From e60ec432df84011ad8e9a3394c5cc06c36214fbc Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 26 May 2023 11:55:44 +0100 Subject: [PATCH 001/154] basic MSMR model with fast diffusion --- .../lithium_ion/basic_spm_msmr.py | 262 ++++++++++++++++++ 1 file changed, 262 insertions(+) create mode 100644 pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py new file mode 100644 index 0000000000..0efe58d452 --- /dev/null +++ b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py @@ -0,0 +1,262 @@ +# +# Basic Single Particle MSMR Model (SPMSMR) +# +import pybamm + + +def electrolyte_diffusivity_Nyman2008(c_e, T): + D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10 + return D_c_e + + +def electrolyte_conductivity_Nyman2008(c_e, T): + sigma_e = ( + 0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000) + ) + return sigma_e + + +def x_n(U): + T = 298.15 + f = pybamm.constants.F / (pybamm.constants.R * T) + xj = 0 + for i in range(6): + U0 = pybamm.Parameter(f"U0_n_{i}") + w = pybamm.Parameter(f"w_n_{i}") + Xj = pybamm.Parameter(f"Xj_n_{i}") + + xj += Xj / (1 + pybamm.exp(f * (U - U0) / w)) + + return xj + + +def dxdU_n(U): + T = 298.15 + f = pybamm.constants.F / (pybamm.constants.R * T) + dxj = 0 + for i in range(6): + U0 = pybamm.Parameter(f"U0_n_{i}") + w = pybamm.Parameter(f"w_n_{i}") + Xj = pybamm.Parameter(f"Xj_n_{i}") + + e = pybamm.exp(f * (U - U0) / w) + dxj += -(f / w) * (Xj * e) / (1 + e) ** 2 + + return dxj + + +def x_p(U): + T = 298.15 + f = pybamm.constants.F / (pybamm.constants.R * T) + xj = 0 + for i in range(4): + U0 = pybamm.Parameter(f"U0_p_{i}") + w = pybamm.Parameter(f"w_p_{i}") + Xj = pybamm.Parameter(f"Xj_p_{i}") + + xj += Xj / (1 + pybamm.exp(f * (U - U0) / w)) + + return xj + + +def dxdU_p(U): + T = 298.15 + f = pybamm.constants.F / (pybamm.constants.R * T) + dxj = 0 + for i in range(4): + U0 = pybamm.Parameter(f"U0_p_{i}") + w = pybamm.Parameter(f"w_p_{i}") + Xj = pybamm.Parameter(f"Xj_p_{i}") + + e = pybamm.exp(f * (U - U0) / w) + dxj += -(f / w) * (Xj * e) / (1 + e) ** 2 + + return dxj + + +def get_parameter_values(): + return { + # cell + "Negative electrode thickness [m]": 7.56e-05, + "Separator thickness [m]": 1.2e-05, + "Positive electrode thickness [m]": 7.56e-05, + "Electrode height [m]": 0.065, + "Electrode width [m]": 1.58, + "Nominal cell capacity [A.h]": 5.0, + "Current function [A]": 5.0, + "Contact resistance [Ohm]": 0, + # negative electrode + "Negative electrode stoichiometry": x_n, + "Negative electrode stoichiometry change [V-1]": dxdU_n, + "U0_n_0": 0.08843, + "Xj_n_0": 0.43336, + "w_n_0": 0.08611, + "U0_n_1": 0.12799, + "Xj_n_1": 0.23963, + "w_n_1": 0.08009, + "U0_n_2": 0.14331, + "Xj_n_2": 0.15018, + "w_n_2": 0.72469, + "U0_n_3": 0.16984, + "Xj_n_3": 0.05462, + "w_n_3": 2.53277, + "U0_n_4": 0.21446, + "Xj_n_4": 0.06744, + "w_n_4": 0.09470, + "U0_n_5": 0.36325, + "Xj_n_5": 0.05476, + "w_n_5": 5.97354, + "Negative electrode conductivity [S.m-1]": 215.0, + "Maximum concentration in negative electrode [mol.m-3]": 33133.0, + "Negative electrode diffusivity [m2.s-1]": 3.3e-14, + "Negative electrode porosity": 0.25, + "Negative electrode active material volume fraction": 0.75, + "Negative particle radius [m]": 5.86e-06, + "Negative electrode Bruggeman coefficient (electrolyte)": 1.5, + "Negative electrode Bruggeman coefficient (electrode)": 0, + "Negative electrode exchange-current density [A.m-2]" "": 2.7, + "Negative electrode OCP entropic change [V.K-1]": 0.0, + # positive electrode + "Positive electrode stoichiometry": x_p, + "Positive electrode stoichiometry change [V-1]": dxdU_p, + "U0_p_0": 3.62274, + "Xj_p_0": 0.13442, + "w_p_0": 0.96710, + "U0_p_1": 3.72645, + "Xj_p_1": 0.32460, + "w_p_1": 1.39712, + "U0_p_2": 3.90575, + "Xj_p_2": 0.21118, + "w_p_2": 3.50500, + "U0_p_3": 4.22955, + "Xj_p_3": 0.32980, + "w_p_3": 5.52757, + "Positive electrode conductivity [S.m-1]": 0.18, + "Maximum concentration in positive electrode [mol.m-3]": 63104.0, + "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive electrode porosity": 0.335, + "Positive electrode active material volume fraction": 0.665, + "Positive particle radius [m]": 5.22e-06, + "Positive electrode Bruggeman coefficient (electrolyte)": 1.5, + "Positive electrode Bruggeman coefficient (electrode)": 0, + "Positive electrode exchange-current density [A.m-2]" "": 5, + "Positive electrode OCP entropic change [V.K-1]": 0.0, + # separator + "Separator porosity": 0.47, + "Separator Bruggeman coefficient (electrolyte)": 1.5, + # electrolyte + "Initial concentration in electrolyte [mol.m-3]": 1000.0, + "Cation transference number": 0.2594, + "Thermodynamic factor": 1.0, + "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Nyman2008, + "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Nyman2008, + # experiment + "Reference temperature [K]": 298.15, + "Total heat transfer coefficient [W.m-2.K-1]": 10.0, + "Ambient temperature [K]": 298.15, + "Number of electrodes connected in parallel to make a cell": 1.0, + "Number of cells connected in series to make a battery": 1.0, + "Lower voltage cut-off [V]": 1, + "Upper voltage cut-off [V]": 5, + "Initial temperature [K]": 298.15, + } + + +class BasicSPMSMR(pybamm.lithium_ion.BaseModel): + def __init__(self, name="Single Particle MSMR Model"): + super().__init__({}, name) + param = self.param + + ###################### + # Variables + ###################### + Q = pybamm.Variable("Discharge capacity [A.h]") + U_n = pybamm.Variable("X-averaged negative electrode OCP [V]") + U_p = pybamm.Variable("X-averaged positive electrode OCP [V]") + + # Current density + i_cell = param.current_density_with_time + a_n = 3 * param.n.prim.epsilon_s_av / param.n.prim.R_typ + a_p = 3 * param.p.prim.epsilon_s_av / param.p.prim.R_typ + j_n = i_cell / (param.n.L * a_n) + j_p = -i_cell / (param.p.L * a_p) + + ###################### + # State of Charge + ###################### + I = param.current_with_time + # The `rhs` dictionary contains differential equations, with the key being the + # variable in the d/dt + self.rhs[Q] = I / 3600 + # Initial conditions must be provided for the ODEs + self.initial_conditions[Q] = pybamm.Scalar(0) + + ###################### + # Particles + ###################### + + def dxdU_n(U_n): + inputs = {"Negative electrode OCP [V]": U_n} + return pybamm.FunctionParameter( + "Negative electrode stoichiometry change [V-1]", inputs=inputs + ) + + def dxdU_p(U_p): + inputs = {"Positive electrode OCP [V]": U_p} + return pybamm.FunctionParameter( + "Positive electrode stoichiometry change [V-1]", inputs=inputs + ) + + # Fast diffusion limit + F = param.F + R_n = pybamm.x_average(param.n.prim.R) + R_p = pybamm.x_average(param.p.prim.R) + c_n_max = param.n.prim.c_max + c_p_max = param.p.prim.c_max + self.rhs[U_n] = (-3 * j_n / F / R_n / c_n_max) / dxdU_n(U_n) + self.rhs[U_p] = (-3 * j_p / F / R_p / c_p_max) / dxdU_p(U_p) + self.initial_conditions[U_n] = 0.1 + self.initial_conditions[U_p] = 4 + + ###################### + # (Some) variables + ###################### + phi_s_n = 0 + phi_s_p = U_p - U_n + V = phi_s_p + + self.variables = { + "Discharge capacity [A.h]": Q, + "Current [A]": I, + "Negative electrode potential [V]": pybamm.PrimaryBroadcast( + phi_s_n, "negative electrode" + ), + "Positive electrode potential [V]": pybamm.PrimaryBroadcast( + phi_s_p, "positive electrode" + ), + "Voltage [V]": V, + } + self.events += [ + pybamm.Event("Minimum voltage [V]", V - param.voltage_low_cut), + pybamm.Event("Maximum voltage [V]", param.voltage_high_cut - V), + ] + + @property + def default_parameter_values(self): + return pybamm.ParameterValues(get_parameter_values()) + + @property + def default_quick_plot_variables(self): + return [ + "Current [A]", + "Negative electrode potential [V]", + "Positive electrode potential [V]", + "Voltage [V]", + ] + + +model = BasicSPMSMR() + +sim = pybamm.Simulation(model) +sim.solve([0, 1800]) +sim.plot() From 094f2b676ced2b3aa3d4f7081696d8c1c9c69d14 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 26 May 2023 15:12:37 +0100 Subject: [PATCH 002/154] run test simulation --- .../lithium_ion/basic_spm_msmr.py | 12 +- .../lithium_ion/msmr.ipynb | 451 ++++++++++++++++++ 2 files changed, 461 insertions(+), 2 deletions(-) create mode 100644 pybamm/models/full_battery_models/lithium_ion/msmr.ipynb diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py index 0efe58d452..ec7faf2fe7 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py @@ -106,6 +106,8 @@ def get_parameter_values(): "U0_n_5": 0.36325, "Xj_n_5": 0.05476, "w_n_5": 5.97354, + "Negative electrode stoichiometry at 0% SOC": 0.03, + "Negative electrode stoichiometry at 100% SOC": 0.9, "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, "Negative electrode diffusivity [m2.s-1]": 3.3e-14, @@ -131,6 +133,8 @@ def get_parameter_values(): "U0_p_3": 4.22955, "Xj_p_3": 0.32980, "w_p_3": 5.52757, + "Positive electrode stoichiometry at 0% SOC": 0.85, + "Positive electrode stoichiometry at 100% SOC": 0.1, "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, "Positive electrode diffusivity [m2.s-1]": 4e-15, @@ -215,8 +219,12 @@ def dxdU_p(U_p): c_p_max = param.p.prim.c_max self.rhs[U_n] = (-3 * j_n / F / R_n / c_n_max) / dxdU_n(U_n) self.rhs[U_p] = (-3 * j_p / F / R_p / c_p_max) / dxdU_p(U_p) - self.initial_conditions[U_n] = 0.1 - self.initial_conditions[U_p] = 4 + self.initial_conditions[U_n] = pybamm.Parameter( + "Initial negative electrode potential [V]" + ) + self.initial_conditions[U_p] = pybamm.Parameter( + "Initial positive electrode potential [V]" + ) ###################### # (Some) variables diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb b/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb new file mode 100644 index 0000000000..5e9a36c5e9 --- /dev/null +++ b/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb @@ -0,0 +1,451 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import pybamm\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def electrolyte_diffusivity_Nyman2008(c_e, T):\n", + " D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10\n", + " return D_c_e\n", + "\n", + "\n", + "def electrolyte_conductivity_Nyman2008(c_e, T):\n", + " sigma_e = (\n", + " 0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000)\n", + " )\n", + " return sigma_e\n", + "\n", + "\n", + "def x_n(U):\n", + " T = 298.15\n", + " f = pybamm.constants.F / (pybamm.constants.R * T)\n", + " xj = 0\n", + " for i in range(6):\n", + " U0 = pybamm.Parameter(f\"U0_n_{i}\")\n", + " w = pybamm.Parameter(f\"w_n_{i}\")\n", + " Xj = pybamm.Parameter(f\"Xj_n_{i}\")\n", + "\n", + " xj += Xj / (1 + pybamm.exp(f * (U - U0) / w))\n", + "\n", + " return xj\n", + "\n", + "\n", + "def dxdU_n(U):\n", + " T = 298.15\n", + " f = pybamm.constants.F / (pybamm.constants.R * T)\n", + " dxj = 0\n", + " for i in range(6):\n", + " U0 = pybamm.Parameter(f\"U0_n_{i}\")\n", + " w = pybamm.Parameter(f\"w_n_{i}\")\n", + " Xj = pybamm.Parameter(f\"Xj_n_{i}\")\n", + "\n", + " e = pybamm.exp(f * (U - U0) / w)\n", + " dxj += -(f / w) * (Xj * e) / (1 + e) ** 2\n", + "\n", + " return dxj\n", + "\n", + "\n", + "def x_p(U):\n", + " T = 298.15\n", + " f = pybamm.constants.F / (pybamm.constants.R * T)\n", + " xj = 0\n", + " for i in range(4):\n", + " U0 = pybamm.Parameter(f\"U0_p_{i}\")\n", + " w = pybamm.Parameter(f\"w_p_{i}\")\n", + " Xj = pybamm.Parameter(f\"Xj_p_{i}\")\n", + "\n", + " xj += Xj / (1 + pybamm.exp(f * (U - U0) / w))\n", + "\n", + " return xj\n", + "\n", + "\n", + "def dxdU_p(U):\n", + " T = 298.15\n", + " f = pybamm.constants.F / (pybamm.constants.R * T)\n", + " dxj = 0\n", + " for i in range(4):\n", + " U0 = pybamm.Parameter(f\"U0_p_{i}\")\n", + " w = pybamm.Parameter(f\"w_p_{i}\")\n", + " Xj = pybamm.Parameter(f\"Xj_p_{i}\")\n", + "\n", + " e = pybamm.exp(f * (U - U0) / w)\n", + " dxj += -(f / w) * (Xj * e) / (1 + e) ** 2\n", + "\n", + " return dxj\n", + "\n", + "\n", + "def get_parameter_values():\n", + " return {\n", + " # cell\n", + " \"Negative electrode thickness [m]\": 7.56e-05,\n", + " \"Separator thickness [m]\": 1.2e-05,\n", + " \"Positive electrode thickness [m]\": 7.56e-05,\n", + " \"Electrode height [m]\": 0.065,\n", + " \"Electrode width [m]\": 1.58,\n", + " \"Nominal cell capacity [A.h]\": 5.0,\n", + " \"Current function [A]\": 5.0,\n", + " \"Contact resistance [Ohm]\": 0,\n", + " # negative electrode\n", + " \"Negative electrode stoichiometry\": x_n,\n", + " \"Negative electrode stoichiometry change [V-1]\": dxdU_n,\n", + " \"U0_n_0\": 0.08843,\n", + " \"Xj_n_0\": 0.43336,\n", + " \"w_n_0\": 0.08611,\n", + " \"U0_n_1\": 0.12799,\n", + " \"Xj_n_1\": 0.23963,\n", + " \"w_n_1\": 0.08009,\n", + " \"U0_n_2\": 0.14331,\n", + " \"Xj_n_2\": 0.15018,\n", + " \"w_n_2\": 0.72469,\n", + " \"U0_n_3\": 0.16984,\n", + " \"Xj_n_3\": 0.05462,\n", + " \"w_n_3\": 2.53277,\n", + " \"U0_n_4\": 0.21446,\n", + " \"Xj_n_4\": 0.06744,\n", + " \"w_n_4\": 0.09470,\n", + " \"U0_n_5\": 0.36325,\n", + " \"Xj_n_5\": 0.05476,\n", + " \"w_n_5\": 5.97354,\n", + " \"Negative electrode stoichiometry at 0% SOC\": 0.03,\n", + " \"Negative electrode stoichiometry at 100% SOC\": 0.9,\n", + " \"Negative electrode conductivity [S.m-1]\": 215.0,\n", + " \"Maximum concentration in negative electrode [mol.m-3]\": 33133.0,\n", + " \"Negative electrode diffusivity [m2.s-1]\": 3.3e-14,\n", + " \"Negative electrode porosity\": 0.25,\n", + " \"Negative electrode active material volume fraction\": 0.75,\n", + " \"Negative particle radius [m]\": 5.86e-06,\n", + " \"Negative electrode Bruggeman coefficient (electrolyte)\": 1.5,\n", + " \"Negative electrode Bruggeman coefficient (electrode)\": 0,\n", + " \"Negative electrode exchange-current density [A.m-2]\" \"\": 2.7,\n", + " \"Negative electrode OCP entropic change [V.K-1]\": 0.0,\n", + " # positive electrode\n", + " \"Positive electrode stoichiometry\": x_p,\n", + " \"Positive electrode stoichiometry change [V-1]\": dxdU_p,\n", + " \"U0_p_0\": 3.62274,\n", + " \"Xj_p_0\": 0.13442,\n", + " \"w_p_0\": 0.96710,\n", + " \"U0_p_1\": 3.72645,\n", + " \"Xj_p_1\": 0.32460,\n", + " \"w_p_1\": 1.39712,\n", + " \"U0_p_2\": 3.90575,\n", + " \"Xj_p_2\": 0.21118,\n", + " \"w_p_2\": 3.50500,\n", + " \"U0_p_3\": 4.22955,\n", + " \"Xj_p_3\": 0.32980,\n", + " \"w_p_3\": 5.52757,\n", + " \"Positive electrode stoichiometry at 0% SOC\": 0.85,\n", + " \"Positive electrode stoichiometry at 100% SOC\": 0.1,\n", + " \"Positive electrode conductivity [S.m-1]\": 0.18,\n", + " \"Maximum concentration in positive electrode [mol.m-3]\": 63104.0,\n", + " \"Positive electrode diffusivity [m2.s-1]\": 4e-15,\n", + " \"Positive electrode porosity\": 0.335,\n", + " \"Positive electrode active material volume fraction\": 0.665,\n", + " \"Positive particle radius [m]\": 5.22e-06,\n", + " \"Positive electrode Bruggeman coefficient (electrolyte)\": 1.5,\n", + " \"Positive electrode Bruggeman coefficient (electrode)\": 0,\n", + " \"Positive electrode exchange-current density [A.m-2]\" \"\": 5,\n", + " \"Positive electrode OCP entropic change [V.K-1]\": 0.0,\n", + " # separator\n", + " \"Separator porosity\": 0.47,\n", + " \"Separator Bruggeman coefficient (electrolyte)\": 1.5,\n", + " # electrolyte\n", + " \"Initial concentration in electrolyte [mol.m-3]\": 1000.0,\n", + " \"Cation transference number\": 0.2594,\n", + " \"Thermodynamic factor\": 1.0,\n", + " \"Electrolyte diffusivity [m2.s-1]\": electrolyte_diffusivity_Nyman2008,\n", + " \"Electrolyte conductivity [S.m-1]\": electrolyte_conductivity_Nyman2008,\n", + " # experiment\n", + " \"Reference temperature [K]\": 298.15,\n", + " \"Total heat transfer coefficient [W.m-2.K-1]\": 10.0,\n", + " \"Ambient temperature [K]\": 298.15,\n", + " \"Number of electrodes connected in parallel to make a cell\": 1.0,\n", + " \"Number of cells connected in series to make a battery\": 1.0,\n", + " \"Lower voltage cut-off [V]\": 2.5,\n", + " \"Upper voltage cut-off [V]\": 4.5,\n", + " \"Initial temperature [K]\": 298.15,\n", + " }\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class BasicSPMSMR(pybamm.lithium_ion.BaseModel):\n", + " def __init__(self, name=\"Single Particle MSMR Model\"):\n", + " super().__init__({}, name)\n", + " param = self.param\n", + "\n", + " ######################\n", + " # Variables\n", + " ######################\n", + " Q = pybamm.Variable(\"Discharge capacity [A.h]\")\n", + " U_n = pybamm.Variable(\"X-averaged negative electrode OCP [V]\")\n", + " U_p = pybamm.Variable(\"X-averaged positive electrode OCP [V]\")\n", + "\n", + " # Current density\n", + " i_cell = param.current_density_with_time\n", + " a_n = 3 * param.n.prim.epsilon_s_av / param.n.prim.R_typ\n", + " a_p = 3 * param.p.prim.epsilon_s_av / param.p.prim.R_typ\n", + " j_n = i_cell / (param.n.L * a_n)\n", + " j_p = -i_cell / (param.p.L * a_p)\n", + "\n", + " ######################\n", + " # State of Charge\n", + " ######################\n", + " I = param.current_with_time\n", + " # The `rhs` dictionary contains differential equations, with the key being the\n", + " # variable in the d/dt\n", + " self.rhs[Q] = I / 3600\n", + " # Initial conditions must be provided for the ODEs\n", + " self.initial_conditions[Q] = pybamm.Scalar(0)\n", + "\n", + " ######################\n", + " # Particles\n", + " ######################\n", + "\n", + " def dxdU_n(U_n):\n", + " inputs = {\"Negative electrode OCP [V]\": U_n}\n", + " return pybamm.FunctionParameter(\n", + " \"Negative electrode stoichiometry change [V-1]\", inputs=inputs\n", + " )\n", + "\n", + " def dxdU_p(U_p):\n", + " inputs = {\"Positive electrode OCP [V]\": U_p}\n", + " return pybamm.FunctionParameter(\n", + " \"Positive electrode stoichiometry change [V-1]\", inputs=inputs\n", + " )\n", + "\n", + " # Fast diffusion limit\n", + " F = param.F\n", + " R_n = pybamm.x_average(param.n.prim.R)\n", + " R_p = pybamm.x_average(param.p.prim.R)\n", + " c_n_max = param.n.prim.c_max\n", + " c_p_max = param.p.prim.c_max\n", + " self.rhs[U_n] = (-3 * j_n / F / R_n / c_n_max) / dxdU_n(U_n)\n", + " self.rhs[U_p] = (-3 * j_p / F / R_p / c_p_max) / dxdU_p(U_p)\n", + " self.initial_conditions[U_n] = pybamm.Parameter(\n", + " \"Initial negative electrode potential [V]\"\n", + " )\n", + " self.initial_conditions[U_p] = pybamm.Parameter(\n", + " \"Initial positive electrode potential [V]\"\n", + " )\n", + "\n", + " ######################\n", + " # (Some) variables\n", + " ######################\n", + " phi_s_n = 0\n", + " phi_s_p = U_p - U_n\n", + " V = phi_s_p\n", + "\n", + " self.variables = {\n", + " \"Discharge capacity [A.h]\": Q,\n", + " \"Current [A]\": I,\n", + " \"Negative electrode potential [V]\": pybamm.PrimaryBroadcast(\n", + " phi_s_n, \"negative electrode\"\n", + " ),\n", + " \"Positive electrode potential [V]\": pybamm.PrimaryBroadcast(\n", + " phi_s_p, \"positive electrode\"\n", + " ),\n", + " \"Voltage [V]\": V,\n", + " }\n", + " self.events += [\n", + " pybamm.Event(\"Minimum voltage [V]\", V - param.voltage_low_cut),\n", + " pybamm.Event(\"Maximum voltage [V]\", param.voltage_high_cut - V),\n", + " ]\n", + "\n", + " @property\n", + " def default_parameter_values(self):\n", + " return pybamm.ParameterValues(get_parameter_values())\n", + "\n", + " @property\n", + " def default_quick_plot_variables(self):\n", + " return [\n", + " \"Current [A]\",\n", + " \"Negative electrode potential [V]\",\n", + " \"Positive electrode potential [V]\",\n", + " \"Voltage [V]\",\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WJ0PTpk3DihUrsGjRIsTHxyM7Oxtbtmwx99XOy8tDUZHliDAnTpzAb7/91uqRN6lUikOHDuGuu+5Cv3798MgjjyAhIQG//vorlEplB6vV9S4Nrc0zQ0REXY0H6pyfSi7FK1MHAwA+zTiHnIIqO0dERO5A1pGFnnzySTz55JOtvtZaX+rY2Ng2L4j08PDA1q1bOxKGQ2E3OSIi+5k2bRpKS0uxaNEiqNVqxMfHX3GgTiKxPP7XfKBu27ZtV6yv+UDdJ598gsrKSoSHh+P222/HkiVLnOpAnbMZ3bc77ooLx3cHC/H8NznYNPcmSCWCvcMiIhfWoWSIruShaO4mx2SIiMgeeKDONbxwxwD8cqIEOReq8Pme85iRFG3vkIjIhXXp0NquzNxNjtcMERERdViwjwrzU2IBAMu3nkBptc7OERGRK2MyZCNKdpMjIiKyiT8l9sSQCD9U1zdi6eZj9g6HiFwYkyEbuTSaHJMhIiKi6yGVCFgydTAEAdiYdQF7zpbbOyQiclFMhmyEQ2sTERHZTnyUP+6/oQcA4MVvD6PBwPaViGyPyZCNePDMEBERkU3NT4lFgKccJ4trsHbXOXuHQ0QuiMmQjXgrTQPzVesa7RwJERGRawjwUmDhxAEAgJU/nURRVZ2dIyIiV8NkyEZ8VE3JUH2DnSMhIiJyHfckRGJ4D39o9Qa88gMHUyAi22IyZCO+HnIApmuGdI3sKkdERGQLkqbBFCQC8GNOEXaeLLV3SETkQpgM2YiPUgah6SbZ1fXsKkdERGQrg8L9zDdfXfzdER50JCKbYTJkIxKJAG+Fqaucpo5d5YiIiGwp9fZ+6O6jRG6ZFh/uPGvvcIjIRTAZsqHmrnIanhkiIiKyKV+VHC9MNg2m8O4vp5F/sdbOERGRK2AyZEMcRIGIiKjz3BUXjqRegahvMGLxd0cgiqK9QyIiJ8dkyIZ8VU1nhup4ZoiIiMjWBEHAkqmDoJBK8PPxEmzYX2DvkIjIyTEZsiFfj6ZrhnhmiIiIqFP0CfZB6u39AAAvf3+E3eWI6LowGbKhS2eGmAwRERF1ljmje+GG6ABo9QY889VBGIzsLkdEHcNkyIYCvRUAgKKqejtHQkRE5LqkEgGv3xsPL4UUe89dxKpfTts7JCJyUkyGbKhvsA8A4FRJtZ0jISIicm09Aj3x8pTBAIA3fzqJHbwZKxF1AJMhG+oXakqGThbX2DkSIiIi13dPQiSmj+wBUQSeWpfF64eIyGpMhmyob7A3AKC0WoeSanaVIyIi6mwv3TUQcZF+qKxtwF/+k4kaHUd0JaL2YzJkQ15KGYZG+gEAPtjBu2MTERF1NqVMivceTECglwJHizR47D+Z0Dca7R0WETmJDiVDq1atQnR0NFQqFRITE7F37942y65duxaCIFhMKpXKoowoili0aBHCwsLg4eGB5ORknDp1qiOh2d3ccX0AAB/9louXvjsCXaPBzhERERG5tnB/D/x71g3wVEjx2+kyPLPhIIwcYY6I2sHqZGj9+vVITU3F4sWLceDAAcTFxSElJQUlJSVtLuPr64uioiLzdP78eYvXX3vtNbz99ttYvXo19uzZAy8vL6SkpKC+3vm6mt0+MASpt5nuf7B29zn88b3dOFemtXNURESujwfq3NvQSH+sfjABMomA7w8WYsHGQxxym4iuyepk6I033sCcOXMwa9YsDBw4EKtXr4anpyfWrFnT5jKCICA0NNQ8hYSEmF8TRRErV67ECy+8gClTpmDo0KH49NNPUVhYiE2bNnWoUvYkCAL+3/i+WPPwCAR4ynH4ggZ/+NcunCnloApERJ2FB+oIAG7p1x2v3xcHiQB8tb8AT63LQoOBXeaIqG1WJUN6vR6ZmZlITk6+tAKJBMnJycjIyGhzuZqaGvTs2RNRUVGYMmUKjhw5Yn4tNzcXarXaYp1+fn5ITEy86jod3a39Q7D5qdEYEuGHitoGPPf1IYgij1AREXUGHqijZlPiI/Dun4ZDLhXww6Ei/OU/mdByUAUiaoNVyVBZWRkMBoNFgwEAISEhUKvVrS4TGxuLNWvW4Ntvv8Vnn30Go9GIUaNGoaCgAADMy1mzTp1OB41GYzE5ojA/D3w4YwSUMgn2n69A5vkKe4dERORyHOVAnbO0Te5g0pAwfNDU/v58vAR/fG83h90molZ1+mhySUlJmDFjBuLj4zFmzBhs3LgR3bt3x/vvv9/hdS5duhR+fn7mKSoqyoYR21aonwrjBwQDAPaeu2jnaIiIXI+jHKhzprbJHYyLDcYXc25EkLcSx9XVmLJqF3afKbN3WETkYKxKhoKCgiCVSlFcXGwxv7i4GKGhoe1ah1wux7Bhw3D69GkAMC9nzToXLlyIqqoq85Sfn29NNbrcsKgAAEB2XqV9AyEiIgCdc6DO2domd5DQMwDfPXkTBkf44qJWjwc+2oNXtxzndUREZGZVMqRQKJCQkIC0tDTzPKPRiLS0NCQlJbVrHQaDATk5OQgLCwMAxMTEIDQ01GKdGo0Ge/bsaXOdSqUSvr6+FpMji+/hDwDIuVBl30CIiFyQoxyoc7a2yV2E+3tgw19GYdqIKIgi8F76Gfzxvd3I5UivRIQOdJNLTU3Fhx9+iE8++QTHjh3D448/Dq1Wi1mzZgEAZsyYgYULF5rL//3vf8e2bdtw9uxZHDhwAA8++CDOnz+P2bNnAzBdwDpv3jy88sor+O6775CTk4MZM2YgPDwcU6dOtU0t7SwqwBMAUFKt430PiIhszFEO1JHj8lBI8eo9Q/HeA8Ph5yHHoYIqTFi5E6t+Oc2zRERuTmbtAtOmTUNpaSkWLVoEtVqN+Ph4bNmyxdyvOi8vDxLJpRyroqICc+bMgVqtRkBAABISErB7924MHDjQXGb+/PnQarV49NFHUVlZiZtvvhlbtmy54p4PzqqblwIAYDCKqKprQEDTcyIiso3U1FTMnDkTI0aMwMiRI7Fy5corDtRFRERg6dKlAEwH6m688Ub06dMHlZWVWL58eZsH6vr27YuYmBi8+OKLLnWgzh1NHBKGuCh/zP/6EH47XYblW0/g+4OF+OfdQzC8R4C9wyMiOxBEFxjvWaPRwM/PD1VVVQ7bLWHoS1uhqW/ET6m3oE+wj73DISKyGUfZB7/77rtYvny5+UDd22+/jcTERADA2LFjER0djbVr1wIAnn76aWzcuNHiQN0rr7yCYcOGmdcniiIWL16MDz74wHyg7l//+hf69evXrngcZbvQlURRxMYDF/DKj0dRUdsAQQDuvyEKz9weiyBvpb3DI6LrZM3+l8lQF7l1RTrOlmmx/tEbkdgr0N7hEBHZjDPsg+2B28XxXdTq8Y8fj+G/B0yjCPooZfi/8X0wc1Q0lDKpnaMjoo6yZv/b6UNrk0mgt6lrXLlWb+dIiIiICDB1Y3/9vjhseCwJQyL8UK1rxD83H8ftb+7EtiNq3iydyA0wGeoigV6m0+7lNTo7R0JEREQt3RDdDd/OvQnL7xmK7j5KnC+vxaP/ycSfPtyDg/mV9g6PiDoRk6Eu0nxmqKyGZ4aIiIgcjUQi4N4RUfjlr2Mxd1xvKGQSZJwtx5RVuzD3iwMcipvIRTEZ6iIBnqZkqKKWyRAREZGj8lbK8GxKf/z8zBj8cXgkBAH48VARbntjB17cdBil1ezhQeRKmAx1ER+VaRTzmvpGO0dCRERE1xIZ4InX74vD/54ajXGx3dFoFPGf389jzPJf8Mb2k6jRsT0ncgVMhrqId1MyVM2dJxERkdPoH+qLf88aiS/n3Ii4KH/U6g14O+0Uxrz2Cz7ZfQ76Rt60lciZMRnqIj4qOQCeGSIiInJGSb0DsemJUXjvgeGICfJCuVaPxd8dQfIbO7Ap6wIMRo48R+SMmAx1ER9lUzc5nhkiIiJySoIgYOKQMGx7+ha8MnUwgryVyLtYi3nrszHxrZ3YcpjDcRM5GyZDXcTcTa6+wc6REBER0fWQSyV48Mae2PHsWDybEgtflQwni2vw2GeZmLpqF3aeLGVSROQkmAx1EfMACjwzRERE5BK8lDLMHdcHvz53K54c1weeCikOFlRhxpq9mPbB79h37qK9QySia2Ay1EW8lc1nhpgMERERuRI/Dzn+mhKLnfPH4ZGbY6CQSbA39yLuXZ2Bh/+9F4cvVNk7RCJqA5OhLuKjNA2goGs0cuQZIiIiFxTkrcSLdwzEjmfHYvrIHpBJBKSfKMUd7/yGxz/LxKnianuHSESXYTLURZqvGQLYVY6IiMiVhfl5YOndQ5D2zBj8YVgEBAH432E1UlbuROr6bJwprbF3iETUhMlQF5FKBHgqpAA4vDYREZE76BnohTenxWPLU7cgZVAIjCKwMesCbntjB/7fl1k8U0TkAJgMdaHm64Y0HFGOiIjIbcSG+uD9h0bg+ydvRvIAU1L03cFC3L5yJ+Z+fgDHijT2DpHIbTEZ6kLdvBQAgItavZ0jISIioq42JNIPH80cgR/+72ZMGBQKUQR+zCnCxLd+xaOf7udAC0R2wGSoCzEZIiIiosERflj9UAK2zBuNO4aGQRCAbUeLccc7v+GRtfuQnV9p7xCJ3AaToS7UnAyVMxkiIiJye/1DffHun4Zj+9O3YGp8OCQCkHa8BFNX7cL9H2Qg/UQJb95K1MmYDHWhQPOZIZ2dIyEiIiJH0SfYByvvH4a0Z8binoRIyCQCfj97EQ//ex8mvvUrNmVdQKOBt+Ug6gxMhrpQNy8lAHaTIyIioivFBHlhxb1x2Dl/HGbfHAMvhRTH1dWYtz4bY5anY+2uXNTqOSItkS11KBlatWoVoqOjoVKpkJiYiL1797ZZ9sMPP8To0aMREBCAgIAAJCcnX1H+4YcfhiAIFtOECRM6EppD6+bd1E2uhskQERERtS7c3wMv3DEQuxeMx19v74dALwUuVNbhpe+P4qZlP+PN7Sd5YJXIRqxOhtavX4/U1FQsXrwYBw4cQFxcHFJSUlBSUtJq+fT0dEyfPh2//PILMjIyEBUVhdtvvx0XLlywKDdhwgQUFRWZpy+//LJjNXJggbxmiIio0/BAHbkaP085nry1L3YtuBWvTB2MnoGeqKhtwFtpp5C0NA0LN+bgdAnvVUR0PaxOht544w3MmTMHs2bNwsCBA7F69Wp4enpizZo1rZb//PPP8cQTTyA+Ph79+/fHRx99BKPRiLS0NItySqUSoaGh5ikgIKBjNXJgwT6mbnJFlXV2joSIyLXwQB25MpVcigdv7ImfnxmLd/80DEMi/KBrNOLLvXlIfmMnZq7Zi50nSznYAlEHWJUM6fV6ZGZmIjk5+dIKJBIkJycjIyOjXeuora1FQ0MDunXrZjE/PT0dwcHBiI2NxeOPP47y8nJrQnMK/UJ9AACFVfU8vU1EZEM8UEfuQCoRcMfQcHz35E1Y/+iNuH1gCAQB2HGyFDPW7EXKyp1YtzcP9Q0Ge4dK5DSsSobKyspgMBgQEhJiMT8kJARqtbpd63juuecQHh5ukVBNmDABn376KdLS0vDqq69ix44dmDhxIgyG1r/MOp0OGo3GYnIGvio5YoK8AAA5vLEaEZFNOMqBOmdtm8j5CIKAxF6B+GDGCKT/dSweHhUNL4UUJ4trsGBjDkYt+xlvbDuBkup6e4dK5PC6dDS5ZcuWYd26dfjmm2+gUqnM8++//37cddddGDJkCKZOnYoffvgB+/btQ3p6eqvrWbp0Kfz8/MxTVFRUF9Xg+g2J8AMA7D5TZudIiIhcg6McqHPmtomcV89AL7x01yDsXjgef5s0ABH+Hrio1ePtn0/j5mW/4JmvDuJoIRNzorZYlQwFBQVBKpWiuLjYYn5xcTFCQ0OvuuyKFSuwbNkybNu2DUOHDr1q2V69eiEoKAinT59u9fWFCxeiqqrKPOXn51tTDbuaNCQMALB+Xz409Q12joaIiGx1oM6Z2yZyfn4ecsy5pRd2PDsWq/40HMN7+ENvMOK/Bwow6e1fMf2D37HlsJr3KyK6jFXJkEKhQEJCgkWf6uY+1klJSW0u99prr2HJkiXYsmULRowYcc33KSgoQHl5OcLCwlp9XalUwtfX12JyFrcNDEGvIC9U1jbgHz8cs3c4REROz1EO1Dlz20SuQyaVYPLQMGx84iZ888Qo3DE0DFKJgIyz5Xjss0yMWZ6O99LPoILXLhMB6EA3udTUVHz44Yf45JNPcOzYMTz++OPQarWYNWsWAGDGjBlYuHChufyrr76KF198EWvWrEF0dDTUajXUajVqamoAADU1NXj22Wfx+++/49y5c0hLS8OUKVPQp08fpKSk2KiajkMqEbDsj0MhCMD6/fn46WjxtRciIqI2OcqBOiJHM6xHAN7903DsnD8Oj4/tjQBPOS5U1uHVLcdx49I0zP/6IA7zGmZyc1YnQ9OmTcOKFSuwaNEixMfHIzs7G1u2bDH31c7Ly0NRUZG5/HvvvQe9Xo977rkHYWFh5mnFihUAAKlUikOHDuGuu+5Cv3798MgjjyAhIQG//vorlEqljarpWEbGdMOfb4oBADyz4SAKKmrtHBERkXPjgTqitkX4e+C5Cf2RsXA8XrtnKAaF+0LXaMRX+wtwxzu/4Z73duP7g4VoYBc6ckOC6AKD0ms0Gvj5+aGqqsppuiXoGg24b3UGDhZUIS7KHxv+kgSFrEvHsyAisglH2Qe/++67WL58OdRqNeLj4/H2228jMTERADB27FhER0dj7dq1AIDo6GicP3/+inUsXrwYL730Eurq6jB16lRkZWWhsrIS4eHhuP3227FkyZIrBmpoi6NsF6LLiaKIzPMV+CTjPP6XU4RGo+mnYIivEg8k9sT0kT3Q3cc1D0iTe7Bm/8tkyI7yL9bijnd+Q1VdA2Ym9cTLUwbbOyQiIqs56z64s3G7kDMo1tTj8z15+GLPeZTVmK4jUjRddzRzVDTio/ztGyBRB1iz/+WpCDuK6uaJN+6LAwB8knEea3fl2jkiIiIicichviqk3tYPuxbcipXT4hEfZRqF7pusC5i6ahemrNqFb7IKoGvkjVzJNfHMkANY9ctpLN96AoIAvPfAcEwYzItzich5OPs+uLNwu5CzOphfiU92n8MPh4qgb7qOqJuXAvcmRGL6yB6IbrqBPJGjYjc5JyOKIv626TC+2JMHmUTA6/fFYUp8hL3DIiJqF2ffB3cWbhdydmU1Ony5Jw+f78mDWlNvnn9znyA8kNgDyQNDIJeykxE5HiZDTqjRYMQzGw7i2+xCCAKwcGJ/zL65FyQSwd6hERFdlSvsgzsDtwu5ikaDEb+cKMXne85jx8lSNP9y7O6jxLQRUbh/ZBQiAzztGyRRC0yGnJTRKOLl74/gkwzTCEej+wZh+T1xCPVTXWNJIiL7cZV9sK1xu5Aryr9Yi3X78rB+XwHKanQAAEEAxvbrjj8l9sS42O6Q8WwR2RmTIScmiiI+25OHV344Cl2jESq5BI/cHINHR/eGn6fc3uEREV3BlfbBtsTtQq5M32jET8eK8fme89h1utw8P8xPhXsSIvHH4ZG8tojshsmQCzhdUo3n/puDzPMVAAAPuRT3JERi1k3R6NXd287RERFd4or7YFvgdiF3kVumxZd787Bhfz4qahvM80dGd8M9CZGYNDQM3kqZHSMkd8NkyEWIoojtR4vx5k+ncKxIAwCQSgQsvnMgZiRF2zc4IqImrroPvl7cLuRu6hsM2Ha0GF9nFuDXU5euLfKQSzFxSCjuTYhCYkw3Xg9NnY7JkIsRRREZZ8vxwc6zSD9RCqlEwHdP3oRB4X72Do2IyOX3wR3F7ULurKiqDhsPXMB/Mwtwtkxrnh8Z4IG7h0Xgzrhw9A3xsWOE5MqYDLkoURTx+GcHsOWIGvckRGLFvXH2DomIyG32wdbidiEy/XY5kFeJrzPz8f3BItToGs2v9Q/1wZ1x4bhzaDh6BHI0OrIda/a/7MDpRARBwIxRPbHliBppx4rRaDByxBYiIiJyWIIgIKFnABJ6BmDRHYOw7aga32UXYuepUhxXV+O4+gSWbz2B+Ch/3BkXjklDQhHm52HvsMmNMBlyMiOju8HPQ46K2gbkXKjCsB4B9g6JiIiI6Jo8FFJMiY/AlPgIVNbqsfWIGt8dLETGmXJk51ciO78SS344irhIP9w2MAS3DwpF32BvCAKvMaLOw2TIycikEtwQ3Q0/HSvG/nMVTIaIiIjI6fh7KjDthh6YdkMPlFTX4385anx/sBCZeRU4WFCFgwVVWLHtJKIDPc2J0fAeAZBy8AWyMSZDTmhEdAB+OlaMvecuYs4tvewdDhEREVGHBfuoMHNUNGaOikZJdT3SjpVg+9Fi/Ha6DOfKa/Hhr7n48Ndc+HvKcXOfINzStztG9wtidzqyCSZDTujGXoEAgN2ny1DfYIBKLrVzRERERETXL9hHhekje2D6yB6o0TVi58lSbD9ajLRjxaisbcAPh4rww6EiAEC/EG+M7tsdo/sG4YbobvDivYyoA/hf44SGRvghxFeJYo0O6SdKMWFwqL1DIiIiIrIpb6UMk4aEYdKQMDQYjDiYX4mdJ0ux41QZDhVU4mRxDU4W1+Dj33IhlQgYHO6LkTHdMDImEDdEB8DfU2HvKpAT4NDaTmrp5mN4f+dZBHkrMS+5L5J6B6JnN0+OLkdEXc4d98Htwe1C1HkqtHrsOlOGnSdLset0OS5U1l1Rpn+oD4b3DEB8pD+GRvmhb7APrzlyE7zPkBuo0TXirnd/w9nSSzcyU8gk6NPdG/1CvNGruzd6dPNEj0BP9OjmiUAvBUdjIaJO4Y774PbgdiHqOgUVtdh37iL25lZgb245zrT4fdTMUyHF4HA/xEX5YUikPwaE+iAmyIsHkl0QkyE3UV3fgE8zziP9RAkOX9CgrsHQZlkvhRRR3TwRGeCJMD8VQv1UCPFVIdTX9DjUTwVv9rUlog5w133wtXC7ENlPabUO+89dRHZBJQ7mVyKnoApa/ZW/kxRSCXoHe6N/qA/6h/ogtmkK9VXxILITYzLkhoxGEQUVdThRXI2TxdU4X65F3sVa5JXXokhTj/Z8yt5KGUJ8lQjyViLQW4FAr+a/CgR6K9HNS4EgbwW6eSnh7yGHhKeaiQjcB7eF24XIcRiMIs6W1iA7vxKHCqpwuLAKJ9XVrSZIAOAhl6JnoCdigrwQHeSFmEAvxHT3QnSgF4K82dvG0XV6MrRq1SosX74carUacXFxeOeddzBy5Mg2y2/YsAEvvvgizp07h759++LVV1/FpEmTzK+LoojFixfjww8/RGVlJW666Sa899576Nu3b7viYYNzdbpGAy5U1CHvYi0KKupQrKlHUVU9ijX1UFeZpmpdo1XrlEoEBHjK4efR+uTb4rG/p8LiNZVcwp0IkQvhPrh13C5Ejq35QPJxtQYn1NU4XlyN40UanCuvhcHY9s9jD7kUYf4qRPh7IMxPhXB/D4T7eSDc3wNh/iqE+3nAQ8GRfu3Jmv2v1f2i1q9fj9TUVKxevRqJiYlYuXIlUlJScOLECQQHB19Rfvfu3Zg+fTqWLl2KO+64A1988QWmTp2KAwcOYPDgwQCA1157DW+//TY++eQTxMTE4MUXX0RKSgqOHj0KlUplbYh0GaVMil7dTdcRtUWra4RaU4/iqnqUafW4WKNDuVZvmmp0KK/R46JWj7IaHTT1jTAYRZTV6FFWo7c6HplEgLdKBi+FDD4qGbyUMni3mLyUMnirZPBWSuGtlMNLKTWVU8jMy3kopFDJpfCQS6GQsa8vETnegToicmwSiWC6tjrQE7cPujQyb4PBiPyLtThXrkVuWS3OlWmbHmtxobIOdQ0GnC3VWly3fTkvhRRBPkoEeikQ5K1EkI+p502Qt+l5oJcCfi0OKnvIpTxQbCdWnxlKTEzEDTfcgHfffRcAYDQaERUVhf/7v//DggULrig/bdo0aLVa/PDDD+Z5N954I+Lj47F69WqIoojw8HA888wz+Otf/woAqKqqQkhICNauXYv777//mjHx6FvX0jcaUVGrR3mNHlV1DU1Ty8cNqKprND/WtJh/tSMtHSWTCPCQS6FSmJKjS48l8FTITM/lUngoJBavK6QSKGUSKJonqSmxspx35fOWj7njInKMffD69esxY8YMiwN1GzZsuOqBultuucXiQN2rr75qcaDu1VdfxdKlSy0O1OXk5LT7QJ0jbBcisq36BgPUVfUorKxDYdPfoqo6XKisR1FlHQor69rsenc1cqkAX9Wl3jWXetiYDhJ7KWTwVEjhqZDBS2n6a3ouhZfS9Lj5YLFCKnH7Sxk6rZucXq+Hp6cnvv76a0ydOtU8f+bMmaisrMS33357xTI9evRAamoq5s2bZ563ePFibNq0CQcPHsTZs2fRu3dvZGVlIT4+3lxmzJgxiI+Px1tvvXXNuNjgOAdRFKHVG1Bd3wCtrhHV9Y3Q6gyo0TWgRmdATX1D0+uN0OoaUdM81TdCqzf9rdY1ok5vQK2+EZ2QV1mtOSmSSgTIJAJkUgEyiQQyqQCpRIBcYnpN3vTc4jVpy9ckkEuaykglkDU9FgRAKgiQND2WCILpuQAIgmB6Lrn0WCKgabkrHzcv27weiaTpb9MkCIAAwJTftXwuoHmXKghNE0wvtvp6i2XR4nnTU1P5y97r0rKX1idctuwV67/Kfv5SRJfNv+oybcy/antiu/cxLdfG+q66TFvv0/ZSrS2jkEkQ4tuxM/GOsA/mgToicgSiKKJa14jyGlNvmvIaHUpr9Cir1qFcq0NZtWn+Ra0emnrTgeIGg+1/0MilApQyqfmArlImgVJ26aCvUt58wFdqPshr8Vulxe8R02+bpt8qUsvfKha/fZrmN/8ukbb4bSKRtPG4uYwETb9RLv1+kQiCOSm0Vqd1kysrK4PBYEBISIjF/JCQEBw/frzVZdRqdavl1Wq1+fXmeW2VuZxOp4NOpzM/12g01lSD7EQQBHNXuOsliiIaDCLqGgyo0xss/ta3Mq/5cX2Lx3qDEboGI/QGI/SNpklnfmyArmley9cbL8vAmucTObvhPfyx8Ymb7B1Gh+j1emRmZmLhwoXmeRKJBMnJycjIyGh1mYyMDKSmplrMS0lJwaZNmwAAubm5UKvVSE5ONr/u5+eHxMREZGRktJoMsW0iIkEwneHxVckRE+R1zfKiaPotY+5ZU9sATb1l75pafSO0egNqdU1/9aaDyXV6A7T6RtTqDdDqGqFr8XukwSCiwdCIGt1V3twJ/L/xfZF6W79OfQ+nHEt56dKlePnll+0dBtmRIAhQyAQoZJIOHTHoKINRRENTEqUzGEwJkkFEo9GUKJkeizAYjWgwiDAYxab5LV83muY3lW00mtZhMIpoMBphMIhoMIoQRdM8owiLx0ax6TWx9deMFuXQ9FqL15sfm+dfWo8oAiKaHgPmUQhFmJ40zxMhXnqtxTKwWObK9aHF+sTL1mdel3m9l8fQcn1X+5Raf/Fqy7T10tVOnLe9zFXepwPra/sF28atlDnvxb6OcqCObRMRWUsQhKYubzKE+Xlc17oaDUbUNRjMB3J1DUbzgV1d00FeXaOh6XnzbxkjdA2mA8TNvz8MRmOL3zOm3z2mv02vXfYbxtDi902DocVvFONlvzsu+41i8bhFGYPR1N4bRREKaed397MqGQoKCoJUKkVxcbHF/OLiYoSGhra6TGho6FXLN/8tLi5GWFiYRZmW3eZaWrhwocURPY1Gg6ioKGuqQtQhUokAqcR0DRLQdUkYETk+tk1EZE8yqQQ+vIGs1azaYgqFAgkJCUhLSzPPMxqNSEtLQ1JSUqvLJCUlWZQHgO3bt5vLx8TEIDQ01KKMRqPBnj172lynUqmEr6+vxURERO6psw/UtXedbJuIiJyP1eljamoqPvzwQ3zyySc4duwYHn/8cWi1WsyaNQsAMGPGDIt+20899RS2bNmC119/HcePH8dLL72E/fv348knnwRgOj04b948vPLKK/juu++Qk5ODGTNmIDw83GKQBiIiotY4yoE6IiJyPlZfMzRt2jSUlpZi0aJFUKvViI+Px5YtW8z9qvPy8iCRXMqxRo0ahS+++AIvvPACnn/+efTt2xebNm0yD10KAPPnz4dWq8Wjjz6KyspK3HzzzdiyZQvvMURERO2SmpqKmTNnYsSIERg5ciRWrlx5xYG6iIgILF26FIDpQN2YMWPw+uuvY/LkyVi3bh3279+PDz74AIDlgbq+ffuah9bmgToiItdi9X2GHFFVVRX8/f2Rn5/PbglERF2s+dqYyspK+Pn52S2Od99913zT1fj4eLz99ttITEwEAIwdOxbR0dFYu3atufyGDRvwwgsvmG+6+tprr7V609UPPvjAfKDuX//6F/r1a9/IRmybiIjsw5p2ySWSoYKCAl6kSkRkZ/n5+YiMjLR3GA6DbRMRkX21p11yiWTIaDSisLAQPj4+bd60sDXNWaOrHbVjvZyLK9bLFesEsF5tEUUR1dXVCA8Pt+gm7e462jZ1Flf5/2U9HAvr4VhYDxNr2iWnvM/Q5SQSyXUdjXTVUX9YL+fiivVyxToBrFdr7Nk9zlFdb9vUWVzl/5f1cCysh2NhPdrfLvEQHhERERERuSUmQ0RERERE5JbcOhlSKpVYvHgxlEqlvUOxKdbLubhivVyxTgDrRc7NVT5n1sOxsB6OhfWwnksMoEBERERERGQttz4zRERERERE7ovJEBERERERuSUmQ0RERERE5JaYDBERERERkVtyuWRo1apViI6OhkqlQmJiIvbu3XvV8hs2bED//v2hUqkwZMgQbN682eJ1URSxaNEihIWFwcPDA8nJyTh16lRnVqFV1tTrww8/xOjRoxEQEICAgAAkJydfUf7hhx+GIAgW04QJEzq7GhasqdPatWuviFelUlmUccbPauzYsVfUSxAETJ482VzG3p/Vzp07ceeddyI8PByCIGDTpk3XXCY9PR3Dhw+HUqlEnz59sHbt2ivKWPtdtTVr67Vx40bcdttt6N69O3x9fZGUlIStW7dalHnppZeu+Kz69+/fibW4krX1Sk9Pb/V/UK1WW5Sz9+dFlt577z0MHTrUfEPCpKQk/O9//2uzvKPuQ62thzPsMwFg2bJlEAQB8+bNu2o5R/8N0p56OMNvjvbUw1G/Iy21px6O+B3pSNvYld8Nl0qG1q9fj9TUVCxevBgHDhxAXFwcUlJSUFJS0mr53bt3Y/r06XjkkUeQlZWFqVOnYurUqTh8+LC5zGuvvYa3334bq1evxp49e+Dl5YWUlBTU19d3VbWsrld6ejqmT5+OX375BRkZGYiKisLtt9+OCxcuWJSbMGECioqKzNOXX37ZFdUBYH2dANNdiFvGe/78eYvXnfGz2rhxo0WdDh8+DKlUinvvvdeinD0/K61Wi7i4OKxatapd5XNzczF58mSMGzcO2dnZmDdvHmbPnm2ROHTk87c1a+u1c+dO3Hbbbdi8eTMyMzMxbtw43HnnncjKyrIoN2jQIIvP6rfffuuM8Ntkbb2anThxwiLu4OBg82uO8HmRpcjISCxbtgyZmZnYv38/br31VkyZMgVHjhxpcxlH3IdaWw9n2Gfu27cP77//PoYOHXrVco7+G6S99XD03xztrQfgmN+RZu2th6N+R6xpG7v8uyG6kJEjR4pz5841PzcYDGJ4eLi4dOnSVsvfd9994uTJky3mJSYmin/5y19EURRFo9EohoaGisuXLze/XllZKSqVSvHLL7/shBq0ztp6Xa6xsVH08fERP/nkE/O8mTNnilOmTLF1qO1mbZ3+/e9/i35+fm2uz1U+qzfffFP08fERa2pqzPPs/Vm1BED85ptvrlpm/vz54qBBgyzmTZs2TUxJSTE/v97tZGvtqVdrBg4cKL788svm54sXLxbj4uJsF9h1ak+9fvnlFxGAWFFR0WYZR/u8qHUBAQHiRx991OprzrIPFcWr1+NyjrbPrK6uFvv27Stu375dHDNmjPjUU0+1WdaRf4NYU4/LOdJvDmvq4cjfkev5PBzhO2Jt29jV3w2XOTOk1+uRmZmJ5ORk8zyJRILk5GRkZGS0ukxGRoZFeQBISUkxl8/NzYVarbYo4+fnh8TExDbXaWsdqdflamtr0dDQgG7dulnMT09PR3BwMGJjY/H444+jvLzcprG3paN1qqmpQc+ePREVFXXFkUNX+aw+/vhj3H///fDy8rKYb6/PqiOu9b2yxXZyBEajEdXV1Vd8r06dOoXw8HD06tULDzzwAPLy8uwUoXXi4+MRFhaG2267Dbt27TLPd5XPy5UZDAasW7cOWq0WSUlJbZZz9H1oe+vRkqPtM+fOnYvJkydfsQ9sjSP/BrGmHpdzpN8c1tbDUb8j1/N5OMp3xJq2sau/GzKrl3BQZWVlMBgMCAkJsZgfEhKC48ePt7qMWq1utXxzX/nmv1cr09k6Uq/LPffccwgPD7f4p5kwYQLuvvtuxMTE4MyZM3j++ecxceJEZGRkQCqV2rQOl+tInWJjY7FmzRoMHToUVVVVWLFiBUaNGoUjR44gMjLSJT6rvXv34vDhw/j4448t5tvzs+qItr5XGo0GdXV1qKiouO7/aUewYsUK1NTU4L777jPPS0xMxNq1axEbG4uioiK8/PLLGD16NA4fPgwfHx87Rtu2sLAwrF69GiNGjIBOp8NHH32EsWPHYs+ePRg+fLhN9kHUOXJycpCUlIT6+np4e3vjm2++wcCBA1st68j7UGvq0ZKj7TPXrVuHAwcOYN++fe0q76i/Qaytx+Uc5TeHtfVw1O/I9XwejvIdsbZt7OrvhsskQ9S6ZcuWYd26dUhPT7e4EPD+++83Px4yZAiGDh2K3r17Iz09HePHj7dHqFeVlJRkcaRw1KhRGDBgAN5//30sWbLEjpHZzscff4whQ4Zg5MiRFvOd7bNyB1988QVefvllfPvttxbX1kycONH8eOjQoUhMTETPnj3x1Vdf4ZFHHrFHqNcUGxuL2NhY8/NRo0bhzJkzePPNN/Gf//zHjpHRtcTGxiI7OxtVVVX4+uuvMXPmTOzYsaPVRMKR96HW1KMlR9pn5ufn46mnnsL27duvuOjemVxvPRzlN0dH6uGI35Hr/Twc5Tvi6G2jy3STCwoKglQqRXFxscX84uJihIaGtrpMaGjoVcs3/7VmnbbWkXo1W7FiBZYtW4Zt27Zd84K7Xr16ISgoCKdPn77umK/leurUTC6XY9iwYeZ4nf2z0mq1WLduXbt2Cl35WXVEW98rX19feHh42OTzt6d169Zh9uzZ+Oqrr67ZZcHf3x/9+vVz2M+qLSNHjjTH7OyflytTKBTo06cPEhISsHTpUsTFxeGtt95q17KOtA/tSD0cbZ+ZmZmJkpISDB8+HDKZDDKZDDt27MDbb78NmUwGg8FwxTKO+BukI/Vo5ki/Oa6nHs0c4TtyPfVwtO9IS9dqG7v6u+EyyZBCoUBCQgLS0tLM84xGI9LS0trse5yUlGRRHgC2b99uLh8TE4PQ0FCLMhqNBnv27Gl3f+br1ZF6AaZRNpYsWYItW7ZgxIgR13yfgoIClJeXIywszCZxX01H69SSwWBATk6OOV5n/qwA0xCSOp0ODz744DXfpys/q4641vfKFp+/vXz55ZeYNWsWvvzyS4thSttSU1ODM2fOOOxn1Zbs7GxzzM78ebkbo9EInU7XrrKOuA9t1p56ONo+c/z48cjJyUF2drZ5GjFiBB544AFkZ2e32vXIEX+DdKQegOP95uhoPVpyhO/I9dTD0b4jLV2rbezy74bVQy44sHXr1olKpVJcu3atePToUfHRRx8V/f39RbVaLYqiKD700EPiggULzOV37dolymQyccWKFeKxY8fExYsXi3K5XMzJyTGXWbZsmejv7y9+++234qFDh8QpU6aIMTExYl1dncPWa9myZaJCoRC//vprsaioyDxVV1eLomgaleSvf/2rmJGRIebm5oo//fSTOHz4cLFv375ifX29Q9bp5ZdfFrdu3SqeOXNGzMzMFO+//35RpVKJR44csai3s31WzW6++WZx2rRpV8x3hM+qurpazMrKErOyskQA4htvvCFmZWWJ58+fF0VRFBcsWCA+9NBD5vJnz54VPT09xWeffVY8duyYuGrVKlEqlYpbtmwxl7nWdnLEen3++eeiTCYTV61aZfG9qqysNJd55plnxPT0dDE3N1fctWuXmJycLAYFBYklJSUOW68333xT3LRpk3jq1CkxJydHfOqpp0SJRCL+9NNP5jKO8HmRpQULFog7duwQc3NzxUOHDokLFiwQBUEQt23bJoqi8+xDra1HM0feZza7fNQvZ/0Ncq16OMNvjvbUw1G/I9bWo5kjfUeu1Tba+7vhUsmQKIriO++8I/bo0UNUKBTiyJEjxd9//9382pgxY8SZM2dalP/qq6/Efv36iQqFQhw0aJD4448/WrxuNBrFF198UQwJCRGVSqU4fvx48cSJE11RFQvW1Ktnz54igCumxYsXi6IoirW1teLtt98udu/eXZTL5WLPnj3FOXPmdPkPG2vqNG/ePHPZkJAQcdKkSeKBAwcs1ueMn5UoiuLx48dFAObGvyVH+Kyah16+fGqux8yZM8UxY8ZcsUx8fLyoUCjEXr16if/+97+vWO/VtlNXsLZeY8aMuWp5UTQNIR4WFiYqFAoxIiJCnDZtmnj69GmHrterr74q9u7dW1SpVGK3bt3EsWPHij///PMV67X350WW/vznP4s9e/YUFQqF2L17d3H8+PEW+xBn2YdaWw9RdPx9ZrPLf7Q662+Qa9XDWX5zXKsejvoduVx7/q8c7TtyrbbR3t8NQRRF0frzSURERERERM7NZa4ZIiIiIiIisgaTISIiIiIicktMhoiIiIiIyC0xGSIiIiIiIrfEZIiIiIiIiNySzN4B2ILRaERhYSF8fHwgCIK9wyEiciuiKKK6uhrh4eGQSHiMrRnbJiIi+7CmXXKJZKiwsBBRUVH2DoOIyK3l5+cjMjLS3mE4DLZNRET21Z52ySWSIR8fHwCmCvv6+to5GiIi96LRaBAVFWXeF5MJ2yYiIvuwpl2yeTK0c+dOLF++HJmZmSgqKsI333yDqVOntlk+PT0d48aNu2J+UVERQkND2/Wezd0PfH192eAQEdmJI3cFe+mll/Dyyy9bzIuNjcXx48fbXGbDhg148cUXce7cOfTt2xevvvoqJk2a1O73ZNtERGRf7WmXbN65W6vVIi4uDqtWrbJquRMnTqCoqMg8BQcH2zo0IiJyY4MGDbJoZ3777bc2y+7evRvTp0/HI488gqysLEydOhVTp07F4cOHuzBiIiLqbDY/MzRx4kRMnDjR6uWCg4Ph7+9v63CIiIgAADKZrN09Dt566y1MmDABzz77LABgyZIl2L59O959912sXr26M8MkIqIu5DDD/sTHxyMsLAy33XYbdu3a1aXvnXn+Ij7fcx7Z+ZVd+r5ERNR1Tp06hfDwcPTq1QsPPPAA8vLy2iybkZGB5ORki3kpKSnIyMjo7DDN/pdThE8zzqGkur7L3pOIyN3YfQCFsLAwrF69GiNGjIBOp8NHH32EsWPHYs+ePRg+fHiry+h0Ouh0OvNzjUZzXTH8eEiNNbty8cTY3oiP8r+udRERkeNJTEzE2rVrERsbi6KiIrz88ssYPXo0Dh8+3OoFtmq1GiEhIRbzQkJCoFar23wPW7dNK7adwJlSLfqF+CDYR3Vd6yIiotbZPRmKjY1FbGys+fmoUaNw5swZvPnmm/jPf/7T6jJLly694kJYWxBtvkYiInIELbtvDx06FImJiejZsye++uorPPLIIzZ5D1u3Td5KUxOt1TXabJ1ERGTJYbrJtTRy5EicPn26zdcXLlyIqqoq85Sfn9+F0RERkbPz9/dHv3792mxrQkNDUVxcbDGvuLj4qtcc2bpt8lQ0JUN6w3Wth4iI2uaQyVB2djbCwsLafF2pVJqHKrXFkKXNo+6JPDVEROQWampqcObMmTbbmqSkJKSlpVnM2759O5KSktpcp63bJi+eGSIi6nQ27yZXU1NjcaQtNzcX2dnZ6NatG3r06IGFCxfiwoUL+PTTTwEAK1euRExMDAYNGoT6+np89NFH+Pnnn7Ft2zZbh0ZERG7qr3/9K+6880707NkThYWFWLx4MaRSKaZPnw4AmDFjBiIiIrB06VIAwFNPPYUxY8bg9ddfx+TJk7Fu3Trs378fH3zwQZfF7K2UAgBq6pkMERF1FpsnQ/v377e4iWpqaioAYObMmVi7di2KioosRvDR6/V45plncOHCBXh6emLo0KH46aefWr0Ra2dx3NsEEhGRLRQUFGD69OkoLy9H9+7dcfPNN+P3339H9+7dAQB5eXmQSC51lhg1ahS++OILvPDCC3j++efRt29fbNq0CYMHD+6ymP085ACAqrqGLntPIiJ3Y/NkaOzYsRCv0t9s7dq1Fs/nz5+P+fPn2zqMDhE5hAIRkUtat27dVV9PT0+/Yt69996Le++9t5MiujZ/TwUA4GKt3m4xEBG5Ooe8ZoiIiMjddfMyJUOVTIaIiDoNkyFcGkCBJ4aIiMhR+HuauslVaNlNjoioszAZIiIickABTd3kKnhmiIio0zAZAiA0nRriiSEiInIUzd3kymp0do6EiMh1MRkiIiJyQOH+HgCAsho96ht441Uios7AZKiFq42CR0RE1JUCPOVQyU3NtLqq3s7REBG5JiZD4H2GiIjI8QiCYD47VFhZZ+doiIhcE5OhFnhiiIiIHElEUzJUwGSIiKhTMBkCeGqIiIgcUs9ATwDA2VKtnSMhInJNTIZa4IkhIiJyJP1CfAAAp4qr7RwJEZFrYjIEQGg6NcRuckRE5Ej6BpuSoZMlTIaIiDoDkyEAkqZuciLPDRERkQPpF+INAMi/WIcaXaOdoyEicj1MhgBIBJ4ZIiIixxPorTQPonAov9K+wRARuSAmQwCaciEYmQ0REZGDGd4zAACw/3yFnSMhInI9TIZgupcDwDNDRETkeBJ6+ANgMkRE1BmYDOHSyNo8M0RERI5mZEwgAGBvbjnqGwx2joaIyLUwGUKLa4bsHAcREdHlBoT5INRXhfoGIzLOlts7HCIil8JkCC1Gk+OZISIicjCCIODWAcEAgLRjxXaOhojItTAZQosBFIz2jYOIiKg1tw0MAQBszlFD38jGiojIVpgMocUACuwoR0REDmh0nyAE+yhxUavHz8dL7B0OEZHLYDKES9cMGZkLERGRA5JJJfjD8AgAwFf78+0cDRGR62AyBN5niIiIHN/9N/SAIAA/Hy/ByeJqe4dDROQSmAzh0gAK7CVHRESOKibICykDQwEAq3ecsXM0RESugckQWnaTYzZERESO6/GxvQEA32YX4hTPDhERXTcmQy3wmiEiInJkcVH+uG1gCAxGEf/YfMze4RAROT0mQ+BNV4mIyHk8P2kA5FIB6SdKsfWI2t7hEBE5NSZDuHTNELvJERGRo4sJ8sLs0b0AAH/7JgcXtXo7R0RE5LyYDKHFfYaYDBERkRN4anxf9A32RlmNHs9vzGH7RUTUQUyGcOnMENsSIiJyBiq5FG/cFw+5VMCWI2qs3nHW3iERETklJkO4dGbIwBEUiIhc3rJlyyAIAubNm9dmmbVr10IQBItJpVJ1XZDtMCTSD4vvHAQAeG3rcfxyosTOEREROR8mQwDkUlMy1MhkiIjIpe3btw/vv/8+hg4des2yvr6+KCoqMk/nz5/vggit80BiD9x/QxREEXjiswPIPH/R3iERETkVJkMAZBLTZmgwGO0cCRERdZaamho88MAD+PDDDxEQEHDN8oIgIDQ01DyFhIR0QZTWEQQBL08ZhFv6dUddgwEP/3sfjhRW2TssIiKnYfNkaOfOnbjzzjsRHh4OQRCwadOmay6Tnp6O4cOHQ6lUok+fPli7dq2tw7oqWfOZIQPPDBERuaq5c+di8uTJSE5Oblf5mpoa9OzZE1FRUZgyZQqOHDly1fI6nQ4ajcZi6gpKmRSrHxyOG6IDUF3fiD99uIdniIiI2snmyZBWq0VcXBxWrVrVrvK5ubmYPHkyxo0bh+zsbMybNw+zZ8/G1q1bbR1am+RS02ZoNPLMEBGRK1q3bh0OHDiApUuXtqt8bGws1qxZg2+//RafffYZjEYjRo0ahYKCgjaXWbp0Kfz8/MxTVFSUrcK/Jk+FDB8/fAOG9/BHVV0DHvhoD9KOFXfZ+xMROStB7MTxOAVBwDfffIOpU6e2Wea5557Djz/+iMOHD5vn3X///aisrMSWLVva9T4ajQZ+fn6oqqqCr6+v1XFuO6LGo//JRHyUPzbNvcnq5YmI3Nn17oM7W35+PkaMGIHt27ebrxUaO3Ys4uPjsXLlynato6GhAQMGDMD06dOxZMmSVsvodDrodDrzc41Gg6ioqC7dLnV6A+Z+cQA/Hy+BVCLg+UkD8Oebos0DBRERuQNr2iW7XzOUkZFxRZeFlJQUZGRktLmMrbsiNJ8Z4mhyRESuJzMzEyUlJRg+fDhkMhlkMhl27NiBt99+GzKZDAaD4ZrrkMvlGDZsGE6fPt1mGaVSCV9fX4upq3kopHj/oQTckxAJg1HEkh+O4ql12ajVN3Z5LEREzsDuyZBarb7iotSQkBBoNBrU1dW1uoytuyI0XzPEARSIiFzP+PHjkZOTg+zsbPM0YsQIPPDAA8jOzoZUKr3mOgwGA3JychAWFtYFEV8fuVSC5fcMxaI7BkImEfDdwUL8YdVuHFd3zTVMRETOxO7JUEcsXLgQVVVV5ik/P/+61tc8mhyH1iYicj0+Pj4YPHiwxeTl5YXAwEAMHjwYADBjxgwsXLjQvMzf//53bNu2DWfPnsWBAwfw4IMP4vz585g9e7a9qmEVQRDw55tj8MWcGxHkrcSJ4mrc9c4uvL/jDHtBEBG1YPdkKDQ0FMXFlhd5FhcXw9fXFx4eHq0uY+uuCOb7DPHMEBGRW8rLy0NRUZH5eUVFBebMmYMBAwZg0qRJ0Gg02L17NwYOHGjHKK03MqYbNj91M8b3D4beYMTS/x3H/R9kILdMa+/QiIgcgszeASQlJWHz5s0W87Zv346kpKQui0Embb7PEI+WERG5g/T09Ks+f/PNN/Hmm292XUCdKNhHhY9mjsCG/QV4+fsj2HeuAilv7sRfxvTC3HF9oJJfu5sgEZGrsvmZoZqaGnOfbMA0dHZ2djby8vIAmLq4zZgxw1z+sccew9mzZzF//nwcP34c//rXv/DVV1/h6aeftnVobZJJms4McWhtIiJyQYIg4L4borBl3i24pV936A1GvPPzaSS/sQM/HeUQ3ETkvmyeDO3fvx/Dhg3DsGHDAACpqakYNmwYFi1aBAAoKioyJ0YAEBMTgx9//BHbt29HXFwcXn/9dXz00UdISUmxdWhtUspMm0HXyGSIiIhcV1Q3T3wy6wasfnA4wv1UKKiow+xP9+OBj35HTkGVvcMjIupynXqfoa5yvfe4KKioxc2v/gKlTIITr0zshAiJiFyXo99nyF4cfbvU6hvxdtpprPktF/qma2bvGBqGv94ei+ggLztHR0TUcU51nyFH4NHUX1rXaISRo+wQEZEb8FTIsGBif6Q9MwZ3D4uAIAA/HCpC8hs78OKmwyisbP32FkREroTJEEw3qWtW33jtm+8RERG5iqhunnhjWjx+/L/RGBvbHY1GEf/5/TzGLP8FCzceQl55rb1DJCLqNEyGAKhkl5KhWj2TISIicj8Dw32xdtZIfDnnRtzYqxsaDCK+3JuPca+nI/WrbJwuqbF3iERENmf3obUdgUQiQCWXoL7BiDomQ0RE5MaSegciqXcS9p27iHd/Po0dJ0ux8cAFfJN1AckDQvDIzTFIjOkGQRDsHSoR0XVjMtTEQy41JUMNTIaIiIhuiO6GT/48EgfzK/HOz6fx07FibD9qmgZH+OKRm2MweUg4FDJ2MiEi58U9WJPmQRR4ZoiIiOiSuCh/fDRzBH5KHYM/JfaASi7B4QsaPL3+IEa/9jNW/XIaFVq9vcMkIuoQJkNNmgdR0Oob7RwJERGR4+kT7I1//mEIMhaMx7MpsQj2UaJYo8PyrSeQuDQNqeuzkXn+Ilzgjh1E5EbYTa6Jn4ccAKCpYzJERETUlgAvBeaO64M5o3vhh0OFWLMrF4cvaLAx6wI2Zl1A/1AfPHhjT0wdFgFvJX9mEJFj416qyaVkqMHOkRARETk+hUyCu4dH4u7hkTiYX4nPfj+P7w8V4ri6Gi9sOoylm49h6rAIPJDYEwPDHe+ms0REAJMhs+ZkqLKO/Z6JiIisERflj7gof7wweSD+e6AAn+85jzOlWny+Jw+f78nD8B7+uP+GHpg0NIxni4jIoXCP1MTfUwEAqOKZISIiog7x85TjzzfHYNZN0fj97EV8tuc8th5W40BeJQ7kVeKl749g8pAw3HdDFEb0DODw3ERkd0yGmvg2nRliMkRERHR9BEFoul9RIEqq6/F1ZgG+3l+As2VabMgswIbMAsQEeeGehEj8cXgkQv1U9g6ZiNwUk6Em/uZkiAMoEBER2UqwjwpPjO2Dx8f0Rub5Cny1Px8/HCpCbpkWy7eewOvbTmBMv+64b0QUxg8I4X2LiKhLMRlq0s3L1E2uvEZn50iIiIhcjyAIGBHdDSOiu2HxnYPwY04RNuzPx75zFfjlRCl+OVGKbl4KTI2PwH03RKJ/KAddIKLOx2SoSbCvEgBQrKm3cyRERESuzUspw30jonDfiCicLa3BhswC/DezACXVOqzZlYs1u3IxNNIP9yRE4s6h4QhoOmBJRGRrTIaahPia+iuXaHhmiIiIqKv06u6N5yb0xzO39cPOU6XYsL8APx0rxqGCKhwqqMKSH45iXGww7h4egXH9g6GUSe0dMhG5ECZDTZqToWpdI7S6Rnhx6E8iIqIuI5NKcGv/ENzaPwTlNTpsyi7ExgMFOFKowbajxdh2tBh+HnLcGReGu4dHYliUP0ejI6Lrxl/8TbyVMngppNDqDSip1iGGyRAREZFdBHor8cjNMXjk5hicUFdjY1YBNmVdQLFGh89+z8Nnv+chJsgLdw+LwNRhEYjq5mnvkInISXHIlhaazw4VVdXZORIiIiICgNhQHyycOAC7F4zHfx4ZiT8Mi4CHXIrcMi1e334So1/7Bfe9n4H1+/KgqeftMYjIOjz90UJEgAfOlmmRf7EW6G3vaIiIiKiZVCJgdN/uGN23O5ZMbcTWw2pszCrA7jPl2Jt7EXtzL2LRt0dw+6BQ3D08AqP7BEEm5TFfIro6JkMtxAR54ddTZThXXmvvUIiIiKgN3koZ/pgQiT8mRKKwsg6bsi9g44ELOF1Sg+8PFuL7g4UI8lZiSnw4/jAsAoPCfXl9ERG1islQCz0DvQAA58u1do6EiIiI2iPc38N8U9ecC1XYeOACvjtYiLIaHT7+LRcf/5aLXt29MCUuAnfFhyMmyMveIRORA2Ey1EJ0oOkCzHNlPDNERETkTARBwNBIfwyN9MffJg/AjhOl+CbrAn46VoyzpVq8+dNJvPnTScRF+uGu+AjcOTQMwU3XChOR+2Iy1EJ009Gic+VaiKLIU+pEREROSC6VIHlgCJIHhqBG14htR9T4NrsQv50uw8GCKhwsqMI/fjyKpN6BmBIXgZTBofDzkNs7bCKyAyZDLfTo5gmFVIJavQEFFXUcqpOIiMjJeStluHt4JO4eHomyGh025xTh2+xCZJ6vwK7T5dh1uhwvbDqMcf27Y0p8BG7tHwyVnDd2JXIXTIZakEsl6BvijSOFGhwp1DAZIiIiciFB3krMSIrGjKRo5F+sxXcHC/FddiFOFFdj65FibD1SDC+FFLcOCMGkwaEYGxsMDwUTIyJXxjEnLzMwzBcAcLRIY+dIiIioMyxbtgyCIGDevHlXLbdhwwb0798fKpUKQ4YMwebNm7smQOoSUd08MXdcH2x9+hZsmTcaT4ztjQh/D2j1Bnx/sBCPf34ACa9sx9wvDmBzThHq9AZ7h0xEnYBnhi4zMNwXyASOMRkiInI5+/btw/vvv4+hQ4detdzu3bsxffp0LF26FHfccQe++OILTJ06FQcOHMDgwYO7KFrqKv1DfdF/gi+eTYnFwYIqbM4pwuacIhRU1OHHQ0X48VARPORS3No/GJOGhGFc/+7wVPAnFJErEERRFO0dxPXSaDTw8/NDVVUVfH19r2tde86WY9oHvyPEV4nfF47nIApERNdgy31wZ6qpqcHw4cPxr3/9C6+88gri4+OxcuXKVstOmzYNWq0WP/zwg3nejTfeiPj4eKxevbpd7+cs24VaJ4oici5U4cemxCj/Yp35NQ+5FOP6d8ekIWEYGxsMbyUTIyJHYs3+l9/eywyN9IdMIqBYo+MgCkRELmTu3LmYPHkykpOT8corr1y1bEZGBlJTUy3mpaSkYNOmTW0uo9PpoNPpzM81GvYwcGYth+peMKE/Dl/QmBOjvIu12JyjxuYcNRRSCUb1CcTtA0ORPCCYw3UTORkmQ5fxUEgxOMIP2fmV2H/+IpMhIiIXsG7dOhw4cAD79u1rV3m1Wo2QkBCLeSEhIVCr1W0us3TpUrz88svXFSc5JkEQMCTSD0Mi/fDchFgcKTQlRlsOq5FbpkX6iVKknyjF898Aw3r447aBIbh9YCj6BHvbO3QiugYmQ624IToA2fmV2JtbgT8Mi7R3OEREdB3y8/Px1FNPYfv27VCpOu+o/cKFCy3OJmk0GkRFRXXa+5F9CIKAwRF+GBzhh/kpsThTWoNtR4ux7UgxsvMrkZVnml7bcgK9grxw26AQ3D4wBMOiAiCRsOs9kaPptNHkVq1ahejoaKhUKiQmJmLv3r1tll27di0EQbCYOrPBupYR0d0AAPvOXbRbDEREZBuZmZkoKSnB8OHDIZPJIJPJsGPHDrz99tuQyWQwGK4cJSw0NBTFxcUW84qLixEaGtrm+yiVSvj6+lpM5NoEQUCfYB88MbYPNs29CXueH49//GEwxvTrDrlUwNkyLd7fcRZ/fC8DI/+ZhgX/PYSfjxejvoEj0xE5ik45M7R+/XqkpqZi9erVSExMxMqVK5GSkoITJ04gODi41WV8fX1x4sQJ83N7DlwwMrobBAE4XVKDoqo6hPl52C0WIiK6PuPHj0dOTo7FvFmzZqF///547rnnIJVeeR+ZpKQkpKWlWQy/vX37diQlJXV2uOTEQnxVeCCxJx5I7Inq+gbsOFmKbUeK8cvxEpTV6LBuXz7W7cuHp0KKm/sEYfyAYIyL5XVGRPbUKcnQG2+8gTlz5mDWrFkAgNWrV+PHH3/EmjVrsGDBglaXEQThqkfculKAlwLxUf7IyqtE+olSTB/Zw94hERFRB/n4+FwxHLaXlxcCAwPN82fMmIGIiAgsXboUAPDUU09hzJgxeP311zF58mSsW7cO+/fvxwcffNDl8ZNz8lHJccfQcNwxNBz6RiP25JZj25FibD9aDLWm3tS17qjp7OOQCD/c2j8Y4wcEY3C4H7vTEXUhmydDer0emZmZWLhwoXmeRCJBcnIyMjIy2lyupqYGPXv2hNFoxPDhw/HPf/4TgwYNarVsV4zYM7ZfcFMyVMJkiIjIxeXl5UEiudRzfNSoUfjiiy/wwgsv4Pnnn0ffvn2xadMm3mOIOkQhk2B03+4Y3bc7/j5lEI4UapB2rAQ/Hy/GwYIq5FwwTW+lnUJ3HyVujQ3GuP7BGN03CF4ctpuoU9n8PkOFhYWIiIjA7t27LboTzJ8/Hzt27MCePXuuWCYjIwOnTp3C0KFDUVVVhRUrVmDnzp04cuQIIiOvHMDgpZdeanXEHlvey+FQQSXuencXvBRSZC26HQpZp11eRUTk1Hg/ndZxu1B7lFTXI/1EKX4+VoJfT5VCq790PZFCKkFir24Y3z8Yt/YPQY9AjnBL1B5Od5+hpKQki8Rp1KhRGDBgAN5//30sWbLkivJdMWLP4HA/BHkrUVajw+4zZRgb2/q1TkREREQdFeyjwn0jonDfiCjoGg3Ym3ux6axRCfIu1uLXU2X49VQZXvr+KPoGe+PW/sG4tX8wEnoGQCblgVqi62XzZCgoKAhSqdTqUXhaksvlGDZsGE6fPt3q60qlEkql8rpjvRqJRMDEwaH4z+/n8cOhIiZDRERE1KmUMqm5O93iOwfiTKkWPx8vRtqxEuw/X4FTJTU4VVKD93eeha9KhtH9umNM0xTCQRiIOsTmhxQUCgUSEhKQlpZmnmc0GpGWltbuUXgMBgNycnIQFhZm6/CscsdQ0/tvPaKGrpHDYBIREVHXMA3b7Y1Hb+mN9X9JwoEXbsPb04fhD8Mi4O8ph6a+ET8eKsL8rw8h8Z9pmLByJ5ZuPobdp8v4m4XICp3STS41NRUzZ87EiBEjMHLkSKxcuRJardY8utzlo/b8/e9/x4033og+ffqgsrISy5cvx/nz5zF79uzOCK/dbojuhhBfJYo1Ouw8WYbbBoZceyEiIiIiG/PzlOOuuHDcFRcOg1FEVl4Fdp4sxY5TZThUUInj6mocV1fj/Z1n4amQYlTvQNzSrztu6hOEXkFedr1lCZEj65RkaNq0aSgtLcWiRYugVqsRHx+PLVu2ICTElExcPmpPRUUF5syZA7VajYCAACQkJGD37t0YOHBgZ4TXbhKJgMlDwrFmVy7+m1nAZIiIiIjsTioRMCK6G0ZEd0Pq7bG4qNXj11Ol2HGyFDtPlqGsRoefjpXgp2MlAIBgHyVu7BWIpN6BSOoViJ6BnkyOiJrYfDQ5e+jMEXuOqzWYsPJXyCQCdi+8FcE+7JNLRNQSR01rHbcL2YPRKOKYWtOUGJXiQF4l9I1GizKhviok9Q7EiOgAxEf5IzbEh4MxkEtxutHkHFn/UF8M62G6AevXmQV4Ymwfe4dERERE1CqJRMCgcD8MCvfDE2P7oL7BgKy8SmScLcfvZ8uRnVcJtaYe32RdwDdZFwAAHnIphkb6Ib6HP4ZFBWB4D38Ec0AGchNMhtph+sgeyMqrxJd78/DYLb15Z2giIiJyCiq51NQ9rncgAKBOb8CBvArsOVuOrPxKZOdVolrXiD25F7En96J5ue4+SvQP9cHAMF8MCPNF/zAf9O7uDTnPIJGLYTLUDncODccrPxxF/sU6bD9WjJRB7RsinIiIiMiReCikuKlPEG7qEwTA1K3uTGkNsvIqkZVfiay8CpwsrkZptQ6l1Tr8eqrMvKxcKqBPsA/6BnsjJsgLvbp7oXd302MvJX9SknPif247eCikeCipJ1b9cgbvpZ/B7QNDeOEhEREROT2JREDfEB/0DfHBfTeYbmBfq280jU5XVI1jRRocK9LguLoaNbpG8/PLhfqq0Ku7F2KCTFNkgCciAzwQFeAJP095V1eLqN2YDLXTw6Ni8OGvucjOr8Se3Iu4sVegvUMiIiIisjlPhQzDewRgeI8A8zxRFFFQUYdjRRqcLdPibGkNzpZqcbZMi4taPdSaeqg19dh9pvyK9fmoZObkyDSZHkf4eyDYV4lALyWkvASB7ITJUDt191HivhGR+Oz3PLy5/STWPXojzw4RERGRWxAEAVHdPBHVzfOK1ypr9U0JkilJyrtYi/yKOlyoqEVZjR7V9W2fUQJMQ4UHeSsQ7KNCsI8Swb5K0+Pmvz5KhPiqEOSt4Kh3ZHNMhqzw+Ng++Gp/AfbkXsQvJ0pwa3/ed4iIiIjcm7+nAsN7KCzOJDWr1TfiQkUdCirqUFBRi4KKOuQ3/S2srEO5Vg+DUUSxRodije6q7yMIQKCXAt3NCVLLpElpnh/sq4RSJu2s6pKLYTJkhQh/D8y6KRrv7ziLpZuP45a+3XmEgoiIiKgNngqZ+Zqk1jQYjCiv0aNYU4+Sah1KqutRotGhpFqH0up6FGtM88pqTElTWY0eZTV6HCu6+vv6e8pNidFlZ5iaHzcnUh4KJk3ujsmQlZ4Y2wfr9+XjVEkN/r3rHObc0sveIRERERE5JblUglA/FUL9rn5fI4NRxEWtvkWydClpKmlKmppHwNMbjKisbUBlbQNOFtdcdb0+Shm6N51ZCvFVWSRQ3VvM81bKeHmEi2IyZCU/DzkWTuyP5/6bg9e3n8Dtg0LQM9DL3mERERERuSypREB3H1OCMii87XKiKKKytsEiSWpOnEovm1ffYES1rhHVpY04W6q96vt7yKXm7njBviqENJ1dCvFVNU1KhPqp4KngT2tnw0+sA+4bEYVvswux+0w5nv36EL6YncjuckRERER2JggCArwUCPBSIDa09a55gClpqtY1XnaWyfJsU0m1DiUaHWp0jahrMOB8eS3Ol9de9f19lDIENyVGIT4qhPipEOJjeh7clDgF+yh581oHwmSoAwRBwNK7h2DSW79ib+5FrPzpFP6aEmvvsIiIiIioHQRBgK9KDl+VHH2Cva9atlbfeEWXvJLqehRX1TcN/GAaVrxWbzCfaTpzlTNNzQNBhPqpEO7ngXB/05Dj4f6m4cbD/T0Q5K1gt7wuwmSog3oGemHZH4fi/77Mwru/nMbgCD9MGBxq77CIiIiIyIY8FTJEB8kQHXT1yyJqdI1QV9WjpCk5ak6ULk2mJKrBcGkgiMMXWh9uXCGTIMKcHKkQ4e9p+tt0f6YwPw8oZDy7ZAtMhq7DnXHh2H/uIj7JOI//ty4Ln89OxA3R3ewdFhERERF1MW+lDH2Cva96psloFFFR23ST2qp6FFbWoaCyDoWV9bhQUYvCynoUV9dD32hEbpkWuWWtn2ESBKC7txIRTWeUIv0tzyxFBHjAV8VBH9qDydB1evGOgbhQWY+fjhVj1r/34YMZCRjVO8jeYRERERGRg5FIBAR6KxHorcSgcL9Wy+gbjSjW1JvvxXSh8tLfC5V1uFBRB12jsanbng5ZeZWtrsdbKbt0ZqlFN7yIpmQp2EcFqYTJkiCKomjvIK6XRqOBn58fqqqq4Ovr2+XvX6c34M9r9yHjbDkUUglmj47BmH7d4ecph7dSBm+lDF5KGS+WIyKXZO99sKPidiGiziCKpmHGm5MkU9JUjwuVtU1/63BRq7/memQSAaF+KkT4eyAywBNR3Zr+BnggqpsnQnydN1myZv/LZMhG6hsMSP0qG5tz1G2WUcokpuRIJYOXwvS3ZbLk0zTfSyk1PW56zUclQ6CXafhG3hyMiByNI+yDHRG3CxHZS53eYHFGqbDpjFLz2SV1VT0ajVdPAeRSAeH+HohqkShFNiVKvYK84O+p6KLaWM+a/S+7ydmISi7Fqj8Nx5bDavz3QAFOldRAq2tEdX0jdI1GAICu0Qhdox7l7cjW2xLso8T4ASFIGRSCUb2DePEcEREREVnwUEivev2SwSiipLrefGapoKIO+RdrTX8ranGhog4NBvGqw4kHeMoRE+SFmCBv9OruhZggL/Tq7oXe3b2dqjcUzwx1gQaDEVpdI2qapuYkSaszoEbXgBqdATX1jdDqm+dfKltT34hqXQPKqvWoazBYrNdHKcO4/sFIHhiCm3oHItBbaacaEpE7c/R9sL1wuxCRszIYRRRr6pF/sRb5lyVKeeW1UGvq21xWIZWgT7A3Bob7YkCYLwaG+WJwhC98VPIui5/d5FxQ883BsvMqsfWIGtuOFqO0WmdRZlC4LxJjAjE00g9DIv0QE+gFiZP29SQi5+EO++CO4HYhIldVq2/EubJa5JZpcba0xvS3TIvTJTWo0TVeUV4iAAPCfHFDdDeM6h2Im/sGwVPReR3UmAy5AaNRRFZ+JbYdUWPHyVIcV1dfUaZ5iMfmU5a9u3uhV3dv9Az0hFLGa4+IyDbccR/cHtwuRORuRFFEQUUdjhRqcKzINB0p1OBCZZ1FOaVMgpv7BOGPCZG4bWCIzbvVMRlyQyXV9cg4U46svErkXKjCkcIq1DcYWy0rEYCIAA9E+ntajE9vvpGXv4rJEhG1G/fBreN2ISIyUVfVY++5i9ibW470E6UoqLiUHHX3UeKxMb3x4I09bPb7k8kQodFgxJlSLc6U1uBMSQ3ONp3GPFOqbfX05eWCfZTmm3Y1J0ohvip081Kgm5cCgV4K+Krk7IZHRNwHt4HbhYjoSqIo4kRxNb4/WIiv9heYL/voE+yNt+8fhoHh17+/ZDJEbRJFEaXVOpwrr8WFytoWwyya7nx8obKuzTNKl5NKBAR4KuDnIYNKLoVSJjH/VcqkUMolUMokkEslkEoESASh6a/ppmPSpueC0PwYpsdNr0mayjYvK2kqc2k9gnk9LdcpaVFGKgiX1tm8fovl0eK9Li1v+Z6X5sskkkt/BfDOzkRwjn3we++9h/feew/nzp0DAAwaNAiLFi3CxIkTWy2/du1azJo1y2KeUqlEfX3bFw1fzhm2CxGRPTUYjPg6swCvbzuJshodvBRSfPrISCT07HZd6+XQ2tQmQRAQ7KtCsK8KwJX/aM038mq+eVfLG3mVVOtwUavHxRo9qnWNMBhFlNXoUFaju/KN3MQViZIAyKQSSAQBMklT0tXyNYkESrkEKnOyKIVKbkoiVXIJvJVyBHopEOitQIivCtFBXgjzVfEMHNF1ioyMxLJly9C3b1+IoohPPvkEU6ZMQVZWFgYNGtTqMr6+vjhx4oT5OQ9+EBHZllwqwfSRPZAyKBRPfJ6J389exCOf7Me2ebc0/VbtfEyGyIIgCAj0ViLQW4khkX5tltM1GlChbUC5VoequgboG42obzBC12houp+SEboG02N9oxFGUYTBKMIgihBF05CNBqMIUTTNMxhNidgVZUTx0nwjYBRF87qMogijETCIIoxNzw0iLj1uLtM0zyC2WOaKdTe9p3jl46sxGEUYIAIGAGjfGTVr+ahkGBrph7hIfwyN9MfgCF9E+HvwhxmRFe68806L5//4xz/w3nvv4ffff28zGRIEAaGhoV0RHhGRW+vmpcC/Hx6Je9/fjcMXNPjn5mNYef+wLnlvJkPUIUqZFKF+UoT6dU3Wbk/NiVRz0tScfDUaL/1t7bWW85ofNxpE6A0G1DcYUd+ULNY3XHpeXd+Icq0O5TV6FFbWIe9iLarrG7HrdDl2nS43x+TnIUffYG/zNVx+nnKoZFIoZBIopBLT3xaP5VJTl0WZ1NT1TxBMXQkFwNwdURCauwYCAi6Vae52KKC5DMxdCAUBl8oJl9Zzad6lZVou23IZ03p51J26jsFgwIYNG6DVapGUlNRmuZqaGvTs2RNGoxHDhw/HP//5zzYTJwDQ6XTQ6S6dKddoNDaNm4jIlXkopFj6h6G4893f8P2hIsyf0B/h/h6d/r5MhoiuQSIRIIEAuR0G2GswGHGyuBoH86twqKASBwuqcKq4GlV1Ddh/vqLrA+pEFgkULBMqiSAAlz2/lGRZJmet/TWvA5cleM0J4VUSvPYkdi0TSfM8tEgcm65XE3D5uprXb1mnlu8rkwiQSgXIJaZr72RSU/dLUzdMCWRN81o+b37dQyGFt1IGr6bJWymD1I27XObk5CApKQn19fXw9vbGN998g4EDB7ZaNjY2FmvWrMHQoUNRVVWFFStWYNSoUThy5AgiIyNbXWbp0qV4+eWXO7MKREQubUikH26IDsC+cxXYfrQYM0dFd/p7cgAFIiejazTgVHENzpVrUVHbgAqtHpW1DdAbDNA3dUvUG4zQN4pNf03zGwwiGgymrnzm7oNNXRLFy563/Gs0vy5ChOlMWcvXmudfvg5yTKG+KsSG+uDu4RG4Ky7cJmfknGUfrNfrkZeXh6qqKnz99df46KOPsGPHjjYTopYaGhowYMAATJ8+HUuWLGm1TGtnhqKiohx+uxAROZL3d5zB0v8dR/KAYHw084YOrYMDKBC5MKVMisERfhgc0fY1XY6gZXJ09STryr+tJ16m14AW6226jgy4PMFrXq7t979aHM3LmNdrbJE4tnz/pmVaSxxNjy/F01byaV4Hmq93u7RMcxfLRkPTX6OIRqPR4rnBaEpyL3/eaBRRpzdAq2+EVteIBoOpPmpNPdSaeuw4WYrfTpVh2R+Hus3ZIoVCgT59+gAAEhISsG/fPrz11lt4//33r7msXC7HsGHDcPr06TbLKJVKKJVKm8VLROSO4qP8AQDHiqq75P2YDBFRpzANYw5I4R4/tB2drtF0Tdr58lrsOFmKd38+hQ2ZBQj0VmLBxP72Ds8ujEajxZmcqzEYDMjJycGkSZM6OSoiIvfWP9R0JudCZR009Q3wVck79f0knbXiVatWITo6GiqVComJidi7d+9Vy2/YsAH9+/eHSqXCkCFDsHnz5s4KjYjI7ShlUgR5K5HQMwCpt/XDG/fFAwBW7ziD/ecu2je4LrBw4ULs3LkT586dQ05ODhYuXIj09HQ88MADAIAZM2Zg4cKF5vJ///vfsW3bNpw9exYHDhzAgw8+iPPnz2P27Nn2qgIRkVvw8zTdZgQACi7Wdfr7dUoytH79eqSmpmLx4sU4cOAA4uLikJKSgpKSklbL7969G9OnT8cjjzyCrKwsTJ06FVOnTsXhw4c7IzwiIrc3dVgEpo2IAgC89P0RuMDlo1dVUlKCGTNmIDY2FuPHj8e+ffuwdetW3HbbbQCAvLw8FBUVmctXVFRgzpw5GDBgACZNmgSNRoPdu3e36/oiIiK6Pt19TF2OS7vgXpadMoBCYmIibrjhBrz77rsATF0RoqKi8H//939YsGDBFeWnTZsGrVaLH374wTzvxhtvRHx8PFavXn3N93OWi3eJiBxJWY0OY177BVq9AZ/PTsRNfYI6tB7ug1vH7UJE1DEPfbwHv54qw4p743BPQusjeF6NNftfm58Z0uv1yMzMRHJy8qU3kUiQnJyMjIyMVpfJyMiwKA8AKSkpbZYnIqLrF+StxB+bGpm1u8/ZNxgiIqIm5jND1Z1/ZsjmyVBZWRkMBgNCQkIs5oeEhECtVre6jFqttqq8TqeDRqOxmIiIyHozkqIhCECjwYjGpqHXiYiI7Km7jxIecqn5liCdySlHk+ON7YiIbKNPsDd2L7gVYX6df5dvIiKi9nj29lgsmNDfJvfCuxabnxkKCgqCVCpFcXGxxfzi4mKEhoa2ukxoaKhV5RcuXIiqqirzlJ+fb5vgiYjcEBMhIiJyJDKppEsSIaATkiGFQoGEhASkpaWZ5xmNRqSlpSEpKanVZZKSkizKA8D27dvbLK9UKuHr62sxERERERERWaNTusmlpqZi5syZGDFiBEaOHImVK1dCq9Vi1qxZAEz3c4iIiMDSpUsBAE899RTGjBmD119/HZMnT8a6deuwf/9+fPDBB50RHhERERERUeckQ9OmTUNpaSkWLVoEtVqN+Ph4bNmyxTxIQl5eHiSSSyelRo0ahS+++AIvvPACnn/+efTt2xebNm3C4MGD2/V+zaODcyAFIqKu17zvdfV7FVmLbRMRkX1Y0y51yn2GulpBQQGioqLsHQYRkVvLz89HZKT194NwVWybiIjsqz3tkkskQ0ajEYWFhfDx8enQxVYajQZRUVHIz8932+uPuA24Ddy9/gC3AdCxbSCKIqqrqxEeHm5x1t/dsW26Pqy/e9cf4DZg/Ttef2vaJaccWvtyEonEJkcjORgDtwHAbeDu9Qe4DQDrt4Gfn18nRuOc2DbZBuvv3vUHuA1Y/47Vv73tEg/hERERERGRW2IyREREREREbonJEEz3LVq8eDGUSqW9Q7EbbgNuA3evP8BtAHAbOBJ3/yxYf/euP8BtwPp3Tf1dYgAFIiIiIiIia/HMEBERERERuSUmQ0RERERE5JaYDBERERERkVtiMkRERERERG7JbZKhVatWITo6GiqVComJidi7d+9Vy2/YsAH9+/eHSqXCkCFDsHnz5i6KtPNYsw0+/PBDjB49GgEBAQgICEBycvI1t5kzsPb/oNm6desgCAKmTp3auQF2MmvrX1lZiblz5yIsLAxKpRL9+vVz+u+Ctdtg5cqViI2NhYeHB6KiovD000+jvr6+i6K1rZ07d+LOO+9EeHg4BEHApk2brrlMeno6hg8fDqVSiT59+mDt2rWdHqc7cfe2yd3bJbZJbJPYJjlAmyS6gXXr1okKhUJcs2aNeOTIEXHOnDmiv7+/WFxc3Gr5Xbt2iVKpVHzttdfEo0ePii+88IIol8vFnJycLo7cdqzdBn/605/EVatWiVlZWeKxY8fEhx9+WPTz8xMLCgq6OHLbsXYbNMvNzRUjIiLE0aNHi1OmTOmaYDuBtfXX6XTiiBEjxEmTJom//fabmJubK6anp4vZ2dldHLntWLsNPv/8c1GpVIqff/65mJubK27dulUMCwsTn3766S6O3DY2b94s/u1vfxM3btwoAhC/+eabq5Y/e/as6OnpKaampopHjx4V33nnHVEqlYpbtmzpmoBdnLu3Te7eLrFNYpvENskx2iS3SIZGjhwpzp071/zcYDCI4eHh4tKlS1stf99994mTJ0+2mJeYmCj+5S9/6dQ4O5O12+ByjY2Noo+Pj/jJJ590VoidriPboLGxURw1apT40UcfiTNnznTqhsfa+r/33ntir169RL1e31Uhdjprt8HcuXPFW2+91WJeamqqeNNNN3VqnF2hPQ3P/PnzxUGDBlnMmzZtmpiSktKJkbkPd2+b3L1dYpvENolt0iX2bJNcvpucXq9HZmYmkpOTzfMkEgmSk5ORkZHR6jIZGRkW5QEgJSWlzfKOriPb4HK1tbVoaGhAt27dOivMTtXRbfD3v/8dwcHBeOSRR7oizE7Tkfp/9913SEpKwty5cxESEoLBgwfjn//8JwwGQ1eFbVMd2QajRo1CZmamudvC2bNnsXnzZkyaNKlLYrY3V9sXOhJ3b5vcvV1im8Q2iW2S9TprHyi7rqWdQFlZGQwGA0JCQizmh4SE4Pjx460uo1arWy2vVqs7Lc7O1JFtcLnnnnsO4eHhV/wTOouObIPffvsNH3/8MbKzs7sgws7VkfqfPXsWP//8Mx544AFs3rwZp0+fxhNPPIGGhgYsXry4K8K2qY5sgz/96U8oKyvDzTffDFEU0djYiMceewzPP/98V4Rsd23tCzUaDerq6uDh4WGnyJyfu7dN7t4usU1im8Q2yXqd1Sa5/Jkhun7Lli3DunXr8M0330ClUtk7nC5RXV2Nhx56CB9++CGCgoLsHY5dGI1GBAcH44MPPkBCQgKmTZuGv/3tb1i9erW9Q+sy6enp+Oc//4l//etfOHDgADZu3Igff/wRS5YssXdoRG7N3doltklskwC2SZ3F5c8MBQUFQSqVori42GJ+cXExQkNDW10mNDTUqvKOriPboNmKFSuwbNky/PTTTxg6dGhnhtmprN0GZ86cwblz53DnnXea5xmNRgCATCbDiRMn0Lt3784N2oY68j8QFhYGuVwOqVRqnjdgwACo1Wro9XooFIpOjdnWOrINXnzxRTz00EOYPXs2AGDIkCHQarV49NFH8be//Q0SiWsfT2prX+jr68uzQtfJ3dsmd2+X2CaxTWKbZL3OapNce6sBUCgUSEhIQFpamnme0WhEWloakpKSWl0mKSnJojwAbN++vc3yjq4j2wAAXnvtNSxZsgRbtmzBiBEjuiLUTmPtNujfvz9ycnKQnZ1tnu666y6MGzcO2dnZiIqK6srwr1tH/gduuukmnD592tzgAsDJkycRFhbmdI0O0LFtUFtbe0Xj0twQm673dG2uti90JO7eNrl7u8Q2iW0S2yTrddo+8LqGX3AS69atE5VKpbh27Vrx6NGj4qOPPir6+/uLarVaFEVRfOihh8QFCxaYy+/atUuUyWTiihUrxGPHjomLFy926uFLRdH6bbBs2TJRoVCIX3/9tVhUVGSeqqur7VWF62btNrics4/cY2398/LyRB8fH/HJJ58UT5w4If7www9icHCw+Morr9irCtfN2m2wePFi0cfHR/zyyy/Fs2fPitu2bRN79+4t3nffffaqwnWprq4Ws7KyxKysLBGA+MYbb4hZWVni+fPnRVEUxQULFogPPfSQuXzzMKbPPvuseOzYMXHVqlUcWtuG3L1tcvd2iW0S2yS2SY7RJrlFMiSKovjOO++IPXr0EBUKhThy5Ejx999/N782ZswYcebMmRblv/rqK7Ffv36iQqEQBw0aJP74449dHLHtWbMNevbsKQK4Ylq8eHHXB25D1v4ftOTsDY8oWl//3bt3i4mJiaJSqRR79eol/uMf/xAbGxu7OGrbsmYbNDQ0iC+99JLYu3dvUaVSiVFRUeITTzwhVlRUdH3gNvDLL7+0+r1urvPMmTPFMWPGXLFMfHy8qFAoxF69eon//ve/uzxuV+bubZO7t0tsk9gmsU2yf5skiKIbnFcjIiIiIiK6jMtfM0RERERERNQaJkNEREREROSWmAwREREREZFbYjJERERERERuickQERERERG5JSZDRERERETklpgMERERERGRW2IyREREREREbonJEBERERERuSUmQ0RERERE5JaYDBERERERkVtiMkRERERERG7p/wNYxGLF+tGQjAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = BasicSPMSMR()\n", + "parameter_values = model.default_parameter_values\n", + "\n", + "fig, ax = plt.subplots(2, 2, figsize=(10, 4))\n", + "\n", + "U = pybamm.linspace(0.01, 1.5, 500)\n", + "x_eval = parameter_values.evaluate(x_n(U)).flatten()\n", + "U_eval = U.evaluate().flatten()\n", + "ax[0, 0].plot(U_eval, x_eval, label=\"x_n\")\n", + "ax[1, 0].plot(x_eval, U_eval, label=\"x_n\")\n", + "\n", + "U = pybamm.linspace(3.4, 5, 500)\n", + "x_eval = parameter_values.evaluate(x_p(U)).flatten()\n", + "U_eval = U.evaluate().flatten()\n", + "ax[0, 1].plot(U_eval, x_eval, label=\"x_p\")\n", + "ax[1, 1].plot(x_eval, U_eval, label=\"x_p\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.08494793698397733 4.350779153249299\n" + ] + } + ], + "source": [ + "soc_model = pybamm.BaseModel()\n", + "U_n = pybamm.Variable(\"U_n\")\n", + "U_p = pybamm.Variable(\"U_p\")\n", + "soc_model.variables = {\"U_n\": U_n, \"U_p\": U_p}\n", + "x_0 = parameter_values[\"Negative electrode stoichiometry at 0% SOC\"]\n", + "x_100 = parameter_values[\"Negative electrode stoichiometry at 100% SOC\"]\n", + "y_0 = parameter_values[\"Positive electrode stoichiometry at 0% SOC\"]\n", + "y_100 = parameter_values[\"Positive electrode stoichiometry at 100% SOC\"]\n", + "initial_soc = pybamm.InputParameter(\"Initial soc\")\n", + "x = x_0 + initial_soc * (x_100 - x_0)\n", + "y = y_0 - initial_soc * (y_0 - y_100)\n", + "soc_model.algebraic = {U_n: x - x_n(U_n), U_p: y - x_p(U_p)}\n", + "soc_model.initial_conditions = {U_n: pybamm.Scalar(0), U_p: pybamm.Scalar(4)}\n", + "parameter_values.process_model(soc_model)\n", + "soc_sol = pybamm.AlgebraicSolver(tol=1e-6).solve(soc_model, inputs={\"Initial soc\": 1})\n", + "U_n, U_p = soc_sol[\"U_n\"].data[0], soc_sol[\"U_p\"].data[0]\n", + "\n", + "parameter_values.update(\n", + " {\n", + " \"Initial negative electrode potential [V]\": U_n,\n", + " \"Initial positive electrode potential [V]\": U_p,\n", + " },\n", + " check_already_exists=False,\n", + ")\n", + "print(U_n, U_p)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b6e3407010354cb29dd74122fa53869b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=3300.0, step=33.0), Output()), _dom_classes=…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", + "sim.solve([0, 3300])\n", + "sim.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "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": "3.9.16" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 6387858eaa3b5c5b262c05060f9f09cfe6742c16 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 31 May 2023 15:36:42 +0100 Subject: [PATCH 003/154] particle problem in basic SP MSMR --- .../lithium_ion/basic_spm_msmr.py | 148 +- .../lithium_ion/msmr.ipynb | 1471 +++++++++++++---- 2 files changed, 1276 insertions(+), 343 deletions(-) diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py index ec7faf2fe7..1b9fbe9920 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py @@ -171,12 +171,49 @@ def __init__(self, name="Single Particle MSMR Model"): super().__init__({}, name) param = self.param + ###################### + # Parameters + ###################### + + def x_n_fun(U_n): + inputs = {"Negative electrode OCP [V]": U_n} + return pybamm.FunctionParameter( + "Negative electrode stoichiometry", inputs=inputs + ) + + def x_p_fun(U_p): + inputs = {"Positive electrode OCP [V]": U_p} + return pybamm.FunctionParameter( + "Positive electrode stoichiometry", inputs=inputs + ) + + def dxdU_n_fun(U_n): + inputs = {"Negative electrode OCP [V]": U_n} + return pybamm.FunctionParameter( + "Negative electrode stoichiometry change [V-1]", inputs=inputs + ) + + def dxdU_p_fun(U_p): + inputs = {"Positive electrode OCP [V]": U_p} + return pybamm.FunctionParameter( + "Positive electrode stoichiometry change [V-1]", inputs=inputs + ) + ###################### # Variables ###################### Q = pybamm.Variable("Discharge capacity [A.h]") - U_n = pybamm.Variable("X-averaged negative electrode OCP [V]") - U_p = pybamm.Variable("X-averaged positive electrode OCP [V]") + U_n_dist = pybamm.Variable( + "X-averaged negative particle OCP [V]", domain="negative particle" + ) + U_p_dist = pybamm.Variable( + "X-averaged positive particle OCP [V]", domain="positive particle" + ) + U_n = pybamm.surf(U_n_dist) + U_p = pybamm.surf(U_p_dist) + + # Constant temperature + T = param.T_init # Current density i_cell = param.current_density_with_time @@ -199,30 +236,43 @@ def __init__(self, name="Single Particle MSMR Model"): # Particles ###################### - def dxdU_n(U_n): - inputs = {"Negative electrode OCP [V]": U_n} - return pybamm.FunctionParameter( - "Negative electrode stoichiometry change [V-1]", inputs=inputs - ) - - def dxdU_p(U_p): - inputs = {"Positive electrode OCP [V]": U_p} - return pybamm.FunctionParameter( - "Positive electrode stoichiometry change [V-1]", inputs=inputs - ) - - # Fast diffusion limit F = param.F - R_n = pybamm.x_average(param.n.prim.R) - R_p = pybamm.x_average(param.p.prim.R) + f = F / (param.R * T) c_n_max = param.n.prim.c_max c_p_max = param.p.prim.c_max - self.rhs[U_n] = (-3 * j_n / F / R_n / c_n_max) / dxdU_n(U_n) - self.rhs[U_p] = (-3 * j_p / F / R_p / c_p_max) / dxdU_p(U_p) - self.initial_conditions[U_n] = pybamm.Parameter( + x_n = x_n_fun(U_n_dist) + x_p = x_p_fun(U_p_dist) + dxdU_n = dxdU_n_fun(U_n_dist) + dxdU_p = dxdU_p_fun(U_p_dist) + c_n = c_n_max * x_n + c_p = c_p_max * x_p + D_n = param.n.prim.D(c_n, T) + D_p = param.p.prim.D(c_p, T) + N_n = c_n_max * x_n * (1 - x_n) * f * D_n * pybamm.grad(U_n_dist) + N_p = c_p_max * x_p * (1 - x_p) * f * D_p * pybamm.grad(U_p_dist) + + self.rhs[U_n_dist] = -pybamm.div(N_n) / c_n_max / dxdU_n + self.rhs[U_p_dist] = -pybamm.div(N_p) / c_p_max / dxdU_p + + self.boundary_conditions[U_n_dist] = { + "left": (pybamm.Scalar(0), "Neumann"), + "right": ( + (j_n / F) / pybamm.surf(c_n_max * x_n * (1 - x_n) * f * D_n), + "Neumann", + ), + } + self.boundary_conditions[U_p_dist] = { + "left": (pybamm.Scalar(0), "Neumann"), + "right": ( + (j_p / F) / pybamm.surf(c_p_max * x_p * (1 - x_p) * f * D_p), + "Neumann", + ), + } + + self.initial_conditions[U_n_dist] = pybamm.Parameter( "Initial negative electrode potential [V]" ) - self.initial_conditions[U_p] = pybamm.Parameter( + self.initial_conditions[U_p_dist] = pybamm.Parameter( "Initial positive electrode potential [V]" ) @@ -236,6 +286,16 @@ def dxdU_p(U_p): self.variables = { "Discharge capacity [A.h]": Q, "Current [A]": I, + "X-averaged negative electrode stoichiometry": x_n, + "X-averaged positive electrode stoichiometry": x_p, + "X-averaged negative particle concentration": c_n, + "X-averaged positive particle concentration": c_p, + "X-averaged negative electrode stoichiometry change [V-1]": dxdU_n, + "X-averaged positive electrode stoichiometry change [V-1]": dxdU_p, + "X-averaged negative particle OCP [V]": U_n_dist, + "X-averaged positive particle OCP [V]": U_p_dist, + "X-averaged negative electrode OCP [V]": U_n, + "X-averaged positive electrode OCP [V]": U_p, "Negative electrode potential [V]": pybamm.PrimaryBroadcast( phi_s_n, "negative electrode" ), @@ -256,15 +316,49 @@ def default_parameter_values(self): @property def default_quick_plot_variables(self): return [ + "X-averaged negative electrode stoichiometry", + "X-averaged positive electrode stoichiometry", + "X-averaged negative particle OCP [V]", + "X-averaged positive particle OCP [V]", + "X-averaged negative electrode OCP [V]", + "X-averaged positive electrode OCP [V]", "Current [A]", - "Negative electrode potential [V]", - "Positive electrode potential [V]", "Voltage [V]", ] -model = BasicSPMSMR() +if __name__ == "__main__": + model = BasicSPMSMR() + parameter_values = model.default_parameter_values + + soc_model = pybamm.BaseModel() + U_n = pybamm.Variable("U_n") + U_p = pybamm.Variable("U_p") + soc_model.variables = {"U_n": U_n, "U_p": U_p} + x_0 = parameter_values["Negative electrode stoichiometry at 0% SOC"] + x_100 = parameter_values["Negative electrode stoichiometry at 100% SOC"] + y_0 = parameter_values["Positive electrode stoichiometry at 0% SOC"] + y_100 = parameter_values["Positive electrode stoichiometry at 100% SOC"] + initial_soc = pybamm.InputParameter("Initial soc") + x = x_0 + initial_soc * (x_100 - x_0) + y = y_0 - initial_soc * (y_0 - y_100) + soc_model.algebraic = {U_n: x - x_n(U_n), U_p: y - x_p(U_p)} + soc_model.initial_conditions = {U_n: pybamm.Scalar(0), U_p: pybamm.Scalar(4)} + parameter_values.process_model(soc_model) + soc_sol = pybamm.AlgebraicSolver(tol=1e-6).solve( + soc_model, inputs={"Initial soc": 1} + ) + U_n, U_p = soc_sol["U_n"].data[0], soc_sol["U_p"].data[0] + + parameter_values.update( + { + "Initial negative electrode potential [V]": U_n, + "Initial positive electrode potential [V]": U_p, + }, + check_already_exists=False, + ) + print(U_n, U_p) -sim = pybamm.Simulation(model) -sim.solve([0, 1800]) -sim.plot() + sim = pybamm.Simulation(model, parameter_values=parameter_values) + sim.solve([0, 3300]) + sim.plot() diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb b/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb index 5e9a36c5e9..6303bcbc26 100644 --- a/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb +++ b/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb @@ -2,343 +2,1207 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pybamm\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "from basic_spm_msmr import BasicSPMSMR\n", + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "def electrolyte_diffusivity_Nyman2008(c_e, T):\n", - " D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10\n", - " return D_c_e\n", - "\n", - "\n", - "def electrolyte_conductivity_Nyman2008(c_e, T):\n", - " sigma_e = (\n", - " 0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000)\n", - " )\n", - " return sigma_e\n", - "\n", - "\n", - "def x_n(U):\n", - " T = 298.15\n", - " f = pybamm.constants.F / (pybamm.constants.R * T)\n", - " xj = 0\n", - " for i in range(6):\n", - " U0 = pybamm.Parameter(f\"U0_n_{i}\")\n", - " w = pybamm.Parameter(f\"w_n_{i}\")\n", - " Xj = pybamm.Parameter(f\"Xj_n_{i}\")\n", - "\n", - " xj += Xj / (1 + pybamm.exp(f * (U - U0) / w))\n", - "\n", - " return xj\n", - "\n", - "\n", - "def dxdU_n(U):\n", - " T = 298.15\n", - " f = pybamm.constants.F / (pybamm.constants.R * T)\n", - " dxj = 0\n", - " for i in range(6):\n", - " U0 = pybamm.Parameter(f\"U0_n_{i}\")\n", - " w = pybamm.Parameter(f\"w_n_{i}\")\n", - " Xj = pybamm.Parameter(f\"Xj_n_{i}\")\n", - "\n", - " e = pybamm.exp(f * (U - U0) / w)\n", - " dxj += -(f / w) * (Xj * e) / (1 + e) ** 2\n", - "\n", - " return dxj\n", - "\n", - "\n", - "def x_p(U):\n", - " T = 298.15\n", - " f = pybamm.constants.F / (pybamm.constants.R * T)\n", - " xj = 0\n", - " for i in range(4):\n", - " U0 = pybamm.Parameter(f\"U0_p_{i}\")\n", - " w = pybamm.Parameter(f\"w_p_{i}\")\n", - " Xj = pybamm.Parameter(f\"Xj_p_{i}\")\n", - "\n", - " xj += Xj / (1 + pybamm.exp(f * (U - U0) / w))\n", - "\n", - " return xj\n", - "\n", - "\n", - "def dxdU_p(U):\n", - " T = 298.15\n", - " f = pybamm.constants.F / (pybamm.constants.R * T)\n", - " dxj = 0\n", - " for i in range(4):\n", - " U0 = pybamm.Parameter(f\"U0_p_{i}\")\n", - " w = pybamm.Parameter(f\"w_p_{i}\")\n", - " Xj = pybamm.Parameter(f\"Xj_p_{i}\")\n", - "\n", - " e = pybamm.exp(f * (U - U0) / w)\n", - " dxj += -(f / w) * (Xj * e) / (1 + e) ** 2\n", - "\n", - " return dxj\n", - "\n", - "\n", - "def get_parameter_values():\n", - " return {\n", - " # cell\n", - " \"Negative electrode thickness [m]\": 7.56e-05,\n", - " \"Separator thickness [m]\": 1.2e-05,\n", - " \"Positive electrode thickness [m]\": 7.56e-05,\n", - " \"Electrode height [m]\": 0.065,\n", - " \"Electrode width [m]\": 1.58,\n", - " \"Nominal cell capacity [A.h]\": 5.0,\n", - " \"Current function [A]\": 5.0,\n", - " \"Contact resistance [Ohm]\": 0,\n", - " # negative electrode\n", - " \"Negative electrode stoichiometry\": x_n,\n", - " \"Negative electrode stoichiometry change [V-1]\": dxdU_n,\n", - " \"U0_n_0\": 0.08843,\n", - " \"Xj_n_0\": 0.43336,\n", - " \"w_n_0\": 0.08611,\n", - " \"U0_n_1\": 0.12799,\n", - " \"Xj_n_1\": 0.23963,\n", - " \"w_n_1\": 0.08009,\n", - " \"U0_n_2\": 0.14331,\n", - " \"Xj_n_2\": 0.15018,\n", - " \"w_n_2\": 0.72469,\n", - " \"U0_n_3\": 0.16984,\n", - " \"Xj_n_3\": 0.05462,\n", - " \"w_n_3\": 2.53277,\n", - " \"U0_n_4\": 0.21446,\n", - " \"Xj_n_4\": 0.06744,\n", - " \"w_n_4\": 0.09470,\n", - " \"U0_n_5\": 0.36325,\n", - " \"Xj_n_5\": 0.05476,\n", - " \"w_n_5\": 5.97354,\n", - " \"Negative electrode stoichiometry at 0% SOC\": 0.03,\n", - " \"Negative electrode stoichiometry at 100% SOC\": 0.9,\n", - " \"Negative electrode conductivity [S.m-1]\": 215.0,\n", - " \"Maximum concentration in negative electrode [mol.m-3]\": 33133.0,\n", - " \"Negative electrode diffusivity [m2.s-1]\": 3.3e-14,\n", - " \"Negative electrode porosity\": 0.25,\n", - " \"Negative electrode active material volume fraction\": 0.75,\n", - " \"Negative particle radius [m]\": 5.86e-06,\n", - " \"Negative electrode Bruggeman coefficient (electrolyte)\": 1.5,\n", - " \"Negative electrode Bruggeman coefficient (electrode)\": 0,\n", - " \"Negative electrode exchange-current density [A.m-2]\" \"\": 2.7,\n", - " \"Negative electrode OCP entropic change [V.K-1]\": 0.0,\n", - " # positive electrode\n", - " \"Positive electrode stoichiometry\": x_p,\n", - " \"Positive electrode stoichiometry change [V-1]\": dxdU_p,\n", - " \"U0_p_0\": 3.62274,\n", - " \"Xj_p_0\": 0.13442,\n", - " \"w_p_0\": 0.96710,\n", - " \"U0_p_1\": 3.72645,\n", - " \"Xj_p_1\": 0.32460,\n", - " \"w_p_1\": 1.39712,\n", - " \"U0_p_2\": 3.90575,\n", - " \"Xj_p_2\": 0.21118,\n", - " \"w_p_2\": 3.50500,\n", - " \"U0_p_3\": 4.22955,\n", - " \"Xj_p_3\": 0.32980,\n", - " \"w_p_3\": 5.52757,\n", - " \"Positive electrode stoichiometry at 0% SOC\": 0.85,\n", - " \"Positive electrode stoichiometry at 100% SOC\": 0.1,\n", - " \"Positive electrode conductivity [S.m-1]\": 0.18,\n", - " \"Maximum concentration in positive electrode [mol.m-3]\": 63104.0,\n", - " \"Positive electrode diffusivity [m2.s-1]\": 4e-15,\n", - " \"Positive electrode porosity\": 0.335,\n", - " \"Positive electrode active material volume fraction\": 0.665,\n", - " \"Positive particle radius [m]\": 5.22e-06,\n", - " \"Positive electrode Bruggeman coefficient (electrolyte)\": 1.5,\n", - " \"Positive electrode Bruggeman coefficient (electrode)\": 0,\n", - " \"Positive electrode exchange-current density [A.m-2]\" \"\": 5,\n", - " \"Positive electrode OCP entropic change [V.K-1]\": 0.0,\n", - " # separator\n", - " \"Separator porosity\": 0.47,\n", - " \"Separator Bruggeman coefficient (electrolyte)\": 1.5,\n", - " # electrolyte\n", - " \"Initial concentration in electrolyte [mol.m-3]\": 1000.0,\n", - " \"Cation transference number\": 0.2594,\n", - " \"Thermodynamic factor\": 1.0,\n", - " \"Electrolyte diffusivity [m2.s-1]\": electrolyte_diffusivity_Nyman2008,\n", - " \"Electrolyte conductivity [S.m-1]\": electrolyte_conductivity_Nyman2008,\n", - " # experiment\n", - " \"Reference temperature [K]\": 298.15,\n", - " \"Total heat transfer coefficient [W.m-2.K-1]\": 10.0,\n", - " \"Ambient temperature [K]\": 298.15,\n", - " \"Number of electrodes connected in parallel to make a cell\": 1.0,\n", - " \"Number of cells connected in series to make a battery\": 1.0,\n", - " \"Lower voltage cut-off [V]\": 2.5,\n", - " \"Upper voltage cut-off [V]\": 4.5,\n", - " \"Initial temperature [K]\": 298.15,\n", - " }\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "class BasicSPMSMR(pybamm.lithium_ion.BaseModel):\n", - " def __init__(self, name=\"Single Particle MSMR Model\"):\n", - " super().