THN20181012_GEMZPM_pgdisturbance_triplicates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compile triplicates for synthetic feedback circuit \n",
    "# positive (pg) disturbance\n",
    "# dilution, sample every 25 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from FlowCytometryTools import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import seaborn as sns\n",
    "import pickle as pkl\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy import stats\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "path = \"C:/Users/tayng/Documents/PythonScripts/cytometer\"\n",
    "\n",
    "if not path in sys.path:\n",
    "    sys.path.insert(1, path)\n",
    "    os.chdir(path)\n",
    "    from AHN_FlowTools import *\n",
    "del path\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks')\n",
    "%matplotlib inline\n",
    "\n",
    "os.chdir(\"C:/Users/tayng/Documents/PythonScripts/cytometer/CAGEKEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# update data directory\n",
    "datadir = os.path.normpath(\"C:/Users/tayng/Documents/Data/cytometer/CAGEKEY/THN20181012_GEMZPM_pgdisturbance_triplicates/\")+os.sep\n",
    " \n",
    "allfiles = {}\n",
    "date1 = '20180704'\n",
    "date2 = '20180705'\n",
    "date3 = '20181011'\n",
    "\n",
    "experiment = [date1, date2, date3]\n",
    "date = '20180704'\n",
    "\n",
    "for idx, exp in enumerate(experiment): \n",
    "    filepath = datadir + os.sep +'*.fcs'\n",
    "    files = glob.glob(filepath)\n",
    "    assert len(files)>0, \"No filenames recovered - check file path: \" + filepath\n",
    "    allfiles[exp] = [files[idx]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "allexp = {}\n",
    "\n",
    "for exp in experiment: # iterate through experiments (fcs files)\n",
    "    for f in allfiles[exp]:\n",
    "        allexp[exp] = FCMeasurement(ID = exp, datafile = f)\n",
    "        \n",
    "    allexp[exp].data = allexp[exp].data.loc[:, ['Time','SSC-H','FITC-H','mCherry-H','FSC-H']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# settings\n",
    "og_num = 1;        # number of outgrowth timepoints\n",
    "samp_pd = 25;      # length of time b/w samples in min\n",
    "summary_idx  = 1   # check summaryfile from labview\n",
    "tot_reactors = pd.Series(range(1,9)) \n",
    "\n",
    "# read summary file\n",
    "summaryfilepath = {}\n",
    "summaryfile = {}\n",
    "paths = {}\n",
    "tot_timepoints = {}\n",
    "paths[date1] = \"THN20180704_summaryfile.csv\"\n",
    "paths[date2] = \"THN20180705_summaryfile.csv\"\n",
    "paths[date3] = \"THN20181011_summaryfile.csv\"\n",
    "\n",
    "for exp in experiment:\n",
    "    summaryfilepath[exp] = os.path.normpath(datadir + paths[exp])\n",
    "    summaryfile[exp] = pd.read_csv(summaryfilepath[exp], names=['timepoint','valve','start','end'])\n",
    "    summaryfile[exp]['valve'] = summaryfile[exp]['valve'].astype(int)\n",
    "    summaryfile[exp]['timepoint'] = summaryfile[exp]['timepoint'].astype(int)\n",
    "\n",
    "tot_timepoints = pd.Series(range(0,49))\n",
    "\n",
    "# create sample_order (valve # : order #)\n",
    "valves = [5, 4, 7, 6, 11, 12, 2, 1]\n",
    "order = [1, 2, 3, 4, 5, 6, 7, 8] # update\n",
    "sample_order = {}\n",
    "\n",
    "for i in range(len(valves)):\n",
    "    sample_order[valves[i]] = order[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# legend \n",
    "labels = ['feedback: no disturbance',\n",
    "          'feedback: 1.56nM Pg',\n",
    "          'feedback: 3.13nM Pg',\n",
