    "In \\[33\\]: objJac, output = objSIR.jac(full_output=True)\n",
-    "\n",
-    "## Gradient\n",
-    "\n",
-    "Just the sensitivities alone are not enough to obtain the gradient, but\n",
-    "we are $90\\%$ there. Differentiating the loss function\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{dL}{d\\theta} &= \\nabla_{\\theta} \\sum_{i=1}^{N}\\frac{dl}{dg} \\\\\n",
-    "                   &= \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial l}{\\partial \\theta} \\\\\n",
-    "                   &= \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial x}\\frac{dx}{d\\theta} + \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial \\theta}\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "via chain rule. When $\\frac{\\partial g}{\\partial \\theta} = 0$, the total\n",
-    "gradient simplifies to\n",
-    "\n",
-    "$$\\frac{dL}{d\\theta} = \\sum_{i=1}^{N} \\frac{\\partial l}{\\partial g}\\frac{\\partial g}{\\partial x}\\frac{dx}{d\\theta}$$\n",
-    "\n",
-    "Obviously, the time indicies are dropped above but all the terms above\n",
-    "are evaluated only at the observed time points. More concretely, this\n",
-    "means that\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{\\partial l(x(j),\\theta)}{\\partial g} = \\left\\{ \\begin{array}{ll} -2(y_{i} - x(j)) & , \\; j = t_{i} \\\\ 0 & \\; \\text{otherwise} \\end{array} \\right.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "When $g(\\cdot)$ is an identity function (which is assumed to be the case\n",
-    "in `SquareLoss`)\n",
-    "\n",
-    "$$\\frac{\\partial g(x(t_{i}),\\theta)}{\\partial x} = I_{d}$$\n",
-    "\n",
-    "then the gradient simplifies even further as it is simply\n",
-    "\n",
-    "$$\\frac{dL}{d\\theta} = -2\\mathbf{e}^{\\top}\\mathbf{S}$$\n",
-    "\n",
-    "where $\\mathbf{e}$ is the vector of residuals and\n",
-    "$\\mathbf{S} = \\left[\\mathbf{s}_{1},\\mathbf{s}_{2},\\ldots,\\mathbf{s}_{n}\\right]$\n",
-    "with elements\n",
-    "\n",
-    "$$\\mathbf{s}_{i} = \\frac{dx}{d\\theta}(t_{i}),$$\n",
-    "\n",
-    "the solution of the forward sensitivies at time $t_{i}$, obtained from\n",
-    "solving the coupled ode as mentioned previously.\n",
-    "\n",
-    "## Jacobian\n",
-    "\n",
-    "Now note how the gradient simplifies to $-2\\mathbf{e}^{\\top}\\mathbf{S}$.\n",
-    "Recall that a standard result in non-linear programming states that the\n",
-    "gradient of a sum of sqaures objective function $L(\\theta,y,x)$ is\n",
-    "\n",
-    "$$\\nabla_{\\theta} L(\\theta,y,x) = -2(\\mathbf{J}^{T} \\left[\\mathbf{y} - \\mathbf{f}(x,\\boldsymbol{\\theta}) \\right] )^{\\top}$$\n",
-    "\n",
-    "with $f(x,\\theta)$ our non-linear function and $J$ our Jacobian with\n",
-    "elements\n",
-    "\n",
-    "$$J_{i} = \\frac{\\partial f(x_{i},\\boldsymbol{\\theta})}{\\partial \\boldsymbol{\\theta}}.$$\n",
-    "\n",
-    "This is exactly what we have seen previously, substituting in reveals\n",
-    "that $J = \\mathbf{S}$. Hence, the Jacobian is (a necessary)by product\n",
-    "when we wish to obtain the gradient. In fact, this is exactly how we\n",
-    "proceed in `sensitivity ` where it makes\n",
-    "an internal call to `jac ` to obtain the Jacobian\n",
-    "first. This allows the end user to have more options when choosing which\n",
-    "type of algorithms to use, i.e. Gauss-Newton or Levenberg-Marquardt.\n",
-    "\n",
-    "To check that the output is in fact the same\n",
-    "\n",
-    "In \\[1\\]: objJac.transpose().dot(-2\\*output\\['resid'\\]) - gradSens\n",
-    "\n",
-    "## Adjoint\n",
-    "\n",
-    "When the number of parameters increases, the number of sensitivies also\n",
-    "increases. The time required scales directly with the number of\n",
-    "parameters. We describe another method which does not depend on the\n",
-    "number of parameters, but rather, the number of states and observations.\n",
-    "\n",
-    "The full derivations will not be shown here, but we aim to provide\n",
-    "enough information to work out the steps performed in the our code. Let\n",
-    "write our optimization problem as\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "min_{\\theta} \\quad & \\int_{t_{0}}^{T} l(x_{0},\\theta,x(t)) dt \\\\\n",
-    "s.t. \\quad & \\dot{x} = f(x,\\theta)\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "which is identical to the original problem but in a continuous setting.\n",
-    "Now write the constrained problem in the Lagrangian form\n",
-    "\n",
-    "$$min_{\\theta} \\; L(\\theta) + \\int_{t_{0}}^{T} \\lambda^{\\top}(\\dot{x} - f(x,\\theta))$$\n",
-    "\n",
-    "with Lagrangian multiplier $\\lambda \\ge 0$. After some algebraic\n",
-    "manipulation, it can be shown that the total derivative of the\n",
-    "Lagrangian function is\n",
-    "\n",
-    "$$\\frac{dL}{d\\theta} = \\int_{t_{0}}^{T} \\left(\\frac{\\partial l}{\\partial \\theta} - \\lambda^{\\top}\\frac{\\partial f}{\\partial \\theta} \\right) dt.$$\n",
-    "\n",
-    "Using previously defined loss functions (the identity), the first term\n",
-    "is zero and evaluating $\\frac{\\partial f}{\\partial \\theta}$ is trivial.\n",
-    "What remains is the calculation of $\\lambda(t)$ for\n",
-    "$t \\in \\left[t_{0},T\\right]$.\n",
-    "\n",
-    "Although this still seem to be ill-posed problem when Looking at the\n",
-    "Lagrangian function, one can actually obtain the *adjoint equation*,\n",
-    "after certain assumptions and\n",
-    "\n",
-    "$$\\frac{d\\lambda^{\\top}}{dt} = \\frac{\\partial l}{\\partial x} - \\lambda^{\\top}\\frac{\\partial f}{\\partial \\theta}.$$\n",
-    "\n",
-    "which is again an integration. An unfortunate situation arise here for\n",
-    "non-linear systems because we use the minus Jacobian in the adjoint\n",
-    "equation. So if the eigenvalues of the Jacobian indicate that our\n",
-    "original ode is stable, such as -1, the minus eigenvalues (now 1)\n",
-    "implies that the adjoint equation is not stable. Therefore, one must\n",
-    "integrate backward in time to solve the adjoint equation and it cannot\n",
-    "be solved simultaneously as the ode, unlike the forward sensitivity\n",
-    "equations.\n",
-    "\n",
-    "Given a non-linearity ode, we must store information about the states\n",
-    "between $t_{0}$ and $T$ in order to perform the integration. There are\n",
-    "two options, both require storing many evaluated $x(j)$ within the\n",
-    "interval $\\left[t_{0},T\\right]$. Unfortunately, only one is available;\n",
-    "interpolation over all states and integrate using the interpolating\n",
-    "functions. The alternative of using observed $x(j)'s$ at fixed points is\n",
-    "not competitive because we are unable to use fortran routines for the\n",
-    "integration\n",
-    "\n",
-    "The method of choice here to perform the adjoint calcuation is to run a\n",
-    "forward integration, then perform an interpolation using splines with\n",
-    "explicit knots at the observed time points.\n",
-    "\n",
-    "In \\[326\\]: odeSIRAdjoint, outputAdjoint =\n",
-    "objSIR.adjoint(full_output=True)\n",
-    "\n",
-    "This is because evaluating the Jacobian may be expensive and Runge-kutta\n",
-    "method suffers as the complexity increases. In non-linear model such as\n",
-    "those found in epidemiology, each element of the Jacobian may be the\n",
-    "result of a complicated equation where linear step method will shine as\n",
-    "it makes as little function evaluation as possible. Note that\n",
-    "derivations in the literature, the initial condition when evaluating the\n",
-    "adjoint equation is $\\lambda(T)=0$. But in our code we used\n",
-    "$\\lambda(T) = -2(y(T)-x(T))$. Recall that we have observation $y(T)$ and\n",
-    "simulation $x(T)$, so that the adjoint equation evaluated at time $T$\n",
-    "\n",
-    "$$\\frac{\\partial \\lambda^{\\top}}{\\partial t} \\Big|_{T} = -2(y-f(x,\\theta))\\Big|_{T}  - \\lambda(T)\\frac{\\partial f}{\\partial \\theta}\\Big|_{T}$$\n",
-    "\n",
-    "with the second term equal to zero. Integration under step size $h$\n",
-    "implies that\n",
-    "$\\lambda(T) \\approx \\lim_{h \\to 0} \\lambda(T-h) = -2(y(T)-x(T))$.\n",
-    "\n",
-    "## Time Comparison\n",
-    "\n",
-    "A simple time comparison between the different methods reveals that the\n",
-    "forward sensitivity method dominates the others by a wide margin. It\n",
-    "will be tempting to conclude that it is the best and should be the\n",
-    "default at all times but that is not true, due to the complexity of each\n",
-    "method mentioned previously. We leave it to the end user to find out the\n",
-    "best method for their specific problem.\n",
-    "\n",
-    "In \\[319\\]: %timeit gradSens = objSIR.sensitivity()\n",
-    "\n",
-    "In \\[326\\]: %timeit odeSIRAdjoint,outputAdjoint =\n",
-    "objSIR.adjoint(full_output=True)\n",
-    "\n",
-    "## Hessian\n",
-    "\n",
-    "The Hessian is defined by\n",
-    "\n",
-    "$$\\frac{\\partial^{2} l}{\\partial \\theta^{2}} = \\left( \\frac{\\partial l}{\\partial x} \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + \\frac{\\partial x}{\\partial \\theta}^{\\top}\\frac{\\partial^{2} l}{\\partial x^{2}}\\frac{\\partial x}{\\partial \\theta}$$\n",
-    "\n",
-    "where $\\otimes$ is the Kronecker product. Note that $\\nabla_{\\theta} x$\n",
-    "is the sensitivity and the second order sensitivities can be found again\n",
-    "via the forward method, which involve another set of ode's, namely the\n",
-    "forward-forward sensitivities\n",
-    "\n",
-    "$$\\frac{\\partial}{\\partial t}\\left(\\frac{\\partial^{2} x}{\\partial \\theta^{2}}\\right) = \\left( \\frac{\\partial f}{\\partial x} \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + \\left( I_{d} \\otimes \\frac{\\partial x}{\\partial \\theta}^{\\top} \\right) \\frac{\\partial^{2} f}{\\partial x^{2}} \\frac{\\partial x}{\\partial \\theta}.$$\n",
-    "\n",
-    "From before, we know that\n",
-    "\n",
-    "$$\\frac{\\partial l}{\\partial x} = (-2y+2x)  \\quad and \\quad \\frac{\\partial^{2} l}{\\partial x^{2}} = 2I_{d}$$\n",
-    "\n",
-    "so our Hessian reduces to\n",
-    "\n",
-    "$$\\frac{\\partial^{2} l}{\\partial \\theta^{2}} = \\left( \\left(-2y+2x\\right) \\otimes I_{p} \\right) \\frac{\\partial^{2} x}{\\partial \\theta^{2}} + 2S^{\\top}S,$$\n",
-    "\n",
-    "where the second term is a good approximation to the Hessian as\n",
-    "mentioned previously. This is the only implementation in place so far\n",
-    "even though obtaining the estimate this way is relatively slow.\n",
-    "\n",
-    "Just to demonstate how it works, lets look at the Hessian at the optimal\n",
-    "point. First, we obtain the optimal value\n",
-    "\n",
-    "In \\[211\\]: import scipy.linalg,scipy.optimize\n",
-    "\n",
-    "In \\[212\\]: boxBounds = \\[(0.0, 2.0), (0.0, 2.0)\\]\n",
-    "\n",
-    "In \\[213\\]: res = scipy.optimize.minimize(fun=objSIR.cost,  \n",
-    ".….: jac=objSIR.sensitivity, .….: x0=theta, .….: bounds=boxBounds, .….:\n",
-    "method='L-BFGS-B')\n",
-    "\n",
-    "Then compare again the least square estimate of the covariance matrix\n",
-    "against our version\n",
-    "\n",
-    "In \\[211\\]: resLS, cov_x, infodict, mesg, ier =\n",
-    "scipy.optimize.leastsq(func=objSIR.residual, x0=res\\['x'\\],\n",
-    "full_output=True)\n",
-    "\n",
-    "In \\[212\\]: HJTJ, outputHJTJ = objSIR.hessian(full_output=True)\n",
-    "\n",
-    "In \\[311\\]: print(scipy.linalg.inv(HJTJ))\n",
-    "\n",
-    "In \\[312\\]: print(cov_x)\n",
-    "\n",
-    "also note the difference between the Hessian and the approximation using\n",
-    "the Jacobian, which is in fact what the least squares routine uses.\n",
-    "\n",
-    "In \\[313\\]: print(scipy.linalg.inv(outputHJTJ\\['JTJ'\\]))"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/sir.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/sir.ipynb
deleted file mode 100644
index 0767ca96..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/sir.ipynb
+++ /dev/null
@@ -1,867 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Motivating Example: SIR Model\n",
-    "\n",
-    "## Defining the model\n",
-    "\n",
-    "First, we are going to go through an SIR model to show the functionality\n",
-    "of the package. The SIR model is defined by the following equations\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{dS}{dt} &= -\\beta SI \\\\\n",
-    "\\frac{dI}{dt} &= \\beta SI- \\gamma I \\\\\n",
-    "\\frac{dR}{dt} &= \\gamma I.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "We can set this up as follows:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "586834ab",
-   "metadata": {},
-   "source": [
-    "1. import the classes required to define the transitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "4c80a36a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import Transition, TransitionType"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ec4c9d0",
-   "metadata": {},
-   "source": [
-    "2. define our states, in this case the population states of **S**usceptible, **I**nfected and **R**emoved"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "3ce016f2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stateList = ['S', 'I', 'R']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "021a1927",
-   "metadata": {},
-   "source": [
-    "3. define the set of parameters for our model, here there are only two"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "441e2287",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "paramList = ['beta', 'gamma']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfd736b2",
-   "metadata": {},
-   "source": [
-    "4. specify the transitions of the modelled states; this will form our ODE system"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "b5f80ab0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "odeList = [\n",
-    "    Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE),\n",
-    "    Transition(origin='I',equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE),\n",
-    "    Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE) \n",
-    "    ]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21629e7b",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "Here, we have invoked a class from `Transition` to define the ODE object. We proceed here and ignore the details for now. The details of defining a transition object will be covered later in {doc}`transition`. Both the set of states and parameters should be defined when constructing the object, even though not explicitly enforced, to help clarify what we are trying to model. Similarly, this holds for the rest, such as the derived parameters and transitions, where we force the end user to input the different type of transition/process via the correct argument. See {ref}`transition:defining-the-equations` for an example when the input is wrong.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "69bfb7c3",