__init__({}, name)\n", - " param = self.param\n", - "\n", - " ######################\n", - " # Variables\n", - " ######################\n", - " Q = pybamm.Variable(\"Discharge capacity [A.h]\")\n", - " U_n = pybamm.Variable(\"X-averaged negative electrode OCP [V]\")\n", - " U_p = pybamm.Variable(\"X-averaged positive electrode OCP [V]\")\n", - "\n", - " # Current density\n", - " i_cell = param.current_density_with_time\n", - " a_n = 3 * param.n.prim.epsilon_s_av / param.n.prim.R_typ\n", - " a_p = 3 * param.p.prim.epsilon_s_av / param.p.prim.R_typ\n", - " j_n = i_cell / (param.n.L * a_n)\n", - " j_p = -i_cell / (param.p.L * a_p)\n", - "\n", - " ######################\n", - " # State of Charge\n", - " ######################\n", - " I = param.current_with_time\n", - " # The `rhs` dictionary contains differential equations, with the key being the\n", - " # variable in the d/dt\n", - " self.rhs[Q] = I / 3600\n", - " # Initial conditions must be provided for the ODEs\n", - " self.initial_conditions[Q] = pybamm.Scalar(0)\n", - "\n", - " ######################\n", - " # Particles\n", - " ######################\n", - "\n", - " def dxdU_n(U_n):\n", - " inputs = {\"Negative electrode OCP [V]\": U_n}\n", - " return pybamm.FunctionParameter(\n", - " \"Negative electrode stoichiometry change [V-1]\", inputs=inputs\n", - " )\n", - "\n", - " def dxdU_p(U_p):\n", - " inputs = {\"Positive electrode OCP [V]\": U_p}\n", - " return pybamm.FunctionParameter(\n", - " \"Positive electrode stoichiometry change [V-1]\", inputs=inputs\n", - " )\n", - "\n", - " # Fast diffusion limit\n", - " F = param.F\n", - " R_n = pybamm.x_average(param.n.prim.R)\n", - " R_p = pybamm.x_average(param.p.prim.R)\n", - " c_n_max = param.n.prim.c_max\n", - " c_p_max = param.p.prim.c_max\n", - " self.rhs[U_n] = (-3 * j_n / F / R_n / c_n_max) / dxdU_n(U_n)\n", - " self.rhs[U_p] = (-3 * j_p / F / R_p / c_p_max) / dxdU_p(U_p)\n", - " self.initial_conditions[U_n] = pybamm.Parameter(\n", - " \"Initial negative electrode potential [V]\"\n", - " )\n", - " self.initial_conditions[U_p] = pybamm.Parameter(\n", - " \"Initial positive electrode potential [V]\"\n", - " )\n", - "\n", - " ######################\n", - " # (Some) variables\n", - " ######################\n", - " phi_s_n = 0\n", - " phi_s_p = U_p - U_n\n", - " V = phi_s_p\n", - "\n", - " self.variables = {\n", - " \"Discharge capacity [A.h]\": Q,\n", - " \"Current [A]\": I,\n", - " \"Negative electrode potential [V]\": pybamm.PrimaryBroadcast(\n", - " phi_s_n, \"negative electrode\"\n", - " ),\n", - " \"Positive electrode potential [V]\": pybamm.PrimaryBroadcast(\n", - " phi_s_p, \"positive electrode\"\n", - " ),\n", - " \"Voltage [V]\": V,\n", - " }\n", - " self.events += [\n", - " pybamm.Event(\"Minimum voltage [V]\", V - param.voltage_low_cut),\n", - " pybamm.Event(\"Maximum voltage [V]\", param.voltage_high_cut - V),\n", - " ]\n", - "\n", - " @property\n", - " def default_parameter_values(self):\n", - " return pybamm.ParameterValues(get_parameter_values())\n", - "\n", - " @property\n", - " def default_quick_plot_variables(self):\n", - " return [\n", - " \"Current [A]\",\n", - " \"Negative electrode potential [V]\",\n", - " \"Positive electrode potential [V]\",\n", - " \"Voltage [V]\",\n", - " ]" + "model = BasicSPMSMR()\n", + "parameter_values = model.default_parameter_values" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using matplotlib backend: MacOSX\n" + ] + }, { "data": { "text/plain": [ - "[]" + "(0.0, 5.0)" ] }, - "execution_count": 21, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { - "data": { - "image/png": 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WJ0PTpk3DihUrsGjRIsTHxyM7Oxtbtmwx99XOy8tDUZHliDAnTpzAb7/91uqRN6lUikOHDuGuu+5Cv3798MgjjyAhIQG//vorlEplB6vV9S4Nrc0zQ0REXY0H6pyfSi7FK1MHAwA+zTiHnIIqO0dERO5A1pGFnnzySTz55JOtvtZaX+rY2Ng2L4j08PDA1q1bOxKGQ2E3OSIi+5k2bRpKS0uxaNEiqNVqxMfHX3GgTiKxPP7XfKBu27ZtV6yv+UDdJ598gsrKSoSHh+P222/HkiVLnOpAnbMZ3bc77ooLx3cHC/H8NznYNPcmSCWCvcMiIhfWoWSIruShaO4mx2SIiMgeeKDONbxwxwD8cqIEOReq8Pme85iRFG3vkIjIhXXp0NquzNxNjtcMERERdViwjwrzU2IBAMu3nkBptc7OERGRK2MyZCNKdpMjIiKyiT8l9sSQCD9U1zdi6eZj9g6HiFwYkyEbuTSaHJMhIiKi6yGVCFgydTAEAdiYdQF7zpbbOyQiclFMhmyEQ2sTERHZTnyUP+6/oQcA4MVvD6PBwPaViGyPyZCNePDMEBERkU3NT4lFgKccJ4trsHbXOXuHQ0QuiMmQjXgrTQPzVesa7RwJERGRawjwUmDhxAEAgJU/nURRVZ2dIyIiV8NkyEZ8VE3JUH2DnSMhIiJyHfckRGJ4D39o9Qa88gMHUyAi22IyZCO+HnIApmuGdI3sKkdERGQLkqbBFCQC8GNOEXaeLLV3SETkQpgM2YiPUgah6SbZ1fXsKkdERGQrg8L9zDdfXfzdER50JCKbYTJkIxKJAG+Fqaucpo5d5YiIiGwp9fZ+6O6jRG6ZFh/uPGvvcIjIRTAZsqHmrnIanhkiIiKyKV+VHC9MNg2m8O4vp5F/sdbOERGRK2AyZEMcRIGIiKjz3BUXjqRegahvMGLxd0cgiqK9QyIiJ8dkyIZ8VU1nhup4ZoiIiMjWBEHAkqmDoJBK8PPxEmzYX2DvkIjIyTEZsiFfj6ZrhnhmiIiIqFP0CfZB6u39AAAvf3+E3eWI6LowGbKhS2eGmAwRERF1ljmje+GG6ABo9QY889VBGIzsLkdEHcNkyIYCvRUAgKKqejtHQkRE5LqkEgGv3xsPL4UUe89dxKpfTts7JCJyUkyGbKhvsA8A4FRJtZ0jISIicm09Aj3x8pTBAIA3fzqJHbwZKxF1AJMhG+oXakqGThbX2DkSIiIi13dPQiSmj+wBUQSeWpfF64eIyGpMhmyob7A3AKC0WoeSanaVIyIi6mwv3TUQcZF+qKxtwF/+k4kaHUd0JaL2YzJkQ15KGYZG+gEAPtjBu2MTERF1NqVMivceTECglwJHizR47D+Z0Dca7R0WETmJDiVDq1atQnR0NFQqFRITE7F37942y65duxaCIFhMKpXKoowoili0aBHCwsLg4eGB5ORknDp1qiOh2d3ccX0AAB/9louXvjsCXaPBzhERERG5tnB/D/x71g3wVEjx2+kyPLPhIIwcYY6I2sHqZGj9+vVITU3F4sWLceDAAcTFxSElJQUlJSVtLuPr64uioiLzdP78eYvXX3vtNbz99ttYvXo19uzZAy8vL6SkpKC+3vm6mt0+MASpt5nuf7B29zn88b3dOFemtXNURESujwfq3NvQSH+sfjABMomA7w8WYsHGQxxym4iuyepk6I033sCcOXMwa9YsDBw4EKtXr4anpyfWrFnT5jKCICA0NNQ8hYSEmF8TRRErV67ECy+8gClTpmDo0KH49NNPUVhYiE2bNnWoUvYkCAL+3/i+WPPwCAR4ynH4ggZ/+NcunCnloApERJ2FB+oIAG7p1x2v3xcHiQB8tb8AT63LQoOBXeaIqG1WJUN6vR6ZmZlITk6+tAKJBMnJycjIyGhzuZqaGvTs2RNRUVGYMmUKjhw5Yn4tNzcXarXaYp1+fn5ITEy86jod3a39Q7D5qdEYEuGHitoGPPf1IYgij1AREXUGHqijZlPiI/Dun4ZDLhXww6Ei/OU/mdByUAUiaoNVyVBZWRkMBoNFgwEAISEhUKvVrS4TGxuLNWvW4Ntvv8Vnn30Go9GIUaNGoaCgAADMy1mzTp1OB41GYzE5ojA/D3w4YwSUMgn2n69A5vkKe4dERORyHOVAnbO0Te5g0pAwfNDU/v58vAR/fG83h90molZ1+mhySUlJmDFjBuLj4zFmzBhs3LgR3bt3x/vvv9/hdS5duhR+fn7mKSoqyoYR21aonwrjBwQDAPaeu2jnaIiIXI+jHKhzprbJHYyLDcYXc25EkLcSx9XVmLJqF3afKbN3WETkYKxKhoKCgiCVSlFcXGwxv7i4GKGhoe1ah1wux7Bhw3D69GkAMC9nzToXLlyIqqoq85Sfn29NNbrcsKgAAEB2XqV9AyEiIgCdc6DO2domd5DQMwDfPXkTBkf44qJWjwc+2oNXtxzndUREZGZVMqRQKJCQkIC0tDTzPKPRiLS0NCQlJbVrHQaDATk5OQgLCwMAxMTEIDQ01GKdGo0Ge/bsaXOdSqUSvr6+FpMji+/hDwDIuVBl30CIiFyQoxyoc7a2yV2E+3tgw19GYdqIKIgi8F76Gfzxvd3I5UivRIQOdJNLTU3Fhx9+iE8++QTHjh3D448/Dq1Wi1mzZgEAZsyYgYULF5rL//3vf8e2bdtw9uxZHDhwAA8++CDOnz+P2bNnAzBdwDpv3jy88sor+O6775CTk4MZM2YgPDwcU6dOtU0t7SwqwBMAUFKt430PiIhszFEO1JHj8lBI8eo9Q/HeA8Ph5yHHoYIqTFi5E6t+Oc2zRERuTmbtAtOmTUNpaSkWLVoEtVqN+Ph4bNmyxdyvOi8vDxLJpRyroqICc+bMgVqtRkBAABISErB7924MHDjQXGb+/PnQarV49NFHUVlZiZtvvhlbtmy54p4PzqqblwIAYDCKqKprQEDTcyIiso3U1FTMnDkTI0aMwMiRI7Fy5corDtRFRERg6dKlAEwH6m688Ub06dMHlZWVWL58eZsH6vr27YuYmBi8+OKLLnWgzh1NHBKGuCh/zP/6EH47XYblW0/g+4OF+OfdQzC8R4C9wyMiOxBEFxjvWaPRwM/PD1VVVQ7bLWHoS1uhqW/ET6m3oE+wj73DISKyGUfZB7/77rtYvny5+UDd22+/jcTERADA2LFjER0djbVr1wIAnn76aWzcuNHiQN0rr7yCYcOGmdcniiIWL16MDz74wHyg7l//+hf69evXrngcZbvQlURRxMYDF/DKj0dRUdsAQQDuvyEKz9weiyBvpb3DI6LrZM3+l8lQF7l1RTrOlmmx/tEbkdgr0N7hEBHZjDPsg+2B28XxXdTq8Y8fj+G/B0yjCPooZfi/8X0wc1Q0lDKpnaMjoo6yZv/b6UNrk0mgt6lrXLlWb+dIiIiICDB1Y3/9vjhseCwJQyL8UK1rxD83H8ftb+7EtiNq3iydyA0wGeoigV6m0+7lNTo7R0JEREQt3RDdDd/OvQnL7xmK7j5KnC+vxaP/ycSfPtyDg/mV9g6PiDoRk6Eu0nxmqKyGZ4aIiIgcjUQi4N4RUfjlr2Mxd1xvKGQSZJwtx5RVuzD3iwMcipvIRTEZ6iIBnqZkqKKWyRAREZGj8lbK8GxKf/z8zBj8cXgkBAH48VARbntjB17cdBil1ezhQeRKmAx1ER+VaRTzmvpGO0dCRERE1xIZ4InX74vD/54ajXGx3dFoFPGf389jzPJf8Mb2k6jRsT0ncgVMhrqId1MyVM2dJxERkdPoH+qLf88aiS/n3Ii4KH/U6g14O+0Uxrz2Cz7ZfQ76Rt60lciZMRnqIj4qOQCeGSIiInJGSb0DsemJUXjvgeGICfJCuVaPxd8dQfIbO7Ap6wIMRo48R+SMmAx1ER9lUzc5nhkiIiJySoIgYOKQMGx7+ha8MnUwgryVyLtYi3nrszHxrZ3YcpjDcRM5GyZDXcTcTa6+wc6REBER0fWQSyV48Mae2PHsWDybEgtflQwni2vw2GeZmLpqF3aeLGVSROQkmAx1EfMACjwzRERE5BK8lDLMHdcHvz53K54c1weeCikOFlRhxpq9mPbB79h37qK9QySia2Ay1EW8lc1nhpgMERERuRI/Dzn+mhKLnfPH4ZGbY6CQSbA39yLuXZ2Bh/+9F4cvVNk7RCJqA5OhLuKjNA2goGs0cuQZIiIiFxTkrcSLdwzEjmfHYvrIHpBJBKSfKMUd7/yGxz/LxKnianuHSESXYTLURZqvGQLYVY6IiMiVhfl5YOndQ5D2zBj8YVgEBAH432E1UlbuROr6bJwprbF3iETUhMlQF5FKBHgqpAA4vDYREZE76BnohTenxWPLU7cgZVAIjCKwMesCbntjB/7fl1k8U0TkAJgMdaHm64Y0HFGOiIjIbcSG+uD9h0bg+ydvRvIAU1L03cFC3L5yJ+Z+fgDHijT2DpHIbTEZ6kLdvBQAgItavZ0jISIioq42JNIPH80cgR/+72ZMGBQKUQR+zCnCxLd+xaOf7udAC0R2wGSoCzEZIiIiosERflj9UAK2zBuNO4aGQRCAbUeLccc7v+GRtfuQnV9p7xCJ3AaToS7UnAyVMxkiIiJye/1DffHun4Zj+9O3YGp8OCQCkHa8BFNX7cL9H2Qg/UQJb95K1MmYDHWhQPOZIZ2dIyEiIiJH0SfYByvvH4a0Z8binoRIyCQCfj97EQ//ex8mvvUrNmVdQKOBt+Ug6gxMhrpQNy8lAHaTIyIioivFBHlhxb1x2Dl/HGbfHAMvhRTH1dWYtz4bY5anY+2uXNTqOSItkS11KBlatWoVoqOjoVKpkJiYiL1797ZZ9sMPP8To0aMREBCAgIAAJCcnX1H+4YcfhiAIFtOECRM6EppD6+bd1E2uhskQERERtS7c3wMv3DEQuxeMx19v74dALwUuVNbhpe+P4qZlP+PN7Sd5YJXIRqxOhtavX4/U1FQsXrwYBw4cQFxcHFJSUlBSUtJq+fT0dEyfPh2//PILMjIyEBUVhdtvvx0XLlywKDdhwgQUFRWZpy+//LJjNXJggbxmiIio0/BAHbkaP085nry1L3YtuBWvTB2MnoGeqKhtwFtpp5C0NA0LN+bgdAnvVUR0PaxOht544w3MmTMHs2bNwsCBA7F69Wp4enpizZo1rZb//PPP8cQTTyA+Ph79+/fHRx99BKPRiLS0NItySqUSoaGh5ikgIKBjNXJgwT6mbnJFlXV2joSIyLXwQB25MpVcigdv7ImfnxmLd/80DEMi/KBrNOLLvXlIfmMnZq7Zi50nSznYAlEHWJUM6fV6ZGZmIjk5+dIKJBIkJycjIyOjXeuora1FQ0MDunXrZjE/PT0dwcHBiI2NxeOPP47y8nJrQnMK/UJ9AACFVfU8vU1EZEM8UEfuQCoRcMfQcHz35E1Y/+iNuH1gCAQB2HGyFDPW7EXKyp1YtzcP9Q0Ge4dK5DSsSobKyspgMBgQEhJiMT8kJARqtbpd63juuecQHh5ukVBNmDABn376KdLS0vDqq69ix44dmDhxIgyG1r/MOp0OGo3GYnIGvio5YoK8AAA5vLEaEZFNOMqBOmdtm8j5CIKAxF6B+GDGCKT/dSweHhUNL4UUJ4trsGBjDkYt+xlvbDuBkup6e4dK5PC6dDS5ZcuWYd26dfjmm2+gUqnM8++//37cddddGDJkCKZOnYoffvgB+/btQ3p6eqvrWbp0Kfz8/MxTVFRUF9Xg+g2J8AMA7D5TZudIiIhcg6McqHPmtomcV89AL7x01yDsXjgef5s0ABH+Hrio1ePtn0/j5mW/4JmvDuJoIRNzorZYlQwFBQVBKpWiuLjYYn5xcTFCQ0OvuuyKFSuwbNkybNu2DUOHDr1q2V69eiEoKAinT59u9fWFCxeiqqrKPOXn51tTDbuaNCQMALB+Xz409Q12joaIiGx1oM6Z2yZyfn4ecsy5pRd2PDsWq/40HMN7+ENvMOK/Bwow6e1fMf2D37HlsJr3KyK6jFXJkEKhQEJCgkWf6uY+1klJSW0u99prr2HJkiXYsmULRowYcc33KSgoQHl5OcLCwlp9XalUwtfX12JyFrcNDEGvIC9U1jbgHz8cs3c4REROz1EO1Dlz20SuQyaVYPLQMGx84iZ888Qo3DE0DFKJgIyz5Xjss0yMWZ6O99LPoILXLhMB6EA3udTUVHz44Yf45JNPcOzYMTz++OPQarWYNWsWAGDGjBlYuHChufyrr76KF198EWvWrEF0dDTUajXUajVqamoAADU1NXj22Wfx+++/49y5c0hLS8OUKVPQp08fpKSk2KiajkMqEbDsj0MhCMD6/fn46WjxtRciIqI2OcqBOiJHM6xHAN7903DsnD8Oj4/tjQBPOS5U1uHVLcdx49I0zP/6IA7zGmZyc1YnQ9OmTcOKFSuwaNEixMfHIzs7G1u2bDH31c7Ly0NRUZG5/HvvvQe9Xo977rkHYWFh5mnFihUAAKlUikOHDuGuu+5Cv3798MgjjyAhIQG//vorlEqljarpWEbGdMOfb4oBADyz4SAKKmrtHBERkXPjgTqitkX4e+C5Cf2RsXA8XrtnKAaF+0LXaMRX+wtwxzu/4Z73duP7g4VoYBc6ckOC6AKD0ms0Gvj5+aGqqsppuiXoGg24b3UGDhZUIS7KHxv+kgSFrEvHsyAisglH2Qe/++67WL58OdRqNeLj4/H2228jMTERADB27FhER0dj7dq1AIDo6GicP3/+inUsXrwYL730Eurq6jB16lRkZWWhsrIS4eHhuP3227FkyZIrBmpoi6NsF6LLiaKIzPMV+CTjPP6XU4RGo+mnYIivEg8k9sT0kT3Q3cc1D0iTe7Bm/8tkyI7yL9bijnd+Q1VdA2Ym9cTLUwbbOyQiIqs56z64s3G7kDMo1tTj8z15+GLPeZTVmK4jUjRddzRzVDTio/ztGyBRB1iz/+WpCDuK6uaJN+6LAwB8knEea3fl2jkiIiIicichviqk3tYPuxbcipXT4hEfZRqF7pusC5i6ahemrNqFb7IKoGvkjVzJNfHMkANY9ctpLN96AoIAvPfAcEwYzItzich5OPs+uLNwu5CzOphfiU92n8MPh4qgb7qOqJuXAvcmRGL6yB6IbrqBPJGjYjc5JyOKIv626TC+2JMHmUTA6/fFYUp8hL3DIiJqF2ffB3cWbhdydmU1Ony5Jw+f78mDWlNvnn9znyA8kNgDyQNDIJeykxE5HiZDTqjRYMQzGw7i2+xCCAKwcGJ/zL65FyQSwd6hERFdlSvsgzsDtwu5ikaDEb+cKMXne85jx8lSNP9y7O6jxLQRUbh/ZBQiAzztGyRRC0yGnJTRKOLl74/gkwzTCEej+wZh+T1xCPVTXWNJIiL7cZV9sK1xu5Aryr9Yi3X78rB+XwHKanQAAEEAxvbrjj8l9sS42O6Q8WwR2RmTIScmiiI+25OHV344Cl2jESq5BI/cHINHR/eGn6fc3uEREV3BlfbBtsTtQq5M32jET8eK8fme89h1utw8P8xPhXsSIvHH4ZG8tojshsmQCzhdUo3n/puDzPMVAAAPuRT3JERi1k3R6NXd287RERFd4or7YFvgdiF3kVumxZd787Bhfz4qahvM80dGd8M9CZGYNDQM3kqZHSMkd8NkyEWIoojtR4vx5k+ncKxIAwCQSgQsvnMgZiRF2zc4IqImrroPvl7cLuRu6hsM2Ha0GF9nFuDXU5euLfKQSzFxSCjuTYhCYkw3Xg9NnY7JkIsRRREZZ8vxwc6zSD9RCqlEwHdP3oRB4X72Do2IyOX3wR3F7ULurKiqDhsPXMB/Mwtwtkxrnh8Z4IG7h0Xgzrhw9A3xsWOE5MqYDLkoURTx+GcHsOWIGvckRGLFvXH2DomIyG32wdbidiEy/XY5kFeJrzPz8f3BItToGs2v9Q/1wZ1x4bhzaDh6BHI0OrIda/a/7MDpRARBwIxRPbHliBppx4rRaDByxBYiIiJyWIIgIKFnABJ6BmDRHYOw7aga32UXYuepUhxXV+O4+gSWbz2B+Ch/3BkXjklDQhHm52HvsMmNMBlyMiOju8HPQ46K2gbkXKjCsB4B9g6JiIiI6Jo8FFJMiY/AlPgIVNbqsfWIGt8dLETGmXJk51ciO78SS344irhIP9w2MAS3DwpF32BvCAKvMaLOw2TIycikEtwQ3Q0/HSvG/nMVTIaIiIjI6fh7KjDthh6YdkMPlFTX4385anx/sBCZeRU4WFCFgwVVWLHtJKIDPc2J0fAeAZBy8AWyMSZDTmhEdAB+OlaMvecuYs4tvewdDhEREVGHBfuoMHNUNGaOikZJdT3SjpVg+9Fi/Ha6DOfKa/Hhr7n48Ndc+HvKcXOfINzStztG9wtidzqyCSZDTujGXoEAgN2ny1DfYIBKLrVzRERERETXL9hHhekje2D6yB6o0TVi58lSbD9ajLRjxaisbcAPh4rww6EiAEC/EG+M7tsdo/sG4YbobvDivYyoA/hf44SGRvghxFeJYo0O6SdKMWFwqL1DIiIiIrIpb6UMk4aEYdKQMDQYjDiYX4mdJ0ux41QZDhVU4mRxDU4W1+Dj33IhlQgYHO6LkTHdMDImEDdEB8DfU2HvKpAT4NDaTmrp5mN4f+dZBHkrMS+5L5J6B6JnN0+OLkdEXc4d98Htwe1C1HkqtHrsOlOGnSdLset0OS5U1l1Rpn+oD4b3DEB8pD+GRvmhb7APrzlyE7zPkBuo0TXirnd/w9nSSzcyU8gk6NPdG/1CvNGruzd6dPNEj0BP9OjmiUAvBUdjIaJO4Y774PbgdiHqOgUVtdh37iL25lZgb245zrT4fdTMUyHF4HA/xEX5YUikPwaE+iAmyIsHkl0QkyE3UV3fgE8zziP9RAkOX9CgrsHQZlkvhRRR3TwRGeCJMD8VQv1UCPFVIdTX9DjUTwVv9rUlog5w133wtXC7ENlPabUO+89dRHZBJQ7mVyKnoApa/ZW/kxRSCXoHe6N/qA/6h/ogtmkK9VXxILITYzLkhoxGEQUVdThRXI2TxdU4X65F3sVa5JXXokhTj/Z8yt5KGUJ8lQjyViLQW4FAr+a/CgR6K9HNS4EgbwW6eSnh7yGHhKeaiQjcB7eF24XIcRiMIs6W1iA7vxKHCqpwuLAKJ9XVrSZIAOAhl6JnoCdigrwQHeSFmEAvxHT3QnSgF4K82dvG0XV6MrRq1SosX74carUacXFxeOeddzBy5Mg2y2/YsAEvvvgizp07h759++LVV1/FpEmTzK+LoojFixfjww8/RGVlJW666Sa899576Nu3b7viYYNzdbpGAy5U1CHvYi0KKupQrKlHUVU9ijX1UFeZpmpdo1XrlEoEBHjK4efR+uTb4rG/p8LiNZVcwp0IkQvhPrh13C5Ejq35QPJxtQYn1NU4XlyN40UanCuvhcHY9s9jD7kUYf4qRPh7IMxPhXB/D4T7eSDc3wNh/iqE+3nAQ8GRfu3Jmv2v1f2i1q9fj9TUVKxevRqJiYlYuXIlUlJScOLECQQHB19Rfvfu3Zg+fTqWLl2KO+64A1988QWmTp2KAwcOYPDgwQCA1157DW+//TY++eQTxMTE4MUXX0RKSgqOHj0KlUplbYh0GaVMil7dTdcRtUWra4RaU4/iqnqUafW4WKNDuVZvmmp0KK/R46JWj7IaHTT1jTAYRZTV6FFWo7c6HplEgLdKBi+FDD4qGbyUMni3mLyUMnirZPBWSuGtlMNLKTWVU8jMy3kopFDJpfCQS6GQsa8vETnegToicmwSiWC6tjrQE7cPujQyb4PBiPyLtThXrkVuWS3OlWmbHmtxobIOdQ0GnC3VWly3fTkvhRRBPkoEeikQ5K1EkI+p502Qt+l5oJcCfi0OKnvIpTxQbCdWnxlKTEzEDTfcgHfffRcAYDQaERUVhf/7v//DggULrig/bdo0aLVa/PDDD+Z5N954I+Lj47F69WqIoojw8HA888wz+Otf/woAqKqqQkhICNauXYv777//mjHx6FvX0jcaUVGrR3mNHlV1DU1Ty8cNqKprND/WtJh/tSMtHSWTCPCQS6FSmJKjS48l8FTITM/lUngoJBavK6QSKGUSKJonqSmxspx35fOWj7njInKMffD69esxY8YMiwN1GzZsuOqBultuucXiQN2rr75qcaDu1VdfxdKlSy0O1OXk5LT7QJ0jbBcisq36BgPUVfUorKxDYdPfoqo6XKisR1FlHQor69rsenc1cqkAX9Wl3jWXetiYDhJ7KWTwVEjhqZDBS2n6a3ouhZfS9Lj5YLFCKnH7Sxk6rZucXq+Hp6cnvv76a0ydOtU8f+bMmaisrMS33357xTI9evRAamoq5s2bZ563ePFibNq0CQcPHsTZs2fRu3dvZGVlIT4+3lxmzJgxiI+Px1tvvXXNuNjgOAdRFKHVG1Bd3wCtrhHV9Y3Q6gyo0TWgRmdATX1D0+uN0OoaUdM81TdCqzf9rdY1ok5vQK2+EZ2QV1mtOSmSSgTIJAJkUgEyiQQyqQCpRIBcYnpN3vTc4jVpy9ckkEuaykglkDU9FgRAKgiQND2WCILpuQAIgmB6Lrn0WCKgabkrHzcv27weiaTpb9MkCIAAwJTftXwuoHmXKghNE0wvtvp6i2XR4nnTU1P5y97r0rKX1idctuwV67/Kfv5SRJfNv+oybcy/antiu/cxLdfG+q66TFvv0/ZSrS2jkEkQ4tuxM/GOsA/mgToicgSiKKJa14jyGlNvmvIaHUpr9Cir1qFcq0NZtWn+Ra0emnrTgeIGg+1/0MilApQyqfmArlImgVJ26aCvUt58wFdqPshr8Vulxe8R02+bpt8qUsvfKha/fZrmN/8ukbb4bSKRtPG4uYwETb9RLv1+kQiCOSm0Vqd1kysrK4PBYEBISIjF/JCQEBw/frzVZdRqdavl1Wq1+fXmeW2VuZxOp4NOpzM/12g01lSD7EQQBHNXuOsliiIaDCLqGgyo0xss/ta3Mq/5cX2Lx3qDEboGI/QGI/SNpklnfmyArmley9cbL8vAmucTObvhPfyx8Ymb7B1Gh+j1emRmZmLhwoXmeRKJBMnJycjIyGh1mYyMDKSmplrMS0lJwaZNmwAAubm5UKvVSE5ONr/u5+eHxMREZGRktJoMsW0iIkEwneHxVckRE+R1zfKiaPotY+5ZU9sATb1l75pafSO0egNqdU1/9aaDyXV6A7T6RtTqDdDqGqFr8XukwSCiwdCIGt1V3twJ/L/xfZF6W79OfQ+nHEt56dKlePnll+0dBtmRIAhQyAQoZJIOHTHoKINRRENTEqUzGEwJkkFEo9GUKJkeizAYjWgwiDAYxab5LV83muY3lW00mtZhMIpoMBphMIhoMIoQRdM8owiLx0ax6TWx9deMFuXQ9FqL15sfm+dfWo8oAiKaHgPmUQhFmJ40zxMhXnqtxTKwWObK9aHF+sTL1mdel3m9l8fQcn1X+5Raf/Fqy7T10tVOnLe9zFXepwPra/sF28atlDnvxb6OcqCObRMRWUsQhKYubzKE+Xlc17oaDUbUNRjMB3J1DUbzgV1d00FeXaOh6XnzbxkjdA2mA8TNvz8MRmOL3zOm3z2mv02vXfYbxtDi902DocVvFONlvzsu+41i8bhFGYPR1N4bRREKaed397MqGQoKCoJUKkVxcbHF/OLiYoSGhra6TGho6FXLN/8tLi5GWFiYRZmW3eZaWrhwocURPY1Gg6ioKGuqQtQhUokAqcR0DRLQdUkYETk+tk1EZE8yqQQ+vIGs1azaYgqFAgkJCUhLSzPPMxqNSEtLQ1JSUqvLJCUlWZQHgO3bt5vLx8TEIDQ01KKMRqPBnj172lynUqmEr6+vxURERO6psw/UtXedbJuIiJyP1eljamoqPvzwQ3zyySc4duwYHn/8cWi1WsyaNQsAMGPGDIt+20899RS2bNmC119/HcePH8dLL72E/fv348knnwRgOj04b948vPLKK/juu++Qk5ODGTNmIDw83GKQBiIiotY4yoE6IiJyPlZfMzRt2jSUlpZi0aJFUKvViI+Px5YtW8z9qvPy8iCRXMqxRo0ahS+++AIvvPACnn/+efTt2xebNm0yD10KAPPnz4dWq8Wjjz6KyspK3HzzzdiyZQvvMURERO2SmpqKmTNnYsSIERg5ciRWrlx5xYG6iIgILF26FIDpQN2YMWPw+uuvY/LkyVi3bh3279+PDz74AIDlgbq+ffuah9bmgToiItdi9X2GHFFVVRX8/f2Rn5/PbglERF2s+dqYyspK+Pn52S2Od99913zT1fj4eLz99ttITEwEAIwdOxbR0dFYu3atufyGDRvwwgsvmG+6+tprr7V609UPPvjAfKDuX//6F/r1a9/IRmybiIjsw5p2ySWSoYKCAl6kSkRkZ/n5+YiMjLR3GA6DbRMRkX21p11yiWTIaDSisLAQPj4+bd60sDXNWaOrHbVjvZyLK9bLFesEsF5tEUUR1dXVCA8Pt+gm7e462jZ1Flf5/2U9HAvr4VhYDxNr2iWnvM/Q5SQSyXUdjXTVUX9YL+fiivVyxToBrFdr7Nk9zlFdb9vUWVzl/5f1cCysh2NhPdrfLvEQHhERERERuSUmQ0RERERE5JbcOhlSKpVYvHgxlEqlvUOxKdbLubhivVyxTgDrRc7NVT5n1sOxsB6OhfWwnksMoEBERERERGQttz4zRERERERE7ovJEBERERERuSUmQ0RERERE5JaYDBERERERkVtyuWRo1apViI6OhkqlQmJiIvbu3XvV8hs2bED//v2hUqkwZMgQbN682eJ1URSxaNEihIWFwcPDA8nJyTh16lRnVqFV1tTrww8/xOjRoxEQEICAgAAkJydfUf7hhx+GIAgW04QJEzq7GhasqdPatWuviFelUlmUccbPauzYsVfUSxAETJ482VzG3p/Vzp07ceeddyI8PByCIGDTpk3XXCY9PR3Dhw+HUqlEnz59sHbt2ivKWPtdtTVr67Vx40bcdttt6N69O3x9fZGUlIStW7dalHnppZeu+Kz69+/fibW4krX1Sk9Pb/V/UK1WW5Sz9+dFlt577z0MHTrUfEPCpKQk/O9//2uzvKPuQ62thzPsMwFg2bJlEAQB8+bNu2o5R/8N0p56OMNvjvbUw1G/Iy21px6O+B3pSNvYld8Nl0qG1q9fj9TUVCxevBgHDhxAXFwcUlJSUFJS0mr53bt3Y/r06XjkkUeQlZWFqVOnYurUqTh8+LC5zGuvvYa3334bq1evxp49e+Dl5YWUlBTU19d3VbWsrld6ejqmT5+OX375BRkZGYiKisLtt9+OCxcuWJSbMGECioqKzNOXX37ZFdUBYH2dANNdiFvGe/78eYvXnfGz2rhxo0WdDh8+DKlUinvvvdeinD0/K61Wi7i4OKxatapd5XNzczF58mSMGzcO2dnZmDdvHmbPnm2ROHTk87c1a+u1c+dO3Hbbbdi8eTMyMzMxbtw43HnnncjKyrIoN2jQIIvP6rfffuuM8Ntkbb2anThxwiLu4OBg82uO8HmRpcjISCxbtgyZmZnYv38/br31VkyZMgVHjhxpcxlH3IdaWw9n2Gfu27cP77//PoYOHXrVco7+G6S99XD03xztrQfgmN+RZu2th6N+R6xpG7v8uyG6kJEjR4pz5841PzcYDGJ4eLi4dOnSVsvfd9994uTJky3mJSYmin/5y19EURRFo9EohoaGisuXLze/XllZKSqVSvHLL7/shBq0ztp6Xa6xsVH08fERP/nkE/O8mTNnilOmTLF1qO1mbZ3+/e9/i35+fm2uz1U+qzfffFP08fERa2pqzPPs/Vm1BED85ptvrlpm/vz54qBBgyzmTZs2TUxJSTE/v97tZGvtqVdrBg4cKL788svm54sXLxbj4uJsF9h1ak+9fvnlFxGAWFFR0WYZR/u8qHUBAQHiRx991OprzrIPFcWr1+NyjrbPrK6uFvv27Stu375dHDNmjPjUU0+1WdaRf4NYU4/LOdJvDmvq4cjfkev5PBzhO2Jt29jV3w2XOTOk1+uRmZmJ5ORk8zyJRILk5GRkZGS0ukxGRoZFeQBISUkxl8/NzYVarbYo4+fnh8TExDbXaWsdqdflamtr0dDQgG7dulnMT09PR3BwMGJjY/H444+jvLzcprG3paN1qqmpQc+ePREVFXXFkUNX+aw+/vhj3H///fDy8rKYb6/PqiOu9b2yxXZyBEajEdXV1Vd8r06dOoXw8HD06tULDzzwAPLy8uwUoXXi4+MRFhaG2267Dbt27TLPd5XPy5UZDAasW7cOWq0WSUlJbZZz9H1oe+vRkqPtM+fOnYvJkydfsQ9sjSP/BrGmHpdzpN8c1tbDUb8j1/N5OMp3xJq2sau/GzKrl3BQZWVlMBgMCAkJsZgfEhKC48ePt7qMWq1utXxzX/nmv1cr09k6Uq/LPffccwgPD7f4p5kwYQLuvvtuxMTE4MyZM3j++ecxceJEZGRkQCqV2rQOl+tInWJjY7FmzRoMHToUVVVVWLFiBUaNGoUjR44gMjLSJT6rvXv34vDhw/j4448t5tvzs+qItr5XGo0GdXV1qKiouO7/aUewYsUK1NTU4L777jPPS0xMxNq1axEbG4uioiK8/PLLGD16NA4fPgwfHx87Rtu2sLAwrF69GiNGjIBOp8NHH32EsWPHYs+ePRg+fLhN9kHUOXJycpCUlIT6+np4e3vjm2++wcCBA1st68j7UGvq0ZKj7TPXrVuHAwcOYN++fe0q76i/Qaytx+Uc5TeHtfVw1O/I9XwejvIdsbZt7OrvhsskQ9S6ZcuWYd26dUhPT7e4EPD+++83Px4yZAiGDh2K3r17Iz09HePHj7dHqFeVlJRkcaRw1KhRGDBgAN5//30sWbLEjpHZzscff4whQ4Zg5MiRFvOd7bNyB1988QVefvllfPvttxbX1kycONH8eOjQoUhMTETPnj3x1Vdf4ZFHHrFHqNcUGxuL2NhY8/NRo0bhzJkzePPNN/Gf//zHjpHRtcTGxiI7OxtVVVX4+uuvMXPmTOzYsaPVRMKR96HW1KMlR9pn5ufn46mnnsL27duvuOjemVxvPRzlN0dH6uGI35Hr/Twc5Tvi6G2jy3STCwoKglQqRXFxscX84uJihIaGtrpMaGjoVcs3/7VmnbbWkXo1W7FiBZYtW4Zt27Zd84K7Xr16ISgoCKdPn77umK/leurUTC6XY9iwYeZ4nf2z0mq1WLduXbt2Cl35WXVEW98rX19feHh42OTzt6d169Zh9uzZ+Oqrr67ZZcHf3x/9+vVz2M+qLSNHjjTH7OyflytTKBTo06cPEhISsHTpUsTFxeGtt95q17KOtA/tSD0cbZ+ZmZmJkpISDB8+HDKZDDKZDDt27MDbb78NmUwGg8FwxTKO+BukI/Vo5ki/Oa6nHs0c4TtyPfVwtO9IS9dqG7v6u+EyyZBCoUBCQgLS0tLM84xGI9LS0trse5yUlGRRHgC2b99uLh8TE4PQ0FCLMhqNBnv27Gl3f+br1ZF6AaZRNpYsWYItW7ZgxIgR13yfgoIClJeXIywszCZxX01H69SSwWBATk6OOV5n/qwA0xCSOp0ODz744DXfpys/q4641vfKFp+/vXz55ZeYNWsWvvzyS4thSttSU1ODM2fOOOxn1Zbs7GxzzM78ebkbo9EInU7XrrKOuA9t1p56ONo+c/z48cjJyUF2drZ5GjFiBB544AFkZ2e32vXIEX+DdKQegOP95uhoPVpyhO/I9dTD0b4jLV2rbezy74bVQy44sHXr1olKpVJcu3atePToUfHRRx8V/f39RbVaLYqiKD700EPiggULzOV37dolymQyccWKFeKxY8fExYsXi3K5XMzJyTGXWbZsmejv7y9+++234qFDh8QpU6aIMTExYl1dncPWa9myZaJCoRC//vprsaioyDxVV1eLomgaleSvf/2rmJGRIebm5oo//fSTOHz4cLFv375ifX29Q9bp5ZdfFrdu3SqeOXNGzMzMFO+//35RpVKJR44csai3s31WzW6++WZx2rRpV8x3hM+qurpazMrKErOyskQA4htvvCFmZWWJ58+fF0VRFBcsWCA+9NBD5vJnz54VPT09xWeffVY8duyYuGrVKlEqlYpbtmwxl7nWdnLEen3++eeiTCYTV61aZfG9qqysNJd55plnxPT0dDE3N1fctWuXmJycLAYFBYklJSUOW68333xT3LRpk3jq1CkxJydHfOqpp0SJRCL+9NNP5jKO8HmRpQULFog7duwQc3NzxUOHDokLFiwQBUEQt23bJoqi8+xDra1HM0feZza7fNQvZ/0Ncq16OMNvjvbUw1G/I9bWo5kjfUeu1Tba+7vhUsmQKIriO++8I/bo0UNUKBTiyJEjxd9//9382pgxY8SZM2dalP/qq6/Efv36iQqFQhw0aJD4448/WrxuNBrFF198UQwJCRGVSqU4fvx48cSJE11RFQvW1Ktnz54igCumxYsXi6IoirW1teLtt98udu/eXZTL5WLPnj3FOXPmdPkPG2vqNG/ePHPZkJAQcdKkSeKBAwcs1ueMn5UoiuLx48dFAObGvyVH+Kyah16+fGqux8yZM8UxY8ZcsUx8fLyoUCjEXr16if/+97+vWO/VtlNXsLZeY8aMuWp5UTQNIR4WFiYqFAoxIiJCnDZtmnj69GmHrterr74q9u7dW1SpVGK3bt3EsWPHij///PMV67X350WW/vznP4s9e/YUFQqF2L17d3H8+PEW+xBn2YdaWw9RdPx9ZrPLf7Q662+Qa9XDWX5zXKsejvoduVx7/q8c7TtyrbbR3t8NQRRF0frzSURERERERM7NZa4ZIiIiIiIisgaTISIiIiIicktMhoiIiIiIyC0xGSIiIiIiIrfEZIiIiIiIiNySzN4B2ILRaERhYSF8fHwgCIK9wyEiciuiKKK6uhrh4eGQSHiMrRnbJiIi+7CmXXKJZKiwsBBRUVH2DoOIyK3l5+cjMjLS3mE4DLZNRET21Z52ySWSIR8fHwCmCvv6+to5GiIi96LRaBAVFWXeF5MJ2yYiIvuwpl2yeTK0c+dOLF++HJmZmSgqKsI333yDqVOntlk+PT0d48aNu2J+UVERQkND2/Wezd0PfH192eAQEdmJI3cFe+mll/Dyyy9bzIuNjcXx48fbXGbDhg148cUXce7cOfTt2xevvvoqJk2a1O73ZNtERGRf7WmXbN65W6vVIi4uDqtWrbJquRMnTqCoqMg8BQcH2zo0IiJyY4MGDbJoZ3777bc2y+7evRvTp0/HI488gqysLEydOhVTp07F4cOHuzBiIiLqbDY/MzRx4kRMnDjR6uWCg4Ph7+9v63CIiIgAADKZrN09Dt566y1MmDABzz77LABgyZIl2L59O959912sXr26M8MkIqIu5DDD/sTHxyMsLAy33XYbdu3a1aXvnXn+Ij7fcx7Z+ZVd+r5ERNR1Tp06hfDwcPTq1QsPPPAA8vLy2iybkZGB5ORki3kpKSnIyMjo7DDN/pdThE8zzqGkur7L3pOIyN3YfQCFsLAwrF69GiNGjIBOp8NHH32EsWPHYs+ePRg+fHiry+h0Ouh0OvNzjUZzXTH8eEiNNbty8cTY3oiP8r+udRERkeNJTEzE2rVrERsbi6KiIrz88ssYPXo0Dh8+3OoFtmq1GiEhIRbzQkJCoFar23wPW7dNK7adwJlSLfqF+CDYR3Vd6yIiotbZPRmKjY1FbGys+fmoUaNw5swZvPnmm/jPf/7T6jJLly694kJYWxBtvkYiInIELbtvDx06FImJiejZsye++uorPPLIIzZ5D1u3Td5KUxOt1TXabJ1ERGTJYbrJtTRy5EicPn26zdcXLlyIqqoq85Sfn9+F0RERkbPz9/dHv3792mxrQkNDUVxcbDGvuLj4qtcc2bpt8lQ0JUN6w3Wth4iI2uaQyVB2djbCwsLafF2pVJqHKrXFkKXNo+6JPDVEROQWampqcObMmTbbmqSkJKSlpVnM2759O5KSktpcp63bJi+eGSIi6nQ27yZXU1NjcaQtNzcX2dnZ6NatG3r06IGFCxfiwoUL+PTTTwEAK1euRExMDAYNGoT6+np89NFH+Pnnn7Ft2zZbh0ZERG7qr3/9K+6880707NkThYWFWLx4MaRSKaZPnw4AmDFjBiIiIrB06VIAwFNPPYUxY8bg9ddfx+TJk7Fu3Trs378fH3zwQZfF7K2UAgBq6pkMERF1FpsnQ/v377e4iWpqaioAYObMmVi7di2KioosRvDR6/V45plncOHCBXh6emLo0KH46aefWr0Ra2dx3NsEEhGRLRQUFGD69OkoLy9H9+7dcfPNN+P3339H9+7dAQB5eXmQSC51lhg1ahS++OILvPDCC3j++efRt29fbNq0CYMHD+6ymP085ACAqrqGLntPIiJ3Y/NkaOzYsRCv0t9s7dq1Fs/nz5+P+fPn2zqMDhE5hAIRkUtat27dVV9PT0+/Yt69996Le++9t5MiujZ/TwUA4GKt3m4xEBG5Ooe8ZoiIiMjddfMyJUOVTIaIiDoNkyFcGkCBJ4aIiMhR+HuauslVaNlNjoioszAZIiIickABTd3kKnhmiIio0zAZAiA0nRriiSEiInIUzd3kymp0do6EiMh1MRkiIiJyQOH+HgCAsho96ht441Uios7AZKiFq42CR0RE1JUCPOVQyU3NtLqq3s7REBG5JiZD4H2GiIjI8QiCYD47VFhZZ+doiIhcE5OhFnhiiIiIHElEUzJUwGSIiKhTMBkCeGqIiIgcUs9ATwDA2VKtnSMhInJNTIZa4IkhIiJyJP1CfAAAp4qr7RwJEZFrYjIEQGg6NcRuckRE5Ej6BpuSoZMlTIaIiDoDkyEAkqZuciLPDRERkQPpF+INAMi/WIcaXaOdoyEicj1MhgBIBJ4ZIiIixxPorTQPonAov9K+wRARuSAmQwCaciEYmQ0REZGDGd4zAACw/3yFnSMhInI9TIZgupcDwDNDRETkeBJ6+ANgMkRE1BmYDOHSyNo8M0RERI5mZEwgAGBvbjnqGwx2joaIyLUwGUKLa4bsHAcREdHlBoT5INRXhfoGIzLOlts7HCIil8JkCC1Gk+OZISIicjCCIODWAcEAgLRjxXaOhojItTAZQosBFIz2jYOIiKg1tw0MAQBszlFD38jGiojIVpgMocUACuwoR0REDmh0nyAE+yhxUavHz8dL7B0OEZHLYDKES9cMGZkLERGRA5JJJfjD8AgAwFf78+0cDRGR62AyBN5niIiIHN/9N/SAIAA/Hy/ByeJqe4dDROQSmAzh0gAK7CVHRESOKibICykDQwEAq3ecsXM0RESugckQWnaTYzZERESO6/GxvQEA32YX4hTPDhERXTcmQy3wmiEiInJkcVH+uG1gCAxGEf/YfMze4RAROT0mQ+BNV4mIyHk8P2kA5FIB6SdKsfWI2t7hEBE5NSZDuHTNELvJERGRo4sJ8sLs0b0AAH/7JgcXtXo7R0RE5LyYDKHFfYaYDBERkRN4anxf9A32RlmNHs9vzGH7RUTUQUyGcOnMENsSIiJyBiq5FG/cFw+5VMCWI2qs3nHW3iERETklJkO4dGbIwBEUiIhc3rJlyyAIAubNm9dmmbVr10IQBItJpVJ1XZDtMCTSD4vvHAQAeG3rcfxyosTOEREROR8mQwDkUlMy1MhkiIjIpe3btw/vv/8+hg4des2yvr6+KCoqMk/nz5/vggit80BiD9x/QxREEXjiswPIPH/R3iERETkVJkMAZBLTZmgwGO0cCRERdZaamho88MAD+PDDDxEQEHDN8oIgIDQ01DyFhIR0QZTWEQQBL08ZhFv6dUddgwEP/3sfjhRW2TssIiKnYfNkaOfOnbjzzjsRHh4OQRCwadOmay6Tnp6O4cOHQ6lUok+fPli7dq2tw7oqWfOZIQPPDBERuaq5c+di8uTJSE5Oblf5mpoa9OzZE1FRUZgyZQqOHDly1fI6nQ4ajcZi6gpKmRSrHxyOG6IDUF3fiD99uIdniIiI2snmyZBWq0VcXBxWrVrVrvK5ubmYPHkyxo0bh+zsbMybNw+zZ8/G1q1bbR1am+RS02ZoNPLMEBGRK1q3bh0OHDiApUuXtqt8bGws1qxZg2+//RafffYZjEYjRo0ahYKCgjaXWbp0Kfz8/MxTVFSUrcK/Jk+FDB8/fAOG9/BHVV0DHvhoD9KOFXfZ+xMROStB7MTxOAVBwDfffIOpU6e2Wea5557Djz/+iMOHD5vn3X///aisrMSWLVva9T4ajQZ+fn6oqqqCr6+v1XFuO6LGo//JRHyUPzbNvcnq5YmI3Nn17oM7W35+PkaMGIHt27ebrxUaO3Ys4uPjsXLlynato6GhAQMGDMD06dOxZMmSVsvodDrodDrzc41Gg6ioqC7dLnV6A+Z+cQA/Hy+BVCLg+UkD8Oebos0DBRERuQNr2iW7XzOUkZFxRZeFlJQUZGRktLmMrbsiNJ8Z4mhyRESuJzMzEyUlJRg+fDhkMhlkMhl27NiBt99+GzKZDAaD4ZrrkMvlGDZsGE6fPt1mGaVSCV9fX4upq3kopHj/oQTckxAJg1HEkh+O4ql12ajVN3Z5LEREzsDuyZBarb7iotSQkBBoNBrU1dW1uoytuyI0XzPEARSIiFzP+PHjkZOTg+zsbPM0YsQIPPDAA8jOzoZUKr3mOgwGA3JychAWFtYFEV8fuVSC5fcMxaI7BkImEfDdwUL8YdVuHFd3zTVMRETOxO7JUEcsXLgQVVVV5ik/P/+61tc8mhyH1iYicj0+Pj4YPHiwxeTl5YXAwEAMHjwYADBjxgwsXLjQvMzf//53bNu2DWfPnsWBAwfw4IMP4vz585g9e7a9qmEVQRDw55tj8MWcGxHkrcSJ4mrc9c4uvL/jDHtBEBG1YPdkKDQ0FMXFlhd5FhcXw9fXFx4eHq0uY+uuCOb7DPHMEBGRW8rLy0NRUZH5eUVFBebMmYMBAwZg0qRJ0Gg02L17NwYOHGjHKK03MqYbNj91M8b3D4beYMTS/x3H/R9kILdMa+/QiIgcgszeASQlJWHz5s0W87Zv346kpKQui0Embb7PEI+WERG5g/T09Ks+f/PNN/Hmm292XUCdKNhHhY9mjsCG/QV4+fsj2HeuAilv7sRfxvTC3HF9oJJfu5sgEZGrsvmZoZqaGnOfbMA0dHZ2djby8vIAmLq4zZgxw1z+sccew9mzZzF//nwcP34c//rXv/DVV1/h6aeftnVobZJJms4McWhtIiJyQYIg4L4borBl3i24pV936A1GvPPzaSS/sQM/HeUQ3ETkvmyeDO3fvx/Dhg3DsGHDAACpqakYNmwYFi1aBAAoKioyJ0YAEBMTgx9//BHbt29HXFwcXn/9dXz00UdISUmxdWhtUspMm0HXyGSIiIhcV1Q3T3wy6wasfnA4wv1UKKiow+xP9+OBj35HTkGVvcMjIupynXqfoa5yvfe4KKioxc2v/gKlTIITr0zshAiJiFyXo99nyF4cfbvU6hvxdtpprPktF/qma2bvGBqGv94ei+ggLztHR0TUcU51nyFH4NHUX1rXaISRo+wQEZEb8FTIsGBif6Q9MwZ3D4uAIAA/HCpC8hs78OKmwyisbP32FkREroTJEEw3qWtW33jtm+8RERG5iqhunnhjWjx+/L/RGBvbHY1GEf/5/TzGLP8FCzceQl55rb1DJCLqNEyGAKhkl5KhWj2TISIicj8Dw32xdtZIfDnnRtzYqxsaDCK+3JuPca+nI/WrbJwuqbF3iERENmf3obUdgUQiQCWXoL7BiDomQ0RE5MaSegciqXcS9p27iHd/Po0dJ0ux8cAFfJN1AckDQvDIzTFIjOkGQRDsHSoR0XVjMtTEQy41JUMNTIaIiIhuiO6GT/48EgfzK/HOz6fx07FibD9qmgZH+OKRm2MweUg4FDJ2MiEi58U9WJPmQRR4ZoiIiOiSuCh/fDRzBH5KHYM/JfaASi7B4QsaPL3+IEa/9jNW/XIaFVq9vcMkIuoQJkNNmgdR0Oob7RwJERGR4+kT7I1//mEIMhaMx7MpsQj2UaJYo8PyrSeQuDQNqeuzkXn+Ilzgjh1E5EbYTa6Jn4ccAKCpYzJERETUlgAvBeaO64M5o3vhh0OFWLMrF4cvaLAx6wI2Zl1A/1AfPHhjT0wdFgFvJX9mEJFj416qyaVkqMHOkRARETk+hUyCu4dH4u7hkTiYX4nPfj+P7w8V4ri6Gi9sOoylm49h6rAIPJDYEwPDHe+ms0REAJMhs+ZkqLKO/Z6JiIisERflj7gof7wweSD+e6AAn+85jzOlWny+Jw+f78nD8B7+uP+GHpg0NIxni4jIoXCP1MTfUwEAqOKZISIiog7x85TjzzfHYNZN0fj97EV8tuc8th5W40BeJQ7kVeKl749g8pAw3HdDFEb0DODw3ERkd0yGmvg2nRliMkRERHR9BEFoul9RIEqq6/F1ZgG+3l+As2VabMgswIbMAsQEeeGehEj8cXgkQv1U9g6ZiNwUk6Em/uZkiAMoEBER2UqwjwpPjO2Dx8f0Rub5Cny1Px8/HCpCbpkWy7eewOvbTmBMv+64b0QUxg8I4X2LiKhLMRlq0s3L1E2uvEZn50iIiIhcjyAIGBHdDSOiu2HxnYPwY04RNuzPx75zFfjlRCl+OVGKbl4KTI2PwH03RKJ/KAddIKLOx2SoSbCvEgBQrKm3cyRERESuzUspw30jonDfiCicLa3BhswC/DezACXVOqzZlYs1u3IxNNIP9yRE4s6h4QhoOmBJRGRrTIaahPia+iuXaHhmiIiIqKv06u6N5yb0xzO39cPOU6XYsL8APx0rxqGCKhwqqMKSH45iXGww7h4egXH9g6GUSe0dMhG5ECZDTZqToWpdI7S6Rnhx6E8iIqIuI5NKcGv/ENzaPwTlNTpsyi7ExgMFOFKowbajxdh2tBh+HnLcGReGu4dHYliUP0ejI6Lrxl/8TbyVMngppNDqDSip1iGGyRAREZFdBHor8cjNMXjk5hicUFdjY1YBNmVdQLFGh89+z8Nnv+chJsgLdw+LwNRhEYjq5mnvkInISXHIlhaazw4VVdXZORIiIiICgNhQHyycOAC7F4zHfx4ZiT8Mi4CHXIrcMi1e334So1/7Bfe9n4H1+/KgqeftMYjIOjz90UJEgAfOlmmRf7EW6G3vaIiIiKiZVCJgdN/uGN23O5ZMbcTWw2pszCrA7jPl2Jt7EXtzL2LRt0dw+6BQ3D08AqP7BEEm5TFfIro6JkMtxAR54ddTZThXXmvvUIiIiKgN3koZ/pgQiT8mRKKwsg6bsi9g44ELOF1Sg+8PFuL7g4UI8lZiSnw4/jAsAoPCfXl9ERG1islQCz0DvQAA58u1do6EiIiI2iPc38N8U9ecC1XYeOACvjtYiLIaHT7+LRcf/5aLXt29MCUuAnfFhyMmyMveIRORA2Ey1EJ0oOkCzHNlPDNERETkTARBwNBIfwyN9MffJg/AjhOl+CbrAn46VoyzpVq8+dNJvPnTScRF+uGu+AjcOTQMwU3XChOR+2Iy1EJ009Gic+VaiKLIU+pEREROSC6VIHlgCJIHhqBG14htR9T4NrsQv50uw8GCKhwsqMI/fjyKpN6BmBIXgZTBofDzkNs7bCKyAyZDLfTo5gmFVIJavQEFFXUcqpOIiMjJeStluHt4JO4eHomyGh025xTh2+xCZJ6vwK7T5dh1uhwvbDqMcf27Y0p8BG7tHwyVnDd2JXIXTIZakEsl6BvijSOFGhwp1DAZIiIiciFB3krMSIrGjKRo5F+sxXcHC/FddiFOFFdj65FibD1SDC+FFLcOCMGkwaEYGxsMDwUTIyJXxjEnLzMwzBcAcLRIY+dIiIioMyxbtgyCIGDevHlXLbdhwwb0798fKpUKQ4YMwebNm7smQOoSUd08MXdcH2x9+hZsmTcaT4ztjQh/D2j1Bnx/sBCPf34ACa9sx9wvDmBzThHq9AZ7h0xEnYBnhi4zMNwXyASOMRkiInI5+/btw/vvv4+hQ4detdzu3bsxffp0LF26FHfccQe++OILTJ06FQcOHMDgwYO7KFrqKv1DfdF/gi+eTYnFwYIqbM4pwuacIhRU1OHHQ0X48VARPORS3No/GJOGhGFc/+7wVPAnFJErEERRFO0dxPXSaDTw8/NDVVUVfH19r2tde86WY9oHvyPEV4nfF47nIApERNdgy31wZ6qpqcHw4cPxr3/9C6+88gri4+OxcuXKVstOmzYNWq0WP/zwg3nejTfeiPj4eKxevbpd7+cs24VaJ4oici5U4cemxCj/Yp35NQ+5FOP6d8ekIWEYGxsMbyUTIyJHYs3+l9/eywyN9IdMIqBYo+MgCkRELmTu3LmYPHkykpOT8corr1y1bEZGBlJTUy3mpaSkYNOmTW0uo9PpoNPpzM81GvYwcGYth+peMKE/Dl/QmBOjvIu12JyjxuYcNRRSCUb1CcTtA0ORPCCYw3UTORkmQ5fxUEgxOMIP2fmV2H/+IpMhIiIXsG7dOhw4cAD79u1rV3m1Wo2QkBCLeSEhIVCr1W0us3TpUrz88svXFSc5JkEQMCTSD0Mi/fDchFgcKTQlRlsOq5FbpkX6iVKknyjF898Aw3r447aBIbh9YCj6BHvbO3QiugYmQ624IToA2fmV2JtbgT8Mi7R3OEREdB3y8/Px1FNPYfv27VCpOu+o/cKFCy3OJmk0GkRFRXXa+5F9CIKAwRF+GBzhh/kpsThTWoNtR4ux7UgxsvMrkZVnml7bcgK9grxw26AQ3D4wBMOiAiCRsOs9kaPptNHkVq1ahejoaKhUKiQmJmLv3r1tll27di0EQbCYOrPBupYR0d0AAPvOXbRbDEREZBuZmZkoKSnB8OHDIZPJIJPJsGPHDrz99tuQyWQwGK4cJSw0NBTFxcUW84qLixEaGtrm+yiVSvj6+lpM5NoEQUCfYB88MbYPNs29CXueH49//GEwxvTrDrlUwNkyLd7fcRZ/fC8DI/+ZhgX/PYSfjxejvoEj0xE5ik45M7R+/XqkpqZi9erVSExMxMqVK5GSkoITJ04gODi41WV8fX1x4sQJ83N7DlwwMrobBAE4XVKDoqo6hPl52C0WIiK6PuPHj0dOTo7FvFmzZqF///547rnnIJVeeR+ZpKQkpKWlWQy/vX37diQlJXV2uOTEQnxVeCCxJx5I7Inq+gbsOFmKbUeK8cvxEpTV6LBuXz7W7cuHp0KKm/sEYfyAYIyL5XVGRPbUKcnQG2+8gTlz5mDWrFkAgNWrV+PHH3/EmjVrsGDBglaXEQThqkfculKAlwLxUf7IyqtE+olSTB/Zw94hERFRB/n4+FwxHLaXlxcCAwPN82fMmIGIiAgsXboUAPDUU09hzJgxeP311zF58mSsW7cO+/fvxwcffNDl8ZNz8lHJccfQcNwxNBz6RiP25JZj25FibD9aDLWm3tS17qjp7OOQCD/c2j8Y4wcEY3C4H7vTEXUhmydDer0emZmZWLhwoXmeRCJBcnIyMjIy2lyupqYGPXv2hNFoxPDhw/HPf/4TgwYNarVsV4zYM7ZfcFMyVMJkiIjIxeXl5UEiudRzfNSoUfjiiy/wwgsv4Pnnn0ffvn2xadMm3mOIOkQhk2B03+4Y3bc7/j5lEI4UapB2rAQ/Hy/GwYIq5FwwTW+lnUJ3HyVujQ3GuP7BGN03CF4ctpuoU9n8PkOFhYWIiIjA7t27LboTzJ8/Hzt27MCePXuuWCYjIwOnTp3C0KFDUVVVhRUrVmDnzp04cuQIIiOvHMDgpZdeanXEHlvey+FQQSXuencXvBRSZC26HQpZp11eRUTk1Hg/ndZxu1B7lFTXI/1EKX4+VoJfT5VCq790PZFCKkFir24Y3z8Yt/YPQY9AjnBL1B5Od5+hpKQki8Rp1KhRGDBgAN5//30sWbLkivJdMWLP4HA/BHkrUVajw+4zZRgb2/q1TkREREQdFeyjwn0jonDfiCjoGg3Ym3ux6axRCfIu1uLXU2X49VQZXvr+KPoGe+PW/sG4tX8wEnoGQCblgVqi62XzZCgoKAhSqdTqUXhaksvlGDZsGE6fPt3q60qlEkql8rpjvRqJRMDEwaH4z+/n8cOhIiZDRERE1KmUMqm5O93iOwfiTKkWPx8vRtqxEuw/X4FTJTU4VVKD93eeha9KhtH9umNM0xTCQRiIOsTmhxQUCgUSEhKQlpZmnmc0GpGWltbuUXgMBgNycnIQFhZm6/CscsdQ0/tvPaKGrpHDYBIREVHXMA3b7Y1Hb+mN9X9JwoEXbsPb04fhD8Mi4O8ph6a+ET8eKsL8rw8h8Z9pmLByJ5ZuPobdp8v4m4XICp3STS41NRUzZ87EiBEjMHLkSKxcuRJardY8utzlo/b8/e9/x4033og+ffqgsrISy5cvx/nz5zF79uzOCK/dbojuhhBfJYo1Ouw8WYbbBoZceyEiIiIiG/PzlOOuuHDcFRcOg1FEVl4Fdp4sxY5TZThUUInj6mocV1fj/Z1n4amQYlTvQNzSrztu6hOEXkFedr1lCZEj65RkaNq0aSgtLcWiRYugVqsRHx+PLVu2ICTElExcPmpPRUUF5syZA7VajYCAACQkJGD37t0YOHBgZ4TXbhKJgMlDwrFmVy7+m1nAZIiIiIjsTioRMCK6G0ZEd0Pq7bG4qNXj11Ol2HGyFDtPlqGsRoefjpXgp2MlAIBgHyVu7BWIpN6BSOoViJ6BnkyOiJrYfDQ5e+jMEXuOqzWYsPJXyCQCdi+8FcE+7JNLRNQSR01rHbcL2YPRKOKYWtOUGJXiQF4l9I1GizKhviok9Q7EiOgAxEf5IzbEh4MxkEtxutHkHFn/UF8M62G6AevXmQV4Ymwfe4dERERE1CqJRMCgcD8MCvfDE2P7oL7BgKy8SmScLcfvZ8uRnVcJtaYe32RdwDdZFwAAHnIphkb6Ib6HP4ZFBWB4D38Ec0AGchNMhtph+sgeyMqrxJd78/DYLb15Z2giIiJyCiq51NQ9rncgAKBOb8CBvArsOVuOrPxKZOdVolrXiD25F7En96J5ue4+SvQP9cHAMF8MCPNF/zAf9O7uDTnPIJGLYTLUDncODccrPxxF/sU6bD9WjJRB7RsinIiIiMiReCikuKlPEG7qEwTA1K3uTGkNsvIqkZVfiay8CpwsrkZptQ6l1Tr8eqrMvKxcKqBPsA/6BnsjJsgLvbp7oXd302MvJX9SknPif247eCikeCipJ1b9cgbvpZ/B7QNDeOEhEREROT2JREDfEB/0DfHBfTeYbmBfq280jU5XVI1jRRocK9LguLoaNbpG8/PLhfqq0Ku7F2KCTFNkgCciAzwQFeAJP095V1eLqN2YDLXTw6Ni8OGvucjOr8Se3Iu4sVegvUMiIiIisjlPhQzDewRgeI8A8zxRFFFQUYdjRRqcLdPibGkNzpZqcbZMi4taPdSaeqg19dh9pvyK9fmoZObkyDSZHkf4eyDYV4lALyWkvASB7ITJUDt191HivhGR+Oz3PLy5/STWPXojzw4RERGRWxAEAVHdPBHVzfOK1ypr9U0JkilJyrtYi/yKOlyoqEVZjR7V9W2fUQJMQ4UHeSsQ7KNCsI8Swb5K0+Pmvz5KhPiqEOSt4Kh3ZHNMhqzw+Ng++Gp/AfbkXsQvJ0pwa3/ed4iIiIjcm7+nAsN7KCzOJDWr1TfiQkUdCirqUFBRi4KKOuQ3/S2srEO5Vg+DUUSxRodije6q7yMIQKCXAt3NCVLLpElpnh/sq4RSJu2s6pKLYTJkhQh/D8y6KRrv7ziLpZuP45a+3XmEgoiIiKgNngqZ+Zqk1jQYjCiv0aNYU4+Sah1KqutRotGhpFqH0up6FGtM88pqTElTWY0eZTV6HCu6+vv6e8pNidFlZ5iaHzcnUh4KJk3ujsmQlZ4Y2wfr9+XjVEkN/r3rHObc0sveIRERERE5JblUglA/FUL9rn5fI4NRxEWtvkWydClpKmlKmppHwNMbjKisbUBlbQNOFtdcdb0+Shm6N51ZCvFVWSRQ3VvM81bKeHmEi2IyZCU/DzkWTuyP5/6bg9e3n8Dtg0LQM9DL3mERERERuSypREB3H1OCMii87XKiKKKytsEiSWpOnEovm1ffYES1rhHVpY04W6q96vt7yKXm7njBviqENJ1dCvFVNU1KhPqp4KngT2tnw0+sA+4bEYVvswux+0w5nv36EL6YncjuckRERER2JggCArwUCPBSIDa09a55gClpqtY1XnaWyfJsU0m1DiUaHWp0jahrMOB8eS3Ol9de9f19lDIENyVGIT4qhPipEOJjeh7clDgF+yh581oHwmSoAwRBwNK7h2DSW79ib+5FrPzpFP6aEmvvsIiIiIioHQRBgK9KDl+VHH2Cva9atlbfeEWXvJLqehRX1TcN/GAaVrxWbzCfaTpzlTNNzQNBhPqpEO7ngXB/05Dj4f6m4cbD/T0Q5K1gt7wuwmSog3oGemHZH4fi/77Mwru/nMbgCD9MGBxq77CIiIiIyIY8FTJEB8kQHXT1yyJqdI1QV9WjpCk5ak6ULk2mJKrBcGkgiMMXWh9uXCGTIMKcHKkQ4e9p+tt0f6YwPw8oZDy7ZAtMhq7DnXHh2H/uIj7JOI//ty4Ln89OxA3R3ewdFhERERF1MW+lDH2Cva96psloFFFR23ST2qp6FFbWoaCyDoWV9bhQUYvCynoUV9dD32hEbpkWuWWtn2ESBKC7txIRTWeUIv0tzyxFBHjAV8VBH9qDydB1evGOgbhQWY+fjhVj1r/34YMZCRjVO8jeYRERERGRg5FIBAR6KxHorcSgcL9Wy+gbjSjW1JvvxXSh8tLfC5V1uFBRB12jsanbng5ZeZWtrsdbKbt0ZqlFN7yIpmQp2EcFqYTJkiCKomjvIK6XRqOBn58fqqqq4Ovr2+XvX6c34M9r9yHjbDkUUglmj47BmH7d4ecph7dSBm+lDF5KGS+WIyKXZO99sKPidiGiziCKpmHGm5MkU9JUjwuVtU1/63BRq7/memQSAaF+KkT4eyAywBNR3Zr+BnggqpsnQnydN1myZv/LZMhG6hsMSP0qG5tz1G2WUcokpuRIJYOXwvS3ZbLk0zTfSyk1PW56zUclQ6CXafhG3hyMiByNI+yDHRG3CxHZS53eYHFGqbDpjFLz2SV1VT0ajVdPAeRSAeH+HohqkShFNiVKvYK84O+p6KLaWM+a/S+7ydmISi7Fqj8Nx5bDavz3QAFOldRAq2tEdX0jdI1GAICu0Qhdox7l7cjW2xLso8T4ASFIGRSCUb2DePEcEREREVnwUEivev2SwSiipLrefGapoKIO+RdrTX8ranGhog4NBvGqw4kHeMoRE+SFmCBv9OruhZggL/Tq7oXe3b2dqjcUzwx1gQaDEVpdI2qapuYkSaszoEbXgBqdATX1jdDqm+dfKltT34hqXQPKqvWoazBYrNdHKcO4/sFIHhiCm3oHItBbaacaEpE7c/R9sL1wuxCRszIYRRRr6pF/sRb5lyVKeeW1UGvq21xWIZWgT7A3Bob7YkCYLwaG+WJwhC98VPIui5/d5FxQ883BsvMqsfWIGtuOFqO0WmdRZlC4LxJjAjE00g9DIv0QE+gFiZP29SQi5+EO++CO4HYhIldVq2/EubJa5JZpcba0xvS3TIvTJTWo0TVeUV4iAAPCfHFDdDeM6h2Im/sGwVPReR3UmAy5AaNRRFZ+JbYdUWPHyVIcV1dfUaZ5iMfmU5a9u3uhV3dv9Az0hFLGa4+IyDbccR/cHtwuRORuRFFEQUUdjhRqcKzINB0p1OBCZZ1FOaVMgpv7BOGPCZG4bWCIzbvVMRlyQyXV9cg4U46svErkXKjCkcIq1DcYWy0rEYCIAA9E+ntajE9vvpGXv4rJEhG1G/fBreN2ISIyUVfVY++5i9ibW470E6UoqLiUHHX3UeKxMb3x4I09bPb7k8kQodFgxJlSLc6U1uBMSQ3ONp3GPFOqbfX05eWCfZTmm3Y1J0ohvip081Kgm5cCgV4K+Krk7IZHRNwHt4HbhYjoSqIo4kRxNb4/WIiv9heYL/voE+yNt+8fhoHh17+/ZDJEbRJFEaXVOpwrr8WFytoWwyya7nx8obKuzTNKl5NKBAR4KuDnIYNKLoVSJjH/VcqkUMolUMokkEslkEoESASh6a/ppmPSpueC0PwYpsdNr0mayjYvK2kqc2k9gnk9LdcpaVFGKgiX1tm8fovl0eK9Li1v+Z6X5sskkkt/BfDOzkRwjn3we++9h/feew/nzp0DAAwaNAiLFi3CxIkTWy2/du1azJo1y2KeUqlEfX3bFw1fzhm2CxGRPTUYjPg6swCvbzuJshodvBRSfPrISCT07HZd6+XQ2tQmQRAQ7KtCsK8KwJX/aM038mq+eVfLG3mVVOtwUavHxRo9qnWNMBhFlNXoUFaju/KN3MQViZIAyKQSSAQBMklT0tXyNYkESrkEKnOyKIVKbkoiVXIJvJVyBHopEOitQIivCtFBXgjzVfEMHNF1ioyMxLJly9C3b1+IoohPPvkEU6ZMQVZWFgYNGtTqMr6+vjhx4oT5OQ9+EBHZllwqwfSRPZAyKBRPfJ6J389exCOf7Me2ebc0/VbtfEyGyIIgCAj0ViLQW4khkX5tltM1GlChbUC5VoequgboG42obzBC12houp+SEboG02N9oxFGUYTBKMIgihBF05CNBqMIUTTNMxhNidgVZUTx0nwjYBRF87qMogijETCIIoxNzw0iLj1uLtM0zyC2WOaKdTe9p3jl46sxGEUYIAIGAGjfGTVr+ahkGBrph7hIfwyN9MfgCF9E+HvwhxmRFe68806L5//4xz/w3nvv4ffff28zGRIEAaGhoV0RHhGRW+vmpcC/Hx6Je9/fjcMXNPjn5mNYef+wLnlvJkPUIUqZFKF+UoT6dU3Wbk/NiVRz0tScfDUaL/1t7bWW85ofNxpE6A0G1DcYUd+ULNY3XHpeXd+Icq0O5TV6FFbWIe9iLarrG7HrdDl2nS43x+TnIUffYG/zNVx+nnKoZFIoZBIopBLT3xaP5VJTl0WZ1NT1TxBMXQkFwNwdURCauwYCAi6Vae52KKC5DMxdCAUBl8oJl9Zzad6lZVou23IZ03p51J26jsFgwIYNG6DVapGUlNRmuZqaGvTs2RNGoxHDhw/HP//5zzYTJwDQ6XTQ6S6dKddoNDaNm4jIlXkopFj6h6G4893f8P2hIsyf0B/h/h6d/r5MhoiuQSIRIIEAuR0G2GswGHGyuBoH86twqKASBwuqcKq4GlV1Ddh/vqLrA+pEFgkULBMqiSAAlz2/lGRZJmet/TWvA5cleM0J4VUSvPYkdi0TSfM8tEgcm65XE3D5uprXb1mnlu8rkwiQSgXIJaZr72RSU/dLUzdMCWRN81o+b37dQyGFt1IGr6bJWymD1I27XObk5CApKQn19fXw9vbGN998g4EDB7ZaNjY2FmvWrMHQoUNRVVWFFStWYNSoUThy5AgiIyNbXWbp0qV4+eWXO7MKREQubUikH26IDsC+cxXYfrQYM0dFd/p7cgAFIiejazTgVHENzpVrUVHbgAqtHpW1DdAbDNA3dUvUG4zQN4pNf03zGwwiGgymrnzm7oNNXRLFy563/Gs0vy5ChOlMWcvXmudfvg5yTKG+KsSG+uDu4RG4Ky7cJmfknGUfrNfrkZeXh6qqKnz99df46KOPsGPHjjYTopYaGhowYMAATJ8+HUuWLGm1TGtnhqKiohx+uxAROZL3d5zB0v8dR/KAYHw084YOrYMDKBC5MKVMisERfhgc0fY1XY6gZXJ09STryr+tJ16m14AW6226jgy4PMFrXq7t979aHM3LmNdrbJE4tnz/pmVaSxxNjy/F01byaV4Hmq93u7RMcxfLRkPTX6OIRqPR4rnBaEpyL3/eaBRRpzdAq2+EVteIBoOpPmpNPdSaeuw4WYrfTpVh2R+Hus3ZIoVCgT59+gAAEhISsG/fPrz11lt4//33r7msXC7HsGHDcPr06TbLKJVKKJVKm8VLROSO4qP8AQDHiqq75P2YDBFRpzANYw5I4R4/tB2drtF0Tdr58lrsOFmKd38+hQ2ZBQj0VmLBxP72Ds8ujEajxZmcqzEYDMjJycGkSZM6OSoiIvfWP9R0JudCZR009Q3wVck79f0knbXiVatWITo6GiqVComJidi7d+9Vy2/YsAH9+/eHSqXCkCFDsHnz5s4KjYjI7ShlUgR5K5HQMwCpt/XDG/fFAwBW7ziD/ecu2je4LrBw4ULs3LkT586dQ05ODhYuXIj09HQ88MADAIAZM2Zg4cKF5vJ///vfsW3bNpw9exYHDhzAgw8+iPPnz2P27Nn2qgIRkVvw8zTdZgQACi7Wdfr7dUoytH79eqSmpmLx4sU4cOAA4uLikJKSgpKSklbL7969G9OnT8cjjzyCrKwsTJ06FVOnTsXhw4c7IzwiIrc3dVgEpo2IAgC89P0RuMDlo1dVUlKCGTNmIDY2FuPHj8e+ffuwdetW3HbbbQCAvLw8FBUVmctXVFRgzpw5GDBgACZNmgSNRoPdu3e36/oiIiK6Pt19TF2OS7vgXpadMoBCYmIibrjhBrz77rsATF0RoqKi8H//939YsGDBFeWnTZsGrVaLH374wTzvxhtvRHx8PFavXn3N93OWi3eJiBxJWY0OY177BVq9AZ/PTsRNfYI6tB7ug1vH7UJE1DEPfbwHv54qw4p743BPQusjeF6NNftfm58Z0uv1yMzMRHJy8qU3kUiQnJyMjIyMVpfJyMiwKA8AKSkpbZYnIqLrF+StxB+bGpm1u8/ZNxgiIqIm5jND1Z1/ZsjmyVBZWRkMBgNCQkIs5oeEhECtVre6jFqttqq8TqeDRqOxmIiIyHozkqIhCECjwYjGpqHXiYiI7Km7jxIecqn5liCdySlHk+ON7YiIbKNPsDd2L7gVYX6df5dvIiKi9nj29lgsmNDfJvfCuxabnxkKCgqCVCpFcXGxxfzi4mKEhoa2ukxoaKhV5RcuXIiqqirzlJ+fb5vgiYjcEBMhIiJyJDKppEsSIaATkiGFQoGEhASkpaWZ5xmNRqSlpSEpKanVZZKSkizKA8D27dvbLK9UKuHr62sxERERERERWaNTusmlpqZi5syZGDFiBEaOHImVK1dCq9Vi1qxZAEz3c4iIiMDSpUsBAE899RTGjBmD119/HZMnT8a6deuwf/9+fPDBB50RHhERERERUeckQ9OmTUNpaSkWLVoEtVqN+Ph4bNmyxTxIQl5eHiSSSyelRo0ahS+++AIvvPACnn/+efTt2xebNm3C4MGD2/V+zaODcyAFIqKu17zvdfV7FVmLbRMRkX1Y0y51yn2GulpBQQGioqLsHQYRkVvLz89HZKT194NwVWybiIjsqz3tkkskQ0ajEYWFhfDx8enQxVYajQZRUVHIz8932+uPuA24Ddy9/gC3AdCxbSCKIqqrqxEeHm5x1t/dsW26Pqy/e9cf4DZg/Ttef2vaJaccWvtyEonEJkcjORgDtwHAbeDu9Qe4DQDrt4Gfn18nRuOc2DbZBuvv3vUHuA1Y/47Vv73tEg/hERERERGRW2IyREREREREbonJEEz3LVq8eDGUSqW9Q7EbbgNuA3evP8BtAHAbOBJ3/yxYf/euP8BtwPp3Tf1dYgAFIiIiIiIia/HMEBERERERuSUmQ0RERERE5JaYDBERERERkVtiMkRERERERG7JbZKhVatWITo6GiqVComJidi7d+9Vy2/YsAH9+/eHSqXCkCFDsHnz5i6KtPNYsw0+/PBDjB49GgEBAQgICEBycvI1t5kzsPb/oNm6desgCAKmTp3auQF2MmvrX1lZiblz5yIsLAxKpRL9+vVz+u+Ctdtg5cqViI2NhYeHB6KiovD000+jvr6+i6K1rZ07d+LOO+9EeHg4BEHApk2brrlMeno6hg8fDqVSiT59+mDt2rWdHqc7cfe2yd3bJbZJbJPYJjlAmyS6gXXr1okKhUJcs2aNeOTIEXHOnDmiv7+/WFxc3Gr5Xbt2iVKpVHzttdfEo0ePii+88IIol8vFnJycLo7cdqzdBn/605/EVatWiVlZWeKxY8fEhx9+WPTz8xMLCgq6OHLbsXYbNMvNzRUjIiLE0aNHi1OmTOmaYDuBtfXX6XTiiBEjxEmTJom//fabmJubK6anp4vZ2dldHLntWLsNPv/8c1GpVIqff/65mJubK27dulUMCwsTn3766S6O3DY2b94s/u1vfxM3btwoAhC/+eabq5Y/e/as6OnpKaampopHjx4V33nnHVEqlYpbtmzpmoBdnLu3Te7eLrFNYpvENskx2iS3SIZGjhwpzp071/zcYDCI4eHh4tKlS1stf99994mTJ0+2mJeYmCj+5S9/6dQ4O5O12+ByjY2Noo+Pj/jJJ590VoidriPboLGxURw1apT40UcfiTNnznTqhsfa+r/33ntir169RL1e31Uhdjprt8HcuXPFW2+91WJeamqqeNNNN3VqnF2hPQ3P/PnzxUGDBlnMmzZtmpiSktKJkbkPd2+b3L1dYpvENolt0iX2bJNcvpucXq9HZmYmkpOTzfMkEgmSk5ORkZHR6jIZGRkW5QEgJSWlzfKOriPb4HK1tbVoaGhAt27dOivMTtXRbfD3v/8dwcHBeOSRR7oizE7Tkfp/9913SEpKwty5cxESEoLBgwfjn//8JwwGQ1eFbVMd2QajRo1CZmamudvC2bNnsXnzZkyaNKlLYrY3V9sXOhJ3b5vcvV1im8Q2iW2S9TprHyi7rqWdQFlZGQwGA0JCQizmh4SE4Pjx460uo1arWy2vVqs7Lc7O1JFtcLnnnnsO4eHhV/wTOouObIPffvsNH3/8MbKzs7sgws7VkfqfPXsWP//8Mx544AFs3rwZp0+fxhNPPIGGhgYsXry4K8K2qY5sgz/96U8oKyvDzTffDFEU0djYiMceewzPP/98V4Rsd23tCzUaDerq6uDh4WGnyJyfu7dN7t4usU1im8Q2yXqd1Sa5/Jkhun7Lli3DunXr8M0330ClUtk7nC5RXV2Nhx56CB9++CGCgoLsHY5dGI1GBAcH44MPPkBCQgKmTZuGv/3tb1i9erW9Q+sy6enp+Oc//4l//etfOHDgADZu3Igff/wRS5YssXdoRG7N3doltklskwC2SZ3F5c8MBQUFQSqVori42GJ+cXExQkNDW10mNDTUqvKOriPboNmKFSuwbNky/PTTTxg6dGhnhtmprN0GZ86cwblz53DnnXea5xmNRgCATCbDiRMn0Lt3784N2oY68j8QFhYGuVwOqVRqnjdgwACo1Wro9XooFIpOjdnWOrINXnzxRTz00EOYPXs2AGDIkCHQarV49NFH8be//Q0SiWsfT2prX+jr68uzQtfJ3dsmd2+X2CaxTWKbZL3OapNce6sBUCgUSEhIQFpamnme0WhEWloakpKSWl0mKSnJojwAbN++vc3yjq4j2wAAXnvtNSxZsgRbtmzBiBEjuiLUTmPtNujfvz9ycnKQnZ1tnu666y6MGzcO2dnZiIqK6srwr1tH/gduuukmnD592tzgAsDJkycRFhbmdI0O0LFtUFtbe0Xj0twQm673dG2uti90JO7eNrl7u8Q2iW0S2yTrddo+8LqGX3AS69atE5VKpbh27Vrx6NGj4qOPPir6+/uLarVaFEVRfOihh8QFCxaYy+/atUuUyWTiihUrxGPHjomLFy926uFLRdH6bbBs2TJRoVCIX3/9tVhUVGSeqqur7VWF62btNrics4/cY2398/LyRB8fH/HJJ58UT5w4If7www9icHCw+Morr9irCtfN2m2wePFi0cfHR/zyyy/Fs2fPitu2bRN79+4t3nffffaqwnWprq4Ws7KyxKysLBGA+MYbb4hZWVni+fPnRVEUxQULFogPPfSQuXzzMKbPPvuseOzYMXHVqlUcWtuG3L1tcvd2iW0S2yS2SY7RJrlFMiSKovjOO++IPXr0EBUKhThy5Ejx999/N782ZswYcebMmRblv/rqK7Ffv36iQqEQBw0aJP74449dHLHtWbMNevbsKQK4Ylq8eHHXB25D1v4ftOTsDY8oWl//3bt3i4mJiaJSqRR79eol/uMf/xAbGxu7OGrbsmYbNDQ0iC+99JLYu3dvUaVSiVFRUeITTzwhVlRUdH3gNvDLL7+0+r1urvPMmTPFMWPGXLFMfHy8qFAoxF69eon//ve/uzxuV+bubZO7t0tsk9gmsU2yf5skiKIbnFcjIiIiIiK6jMtfM0RERERERNQaJkNEREREROSWmAwREREREZFbYjJERERERERuickQERERERG5JSZDRERERETklpgMERERERGRW2IyREREREREbonJEBERERERuSUmQ0RERERE5JaYDBERERERkVtiMkRERERERG7p/wNYxGLF+tGQjAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-31 15:10:33.725 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.738 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.750 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.763 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.775 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.787 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.800 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.813 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.825 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.837 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.849 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.861 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.886 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.898 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.910 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.922 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.933 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.946 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.958 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.971 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.983 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:33.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.008 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.021 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.033 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.045 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.058 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.071 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.084 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.097 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.110 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.123 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.136 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.150 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.163 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.176 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.188 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.201 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.214 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.227 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.240 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.253 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.266 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.279 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.292 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.305 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.318 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.331 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.344 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.357 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.370 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.383 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.396 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.409 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.422 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.435 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.448 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.462 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.475 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.488 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.502 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.516 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.529 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.542 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.555 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.568 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.581 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.594 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.607 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.620 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.633 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.646 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.659 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.672 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.685 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.697 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.710 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.724 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.738 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.751 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.764 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.777 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.790 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.803 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.816 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.828 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.841 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.854 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.867 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.879 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.892 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.905 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.918 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.931 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.944 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.957 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.970 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.983 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:34.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.009 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.022 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.035 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.048 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.062 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.075 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.088 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.102 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.116 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.129 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.142 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.155 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.169 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.183 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.197 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.210 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.224 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.237 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.250 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.263 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.277 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.291 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.305 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.319 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.334 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.347 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.361 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.376 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.390 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.404 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.418 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.431 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.444 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.458 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.471 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.484 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.497 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.511 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.524 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.537 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.550 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.563 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.577 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.591 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.604 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.618 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.630 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.643 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.655 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.668 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.681 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.694 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.708 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.721 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.734 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.747 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.761 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.774 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.787 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.800 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.813 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.825 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.838 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.851 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.888 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.901 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.913 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.926 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.938 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.951 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.964 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.977 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:35.990 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.004 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.017 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.030 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.043 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.056 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.069 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.081 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.093 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.105 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.130 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.143 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.155 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.167 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.180 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.193 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.206 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.220 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.233 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.246 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.258 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.271 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.284 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.297 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.311 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.324 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.339 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.351 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.364 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.378 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.391 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.405 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.419 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.432 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.446 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.459 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.472 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.485 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.499 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.512 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.526 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.540 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.554 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.567 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.581 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.594 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.609 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.628 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.641 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.655 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.668 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.681 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.694 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.707 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.721 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.734 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.747 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.761 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.775 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.788 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.802 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.817 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.831 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.845 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.859 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.873 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.886 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.899 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.912 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.926 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.938 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.952 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.966 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.980 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:36.994 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.008 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.022 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.036 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.051 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.064 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.078 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.091 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.104 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.131 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.144 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.158 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.172 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.185 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.199 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.212 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.226 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.241 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.254 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.268 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.281 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.294 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.308 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.321 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.335 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.349 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.362 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.376 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.390 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.403 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.416 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.430 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.443 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.457 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.471 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.485 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.497 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.510 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.524 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.537 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.551 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.564 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.577 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.590 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.604 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.617 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.631 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.644 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.658 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.672 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.685 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.697 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.711 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.724 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.736 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.749 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.763 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.775 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.788 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.801 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.814 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.827 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.841 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.854 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.868 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.881 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.894 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.907 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.920 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.933 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.947 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.960 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.973 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.986 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:37.999 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.012 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.027 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.040 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.054 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.067 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.080 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.093 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.105 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.131 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.144 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.157 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.170 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.183 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.196 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.209 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.222 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.234 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.247 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.261 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.275 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.288 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.302 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.316 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.329 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.342 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.355 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.368 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.381 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.394 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.408 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.422 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.434 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.447 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.461 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.474 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.488 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.501 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.514 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.527 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.540 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.553 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.566 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.579 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.593 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.606 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.620 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.633 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.646 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.659 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.673 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.687 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.701 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.714 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.727 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.741 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.754 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.767 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.780 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.792 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.804 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.817 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.829 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.842 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.855 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.867 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.880 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.893 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.906 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.920 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.933 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.947 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.960 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.973 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:38.987 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.000 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.014 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.027 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.041 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.055 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.068 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.082 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.095 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.109 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.122 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.136 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.150 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.164 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.178 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.191 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.204 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.218 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.231 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.244 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.258 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.272 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.286 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.300 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.313 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.327 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.340 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.352 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.365 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.377 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.390 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.403 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.416 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.429 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.443 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.455 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.468 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.481 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.494 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.507 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.520 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.533 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.546 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.559 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.572 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.584 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.597 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.610 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.622 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.635 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.647 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.660 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.674 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.686 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.699 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.713 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.726 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.738 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.752 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.764 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.777 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.790 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.803 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.816 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.829 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.843 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.855 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.868 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.881 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.894 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.907 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.920 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.934 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.947 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.960 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.973 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.986 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:39.999 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.012 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.025 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.038 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.051 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.064 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.078 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.091 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.104 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.132 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.145 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.158 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.172 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.185 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.197 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.211 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.224 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.237 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.250 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.263 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.277 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.290 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.304 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.317 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.331 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.346 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.360 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.374 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.387 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.400 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.413 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.427 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.441 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.454 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.467 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.481 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.495 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.508 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.522 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.534 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.547 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.561 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.575 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.588 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.601 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.614 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.627 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.641 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.654 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.668 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.681 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.694 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.707 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.720 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.733 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.746 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.759 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.772 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.784 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.797 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.810 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.822 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.835 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.847 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.861 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.887 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.900 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.914 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.927 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.941 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.954 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.968 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.981 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:40.995 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.009 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.023 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.036 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.049 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.062 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.075 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.089 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.103 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.116 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.130 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.144 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.158 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.172 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.186 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.200 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.214 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.228 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.241 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.254 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.268 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.281 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.294 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.308 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.322 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.335 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.349 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.362 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.375 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.388 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.401 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.414 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.426 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.439 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.452 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.464 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.477 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.489 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.501 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.514 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.526 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.539 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.551 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.564 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.577 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.590 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.603 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.616 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.629 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.643 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.656 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.669 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.683 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.696 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.710 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.723 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.737 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.751 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.764 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.776 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.789 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.801 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.814 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.826 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.839 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.851 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.864 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.877 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.890 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.903 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.915 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.929 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.942 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.955 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.969 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.982 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:41.