    "          'feedback: 6.25nM Pg',\n",
    "          'no feedback: no disturbance',\n",
    "          'no feedback: 1.56nM Pg',\n",
    "          'no feedback: 3.13nM Pg',\n",
    "          'no feedback: 6.25nM Pg'];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create binned_data\n",
    "binned_data = {}\n",
    "for exp in experiment:\n",
    "    binned_data[exp] = pd.DataFrame(index=tot_reactors,columns=tot_timepoints)\n",
    "\n",
    "for exp in experiment:\n",
    "    for i, row in enumerate(summaryfile[exp].values): # iterate through summary file\n",
    "        d = allexp[exp].data[allexp[exp].data.Time.between(summaryfile[exp].iloc[i,2],summaryfile[exp].iloc[i,3])]\n",
    "        timepoint = summaryfile[exp].iloc[i,0] - summary_idx \n",
    "        if timepoint < 49:\n",
    "            reactor_num = sample_order[summaryfile[exp].iloc[i,1]] # sample order gives the order of reactor (indexed at valve num)\n",
    "            binned_data[exp].set_value(reactor_num, timepoint, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot some things!!! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# delete outgrowth timepoint(s)\n",
    "t = ((tot_timepoints)*samp_pd)/60.0 \n",
    "t_cut = t[20:50]-10.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_color():\n",
    "    color=iter(sns.color_palette('hls',8))\n",
    "    return(color)\n",
    "\n",
    "def get_YFP_color():\n",
    "    CL_color = iter(sns.color_palette(sns.color_palette(\"YlOrRd\", 8)))\n",
    "    return(CL_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_eventrate(reactor_num, timepoint):\n",
    "    Ns = binned_data.loc[reactor_num,timepoint].size\n",
    "    dt = binned_data.loc[reactor_num,timepoint].iloc[-1,0]-binned_data.loc[reactor_num,timepoint].iloc[0,0]\n",
    "    dt = dt/10.0\n",
    "            \n",
    "    events_all.loc[reactor_num,timepoint] = Ns;\n",
    "    event_rate.loc[reactor_num,timepoint] = Ns/dt\n",
    "    return(event_rate)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot norm data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_norm_data(exp, reactor_num, timepoint, datatype):\n",
    "    tp_data = binned_data[exp].loc[reactor_num, timepoint]\n",
    "    tp_data = tp_data[tp_data[datatype] > 0] \n",
    "    tp_data = tp_data[tp_data['SSC-H'] > 1000]\n",
    "    \n",
    "    tot_events = tp_data.size\n",
    "    time = np.median(tp_data.loc[:,'Time'])\n",
    "    reading = tp_data.loc[:, datatype]\n",
    "    \n",
    "    raw_data = np.median(reading)\n",
    "    ssc = tp_data.loc[:,'SSC-H']\n",
    "    d = pd.Series(reading/ssc)\n",
    "            \n",
    "    d.dropna(how='any')    #to drop if all values in the row are nan\n",
    "    yerr = np.std(d)\n",
    "    d = np.median(d)\n",
    "    return(d, raw_data, time, yerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get normalized data data\n",
    "rows = tot_reactors\n",
    "cols = tot_timepoints\n",
    "empty = pd.DataFrame(index = rows, columns = cols)\n",
    "\n",
    "FITC = {}\n",
    "for exp in experiment:\n",
    "    FITC[exp] = pd.Panel({'time':empty, 'raw':empty, 'med':empty, 'sd':empty, 'ravg':empty})\n",
    "\n",
    "    for reactor_num in tot_reactors:    \n",
    "        for timepoint in tot_timepoints:\n",
    "        \n",
    "            data, raw_data, time, yerr = get_norm_data(exp, reactor_num, timepoint, 'FITC-H')\n",
    "            FITC[exp].time.set_value(reactor_num, timepoint, time)\n",