-   "metadata": {},
-   "source": [
-    "\n",
-    "5. import the ode module"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "f78c33d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import DeterministicOde"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "477d6f84",
-   "metadata": {},
-   "source": [
-    "6. initialize the model, which constructs our ODE system from all the information we have provided"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "c9fbcce0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = DeterministicOde(stateList, paramList, ode=odeList)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4bf43064",
-   "metadata": {},
-   "source": [
-    "That is all the information required to define a simple SIR model. We can verify the model using"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "6ad54e09",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "143a6871",
-   "metadata": {},
-   "source": [
-    "where we can see the equations corresponding to their respective $S,I$ and $R$ state. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08207474",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "The ordering of the equations is in the standard $S,I,R$ sequence because of how we defined the states initially. \n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7054e58a",
-   "metadata": {},
-   "source": [
-    "We can rearrange the state list,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "07f81fd1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}I \\gamma\\\\- I S \\beta\\\\I S \\beta - I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[           I*gamma],\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma]])"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# now we are going to define in a different order. note that the output ode changed with the input state\n",
-    "stateList = ['R', 'S', 'I']\n",
-    "\n",
-    "model = DeterministicOde(stateList, paramList, ode=odeList)\n",
-    "\n",
-    "model.get_ode_eqn()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3f587d4",
-   "metadata": {},
-   "source": [
-    "and find that the set of ODEs comes out in the order that we specified. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1175c832",
-   "metadata": {},
-   "source": [
-    "```{tip}\n",
-    "In addition to showing the equation form of the ODEs, we can also display them as either symbols or latex code, which can save some extra typing when porting the equations to another document.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "960fdc0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "⎡dR/dt=      I⋅γ    ⎤\n",
-      "⎢                   ⎥\n",
-      "⎢dS/dt=    -I⋅S⋅β   ⎥\n",
-      "⎢                   ⎥\n",
-      "⎣dI/dt=  I⋅S⋅β - I⋅γ⎦\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.print_ode()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "93e32c75",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{array}{cc}dR/dt= & I \\gamma\\\\dS/dt= & - I S \\beta\\\\dI/dt= & I S \\beta - I \\gamma\\end{array}\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.print_ode(True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79c154ba",
-   "metadata": {},
-   "source": [
-    "#TODO links to unroll\n",
-    "Here the SIR model was provided to PyGOM as a set ODEs by using the `Transition` to define them.  \n",
-    "We have also provided the capability to obtain a *best guess* transition matrix when only the ODEs are available. See the section `unrollOde` for more information, and in particular `unrollSimple` for the continuing demonstration of the SIR model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51ed6fa2",
-   "metadata": {},
-   "source": [
-    "## Extracting model information\n",
-    "\n",
-    "We may wish to determine if the set of ODEs are linear. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "61684654",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.linear_ode()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4cf58543",
-   "metadata": {},
-   "source": [
-    "Since we know that the SIR model is not linear, we may want to have a look at the Jacobian.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "1c8ff090",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & \\gamma\\\\0 & - I \\beta & - S \\beta\\\\0 & I \\beta & S \\beta - \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[0,       0,          gamma],\n",
-       "[0, -I*beta,        -S*beta],\n",
-       "[0,  I*beta, S*beta - gamma]])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_jacobian_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bab79c98",
-   "metadata": {},
-   "source": [
-    "Or maybe we want to know the gradient."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "abb02502",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & I\\\\- I S & 0\\\\I S & - I\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[   0,  I],\n",
-       "[-I*S,  0],\n",
-       "[ I*S, -I]])"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_grad_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d0052ec",
-   "metadata": {},
-   "source": [
-    "```{Warning}\n",
-    "Invoking the functions that compute the derivatives, $f(x)$, `model.ode()` or `model.jacobian()`, will return an error\n",
-    "\n",
-    "These functions are used to solve the ODEs numerically, and require values of initial state values, time, and parameter values.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35abe902",
-   "metadata": {},
-   "source": [
-    "For setting initial conditions, the numeric values of the states **must** be set in the same order that list of states appear. We can use the following to check the state ordering, as well as displaying all of the states that we have included."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "e703c888",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ODEVariable('R', 'R', None, True),\n",
-       " ODEVariable('S', 'S', None, True),\n",
-       " ODEVariable('I', 'I', None, True)]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.state_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54e681d0",
-   "metadata": {},
-   "source": [
-    "#TODO unsure if this is needed\n",
-    "There is currently no mechanism to set the numeric initial conditions the states when the states are defined. This is because of an implementation issue with external package, such as solving an initial value problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13bed7f9",
-   "metadata": {},
-   "source": [
-    "## Initial value problem\n",
-    "\n",
-    "By setting the state initial conditions, time, and parameters, we can evaluate our model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04fb8dd9",
-   "metadata": {},
-   "source": [
-    "1. Define the model parameters. (We can call the parameters to check what we must provide.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "46042ebf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "9ec016b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{beta: 0.5, gamma: 0.3333333333333333}"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "paramEval = [('beta',0.5), ('gamma',1.0/3.0)]\n",
-    "\n",
-    "model.parameters = paramEval\n",
-    "\n",
-    "model.parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "efcdac90",
-   "metadata": {},
-   "source": [
-    "2. Provide initial conditions for the states."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "c43074c6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4.23333333e-07, -6.35000000e-07,  2.11666667e-07])"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "initialState = [0, 1, 1.27e-6]\n",
-    "    \n",
-    "model.ode(state=initialState, t=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a8dc4681",
-   "metadata": {},
-   "source": [
-    "\n",
-    "3. Implement an ODE solver.\n",
-    "\n",
-    "We are well equipped to solve an initial value problem, using the standard numerical integrator such as `odeint ` from `scipy.integrate`. We also used `matplotlib.pyplot` for plotting and `linspace ` to create the time vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "a14b9901",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.integrate\n",
-    "import numpy\n",
-    "\n",
-    "t = numpy.linspace(0, 150, 100)\n",
-    "\n",
-    "solution = scipy.integrate.odeint(model.ode, initialState, t)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4feebae",
-   "metadata": {},
-   "source": [
-    "We can plot our solution to observe a standard SIR shape."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "8303885c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.figure()\n",
-    "\n",
-    "plt.plot(t, solution[:,0], label='R')\n",
-    "\n",
-    "plt.plot(t, solution[:,1], label='S')\n",
-    "\n",
-    "plt.plot(t, solution[:,2], label='I')\n",
-    "\n",
-    "plt.xlabel('Time')\n",
-    "\n",
-    "plt.ylabel('Population proportion')\n",
-    "\n",
-    "plt.title('Standard SIR model')\n",
-    "\n",
-    "plt.legend(loc=0)\n",
-    "\n",
-    "#@savefig sir_plot.png In \n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "#plt.close()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba849579",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can integrate and plot via the **ode** object which we initialized earlier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "efddd3a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model.initial_values = (initialState, t[0])\n",
-    "\n",
-    "model.parameters = paramEval\n",
-    "\n",
-    "solution = model.integrate(t[1::])\n",
-    "\n",
-    "model.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4dd6e250",
-   "metadata": {},
-   "source": [
-    "We could solve the ODEs above using the Jacobian as well. Unfortunately, it does not help because the number of times the Jacobian was evaluated was zero, as expected given that our set of equations are not stiff."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "7a7a568f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.58 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "#TODO what does this show?\n",
-    "%timeit solution1, output1 = scipy.integrate.odeint(model.ode, initialState, t, full_output=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "91b7f9d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.84 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "%timeit solution2, output2 = scipy.integrate.odeint(model.ode, initialState, t, Dfun=model.jacobian, mu=None, ml=None, full_output=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "3403884c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.68 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "%timeit solution3, output3 = model.integrate(t, full_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6b9ee47",
-   "metadata": {},
-   "source": [
-    "It is important to note that we return our Jacobian as a dense square matrix. Hence, the two argument (mu,ml) for the ODE solver was set to `None` to let it know the output explicitly."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ca18fa3",
-   "metadata": {},
-   "source": [
-    "## Solving the forward sensitivity equation\n",
-    "\n",
-    "The sensitivity equations are also solved as an initial value problem. Let us redefine the model in the standard SIR order and we solve it with the sensitivity all set at zero, i.e. we do not wish to infer the initial value of the states."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "87173626",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stateList = ['S', 'I', 'R']\n",
-    "\n",
-    "model = DeterministicOde(stateList, paramList, ode=odeList)\n",
-    "\n",
-    "initialState = [1, 1.27e-6, 0]\n",
-    "\n",
-    "paramEval = [('beta', 0.5), ('gamma', 1.0/3.0)]\n",
-    "\n",
-    "model.parameters = paramEval\n",
-    "\n",
-    "solution = scipy.integrate.odeint(model.ode_and_sensitivity, numpy.append(initialState, numpy.zeros(6)), t)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "8b63ba62",
-   "metadata": {
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "{\n",
-    "    \"tags\": [\n",
-    "        \"hide-input\",\n",
-    "    ]\n",
-    "}\n",
-    "f,axarr = plt.subplots(3,3);\n",
-    "\n",
-    "f.text(0.5,0.975,'SIR with forward sensitivity solved via ode',fontsize=16,horizontalalignment='center',verticalalignment='top')\n",
-    "\n",
-    "axarr[0,0].plot(t, solution[:,0])\n",
-    "\n",
-    "axarr[0,0].set_title('S')\n",
-    "\n",
-    "axarr[0,1].plot(t, solution[:,1])\n",
-    "\n",
-    "axarr[0,1].set_title('I')\n",
-    "\n",
-    "axarr[0,2].plot(t, solution[:,2]);\n",
-    "\n",
-    "axarr[0,2].set_title('R')\n",
-    "\n",
-    "axarr[1,0].plot(t, solution[:,3])\n",
-    "\n",
-    "axarr[1,0].set_title(r'state S parameter $beta$')\n",
-    "\n",
-    "axarr[2,0].plot(t, solution[:,4])\n",
-    "\n",
-    "axarr[2,0].set_title(r'state S parameter $gamma$')\n",
-    "\n",
-    "axarr[1,1].plot(t, solution[:,5])\n",
-    "\n",
-    "axarr[1,1].set_title(r'state I parameter $beta$')\n",
-    "\n",
-    "axarr[2,1].plot(t, solution[:,6])\n",
-    "\n",
-    "axarr[2,1].set_title(r'state I parameter $gamma$')\n",
-    "\n",
-    "axarr[1,2].plot(t, solution[:,7])\n",
-    "\n",
-    "axarr[1,2].set_title(r'state R parameter $beta$')\n",
-    "\n",
-    "axarr[2,2].plot(t, solution[:,8])\n",
-    "\n",
-    "axarr[2,2].set_title(r'state R parameter $gamma$')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f64d869",
-   "metadata": {},
-   "source": [
-    "#TODO links\n",
-    "This concludes the introductory example and we will be moving on to look at parameter estimation next in {doc}`estimate1` and the most important part in terms of setting up the ODE object; defining the equations in various different ways in {doc}`transition`."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.15 ('sphinx-doc')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/stochastic.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/stochastic.ipynb
deleted file mode 100644
index f037f47b..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/stochastic.ipynb
+++ /dev/null
@@ -1,617 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stochastic representation of ODEs\n",
-    "\n",
-    "There are multiple interpretations of the stochasticity of a deterministic\n",
-    "ODE. We have implemented two of the most common interpretations; when the\n",
-    "parameters are realizations of some underlying distribution, and when we\n",