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.010 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.024 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.037 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.052 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.064 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.077 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.090 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.103 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.117 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.130 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.143 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.157 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.171 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.183 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.197 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.211 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.225 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.239 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.253 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.268 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.281 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.294 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.308 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.321 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.335 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.347 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.361 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.375 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.389 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.402 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.414 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.427 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.439 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.450 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.463 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.476 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.488 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.501 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.514 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.527 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.540 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.553 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.566 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.580 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.593 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.607 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.620 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.634 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.647 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.661 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.675 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.690 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.704 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.717 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.730 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.744 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.758 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.772 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.786 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.799 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.813 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.827 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.841 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.854 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.868 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.881 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.895 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.908 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.921 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.934 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.947 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.960 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.973 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:42.987 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.001 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.014 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.027 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.040 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.053 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.066 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.079 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.091 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.104 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.117 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.129 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.142 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.155 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.168 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.181 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.194 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.207 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.221 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.234 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.247 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.261 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.275 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.289 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.302 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.315 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.329 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.342 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.355 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.368 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.382 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.395 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.409 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.422 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.435 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.449 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.462 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.476 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.490 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.504 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.519 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.532 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.546 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.559 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.572 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.586 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.599 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.612 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.627 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.641 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.654 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.668 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.681 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.695 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.707 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.721 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.734 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.747 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.761 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.774 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.787 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.801 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.815 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.829 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.843 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.857 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.871 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.884 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.897 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.911 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.925 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.938 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.951 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.964 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.977 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:43.989 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.001 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.013 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.026 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.038 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.050 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.063 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.075 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.088 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.101 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.114 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.127 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.141 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.153 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.165 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.179 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.192 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.205 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.219 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.233 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.247 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.261 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.275 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.289 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.303 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.318 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.332 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.346 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.359 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.373 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.387 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.401 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.415 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.429 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.442 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.455 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.469 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.483 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.497 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.511 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.525 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.539 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.552 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.565 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.579 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.592 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.606 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.650 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.663 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.676 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.689 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.702 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.715 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.729 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.742 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.755 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.768 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.781 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.794 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.807 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.821 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.834 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.847 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.861 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.888 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.902 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.916 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.929 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.943 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.957 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.970 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.983 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:44.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.010 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.023 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.039 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.052 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.066 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.079 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.093 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.107 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.121 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.135 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.149 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.162 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.176 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.189 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.202 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.215 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.229 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.242 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.255 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.269 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.283 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.296 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.310 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.324 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.338 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.351 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.364 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.378 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.391 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.404 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.418 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.431 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.445 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.458 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.472 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.486 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.500 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.514 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.529 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.543 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.557 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.572 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.584 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.597 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.611 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.625 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.639 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.652 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.666 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.679 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.693 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.707 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.721 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.735 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.750 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.764 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.778 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.792 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.805 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.819 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.833 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.846 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.860 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.888 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.901 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.914 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.928 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.942 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.955 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.969 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.983 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:45.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.009 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.022 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.035 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.049 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.063 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.077 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.091 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.104 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.132 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.146 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.159 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.173 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.187 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.200 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.213 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.226 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.240 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.253 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.266 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.279 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.293 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.307 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.320 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.333 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.347 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.360 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.374 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.388 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.401 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.414 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.427 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.441 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.454 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.467 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.481 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.494 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.507 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.521 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.534 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.546 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.559 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.572 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.584 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.597 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.609 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.622 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.635 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.647 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.660 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.674 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.687 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.700 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.714 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.727 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.740 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.754 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.768 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.781 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.795 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.808 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.822 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.835 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.849 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.862 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.876 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.890 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.903 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.916 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.930 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "2023-05-31 15:10:46.944 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", + "Traceback (most recent call last):\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/tornado/ioloop.py\", line 738, in _run_callback\n", + " ret = callback()\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 458, in advance_eventloop\n", + " eventloop(self)\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/ipykernel/eventloops.py\", line 353, in loop_cocoa\n", + " if kernel.shell_stream.flush(limit=1):\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/eventloop/zmqstream.py\", line 533, in flush\n", + " self._rebuild_io_state()\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/eventloop/zmqstream.py\", line 698, in _rebuild_io_state\n", + " self._update_handler(state)\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/eventloop/zmqstream.py\", line 715, in _update_handler\n", + " if state & self.socket.events:\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/sugar/attrsettr.py\", line 55, in __getattr__\n", + " return self._get_attr_opt(upper_key, opt)\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/sugar/attrsettr.py\", line 67, in _get_attr_opt\n", + " return self.get(opt)\n", + " File \"zmq/backend/cython/socket.pyx\", line 481, in zmq.backend.cython.socket.Socket.get\n", + "RecursionError: maximum recursion depth exceeded\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n", + " app_log.error(\"Exception in callback %r\", callback, exc_info=True)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1475, in error\n", + " self._log(ERROR, msg, args, **kwargs)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1587, in _log\n", + " record = self.makeRecord(self.name, level, fn, lno, msg, args,\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1556, in makeRecord\n", + " rv = _logRecordFactory(name, level, fn, lno, msg, args, exc_info, func,\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 308, in __init__\n", + " if (args and len(args) == 1 and isinstance(args[0], collections.abc.Mapping)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/abc.py\", line 119, in __instancecheck__\n", + " return _abc_instancecheck(cls, instance)\n", + "RecursionError: maximum recursion depth exceeded\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1779, in call_exception_handler\n", + " self.default_exception_handler(context)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1750, in default_exception_handler\n", + " value = repr(value)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 61, in __repr__\n", + " info = self._repr_info()\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 112, in _repr_info\n", + " info = super()._repr_info()\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 51, in _repr_info\n", + " info.append(format_helpers._format_callback_source(\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/format_helpers.py\", line 23, in _format_callback_source\n", + " func_repr = _format_callback(func, args, None)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/format_helpers.py\", line 56, in _format_callback\n", + " func_repr += _format_args_and_kwargs(args, kwargs)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/format_helpers.py\", line 38, in _format_args_and_kwargs\n", + " items.extend(reprlib.repr(arg) for arg in args)\n", + "RecursionError: maximum recursion depth exceeded\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n", + " return _run_code(code, main_globals, None,\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py\", line 87, in _run_code\n", + " exec(code, run_globals)\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/ipykernel_launcher.py\", line 17, in \n", + " app.launch_new_instance()\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n", + " app.start()\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 725, in start\n", + " self.io_loop.start()\n", + " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 195, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", + " self._run_once()\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n", + " handle._run()\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 94, in _run\n", + " self._loop.call_exception_handler(context)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1786, in call_exception_handler\n", + " logger.error('Exception in default exception handler',\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1475, in error\n", + " self._log(ERROR, msg, args, **kwargs)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1589, in _log\n", + " self.handle(record)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1599, in handle\n", + " self.callHandlers(record)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1669, in callHandlers\n", + " lastResort.handle(record)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 952, in handle\n", + " self.emit(record)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1083, in emit\n", + " msg = self.format(record)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 927, in format\n", + " return fmt.format(record)\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 664, in format\n", + " if self.usesTime():\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 632, in usesTime\n", + " return self._style.usesTime()\n", + " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 422, in usesTime\n", + " return self._fmt.find(self.asctime_search) >= 0\n", + "RecursionError: maximum recursion depth exceeded while calling a Python object\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] } ], "source": [ - "model = BasicSPMSMR()\n", - "parameter_values = model.default_parameter_values\n", + "%matplotlib\n", + "\n", + "x_n = parameter_values[\"Negative electrode stoichiometry\"]\n", + "x_p = parameter_values[\"Positive electrode stoichiometry\"]\n", "\n", "fig, ax = plt.subplots(2, 2, figsize=(10, 4))\n", "\n", "U = pybamm.linspace(0.01, 1.5, 500)\n", "x_eval = parameter_values.evaluate(x_n(U)).flatten()\n", "U_eval = U.evaluate().flatten()\n", + "dUdx_eval = -np.gradient(U_eval, x_eval)\n", "ax[0, 0].plot(U_eval, x_eval, label=\"x_n\")\n", - "ax[1, 0].plot(x_eval, U_eval, label=\"x_n\")\n", + "ax[0, 0].set_xlabel(\"U_n\")\n", + "ax[0, 0].set_ylabel(\"x_n\")\n", + "ax[1, 0].plot(x_eval, dUdx_eval, label=\"x_n\")\n", + "ax[1, 0].set_xlabel(\"x_n\")\n", + "ax[1, 0].set_ylabel(\"dU_n/dx_n\")\n", + "ax[1, 0].set_ylim([0, 5])\n", "\n", "U = pybamm.linspace(3.4, 5, 500)\n", "x_eval = parameter_values.evaluate(x_p(U)).flatten()\n", "U_eval = U.evaluate().flatten()\n", + "dUdx_eval = -np.gradient(U_eval, x_eval)\n", "ax[0, 1].plot(U_eval, x_eval, label=\"x_p\")\n", - "ax[1, 1].plot(x_eval, U_eval, label=\"x_p\")" + "ax[0, 1].set_xlabel(\"U_p\")\n", + "ax[0, 1].set_ylabel(\"x_p\")\n", + "ax[1, 1].plot(x_eval, dUdx_eval, label=\"x_p\")\n", + "ax[1, 1].set_xlabel(\"x_p\")\n", + "ax[1, 1].set_ylabel(\"dU_p/dx_p\")\n", + "ax[1, 1].set_ylim([0, 5])\n" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.08494793698397733 4.350779153249299\n" + "ename": "NameError", + "evalue": "name 'pybamm' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m soc_model \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mBaseModel()\n\u001b[1;32m 2\u001b[0m U_n \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mVariable(\u001b[39m\"\u001b[39m\u001b[39mU_n\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 3\u001b[0m U_p \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mVariable(\u001b[39m\"\u001b[39m\u001b[39mU_p\u001b[39m\u001b[39m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'pybamm' is not defined" ] } ], @@ -372,34 +1236,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b6e3407010354cb29dd74122fa53869b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=3300.0, step=33.0), Output()), _dom_classes=…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", "sim.solve([0, 3300])\n", From d1b20a7e2e849d3639ee3d2310eca8b40e1065ea Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 13 Jun 2023 10:51:15 +0100 Subject: [PATCH 004/154] update example --- .../lithium_ion/basic_spm_msmr.py | 55 +++++++++++++++++-- scripts/install_KLU_Sundials.py | 34 ++++++++++++ 2 files changed, 83 insertions(+), 6 deletions(-) diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py index 1b9fbe9920..718893b723 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py @@ -286,8 +286,12 @@ def dxdU_p_fun(U_p): self.variables = { "Discharge capacity [A.h]": Q, "Current [A]": I, - "X-averaged negative electrode stoichiometry": x_n, - "X-averaged positive electrode stoichiometry": x_p, + "X-averaged negative particle stoichiometry": x_n, + "X-averaged positive particle stoichiometry": x_p, + "X-averaged negative electrode extent of lithiation": pybamm.r_average(x_n), + "X-averaged positive electrode extent of lithiation": pybamm.r_average(x_p), + "X-averaged negative particle surface stoichiometry": pybamm.surf(x_n), + "X-averaged positive particle surface stoichiometry": pybamm.surf(x_p), "X-averaged negative particle concentration": c_n, "X-averaged positive particle concentration": c_p, "X-averaged negative electrode stoichiometry change [V-1]": dxdU_n, @@ -304,6 +308,24 @@ def dxdU_p_fun(U_p): ), "Voltage [V]": V, } + + # x_n + for i in range(6): + U0 = pybamm.Parameter(f"U0_n_{i}") + w = pybamm.Parameter(f"w_n_{i}") + Xj = pybamm.Parameter(f"Xj_n_{i}") + + self.variables[f"x{i}_n"] = Xj / (1 + pybamm.exp(f * (U_n - U0) / w)) + + # x_p + for i in range(4): + U0 = pybamm.Parameter(f"U0_p_{i}") + w = pybamm.Parameter(f"w_p_{i}") + Xj = pybamm.Parameter(f"Xj_p_{i}") + + self.variables[f"x{i}_p"] = Xj / (1 + pybamm.exp(f * (U_p - U0) / w)) + + # events self.events += [ pybamm.Event("Minimum voltage [V]", V - param.voltage_low_cut), pybamm.Event("Maximum voltage [V]", param.voltage_high_cut - V), @@ -316,8 +338,8 @@ def default_parameter_values(self): @property def default_quick_plot_variables(self): return [ - "X-averaged negative electrode stoichiometry", - "X-averaged positive electrode stoichiometry", + "X-averaged negative particle stoichiometry", + "X-averaged positive particle stoichiometry", "X-averaged negative particle OCP [V]", "X-averaged positive particle OCP [V]", "X-averaged negative electrode OCP [V]", @@ -350,15 +372,36 @@ def default_quick_plot_variables(self): ) U_n, U_p = soc_sol["U_n"].data[0], soc_sol["U_p"].data[0] + def current(t): + return 5 * (t < 3000) + parameter_values.update( { "Initial negative electrode potential [V]": U_n, "Initial positive electrode potential [V]": U_p, + "Current function [A]": current, }, check_already_exists=False, ) print(U_n, U_p) sim = pybamm.Simulation(model, parameter_values=parameter_values) - sim.solve([0, 3300]) - sim.plot() + sim.solve([0, 4000]) + sim.plot( + [ + [ + "X-averaged negative electrode extent of lithiation", + "X-averaged negative particle surface stoichiometry", + ], + [ + "X-averaged positive electrode extent of lithiation", + "X-averaged positive particle surface stoichiometry", + ], + "X-averaged negative electrode OCP [V]", + "X-averaged positive electrode OCP [V]", + [f"x{i}_n" for i in range(6)], + [f"x{i}_p" for i in range(4)], + "Current [A]", + "Voltage [V]", + ] + ) diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py index a97da77faf..96e60aeb0e 100755 --- a/scripts/install_KLU_Sundials.py +++ b/scripts/install_KLU_Sundials.py @@ -2,6 +2,7 @@ import subprocess import tarfile import argparse +import platform try: # wget module is required to download SUNDIALS or SuiteSparse. @@ -113,6 +114,39 @@ def download_extract_library(url, download_dir): "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR, ] +# try to find OpenMP on mac +if platform.system() == "Darwin": + # flags to find OpenMP on mac + if platform.processor() == "arm": + LDFLAGS = "-L/opt/homebrew/opt/libomp/lib" + CPPFLAGS = "-I/opt/homebrew/opt/libomp/include" + OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include" + OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include" + OpenMP_C_LIB_NAMES = "omp" + OpenMP_CXX_LIB_NAMES = "omp" + OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib" + OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib" + elif platform.processor() == "i386": + LDFLAGS = "-L/usr/local/opt/libomp/lib" + CPPFLAGS = "-I/usr/local/opt/libomp/include" + OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" + OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" + OpenMP_C_LIB_NAMES = "omp" + OpenMP_CXX_LIB_NAMES = "omp" + OpenMP_libomp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib" + OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib" + + cmake_args += [ + "-DLDFLAGS=" + LDFLAGS, + "-DCPPFLAGS=" + CPPFLAGS, + "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS, + "-DOpenMP_CXX_FLAGS=" + OpenMP_CXX_FLAGS, + "-DOpenMP_C_LIB_NAMES=" + OpenMP_C_LIB_NAMES, + "-DOpenMP_CXX_LIB_NAMES=" + OpenMP_CXX_LIB_NAMES, + "-DOpenMP_libomp_LIBRARY=" + OpenMP_libomp_LIBRARY, + "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY, + ] + # SUNDIALS are built within download_dir 'build_sundials' in the PyBaMM root # download_dir build_dir = os.path.abspath(os.path.join(download_dir, "build_sundials")) From e0e42d19258b0ed127bb990f2d64ef0bc7998eca Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 14 Jun 2023 12:10:08 +0100 Subject: [PATCH 005/154] MSMR submodels for diffusion and ocp --- pybamm/CITATIONS.txt | 11 + .../open_circuit_potential/__init__.py | 1 + .../open_circuit_potential/msmr_ocp.py | 72 ++ pybamm/models/submodels/particle/__init__.py | 1 + .../submodels/particle/base_particle.py | 112 +-- .../submodels/particle/fickian_diffusion.py | 7 +- .../submodels/particle/msmr_diffusion.py | 643 ++++++++++++++++++ 7 files changed, 805 insertions(+), 42 deletions(-) create mode 100644 pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py create mode 100644 pybamm/models/submodels/particle/msmr_diffusion.py diff --git a/pybamm/CITATIONS.txt b/pybamm/CITATIONS.txt index 546f91f0a1..208e4d1333 100644 --- a/pybamm/CITATIONS.txt +++ b/pybamm/CITATIONS.txt @@ -36,6 +36,17 @@ doi = {10.1007/s12532-018-0139-4}, } +@article{Baker2018, + title={Multi-species, multi-reaction model for porous intercalation electrodes: Part I. Model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode}, + author={Baker, Daniel R and Verbrugge, Mark W}, + journal={Journal of The Electrochemical Society}, + volume={165}, + number={16}, + pages={A3952}, + year={2018}, + publisher={IOP Publishing} +} + @article{BrosaPlanella2021, title = {Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods}, author = {Brosa Planella, Ferran and Sheikh, Muhammad and Widanage, W. Dhammika}, diff --git a/pybamm/models/submodels/interface/open_circuit_potential/__init__.py b/pybamm/models/submodels/interface/open_circuit_potential/__init__.py index d644b87e76..5f8a409bba 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/__init__.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/__init__.py @@ -1,3 +1,4 @@ from .base_ocp import BaseOpenCircuitPotential from .single_ocp import SingleOpenCircuitPotential from .current_sigmoid_ocp import CurrentSigmoidOpenCircuitPotential +from .msmr_ocp import MSMROpenCircuitPotential diff --git a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py new file mode 100644 index 0000000000..4fffd55af7 --- /dev/null +++ b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py @@ -0,0 +1,72 @@ +# +# Open-circuit potential from the Multi-Species Multi-Reaction framework +# +import pybamm +from . import BaseOpenCircuitPotential + + +class MSMROpenCircuitPotential(BaseOpenCircuitPotential): + """ + Class for open-circuit potential within the Multi-Species Multi-Reaction + framework [1]_. + + References + ---------- + .. [1] DR Baker and MW Verbrugge. "Multi-species, multi-reaction model for porous + intercalation electrodes: Part I. Model formulation and a perturbation + solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium + manganese oxide electrode." Journal of The Electrochemical Society, + 165(16):A3952, 2019 + """ + + def get_coupled_variables(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + if self.reaction == "lithium-ion main": + T = variables[f"{Domain} electrode temperature [K]"] + # For "particle-size distribution" models, take distribution version + # of c_s_surf that depends on particle size. + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "distribution": + sto_surf = variables[ + f"{Domain} {phase_name}particle surface stoichiometry distribution" + ] + ocp_surf = variables[ + f"{Domain} {phase_name}particle surface open-circuit potential " + "distribution [V]" + ] + # If variable was broadcast, take only the orphan + if ( + isinstance(sto_surf, pybamm.Broadcast) + and isinstance(ocp_surf, pybamm.Broadcast) + and isinstance(T, pybamm.Broadcast) + ): + sto_surf = sto_surf.orphans[0] + ocp_surf = ocp_surf.orphans[0] + T = T.orphans[0] + T = pybamm.PrimaryBroadcast(T, [f"{domain} particle size"]) + else: + sto_surf = variables[ + f"{Domain} {phase_name}particle surface stoichiometry" + ] + ocp_surf = variables[ + f"{Domain} {phase_name}particle surface open-circuit potential [V]" + ] + # If variable was broadcast, take only the orphan + if ( + isinstance(sto_surf, pybamm.Broadcast) + and isinstance(ocp_surf, pybamm.Broadcast) + and isinstance(T, pybamm.Broadcast) + ): + sto_surf = sto_surf.orphans[0] + ocp_surf = ocp_surf.orphans[0] + T = T.orphans[0] + + ocp_bulk = variables[ + f"Average {domain} {phase_name}particle open-circuit potential [V]" + ] + dUdT = self.phase_param.dUdT(sto_surf) + + variables.update(self._get_standard_ocp_variables(ocp_surf, ocp_bulk, dUdT)) + return variables diff --git a/pybamm/models/submodels/particle/__init__.py b/pybamm/models/submodels/particle/__init__.py index 7f3c19953d..237b2c19c8 100644 --- a/pybamm/models/submodels/particle/__init__.py +++ b/pybamm/models/submodels/particle/__init__.py @@ -3,3 +3,4 @@ from .polynomial_profile import PolynomialProfile from .x_averaged_polynomial_profile import XAveragedPolynomialProfile from .total_particle_concentration import TotalConcentration +from .msmr_diffusion import MSMRDiffusion diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py index 3ef6ee4d70..ca28cb7695 100644 --- a/pybamm/models/submodels/particle/base_particle.py +++ b/pybamm/models/submodels/particle/base_particle.py @@ -36,7 +36,7 @@ def _get_effective_diffusivity(self, c, T): # Get diffusivity D = phase_param.D(c, T) - # Account for stress-induced difftusion by defining a multiplicative + # Account for stress-induced diffusion by defining a multiplicative # "stress factor" stress_option = getattr(self.options, domain)["stress-induced diffusion"] @@ -58,7 +58,7 @@ def _get_standard_concentration_variables( """ All particle submodels must provide the particle concentration as an argument to this method. Some submodels solve for quantities other than the concentration - itself, for example the 'XAveragedFickianDiffusion' models solves for the + itself, for example the 'XAveragedPolynomialProfile' models solves for the x-averaged concentration. In such cases the variables being solved for (set in 'get_fundamental_variables') must also be passed as keyword arguments. If not passed as keyword arguments, the various average concentrations and surface @@ -85,44 +85,63 @@ def _get_standard_concentration_variables( c_s_av = pybamm.r_average(c_s_xav) variables = { - f"{Domain} {phase_name}particle stoichiometry": c_s / c_scale, - f"{Domain} {phase_name}particle concentration": c_s / c_scale, + # Dimensional concentration f"{Domain} {phase_name}particle concentration [mol.m-3]": c_s, - f"X-averaged {domain} {phase_name}particle concentration": c_s_xav - / c_scale, f"X-averaged {domain} {phase_name}particle " "concentration [mol.m-3]": c_s_xav, - f"R-averaged {domain} {phase_name}particle concentration": c_s_rav - / c_scale, f"R-averaged {domain} {phase_name}particle " "concentration [mol.m-3]": c_s_rav, - f"Average {domain} {phase_name}particle concentration": c_s_av / c_scale, f"Average {domain} {phase_name}particle concentration [mol.m-3]": c_s_av, - f"{Domain} {phase_name}particle surface stoichiometry": c_s_surf / c_scale, - f"{Domain} {phase_name}particle surface concentration": c_s_surf / c_scale, f"{Domain} {phase_name}particle surface concentration [mol.m-3]": c_s_surf, f"X-averaged {domain} {phase_name}particle " - "surface concentration": c_s_surf_av / c_scale, - f"X-averaged {domain} {phase_name}particle " "surface concentration [mol.m-3]": c_s_surf_av, - f"{Domain} electrode extent of lithiation": c_s_rav / c_scale, - f"X-averaged {domain} electrode extent of lithiation": c_s_av / c_scale, - f"Minimum {domain} {phase_name}particle concentration": pybamm.min(c_s) - / c_scale, - f"Maximum {domain} {phase_name}particle concentration": pybamm.max(c_s) - / c_scale, f"Minimum {domain} {phase_name}particle concentration [mol.m-3]" "": pybamm.min(c_s), f"Maximum {domain} {phase_name}particle concentration [mol.m-3]" "": pybamm.max(c_s), f"Minimum {domain} {phase_name}particle " + f"Minimum {domain} {phase_name}particle " + "surface concentration [mol.m-3]": pybamm.min(c_s_surf), + f"Maximum {domain} {phase_name}particle " + "surface concentration [mol.m-3]": pybamm.max(c_s_surf), + # Dimensionless concentration + f"{Domain} {phase_name}particle concentration": c_s / c_scale, + f"X-averaged {domain} {phase_name}particle concentration": c_s_xav + / c_scale, + f"R-averaged {domain} {phase_name}particle concentration": c_s_rav + / c_scale, + f"Average {domain} {phase_name}particle concentration": c_s_av / c_scale, + f"{Domain} {phase_name}particle surface concentration": c_s_surf / c_scale, + f"X-averaged {domain} {phase_name}particle " + "surface concentration": c_s_surf_av / c_scale, + f"Minimum {domain} {phase_name}particle concentration": pybamm.min(c_s) + / c_scale, + f"Maximum {domain} {phase_name}particle concentration": pybamm.max(c_s) + / c_scale, "surface concentration": pybamm.min(c_s_surf) / c_scale, f"Maximum {domain} {phase_name}particle " "surface concentration": pybamm.max(c_s_surf) / c_scale, + # Stoichiometry (equivalent to dimensionless concentration) + f"{Domain} {phase_name}particle stoichiometry": c_s / c_scale, + f"X-averaged {domain} {phase_name}particle stoichiometry": c_s_xav + / c_scale, + f"R-averaged {domain} {phase_name}particle stoichiometry": c_s_rav + / c_scale, + f"Average {domain} {phase_name}particle stoichiometry": c_s_av / c_scale, + f"{Domain} {phase_name}particle surface stoichiometry": c_s_surf / c_scale, + f"X-averaged {domain} {phase_name}particle " + "surface stoichiometry": c_s_surf_av / c_scale, + f"Minimum {domain} {phase_name}particle stoichiometry": pybamm.min(c_s) + / c_scale, + f"Maximum {domain} {phase_name}particle stoichiometry": pybamm.max(c_s) + / c_scale, f"Minimum {domain} {phase_name}particle " - "surface concentration [mol.m-3]": pybamm.min(c_s_surf), + "surface stoichiometry": pybamm.min(c_s_surf) / c_scale, f"Maximum {domain} {phase_name}particle " - "surface concentration [mol.m-3]": pybamm.max(c_s_surf), + "surface stoichiometry": pybamm.max(c_s_surf) / c_scale, + # Electrode extent of lithiation + f"{Domain} electrode extent of lithiation": c_s_rav / c_scale, + f"X-averaged {domain} electrode extent of lithiation": c_s_av / c_scale, } return variables @@ -289,7 +308,7 @@ def _get_standard_concentration_distribution_variables(self, c_s): c_s_surf_xav_distribution, [f"{domain} {phase_name}particle"] ) - # Concentration distribution in all domains. + # Concentration distribution in all domains c_s_distribution = pybamm.PrimaryBroadcast( c_s_surf_distribution, [f"{domain} {phase_name}particle"] ) @@ -315,32 +334,49 @@ def _get_standard_concentration_distribution_variables(self, c_s): c_s_av_distribution = pybamm.x_average(c_s_rav_distribution) variables = { - f"Average {domain} {phase_name}particle concentration " - "distribution": c_s_av_distribution / c_scale, - f"Average {domain} {phase_name}particle concentration " - "distribution [mol.m-3]": c_s_av_distribution, - f"{Domain} {phase_name}particle concentration " - "distribution": c_s_distribution / c_scale, + # Dimensional concentration f"{Domain} {phase_name}particle concentration distribution " "[mol.m-3]": c_s_distribution, - f"R-averaged {domain} {phase_name}particle concentration " - "distribution": c_s_rav_distribution / c_scale, - f"R-averaged {domain} {phase_name}particle concentration distribution " - "[mol.m-3]": c_s_rav_distribution, - f"X-averaged {domain} {phase_name}particle concentration " - "distribution": c_s_xav_distribution / c_scale, f"X-averaged {domain} {phase_name}particle concentration distribution " "[mol.m-3]": c_s_xav_distribution, - f"X-averaged {domain} {phase_name}particle surface concentration" - " distribution": c_s_surf_xav_distribution / c_scale, + f"R-averaged {domain} {phase_name}particle concentration distribution " + "[mol.m-3]": c_s_rav_distribution, + f"Average {domain} {phase_name}particle concentration " + "distribution [mol.m-3]": c_s_av_distribution, + f"{Domain} {phase_name}particle surface concentration" + " distribution [mol.m-3]": c_s_surf_distribution, f"X-averaged {domain} {phase_name}particle surface concentration " "distribution [mol.m-3]": c_s_surf_xav_distribution, + # Dimensionless concentration + f"{Domain} {phase_name}particle concentration " + "distribution": c_s_distribution / c_scale, + f"X-averaged {domain} {phase_name}particle concentration " + "distribution": c_s_xav_distribution / c_scale, + f"R-averaged {domain} {phase_name}particle concentration " + "distribution": c_s_rav_distribution / c_scale, + f"Average {domain} {phase_name}particle concentration " + "distribution": c_s_av_distribution / c_scale, f"{Domain} {phase_name}particle surface concentration" " distribution": c_s_surf_distribution / c_scale, + f"X-averaged {domain} {phase_name}particle surface concentration" + " distribution": c_s_surf_xav_distribution / c_scale, + # Stoichiometry (equivalent to dimensionless concentration) + f"{Domain} {phase_name}particle stoichiometry " + "distribution": c_s_distribution / c_scale, + f"X-averaged {domain} {phase_name}particle stoichiometry " + "distribution": c_s_xav_distribution / c_scale, + f"R-averaged {domain} {phase_name}particle stoichiometry " + "distribution": c_s_rav_distribution / c_scale, + f"Average {domain} {phase_name}particle stoichiometry " + "distribution": c_s_av_distribution / c_scale, f"{Domain} {phase_name}particle surface stoichiometry" " distribution": c_s_surf_distribution / c_scale, - f"{Domain} {phase_name}particle surface concentration" - " distribution [mol.m-3]": c_s_surf_distribution, + f"X-averaged {domain} {phase_name}particle surface stoichiometry" + " distribution": c_s_surf_xav_distribution / c_scale, + # Electrode extent of lithiation + f"{Domain} electrode extent of lithiation": c_s_rav_distribution / c_scale, + f"X-averaged {domain} electrode extent of lithiation": c_s_av_distribution + / c_scale, } return variables diff --git a/pybamm/models/submodels/particle/fickian_diffusion.py b/pybamm/models/submodels/particle/fickian_diffusion.py index c85716373a..a105d55b6b 100644 --- a/pybamm/models/submodels/particle/fickian_diffusion.py +++ b/pybamm/models/submodels/particle/fickian_diffusion.py @@ -122,6 +122,7 @@ def get_fundamental_variables(self): if self.x_average is True: c_s = pybamm.SecondaryBroadcast(c_s, [f"{domain} electrode"]) + # Standard concentration variables (size-independent) variables.update(self._get_standard_concentration_variables(c_s)) return variables @@ -169,7 +170,6 @@ def get_coupled_variables(self, variables): f"{Domain} {phase_name}particle " "concentration distribution [mol.m-3]" ] - # broadcast T to "particle size" domain then again into "particle" T = pybamm.PrimaryBroadcast( variables[f"{Domain} electrode temperature [K]"], @@ -185,7 +185,6 @@ def get_coupled_variables(self, variables): f"X-averaged {domain} {phase_name}particle " "concentration distribution [mol.m-3]" ] - # broadcast to "particle size" domain then again into "particle" T = pybamm.PrimaryBroadcast( variables[f"X-averaged {domain} electrode temperature [K]"], @@ -206,7 +205,7 @@ def get_coupled_variables(self, variables): 1 / (R_broad_nondim**2) ) * pybamm.div(N_s), - f"{Domain} {phase_name}particle bc [mol.m-2]": -j + f"{Domain} {phase_name}particle bc [mol.m-4]": -j * R_nondim / param.F / pybamm.surf(D_eff), @@ -285,7 +284,7 @@ def set_boundary_conditions(self, variables): "concentration distribution [mol.m-3]" ] - rbc = variables[f"{Domain} {phase_name}particle bc [mol.m-2]"] + rbc = variables[f"{Domain} {phase_name}particle bc [mol.m-4]"] self.boundary_conditions = { c_s: {"left": (pybamm.Scalar(0), "Neumann"), "right": (rbc, "Neumann")} } diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py new file mode 100644 index 0000000000..15efab981f --- /dev/null +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -0,0 +1,643 @@ +# +# Class for particles using the MSMR model +# +import pybamm +from .base_particle import BaseParticle + + +class MSMRDiffusion(BaseParticle): + """ + Class for molar conservation in particles within the Multi-Species Multi-Reaction + framework [1]_. + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + domain : str + The domain of the model either 'Negative' or 'Positive' + options: dict + A dictionary of options to be passed to the model. + See :class:`pybamm.BaseBatteryModel` + phase : str, optional + Phase of the particle (default is "primary") + x_average : bool + Whether the particle concentration is averaged over the x-direction + + References + ---------- + .. [1] DR Baker and MW Verbrugge. "Multi-species, multi-reaction model for porous + intercalation electrodes: Part I. Model formulation and a perturbation + solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium + manganese oxide electrode." Journal of The Electrochemical Society, + 165(16):A3952, 2019 + """ + + def __init__(self, param, domain, options, phase="primary", x_average=False): + super().__init__(param, domain, options, phase) + self.x_average = x_average + + pybamm.citations.register("Baker2018") + + def get_fundamental_variables(self): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + variables = {} + + # Define "particle" open-circuit potential variables. In the MSMR model, we + # solve for the potential as a function of position within the electrode and + # particles (and particle-size distribution, if applicable). The potential is + # then used to calculate the stoichiometry, which is used to calculate the + # particle concentration. + c_max = self.phase_param.c_max + if self.size_distribution is False: + if self.x_average is False: + U = pybamm.Variable( + f"{Domain} {phase_name}particle open-circuit potential [V]", + f"{domain} {phase_name}particle", + auxiliary_domains={ + "secondary": f"{domain} electrode", + "tertiary": "current collector", + }, + ) + U.print_name = f"U_{domain[0]}" + else: + U_xav = pybamm.Variable( + f"X-averaged {domain} {phase_name}particle open-circuit " + "potential [V]", + f"{domain} {phase_name}particle", + auxiliary_domains={"secondary": "current collector"}, + ) + U_xav.print_name = f"U_{domain[0]}_xav" + U = pybamm.SecondaryBroadcast(U_xav, f"{domain} electrode") + else: + if self.x_average is False: + U_distribution = pybamm.Variable( + f"{Domain} {phase_name}particle " + "open-circuit potential distribution [V]", + domain=f"{domain} {phase_name}particle", + auxiliary_domains={ + "secondary": f"{domain} {phase_name}particle size", + "tertiary": f"{domain} electrode", + "quaternary": "current collector", + }, + ) + R = pybamm.SpatialVariable( + f"R_{domain[0]}", + domain=[f"{domain} {phase_name}particle size"], + auxiliary_domains={ + "secondary": f"{domain} electrode", + "tertiary": "current collector", + }, + coord_sys="cartesian", + ) + variables = self._get_distribution_variables(R) + f_v_dist = variables[ + f"{Domain} volume-weighted {phase_name}" + "particle-size distribution [m-1]" + ] + else: + U_distribution = pybamm.Variable( + f"X-averaged {domain} {phase_name}particle " + "open-circuit potential distribution [V]", + domain=f"{domain} {phase_name}particle", + auxiliary_domains={ + "secondary": f"{domain} {phase_name}particle size", + "tertiary": "current collector", + }, + ) + R = pybamm.SpatialVariable( + f"R_{domain[0]}", + domain=[f"{domain} {phase_name}particle size"], + auxiliary_domains={"secondary": "current collector"}, + coord_sys="cartesian", + ) + variables = self._get_distribution_variables(R) + f_v_dist = variables[ + f"X-averaged {domain} volume-weighted {phase_name}" + "particle-size distribution [m-1]" + ] + + # Standard potential distribution_variables + variables.update( + self._get_standard_potential_distribution_variables(U_distribution) + ) + + # Calculate the stoichiometry distribution from the potential distribution + x_distribution = self.phase_param.x(U_distribution) + dxdU_distribution = self.phase_param.dxdU(U_distribution) + + # Standard stoichiometry and concentration distribution variables + # (size-dependent) + c_s_distribution = x_distribution * c_max + variables.update( + self._get_standard_concentration_distribution_variables( + c_s_distribution + ) + ) + variables.update( + self._get_standard_differential_stoichiometry_distribution_variables( + dxdU_distribution + ) + ) + + # Standard size-averaged variables. Average potentials using + # the volume-weighted distribution since they are volume-based + # quantities. Necessary for output variables "Total lithium in + # negative electrode [mol]", etc, to be calculated correctly + U = pybamm.Integral(f_v_dist * U_distribution, R) + if self.x_average is True: + U = pybamm.SecondaryBroadcast(U, [f"{domain} electrode"]) + + # Standard potential variables + variables.update(self._get_standard_potential_variables(U)) + + # Calculate the stoichiometry from the potential + x = self.phase_param.x(U) + dxdU = self.phase_param.dxdU(U) + + # Standard stoichiometry and concentration variables (size-independent) + c_s = x * c_max + variables.update(self._get_standard_concentration_variables(c_s)) + variables.update(self._get_standard_differential_stoichiometry_variables(dxdU)) + + return variables + + def get_coupled_variables(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + param = self.param + + if self.size_distribution is False: + if self.x_average is False: + x = variables[f"{Domain} {phase_name} particle stoichiometry"] + dxdU = variables[ + f"{Domain} {phase_name} particle differential stoichiometry [V-1]" + ] + U = variables[ + f"{Domain} {phase_name} particle open-circuit potential [V]" + ] + T = pybamm.PrimaryBroadcast( + variables[f"{Domain} electrode temperature [K]"], + [f"{domain} {phase_name}particle"], + ) + R_nondim = variables[f"{Domain} {phase_name}particle radius"] + j = variables[ + f"{Domain} electrode {phase_name}" + "interfacial current density [A.m-2]" + ] + else: + x = variables[f"X-averaged {domain} {phase_name}particle stoichiometry"] + dxdU = variables[ + f"X-averaged {domain} {phase_name}particle differential " + "stoichiometry [V-1]" + ] + U = variables[ + f"X-averaged {domain} {phase_name}particle open-circuit " + "potential [V]" + ] + T = pybamm.PrimaryBroadcast( + variables[f"X-averaged {domain} electrode temperature [K]"], + [f"{domain} {phase_name}particle"], + ) + R_nondim = 1 + j = variables[ + f"X-averaged {domain} electrode {phase_name}" + "interfacial current density [A.m-2]" + ] + R_broad_nondim = R_nondim + else: + R_nondim = variables[f"{Domain} {phase_name}particle sizes"] + R_broad_nondim = pybamm.PrimaryBroadcast( + R_nondim, [f"{domain} {phase_name}particle"] + ) + if self.x_average is False: + x = variables[ + f"{Domain} {phase_name}particle stoichiometry distribution" + ] + dxdU = variables[ + f"{Domain} {phase_name}particle differential stoichiometry " + "distribution [V-1]" + ] + U = variables[ + f"{Domain} {phase_name}particle open-circuit potential " + "distribution [V]" + ] + # broadcast T to "particle size" domain then again into "particle" + T = pybamm.PrimaryBroadcast( + variables[f"{Domain} electrode temperature [K]"], + [f"{domain} {phase_name}particle size"], + ) + T = pybamm.PrimaryBroadcast(T, [f"{domain} {phase_name}particle"]) + j = variables[ + f"{Domain} electrode {phase_name}interfacial " + "current density distribution [A.m-2]" + ] + else: + x = variables[ + f"X-averaged {domain} {phase_name}particle " + "stoichiometry distribution" + ] + dxdU = variables[ + f"X-averaged {domain} {phase_name}particle " + "differential stoichiometry distribution [V-1]" + ] + U = variables[ + f"X-averaged {domain} {phase_name}particle " + "open-circuit potential distribution [V]" + ] + # broadcast to "particle size" domain then again into "particle" + T = pybamm.PrimaryBroadcast( + variables[f"X-averaged {domain} electrode temperature [K]"], + [f"{domain} {phase_name}particle size"], + ) + T = pybamm.PrimaryBroadcast(T, [f"{domain} {phase_name}particle"]) + j = variables[ + f"X-averaged {domain} electrode {phase_name}interfacial " + "current density distribution [A.m-2]" + ] + + # Note: diffusivity is given as a function of concentration here, + # not stoichiometry + c_max = self.phase_param.c_max + D_eff = self._get_effective_diffusivity(x * c_max, T) + f = self.param.F / (self.param.R * T) + N_s = c_max * x * (1 - x) * f * D_eff * pybamm.grad(U) + variables.update( + { + f"{Domain} {phase_name}particle rhs [V.s-1]": -( + 1 / (R_broad_nondim**2) + ) + * pybamm.div(N_s) + / c_max + / dxdU, + f"{Domain} {phase_name}particle bc [V.m-1]": -j + * R_nondim + / param.F + / pybamm.surf(c_max * x * (1 - x) * f * D_eff), + } + ) + + if self.size_distribution is True: + # Size-dependent flux variables + variables.update( + self._get_standard_diffusivity_distribution_variables(D_eff) + ) + variables.update(self._get_standard_flux_distribution_variables(N_s)) + # Size-averaged flux variables + R = variables[f"{Domain} {phase_name}particle sizes [m]"] + f_a_dist = self.phase_param.f_a_dist(R) + D_eff = pybamm.Integral(f_a_dist * D_eff, R) + N_s = pybamm.Integral(f_a_dist * N_s, R) + + if self.x_average is True: + D_eff = pybamm.SecondaryBroadcast(D_eff, [f"{domain} electrode"]) + N_s = pybamm.SecondaryBroadcast(N_s, [f"{domain} electrode"]) + + variables.update(self._get_standard_diffusivity_variables(D_eff)) + variables.update(self._get_standard_flux_variables(N_s)) + + return variables + + def set_rhs(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + if self.size_distribution is False: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle open-circuit potential [V]" + ] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle open-circuit " + "potential [V]" + ] + else: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle " + "open-circuit potential distribution [V]" + ] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " + "open-circuit potential distribution [V]" + ] + self.rhs = {U: variables[f"{Domain} {phase_name}particle rhs [V.s-1]"]} + + def set_boundary_conditions(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + if self.size_distribution is False: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle open-circuit potential [V]" + ] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle open-circuit " + "potential [V]" + ] + else: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle " + "open-circuit potential distribution [V]" + ] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " + "open-circuit potential distribution [V]" + ] + + rbc = variables[f"{Domain} {phase_name}particle bc [V.m-1]"] + self.boundary_conditions = { + U: {"left": (pybamm.Scalar(0), "Neumann"), "right": (rbc, "Neumann")} + } + + def set_initial_conditions(self, variables): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + U_init = self.phase_param.U_init + if self.size_distribution is False: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle open-circuit potential [V]" + ] + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle open-circuit " + "potential [V]" + ] + U_init = pybamm.x_average(U_init) + else: + if self.x_average is False: + U = variables[ + f"{Domain} {phase_name}particle " + "concentration distribution [mol.m-3]" + ] + U_init = pybamm.SecondaryBroadcast( + U_init, f"{domain} {phase_name}particle size" + ) + else: + U = variables[ + f"X-averaged {domain} {phase_name}particle " + "concentration distribution [mol.m-3]" + ] + + U_init = pybamm.SecondaryBroadcast( + pybamm.x_average(U_init), f"{domain} {phase_name}particle size" + ) + self.initial_conditions = {U: U_init} + + def _get_standard_potential_variables(self, U): + """ + A private function to obtain the standard variables which can be derived from + the potential. + """ + domain, Domain = self.domain_Domain + phase_name = self.phase_name + U_surf = pybamm.surf(U) + U_surf_av = pybamm.x_average(U_surf) + U_xav = pybamm.x_average(U) + U_rav = pybamm.r_average(U) + U_av = pybamm.r_average(U_xav) + variables = { + f"{Domain} {phase_name}particle open-circuit potential [V]": U, + f"X-averaged {domain} {phase_name}particle " + "open-circuit potential [V]": U_xav, + f"R-averaged {domain} {phase_name}particle " + "open-circuit potential [V]": U_rav, + f"Average {domain} {phase_name}particle open-circuit potential [V]": U_av, + f"{Domain} {phase_name}particle surface open-circuit potential [V]": U_surf, + f"X-averaged {domain} {phase_name}particle " + "surface open-circuit potential [V]": U_surf_av, + f"Minimum {domain} {phase_name}particle open-circuit potential [V]" + "": pybamm.min(U), + f"Maximum {domain} {phase_name}particle open-circuit potential [V]" + "": pybamm.max(U), + f"Minimum {domain} {phase_name}particle " + f"Minimum {domain} {phase_name}particle " + "surface open-circuit potential [V]": pybamm.min(U_surf), + f"Maximum {domain} {phase_name}particle " + "surface open-circuit potential [V]": pybamm.max(U_surf), + } + return variables + + def _get_standard_potential_distribution_variables(self, U): + """ + A private function to obtain the standard variables which can be derived from + the potential distribution in particle size. + """ + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + # Broadcast and x-average when necessary + if U.domain == [f"{domain} {phase_name}particle size"] and U.domains[ + "secondary" + ] != [f"{domain} electrode"]: + # X-avg potential distribution + U_xav_distribution = pybamm.PrimaryBroadcast( + U, [f"{domain} {phase_name}particle"] + ) + + # Surface open-circuit potential distribution variables + U_surf_xav_distribution = U + U_surf_distribution = pybamm.SecondaryBroadcast( + U_surf_xav_distribution, [f"{domain} electrode"] + ) + + # Open-circuit potential distribution in all domains. + U_distribution = pybamm.PrimaryBroadcast( + U_surf_distribution, [f"{domain} {phase_name}particle"] + ) + elif U.domain == [f"{domain} {phase_name}particle"] and ( + U.domains["tertiary"] != [f"{domain} electrode"] + ): + # X-avg open-circuit potential distribution + U_xav_distribution = U + + # Surface open-circuit potential distribution variables + U_surf_xav_distribution = pybamm.surf(U_xav_distribution) + U_surf_distribution = pybamm.SecondaryBroadcast( + U_surf_xav_distribution, [f"{domain} electrode"] + ) + + # open-circuit potential distribution in all domains + U_distribution = pybamm.TertiaryBroadcast( + U_xav_distribution, [f"{domain} electrode"] + ) + elif U.domain == [f"{domain} {phase_name}particle size"] and U.domains[ + "secondary" + ] == [f"{domain} electrode"]: + # Surface open-circuit potential distribution variables + U_surf_distribution = U + U_surf_xav_distribution = pybamm.x_average(U) + + # X-avg open-circuit potential distribution + U_xav_distribution = pybamm.PrimaryBroadcast( + U_surf_xav_distribution, [f"{domain} {phase_name}particle"] + ) + + # Open-circuit potential distribution in all domains + U_distribution = pybamm.PrimaryBroadcast( + U_surf_distribution, [f"{domain} {phase_name}particle"] + ) + else: + U_distribution = U + + # x-average the *tertiary* domain. + # NOTE: not yet implemented. Make 0.5 everywhere + U_xav_distribution = pybamm.FullBroadcast( + 0.5, + [f"{domain} {phase_name}particle"], + { + "secondary": f"{domain} {phase_name}particle size", + "tertiary": "current collector", + }, + ) + + # Surface open-circuit potential distribution variables + U_surf_distribution = pybamm.surf(U) + U_surf_xav_distribution = pybamm.x_average(U_surf_distribution) + + U_rav_distribution = pybamm.r_average(U_distribution) + U_av_distribution = pybamm.x_average(U_rav_distribution) + + variables = { + f"{Domain} {phase_name}particle open-circuit potential distribution " + "[V]": U_distribution, + f"X-averaged {domain} {phase_name}particle open-circuit potential " + "distribution [V]": U_xav_distribution, + f"R-averaged {domain} {phase_name}particle open-circuit potential " + "distribution [V]": U_rav_distribution, + f"Average {domain} {phase_name}particle open-circuit potential " + "distribution [V]": U_av_distribution, + f"{Domain} {phase_name}particle surface open-circuit potential" + " distribution [V]": U_surf_distribution, + f"X-averaged {domain} {phase_name}particle surface open-circuit potential " + "distribution [V]": U_surf_xav_distribution, + } + return variables + + def _get_standard_differential_stoichiometry_variables(self, dxdU): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + dxdU_surf = pybamm.surf(dxdU) + dxdU_surf_av = pybamm.x_average(dxdU_surf) + dxdU_xav = pybamm.x_average(dxdU) + dxdU_rav = pybamm.r_average(dxdU) + dxdU_av = pybamm.r_average(dxdU_xav) + + variables = { + f"{Domain} {phase_name}particle differential stoichiometry [V-1]": dxdU, + f"X-averaged {domain} {phase_name}particle " + "differential stoichiometry [V-1]": dxdU_xav, + f"R-averaged {domain} {phase_name}particle " + "differential stoichiometry [V-1]": dxdU_rav, + f"Average {domain} {phase_name}particle differential " + "stoichiometry [V-1]": dxdU_av, + f"{Domain} {phase_name}particle surface differential " + "stoichiometry [V-1]": dxdU_surf, + f"X-averaged {domain} {phase_name}particle " + "surface differential stoichiometry [V-1]": dxdU_surf_av, + } + + return variables + + def _get_standard_differential_stoichiometry_distribution_variables(self, dxdU): + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + # Broadcast and x-average when necessary + if dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[ + "secondary" + ] != [f"{domain} electrode"]: + # X-avg differential stoichiometry distribution + dxdU_xav_distribution = pybamm.PrimaryBroadcast( + dxdU, [f"{domain} {phase_name}particle"] + ) + + # Surface differential stoichiometry distribution variables + dxdU_surf_xav_distribution = dxdU + dxdU_surf_distribution = pybamm.SecondaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} electrode"] + ) + + # Differential stoichiometry distribution in all domains. + dxdU_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_distribution, [f"{domain} {phase_name}particle"] + ) + elif dxdU.domain == [f"{domain} {phase_name}particle"] and ( + dxdU.domains["tertiary"] != [f"{domain} electrode"] + ): + # X-avg differential stoichiometry distribution + dxdU_xav_distribution = dxdU + + # Surface differential stoichiometry distribution variables + dxdU_surf_xav_distribution = pybamm.surf(dxdU_xav_distribution) + dxdU_surf_distribution = pybamm.SecondaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} electrode"] + ) + + # Differential stoichiometry distribution in all domains. + dxdU_distribution = pybamm.TertiaryBroadcast( + dxdU_xav_distribution, [f"{domain} electrode"] + ) + elif dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[ + "secondary" + ] == [f"{domain} electrode"]: + # Surface differential stoichiometry distribution variables + dxdU_surf_distribution = dxdU + dxdU_surf_xav_distribution = pybamm.x_average(dxdU) + + # X-avg differential stoichiometry distribution + dxdU_xav_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} {phase_name}particle"] + ) + + # Differential stoichiometry distribution in all domains + dxdU_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_distribution, [f"{domain} {phase_name}particle"] + ) + else: + dxdU_distribution = dxdU + + # x-average the *tertiary* domain. + # NOTE: not yet implemented. Make 0.5 everywhere + dxdU_xav_distribution = pybamm.FullBroadcast( + 0.5, + [f"{domain} {phase_name}particle"], + { + "secondary": f"{domain} {phase_name}particle size", + "tertiary": "current collector", + }, + ) + + # Surface differential stoichiometry distribution variables + dxdU_surf_distribution = pybamm.surf(dxdU) + dxdU_surf_xav_distribution = pybamm.x_average(dxdU_surf_distribution) + + dxdU_rav_distribution = pybamm.r_average(dxdU_distribution) + dxdU_av_distribution = pybamm.x_average(dxdU_rav_distribution) + + variables = { + f"{Domain} {phase_name}particle differential stoichiometry distribution " + "[V-1]": dxdU_distribution, + f"X-averaged {domain} {phase_name}particle differential stoichiometry " + "distribution [V-1]": dxdU_xav_distribution, + f"R-averaged {domain} {phase_name}particle differential stoichiometry " + "distribution [V-1]": dxdU_rav_distribution, + f"Average {domain} {phase_name}particle differential stoichiometry " + "distribution [V-1]": dxdU_av_distribution, + f"{Domain} {phase_name}particle surface differential stoichiometry" + " distribution [V-1]": dxdU_surf_distribution, + f"X-averaged {domain} {phase_name}particle surface differential " + "stoichiometry distribution [V-1]": dxdU_surf_xav_distribution, + } + return variables From c644be8e661e0333073ad8d23909429210bc11ce Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 14 Jun 2023 14:18:06 +0100 Subject: [PATCH 006/154] MSMR model options + example --- examples/scripts/MSMR.py | 9 + examples/scripts/MSMR_example.py | 167 ++++++++++++++++++ .../full_battery_models/base_battery_model.py | 38 +++- .../lithium_ion/base_lithium_ion_model.py | 2 + .../lithium_ion/basic_spm_msmr.py | 6 +- .../full_battery_models/lithium_ion/dfn.py | 4 + .../lithium_ion/newman_tobias.py | 4 + .../full_battery_models/lithium_ion/spm.py | 4 + .../submodels/particle/msmr_diffusion.py | 4 +- pybamm/parameters/lithium_ion_parameters.py | 34 +++- 10 files changed, 259 insertions(+), 13 deletions(-) create mode 100644 examples/scripts/MSMR.py create mode 100644 examples/scripts/MSMR_example.py diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py new file mode 100644 index 0000000000..a5ee302f18 --- /dev/null +++ b/examples/scripts/MSMR.py @@ -0,0 +1,9 @@ +import pybamm +from MSMR_example import get_parameter_values + +pybamm.set_logging_level("DEBUG") +model = pybamm.lithium_ion.SPM({"open-circuit potential": "MSMR", "particle": "MSMR"}) +parameter_values = pybamm.ParameterValues(get_parameter_values()) +sim = pybamm.Simulation(model, parameter_values=parameter_values) +sim.solve([0, 3000]) +sim.plot() diff --git a/examples/scripts/MSMR_example.py b/examples/scripts/MSMR_example.py new file mode 100644 index 0000000000..46d15962a4 --- /dev/null +++ b/examples/scripts/MSMR_example.py @@ -0,0 +1,167 @@ +import pybamm + + +def electrolyte_diffusivity_Nyman2008(c_e, T): + D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10 + return D_c_e + + +def electrolyte_conductivity_Nyman2008(c_e, T): + sigma_e = ( + 0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000) + ) + return sigma_e + + +def x_n(U): + T = 298.15 + f = pybamm.constants.F / (pybamm.constants.R * T) + xj = 0 + for i