    "            FITC[exp].raw.set_value(reactor_num, timepoint, raw_data)\n",
    "            FITC[exp].med.set_value(reactor_num, timepoint, data)\n",
    "            FITC[exp].sd.set_value(reactor_num, timepoint, yerr)\n",
    "            FITC[exp].ravg.set_value(reactor_num, timepoint, yerr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get mean and sd data\n",
    "FITC_all = {}\n",
    "FITC_all = pd.Panel({'time':empty, 'med':empty, 'sd':empty, 'ravg':empty})\n",
    "\n",
    "for reactor_num in tot_reactors:\n",
    "    for timepoint in tot_timepoints:\n",
    "        d1 = FITC[date1].med.loc[reactor_num, timepoint]\n",
    "        d2 = FITC[date2].med.loc[reactor_num, timepoint]\n",
    "        d3 = FITC[date3].med.loc[reactor_num, timepoint]\n",
    "        davg = np.mean([d1, d2, d3], dtype=np.float64)\n",
    "        dyerr = np.std([d1, d2, d3], dtype=np.float64)\n",
    "\n",
    "        FITC_all.med.set_value(reactor_num, timepoint, davg)\n",
    "        FITC_all.sd.set_value(reactor_num, timepoint, dyerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tayng\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=3,center=True).mean()\n",
      "  \"\"\"\n",
      "C:\\Users\\tayng\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=3,center=True).mean()\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "def rolling_mean(readout, reactor_num, N):\n",
    "    t_last = len(t)-1\n",
    "\n",
    "    if readout == 'FITC-H':\n",
    "        ravg = pd.rolling_mean(FITC_all.med.loc[reactor_num, :], N, center=True)\n",
    "        ravg[0] = (FITC_all.med.loc[reactor_num, 0]+FITC_all.med.loc[reactor_num, 1])/2\n",
    "        ravg[t_last] = (FITC_all.med.loc[reactor_num, t_last-1]+FITC_all.med.loc[reactor_num, t_last])/2\n",
    "        \n",
    "    return(ravg)\n",
    "\n",
    "window = 3;\n",
    "for reactor_num in range(1,9):\n",
    "    FITC_all.ravg.loc[reactor_num, :] = rolling_mean('FITC-H',reactor_num, window)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot on log10 yscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yGoJWQIHgMf0z2KXAB44M2hYHBJqlHXKDtWDwM4fyIHhcrhyy2BB5V0P4lEyL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x350c9d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on log 10 scale output over time\n",
    "# rep 2\n",
    "fig, ax = plt.subplots(2, 1, sharex = True, sharey = True, figsize = (2, 3.))\n",
    "YFP_color = get_YFP_color()\n",
    "YFP_color_OL = get_YFP_color()\n",
    "c_YFP = next(YFP_color)\n",
    "c_YFP_OL = next(YFP_color_OL)\n",
    "\n",
    "ms = 3; lw = 1. ; mew = 1; cs = 1.5; ct = 1;\n",
    "\n",
    "for reactor_num in tot_reactors: \n",
    "    if reactor_num == 1:\n",
    "        ax[0].errorbar(t_cut, FITC_all.med.loc[reactor_num, 20:50], FITC_all.sd.loc[reactor_num, 20:50],\n",
    "                       elinewidth=1, linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = 'grey')\n",
    "        ax[0].plot(t_cut, FITC_all.ravg.loc[reactor_num, 20:50], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 2 or reactor_num == 3 or reactor_num == 4:\n",
    "        c_YFP = next(YFP_color)\n",
    "        ax[0].errorbar(t_cut, FITC_all.med.loc[reactor_num, 20:50], FITC_all.sd.loc[reactor_num, 20:50],\n",
    "                       elinewidth=1, linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax[0].plot(t_cut, FITC_all.ravg.loc[reactor_num, 20:50], label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    if reactor_num == 5:\n",