-    "have a so called chemical master equation where each transition\n",
-    "represent a jump. Again, we use the standard SIR example as previously\n",
-    "seen in {doc}`sir`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d945f972",
-   "metadata": {},
-   "source": [
-    "#TODO add comment about using a density or frequency formulation (use of population size) - currently unexplained"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "3f44792d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import SimulateOde, Transition, TransitionType\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "# initial conditions\n",
-    "Ix0 = [1, 1.27e-6, 0]\n",
-    "\n",
-    "# time period\n",
-    "t = np.linspace(0, 150, 100)\n",
-    "\n",
-    "stateList = ['S', 'I', 'R']\n",
-    "\n",
-    "paramList = ['beta', 'gamma']\n",
-    "\n",
-    "transitionList = [Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T), \n",
-    "                  Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)]\n",
-    "\n",
-    "# create the ODEs\n",
-    "odeS = SimulateOde(stateList, paramList, transition=transitionList)\n",
-    "\n",
-    "# assign parameter values\n",
-    "odeS.parameters = [0.5, 1.0/3.0]\n",
-    "\n",
-    "# assign initial values\n",
-    "odeS.initial_values = (Ix0, t[0])\n",
-    "\n",
-    "# solve deterministic solution\n",
-    "solutionReference = odeS.integrate(t[1::], full_output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e516f09",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Stochastic Parameters\n",
-    "\n",
-    "In our first implementation, we assume that the parameters follow some\n",
-    "underlying distribution. Given that both $\\beta$ and $\\gamma$ in our SIR\n",
-    "model has to be non-negative, it seems natural to use a Gamma\n",
-    "distribution. We make use of the familiar syntax from\n",
-    "[R](http://www.r-project.org/) to define our distribution.\n",
-    "Unfortunately, we have to define it via a tuple, where the first item is the\n",
-    "function handle (name) and the second the parameters. \n",
-    "\n",
-    "```{note}\n",
-    "The parameters can be defined as either a dictionary or as the same sequence\n",
-    "as [R](http://www.r-project.org/) (without specifying the arguments), which for the [Gamma distribution](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/GammaDist.html) is the shape followed by the rate.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "a3e9bc81",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom.utilR import rgamma\n",
-    "\n",
-    "d = dict()\n",
-    "\n",
-    "# E(X) = 0.5 = shape * scale = shape / rate \n",
-    "d['beta'] = (rgamma,{'shape':100.0, 'rate':200.0})\n",
-    "\n",
-    "# E(X) = 1.0/3.0 = shape / rate\n",
-    "d['gamma'] = (rgamma,(100.0, 300.0))\n",
-    "\n",
-    "odeS.parameters = d\n",
-    "\n",
-    "Ymean, Yall = odeS.simulate_param(t[1::], 10, full_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2221479d",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "Note that a message may be printed above where PyGOM is trying to connect to an\n",
-    "mpi backend, as our module has the capability to compute in parallel\n",
-    "using the IPython. We have simulated a total of 10 different solutions\n",
-    "using different parameters, the plots can be seen below.\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "8849566b",
-   "metadata": {
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, axarr = plt.subplots(1,3, layout='constrained')\n",
-    "\n",
-    "#TODO tidy up plots\n",
-    "for solution in Yall:  \n",
-    "    axarr[0].plot(t, solution[:,0]) \n",
-    "    axarr[1].plot(t, solution[:,1]) \n",
-    "    axarr[2].plot(t, solution[:,2])\n",
-    "\n",
-    "for idx, state in enumerate(stateList):\n",
-    "    axarr[idx].set_title(state)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fd28659",
-   "metadata": {},
-   "source": [
-    "\n",
-    "We then see how the expected results, using the sample average of the\n",
-    "simulations\n",
-    "\n",
-    "$$\\tilde{x}(T) = \\mathbb{E}\\left[ \\int_{t_{0}}^{T} f(\\theta,x,t) dt \\right]$$\n",
-    "\n",
-    "differs from the reference solution\n",
-    "\n",
-    "$$\\hat{x}(T) = \\int_{t_{0}}^{T} f(\\mathbb{E}\\left[ \\theta \\right],x,t) dt$$ "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "4e6f4164",
-   "metadata": {
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, axarr = plt.subplots(1,3, layout='constrained')\n",
-    "\n",
-    "for i in range(3): \n",
-    "    axarr[i].plot(t, Ymean[:,i] - solutionReference[:,i])\n",
-    "    axarr[i].set_title(stateList[i])\n",
-    "\n",
-    "plt.show()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "ee4d496d",
-   "metadata": {
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, axarr = plt.subplots(1,3, layout='constrained', sharey=False)\n",
-    "\n",
-    "for i in range(3): \n",
-    "    axarr[i].plot(t, Ymean[:,i], label='sample average')\n",
-    "    axarr[i].plot(t, solutionReference[:,i], label='reference')\n",
-    "    axarr[i].set_title(stateList[i])\n",
-    "\n",
-    "axarr[2].legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa230d7f",
-   "metadata": {},
-   "source": [
-    "The difference between the deterministic reference and averaged solutions is relatively large, especially for the $S$ state. \n",
-    "We can decrease this difference as we increase the number of simulations, and also by using more sophisticated sampling methods for the generation of random\n",
-    "variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39d66f12",
-   "metadata": {},
-   "source": [
-    "```{tip}\n",
-    "In addition to using the built-in functions to represent stochasticity,\n",
-    "we can also use standard frozen distributions from scipy. Note that it\n",
-    "must be a frozen distribution as that is the only for the parameters of\n",
-    "the distributions to propagate through the model.\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "b8a806bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as st\n",
-    "\n",
-    "d = dict()\n",
-    "\n",
-    "d['beta'] = st.gamma(a=100.0, scale=1.0/200.0)\n",
-    "\n",
-    "d['gamma'] = st.gamma(a=100.0, scale=1.0/300.0)\n",
-    "\n",
-    "odeS.parameters = d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "030d6487",
-   "metadata": {},
-   "source": [
-    "There may be scenarios where only some of the parameters are\n",
-    "stochastic. Let's say that the $\\gamma$ parameter is fixed at $1/3$,\n",
-    "then we can replace the distribution information with a scalar. A quick\n",
-    "visual inspection at the resulting plot suggests that the system of ODEs\n",
-    "potentially has less variation when compared to the case where both\n",
-    "parameters are stochastic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "fa0d6ee2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d['gamma'] = 1.0/3.0\n",
-    "\n",
-    "odeS.parameters = d\n",
-    "\n",
-    "YmeanSingle, YallSingle = odeS.simulate_param(t[1::], 5, full_output=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "eb4e969a",
-   "metadata": {
-    "tags": [
-     "hide-input"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, axarr = plt.subplots(1,3)\n",
-    "\n",
-    "for solution in YallSingle:  \n",
-    "    axarr[0].plot(t,solution[:,0]) \n",
-    "    axarr[1].plot(t,solution[:,1]) \n",
-    "    axarr[2].plot(t,solution[:,2])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f01a3d9",
-   "metadata": {},
-   "source": [
-    "## Continuous Markov Representation\n",
-    "\n",
-    "Another common method of introducing stochasticity into a set of ODEs is\n",
-    "by assuming each movement in the system is a result of a jump process.\n",
-    "More concretely, the probabilty of a move for transition $j$ is governed\n",
-    "by an exponential distribution such that\n",
-    "\n",
-    "$$\\Pr(\\text{process $j$ jump within time } \\tau) = \\lambda_{j} e^{-\\lambda_{j} \\tau},$$\n",
-    "\n",
-    "where $\\lambda_{j}$ is the rate of transition for process $j$ and $\\tau$\n",
-    "the time elapsed after current time $t$.\n",
-    "\n",
-    "Two of the commmon algorithms for the jump process have been\n",
-    "implemented for use during simulation; the reaction method [\\[Gillespie1977\\]]() and the $\\tau$-Leap method\n",
-    "[\\[Cao2006\\]](). The two change interactively depending on the size of the states."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "a0a0bcbc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x0 = [2362206.0, 3.0, 0.0]\n",
-    "\n",
-    "stateList = ['S', 'I', 'R']\n",
-    "\n",
-    "paramList = ['beta', 'gamma', 'N']\n",
-    "\n",
-    "transitionList = [Transition(origin='S', destination='I', equation='beta*S*I/N', transition_type=TransitionType.T),\n",
-    "                  Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)\n",
-    "                  ]\n",
-    "\n",
-    "odeS = SimulateOde(stateList, paramList, transition=transitionList)\n",
-    "\n",
-    "odeS.parameters = [0.5, 1.0/3.0, x0[0]]\n",
-    "\n",
-    "odeS.initial_values = (x0, t[0])\n",
-    "\n",
-    "solutionReference = odeS.integrate(t[1::])\n",
-    "\n",
-    "simX, simT = odeS.simulate_jump(t[1:10], 10, full_output=True)\n",
-    "\n",
-    "f, axarr = plt.subplots(1, 3)\n",
-    "\n",
-    "for solution in simX:  \n",
-    "    axarr[0].plot(t[:9], solution[:,0])\n",
-    "    axarr[1].plot(t[:9], solution[:,1]) \n",
-    "    axarr[2].plot(t[:9], solution[:,2])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e18e4fa7",
-   "metadata": {},
-   "source": [
-    "Above, we see 10 different simulations, again using the SIR model but\n",
-    "without standardization of the initial conditions (we include $N$ as a parameter in our model). \n",
-    "For demonstration purposes, we restrict our time\n",
-    "frame to be only the first 10 time points so that the individual changes or stochasticity\n",
-    "can be seen more clearly. If we use the same time frame as the one\n",
-    "used previously for the deterministic system (as shown below), the\n",
-    "trajectories are smoothed out and we no longer observe the *jumps*.\n",
-    "Looking at the raw trajectories of the ODEs below, we see that the\n",
-    "mean from a jump process is very different to the deterministic\n",
-    "solution. This occurs because the jump process above was able\n",
-    "to fully remove all the initial infected individuals before any new\n",
-    "ones occured."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "525a069b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "simX,simT = odeS.simulate_jump(t, 5, full_output=True)\n",
-    "\n",
-    "simMean = np.mean(simX, axis=0)\n",
-    "\n",
-    "f, axarr = plt.subplots(1,3)\n",
-    "\n",
-    "for solution in simX:  \n",
-    "    axarr[0].plot(t, solution[:,0]) \n",
-    "    axarr[1].plot(t, solution[:,1]) \n",
-    "    axarr[2].plot(t, solution[:,2])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbef64f3",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Repeatable Simulation\n",
-    "\n",
-    "One of the possible uses of compartmental models is to generate\n",
-    "forecasts. Although most of the time the requirement would be to have\n",
-    "(at least point-wise) convergence in the limit, reproducibility is also\n",
-    "important. For both types of interpretation explained above, we have\n",
-    "given the package the capability to repeat the simulations by setting a\n",
-    "seed. When the assumption is that the parameters follows some sort of\n",
-    "distribution, we can set the seed which governs the global state of\n",
-    "the random number generator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "415cab81",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x0 = [2362206.0, 3.0, 0.0]\n",
-    "\n",
-    "odeS = SimulateOde(stateList, paramList, transition=transitionList)\n",
-    "\n",
-    "d = {'beta': st.gamma(a=100.0, scale=1.0/200.0), 'gamma':st.gamma(a=100.0, scale=1.0/300.0), 'N': x0[0]}\n",
-    "\n",
-    "odeS.parameters = d\n",
-    "\n",
-    "odeS.initial_values = (x0, t[0])\n",
-    "\n",
-    "Ymean, Yall = odeS.simulate_param(t[1::], 10, full_output=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "7a51e644",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Different in the simulations and the mean: (81103473.8935847, 52437230.37979218) \n",
-      "Different in the simulations and the mean using same seed: (0.0, 0.0) \n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "np.random.seed(1)\n",
-    "\n",
-    "Ymean1, Yall1 = odeS.simulate_param(t[1::], 10, full_output=True)\n",
-    "\n",
-    "np.random.seed(1)\n",
-    "\n",
-    "Ymean2, Yall2 = odeS.simulate_param(t[1::], 10, full_output=True)\n",
-    "\n",
-    "sim_diff = [np.linalg.norm(Yall[i] - yi) for i, yi in enumerate(Yall1)]\n",
-    "\n",
-    "sim_diff12 = [np.linalg.norm(Yall2[i] - yi) for i, yi in enumerate(Yall1)]\n",
-    "\n",
-    "print(\"Different in the simulations and the mean: (%s, %s) \" %(np.sum(sim_diff), np.sum(np.abs(Ymean1 - Ymean))))\n",
-    "\n",
-    "print(\"Different in the simulations and the mean using same seed: (%s, %s) \" % (np.sum(sim_diff12), np.sum(np.abs(Ymean2 - Ymean1))))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1fe54a8",
-   "metadata": {},
-   "source": [
-    "In the alternative interpretation, setting the global seed is\n",
-    "insufficient. Unlike simulation based on the parameters, where we can\n",
-    "pre-generate all the parameter values and send them off to individual\n",
-    "processes in the parallel backend, this is prohibitive here. In a\n",
-    "nutshell, the seed does not propagate when using a parallel backend\n",
-    "because each *integration* requires an unknown number of random samples.\n",
-    "Therefore, we have an additional flag **parallel** in the function\n",
-    "signature. By ensuring that the computation runs in serial, we can make\n",
-    "use of the global seed and generate identical runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "d5a9e235",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Difference in simulation: 2534.4614923250074\n",
-      "Difference in simulation using same seed: 0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "x0 = [2362206.0, 3.0, 0.0]\n",
-    "\n",
-    "odeS = SimulateOde(stateList, paramList, transition=transitionList)\n",
-    "\n",
-    "odeS.parameters = [0.5, 1.0/3.0, x0[0]]\n",
-    "\n",
-    "odeS.initial_values = (x0, t[0])\n",
-    "\n",
-    "simX, simT = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)\n",
-    "\n",