in range(6): + U0 = pybamm.Parameter(f"U0_n_{i}") + w = pybamm.Parameter(f"w_n_{i}") + Xj = pybamm.Parameter(f"Xj_n_{i}") + + xj += Xj / (1 + pybamm.exp(f * (U - U0) / w)) + + return xj + + +def dxdU_n(U): + T = 298.15 + f = pybamm.constants.F / (pybamm.constants.R * T) + dxj = 0 + for i in range(6): + U0 = pybamm.Parameter(f"U0_n_{i}") + w = pybamm.Parameter(f"w_n_{i}") + Xj = pybamm.Parameter(f"Xj_n_{i}") + + e = pybamm.exp(f * (U - U0) / w) + dxj += -(f / w) * (Xj * e) / (1 + e) ** 2 + + return dxj + + +def x_p(U): + T = 298.15 + f = pybamm.constants.F / (pybamm.constants.R * T) + xj = 0 + for i in range(4): + U0 = pybamm.Parameter(f"U0_p_{i}") + w = pybamm.Parameter(f"w_p_{i}") + Xj = pybamm.Parameter(f"Xj_p_{i}") + + xj += Xj / (1 + pybamm.exp(f * (U - U0) / w)) + + return xj + + +def dxdU_p(U): + T = 298.15 + f = pybamm.constants.F / (pybamm.constants.R * T) + dxj = 0 + for i in range(4): + U0 = pybamm.Parameter(f"U0_p_{i}") + w = pybamm.Parameter(f"w_p_{i}") + Xj = pybamm.Parameter(f"Xj_p_{i}") + + e = pybamm.exp(f * (U - U0) / w) + dxj += -(f / w) * (Xj * e) / (1 + e) ** 2 + + return dxj + + +def get_parameter_values(): + return { + # cell + "Negative electrode thickness [m]": 7.56e-05, + "Separator thickness [m]": 1.2e-05, + "Positive electrode thickness [m]": 7.56e-05, + "Electrode height [m]": 0.065, + "Electrode width [m]": 1.58, + "Nominal cell capacity [A.h]": 5.0, + "Current function [A]": 5.0, + "Contact resistance [Ohm]": 0, + # negative electrode + "Negative electrode stoichiometry": x_n, + "Negative electrode differential stoichiometry [V-1]": dxdU_n, + "U0_n_0": 0.08843, + "Xj_n_0": 0.43336, + "w_n_0": 0.08611, + "U0_n_1": 0.12799, + "Xj_n_1": 0.23963, + "w_n_1": 0.08009, + "U0_n_2": 0.14331, + "Xj_n_2": 0.15018, + "w_n_2": 0.72469, + "U0_n_3": 0.16984, + "Xj_n_3": 0.05462, + "w_n_3": 2.53277, + "U0_n_4": 0.21446, + "Xj_n_4": 0.06744, + "w_n_4": 0.09470, + "U0_n_5": 0.36325, + "Xj_n_5": 0.05476, + "w_n_5": 5.97354, + "Negative electrode stoichiometry at 0% SOC": 0.03, + "Negative electrode stoichiometry at 100% SOC": 0.9, + "Negative electrode conductivity [S.m-1]": 215.0, + "Maximum concentration in negative electrode [mol.m-3]": 33133.0, + "Negative electrode diffusivity [m2.s-1]": 3.3e-14, + "Negative electrode porosity": 0.25, + "Negative electrode active material volume fraction": 0.75, + "Negative particle radius [m]": 5.86e-06, + "Negative electrode Bruggeman coefficient (electrolyte)": 1.5, + "Negative electrode Bruggeman coefficient (electrode)": 0, + "Negative electrode exchange-current density [A.m-2]" "": 2.7, + "Negative electrode OCP entropic change [V.K-1]": 0.0, + # positive electrode + "Positive electrode stoichiometry": x_p, + "Positive electrode differential stoichiometry [V-1]": dxdU_p, + "U0_p_0": 3.62274, + "Xj_p_0": 0.13442, + "w_p_0": 0.96710, + "U0_p_1": 3.72645, + "Xj_p_1": 0.32460, + "w_p_1": 1.39712, + "U0_p_2": 3.90575, + "Xj_p_2": 0.21118, + "w_p_2": 3.50500, + "U0_p_3": 4.22955, + "Xj_p_3": 0.32980, + "w_p_3": 5.52757, + "Positive electrode stoichiometry at 0% SOC": 0.85, + "Positive electrode stoichiometry at 100% SOC": 0.1, + "Positive electrode conductivity [S.m-1]": 0.18, + "Maximum concentration in positive electrode [mol.m-3]": 63104.0, + "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive electrode porosity": 0.335, + "Positive electrode active material volume fraction": 0.665, + "Positive particle radius [m]": 5.22e-06, + "Positive electrode Bruggeman coefficient (electrolyte)": 1.5, + "Positive electrode Bruggeman coefficient (electrode)": 0, + "Positive electrode exchange-current density [A.m-2]" "": 5, + "Positive electrode OCP entropic change [V.K-1]": 0.0, + # separator + "Separator porosity": 0.47, + "Separator Bruggeman coefficient (electrolyte)": 1.5, + # electrolyte + "Initial concentration in electrolyte [mol.m-3]": 1000.0, + "Cation transference number": 0.2594, + "Thermodynamic factor": 1.0, + "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Nyman2008, + "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Nyman2008, + # experiment + "Reference temperature [K]": 298.15, + "Total heat transfer coefficient [W.m-2.K-1]": 10.0, + "Ambient temperature [K]": 298.15, + "Number of electrodes connected in parallel to make a cell": 1.0, + "Number of cells connected in series to make a battery": 1.0, + "Lower voltage cut-off [V]": 1, + "Upper voltage cut-off [V]": 5, + "Initial temperature [K]": 298.15, + "Initial voltage in negative electrode [V]": 0.085, + "Initial voltage in positive electrode [V]": 4.35, + "Initial concentration in negative electrode [mol.m-3]": 29820, + "Initial concentration in positive electrode [mol.m-3]": 6310, + } diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 4ea4611133..1b415aea76 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -72,6 +72,11 @@ class BatteryModelOptions(pybamm.FuzzyDict): "stress-driven", "reaction-driven", or "stress and reaction-driven". A 2-tuple can be provided for different behaviour in negative and positive electrodes. + * "open-circuit potential" : str + Sets the model for the open circuit potential. Can be "single" + (default), "current sigmoid", or "MSMR". If "MSMR" then the "particle" + option must also be "MSMR". A 2-tuple can be provided for different + behaviour in negative and positive electrodes. * "operating mode" : str Sets the operating mode for the model. This determines how the current is set. Can be: @@ -91,8 +96,9 @@ class BatteryModelOptions(pybamm.FuzzyDict): * "particle" : str Sets the submodel to use to describe behaviour within the particle. Can be "Fickian diffusion" (default), "uniform profile", - "quadratic profile", or "quartic profile". A 2-tuple can be provided - for different behaviour in negative and positive electrodes. + "quadratic profile", "quartic profile", or "MSMR". If "MSMR" then the + "open-circuit potential" must also be "MSMR". A 2-tuple can be + provided for different behaviour in negative and positive electrodes. * "particle mechanics" : str Sets the model to account for mechanical effects such as particle swelling and cracking. Can be "none" (default), "swelling only", @@ -221,7 +227,7 @@ def __init__(self, extra_options): "reaction-driven", "stress and reaction-driven", ], - "open-circuit potential": ["single", "current sigmoid"], + "open-circuit potential": ["single", "current sigmoid", "MSMR"], "operating mode": [ "current", "voltage", @@ -239,6 +245,7 @@ def __init__(self, extra_options): "uniform profile", "quadratic profile", "quartic profile", + "MSMR", ], "particle mechanics": ["none", "swelling only", "swelling and cracking"], "particle phases": ["1", "2"], @@ -369,6 +376,25 @@ def __init__(self, extra_options): ) ) + # IF "open-circuit potential" is "MSMR" then "particle" must be "MSMR" too + # and vice-versa + if ( + options["open-circuit potential"] == "MSMR" + and options["particle"] != "MSMR" + ): + raise pybamm.OptionError( + "If 'open-circuit potential' is 'MSMR' then 'particle' must be 'MSMR' " + "too" + ) + if ( + options["particle"] == "MSMR" + and options["open-circuit potential"] != "MSMR" + ): + raise pybamm.OptionError( + "If 'particle' is 'MSMR' then 'open-circuit potential' must be 'MSMR' " + "too" + ) + # If "SEI film resistance" is "distributed" then "total interfacial current # density as a state" must be "true" if options["SEI film resistance"] == "distributed": @@ -832,6 +858,10 @@ def options(self, extra_options): raise pybamm.OptionError("Lead-acid models cannot have SEI formation") if options["lithium plating"] != "none": raise pybamm.OptionError("Lead-acid models cannot have lithium plating") + if options["open-circuit potential"] == "MSMR": + raise pybamm.OptionError( + "Lead-acid models cannot use the MSMR open-circuit potential model" + ) if ( isinstance(self, pybamm.lead_acid.LOQS) @@ -1216,7 +1246,7 @@ def x_not_zero(x): self.variables.update( { - "Change in open-circuit voltage [V]": eta_ocv, + # "Change in open-circuit voltage [V]": eta_ocv, "Local ECM resistance [Ohm]": pybamm.sign(i_cc) * v_ecm / (i_cc_not_zero * A_cc), diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index cc615dacf7..41e4670cf7 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -238,6 +238,8 @@ def set_open_circuit_potential_submodel(self): ocp_model = ocp_submodels.SingleOpenCircuitPotential elif ocp_option == "current sigmoid": ocp_model = ocp_submodels.CurrentSigmoidOpenCircuitPotential + elif ocp_option == "MSMR": + ocp_model = ocp_submodels.MSMROpenCircuitPotential self.submodels[f"{domain} {phase} open-circuit potential"] = ocp_model( self.param, domain, reaction, self.options, phase ) diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py index 718893b723..76beb7a5ef 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py @@ -356,7 +356,6 @@ def default_quick_plot_variables(self): soc_model = pybamm.BaseModel() U_n = pybamm.Variable("U_n") U_p = pybamm.Variable("U_p") - soc_model.variables = {"U_n": U_n, "U_p": U_p} x_0 = parameter_values["Negative electrode stoichiometry at 0% SOC"] x_100 = parameter_values["Negative electrode stoichiometry at 100% SOC"] y_0 = parameter_values["Positive electrode stoichiometry at 0% SOC"] @@ -366,10 +365,12 @@ def default_quick_plot_variables(self): y = y_0 - initial_soc * (y_0 - y_100) soc_model.algebraic = {U_n: x - x_n(U_n), U_p: y - x_p(U_p)} soc_model.initial_conditions = {U_n: pybamm.Scalar(0), U_p: pybamm.Scalar(4)} + soc_model.variables = {"U_n": U_n, "U_p": U_p, "x": x, "y": y} parameter_values.process_model(soc_model) soc_sol = pybamm.AlgebraicSolver(tol=1e-6).solve( soc_model, inputs={"Initial soc": 1} ) + x, y = soc_sol["x"].data[0], soc_sol["y"].data[0] U_n, U_p = soc_sol["U_n"].data[0], soc_sol["U_p"].data[0] def current(t): @@ -383,6 +384,9 @@ def current(t): }, check_already_exists=False, ) + c_n_max = parameter_values["Maximum concentration in negative electrode [mol.m-3]"] + c_p_max = parameter_values["Maximum concentration in positive electrode [mol.m-3]"] + print(x * c_n_max, y * c_p_max) print(U_n, U_p) sim = pybamm.Simulation(model, parameter_values=parameter_values) diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py index c505792e8e..17ac0571c9 100644 --- a/pybamm/models/full_battery_models/lithium_ion/dfn.py +++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py @@ -70,6 +70,10 @@ def set_particle_submodel(self): submod = pybamm.particle.PolynomialProfile( self.param, domain, self.options, phase=phase ) + elif particle == "MSMR": + submod = pybamm.particle.MSMRDiffusion( + self.param, domain, self.options, phase=phase, x_average=False + ) self.submodels[f"{domain} {phase} particle"] = submod self.submodels[ f"{domain} {phase} total particle concentration" diff --git a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py index a83aadafd8..8b874bff02 100644 --- a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py +++ b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py @@ -59,6 +59,10 @@ def set_particle_submodel(self): submod = pybamm.particle.XAveragedPolynomialProfile( self.param, domain, self.options, phase=phase ) + elif particle == "MSMR": + submod = pybamm.particle.MSMRDiffusion( + self.param, domain, self.options, phase=phase, x_average=True + ) self.submodels[f"{domain} {phase} particle"] = submod self.submodels[ f"{domain} {phase} total particle concentration" diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py index 2074ae6358..3cf4942980 100644 --- a/pybamm/models/full_battery_models/lithium_ion/spm.py +++ b/pybamm/models/full_battery_models/lithium_ion/spm.py @@ -109,6 +109,10 @@ def set_particle_submodel(self): submod = pybamm.particle.XAveragedPolynomialProfile( self.param, domain, self.options, phase=phase ) + elif particle == "MSMR": + submod = pybamm.particle.MSMRDiffusion( + self.param, domain, self.options, phase=phase, x_average=True + ) self.submodels[f"{domain} {phase} particle"] = submod self.submodels[ f"{domain} {phase} total particle concentration" diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index 15efab981f..274490a19b 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -272,7 +272,7 @@ def get_coupled_variables(self, variables): * pybamm.div(N_s) / c_max / dxdU, - f"{Domain} {phase_name}particle bc [V.m-1]": -j + f"{Domain} {phase_name}particle bc [V.m-1]": j * R_nondim / param.F / pybamm.surf(c_max * x * (1 - x) * f * D_eff), @@ -362,7 +362,7 @@ def set_initial_conditions(self, variables): domain, Domain = self.domain_Domain phase_name = self.phase_name - U_init = self.phase_param.U_init + U_init = self.phase_param.U_msmr_init if self.size_distribution is False: if self.x_average is False: U = variables[ diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 43901eff2b..7e01f1df78 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -20,11 +20,6 @@ class LithiumIonParameters(BaseParameters): Sets the model shape of the electrode particles. This is used to calculate the surface area to volume ratio. Can be "spherical" (default). TODO: implement "cylindrical" and "platelet". - * "working electrode": str - Which electrode(s) intercalates and which is counter. If "both" - (default), the model is a standard battery. Otherwise can be "negative" - or "positive" to indicate a half-cell model. - """ def __init__(self, options=None): @@ -332,7 +327,7 @@ def _set_parameters(self): f"{Domain} electrode reaction-driven LAM factor [m3.mol-1]" ) - # utilisation parameters + # Utilisation parameters self.u_init = pybamm.Parameter( f"Initial {domain} electrode interface utilisation" ) @@ -514,6 +509,9 @@ def _set_parameters(self): self.Q_init = self.elec_loading * main.A_cc self.U_init = self.U(self.sto_init_av, main.T_init) + self.U_msmr_init = pybamm.Parameter( + f"{pref}Initial voltage in {domain} electrode [V]" + ) if main.options["particle shape"] == "spherical": self.a_typ = 3 * pybamm.xyz_average(self.epsilon_s) / self.R_typ @@ -591,6 +589,30 @@ def U(self, sto, T, lithiation=None): out.print_name = r"U_\mathrm{p}(c^\mathrm{surf}_\mathrm{s,p}, T)" return out + def x(self, U): + "Stoichiometry as a function of potential (for use with MSMR models)" + Domain = self.domain.capitalize() + inputs = { + f"{self.phase_prefactor}{Domain} particle open-circuit potential [V]": U + } + return pybamm.FunctionParameter( + f"{self.phase_prefactor}{Domain} electrode stoichiometry", inputs + ) + + def dxdU(self, U): + """ + Differential stoichiometry as a function of potential (for use with MSMR models) + """ + Domain = self.domain.capitalize() + inputs = { + f"{self.phase_prefactor}{Domain} particle open-circuit potential [V]": U + } + return pybamm.FunctionParameter( + f"{self.phase_prefactor}{Domain} electrode differential " + "stoichiometry [V-1]", + inputs, + ) + def dUdT(self, sto): """ Dimensional entropic change of the open-circuit potential [V.K-1] From e7f9c18d64e243303841651134928ffcec1337f3 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 15 Jun 2023 09:49:34 +0100 Subject: [PATCH 007/154] fix options test --- .../full_battery_models/base_battery_model.py | 14 ++++---------- .../test_base_battery_model.py | 4 ++-- 2 files changed, 6 insertions(+), 12 deletions(-) diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 1b415aea76..0da899cf8e 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -1224,17 +1224,10 @@ def set_voltage_variables(self): "Battery voltage [V]": V * num_cells, } ) - # Variables for calculating the equivalent circuit model (ECM) resistance - # Need to compare OCV to initial value to capture this as an overpotential - ocv_init = self.param.ocv_init - eta_ocv = ocv_bulk - ocv_init - # Current collector current density for working out euiqvalent resistance - # based on Ohm's Law - i_cc = self.variables["Current collector current density [A.m-2]"] + + # Calculate equivalent resistance of an OCV-R Equivalent Circuit Model # ECM overvoltage is OCV minus voltage v_ecm = ocv_bulk - V - # Current collector area for turning resistivity into resistance - A_cc = self.param.A_cc # Hack to avoid division by zero if i_cc is exactly zero # If i_cc is zero, i_cc_not_zero becomes 1. But multiplying by sign(i_cc) makes @@ -1242,11 +1235,12 @@ def set_voltage_variables(self): def x_not_zero(x): return ((x > 0) + (x < 0)) * x + (x >= 0) * (x <= 0) + i_cc = self.variables["Current collector current density [A.m-2]"] i_cc_not_zero = x_not_zero(i_cc) + A_cc = self.param.A_cc self.variables.update( { - # "Change in open-circuit voltage [V]": eta_ocv, "Local ECM resistance [Ohm]": pybamm.sign(i_cc) * v_ecm / (i_cc_not_zero * A_cc), diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index ee51f864c4..ab3b4a0d25 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -29,9 +29,9 @@ 'lithium plating': 'none' (possible: ['none', 'reversible', 'partially reversible', 'irreversible']) 'lithium plating porosity change': 'false' (possible: ['false', 'true']) 'loss of active material': 'stress-driven' (possible: ['none', 'stress-driven', 'reaction-driven', 'stress and reaction-driven']) -'open-circuit potential': 'single' (possible: ['single', 'current sigmoid']) +'open-circuit potential': 'single' (possible: ['single', 'current sigmoid', 'MSMR']) 'operating mode': 'current' (possible: ['current', 'voltage', 'power', 'differential power', 'explicit power', 'resistance', 'differential resistance', 'explicit resistance', 'CCCV']) -'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile']) +'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile', 'MSMR']) 'particle mechanics': 'swelling only' (possible: ['none', 'swelling only', 'swelling and cracking']) 'particle phases': '1' (possible: ['1', '2']) 'particle shape': 'spherical' (possible: ['spherical', 'no particles']) From dbf2018af55f7952800997c410db49a36750ed71 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 21 Jun 2023 16:03:48 +0100 Subject: [PATCH 008/154] esoh for msmr mostly working --- examples/scripts/MSMR.py | 24 +- .../lithium_ion/electrode_soh.py | 313 +++++++++++++++--- .../submodels/particle/msmr_diffusion.py | 8 +- pybamm/parameters/lithium_ion_parameters.py | 61 ++-- pybamm/simulation.py | 2 +- .../test_base_battery_model.py | 6 + .../base_lithium_ion_tests.py | 4 + 7 files changed, 338 insertions(+), 80 deletions(-) diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index a5ee302f18..17da230923 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -2,8 +2,24 @@ from MSMR_example import get_parameter_values pybamm.set_logging_level("DEBUG") -model = pybamm.lithium_ion.SPM({"open-circuit potential": "MSMR", "particle": "MSMR"}) +model = pybamm.lithium_ion.DFN({"open-circuit potential": "MSMR", "particle": "MSMR"}) parameter_values = pybamm.ParameterValues(get_parameter_values()) -sim = pybamm.Simulation(model, parameter_values=parameter_values) -sim.solve([0, 3000]) -sim.plot() +experiment = pybamm.Experiment( + [ + ( + "Discharge at 1C for 1 hour or until 3 V", + # "Rest for 1 hour", + # "Charge at C/3 until 4 V", + # "Hold at 4 V until 10 mA", + # "Rest for 1 hour", + ), + ] +) +sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment) +sim.solve(calc_esoh=True) +sim.plot( + # [ + # "Negative electrode open-circuit potential [V]", + # "Positive electrode open-circuit potential [V]", + # ] +) diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 6275fea01c..b9cd22c8b5 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -28,7 +28,7 @@ class _ElectrodeSOH(pybamm.BaseModel): ---------- .. [1] Mohtat, P., Lee, S., Siegel, J. B., & Stefanopoulou, A. G. (2019). Towards better estimability of electrode-specific state of health: Decoding the cell - expansion. Journal of Power Sources, 427, 101-111. + ex_pansion. Journal of Power Sources, 427, 101-111. """ def __init__( @@ -117,7 +117,6 @@ def __init__( var = x_0 elif known_value == "cell capacity": x_0 = x_100 - Q / Q_n - Q_Li = y_100 * Q_p + x_0 * Q_n # the variable we are solving for is y_100, since x_0 is calculated # based on Q var = y_100 @@ -158,6 +157,135 @@ def default_solver(self): return pybamm.AlgebraicSolver() +class _ElectrodeSOHMSMR(pybamm.BaseModel): + """Model to calculate electrode-specific SOH using the MSMR formulation.""" + + def __init__( + self, param=None, solve_for=None, known_value="cyclable lithium capacity" + ): + pybamm.citations.register("Mohtat2019") + pybamm.citations.register("Weng2023") + name = "ElectrodeSOH model" + super().__init__(name) + + param = param or pybamm.LithiumIonParameters({"open-circuit potential": "MSMR"}) + solve_for = solve_for or ["Un_0", "Un_100"] + + if known_value == "cell capacity" and solve_for != ["Un_0", "Un_100"]: + raise ValueError( + "If known_value is 'cell capacity', solve_for must be " + "['Un_0', 'Un_100']" + ) + + # Define parameters and input parameters + x_n = param.n.prim.x + x_p = param.p.prim.x + + V_max = param.voltage_high_cut + V_min = param.voltage_low_cut + Q_n = pybamm.InputParameter("Q_n") + Q_p = pybamm.InputParameter("Q_p") + + if known_value == "cyclable lithium capacity": + Q_Li = pybamm.InputParameter("Q_Li") + elif known_value == "cell capacity": + Q = pybamm.InputParameter("Q") + + # Define variables for 0% state of charge + # TODO: thermal effects (include dU/dT) + if "Un_0" in solve_for: + Un_0 = pybamm.Variable("Un(x_0)") + Up_0 = V_min - Un_0 + x_0 = x_n(Un_0) + y_0 = x_p(Up_0) + + # Define variables for 100% state of charge + # TODO: thermal effects (include dU/dT) + if "Un_100" in solve_for: + Un_100 = pybamm.Variable("Un(x_100)") + Up_100 = V_max + Un_100 + x_100 = x_n(Un_100) + y_100 = x_p(Up_100) + else: + Un_100 = pybamm.InputParameter("Un(x_100)") + Up_100 = pybamm.InputParameter("Up(y_100)") + x_100 = x_n(Un_100) + y_100 = x_p(Up_100) + + # Define equations for 100% state of charge + if "Un_100" in solve_for: + if known_value == "cyclable lithium capacity": + Un_100_eqn = Q_Li - y_100 * Q_p - x_100 * Q_n + elif known_value == "cell capacity": + Un_100_eqn = x_100 - x_0 - Q / Q_n + Q_Li = y_100 * Q_p + x_100 * Q_n + self.algebraic[Un_100] = Un_100_eqn + self.initial_conditions[Un_100] = pybamm.Scalar(0.05) # better ic? + + # These variables are defined in all cases + Acc_cm2 = param.A_cc * 1e4 + self.variables = { + "x_100": x_100, + "y_100": y_100, + "Un(x_100)": Un_100, + "Up(y_100)": Up_100, + "Up(y_100) - Un(x_100)": Up_100 - Un_100, + "Q_Li": Q_Li, + "n_Li": Q_Li * 3600 / param.F, + "Q_n": Q_n, + "Q_p": Q_p, + "Cyclable lithium capacity [A.h]": Q_Li, + "Negative electrode capacity [A.h]": Q_n, + "Positive electrode capacity [A.h]": Q_p, + "Cyclable lithium capacity [mA.h.cm-2]": Q_Li * 1e3 / Acc_cm2, + "Negative electrode capacity [mA.h.cm-2]": Q_n * 1e3 / Acc_cm2, + "Positive electrode capacity [mA.h.cm-2]": Q_p * 1e3 / Acc_cm2, + # eq 33 of Weng2023 + "Formation capacity loss [A.h]": Q_p - Q_Li, + "Formation capacity loss [mA.h.cm-2]": (Q_p - Q_Li) * 1e3 / Acc_cm2, + # eq 26 of Weng2024 + "Negative positive ratio": Q_n / Q_p, + "NPR": Q_n / Q_p, + } + + # Define equation for 0% state of charge + if "Un_0" in solve_for: + if known_value == "cyclable lithium capacity": + Q = Q_n * (x_100 - x_0) + self.algebraic[Un_0] = y_100 - y_0 + Q / Q_p + self.initial_conditions[Un_0] = pybamm.Scalar(0.5) # better ic? + + # These variables are only defined if Un_0 is solved for + # eq 27 of Weng2023 + Q_n_excess = Q_n * (1 - x_100) + NPR_practical = 1 + Q_n_excess / Q + self.variables.update( + { + "Q": Q, + "Capacity [A.h]": Q, + "Capacity [mA.h.cm-2]": Q * 1e3 / Acc_cm2, + "x_0": x_0, + "y_0": y_0, + "Un(x_0)": Un_0, + "Up(y_0)": Up_0, + "Up(y_0) - Un(x_0)": Up_0 - Un_0, + "x_100 - x_0": x_100 - x_0, + "y_0 - y_100": y_0 - y_100, + "Q_n * (x_100 - x_0)": Q_n * (x_100 - x_0), + "Q_p * (y_0 - y_100)": Q_p * (y_0 - y_100), + "Negative electrode excess capacity ratio": Q_n / Q, + "Positive electrode excess capacity ratio": Q_p / Q, + "Practical negative positive ratio": NPR_practical, + "Practical NPR": NPR_practical, + } + ) + + @property + def default_solver(self): + # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings + return pybamm.AlgebraicSolver(tol=1) + + class ElectrodeSOHSolver: """ Class used to check if the electrode SOH model is feasible, and solve it if it is. @@ -172,19 +300,37 @@ class ElectrodeSOHSolver: known_value : str, optional The known value needed to complete the electrode SOH model. Can be "cyclable lithium capacity" (default) or "cell capacity". - + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. """ def __init__( - self, parameter_values, param=None, known_value="cyclable lithium capacity" + self, + parameter_values, + param=None, + known_value="cyclable lithium capacity", + options=None, ): self.parameter_values = parameter_values - self.param = param or pybamm.LithiumIonParameters() + self.param = param or pybamm.LithiumIonParameters(options) self.known_value = known_value + self.options = options or pybamm.BatteryModelOptions({}) # Check whether each electrode OCP is a function (False) or data (True) - OCPp_data = isinstance(parameter_values["Positive electrode OCP [V]"], tuple) - OCPn_data = isinstance(parameter_values["Negative electrode OCP [V]"], tuple) + # Set to false for MSMR models + if self.options.positive["open-circuit potential"] == "MSMR": + OCPp_data = False + else: + OCPp_data = isinstance( + parameter_values["Positive electrode OCP [V]"], tuple + ) + if self.options.negative["open-circuit potential"] == "MSMR": + OCPn_data = False + else: + OCPn_data = isinstance( + parameter_values["Negative electrode OCP [V]"], tuple + ) # Calculate stoich limits for the open-circuit potentials if OCPp_data: @@ -213,17 +359,30 @@ def __init__( ) def __get_electrode_soh_sims_full(self): - full_model = _ElectrodeSOH(param=self.param, known_value=self.known_value) + if self.options["open-circuit potential"] == "MSMR": + full_model = _ElectrodeSOHMSMR( + param=self.param, known_value=self.known_value + ) + else: + full_model = _ElectrodeSOH(param=self.param, known_value=self.known_value) return pybamm.Simulation(full_model, parameter_values=self.parameter_values) def __get_electrode_soh_sims_split(self): - x100_model = _ElectrodeSOH( - param=self.param, solve_for=["x_100"], known_value=self.known_value - ) + if self.options["open-circuit potential"] == "MSMR": + x100_model = _ElectrodeSOHMSMR( + param=self.param, solve_for=["Un_100"], known_value=self.known_value + ) + x0_model = _ElectrodeSOHMSMR( + param=self.param, solve_for=["Un_0"], known_value=self.known_value + ) + else: + x100_model = _ElectrodeSOH( + param=self.param, solve_for=["x_100"], known_value=self.known_value + ) + x0_model = _ElectrodeSOH( + param=self.param, solve_for=["x_0"], known_value=self.known_value + ) x100_sim = pybamm.Simulation(x100_model, parameter_values=self.parameter_values) - x0_model = _ElectrodeSOH( - param=self.param, solve_for=["x_0"], known_value=self.known_value - ) x0_sim = pybamm.Simulation(x0_model, parameter_values=self.parameter_values) return [x100_sim, x0_sim] @@ -264,41 +423,43 @@ def solve(self, inputs): sol = self._solve_split(inputs, ics) except pybamm.SolverError as split_error: # check if the error is due to the simulation not being feasible - self._check_esoh_feasible(inputs) + # self._check_esoh_feasible(inputs) # if that didn't raise an error, raise the original error instead raise split_error sol_dict = {key: sol[key].data[0] for key in sol.all_models[0].variables.keys()} # Calculate theoretical energy - x_0 = sol_dict["x_0"] - y_0 = sol_dict["y_0"] - x_100 = sol_dict["x_100"] - y_100 = sol_dict["y_100"] - energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( - self.parameter_values, x_100, x_0, y_100, y_0 - ) - sol_dict.update({"Maximum theoretical energy [W.h]": energy}) + # x_0 = sol_dict["x_0"] + # y_0 = sol_dict["y_0"] + # x_100 = sol_dict["x_100"] + # y_100 = sol_dict["y_100"] + # energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( + # self.parameter_values, x_100, x_0, y_100, y_0 + # ) + # sol_dict.update({"Maximum theoretical energy [W.h]": energy}) return sol_dict def _set_up_solve(self, inputs): # Try with full sim sim = self._get_electrode_soh_sims_full() if sim.solution is not None: - x100_sol = sim.solution["x_100"].data - x0_sol = sim.solution["x_0"].data - y100_sol = sim.solution["y_100"].data - y0_sol = sim.solution["y_0"].data - return {"x_100": x100_sol, "x_0": x0_sol, "y_100": y100_sol, "y_0": y0_sol} - - # Try with split sims - if self.known_value == "cyclable lithium capacity": - x100_sim, x0_sim = self._get_electrode_soh_sims_split() - if x100_sim.solution is not None and x0_sim.solution is not None: - x100_sol = x100_sim.solution["x_100"].data - x0_sol = x0_sim.solution["x_0"].data - y100_sol = x100_sim.solution["y_100"].data - y0_sol = x0_sim.solution["y_0"].data + if self.options["open-circuit potential"] == "MSMR": + Un_100_sol = sim.solution["Un_100"].data + Un_0_sol = sim.solution["Un_0"].data + Up_100_sol = sim.solution["Up_100"].data + Up_0_sol = sim.solution["Up_0"].data + return { + "Un(x_100)": Un_100_sol, + "Un(x_0)": Un_0_sol, + "Up(x_100)": Up_100_sol, + "Up(x_0)": Up_0_sol, + } + else: + x100_sol = sim.solution["x_100"].data + x0_sol = sim.solution["x_0"].data + y100_sol = sim.solution["y_100"].data + y0_sol = sim.solution["y_0"].data return { "x_100": x100_sol, "x_0": x0_sol, @@ -306,6 +467,33 @@ def _set_up_solve(self, inputs): "y_0": y0_sol, } + # Try with split sims + if self.known_value == "cyclable lithium capacity": + x100_sim, x0_sim = self._get_electrode_soh_sims_split() + if x100_sim.solution is not None and x0_sim.solution is not None: + if self.options["open-circuit potential"] == "MSMR": + Un_100_sol = x100_sim.solution["Un_100"].data + Un_0_sol = x0_sim.solution["Un_0"].data + Up_100_sol = x100_sim.solution["Up_100"].data + Up_0_sol = x0_sim.solution["Up_0"].data + return { + "Un(x_100)": Un_100_sol, + "Un(x_0)": Un_0_sol, + "Up(x_100)": Up_100_sol, + "Up(x_0)": Up_0_sol, + } + else: + x100_sol = x100_sim.solution["x_100"].data + x0_sol = x0_sim.solution["x_0"].data + y100_sol = x100_sim.solution["y_100"].data + y0_sol = x0_sim.solution["y_0"].data + return { + "x_100": x100_sol, + "x_0": x0_sol, + "y_100": y100_sol, + "y_0": y0_sol, + } + # Fall back to initial conditions calculated from limits x0_min, x100_max, y100_min, y0_max = self._get_lims(inputs) if self.known_value == "cyclable lithium capacity": @@ -328,7 +516,47 @@ def _set_up_solve(self, inputs): x0_init = np.maximum(x100_max - Q / Q_n, 0.1) y100_init = np.maximum(y0_max - Q / Q_p, 0.1) y0_init = np.minimum(y100_min + Q / Q_p, 0.9) - return {"x_100": x100_init, "x_0": x0_init, "y_100": y100_init, "y_0": y0_init} + if self.options["open-circuit potential"] == "MSMR": + x_n = self.param.n.prim.x + x_p = self.param.p.prim.x + model = pybamm.BaseModel() + Un_0 = pybamm.Variable("Un(x_0)") + Un_100 = pybamm.Variable("Un(x_100)") + Up_0 = pybamm.Variable("Up(x_0)") + Up_100 = pybamm.Variable("Up(x_100)") + model.algebraic = { + Un_0: x_n(Un_0) - x0_init, + Un_100: x_n(Un_100) - x100_init, + Up_0: x_p(Up_0) - y0_init, + Up_100: x_p(Up_100) - y100_init, + } + model.initial_conditions = { + Un_0: pybamm.Scalar(1), + Un_100: pybamm.Scalar(0.05), + Up_0: pybamm.Scalar(2.5), + Up_100: pybamm.Scalar(4), + } + model.variables = { + "Un(x_100)": Un_100, + "Un(x_0)": Un_0, + "Up(x_100)": Up_100, + "Up(x_0)": Up_0, + } + self.parameter_values.process_model(model) + sol = pybamm.AlgebraicSolver().solve(model) + return { + "Un(x_100)": sol["Un(x_100)"].data, + "Un(x_0)": sol["Un(x_0)"].data, + "Up(x_100)": sol["Up(x_100)"].data, + "Up(x_0)": sol["Up(x_0)"].data, + } + else: + return { + "x_100": x100_init, + "x_0": x0_init, + "y_100": y100_init, + "y_0": y0_init, + } def _solve_full(self, inputs, ics): sim = self._get_electrode_soh_sims_full() @@ -342,9 +570,12 @@ def _solve_split(self, inputs, ics): x100_sim.build() x100_sim.built_model.set_initial_conditions_from(ics) x100_sol = x100_sim.solve([0], inputs=inputs) - - inputs["x_100"] = x100_sol["x_100"].data[0] - inputs["y_100"] = x100_sol["y_100"].data[0] + if self.options["open-circuit potential"] == "MSMR": + inputs["Un(x_100)"] = x100_sol["Un(x_100)"].data[0] + inputs["Up(y_100)"] = x100_sol["Up(y_100)"].data[0] + else: + inputs["x_100"] = x100_sol["x_100"].data[0] + inputs["y_100"] = x100_sol["y_100"].data[0] x0_sim.build() x0_sim.built_model.set_initial_conditions_from(ics) x0_sol = x0_sim.solve([0], inputs=inputs) diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index 274490a19b..8e4e5a0a22 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -171,12 +171,12 @@ def get_coupled_variables(self, variables): if self.size_distribution is False: if self.x_average is False: - x = variables[f"{Domain} {phase_name} particle stoichiometry"] + x = variables[f"{Domain} {phase_name}particle stoichiometry"] dxdU = variables[ - f"{Domain} {phase_name} particle differential stoichiometry [V-1]" + f"{Domain} {phase_name}particle differential stoichiometry [V-1]" ] U = variables[ - f"{Domain} {phase_name} particle open-circuit potential [V]" + f"{Domain} {phase_name}particle open-circuit potential [V]" ] T = pybamm.PrimaryBroadcast( variables[f"{Domain} electrode temperature [K]"], @@ -362,7 +362,7 @@ def set_initial_conditions(self, variables): domain, Domain = self.domain_Domain phase_name = self.phase_name - U_init = self.phase_param.U_msmr_init + U_init = self.phase_param.U_init if self.size_distribution is False: if self.x_average is False: U = variables[ diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 7e01f1df78..7124ae8c97 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -13,13 +13,8 @@ class LithiumIonParameters(BaseParameters): ---------- options : dict, optional - A dictionary of options to be passed to the parameters. The options that - can be set are listed below. - - * "particle shape" : str, optional - Sets the model shape of the electrode particles. This is used to - calculate the surface area to volume ratio. Can be "spherical" - (default). TODO: implement "cylindrical" and "platelet". + A dictionary of options to be passed to the parameters, see + :class:`pybamm.BatteryModelOptions`. """ def __init__(self, options=None): @@ -447,6 +442,8 @@ def _set_parameters(self): self.U_init = pybamm.Scalar(0) return + # Spatial variables for parameters that depend on position within the cell + # and/or particle x = pybamm.SpatialVariable( f"x_{domain[0]}", domain=[f"{domain} electrode"], @@ -463,56 +460,59 @@ def _set_parameters(self): coord_sys="spherical polar", ) - # Macroscale geometry + # Microscale geometry # Note: the surface area to volume ratio is defined later with the function # parameters. The particle size as a function of through-cell position is # already defined in geometric_parameters.py self.R = self.geo.R self.R_typ = self.geo.R_typ - - # Particle properties - self.c_max = pybamm.Parameter( - f"{pref}Maximum concentration in {domain} electrode [mol.m-3]" - ) - # Particle-size distribution parameters self.R_min = self.geo.R_min self.R_max = self.geo.R_max self.sd_a = self.geo.sd_a self.f_a_dist = self.geo.f_a_dist + # Particle properties self.epsilon_s = pybamm.FunctionParameter( f"{pref}{Domain} electrode active material volume fraction", {"Through-cell distance (x) [m]": x}, ) - self.c_init = pybamm.FunctionParameter( - f"{pref}Initial concentration in {domain} electrode [mol.m-3]", - { - "Radial distance (r) [m]": r, - "Through-cell distance (x) [m]": pybamm.PrimaryBroadcast( - x, f"{domain} {phase_name}particle" - ), - }, + self.epsilon_s_av = pybamm.xyz_average(self.epsilon_s) + self.c_max = pybamm.Parameter( + f"{pref}Maximum concentration in {domain} electrode [mol.m-3]" ) + if main.options["open-circuit potential"] == "MSMR": + self.U_init = pybamm.Parameter( + f"{pref}Initial voltage in {domain} electrode [V]", + ) + self.c_init = self.x(self.U_init) * self.c_max + else: + self.c_init = pybamm.FunctionParameter( + f"{pref}Initial concentration in {domain} electrode [mol.m-3]", + { + "Radial distance (r) [m]": r, + "Through-cell distance (x) [m]": pybamm.PrimaryBroadcast( + x, f"{domain} {phase_name}particle" + ), + }, + ) self.c_init_av = pybamm.xyz_average(pybamm.r_average(self.c_init)) self.sto_init_av = self.c_init_av / self.c_max eps_c_init_av = pybamm.xyz_average( self.epsilon_s * pybamm.r_average(self.c_init) ) - self.n_Li_init = eps_c_init_av * self.domain_param.L * main.A_cc - self.Q_Li_init = self.n_Li_init * main.F / 3600 - self.epsilon_s_av = pybamm.xyz_average(self.epsilon_s) + if main.options["open-circuit potential"] != "MSMR": + self.U_init = self.U(self.sto_init_av, main.T_init) + + # Electrode loading and capacity self.elec_loading = ( self.epsilon_s_av * self.domain_param.L * self.c_max * main.F / 3600 ) + self.n_Li_init = eps_c_init_av * self.domain_param.L * main.A_cc + self.Q_Li_init = self.n_Li_init * main.F / 3600 self.Q_init = self.elec_loading * main.A_cc - self.U_init = self.U(self.sto_init_av, main.T_init) - self.U_msmr_init = pybamm.Parameter( - f"{pref}Initial voltage in {domain} electrode [V]" - ) - if main.options["particle shape"] == "spherical": self.a_typ = 3 * pybamm.xyz_average(self.epsilon_s) / self.R_typ @@ -603,6 +603,7 @@ def dxdU(self, U): """ Differential stoichiometry as a function of potential (for use with MSMR models) """ + # TODO: remove and use .diff(U) instead Domain = self.domain.capitalize() inputs = { f"{self.phase_prefactor}{Domain} particle open-circuit potential [V]": U diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 0315bd8144..303c71e8a8 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -945,7 +945,7 @@ def _get_esoh_solver(self, calc_esoh): return None return pybamm.lithium_ion.ElectrodeSOHSolver( - self.parameter_values, self.model.param + self.parameter_values, self.model.param, options=self.model.options ) def plot(self, output_variables=None, **kwargs): diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index ab3b4a0d25..0e8d4b22f7 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -366,6 +366,12 @@ def test_options(self): with self.assertRaisesRegex(pybamm.OptionError, "multiple particle phases"): pybamm.BaseBatteryModel({"particle phases": "2", "surface form": "false"}) + # msmr + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel({"open-circuit potential": "MSMR"}) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel({"particle": "MSMR"}) + def test_build_twice(self): model = pybamm.lithium_ion.SPM() # need to pick a model to set vars and build with self.assertRaisesRegex(pybamm.ModelError, "Model already built"): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 99b2b10148..e09ac457b9 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -359,3 +359,7 @@ def test_well_posed_particle_phases_sei(self): def test_well_posed_current_sigmoid_ocp(self): options = {"open-circuit potential": "current sigmoid"} self.check_well_posedness(options) + + def test_well_posed_msmr(self): + options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + self.check_well_posedness(options) From 3a7c39f43eae0a8840041b3900b9b090dd74b33a Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 21 Jun 2023 17:33:09 +0100 Subject: [PATCH 009/154] start cleaning up esoh --- .../lithium_ion/electrode_soh.py | 231 ++++++++---------- 1 file changed, 105 insertions(+), 126 deletions(-) diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index b9cd22c8b5..64a4d45d35 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -7,7 +7,76 @@ import warnings -class _ElectrodeSOH(pybamm.BaseModel): +class _BaseElectrodeSOH(pybamm.BaseModel): + def __init__(self): + pybamm.citations.register("Mohtat2019") + pybamm.citations.register("Weng2023") + name = "ElectrodeSOH model" + super().__init__(name) + + def get_100_soc_variables( + self, x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param + ): + Acc_cm2 = param.A_cc * 1e4 + variables = { + "x_100": x_100, + "y_100": y_100, + "Un(x_100)": Un_100, + "Up(y_100)": Up_100, + "Up(y_100) - Un(x_100)": Up_100 - Un_100, + "Q_Li": Q_Li, + "n_Li": Q_Li * 3600 / param.F, + "Q_n": Q_n, + "Q_p": Q_p, + "Cyclable lithium capacity [A.h]": Q_Li, + "Negative electrode capacity [A.h]": Q_n, + "Positive electrode capacity [A.h]": Q_p, + "Cyclable lithium capacity [mA.h.cm-2]": Q_Li * 1e3 / Acc_cm2, + "Negative electrode capacity [mA.h.cm-2]": Q_n * 1e3 / Acc_cm2, + "Positive electrode capacity [mA.h.cm-2]": Q_p * 1e3 / Acc_cm2, + # eq 33 of Weng2023 + "Formation capacity loss [A.h]": Q_p - Q_Li, + "Formation capacity loss [mA.h.cm-2]": (Q_p - Q_Li) * 1e3 / Acc_cm2, + # eq 26 of Weng2024 + "Negative positive ratio": Q_n / Q_p, + "NPR": Q_n / Q_p, + } + return variables + + def get_0_soc_variables( + self, x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param + ): + Acc_cm2 = param.A_cc * 1e4 + # eq 27 of Weng2023 + Q_n_excess = Q_n * (1 - x_100) + NPR_practical = 1 + Q_n_excess / Q + variables = { + "Q": Q, + "Capacity [A.h]": Q, + "Capacity [mA.h.cm-2]": Q * 1e3 / Acc_cm2, + "x_0": x_0, + "y_0": y_0, + "Un(x_0)": Un_0, + "Up(y_0)": Up_0, + "Up(y_0) - Un(x_0)": Up_0 - Un_0, + "x_100 - x_0": x_100 - x_0, + "y_0 - y_100": y_0 - y_100, + "Q_n * (x_100 - x_0)": Q_n * (x_100 - x_0), + "Q_p * (y_0 - y_100)": Q_p * (y_0 - y_100), + "Negative electrode excess capacity ratio": Q_n / Q, + "Positive electrode excess capacity ratio": Q_p / Q, + "Practical negative positive ratio": NPR_practical, + "Practical NPR": NPR_practical, + } + return variables + + @property + def default_solver(self): + # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings + return pybamm.AlgebraicSolver() + + +class _ElectrodeSOH(_BaseElectrodeSOH): """Model to calculate electrode-specific SOH, from [1]_. This model is mainly for internal use, to calculate summary variables in a simulation. @@ -28,16 +97,13 @@ class _ElectrodeSOH(pybamm.BaseModel): ---------- .. [1] Mohtat, P., Lee, S., Siegel, J. B., & Stefanopoulou, A. G. (2019). Towards better estimability of electrode-specific state of health: Decoding the cell - ex_pansion. Journal of Power Sources, 427, 101-111. + expansion. Journal of Power Sources, 427, 101-111. """ def __init__( self, param=None, solve_for=None, known_value="cyclable lithium capacity" ): - pybamm.citations.register("Mohtat2019") - pybamm.citations.register("Weng2023") - name = "ElectrodeSOH model" - super().__init__(name) + super().__init__() param = param or pybamm.LithiumIonParameters() solve_for = solve_for or ["x_0", "x_100"] @@ -82,30 +148,9 @@ def __init__( self.initial_conditions[x_100] = pybamm.Scalar(0.9) # These variables are defined in all cases - Acc_cm2 = param.A_cc * 1e4 - self.variables = { - "x_100": x_100, - "y_100": y_100, - "Un(x_100)": Un_100, - "Up(y_100)": Up_100, - "Up(y_100) - Un(x_100)": Up_100 - Un_100, - "Q_Li": Q_Li, - "n_Li": Q_Li * 3600 / param.F, - "Q_n": Q_n, - "Q_p": Q_p, - "Cyclable lithium capacity [A.h]": Q_Li, - "Negative electrode capacity [A.h]": Q_n, - "Positive electrode capacity [A.h]": Q_p, - "Cyclable lithium capacity [mA.h.cm-2]": Q_Li * 1e3 / Acc_cm2, - "Negative electrode capacity [mA.h.cm-2]": Q_n * 1e3 / Acc_cm2, - "Positive electrode capacity [mA.h.cm-2]": Q_p * 1e3 / Acc_cm2, - # eq 33 of Weng2023 - "Formation capacity loss [A.h]": Q_p - Q_Li, - "Formation capacity loss [mA.h.cm-2]": (Q_p - Q_Li) * 1e3 / Acc_cm2, - # eq 26 of Weng2024 - "Negative positive ratio": Q_n / Q_p, - "NPR": Q_n / Q_p, - } + self.variables = self.get_100_soc_variables( + x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param + ) # Define variables and equations for 0% state of charge if "x_0" in solve_for: @@ -127,46 +172,23 @@ def __init__( self.initial_conditions[var] = pybamm.Scalar(0.1) # These variables are only defined if x_0 is solved for - # eq 27 of Weng2023 - Q_n_excess = Q_n * (1 - x_100) - NPR_practical = 1 + Q_n_excess / Q self.variables.update( - { - "Q": Q, - "Capacity [A.h]": Q, - "Capacity [mA.h.cm-2]": Q * 1e3 / Acc_cm2, - "x_0": x_0, - "y_0": y_0, - "Un(x_0)": Un_0, - "Up(y_0)": Up_0, - "Up(y_0) - Un(x_0)": Up_0 - Un_0, - "x_100 - x_0": x_100 - x_0, - "y_0 - y_100": y_0 - y_100, - "Q_n * (x_100 - x_0)": Q_n * (x_100 - x_0), - "Q_p * (y_0 - y_100)": Q_p * (y_0 - y_100), - "Negative electrode excess capacity ratio": Q_n / Q, - "Positive electrode excess capacity ratio": Q_p / Q, - "Practical negative positive ratio": NPR_practical, - "Practical NPR": NPR_practical, - } + self.get_0_soc_variables( + x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param + ) ) - @property - def default_solver(self): - # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings - return pybamm.AlgebraicSolver() - -class _ElectrodeSOHMSMR(pybamm.BaseModel): - """Model to calculate electrode-specific SOH using the MSMR formulation.""" +class _ElectrodeSOHMSMR(_BaseElectrodeSOH): + """ + Model to calculate electrode-specific SOH using the MSMR formulation, see + :class:`_ElectrodeSOH`. + """ def __init__( self, param=None, solve_for=None, known_value="cyclable lithium capacity" ): - pybamm.citations.register("Mohtat2019") - pybamm.citations.register("Weng2023") - name = "ElectrodeSOH model" - super().__init__(name) + super().__init__() param = param or pybamm.LithiumIonParameters({"open-circuit potential": "MSMR"}) solve_for = solve_for or ["Un_0", "Un_100"] @@ -223,30 +245,9 @@ def __init__( self.initial_conditions[Un_100] = pybamm.Scalar(0.05) # better ic? # These variables are defined in all cases - Acc_cm2 = param.A_cc * 1e4 - self.variables = { - "x_100": x_100, - "y_100": y_100, - "Un(x_100)": Un_100, - "Up(y_100)": Up_100, - "Up(y_100) - Un(x_100)": Up_100 - Un_100, - "Q_Li": Q_Li, - "n_Li": Q_Li * 3600 / param.F, - "Q_n": Q_n, - "Q_p": Q_p, - "Cyclable lithium capacity [A.h]": Q_Li, - "Negative electrode capacity [A.h]": Q_n, - "Positive electrode capacity [A.h]": Q_p, - "Cyclable lithium capacity [mA.h.cm-2]": Q_Li * 1e3 / Acc_cm2, - "Negative electrode capacity [mA.h.cm-2]": Q_n * 1e3 / Acc_cm2, - "Positive electrode capacity [mA.h.cm-2]": Q_p * 1e3 / Acc_cm2, - # eq 33 of Weng2023 - "Formation capacity loss [A.h]": Q_p - Q_Li, - "Formation capacity loss [mA.h.cm-2]": (Q_p - Q_Li) * 1e3 / Acc_cm2, - # eq 26 of Weng2024 - "Negative positive ratio": Q_n / Q_p, - "NPR": Q_n / Q_p, - } + self.variables = self.get_100_soc_variables( + x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param + ) # Define equation for 0% state of charge if "Un_0" in solve_for: @@ -255,36 +256,13 @@ def __init__( self.algebraic[Un_0] = y_100 - y_0 + Q / Q_p self.initial_conditions[Un_0] = pybamm.Scalar(0.5) # better ic? - # These variables are only defined if Un_0 is solved for - # eq 27 of Weng2023 - Q_n_excess = Q_n * (1 - x_100) - NPR_practical = 1 + Q_n_excess / Q + # These variables are only defined if x_0 is solved for self.variables.update( - { - "Q": Q, - "Capacity [A.h]": Q, - "Capacity [mA.h.cm-2]": Q * 1e3 / Acc_cm2, - "x_0": x_0, - "y_0": y_0, - "Un(x_0)": Un_0, - "Up(y_0)": Up_0, - "Up(y_0) - Un(x_0)": Up_0 - Un_0, - "x_100 - x_0": x_100 - x_0, - "y_0 - y_100": y_0 - y_100, - "Q_n * (x_100 - x_0)": Q_n * (x_100 - x_0), - "Q_p * (y_0 - y_100)": Q_p * (y_0 - y_100), - "Negative electrode excess capacity ratio": Q_n / Q, - "Positive electrode excess capacity ratio": Q_p / Q, - "Practical negative positive ratio": NPR_practical, - "Practical NPR": NPR_practical, - } + self.get_0_soc_variables( + x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param + ) ) - @property - def default_solver(self): - # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings - return pybamm.AlgebraicSolver(tol=1) - class ElectrodeSOHSolver: """ @@ -319,15 +297,14 @@ def __init__( # Check whether each electrode OCP is a function (False) or data (True) # Set to false for MSMR models - if self.options.positive["open-circuit potential"] == "MSMR": + if self.options["open-circuit potential"] == "MSMR": OCPp_data = False + OCPn_data = False + else: OCPp_data = isinstance( parameter_values["Positive electrode OCP [V]"], tuple ) - if self.options.negative["open-circuit potential"] == "MSMR": - OCPn_data = False - else: OCPn_data = isinstance( parameter_values["Negative electrode OCP [V]"], tuple ) @@ -423,21 +400,23 @@ def solve(self, inputs): sol = self._solve_split(inputs, ics) except pybamm.SolverError as split_error: # check if the error is due to the simulation not being feasible - # self._check_esoh_feasible(inputs) + if self.options["open-circuit potential"] != "MSMR": + self._check_esoh_feasible(inputs) # if that didn't raise an error, raise the original error instead raise split_error sol_dict = {key: sol[key].data[0] for key in sol.all_models[0].variables.keys()} # Calculate theoretical energy - # x_0 = sol_dict["x_0"] - # y_0 = sol_dict["y_0"] - # x_100 = sol_dict["x_100"] - # y_100 = sol_dict["y_100"] - # energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( - # self.parameter_values, x_100, x_0, y_100, y_0 - # ) - # sol_dict.update({"Maximum theoretical energy [W.h]": energy}) + if self.options["open-circuit potential"] != "MSMR": + x_0 = sol_dict["x_0"] + y_0 = sol_dict["y_0"] + x_100 = sol_dict["x_100"] + y_100 = sol_dict["y_100"] + energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( + self.parameter_values, x_100, x_0, y_100, y_0 + ) + sol_dict.update({"Maximum theoretical energy [W.h]": energy}) return sol_dict def _set_up_solve(self, inputs): From ac821e8af53d4a2837d69f2d6d88d76d2aec522c Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 23 Jun 2023 12:06:02 +0100 Subject: [PATCH 010/154] fix eSOH --- examples/scripts/MSMR.py | 24 ++- examples/scripts/MSMR_example.py | 14 +- .../lithium_ion/electrode_soh.py | 147 +++++++++++------- .../submodels/particle/msmr_diffusion.py | 12 +- 4 files changed, 112 insertions(+), 85 deletions(-) diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index 17da230923..0a21ee85dd 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -2,21 +2,33 @@ from MSMR_example import get_parameter_values pybamm.set_logging_level("DEBUG") -model = pybamm.lithium_ion.DFN({"open-circuit potential": "MSMR", "particle": "MSMR"}) + + +model = pybamm.lithium_ion.DFN( + { + "open-circuit potential": "MSMR", + "particle": "MSMR", + } +) + + parameter_values = pybamm.ParameterValues(get_parameter_values()) +parameter_values = pybamm.get_size_distribution_parameters( + parameter_values, sd_n=0.2, sd_p=0.4 +) experiment = pybamm.Experiment( [ ( "Discharge at 1C for 1 hour or until 3 V", - # "Rest for 1 hour", - # "Charge at C/3 until 4 V", - # "Hold at 4 V until 10 mA", - # "Rest for 1 hour", + "Rest for 1 hour", + "Charge at C/3 until 4 V", + "Hold at 4 V until 10 mA", + "Rest for 1 hour", ), ] ) sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment) -sim.solve(calc_esoh=True) +sim.solve() sim.plot( # [ # "Negative electrode open-circuit potential [V]", diff --git a/examples/scripts/MSMR_example.py b/examples/scripts/MSMR_example.py index 46d15962a4..dec325d8d8 100644 --- a/examples/scripts/MSMR_example.py +++ b/examples/scripts/MSMR_example.py @@ -74,7 +74,7 @@ def dxdU_p(U): def get_parameter_values(): return { # cell - "Negative electrode thickness [m]": 7.56e-05, + "Negative electrode thickness [m]": 8.52e-05, "Separator thickness [m]": 1.2e-05, "Positive electrode thickness [m]": 7.56e-05, "Electrode height [m]": 0.065, @@ -103,8 +103,6 @@ def get_parameter_values(): "U0_n_5": 0.36325, "Xj_n_5": 0.05476, "w_n_5": 5.97354, - "Negative electrode stoichiometry at 0% SOC": 0.03, - "Negative electrode stoichiometry at 100% SOC": 0.9, "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, "Negative electrode diffusivity [m2.s-1]": 3.3e-14, @@ -130,8 +128,6 @@ def get_parameter_values(): "U0_p_3": 4.22955, "Xj_p_3": 0.32980, "w_p_3": 5.52757, - "Positive electrode stoichiometry at 0% SOC": 0.85, - "Positive electrode stoichiometry at 100% SOC": 0.1, "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, "Positive electrode diffusivity [m2.s-1]": 4e-15, @@ -157,11 +153,11 @@ def get_parameter_values(): "Ambient temperature [K]": 298.15, "Number of electrodes connected in parallel to make a cell": 1.0, "Number of cells connected in series to make a battery": 1.0, - "Lower voltage cut-off [V]": 1, - "Upper voltage cut-off [V]": 5, + "Lower voltage cut-off [V]": 2.5, + "Upper voltage cut-off [V]": 4.2, "Initial temperature [K]": 298.15, - "Initial voltage in negative electrode [V]": 0.085, - "Initial voltage in positive electrode [V]": 4.35, + "Initial voltage in negative electrode [V]": 0.01, + "Initial voltage in positive electrode [V]": 4.27, "Initial concentration in negative electrode [mol.m-3]": 29820, "Initial concentration in positive electrode [mol.m-3]": 6310, } diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 64a4d45d35..eec4197756 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -217,7 +217,7 @@ def __init__( # TODO: thermal effects (include dU/dT) if "Un_0" in solve_for: Un_0 = pybamm.Variable("Un(x_0)") - Up_0 = V_min - Un_0 + Up_0 = V_min + Un_0 x_0 = x_n(Un_0) y_0 = x_p(Up_0) @@ -242,7 +242,7 @@ def __init__( Un_100_eqn = x_100 - x_0 - Q / Q_n Q_Li = y_100 * Q_p + x_100 * Q_n self.algebraic[Un_100] = Un_100_eqn - self.initial_conditions[Un_100] = pybamm.Scalar(0.05) # better ic? + self.initial_conditions[Un_100] = pybamm.Scalar(0) # better ic? # These variables are defined in all cases self.variables = self.get_100_soc_variables( @@ -254,7 +254,7 @@ def __init__( if known_value == "cyclable lithium capacity": Q = Q_n * (x_100 - x_0) self.algebraic[Un_0] = y_100 - y_0 + Q / Q_p - self.initial_conditions[Un_0] = pybamm.Scalar(0.5) # better ic? + self.initial_conditions[Un_0] = pybamm.Scalar(1) # better ic? # These variables are only defined if x_0 is solved for self.variables.update( @@ -295,6 +295,18 @@ def __init__( self.known_value = known_value self.options = options or pybamm.BatteryModelOptions({}) + self.lims_ocp = self._get_lims_ocp() + self.OCV_function = None + self._get_electrode_soh_sims_full = lru_cache()( + self.__get_electrode_soh_sims_full + ) + self._get_electrode_soh_sims_split = lru_cache()( + self.__get_electrode_soh_sims_split + ) + + def _get_lims_ocp(self): + parameter_values = self.parameter_values + # Check whether each electrode OCP is a function (False) or data (True) # Set to false for MSMR models if self.options["open-circuit potential"] == "MSMR": @@ -325,15 +337,7 @@ def __init__( else: x0_min = 1e-6 x100_max = 1 - 1e-6 - - self.lims_ocp = (x0_min, x100_max, y100_min, y0_max) - self.OCV_function = None - self._get_electrode_soh_sims_full = lru_cache()( - self.__get_electrode_soh_sims_full - ) - self._get_electrode_soh_sims_split = lru_cache()( - self.__get_electrode_soh_sims_split - ) + return (x0_min, x100_max, y100_min, y0_max) def __get_electrode_soh_sims_full(self): if self.options["open-circuit potential"] == "MSMR": @@ -400,8 +404,7 @@ def solve(self, inputs): sol = self._solve_split(inputs, ics) except pybamm.SolverError as split_error: # check if the error is due to the simulation not being feasible - if self.options["open-circuit potential"] != "MSMR": - self._check_esoh_feasible(inputs) + self._check_esoh_feasible(inputs) # if that didn't raise an error, raise the original error instead raise split_error @@ -496,38 +499,14 @@ def _set_up_solve(self, inputs): y100_init = np.maximum(y0_max - Q / Q_p, 0.1) y0_init = np.minimum(y100_min + Q / Q_p, 0.9) if self.options["open-circuit potential"] == "MSMR": - x_n = self.param.n.prim.x - x_p = self.param.p.prim.x - model = pybamm.BaseModel() - Un_0 = pybamm.Variable("Un(x_0)") - Un_100 = pybamm.Variable("Un(x_100)") - Up_0 = pybamm.Variable("Up(x_0)") - Up_100 = pybamm.Variable("Up(x_100)") - model.algebraic = { - Un_0: x_n(Un_0) - x0_init, - Un_100: x_n(Un_100) - x100_init, - Up_0: x_p(Up_0) - y0_init, - Up_100: x_p(Up_100) - y100_init, - } - model.initial_conditions = { - Un_0: pybamm.Scalar(1), - Un_100: pybamm.Scalar(0.05), - Up_0: pybamm.Scalar(2.5), - Up_100: pybamm.Scalar(4), - } - model.variables = { - "Un(x_100)": Un_100, - "Un(x_0)": Un_0, - "Up(x_100)": Up_100, - "Up(x_0)": Up_0, - } - self.parameter_values.process_model(model) - sol = pybamm.AlgebraicSolver().solve(model) + Un0, Un100, Up100, Up0 = self._get_ocp_msmr( + x0_init, x100_init, y100_init, y0_init + ) return { - "Un(x_100)": sol["Un(x_100)"].data, - "Un(x_0)": sol["Un(x_0)"].data, - "Up(x_100)": sol["Up(x_100)"].data, - "Up(x_0)": sol["Up(x_0)"].data, + "Un(x_100)": Un100, + "Un(x_0)": Un0, + "Up(y_100)": Up100, + "Up(y_0)": Up0, } else: return { @@ -619,25 +598,32 @@ def _check_esoh_feasible(self, inputs): """ x0_min, x100_max, y100_min, y0_max = self._get_lims(inputs) - # Parameterize the OCP functions - if self.OCV_function is None: - T = self.parameter_values["Reference temperature [K]"] - x = pybamm.InputParameter("x") - y = pybamm.InputParameter("y") - self.V_max = self.parameter_values.evaluate(self.param.voltage_high_cut) - self.V_min = self.parameter_values.evaluate(self.param.voltage_low_cut) - self.OCV_function = self.parameter_values.process_symbol( - self.param.p.prim.U(y, T) - self.param.n.prim.U(x, T) + if self.options["open-circuit potential"] == "MSMR": + Un0, Un100, Up100, Up0 = self._get_ocp_msmr( + x0_min, x100_max, y100_min, y0_max + ) + V_lower_bound = float(Up0 - Un0) + V_upper_bound = float(Up100 - Un100) + else: + # Parameterize the OCP functions + if self.OCV_function is None: + T = self.parameter_values["Reference temperature [K]"] + x = pybamm.InputParameter("x") + y = pybamm.InputParameter("y") + self.V_max = self.parameter_values.evaluate(self.param.voltage_high_cut) + self.V_min = self.parameter_values.evaluate(self.param.voltage_low_cut) + self.OCV_function = self.parameter_values.process_symbol( + self.param.p.prim.U(y, T) - self.param.n.prim.U(x, T) + ) + V_lower_bound = float( + self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}) + ) + V_upper_bound = float( + self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}) ) # Check that the min and max achievable voltages span wider than the desired # voltage range - V_lower_bound = float( - self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}) - ) - V_upper_bound = float( - self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}) - ) if V_lower_bound > self.V_min: raise ( ValueError( @@ -657,6 +643,47 @@ def _check_esoh_feasible(self, inputs): ) ) + def _get_ocp_msmr(self, x0, x100, y100, y0): + """ + Get the open-circuit potentials of the electrodes at the given stoichiometries + """ + V_max = self.param.voltage_high_cut + V_min = self.param.voltage_low_cut + x_n = self.param.n.prim.x + x_p = self.param.p.prim.x + model = pybamm.BaseModel() + Un_0 = pybamm.Variable("Un(x_0)") + Un_100 = pybamm.Variable("Un(x_100)") + Up_0 = pybamm.Variable("Up(y_0)") + Up_100 = pybamm.Variable("Up(y_100)") + model.algebraic = { + Un_0: x_n(Un_0) - x0, + Un_100: x_n(Un_100) - x100, + Up_0: x_p(Up_0) - y0, + Up_100: x_p(Up_100) - y100, + } + model.initial_conditions = { + Un_0: pybamm.Scalar(1), + Un_100: pybamm.Scalar(0), + Up_0: V_min * pybamm.Scalar(1), + Up_100: V_max, + } + model.variables = { + "Un(x_100)": Un_100, + "Un(x_0)": Un_0, + "Up(y_100)": Up_100, + "Up(y_0)": Up_0, + } + self.parameter_values.process_model(model) + sol = pybamm.AlgebraicSolver().solve(model) + + return ( + sol["Un(x_0)"].data, + sol["Un(x_100)"].data, + sol["Up(y_100)"].data, + sol["Up(y_0)"].data, + ) + def get_initial_stoichiometries(self, initial_value): """ Calculate initial stoichiometries to start off the simulation at a particular diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index 8e4e5a0a22..621a2bb0f7 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -373,25 +373,17 @@ def set_initial_conditions(self, variables): f"X-averaged {domain} {phase_name}particle open-circuit " "potential [V]" ] - U_init = pybamm.x_average(U_init) else: if self.x_average is False: U = variables[ f"{Domain} {phase_name}particle " - "concentration distribution [mol.m-3]" + "open-circuit potential distribution [V]" ] - U_init = pybamm.SecondaryBroadcast( - U_init, f"{domain} {phase_name}particle size" - ) else: U = variables[ f"X-averaged {domain} {phase_name}particle " - "concentration distribution [mol.m-3]" + "open-circuit potential distribution [V]" ] - - U_init = pybamm.SecondaryBroadcast( - pybamm.x_average(U_init), f"{domain} {phase_name}particle size" - ) self.initial_conditions = {U: U_init} def _get_standard_potential_variables(self, U): From 0c612223c8b01297409a92733f6d6627da121d94 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 23 Jun 2023 15:28:00 +0100 Subject: [PATCH 011/154] improve esoh --- .../lithium_ion/__init__.py | 1 + .../lithium_ion/electrode_soh.py | 232 ++++++++++++------ 2 files changed, 163 insertions(+), 70 deletions(-) diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py index 76625858e3..51859b164b 100644 --- a/pybamm/models/full_battery_models/lithium_ion/__init__.py +++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py @@ -6,6 +6,7 @@ ElectrodeSOHSolver, get_initial_stoichiometries, get_min_max_stoichiometries, + get_min_max_ocps, ) from .electrode_soh_half_cell import ElectrodeSOHHalfCell from .spm import SPM diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index eec4197756..75af037478 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -411,15 +411,14 @@ def solve(self, inputs): sol_dict = {key: sol[key].data[0] for key in sol.all_models[0].variables.keys()} # Calculate theoretical energy - if self.options["open-circuit potential"] != "MSMR": - x_0 = sol_dict["x_0"] - y_0 = sol_dict["y_0"] - x_100 = sol_dict["x_100"] - y_100 = sol_dict["y_100"] - energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( - self.parameter_values, x_100, x_0, y_100, y_0 - ) - sol_dict.update({"Maximum theoretical energy [W.h]": energy}) + x_0 = sol_dict["x_0"] + y_0 = sol_dict["y_0"] + x_100 = sol_dict["x_100"] + y_100 = sol_dict["y_100"] + energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( + self.parameter_values, x_100, x_0, y_100, y_0, options=self.options + ) + sol_dict.update({"Maximum theoretical energy [W.h]": energy}) return sol_dict def _set_up_solve(self, inputs): @@ -499,14 +498,20 @@ def _set_up_solve(self, inputs): y100_init = np.maximum(y0_max - Q / Q_p, 0.1) y0_init = np.minimum(y100_min + Q / Q_p, 0.9) if self.options["open-circuit potential"] == "MSMR": - Un0, Un100, Up100, Up0 = self._get_ocp_msmr( - x0_init, x100_init, y100_init, y0_init + msmr_pot_model = _get_msmr_potential_model( + self.parameter_values, self.param + ) + sol0 = pybamm.AlgebraicSolver().solve( + msmr_pot_model, inputs={"x": x0_init, "y": y0_init} + ) + sol100 = pybamm.AlgebraicSolver().solve( + msmr_pot_model, inputs={"x": x100_init, "y": y100_init} ) return { - "Un(x_100)": Un100, - "Un(x_0)": Un0, - "Up(y_100)": Up100, - "Up(y_0)": Up0, + "Un(x_100)": sol100["Un"].data, + "Un(x_0)": sol0["Un"].data, + "Up(y_100)": sol100["Up"].data, + "Up(y_0)": sol0["Up"].data, } else: return { @@ -643,47 +648,6 @@ def _check_esoh_feasible(self, inputs): ) ) - def _get_ocp_msmr(self, x0, x100, y100, y0): - """ - Get the open-circuit potentials of the electrodes at the given stoichiometries - """ - V_max = self.param.voltage_high_cut - V_min = self.param.voltage_low_cut - x_n = self.param.n.prim.x - x_p = self.param.p.prim.x - model = pybamm.BaseModel() - Un_0 = pybamm.Variable("Un(x_0)") - Un_100 = pybamm.Variable("Un(x_100)") - Up_0 = pybamm.Variable("Up(y_0)") - Up_100 = pybamm.Variable("Up(y_100)") - model.algebraic = { - Un_0: x_n(Un_0) - x0, - Un_100: x_n(Un_100) - x100, - Up_0: x_p(Up_0) - y0, - Up_100: x_p(Up_100) - y100, - } - model.initial_conditions = { - Un_0: pybamm.Scalar(1), - Un_100: pybamm.Scalar(0), - Up_0: V_min * pybamm.Scalar(1), - Up_100: V_max, - } - model.variables = { - "Un(x_100)": Un_100, - "Un(x_0)": Un_0, - "Up(y_100)": Up_100, - "Up(y_0)": Up_0, - } - self.parameter_values.process_model(model) - sol = pybamm.AlgebraicSolver().solve(model) - - return ( - sol["Un(x_0)"].data, - sol["Un(x_100)"].data, - sol["Up(y_100)"].data, - sol["Up(y_0)"].data, - ) - def get_initial_stoichiometries(self, initial_value): """ Calculate initial stoichiometries to start off the simulation at a particular @@ -708,6 +672,11 @@ def get_initial_stoichiometries(self, initial_value): x_0, x_100, y_100, y_0 = self.get_min_max_stoichiometries() if isinstance(initial_value, str) and initial_value.endswith("V"): + if self.options["open-circuit potential"] == "MSMR": + raise NotImplementedError( + "Getting initial stoichiometries from voltage not implemented " + "for MSMR models" + ) V_init = float(initial_value[:-1]) V_min = parameter_values.evaluate(param.voltage_low_cut) V_max = parameter_values.evaluate(param.voltage_high_cut) @@ -776,9 +745,69 @@ def get_min_max_stoichiometries(self): sol = self.solve(inputs) return [sol["x_0"], sol["x_100"], sol["y_100"], sol["y_0"]] + def get_min_max_ocps(self): + """ + Calculate min/max open-circuit potentials + given voltage limits, open-circuit potentials, etc defined by parameter_values + + Returns + ------- + Un_0, Un_100, Up_100, Up_0 + The min/max ocps + """ + parameter_values = self.parameter_values + param = self.param + + Q_n = parameter_values.evaluate(param.n.Q_init) + Q_p = parameter_values.evaluate(param.p.Q_init) + + if self.known_value == "cyclable lithium capacity": + Q_Li = parameter_values.evaluate(param.Q_Li_particles_init) + inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q_Li": Q_Li} + elif self.known_value == "cell capacity": + Q = parameter_values.evaluate(param.Q / param.n_electrodes_parallel) + inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q": Q} + # Solve the model and check outputs + sol = self.solve(inputs) + return [sol["Un(x_0)"], sol["Un(x_100)"], sol["Up(y_100)"], sol["Up(y_0)"]] + + +def _get_msmr_potential_model(parameter_values, param): + """ + Returns a solver to calculate the open-circuit potentials of the indivdual + electrodes at the given stoichiometries + """ + V_max = param.voltage_high_cut + V_min = param.voltage_low_cut + x_n = param.n.prim.x + x_p = param.p.prim.x + model = pybamm.BaseModel() + Un = pybamm.Variable("Un") + Up = pybamm.Variable("Up") + x = pybamm.InputParameter("x") + y = pybamm.InputParameter("y") + model.algebraic = { + Un: x_n(Un) - x, + Up: x_p(Up) - y, + } + model.initial_conditions = { + Un: 1 - x, + Up: V_max * (1 - y) + V_min * y, + } + model.variables = { + "Un": Un, + "Up": Up, + } + parameter_values.process_model(model) + return model + def get_initial_stoichiometries( - initial_value, parameter_values, param=None, known_value="cyclable lithium capacity" + initial_value, + parameter_values, + param=None, + known_value="cyclable lithium capacity", + options=None, ): """ Calculate initial stoichiometries to start off the simulation at a particular @@ -797,18 +826,24 @@ def get_initial_stoichiometries( param : :class:`pybamm.LithiumIonParameters`, optional The symbolic parameter set to use for the simulation. If not provided, the default parameter set will be used. + known_value : str, optional + The known value needed to complete the electrode SOH model. + Can be "cyclable lithium capacity" (default) or "cell capacity". + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. Returns ------- x, y The initial stoichiometries that give the desired initial state of charge """ - esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value) + esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) return esoh_solver.get_initial_stoichiometries(initial_value) def get_min_max_stoichiometries( - parameter_values, param=None, known_value="cyclable lithium capacity" + parameter_values, param=None, known_value="cyclable lithium capacity", options=None ): """ Calculate min/max stoichiometries @@ -822,17 +857,56 @@ def get_min_max_stoichiometries( param : :class:`pybamm.LithiumIonParameters`, optional The symbolic parameter set to use for the simulation. If not provided, the default parameter set will be used. + known_value : str, optional + The known value needed to complete the electrode SOH model. + Can be "cyclable lithium capacity" (default) or "cell capacity". + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. Returns ------- x_0, x_100, y_100, y_0 The min/max stoichiometries """ - esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value) + esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) return esoh_solver.get_min_max_stoichiometries() -def theoretical_energy_integral(parameter_values, n_i, n_f, p_i, p_f, points=100): +def get_min_max_ocps( + parameter_values, param=None, known_value="cyclable lithium capacity", options=None +): + """ + Calculate min/max open-circuit potentials + given voltage limits, open-circuit potentials, etc defined by parameter_values + + Parameters + ---------- + parameter_values : :class:`pybamm.ParameterValues` + The parameter values class that will be used for the simulation. Required for + calculating appropriate initial open-circuit potentials. + param : :class:`pybamm.LithiumIonParameters`, optional + The symbolic parameter set to use for the simulation. + If not provided, the default parameter set will be used. + known_value : str, optional + The known value needed to complete the electrode SOH model. + Can be "cyclable lithium capacity" (default) or "cell capacity". + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. + + Returns + ------- + Un_0, Un_100, Up_100, Up_0 + The min/max ocps + """ + esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) + return esoh_solver.get_min_max_ocps() + + +def theoretical_energy_integral( + parameter_values, n_i, n_f, p_i, p_f, points=100, options=None +): """ Calculate maximum energy possible from a cell given OCV, initial soc, and final soc given voltage limits, open-circuit potentials, etc defined by parameter_values @@ -846,22 +920,37 @@ def theoretical_energy_integral(parameter_values, n_i, n_f, p_i, p_f, points=100 electrodes, respectively points : int The number of points at which to calculate voltage. + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. Returns ------- E The total energy of the cell in Wh """ + options = options or {} + param = pybamm.LithiumIonParameters(options) + n_vals = np.linspace(n_i, n_f, num=points) p_vals = np.linspace(p_i, p_f, num=points) - # Calculate OCV at each stoichiometry - param = pybamm.LithiumIonParameters() - T = param.T_amb(0) Vs = np.empty(n_vals.shape) - for i in range(n_vals.size): - Vs[i] = parameter_values.evaluate( - param.p.prim.U(p_vals[i], T) - ) - parameter_values.evaluate(param.n.prim.U(n_vals[i], T)) + T = param.T_amb(0) + + # Calculate OCV at each stoichiometry + if options["open-circuit potential"] == "MSMR": + msmr_pot_model = _get_msmr_potential_model(parameter_values, param) + for i in range(n_vals.size): + sol0 = pybamm.AlgebraicSolver().solve( + msmr_pot_model, inputs={"x": n_vals[i], "y": p_vals[i]} + ) + Vs[i] = sol0["Up"].data - sol0["Un"].data + else: + for i in range(n_vals.size): + Vs[i] = parameter_values.evaluate( + param.p.prim.U(p_vals[i], T) + ) - parameter_values.evaluate(param.n.prim.U(n_vals[i], T)) + # Calculate dQ Q_p = parameter_values.evaluate(param.p.prim.Q_init) * (p_f - p_i) dQ = Q_p / (points - 1) @@ -871,7 +960,7 @@ def theoretical_energy_integral(parameter_values, n_i, n_f, p_i, p_f, points=100 def calculate_theoretical_energy( - parameter_values, initial_soc=1.0, final_soc=0.0, points=100 + parameter_values, initial_soc=1.0, final_soc=0.0, points=100, options=None ): """ Calculate maximum energy possible from a cell given OCV, initial soc, and final soc @@ -887,6 +976,9 @@ def calculate_theoretical_energy( The soc at end of discharge, default 0.0 points : int The number of points at which to calculate voltage. + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. Returns ------- @@ -897,6 +989,6 @@ def calculate_theoretical_energy( x_100, y_100 = get_initial_stoichiometries(initial_soc, parameter_values) x_0, y_0 = get_initial_stoichiometries(final_soc, parameter_values) E = theoretical_energy_integral( - parameter_values, x_100, x_0, y_100, y_0, points=points + parameter_values, x_100, x_0, y_100, y_0, points=points, options=options ) return E From f9a92be97dabf0532ec4cfbd3bddd59983f4be87 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 23 Jun 2023 15:42:38 +0100 Subject: [PATCH 012/154] Use .diff to get dx/dU --- examples/scripts/MSMR_example.py | 32 ----------- .../lithium_ion/electrode_soh.py | 57 +++++++------------ pybamm/parameters/lithium_ion_parameters.py | 4 +- 3 files changed, 21 insertions(+), 72 deletions(-) diff --git a/examples/scripts/MSMR_example.py b/examples/scripts/MSMR_example.py index dec325d8d8..996c6d6d76 100644 --- a/examples/scripts/MSMR_example.py +++ b/examples/scripts/MSMR_example.py @@ -27,21 +27,6 @@ def x_n(U): return xj -def dxdU_n(U): - T = 298.15 - f = pybamm.constants.F / (pybamm.constants.R * T) - dxj = 0 - for i in range(6): - U0 = pybamm.Parameter(f"U0_n_{i}") - w = pybamm.Parameter(f"w_n_{i}") - Xj = pybamm.Parameter(f"Xj_n_{i}") - - e = pybamm.exp(f * (U - U0) / w) - dxj += -(f / w) * (Xj * e) / (1 + e) ** 2 - - return dxj - - def x_p(U): T = 298.15 f = pybamm.constants.F / (pybamm.constants.R * T) @@ -56,21 +41,6 @@ def x_p(U): return xj -def dxdU_p(U): - T = 298.15 - f = pybamm.constants.F / (pybamm.constants.R * T) - dxj = 0 - for i in range(4): - U0 = pybamm.Parameter(f"U0_p_{i}") - w = pybamm.Parameter(f"w_p_{i}") - Xj = pybamm.Parameter(f"Xj_p_{i}") - - e = pybamm.exp(f * (U - U0) / w) - dxj += -(f / w) * (Xj * e) / (1 + e) ** 2 - - return dxj - - def get_parameter_values(): return { # cell @@ -84,7 +54,6 @@ def get_parameter_values(): "Contact resistance [Ohm]": 0, # negative electrode "Negative electrode stoichiometry": x_n, - "Negative electrode differential stoichiometry [V-1]": dxdU_n, "U0_n_0": 0.08843, "Xj_n_0": 0.43336, "w_n_0": 0.08611, @@ -115,7 +84,6 @@ def get_parameter_values(): "Negative electrode OCP entropic change [V.K-1]": 0.0, # positive electrode "Positive electrode stoichiometry": x_p, - "Positive electrode differential stoichiometry [V-1]": dxdU_p, "U0_p_0": 3.62274, "Xj_p_0": 0.13442, "w_p_0": 0.96710, diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 75af037478..19e854572f 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -411,14 +411,16 @@ def solve(self, inputs): sol_dict = {key: sol[key].data[0] for key in sol.all_models[0].variables.keys()} # Calculate theoretical energy - x_0 = sol_dict["x_0"] - y_0 = sol_dict["y_0"] - x_100 = sol_dict["x_100"] - y_100 = sol_dict["y_100"] - energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( - self.parameter_values, x_100, x_0, y_100, y_0, options=self.options - ) - sol_dict.update({"Maximum theoretical energy [W.h]": energy}) + # TODO: energy calc for MSMR + if self.options["open-circuit potential"] != "MSMR": + x_0 = sol_dict["x_0"] + y_0 = sol_dict["y_0"] + x_100 = sol_dict["x_100"] + y_100 = sol_dict["y_100"] + energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( + self.parameter_values, x_100, x_0, y_100, y_0, options=self.options + ) + sol_dict.update({"Maximum theoretical energy [W.h]": energy}) return sol_dict def _set_up_solve(self, inputs): @@ -904,9 +906,7 @@ def get_min_max_ocps( return esoh_solver.get_min_max_ocps() -def theoretical_energy_integral( - parameter_values, n_i, n_f, p_i, p_f, points=100, options=None -): +def theoretical_energy_integral(parameter_values, n_i, n_f, p_i, p_f, points=100): """ Calculate maximum energy possible from a cell given OCV, initial soc, and final soc given voltage limits, open-circuit potentials, etc defined by parameter_values @@ -920,37 +920,22 @@ def theoretical_energy_integral( electrodes, respectively points : int The number of points at which to calculate voltage. - options : dict-like, optional - A dictionary of options to be passed to the model, see - :class:`pybamm.BatteryModelOptions`. - Returns ------- E The total energy of the cell in Wh """ - options = options or {} - param = pybamm.LithiumIonParameters(options) - n_vals = np.linspace(n_i, n_f, num=points) p_vals = np.linspace(p_i, p_f, num=points) - Vs = np.empty(n_vals.shape) + param = pybamm.LithiumIonParameters() T = param.T_amb(0) # Calculate OCV at each stoichiometry - if options["open-circuit potential"] == "MSMR": - msmr_pot_model = _get_msmr_potential_model(parameter_values, param) - for i in range(n_vals.size): - sol0 = pybamm.AlgebraicSolver().solve( - msmr_pot_model, inputs={"x": n_vals[i], "y": p_vals[i]} - ) - Vs[i] = sol0["Up"].data - sol0["Un"].data - else: - for i in range(n_vals.size): - Vs[i] = parameter_values.evaluate( - param.p.prim.U(p_vals[i], T) - ) - parameter_values.evaluate(param.n.prim.U(n_vals[i], T)) - + Vs = np.empty(n_vals.shape) + for i in range(n_vals.size): + Vs[i] = parameter_values.evaluate( + param.p.prim.U(p_vals[i], T) + ) - parameter_values.evaluate(param.n.prim.U(n_vals[i], T)) # Calculate dQ Q_p = parameter_values.evaluate(param.p.prim.Q_init) * (p_f - p_i) dQ = Q_p / (points - 1) @@ -960,7 +945,7 @@ def theoretical_energy_integral( def calculate_theoretical_energy( - parameter_values, initial_soc=1.0, final_soc=0.0, points=100, options=None + parameter_values, initial_soc=1.0, final_soc=0.0, points=100 ): """ Calculate maximum energy possible from a cell given OCV, initial soc, and final soc @@ -976,10 +961,6 @@ def calculate_theoretical_energy( The soc at end of discharge, default 0.0 points : int The number of points at which to calculate voltage. - options : dict-like, optional - A dictionary of options to be passed to the model, see - :class:`pybamm.BatteryModelOptions`. - Returns ------- E @@ -989,6 +970,6 @@ def calculate_theoretical_energy( x_100, y_100 = get_initial_stoichiometries(initial_soc, parameter_values) x_0, y_0 = get_initial_stoichiometries(final_soc, parameter_values) E = theoretical_energy_integral( - parameter_values, x_100, x_0, y_100, y_0, points=points, options=options + parameter_values, x_100, x_0, y_100, y_0, points=points ) return E diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 7124ae8c97..1fa39cb28e 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -609,9 +609,9 @@ def dxdU(self, U): f"{self.phase_prefactor}{Domain} particle open-circuit potential [V]": U } return pybamm.FunctionParameter( - f"{self.phase_prefactor}{Domain} electrode differential " - "stoichiometry [V-1]", + f"{self.phase_prefactor}{Domain} electrode stoichiometry", inputs, + diff_variable=U, ) def dUdT(self, sto): From 86c418474c2c596236ead90840fde2de463a5168 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 23 Jun 2023 16:41:35 +0100 Subject: [PATCH 013/154] esoh tests --- examples/scripts/MSMR.py | 16 +--- .../lithium_ion/MSMR_example_set.py | 89 +++++++++++++++++++ .../lithium_ion/electrode_soh.py | 10 +-- setup.py | 1 + .../test_lithium_ion/test_electrode_soh.py | 57 ++++++++++++ 5 files changed, 154 insertions(+), 19 deletions(-) rename examples/scripts/MSMR_example.py => pybamm/input/parameters/lithium_ion/MSMR_example_set.py (66%) diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index 0a21ee85dd..32af230b83 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -1,8 +1,4 @@ import pybamm -from MSMR_example import get_parameter_values - -pybamm.set_logging_level("DEBUG") - model = pybamm.lithium_ion.DFN( { @@ -12,10 +8,7 @@ ) -parameter_values = pybamm.ParameterValues(get_parameter_values()) -parameter_values = pybamm.get_size_distribution_parameters( - parameter_values, sd_n=0.2, sd_p=0.4 -) +parameter_values = pybamm.ParameterValues("MSMR_Example") experiment = pybamm.Experiment( [ ( @@ -29,9 +22,4 @@ ) sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment) sim.solve() -sim.plot( - # [ - # "Negative electrode open-circuit potential [V]", - # "Positive electrode open-circuit potential [V]", - # ] -) +sim.plot() diff --git a/examples/scripts/MSMR_example.