    "        ax[1].errorbar(t_cut, FITC_all.med.loc[reactor_num, 20:50], FITC_all.sd.loc[reactor_num, 20:50], elinewidth=1, \n",
    "                       linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize=ms, \n",
    "                       markeredgewidth=0.5, markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax[1].plot(t_cut, FITC_all.ravg.loc[reactor_num, 20:50], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 6 or reactor_num == 7 or reactor_num ==8: \n",
    "        c_YFP_OL = next(YFP_color_OL)\n",
    "        ax[1].errorbar(t_cut, FITC_all.med.loc[reactor_num, 20:50], FITC_all.sd.loc[reactor_num, 20:50], elinewidth=1, \n",
    "                       linestyle='none', marker='o', capsize=cs, capthick=ct, color = c_YFP_OL, markersize=ms, \n",
    "                       markeredgewidth=0.5, markerfacecolor = 'none', markeredgecolor=c_YFP_OL)\n",
    "        ax[1].plot(t_cut, FITC_all.ravg.loc[reactor_num, 20:50], label='_nolegend_', linewidth = lw, color=c_YFP_OL)\n",
    "\n",
    "#ax[0].set_ylabel('Normalized YFP')\n",
    "#ax[0].set_title('Closed loop') \n",
    "#ax[1].set_title('Open loop') \n",
    "\n",
    "ax[1].set_xticks(np.arange(-2, 12, step=2))\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].set_yscale('log')\n",
    "    #ax[i].legend(['no disturbance','low','med','high'],loc='upper left')\n",
    "\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    #ax[i].set_xlabel('Time (hrs)')\n",
    "    \n",
    "    if i == 0: \n",
    "        ax[i].set_ylim([.18, 2.9])\n",
    "        ax[i].set_xlim([-2.0, 10.3])\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_log10scaleoutput_compareDose'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get mean and sd data\n",
    "ssFITC = {}\n",
    "empty = pd.DataFrame(index = rows, columns = range(1,4)) # columns are triplicates\n",
    "ssFITC = pd.Panel({'avg':empty, 'precision':empty})\n",
    "\n",
    "for reactor_num in tot_reactors:\n",
    "        \n",
    "    d1 = np.mean(FITC[date1].med.loc[reactor_num, 43:49]);\n",
    "    d2 = np.mean(FITC[date2].med.loc[reactor_num, 43:49]);\n",
    "    d3 = np.mean(FITC[date3].med.loc[reactor_num, 43:49]);\n",
    "\n",
    "    ssFITC.avg.set_value(reactor_num, 1, d1)\n",
    "    ssFITC.avg.set_value(reactor_num, 2, d2)\n",
    "    ssFITC.avg.set_value(reactor_num, 3, d3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tayng\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "# plot precision\n",
    "i1 = 6.25/8;\n",
    "i2 = [(6.25/8), (6.25/4), (6.25/2), 6.25, (6.25/8), (6.25/4), (6.25/2), 6.25];\n",
    "\n",
    "for j in range(1,4): # iterate through triplicates\n",
    "    for i, reactor_num in enumerate(tot_reactors): \n",
    "    \n",
    "        # no disturbance value\n",
    "        CL_NoDisturbance = ssFITC.avg.loc[1, j]; \n",
    "        OL_NoDisturbance = ssFITC.avg.loc[5, j];\n",
    "    \n",
    "        disturbance = ssFITC.avg.loc[reactor_num, j];\n",
    "\n",
    "        if reactor_num == 1 or reactor_num == 2 or reactor_num == 3 or reactor_num == 4: \n",
    "            error = np.abs((disturbance-CL_NoDisturbance)/CL_NoDisturbance);\n",
    "        \n",
    "        elif reactor_num == 5 or reactor_num == 6 or reactor_num == 7 or reactor_num == 8:\n",
    "            error = np.abs((disturbance-OL_NoDisturbance)/OL_NoDisturbance);\n",
    "        \n",
    "        inorm = (i2[i]-i1)/i1; \n",
    "        precision = (error/inorm)**-1;\n",
    "        ssFITC.precision.set_value(reactor_num, j, precision)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssFITCprecision = {}\n",