-    "np.random.seed(1)\n",
-    "\n",
-    "simX1, simT1 = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)\n",
-    "\n",
-    "np.random.seed(1)\n",
-    "\n",
-    "simX2, simT2 = odeS.simulate_jump(t[1:10], 10, parallel=False, full_output=True)\n",
-    "\n",
-    "sim_diff = [np.linalg.norm(simX[i] - x1) for i, x1 in enumerate(simX1)]\n",
-    "\n",
-    "sim_diff12 = [np.linalg.norm(simX2[i] - x1) for i, x1 in enumerate(simX1)]\n",
-    "\n",
-    "print(\"Difference in simulation: %s\" %np.sum(np.abs(sim_diff)))\n",
-    "\n",
-    "print(\"Difference in simulation using same seed: %s\" %np.sum(np.abs(sim_diff12)))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.15 ('sphinx-doc')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/transition.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/transition.ipynb
deleted file mode 100644
index 2ed607d2..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/transition.ipynb
+++ /dev/null
@@ -1,452 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transition Object\n",
-    "\n",
-    "\n",
-    "The most important part of setting up the model is to correctly define the set ODEs, which is based solely on the classes defined in `transition`. These transitions describe how members of one state can move into another state. We choose to define the models using transitions because it enables the computer to do our bookeeping, therefore reducing the error of, for example, including a flow out of one state, but forgetting to include it in the recipient state. (This does not apply to birth and death processes.) \n",
-    "\n",
-    "\n",
-    "All transitions that get fed into the ODE system need to be defined as a transition object, `Transition`. It takes a total of four input arguments:\n",
-    "\n",
-    "1.  The origin state\n",
-    "2.  Equation that describe the process\n",
-    "3.  The type of transition\n",
-    "4.  The destination state\n",
-    "\n",
-    "where the first three are mandatory. To demonstrate, we go back to the SIR model defined previously in the section {doc}`sir`. Recall that the set of ODEs are\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    " \\frac{d S}{d t} &= - beta SI \\\\\n",
-    "\\frac{d I}{d t} &= beta SI - \\gamma I \\\\\n",
-    "\\frac{d R}{d t} &= \\gamma I.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "We can define the set of ODEs, as seen previously, via"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "68d41d64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import Transition, TransitionType, common_models\n",
-    "\n",
-    "ode1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.ODE)\n",
-    "\n",
-    "ode2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.ODE)\n",
-    "\n",
-    "ode3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.ODE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d393a33a",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Note that we need to state explicitly the type of equation we are\n",
-    "inputting, which is simply of type **ODE** in this case. We can confirm\n",
-    "this has been entered correctly by putting it into `DeterministicOde`,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3b0801dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import DeterministicOde\n",
-    "\n",
-    "stateList = ['S', 'I', 'R']\n",
-    "\n",
-    "paramList = ['beta', 'gamma']\n",
-    "\n",
-    "model = DeterministicOde(stateList, paramList, ode=[ode1, ode2, ode3])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e764826",
-   "metadata": {},
-   "source": [
-    "\n",
-    "and then checking it.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5782a96f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c693cbc7",
-   "metadata": {},
-   "source": [
-    "Now we are going to show the different ways of defining the same set of\n",
-    "ODEs.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa631d9f",
-   "metadata": {},
-   "source": [
-    "(transition:defining-the-equations)=\n",
-    "## Defining the equations\n",
-    "\n",
-    "We first recognize that the set of ODEs defining the SIR model are the result of\n",
-    "two transitions,\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "S \\rightarrow I &= \\beta SI \\\\\n",
-    "I \\rightarrow R &= \\gamma I\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "where $S \\rightarrow I$ denotes a transition from state $S$ to state\n",
-    "$I$. Therefore, we can define our model by these two transitions,\n",
-    "but they need to be passed as the `transition`\n",
-    "argument instead of the `ode` argument of `DeterministicOde` or `SimulateOde`.\n",
-    "\n",
-    "```{note}\n",
-    "We are initializing the model using the `SimulateOde` class, rather than `DeterministicOde`, because the stochastic implementation has more available operations on transitions.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6966fc5e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pygom import SimulateOde\n",
-    "\n",
-    "t1 = Transition(origin='S', destination='I', equation='beta*S*I', transition_type=TransitionType.T)\n",
-    "\n",
-    "t2 = Transition(origin='I', destination='R', equation='gamma*I', transition_type=TransitionType.T)\n",
-    "\n",
-    "modelTrans = SimulateOde(stateList, paramList, transition=[t1, t2])\n",
-    "\n",
-    "modelTrans.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b896d5c9",
-   "metadata": {},
-   "source": [
-    "\n",
-    "We can see that the resulting ODE is exactly the same, as expected. The\n",
-    "transition matrix that defines this process can be visualized\n",
-    "using graphviz. Because only certain renderers permit the use of sub and\n",
-    "superscript, operators such as $**$ are left as they are in the\n",
-    "equation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "1e17eb7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & I S \\beta & 0\\\\0 & 0 & I \\gamma\\\\0 & 0 & 0\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[0, I*S*beta,       0],\n",
-       "[0,        0, I*gamma],\n",
-       "[0,        0,       0]])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "modelTrans.get_transition_matrix()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "570ceed5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": "\n\n\n\n\n\n\n\n\nS\n\nS\n\n\n\nI\n\nI\n\n\n\nS->I\n\n\nβ*S*I\n\n\n\nR\n\nR\n\n\n\nI->R\n\n\nγ*I\n\n\n\n",
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "# TODO why are two images produced? issue #75\n",
-    "modelTrans.get_transition_graph(show=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d982b87",
-   "metadata": {},
-   "source": [
-    "```{warning}\n",
-    "The execution will error if the incorrect `TransitionType` is used against the wrong argument.\n",
-    "\n",
-    "`modelTrans = DeterministicOde(stateList, paramList, ode=[t1, t2])`\n",
-    "\n",
-    "Here the error occurs because `t1` and `t2` used `transition_type=TransitionType.T` argument, but `DeterministicOde` is expecting a `TransitionType.ODE` argument.\n",
-    "\n",
-    "Similarly `DeterministicOde(stateList, paramList, transition=[ode1, ode2, ode3])` would fail.\n",
-    "\n",
-    "This therefore forces us to construct our model carefully.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d590f4d5",
-   "metadata": {},
-   "source": [
-    "The third option is to reframe the system as a set of birth processes, using `transition_type=TransitionType.B`. For this simple example, this formulation takes a similar form to defining using ODE equations.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "9f24cb0a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "birth1 = Transition(origin='S', equation='-beta*S*I', transition_type=TransitionType.B)\n",
-    "\n",
-    "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n",
-    "\n",
-    "birth3 = Transition(origin='R', equation='gamma*I', transition_type=TransitionType.B)\n",
-    "\n",
-    "modelBirth = DeterministicOde(stateList, paramList, birth_death=[birth1, birth2, birth3])\n",
-    "\n",
-    "modelBirth.get_ode_eqn()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1d61012",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can use the negative of the equation to configure the ODEs to represent death processes. Since the death process is the removal of a flow, we take the negative of the birth process alongside using `transition_type=TransitionType.D`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "9a3b3758",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}- I S \\beta\\\\I S \\beta - I \\gamma\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[         -I*S*beta],\n",
-       "[I*S*beta - I*gamma],\n",
-       "[           I*gamma]])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "death1 = Transition(origin='S', equation='beta*S*I', transition_type=TransitionType.D)\n",
-    "\n",
-    "birth2 = Transition(origin='I', equation='beta*S*I - gamma*I', transition_type=TransitionType.B)\n",
-    "\n",
-    "death3 = Transition(origin='R', equation='-gamma*I', transition_type=TransitionType.D)\n",
-    "\n",
-    "modelBD = DeterministicOde(stateList, paramList, birth_death=[death1, birth2, death3])\n",
-    "\n",
-    "modelBD.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c8d48b8",
-   "metadata": {},
-   "source": [
-    "\n",
-    "We can see that all four approaches have yielded the same set of ODEs at the end."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23a105af",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Model Addition\n",
-    "\n",
-    "Because we allow the separation of transitions between states and birth/death processes, the birth/death processes can be added later on. The following example takes the model that was defined using transitions (`modelTrans`) and includes a birth process to the $S$ state, and death processes to the $S$ and $I$ states."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "3a6a15ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}B - I S \\beta - S \\mu\\\\I S \\beta - I \\gamma - I \\mu\\\\I \\gamma\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[      B - I*S*beta - S*mu],\n",
-       "[I*S*beta - I*gamma - I*mu],\n",
-       "[                  I*gamma]])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "modelBD2 = modelTrans\n",
-    "\n",
-    "modelBD2.param_list = paramList + ['mu', 'B']\n",
-    "\n",
-    "birthDeathList = [Transition(origin='S', equation='B', transition_type=TransitionType.B),  \n",
-    "                  Transition(origin='S', equation='mu*S', transition_type=TransitionType.D), \n",
-    "                  Transition(origin='I', equation='mu*I', transition_type=TransitionType.D)\n",
-    "                  ]\n",
-    "\n",
-    "modelBD2.birth_death_list = birthDeathList\n",
-    "\n",
-    "modelBD2.get_ode_eqn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "076bdc12",
-   "metadata": {},
-   "source": [
-    "\n",
-    "This demonstrates that we can approach our in stages. Start off with a standard closed system using `TransitionType.T`, and then extend it with additional flows that interact with the populations' surrounding environments using `TransitionType.B` or `TransitionType.D`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec63c1e1",
-   "metadata": {},
-   "source": [
-    "## Transition type summary\n",
-    "\n",
-    "In summary, there are four different types of transitions allowed. These are B, D, ODE and T, which are defined in an enum class also located in `transition`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "ce5787f8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TransitionType.B = Birth process\n",
-      "TransitionType.D = Death process\n",
-      "TransitionType.T = Between states\n",
-      "TransitionType.ODE = ODE _equation\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pygom import transition\n",
-    "\n",
-    "for i in transition.TransitionType:  \n",
-    "    print(str(i) + \" = \" + i.value)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89607b7d",
-   "metadata": {},
-   "source": [
-    "Each birth process is added to the origin state, while each death\n",
-    "process is deducted from the origin state (alternatively added to the state after\n",
-    "multiplying the flow with a negative sign). An ODE type is also added to the state, but we forbid the number of input ODEs to be greater than the number of states inputted. These strict definitions should help us to improve the bookeeping of states and flows when we have models of greater complexity."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.15 ('sphinx-doc')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "4dc1e323c80fe09539c74ad5c5a7c7d8d9ff99e04f7b3dbd3680daf878629d6e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollBD.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollBD.ipynb
deleted file mode 100644
index ea56ca47..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollBD.ipynb
+++ /dev/null
@@ -1,79 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ODE With Birth and Death Process\n",
-    "\n",
-    "We follow on from the SIR model of `unrollSimple` but with additional\n",
-    "birth and death processes.\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{dS}{dt} &= -\\beta SI + B - \\mu S\\\\\n",
-    "\\frac{dI}{dt} &= \\beta SI - \\gamma I - \\mu I\\\\\n",
-    "\\frac{dR}{dt} &= \\gamma I.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "which consists of two transitions and three birth and death process\n",
-    "\n",
-    "digraph SIR_Model {  \n",
-    "rankdir=LR; size=\"8\" node \\[shape = circle\\]; S -\\> I \\[ label = \"βSI\"\n",
-    "\\]; I -\\> R \\[ label = \"γI\" \\]; B \\[height=0 margin=0 shape=plaintext\n",
-    "width=0\\]; B -\\> S; \"S\\**2*μ\" \\[height=0 margin=0 shape=plaintext\n",
-    "width=0\\]; S -\\> \"S\\**2*μ\"; \"I\\*μ\" \\[height=0 margin=0 shape=plaintext\n",
-    "width=0\\]; I -\\> \"I\\*μ\";\n",
-    "\n",
-    "}\n",
-    "\n",
-    "Let's define this in terms of ODEs, and unroll it back to the individual\n",
-    "processes.\n",
-    "\n",
-    "In \\[1\\]: from pygom import Transition, TransitionType, SimulateOde,\n",
-    "common_models\n",
-    "\n",
-    "In \\[1\\]: import matplotlib.pyplot as plt\n",
-    "\n",
-    "In \\[1\\]: stateList = \\['S', 'I', 'R'\\]\n",
-    "\n",
-    "In \\[1\\]: paramList = \\['beta', 'gamma', 'B', 'mu'\\]\n",
-    "\n",
-    "In \\[1\\]: odeList = \\[  \n",
-    "...: Transition(origin='S', ...: equation='-beta\\*S\\*I + B - mu\\*S',\n",
-    "...: transition_type=TransitionType.ODE), ...: Transition(origin='I',\n",
-    "...: equation='beta\\*S\\*I - gamma\\*I - mu\\*I', ...:\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='R', ...:\n",