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py similarity index 66% rename from examples/scripts/MSMR_example.py rename to pybamm/input/parameters/lithium_ion/MSMR_example_set.py index 996c6d6d76..a01f0e10d8 100644 --- a/examples/scripts/MSMR_example.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -2,18 +2,83 @@ def electrolyte_diffusivity_Nyman2008(c_e, T): + """ + Diffusivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data + comes from [1] + + References + ---------- + .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and + modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte," + Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008. + + Parameters + ---------- + c_e: :class:`pybamm.Symbol` + Dimensional electrolyte concentration + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10 + + # Nyman et al. (2008) does not provide temperature dependence + return D_c_e def electrolyte_conductivity_Nyman2008(c_e, T): + """ + Conductivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data + comes from [1]. + + References + ---------- + .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and + modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte," + Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008. + + Parameters + ---------- + c_e: :class:`pybamm.Symbol` + Dimensional electrolyte concentration + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + sigma_e = ( 0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000) ) + + # Nyman et al. (2008) does not provide temperature dependence + return sigma_e def x_n(U): + """ + Graphite stoichiometry as a function of potential. + + Parameters + ---------- + :class:`pybamm.Symbol` + Potential [V] + + Returns + ------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry + """ T = 298.15 f = pybamm.constants.F / (pybamm.constants.R * T) xj = 0 @@ -28,6 +93,19 @@ def x_n(U): def x_p(U): + """ + NMC stoichiometry as a function of potential. + + Parameters + ---------- + :class:`pybamm.Symbol` + Potential [V] + + Returns + ------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry + """ T = 298.15 f = pybamm.constants.F / (pybamm.constants.R * T) xj = 0 @@ -42,6 +120,17 @@ def x_p(U): def get_parameter_values(): + """ + Example parameter values for use with MSMR models. The values are loosely based on + the LG M50 cell, from the paper + + Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. + Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for + Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The + Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050. + + and references therein. + """ return { # cell "Negative electrode thickness [m]": 8.52e-05, diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 19e854572f..24b90b9c74 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -418,7 +418,7 @@ def solve(self, inputs): x_100 = sol_dict["x_100"] y_100 = sol_dict["y_100"] energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral( - self.parameter_values, x_100, x_0, y_100, y_0, options=self.options + self.parameter_values, x_100, x_0, y_100, y_0 ) sol_dict.update({"Maximum theoretical energy [W.h]": energy}) return sol_dict @@ -428,10 +428,10 @@ def _set_up_solve(self, inputs): sim = self._get_electrode_soh_sims_full() if sim.solution is not None: if self.options["open-circuit potential"] == "MSMR": - Un_100_sol = sim.solution["Un_100"].data - Un_0_sol = sim.solution["Un_0"].data - Up_100_sol = sim.solution["Up_100"].data - Up_0_sol = sim.solution["Up_0"].data + Un_100_sol = sim.solution["Un(x_100)"].data + Un_0_sol = sim.solution["Un(x_0)"].data + Up_100_sol = sim.solution["Up(y_100)"].data + Up_0_sol = sim.solution["Up(y_0)"].data return { "Un(x_100)": Un_100_sol, "Un(x_0)": Un_0_sol, diff --git a/setup.py b/setup.py index 2db28f2f6d..7b51e717af 100644 --- a/setup.py +++ b/setup.py @@ -260,6 +260,7 @@ def compile_KLU(): "Ramadass2004 = pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values", # noqa: E501 "Xu2019 = pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values", # noqa: E501 "ECM_Example = pybamm.input.parameters.ecm.example_set:get_parameter_values", # noqa: E501 + "MSMR_Example = pybamm.input.parameters.lithium_ion.MSMR_example_set:get_parameter_values", # noqa: E501 ], }, ) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index 1fae0db77d..05bc2f8b7c 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -137,6 +137,63 @@ def test_error(self): esoh_solver.solve(inputs) +class TestElectrodeSOHMSMR(TestCase): + def test_known_solution(self): + options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + param = pybamm.LithiumIonParameters(options=options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + + esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( + parameter_values, param, options=options + ) + + Vmin = 2.5 + Vmax = 4.2 + Q_n = parameter_values.evaluate(param.n.Q_init) + Q_p = parameter_values.evaluate(param.p.Q_init) + Q_Li = parameter_values.evaluate(param.Q_Li_particles_init) + + inputs = {"Q_Li": Q_Li, "Q_n": Q_n, "Q_p": Q_p} + + # Solve the model and check outputs + sol = esoh_solver.solve(inputs) + + self.assertAlmostEqual(sol["Up(y_100) - Un(x_100)"], Vmax, places=5) + self.assertAlmostEqual(sol["Up(y_0) - Un(x_0)"], Vmin, places=5) + self.assertAlmostEqual(sol["Q_Li"], Q_Li, places=5) + + # Solve with split esoh and check outputs + ics = esoh_solver._set_up_solve(inputs) + sol_split = esoh_solver._solve_split(inputs, ics) + for key in sol: + if key != "Maximum theoretical energy [W.h]": + self.assertAlmostEqual(sol[key], sol_split[key].data[0], places=5) + + def test_known_solution_cell_capacity(self): + options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + param = pybamm.LithiumIonParameters(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + + esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( + parameter_values, param, known_value="cell capacity", options=options + ) + + Vmin = 2.5 + Vmax = 4.2 + Q_n = parameter_values.evaluate(param.n.Q_init) + Q_p = parameter_values.evaluate(param.p.Q_init) + Q = parameter_values.evaluate(param.Q) + + inputs = {"Q": Q, "Q_n": Q_n, "Q_p": Q_p} + + # Solve the model and check outputs + sol = esoh_solver.solve(inputs) + + self.assertAlmostEqual(sol["Up(y_100) - Un(x_100)"], Vmax, places=5) + self.assertAlmostEqual(sol["Up(y_0) - Un(x_0)"], Vmin, places=5) + self.assertAlmostEqual(sol["Q"], Q, places=5) + + class TestElectrodeSOHHalfCell(TestCase): def test_known_solution(self): model = pybamm.lithium_ion.ElectrodeSOHHalfCell("positive") From 42c743883b713d231014e85fa9dda9174dd94ba5 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 4 Jul 2023 23:52:41 +0100 Subject: [PATCH 014/154] fix diff for broadcasts --- examples/scripts/MSMR.py | 3 +- pybamm/expression_tree/broadcasts.py | 21 + pybamm/expression_tree/unary_operators.py | 2 +- .../lithium_ion/MSMR_example_set.py | 6 +- .../lithium_ion/electrode_soh.py | 24 +- .../lithium_ion/msmr.ipynb | 1290 ----------------- .../open_circuit_potential/msmr_ocp.py | 9 +- .../submodels/particle/msmr_diffusion.py | 259 ++-- pybamm/parameters/lithium_ion_parameters.py | 7 +- test_callback.log | 0 .../base_lithium_ion_tests.py | 10 + .../test_expression_tree/test_broadcasts.py | 8 + 12 files changed, 181 insertions(+), 1458 deletions(-) delete mode 100644 pybamm/models/full_battery_models/lithium_ion/msmr.ipynb delete mode 100644 test_callback.log diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index 32af230b83..dc7b0ddaa4 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -1,6 +1,7 @@ import pybamm -model = pybamm.lithium_ion.DFN( +pybamm.set_logging_level("DEBUG") +model = pybamm.lithium_ion.SPM( { "open-circuit potential": "MSMR", "particle": "MSMR", diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py index 7fb34a57b8..622876c262 100644 --- a/pybamm/expression_tree/broadcasts.py +++ b/pybamm/expression_tree/broadcasts.py @@ -45,6 +45,27 @@ def _sympy_operator(self, child): """Override :meth:`pybamm.UnaryOperator._sympy_operator`""" return child + def diff(self, variable): + """ + Override :meth:`pybamm.SpatialOperator.diff()` to reinstate behaviour of + :meth:`pybamm.Symbol.diff()`. + """ + if variable == self: + return pybamm.Scalar(1) + elif any(variable == x for x in self.pre_order()): + return self._diff(variable) + elif variable == pybamm.t and self.has_symbol_of_classes( + (pybamm.VariableBase, pybamm.StateVectorBase) + ): + return self._diff(variable) + else: + return pybamm.Scalar(0) + + def _diff(self, variable): + """See :meth:`pybamm.Symbol._diff()`.""" + # Differentiate the child and broadcast the result in the same way + return self._unary_new_copy(self.child.diff(variable)) + class PrimaryBroadcast(Broadcast): """ diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py index d8550bb8ae..ffa14ce007 100644 --- a/pybamm/expression_tree/unary_operators.py +++ b/pybamm/expression_tree/unary_operators.py @@ -341,7 +341,7 @@ def __init__(self, name, child, domains=None): def diff(self, variable): """See :meth:`pybamm.Symbol.diff()`.""" - # We shouldn't need this + # We shouldn't need this, except for Broadcasts raise NotImplementedError diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index a01f0e10d8..e8b07edf9c 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -212,9 +212,9 @@ def get_parameter_values(): "Number of cells connected in series to make a battery": 1.0, "Lower voltage cut-off [V]": 2.5, "Upper voltage cut-off [V]": 4.2, + "Open-circuit voltage at 0% SOC [V]": 2.5, + "Open-circuit voltage at 100% SOC [V]": 4.2, "Initial temperature [K]": 298.15, "Initial voltage in negative electrode [V]": 0.01, - "Initial voltage in positive electrode [V]": 4.27, - "Initial concentration in negative electrode [mol.m-3]": 29820, - "Initial concentration in positive electrode [mol.m-3]": 6310, + "Initial voltage in positive electrode [V]": 4.19, } diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 81c05b907c..194b013dc6 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -196,8 +196,8 @@ def __init__( ) # Define parameters and input parameters - x_n = param.n.prim.x - x_p = param.p.prim.x + X_n = param.n.prim.X + X_p = param.p.prim.X V_max = param.voltage_high_cut V_min = param.voltage_low_cut @@ -214,21 +214,21 @@ def __init__( if "Un_0" in solve_for: Un_0 = pybamm.Variable("Un(x_0)") Up_0 = V_min + Un_0 - x_0 = x_n(Un_0) - y_0 = x_p(Up_0) + x_0 = X_n(Un_0) + y_0 = X_p(Up_0) # Define variables for 100% state of charge # TODO: thermal effects (include dU/dT) if "Un_100" in solve_for: Un_100 = pybamm.Variable("Un(x_100)") Up_100 = V_max + Un_100 - x_100 = x_n(Un_100) - y_100 = x_p(Up_100) + x_100 = X_n(Un_100) + y_100 = X_p(Up_100) else: Un_100 = pybamm.InputParameter("Un(x_100)") Up_100 = pybamm.InputParameter("Up(y_100)") - x_100 = x_n(Un_100) - y_100 = x_p(Up_100) + x_100 = X_n(Un_100) + y_100 = X_p(Up_100) # Define equations for 100% state of charge if "Un_100" in solve_for: @@ -791,16 +791,16 @@ def _get_msmr_potential_model(parameter_values, param): """ V_max = param.voltage_high_cut V_min = param.voltage_low_cut - x_n = param.n.prim.x - x_p = param.p.prim.x + X_n = param.n.prim.X + X_p = param.p.prim.X model = pybamm.BaseModel() Un = pybamm.Variable("Un") Up = pybamm.Variable("Up") x = pybamm.InputParameter("x") y = pybamm.InputParameter("y") model.algebraic = { - Un: x_n(Un) - x, - Up: x_p(Up) - y, + Un: X_n(Un) - x, + Up: X_p(Up) - y, } model.initial_conditions = { Un: 1 - x, diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb b/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb deleted file mode 100644 index 6303bcbc26..0000000000 --- a/pybamm/models/full_battery_models/lithium_ion/msmr.ipynb +++ /dev/null @@ -1,1290 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pybamm\n", - "import matplotlib.pyplot as plt\n", - "from basic_spm_msmr import BasicSPMSMR\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model = BasicSPMSMR()\n", - "parameter_values = model.default_parameter_values" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using matplotlib backend: MacOSX\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.0, 5.0)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-05-31 15:10:33.725 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:33.738 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:33.750 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - 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"2023-05-31 15:10:33.983 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:33.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.008 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.021 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.033 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.045 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.058 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.071 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.084 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.097 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.110 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.123 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.136 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.150 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.163 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.176 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.188 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.201 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.214 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.227 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.240 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.253 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.266 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.279 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.292 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.305 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.318 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.331 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.344 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.357 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.370 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.383 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.396 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.409 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.422 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.435 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.448 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.462 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.475 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.488 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.502 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.516 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.529 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.542 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.555 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.568 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.581 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.594 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.607 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.620 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.633 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.646 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.659 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.672 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.685 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.697 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.710 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.724 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.738 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.751 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.764 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.777 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.790 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.803 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.816 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.828 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.841 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.854 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.867 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.879 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.892 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.905 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.918 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.931 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.944 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.957 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.970 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.983 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:34.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.009 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.022 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.035 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.048 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.062 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.075 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.088 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.102 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.116 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.129 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.142 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.155 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.169 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.183 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.197 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.210 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.224 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.237 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.250 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.263 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.277 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.291 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.305 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.319 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.334 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.347 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.361 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.376 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.390 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.404 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.418 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.431 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.444 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.458 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.471 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.484 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.497 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.511 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.524 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.537 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.550 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.563 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.577 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.591 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.604 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.618 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.630 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.643 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.655 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.668 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.681 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.694 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.708 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.721 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.734 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.747 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.761 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.774 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.787 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.800 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.813 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.825 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.838 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.851 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.888 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.901 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.913 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.926 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.938 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.951 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.964 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.977 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:35.990 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.004 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.017 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.030 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.043 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.056 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.069 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.081 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.093 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.105 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.130 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.143 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.155 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.167 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.180 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.193 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.206 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.220 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.233 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.246 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.258 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.271 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.284 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.297 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.311 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.324 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.339 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.351 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.364 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.378 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.391 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.405 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.419 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.432 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.446 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.459 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.472 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.485 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.499 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.512 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.526 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.540 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.554 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.567 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.581 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.594 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.609 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.628 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.641 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.655 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.668 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.681 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.694 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.707 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.721 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.734 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.747 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.761 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.775 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.788 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.802 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.817 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.831 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.845 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.859 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.873 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.886 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.899 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.912 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.926 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.938 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.952 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.966 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.980 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:36.994 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.008 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.022 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.036 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.051 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.064 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.078 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.091 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.104 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.131 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.144 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.158 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.172 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.185 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.199 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.212 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.226 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.241 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.254 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.268 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.281 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.294 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.308 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.321 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.335 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.349 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.362 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.376 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.390 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.403 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.416 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.430 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.443 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.457 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.471 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.485 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.497 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.510 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.524 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.537 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.551 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.564 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.577 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.590 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.604 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.617 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.631 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.644 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.658 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.672 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.685 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.697 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.711 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.724 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.736 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.749 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.763 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.775 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.788 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.801 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.814 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.827 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.841 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.854 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.868 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.881 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.894 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.907 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.920 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.933 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.947 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.960 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.973 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.986 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:37.999 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.012 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.027 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.040 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.054 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.067 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.080 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.093 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.105 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.131 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.144 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.157 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.170 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.183 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.196 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.209 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.222 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.234 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.247 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.261 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.275 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.288 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.302 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.316 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.329 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.342 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.355 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.368 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.381 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.394 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.408 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.422 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.434 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.447 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.461 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.474 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.488 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.501 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.514 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.527 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.540 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.553 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.566 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.579 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.593 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.606 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.620 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.633 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.646 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.659 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.673 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.687 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.701 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.714 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.727 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.741 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.754 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.767 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.780 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.792 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.804 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.817 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.829 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.842 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.855 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.867 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.880 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.893 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.906 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.920 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.933 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.947 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.960 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.973 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:38.987 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.000 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.014 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.027 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.041 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.055 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.068 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.082 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.095 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.109 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.122 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.136 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.150 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.164 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.178 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.191 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.204 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.218 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.231 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.244 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.258 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.272 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.286 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.300 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.313 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.327 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.340 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.352 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.365 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.377 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.390 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.403 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.416 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.429 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.443 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.455 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.468 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.481 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.494 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.507 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.520 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.533 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.546 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.559 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.572 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.584 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.597 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.610 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.622 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.635 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.647 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.660 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.674 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.686 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.699 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.713 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.726 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.738 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.752 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.764 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.777 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.790 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.803 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.816 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.829 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.843 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.855 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.868 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.881 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.894 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.907 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.920 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.934 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.947 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.960 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.973 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.986 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:39.999 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.012 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.025 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.038 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.051 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.064 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.078 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.091 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.104 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.132 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.145 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.158 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.172 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.185 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.197 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.211 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.224 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.237 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.250 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.263 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.277 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.290 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.304 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.317 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.331 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.346 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.360 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.374 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.387 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.400 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.413 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.427 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.441 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.454 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.467 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.481 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.495 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.508 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.522 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.534 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.547 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.561 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.575 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.588 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.601 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.614 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.627 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.641 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.654 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.668 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.681 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.694 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.707 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.720 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.733 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.746 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.759 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.772 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.784 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.797 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.810 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.822 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.835 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.847 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.861 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.887 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.900 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.914 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.927 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.941 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.954 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.968 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.981 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:40.995 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.009 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.023 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.036 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.049 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.062 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.075 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.089 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.103 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.116 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.130 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.144 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.158 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.172 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.186 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.200 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.214 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.228 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.241 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.254 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.268 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.281 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.294 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.308 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.322 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.335 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.349 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.362 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.375 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.388 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.401 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.414 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.426 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.439 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.452 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.464 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.477 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.489 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.501 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.514 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.526 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.539 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.551 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.564 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.577 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.590 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.603 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.616 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.629 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.643 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.656 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.669 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.683 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.696 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.710 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.723 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.737 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.751 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.764 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.776 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.789 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.801 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.814 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.826 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.839 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.851 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.864 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.877 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.890 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.903 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.915 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.929 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.942 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.955 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.969 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.982 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:41.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.010 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.024 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.037 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.052 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.064 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.077 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.090 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.103 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.117 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.130 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.143 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.157 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.171 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.183 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.197 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.211 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.225 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.239 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.253 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.268 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.281 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.294 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.308 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.321 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.335 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.347 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.361 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.375 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.389 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.402 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.414 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.427 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.439 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.450 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.463 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.476 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.488 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.501 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.514 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.527 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.540 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.553 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.566 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.580 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.593 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.607 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.620 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.634 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.647 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.661 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.675 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.690 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.704 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.717 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.730 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.744 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.758 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.772 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.786 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.799 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.813 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.827 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.841 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.854 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.868 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.881 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.895 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.908 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.921 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.934 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.947 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.960 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.973 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:42.987 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.001 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.014 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.027 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.040 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.053 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.066 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.079 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.091 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.104 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.117 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.129 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.142 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.155 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.168 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.181 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.194 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.207 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.221 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.234 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.247 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.261 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.275 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.289 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.302 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.315 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.329 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.342 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.355 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.368 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.382 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.395 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.409 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.422 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.435 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.449 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.462 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.476 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.490 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.504 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.519 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.532 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.546 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.559 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.572 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.586 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.599 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.612 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.627 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.641 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.654 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.668 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.681 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.695 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.707 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.721 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.734 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.747 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.761 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.774 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.787 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.801 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.815 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.829 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.843 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.857 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.871 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.884 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.897 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.911 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.925 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.938 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.951 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.964 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.977 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:43.989 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.001 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.013 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.026 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.038 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.050 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.063 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.075 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.088 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.101 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.114 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.127 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.141 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.153 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.165 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.179 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.192 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.205 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.219 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.233 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.247 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.261 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.275 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.289 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.303 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.318 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.332 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.346 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.359 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.373 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.387 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.401 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.415 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.429 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.442 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.455 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.469 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.483 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.497 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.511 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.525 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.539 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.552 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.565 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.579 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.592 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.606 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.650 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.663 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.676 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.689 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.702 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.715 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.729 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.742 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.755 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.768 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.781 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.794 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.807 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.821 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.834 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.847 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.861 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.888 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.902 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.916 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.929 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.943 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.957 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.970 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.983 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:44.