    "empty = pd.DataFrame(index = rows, columns = [0]) # columns are triplicates\n",
    "ssFITCprecision = pd.Panel({'avg':empty, 'sd':empty})\n",
    "\n",
    "for reactor_num in tot_reactors:\n",
    "        \n",
    "    d1 = ssFITC.precision.loc[reactor_num, 1];\n",
    "    d2 = ssFITC.precision.loc[reactor_num, 2];\n",
    "    d3 = ssFITC.precision.loc[reactor_num, 3];\n",
    "    \n",
    "    davg = np.mean([d1, d2, d3], dtype=np.float64)\n",
    "    dyerr = np.std([d1, d2, d3], dtype=np.float64)\n",
    "\n",
    "    ssFITCprecision.avg.set_value(reactor_num, 0, davg)\n",
    "    ssFITCprecision.sd.set_value(reactor_num, 0, dyerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000724856409396\n",
      "6.59693218374e-05\n",
      "2.36453953669e-05\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAABrCAYAAACBtCeBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNOP/Fr7NQcLMe78AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37175c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot precision\n",
    "fig, ax = plt.subplots(figsize = (1, 1.2))\n",
    "#fig, ax = plt.subplots(figsize = (5, 5))\n",
    "\n",
    "index = np.arange(3)\n",
    "bar_width = 0.35;\n",
    "cs = 1;\n",
    "ct = 1;\n",
    "space = np.array([0.2,0.5,0.8]);\n",
    "lwidth = 0.5;\n",
    "mwidth = 0.3;\n",
    "size = 4;\n",
    "\n",
    "CL_barlist = ax.bar(index, ssFITCprecision.avg.loc[2:4, 0], bar_width, yerr=ssFITCprecision.sd.loc[2:4, 0],\n",
    "                    linewidth = lwidth, error_kw=dict(lw=lwidth, capsize = cs, capthick = ct), color='y', label='Closed loop', fill=False)\n",
    "OL_barlist = ax.bar(index + bar_width, ssFITCprecision.avg.loc[6:8, 0], bar_width, yerr=ssFITCprecision.sd.loc[6:8, 0], \n",
    "                    linewidth = lwidth, error_kw=dict(lw=lwidth, capsize = cs, capthick = ct), fill=False, color='r', label='Open loop')\n",
    "\n",
    "# add the individual data points\n",
    "for idx, reactor_num in enumerate(range(2,5)):    \n",
    "    for i in index: # closed loop reactors\n",
    "        x = space[i]*bar_width+idx-bar_width/2\n",
    "        CL_scatter = ax.scatter(x, ssFITC.precision.loc[reactor_num, i+1], \n",
    "                                color='none', edgecolor = 'black', linewidth = mwidth, s=size)\n",
    "        \n",
    "for idx, reactor_num in enumerate(range(6,9)): # open loop reactors\n",
    "    for i in index:\n",
    "        x = space[i]*bar_width+idx+bar_width/2\n",
    "        OL_scatter = ax.scatter(x, ssFITC.precision.loc[reactor_num, i+1], \n",
    "                               color='none', edgecolor = 'black', linewidth = mwidth, s =size)\n",
    "\n",
    "for i in range(2,5):\n",
    "    t1 = ssFITC.avg.loc[i, 1:3];\n",
    "    t2 = ssFITC.avg.loc[i+4, 1:3];\n",
    "    t, p = stats.ttest_ind(t1, t2)\n",
    "    print(p)\n",
    "\n",
    "YFP_color = get_YFP_color()\n",
    "for i in range(3):\n",
    "    c_YFP = next(YFP_color)\n",
    "    CL_barlist[i].set_color(c_YFP)\n",
    "    OL_barlist[i].set_color(c_YFP)\n",
    "    OL_barlist[i].set_hatch('/')\n",
    "\n",