-    "equation='gamma\\*I', ...: transition_type=TransitionType.ODE) ...: \\]\n",
-    "\n",
-    "In \\[1\\]: ode = SimulateOde(stateList, paramList, ode=odeList)\n",
-    "\n",
-    "In \\[1\\]: ode2 = ode.get_unrolled_obj()\n",
-    "\n",
-    "In \\[1\\]: f = plt.figure()\n",
-    "\n",
-    "@savefig sir_unrolled_transition_graph.png In \\[1\\]:\n",
-    "ode2.get_transition_graph()\n",
-    "\n",
-    "In \\[1\\]: plt.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.11.0 ('pygom-dev')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.11.0"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "fc17efc1b95c7b1781d7b7ce7a7c317a91a744529621a3902877be6d49554249"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollHard.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollHard.ipynb
deleted file mode 100644
index 64e14bd0..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollHard.ipynb
+++ /dev/null
@@ -1,90 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hard Problem\n",
-    "\n",
-    "Now we turn to a harder problem that does not have a one to one mapping\n",
-    "between all the transitions and the terms in the ODEs. We use the model\n",
-    "in `Influenza_SLIARN`, defined by\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{dS}{dt} &= -S \\beta (I + \\delta A) \\\\    \n",
-    "\\frac{dL}{dt} &= S \\beta (I + \\delta A) - \\kappa L \\\\  \n",
-    "\\frac{dI}{dt} &= p \\kappa L - \\alpha I \\\\\n",
-    "\\frac{dA}{dt} &= (1 - p) \\kappa L - \\eta A \\\\\n",
-    "\\frac{dR}{dt} &= f \\alpha I + \\eta A \\\\\n",
-    "\\frac{dN}{dt} &= -(1 - f) \\alpha I.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "The outflow of state **L**, $\\kappa L$, is composed of two transitions,\n",
-    "one to **I** and the other to **A** but the ode of **L** only reflects\n",
-    "the total flow going out of the state. Same can be said for state **I**,\n",
-    "where the flow $\\alpha I$ goes to both **R** and **N**. Graphically, it\n",
-    "is a rather simple process as shown below.\n",
-    "\n",
-    "digraph SLIARD_Model {  \n",
-    "labelloc = \"t\"; label = \"Original transitions\"; rankdir=LR; size=\"8\"\n",
-    "node \\[shape = circle\\]; S -\\> L \\[ label = \"-Sβ(I + δA)/N\" \\]; L -\\> I\n",
-    "\\[ label = \"κLp\" \\]; L -\\> A \\[ label = \"κL(1-p)\" \\]; I -\\> R \\[ label =\n",
-    "\"αIf\" \\]; I -\\> D \\[ label = \"αI(1-f)\" \\]; A -\\> R \\[ label = \"ηA\" \\];\n",
-    "\n",
-    "}\n",
-    "\n",
-    "We slightly change the model by introducing a new state **D** to convert\n",
-    "it into a closed system. The combination of state **D** and **N** is a\n",
-    "constant, the total population. So we can remove **N** and this new\n",
-    "system consist of six transitions. We define them explicitly as ODEs and\n",
-    "unroll them into transitions.\n",
-    "\n",
-    "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n",
-    "\n",
-    "In \\[1\\]: stateList = \\['S', 'L', 'I', 'A', 'R', 'D'\\]\n",
-    "\n",
-    "In \\[2\\]: paramList = \\['beta', 'p', 'kappa', 'alpha', 'f', 'delta',\n",
-    "'epsilon', 'N'\\]\n",
-    "\n",
-    "In \\[3\\]: odeList = \\[  \n",
-    "...: Transition(origin='S', equation='- beta\\*S/N\\*(I + delta\\*A)',\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='L',\n",
-    "equation='beta\\*S/N\\*(I + delta\\*A) - kappa\\*L',\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='I',\n",
-    "equation='p\\*kappa\\*L - alpha\\*I', transition_type=TransitionType.ODE),\n",
-    "...: Transition(origin='A', equation='(1 - p)*kappa* L - epsilon\\*A',\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='R',\n",
-    "equation='f\\*alpha\\*I + epsilon\\*A',\n",
-    "transition_type=TransitionType.ODE), ...: Transition(origin='D',\n",
-    "equation='(1 - f)*alpha*I', transition_type=TransitionType.ODE) \\]\n",
-    "\n",
-    "In \\[4\\]: ode = SimulateOde(stateList, paramList, ode=odeList)\n",
-    "\n",
-    "In \\[5\\]: ode.get_transition_matrix()\n",
-    "\n",
-    "In \\[6\\]: ode2 = ode.get_unrolled_obj()\n",
-    "\n",
-    "In \\[7\\]: ode2.get_transition_matrix()\n",
-    "\n",
-    "In \\[8\\]: ode2.get_ode_eqn()\n",
-    "\n",
-    "After unrolling the odes, we have the following transition graph\n",
-    "\n",
-    "@savefig sir_unrolled_transition_graph_hard.png In \\[1\\]:\n",
-    "ode2.get_transition_graph()\n",
-    "\n",
-    "In \\[2\\]: plt.close()\n",
-    "\n",
-    "In \\[3\\]: print(sum(ode.get_ode_eqn() - ode2.get_ode_eqn()).simplify())\n",
-    "\\# difference\n",
-    "\n",
-    "which is exactly the same apart from slightly weird arrangement of\n",
-    "symbols in some of the equations. The last line with a value of zero\n",
-    "also reaffirms the result."
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollSimple.ipynb b/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollSimple.ipynb
deleted file mode 100644
index 88bbd592..00000000
--- a/docs/pygom-doc/_build/html/_sources/notebooks/unroll/unrollSimple.ipynb
+++ /dev/null
@@ -1,61 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Simple Problem\n",
-    "\n",
-    "For a simple problem, we consider the SIR model defined by\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\frac{dS}{dt} &= -\\beta SI \\\\\n",
-    "\\frac{dI}{dt} &= \\beta SI - \\gamma I \\\\\n",
-    "\\frac{dR}{dt} &= \\gamma I.\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "which consists of two transitions\n",
-    "\n",
-    "digraph SIR_Model {  \n",
-    "rankdir=LR; size=\"8\" node \\[shape = circle\\]; S -\\> I \\[ label = \"βSI\"\n",
-    "\\]; I -\\> R \\[ label = \"γI\" \\];\n",
-    "\n",
-    "}\n",
-    "\n",
-    "Let's define this using the code block below\n",
-    "\n",
-    "In \\[1\\]: from pygom import SimulateOde, Transition, TransitionType\n",
-    "\n",
-    "In \\[2\\]: ode1 = Transition(origin='S', equation='-beta\\*S\\*I',\n",
-    "transition_type=TransitionType.ODE)\n",
-    "\n",
-    "In \\[3\\]: ode2 = Transition(origin='I', equation='beta\\*S\\*I -\n",
-    "gamma\\*I', transition_type=TransitionType.ODE)\n",
-    "\n",
-    "In \\[4\\]: ode3 = Transition(origin='R', equation='gamma\\*I',\n",
-    "transition_type=TransitionType.ODE)\n",
-    "\n",
-    "In \\[6\\]: stateList = \\['S', 'I', 'R'\\]\n",
-    "\n",
-    "In \\[7\\]: paramList = \\['beta', 'gamma'\\]\n",
-    "\n",
-    "In \\[8\\]: ode = SimulateOde(stateList,  \n",
-    "...: paramList, ...: ode=\\[ode1, ode2, ode3\\])\n",
-    "\n",
-    "In \\[9\\]: ode.get_transition_matrix()\n",
-    "\n",
-    "and the last line shows that the transition matrix is empty. This is the\n",
-    "expected result because `SimulateOdeModel` was not initialized using\n",
-    "transitions. We populate the transition matrix below and demonstrate the\n",
-    "difference.\n",
-    "\n",
-    "In \\[1\\]: ode = ode.get_unrolled_obj()\n",
-    "\n",
-    "In \\[2\\]: ode.get_transition_matrix()"
-   ]
-  }
- ],
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {}
-}
diff --git a/docs/pygom-doc/_build/html/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/docs/pygom-doc/_build/html/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
deleted file mode 100644
index 3225661c..00000000
--- a/docs/pygom-doc/_build/html/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
+++ /dev/null
@@ -1 +0,0 @@
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index 9e364ed3..00000000
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-  -moz-user-select: none; /* Firefox */
-  -ms-user-select: none; /* IE10+ */
-}
-
-div.code-block-caption span.caption-number {
-    padding: 0.1em 0.3em;
-    font-style: italic;
-}
-
-div.code-block-caption span.caption-text {
-}
-
-div.literal-block-wrapper {
-    margin: 1em 0;
-}
-
-code.xref, a code {
-    background-color: transparent;
-    font-weight: bold;
-}
-
-h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
-    background-color: transparent;
-}
-
-.viewcode-link {
-    float: right;
-}
-
-.viewcode-back {
-    float: right;
-    font-family: sans-serif;
-}
-
-div.viewcode-block:target {
-    margin: -1px -10px;
-    padding: 0 10px;
-}
-
-/* -- math display ---------------------------------------------------------- */
-
-img.math {
-    vertical-align: middle;
-}
-
-div.body div.math p {
-    text-align: center;
-}
-
-span.eqno {
-    float: right;
-}
-
-span.eqno a.headerlink {
-    position: absolute;
-    z-index: 1;
-}
-
-div.math:hover a.headerlink {
-    visibility: visible;
-}
-
-/* -- printout stylesheet --------------------------------------------------- */
-
-@media print {
-    div.document,
-    div.documentwrapper,
-    div.bodywrapper {
-        margin: 0 !important;
-        width: 100%;
-    }
-
-    div.sphinxsidebar,
-    div.related,
-    div.footer,
-    #top-link {
-        display: none;
-    }
-}
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/_static/check-solid.svg b/docs/pygom-doc/_build/html/_static/check-solid.svg
deleted file mode 100644
index 92fad4b5..00000000
--- a/docs/pygom-doc/_build/html/_static/check-solid.svg
+++ /dev/null
@@ -1,4 +0,0 @@
-
-  
-  
-
diff --git a/docs/pygom-doc/_build/html/_static/clipboard.min.js b/docs/pygom-doc/_build/html/_static/clipboard.min.js
deleted file mode 100644
index 54b3c463..00000000
--- a/docs/pygom-doc/_build/html/_static/clipboard.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
- * clipboard.js v2.0.8
- * https://clipboardjs.com/
- *
- * Licensed MIT © Zeno Rocha
- */
-!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1
-  
-  
-  
-
diff --git a/docs/pygom-doc/_build/html/_static/copybutton.css b/docs/pygom-doc/_build/html/_static/copybutton.css
deleted file mode 100644
index f1916ec7..00000000
--- a/docs/pygom-doc/_build/html/_static/copybutton.css
+++ /dev/null
@@ -1,94 +0,0 @@
-/* Copy buttons */
-button.copybtn {
-    position: absolute;
-    display: flex;
-    top: .3em;
-    right: .3em;
-    width: 1.7em;
-    height: 1.7em;
-	opacity: 0;
-    transition: opacity 0.3s, border .3s, background-color .3s;
-    user-select: none;
-    padding: 0;
-    border: none;
-    outline: none;
-    border-radius: 0.4em;
-    /* The colors that GitHub uses */
-    border: #1b1f2426 1px solid;
-    background-color: #f6f8fa;
-    color: #57606a;
-}
-
-button.copybtn.success {
-    border-color: #22863a;
-    color: #22863a;
-}
-
-button.copybtn svg {
-    stroke: currentColor;
-    width: 1.5em;
-    height: 1.5em;
-    padding: 0.1em;
-}
-
-div.highlight  {
-    position: relative;
-}
-
-/* Show the copybutton */
-.highlight:hover button.copybtn, button.copybtn.success {
-	opacity: 1;
-}
-
-.highlight button.copybtn:hover {
-    background-color: rgb(235, 235, 235);
-}
-
-.highlight button.copybtn:active {
-    background-color: rgb(187, 187, 187);
-}
-
-/**
- * A minimal CSS-only tooltip copied from:
- *   https://codepen.io/mildrenben/pen/rVBrpK
- *
- * To use, write HTML like the following:
- *
- * 
          Short
          
- */
- .o-tooltip--left {
-  position: relative;
- }
-
- .o-tooltip--left:after {
-    opacity: 0;
-    visibility: hidden;
-    position: absolute;
-    content: attr(data-tooltip);
-    padding: .2em;
-    font-size: .8em;
-    left: -.2em;
-    background: grey;
-    color: white;
-    white-space: nowrap;
-    z-index: 2;
-    border-radius: 2px;
-    transform: translateX(-102%) translateY(0);
-    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
-}
-
-.o-tooltip--left:hover:after {
-    display: block;
-    opacity: 1;
-    visibility: visible;
-    transform: translateX(-100%) translateY(0);
-    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
-    transition-delay: .5s;
-}
-
-/* By default the copy button shouldn't show up when printing a page */
-@media print {
-    button.copybtn {
-        display: none;
-    }
-}
diff --git a/docs/pygom-doc/_build/html/_static/copybutton.js b/docs/pygom-doc/_build/html/_static/copybutton.js
deleted file mode 100644
index 2ea7ff3e..00000000
--- a/docs/pygom-doc/_build/html/_static/copybutton.js
+++ /dev/null
@@ -1,248 +0,0 @@
-// Localization support
-const messages = {
-  'en': {
-    'copy': 'Copy',
-    'copy_to_clipboard': 'Copy to clipboard',
-    'copy_success': 'Copied!',
-    'copy_failure': 'Failed to copy',
-  },
-  'es' : {
-    'copy': 'Copiar',
-    'copy_to_clipboard': 'Copiar al portapapeles',
-    'copy_success': '¡Copiado!',
-    'copy_failure': 'Error al copiar',
-  },
-  'de' : {
-    'copy': 'Kopieren',
-    'copy_to_clipboard': 'In die Zwischenablage kopieren',
-    'copy_success': 'Kopiert!',
-    'copy_failure': 'Fehler beim Kopieren',
-  },
-  'fr' : {
-    'copy': 'Copier',
-    'copy_to_clipboard': 'Copier dans le presse-papier',
-    'copy_success': 'Copié !',
-    'copy_failure': 'Échec de la copie',
-  },
-  'ru': {
-    'copy': 'Скопировать',
-    'copy_to_clipboard': 'Скопировать в буфер',
-    'copy_success': 'Скопировано!',
-    'copy_failure': 'Не удалось скопировать',
-  },
-  'zh-CN': {
-    'copy': '复制',
-    'copy_to_clipboard': '复制到剪贴板',
-    'copy_success': '复制成功!',
-    'copy_failure': '复制失败',
-  },
-  'it' : {
-    'copy': 'Copiare',
-    'copy_to_clipboard': 'Copiato negli appunti',
-    'copy_success': 'Copiato!',
-    'copy_failure': 'Errore durante la copia',
-  }
-}
-
-let locale = 'en'
-if( document.documentElement.lang !== undefined
-    && messages[document.documentElement.lang] !== undefined ) {
-  locale = document.documentElement.lang
-}
-
-let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
-if (doc_url_root == '#') {
-    doc_url_root = '';
-}
-
-/**
- * SVG files for our copy buttons
- */
-let iconCheck = `
-  ${messages[locale]['copy_success']}
-  
-  
-`
-
-// If the user specified their own SVG use that, otherwise use the default
-let iconCopy = ``;
-if (!iconCopy) {
-  iconCopy = `
-  ${messages[locale]['copy_to_clipboard']}
-  
-  
-  
-`
-}
-
-/**
- * Set up copy/paste for code blocks
- */
-
-const runWhenDOMLoaded = cb => {
-  if (document.readyState != 'loading') {
-    cb()
-  } else if (document.addEventListener) {
-    document.addEventListener('DOMContentLoaded', cb)
-  } else {
-    document.attachEvent('onreadystatechange', function() {
-      if (document.readyState == 'complete') cb()
-    })
-  }
-}
-
-const codeCellId = index => `codecell${index}`
-
-// Clears selected text since ClipboardJS will select the text when copying
-const clearSelection = () => {
-  if (window.getSelection) {
-    window.getSelection().removeAllRanges()
-  } else if (document.selection) {
-    document.selection.empty()
-  }
-}
-
-// Changes tooltip text for a moment, then changes it back
-// We want the timeout of our `success` class to be a bit shorter than the
-// tooltip and icon change, so that we can hide the icon before changing back.