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.010 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.023 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.039 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.052 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.066 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.079 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.093 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.107 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.121 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.135 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.149 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.162 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.176 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.189 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.202 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.215 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.229 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.242 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.255 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.269 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.283 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.296 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.310 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.324 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.338 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.351 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.364 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.378 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.391 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.404 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.418 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.431 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.445 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.458 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.472 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.486 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.500 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.514 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.529 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.543 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.557 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.572 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.584 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.597 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.611 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.625 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.639 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.652 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.666 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.679 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.693 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.707 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.721 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.735 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.750 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.764 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.778 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.792 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.805 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.819 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.833 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.846 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.860 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.874 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.888 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.901 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.914 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.928 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.942 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.955 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.969 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.983 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:45.996 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.009 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.022 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.035 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.049 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.063 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.077 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.091 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.104 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.118 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.132 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.146 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.159 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.173 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.187 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.200 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.213 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.226 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.240 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.253 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.266 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.279 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.293 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.307 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.320 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.333 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.347 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.360 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.374 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.388 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.401 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.414 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.427 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.441 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.454 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.467 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.481 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.494 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.507 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.521 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.534 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.546 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.559 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.572 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.584 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.597 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.609 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.622 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.635 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.647 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.660 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.674 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.687 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.700 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.714 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.727 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.740 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.754 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.768 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.781 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.795 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.808 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.822 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.835 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.849 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.862 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.876 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.890 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.903 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.916 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.930 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "2023-05-31 15:10:46.944 Python[90554:1895843] *** Assertion failure in +[NSEvent otherEventWithType:location:modifierFlags:timestamp:windowNumber:context:subtype:data1:data2:], NSEvent.m:647\n", - "Traceback (most recent call last):\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/tornado/ioloop.py\", line 738, in _run_callback\n", - " ret = callback()\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 458, in advance_eventloop\n", - " eventloop(self)\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/ipykernel/eventloops.py\", line 353, in loop_cocoa\n", - " if kernel.shell_stream.flush(limit=1):\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/eventloop/zmqstream.py\", line 533, in flush\n", - " self._rebuild_io_state()\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/eventloop/zmqstream.py\", line 698, in _rebuild_io_state\n", - " self._update_handler(state)\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/eventloop/zmqstream.py\", line 715, in _update_handler\n", - " if state & self.socket.events:\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/sugar/attrsettr.py\", line 55, in __getattr__\n", - " return self._get_attr_opt(upper_key, opt)\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/zmq/sugar/attrsettr.py\", line 67, in _get_attr_opt\n", - " return self.get(opt)\n", - " File \"zmq/backend/cython/socket.pyx\", line 481, in zmq.backend.cython.socket.Socket.get\n", - "RecursionError: maximum recursion depth exceeded\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n", - " app_log.error(\"Exception in callback %r\", callback, exc_info=True)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1475, in error\n", - " self._log(ERROR, msg, args, **kwargs)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1587, in _log\n", - " record = self.makeRecord(self.name, level, fn, lno, msg, args,\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1556, in makeRecord\n", - " rv = _logRecordFactory(name, level, fn, lno, msg, args, exc_info, func,\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 308, in __init__\n", - " if (args and len(args) == 1 and isinstance(args[0], collections.abc.Mapping)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/abc.py\", line 119, in __instancecheck__\n", - " return _abc_instancecheck(cls, instance)\n", - "RecursionError: maximum recursion depth exceeded\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1779, in call_exception_handler\n", - " self.default_exception_handler(context)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1750, in default_exception_handler\n", - " value = repr(value)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 61, in __repr__\n", - " info = self._repr_info()\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 112, in _repr_info\n", - " info = super()._repr_info()\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 51, in _repr_info\n", - " info.append(format_helpers._format_callback_source(\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/format_helpers.py\", line 23, in _format_callback_source\n", - " func_repr = _format_callback(func, args, None)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/format_helpers.py\", line 56, in _format_callback\n", - " func_repr += _format_args_and_kwargs(args, kwargs)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/format_helpers.py\", line 38, in _format_args_and_kwargs\n", - " items.extend(reprlib.repr(arg) for arg in args)\n", - "RecursionError: maximum recursion depth exceeded\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n", - " return _run_code(code, main_globals, None,\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py\", line 87, in _run_code\n", - " exec(code, run_globals)\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/ipykernel_launcher.py\", line 17, in \n", - " app.launch_new_instance()\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n", - " app.start()\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 725, in start\n", - " self.io_loop.start()\n", - " File \"/Users/robertwtimms/Documents/PyBaMM/.tox/dev/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 195, in start\n", - " self.asyncio_loop.run_forever()\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n", - " self._run_once()\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n", - " handle._run()\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/events.py\", line 94, in _run\n", - " self._loop.call_exception_handler(context)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/asyncio/base_events.py\", line 1786, in call_exception_handler\n", - " logger.error('Exception in default exception handler',\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1475, in error\n", - " self._log(ERROR, msg, args, **kwargs)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1589, in _log\n", - " self.handle(record)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1599, in handle\n", - " self.callHandlers(record)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1669, in callHandlers\n", - " lastResort.handle(record)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 952, in handle\n", - " self.emit(record)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 1083, in emit\n", - " msg = self.format(record)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 927, in format\n", - " return fmt.format(record)\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 664, in format\n", - " if self.usesTime():\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 632, in usesTime\n", - " return self._style.usesTime()\n", - " File \"/opt/homebrew/Cellar/python@3.9/3.9.16/Frameworks/Python.framework/Versions/3.9/lib/python3.9/logging/__init__.py\", line 422, in usesTime\n", - " return self._fmt.find(self.asctime_search) >= 0\n", - "RecursionError: maximum recursion depth exceeded while calling a Python object\n" - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." - ] - } - ], - "source": [ - "%matplotlib\n", - "\n", - "x_n = parameter_values[\"Negative electrode stoichiometry\"]\n", - "x_p = parameter_values[\"Positive electrode stoichiometry\"]\n", - "\n", - "fig, ax = plt.subplots(2, 2, figsize=(10, 4))\n", - "\n", - "U = pybamm.linspace(0.01, 1.5, 500)\n", - "x_eval = parameter_values.evaluate(x_n(U)).flatten()\n", - "U_eval = U.evaluate().flatten()\n", - "dUdx_eval = -np.gradient(U_eval, x_eval)\n", - "ax[0, 0].plot(U_eval, x_eval, label=\"x_n\")\n", - "ax[0, 0].set_xlabel(\"U_n\")\n", - "ax[0, 0].set_ylabel(\"x_n\")\n", - "ax[1, 0].plot(x_eval, dUdx_eval, label=\"x_n\")\n", - "ax[1, 0].set_xlabel(\"x_n\")\n", - "ax[1, 0].set_ylabel(\"dU_n/dx_n\")\n", - "ax[1, 0].set_ylim([0, 5])\n", - "\n", - "U = pybamm.linspace(3.4, 5, 500)\n", - "x_eval = parameter_values.evaluate(x_p(U)).flatten()\n", - "U_eval = U.evaluate().flatten()\n", - "dUdx_eval = -np.gradient(U_eval, x_eval)\n", - "ax[0, 1].plot(U_eval, x_eval, label=\"x_p\")\n", - "ax[0, 1].set_xlabel(\"U_p\")\n", - "ax[0, 1].set_ylabel(\"x_p\")\n", - "ax[1, 1].plot(x_eval, dUdx_eval, label=\"x_p\")\n", - "ax[1, 1].set_xlabel(\"x_p\")\n", - "ax[1, 1].set_ylabel(\"dU_p/dx_p\")\n", - "ax[1, 1].set_ylim([0, 5])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'pybamm' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m soc_model \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mBaseModel()\n\u001b[1;32m 2\u001b[0m U_n \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mVariable(\u001b[39m\"\u001b[39m\u001b[39mU_n\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 3\u001b[0m U_p \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mVariable(\u001b[39m\"\u001b[39m\u001b[39mU_p\u001b[39m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'pybamm' is not defined" - ] - } - ], - "source": [ - "soc_model = pybamm.BaseModel()\n", - "U_n = pybamm.Variable(\"U_n\")\n", - "U_p = pybamm.Variable(\"U_p\")\n", - "soc_model.variables = {\"U_n\": U_n, \"U_p\": U_p}\n", - "x_0 = parameter_values[\"Negative electrode stoichiometry at 0% SOC\"]\n", - "x_100 = parameter_values[\"Negative electrode stoichiometry at 100% SOC\"]\n", - "y_0 = parameter_values[\"Positive electrode stoichiometry at 0% SOC\"]\n", - "y_100 = parameter_values[\"Positive electrode stoichiometry at 100% SOC\"]\n", - "initial_soc = pybamm.InputParameter(\"Initial soc\")\n", - "x = x_0 + initial_soc * (x_100 - x_0)\n", - "y = y_0 - initial_soc * (y_0 - y_100)\n", - "soc_model.algebraic = {U_n: x - x_n(U_n), U_p: y - x_p(U_p)}\n", - "soc_model.initial_conditions = {U_n: pybamm.Scalar(0), U_p: pybamm.Scalar(4)}\n", - "parameter_values.process_model(soc_model)\n", - "soc_sol = pybamm.AlgebraicSolver(tol=1e-6).solve(soc_model, inputs={\"Initial soc\": 1})\n", - "U_n, U_p = soc_sol[\"U_n\"].data[0], soc_sol[\"U_p\"].data[0]\n", - "\n", - "parameter_values.update(\n", - " {\n", - " \"Initial negative electrode potential [V]\": U_n,\n", - " \"Initial positive electrode potential [V]\": U_p,\n", - " },\n", - " check_already_exists=False,\n", - ")\n", - "print(U_n, U_p)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n", - "sim.solve([0, 3300])\n", - "sim.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "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": "3.9.16" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py index 4fffd55af7..d2139d33ca 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py @@ -33,8 +33,7 @@ def get_coupled_variables(self, variables): f"{Domain} {phase_name}particle surface stoichiometry distribution" ] ocp_surf = variables[ - f"{Domain} {phase_name}particle surface open-circuit potential " - "distribution [V]" + f"{Domain} {phase_name}particle surface potential distribution [V]" ] # If variable was broadcast, take only the orphan if ( @@ -51,7 +50,7 @@ def get_coupled_variables(self, variables): f"{Domain} {phase_name}particle surface stoichiometry" ] ocp_surf = variables[ - f"{Domain} {phase_name}particle surface open-circuit potential [V]" + f"{Domain} {phase_name}particle surface potential [V]" ] # If variable was broadcast, take only the orphan if ( @@ -63,9 +62,7 @@ def get_coupled_variables(self, variables): ocp_surf = ocp_surf.orphans[0] T = T.orphans[0] - ocp_bulk = variables[ - f"Average {domain} {phase_name}particle open-circuit potential [V]" - ] + ocp_bulk = variables[f"Average {domain} {phase_name}particle potential [V]"] dUdT = self.phase_param.dUdT(sto_surf) variables.update(self._get_standard_ocp_variables(ocp_surf, ocp_bulk, dUdT)) diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index 621a2bb0f7..3f3cf0e7d3 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -45,16 +45,16 @@ def get_fundamental_variables(self): variables = {} - # Define "particle" open-circuit potential variables. In the MSMR model, we - # solve for the potential as a function of position within the electrode and - # particles (and particle-size distribution, if applicable). The potential is - # then used to calculate the stoichiometry, which is used to calculate the - # particle concentration. + # Define "particle" potential variables. In the MSMR model, we solve for the + # potential as a function of position within the electrode and particles (and + # particle-size distribution, if applicable). The potential is then used to + # calculate the stoichiometry, which is used to calculate the particle + # concentration. c_max = self.phase_param.c_max if self.size_distribution is False: if self.x_average is False: U = pybamm.Variable( - f"{Domain} {phase_name}particle open-circuit potential [V]", + f"{Domain} {phase_name}particle potential [V]", f"{domain} {phase_name}particle", auxiliary_domains={ "secondary": f"{domain} electrode", @@ -64,8 +64,7 @@ def get_fundamental_variables(self): U.print_name = f"U_{domain[0]}" else: U_xav = pybamm.Variable( - f"X-averaged {domain} {phase_name}particle open-circuit " - "potential [V]", + f"X-averaged {domain} {phase_name}particle " "potential [V]", f"{domain} {phase_name}particle", auxiliary_domains={"secondary": "current collector"}, ) @@ -74,8 +73,7 @@ def get_fundamental_variables(self): else: if self.x_average is False: U_distribution = pybamm.Variable( - f"{Domain} {phase_name}particle " - "open-circuit potential distribution [V]", + f"{Domain} {phase_name}particle " "potential distribution [V]", domain=f"{domain} {phase_name}particle", auxiliary_domains={ "secondary": f"{domain} {phase_name}particle size", @@ -100,7 +98,7 @@ def get_fundamental_variables(self): else: U_distribution = pybamm.Variable( f"X-averaged {domain} {phase_name}particle " - "open-circuit potential distribution [V]", + "potential distribution [V]", domain=f"{domain} {phase_name}particle", auxiliary_domains={ "secondary": f"{domain} {phase_name}particle size", @@ -125,12 +123,12 @@ def get_fundamental_variables(self): ) # Calculate the stoichiometry distribution from the potential distribution - x_distribution = self.phase_param.x(U_distribution) - dxdU_distribution = self.phase_param.dxdU(U_distribution) + X_distribution = self.phase_param.X(U_distribution) + dXdU_distribution = self.phase_param.dXdU(U_distribution) # Standard stoichiometry and concentration distribution variables # (size-dependent) - c_s_distribution = x_distribution * c_max + c_s_distribution = X_distribution * c_max variables.update( self._get_standard_concentration_distribution_variables( c_s_distribution @@ -138,7 +136,7 @@ def get_fundamental_variables(self): ) variables.update( self._get_standard_differential_stoichiometry_distribution_variables( - dxdU_distribution + dXdU_distribution ) ) @@ -154,13 +152,13 @@ def get_fundamental_variables(self): variables.update(self._get_standard_potential_variables(U)) # Calculate the stoichiometry from the potential - x = self.phase_param.x(U) - dxdU = self.phase_param.dxdU(U) + X = self.phase_param.X(U) + dXdU = self.phase_param.dXdU(U) # Standard stoichiometry and concentration variables (size-independent) - c_s = x * c_max + c_s = X * c_max variables.update(self._get_standard_concentration_variables(c_s)) - variables.update(self._get_standard_differential_stoichiometry_variables(dxdU)) + variables.update(self._get_standard_differential_stoichiometry_variables(dXdU)) return variables @@ -171,13 +169,11 @@ def get_coupled_variables(self, variables): if self.size_distribution is False: if self.x_average is False: - x = variables[f"{Domain} {phase_name}particle stoichiometry"] - dxdU = variables[ + X = variables[f"{Domain} {phase_name}particle stoichiometry"] + dXdU = variables[ f"{Domain} {phase_name}particle differential stoichiometry [V-1]" ] - U = variables[ - f"{Domain} {phase_name}particle open-circuit potential [V]" - ] + U = variables[f"{Domain} {phase_name}particle potential [V]"] T = pybamm.PrimaryBroadcast( variables[f"{Domain} electrode temperature [K]"], [f"{domain} {phase_name}particle"], @@ -188,14 +184,13 @@ def get_coupled_variables(self, variables): "interfacial current density [A.m-2]" ] else: - x = variables[f"X-averaged {domain} {phase_name}particle stoichiometry"] - dxdU = variables[ + X = variables[f"X-averaged {domain} {phase_name}particle stoichiometry"] + dXdU = variables[ f"X-averaged {domain} {phase_name}particle differential " "stoichiometry [V-1]" ] U = variables[ - f"X-averaged {domain} {phase_name}particle open-circuit " - "potential [V]" + f"X-averaged {domain} {phase_name}particle " "potential [V]" ] T = pybamm.PrimaryBroadcast( variables[f"X-averaged {domain} electrode temperature [K]"], @@ -213,16 +208,15 @@ def get_coupled_variables(self, variables): R_nondim, [f"{domain} {phase_name}particle"] ) if self.x_average is False: - x = variables[ + X = variables[ f"{Domain} {phase_name}particle stoichiometry distribution" ] - dxdU = variables[ + dXdU = variables[ f"{Domain} {phase_name}particle differential stoichiometry " "distribution [V-1]" ] U = variables[ - f"{Domain} {phase_name}particle open-circuit potential " - "distribution [V]" + f"{Domain} {phase_name}particle potential " "distribution [V]" ] # broadcast T to "particle size" domain then again into "particle" T = pybamm.PrimaryBroadcast( @@ -235,17 +229,17 @@ def get_coupled_variables(self, variables): "current density distribution [A.m-2]" ] else: - x = variables[ + X = variables[ f"X-averaged {domain} {phase_name}particle " "stoichiometry distribution" ] - dxdU = variables[ + dXdU = variables[ f"X-averaged {domain} {phase_name}particle " "differential stoichiometry distribution [V-1]" ] U = variables[ f"X-averaged {domain} {phase_name}particle " - "open-circuit potential distribution [V]" + "potential distribution [V]" ] # broadcast to "particle size" domain then again into "particle" T = pybamm.PrimaryBroadcast( @@ -261,9 +255,9 @@ def get_coupled_variables(self, variables): # Note: diffusivity is given as a function of concentration here, # not stoichiometry c_max = self.phase_param.c_max - D_eff = self._get_effective_diffusivity(x * c_max, T) + D_eff = self._get_effective_diffusivity(X * c_max, T) f = self.param.F / (self.param.R * T) - N_s = c_max * x * (1 - x) * f * D_eff * pybamm.grad(U) + N_s = c_max * X * (1 - X) * f * D_eff * pybamm.grad(U) variables.update( { f"{Domain} {phase_name}particle rhs [V.s-1]": -( @@ -271,11 +265,11 @@ def get_coupled_variables(self, variables): ) * pybamm.div(N_s) / c_max - / dxdU, + / dXdU, f"{Domain} {phase_name}particle bc [V.m-1]": j * R_nondim / param.F - / pybamm.surf(c_max * x * (1 - x) * f * D_eff), + / pybamm.surf(c_max * X * (1 - X) * f * D_eff), } ) @@ -306,24 +300,20 @@ def set_rhs(self, variables): if self.size_distribution is False: if self.x_average is False: - U = variables[ - f"{Domain} {phase_name}particle open-circuit potential [V]" - ] + U = variables[f"{Domain} {phase_name}particle potential [V]"] else: U = variables[ - f"X-averaged {domain} {phase_name}particle open-circuit " - "potential [V]" + f"X-averaged {domain} {phase_name}particle " "potential [V]" ] else: if self.x_average is False: U = variables[ - f"{Domain} {phase_name}particle " - "open-circuit potential distribution [V]" + f"{Domain} {phase_name}particle " "potential distribution [V]" ] else: U = variables[ f"X-averaged {domain} {phase_name}particle " - "open-circuit potential distribution [V]" + "potential distribution [V]" ] self.rhs = {U: variables[f"{Domain} {phase_name}particle rhs [V.s-1]"]} @@ -333,24 +323,20 @@ def set_boundary_conditions(self, variables): if self.size_distribution is False: if self.x_average is False: - U = variables[ - f"{Domain} {phase_name}particle open-circuit potential [V]" - ] + U = variables[f"{Domain} {phase_name}particle potential [V]"] else: U = variables[ - f"X-averaged {domain} {phase_name}particle open-circuit " - "potential [V]" + f"X-averaged {domain} {phase_name}particle " "potential [V]" ] else: if self.x_average is False: U = variables[ - f"{Domain} {phase_name}particle " - "open-circuit potential distribution [V]" + f"{Domain} {phase_name}particle " "potential distribution [V]" ] else: U = variables[ f"X-averaged {domain} {phase_name}particle " - "open-circuit potential distribution [V]" + "potential distribution [V]" ] rbc = variables[f"{Domain} {phase_name}particle bc [V.m-1]"] @@ -365,24 +351,20 @@ def set_initial_conditions(self, variables): U_init = self.phase_param.U_init if self.size_distribution is False: if self.x_average is False: - U = variables[ - f"{Domain} {phase_name}particle open-circuit potential [V]" - ] + U = variables[f"{Domain} {phase_name}particle potential [V]"] else: U = variables[ - f"X-averaged {domain} {phase_name}particle open-circuit " - "potential [V]" + f"X-averaged {domain} {phase_name}particle " "potential [V]" ] else: if self.x_average is False: U = variables[ - f"{Domain} {phase_name}particle " - "open-circuit potential distribution [V]" + f"{Domain} {phase_name}particle " "potential distribution [V]" ] else: U = variables[ f"X-averaged {domain} {phase_name}particle " - "open-circuit potential distribution [V]" + "potential distribution [V]" ] self.initial_conditions = {U: U_init} @@ -399,24 +381,20 @@ def _get_standard_potential_variables(self, U): U_rav = pybamm.r_average(U) U_av = pybamm.r_average(U_xav) variables = { - f"{Domain} {phase_name}particle open-circuit potential [V]": U, - f"X-averaged {domain} {phase_name}particle " - "open-circuit potential [V]": U_xav, - f"R-averaged {domain} {phase_name}particle " - "open-circuit potential [V]": U_rav, - f"Average {domain} {phase_name}particle open-circuit potential [V]": U_av, - f"{Domain} {phase_name}particle surface open-circuit potential [V]": U_surf, + f"{Domain} {phase_name}particle potential [V]": U, + f"X-averaged {domain} {phase_name}particle " "potential [V]": U_xav, + f"R-averaged {domain} {phase_name}particle " "potential [V]": U_rav, + f"Average {domain} {phase_name}particle potential [V]": U_av, + f"{Domain} {phase_name}particle surface potential [V]": U_surf, f"X-averaged {domain} {phase_name}particle " - "surface open-circuit potential [V]": U_surf_av, - f"Minimum {domain} {phase_name}particle open-circuit potential [V]" - "": pybamm.min(U), - f"Maximum {domain} {phase_name}particle open-circuit potential [V]" - "": pybamm.max(U), + "surface potential [V]": U_surf_av, + f"Minimum {domain} {phase_name}particle potential [V]" "": pybamm.min(U), + f"Maximum {domain} {phase_name}particle potential [V]" "": pybamm.max(U), f"Minimum {domain} {phase_name}particle " f"Minimum {domain} {phase_name}particle " - "surface open-circuit potential [V]": pybamm.min(U_surf), + "surface potential [V]": pybamm.min(U_surf), f"Maximum {domain} {phase_name}particle " - "surface open-circuit potential [V]": pybamm.max(U_surf), + "surface potential [V]": pybamm.max(U_surf), } return variables @@ -437,45 +415,45 @@ def _get_standard_potential_distribution_variables(self, U): U, [f"{domain} {phase_name}particle"] ) - # Surface open-circuit potential distribution variables + # Surface potential distribution variables U_surf_xav_distribution = U U_surf_distribution = pybamm.SecondaryBroadcast( U_surf_xav_distribution, [f"{domain} electrode"] ) - # Open-circuit potential distribution in all domains. + # potential distribution in all domains. U_distribution = pybamm.PrimaryBroadcast( U_surf_distribution, [f"{domain} {phase_name}particle"] ) elif U.domain == [f"{domain} {phase_name}particle"] and ( U.domains["tertiary"] != [f"{domain} electrode"] ): - # X-avg open-circuit potential distribution + # X-avg potential distribution U_xav_distribution = U - # Surface open-circuit potential distribution variables + # Surface potential distribution variables U_surf_xav_distribution = pybamm.surf(U_xav_distribution) U_surf_distribution = pybamm.SecondaryBroadcast( U_surf_xav_distribution, [f"{domain} electrode"] ) - # open-circuit potential distribution in all domains + # potential distribution in all domains U_distribution = pybamm.TertiaryBroadcast( U_xav_distribution, [f"{domain} electrode"] ) elif U.domain == [f"{domain} {phase_name}particle size"] and U.domains[ "secondary" ] == [f"{domain} electrode"]: - # Surface open-circuit potential distribution variables + # Surface potential distribution variables U_surf_distribution = U U_surf_xav_distribution = pybamm.x_average(U) - # X-avg open-circuit potential distribution + # X-avg potential distribution U_xav_distribution = pybamm.PrimaryBroadcast( U_surf_xav_distribution, [f"{domain} {phase_name}particle"] ) - # Open-circuit potential distribution in all domains + # potential distribution in all domains U_distribution = pybamm.PrimaryBroadcast( U_surf_distribution, [f"{domain} {phase_name}particle"] ) @@ -493,7 +471,7 @@ def _get_standard_potential_distribution_variables(self, U): }, ) - # Surface open-circuit potential distribution variables + # Surface potential distribution variables U_surf_distribution = pybamm.surf(U) U_surf_xav_distribution = pybamm.x_average(U_surf_distribution) @@ -501,108 +479,107 @@ def _get_standard_potential_distribution_variables(self, U): U_av_distribution = pybamm.x_average(U_rav_distribution) variables = { - f"{Domain} {phase_name}particle open-circuit potential distribution " - "[V]": U_distribution, - f"X-averaged {domain} {phase_name}particle open-circuit potential " + f"{Domain} {phase_name}particle potential distribution [V]": U_distribution, + f"X-averaged {domain} {phase_name}particle potential " "distribution [V]": U_xav_distribution, - f"R-averaged {domain} {phase_name}particle open-circuit potential " + f"R-averaged {domain} {phase_name}particle potential " "distribution [V]": U_rav_distribution, - f"Average {domain} {phase_name}particle open-circuit potential " + f"Average {domain} {phase_name}particle potential " "distribution [V]": U_av_distribution, - f"{Domain} {phase_name}particle surface open-circuit potential" + f"{Domain} {phase_name}particle surface potential" " distribution [V]": U_surf_distribution, - f"X-averaged {domain} {phase_name}particle surface open-circuit potential " + f"X-averaged {domain} {phase_name}particle surface potential " "distribution [V]": U_surf_xav_distribution, } return variables - def _get_standard_differential_stoichiometry_variables(self, dxdU): + def _get_standard_differential_stoichiometry_variables(self, dXdU): domain, Domain = self.domain_Domain phase_name = self.phase_name - dxdU_surf = pybamm.surf(dxdU) - dxdU_surf_av = pybamm.x_average(dxdU_surf) - dxdU_xav = pybamm.x_average(dxdU) - dxdU_rav = pybamm.r_average(dxdU) - dxdU_av = pybamm.r_average(dxdU_xav) + dXdU_surf = pybamm.surf(dXdU) + dXdU_surf_av = pybamm.x_average(dXdU_surf) + dXdU_xav = pybamm.x_average(dXdU) + dXdU_rav = pybamm.r_average(dXdU) + dXdU_av = pybamm.r_average(dXdU_xav) variables = { - f"{Domain} {phase_name}particle differential stoichiometry [V-1]": dxdU, + f"{Domain} {phase_name}particle differential stoichiometry [V-1]": dXdU, f"X-averaged {domain} {phase_name}particle " - "differential stoichiometry [V-1]": dxdU_xav, + "differential stoichiometry [V-1]": dXdU_xav, f"R-averaged {domain} {phase_name}particle " - "differential stoichiometry [V-1]": dxdU_rav, + "differential stoichiometry [V-1]": dXdU_rav, f"Average {domain} {phase_name}particle differential " - "stoichiometry [V-1]": dxdU_av, + "stoichiometry [V-1]": dXdU_av, f"{Domain} {phase_name}particle surface differential " - "stoichiometry [V-1]": dxdU_surf, + "stoichiometry [V-1]": dXdU_surf, f"X-averaged {domain} {phase_name}particle " - "surface differential stoichiometry [V-1]": dxdU_surf_av, + "surface differential stoichiometry [V-1]": dXdU_surf_av, } return variables - def _get_standard_differential_stoichiometry_distribution_variables(self, dxdU): + def _get_standard_differential_stoichiometry_distribution_variables(self, dXdU): domain, Domain = self.domain_Domain phase_name = self.phase_name # Broadcast and x-average when necessary - if dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[ + if dXdU.domain == [f"{domain} {phase_name}particle size"] and dXdU.domains[ "secondary" ] != [f"{domain} electrode"]: # X-avg differential stoichiometry distribution - dxdU_xav_distribution = pybamm.PrimaryBroadcast( - dxdU, [f"{domain} {phase_name}particle"] + dXdU_xav_distribution = pybamm.PrimaryBroadcast( + dXdU, [f"{domain} {phase_name}particle"] ) # Surface differential stoichiometry distribution variables - dxdU_surf_xav_distribution = dxdU - dxdU_surf_distribution = pybamm.SecondaryBroadcast( - dxdU_surf_xav_distribution, [f"{domain} electrode"] + dXdU_surf_xav_distribution = dXdU + dXdU_surf_distribution = pybamm.SecondaryBroadcast( + dXdU_surf_xav_distribution, [f"{domain} electrode"] ) # Differential stoichiometry distribution in all domains. - dxdU_distribution = pybamm.PrimaryBroadcast( - dxdU_surf_distribution, [f"{domain} {phase_name}particle"] + dXdU_distribution = pybamm.PrimaryBroadcast( + dXdU_surf_distribution, [f"{domain} {phase_name}particle"] ) - elif dxdU.domain == [f"{domain} {phase_name}particle"] and ( - dxdU.domains["tertiary"] != [f"{domain} electrode"] + elif dXdU.domain == [f"{domain} {phase_name}particle"] and ( + dXdU.domains["tertiary"] != [f"{domain} electrode"] ): # X-avg differential stoichiometry distribution - dxdU_xav_distribution = dxdU + dXdU_xav_distribution = dXdU # Surface differential stoichiometry distribution variables - dxdU_surf_xav_distribution = pybamm.surf(dxdU_xav_distribution) - dxdU_surf_distribution = pybamm.SecondaryBroadcast( - dxdU_surf_xav_distribution, [f"{domain} electrode"] + dXdU_surf_xav_distribution = pybamm.surf(dXdU_xav_distribution) + dXdU_surf_distribution = pybamm.SecondaryBroadcast( + dXdU_surf_xav_distribution, [f"{domain} electrode"] ) # Differential stoichiometry distribution in all domains. - dxdU_distribution = pybamm.TertiaryBroadcast( - dxdU_xav_distribution, [f"{domain} electrode"] + dXdU_distribution = pybamm.TertiaryBroadcast( + dXdU_xav_distribution, [f"{domain} electrode"] ) - elif dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[ + elif dXdU.domain == [f"{domain} {phase_name}particle size"] and dXdU.domains[ "secondary" ] == [f"{domain} electrode"]: # Surface differential stoichiometry distribution variables - dxdU_surf_distribution = dxdU - dxdU_surf_xav_distribution = pybamm.x_average(dxdU) + dXdU_surf_distribution = dXdU + dXdU_surf_xav_distribution = pybamm.x_average(dXdU) # X-avg differential stoichiometry distribution - dxdU_xav_distribution = pybamm.PrimaryBroadcast( - dxdU_surf_xav_distribution, [f"{domain} {phase_name}particle"] + dXdU_xav_distribution = pybamm.PrimaryBroadcast( + dXdU_surf_xav_distribution, [f"{domain} {phase_name}particle"] ) # Differential stoichiometry distribution in all domains - dxdU_distribution = pybamm.PrimaryBroadcast( - dxdU_surf_distribution, [f"{domain} {phase_name}particle"] + dXdU_distribution = pybamm.PrimaryBroadcast( + dXdU_surf_distribution, [f"{domain} {phase_name}particle"] ) else: - dxdU_distribution = dxdU + dXdU_distribution = dXdU # x-average the *tertiary* domain. # NOTE: not yet implemented. Make 0.5 everywhere - dxdU_xav_distribution = pybamm.FullBroadcast( + dXdU_xav_distribution = pybamm.FullBroadcast( 0.5, [f"{domain} {phase_name}particle"], { @@ -612,24 +589,24 @@ def _get_standard_differential_stoichiometry_distribution_variables(self, dxdU): ) # Surface differential stoichiometry distribution variables - dxdU_surf_distribution = pybamm.surf(dxdU) - dxdU_surf_xav_distribution = pybamm.x_average(dxdU_surf_distribution) + dXdU_surf_distribution = pybamm.surf(dXdU) + dXdU_surf_xav_distribution = pybamm.x_average(dXdU_surf_distribution) - dxdU_rav_distribution = pybamm.r_average(dxdU_distribution) - dxdU_av_distribution = pybamm.x_average(dxdU_rav_distribution) + dXdU_rav_distribution = pybamm.r_average(dXdU_distribution) + dXdU_av_distribution = pybamm.x_average(dXdU_rav_distribution) variables = { f"{Domain} {phase_name}particle differential stoichiometry distribution " - "[V-1]": dxdU_distribution, + "[V-1]": dXdU_distribution, f"X-averaged {domain} {phase_name}particle differential stoichiometry " - "distribution [V-1]": dxdU_xav_distribution, + "distribution [V-1]": dXdU_xav_distribution, f"R-averaged {domain} {phase_name}particle differential stoichiometry " - "distribution [V-1]": dxdU_rav_distribution, + "distribution [V-1]": dXdU_rav_distribution, f"Average {domain} {phase_name}particle differential stoichiometry " - "distribution [V-1]": dxdU_av_distribution, + "distribution [V-1]": dXdU_av_distribution, f"{Domain} {phase_name}particle surface differential stoichiometry" - " distribution [V-1]": dxdU_surf_distribution, + " distribution [V-1]": dXdU_surf_distribution, f"X-averaged {domain} {phase_name}particle surface differential " - "stoichiometry distribution [V-1]": dxdU_surf_xav_distribution, + "stoichiometry distribution [V-1]": dXdU_surf_xav_distribution, } return variables diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 573ff2ac87..2f0e1e58e3 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -487,7 +487,7 @@ def _set_parameters(self): self.U_init = pybamm.Parameter( f"{pref}Initial voltage in {domain} electrode [V]", ) - self.c_init = self.x(self.U_init) * self.c_max + self.c_init = self.X(self.U_init) * self.c_max else: self.c_init = pybamm.FunctionParameter( f"{pref}Initial concentration in {domain} electrode [mol.m-3]", @@ -591,7 +591,7 @@ def U(self, sto, T, lithiation=None): out.print_name = r"U_\mathrm{p}(c^\mathrm{surf}_\mathrm{s,p}, T)" return out - def x(self, U): + def X(self, U): "Stoichiometry as a function of potential (for use with MSMR models)" Domain = self.domain.capitalize() inputs = { @@ -601,11 +601,10 @@ def x(self, U): f"{self.phase_prefactor}{Domain} electrode stoichiometry", inputs ) - def dxdU(self, U): + def dXdU(self, U): """ Differential stoichiometry as a function of potential (for use with MSMR models) """ - # TODO: remove and use .diff(U) instead Domain = self.domain.capitalize() inputs = { f"{self.phase_prefactor}{Domain} particle open-circuit potential [V]": U diff --git a/test_callback.log b/test_callback.log deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index b5dd46101a..6ef8b0c4a3 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -280,3 +280,13 @@ def test_composite_graphite_silicon_sei(self): {f"Primary: {name}": (1 - x) * 0.75, f"Secondary: {name}": x * 0.75} ) self.run_basic_processing_test(options, parameter_values=parameter_values) + + def test_basic_processing_msmr(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + } + parameter_values = pybamm.ParameterValues("MSMR_Example") + model = self.model(options) + modeltest = tests.StandardModelTest(model, parameter_values=parameter_values) + modeltest.test_all(skip_output_tests=True) diff --git a/tests/unit/test_expression_tree/test_broadcasts.py b/tests/unit/test_expression_tree/test_broadcasts.py index f9500a6f90..75e2aad875 100644 --- a/tests/unit/test_expression_tree/test_broadcasts.py +++ b/tests/unit/test_expression_tree/test_broadcasts.py @@ -332,6 +332,14 @@ def test_to_equation(self): a = pybamm.PrimaryBroadcast(0, "test").to_equation() self.assertEqual(a, 0) + def test_diff(self): + a = pybamm.StateVector(slice(0, 1)) + b = pybamm.PrimaryBroadcast(a, "separator") + y = np.array([5]) + d = b.diff(a) + self.assertIsInstance(d, pybamm.PrimaryBroadcast) + self.assertEqual(d.child.evaluate(y=y), 1) + if __name__ == "__main__": print("Add -v for more debug output") From 4c0758d34cef068b479b557a43fc404476f7f084 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 6 Jul 2023 18:58:48 +0100 Subject: [PATCH 015/154] start msmr notebook --- .../examples/notebooks/models/MSMR.ipynb | 295 +++++++++++++ .../lithium_ion/basic_spm_msmr.py | 411 ------------------ .../lithium_ion/electrode_soh.py | 135 ++++-- .../open_circuit_potential/msmr_ocp.py | 10 +- .../submodels/particle/msmr_diffusion.py | 10 +- 5 files changed, 395 insertions(+), 466 deletions(-) create mode 100644 docs/source/examples/notebooks/models/MSMR.ipynb delete mode 100644 pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb new file mode 100644 index 0000000000..639f432396 --- /dev/null +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -0,0 +1,295 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Species Multi-Reaction model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Equations" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example solving MSMR using PyBaMM" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#%pip install pybamm -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.lithium_ion.SPM(\n", + " {\n", + " \"open-circuit potential\": \"MSMR\",\n", + " \"particle\": \"MSMR\",\n", + " }\n", + ")\n", + "\n", + "parameter_values = pybamm.ParameterValues(\"MSMR_Example\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# x_n\n", + "U_n = model.variables[\"Negative electrode open-circuit potential [V]\"]\n", + "T = model.variables[\"Negative electrode temperature [K]\"]\n", + "f = pybamm.constants.F / (pybamm.constants.R * T)\n", + "for i in range(6):\n", + " U0 = pybamm.Parameter(f\"U0_n_{i}\")\n", + " w = pybamm.Parameter(f\"w_n_{i}\")\n", + " Xj = pybamm.Parameter(f\"Xj_n_{i}\")\n", + "\n", + " x_n = Xj / (1 + pybamm.exp(f * (U_n - U0) / w))\n", + " model.variables[f\"x{i}_n\"] = x_n\n", + " model.variables[f\"X-averaged x{i}_n\"] = pybamm.x_average(x_n)\n", + " \n", + "\n", + "# x_p\n", + "U_p = model.variables[\"Positive electrode open-circuit potential [V]\"]\n", + "T = model.variables[\"Positive electrode temperature [K]\"]\n", + "f = pybamm.constants.F / (pybamm.constants.R * T)\n", + "for i in range(4):\n", + " U0 = pybamm.Parameter(f\"U0_p_{i}\")\n", + " w = pybamm.Parameter(f\"w_p_{i}\")\n", + " Xj = pybamm.Parameter(f\"Xj_p_{i}\")\n", + "\n", + " x_p = Xj / (1 + pybamm.exp(f * (U_p - U0) / w))\n", + " model.variables[f\"x{i}_p\"] = x_p\n", + " model.variables[f\"X-averaged x{i}_p\"] = pybamm.x_average(x_p)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "experiment = pybamm.Experiment(\n", + " [\n", + " (\n", + " \"Discharge at 1C for 1 hour or until 3 V\",\n", + " \"Rest for 1 hour\",\n", + " \"Charge at C/3 until 4 V\",\n", + " \"Hold at 4 V until 10 mA\",\n", + " \"Rest for 1 hour\",\n", + " ),\n", + " ]\n", + ")\n", + "sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment)\n", + "sim.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a2be754ae444465a9b5c413582e703f1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=5.86646556905814, step=0.0586646556905814), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.plot(\n", + " [\n", + " \"X-averaged negative particle stoichiometry\",\n", + " \"X-averaged positive particle stoichiometry\",\n", + " \"X-averaged negative particle potential [V]\",\n", + " \"X-averaged positive particle potential [V]\",\n", + " [f\"X-averaged x{i}_n\" for i in range(6)],\n", + " [f\"X-averaged x{i}_p\" for i in range(4)],\n", + " \"X-averaged negative electrode open-circuit potential [V]\",\n", + " \"X-averaged positive electrode open-circuit potential [V]\",\n", + " \"Current [A]\",\n", + " \"Voltage [V]\",\n", + " ]\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison with ideal diffusion and \"standard\" OCV model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [], + "source": [ + "def U_n(sto):\n", + " u_eq = (\n", + " 0.3635 * pybamm.exp(-439.3 * sto)\n", + " + 0.6908\n", + " + 0.5489 * pybamm.tanh(-30.07 * sto)\n", + " - 0.0344 * pybamm.tanh(25.46 * (sto - 0.1713))\n", + " - 0.0276 * pybamm.tanh(14.27 * (sto - 0.5270))\n", + "\n", + " )\n", + " return u_eq\n", + "\n", + "def U_p(sto):\n", + " u_eq = (\n", + " -0.3415 * sto\n", + " + 4.116\n", + " - 0.2835 * pybamm.tanh(4.181 * (sto - 0.2324))\n", + " - 0.1020 * pybamm.tanh(29.61 * (sto - 1))\n", + " )\n", + " return u_eq" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_values_2 = parameter_values.copy()\n", + "parameter_values_2.update({\n", + " \"Negative electrode OCP [V]\": U_n,\n", + " \"Positive electrode OCP [V]\": U_p,\n", + "})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[2] Daniel R Baker and Mark W Verbrugge. Multi-species, multi-reaction model for porous intercalation electrodes: part i. model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode. Journal of The Electrochemical Society, 165(16):A3952, 2018.\n", + "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[8] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", + "\n" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "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": "3.9.16" + }, + "vscode": { + "interpreter": { + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py b/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py deleted file mode 100644 index 76beb7a5ef..0000000000 --- a/pybamm/models/full_battery_models/lithium_ion/basic_spm_msmr.py +++ /dev/null @@ -1,411 +0,0 @@ -# -# Basic Single Particle MSMR Model (SPMSMR) -# -import pybamm - - -def electrolyte_diffusivity_Nyman2008(c_e, T): - D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10 - return D_c_e - - -def electrolyte_conductivity_Nyman2008(c_e, T): - sigma_e = ( - 0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000) - ) - return sigma_e - - -def x_n(U): - T = 298.15 - f = pybamm.constants.F / (pybamm.constants.R * T) - xj = 0 - for i in range(6): - U0 = pybamm.Parameter(f"U0_n_{i}") - w = pybamm.Parameter(f"w_n_{i}") - Xj = pybamm.Parameter(f"Xj_n_{i}") - - xj += Xj / (1 + pybamm.exp(f * (U - U0) / w)) - - return xj - - -def dxdU_n(U): - T = 298.15 - f = pybamm.constants.F / (pybamm.constants.R * T) - dxj = 0 - for i in range(6): - U0 = pybamm.Parameter(f"U0_n_{i}") - w = pybamm.Parameter(f"w_n_{i}") - Xj = pybamm.Parameter(f"Xj_n_{i}") - - e = pybamm.exp(f * (U - U0) / w) - dxj += -(f / w) * (Xj * e) / (1 + e) ** 2 - - return dxj - - -def x_p(U): - T = 298.15 - f = pybamm.constants.F / (pybamm.constants.R * T) - xj = 0 - for i in range(4): - U0 = pybamm.Parameter(f"U0_p_{i}") - w = pybamm.Parameter(f"w_p_{i}") - Xj = pybamm.Parameter(f"Xj_p_{i}") - - xj += Xj / (1 + pybamm.exp(f * (U - U0) / w)) - - return xj - - -def dxdU_p(U): - T = 298.15 - f = pybamm.constants.F / (pybamm.constants.R * T) - dxj = 0 - for i in range(4): - U0 = pybamm.Parameter(f"U0_p_{i}") - w = pybamm.Parameter(f"w_p_{i}") - Xj = pybamm.Parameter(f"Xj_p_{i}") - - e = pybamm.exp(f * (U - U0) / w) - dxj += -(f / w) * (Xj * e) / (1 + e) ** 2 - - return dxj - - -def get_parameter_values(): - return { - # cell - "Negative electrode thickness [m]": 7.56e-05, - "Separator thickness [m]": 1.2e-05, - "Positive electrode thickness [m]": 7.56e-05, - "Electrode height [m]": 0.065, - "Electrode width [m]": 1.58, - "Nominal cell capacity [A.h]": 5.0, - "Current function [A]": 5.0, - "Contact resistance [Ohm]": 0, - # negative electrode - "Negative electrode stoichiometry": x_n, - "Negative electrode stoichiometry change [V-1]": dxdU_n, - "U0_n_0": 0.08843, - "Xj_n_0": 0.43336, - "w_n_0": 0.08611, - "U0_n_1": 0.12799, - "Xj_n_1": 0.23963, - "w_n_1": 0.08009, - "U0_n_2": 0.14331, - "Xj_n_2": 0.15018, - "w_n_2": 0.72469, - "U0_n_3": 0.16984, - "Xj_n_3": 0.05462, - "w_n_3": 2.53277, - "U0_n_4": 0.21446, - "Xj_n_4": 0.06744, - "w_n_4": 0.09470, - "U0_n_5": 0.36325, - "Xj_n_5": 0.05476, - "w_n_5": 5.97354, - "Negative electrode stoichiometry at 0% SOC": 0.03, - "Negative electrode stoichiometry at 100% SOC": 0.9, - "Negative electrode conductivity [S.m-1]": 215.0, - "Maximum concentration in negative electrode [mol.m-3]": 33133.0, - "Negative electrode diffusivity [m2.s-1]": 3.3e-14, - "Negative electrode porosity": 0.25, - "Negative electrode active material volume fraction": 0.75, - "Negative particle radius [m]": 5.86e-06, - "Negative electrode Bruggeman coefficient (electrolyte)": 1.5, - "Negative electrode Bruggeman coefficient (electrode)": 0, - "Negative electrode exchange-current density [A.m-2]" "": 2.7, - "Negative electrode OCP entropic change [V.K-1]": 0.0, - # positive electrode - "Positive electrode stoichiometry": x_p, - "Positive electrode stoichiometry change [V-1]": dxdU_p, - "U0_p_0": 3.62274, - "Xj_p_0": 0.13442, - "w_p_0": 0.96710, - "U0_p_1": 3.72645, - "Xj_p_1": 0.32460, - "w_p_1": 1.39712, - "U0_p_2": 3.90575, - "Xj_p_2": 0.21118, - "w_p_2": 3.50500, - "U0_p_3": 4.22955, - "Xj_p_3": 0.32980, - "w_p_3": 5.52757, - "Positive electrode stoichiometry at 0% SOC": 0.85, - "Positive electrode stoichiometry at 100% SOC": 0.1, - "Positive electrode conductivity [S.m-1]": 0.18, - "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": 4e-15, - "Positive electrode porosity": 0.335, - "Positive electrode active material volume fraction": 0.665, - "Positive particle radius [m]": 5.22e-06, - "Positive electrode Bruggeman coefficient (electrolyte)": 1.5, - "Positive electrode Bruggeman coefficient (electrode)": 0, - "Positive electrode exchange-current density [A.m-2]" "": 5, - "Positive electrode OCP entropic change [V.K-1]": 0.0, - # separator - "Separator porosity": 0.47, - "Separator Bruggeman coefficient (electrolyte)": 1.5, - # electrolyte - "Initial concentration in electrolyte [mol.m-3]": 1000.0, - "Cation transference number": 0.2594, - "Thermodynamic factor": 1.0, - "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Nyman2008, - "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Nyman2008, - # experiment - "Reference temperature [K]": 298.15, - "Total heat transfer coefficient [W.m-2.K-1]": 10.0, - "Ambient temperature [K]": 298.15, - "Number of electrodes connected in parallel to make a cell": 1.0, - "Number of cells connected in series to make a battery": 1.0, - "Lower voltage cut-off [V]": 1, - "Upper voltage cut-off [V]": 5, - "Initial temperature [K]": 298.15, - } - - -class BasicSPMSMR(pybamm.lithium_ion.BaseModel): - def __init__(self, name="Single Particle MSMR Model"): - super().__init__({}, name) - param = self.param - - ###################### - # Parameters - ###################### - - def x_n_fun(U_n): - inputs = {"Negative electrode OCP [V]": U_n} - return pybamm.FunctionParameter( - "Negative electrode stoichiometry", inputs=inputs - ) - - def x_p_fun(U_p): - inputs = {"Positive electrode OCP [V]": U_p} - return pybamm.FunctionParameter( - "Positive electrode stoichiometry", inputs=inputs - ) - - def dxdU_n_fun(U_n): - inputs = {"Negative electrode OCP [V]": U_n} - return pybamm.FunctionParameter( - "Negative electrode stoichiometry change [V-1]", inputs=inputs - ) - - def dxdU_p_fun(U_p): - inputs = {"Positive electrode OCP [V]": U_p} - return pybamm.FunctionParameter( - "Positive electrode stoichiometry change [V-1]", inputs=inputs - ) - - ###################### - # Variables - ###################### - Q = pybamm.Variable("Discharge capacity [A.h]") - U_n_dist = pybamm.Variable( - "X-averaged negative particle OCP [V]", domain="negative particle" - ) - U_p_dist = pybamm.Variable( - "X-averaged positive particle OCP [V]", domain="positive particle" - ) - U_n = pybamm.surf(U_n_dist) - U_p = pybamm.surf(U_p_dist) - - # Constant temperature - T = param.T_init - - # Current density - i_cell = param.current_density_with_time - a_n = 3 * param.n.prim.epsilon_s_av / param.n.prim.R_typ - a_p = 3 * param.p.prim.epsilon_s_av / param.p.prim.R_typ - j_n = i_cell / (param.n.L * a_n) - j_p = -i_cell / (param.p.L * a_p) - - ###################### - # State of Charge - ###################### - I = param.current_with_time - # The `rhs` dictionary contains differential equations, with the key being the - # variable in the d/dt - self.rhs[Q] = I / 3600 - # Initial conditions must be provided for the ODEs - self.initial_conditions[Q] = pybamm.Scalar(0) - - ###################### - # Particles - ###################### - - F = param.F - f = F / (param.R * T) - c_n_max = param.n.prim.c_max - c_p_max = param.p.prim.c_max - x_n = x_n_fun(U_n_dist) - x_p = x_p_fun(U_p_dist) - dxdU_n = dxdU_n_fun(U_n_dist) - dxdU_p = dxdU_p_fun(U_p_dist) - c_n = c_n_max * x_n - c_p = c_p_max * x_p - D_n = param.n.prim.D(c_n, T) - D_p = param.p.prim.D(c_p, T) - N_n = c_n_max * x_n * (1 - x_n) * f * D_n * pybamm.grad(U_n_dist) - N_p = c_p_max * x_p * (1 - x_p) * f * D_p * pybamm.grad(U_p_dist) - - self.rhs[U_n_dist] = -pybamm.div(N_n) / c_n_max / dxdU_n - self.rhs[U_p_dist] = -pybamm.div(N_p) / c_p_max / dxdU_p - - self.boundary_conditions[U_n_dist] = { - "left": (pybamm.Scalar(0), "Neumann"), - "right": ( - (j_n / F) / pybamm.surf(c_n_max * x_n * (1 - x_n) * f * D_n), - "Neumann", - ), - } - self.boundary_conditions[U_p_dist] = { - "left": (pybamm.Scalar(0), "Neumann"), - "right": ( - (j_p / F) / pybamm.surf(c_p_max * x_p * (1 - x_p) * f * D_p), - "Neumann", - ), - } - - self.initial_conditions[U_n_dist] = pybamm.Parameter( - "Initial negative electrode potential [V]" - ) - self.initial_conditions[U_p_dist] = pybamm.Parameter( - "Initial positive electrode potential [V]" - ) - - ###################### - # (Some) variables - ###################### - phi_s_n = 0 - phi_s_p = U_p - U_n - V = phi_s_p - - self.variables = { - "Discharge capacity [A.h]": Q, - "Current [A]": I, - "X-averaged negative particle stoichiometry": x_n, - "X-averaged positive particle stoichiometry": x_p, - "X-averaged negative electrode extent of lithiation": pybamm.r_average(x_n), - "X-averaged positive electrode extent of lithiation": pybamm.r_average(x_p), - "X-averaged negative particle surface stoichiometry": pybamm.surf(x_n), - "X-averaged positive particle surface stoichiometry": pybamm.surf(x_p), - "X-averaged negative particle concentration": c_n, - "X-averaged positive particle concentration": c_p, - "X-averaged negative electrode stoichiometry change [V-1]": dxdU_n, - "X-averaged positive electrode stoichiometry change [V-1]": dxdU_p, - "X-averaged negative particle OCP [V]": U_n_dist, - "X-averaged positive particle OCP [V]": U_p_dist, - "X-averaged negative electrode OCP [V]": U_n, - "X-averaged positive electrode OCP [V]": U_p, - "Negative electrode potential [V]": pybamm.PrimaryBroadcast( - phi_s_n, "negative electrode" - ), - "Positive electrode potential [V]": pybamm.PrimaryBroadcast( - phi_s_p, "positive electrode" - ), - "Voltage [V]": V, - } - - # x_n - for i in range(6): - U0 = pybamm.Parameter(f"U0_n_{i}") - w = pybamm.Parameter(f"w_n_{i}") - Xj = pybamm.Parameter(f"Xj_n_{i}") - - self.variables[f"x{i}_n"] = Xj / (1 + pybamm.exp(f * (U_n - U0) / w)) - - # x_p - for i in range(4): - U0 = pybamm.Parameter(f"U0_p_{i}") - w = pybamm.Parameter(f"w_p_{i}") - Xj = pybamm.Parameter(f"Xj_p_{i}") - - self.variables[f"x{i}_p"] = Xj / (1 + pybamm.exp(f * (U_p - U0) / w)) - - # events - self.events += [ - pybamm.Event("Minimum voltage [V]", V - param.voltage_low_cut), - pybamm.Event("Maximum voltage [V]", param.voltage_high_cut - V), - ] - - @property - def default_parameter_values(self): - return pybamm.ParameterValues(get_parameter_values()) - - @property - def default_quick_plot_variables(self): - return [ - "X-averaged negative particle stoichiometry", - "X-averaged positive particle stoichiometry", - "X-averaged negative particle OCP [V]", - "X-averaged positive particle OCP [V]", - "X-averaged negative electrode OCP [V]", - "X-averaged positive electrode OCP [V]", - "Current [A]", - "Voltage [V]", - ] - - -if __name__ == "__main__": - model = BasicSPMSMR() - parameter_values = model.default_parameter_values - - soc_model = pybamm.BaseModel() - U_n = pybamm.Variable("U_n") - U_p = pybamm.Variable("U_p") - x_0 = parameter_values["Negative electrode stoichiometry at 0% SOC"] - x_100 = parameter_values["Negative electrode stoichiometry at 100% SOC"] - y_0 = parameter_values["Positive electrode stoichiometry at 0% SOC"] - y_100 = parameter_values["Positive electrode stoichiometry at 100% SOC"] - initial_soc = pybamm.InputParameter("Initial soc") - x = x_0 + initial_soc * (x_100 - x_0) - y = y_0 - initial_soc * (y_0 - y_100) - soc_model.algebraic = {U_n: x - x_n(U_n), U_p: y - x_p(U_p)} - soc_model.initial_conditions = {U_n: pybamm.Scalar(0), U_p: pybamm.Scalar(4)} - soc_model.variables = {"U_n": U_n, "U_p": U_p, "x": x, "y": y} - parameter_values.process_model(soc_model) - soc_sol = pybamm.AlgebraicSolver(tol=1e-6).solve( - soc_model, inputs={"Initial soc": 1} - ) - x, y = soc_sol["x"].data[0], soc_sol["y"].data[0] - U_n, U_p = soc_sol["U_n"].data[0], soc_sol["U_p"].data[0] - - def current(t): - return 5 * (t < 3000) - - parameter_values.update( - { - "Initial negative electrode potential [V]": U_n, - "Initial positive electrode potential [V]": U_p, - "Current function [A]": current, - }, - check_already_exists=False, - ) - c_n_max = parameter_values["Maximum concentration in negative electrode [mol.m-3]"] - c_p_max = parameter_values["Maximum concentration in positive electrode [mol.m-3]"] - print(x * c_n_max, y * c_p_max) - print(U_n, U_p) - - sim = pybamm.Simulation(model, parameter_values=parameter_values) - sim.solve([0, 4000]) - sim.plot( - [ - [ - "X-averaged negative electrode extent of lithiation", - "X-averaged negative particle surface stoichiometry", - ], - [ - "X-averaged positive electrode extent of lithiation", - "X-averaged positive particle surface stoichiometry", - ], - "X-averaged negative electrode OCP [V]", - "X-averaged positive electrode OCP [V]", - [f"x{i}_n" for i in range(6)], - [f"x{i}_p" for i in range(4)], - "Current [A]", - "Voltage [V]", - ] - ) diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 194b013dc6..861ab0a26e 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -77,11 +77,10 @@ def default_solver(self): class _ElectrodeSOH(_BaseElectrodeSOH): - """Model to calculate electrode-specific SOH, from :footcite:t:`Mohtat2019`. - - This model is mainly for internal use, to calculate summary variables in a - simulation. - Some of the output variables are defined in [2]_. + """ + Model to calculate electrode-specific SOH, from :footcite:t:`Mohtat2019`. This + model is mainly for internal use, to calculate summary variables in a simulation. + Some of the output variables are defined in :footcite:t:`Weng2023`. .. math:: Q_{Li} = y_{100}Q_p + x_{100}Q_n, @@ -177,13 +176,14 @@ def __init__( class _ElectrodeSOHMSMR(_BaseElectrodeSOH): """ - Model to calculate electrode-specific SOH using the MSMR formulation, see - :class:`_ElectrodeSOH`. + Model to calculate electrode-specific SOH using the MSMR formulation from + :footcite:t:`Baker2018`. See :class:`_ElectrodeSOH` for more details. """ def __init__( self, param=None, solve_for=None, known_value="cyclable lithium capacity" ): + pybamm.citations.register("Baker2018") super().__init__() param = param or pybamm.LithiumIonParameters({"open-circuit potential": "MSMR"}) @@ -757,6 +757,27 @@ def get_min_max_stoichiometries(self): sol = self.solve(inputs) return [sol["x_0"], sol["x_100"], sol["y_100"], sol["y_0"]] + def get_initial_ocps(self, initial_value): + """ + Calculate initial open-circuit potentials to start off the simulation at a + particular state of charge, given voltage limits, open-circuit potentials, etc + defined by parameter_values + + Parameters + ---------- + initial_value : float + Target SOC, must be between 0 and 1. + + Returns + ------- + Un, Up + The initial open-circuit potentials at the desired initial state of charge + """ + # TODO: For "normal" model get init sto and eval OCP. For msmr get init sto and + # use _get_msmr_potential_model to get OCP. This is to be consistent with + # linearly interpolating in sto to define soc + raise NotImplementedError + def get_min_max_ocps(self): """ Calculate min/max open-circuit potentials @@ -784,36 +805,6 @@ def get_min_max_ocps(self): return [sol["Un(x_0)"], sol["Un(x_100)"], sol["Up(y_100)"], sol["Up(y_0)"]] -def _get_msmr_potential_model(parameter_values, param): - """ - Returns a solver to calculate the open-circuit potentials of the indivdual - electrodes at the given stoichiometries - """ - V_max = param.voltage_high_cut - V_min = param.voltage_low_cut - X_n = param.n.prim.X - X_p = param.p.prim.X - model = pybamm.BaseModel() - Un = pybamm.Variable("Un") - Up = pybamm.Variable("Up") - x = pybamm.InputParameter("x") - y = pybamm.InputParameter("y") - model.algebraic = { - Un: X_n(Un) - x, - Up: X_p(Up) - y, - } - model.initial_conditions = { - Un: 1 - x, - Up: V_max * (1 - y) + V_min * y, - } - model.variables = { - "Un": Un, - "Up": Up, - } - parameter_values.process_model(model) - return model - - def get_initial_stoichiometries( initial_value, parameter_values, @@ -885,6 +876,46 @@ def get_min_max_stoichiometries( return esoh_solver.get_min_max_stoichiometries() +def get_initial_ocps( + initial_value, + parameter_values, + param=None, + known_value="cyclable lithium capacity", + options=None, +): + """ + Calculate initial open-circuit potentials to start off the simulation at a + particular state of charge, given voltage limits, open-circuit potentials, etc + defined by parameter_values + + Parameters + ---------- + initial_value : float + Target initial value. + If integer, interpreted as SOC, must be between 0 and 1. + If string e.g. "4 V", interpreted as voltage, must be between V_min and V_max. + parameter_values : :class:`pybamm.ParameterValues` + The parameter values class that will be used for the simulation. Required for + calculating appropriate initial stoichiometries. + param : :class:`pybamm.LithiumIonParameters`, optional + The symbolic parameter set to use for the simulation. + If not provided, the default parameter set will be used. + known_value : str, optional + The known value needed to complete the electrode SOH model. + Can be "cyclable lithium capacity" (default) or "cell capacity". + options : dict-like, optional + A dictionary of options to be passed to the model, see + :class:`pybamm.BatteryModelOptions`. + + Returns + ------- + x, y + The initial stoichiometries that give the desired initial state of charge + """ + esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) + return esoh_solver.get_initial_ocps(initial_value) + + def get_min_max_ocps( parameter_values, param=None, known_value="cyclable lithium capacity", options=None ): @@ -984,3 +1015,33 @@ def calculate_theoretical_energy( parameter_values, x_100, x_0, y_100, y_0, points=points ) return E + + +def _get_msmr_potential_model(parameter_values, param): + """ + Returns a solver to calculate the open-circuit potentials of the individual + electrodes at the given stoichiometries + """ + V_max = param.voltage_high_cut + V_min = param.voltage_low_cut + X_n = param.n.prim.X + X_p = param.p.prim.X + model = pybamm.BaseModel() + Un = pybamm.Variable("Un") + Up = pybamm.Variable("Up") + x = pybamm.InputParameter("x") + y = pybamm.InputParameter("y") + model.algebraic = { + Un: X_n(Un) - x, + Up: X_p(Up) - y, + } + model.initial_conditions = { + Un: 1 - x, + Up: V_max * (1 - y) + V_min * y, + } + model.variables = { + "Un": Un, + "Up": Up, + } + parameter_values.process_model(model) + return model diff --git a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py index d2139d33ca..65dc90df09 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py @@ -8,15 +8,7 @@ class MSMROpenCircuitPotential(BaseOpenCircuitPotential): """ Class for open-circuit potential within the Multi-Species Multi-Reaction - framework [1]_. - - References - ---------- - .. [1] DR Baker and MW Verbrugge. "Multi-species, multi-reaction model for porous - intercalation electrodes: Part I. Model formulation and a perturbation - solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium - manganese oxide electrode." Journal of The Electrochemical Society, - 165(16):A3952, 2019 + framework :footcite:t:`Baker2018`. """ def get_coupled_variables(self, variables): diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index 3f3cf0e7d3..f534909d0d 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -8,7 +8,7 @@ class MSMRDiffusion(BaseParticle): """ Class for molar conservation in particles within the Multi-Species Multi-Reaction - framework [1]_. + framework :footcite:t:`Baker2018`. Parameters ---------- @@ -23,14 +23,6 @@ class MSMRDiffusion(BaseParticle): Phase of the particle (default is "primary") x_average : bool Whether the particle concentration is averaged over the x-direction - - References - ---------- - .. [1] DR Baker and MW Verbrugge. "Multi-species, multi-reaction model for porous - intercalation electrodes: Part I. Model formulation and a perturbation - solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium - manganese oxide electrode." Journal of The Electrochemical Society, - 165(16):A3952, 2019 """ def __init__(self, param, domain, options, phase="primary", x_average=False): From c94d842de7b7a473c458d1cecf10a0fcb4ee9985 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Sun, 9 Jul 2023 12:38:08 +0100 Subject: [PATCH 016/154] improve coverage --- examples/scripts/MSMR.py | 1 - pybamm/expression_tree/broadcasts.py | 16 ----- pybamm/expression_tree/unary_operators.py | 5 -- .../lithium_ion/electrode_soh.py | 59 +++++++++++-------- .../test_expression_tree/test_broadcasts.py | 10 ++++ .../test_base_lead_acid_model.py | 7 +++ .../test_lithium_ion/test_electrode_soh.py | 22 +++++++ .../test_lithium_ion/test_mpm.py | 8 +++ 8 files changed, 82 insertions(+), 46 deletions(-) diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index dc7b0ddaa4..f1d78215bd 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -1,6 +1,5 @@ import pybamm -pybamm.set_logging_level("DEBUG") model = pybamm.lithium_ion.SPM( { "open-circuit potential": "MSMR", diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py index 622876c262..32cf2c002b 100644 --- a/pybamm/expression_tree/broadcasts.py +++ b/pybamm/expression_tree/broadcasts.py @@ -45,22 +45,6 @@ def _sympy_operator(self, child): """Override :meth:`pybamm.UnaryOperator._sympy_operator`""" return child - def diff(self, variable): - """ - Override :meth:`pybamm.SpatialOperator.diff()` to reinstate behaviour of - :meth:`pybamm.Symbol.diff()`. - """ - if variable == self: - return pybamm.Scalar(1) - elif any(variable == x for x in self.pre_order()): - return self._diff(variable) - elif variable == pybamm.t and self.has_symbol_of_classes( - (pybamm.VariableBase, pybamm.StateVectorBase) - ): - return self._diff(variable) - else: - return pybamm.Scalar(0) - def _diff(self, variable): """See :meth:`pybamm.Symbol._diff()`.""" # Differentiate the child and broadcast the result in the same way diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py index ffa14ce007..7f9c45775c 100644 --- a/pybamm/expression_tree/unary_operators.py +++ b/pybamm/expression_tree/unary_operators.py @@ -339,11 +339,6 @@ class with a :class:`Matrix` def __init__(self, name, child, domains=None): super().__init__(name, child, domains) - def diff(self, variable): - """See :meth:`pybamm.Symbol.diff()`.""" - # We shouldn't need this, except for Broadcasts - raise NotImplementedError - class Gradient(SpatialOperator): """ diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 861ab0a26e..4951388d4a 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -603,44 +603,55 @@ def _check_esoh_feasible(self, inputs): """ x0_min, x100_max, y100_min, y0_max = self._get_lims(inputs) - if self.options["open-circuit potential"] == "MSMR": - Un0, Un100, Up100, Up0 = self._get_ocp_msmr( - x0_min, x100_max, y100_min, y0_max + # Parameterize the OCP functions + if self.OCV_function is None: + self.V_max = self.parameter_values.evaluate( + self.param.opc_soc_100_dimensional ) - V_lower_bound = float(Up0 - Un0) - V_upper_bound = float(Up100 - Un100) - else: - # Parameterize the OCP functions - if self.OCV_function is None: + self.V_min = self.parameter_values.evaluate( + self.param.opc_soc_0_dimensional + ) + if self.options["open-circuit potential"] == "MSMR": + # will solve for potentials at the sto limits, so no need + # to store a function + self.OCV_function = "MSMR" + else: T = self.parameter_values["Reference temperature [K]"] x = pybamm.InputParameter("x") y = pybamm.InputParameter("y") - self.V_max = self.parameter_values.evaluate( - self.param.opc_soc_100_dimensional - ) - self.V_min = self.parameter_values.evaluate( - self.param.opc_soc_0_dimensional - ) self.OCV_function = self.parameter_values.process_symbol( self.param.p.prim.U(y, T) - self.param.n.prim.U(x, T) ) + + # Evaluate OCP function + if self.options["open-circuit potential"] == "MSMR": + msmr_pot_model = _get_msmr_potential_model( + self.parameter_values, self.param + ) + sol0 = pybamm.AlgebraicSolver(tol=1e-4).solve( + msmr_pot_model, inputs={"x": x0_min, "y": y0_max} + ) + sol100 = pybamm.AlgebraicSolver(tol=1e-4).solve( + msmr_pot_model, inputs={"x": x100_max, "y": y100_min} + ) + Up0 = sol0["Up"].data[0] + Un0 = sol0["Un"].data[0] + Up100 = sol100["Up"].data[0] + Un100 = sol100["Un"].data[0] + V_lower_bound = float(Up0 - Un0) + V_upper_bound = float(Up100 - Un100) + else: + # address numpy 1.25 deprecation warning: array should have ndim=0 + # before conversion V_lower_bound = float( - self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}) + self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}).item() ) V_upper_bound = float( - self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}) + self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}).item() ) # Check that the min and max achievable voltages span wider than the desired # voltage range - # address numpy 1.25 deprecation warning: array should have ndim=0 - # before conversion - V_lower_bound = float( - self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}).item() - ) - V_upper_bound = float( - self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}).item() - ) if V_lower_bound > self.V_min: raise ( ValueError( diff --git a/tests/unit/test_expression_tree/test_broadcasts.py b/tests/unit/test_expression_tree/test_broadcasts.py index 75e2aad875..81d1210229 100644 --- a/tests/unit/test_expression_tree/test_broadcasts.py +++ b/tests/unit/test_expression_tree/test_broadcasts.py @@ -336,9 +336,19 @@ def test_diff(self): a = pybamm.StateVector(slice(0, 1)) b = pybamm.PrimaryBroadcast(a, "separator") y = np.array([5]) + # diff of broadcast is broadcast of diff d = b.diff(a) self.assertIsInstance(d, pybamm.PrimaryBroadcast) self.assertEqual(d.child.evaluate(y=y), 1) + # diff of broadcast w.r.t. itself is 1 + d = b.diff(b) + self.assertIsInstance(d, pybamm.Scalar) + self.assertEqual(d.evaluate(y=y), 1) + # diff of broadcast of a constant is 0 + c = pybamm.PrimaryBroadcast(pybamm.Scalar(4), "separator") + d = c.diff(a) + self.assertIsInstance(d, pybamm.Scalar) + self.assertEqual(d.evaluate(y=y), 0) if __name__ == "__main__": diff --git a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py index aa62179e05..ec280cdd1f 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py @@ -27,6 +27,13 @@ def test_incompatible_options(self): pybamm.lead_acid.BaseModel({"SEI": "constant"}) with self.assertRaisesRegex(pybamm.OptionError, "lithium plating"): pybamm.lead_acid.BaseModel({"lithium plating": "reversible"}) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.lead_acid.BaseModel( + { + "open-circuit potential": "MSMR", + "particle": "MSMR", + } + ) if __name__ == "__main__": diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index da9529fc82..7baf7ad2f0 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -182,12 +182,18 @@ def test_known_solution(self): self.assertAlmostEqual(sol["Q_Li"], Q_Li, places=5) # Solve with split esoh and check outputs + esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( + parameter_values, param, options=options + ) ics = esoh_solver._set_up_solve(inputs) sol_split = esoh_solver._solve_split(inputs, ics) for key in sol: if key != "Maximum theoretical energy [W.h]": self.assertAlmostEqual(sol[key], sol_split[key].data[0], places=5) + # Check feasibility checks can be performed successfully + esoh_solver._check_esoh_feasible(inputs) + def test_known_solution_cell_capacity(self): options = {"open-circuit potential": "MSMR", "particle": "MSMR"} param = pybamm.LithiumIonParameters(options) @@ -294,6 +300,22 @@ def test_min_max_stoich(self): V = parameter_values.evaluate(param.p.prim.U(y0, T) - param.n.prim.U(x0, T)) self.assertAlmostEqual(V, 2.8) + def test_get_initial_ocp(self): + with self.assertRaises(NotImplementedError): + param = pybamm.LithiumIonParameters() + parameter_values = pybamm.ParameterValues("Mohtat2020") + Un, Up = pybamm.lithium_ion.get_initial_ocps(parameter_values, param) + + def test_min_max_ocp(self): + param = pybamm.LithiumIonParameters() + parameter_values = pybamm.ParameterValues("Mohtat2020") + + Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_stoichiometries( + parameter_values, param + ) + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + self.assertAlmostEqual(Up_0 - Un_0, 2.8) + def test_initial_soc_cell_capacity(self): param = pybamm.LithiumIonParameters() parameter_values = pybamm.ParameterValues("Mohtat2020") diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index 222f587a56..8b7b498453 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -108,6 +108,14 @@ def test_stress_induced_diffusion_not_implemented(self): with self.assertRaises(NotImplementedError): pybamm.lithium_ion.MPM(options) + def test_msmr(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + } + model = pybamm.lithium_ion.MPM(options) + model.check_well_posedness() + class TestMPMExternalCircuits(TestCase): def test_well_posed_voltage(self): From 75835b5f8f74945ffef28a77a91e072c447bd3b9 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Sun, 9 Jul 2023 16:43:44 +0100 Subject: [PATCH 017/154] update esoh --- CHANGELOG.md | 6 ++ .../lithium_ion/__init__.py | 1 + .../lithium_ion/electrode_soh.py | 59 ++++++++++------ .../lithium_ion/electrode_soh_half_cell.py | 4 +- pybamm/parameters/electrical_parameters.py | 4 +- pybamm/parameters/lithium_ion_parameters.py | 4 +- .../test_lithium_ion/test_electrode_soh.py | 70 ++++++++++++++----- 7 files changed, 104 insertions(+), 44 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2b7365f3ec..754788f980 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,11 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) +## Features +- Implement the MSMR model ([#3116](https://github.com/pybamm-team/PyBaMM/pull/3116)) +## Bug fixes + +- Rename `param.opc_soc_0_dimensional` and `param.opc_soc_100_dimensional` to `param.ocp_soc_0_dimensional` and `param.ocp_soc_100_dimensional` (`opc` to `ocp`) ([#3116](https://github.com/pybamm-team/PyBaMM/pull/3116)) + # [v23.5](https://github.com/pybamm-team/PyBaMM/tree/v23.5) - 2023-06-18 ## Features diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py index 51859b164b..8b63222eb9 100644 --- a/pybamm/models/full_battery_models/lithium_ion/__init__.py +++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py @@ -6,6 +6,7 @@ ElectrodeSOHSolver, get_initial_stoichiometries, get_min_max_stoichiometries, + get_initial_ocps, get_min_max_ocps, ) from .electrode_soh_half_cell import ElectrodeSOHHalfCell diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 4951388d4a..15f80ffe4f 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -113,8 +113,8 @@ def __init__( Up = param.p.prim.U T_ref = param.T_ref - V_max = param.opc_soc_100_dimensional - V_min = param.opc_soc_0_dimensional + V_max = param.ocp_soc_100_dimensional + V_min = param.ocp_soc_0_dimensional Q_n = pybamm.InputParameter("Q_n") Q_p = pybamm.InputParameter("Q_p") @@ -308,7 +308,6 @@ def _get_lims_ocp(self): if self.options["open-circuit potential"] == "MSMR": OCPp_data = False OCPn_data = False - else: OCPp_data = isinstance( parameter_values["Positive electrode OCP [V]"], tuple @@ -606,10 +605,10 @@ def _check_esoh_feasible(self, inputs): # Parameterize the OCP functions if self.OCV_function is None: self.V_max = self.parameter_values.evaluate( - self.param.opc_soc_100_dimensional + self.param.ocp_soc_100_dimensional ) self.V_min = self.parameter_values.evaluate( - self.param.opc_soc_0_dimensional + self.param.ocp_soc_0_dimensional ) if self.options["open-circuit potential"] == "MSMR": # will solve for potentials at the sto limits, so no need @@ -695,14 +694,9 @@ def get_initial_stoichiometries(self, initial_value): x_0, x_100, y_100, y_0 = self.get_min_max_stoichiometries() if isinstance(initial_value, str) and initial_value.endswith("V"): - if self.options["open-circuit potential"] == "MSMR": - raise NotImplementedError( - "Getting initial stoichiometries from voltage not implemented " - "for MSMR models" - ) V_init = float(initial_value[:-1]) - V_min = parameter_values.evaluate(param.opc_soc_0_dimensional) - V_max = parameter_values.evaluate(param.opc_soc_100_dimensional) + V_min = parameter_values.evaluate(param.ocp_soc_0_dimensional) + V_max = parameter_values.evaluate(param.ocp_soc_100_dimensional) if not V_min < V_init < V_max: raise ValueError( @@ -713,13 +707,23 @@ def get_initial_stoichiometries(self, initial_value): # Solve simple model for initial soc based on target voltage soc_model = pybamm.BaseModel() soc = pybamm.Variable("soc") - Up = param.p.prim.U - Un = param.n.prim.U - T_ref = parameter_values["Reference temperature [K]"] x = x_0 + soc * (x_100 - x_0) y = y_0 - soc * (y_0 - y_100) - - soc_model.algebraic[soc] = Up(y, T_ref) - Un(x, T_ref) - V_init + if self.options["open-circuit potential"] == "MSMR": + Xn = param.n.prim.X + Xp = param.p.prim.X + Up = pybamm.Variable("Up") + Un = pybamm.Variable("Un") + soc_model.algebraic[Up] = x - Xn(Un) + soc_model.algebraic[Un] = y - Xp(Up) + soc_model.initial_conditions[Un] = 0 + soc_model.initial_conditions[Up] = V_max + soc_model.algebraic[soc] = Up - Un - V_init + else: + Up = param.p.prim.U + Un = param.n.prim.U + T_ref = parameter_values["Reference temperature [K]"] + soc_model.algebraic[soc] = Up(y, T_ref) - Un(x, T_ref) - V_init # initial guess for soc linearly interpolates between 0 and 1 # based on V linearly interpolating between V_max and V_min soc_model.initial_conditions[soc] = (V_init - V_min) / (V_max - V_min) @@ -784,10 +788,23 @@ def get_initial_ocps(self, initial_value): Un, Up The initial open-circuit potentials at the desired initial state of charge """ - # TODO: For "normal" model get init sto and eval OCP. For msmr get init sto and - # use _get_msmr_potential_model to get OCP. This is to be consistent with - # linearly interpolating in sto to define soc - raise NotImplementedError + parameter_values = self.parameter_values + param = self.param + x, y = self.get_initial_stoichiometries(initial_value) + if self.options["open-circuit potential"] == "MSMR": + msmr_pot_model = _get_msmr_potential_model( + self.parameter_values, self.param + ) + sol = pybamm.AlgebraicSolver().solve( + msmr_pot_model, inputs={"x": x, "y": y} + ) + Un = sol["Un"].data[0] + Up = sol["Up"].data[0] + else: + T_ref = parameter_values["Reference temperature [K]"] + Un = parameter_values.evaluate(param.n.prim.U(x, T_ref)) + Up = parameter_values.evaluate(param.p.prim.U(y, T_ref)) + return Un, Up def get_min_max_ocps(self): """ diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py index ed55a2d621..39aad1c896 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py @@ -37,8 +37,8 @@ def __init__(self, working_electrode, name="Electrode-specific SOH model"): U_w = param.p.prim.U Q = Q_w * (x_100 - x_0) - V_max = param.opc_soc_100_dimensional - V_min = param.opc_soc_0_dimensional + V_max = param.ocp_soc_100_dimensional + V_min = param.ocp_soc_0_dimensional self.algebraic = { x_100: U_w(x_100, T_ref) - V_max, diff --git a/pybamm/parameters/electrical_parameters.py b/pybamm/parameters/electrical_parameters.py index e4d574daed..946c47f53b 100644 --- a/pybamm/parameters/electrical_parameters.py +++ b/pybamm/parameters/electrical_parameters.py @@ -30,10 +30,10 @@ def _set_parameters(self): ) self.voltage_low_cut = pybamm.Parameter("Lower voltage cut-off [V]") self.voltage_high_cut = pybamm.Parameter("Upper voltage cut-off [V]") - self.opc_soc_0_dimensional = pybamm.Parameter( + self.ocp_soc_0_dimensional = pybamm.Parameter( "Open-circuit voltage at 0% SOC [V]" ) - self.opc_soc_100_dimensional = pybamm.Parameter( + self.ocp_soc_100_dimensional = pybamm.Parameter( "Open-circuit voltage at 100% SOC [V]" ) # Current as a function of time diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 2f0e1e58e3..d712f7b7d8 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -74,8 +74,8 @@ def _set_parameters(self): self.n_cells = self.elec.n_cells self.voltage_low_cut = self.elec.voltage_low_cut self.voltage_high_cut = self.elec.voltage_high_cut - self.opc_soc_0_dimensional = self.elec.opc_soc_0_dimensional - self.opc_soc_100_dimensional = self.elec.opc_soc_100_dimensional + self.ocp_soc_0_dimensional = self.elec.ocp_soc_0_dimensional + self.ocp_soc_100_dimensional = self.elec.ocp_soc_100_dimensional # Domain parameters for domain in self.domain_params.values(): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index 7baf7ad2f0..f9538ccfb7 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -250,7 +250,7 @@ def test_efficiency(self): ) ) # Real energy should be less than discharge energy, - # and both should be greater than 0 + # and both should be greater than 0 self.assertLess(discharge_energy, theoretical_energy) self.assertLess(0, discharge_energy) self.assertLess(0, theoretical_energy) @@ -300,22 +300,6 @@ def test_min_max_stoich(self): V = parameter_values.evaluate(param.p.prim.U(y0, T) - param.n.prim.U(x0, T)) self.assertAlmostEqual(V, 2.8) - def test_get_initial_ocp(self): - with self.assertRaises(NotImplementedError): - param = pybamm.LithiumIonParameters() - parameter_values = pybamm.ParameterValues("Mohtat2020") - Un, Up = pybamm.lithium_ion.get_initial_ocps(parameter_values, param) - - def test_min_max_ocp(self): - param = pybamm.LithiumIonParameters() - parameter_values = pybamm.ParameterValues("Mohtat2020") - - Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_stoichiometries( - parameter_values, param - ) - self.assertAlmostEqual(Up_100 - Un_100, 4.2) - self.assertAlmostEqual(Up_0 - Un_0, 2.8) - def test_initial_soc_cell_capacity(self): param = pybamm.LithiumIonParameters() parameter_values = pybamm.ParameterValues("Mohtat2020") @@ -342,6 +326,58 @@ def test_error(self): pybamm.lithium_ion.get_initial_stoichiometries("5 A", parameter_values) +class TestGetInitialOCP(TestCase): + def test_get_initial_ocp(self): + param = pybamm.LithiumIonParameters() + parameter_values = pybamm.ParameterValues("Mohtat2020") + Un, Up = pybamm.lithium_ion.get_initial_ocps(1, parameter_values, param) + self.assertAlmostEqual(Up - Un, 4.2) + Un, Up = pybamm.lithium_ion.get_initial_ocps(0, parameter_values, param) + self.assertAlmostEqual(Up - Un, 2.8) + Un, Up = pybamm.lithium_ion.get_initial_ocps("4 V", parameter_values, param) + self.assertAlmostEqual(Up - Un, 4) + + def test_min_max_ocp(self): + param = pybamm.LithiumIonParameters() + parameter_values = pybamm.ParameterValues("Mohtat2020") + + Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps( + parameter_values, param + ) + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + self.assertAlmostEqual(Up_0 - Un_0, 2.8) + + +class TestGetInitialOCPMSMR(TestCase): + def test_get_initial_ocp(self): + options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + param = pybamm.LithiumIonParameters(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + Un, Up = pybamm.lithium_ion.get_initial_ocps( + 1, parameter_values, param, options=options + ) + self.assertAlmostEqual(Up - Un, 4.2, places=5) + Un, Up = pybamm.lithium_ion.get_initial_ocps( + 0, parameter_values, param, options=options + ) + self.assertAlmostEqual(Up - Un, 2.5, places=5) + Un, Up = pybamm.lithium_ion.get_initial_ocps( + "4 V", parameter_values, param, options=options + ) + self.assertAlmostEqual(Up - Un, 4) + + def test_min_max_ocp(self): + options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + param = pybamm.LithiumIonParameters(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + + Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps( + parameter_values, param, options=options + ) + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + self.assertAlmostEqual(Up_0 - Un_0, 2.5) + + if __name__ == "__main__": print("Add -v for more debug output") import sys From 0f7c2abe87693359b49219337c2dc90806817cfb Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Mon, 10 Jul 2023 16:06:22 +0100 Subject: [PATCH 018/154] clean up notebook --- .../examples/notebooks/models/DFN.ipynb | 20 +- .../examples/notebooks/models/MSMR.ipynb | 216 ++++++++++++------ .../examples/notebooks/models/SPM.ipynb | 10 +- .../examples/notebooks/models/SPMe.ipynb | 24 +- 4 files changed, 169 insertions(+), 101 deletions(-) diff --git a/docs/source/examples/notebooks/models/DFN.ipynb b/docs/source/examples/notebooks/models/DFN.ipynb index c96ad08f79..9e9ce2e20a 100644 --- a/docs/source/examples/notebooks/models/DFN.ipynb +++ b/docs/source/examples/notebooks/models/DFN.ipynb @@ -62,22 +62,22 @@ "\n", "#### Current:\n", "$$\n", - "i_{\\text{e,n}}\\big|_{x=0} = 0, \\quad i_{\\text{e,p}}\\big|_{x=1}=0, \\\\\n", + "i_{\\text{e,n}}\\big|_{x=0} = 0, \\quad i_{\\text{e,p}}\\big|_{x=L}=0, \\\\\n", "\\phi_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = \\phi_{\\text{e,s}}\\big|_{x=L_{\\text{n}}}, \\quad i_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = i_{\\text{e,s}}\\big\\vert_{x=L_{\\text{n}}} = I, \\\\ \n", - "\\phi_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}} = \\phi_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}}, \\quad \n", - " i_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}} = i_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}} = I.