    "ax.set_ylim([0, 15.0])\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_precision'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HeC0rSrUEypbsw//XjyCUjdf3YrNx+3KzrGH/1z179vDiiy+yfft2unTpwpVX\nXknbtm1rVKfD4ag/pOKmFX2lbF9W3WxHdkAdsSs88so9LPLaMW/fEMbt3cTZO7/JYb8xbdnDHtpy\nbMSdvPbe8jJC5x/dj//aD+DApyb32trkcf2tC6FpO2iTW0Yk27dvz4oVsZxjp06d2LFjR9J25eTk\ncPy4eQdQVFTEo48+yokTJ8otO3ToUCZNMn1WnTg7HPWfKpm9BAR2dWXHBsrkERgjLCIh4FFgINau\nsCr1Jal7w2s3d2Njr6kML/4Tiw+dxsiWW9l0tBXdGh2g6FhLmngnKCVE50aH+OxYCxYcymN0i/W0\nyznC7uNNadJFaBU6ArvXc6JVT15c24R1Je1o3aYtBw8ejIpkhKysLEpLS6MC2qRJE0pKSvA8j1Sv\nZ05ODidOnKB9+/bs3LmTnj17sm3btjLnitC8eXPatGlTLcGt42LtXkg5Gix12k2rsqGyEYF9Zlof\nzhpzBasXvUnX5mG6eMXsKW1Gu+zDHA97ZHs+Yd8jK+RTGvYoxWPT0db0aLSPpQe7MqTlNpYd7My8\nfbmc23IbA5rv4LFt5b97GzJkCEuWLGHw4MEsXbqU/v37s3LlyjiBHTlyJPPnz6dPnz6sW7eOvn37\nsmbNGiZOnMjrr78eLRuJYrOzs+Oi2yAtW7bkwIEDcdtCoRDhcKy3SPv27dm1a1diUQCaNm3KoEGD\nGDBgAP/5z39Ys2YN2dnZFBcX07FjR0aNGsW7777Lzp076dy5MxMnTmTu3LlJBTlRrPPz88s9NkWc\nwDoaLHVWYG3/2C8CgzXJzLL2mDxgw21TJvP1LqtoETrOwXAOh0sbUXLaSE7bMZeiNsPJ3fc+72aN\npV/JQpb7Z3JBo2Vs87qie3PY2no41/A8Cw90ZUiLz/hV0TDu7L6AeXtz2VDSmm3HW5Gfn8/QoUNZ\ntGgRb731FmPHjmXu3Lnk5+fHrbdq1QqA/fv3k5+fz/vvv8+hQ7Euf8GUARiRHD9+fLSuuXPn8t3v\nfpff/OY3tG3blj179gDQtWtXtm3bRrdu3SgqKqpSpJxucnJy6NOnD3379mXDhg2sWrWKgQMHMmDA\nANasWcPy5cs5fDg2wrN58+aUlJRUJL5OYB0NljojsOUMlUVEnqlMYO+55x7Wr18f3T6+807O8DZF\nUwAtso+z8mAH5u3LZVzrTQxq8Rlv78tl8cGunNdiG2NabyJ7yFQemLWK8W02073RPjrlHGbxwW78\nJ+sccpo1toMFAAALSUlEQVS0KDcHO3DgQHbt2kVRURHNmzePE9RgThXKRn+JdTVv3pwRI0Ywd+5c\nevTowZYtW5LumzBhAm+88QbXXnstM2fO5Morr+TFF1/k4osvZvbs2YRCIXzfx/d9LrnkEl566aUy\n1y6yffLkybz88stMmjSJV155JRp5J6Omwt6yZUu+973vlam22hU6HHWcOiOw5ZGKwFY20MA/8Cn+\nvPuivQz2N+5KyYaFUQF+92BvhjXfSOdGB9l9vCkv7jyTPSea0rnRQa44K4v2l/1PuXXPmzePt956\nK7o+duxYxo0bV63v+sorr7BoUWyYdlCgE/dVFA03btyYo0ePRvcl5m/37t0bdyNIFM5IXUOHDuX+\n+++nS5cubNu2jbvuuov7778/rs3JRHf06NG88847cfvuvPNOHnzwQe6+++7Er+0E1tFgqVRg7djh\nJzBGCD7mJdfKVE9gTRIesGN5gy+5zsEM4btRVcsb+pYWga2MaGS5rYjO2fu5ooPSNrvE7DzjC4RG\nfb/adWeKVKLh8h7NK8ujBtd934/L9SaKdWLZRPEO4iJYx6lGKgJ7GWZo6w12iOrtwaGtDWWoLED4\nnzdBoxZ4595onJ+6DKwzc3DVFlXtgRA8vkMHM634zp07XQ7WcUqSSj/Yf4pIxIYtF9gb2deghsoe\n2AZ7PsE77xtGVE9xYY3Qtm1bbrwxdb/zqh7vcDRkUhpor6onRORpjI3YnwPbj2CMgX8DXAV8JaHc\ni5R120k6VLbqTU8zWxaYZc+RtdsOh8PRYEjZycT6KfYFnrCelCQMlT2AGSpbGWkfKpsO/M3zoU0u\nXqtutd0Uh8PRQKhUYEVkSsDk5TDG4Dby1iMyVHYaZvhr4lQQyXgPuMjWnZahsjXFP7rf+A/0GFHb\nTXE4HA2IVB7N/46ZWfNtzHQOt9nUAKp6GOuso6olVD7VA8A/gInWfNgjNkdOreCXHsOfZyZQ8E4f\nV606tm7dysaNG9m9ezdLly6Nbq9Jl62TTeQ75OXlpeWFocPhqAf9YCuiKt20EgVk69atbNywgZ67\n3qT7nnfwzr8Dr++kCsvm5uayevXquI74ubm5bN68OdrfM9L30/M8evXqFTcAIjc3l02bNpW7Pnbs\nWPr06ZNU6FLpb1uRSFa0b9asWXE3hsGDB3PppZemfN4giccHKaes60XgaLA0CIG999572bv2fXIb\n72PT0dYM6RLmnEDmYfnRnny4pw09G+9n89FW5DU/zPw9nQkTIkSYL3VeS1vvIIfD2TQLnWDhgdNY\ndqgrAM2aNYsb+lkTEgcHdO/enaKioqggd+jQgeLi4qRlE4+NdP6P0LFjx7iyAwcOJD8/n6ZNmzJn\nzpw4AQ0Ow4Wyrl6JdUV8E6ZNm8a6deviBDTxJtGzZ0+2bNlSZvBBKBRi6tSpyW6ETmAdDZYGIbDP\n3XMl/Y8tJsvzKfU9ir2OdPKLCXk+YR8WHspjaPNN0f1/KxYOhRtHBRngus4rovuf/mwgRceMr0Bl\npiqJYhQhFArRt29fVq+OGYW1atWK/fv3R9crMnhJJ+n0LsjLy8P3fTZt2kTHjh05duwY+/btq7wg\nMGHCBEaNGlWmeWlpmMNRB2kQ86GUnPDI8oyAZHk+oWMHCNn1kAf9mnwat/+CNpu4rvMKJrTdyHWd\nVzC806G4/bmNY4LRuXPnuHMlClX37t2ZNm0aEyZMYPDg2GSZ4XCYvXv3xh3bpUsXQiFzyUOhEJMm\nTYpbP/300ymPxHZ06dIlbj1iNJOI53m0bt06blu7du3i1ocNG8aFF14YTQ2MGFH+y75t27ZFI9bi\n4uJo+yP079+/zLYIu3enPBmnw9EgqP3+p2mgac/BlG5cFI1At7YaRvsjb0XXV2efw7n+B9H1fY26\n0gkz6WiW59O0SVNKS7zo/khUC0bILrroorL524R8ZmQZyV1GCB67bt061qwxszuHw2FWrFgRjY7D\n4TA9e/Zk/PjxbNy4kWbNmjF79mzC4TChUIjJkycDROsCmDFjRnT/VVddFd2/b98+PvjgA8DcEIYM\nGUJeXl65Zfv37x/36L5161YWLlxIOBzG87xoPaFQiAEDBkTrBujduzcffvhhtK6cnJy4iH/w4MG0\na9fOvTxznJLUaYGtzA82wpENC9l1omk0h9rM38rMHQOiOdeJQ/PIHjAFf/YtZE/+NX0Bf87tZnpp\nL4smOSF2HYiVH9ijFWfmTUgqoJHPqYpF8Nju3bun1Kuge/fuzJs3L058161bx7hx46J1VbR/1qxZ\ncfXt3r2bUaNGxbV56tSpPPnkk0nzot27d4/uv+GGGwCixwJxglpaWhrXjlatWlFQUJDStXE4Gjp1\nNgdbFT/YZL0