-var timeoutIcon = 2000;
-var timeoutSuccessClass = 1500;
-
-const temporarilyChangeTooltip = (el, oldText, newText) => {
-  el.setAttribute('data-tooltip', newText)
-  el.classList.add('success')
-  // Remove success a little bit sooner than we change the tooltip
-  // So that we can use CSS to hide the copybutton first
-  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
-  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
-}
-
-// Changes the copy button icon for two seconds, then changes it back
-const temporarilyChangeIcon = (el) => {
-  el.innerHTML = iconCheck;
-  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
-}
-
-const addCopyButtonToCodeCells = () => {
-  // If ClipboardJS hasn't loaded, wait a bit and try again. This
-  // happens because we load ClipboardJS asynchronously.
-  if (window.ClipboardJS === undefined) {
-    setTimeout(addCopyButtonToCodeCells, 250)
-    return
-  }
-
-  // Add copybuttons to all of our code cells
-  const COPYBUTTON_SELECTOR = 'div.highlight pre';
-  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
-  codeCells.forEach((codeCell, index) => {
-    const id = codeCellId(index)
-    codeCell.setAttribute('id', id)
-
-    const clipboardButton = id =>
-    ``
-    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
-  })
-
-function escapeRegExp(string) {
-    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-    var regexp;
-    var match;
-
-    // Do we check for line continuation characters and "HERE-documents"?
-    var useLineCont = !!lineContinuationChar
-    var useHereDoc = !!hereDocDelim
-
-    // create regexp to capture prompt and remaining line
-    if (isRegexp) {
-        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
-    } else {
-        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
-    }
-
-    const outputLines = [];
-    var promptFound = false;
-    var gotLineCont = false;
-    var gotHereDoc = false;
-    const lineGotPrompt = [];
-    for (const line of textContent.split('\n')) {
-        match = line.match(regexp)
-        if (match || gotLineCont || gotHereDoc) {
-            promptFound = regexp.test(line)
-            lineGotPrompt.push(promptFound)
-            if (removePrompts && promptFound) {
-                outputLines.push(match[2])
-            } else {
-                outputLines.push(line)
-            }
-            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
-            if (line.includes(hereDocDelim) & useHereDoc)
-                gotHereDoc = !gotHereDoc
-        } else if (!onlyCopyPromptLines) {
-            outputLines.push(line)
-        } else if (copyEmptyLines && line.trim() === '') {
-            outputLines.push(line)
-        }
-    }
-
-    // If no lines with the prompt were found then just use original lines
-    if (lineGotPrompt.some(v => v === true)) {
-        textContent = outputLines.join('\n');
-    }
-
-    // Remove a trailing newline to avoid auto-running when pasting
-    if (textContent.endsWith("\n")) {
-        textContent = textContent.slice(0, -1)
-    }
-    return textContent
-}
-
-
-var copyTargetText = (trigger) => {
-  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
-
-  // get filtered text
-  let exclude = '.linenos';
-
-  let text = filterText(target, exclude);
-  return formatCopyText(text, '', false, true, true, true, '', '')
-}
-
-  // Initialize with a callback so we can modify the text before copy
-  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
-
-  // Update UI with error/success messages
-  clipboard.on('success', event => {
-    clearSelection()
-    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
-    temporarilyChangeIcon(event.trigger)
-  })
-
-  clipboard.on('error', event => {
-    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
-  })
-}
-
-runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/docs/pygom-doc/_build/html/_static/copybutton_funcs.js b/docs/pygom-doc/_build/html/_static/copybutton_funcs.js
deleted file mode 100644
index dbe1aaad..00000000
--- a/docs/pygom-doc/_build/html/_static/copybutton_funcs.js
+++ /dev/null
@@ -1,73 +0,0 @@
-function escapeRegExp(string) {
-    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-export function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-    var regexp;
-    var match;
-
-    // Do we check for line continuation characters and "HERE-documents"?
-    var useLineCont = !!lineContinuationChar
-    var useHereDoc = !!hereDocDelim
-
-    // create regexp to capture prompt and remaining line
-    if (isRegexp) {
-        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
-    } else {
-        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
-    }
-
-    const outputLines = [];
-    var promptFound = false;
-    var gotLineCont = false;
-    var gotHereDoc = false;
-    const lineGotPrompt = [];
-    for (const line of textContent.split('\n')) {
-        match = line.match(regexp)
-        if (match || gotLineCont || gotHereDoc) {
-            promptFound = regexp.test(line)
-            lineGotPrompt.push(promptFound)
-            if (removePrompts && promptFound) {
-                outputLines.push(match[2])
-            } else {
-                outputLines.push(line)
-            }
-            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
-            if (line.includes(hereDocDelim) & useHereDoc)
-                gotHereDoc = !gotHereDoc
-        } else if (!onlyCopyPromptLines) {
-            outputLines.push(line)
-        } else if (copyEmptyLines && line.trim() === '') {
-            outputLines.push(line)
-        }
-    }
-
-    // If no lines with the prompt were found then just use original lines
-    if (lineGotPrompt.some(v => v === true)) {
-        textContent = outputLines.join('\n');
-    }
-
-    // Remove a trailing newline to avoid auto-running when pasting
-    if (textContent.endsWith("\n")) {
-        textContent = textContent.slice(0, -1)
-    }
-    return textContent
-}
diff --git a/docs/pygom-doc/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/docs/pygom-doc/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
deleted file mode 100644
index 3225661c..00000000
--- a/docs/pygom-doc/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css
+++ /dev/null
@@ -1 +0,0 @@
-.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}a.sd-bg-secondary:focus,a.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}.sd-bg-success{background-color:var(--sd-color-success) !important}.sd-bg-text-success{color:var(--sd-color-success-text) !important}button.sd-bg-success:focus,button.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}a.sd-bg-success:focus,a.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}.sd-bg-info{background-color:var(--sd-color-info) !important}.sd-bg-text-info{color:var(--sd-color-info-text) !important}button.sd-bg-info:focus,button.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}a.sd-bg-info:focus,a.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}.sd-bg-warning{background-color:var(--sd-color-warning) !important}.sd-bg-text-warning{color:var(--sd-color-warning-text) !important}button.sd-bg-warning:focus,button.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) !important}a.sd-bg-warning:focus,a.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) !important}.sd-bg-danger{background-color:var(--sd-color-danger) !important}.sd-bg-text-danger{color:var(--sd-color-danger-text) !important}button.sd-bg-danger:focus,button.sd-bg-danger:hover{background-color:var(--sd-color-danger-highlight) !important}a.sd-bg-danger:focus,a.sd-bg-danger:hover{background-color:var(--sd-color-danger-highlight) !important}.sd-bg-light{background-color:var(--sd-color-light) !important}.sd-bg-text-light{color:var(--sd-color-light-text) !important}button.sd-bg-light:focus,button.sd-bg-light:hover{background-color:var(--sd-color-light-highlight) !important}a.sd-bg-light:focus,a.sd-bg-light:hover{background-color:var(--sd-color-light-highlight) !important}.sd-bg-muted{background-color:var(--sd-color-muted) !important}.sd-bg-text-muted{color:var(--sd-color-muted-text) !important}button.sd-bg-muted:focus,button.sd-bg-muted:hover{background-color:var(--sd-color-muted-highlight) !important}a.sd-bg-muted:focus,a.sd-bg-muted:hover{background-color:var(--sd-color-muted-highlight) !important}.sd-bg-dark{background-color:var(--sd-color-dark) 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diff --git a/docs/pygom-doc/_build/html/_static/design-tabs.js b/docs/pygom-doc/_build/html/_static/design-tabs.js
deleted file mode 100644
index 36b38cf0..00000000
--- a/docs/pygom-doc/_build/html/_static/design-tabs.js
+++ /dev/null
@@ -1,27 +0,0 @@
-var sd_labels_by_text = {};
-
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-  for (const label of li) {
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-  for (label of sd_labels_by_text[syncId]) {
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-    label.previousElementSibling.checked = true;
-  }
-  window.localStorage.setItem("sphinx-design-last-tab", syncId);
-}
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-document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/docs/pygom-doc/_build/html/_static/doctools.js b/docs/pygom-doc/_build/html/_static/doctools.js
deleted file mode 100644
index c3db08d1..00000000
--- a/docs/pygom-doc/_build/html/_static/doctools.js
+++ /dev/null
@@ -1,264 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-const _ready = (callback) => {
-  if (document.readyState !== "loading") {
-    callback();
-  } else {
-    document.addEventListener("DOMContentLoaded", callback);
-  }
-};
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const Documentation = {
-  init: () => {
-    Documentation.highlightSearchWords();
-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
-  },
-
-  /**
-   * i18n support
-   */
-  TRANSLATIONS: {},
-  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
-  LOCALE: "unknown",
-
-  // gettext and ngettext don't access this so that the functions
-  // can safely bound to a different name (_ = Documentation.gettext)
-  gettext: (string) => {
-    const translated = Documentation.TRANSLATIONS[string];
-    switch (typeof translated) {
-      case "undefined":
-        return string; // no translation
-      case "string":
-        return translated; // translation exists
-      default:
-        return translated[0]; // (singular, plural) translation tuple exists
-    }
-  },
-
-  ngettext: (singular, plural, n) => {
-    const translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated !== "undefined")
-      return translated[Documentation.PLURAL_EXPR(n)];
-    return n === 1 ? singular : plural;
-  },
-
-  addTranslations: (catalog) => {
-    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
-    Documentation.PLURAL_EXPR = new Function(
-      "n",
-      `return (${catalog.plural_expr})`
-    );
-    Documentation.LOCALE = catalog.locale;
-  },
-
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords: () => {
-    const highlight =
-      new URLSearchParams(window.location.search).get("highlight") || "";
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '
          ' +
-            '' +
-            Documentation.gettext("Hide Search Matches") +
-            "
          "
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    const url = new URL(window.location);
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-  },
-
-  /**
-   * helper function to focus on search bar
-   */
-  focusSearchBar: () => {
-    document.querySelectorAll("input[name=q]")[0]?.focus();
-  },
-
-  /**
-   * Initialise the domain index toggle buttons
-   */
-  initDomainIndexTable: () => {
-    const toggler = (el) => {
-      const idNumber = el.id.substr(7);
-      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
-      if (el.src.substr(-9) === "minus.png") {
-        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
-        toggledRows.forEach((el) => (el.style.display = "none"));
-      } else {
-        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
-        toggledRows.forEach((el) => (el.style.display = ""));
-      }
-    };
-
-    const togglerElements = document.querySelectorAll("img.toggler");
-    togglerElements.forEach((el) =>
-      el.addEventListener("click", (event) => toggler(event.currentTarget))
-    );
-    togglerElements.forEach((el) => (el.style.display = ""));
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
-  },
-
-  initOnKeyListeners: () => {
-    // only install a listener if it is really needed
-    if (
-      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
-      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
-    )
-      return;
-
-    const blacklistedElements = new Set([
-      "TEXTAREA",
-      "INPUT",
-      "SELECT",
-      "BUTTON",
-    ]);
-    document.addEventListener("keydown", (event) => {
-      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
-      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
-
-      if (!event.shiftKey) {
-        switch (event.key) {
-          case "ArrowLeft":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const prevLink = document.querySelector('link[rel="prev"]');
-            if (prevLink && prevLink.href) {
-              window.location.href = prevLink.href;
-              event.preventDefault();
-            }
-            break;
-          case "ArrowRight":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const nextLink = document.querySelector('link[rel="next"]');
-            if (nextLink && nextLink.href) {
-              window.location.href = nextLink.href;
-              event.preventDefault();
-            }
-            break;
-          case "Escape":
-            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-            Documentation.hideSearchWords();
-            event.preventDefault();
-        }
-      }
-
-      // some keyboard layouts may need Shift to get /
-      switch (event.key) {
-        case "/":
-          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-          Documentation.focusSearchBar();
-          event.preventDefault();
-      }
-    });
-  },
-};
-
-// quick alias for translations
-const _ = Documentation.gettext;
-
-_ready(Documentation.init);
diff --git a/docs/pygom-doc/_build/html/_static/documentation_options.js b/docs/pygom-doc/_build/html/_static/documentation_options.js
deleted file mode 100644
index 30637825..00000000
--- a/docs/pygom-doc/_build/html/_static/documentation_options.js
+++ /dev/null
@@ -1,14 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '',
-    LANGUAGE: 'en',
-    COLLAPSE_INDEX: false,
-    BUILDER: 'html',
-    FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
-    HAS_SOURCE: true,
-    SOURCELINK_SUFFIX: '',
-    NAVIGATION_WITH_KEYS: true,
-    SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
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\ No newline at end of file
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deleted file mode 100644
index 60cfe9f2..00000000
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diff --git a/docs/pygom-doc/_build/html/_static/jquery-3.6.0.js b/docs/pygom-doc/_build/html/_static/jquery-3.6.0.js
deleted file mode 100644
index fc6c299b..00000000
--- a/docs/pygom-doc/_build/html/_static/jquery-3.6.0.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-		// Support: Chrome <=57, Firefox <=52
-		// In some browsers, typeof returns "function" for HTML  elements
-		// (i.e., `typeof document.createElement( "object" ) === "function"`).