Itm7dypNPPsm0adM4rfiN+OmjK5hquqFQUa+BynoGVNQTIFnPhxp2RXM5WEeDpc4I\nbE38YBMFNp0Wgo6M4wTW0WCpMykCVX0YeDgddY0bN84JqsPhqHUqFVgRyQH+COQBjYH/VtVZFRaK\nL18jP1iHw+Gor6QSwV4D7FLVKSLSDlgGRAU2VT/YQH2XAU1UdYT1IvgVJtealGTpgYAfbFuA7du3\nJx7iqCdMmDAhD9haFwx/HI50k4rAvgD8zX72gOgfQh3wgx0F3HL11VdXtQpH3WEDcDqwsZbb4XCk\nnVQMtw8CiEhLjNDeHdh3REQuxDhiFQGjE8q+aF9EBUnqB1vNCGaxXfYBSqtRPpHIH7ur5+TWszUN\ndTocdY6UXnKJSA+MC9ajqvqXwPagH2xPjB/sY5VUlzY/WFU9KiKo6vrKj64cW9dGV0/9qMfhqOuk\n4gfbGSOgP1TVPybsbhB+sA6Hw5EJUolgf4x5mXSPiNxjt01S1SMNwQ/W4XA4MkUqOdhbqcHEgvZR\ncHhgPQzcXN36HA6Ho77QENy0CutgXa6ek1OPw1GnqTNDZR0Oh6Oh0RAiWIfD4aiTOIF1OByODOEE\n1uFwODKEE1iHw+HIEE5gHQ6HI0M4gXU4HI4MUWcMt6uLiLQGnsGYyDQCvqeq71ehfFr8aWvqm5uk\nvk7AEmCiqq6u7PgK6rkLuBRzbR5V1SerUUcO8DTmu5UC/6+qbQr6AtvpgJ4CfGAl8G07AMXhaFA0\nhAj2e8AbqjoWuB743yqWj/rTAj/C+NNWh4hv7mjgC9ghxNXBCtrjwJHq1mHrGQeMxNhIjgV6VLOq\ni4BsVR0J/Ay4t4rt+AHwB6CJ3fQgcLe9Vh4V+AE7HPWZhiCwD2HECExEXlLF8nH+tECV/WktLwAR\nr4Y439xq8EuMK9mnNagDIGIl+Q/gJeDlatazBsi20X4r4HgVy0d8gSMMASKTpr1CbC42h6NBUa9S\nBCIyDbg9YfNUVV0sIl0wqYLbqlhtWvxpK/LNrQoicj1QrKqv2cf7mtAByAUmY/xXZ4nImapa1eF7\nBzHpgdW2zslVKZzEF9gLtOEA0LqK7XE46gX1SmBt/rBMDlFEBgDPAd9X1eTzTZdP2vxpy/PNrSI3\nAL6I5AODgJkicqmqVmdenF3AalU9BqiIlAAdgR1VrOd24DVVvct+xzdFZIB1UKsOwXxrS2BvNetx\nOOo09T5FICL9MI/nX1fVV6pRRVr8aSvxzU0ZVR2jqmPtJJHLgGurKa4A7wJfEBFPRE4DmmNEt6rs\nIRbl7wZygKxqtglgqc0PA0wC3qlBXQ5HnaVeRbDlcD/m5cmvRQRgn6pW5aVJuvxpy/XNrWZ9NUZV\nXxaRMcAizM3026panal1HgL+KCLvYHoj/FhVD1VSpiLuAJ4QkUbAx8TmfHM4GhTOTcvhcDgyRL1P\nETgcDkddxQmsw+FwZAgnsA6Hw5EhnMA6HA5HhnAC63A4HBnCCazD4XBkCCewDofDkSH+P8I5za0q\nKRgOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33872978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on log 10 scale output over time\n",
    "# rep 2\n",
    "fig, ax = plt.subplots(figsize = (2, 1.5))\n",
    "YFP_color = get_YFP_color()\n",
    "YFP_color_OL = get_YFP_color()\n",
    "\n",
    "for i in range(3):\n",
    "    c_YFP = next(YFP_color)\n",
    "    c_YFP_OL = next(YFP_color_OL)\n",
    "\n",
    "ms = 4\n",
    "lw = 1.5\n",
    "mew = 1\n",
    "cs = 2\n",
    "ct = 1\n",
    "\n",
    "for reactor_num in tot_reactors: \n",
    "    if reactor_num == 1:\n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 21:49], FITC_all.sd.loc[reactor_num, 21:49], elinewidth=1, \n",
    "                    linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = 'grey')\n",
    "        ax.plot(t_cut, FITC_all.ravg.loc[reactor_num, 21:49], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num == 4:\n",
    "        c_YFP = next(YFP_color)\n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 21:49], FITC_all.sd.loc[reactor_num, 21:49], elinewidth=1, \n",
    "                    linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax.plot(t_cut, FITC_all.ravg.loc[reactor_num, 21:49], label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    if reactor_num == 5:\n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 21:49], FITC_all.sd.loc[reactor_num, 21:49], elinewidth=1, \n",
    "                    linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize = ms, markeredgewidth=1, \n",
    "                    markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax.plot(t_cut, FITC_all.ravg.loc[reactor_num, 21:49], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "    if reactor_num ==8: \n",
    "        c_YFP_OL = next(YFP_color_OL)\n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 21:49], FITC_all.sd.loc[reactor_num, 21:49], elinewidth=1, \n",