-		// We don't want to classify *any* DOM node as a function.
-		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
-		// Plus for old WebKit, typeof returns "function" for HTML collections
-		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
-		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
-			typeof obj.item !== "function";
-	};
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.6.0",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-						[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-	function( _i, name ) {
-		class2type[ "[object " + name + "]" ] = name.toLowerCase();
-	} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem && elem.namespaceURI,
-		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "" +
-				"";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "" +
-				"";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over  to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			//  elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the primary Deferred
-			primary = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						primary.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( primary.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return primary.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
-		}
-
-		return primary.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-						value :
-						value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces ";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting  or other required elements.
-	thead: [ 1, "", "
          " ],
-	col: [ 2, "", "
          " ],
-	tr: [ 2, "", "
          " ],
-	td: [ 3, "", "
          " ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-				dataPriv.get( this, "events" ) || Object.create( null )
-			)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG  instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-						return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-						return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-
-						// Support: Chrome 86+
-						// In Chrome, if an element having a focusout handler is blurred by
-						// clicking outside of it, it invokes the handler synchronously. If
-						// that handler calls `.remove()` on the element, the data is cleared,
-						// leaving `result` undefined. We need to guard against this.
-						return result && result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-	which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		// Suppress native focus or blur as it's already being fired
-		// in leverageNative.
-		_default: function() {
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		//
-		// Support: Firefox 70+
-		// Only Firefox includes border widths
-		// in computed dimensions. (gh-4529)
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
-				tr.style.cssText = "border:1px solid";
-
-				// Support: Chrome 86+
-				// Height set through cssText does not get applied.
-				// Computed height then comes back as 0.
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				// Support: Android 8 Chrome 86+
-				// In our bodyBackground.html iframe,
-				// display for all div elements is set to "inline",
-				// which causes a problem only in Android 8 Chrome 86.
-				// Ensuring the div is display: block
-				// gets around this issue.
-				trChild.style.display = "block";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
-					parseInt( trStyle.borderTopWidth, 10 ) +
-					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-					swap( elem, cssShow, function() {
-						return getWidthOrHeight( elem, dimension, extra );
-					} ) :
-					getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-			) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-				jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-				/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-					animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-
-		doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-							"" :
-							dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-				return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml, parserErrorElem;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {}
-
-	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
-	if ( !xml || parserErrorElem ) {
-		jQuery.error( "Invalid XML: " + (
-			parserErrorElem ?
-				jQuery.map( parserErrorElem.childNodes, function( el ) {
-					return el.textContent;
-				} ).join( "\n" ) :
-				data
-		) );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} ).filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} ).map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-				jQuery( callbackContext ) :
-				jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script but not if jsonp
-			if ( !isSuccess &&
-				jQuery.inArray( "script", s.dataTypes ) > -1 &&
-				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "
-{% endmacro %}
diff --git a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js
deleted file mode 100644
index 766173ab..00000000
--- a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js
+++ /dev/null
@@ -1,3 +0,0 @@
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ve{constructor(t,e){super(),(t=Ht(t))&&(this._element=t,this._config=this._getConfig(e),ge.set(this._element,this.constructor.DATA_KEY,this))}dispose(){ge.remove(this._element,this.constructor.DATA_KEY),de.off(this._element,this.constructor.EVENT_KEY);for(const t of Object.getOwnPropertyNames(this))this[t]=null}_queueCallback(t,e,i=!0){Yt(t,e,i)}_getConfig(t){return t=this._mergeConfigObj(t,this._element),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}static getInstance(t){return ge.get(Ht(t),this.DATA_KEY)}static getOrCreateInstance(t,e={}){return this.getInstance(t)||new this(t,"object"==typeof e?e:null)}static get VERSION(){return"5.2.3"}static get DATA_KEY(){return`bs.${this.NAME}`}static get EVENT_KEY(){return`.${this.DATA_KEY}`}static eventName(t){return`${t}${this.EVENT_KEY}`}}const we=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;de.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),Ft(this))return;const s=Pt(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},Ae=".bs.alert",Ee=`close${Ae}`,Ce=`closed${Ae}`;class Te extends ye{static get NAME(){return"alert"}close(){if(de.trigger(this._element,Ee).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),de.trigger(this._element,Ce),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Te.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}we(Te,"close"),Qt(Te);const Oe='[data-bs-toggle="button"]';class xe extends ye{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=xe.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}de.on(document,"click.bs.button.data-api",Oe,(t=>{t.preventDefault();const e=t.target.closest(Oe);xe.getOrCreateInstance(e).toggle()})),Qt(xe);const ke={find:(t,e=document.documentElement)=>[].concat(...Element.prototype.querySelectorAll.call(e,t)),findOne:(t,e=document.documentElement)=>Element.prototype.querySelector.call(e,t),children:(t,e)=>[].concat(...t.children).filter((t=>t.matches(e))),parents(t,e){const i=[];let n=t.parentNode.closest(e);for(;n;)i.push(n),n=n.parentNode.closest(e);return i},prev(t,e){let i=t.previousElementSibling;for(;i;){if(i.matches(e))return[i];i=i.previousElementSibling}return[]},next(t,e){let i=t.nextElementSibling;for(;i;){if(i.matches(e))return[i];i=i.nextElementSibling}return[]},focusableChildren(t){const e=["a","button","input","textarea","select","details","[tabindex]",'[contenteditable="true"]'].map((t=>`${t}:not([tabindex^="-"])`)).join(",");return this.find(e,t).filter((t=>!Ft(t)&&Wt(t)))}},Le=".bs.swipe",De=`touchstart${Le}`,$e=`touchmove${Le}`,Se=`touchend${Le}`,Ie=`pointerdown${Le}`,Ne=`pointerup${Le}`,Pe={endCallback:null,leftCallback:null,rightCallback:null},je={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class Me extends ve{constructor(t,e){super(),this._element=t,t&&Me.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return Pe}static get DefaultType(){return je}static get NAME(){return"swipe"}dispose(){de.off(this._element,Le)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Xt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Xt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(de.on(this._element,Ie,(t=>this._start(t))),de.on(this._element,Ne,(t=>this._end(t))),this._element.classList.add("pointer-event")):(de.on(this._element,De,(t=>this._start(t))),de.on(this._element,$e,(t=>this._move(t))),de.on(this._element,Se,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const He=".bs.carousel",We=".data-api",Fe="next",Be="prev",ze="left",qe="right",Re=`slide${He}`,Ve=`slid${He}`,Ke=`keydown${He}`,Qe=`mouseenter${He}`,Xe=`mouseleave${He}`,Ye=`dragstart${He}`,Ue=`load${He}${We}`,Ge=`click${He}${We}`,Je="carousel",Ze="active",ti=".active",ei=".carousel-item",ii=ti+ei,ni={ArrowLeft:qe,ArrowRight:ze},si={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},oi={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class ri extends ye{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=ke.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===Je&&this.cycle()}static get Default(){return si}static get DefaultType(){return oi}static get NAME(){return"carousel"}next(){this._slide(Fe)}nextWhenVisible(){!document.hidden&&Wt(this._element)&&this.next()}prev(){this._slide(Be)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?de.one(this._element,Ve,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void de.one(this._element,Ve,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?Fe:Be;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&de.on(this._element,Ke,(t=>this._keydown(t))),"hover"===this._config.pause&&(de.on(this._element,Qe,(()=>this.pause())),de.on(this._element,Xe,(()=>this._maybeEnableCycle()))),this._config.touch&&Me.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of ke.find(".carousel-item img",this._element))de.on(t,Ye,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ze)),rightCallback:()=>this._slide(this._directionToOrder(qe)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new Me(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ni[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ke.findOne(ti,this._indicatorsElement);e.classList.remove(Ze),e.removeAttribute("aria-current");const i=ke.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Ze),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===Fe,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>de.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?Be:Fe:t===ze?Fe:Be}_orderToDirection(t){return Kt()?t===Be?ze:qe:t===Be?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}de.on(document,Ge,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Pt(this);if(!e||!e.classList.contains(Je))return;t.preventDefault();const i=ri.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===be.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),de.on(window,Ue,(()=>{const t=ke.find('[data-bs-ride="carousel"]');for(const e of t)ri.getOrCreateInstance(e)})),Qt(ri);const ai=".bs.collapse",li=`show${ai}`,ci=`shown${ai}`,hi=`hide${ai}`,ui=`hidden${ai}`,di=`click${ai}.data-api`,fi="show",pi="collapse",gi="collapsing",mi=`:scope .${pi} .${pi}`,_i='[data-bs-toggle="collapse"]',bi={parent:null,toggle:!0},vi={parent:"(null|element)",toggle:"boolean"};class yi extends ye{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ke.find(_i);for(const t of i){const e=Nt(t),i=ke.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(de.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(gi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",de.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(de.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),de.trigger(this._element,ui)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}de.on(document,di,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
          "},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const 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t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get 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diff --git a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.LICENSE.txt b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.LICENSE.txt
deleted file mode 100644
index 91ad10aa..00000000
--- a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.LICENSE.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-/*!
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-  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
-  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
-  */
diff --git a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.map b/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.map
deleted file mode 100644
index d83e2f7c..00000000
--- a/docs/pygom-doc/_build/html/_static/scripts/bootstrap.js.map
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= 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (process.env.NODE_ENV !== \"production\") {\n    if (!isHTMLElement(arrowElement)) {\n      console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n    }\n\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n  if (process.env.NODE_ENV !== \"production\") {\n    var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n    if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n      return transitionProperty.indexOf(property) >= 0;\n    })) {\n      console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n    }\n  }\n\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If  has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on \n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n    }\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        }); // Validate the provided modifiers so that the consumer will get warned\n        // if one of the modifiers is invalid for any reason\n\n        if (process.env.NODE_ENV !== \"production\") {\n          var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n            var name = _ref.name;\n            return name;\n          });\n          validateModifiers(modifiers);\n\n          if (getBasePlacement(state.options.placement) === auto) {\n            var flipModifier = state.orderedModifiers.find(function (_ref2) {\n              var name = _ref2.name;\n              return name === 'flip';\n            });\n\n            if (!flipModifier) {\n              console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n            }\n          }\n\n          var _getComputedStyle = getComputedStyle(popper),\n              marginTop = _getComputedStyle.marginTop,\n              marginRight = _getComputedStyle.marginRight,\n              marginBottom = _getComputedStyle.marginBottom,\n              marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n          // cause bugs with positioning, so we'll warn the consumer\n\n\n          if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n            return parseFloat(margin);\n          })) {\n            console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n          }\n        }\n\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          if (process.env.NODE_ENV !== \"production\") {\n            console.error(INVALID_ELEMENT_ERROR);\n          }\n\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n        var __debug_loops__ = 0;\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (process.env.NODE_ENV !== \"production\") {\n            __debug_loops__ += 1;\n\n            if (__debug_loops__ > 100) {\n              console.error(INFINITE_LOOP_ERROR);\n              break;\n            }\n          }\n\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      if (process.env.NODE_ENV !== \"production\") {\n        console.error(INVALID_ELEMENT_ERROR);\n      }\n\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref3) {\n        var name = _ref3.name,\n            _ref3$options = _ref3.options,\n            options = _ref3$options === void 0 ? {} : _ref3$options,\n            effect = _ref3.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.2.3 (https://getbootstrap.com/)\n  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n\n  return prefix;\n};\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n  }\n\n  return selector;\n};\n\nconst getSelectorFromElement = element => {\n  const selector = getSelector(element);\n\n  if (selector) {\n    return document.querySelector(selector) ? selector : null;\n  }\n\n  return null;\n};\n\nconst getElementFromSelector = element => {\n  const selector = getSelector(element);\n  return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  } // Get transition-duration of the element\n\n\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  } // If multiple durations are defined, take the first\n\n\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n\n  return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(object);\n  }\n\n  return null;\n};\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n  const closedDetails = element.closest('details:not([open])');\n\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n\n    if (summary === null) {\n      return false;\n    }\n  }\n\n  return elementIsVisible;\n};\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  } // Can find the shadow root otherwise it'll return the document\n\n\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element;\n  } // when we don't find a shadow root\n\n\n  if (!element.parentNode) {\n    return null;\n  }\n\n  return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n\n  return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\n\nconst execute = callback => {\n  if (typeof callback === 'function') {\n    callback();\n  }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n\n  index += shouldGetNext ? 1 : -1;\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n\n    return fn.apply(element, [event]);\n  };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n\n  return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n\n    callable = wrapFunction(callable);\n  }\n\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n  if (!fn) {\n    return;\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n\n  for (const handlerKey of Object.keys(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      const event = storeElementEvent[handlerKey];\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n\n    for (const keyHandlers of Object.keys(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        const event = storeElementEvent[keyHandlers];\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n\n    let evt = new Event(event, {\n      bubbles,\n      cancelable: true\n    });\n    evt = hydrateObj(evt, args);\n\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n\n    return evt;\n  }\n\n};\n\nfunction hydrateObj(obj, meta) {\n  for (const [key, value] of Object.entries(meta || {})) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n\n        get() {\n          return value;\n        }\n\n      });\n    }\n  }\n\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n\n    const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n\n    instanceMap.set(key, instance);\n  },\n\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n\n    return null;\n  },\n\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n\n  if (value === 'false') {\n    return false;\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n\n  if (value === '' || value === 'null') {\n    return null;\n  }\n\n  if (typeof value !== 'string') {\n    return value;\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n\n    return attributes;\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n\n  static get DefaultType() {\n    return {};\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  }\n\n  _configAfterMerge(config) {\n    return config;\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return { ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const property of Object.keys(configTypes)) {\n      const expectedTypes = configTypes[property];\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n\n    if (!element) {\n      return;\n    }\n\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  } // Public\n\n\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  } // Static\n\n\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n\n  static get VERSION() {\n    return VERSION;\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n\n    if (isDisabled(this)) {\n      return;\n    }\n\n    const target = getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  } // Public\n\n\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  } // Private\n\n\n  _destroyElement() {\n    this._element.remove();\n\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](this);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  } // Public\n\n\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n\n    return parents;\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n\n      previous = previous.previousElementSibling;\n    }\n\n    return [];\n  },\n\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n\n      next = next.nextElementSibling;\n    }\n\n    return [];\n  },\n\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n\n    this._initEvents();\n  } // Getters\n\n\n  static get Default() {\n    return Default$c;\n  }\n\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n\n  static get NAME() {\n    return NAME$d;\n  } // Public\n\n\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  } // Private\n\n\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n\n    this._handleSwipe();\n\n    execute(this._config.endCallback);\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n\n    if (!direction) {\n      return;\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  } // Static\n\n\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n    this._addEventListeners();\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$b;\n  }\n\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n\n  static get NAME() {\n    return NAME$c;\n  } // Public\n\n\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n\n    this._clearInterval();\n  }\n\n  cycle() {\n    this._clearInterval();\n\n    this._updateInterval();\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n\n    this.cycle();\n  }\n\n  to(index) {\n    const items = this._getItems();\n\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive());\n\n    if (activeIndex === index) {\n      return;\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n    this._slide(order, items[index]);\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n\n    super.dispose();\n  } // Private\n\n\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      } // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n\n      this.pause();\n\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key];\n\n    if (direction) {\n      event.preventDefault();\n\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n\n    if (!element) {\n      return;\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n\n    const activeElement = this._getActive();\n\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n    if (nextElement === activeElement) {\n      return;\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement);\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // todo: change tests that use empty divs to avoid this check\n      return;\n    }\n\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n\n    this._setActiveIndicatorElement(nextElementIndex);\n\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n\n  if (slideIndex) {\n    carousel.to(slideIndex);\n\n    carousel._maybeEnableCycle();\n\n    return;\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n\n    carousel._maybeEnableCycle();\n\n    return;\n  }\n\n  carousel.prev();\n\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n    for (const elem of toggleList) {\n      const selector = getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n\n    this._initializeChildren();\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$a;\n  }\n\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n\n  static get NAME() {\n    return NAME$b;\n  } // Public\n\n\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n\n    let activeChildren = []; // find active children\n\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n\n    const dimension = this._getDimension();\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n    this._element.style[dimension] = 0;\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n    this._isTransitioning = true;\n\n    const complete = () => {\n      this._isTransitioning = false;\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n\n    this._queueCallback(complete, this._element, true);\n\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n\n    const dimension = this._getDimension();\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n    for (const trigger of this._triggerArray) {\n      const element = getElementFromSelector(trigger);\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n\n    this._isTransitioning = true;\n\n    const complete = () => {\n      this._isTransitioning = false;\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n\n    this._element.style[dimension] = '';\n\n    this._queueCallback(complete, this._element, true);\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  } // Private\n\n\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n\n    config.parent = getElement(config.parent);\n    return config;\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n    for (const element of children) {\n      const selected = getElementFromSelector(element);\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    const _config = {};\n\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for  elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n\n  const selector = getSelectorFromElement(this);\n  const selectorElements = SelectorEngine.find(selector);\n\n  for (const element of selectorElements) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  } // Getters\n\n\n  static get Default() {\n    return Default$9;\n  }\n\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n\n  static get NAME() {\n    return NAME$a;\n  } // Public\n\n\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._createPopper(); // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n\n    this._element.focus();\n\n    this._element.setAttribute('aria-expanded', true);\n\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n\n    this._completeHide(relatedTarget);\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n\n    super.dispose();\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar();\n\n    if (this._popper) {\n      this._popper.update();\n    }\n  } // Private\n\n\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    } // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy();\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n    this._element.setAttribute('aria-expanded', 'false');\n\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config);\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n\n    return config;\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n\n    let referenceElement = this._element;\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n\n    const popperConfig = this._getPopperConfig();\n\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent;\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    } // We need to trim the value because custom properties can also include spaces\n\n\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n\n    return offset;\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n\n    return { ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    };\n  }\n\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n    if (!items.length) {\n      return;\n    } // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n\n      context._completeHide(relatedTarget);\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n\n    event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n\n      instance._selectMenuItem(event);\n\n      return;\n    }\n\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  } // Public\n\n\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n\n  hide() {\n    const width = this.getWidth();\n\n    this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0;\n  } // Private\n\n\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n\n    this._element.style.overflow = 'hidden';\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n\n      this._saveInitialAttribute(element, styleProperty);\n\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  } // Getters\n\n\n  static get Default() {\n    return Default$8;\n  }\n\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n\n  static get NAME() {\n    return NAME$9;\n  } // Public\n\n\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n\n    this._append();\n\n    const element = this._getElement();\n\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n\n    element.classList.add(CLASS_NAME_SHOW$5);\n\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n    this._element.remove();\n\n    this._isAppended = false;\n  } // Private\n\n\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n\n      this._element = backdrop;\n    }\n\n    return this._element;\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n\n    const element = this._getElement();\n\n    this._config.rootElement.append(element);\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  } // Getters\n\n\n  static get Default() {\n    return Default$7;\n  }\n\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n\n  static get NAME() {\n    return NAME$8;\n  } // Public\n\n\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  } // Private\n\n\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement);\n\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n\n    this._addEventListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default$6;\n  }\n\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n\n  static get NAME() {\n    return NAME$7;\n  } // Public\n\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = true;\n    this._isTransitioning = true;\n\n    this._scrollBar.hide();\n\n    document.body.classList.add(CLASS_NAME_OPEN);\n\n    this._adjustDialog();\n\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = false;\n    this._isTransitioning = true;\n\n    this._focustrap.deactivate();\n\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n\n  dispose() {\n    for (const htmlElement of [window, this._dialog]) {\n      EventHandler.off(htmlElement, EVENT_KEY$4);\n    }\n\n    this._backdrop.dispose();\n\n    this._focustrap.deactivate();\n\n    super.dispose();\n  }\n\n  handleUpdate() {\n    this._adjustDialog();\n  } // Private\n\n\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n\n    this._element.style.display = 'block';\n\n    this._element.removeAttribute('aria-hidden');\n\n    this._element.setAttribute('aria-modal', true);\n\n    this._element.setAttribute('role', 'dialog');\n\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n\n      if (this._config.keyboard) {\n        event.preventDefault();\n        this.hide();\n        return;\n      }\n\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n\n          return;\n        }\n\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none';\n\n    this._element.setAttribute('aria-hidden', true);\n\n    this._element.removeAttribute('aria-modal');\n\n    this._element.removeAttribute('role');\n\n    this._isTransitioning = false;\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n\n      this._resetAdjustments();\n\n      this._scrollBar.reset();\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC);\n\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n\n    this._element.focus();\n  }\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n    const scrollbarWidth = this._scrollBar.getWidth();\n\n    const isBodyOverflowing = scrollbarWidth > 0;\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  } // Static\n\n\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](relatedTarget);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  }); // avoid conflict when clicking modal toggler while another one is open\n\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n\n    this._addEventListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default$5;\n  }\n\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n\n  static get NAME() {\n    return NAME$6;\n  } // Public\n\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = true;\n\n    this._backdrop.show();\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n\n    this._element.setAttribute('aria-modal', true);\n\n    this._element.setAttribute('role', 'dialog');\n\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._focustrap.deactivate();\n\n    this._element.blur();\n\n    this._isShown = false;\n\n    this._element.classList.add(CLASS_NAME_HIDING);\n\n    this._backdrop.hide();\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n      this._element.removeAttribute('aria-modal');\n\n      this._element.removeAttribute('role');\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n\n    this._queueCallback(completeCallback, this._element, true);\n  }\n\n  dispose() {\n    this._backdrop.dispose();\n\n    this._focustrap.deactivate();\n\n    super.dispose();\n  } // Private\n\n\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n\n      this.hide();\n    }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n\n      if (!this._config.keyboard) {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n\n      this.hide();\n    });\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](this);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  if (isDisabled(this)) {\n    return;\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n    }\n\n    return true;\n  } // Check if a regular expression validates the attribute.\n\n\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '
          '\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  } // Getters\n\n\n  static get Default() {\n    return Default$4;\n  }\n\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n\n  static get NAME() {\n    return NAME$5;\n  } // Public\n\n\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n\n  changeContent(content) {\n    this._checkContent(content);\n\n    this._config.content = { ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n\n    const template = templateWrapper.children[0];\n\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n\n    return template;\n  } // Private\n\n\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n\n    this._checkContent(config.content);\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n\n    if (!templateElement) {\n      return;\n    }\n\n    content = this._resolvePossibleFunction(content);\n\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n\n      return;\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n\n    templateElement.textContent = content;\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n\n  _resolvePossibleFunction(arg) {\n    return typeof arg === 'function' ? arg(this) : arg;\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n\n    templateElement.textContent = element.textContent;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 0],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '
          ' + '
          ' + '
          ' + '
          ',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n\n    super(element, config); // Private\n\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null; // Protected\n\n    this.tip = null;\n\n    this._setListeners();\n\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$3;\n  }\n\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n\n  static get NAME() {\n    return NAME$4;\n  } // Public\n\n\n  enable() {\n    this._isEnabled = true;\n  }\n\n  disable() {\n    this._isEnabled = false;\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click;\n\n    if (this._isShown()) {\n      this._leave();\n\n      return;\n    }\n\n    this._enter();\n  }\n\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n\n    this._disposePopper();\n\n    super.dispose();\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    } // todo v6 remove this OR make it optional\n\n\n    this._disposePopper();\n\n    const tip = this._getTipElement();\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n    const {\n      container\n    } = this._config;\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n      if (this._isHovered === false) {\n        this._leave();\n      }\n\n      this._isHovered = false;\n    };\n\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const tip = this._getTipElement();\n\n    tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n\n      this._element.removeAttribute('aria-describedby');\n\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  } // Protected\n\n\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n\n    return this.tip;\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n    if (!tip) {\n      return null;\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n\n    return tip;\n  }\n\n  setContent(content) {\n    this._newContent = content;\n\n    if (this._isShown()) {\n      this._disposePopper();\n\n      this.show();\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({ ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n\n    return this._templateFactory;\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  } // Private\n\n\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n\n  _createPopper(tip) {\n    const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n\n    return offset;\n  }\n\n  _resolvePossibleFunction(arg) {\n    return typeof arg === 'function' ? arg.call(this._element) : arg;\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return { ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    };\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n          context._leave();\n        });\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n\n    if (!title) {\n      return;\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n    this._element.removeAttribute('title');\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n\n    this._isHovered = true;\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n\n    this._isHovered = false;\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n\n    config = { ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n\n    return config;\n  }\n\n  _getDelegateConfig() {\n    const config = {};\n\n    for (const key in this._config) {\n      if (this.constructor.Default[key] !== this._config[key]) {\n        config[key] = this._config[key];\n      }\n    }\n\n    config.selector = false;\n    config.trigger = 'manual'; // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n\n    return config;\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n\n      this._popper = null;\n    }\n\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '
          ' + '
          ' + '
          ' + '
          ' + '
          ',\n  trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n\n  static get NAME() {\n    return NAME$3;\n  } // Overrides\n\n\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  } // Private\n\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  } // Getters\n\n\n  static get Default() {\n    return Default$1;\n  }\n\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n\n  static get NAME() {\n    return NAME$2;\n  } // Public\n\n\n  refresh() {\n    this._initializeTargetsAndObservables();\n\n    this._maybeEnableSmoothScroll();\n\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect();\n\n    super.dispose();\n  } // Private\n\n\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n\n    return config;\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    } // unregister any previous listeners\n\n\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        } // Chrome 60 doesn't support `scrollTo`\n\n\n        root.scrollTop = height;\n      }\n    });\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  } // The logic of selection\n\n\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n      this._process(targetElement(entry));\n    };\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n\n        this._clearActiveClass(targetElement(entry));\n\n        continue;\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n        if (!parentScrollTop) {\n          return;\n        }\n\n        continue;\n      } // if we are scrolling up, pick the smallest offsetTop\n\n\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n\n      const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(anchor.hash, anchor);\n\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n\n    this._clearActiveClass(this._config.target);\n\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n\n    this._activateParents(target);\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both