    "                    linestyle='none', marker='o', capsize=cs, capthick=ct, color = c_YFP_OL, markersize=ms, markeredgewidth=1, \n",
    "                    markerfacecolor = 'none', markeredgecolor=c_YFP_OL)\n",
    "        ax.plot(t_cut, FITC_all.ravg.loc[reactor_num, 21:49], label='_nolegend_', linewidth = lw, color=c_YFP_OL)\n",
    "\n",
    "#ax[0].set_ylabel('Normalized YFP')\n",
    "#ax[0].set_title('Closed loop') \n",
    "#ax[1].set_title('Open loop') \n",
    "\n",
    "ax.set_xticks(np.arange(-2, 12, step=2))\n",
    "ax.set_yscale('log')\n",
    "ax.legend(['Feedback: control','Feedback: high disturbance','No feedback: control','No feedback: high disturbance'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "#ax[i].set_xlabel('Time (hrs)')\n",
    "#ax.set_ylim([.18, 2.8])\n",
    "ax.set_xlim([-2.0, 10.3])\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_onlyHIdisturbance'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AXfuaEzgjtWxiLRcBjWTbOa6Jygu3SUAzTg6OPY3e2BnPQv8b4KvtgF49O4sOh8vUkNNq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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbe1748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalize CL and OL so that they are both at 1. at 1=0\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (2.2, 1.7))\n",
    "YFP_color = get_YFP_color()\n",
    "YFP_color_OL = get_YFP_color()\n",
    "\n",
    "for i in range(3):\n",
    "    c_YFP = next(YFP_color)\n",
    "    c_YFP_OL = next(YFP_color_OL)\n",
    "\n",
    "#plot settings\n",
    "ms = 4; lw = 1.5; mew = 1; cs = 2; ct = 1;\n",
    "\n",
    "for reactor_num in tot_reactors: \n",
    "    if reactor_num == 1: # CL no disturbance\n",
    "        CL_ctrl = FITC_all.med.loc[reactor_num, 24];\n",
    "        CL_ctrl_norm = CL_ctrl/CL_ctrl;\n",
    "        ax.plot(t[1:25], np.ones(len(t[1:25]))*CL_ctrl_norm, ':', color='black',label='_nolegend_');\n",
    "\n",
    "    if reactor_num == 4: # CL disturbance\n",
    "        c_YFP = next(YFP_color);\n",
    "        ax.errorbar(t_cut, (FITC_all.med.loc[reactor_num, 21:49]/CL_ctrl), (FITC_all.sd.loc[reactor_num, 21:49]/CL_ctrl), \n",
    "                    elinewidth=1, \n",
    "                    linestyle='none', marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax.plot(t_cut, (FITC_all.ravg.loc[reactor_num, 21:49]/CL_ctrl), \n",
    "                label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    if reactor_num == 5: # OL no disturbance\n",
    "        OL_ctrl = FITC_all.med.loc[reactor_num, 24];\n",
    "        OL_ctrl_norm = OL_ctrl/OL_ctrl;\n",
    "    #    ax.plot(t_cut, np.ones(len(t_cut))*OL_ctrl_norm, ':', color='black',label='_nolegend_');\n",
    "\n",
    "    if reactor_num ==8: # OL disturbance\n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 21:49]/OL_ctrl, FITC_all.sd.loc[reactor_num, 21:49]/OL_ctrl, elinewidth=1, \n",
    "                    linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize=ms, markeredgewidth=1, \n",
    "                    markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax.plot(t_cut, FITC_all.ravg.loc[reactor_num, 21:49]/OL_ctrl, label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "ax.set_xticks(np.arange(-2, 12, step=2))\n",
    "#ax.set_yscale('log')\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.set_ylim([.8, 4.5])\n",
    "ax.set_xlim([-2.0, 10.3])\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_onlyHIdisturbance_normalized'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
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