\n", + "\\phi_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}} = \\phi_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}}, \\quad \n", + " i_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}} = i_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}} = I.\n", "$$\n", "\n", "#### Concentration in the electrolyte:\n", "$$\n", - "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=1}=0,\\\\ \n", + "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=L}=0,\\\\ \n", "c_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = c_{\\text{e,s}}|_{x=L_{\\text{n}}}, \\quad N_{\\text{e,n}}\\big|_{x=L_{\\text{n}}}=N_{\\text{e,s}}\\big|_{x=L_{\\text{n}}}, \\\\\n", - "c_{\\text{e,s}}|_{x=1-L_{\\text{p}}}=c_{\\text{e,p}}|_{x=1-L_{\\text{p}}}, \\quad N_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}}=N_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}}.\n", + "c_{\\text{e,s}}|_{x=L-L_{\\text{p}}}=c_{\\text{e,p}}|_{x=L-L_{\\text{p}}}, \\quad N_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}}=N_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}}.\n", "$$\n", "\n", "#### Concentration in the electrode active material:\n", "$$\n", - "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \\frac{j_{\\text{k}}}{F}, \\quad \\text{k} \\in \\text{n, p}.\n", + "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ N_{\\text{s,k}}\\big|_{r_{\\text{k}}=R_{\\text{k}}} = \\frac{j_{\\text{k}}}{F}, \\quad \\text{k} \\in \\text{n, p}.\n", "$$\n", "\n", "#### Reference potential:\n", @@ -87,8 +87,8 @@ "#### And the initial conditions:\n", "\n", "$$\n", - "c_{\\text{s,k}}(x,r,0) = c_{\\text{s,k,0}}, \\quad \\phi_{\\text{s,n}}(x,0) = 0, \\quad \\phi_{\\text{s,p}}(x,0) = \\phi_{\\text{s,p,0}}, \\\\ \\text{k} \\in \\text{n, p},\\\\\n", - "\\phi_{\\text{e,k}}(x,0) = \\phi_{\\text{e,0}}, \\quad c_{\\text{e,k}}(x,0) = 1, \\\\ \\text{k} \\in \\text{n, s, p}. \n", + "c_{\\text{s,k}}(x,r,0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},\\\\\n", + "c_{\\text{e,k}}(x,0) = c_{\\text{e,0}}, \\quad \\text{k} \\in \\text{n, s, p}. \n", "$$\n" ] }, @@ -269,7 +269,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -287,7 +287,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 639f432396..b762c32a07 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -13,26 +13,74 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Model Equations" + "## Model Equations\n", + "\n", + "Here we briefly outline the models used for the open-circuit potential and solid phase transport used in the MSMR model, as described in Baker and Verbrugge (2018). The remaining physics is modelled differently depending on which options are selected. By default, the rest of the battery model is as described in Maquis et al. (2019)." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "## Thermodynamics\n", + "The MSMR model is developed by assuming that all electrochemical reactions at the electrode/electrolyte interface in a lithium insertion cell can be expressed in the form \n", + "$$ \\text{Li}^{+} + \\text{e}^{-} + \\text{H}_{j} \\rightleftharpoons (\\text{Li--H})_{j}.$$\n", + "For each species $j$, a vacant host site $\\text{H}_{j}$ can accommodate one lithium leading to a filled host site $(\\text{Li--H})_{j}$. The OCV for this reaction is written as\n", + "$$ U_j = U_j^0 + \\frac{\\omega_j}{f}\\log\\left(\\frac{X_j - x_j}{x_j}\\right),$$\n", + "where $f = (RT)/F$, and $R$, $T$, and $F$ are the universal gas constant, temperature in Kelvin, and Faraday’s constant, respectively. Here $X_j$ represents the total fraction of available host sites which can be occupied by species $j$, $x_j$ is the fraction of filled sites occupied by species $j$, $U_j^0$ is a concentration independent standard electrode potential, and the $\\omega_j$ is an unitless parameter that describes the level of disorder of the reaction represented by gallery $j$. \n", + "\n", + "The equation for each reaction can be inverted to give \n", + "$$x_j = \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]}.$$\n", + "The overall electrode state of charge is given by summing the fractional occupancies \n", + "$$x = \\sum_j x_j = \\sum_j \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]},$$\n", + "which is an explicit closed form expression for the inverse of the OCV. This is opposite to many battery models where one typically gives the OCV as an explicit function of the state of charge (or stoichiometry).\n", + "\n", + "At a particle interface with the electrolyte, local equilibrium requires that \n", + "$$U_j = U(x) \\quad \\forall j.$$" + ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Example solving MSMR using PyBaMM" + "## Solid phase transport\n", + "Within the MSMR framework, the flux within the particles is expressed in terms of gradient of the chemical potential\n", + "$$N = -c_{\\text{T}}x\\frac{D}{RT}\\nabla \\mu + x(N+N_{\\text{H}}),$$\n", + "where $N$ is the flux of lithiated sites, $N_{\\text{H}}$ is the flux of unlithiated sites, $c_{\\text{T}}$ is the total concentration of lithiated and delithiated sites, and $D$ is a diffusion coefficient. Ignoring volumetric expansion during lithiation, the total flux of sites vanishes\n", + "$$N+N_{\\text{H}}.$$ \n", + "It can then be shown that \n", + "$$N = c_{\\text{T}}fDx(1-x)\\frac{\\text{d}U}{\\text{d}x}\\nabla x.$$\n", + "\n", + "A mass balance in the solid phase then gives\n", + "$$\\frac{\\partial x}{\\partial t} = -\\nabla\\cdot\\left(x(1-x)fD\\frac{\\text{d}U}{\\text{d}x}\\nabla x\\right),$$\n", + "which, for a radially symmetric spherical particle, must be solved subject to the boundary conditions\n", + "$$N\\big\\vert_{r=0} = 0, \\quad N\\big\\vert_{r=R} = \\frac{j}{F},$$\n", + "where $j$ is the interfacial current density and $R$ is the particle radius. This must be supplemented with a suitable initial condition for the electrode state of charge.\n", + "\n", + "Solution of this problem requires evaluate of the function $U(x)$ and the derivative $\\text{d}U/\\text{d}x$, but these functions cannot be explicitly integrated. This problem can be avoided by replacing the dependent variable $x$ with a new dependent variable $U$ subject to the transformation \n", + "$$x = \\sum_j \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]}.$$\n", + "This gives the following equation for mass balance within the particles\n", + "$$\\frac{\\text{d}U}{\\text{d}x}\\frac{\\partial U}{\\partial t} = -\\nabla\\cdot\\left(x(1-x)fD\\nabla x\\right),$$\n", + "\n", + "which must be solved along with the transformed boundary and initial conditions." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example solving MSMR using PyBaMM\n", + "Below we show how to set up and solve a CCCV experiment using the MSMR model in PyBaMM. We use an example parameter set based on an Gr vs NMC cell similar to the LG M50.\n", + "\n", + "We begin by importing pybamm and numpy" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -41,13 +89,21 @@ "import numpy as np" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we load in the model. We choose to use the DFN along with our MSMR model for the open-circuit potential and solid phase (particle) transport" + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "model = pybamm.lithium_ion.SPM(\n", + "model = pybamm.lithium_ion.DFN(\n", " {\n", " \"open-circuit potential\": \"MSMR\",\n", " \"particle\": \"MSMR\",\n", @@ -57,9 +113,17 @@ "parameter_values = pybamm.ParameterValues(\"MSMR_Example\")" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also add variables for the individual electrode reaction as described in the MSMR model. We cannot create these variables until _after_ we have chosen some parameter values since we do not know in advance how many reactions have been used to describe thermodynamics of each electrode. The number of reactions is selected as part of parameterizing a particular material." + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -91,18 +155,26 @@ " model.variables[f\"X-averaged x{i}_p\"] = pybamm.x_average(x_p)" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we define our experiment, before creating and solving a simulation" + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -123,20 +195,28 @@ "sim.solve()" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we can plot the results. In the MSMR model we can look at both the potential and stoichiometry as a function of position through the electrode and within the particle" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2be754ae444465a9b5c413582e703f1", + "model_id": "057ac5cd2ebb4149983631548049aa49", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=5.86646556905814, step=0.0586646556905814), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.295989766971717, step=0.06295989766971717)…" ] }, "metadata": {}, @@ -145,10 +225,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -156,17 +236,16 @@ "source": [ "sim.plot(\n", " [\n", - " \"X-averaged negative particle stoichiometry\",\n", - " \"X-averaged positive particle stoichiometry\",\n", - " \"X-averaged negative particle potential [V]\",\n", - " \"X-averaged positive particle potential [V]\",\n", - " [f\"X-averaged x{i}_n\" for i in range(6)],\n", - " [f\"X-averaged x{i}_p\" for i in range(4)],\n", + " \"Negative particle stoichiometry\",\n", + " \"Positive particle stoichiometry\",\n", " \"X-averaged negative electrode open-circuit potential [V]\",\n", - " \"X-averaged positive electrode open-circuit potential [V]\",\n", + " \"X-averaged positive electrode open-circuit potential [V]\", \n", + " \"Negative particle potential [V]\",\n", + " \"Positive particle potential [V]\",\n", " \"Current [A]\",\n", " \"Voltage [V]\",\n", - " ]\n", + " ],\n", + " variable_limits=\"tight\", # make axes tight to plot at each timestep\n", ")" ] }, @@ -175,59 +254,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Comparison with ideal diffusion and \"standard\" OCV model" + "We can also look at the individual reactions" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [ - "def U_n(sto):\n", - " u_eq = (\n", - " 0.3635 * pybamm.exp(-439.3 * sto)\n", - " + 0.6908\n", - " + 0.5489 * pybamm.tanh(-30.07 * sto)\n", - " - 0.0344 * pybamm.tanh(25.46 * (sto - 0.1713))\n", - " - 0.0276 * pybamm.tanh(14.27 * (sto - 0.5270))\n", - "\n", - " )\n", - " return u_eq\n", - "\n", - "def U_p(sto):\n", - " u_eq = (\n", - " -0.3415 * sto\n", - " + 4.116\n", - " - 0.2835 * pybamm.tanh(4.181 * (sto - 0.2324))\n", - " - 0.1020 * pybamm.tanh(29.61 * (sto - 1))\n", - " )\n", - " return u_eq" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "812650ae78554d8a9960e4f8a37c9236", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.295989766971717, step=0.06295989766971717)…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "parameter_values_2 = parameter_values.copy()\n", - "parameter_values_2.update({\n", - " \"Negative electrode OCP [V]\": U_n,\n", - " \"Positive electrode OCP [V]\": U_p,\n", - "})" + "sim.plot(\n", + " [\n", + " [f\"X-averaged x{i}_n\" for i in range(6)],\n", + " [f\"X-averaged x{i}_p\" for i in range(4)],\n", + " \"Current [A]\",\n", + " \"X-averaged negative electrode open-circuit potential [V]\",\n", + " \"X-averaged positive electrode open-circuit potential [V]\",\n", + " \"Voltage [V]\",\n", + " ]\n", + ")" ] }, { @@ -242,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -251,8 +321,8 @@ "text": [ "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", "[2] Daniel R Baker and Mark W Verbrugge. Multi-species, multi-reaction model for porous intercalation electrodes: part i. model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode. Journal of The Electrochemical Society, 165(16):A3952, 2018.\n", - "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", diff --git a/docs/source/examples/notebooks/models/SPM.ipynb b/docs/source/examples/notebooks/models/SPM.ipynb index 1c1a5283a2..167d1da70d 100644 --- a/docs/source/examples/notebooks/models/SPM.ipynb +++ b/docs/source/examples/notebooks/models/SPM.ipynb @@ -28,13 +28,13 @@ "$$\n", "\n", "$$\n", - "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n", + "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ N_{\\text{s,k}}\\big|_{r_{\\text{k}}=R_{\\text{k}}} = \n", "\\begin{cases}\n", "\t\t \\frac{I}{Fa_{\\text{n}}L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n", "\t\t -\\frac{I}{Fa_{\\text{p}}L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n", "\\end{cases} \\\\\n", "c_{\\text{s,k}}(r_{\\text{k}},0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},$$\n", - "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,1]$ is the radial coordinate of the particle in electrode $\\text{k}$. \n", + "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,R_{\\text{k}}]$ is the radial coordinate of the particle in electrode $\\text{k}$. \n", "\n", "### Voltage Expression\n", "The voltage is obtained from the expression: \n", @@ -1104,7 +1104,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -1118,7 +1118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.16" }, "toc": { "base_numbering": 1, @@ -1135,7 +1135,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/docs/source/examples/notebooks/models/SPMe.ipynb b/docs/source/examples/notebooks/models/SPMe.ipynb index f4182b5ffa..9c40fbc827 100644 --- a/docs/source/examples/notebooks/models/SPMe.ipynb +++ b/docs/source/examples/notebooks/models/SPMe.ipynb @@ -25,7 +25,7 @@ "\n", "ii) At the centre of each particle the standard no-flux condition is imposed, and the flux on the surface of the particle is simply the current $I$ divided by the thickness of the electrode $L_{\\text{k}}$, as in the SPM. Since lithium is transferred between the electrolyte and particles, the flux through the particle surface also enters the electrolyte diffusion equation as a source/sink term. There is no transfer of lithium between the electrolyte and current collectors, which leads to no flux boundary conditions on the lithium concentration in the electrolyte $c_{\\text{e,k}}$ at either end of the cell. \n", "\n", - "iii) We must also impose initial conditions which correspond to setting an initial concentration in each particle $c_{\\text{s,k}}(t=0) = c_{\\text{s,k,0}}$, and to having no deviation from the initial (uniform) lithium concentration in the electrolyte $c_{\\text{e,k}}(t=0) = 0$. \n", + "iii) We must also impose initial conditions which correspond to setting an initial concentration in each particle $c_{\\text{s,k}}(t=0) = c_{\\text{s,k,0}}$, and to having no deviation from the initial (uniform) lithium concentration in the electrolyte $c_{\\text{e,k}}(t=0) = c_{\\text{e,0}}$. \n", "\n", "\n", "The model equations for the SPMe read: \n", @@ -33,20 +33,18 @@ "\n", "#### Particles: \n", "$$\n", - "\\mathcal{C}_{\\text{k}} \\frac{\\partial c_{\\text{s,k}}}{\\partial t} = -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n", + "\\frac{\\partial c_{\\text{s,k}}}{\\partial t} = -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n", "N_{\\text{s,k}} = -D_{\\text{s,k}}(c_{\\text{s,k}}) \\frac{\\partial c_{\\text{s,k}}}{\\partial r_{\\text{k}}}, \\quad \\text{k} \\in \\text{n, p},\n", "$$\n", "\n", "$$\n", - "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{R, \\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n", + "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ N_{\\text{s,k}}\\big|_{r_{\\text{k}}=R_{\\text{k}}} = \n", "\\begin{cases}\n", - "\t\t \\frac{I}{L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n", - "\t\t -\\frac{I}{L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n", + "\t\t \\frac{I}{Fa_{\\text{n}}L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n", + "\t\t -\\frac{I}{Fa_{\\text{p}}L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n", "\\end{cases} \\\\\n", - "c_{\\text{s,k}}(r_{\\text{k}},0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},\n", "$$\n", - "\n", - "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,1]$ is the radial coordinate of the particle in electrode $\\text{k}$. All other relevant parameters are given in the table at the end of this notebook.\n", + "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,R_{\\text{k}}]$ is the radial coordinate of the particle in electrode $\\text{k}$. All other relevant parameters are given in the table at the end of this notebook.\n", "\n", "\n", "#### Electrolyte: \n", @@ -66,10 +64,10 @@ "$$\n", "\n", "$$\n", - "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=1}=0, \\\\\n", + "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=L}=0, \\\\\n", "c_{\\text{e,k}}(x,0) = 0, \\quad \\text{k} \\in \\text{n, s, p},\n", "$$\n", - "where $D_{\\text{e}}$ is the diffusion coefficient in the solid, $N_{\\text{e,k}}$ denotes the flux of lithium ions in the electrolyte within the region $\\text{k}$, and $x\\in[0,1]$ is the macroscopic through-cell distance. This equation is also solved subject to continuity of concentration and flux at the electrode/separator interfaces.\n", + "where $D_{\\text{e}}$ is the diffusion coefficient in the solid, $N_{\\text{e,k}}$ denotes the flux of lithium ions in the electrolyte within the region $\\text{k}$, and $x\\in[0,L]$ is the macroscopic through-cell distance. This equation is also solved subject to continuity of concentration and flux at the electrode/separator interfaces.\n", "\n", "### Voltage Expression\n", "The voltage is obtained from the expression: \n", @@ -90,7 +88,7 @@ "where\n", "$$\n", "\\bar{c}_{\\text{e,n}} = \\frac{1}{L_{\\text{n}}}\\int_0^{L_{\\text{n}}} c_{\\text{e,n}} \\, \\text{d}x, \\quad\n", - "\\bar{c}_{\\text{e,p}} = \\frac{1}{L_{\\text{p}}}\\int_{1-L_{\\text{p}}}^{1} c_{\\text{e,p}} \\, \\text{d}x.\n", + "\\bar{c}_{\\text{e,p}} = \\frac{1}{L_{\\text{p}}}\\int_{L-L_{\\text{p}}}^{L} c_{\\text{e,p}} \\, \\text{d}x.\n", "$$\n", "\n", "More details can be found in [[3]](#References)." @@ -248,7 +246,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -266,7 +264,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, From ab612130d467018238184478c478ba7edfb645bc Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Mon, 10 Jul 2023 17:29:11 +0100 Subject: [PATCH 019/154] add helper function for msmr reactions --- .../examples/notebooks/models/MSMR.ipynb | 78 +++++++++---------- .../lithium_ion/MSMR_example_set.py | 2 + .../lithium_ion/base_lithium_ion_model.py | 31 ++++++++ .../test_base_lithium_ion_model.py | 17 ++++ 4 files changed, 85 insertions(+), 43 deletions(-) diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index b762c32a07..ae203fa8f8 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -123,36 +123,28 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "# x_n\n", - "U_n = model.variables[\"Negative electrode open-circuit potential [V]\"]\n", - "T = model.variables[\"Negative electrode temperature [K]\"]\n", - "f = pybamm.constants.F / (pybamm.constants.R * T)\n", - "for i in range(6):\n", - " U0 = pybamm.Parameter(f\"U0_n_{i}\")\n", - " w = pybamm.Parameter(f\"w_n_{i}\")\n", - " Xj = pybamm.Parameter(f\"Xj_n_{i}\")\n", - "\n", - " x_n = Xj / (1 + pybamm.exp(f * (U_n - U0) / w))\n", - " model.variables[f\"x{i}_n\"] = x_n\n", - " model.variables[f\"X-averaged x{i}_n\"] = pybamm.x_average(x_n)\n", - " \n", - "\n", - "# x_p\n", - "U_p = model.variables[\"Positive electrode open-circuit potential [V]\"]\n", - "T = model.variables[\"Positive electrode temperature [K]\"]\n", - "f = pybamm.constants.F / (pybamm.constants.R * T)\n", - "for i in range(4):\n", - " U0 = pybamm.Parameter(f\"U0_p_{i}\")\n", - " w = pybamm.Parameter(f\"w_p_{i}\")\n", - " Xj = pybamm.Parameter(f\"Xj_p_{i}\")\n", - "\n", - " x_p = Xj / (1 + pybamm.exp(f * (U_p - U0) / w))\n", - " model.variables[f\"x{i}_p\"] = x_p\n", - " model.variables[f\"X-averaged x{i}_p\"] = pybamm.x_average(x_p)" + "model.set_msmr_reaction_variables(parameter_values)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The individual reactions are given variables names `xj_k` where `k` can be `n` or `p` to denote the negative or positive electrode, and `j` is the reaction index. E.g. the variable for the second reaction in the negative electrode can be accessed as" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "xn_2 = model.variables[\"x2_n\"]" ] }, { @@ -165,16 +157,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -205,18 +197,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "057ac5cd2ebb4149983631548049aa49", + "model_id": "e2053f131fff43dda0443d2b98bed91f", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.295989766971717, step=0.06295989766971717)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.2959964898302045, step=0.06295996489830205…" ] }, "metadata": {}, @@ -225,10 +217,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -259,18 +251,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "812650ae78554d8a9960e4f8a37c9236", + "model_id": "237dea6bb0594764951f5eb89a2d2d0c", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.295989766971717, step=0.06295989766971717)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.2959964898302045, step=0.06295996489830205…" ] }, "metadata": {}, @@ -279,10 +271,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -312,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [ { diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index e8b07edf9c..4a644c88e3 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -143,6 +143,7 @@ def get_parameter_values(): "Contact resistance [Ohm]": 0, # negative electrode "Negative electrode stoichiometry": x_n, + "Number of reactions in negative electrode": 6, "U0_n_0": 0.08843, "Xj_n_0": 0.43336, "w_n_0": 0.08611, @@ -173,6 +174,7 @@ def get_parameter_values(): "Negative electrode OCP entropic change [V.K-1]": 0.0, # positive electrode "Positive electrode stoichiometry": x_p, + "Number of reactions in positive electrode": 4, "U0_p_0": 3.62274, "Xj_p_0": 0.13442, "w_p_0": 0.96710, diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index 41e4670cf7..83761c25b9 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -413,3 +413,34 @@ def set_convection_submodel(self): self.submodels[ "through-cell convection" ] = pybamm.convection.through_cell.NoConvection(self.param, self.options) + + def set_msmr_reaction_variables(self, parameter_values): + """ + Set variables for the individual MSMR reactions in the negative and + positive electrodes. + + Parameters + ---------- + parameter_values : :class:`pybamm.ParameterValues` + The parameter values to use for the model. + """ + if self.options["open-circuit potential"] != "MSMR": + raise pybamm.OptionError( + "'open-circuit potential' must be 'MSMR' to add MSMR reaction variables" + ) + + for Domain in ["Negative", "Positive"]: + domain = Domain.lower() + suffix = domain[0] + U = self.variables[f"{Domain} electrode open-circuit potential [V]"] + T = self.variables[f"{Domain} electrode temperature [K]"] + N = parameter_values[f"Number of reactions in {domain} electrode"] + f = pybamm.constants.F / (pybamm.constants.R * T) + for i in range(N): + U0 = pybamm.Parameter(f"U0_{suffix}_{i}") + w = pybamm.Parameter(f"w_{suffix}_{i}") + Xj = pybamm.Parameter(f"Xj_{suffix}_{i}") + + x = Xj / (1 + pybamm.exp(f * (U - U0) / w)) + self.variables[f"x{i}_{suffix}"] = x + self.variables[f"X-averaged x{i}_{suffix}"] = pybamm.x_average(x) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py index 315896b29f..b28bebbd49 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py @@ -29,6 +29,23 @@ def test_default_parameters(self): ) os.chdir(cwd) + def test_set_msmr_variables(self): + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.lithium_ion.BaseModel().set_msmr_reaction_variables(None) + + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + } + model = pybamm.lithium_ion.SPM(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + model.set_msmr_reaction_variables(parameter_values) + xn_2 = model.variables["x2_n"] + # For SPM, xn_2 will be a broadcast of the reaction formula, whose child should + # be the parameter "Xj_n_2" + self.assertIsInstance(xn_2.children[0].children[0], pybamm.Parameter) + self.assertEqual(xn_2.children[0].children[0].name, "Xj_n_2") + if __name__ == "__main__": print("Add -v for more debug output") From 7b12a617f5246e1e8a01af850743a0df5cdb8499 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 11 Jul 2023 15:04:24 +0100 Subject: [PATCH 020/154] docs and notebook --- .../open_circuit_potential/msmr_ocp.rst | 6 + .../submodels/particle/msmr_diffusion.rst | 5 + .../examples/notebooks/models/MSMR.ipynb | 118 ++++++++++++++---- .../full_battery_models/base_battery_model.py | 2 +- .../lithium_ion/base_lithium_ion_model.py | 19 ++- 5 files changed, 120 insertions(+), 30 deletions(-) create mode 100644 docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst create mode 100644 docs/source/api/models/submodels/particle/msmr_diffusion.rst diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst new file mode 100644 index 0000000000..5f58e60abc --- /dev/null +++ b/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst @@ -0,0 +1,6 @@ +MSMR Open Circuit Potential +=========================== + + +.. autoclass:: pybamm.open_circuit_potential.MSMROpenCircuitPotential + :members: diff --git a/docs/source/api/models/submodels/particle/msmr_diffusion.rst b/docs/source/api/models/submodels/particle/msmr_diffusion.rst new file mode 100644 index 0000000000..a03bebbcf1 --- /dev/null +++ b/docs/source/api/models/submodels/particle/msmr_diffusion.rst @@ -0,0 +1,5 @@ +MSMR Diffusion +============== + +.. autoclass:: pybamm.particle.MSMRDiffusion + :members: diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index ae203fa8f8..1dd8cae140 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -80,13 +80,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "#%pip install pybamm -q # install PyBaMM if it is not installed\n", + "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import numpy as np" + "import numpy as np\n", + "import matplotlib.pyplot as plt" ] }, { @@ -99,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -123,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -157,16 +158,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -177,8 +178,8 @@ " (\n", " \"Discharge at 1C for 1 hour or until 3 V\",\n", " \"Rest for 1 hour\",\n", - " \"Charge at C/3 until 4 V\",\n", - " \"Hold at 4 V until 10 mA\",\n", + " \"Charge at C/3 until 4.2 V\",\n", + " \"Hold at 4.2 V until 10 mA\",\n", " \"Rest for 1 hour\",\n", " ),\n", " ]\n", @@ -197,18 +198,18 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e2053f131fff43dda0443d2b98bed91f", + "model_id": "caffb51fc074458fb030e1d36fe35d97", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.2959964898302045, step=0.06295996489830205…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094131856986915, step=0.06094131856986915)…" ] }, "metadata": {}, @@ -217,10 +218,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -251,18 +252,18 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "237dea6bb0594764951f5eb89a2d2d0c", + "model_id": "454ed555b45c42c8a754597656fbb1e2", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.2959964898302045, step=0.06295996489830205…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094131856986915, step=0.06094131856986915)…" ] }, "metadata": {}, @@ -271,27 +272,92 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "xns = [f\"Average x{i}_n\" for i in range(6)] # negative electrode reactions: x0_n, x1_n, ..., x5_n\n", + "xps = [f\"Average x{i}_p\" for i in range(4)] # positive electrode reactions: x0_p, x1_p, ..., x3_p\n", "sim.plot(\n", " [\n", - " [f\"X-averaged x{i}_n\" for i in range(6)],\n", - " [f\"X-averaged x{i}_p\" for i in range(4)],\n", + " xns,\n", + " xps,\n", " \"Current [A]\",\n", - " \"X-averaged negative electrode open-circuit potential [V]\",\n", - " \"X-averaged positive electrode open-circuit potential [V]\",\n", + " \"Negative electrode stoichiometry\",\n", + " \"Positive electrode stoichiometry\",\n", " \"Voltage [V]\",\n", " ]\n", ")" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and plot how they sum to give the electrode stoichiometry " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = sim.solution\n", + "time = sol[\"Time [h]\"].data\n", + "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n", + "\n", + "ax[0].plot(time, sol[\"Average negative particle stoichiometry\"].data, \"k-\", label=\"x_n\")\n", + "bottom = 0\n", + "for xn in xns:\n", + " top = bottom + sol[xn].data\n", + " ax[0].fill_between(time, bottom, top, label=xn[-4:])\n", + " bottom = top\n", + "ax[0].set_xlabel(\"Time [h]\")\n", + "ax[0].set_ylabel(\"x_n [-]\")\n", + "ax[0].legend(\n", + " loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n", + ")\n", + "ax[1].plot(time, sol[\"Average positive particle stoichiometry\"].data, \"k-\", label=\"x_p\")\n", + "bottom = 0\n", + "for xp in xps:\n", + " top = bottom + sol[xp].data\n", + " ax[1].fill_between(time, bottom, top, label=xp[-4:])\n", + " bottom = top\n", + "ax[1].set_xlabel(\"Time [h]\")\n", + "ax[1].set_ylabel(\"x_p [-]\")\n", + "ax[1].legend(\n", + " loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n", + ")" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -304,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 0da899cf8e..c77fb0f16a 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -97,7 +97,7 @@ class BatteryModelOptions(pybamm.FuzzyDict): Sets the submodel to use to describe behaviour within the particle. Can be "Fickian diffusion" (default), "uniform profile", "quadratic profile", "quartic profile", or "MSMR". If "MSMR" then the - "open-circuit potential" must also be "MSMR". A 2-tuple can be + "open-circuit potential" option must also be "MSMR". A 2-tuple can be provided for different behaviour in negative and positive electrodes. * "particle mechanics" : str Sets the model to account for mechanical effects such as particle diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index 83761c25b9..1b691afb2f 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -432,7 +432,7 @@ def set_msmr_reaction_variables(self, parameter_values): for Domain in ["Negative", "Positive"]: domain = Domain.lower() suffix = domain[0] - U = self.variables[f"{Domain} electrode open-circuit potential [V]"] + U = self.variables[f"{Domain} particle potential [V]"] T = self.variables[f"{Domain} electrode temperature [K]"] N = parameter_values[f"Number of reactions in {domain} electrode"] f = pybamm.constants.F / (pybamm.constants.R * T) @@ -442,5 +442,18 @@ def set_msmr_reaction_variables(self, parameter_values): Xj = pybamm.Parameter(f"Xj_{suffix}_{i}") x = Xj / (1 + pybamm.exp(f * (U - U0) / w)) - self.variables[f"x{i}_{suffix}"] = x - self.variables[f"X-averaged x{i}_{suffix}"] = pybamm.x_average(x) + x_surf = pybamm.surf(x) + x_surf_av = pybamm.x_average(x_surf) + x_xav = pybamm.x_average(x) + x_rav = pybamm.r_average(x) + x_av = pybamm.r_average(x_xav) + self.variables.update( + { + f"x{i}_{suffix}": x, + f"X-averaged x{i}_{suffix}": x_xav, + f"R-averaged x{i}_{suffix}": x_rav, + f"Average x{i}_{suffix}": x_av, + f"Surface x{i}_{suffix}": x_surf, + f"X-averaged surface x{i}_{suffix}": x_surf_av, + } + ) From a33c3af034b8061af1c420ebaf794c826b36150f Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 12 Jul 2023 12:00:50 +0100 Subject: [PATCH 021/154] fix initial soc at solve --- .../api/models/lithium_ion/electrode_soh.rst | 4 +++ .../examples/notebooks/models/MSMR.ipynb | 35 +++++++++++++------ examples/scripts/MSMR.py | 8 ++--- .../lithium_ion/MSMR_example_set.py | 4 +-- .../lithium_ion/electrode_soh.py | 6 ++-- pybamm/parameters/parameter_values.py | 33 +++++++++++++++-- pybamm/simulation.py | 15 +++++--- .../test_lithium_ion/test_electrode_soh.py | 8 ++--- .../test_parameters/test_parameter_values.py | 14 ++++++++ 9 files changed, 98 insertions(+), 29 deletions(-) diff --git a/docs/source/api/models/lithium_ion/electrode_soh.rst b/docs/source/api/models/lithium_ion/electrode_soh.rst index 8942b2394e..4bf7d57dbe 100644 --- a/docs/source/api/models/lithium_ion/electrode_soh.rst +++ b/docs/source/api/models/lithium_ion/electrode_soh.rst @@ -8,4 +8,8 @@ Electrode SOH models .. autofunction:: pybamm.lithium_ion.get_min_max_stoichiometries +.. autofunction:: pybamm.lithium_ion.get_initial_ocps + +.. autofunction:: pybamm.lithium_ion.get_min_max_ocps + .. footbibliography:: diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 1dd8cae140..b2861f1b81 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -82,7 +82,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", @@ -164,7 +172,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -204,12 +212,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "caffb51fc074458fb030e1d36fe35d97", + "model_id": "35986e0e04f542d2a23ac999c3823d87", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094131856986915, step=0.06094131856986915)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094143683142373, step=0.06094143683142373)…" ] }, "metadata": {}, @@ -218,7 +226,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -258,12 +266,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "454ed555b45c42c8a754597656fbb1e2", + "model_id": "8ff33a3bb89a42bfba1a99c4acd5704d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094131856986915, step=0.06094131856986915)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094143683142373, step=0.06094143683142373)…" ] }, "metadata": {}, @@ -272,7 +280,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -311,7 +319,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -320,7 +328,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -392,6 +400,13 @@ "source": [ "pybamm.print_citations()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index f1d78215bd..1d5fdaf938 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -12,14 +12,14 @@ experiment = pybamm.Experiment( [ ( - "Discharge at 1C for 1 hour or until 3 V", + "Discharge at 1C until 3V", "Rest for 1 hour", - "Charge at C/3 until 4 V", - "Hold at 4 V until 10 mA", + "Charge at C/2 until 4.1 V", + "Hold at 4.1 V until 10 mA", "Rest for 1 hour", ), ] ) sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment) -sim.solve() +sim.solve(initial_soc=0.9) sim.plot() diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index 4a644c88e3..349e28d346 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -212,9 +212,9 @@ def get_parameter_values(): "Ambient temperature [K]": 298.15, "Number of electrodes connected in parallel to make a cell": 1.0, "Number of cells connected in series to make a battery": 1.0, - "Lower voltage cut-off [V]": 2.5, + "Lower voltage cut-off [V]": 2.8, "Upper voltage cut-off [V]": 4.2, - "Open-circuit voltage at 0% SOC [V]": 2.5, + "Open-circuit voltage at 0% SOC [V]": 2.8, "Open-circuit voltage at 100% SOC [V]": 4.2, "Initial temperature [K]": 298.15, "Initial voltage in negative electrode [V]": 0.01, diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 15f80ffe4f..84b328eb7b 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -937,8 +937,8 @@ def get_initial_ocps( Returns ------- - x, y - The initial stoichiometries that give the desired initial state of charge + Un, Up + The initial electrode OCPs that give the desired initial state of charge """ esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) return esoh_solver.get_initial_ocps(initial_value) @@ -969,7 +969,7 @@ def get_min_max_ocps( Returns ------- Un_0, Un_100, Up_100, Up_0 - The min/max ocps + The min/max OCPs """ esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options) return esoh_solver.get_min_max_ocps() diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index 858c6e7c3c..2118bbe54b 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -257,14 +257,15 @@ def set_initial_stoichiometries( param=None, known_value="cyclable lithium capacity", inplace=True, + options=None, ): """ Set the initial stoichiometry of each electrode, based on the initial SOC or voltage """ - param = param or pybamm.LithiumIonParameters() + param = param or pybamm.LithiumIonParameters(options) x, y = pybamm.lithium_ion.get_initial_stoichiometries( - initial_value, self, param=param, known_value=known_value + initial_value, self, param=param, known_value=known_value, options=options ) if inplace: parameter_values = self @@ -280,6 +281,34 @@ def set_initial_stoichiometries( ) return parameter_values + def set_initial_ocps( + self, + initial_value, + param=None, + known_value="cyclable lithium capacity", + inplace=True, + options=None, + ): + """ + Set the initial OCP of each electrode, based on the initial + SOC or voltage + """ + param = param or pybamm.LithiumIonParameters(options) + Un, Up = pybamm.lithium_ion.get_initial_ocps( + initial_value, self, param=param, known_value=known_value, options=options + ) + if inplace: + parameter_values = self + else: + parameter_values = self.copy() + parameter_values.update( + { + "Initial voltage in negative electrode [V]": Un, + "Initial voltage in positive electrode [V]": Up, + } + ) + return parameter_values + def check_parameter_values(self, values): for param in values: if "propotional term" in param: diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 303c71e8a8..7e2296cbc0 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -368,12 +368,19 @@ def set_initial_soc(self, initial_soc): self.op_conds_to_built_models = None self.op_conds_to_built_solvers = None + options = self.model.options param = self.model.param - self.parameter_values = ( - self._unprocessed_parameter_values.set_initial_stoichiometries( - initial_soc, param=param, inplace=False + if options["open-circuit potential"] == "MSMR": + self.parameter_values = self._unprocessed_parameter_values.set_initial_ocps( + initial_soc, param=param, inplace=False, options=options ) - ) + else: + self.parameter_values = ( + self._unprocessed_parameter_values.set_initial_stoichiometries( + initial_soc, param=param, inplace=False, options=options + ) + ) + # Save solved initial SOC in case we need to re-build the model self._built_initial_soc = initial_soc diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index f9538ccfb7..07d2f29b70 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -166,7 +166,7 @@ def test_known_solution(self): parameter_values, param, options=options ) - Vmin = 2.5 + Vmin = 2.8 Vmax = 4.2 Q_n = parameter_values.evaluate(param.n.Q_init) Q_p = parameter_values.evaluate(param.p.Q_init) @@ -203,7 +203,7 @@ def test_known_solution_cell_capacity(self): parameter_values, param, known_value="cell capacity", options=options ) - Vmin = 2.5 + Vmin = 2.8 Vmax = 4.2 Q_n = parameter_values.evaluate(param.n.Q_init) Q_p = parameter_values.evaluate(param.p.Q_init) @@ -360,7 +360,7 @@ def test_get_initial_ocp(self): Un, Up = pybamm.lithium_ion.get_initial_ocps( 0, parameter_values, param, options=options ) - self.assertAlmostEqual(Up - Un, 2.5, places=5) + self.assertAlmostEqual(Up - Un, 2.8, places=5) Un, Up = pybamm.lithium_ion.get_initial_ocps( "4 V", parameter_values, param, options=options ) @@ -375,7 +375,7 @@ def test_min_max_ocp(self): parameter_values, param, options=options ) self.assertAlmostEqual(Up_100 - Un_100, 4.2) - self.assertAlmostEqual(Up_0 - Un_0, 2.5) + self.assertAlmostEqual(Up_0 - Un_0, 2.8) if __name__ == "__main__": diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index ba2a548d0f..5eb3141fe4 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -115,6 +115,20 @@ def test_set_initial_stoichiometries(self): y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"] self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) + def test_set_initial_ocps(self): + options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + param = pybamm.ParameterValues("MSMR_Example") + param_0 = param.set_initial_ocps(0, inplace=False, options=options) + param_100 = param.set_initial_ocps(1, inplace=False, options=options) + + Un_0 = param_0["Initial voltage in negative electrode [V]"] + Up_0 = param_0["Initial voltage in positive electrode [V]"] + self.assertAlmostEqual(Up_0 - Un_0, 2.8) + + Un_100 = param_100["Initial voltage in negative electrode [V]"] + Up_100 = param_100["Initial voltage in positive electrode [V]"] + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + def test_check_parameter_values(self): with self.assertRaisesRegex(ValueError, "propotional term"): pybamm.ParameterValues( From 8396295d1056413815c580aa2acb9a08f79d7880 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 12 Jul 2023 12:53:36 +0100 Subject: [PATCH 022/154] update notebook --- .../examples/notebooks/models/MSMR.ipynb | 31 ++++++++++++------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index b2861f1b81..5a52af7c14 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -75,7 +75,7 @@ "## Example solving MSMR using PyBaMM\n", "Below we show how to set up and solve a CCCV experiment using the MSMR model in PyBaMM. We use an example parameter set based on an Gr vs NMC cell similar to the LG M50.\n", "\n", - "We begin by importing pybamm and numpy" + "We begin by importing pybamm, numpy and matplotlib" ] }, { @@ -169,10 +169,18 @@ "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 275.691 and h = 3.27586e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 275.693 and h = 5.60628e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -190,7 +198,8 @@ " \"Hold at 4.2 V until 10 mA\",\n", " \"Rest for 1 hour\",\n", " ),\n", - " ]\n", + " ],\n", + " period=\"10 seconds\",\n", ")\n", "sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment)\n", "sim.solve()" @@ -212,12 +221,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35986e0e04f542d2a23ac999c3823d87", + "model_id": "b5ec0c8411924c199a371a1b3ba9d130", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094143683142373, step=0.06094143683142373)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094046875941711, step=0.06094046875941711)…" ] }, "metadata": {}, @@ -226,7 +235,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -266,12 +275,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ff33a3bb89a42bfba1a99c4acd5704d", + "model_id": "68c977972a01491993f231dc4235230a", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094143683142373, step=0.06094143683142373)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094046875941711, step=0.06094046875941711)…" ] }, "metadata": {}, @@ -280,7 +289,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -319,7 +328,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -328,7 +337,7 @@ }, { "data": { - "image/png": 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EaWhmloSuM2fO4PXXXwcATBuRBqbHrKxIIoG5foQQoREvvPnmmxgxYgTGjh170fOWL18OrVbrfMXFxQ1QhP4TajOzne4ND0cYx+HEiRNYtWqV0OEQ0m+UzHqIE4c7lxnQzCwJRUuXLkVHRwdSjeEYGhnGO25KuwotZqkAkYWm8PBwcByH8vLybuPl5eWIiuKvZb6Q2WzGxo0bceedd7r9OfPnz0d9fb3zVVRU1K+4hVZdXY2CggIAg3/zV09qjsNDEZEAgCeffBJnz54VOCJC+oeSWQ9ZrQYYz9earaio6HW3MCGDUW5uLt59910AwNTMNN5xiVyBuuphAx1WSJNIJBgzZgy2b9/uHLPZbNi+fTvGj794NYl///vfaGtrw5///Ge3P0cqlUKj0XR7BbPOJQbxYjE0IbD5q6cbNBpcKpejpaUF9913n9DhENIvlMx6qLlRDalIhDClAgBVNCChZfHixbDZbMiMMSI+TMc7HpUyCe0t4oEPLMTNmzcP69atw4YNG5Cbm4u7774bZrMZM2bMAADccccdmD9/Pu+6N998EzfddBPCwvgz7INd5xKDzBCble3EMAwWGaPAAfjkk0+wZcsWoUMixGuUzHqopVEKiVxBSw1IyDl48CA++ugjMHDUle1Jrtagpixl4AMjmD59OlauXInFixcjKysLOTk52Lp1q3NTWGFhIUpLS7tdc/z4cfzwww99WmIwGHXOzIbaEoMLpUiluF1vAADcc889aGlpETgiQrwjEjqAYKSNjEWUVo2jJeW0CYyEjIULFwIAsuKjnRU9LhSRdDUqikLvcW2gmDNnDubMmePy2M6dO3ljaWlpsNvtfo4qcIX6zGyn2eFh2NLYgNOnT+OZZ57B448/LnRIhHiMZma9IFVFUXkuElJ+/PFHbNmyBSzDYMrwVN5xpT4cVSWJAx8YIV6orKxEYWEhACAjhMpyuaJkOTxyfjPYihUrcOrUKYEjIsRzNDPbZ13lh1gurFvjBLvdzitPRMhgYbfbsWDBAgDAZUmxCFfzC+uHxU1CRVHfvhtLZBzC9HaImQ4wsAMI/NlBpb1R6BCID3UuMUgUS6AKwc1fPU1Vq/FRvQK7m5txzz33YPPmzfSZRoIKJbN9pJB1feC2W/SIUCvBsQyamppw9uxZJCYmChccIX709ddf49tvvwXHsrg2g78mVhNhQmVx32qOpkU1wPTpU2Bbmnwdpl/pkxYAyBA6DOIjoVpftjcMw2Ch0YibCgrwxRdf4NNPP8VNN90kdFiE9BktM+gjHVfn/P9NDUpwLItItaNEF62bJYOV3W7HY489BgC4fGgCdAp+Vy9t1CTY7e5ncZJNLYjZOD/oElky+HTOzIb6etkLJUmk+H/nN4PNnTsXZrNZ4IgI6TtKZvtIXXfG+f/bW8RQ6AxU0YAMep9++il+/vlnSEQcrh42lHdcH52AiuKLF+YHAIVahNj/PemPEAnxGM3Muvb3sDCYRCIUFhbiqaeeEjocQvqMktk+khcc7PbPakN0t3WzhAw2VqsVixYtAgBcmZIItYzf1UuhvwoM3M/KptkOgW2q83WIhHisvLwcxcXFYAAMc/F3OpQpWBbzIx3l3FauXIm8vDyBIyKkbyiZ7SOusRYqbdcSY4nSSMksGdQ2btyII0eOQCYWYWIaf1Y2PD4F1SURbu9jCBdB98Vaf4RIiMc6lxgkSSRQsrT5q6drVCpcpVSivb0dc+bMCenybSR4UDLrAZ2i44J/CnOW58rNzUV7e7swQRHiB+3t7ViyZAkAYHLaUCgk/K5eEuUVfbpXSvk2MDarT+MjxFu0xODiGIbBY5FGSBgG27dvx7///W+hQyLELUpmPaC21Tj/v6VVC51CDqmIQ3t7O06ePClgZIT41ttvv41Tp05BJZXgipRE3nHjkOGoKdO7vY/JxEL5/Ud+iJAQ79DmL/fiJRLMNDhaHN9///1obKTSdCSwUTLrAWVtgfP/N9UpwLEcbQIjg05rayueeOIJAMDVw5IhFbuo4Ce6vE/3SsqlWR0SWDpnZkO9WYI7dxoMiBOLUVJSgqVLlwodDiEXRcmsB2RFXWtjrR0c1OFGRGlo3SwZXNauXYvi4mJo5TKMHxrPO25KvQT1lfx2tj3FR9sgy/nGHyES4pXS0lKUlJSABZBOM7MXJWNZPHZ+M9gLL7xAn3EkoFEy6wHRyYNgua6d20q9iWZmyaDS1NSEp59+GgBwbUYKxD26IzEMiw7rZW7vw7BA/L63/BIjId7qvvmLPv7cmahS4RqVClarFbNnz6bNYCRg0W+zB9gOC3T6rg93sSySKhqQQeXFF19EZWUlwlQKXJYUyzsenTYWjTX8drY9DYlqheTkAX+ESIjXOpcY0HrZvns00ggZw+C7777De++9J3Q4hLhEyayHNJJW5/+32boaJ5w+fZo6ppCgVltbi2effRYAMGV4KrgeM1csJ0Jr62i39+FEDGK+f90vMRLSH1TJwHMxYjH+ERYOAHjwwQdRV1cnbECEuEDJrIfUbRXO/9/WrIFaJoVKKoHdbsexY8cEjIyQ/lm5ciXq6+sRpVUjKz6adzwm/XKY6/ntbHtKDq+HqOi4P0IkxGt2u925zICSWc/8P4MBSRIJKioqsHjxYqHDIYSHklkPKapOOf9/U50cnFhMSw1I0CsvL8c///lPAMDUzFSwTPeuXiKJBE31I9zeRyxlEfX1y36JkZD+KCkpQVlZmWPzF1Uy8IiEYbDg/GawNWvW4ODBg26uIGRguai5Q1xpN4UBOYDkTA4w5EoAgN3OQBMRDZNWjfyKatoERoLW8uXLYTabEWfQYni0kXc8Ou0qVBS7b/2Zoi0HV3XO9UGGQc21Y/B5WhOKJU2wwdbfsAfMH7Mk+LXQQZB+6ZyVTZZIIafNXx67XKnEVLUaWxsbMWvWLPz4449g6d8jCRCUzPbRvtFKjP4CEBfmQZLBwdLq6Gik0JhoZpYEtaKiIrz66qsAgGmZaWB6zMqKZXLUVWW4vY9MwSFy60u9Ht9/+6V4JiY4Z3SuF1GHv2DnrC9LSwy89khEJL4zm/HTTz/hrbfewp133il0SIQAoGUGffZVWCkYqWNmSqvtGuckEVSeiwS1ZcuWwWKxYEiEASnGcN5xU+okWFrdf+9NkZ0F21Dt8lj11OBNZMngQJUM+s8oFmN2mKMz2COPPILqate/74QMtIBIZtesWYPExETIZDKMGzcOe/fuvej5L7zwAtLS0iCXyxEXF4f7778fra2tF72mvzoYG2znSxVp2K6qBR0dehjPN04oKytDVVWVX+MgxJdOnjyJ9evXAwCmjeDPyspUatSUp7q9j0ItQtgXrmdlmdhoPJqV1/9gCfESbf7ynT/rDUiWSFBdXY0FCxYIHQ4hAAIgmd20aRPmzZuHJUuW4MCBAxg1ahSmTJmCiooKl+d/8MEHePTRR7FkyRLk5ubizTffxKZNm/DYY4/5PdbaeEcvelVLqXOspUkNmVgEg9Kxy5uWGpBg8vjjj8NqtSLdFIGkcAPveOSQyeiwcC6u7C6VyQXb0uTy2L9/Y0A9498vm4RcTHFxMSoqKsABSJO6X/tNeidmGCw2RgEAXn/9dezbt0/giAgJgGR29erVmDlzJmbMmIGMjAysXbsWCoXCOVvU065duzBhwgT86U9/QmJiIq677jr88Y9/dDub6wtnoxyzVvKyrrJDLY1SSOQKWjdLgs7hw4fxr3/9C4BjrWxPCp0B1aVJbu+j1omh2/qqy2Pmq7LwoYZmZYmwnJu/pFLIaNNSv12qUOBGjQZ2ux133303rFar0CGRECfob7XFYsH+/fuRnZ3tHGNZFtnZ2di9e7fLay6//HLs37/fmbyePn0aW7ZswfXXX+/y/La2NjQ0NHR7eeuw1nGtNL97ZyNtZCyiNLRulgSXRYsWwW63Y2SsCTF6Le94ePxkWDv6MCvb+jNYSxtvnJHJ8MzYMp/ESkh/ULME33sgIhIqlsX+/fvx+uvUJIUIS9BktqqqClarFUZj91JARqMRZWWuPwT/9Kc/4YknnsAVV1wBsViMoUOHYtKkSb0uM1i+fDm0Wq3zFRcX53W8PynKAIYBV3UOClXXhhiZKgomHc3MkuCxd+9efPrpp2AYR13ZntRhkag85/53RR8mguarN10eK/jVSOSJaQ05EZ5z8xfVl/WZCJEI94Y7Now+9thjvS4NJGQgBN3zlp07d+Lpp5/GK6+8ggMHDuDjjz/G5s2bsWzZMpfnz58/H/X19c5XUVGR1z+7ijWDMTkSb62667EKw4U5Z2aPHDkCu93u9c8gZCAsXLgQADAmIRaRGhXvuC56Muw2928PybXfg7HxHzGy4QY8mUwd8YjwaPOX/9yq0yNdKkVdXR0eeeQRocMhIUzQZDY8PBwcx6G8vLzbeHl5OaKiolxes2jRItx+++246667MGLECPz2t7/F008/jeXLl8Nm4xdhl0ql0Gg03V790ZIQCQDQ2OudY+0WPSLUKrAMg4aGhn4lzIT4286dO7Ft2zZwLINrM1J4x3XGWFQW89vZ9hQeKYL6m/dcHtv7qyGoZ2nTFxFeYWEhqqqqIAKQSpu/fEp0wWawt99+Gz/++KPAEZFQJWgyK5FIMGbMGGzfvt05ZrPZsH37dowfP97lNc3NzbyuIxznWNc3EDOiFSbHN3tlY7FzzFyvgohjEaFWAqClBiRw2e12ZzmdcUnxCFMpeOeoIyYCYHjjPSWf2+pynEmMw2rToX7FSTznaYnDuro6zJ49GyaTCVKpFKmpqdiyZcsARTtwOpcYpEilkNLmL5/Lksvxu/PF12fNmoWOjg6BIyKhSPDf7Hnz5mHdunXYsGEDcnNzcffdd8NsNmPGjBkAgDvuuAPz5893nn/DDTfg1VdfxcaNG3HmzBls27YNixYtwg033OBMav3plMHxiyo71/UI1dIqgkJngEnrmPWlTWAkUH3xxRfYtWsXRByLazKSeccNMUmoPMdvZ9tTVBQLxY//dXlsyxQDOpjgaVU7GHha4tBiseDaa69FQUEBPvroIxw/fhzr1q1DTEzMAEfuf7TEwP/mhUdAy3I4dOgQ1qxZI3Q4JAQJ3s52+vTpqKysxOLFi1FWVoasrCxs3brVuSmssLCw20zswoULwTAMFi5ciHPnziEiIgI33HADnnrqqQGJN0ddg8kAJCf3gxl7Czong9WGaERpVUARzcySwGSz2ZyzslckJ0Ir53+4y7VXobnZ/b2GnHSdyNpGpOItw9F+xUk8d2GJQwBYu3YtNm/ejPXr1+PRRx/lnb9+/XrU1NRg165dEIvFAIDExMSBDHnAUCUD/9OLRLg/IgKPl5dh0aJF+MMf/gCTySR0WCSECD4zCwBz5szB2bNn0dbWhj179mDcuHHOYzt37sTbb7/t/GeRSIQlS5YgPz8fLS0tKCwsxJo1a6DT6QYk1v3SUjASCVhzA9Q6sXNcojTSzCwJaP/5z3+Qk5MDqUiEyelDeccjEtJQXRrm9j6x0YBs/1cuj703KSDeUkKKNyUOP/vsM4wfPx6zZ8+G0WhEZmYmnn766YvWC/VlmcOBcuHmr0yZXOBoBrffabUYIZOhsbERDz74oNDhkBBDnzwesjBW2BMcm2O0cotznEGYs3FCbm4urRsiAaWjowOLFy8GAFyVmgSlVMI7RySf4P5GDJBw6F8uD7WOH4nPVfn9ipN4zpsSh6dPn8ZHH30Eq9WKLVu2YNGiRVi1ahWefPLJXn+OL8scDpSCggLU1NRABCBFwv87T3yHYxgsMhrBwNGpc8eOHUKHREIIJbNeaIh1tP1Ud1Q7x9patdAr5ZCIOFgsFuTn04c6CRzvvfce8vLyoJCIMTGN39UrKnkkast1bu+TYOqA9MgP/AMsi7X/1+iDSMlAsNlsiIyMxOuvv44xY8Zg+vTpWLBgAdauXdvrNb4sczhQOpcYpEllkNDmL7/LlMkx/fxT0tmzZ8NisVz8AkJ8hH67vVBidCw1VtSedY411SnAsRyM1AmMBBiLxYKlS5cCACanD4VMLO5+AsPAzvyf2/uwLIP4PW+5PFZ/9WjskgV+cjMYeVPi0GQyITU1tdum2WHDhqGsrKzXBMTXZQ4HQucSgwxaLztg5oZHwMBxyM3NxT//+U+hwyEhgpJZLxzXtwAAZEVdCau1g4M6PBImraMAPW0CI4HijTfeQEFBAdQyKSYkJ/KOR6dcgvoqfuOEnpKiWiA+lcMbZyQSPJ9V4oNIiTe8KXE4YcIE5Ofnd6vNfeLECZhMJkgG0eN4Z+cvSmYHjJbj8EBEBABg6dKlKC4udnMFIf1HyawXflZWAgBE+b+A5brqcSr10YiiTWAkgDQ3Nzu742VnJEMi6l6+jmFZWDouc3sfTsQg5nvX/ddLrxuFY+LK/gdLvOZpicO7774bNTU1mDt3Lk6cOIHNmzfj6aefxuzZs4X6I/gcdf4Szm80Wlwil8NsNuP+++8XOhwSAgQvzRWMjourwKjVYBsbodNzqKlybPYSyyKdm8BoZpYEgjVr1qCsrAx6pRzjkuJ5x2PSxqGqjN84oaeh4Q0QfX2cN86olHh22GmfxEq852mJw7i4OHz55Ze4//77MXLkSMTExGDu3LmDqiXp6dOnUVdXBzHDIJk6fw0olmGwMNKIW8466hh/9dVXuO6664QOiwxilMx6qSMpGtyh49BIWlFz/l+jzWaA6Xwym5+fj+bmZigU7hMFQvyhoaEBK1asAABcl5ECEdejc55IhObm0W7vI5ayMH3zistj+dOGo1h0oP/Bkn6bM2cO5syZ4/LYzp07eWPjx4/HTz/95OeohNO5xCBdKoWEcd/RjvhWukyG2/R6vFtbizlz5uDw4cOQ0pcK4ie0zMBLtTGOpFXd1rXpoq1ZA5VUAqVUArvdjtzcXKHCIwSrV69GTU0NItRKXJLA7+wUnTYBzQ3uH7+m6CrBVRTyxhmDHs8Oob/jJDDREgPhzQkLRzjH4eTJk3juueeEDocMYpTMeqkwwvFNX1HV9Yi1sU4OkUTiXGpA62aJUKqrq7F69WoAwNTMNHA9yhKJJBI01o9wex+pnEPEVy+7PPbLtGTUsi39D5YQP3B2/pJSMisUNcfh4chIAMBTTz2FgoICYQMigxYls146qnXU1JScyekatDPQRETDpKF1s0RYzzzzDBobGxGt02BELL88kyntKrSa3e9aT1EUgaut4I0z0VFYGUdf1khgstlsNDMbIH6l1mCcQoHW1lbMnTtX6HDIIEXJrJf2KEoBAOLCPEhkXTvEFRoTbQIjgiopKcHLLztmU6dmpoLtsV5QIlegvirD7X3kKhHCv3Q9K/vD1Bi0MtTljgSmU6dOoaGhARKGwVBapykohmGwINIIERxtlD///HOhQyKDECWzXqpgzWCMjscnOm3XOCeJoGUGRFBPPfUUWlpakBimxzBTJO94VMpEWFrd7/1MFeWDbarnHxgSj5eNh3wRKiF+ceHmLzFt/hJcslSKvxgcnTPvvfdetLTQ8iTiW5TM9kNrgiNR0HBNzjFrhx5R5xsnlJSUoKamRpDYSGgqKCjAunXrAABTR6SB6fFBLlOpUVOe6vY+Kq0Ihi0vuTy25VodrLD3P1hC/KRziQE1Swgc/wgLR5RIhDNnzmD58uVCh0MGGUpm+6HK5Ci7pTSXOseam1SQicXQK+QAaKkBGVhLly5Fe3s7UozhSI4M4x2PHDIZHRbOxZXdpXQcAmNp5Y3bhqfgbcMxn8RKiL90zsxSG9vAoWRZPHJ+M9gzzzyDkydPChwRGUwome2H0+FWAIC87IRzrKVRBolcQetmyYDLzc3FO++8AwCYlpnGO67Q6lFdmuT2PlqDGLqtr7k89uFkcf+CJMTPbDYbDhxw1D6mmdnAcp1KjQkKJSwWC+655x7Y7fSEh/gGJbP9cEhdBwCQ5v/cbVwTEeNsnkDrZslAWbJkCWw2G4ZHGxEfpuMdD0+YDGtHH2Zlm34CY+Vv7mq/dDg+Vp9wcQUhgePkyZNobGyEjGEwREKbvwIJwzBYYDRCzDD48ssv8d///lfokMggQclsP+yTlgAiEbjqUshVXRtqZGqqaEAG1sGDB/Hvf/8bDBwVDHpSh0Wi8hy/nW1PhggR1Nve4h9gGLw5oc0HkRLiX12bv2QQ0eavgJMokeDO85vB7rvvPpjNZoEjIoMBJbP90My2g4k1AQB0aqtznOXCulU0oEcpxN8WLlwIAMiKj4ZJp+Ed10dPgt3m/tc9uWIHGBd/X81XjMI3ioJ+x0mIv1F92cA30xCGGLEYRUVFWLZsmdDhkEGAktl+aop3bLLR2LtKGLVb9IhUK8EyDOrr63Hu3DmhwiMhYNeuXdiyZQtYhsGU4fxZWa0xBhXn+O1se4owclB9u5F/gOPwymVUlYMEB2fnL0pmA5acZTH//GawVatWUet30m+UzPZTmdHRRUnZWOwca2pQQsRxCFcrAdBSA+I/drsdjz32GADgssRY59+5C6kjJgJ2949bhxZucTled3WWY0kNIQHOarU6N39RMhvYrlapMUmpREdHB2bPnk1PMEm/UDLbTycMjnWEsnNd5YraW8RQ6Ay0CYz43ddff41vv/0WHMvi2uEpvOOGmERUnjO6vY/JxEKx+zPeOCORYPUoerJAgsOJEydgNpshZxgMkbhv10yE9VikEVKGwY4dO7Bxo4unQoT0ESWz/XRAWQUAkJzcD1ww+aUOi0GUhjaBEf+x2+1YsGABAODyofHQna9tfCG59iowcD8rm5T7b5fjpdeNQp64qn+BEjJAOpcYDJPJwNHmr4AXK5HgbwbHUr0HHngADQ0NAkdEghUls/10SFIORi4Ha26ARtdVg1Mij4RJRzOzxH8+++wz7Nu3DxKOw9XDknnHIxJSUV0a7vY+cdF2yHK+4Y0zSiWeHXbaJ7ESMhBovWzw+avBgHixGKWlpXj88ceFDocEKUpm+8nOALZEx+YardziHGeYMOfM7LFjx2C1Wl1eT4g3rFars4LBlamJUMv49TRF8glu78MwQMLB91weOzN1OIpF9S6PERKInJUMpJTMBgspy2KB0bEU6sUXX6TJH+IVSmZ9oD5WCwBQd1Q7x9padTCoFBBzLNra2pCfny9UeGQQ2rRpE44cOQKZWISJaUN5x41DM1Fbrnd7nwRTByS5P/HGGb0OzyTn+SRWQgaC1WrFwYMHAdDMbLC5UqnCtSoVrFYrZs2aRZvBiMcomfWBokhHVyVFbYFzrKlODo7lYKR1s8TH2tvbsWTJEgDApLQhUEhctJjlxru9D8syiPvpTZfHjk5NRTXb3K84CRlIeXl5aG5uhoJhkUibv4LOo5FGyBkGP/zwg7MtNyF9RcmsD+RpHR1MZIVdCau1g4M63EgVDYjPvf3228jPz4dSKsGVKUm846bUS1BfqXZ7n6SoFohPH+KNM8YIrEygL18kuHRt/pLS5q8gZBKLcXeYY43/Qw89hNraWoEjIsGEklkf2KssBwCITv0CTtT1JqrUR1NbW+JTra2teOKJJwAA1wxLhlQs6nacYVh0WC9zex9OxCDmu7Uuj+2dmoAmxuLyGCGBqjOZzaQlBkHrDoMBQyQSVFZWYtGiRUKHQ4KIyP0pBADCxKpej50V1YEx6MHW1EKr41BT1QEAEMsiKZklPvXaa6+huLgYWrkM44fG845Hp12G6nJ+44SekiMaIPr6JG+ciYvBP03+e4oQrzDhSkkE1EG2Jm6kjR5bBzpqYxv8JAyDRUYjZhQV4dVXX8Vf//pXXHLJJUKHRYIAJbN99GB9M/jFi7q0J0RBVFMLjaQVNef/tdpseucyg5MnT6KlpQVyOb8WKCF90dTUhKeeegoAcG1GCsQc1+04y4nQ0jrG7X3EUhZR219xeWzHFCMsTHn/g3Xh/+lG4r6cL8DZg7Cyh2mi0BGQi+jo6EBOTg4ASmaD3TiFEr9Sa7C5sQGzZs3Crl27wLL0EJlcHP0N6aO4ulLEKaJ6PV4d7Zi5VbdVOMdazVqoZVIoJGLYbDbk5dHucOK9F198EZWVlQhTKXBZUizveHT65Wiud/9BnqKrBFdR6OJAIl4N56+h9YXbdCPxwMHPgzORJQEvNzcXLS0tULIsEsQ0ix7sHoqMgJJlsWfPHrz5putNqoRciJJZD4yQ9l6A/myE47GpouqUc6ypXgaRROpcakCbwIi3amtr8dxzzwEApgxPBddjpoITS9DUMNLtfaRyDpFbX3R57H/ZGtj9sG/mUm0KHsr5wvc3JuS8zvWyGVIpWNr8FfQiRWLMOb8Z7NFHH0VVFXUhJBdHyawHRrbbej12WONowyc9k9M1aGegjYyhdbOk31auXIm6ujpEadXIiovmHY9OuwKtTe5npFKUxWDr+R8MthGpeFd3zCexXkgtVmF5wXGakR0Aa9asQWJiImQyGcaNG4e9e/f2eu7bb78NhmG6vWRB/Hi+q/MXLeMaLG7T65EqkaKmpgbz588XOhwS4CiZ9UB6fWWvx/bKSwGGgagwD2Jp179WuSaKynORfqmoqMA///lPAMDUzFSwbPeZJ7FMjvqa4W7vI1eJEL71JZfH/jWJczneXw+IoxFVV+yXe5MumzZtwrx587BkyRIcOHAAo0aNwpQpU1BRUdHrNRqNBqWlpc7X2bNnBzBi3+rc/EWVDAYP0fnNYADwxhtv4Kef+M1dCOlEyawH0ipOgoHrR1i1bAuYaMeaWp2u6xxOHEEzs6Rfli9fDrPZjDiDFsOjjbzjptSJsLS4aJzQQ6ooH2wTvz2t5bLh+FTFr2zQX5dok3Hz0e0+vy/hW716NWbOnIkZM2YgIyMDa9euhUKhwPr163u9hmEYREVFOV9GI//vVjBob293bv7KoGR2UBmjUOAmjQYAMHv2bGoLT3pFyawHVK0NiFZE9nq8JT4CAKDlmpxjVqsOUee7gBUXF6Ours6vMZLBpaioCK+84qg8MC0zDUyP9YBSpQq1FWlu76PUiGD44mX+AYbBWxPafBLrhTiGw4LSEjAIrhJcwchisWD//v3Izs52jrEsi+zsbOzevbvX65qampCQkIC4uDj85je/wdGjRy/6c9ra2tDQ0NDtFQiOHj2KtrY2qFkW8WL3X+pIcHkgIhIalsWBAwewdq3r2tiEUDLroXRpWK/HKkyOWQGludQ5Zm7QQC4RQ6dwHKPZWeKJZcuWwWKxYEiEASlG/gZE49BJaG9zv0Qg1XYYTFsLb7x5wkhslxf4ItRu/qAdjtRyqt4xEKqqqmC1Wnkzq0ajEWVlZS6vSUtLw/r16/Hpp5/ivffeg81mw+WXX47i4t6XhCxfvhxardb5iouL8+mfw1udSwwyZDLa/DUIhYlEmBvumChasGABysv9UzqQBDdKZj2Uau39X9kpg6NZgrz8hHOstUkCqULpnJ2lZJb0VX5+vvMx8bQR/FlZhUaH6rKhbu+j0Yuh3fo6/wDH4dWxdb4ItRutRIPZx3/0+X2J74wfPx533HEHsrKyMHHiRHz88ceIiIjAa6+91us18+fPR319vfNVVFQ0gBH3zrn5S0pLDAarP+h0yJBKUV9fj4cffljocEgAomTWQ6nNjb0ey1HXAACkJ3/uNq6JiKXyXMRjjz/+OKxWK9KjIpAUbuAdD0+aDGt7H2ZlzXvAdvDb09ZPzsIe6TmfxHqhuyWx0DZTX/WBEh4eDo7jeDNW5eXliIrqvTb2hcRiMUaPHo38/Pxez5FKpdBoNN1egYA6fw1+HMNgkTEKDIB33nkH33//vdAhkQBDyayHUmt6n43YLy0FI5GAqy6FQt3VXE2m6qpoQDOzpC+OHDmCDz74AAAwdQR/TazKEI7Kcwlu72MIF0G9zcUmILEYL4wq5Y/3U6IyGn+gTV8DSiKRYMyYMdi+vevfu81mw/bt2zF+/Pg+3cNqteLw4cMwmUz+CtMvLBYLfvnlFwCUzA52o+Ry3KLVAgBmzZqF9vZ2gSMigYSSWQ/FVZ+FQqRweczCWGFPcNQA1aq6dl0yXHi3mVl7kPWlJwNv0aJFsNvtGBkbhVi9lnfcEDsZ9osseemUXPUtGBd/3yquHYWjkt7LNnnr/jYxxDb6kBlo8+bNw7p167Bhwwbk5ubi7rvvhtlsxowZMwAAd9xxR7danU888QS++uornD59GgcOHMCf//xnnD17FnfddZdQfwSvHDlyBBaLBRqWRRxt/hr07o+IhI7jcOTIEbz8sosNrSRkUTLrIQZ2JF+krW1jjB4AoLF3lUBqb9MiUqMCyzCora1FaanvZ8TI4LFv3z588sknYBhgSmYq77g20oSKYn47254iIkVQ7fyAN87IZVg93Pc1RS/VpuDqk/T4TwjTp0/HypUrsXjxYmRlZSEnJwdbt251bgorLCzs9r5TW1uLmTNnYtiwYbj++uvR0NCAXbt2ISMjQ6g/glcuXGLQc005GXx0HId55zeDLVmyBCUlJQJHRAIFJbNeSOGUvR4riXLMDigbupYjmBtUEHMcwlWOGV1aN0suZsGCBQCAMQmxMJ7fOHghjXES+tJ3dmix6xayRVNG4rTIt2taGTB4sNL3M72k7+bMmYOzZ8+ira0Ne/bswbhx45zHdu7cibffftv5z88//7zz3LKyMmzevBmjR48WIOr+6er8RUsMQsXNWi1GyWRobGzEAw88IHQ4JEBQMuuF1PaOXo8d17cCAGQluc4xS6sISn0YNU8gbn377bfYtm0bOJbBtRkpvON6Uzwqit1v6omKYqHY9QlvnFGr8WzqCf4F/TRVPxzDz9GXNDKwKJkNPez5zWAsgI0bN3ZbK05CFyWzXkht4Pe27/Sz0jE7JTm5Hxc+9VKHxVAySy7Kbrc7Z2XHJsUhTMVfm60wTOy1C92Fhpz42OX4yWnDUHZBUw9fkLASzC2gv9NkYLW1tTmfclFZrtCSIZPhVp0OgOOJhMXCr9ZCQktAJLNr1qxBYmIiZDIZxo0bh7179170/Lq6OsyePRsmkwlSqRSpqanYsmXLAEULpFSc6vVYnrgKjFoN1twAta5rQ4JYHumsaEDLDIgrX3zxBX788UeIOBbZLmZlw+NTUF0S4fY+sdGA7MA23jhj0OOZpGM+ifVCt2rSEFNT6PP7EnIxhw8fRnt7O7Qsixja/BVy7g2PQBjHIS8vD6tXrxY6HCIwwZPZTZs2Yd68eViyZAkOHDiAUaNGYcqUKaiocL3+zmKx4Nprr0VBQQE++ugjHD9+HOvWrUNMTMyAxaxtqYNRzu/G1Kkj8XxFA/mF3xa7lhkcO3aMekyTbmw2GxYuXAgAmJCcCK2cP9MkUU5wfyMGSDj0L5eHDk1LQT3b2q84e1KLVfjb8V0+vSchfdG5+StTJqfNXyFIw3F4MMLRXn7ZsmUoLKQv1KFM8GR29erVmDlzJmbMmIGMjAysXbsWCoXC2fmop/Xr16OmpgaffPIJJkyYgMTEREycOBGjRo0a0LhTpL0ns7UxjqRV3VHtHGtr1SBMqYSIY9HS0oLTp0/7PUYSPD7++GMcPHgQUpEIV6fzu3oZhwxHTRm/cUJPCSYrpEd+4I0zUZFYFev7JwIzZQnUIIEIonO9bAatlw1ZN2o0GCOXo7m5Gffff7/Q4RABCZrMWiwW7N+/H9nZ2c4xlmWRnZ2N3bt3u7zms88+w/jx4zF79mwYjUZkZmbi6aef7nWms62tDQ0NDd1evpCG3h9rFUY4ZgkUtQXOsaY6JTgRB6NGBYDWzZIuVqsVixYtAgBclZoEpVTCP0nkvvg9yzKI3+P6S+BPU+PRzPq2/muUPAJ/OrbDp/ckpK86k9lMSmZDFsMwWGQ0goNjQmDr1q1Ch0QEImgyW1VVBavV6qyF2MloNKKsrMzlNadPn8ZHH30Eq9WKLVu2YNGiRVi1ahWefPJJl+cvX74cWq3W+YqLi/NJ7Cktzb0eO6JztLyVne2aCbN1sNBERMGkdbSApHWzpNN7772HvLw8KCRiTExL4h03pYxGfaX71qFJUS0Qn8rhjTPxMfhn1CFfhNrNHLsO0g7fLlsgpC9aW1udEwI0MxvaUqUy/FnvqO8+Z84ctLbSe1IoEnyZgadsNhsiIyPx+uuvY8yYMZg+fToWLFiAtWvXujx//vz5qK+vd76KinpvR+uJlNreGx/slTsScdHpQ+BEXWu5lLpoRNHMLLmAxWLB448/DgCYnD4Ush4bWRiGhdV+mdv7sByD6B9ed3nsm+uM6GBs/Y71QqmqeNyQR7OyRBiHDh1CR0cH9ByHaJHI/QVkUJsdFo4IToRTp07h2WefFTocIgBBk9nw8HBwHIfy8vJu4+Xl5YiKcl1L02QyITU1FRzHOceGDRuGsrIyl+U5pFIpNBpNt5cvJFWegoh1/SZawTWBMRnBdlig1XfFKZJGwKSjmVnS5Y033kBBQQHUMikmJCfyjkenXYaGapXb+yRHNkJceJx/ICUJa8N9Pyt7X7MVrN23CTIhfUWdv8iFVByHRyIdm8GWL19Oe1JCkKDJrEQiwZgxY7oVPbbZbNi+fTvGj3e9RnDChAnIz8+Hzdb1QXrixAmYTCZIJC7WGvqJ2NaOJEV0r8dbExy/WFpxi3PMZgtD1PmOTidPnqTHISGuubnZuTwme1gyJCKu23GW49Daeonb+4gkLKK+ecXlsf9do+pLszCPjNWm4spTrte0EzIQnM0SqL4sOW+aWo1xCgVaW1sxd+5cocMhA0zwZQbz5s3DunXrsGHDBuTm5uLuu++G2WzGjBkzAAB33HEH5s+f7zz/7rvvRk1NDebOnYsTJ05g8+bNePrppzF79uwBjz1F3Pssb4VJDgBQtXWVGGtpUkMjl0IuFsFqteL4cRczaSRkvPLKKygtLYVeIce4IfG849Fp42Gul7u9T4q+GqLys7xx24hUvKvPdXFF/9xfWe7+JEL8iDp/kZ4YhsGiSCPEDIPPP/8cn332mdAhkQEkeDI7ffp0rFy5EosXL0ZWVhZycnKwdetW56awwsJClJZ2rU+Ni4vDl19+iX379mHkyJG49957MXfuXDz66KMDHnvKRUrFngpztLyVV+Y7x8z1MoilMkTRJrCQ19DQgOXLlwMArhueAhHX/VeRE4thbnJfbk4i4xC57SWXxzZN9P1awuv0w5FJbWuJgFpaWnD06FEAVMmAdDdEKsVfzm8Gu/fee9Hc3PtGbTK4BMTK+Tlz5mDOnDkuj+3cuZM3Nn78ePz0009+jsq91Ka6Xo/9oq7FJACyM78AQyeeH2WgiYhGlFaFM1U1tAkshD3//POoqalBhFqJSxL4DT+i065E5Tmp2/ukqM+Bq+HPlLaPycB/1Sd8EmsnESPCva7W5RIygH755RdYrVaEcRyMftr8xUgkgFgM2O1+ub+v2NvbgXbfltwLdv8IC8fnDQ04e/Ysnn766V4rHZHBJSCS2WCVWl0I6F1Pbv8sLQFEIoiKjkOSycHS4pjGlWtMVJ4rxFVXV2PVqlUAgCmZqeDY7n+HxFIZGmoz3N5HphQh4gvXs7JvXeH7D7jf6YYh4fRmn9+XEE9cuMTAX5u/vv7LcLwWHvjvz1e2xuOelwqBjg6hQwkYCpbF/Egj5pacw3PPPYc77rgDqampQodF/EzwZQbBLKquGGqx653mbYwVOD/jpr9gaS0nDne2taWZ2dD0zDPPoLGxEdE6DUbGmnjHTWlXoa3Z/WbGVMkpsC6eDrRMGIWvFWd8EaqTXCTHP07s8+k9CfHGhZUM/OVHret26oHme1khSq93v0k01GSrVLhCqYTFYsGcOXNgD/AZdtJ/fZqZ9aZrlq9KYAW6FEUUDtTnuzzWGGeA+tRZqNlGlEMBAOjo0DuT2cLCQtTX10Or1Q5YvERYpaWlePnllwEAUzNTwfaYWZIqVairTHd7H6VGBMMXa/gHWBZrx9b7JNYL3aFMRngTzcr2x6FDnpdIy8jIgIjqqHbj781fjF6HI+Lg2eT4+LBcvL4nEvby4EjABwLDMFgQacRvCs5g27Zt+M9//oNbbrlF6LCIH/XpXVKn03n0OIdhGJw4cQJDhgzxOrBgkcLKcaCXYyVRYqQBUDWXAEgGADQ3qKCQiKGVy1Df0oqjR4/i8ssvH6hwicCeeuoptLS0ICFMh2GmSN5x49BJqChy/2uZYs8F22rmjTdMysJumW/ryuolWszI/c6n9wxFWVlZYBimz7NELMuGzPtoXzU3N+PYsWMA/Lf5qy05FkCeX+7tD7VsC774zVBMfZ2S2QslSCS402DAq9XVuO+++zB16lSoVO5rdpPg1Oev/B999BEMBoPb8+x2O66//vp+BRVMUtv4jRo6Hde3Ig2AvPQ4oHAks61mKaRKFaK0atS3tOLw4cOUzIaIgoICvP66o0vXtBHpvC+Ico0O1WVD3d5HoxdD9z8XdWVFIvxztOs20P3xN0kMlG2Bv34wGOzZswcRERFuz7Pb7cjMzByAiIJLTk4ObDYbwjkOkSKx+wu8UBqv8Mt9/Wl92BFMGj8Sst2+b5ASzGYawvC/hgYUnzuHJ554grqDDWJ9SmYTEhJw1VVXISwsrE83HTJkCMRi/7zRBJrUhqpe/y3uU5bjRgCS/P3AyF85xzURsYjSqnG8rJLWzYaQpUuXor29HSnGcCRH8n+XIpImo6KQc3FldyktP4Pt4H+JqsrOwmFJji9CdYpRGDH92Dc+vWeomjhxIpKTk6HT6fp0/lVXXQW53H2d4VDSucTAnyW5joa3+e3e/rRiQiWW5shhb2lxf3KIkLEsFkQacfe5Yjz//PP4y1/+guHDhwsdFvGDPm0AO3PmTJ8TWcCxsSkuLs7roIJJcuWpXo8dF1eDUavB1ZRDqenKeGWqKNoEFmLy8vLwzjvvAACmZabxjqsMEag8l+D2PvowETRfvckbZ2QyrBpR2P9Ae5htU0Ns7f3pA+m7HTt29DmRBYAtW7bAZOJvEAxlXZu//Jfkf68pdX9SADomrkTub0YIHUbAmahS4WqVCh0dHZg9ezZtBhuk+lXNoLi4uFtb2VCkam1AtJy/9rFTR5Kj5a1OeUGHBTYMpvPJ7OHDh+mXKwQsWbIENpsNw6ONiA/T8Y7rYyfDbnX/65hc9wMYG79bx7kpI3FKVOOLUJ1SVfH4Vd5On96T8P34449oawvO2cCB5vfNX5HhPv89GkhPJv4CDHX/pTjUzI+MhIxh8O233+KDDz4QOhziB/1KZjMyMlBQUOCjUIJXqqz3WevaGEfSqrbXOcfa23QwqlVgGEfN0fLy4Nk5Szx38OBBfPjhh2DgqGDQkzYyGpXF/MYJPYVHiqDe/i5vnFEp8Vya64oa/TG32QbWHtpfVgfCtGnTcO7cOaHDCHhNTU3Iy3NszPJXMtucHO2X+w4UC2PFO9fLAT/V3w1WMWIJ/nb+6fKDDz6I+nrfV3whwupXMkszig4ptt6XHp89v9dDUV/kHDPXKSEWcQhXKQFQ84TBbtGiRQCArPhomHT8knUa40TA7v7DJ/ncly7HT00bjnOc5+XzLmaMNgVXndrl03sS1+h9tG86N39FikSI8FO5suLY4G+P+7kqH7XXUu3Znv6qNyBRLEFZWRmWLFkidDjEx6hpgg+ktPBLJHU6om0CAMjPHXWOtVtEUBnCEaWhdbOD3a5du7B582awDIPrhvNnZfXRCagojnJ7n6goFoofP+aNM3odnhvi+zJC91dX+fyehPSHv5cYAMDh8Ga/3XsgPZ51GoxeJ3QYAUXCslhoNAIAXnrpJfzyyy8CR0R8qV/J7GOPPdancl2DXWptSa/H9igcmwnEJ/aDueDftsoQ49wERjOzg5PdbseCBQsAAJclxiJCreSdo9BfBQbuZ2WHnPyvy/Ej01JRzfr2A/hqfQZGFdEb/UB57bXXYDz/IUt617n5K1Pqv2T2W9XgWO5RyjXih9+6L/MXai5XKjFFrYbNZsOsWbNCfs/PYNKvZHb+/Pke7c4drBKqTkPCum4/WsWawZiMYFvN0Oi6ypVJ5JFU0WCQ2759O3bu3AmOZZGdkcI7HpGQiuoS9zVHY6IB2f6veONMZDhWxfv27w7HcJhb3HuFDuJ7f/rTn6BU8r/okO46Z2Yz/LX5KzYapVyjX+4thH8af0HH6GFChxFwHomIhJxlsWvXLmzYsEHocIiP9CmZnTdvHszm3h+l9zR//nzU1ATvjlBPiWwdSFL2XkKnNcFR7UAr69qxbEdXRYOjR4/SN8RB5sJZ2fFD46FX8ksJieQT3N+IARIPb3R56OepSWhifFs260ZdBoZUnPTpPYnDzTff7FFr8Ntuuw0VFX3v6rRmzRokJiZCJpNh3Lhx2Lt3b5+u27hxIxiGwU033dTnnzXQGhsbcfz4cQD+W2bQNGTwzY6/MLkZCJGa730VJRZj1vnNYA8//HBI5SqDWZ+S2X/+859obu77o8w1a9agrq7O25iCUopI3euxCpMjkVG3d61DbGvRIkylgIhl0dzcjDNnzvg9RjJwPvvsM+zduxcSjsM1w5J5x41DMlFbrnd7nwSTFdLD3/PGmdhoPB/t224/ElaCWadpeYG/fPrpp6isrERDQ4PbV319Pf73v/+hqampT/fetGkT5s2bhyVLluDAgQMYNWoUpkyZ4jYZLigowIMPPogrr7zSF39Evzl48CDsdjuiRCKE+2nz19mYwZf07ZWew9kbRgsdRsC5Q2/AUIkEVVVVzkkHEtz6lMza7XakpqbCYDD06eXJLO5gkdrR+47kU2EdAABFTVfC2lgnh1gsRqTG0SualhoMHjabzVnB4IqURKhlUv5Jov9zex+GBeL2ve3y2LdTomBh+PVm++OPmnRE1RX79J6kS+f7qF6vd/vy9H109erVmDlzJmbMmIGMjAysXbsWCoUC69ev7/Uaq9WK2267DUuXLsWQIUN88Uf0m4HY/HUwfPAsMbjQ0tQjYGKDu+SYr4kZBovOr1N/7bXXnH+/SPDq01fct956y+Mbh9qGhhRzXa/HDqprMAmA7OxhIHYcAMBuZaGJMMGkVaOkrgGHDx/Gb37zmwGJlfjXpk2bcPjwYcjEIkxK52/CMKVegtpKfomunpKiLJB8c4B/YGgCXo307aZBlViJu45TKS5/2rFjh8fXxMS4rz9ssViwf/9+zJ8/3znGsiyys7Oxe/fuXq974oknEBkZiTvvvBPff8+f/e+pra2tW3MHT5ZM9FdX5y8/JbMsi+8Ug/OLXBNjwX9uNODmV3rfqByKxiqU+LVag88bGzBr1izs3r0bHOe+nTgJTH1KZv/yl7/4O46gl1J1FjC4/te5X1oKiMUQnT4ELomFtd2xPlahM9EmsEGmvb0dixcvBgBMShsChaT7o0uGYdFuHev2PizHIPbHdS6PfZGtgxW+3XX9/+RJ0DXn+vSepLuJEyf65b5VVVWwWq28CQSj0ehsMtDTDz/8gDfffBM5OTl9/jnLly/H0qVL+xOq1/w+M5sYi1p28CZ7G7V5mHJVFpTf5QgdSkB5KDISO81N2LdvH9544w38/e9/Fzok4iWqM+sjxvoSaCWuZ9ssjBVIjAFjs0Kn6/pXLpJEUHmuQWbDhg3Iz8+HUirBlSlJvOPR6WPRVKNwe5+hkU0QnT3GG7cPS8ZbhqMurvBemFSP23O/9ek9SeBqbGzE7bffjnXr1iE8PLzP182fPx/19fXOV1FRkfuLfKC+vh4nTpwAAAz3U1mu+iHuq4oEuyfHlYBRq4QOI6BEiES45/zvwPz581FZWSlwRMRblMz6UIq896UVDbGOerxacddGOpvN4KxocOLECerPHuRaW1vxxBNPAACuSR8Kqbj7TD3LidDa4r4zj0jCwrTzFZfHPrraxfrbfvqbyAiFJfTWuQ8W4eHh4DiO1xa7vLwcUVH8hhynTp1CQUEBbrjhBohEIohEIrzzzjv47LPPIBKJcOqU69JsUqkUGo2m22sgHDx4EAAQLRLB4KfNX6ejBn/711OiGhz8DZXq6umPOj3SpFLU1tbi0UcfFToc4iVKZn0ohel91qDE6HgTVrZ2feC0NKmhlcsgE4vQ0dHhLD1DgtNrr72GoqIiaOUyjE9O4B2PSZ8Ac737maVkQw1EpQW88Y5LMvBvjW//jsQojPj9Mc/XcpLAIZFIMGbMGGzfvt05ZrPZsH37dowfP553fnp6Og4fPoycnBzn68Ybb8TkyZORk5ODuLi4gQzfrYHY/HUgbODW/wrpmbgc2IdRM4ULiRgGi88v0Vm/fj127aK9A8GIklkfSrX0PrN6XN8CAFBU5jvHmuplkMjktG52EDCbzXj66acBANdmpEDcYyOBSCJFY/0It/eRyDhEffWSy2Mbruzof6A9zLapIba1+/y+ZGDNmzcP69atw4YNG5Cbm4u7774bZrMZM2bMAADccccdzg1iMpkMmZmZ3V46nQ5qtRqZmZmQSFw3gBFK1+Yvfq1mnxCJ8L1sYJZMCM0KO9ZOYQDa6NTNaLkCv9VoAQCzZ89GR4fv32uJf1Ey60Op9b2vt9mndNR7lJ466BxjwEATGeNcakDJbPB68cUXUVFRgTCVApclxfKOR6ddhVaz+yQhRV0CtqaMN95y+Uh8qTjtk1idP0sVj1/l7fTpPYlnKioq8P333+P777/3qEFCT9OnT8fKlSuxePFiZGVlIScnB1u3bnVuCissLERpaamvwh5Q/p6ZtQ+NRzMbOl/odsgLUD7V/XKnUPNARAQ0LIucnBy8+uqrQodDPOTxAiSz2YwVK1Zg+/btqKio4HWuOn3atx+4wSS5Ih9sbARsdn43rxPiajAaDUQlpyAdxaGtxVEjVK6OQpSGNoEFs7q6Ojz77LMAgCnDU8Gx3b8jShVK1FW5X6smU4oQ8cWL/AMsi9fG+b4G5r0tdrAu/q4S/2tsbMSsWbOwceNGWK2O9wKO4zB9+nSsWbMGWq3W43vOmTMHc+bMcXls586dF7327bff9vjnDYS6ujrk5zueZvkrma1JNAAo9Mu9A9Xjmcexdl847BVV7k8OEQaRCPdFROCJ8nIsXLgQv//9712uOSeByeNk9q677sK3336L22+/HSaTCQwz+BfO95XCYkaMfDiKmvkzawDQkWQC90sDdBqg3LHqAKwogpYZBLmVK1eirq4ORo0KWXH84uTGlMmoKHT/q5YqOQ22qY433jAxC7tkvu32NVqbjEk53/j0nqTv7rrrLhw8eBCff/65c13r7t27MXfuXPz973/Hxo2uWxiHmgMHHHWWY8Vi6Pz0aPxU753IB61qthnbfjME2esomb3Q77U6fFxfjyMNDXjooYfw7rvvCh0S6SOPk9kvvvgCmzdvxoQJfegrH4JSJYZek9maaDUifgE0bAPKoQQAdHTonMlsQUEBGhsboVb33hqXBJaKigq88MILAICpmWlg2e5f7hQ6A6pL3G+4UGpEMHzxMv+ASIQXLynnj/fT3Jp6n9+T9N3nn3+OL7/8EldccYVzbMqUKVi3bh2mTp0qYGSBpXOJQYafSnIBwB59jd/uHcheDz+CK8dmQrqXJlE6cQyDRZFG3Fp4Fu+99x7uuusuv9WHJr7l8ZrZzlaLxLU0e++zB2cjHf+rNHcV5zbXq6CUSqA53/L06FHf1hAl/rV8+XKYzWbE6rXIjOGXZguPvxrWDvczSqn2Y2Bb+eWxqrOzcEji22T2Cl06xhTu9+k9iWfCwsJcLiXQarXQ6/UCRBSYOpPZTD8tMWDkMuyWDs7OX33xzFW1YPxYJSIYjZDL8XutDoBjM1h7e+ispw5mHiezy5Ytw+LFi9Hc3Oz+5BCUau69xMsRrWPdo7y0qytPW7MEcrWGmicEoeLiYudGgWkj0nhLbrSRJlQWuy9zpNGLof2Cv+GAkUqxOtO3u6wZMJhbFrof3oFi4cKFmDdvHsrKup7ilJWV4aGHHsKiRYsEjCywdFYyyPBTwmVNjkcHE7rrxo+Iy3H8NyOFDiPg3BcRAT3H4ejRo3jxRRf7GEjA8XiZwapVq3Dq1CkYjUYkJiZCLO7errNzjVOoSq0+C/Syd2OPvBT/j2EgPfEzMPoG57g6PBZRWjVOlFfRutkgsmzZMrS1tWFIhAGpRn4nJY1xMiqL3a8pT2n5GWyHhTdect1InBQfdHGF96bqhyP9wBaf3pN47tVXX0V+fj7i4+MRHx8PwFFxQCqVorKyEq+99prz3FB9T62pqXFuKPbX5q/KBM832g02y5J+wbtD4oHTobUJ7mJ0HId5ERFYVFaGxx9/HLfeeitiYmKEDotchMfJ7E033eSHMAaP2JoiqMLT0dTu4pEx2wzGZARbUgalRgRzg6OWnVRpdJbnopnZ4JCfn48333wTADAtkz8ra4hJQkWxEe5SWX2YCJr/vskbZ1RKPDvMdScmb4kYEeYU5vr0nsQ79D7qXmcSHycWQ+unzV/HjVa/3DeYtDFWvPcrJf78MgPY7UKHEzB+q9HiP3X1yGlqwrx587Bp0yahQyIX4XEyu2TJkj6d969//Qs33ngjlEqlx0EFMwZ2pCpMOFCf7/J4S0IkZCVl0Ck74FyRwIZRRYMg8/jjj8NqtSI9KgJJEfw15HLtVWhudj8rm1z7PRgb/wP19NThOMf5dkbut7phiD+92af3JN7x5H3UbDaH3PsoMDCdv37S0m5+APhMdRK/zr4Eum20lr4TyzBYZDTi92cL8OGHH2LmzJnIzs4WOizSC781Tfj73//O6xUeKtIYRa/HKkyON2a1vc451t6mg1GjBgOgsrKyX8XTif8dOXIEH3zwAQBg6og03vGIxHRUl4a5vU94pAjqb97jjTM6LZ4dmufiCu/JOCn+cSo0H1cHs1B+H/V3MsuoVdgvKXF/YohYOvoMGL1O6DACyjCZDH/SOTZkzp49G21tvXf5JMLyWzJrD+HHFeltrb0eO2VwLC1Q1p11jjXVKiERixCmciTBtNQgsC1atAh2ux0jY6MQq+evueOkfStbl3xuq8vxo9PSUM36doPlH9WpiKwPzg5QoSyU30c7N3/5q5JBe0o87FQm3ekc14Afb3JfRjDU3BMejjCOw4kTJ7Bq1SqhwyG9oHa2fpBW13vSkKN21DSUFR9zjnW0c1AbqHlCMNi3bx8++eQTMAwwJTOVdzwqeRTqKtxvKomKYqH48b+8ccYYgZUJvv3vrxarcOfxXT69JyH+VF1djYKCAgD+qzFbFq/yy32D2QtRv8A6Kl3oMAKKmuPwUISjruaTTz6Js2fPurmCCIGSWT9IKc+HiHG9HHm/tBQQiyE5ub9bgX2lIZrKcwWBhQsXAgAuiY+BUdOjuQXDwIb/69N9hpzkJ7IAsG9qApoYfmWD/vh/8kRom2t9ek9C/KlzVjZBLIbaT5u/jkX69vdssHj+mhagR5WiUHeDRoPL5HK0tLTgvvvuEzoc4gIls34gsbYhSclvawoAFsYKJMaCaWuBRt+V8ErkkTQzG+C+/fZbfPXVV+BYBtcN58/KxqSNRUO1+406sdGAbP9XvHEmLhovmHz7RSZMqsefc7/16T0J8beB2Pz1g8Z1p8ZQt1d6DoU3jBY6jIDCMAwWGqMgAvDJJ59gyxYqbxhoKJn1k3Rx74+aG+IcC8q10q61tXZ7mLM815EjR2CzhW4h70Bkt9uxYMECAMDYpDjn+uZOLMehpXWM+xsxQMIv77s8tGNKlOPLjg/9TRwFhYVfJo6QQOb3zV9hBuSJqZJBbx5PPQIm1vWETKhKkUpxu95Rueaee+5BS0uLwBGRC/ktmU1ISOA1VAglae29JyXnIh0zsqr2rjfT1mYNwlVKcCwLs9lM63ICzNatW/Hjjz9CxLHIzkjhHY9On4DmevcfvAmmDkiPuli/mpKIV8MP+SJUpxiFEb8/tsOn9yQDK1TfR7s2f8n9cv/WFCqAfzFNjAX/uZHa1vc0KzwcRpEIp0+fxjPPPCN0OOQCHiezO3b0/uF4YdeaI0eOIC7OfSvPwWpYY3Wvx/IMjm90iuozzrGmOjnEYgmMGsemBFo3GzhsNptzVnZCciK08u5JKyeWwFw/wu19WJZB/J63XB77/Bqtz3dWz7JpILbSusBARO+jvauoqEBhoaMb1TCp1C8/41ysf5LkwWSjNg/mq2i5wYWULItHIh2bwVasWIFTp3zb2IZ4z+NkdurUqXjooYfQ3t7uHKuqqsINN9yARx991KfBBbO08pO9HvtZ4agjKz37i3PMbmOhjYxG1PlkltbNBo6PP/4YBw8ehFQkwtXp/NI10elXosXs/kM3KaoF4lM5vHFbZire0R/1RahOyao4/DqPZmUDFb2P9q5zVjZJIoHKT5u/jkT0Xj6RdFk2rhiMmqo+XGiKSo3xCgXa2tpwzz33hHT5vEDicQewHTt24I477sC2bdvwwQcf4MyZM7jzzjuRlpaGnJwcP4QYGAr14xBfvLfP52tb6hAtT0ZJC78BwklxNRitBuIzRyEayqLD4lgfq9CaEKXVACihZDZAWK1WLF68GABwVWoSlFJJt+NimRz1VcPd3ocTs4j5bq3LY/+a7PsP7DmtDFh7YK27tovk2GP6M/7VMBxlbTLYETwfArfZL8FvfHi/UH0f7YvOZHa4n0pyAcB3amqW0BenRbU4cNNojH53n9ChBAzHZjAjbioowBdffIFPP/2U2lMHAI+T2csvvxw5OTn4xz/+gUsuuQQ2mw3Lli3Dww8/zOtPP5jMr7oO73Gvg7H2vQNImjTcZTILAB2J0eB+yYNOx6KqwpF0cJIIRGlpmUEgef/995GbmwuFRIyrUpN4x02pk1BR5P7XKDmsFqJi/my95bJMfKrybbevkZqhuOaXwJqVtUs1eFC2FP85aRQ6FK9cb/Xt4+5QfR/tC79v/jIZUcj1vgyMdPdsbA4+GJYMJtd1i/ZQlCSRYobegNdrqjF37lxce+21IdlyOpB4tQHsxIkT+PnnnxEbGwuRSITjx4+judm3HYsCTYuVg9mQ4dE1w+y9z7hVxziSVo2oa6e51ap3lufKy8uDxULrHYVksViwZMkSAMDk9KGQS7pvxJGpNKgp55fo6kki4xC17SX+AYbB+st9/7hzbn2Tz+/ZH3aGwwr1Y/hPeXAmsv4Siu+jfeGcmfVTMmseGuWX+w5WVtjxyhQ7IPJ47mtQ+3tYGKJFIhQWFuKpp54SOpyQ53Eyu2LFCowfPx7XXnstjhw5gr179+LgwYMYOXIkdu/e7Y8YA8ZZmWedUdLN9b3fK8Lxv6qWrr7rLY0a6BVySEUidHR04MSJE17FSXzjzTffREFBAdQyKSYkJ/KORw6ZhA6L+yUCKapz4Kr5XeHMV47CN4oCH0TaZbwuDWMLAuuR4O7YO/FacbzQYQSUUH4fvZiysjIUFxeDATDMT8lsYax/NpUNZt/Kz6J0Gm0Gu5CcZTE/0vEFfeXKlcjL8+0TNuIZj5PZf/7zn/jkk0/w0ksvQSaTITMzE3v37sXNN9+MSZMm+SHEwHGgY4hH56dXFvR67Ii2EQCgqOh69NzcKIVUoXQuNaB1s8JpaWnBsmXLAADXDEuGRNQ9aVXqDKgqdf/3QaESIWLri/wDHIc1l9b4JNZODBjMrQisQvDN4aPwl1MThQ4j4ITy++jFXLj5S8n6p3JkTnhgPbkIFo9n5IExRgodRkC5WqXCRKUS7e3tmDNnDm0GE5DH7xaHDx/GtGnTuo2JxWI899xz+OorflejvlizZg0SExMhk8kwbtw47N3bt41WGzduBMMwA7b4+vtGzx5PmeqKoZVoXB77SV4CMAwkpw90G9dExMCkdVxD62aFs2bNGpSWlkKvkOP/hvBLI4XFXw1bh/tfn1QuD2wTf4a+7uos/Cz17SaUbH0Ghp8LnL8zdlaMeW13od0W2mtAXfHH++hg0FVf1k+bv1gW3yqL/XPvQa6WbcEXv6FGChdiGAbzI42QMAy2b9+ODz/8UOiQQpbHyWx4eHivxyZO9HwGZtOmTZg3bx6WLFmCAwcOYNSoUZgyZQoqKlxvnOpUUFCABx98EFdeeaXHP9Nb39boYRd59iabrnCdANeyLWCioyAqLYBM2bUWSaaOoplZgTU0NGDFihUAgGuHp0DUozyQJiIKlcXua39q9GLovniFN85IpVg16pxvgj2PYzjMcbHBTEi/xPwRWyvDhA4jIPn6fXSw8Pvmr7hoVLO0Ltlb68OOoHX8SKHDCCjxEglmGhzvc/PmzUNjY6PAEYUmwdvZrl69GjNnzsSMGTOQkZGBtWvXQqFQYP369b1eY7Vacdttt2Hp0qUYMsSzR//90WZj0aLjd3+6mGHo/U25OcGxcFan7iqhxHLh58tzUTIrlBdeeAHV1dWIUCsxJoHfKUgbNQn2PnQ4SGneB9bCr35RMmUUjvu4leaNugwMqQic3cZWZRRmnr1G6DBIkPF3Wa76ofSYvL+enlABRk5NJy50l8GAOLEYJSUlWLp0qdDhhCRBk1mLxYL9+/cjOzvbOcayLLKzsy+6CeKJJ55AZGQk7rzzzoEIs5syuWfJbHqLuddjFSbHG7aGaXCOtVt0MJ2vaHD69Gk0NdH6roFUXV2NlStXAgCmZKaC67FuT2+KR0Wxye19DOEiaLbxv5AxahVWpPt2BlXCSjDr9C/uTxxA/9LeiUpL6LVhJd4rKSlBSUkJWADpfpqZLYj2TxOGUJInrsKR37rveBhKpCyLBec3g73wwgs0ESUAQZPZqqoqWK1WGI3dS/YYjUaUlbneyPLDDz/gzTffxLp16/r0M9ra2tDQ0NDt1R/H7Z61lhxW0/v6rHyDo/uPsrHrkXNTgwpKqQRqmWPH7bFjx7yIknjr2WefRWNjI6J1GoyM5SetCsNEMHA/K5tctROMzcobz5+agVLOt4+hpmvSEVUXOOsAzRFZWHTGszJ2pP882Xvw8ccf49JLL4VOp4NSqURWVhbefffdAYyWr3NWdohEAoWfNn/tN/Tv/Z84PBWfA6Tw626HsqtUKmSrVLBarZg9ezZtBhtggi8z8ERjYyNuv/12rFu37qJrzi60fPlyaLVa56u/fc73tXi2AD6x8hTknOtZhoNqx252eUmuc6y9RQyFzuCsN0ubwAZOaWkpXnrJUQ92amYq2B7F68PjklFdEuH2PpFGDqqd/+KNs+EGrBji2y8nSpECM08EVimnZR2392kZBvEdT/ceGAwGLFiwALt378ahQ4cwY8YMzJgxA19++eUAR97F75u/RCJ8ryjyz71DTAdjw5vXiwE/fekIVo9GGiFnGHz33Xd47733hA4npAj6NzE8PBwcx6G8vLzbeHl5OaKi+BunTp06hYKCAtxwww0QiUQQiUR455138Nlnn0EkEuHUqVO8a+bPn4/6+nrnq6iof29m39S4T2YuxNptSFG6ToAPSErBSKWQnNyPCyf71IZoRGkcySw9rhg4Tz31FFpaWpAQpsMwE39tnVjVt82GQ8/8z+X4z9cPRT3r2yYJdyiHQm8OnG5GJTFTsbHU/TIM4lue7j2YNGkSfvvb32LYsGEYOnQo5s6di5EjR+KHH34Y4Mi7dG7+yvBTMmsfEocmhhrR+MqXitOonDpG6DACSrRYjH+EOSbaHnzwQdTV1QkbUAgRNJmVSCQYM2YMtm/f7hyz2WzYvn07xo8fzzs/PT0dhw8fRk5OjvN14403YvLkycjJyXE56yqVSqHRaLq9+qOgRQaryrMP63TOdZu7DsYGe0I02MYaqDRd6wslSqNz3SzNzA6MgoICvP766wCAaSPSeC1FI5MyUFumd3ufGBMD+d7NvHEmMRarow/5Jtjz9BIt/pL7nU/v2R92Tor7qm8SOoyQ4+3eg052ux3bt2/H8ePHcdVVV/V6nq+XbPWMoTOZzZT5Z3NRTRJV1vC1JSNOgA2nf68X+ovBgCSJBBUVFVi8eLHQ4YQMwZ8RzJs3D+vWrcOGDRuQm5uLu+++G2azGTNmzAAA3HHHHZg/fz4AOIuLX/jS6XRQq9XIzMyERCIZkJjr1B5uAmvrfTagPtaRIOmU7c4xBmHOZQY0MzswnnjiCbS3tyMlMgzJkfwlLIz4cvc3YYDEoxtdHvp8ahgsDH8NbX/cJYmBsi1wysAcjv4D9tb178si8Zw3ew8AoL6+HiqVChKJBL/61a/w0ksv4dprr+31fF8v2bpQSUkJysvLwQFIk/qnQ9cpemDgc1WsGV//lrr7XUjCMFh4fjPYmjVrcPDgQYEjCg2CJ7PTp0/HypUrsXjxYmRlZSEnJwdbt251vjEXFhaitJTfClRIhWLPFr4Pq+v9A+Wc0VFjVm3t6gbV1qqF8Xyt2fLyclRWVnoRJemrvLw8bNiwAQAwdUQa77gpZTTqK90naQkmK6SH+DOl7WMysEF/tP+BXsAoD8etx3b49J79YZPpcXfR1UKHQTygVquRk5ODffv24amnnsK8efOwc+fOXs/39ZKtC3XOyg6VSCH30zrMPXrfdtwjDmvDD8MyNlPoMALKeKUS09Rq2Gw2zJo1Czabzf1FpF9E7k/xvzlz5mDOnDkuj13szRUA3n77bd8H5MaR9lh40qU6pfwkRPHR6LB38I7l6ZuRAUBRVwjAAABoqlNAJpYgTKlAtbkZR44cweTJk30SO+FbsmQJbDYbhkcbkRDWfSkBw7DosI11ew+GBeL2vc0/IBLhxYm9l2fz1iwYILHya9gK5evw23Eun3reC8HTvQedWJZFcnIyACArKwu5ublYvnx5r+10pVIppH6aNXXWl/VXswS5DLulgVPxY7BZcVUtlhyWwd7i2z0BwezhiEh8azbjp59+wltvvSVIKdFQIvjMbDDa1WR0f9IFpB2tSFS6fsa1V+n4AJIXdS0nsHZw0EQYaanBAMjJycGHH34IBo66sj1Fp12GxhrXa54vNCSqFZKTB3jjZVNHY4/Ut92+EpUx+E1u4MzKdmjicf8Z9wk/8Q9P9x70xmazoa1NmC9I/u781ZGcgA6GZsf85Yi4HHm/oc5gFzKKxZhzfjPYI488gurqwNmoOxhRMuuFnTV62FnPCsIPE+tcjp8W1YLR6yDKPwCW69p0pNRFU3muAbBw4UIAQFZ8NKJ13ZcSsJwILa3ud+tyIgYx37/OG2f0OjyZ4fsWs3PaJeDsvl1/2x/vKf8Cs5XeSoTkyd4DwLH+ddu2bTh9+jRyc3OxatUqvPvuu/jzn/884LFfuPnLX8lsZQKt5fa3ZYm/AEMThA4joNym1yNFIkV1dTUee+wxocMZ1OgTyAstVg5tuqEeXZPW3nvy0Z5oAmtpg1bX1Z1GJIt0VjSgmVn/2L17NzZv3gyWYXDdcBezsumXo7ne/Yfr0IgGiIqO88Z/viEZFZxvO7hlqBNx3fHAqWDQHD4SSwvShQ4j5Hm698BsNmPWrFkYPnw4JkyYgP/85z947733cNdddw147MXFxaisrIQI/tv8lRcVOF/+BisLY8Xbv5IBDNWY7iRmGCw6/zu4bt26izYyIf0TEGtmg1GlIhlxNXl9Pn9YYzV6axxVHaOC8SCgkbai9vx/EpvN0G2Zgd1u55WLIt6z2+3Ob8qXJsYiQt19KQEnlqCpwf1jM7GUhWn7K7xxJikeq3xcigsA5ja2gUHgdJZZZb+NGiQECE/2Hjz55JN48sknByAq95ybv6RSyPy0+WuX1nXzCOJbW5SncMOUMQjb+rPQoQSMSxUK3KjR4LOGBsyaNQt79uwBx1FbZV+jmVkv5TOelSNJK+/9cXNBuCM5UVu6qha0mDWIUCvBsQwaGxtRWFjoXaDEpe3bt2Pnzp3gWBbXZvBLrUWnXYnWJvel3lK0FeAq+P9tPp2q8/kavXG6VFx+Zo9P79kf1aaJePOc78ozkdDk785fjEaDHEnvFWWIby0ZdRJsuEHoMALKgxGRULMs9u/f76xnTnyLklkvHWiL8eh8bUsdYhSuN44d1jiKjyuqTjvHzHUySGUyRKodJbpo3azv2O12LFiwAAAwfmg89MruRdrFMjnqazLc3keq4BD55Uu88Y5LMvCezrdtawFgbmXgzC7ZGRYLm34ndBhkEHCul5X6J5m1pNIXroFUwZqx/SZaO3uhcJEI94Y7uoc+9thjvbaZJt6jZNZL39Z51tYWANKlrjul7FGUACwL6ZmcC0YZaCJiqKKBH/zvf//D3r17IeE4XDMsmXfclDoRlhb3G/xS5YVg66u6DzIMXr/K9zvCs/XDMaLY98sWvHU25gZ8UclvLkGIJwZi81dpnPtqJMS3Xo04DMtlVHv2QrfqdBgmlaKurg6PPPKI0OEMOpTMeulQgwo2mc6ja9J72fFdz7SCiTFBVJgLsbTrHLnGRBUNfMxmszkrGFyRkgi1rPuGE5lKjdpy/mawnhRqEcK2vswbb5g0GjvlZ30T7Hkcw+GeYt9XRfCWXSTDfRW/EjoMMggUFhaiurrar5u/jkQGTj3mUPLMVTVg5P75ghKMOIbBYqOj7vPbb7+NH3/8UeCIBhdKZvuhSes+6bnQMHN9r8fM8eFg7HbodF2baThJBM3M+timTZtw+PBhyMQiTErnV6SIHDIZ7Rb3+yJT2Tyw5u696RmpFKvH+L5b3Q26DAypyPf5fb11KPr3yGlQCR0GGQQ6Z2VTpFJI/LT56ztNiV/uSy7usKQCuVR7tptRcjlu0WoBALNmzUJHB7+REvEOJbP9UCLxrK1teuWZXo+VmxyzEhquq1tUR7vOWZ4rLy8P7e3tXkRJOnV0dGDJkiUAgElpQ6CQdF9KoNDqUV3q/r+pWieG7gt+BYNzU0fhmNi3rYclrASzzgTO8gK7VIvZhdS2lvhGZzLrt81fxgicFtX65d7EvScTfwGSE4UOI6DcHx4BLcvh0KFDWLNmjdDhDBqUzPZDnt2zjQXG+hIYpDqXx06EWQAAquaumb3mBjV0CjmkIg4WiwUnTwbOo+ZgtGHDBpw8eRJKqQRXpPCT1vCEybB2uC+Zktp2AKyl+6NLRqPBirQTPou103RNOky1RT6/r7d2Rv4Zxa3Utpb4RlcbW7mbM73TnBztl/uSvrEwVrx1vRTw06x7MNKLRLg/wrHnZtGiRd3qPxPv0d+wfthrdt2i9mLS5a4rGhxQOVrdycu6iu+3miVQqLW01MAH2trasHTpUgDANelDIRN3X0qgDotE5Tn35dZ0YWJovlzHGz92fTrKfNwgQSlSYOaJ3T69Z39YVdG498w4ocMgg8RAbP4qiqUvXkL7QnkKlVPdd1IMJbdotRghk6GxsREPPvig0OEMCpTM9sM3NWGw99YJoRfD4Lp26SFJGRi5HNKT3YtNa8JjEKWhTWD99dprr6GoqAhauQzjk/llY3Qxk2G3uf91SKn7AYytezchJioSzyT4/r/NX5RDoDcHTj/v/2hvR2MH9VkhvlFQUIDa2lqIGQYpEvc1nb2RE252fxLxuyUjToCJoOonndjzm8EYAB988AF27NghdEhBj5LZfihrk8CqifXomvRm12+uVthhTYwBV10KhaorYZCqomhmtp/MZjOeeuopAMC1GSkQ9+i+ojPGorLI/ePI8EgR1Nvf4Y3/OC0Ozaxv1zMbpDrckfu9T+/ZH236VCw4Q5s5iO90zsqmSvy0+YthsFNV7Pv7Eo9VsWZ8+VvPPisHu+EyGW7V6QAAs2fPhsViETagIEfTLP1Uq0pBREPf1zSm1xQBvWwEr4vTwZALaNVWNJ9/Ys2wYVSeq59efPFFVFRUIEylwGVJ/DdUdcREtJ5zP8M+tOQrF4MJeCnK9xu0ZoqjoWwLnI1f6yR/RruN2tYS3/H3EgMmPgZVrHedv8ZoUzCElQVQ42jXztpasa8+OPZSvBF2BFeNHwnZ7sB5XxPaveER+LKxEbm5uXjhhRfw8MMPCx1S0KJktp8KuER40j4hofI0FLo0NHc0844VRTIwANDY61EKDQDA0qZzJrOnT5+G2WyGUklFwPuqrq4Ozz77LADguuEp4HrMABliklBxLtLtYpGoKA7Kjf/hjf/vOi2sOOercAEA0fJITD/2jU/v2R9NkWOw8iy/uQQh/eHvNrb1yZEAvEtm59TW4dKz+30bkB80S5S4JWU4ipqDo13vUxMq8OQvCtib+Z9/oUjLcXggIgILysrwxBNP4I9//CPi4qhjnTdomUE/He7wrK0tAzvSlK4faR/TOZYgKBu7Ho011Sqhlsugkkpgt9tx7Jjv26QOZqtWrUJdXR2MGhVGx/H/W8m1V4Lpw7rnpPz/8saso9Lxrh/a1s62ayC2Bs4jp2c6bhU6BDLI2O12ZzKb4adk9pTJu483ESPC8NJcH0fjHwqLGU80Wfv0HhYIjourcOhm6gx2od9otLhELofZbMa8efOEDidoUTLbTz80uK5OcDHpjOsyND8pHCU6ZOe6EqSOdg7qsEhaN+uFiooKPP/88wCAqZlpYNnub/gRCWmoLnW/KSEmGpD//CVv/J2Jvn8ImaKKx6/zdvr8vt6qjL4a75Z49oWNEHdOnz6Nuro6SBgGyX7q/LXf0OD+JBdSVbGQW4Jn5vDSs/txm36E0GH02dNxB2Efxm9YE6pYhsGiSCM4AB999BG++srFcjbiFiWz/fRjrQ52zrM342FtrtsrlnKNYIwRkBzfB+aCvEulj3Y2T6Bktu9WrFgBs9mMWL0WmTH8Lx0i+QT3N2GAxMMbecMtE0bhC+UpX4TZzdwWO1i7zef39YadYbGg4WahwyCDUOd62TSpFBLGD7OKYjG+UxR6dekoTu3jYPxv7uHtSOjliV+gscKOV6YAENEqx05pMhlu0+sBAHPmzEFbLzkC6R0ls/3UZmPRqvNsPeGw2t7bK7YmGMG2NEGt6/pFF8sjaROYh4qLi/HKK44uXdNGpIHp8YFpHDoCteU6t/dJMFkhPdyjqgDHYc24Oh9F2uUSbTIm5gdOv+6zMTfgqyqD0GGQQcjfm7/syfFoZbxrFZrVEjyzsp1k7S1YVt8GlgmOj/Rv5Wdx7leXCB1GQJkTFo4IToSTJ0/iueeeEzqcoBMcf/MDXIXcs0cmQ8tPQsyKXR6rNDmWIGjlXaWe7DYDLTPw0LJly9DW1oYhEQakGl0sJWD/z+09GBaI3/sWb7z26izslfp20xcA3F8dOG037ZwU8yp/JXQYZJDq6vzln2S2KlHv9bWjKnz/xGUgjC46iDu0wbMedXH6UTAxnjceGqxUHIeHIyMBAE899RTOnDkjcETBhZJZH8hn+EX4L0Zsa0dyL4+EToU7ZhPUHVXOsZYWLYznGyeUlpaiujpwCukHolOnTmH9+vUAgGmZ/FlZU+oY1Fe5f5Q4JKoN4vyD3cYYqRSrR/k+kZ2sz0BW0UH3Jw6QI9G34EB9LzXkCOkHm83WlcxK/ZPMnjB5t549QmZATI13yxMCwZzDXyNJGRxr3BvZNnz0mzChwwgo16vVGKdQoLW1Fffdd5/Q4QQVSmZ94ECrF21tRRqX4wc1dQAAZU2Bc8xcK4dSLoNe6Zi1pdnZi3v88cfR0dGBtKgIJEV0f0zOMCzaOy5zew9OxCD6h9d44+emjsJxcZWLK7zHMRzmFp/26T37wy5VY+65a4QOgwxS+fn5aGhogJRhMNRPm7926Sq9um6UPLhnCqUdrXiyrhkcw7k/OQBs0uah4WpabtCJYRgsiDRCBOCzzz7D559/LnRIQYOSWR/4pjbS42uGWVyv5/pZWgKIxZCe7Sosbbcz0ETGODeB0brZ3h05cgTvv/8+AMesbE/R6ePQVKtwe5+hEQ0QFx7vNsao1ViRdsI3gV7gBl0Ghlb4/r7e+jHyTzjd7J8ZM0I6Z2XTpVKI/bD5i1Gr8bOk930JFzPKu2W2AWVk8S/4f9oMocPosyWXFoDR64QOI2AkS6X4i8ExCXPvvfeipaVF4IiCAyWzPpDbpIBN7tlGmWH1FS7HLYwVSIiB+NQv4MRd/3kUWhOiNLRu1p3FixfDbrdjRGwUYg3absdYToTWltFu7yGSsIj65lXe+PFpw1DGNfksVgCQclLMPp3j03v2h1UZiblnLxc6DDKI+XvzlyU1DnYvc+Ss2uBoPuDO7EPbkKwKjuL757gGfHczleq60D/CwhElEuHMmTNYvny50OEEBUpmfaRRk+rR+anlJ3rdedoQZwBjs0Kn6zrOiSNh0lEyezH79u3Df//7XzAApgzn//eISb8c5nr3H6Ap+iqIys92G2PDw/DcEN83SPiTOhVRdb5fg+utL/R/RrXF9eZEQnzB35u/ShK8W+stZsXIKBkcTWnEVgueqq6DiAmO8lcvRf4Cy2XDhQ4jYChZFo+e3wz2zDPP4OTJ4GhZLCRKZn3knCTJo/MVFjMSe9kEdi7K8QakFXeViLF26LrNzNrtgd41fOAtXLgQAHBJQoyz+kMnTixBU737wuISOYfIr17ijf98/RDUM62+CfQ8rUSDu/J+8Ok9+6Ndk4CHzrifuSbEW902f/kpmT0S6d3v6TBVPCTWwVPfM6PkKO7SDBM6jD5bflUtGLnrhkKh6FqVGlcolLBYLLjnnnvoM98NSmZ9JNfm+SOddLHO5Xie3pHEKlvLnWPNTWpEqFVgGQb19fUoLi52eW2o+u677/DVV1+BZRhc52JWNjr9SrSY3W82SVUUg6vtvgSEiYvG6uhDvVzhvZnSWGha6n1+X2/9S3k7WqzBsXGEBKcTJ06gqakJMobBEIl/Nn/tVHu5XpZV+jgS4f3t0FdIV3tWbUcoRyUVOPzb4Ckt5m8Mw+AxoxFihsGXX36J//6X31KddKFk1kd+Mkd5fM2wDtednvYoHUmsojLfOdbSKIVSrUaE2vGGS5vAutjtdixYsAAAMG5IHMJU3Td4iWVy1Fe5f4QlV4oQ/iV/VvabKUbHWmYfipZH4o9Hd/j0nv3RGpaBJQXBM4tDglPnrOwwqQwif2z+io7CWVGdV9dmmb1rfxvIxLZ2PFlR1Wtd80DzVEIOtbq9QKJEgjvPbwa77777YDabBY4ocFEy6yM7a8Jgh2dvzsMaXdeLPS2qBaPXQXrqQLdxTUQsTFpHSS9aN9tl69at+OGHHyDmWGQPS+EdN6VOhKXV/dqxFHE+2KYeM6WpSVgb7vsvDvfYNAH1SPNV7k+we7trhgSENWvWIDExETKZDOPGjcPevXt7PXfdunW48sorodfrodfrkZ2dfdHzfaVz81eGn5YYNCV7PqnQaXRZ4FQU8aW0slz8Q8Wv7BKIrLDjpak2anV7gZmGMMSIxSgqKsKyZcuEDidgUTLrI5UWMTo08R5dk17W+6Lu9qRoiEpOQ6roeuwrUxkRpXVsbqCZWQe73e5cK3v50ARoFd0/JKVKFWrL3W/OU2lFCPviZd74J9kqr3dG9yZDnYhf5QXOrGyDcSz+WThE6DBIP2zatAnz5s3DkiVLcODAAYwaNQpTpkxBRYXrqik7d+7EH//4R+zYsQO7d+9GXFwcrrvuOpw759/NiJ3JbKafktmCWIlX10XLIxHRMDgqGbjy18NfYbjGs30dQvlBVoSzN1Lt2U5ylsVj5zeDrVq1Crm5uQJHFJgomfWhGhV/VvBitC11iFG4rlFbFe1YTqDTdC36ZrgIamvbw8cff4wDBw5AKuJw9bBk3nHj0Mlot/RhVrbjEJi27vX8rKPS8YHW928cD9WbwSBwFvOvsEwXOgTST6tXr8bMmTMxY8YMZGRkYO3atVAoFM5OeD29//77mDVrFrKyspCeno433ngDNpsN27dv91uMVqsVBw86utz5a/PX/jDvlgqMknleKzyYiGwdeLq0BFLOP+uUfW1JymEwicFRWmwgTFapMVmpQkdHB2bPnk2bwVygZNaHCrhEj68ZJg13OX46wrGeVsM0OsfaLVrnMoPc3Fx0dAyCCt/9YLVasWjRIgDAValDoJR2n5VRaPWoLnU/46g1iKHbyu/29c5E379hZOuH49Kz+31+X29VRF+DD0qDu+tRqLNYLNi/fz+ys7OdYyzLIjs7G7t37+7TPZqbm9He3g6DwbN62Z44fvw4zGYz5AyDJIl3M6gXJRJhp9K7VrSj2n27Jj4QDak4iTkK/hf+QNTMtuOtGxUASylKp/mRkZAyDHbs2IF33nlH6HACDv1N8aFf2j3viT3M6vo/QY62DgCgaup67NdUp4ZeKYdExKGtrQ35+fkurw0V77//PnJzcyGXiHFVKv8RWnjCZFg73O/OT2naA8ba/YtB6/iR+EJ5ymexAo4GCQ8UHPXpPfvDzrBY2PBbocMg/VRVVQWr1Qqj0dht3Gg0oqysb4/OH3nkEURHR3dLiHtqa2tDQ0NDt5cnnJu/ZDJwftj8ZR8ajybG4tW1WdWBU+vZn+44/CVGa4Mjod2iPIWKaWOEDiNgxEok+EdYGADg7rvvxr///W+aob0ArbL2oR8ajPibh9dkNNW6HN8rLcEskQjyklxA7Vjz2d7GQW2IgFGjRlFNHY4cOYL09PR+Rh2cLBYLHn/8cQDA5LShkEu679ZVh0Wi8pz7NcyGcBHU/+nxKJZlsfb/Gl1f0A93qlIRm7/Z5/f1VmHMr/FVvv9m4khwWLFiBTZu3IidO3dCdpHH/8uXL8fSpUu9/jn+7vxVNTQMgOczs3JOhrSyPN8HFIBYuw1PFp/BLToZWqy+rZvtDwsyc/HGwSjYSwbvemZP3GUIQ05LC741m/GHP/wBJpMJCQkJzt9bhmHAnP+ieOH/DySTJ0/GY4895vP7UjLrQz/WamBXKcC0N7s/+byMspNApII33sy2A/HRkJz4Gbj0N+hcYqkOi4ZJq0JRTR0OHz6MW265xVfhB5X169fjzJkzUMukmJDCr6Ooi5mMyiL3Dx6Sq3aC6fHttmFyFnbJfFtXNkUVj7sOb/PpPfvDzknwQOWvhA6D+EB4eDg4jkN5eXm38fLyckRFXXx3/8qVK7FixQp8/fXXGDly5EXPnT9/PubNm+f854aGBsTF9X1dY11dHTiGwXCpf5LZ41HeLRUYroqDyDY4Kxm4El91BveZpmB5U+BvJKpnW/Hv3ybiljWUzAIAxzB4MSYWr1RV4Z3aGpSWlqK0tFTosDwSERHhl/tSMutDVjuLFm0yFFV9T4QM5ipEycegrKWSd6wpLgyq04VQa8VorGsHAEjkRkSFeHmulpYWZ4mSa4YlQ9qjjIvOGIvKItfd1S4UYeSg2vSv7oNiMZ7P8u0bp06ixeqyUoit3j0C9Yfc6N/h55Nq9yeSgCeRSDBmzBhs374dN910EwA4N3PNmTOn1+ueffZZPPXUU/jyyy9x6aWXuv05UqkUUqn3G4g2bNiAJbGxqP/3R17f42J+0PHfQ/sii/FPch3I/nj4K3yTdQ321Ad+Ev+hJg/Z146Bblvg7DUQkphhMDciAn8LC8PxtjZUdXSg3W6HHc45L9hhD6Atxl0UY8cia9Ysv9ybklkfK5MnYwg8m9XLkEW4TGbPmcRIA6BVtKOxzjFmR5izokGolud65ZVXUFJSAp1Cjv8bwp8ZUkVMROs5949XhhZu4Y1VXJeFo5KDPokTAIaqYvHP0jIkVJ322T37yy5W4r6S3tdGkuAzb948/OUvf8Gll16KsWPH4oUXXoDZbMaMGTMAAHfccQdiYmKwfPlyAI5+74sXL8YHH3yAxMRE59palUoFlUrltzjlYjHa/LCph9FpcUDq3QxVVoPrpV6DGQM7lp09jpsjVGhqD/xC/AtHn8KaX8Jhr6gSOpSAIWdZZAVZ+19tZiair7jCL/emDWA+dtzuWa1ZAMjoZRNYrsFRKkptrXGOtbVoYTqfzObn56OlpcXltYNVQ0OD8wP5uuEpEHHdN3gZYpJQdc7o6tJuTCYWit2fdRtj5HKszDjjs1gv0SbjvZNHAiqRBYB9UbfihDm43gTJxU2fPh0rV67E4sWLkZWVhZycHGzdutW5KaywsLDb48hXX30VFosFt9xyC0wmk/O1cuVKof4I/dKa5vn7LgAwYDCqNDTWy/Zkqi3CQyLPNy0LoYJrwme/c/+0jYQumpn1sT3N0Zjm4TW9bgJTlOMmAMraAgCOjTqNdXJoFHIopRKY2yw4duwYxowJnR2fL7zwAqqrqxGhVmJMAv+NWK69Cs19WLKclMd/1Hl22ggUiA64ONtzqap4rMn7GarWwGqRaZPpMbfoSqHDIH4wZ86cXpcV7Ny5s9s/FxQU+D+gAVQU792XswRlNHTNfStfNhjdfOxrfD16Cr6vC/z1s+/qjmHi1ZdA841v3qPJ4EIzsz62rcbzxc3De+kEli+uAaPXQVbUtTbWbmWhjYxGlMbxKDCU1s3W1NRg1apVAIApw1PB9XhcGZGYhurSMLf3iY0GZAe7F4dntBo8m+yb9WMGqR5rigoCLpEFgJ0Rt6G01Q81PgkR0MFI7x6VZ0momsfSU79AK9EIHUafLLz0NNhw9+/xJPRQMutj51ql6FB79ujGYK6CSe46CW5Piobo5EGwXNcaUKUu2tk8IZTWzT777LNoaGhAtE6DkXH8Qv8i2QS392AYIDHnPd74sevTUcE19TtGjuHwXKsEUXXF/b6Xr1lVJsw9M1boMAjxLZbFN6oiry7Nam3zcTDBJ6KhDAsY1817Ak0Z14T/3RIrdBgkAFEy6wc16jSPr8nspZ1iZYwSbIcFOn3X2lBOEnptbUtLS/Hiiy8CAKZmpoLtUT8vKnkkast1bu+TYOqA5Fj3x4pMRDhWxvummcHd6mEYW7DPJ/fytU+1f0ZjB60sIoPMkHhUs30vh3ih0VVnfRxMcJqWtxNT9MOFDqNPNuiPouHqS4QOgwQYSmb94DTnvoVqT8N76Ux7Ovx8W1tJV4Frq9UQcsns008/jZaWFiSE6TDM1CPxZxjYmHFu78GyDOL2vMUb/3laEhrZ/s/QXKZNwcxDW/t9H39o1w7B/DOjhA6DEJ+rTvZuVlEr0SCpIrS7KF5oYd4ehEuDY9nFY5edAhMRHLPJZGBQMusHByx9LyTeaUSD65IjB3SOzWHqtq6C6C1NakRpHWtmz507h9rawV1a5uzZs3jttdcAANNGpPG6mkSnjkFDlfuaqUlRLRCfyuk2xsRF4/no/jdI0EjUeLogD6zd1u97+cO7itvQZqNfdzL45EV7V1FzpCIaTEBW4xSGrrkGj3cohQ6jTypYMz65hb/UjIQu+nTzg6/rLt51x5WMsuNgwK+Nuld6DhCLIa865RwzN0ihVmugVzh28A722dmlS5eivb0dKZFhSI7s/m2cYVlYLJe5vQcnYhDz/eu88R3XRcHCeNc56EILmEhE1QVmf/eWsEwsKwjNtsdk8PtWX+7+JBeybLTkpqeJ+T/iZv0IocPok/d1uai9LnQq+ZCLC4hkds2aNUhMTIRMJsO4ceOwd+/eXs9dt24drrzySuj1euj1emRnZ1/0fCEcqFfDJtN7dI2qtQGJSn4dvTbGCiTGQHo6xznGgIE2MjYkmiccP34cGzZsAABMHcFfixyT/n9oqnNflmdoRANERce7D6Yk4tWI/s/KXqcfjuvzdvT7Pv6yhv0T7PbA69FNSH8xBj1yJN517Btd713HsMHu4aPfIUbhvlZ3IHhs9Ekw0Z5PHpHBR/BkdtOmTZg3bx6WLFmCAwcOYNSoUZgyZQoqKipcnr9z50788Y9/xI4dO7B7927ExcXhuuuuw7lzgTUr1qAb5vE1I3opE9MQZ4C46Dgksq5NYDJ1lLN5wmCemV2yZAlsNhuGRxuRENb9CwInFsPclOX2HiIJC9P2V3jjn1+jRX9zPINUj4XHA+vL1IUajGPxclGi0GEQ4hctaZ4v6QIAESNCZolvNn0ONsq2Riwzw+WTwkBTzTbjg5sNjjI1JKQJnsyuXr0aM2fOxIwZM5CRkYG1a9dCoVBg/fr1Ls9///33MWvWLGRlZSE9PR1vvPGGsw95ICkQJ3t8zQhLu8vxYqPjcZhO2zXGcuGDfmY2JycHmzZtAgNgSmYq73h02hVoaXTfVz1FXwWuorDbmC0zFe/o+/9htsCuhd5c3e/7+MsKy3ShQyDEb84kuv/9dyVdHQe5xbsKCKHgsoJ9+LMuOJYb/Fd9AqU3uF9qRgY3QZNZi8WC/fv3Izu7q088y7LIzs7G7t1968rS3NyM9vZ2GAyuZzXb2trQ0NDQ7TUQDnQkeHzNiJoSl+PHDI43XQ3X6Bxrt+i7VTSw2wffRoZFixYBAEbFRSNa172ot1gmQ0Ot+1IyEjmHyK9e4o3/azLn4mzPXKsfjuuOf9fv+/hLZfTV+KCUNkmQwWtPRL1X12Wx7jeMhrq5h7/GEFVw1HSdP+wwMMTzz1wyeAiazFZVVcFqtTr7h3cyGo0oK+vbOqhHHnkE0dHR3RLiCy1fvhxardb5iovz7rGUp7bXed5HOrXsOKSclDe+R+H4d6EydyW75noVItVKsAyDurq6gFtm0V+7d+/G559/DpZhMCUzhXfclDoRbc3uO1mlKovB1XZfsmK5bDg+VbnuutZXGokaj53Y3697+JOdYbGo8bdCh0GI3zBSKb5ReFcnNqs58LrzBRppRyuerm6AiA38jXLNbDvW3CgCxGKhQyECEXyZQX+sWLECGzduxH//+1/IZK4fN82fPx/19fXOV1GRd51iPLWrTgO71LMWgWJbO4Yp+d+Ez4rqwIYbIC/t2sBkaRVBGxaBcLWjlMpgWze7YMECAMClibGIUKu6HZMqVaitcL87X64SIXxrj1lZhsFbE/pfU/ZBzoTwJtfrugNBccz12FpJbR/J4NWRlohWppcC3W6M7qWFOOlueMkR/F0VHJVQvpWfRe4to4UOgwhE0GQ2PDwcHMehvLx7aZXy8nJERV18h+LKlSuxYsUKfPXVVxg5cmSv50mlUmg0mm6vgWC3M2jQZXh83QhW4XK8LdEEycmfu42pwmIG5Saw7du3Y8eOHeBYFtdm8GdljUOvRnub+2UCqaJ8sE3dH0M2TxiF7fKCfsU3VpuK3x77ul/38Cc7K8aDVb8WOgxC/KpkqNb9SS7EKIyIrC/1cTSD112Hv8JIjeeNgITweMIBdIz2fPM1CX6CJrMSiQRjxozptnmrczPX+PHje73u2WefxbJly7B161ZceumlAxGqV85I+JuW3BlpbnI5XhmjBFdbAaWm65GPRGFElGZwbQKz2+3OWdnxQ+KhV3Yvu6XUGVBdmuT2PiqtCIYtPWZlOQ6vjO1fgwkpJ8Xi4tP9uoe/nYj5LfbUDcyXNkKEciCqxavrsqQRPo5kcBPZOvD0uULIOe822w0kOwMsya4Bo6X3v1Aj+DKDefPmYd26ddiwYQNyc3Nx9913w2w2Y8aMGQCAO+64A/Pnz3ee/8wzz2DRokVYv349EhMTUVZWhrKyMjQ1uU4ChfRzu/ukq6esilMux0+FOx6n6ZQXFvgPg0k3uGZm//e//2HPnj2QcByuHjaUdzws/mpYO9zPyqZYD4OxtHYbq5uc5WhC0Q8zlSlIqArcZNYuVuC+0uuEDoMQ/2JZfKkpdH+eC5dYvFuaEMoSqk5jnixR6DD65KSoGltvDY6ZZOI7giez06dPx8qVK7F48WJkZWUhJycHW7dudW4KKywsRGlp1yOhV199FRaLBbfccgtMJpPztXLlSqH+CL3aWuv5JrCoumJEyvg9pw9qzre1tXfNLLa1ap0zs8eOHYPV2v9OVkKy2WzOCgZXpCRCI+8+E6CNNKGy2P0GPq1BDN0Xa7sPisV4Ict1tYi+GqKKxV+PBO7yAgA4GPUH5Da5XqpCyKCRnIAq1uzVpVmV3iXBoe7WI19hgi441s++aTiCmusC96kt8b2A2KY4Z84czJkzx+WxnTt3dvvngoIC/wfkIz/Xq2HTh4NtqfLoulFyI7a1dr9mn7QEEIuhrDsLQAcAaKpVIFyrhphj0drailOnTiE11fOlDYHiww8/xKFDhyATizApjf/NWh05GVXn3BfHTmn6CYy1++xLxXVZOCY+2K/4FjZYILZa+nUPf7JLtbi3aKLQYRDidxUp4QA838yrFquQXJHn+4BCxLJTh3CzKQJ1Fu9Kog2kRy7Jwxsn4mAvGJhN30RYgs/MDna1Ove1UHsa1cGvGdvZ1lZWfMw5ZrOy0EaYYBwE62Y7OjqwePFiAMCktCFQSLuX3TLEJKHynPsWi4YIEdTb3uo2xsjleC7jTL/iu1E/Aped/dn9iQL6LvJPKG7ll3YjZLDJiXHdYMadUcpYsHabj6MJHRENZVhs96xVu1DqmVb887ciMFJ6TwwFlMz62XFRmsfXjKp1XWO3Ic4AyYmfwbJds5NKfXS35gnBasOGDTh58iSUUgmuSOGvNZZpJvapvWJyxTdgejSQODttBM6K6ryOTSNR44Hje7y+fiBYlZG4r6D3TZOEDBoMg61a72bbRtsC4mFkULv2xHe4UR8c3cF+kBXhwPTeqx2RwYOSWT/7sTXR42uGlxxz2TzhXKQIjKUVGn3XG7JIEhn05bna2trwxBNPAACuTh8Kmbj7B07kkAzUlLnu8NbtPCMH1bebuo0xGg2eTT7Rr/jmiqJhMHu2VGSgfaH/M2rb6YOahICkOBSLvHvMPbqu3P1JxK35x75HjML9k7JAsDzmIMxXZgkdBvEzSmb97NNKE+x9mFG8kNhqwXAVf6NTZ1tbrbRrl77VanDOzAbrMoPXX38dhYWF0MpluHxoj5aEDANGNKFP9xlydjNvLPf6dFRw3le6yNQk4ZZj292fKKAOTTweOkPFwkloqEz3LokSsSKMKDnm/kTilqq1AU83WsEywZFCPHT5GTAx1Np7MAuOv4lBrLhVinad52VCsiDnjf2kdCw/UFsqnWMtTWrnzOzJkyfR2trKuy6Qmc1mPPnkkwCA7IxkiEXdy25Fp45BfaX7PuomEwPFT//rNsZEhOO5hKNex8YyLBZW1QT8GruNqtvRYnVfroyQweBQjHeltTJU8ZC1e1eblvBdUngAd2o83xMihCrWjFdvUYKRuG+BToITJbMDoESV6fE1Yxr5xf3PiurAhBmgqO6qc2pukMKg00EhEcNmsyE3N7dfsQ60l156CRUVFQhTKjA2qftsNMtxsLRf1qf7JOX+mze2f1oSGlnvW9f+Tjccw88F9mx3qyEdS85QxxsSIhgGX+i9Wy87hlX6OBhy96GvMFzjeT11IXyjKMCBW0cJHQbxE0pmB0AO+C1Z3ckqOeryEY4lyQTpmRznPzNgoDPGBeUmsLq6Ojz77LMAgOsyU8Cx3f+80ekT0FTLn6HuKS7aDlnOjm5jTGw0no8+5HVseokWc/N2eX39QFknvg1WO/0akxCRFI9Crs6rSy9pDPxyUsFGbGvHipJiyEXu36cDwfKYg2iaSEuyBiP6FBwAW+sT3J/Ug6alHiku1s1WxighOnsMYmnXfzqZ2hSU62ZXrVqF2tpaGDUqjI6L6XZMJJGiqd79LlSGARIObOCN75wS5Shn5qV7RUZom/vX+tbfmiIuwaqz/C5phAxWFcMivbqOAYPRJd4vOSK9S6w8hYcl8UKH0Wf3/99JMInum++Q4ELJ7ADYVqWHXep5r+hLOS1vLD+8A4zdDp2ua1MZy4UFXUWDyspKvPDCCwCAqZmp3cqNAUB0+kS0mt2vb0o0tUOSt6/7YEoiXonwflZ2uCYJNx/7xuvrB8pK661Ch0DIgNof613TkqGqmID/chrMbjm6DdfoM4QOo0/q2VasvJkFo6BOiYMJJbMDwGpnUWvwfK3OZU38x2KdbW21bNcO/XaLPuhmZlesWIGmpibE6rXIjInqdkyqVKG2wv06UJZjELtrHW/8f9ka2D0rIOHEgMGC6tqA3/RVY7oSb5fECh0GCSBr1qxBYmIiZDIZxo0bh7179/Z67tGjR/G73/0OiYmJYBjG+cUyoHEcNmvPenXpJeLgKPQfzB7P2+uyFXsg2iM9h223BW+3TMJHyewAyRV5/q310uLDvHWz+6QlYCQSqJrPOcfM9SpEne8CVlxcjOrqalit1oB9FRYWYs2aNQCAaSPSwDDdM0/j0KvR3uZ+d/7QyCaIC7o/OrSNSMW7Ou/L79ykz8SIYu9ndQeCHQyWNv9O6DBIANm0aRPmzZuHJUuW4MCBAxg1ahSmTJmCiooKl+c3NzdjyJAhWLFiBaKiolyeE2jsqUlel9m7pNns42hIT7rmGixv4YKmXNfr4UdQcuNYocMgPhIcf+sGge3Nnpfn0rbUYZi6+3pbC2OFPTEG8tLjXWOtIoQbo6BTyAAA4eHhEIlEAftKSEhAW1sbksINSDV2/yavMoSjqsT9vyuRhEX0zld54x9M9L5ElVqswtwTvc9mBYrSmCn4tNy7tYNkcFq9ejVmzpyJGTNmICMjA2vXroVCocD69etdnn/ZZZfhueeew6233gppkLT7LE5z3zilN2PKTvowEtKbsQX7MEMbHOW6AODBjBx0XBIcyyPIxVEyO0D+U2GCnRV7fN14VsUbq483QHq8e9KlMsRgZGzwFIUWcSx+PSqdNyurj70GNqv7v5bJ+mpwpWe6jbWNzcRnau8/tP4hjUdYU6X7EwVkZ0V4tPZGocMgAcRisWD//v3Izs52jrEsi+zsbOzevdtnP6etrQ0NDQ3dXgNpV4x3s6sxCiOi6s65P5H4xJxfvsRIjeeTN0LoYGx4KLsMTHRwPJ0gvaP+lwOkvl0Ec9QIqCoPeHTdhOoyvNFjsrEokkVmfRVUWhGa6h0FxKUKI27MysC1GSmw2+2+CttvxCIOYq77H0xvikdlUbTbayVyDsZtL3UfZBi8ebn3BdGHqmLxp6Nfe339QDkd8xt8d1IndBgkgFRVVcFqtcJo7N4Zy2g0Ii8vz2c/Z/ny5Vi6dKnP7ucJRi7DF6rT7k90YYyUnmIMJJGtAyuKz+APYUo0tQf+8o5zXANe/kMC5rwmg70luJoOkS40MzuATsjdl5rqKav4F6jE3Yt9HzE43iB0yq5OOHaEAQDkEjEUUknAv3omsgCgMEwC+tD6N1VZDK6me4/1pquysFPu3eYQAHjYbIPI5l1noYFiF8lwX9lUocMgIWr+/Pn4/+3deXhTZdo/8O/J0qRburdpoaXFlhbK0kJZCr4DKss7oK+8MwqD7AjKUoHBEVGRZRZwZtqhICCiP4TxHQZlxsGRQZEpRVA2WYpl2NciXdm6t2mT8/ujUogsTdIkJyf5fq4rF+T0LHfyJHfunDznecrLy5tvV67YNnmBLeo7P4IaRYNN26bV2T5xCtkm+vplvKmQz9nOr7wvY8eYpKaxHkmWWMw60c5a66+eVJka0cPXfAy/vT6FAABd4/XmZfW11g/95UrC4zriemHLV8L6+KkQ+vkK84UqFVak2d49YEBgR/S9sN/m7Z3lu6hnkV/JWYzIXGhoKJRKJUpKzL/glZSU2PXiLo1GA51OZ3ZzljMJtg+jlFZyzo6RkKWGnsrF/wZ1kToMi60NPY5LP7NsxklyPSxmneijkjYQFdb37OjVaP5tsVhZBUEfDt8bd352q7zpA4VSpr1GBAGC+lGLVu2gPAVFtXlfvbJBKcjzKrbp0GqFGr+6csambZ1J1Ojw0pXHpA6DXJCXlxd69OiBnJyc5mUmkwk5OTlIT0+XMDL72a6//6gMLQnXhiL6+iX7BkMWe+14Ltr7yWcIwVcTjqDm0RSpwyAbsJh1ojKDGtUh1n9T7V1278/ntXER0N41ra1oUkAXJp8LwO7WJrEnysv8W1xPF6RG4OerzZYJ3t7I6mx794LndB3R7trFlleU2Nfhz6GgVit1GOSi5syZg/feew8bNmzAyZMnMW3aNFRXV2PixIkAgHHjxuG1115rXt9gMCAvLw95eXkwGAy4evUq8vLycO6c653FFPThOKCx7QKuNG/5/NTtjrwNNfhj6Q1olfIYMUMUgFmPnoOYxJkV5YbFrJOd1qZYvU2H4lMI8DL/Sa84yhuqi/lm09r6BrZ88ZSrUarVqK1Ns2jdhJpvoTCY93+79NMuuKCybWafIK8AvHhyj03bOpPJJwyzL7vHGTZyjJEjRyIzMxMLFixASkoK8vLy8MUXXzRfFFZQUICioqLm9QsLC5GamorU1FQUFRUhMzMTqampmDx5slQP4YGud7N9qtSe9bb1syX76VByCvM01k/pLpVyoQ6vP1UJIYIXDsoJi1kn216baPU2AkR09zH/qeZUiAGCyYigu6a1VWrCWh2fs7VJ6o+aipbPOAaHqqD78n2zZUJQIN5KsP1q7RnqSPjX3TvLmqvZFjwW1w3WD+tGniUjIwOXL19GfX09Dhw4gN69ezf/bdeuXVi/fn3z/djYWIiieM9t165dzg+8BQfb2X5hZk/2l3UJP//PvzEsqLPUYVjsvOoGVjznB8GX1yjIBYtZJ/trcRREG35y6fmjfH5Q13TBU4DiTv/RxgbbBxWXgtZPh5tllg2wHV+aA+FHQ459N6wDritqbDp2vF80nvlPTssrSqxRF4NXLqZKHQaRNNRqfBpk25Bc4dpQWXQh8hQLjn8lq/6ze7QF+Pv4OEAl02tRPAyLWSerbFShPKyH1dv1uFZgdv+4ugSCTge/yjvLaypb7nfqSsLbD7Ro2toIvQJ+uz82Wya0icQf29o+7ezLNSYoRaPN2zvLJr+xqDXaPqsZkZw1dE2w+QtrL/aXdSk+hmpklV6Ht1I+ff83BZzCvrHdpA6DLMBiVgJH1NafaUsqPgV/tflsYA3to+BTcKegq6vSQON774xhrihQH43S7y3rR/XImU/uWZYzVI86wbafH/sFJuHR8/abGclR6oKTsPBiR6nDIJLMqY6257NedQY7RkL2EF9yGm+q5XN2FgCW6Y/h3IheUodBLeD5cwn87VYiHrdyG4VoQqpvNHbfOtm8rCzaH/rtB6H4r1/AZGz6CT7ikQEwGcshCCoICjUANZq+syhgyYQEzlJXE4666y3HEx0lQrtrh9kyU3IC1oTm23RclaDCK1cv2bSts72nHg2jyO+b5Ln+obd9Gtpexa4/5J4neurkThzrPgwf3bQth0vh9UeOYNXQngjb9q3UodADsJiVwLayUBhDIqCsLml55bv0NCqw+677Z8ONiDTUIShYietlTWcpS69YPzGDqxIUQLvDG+5Z/uHjtv/s/vPAjnjkwr9aE5ZTVIanIesyh4chDxYfi+Pq723atK2PHm0uHrRzQGQvrx7bgZOd++K7Ctv6Q0sho+tRvF+TCv9dR6UOhe6Dp30kciWoj9Xb9Lxunti/DWiaASxQXWWXmFxNe30dvE6bfxOu/kkK/uVn2xXK/mo/TD99wB6hOdwfGkdKHQKRpC53s31opN4yHNnFk6iNBmRdPotgTZDUoVhMFIBpfU6gLt36aenJ8VjMSiTHaH2n8qTCE/BT3xkq5JCmEIK3N3QVtk8a4KqUagXa7F5jtkzQaJDZ07aZgABgirYdgquvtTY0h7sWNQAfFraROgwiSW2Ntv292qe62o6RkCPob11FZq0aKkE+PxAbBCOm/uQsGnp0kjoU+hEWsxJ5v6i91VPbKkUjevjeGUDcCBGN8THwuex+P3skhNyA6vuzZssuDeuG/3jZVsy29dFjjAyG4hIFBeZX/lzqMIgkJbSJxFfeBS2veB8KQYE+V2wf6YScp+flQ/iVb4LUYVilRtGAqQMvozGVF+e6EhazEimq80JFmGUzX92tp9G8ycra6aA+eQAqtfs0pZe3EhFfrjBbJoSH4rcJJ2ze55wGLdRG17+6uaDNk/iiLETqMIgkdaVntM3bJvnFILDmhh2jIUcanb8dPw+yfpp3KVUq6jFtyBUWtC7EfSogGdqv7t3ySj/Sq8y8S8GZCCMUjQYEB7vOSAWtlehTAOUN84vjcv8nBuWKOpv2lxaQgEFndre8osREpRdeLhsmdRhEkvtn7HWbt+2nkNd42wS8cWwHugfESx2GVcqFOrw4uAANaZZN/EOOxWJWQv/vmvXf6pKKTiLQK6D5/r7AppnAgoSbdotLSr46FUK2mZ+VNXZNxOow2342VAgKzC0panlFF3Ai6hkcKucHMXk2ITYau7xtvw6g3w15vN/pDrXRgOyzx9DWR14TXVQq6jHliQuo7cuLwqTGYlZCB2/pUBdsXUErQERP3zuDTh9RF0HQ6aC7dtre4Umigykfitq7RmdQKrHmcdvnZn86MBkdi2zvnuAsopcfXro6UOowiCR3tlekzdv6q/3Q7Xv2l5WjoOrrWFV2857JgVxdjaIBz//kFMoHWj+zJ9kPi1mJHfX9L6u36VN/p7gTBcDQIRreJ1z/Z/SWBIaoEfi5+QgG1wan4isbz9L4qnww84w8BrneGzEaF2rkM80jkUMoFPhzjG0XfgFAul87qEy2f/klabUvPYvlBl+oFWqpQ7FKo2DClJ7HcOV/OVOYVFjMSuyDm9Z3fE8vNr/Kv7CdL1SFF+AXIJ8hTu6nw42vIBjvfBAJQYH4dRfbxpQFgBe84xBaZftQXs5i8gnDzMt9pQ6DSHIN3TvilNr2Ibl+Uuv6F3nSw/W89C1+q46B4EIzVlrq5aQj+HZCGqCS92exHLGYldiX10JQH2TdrF3R1y+Z9S06Gl4DAAjzrbVrbM4UoVfCL/cvZssO/k88ipW2TQgR4xOJsTIYigsAtgWPxXWDvM5EEDlCbnfb3wdKQYmfFOTZLxiSzNBTuXjFT54jBfwxMg+bpnaAEBjQ8spkNyxmXcBhvwFWb9P3rhlu/q27AggCAivO2zEq53rkzN/M7osdH8Gfoo7ZvL9X6tWyGIqrISAOr1xMlToMIskJEWH4c/BJm7fvpotDULXtoyCQaxmb/wVeCJDXkF23feJ/Bosn+wEd4qQOxWOwmHUBq69bX8z0q6xo/n+pohpCbDT8/rPLjlE5T0yUCdoj/76zQKHAe4MVMEK0aX/9ApMw4NzXdorOsf7sMxa1RqXUYRBJ7syAOBgEo83bDzR62TEacgUv5f0LYwPlOVLAcXUJJv2sBNf/2/rx5Ml6LGZdwNc3AlATat30tn0Kjpp1kr/WIRzqi/my6zerUApot/89s2XXB3fHv30u2rQ/tUKNed/L4wx1dVgKfnspUeowiCQn+PpieaztI7IIEDDoSr4dIyJXMffoVoyWaUFbJRgwLTUPO15IhRCgkzoct8Zi1kXs1D5h1fo+9VXorrvzE8bxtk1nNDo02P7TvBQSwiugvnBnKB0hOAiLu9p+0ddYXRJiy+RRzP7R+BxEUX4XORDZW8HgZJQqqm3evpuuPfS3rtoxInIl845uxQSZFrQA8F5IPl59QYO6dPk+BlfHYtZF/OFqZ4hKjVXb9G+8cxb2y+BCAEDgttWIj5THhWAabyX027PNln09vL3NF33pvcPwoky6WpRFPYb1hW1bXpHIzQn+/shMONvyig/xVKO8fpEi6718dCte0nWWOgybXVDdxLgBJ5AzJRVCWKjU4bgdFrMuoqBWiyL9Y1ZtM6DwzsUSZ1XXIbRrKo7abn4dcVENdo3PEZI056C8Udx8vzG1I5ZH2H5m+dVGH/gYbD+74yyioMS88p9LHQaRSzj+VEcUKStt3l6j1GDIBXmMJ02t88KxbfiNdweoFPL98vJuaD5emFSP74f3gqCx7gQWPRiLWReyrs66Yjb6+mV08Itpvl/aqWm4LkWjAXEbZ6Oncj8iIwWovFyvmcPCVQj+LLv5vqDRIOtx287IAsBPAjti4Jk9dojM8c61/RlyrgdLHQaR9BLisDSmdV2jhug6IKD2ln3iIZc3/MS/8V5jEII1QVKHYrObilrM6XgEb74UhJuDe3BcWjvgM+hC3r8ajbmRCfC6aflPboMUOpz54f/7Yw146q6/+ed8iI74EEmCAFNoG5h0oRDVakBQAoIAUcJBqVXfnTebIOH08G447HXEpn35qHzw5sX/2Cs0hxI1/phe+N9Sh0EkOUGjwfIn0aoRDADguWLbZggk+Uq7fBgfB0RhblwSjpTbfo2F1E6pr+HFHtfQuWsEpp2IQnhuPsTaOqnDkiUWsy5mm+/TGH4z0+L1/7vwNFb9MJX1PwLO4X+8tfe8GQRRhLLseyjLvrdnqHYjJj2CRXF5Nm8/RxMD/a0v7ReQA+WGj8PZs95Sh0Ekub3PdcbX2tadle0TmIjkozvsFBHJSUR5IdYdK8G6rkPwTtVpNJhcv2vdgxxXl2BGtxLoO/vhxe+7ovPeQoiXXPPz2lW53u/PHm7h5a4w+VjeOTy27Dw6/zCqQZVgQFl/eXWQFzQaZP/UiEbBZNP2fQMTMeK4PD7MGgLa46UL6VKHQSS5cyN6YZm+dYWsAAGzS4tbXpHcllI0YsqxbfhbpYD0QPkPc1isrMLidkfw7KhirJuZiNJhPSGE82IxS7hEMbtq1SrExsZCq9Wid+/eOHjw4EPX37x5M5KSkqDVatGlSxds27bNSZE6XnmDCrtCRlq1zf8a73Qin5tyAqZO8fYOy2GOPtsV+7S2fQMN0QThd+e+g2Dj5ArOlql+AdVGl3jLkRuSRR5VKJE/phdef8S2LkV3GxHUGclXHTO2rMknFKJK65B9k/21Lz2HtUd34F0hCl11j0gdjl184XseGV2PYsSkW3h/ZgdcfKYXTJ07sH/tA0j+rHz00UeYM2cO1qxZg969eyM7OxtDhgzB6dOnER4efs/6e/fuxahRo7B06VI8+eST2LhxI4YPH44jR46gc2d5nZV8kF9e6o0jAZ9AWVVk0fpPnvkG2bHtUNlQhSrBgF8Nu4k/qhOhPGb7IOTOUP2TVCyJPmrTthqlBsuqFQitLLFzVI5xOnok3j0b0/KKRDaQSx7d/JgX3rvQ+kI2WReHX+XvtENE5kSlBgejxmLc+QGoNykgCCK0ChNUggiF0HT2R4AIQcA9Vxw03XfuF2t/lQnDQovwqOYi4hvPIrDiJJTVpU6NwZX0vbAffQHkRadgc1gb/LvyPGoaa6QOq1VEAfjS9wK+TACQAAQ8qUVqXQSiDD7wbVRBZRLg7NedrWI6hiLKQfsWRFGU9Fno3bs3evbsiZUrVwIATCYToqOj8dJLL2HevHn3rD9y5EhUV1dj69atzcv69OmDlJQUrFmzpsXjVVRUICAgAOXl5dDpLJ+R42erv8GRglsWr99a82NPY3LxYovXX91tKN6pON58XxCBcbeS0eeiGoHFlVBW1kKoMwBGEwARkLbZUZPQBhmPnkOlot7qbbVKDbLEMPzk/F4HRGZ/JVEDMeDyeE5bK2OLnuqECf0sn2fd1jxjK2fnUcC2x/ibfb/Bx2c+tmjdB+kV0AF/On3IriMYGH31yA8ejAUlj+K7Cj+77VcK7X3q8F+B19HVuwxxilLoTcXQGUrgXVsMRXUpBLF1F9zJSZ3aG/tie+Ab/wAcariJC1VXIcqk8HNHP0v4GRb3tbyusSbHSHpm1mAw4PDhw3jttdealykUCgwcOBD79u277zb79u3DnDlzzJYNGTIEW7Zsue/69fX1qK+/UzBVVFS0PnAn+O2lRPRPeAYJV/5m0foTTn6FHYkpOFd1BUDTt7kNQf/BBpcdveSWTVv1CEjA60VX0aHEtQtZUaFCVWgKtiiHYMHFTpzpixzGGXkUkDaXeim8kKqLxTM1jRiSl2Nz1yJRUMLkG4EanyiUaWJwUmyHz8vj8K9rIRCvu8d79EKNFhdq2gBoc8/flIIJj/jUo71PDWI01YhQ1yBMWY1AoQY6VMMXNfAWa6A11cDLWAuVsRaqxhoojLVQNNZDMNYCjfUQTI33HtgFaRtq8djZr3F70MsqrQ6nwxNw3j8YV7w0uCoYUWasw7XGatw0VKBa5mdxPZmkxey1a9dgNBoRERFhtjwiIgKnTp267zbFxcX3Xb+4+P4XAixduhSLF1v+TeBBnn+0PZ6scO6QGd+gI0K7/RRBjdfQ0s8IPgDWq72xw0eDOjf55i0IAiACakFAIJToaGhEdG0FoJcsoh++1f8wrNkPw5uZoECjoIZBoUG14ItSMQinDOGoMTW9vd5MlipespdecSFSh/BAzsijgH1y6ZDYIYgLaDrD3fT+bno/CQAUEKAAoBIEqCHAGwJ0ooCwxka0rauE2vTDRaJxPzV7H4qCABEKiIICjVDBJKjQIKhhgBfqBA2qRS0qRG/cMPmitNEHxh99sez+w81TXfvhZg21wgSt0AhvRQM0ghFeghEaGKESGqGGEWoYoYQRasEIJUxQwAQljFCKTf+/+yaIYtO/MEEAfvi3afkPLdz8S2Lz/R+W3XkNAbc/I+/X3aPpFSbCD0CPH2730ygoUK3SoFqlQq1CgTpBiXoBqBcENEBEowA0AjDh9r8iTD+8Go0AxNvnfcWmE0qi2LQMd/+/Ocr7fKYLAiT+sdyh4oMcdz2P5H1mHe21114zOwNRUVGB6Ohoq/czrGukPcOyQnuL1wwA8IzjAiHc6Sd398ehEoAagDea2iAKQIpzwyJyOHvk0l6RvdArslerY7nf+xBoeh8S2UqFphweIHUgZDVJi9nQ0FAolUqUlJhfxFNSUgK9/v6n3/R6vVXrazQaaDhlHBG5KWfkUYC5lIhcl6TjBHl5eaFHjx7IyclpXmYymZCTk4P09PuPx5menm62PgDs2LHjgesTEbkz5lEi8nSSdzOYM2cOxo8fj7S0NPTq1QvZ2dmorq7GxIkTAQDjxo1DmzZtsHTpUgDArFmz0L9/f2RlZWHYsGHYtGkTDh06hLVr10r5MIiIJMM8SkSeTPJiduTIkSgrK8OCBQtQXFyMlJQUfPHFF80XJxQUFEChuHMCuW/fvti4cSPmz5+P119/HQkJCdiyZYvbjDFLRGQt5lEi8mSSjzPrbM4e/5GIPI8n5BlPeIxEJB1rcgzn1iQiIiIi2WIxS0RERESyxWKWiIiIiGSLxSwRERERyRaLWSIiIiKSLRazRERERCRbko8z62y3RyKrqKiQOBIicle384s7j3zIXEpEjmRNHvW4YrayshIAEB0dLXEkROTuKisrERAQIHUYDsFcSkTOYEke9bhJE0wmEwoLC+Hv7w9BECzapqKiAtHR0bhy5QoHB7cDPp/2xefTvuzxfIqiiMrKSkRFRZnNvOVOrM2lfJ3aF59P++Nzal+tfT6tyaMed2ZWoVCgbdu2Nm2r0+n4ArcjPp/2xefTvlr7fLrrGdnbbM2lfJ3aF59P++Nzal+teT4tzaPuecqAiIiIiDwCi1kiIiIiki0WsxbQaDRYuHAhNBqN1KG4BT6f9sXn0774fDoGn1f74vNpf3xO7cuZz6fHXQBGRERERO6DZ2aJiIiISLZYzBIRERGRbLGYJSIiIiLZYjFLRERERLLFYpaIiIiIZIvFrAVWrVqF2NhYaLVa9O7dGwcPHpQ6JFlaunQpevbsCX9/f4SHh2P48OE4ffq01GG5jbfeeguCIGD27NlShyJbV69exZgxYxASEgJvb2906dIFhw4dkjost8A8ah/Mo47FPNp6UuRRFrMt+OijjzBnzhwsXLgQR44cQbdu3TBkyBCUlpZKHZrsfPXVV5gxYwb279+PHTt2oKGhAYMHD0Z1dbXUocnet99+i3fffRddu3aVOhTZunnzJvr16we1Wo3PP/8cJ06cQFZWFoKCgqQOTfaYR+2HedRxmEdbT7I8KtJD9erVS5wxY0bzfaPRKEZFRYlLly6VMCr3UFpaKgIQv/rqK6lDkbXKykoxISFB3LFjh9i/f39x1qxZUockS6+++qr46KOPSh2GW2IedRzmUftgHrUPqfIoz8w+hMFgwOHDhzFw4MDmZQqFAgMHDsS+ffskjMw9lJeXAwCCg4MljkTeZsyYgWHDhpm9Tsl6//znP5GWloZnn30W4eHhSE1NxXvvvSd1WLLHPOpYzKP2wTxqH1LlURazD3Ht2jUYjUZERESYLY+IiEBxcbFEUbkHk8mE2bNno1+/fujcubPU4cjWpk2bcOTIESxdulTqUGTvwoULeOedd5CQkIDt27dj2rRpmDlzJjZs2CB1aLLGPOo4zKP2wTxqP1LlUZVD9070ADNmzMDx48fx9ddfSx2KbF25cgWzZs3Cjh07oNVqpQ5H9kwmE9LS0rBkyRIAQGpqKo4fP441a9Zg/PjxEkdHdC/m0dZjHrUvqfIoz8w+RGhoKJRKJUpKSsyWl5SUQK/XSxSV/GVkZGDr1q3Izc1F27ZtpQ5Htg4fPozS0lJ0794dKpUKKpUKX331FVasWAGVSgWj0Sh1iLISGRmJTp06mS3r2LEjCgoKJIrIPTCPOgbzqH0wj9qXVHmUxexDeHl5oUePHsjJyWleZjKZkJOTg/T0dAkjkydRFJGRkYF//OMf2LlzJ+Li4qQOSdaeeOIJ5OfnIy8vr/mWlpaG0aNHIy8vD0qlUuoQZaVfv373DHF05swZtGvXTqKI3APzqH0xj9oX86h9SZVH2c2gBXPmzMH48eORlpaGXr16ITs7G9XV1Zg4caLUocnOjBkzsHHjRnz66afw9/dv7i8XEBAAb29viaOTH39//3v6yfn6+iIkJIT952zwy1/+En379sWSJUswYsQIHDx4EGvXrsXatWulDk32mEfth3nUvphH7UuyPOr08RNk6O233xZjYmJELy8vsVevXuL+/fulDkmWANz39sEHH0gdmtvgkDKt89lnn4mdO3cWNRqNmJSUJK5du1bqkNwG86h9MI86HvNo60iRRwVRFEXHlstERERERI7BPrNEREREJFssZomIiIhItljMEhEREZFssZglIiIiItliMUtEREREssViloiIiIhki8UsEREREckWi1lySxMmTMDw4cOdftz169dDEAQIgoDZs2c3L4+NjUV2dvZDt729XWBgoENjJCKyBPMoyQWnsyXZEQThoX9fuHAhli9fDqnmA9HpdDh9+jR8fX2t2q6oqAgfffQRFi5c6KDIiIiaMI+SO2ExS7JTVFTU/P+PPvoICxYswOnTp5uX+fn5wc/PT4rQADR9SOj1equ30+v1CAgIcEBERETmmEfJnbCbAcmOXq9vvgUEBDQnvds3Pz+/e34eGzBgAF566SXMnj0bQUFBiIiIwHvvvYfq6mpMnDgR/v7+iI+Px+eff252rOPHj+OnP/0p/Pz8EBERgbFjx+LatWs2xV1TU4NJkybB398fMTExWLt2bWueBiIimzGPkjthMUseY8OGDQgNDcXBgwfx0ksvYdq0aXj22WfRt29fHDlyBIMHD8bYsWNRU1MDALh16xYef/xxpKam4tChQ/jiiy9QUlKCESNG2HT8rKwspKWl4ejRo5g+fTqmTZtmdiaEiMjVMY+SK2IxSx6jW7dumD9/PhISEvDaa69Bq9UiNDQUU6ZMQUJCAhYsWIDr16/ju+++AwCsXLkSqampWLJkCZKSkpCamop169YhNzcXZ86csfr4Q4cOxfTp0xEfH49XX30VoaGhyM3NtffDJCJyGOZRckXsM0seo2vXrs3/VyqVCAkJQZcuXZqXRUREAABKS0sBAMeOHUNubu59+42dP38eHTp0sPn4t3/Su30sIiI5YB4lV8RiljyGWq02uy8Igtmy21f3mkwmAEBVVRWeeuop/P73v79nX5GRkXY5/u1jERHJAfMouSIWs0QP0L17d/z9739HbGwsVCq+VYiIrMU8Ss7APrNEDzBjxgzcuHEDo0aNwrfffovz589j+/btmDhxIoxGo9ThERG5POZRcgYWs0QPEBUVhW+++QZGoxGDBw9Gly5dMHv2bAQGBkKh4FuHiKglzKPkDIIo1fQeRG5o/fr1mD17Nm7duiXJ9kREcsc8Stbi1yIiOysvL4efnx9effVVq7bz8/PD1KlTHRQVEZF8MI+SNXhmlsiOKisrUVJSAgAIDAxEaGioxdueO3cOQNNwN3FxcQ6Jj4jI1TGPkrVYzBIRERGRbLGbARERERHJFotZIiIiIpItFrNEREREJFssZomIiIhItljMEhEREZFssZglIiIiItliMUtEREREssViloiIiIhkSyV1AFIRRRGNjY0wGo1Sh0JEbkSpVEKlUkEQBKlDcTjmUSJyFGtyqUcWswaDAUVFRaipqZE6FCJyQz4+PoiMjISXl5fUoTgM8ygROZqludTjprM1mUw4e/YslEolwsLC4OXl5RFnUIjI8URRhMFgQFlZGYxGIxISEqBQuF9vLuZRInIka3Opx52ZNRgMMJlMiI6Oho+Pj9ThEJGb8fb2hlqtxuXLl2EwGKDVaqUOye6YR4nI0azJpe53ysBC7ni2hIhcg6fkF095nEQkDUtzDDMREREREckWi1kiIiIiki2P6zNL8tFlQxenHSt/fL7TjiVHq6budOrxZqx53KnHI3JbiwKceKxy5x1Lhk4mdXTq8TqeOunU40mJZ2aJHOCTTz7BoEGDEBYWBp1Oh/T0dGzfvl3qsDzeN998A5VKhZSUFKlDIaIWFBUV4bnnnkOHDh2gUCgwe/ZsqUPyaF9//TX69euHkJAQeHt7IykpCcuWLZM6LAAsZokcYvfu3Rg0aBC2bduGw4cP47HHHsNTTz2Fo0ePSh2ax7p16xbGjRuHJ554QupQiMgC9fX1CAsLw/z589GtWzepw/F4vr6+yMjIwO7du3Hy5EnMnz8f8+fPx9q1a6UOjcUs0DSeWXV1tSQ3S4f5LSsrg16vx5IlS5qX7d27F15eXsjJyXnotosWLUJKSgo+/PBDxMbGIiAgAL/4xS9QWVnZqufNk7XUHtnZ2Zg7dy569uyJhIQELFmyBAkJCfjss88s2v+AAQMwc+ZMzJ07F8HBwdDr9Vi0aJGDHo28WfremDp1Kp577jmkp6dbtX+2hWU8JY++++67zUOSjRgxAuXl/GndVi21R2xsLJYvX45x48YhIMD67hITJkzA8OHDsXjx4uZfyaZOnQqDwWDPh+E2WmqP1NRUjBo1CsnJyYiNjcWYMWMwZMgQ7Nmzx6L9DxgwABkZGcjIyEBAQABCQ0Px5ptvWvz+fRj2mQVQU1MDPz8/SY5dVVUFX1/fFtcLCwvDunXrMHz4cAwePBiJiYkYO3YsMjIyLDrTdP78eWzZsgVbt27FzZs3MWLECLz11lv43e9+Z4+H4XGsbQ+TyYTKykoEBwdbfIwNGzZgzpw5OHDgAPbt24cJEyagX79+GDRokD0fiuxZ0hYffPABLly4gP/7v//Db3/7W6uPwbZomSfk0XPnzuHjjz/GZ599hoqKCjz//POYPn06/vKXv9jjYXic1raHJXJycqDVarFr1y5cunQJEydOREhICD/77sPa9jh69Cj27t1rVU7dsGEDnn/+eRw8eBCHDh3CCy+8gJiYGEyZMqVVsbOYlZGhQ4diypQpGD16NNLS0uDr64ulS5datK3JZML69evh7+8PABg7dixycnL4hm4Fa9ojMzMTVVVVGDFihMX779q1KxYuXAgASEhIwMqVK5GTk8MC6j4e1hZnz57FvHnzsGfPHqhUtqU8toX7aE0eraurw5///Ge0adMGAPD2229j2LBhyMrKgl6vd2TYbqs17WEJLy8vrFu3Dj4+PkhOTsavf/1rvPLKK/jNb37DcZLvw5L2aNu2LcrKytDY2IhFixZh8uTJFu8/Ojoay5YtgyAISExMRH5+PpYtW8Zi1h58fHxQVVUl2bGtkZmZic6dO2Pz5s04fPgwNBqNRdvFxsY2F7IAEBkZidLSUquOTfeypD02btyIxYsX49NPP0V4eLjF++7atavZfbbZw92vLYxGI5577jksXrwYHTp0sHnfbIuWeUIejYmJaS5kASA9PR0mkwmnT59mMdsKtraHJbp162b2+khPT0dVVRWuXLmCdu3a2e047qSl9tizZw+qqqqwf/9+zJs3D/Hx8Rg1apRF++7Tp4/Z1Nfp6enIysqC0WiEUqm0OWYWswAEQbDoJypXcP78eRQWFsJkMuHSpUvo0sWy4avUarXZfUEQYDKZHBGiR2mpPTZt2oTJkydj8+bNGDhwoFX7ZptZ535tUVlZiUOHDuHo0aPIyMgA0PQrhSiKUKlU+PLLL/H44y0PA8a2aJkn5FFyDLaHa2mpPeLi4gAAXbp0QUlJCRYtWmRxMesoLGZlxGAwYMyYMRg5ciQSExMxefJk5OfnW3W2j+ynpfb461//ikmTJmHTpk0YNmyYxNG6twe1RWhoKPLzzccQXr16NXbu3Im//e1vzUmZPEdr8mhBQQEKCwsRFRUFANi/fz8UCgUSExMdHbbbcvTn2rFjx1BbWwtvb28ATW3m5+eH6Ohou+zf3VjbHiaTCfX19Rbv/8CBA2b39+/fj4SEhFadlQVYzMrKG2+8gfLycqxYsQJ+fn7Ytm0bJk2ahK1bt0odmkd6WHts3LgR48ePx/Lly9G7d28UFxcDALy9vW26Kpce7mFt0blzZ7N1w8PDodVq71lOnqE1eVSr1WL8+PHIzMxERUUFZs6ciREjRrCLQSu01B55eXkAmi7yKysrQ15eHry8vNCpUyeL9m8wGPD8889j/vz5uHTpEhYuXIiMjAz2l32Ah7XHqlWrEBMTg6SkJABNQ1BmZmZi5syZFu+/oKAAc+bMwYsvvogjR47g7bffRlZWVusDFz1MbW2teOLECbG2tlbqUKySm5srqlQqcc+ePc3LLl68KOp0OnH16tUP3XbhwoVit27dzJYtW7ZMbNeunQMi9QwttUf//v1FAPfcxo8fb9H++/fvL86aNcts2dNPP23x9p7E2vfG/d4PD2NLW8g1z1hKro/PHnl09erVYlRUlKjVasVnnnlGvHHjhqPDdluWtMf98qiln13jx48Xn376aXHBggViSEiI6OfnJ06ZMkWsq6tzxMORvZbaY8WKFWJycrLo4+Mj6nQ6MTU1VVy9erVoNBot2n///v3F6dOni1OnThV1Op0YFBQkvv7666LJZHrgNpbmGkEU7TDAl4zU1dXh4sWLiIuLg1arlTocInJD7p5n3P3x3c+iRYuwZcuW5jOF5PomTJiAW7duYcuWLVKHQmgaZzYlJQXZ2dkWb2NpruF5diIiIiKSLfaZdQPJycm4fPnyff/27rvvYvTo0U6OiB6moKDgof29Tpw4gZiYGCdG5LnYFnRbS3mUXM/DJun4/PPPnRgJWZJLHYndDNzA5cuX0dDQcN+/RUREmI0vS9JrbGzEpUuXHvj32NhYmwf3J+s4qi3cMc/czR0fH/Oo/Jw7d+6Bf2vTpk3zCAbkeFLnUn5iugEO/CwvKpUK8fHxUodBYFvQHcyj8sP3ruuQOpeyzywRERERyRaLWSIiIiKSLRazRERERCRbLGaJiIiISLZYzBIRERGRbHE0g7vEzvuXU4936a1hTj2e3JxM6ui0Y3U8ddJpx5KjrJFPOvV4L3+01anHI/thHnUtXTZ0cdqx8sfnO+1YRHfjmVk3smvXLnTv3h0ajQbx8fFYv3691CF5rK+//hr9+vVDSEgIvL29kZSUhGXLlkkdlkfatWsXBEG451ZcXCx1aOSCmEddxyeffIJBgwYhLCwMOp0O6enp2L59u9RhkQtiMesmLl68iGHDhuGxxx5DXl4eZs+ejcmTJ/ONLxFfX19kZGRg9+7dOHnyJObPn4/58+dj7dq1UofmsU6fPo2ioqLmW3h4uNQhkYthHnUtu3fvxqBBg7Bt2zYcPnwYjz32GJ566ikcPXpU6tDIxbCYlYmysjLo9XosWbKkednevXvh5eWFnJwcrFmzBnFxccjKykLHjh2RkZGBZ555xuKzgQMGDMDMmTMxd+5cBAcHQ6/XY9GiRQ56NPLXUnukpqZi1KhRSE5ORmxsLMaMGYMhQ4Zgz549Fu2f7WG5ltritvDwcOj1+uabQmFZ+mNbuA9n5NGMjAxkZGQgICAAoaGhePPNN+FhE21arKX2yM7Oxty5c9GzZ08kJCRgyZIlSEhIwGeffWbR/tkenoPFrEyEhYVh3bp1WLRoEQ4dOoTKykqMHTsWGRkZeOKJJ7Bv3z4MHDjQbJshQ4Zg3759Fh9jw4YN8PX1xYEDB/CHP/wBv/71r7Fjxw57PxS30FJ7/NjRo0exd+9e9O/f3+JjsD0sY2lbpKSkIDIyEoMGDcI333xj1THYFu7BWXlUpVLh4MGDWL58Of70pz/h/ffft/dDcQvW5lGTyYTKykoEBwdbfAy2h2fgBWAyMnToUEyZMgWjR49GWloafH19sXTpUgBAcXExIiIizNaPiIhARUUFamtrLZqjumvXrli4cCEAICEhAStXrkROTg4GDRpk/wfjBh7WHre1bdsWZWVlaGxsxKJFizB58mSL98/2sNzD2iIyMhJr1qxBWloa6uvr8f7772PAgAE4cOAAunfvbtH+2Rbuw9F5NDo6GsuWLYMgCEhMTER+fj6WLVuGKVOmOOTxyJ0lefS2zMxMVFVVYcSIERbvn+3hGVjMykxmZiY6d+6MzZs34/Dhw9BoNHbbd9euXc3uR0ZGorS01G77d0cttceePXtQVVWF/fv3Y968eYiPj8eoUaMs2jfbwzoPaovExEQkJiY2r9e3b1+cP38ey5Ytw4cffmjRvtkW7sWRebRPnz4QBKH5fnp6OrKysmA0GqFUKu12HHdiSXts3LgRixcvxqeffmpVf3e2h2dgNwOZOX/+PAoLC2EymXDp0qXm5Xq9HiUlJWbrlpSUQKfTWXQ2AQDUarXZfUEQYDKZWh2zO3tQe9wWFxeHLl26YMqUKfjlL39pVV9Ltod1WmqLu/Xq1Qvnzp2zeN9sC/fiyDxK1mvpvbtp0yZMnjwZH3/88T3dQIgAnpmVFYPBgDFjxmDkyJFITEzE5MmTkZ+fj/DwcKSnp2Pbtm1m6+/YsQPp6ekSRev+HtYe92MymVBfX+/kKD2DtW2Rl5eHyMhIJ0dJrsDRefTAgQNm9/fv34+EhASeBXyAlt67f/3rXzFp0iRs2rQJw4ZZP6Yw28MzsJiVkTfeeAPl5eVYsWIF/Pz8sG3bNkyaNAlbt27F1KlTsXLlSsydOxeTJk3Czp078fHHH+Nf/3LuAOae5GHtsWrVKsTExCApKQlA0xAzmZmZmDlzpsRRu6eHtUV2djbi4uKQnJyMuro6vP/++9i5cye+/PJLqcMmCTg6jxYUFGDOnDl48cUXceTIEbz99tvIyspy4COSt4e1x8aNGzF+/HgsX74cvXv3bh4b2tvbGwEBARbtn+3hIUQPU1tbK544cUKsra2VOhSr5ObmiiqVStyzZ0/zsosXL4o6nU5cvXp18zopKSmil5eX2L59e/GDDz6weP/9+/cXZ82aZbbs6aefFsePH2+H6N1PS+2xYsUKMTk5WfTx8RF1Op2Ympoqrl69WjQajRbtn+1huZba4ve//734yCOPiFqtVgwODhYHDBgg7ty50+L929IWcs0zlpLr43NGHp0+fbo4depUUafTiUFBQeLrr78umkwmez8Ut9BSe/Tv318EcM/N0jzI9pA/S3ONIIqeNeBaXV0dLl68iLi4OGi1WqnDISI35O55xt0fn60GDBiAlJQUZGdnSx0Kge3hDizNNbwAjIiIiIhki31mPUBBQQE6der0wL+fOHECMTExTozIs7E9XAfbgixlyWuFnIftQXdjMesBoqKikJeX99C/k/OwPVwH24IsZclrZdeuXU6Lx9OxPehuLGY9gEqlQnx8vNRh0A/YHq6DbUGW4mvFtbA96G7sM0tERDbxsOuHicjJLM0xLGaJiMgqt2dEq6mpkTgSInJnt3PMj2dh/DF2MyAiIqsolUoEBgaitLQUAODj4wNBECSOiojchSiKqKmpQWlpKQIDA1ucsY3FLBERWU2v1wNAc0FLRGRvgYGBzbnmYVjMEhGR1QRBQGRkJMLDw9HQ0CB1OETkZtRqdYtnZG9jMUtERDZTKpUWf+AQETkCi9m7LQpw8vHK7baroqIivPzyyzh06BDOnTuHmTNncgo/IiIicnsczcBN1NfXIywsDPPnz0e3bt2kDoeIiIjIKVjMykRZWRn0ej2WLFnSvGzv3r3w8vJCTk4OYmNjsXz5cowbNw4BAdafYZ4wYQKGDx+OzMxMREZGIiQkBDNmzGBfOCIiInJp7GYgE2FhYVi3bh2GDx+OwYMHIzExEWPHjkVGRgaeeOIJuxwjNzcXkZGRyM3Nxblz5zBy5EikpKRgypQpdtk/ERERkb2xmJWRoUOHYsqUKRg9ejTS0tLg6+uLpUuX2m3/QUFBWLlyJZRKJZKSkjBs2DDk5OSwmCUiIiKXxW4GMpOZmYnGxkZs3rwZf/nLX6DRaOy27+TkZLOrkiMjIzmGJBEREbk0FrMyc/78eRQWFsJkMuHSpUt23fePp4sTBAEmk8muxyAiIiKyJ3YzkBGDwYAxY8Zg5MiRSExMxOTJk5Gfn4/w8HCpQyMiIiKSBItZGXnjjTdQXl6OFStWwM/PD9u2bcOkSZOwdetWAEBeXh4AoKqqCmVlZcjLy4OXlxc6deokYdREREREjsNi9m52nMTA3nbt2oXs7Gzk5uZCp9MBAD788EN069YN77zzDqZNm4bU1NTm9Q8fPoyNGzeiXbt2du+OQEREROQqBFEURamDcKa6ujpcvHgRcXFx0Gq1UodDRG6IeYaIyHl4ARgRERERyRaLWQ/h5+f3wNuePXukDo+IiIjIJuwz6yFuXxx2P23atHFeIERERER2xGLWQ8THx0sdAhEREZHdeWw3Aw+77o2InIj5hYjIeTyumL09y1VNTY3EkRCRu7qdX348qx4REdmfx3UzUCqVCAwMRGlpKQDAx8cHgiBIHBURuQNRFFFTU4PS0lIEBgZCqVRKHRIRkdvzuHFmgaYPnOLiYty6dUvqUIjIDQUGBkKv1/OLMhGRE3hkMXub0WhEQ0OD1GEQkRtRq9U8I0tE5EQeXcwSERERkbx53AVgREREROQ+WMwSERERkWyxmCUiIiIi2WIxS0RERESyxWKWiIiIiGSLxSwRERERyRaLWSIiIiKSrf8PBhp3KOCNtMYAAAAASUVORK5CYII=", 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", 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" ] From 917a53b910ad637d4ebdb0d21e8d2efce402e97c Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 12 Jul 2023 15:06:36 +0100 Subject: [PATCH 023/154] fix test --- .../test_lithium_ion/test_dfn.py | 8 ++++++++ .../test_lithium_ion/test_electrode_soh.py | 16 ++++++++++++++++ .../test_parameters/test_parameter_values.py | 6 +++--- tests/unit/test_simulation.py | 8 ++++++++ 4 files changed, 35 insertions(+), 3 deletions(-) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py index 51a9b88d69..f8c2124079 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py @@ -43,6 +43,14 @@ def test_well_posed_external_circuit_explicit_resistance(self): options = {"operating mode": "explicit resistance"} self.check_well_posedness(options) + def test_well_posed_msmr_with_psd(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "particle size": "distribution", + } + self.check_well_posedness(options) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index 07d2f29b70..8ef5e9c5e6 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -300,6 +300,16 @@ def test_min_max_stoich(self): V = parameter_values.evaluate(param.p.prim.U(y0, T) - param.n.prim.U(x0, T)) self.assertAlmostEqual(V, 2.8) + x0, x100, y100, y0 = pybamm.lithium_ion.get_min_max_stoichiometries( + parameter_values, + param, + known_value="cell capacity", + ) + V = parameter_values.evaluate(param.p.prim.U(y100, T) - param.n.prim.U(x100, T)) + self.assertAlmostEqual(V, 4.2) + V = parameter_values.evaluate(param.p.prim.U(y0, T) - param.n.prim.U(x0, T)) + self.assertAlmostEqual(V, 2.8) + def test_initial_soc_cell_capacity(self): param = pybamm.LithiumIonParameters() parameter_values = pybamm.ParameterValues("Mohtat2020") @@ -377,6 +387,12 @@ def test_min_max_ocp(self): self.assertAlmostEqual(Up_100 - Un_100, 4.2) self.assertAlmostEqual(Up_0 - Un_0, 2.8) + Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps( + parameter_values, param, known_value="cell capacity", options=options + ) + self.assertAlmostEqual(Up_100 - Un_100, 4.2) + self.assertAlmostEqual(Up_0 - Un_0, 2.8) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index 9da2b7a879..d6406ca05f 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -121,9 +121,9 @@ def test_set_initial_stoichiometries(self): def test_set_initial_ocps(self): options = {"open-circuit potential": "MSMR", "particle": "MSMR"} - param = pybamm.ParameterValues("MSMR_Example") - param_0 = param.set_initial_ocps(0, inplace=False, options=options) - param_100 = param.set_initial_ocps(1, inplace=False, options=options) + param_100 = pybamm.ParameterValues("MSMR_Example") + param_100.set_initial_ocps(1, inplace=True, options=options) + param_0 = param_100.set_initial_ocps(0, inplace=False, options=options) Un_0 = param_0["Initial voltage in negative electrode [V]"] Up_0 = param_0["Initial voltage in positive electrode [V]"] diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index 83ec42ef6c..64d5de3456 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -203,6 +203,14 @@ def test_solve_with_initial_soc(self): sim.build(initial_soc=0.5) self.assertEqual(sim._built_initial_soc, 0.5) + # test with MSMR + options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + model = pybamm.lithium_ion.SPM(options) + param = pybamm.ParameterValues("MSMR_Example") + sim = pybamm.Simulation(model, parameter_values=param) + sim.build(initial_soc=0.5) + self.assertEqual(sim._built_initial_soc, 0.5) + def test_solve_with_inputs(self): model = pybamm.lithium_ion.SPM() param = model.default_parameter_values From 8c95a6dd95ea23a6315a5b49bad9fe4809e78f49 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 18 Jul 2023 12:09:06 +0100 Subject: [PATCH 024/154] add individual reaction params --- examples/scripts/MSMR.py | 1 + pybamm/CITATIONS.bib | 11 ++ .../lithium_ion/MSMR_example_set.py | 72 ++------- .../full_battery_models/base_battery_model.py | 6 + .../lithium_ion/electrode_soh.py | 32 ++-- .../open_circuit_potential/msmr_ocp.py | 4 +- .../submodels/particle/msmr_diffusion.py | 141 +++++++++--------- pybamm/parameters/lithium_ion_parameters.py | 80 ++++++---- tests/unit/test_citations.py | 19 +++ 9 files changed, 192 insertions(+), 174 deletions(-) diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index 1d5fdaf938..ad99de4be1 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -4,6 +4,7 @@ { "open-circuit potential": "MSMR", "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), } ) diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib index b3ca67c061..21740584b5 100644 --- a/pybamm/CITATIONS.bib +++ b/pybamm/CITATIONS.bib @@ -513,6 +513,17 @@ @article{Valoen2005 publisher={IOP Publishing} } +@article{Verbrugge2017, + title={Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide}, + author={Verbrugge, Mark and Baker, Daniel and Koch, Brian and Xiao, Xingcheng and Gu, Wentian}, + journal={Journal of The Electrochemical Society}, + volume={164}, + number={11}, + pages={E3243}, + year={2017}, + publisher={IOP Publishing} +} + @article{Virtanen2020, title = {{SciPy 1.0: fundamental algorithms for scientific computing in Python}}, author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others}, diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index 349e28d346..89736c6110 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -1,6 +1,3 @@ -import pybamm - - def electrolyte_diffusivity_Nyman2008(c_e, T): """ Diffusivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data @@ -65,64 +62,19 @@ def electrolyte_conductivity_Nyman2008(c_e, T): return sigma_e -def x_n(U): - """ - Graphite stoichiometry as a function of potential. - - Parameters - ---------- - :class:`pybamm.Symbol` - Potential [V] - - Returns - ------- - sto: :class:`pybamm.Symbol` - Electrode stochiometry - """ - T = 298.15 - f = pybamm.constants.F / (pybamm.constants.R * T) - xj = 0 - for i in range(6): - U0 = pybamm.Parameter(f"U0_n_{i}") - w = pybamm.Parameter(f"w_n_{i}") - Xj = pybamm.Parameter(f"Xj_n_{i}") - - xj += Xj / (1 + pybamm.exp(f * (U - U0) / w)) - - return xj - - -def x_p(U): - """ - NMC stoichiometry as a function of potential. - - Parameters - ---------- - :class:`pybamm.Symbol` - Potential [V] - - Returns - ------- - sto: :class:`pybamm.Symbol` - Electrode stochiometry +def get_parameter_values(): """ - T = 298.15 - f = pybamm.constants.F / (pybamm.constants.R * T) - xj = 0 - for i in range(4): - U0 = pybamm.Parameter(f"U0_p_{i}") - w = pybamm.Parameter(f"w_p_{i}") - Xj = pybamm.Parameter(f"Xj_p_{i}") - - xj += Xj / (1 + pybamm.exp(f * (U - U0) / w)) + Example parameter values for use with MSMR models. The thermodynamic parameters + are for Graphite and NMC622, and are taken from Table 1 of the paper - return xj + Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao and Wentian Gu. + Thermodynamic Model for Substitutional Materials: Application to Lithiated + Graphite, Spinel Manganese Oxide, Iron Phosphate, and Layered + Nickel-Manganese-Cobalt Oxide. Journal of The Electrochemical Society, + 164(11):3243-3253, 2017. doi:10.1149/2.0341708jes. - -def get_parameter_values(): - """ - Example parameter values for use with MSMR models. The values are loosely based on - the LG M50 cell, from the paper + The remaining value are based on a parameterization of the LG M50 cell, from the + paper Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for @@ -142,7 +94,6 @@ def get_parameter_values(): "Current function [A]": 5.0, "Contact resistance [Ohm]": 0, # negative electrode - "Negative electrode stoichiometry": x_n, "Number of reactions in negative electrode": 6, "U0_n_0": 0.08843, "Xj_n_0": 0.43336, @@ -173,7 +124,6 @@ def get_parameter_values(): "Negative electrode exchange-current density [A.m-2]" "": 2.7, "Negative electrode OCP entropic change [V.K-1]": 0.0, # positive electrode - "Positive electrode stoichiometry": x_p, "Number of reactions in positive electrode": 4, "U0_p_0": 3.62274, "Xj_p_0": 0.13442, @@ -219,4 +169,6 @@ def get_parameter_values(): "Initial temperature [K]": 298.15, "Initial voltage in negative electrode [V]": 0.01, "Initial voltage in positive electrode [V]": 4.19, + # citations + "citations": ["Verbrugge2017", "Baker2018", "Chen2020"], } diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index c77fb0f16a..1f4958f426 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -72,6 +72,10 @@ class BatteryModelOptions(pybamm.FuzzyDict): "stress-driven", "reaction-driven", or "stress and reaction-driven". A 2-tuple can be provided for different behaviour in negative and positive electrodes. + * "number of MSMR reactions" : int + Sets the number of reactions to use in the MSMR model in each electrode. + A 2-tuple can be provided to give a different number of reactions in + the negative and positive electrodes. Default is "none". * "open-circuit potential" : str Sets the model for the open circuit potential. Can be "single" (default), "current sigmoid", or "MSMR". If "MSMR" then the "particle" @@ -227,6 +231,7 @@ def __init__(self, extra_options): "reaction-driven", "stress and reaction-driven", ], + "number of MSMR reactions": ["none", "1", "2", "3", "4", "5", "6"], "open-circuit potential": ["single", "current sigmoid", "MSMR"], "operating mode": [ "current", @@ -572,6 +577,7 @@ def __init__(self, extra_options): "intercalation kinetics", "interface utilisation", "loss of active material", + "number of MSMR reactions", "open-circuit potential", "particle", "particle mechanics", diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py index 84b328eb7b..9536550031 100644 --- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py +++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py @@ -196,8 +196,8 @@ def __init__( ) # Define parameters and input parameters - X_n = param.n.prim.X - X_p = param.p.prim.X + x_n = param.n.prim.x + x_p = param.p.prim.x V_max = param.voltage_high_cut V_min = param.voltage_low_cut @@ -214,21 +214,21 @@ def __init__( if "Un_0" in solve_for: Un_0 = pybamm.Variable("Un(x_0)") Up_0 = V_min + Un_0 - x_0 = X_n(Un_0) - y_0 = X_p(Up_0) + x_0 = x_n(Un_0) + y_0 = x_p(Up_0) # Define variables for 100% state of charge # TODO: thermal effects (include dU/dT) if "Un_100" in solve_for: Un_100 = pybamm.Variable("Un(x_100)") Up_100 = V_max + Un_100 - x_100 = X_n(Un_100) - y_100 = X_p(Up_100) + x_100 = x_n(Un_100) + y_100 = x_p(Up_100) else: Un_100 = pybamm.InputParameter("Un(x_100)") Up_100 = pybamm.InputParameter("Up(y_100)") - x_100 = X_n(Un_100) - y_100 = X_p(Up_100) + x_100 = x_n(Un_100) + y_100 = x_p(Up_100) # Define equations for 100% state of charge if "Un_100" in solve_for: @@ -710,12 +710,12 @@ def get_initial_stoichiometries(self, initial_value): x = x_0 + soc * (x_100 - x_0) y = y_0 - soc * (y_0 - y_100) if self.options["open-circuit potential"] == "MSMR": - Xn = param.n.prim.X - Xp = param.p.prim.X + xn = param.n.prim.x + xp = param.p.prim.x Up = pybamm.Variable("Up") Un = pybamm.Variable("Un") - soc_model.algebraic[Up] = x - Xn(Un) - soc_model.algebraic[Un] = y - Xp(Up) + soc_model.algebraic[Up] = x - xn(Un) + soc_model.algebraic[Un] = y - xp(Up) soc_model.initial_conditions[Un] = 0 soc_model.initial_conditions[Up] = V_max soc_model.algebraic[soc] = Up - Un - V_init @@ -1052,16 +1052,16 @@ def _get_msmr_potential_model(parameter_values, param): """ V_max = param.voltage_high_cut V_min = param.voltage_low_cut - X_n = param.n.prim.X - X_p = param.p.prim.X + x_n = param.n.prim.x + x_p = param.p.prim.x model = pybamm.BaseModel() Un = pybamm.Variable("Un") Up = pybamm.Variable("Up") x = pybamm.InputParameter("x") y = pybamm.InputParameter("y") model.algebraic = { - Un: X_n(Un) - x, - Up: X_p(Up) - y, + Un: x_n(Un) - x, + Up: x_p(Up) - y, } model.initial_conditions = { Un: 1 - x, diff --git a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py index 65dc90df09..2ac87279f2 100644 --- a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py +++ b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py @@ -8,7 +8,9 @@ class MSMROpenCircuitPotential(BaseOpenCircuitPotential): """ Class for open-circuit potential within the Multi-Species Multi-Reaction - framework :footcite:t:`Baker2018`. + framework :footcite:t:`Baker2018`. The thermodynamic model is presented in + :footcite:t:`Verbrugge2017`, along with parameter values for a number of + substitutional materials. """ def get_coupled_variables(self, variables): diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index f534909d0d..c9beb8fe7e 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -8,7 +8,9 @@ class MSMRDiffusion(BaseParticle): """ Class for molar conservation in particles within the Multi-Species Multi-Reaction - framework :footcite:t:`Baker2018`. + framework :footcite:t:`Baker2018`. The thermodynamic model is presented in + :footcite:t:`Verbrugge2017`, along with parameter values for a number of + substitutional materials. Parameters ---------- @@ -30,6 +32,7 @@ def __init__(self, param, domain, options, phase="primary", x_average=False): self.x_average = x_average pybamm.citations.register("Baker2018") + pybamm.citations.register("Verbrugge2017") def get_fundamental_variables(self): domain, Domain = self.domain_Domain @@ -115,12 +118,12 @@ def get_fundamental_variables(self): ) # Calculate the stoichiometry distribution from the potential distribution - X_distribution = self.phase_param.X(U_distribution) - dXdU_distribution = self.phase_param.dXdU(U_distribution) + x_distribution = self.phase_param.x(U_distribution) + dxdU_distribution = self.phase_param.dxdU(U_distribution) # Standard stoichiometry and concentration distribution variables # (size-dependent) - c_s_distribution = X_distribution * c_max + c_s_distribution = x_distribution * c_max variables.update( self._get_standard_concentration_distribution_variables( c_s_distribution @@ -128,7 +131,7 @@ def get_fundamental_variables(self): ) variables.update( self._get_standard_differential_stoichiometry_distribution_variables( - dXdU_distribution + dxdU_distribution ) ) @@ -144,13 +147,13 @@ def get_fundamental_variables(self): variables.update(self._get_standard_potential_variables(U)) # Calculate the stoichiometry from the potential - X = self.phase_param.X(U) - dXdU = self.phase_param.dXdU(U) + x = self.phase_param.x(U) + dxdU = self.phase_param.dxdU(U) # Standard stoichiometry and concentration variables (size-independent) - c_s = X * c_max + c_s = x * c_max variables.update(self._get_standard_concentration_variables(c_s)) - variables.update(self._get_standard_differential_stoichiometry_variables(dXdU)) + variables.update(self._get_standard_differential_stoichiometry_variables(dxdU)) return variables @@ -161,8 +164,8 @@ def get_coupled_variables(self, variables): if self.size_distribution is False: if self.x_average is False: - X = variables[f"{Domain} {phase_name}particle stoichiometry"] - dXdU = variables[ + x = variables[f"{Domain} {phase_name}particle stoichiometry"] + dxdU = variables[ f"{Domain} {phase_name}particle differential stoichiometry [V-1]" ] U = variables[f"{Domain} {phase_name}particle potential [V]"] @@ -176,8 +179,8 @@ def get_coupled_variables(self, variables): "interfacial current density [A.m-2]" ] else: - X = variables[f"X-averaged {domain} {phase_name}particle stoichiometry"] - dXdU = variables[ + x = variables[f"X-averaged {domain} {phase_name}particle stoichiometry"] + dxdU = variables[ f"X-averaged {domain} {phase_name}particle differential " "stoichiometry [V-1]" ] @@ -200,10 +203,10 @@ def get_coupled_variables(self, variables): R_nondim, [f"{domain} {phase_name}particle"] ) if self.x_average is False: - X = variables[ + x = variables[ f"{Domain} {phase_name}particle stoichiometry distribution" ] - dXdU = variables[ + dxdU = variables[ f"{Domain} {phase_name}particle differential stoichiometry " "distribution [V-1]" ] @@ -221,11 +224,11 @@ def get_coupled_variables(self, variables): "current density distribution [A.m-2]" ] else: - X = variables[ + x = variables[ f"X-averaged {domain} {phase_name}particle " "stoichiometry distribution" ] - dXdU = variables[ + dxdU = variables[ f"X-averaged {domain} {phase_name}particle " "differential stoichiometry distribution [V-1]" ] @@ -247,9 +250,9 @@ def get_coupled_variables(self, variables): # Note: diffusivity is given as a function of concentration here, # not stoichiometry c_max = self.phase_param.c_max - D_eff = self._get_effective_diffusivity(X * c_max, T) + D_eff = self._get_effective_diffusivity(x * c_max, T) f = self.param.F / (self.param.R * T) - N_s = c_max * X * (1 - X) * f * D_eff * pybamm.grad(U) + N_s = c_max * x * (1 - x) * f * D_eff * pybamm.grad(U) variables.update( { f"{Domain} {phase_name}particle rhs [V.s-1]": -( @@ -257,11 +260,11 @@ def get_coupled_variables(self, variables): ) * pybamm.div(N_s) / c_max - / dXdU, + / dxdU, f"{Domain} {phase_name}particle bc [V.m-1]": j * R_nondim / param.F - / pybamm.surf(c_max * X * (1 - X) * f * D_eff), + / pybamm.surf(c_max * x * (1 - x) * f * D_eff), } ) @@ -485,93 +488,93 @@ def _get_standard_potential_distribution_variables(self, U): } return variables - def _get_standard_differential_stoichiometry_variables(self, dXdU): + def _get_standard_differential_stoichiometry_variables(self, dxdU): domain, Domain = self.domain_Domain phase_name = self.phase_name - dXdU_surf = pybamm.surf(dXdU) - dXdU_surf_av = pybamm.x_average(dXdU_surf) - dXdU_xav = pybamm.x_average(dXdU) - dXdU_rav = pybamm.r_average(dXdU) - dXdU_av = pybamm.r_average(dXdU_xav) + dxdU_surf = pybamm.surf(dxdU) + dxdU_surf_av = pybamm.x_average(dxdU_surf) + dxdU_xav = pybamm.x_average(dxdU) + dxdU_rav = pybamm.r_average(dxdU) + dxdU_av = pybamm.r_average(dxdU_xav) variables = { - f"{Domain} {phase_name}particle differential stoichiometry [V-1]": dXdU, + f"{Domain} {phase_name}particle differential stoichiometry [V-1]": dxdU, f"X-averaged {domain} {phase_name}particle " - "differential stoichiometry [V-1]": dXdU_xav, + "differential stoichiometry [V-1]": dxdU_xav, f"R-averaged {domain} {phase_name}particle " - "differential stoichiometry [V-1]": dXdU_rav, + "differential stoichiometry [V-1]": dxdU_rav, f"Average {domain} {phase_name}particle differential " - "stoichiometry [V-1]": dXdU_av, + "stoichiometry [V-1]": dxdU_av, f"{Domain} {phase_name}particle surface differential " - "stoichiometry [V-1]": dXdU_surf, + "stoichiometry [V-1]": dxdU_surf, f"X-averaged {domain} {phase_name}particle " - "surface differential stoichiometry [V-1]": dXdU_surf_av, + "surface differential stoichiometry [V-1]": dxdU_surf_av, } return variables - def _get_standard_differential_stoichiometry_distribution_variables(self, dXdU): + def _get_standard_differential_stoichiometry_distribution_variables(self, dxdU): domain, Domain = self.domain_Domain phase_name = self.phase_name # Broadcast and x-average when necessary - if dXdU.domain == [f"{domain} {phase_name}particle size"] and dXdU.domains[ + if dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[ "secondary" ] != [f"{domain} electrode"]: # X-avg differential stoichiometry distribution - dXdU_xav_distribution = pybamm.PrimaryBroadcast( - dXdU, [f"{domain} {phase_name}particle"] + dxdU_xav_distribution = pybamm.PrimaryBroadcast( + dxdU, [f"{domain} {phase_name}particle"] ) # Surface differential stoichiometry distribution variables - dXdU_surf_xav_distribution = dXdU - dXdU_surf_distribution = pybamm.SecondaryBroadcast( - dXdU_surf_xav_distribution, [f"{domain} electrode"] + dxdU_surf_xav_distribution = dxdU + dxdU_surf_distribution = pybamm.SecondaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} electrode"] ) # Differential stoichiometry distribution in all domains. - dXdU_distribution = pybamm.PrimaryBroadcast( - dXdU_surf_distribution, [f"{domain} {phase_name}particle"] + dxdU_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_distribution, [f"{domain} {phase_name}particle"] ) - elif dXdU.domain == [f"{domain} {phase_name}particle"] and ( - dXdU.domains["tertiary"] != [f"{domain} electrode"] + elif dxdU.domain == [f"{domain} {phase_name}particle"] and ( + dxdU.domains["tertiary"] != [f"{domain} electrode"] ): # X-avg differential stoichiometry distribution - dXdU_xav_distribution = dXdU + dxdU_xav_distribution = dxdU # Surface differential stoichiometry distribution variables - dXdU_surf_xav_distribution = pybamm.surf(dXdU_xav_distribution) - dXdU_surf_distribution = pybamm.SecondaryBroadcast( - dXdU_surf_xav_distribution, [f"{domain} electrode"] + dxdU_surf_xav_distribution = pybamm.surf(dxdU_xav_distribution) + dxdU_surf_distribution = pybamm.SecondaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} electrode"] ) # Differential stoichiometry distribution in all domains. - dXdU_distribution = pybamm.TertiaryBroadcast( - dXdU_xav_distribution, [f"{domain} electrode"] + dxdU_distribution = pybamm.TertiaryBroadcast( + dxdU_xav_distribution, [f"{domain} electrode"] ) - elif dXdU.domain == [f"{domain} {phase_name}particle size"] and dXdU.domains[ + elif dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[ "secondary" ] == [f"{domain} electrode"]: # Surface differential stoichiometry distribution variables - dXdU_surf_distribution = dXdU - dXdU_surf_xav_distribution = pybamm.x_average(dXdU) + dxdU_surf_distribution = dxdU + dxdU_surf_xav_distribution = pybamm.x_average(dxdU) # X-avg differential stoichiometry distribution - dXdU_xav_distribution = pybamm.PrimaryBroadcast( - dXdU_surf_xav_distribution, [f"{domain} {phase_name}particle"] + dxdU_xav_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_xav_distribution, [f"{domain} {phase_name}particle"] ) # Differential stoichiometry distribution in all domains - dXdU_distribution = pybamm.PrimaryBroadcast( - dXdU_surf_distribution, [f"{domain} {phase_name}particle"] + dxdU_distribution = pybamm.PrimaryBroadcast( + dxdU_surf_distribution, [f"{domain} {phase_name}particle"] ) else: - dXdU_distribution = dXdU + dxdU_distribution = dxdU # x-average the *tertiary* domain. # NOTE: not yet implemented. Make 0.5 everywhere - dXdU_xav_distribution = pybamm.FullBroadcast( + dxdU_xav_distribution = pybamm.FullBroadcast( 0.5, [f"{domain} {phase_name}particle"], { @@ -581,24 +584,24 @@ def _get_standard_differential_stoichiometry_distribution_variables(self, dXdU): ) # Surface differential stoichiometry distribution variables - dXdU_surf_distribution = pybamm.surf(dXdU) - dXdU_surf_xav_distribution = pybamm.x_average(dXdU_surf_distribution) + dxdU_surf_distribution = pybamm.surf(dxdU) + dxdU_surf_xav_distribution = pybamm.x_average(dxdU_surf_distribution) - dXdU_rav_distribution = pybamm.r_average(dXdU_distribution) - dXdU_av_distribution = pybamm.x_average(dXdU_rav_distribution) + dxdU_rav_distribution = pybamm.r_average(dxdU_distribution) + dxdU_av_distribution = pybamm.x_average(dxdU_rav_distribution) variables = { f"{Domain} {phase_name}particle differential stoichiometry distribution " - "[V-1]": dXdU_distribution, + "[V-1]": dxdU_distribution, f"X-averaged {domain} {phase_name}particle differential stoichiometry " - "distribution [V-1]": dXdU_xav_distribution, + "distribution [V-1]": dxdU_xav_distribution, f"R-averaged {domain} {phase_name}particle differential stoichiometry " - "distribution [V-1]": dXdU_rav_distribution, + "distribution [V-1]": dxdU_rav_distribution, f"Average {domain} {phase_name}particle differential stoichiometry " - "distribution [V-1]": dXdU_av_distribution, + "distribution [V-1]": dxdU_av_distribution, f"{Domain} {phase_name}particle surface differential stoichiometry" - " distribution [V-1]": dXdU_surf_distribution, + " distribution [V-1]": dxdU_surf_distribution, f"X-averaged {domain} {phase_name}particle surface differential " - "stoichiometry distribution [V-1]": dXdU_surf_xav_distribution, + "stoichiometry distribution [V-1]": dxdU_surf_xav_distribution, } return variables diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index d712f7b7d8..6bf7469702 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -361,6 +361,7 @@ def __init__(self, phase, domain_param): self.geo = domain_param.geo.prim elif self.phase == "secondary": self.geo = domain_param.geo.sec + self.options = getattr(self.main_param.options, self.domain) def _set_parameters(self): main = self.main_param @@ -483,11 +484,11 @@ def _set_parameters(self): self.c_max = pybamm.Parameter( f"{pref}Maximum concentration in {domain} electrode [mol.m-3]" ) - if main.options["open-circuit potential"] == "MSMR": + if self.options["open-circuit potential"] == "MSMR": self.U_init = pybamm.Parameter( f"{pref}Initial voltage in {domain} electrode [V]", ) - self.c_init = self.X(self.U_init) * self.c_max + self.c_init = self.x(self.U_init) * self.c_max else: self.c_init = pybamm.FunctionParameter( f"{pref}Initial concentration in {domain} electrode [mol.m-3]", @@ -504,7 +505,7 @@ def _set_parameters(self): self.epsilon_s * pybamm.r_average(self.c_init) ) - if main.options["open-circuit potential"] != "MSMR": + if self.options["open-circuit potential"] != "MSMR": self.U_init = self.U(self.sto_init_av, main.T_init) # Electrode loading and capacity @@ -515,7 +516,7 @@ def _set_parameters(self): self.Q_Li_init = self.n_Li_init * main.F / 3600 self.Q_init = self.elec_loading * main.A_cc - if main.options["particle shape"] == "spherical": + if self.options["particle shape"] == "spherical": self.a_typ = 3 * pybamm.xyz_average(self.epsilon_s) / self.R_typ def D(self, c_s, T): @@ -591,30 +592,6 @@ def U(self, sto, T, lithiation=None): out.print_name = r"U_\mathrm{p}(c^\mathrm{surf}_\mathrm{s,p}, T)" return out - def X(self, U): - "Stoichiometry as a function of potential (for use with MSMR models)" - Domain = self.domain.capitalize() - inputs = { - f"{self.phase_prefactor}{Domain} particle open-circuit potential [V]": U - } - return pybamm.FunctionParameter( - f"{self.phase_prefactor}{Domain} electrode stoichiometry", inputs - ) - - def dXdU(self, U): - """ - Differential stoichiometry as a function of potential (for use with MSMR models) - """ - Domain = self.domain.capitalize() - inputs = { - f"{self.phase_prefactor}{Domain} particle open-circuit potential [V]": U - } - return pybamm.FunctionParameter( - f"{self.phase_prefactor}{Domain} electrode stoichiometry", - inputs, - diff_variable=U, - ) - def dUdT(self, sto): """ Dimensional entropic change of the open-circuit potential [V.K-1] @@ -630,6 +607,53 @@ def dUdT(self, sto): inputs, ) + def x_j(self, U, index): + "Fractional occupancy of site j as a function of potential" + domain = self.domain + subscript = domain[0] + T = self.main_param.T_ref + f = self.main_param.F / (self.main_param.R * T) + U0 = pybamm.Parameter(f"U0_{subscript}_{index}") + w = pybamm.Parameter(f"w_{subscript}_{index}") + Xj = pybamm.Parameter(f"Xj_{subscript}_{index}") + # Equation 5, Baker et al 2018 + xj = Xj / (1 + pybamm.exp(f * (U - U0) / w)) + return xj + + def dxdU_j(self, U, index): + "Derivative of fractional occupancy of site j as a function of potential" + domain = self.domain + subscript = domain[0] + T = self.main_param.T_ref + f = self.main_param.F / (self.main_param.R * T) + U0 = pybamm.Parameter(f"U0_{subscript}_{index}") + w = pybamm.Parameter(f"w_{subscript}_{index}") + Xj = pybamm.Parameter(f"Xj_{subscript}_{index}") + e = pybamm.exp(f * (U - U0) / w) + # Equation 25, Baker et al 2018 + dxjdU = -(f / w) * (Xj * e) / (1 + e) ** 2 + return dxjdU + + def x(self, U): + "Stoichiometry as a function of potential (for use with MSMR models)" + N = int(self.options["number of MSMR reactions"]) + # Equation 6, Baker et al 2018 + x = 0 + for i in range(N): + x += self.x_j(U, i) + return x + + def dxdU(self, U): + """ + Differential stoichiometry as a function of potential (for use with MSMR models) + """ + N = int(self.options["number of MSMR reactions"]) + # Equation 25, Baker et al 2018 + dxdU = 0 + for i in range(N): + dxdU += self.dxdU_j(U, i) + return dxdU + def t_change(self, sto): """ Volume change for the electrode; sto should be R-averaged diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py index d388f8d2ba..a4c676576f 100644 --- a/tests/unit/test_citations.py +++ b/tests/unit/test_citations.py @@ -338,6 +338,18 @@ def test_sripad_2020(self): self.assertIn("Sripad2020", citations._papers_to_cite) self.assertIn("Sripad2020", citations._citation_tags.keys()) + def test_msmr(self): + citations = pybamm.citations + + citations._reset() + self.assertNotIn("Baker2018", citations._papers_to_cite) + self.assertNotIn("Verbrugge2017", citations._papers_to_cite) + pybamm.particle.MSMRDiffusion(None, "negative", None, None, None) + self.assertIn("Baker2018", citations._papers_to_cite) + self.assertIn("Baker2018", citations._citation_tags.keys()) + self.assertIn("Verbrugge2017", citations._papers_to_cite) + self.assertIn("Verbrugge2017", citations._citation_tags.keys()) + def test_parameter_citations(self): citations = pybamm.citations @@ -379,6 +391,13 @@ def test_parameter_citations(self): self.assertIn("ORegan2022", citations._papers_to_cite) self.assertIn("ORegan2022", citations._citation_tags.keys()) + citations._reset() + pybamm.ParameterValues("MSMR_Example") + self.assertIn("Baker2018", citations._papers_to_cite) + self.assertIn("Baker2018", citations._citation_tags.keys()) + self.assertIn("Verbrugge2017", citations._papers_to_cite) + self.assertIn("Verbrugge2017", citations._citation_tags.keys()) + def test_solver_citations(self): # Test that solving each solver adds the right citations citations = pybamm.citations From 085543f3b2df314903ee9d245886c8c49105e984 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 18 Jul 2023 16:46:17 +0100 Subject: [PATCH 025/154] fix setting by reaction index --- .../examples/notebooks/models/MSMR.ipynb | 85 ++++++++----------- .../submodels/particle/msmr_diffusion.py | 62 +++++++++++++- .../base_lithium_ion_tests.py | 6 +- .../test_base_lithium_ion_model.py | 17 ---- .../test_lithium_ion/test_dfn.py | 1 + .../test_lithium_ion/test_electrode_soh.py | 24 +++++- .../test_lithium_ion/test_mpm.py | 1 + .../test_parameters/test_parameter_values.py | 6 +- tests/unit/test_simulation.py | 6 +- 9 files changed, 134 insertions(+), 74 deletions(-) diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 5a52af7c14..1dc3178d16 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -87,6 +87,9 @@ "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -116,6 +119,7 @@ " {\n", " \"open-circuit potential\": \"MSMR\",\n", " \"particle\": \"MSMR\",\n", + " \"number of MSMR reactions\": (\"6\", \"4\"),\n", " }\n", ")\n", "\n", @@ -127,33 +131,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also add variables for the individual electrode reaction as described in the MSMR model. We cannot create these variables until _after_ we have chosen some parameter values since we do not know in advance how many reactions have been used to describe thermodynamics of each electrode. The number of reactions is selected as part of parameterizing a particular material." + "In the MSMR model, the individual reactions are given variables names `x_k_j` where `k` can be `n` or `p` to denote the negative or positive electrode, and `j` is the reaction index. E.g. the variable for the second reaction in the negative electrode can be accessed as" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model.set_msmr_reaction_variables(parameter_values)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The individual reactions are given variables names `xj_k` where `k` can be `n` or `p` to denote the negative or positive electrode, and `j` is the reaction index. E.g. the variable for the second reaction in the negative electrode can be accessed as" - ] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "xn_2 = model.variables[\"x2_n\"]" + "xn_2 = model.variables[\"x_n_2\"]" ] }, { @@ -166,24 +153,24 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "At t = 275.691 and h = 3.27586e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 275.693 and h = 5.60628e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 275.534 and h = 8.78023e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 275.531 and h = 7.24097e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -215,18 +202,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b5ec0c8411924c199a371a1b3ba9d130", + "model_id": "fcc2a79e22aa429bbab4fe2506d4724c", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094046875941711, step=0.06094046875941711)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.093991658846746, step=0.06093991658846746)…" ] }, "metadata": {}, @@ -235,10 +222,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -269,18 +256,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68c977972a01491993f231dc4235230a", + "model_id": "575d8fe02ded48a48dd1831c728e5766", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.094046875941711, step=0.06094046875941711)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.093991658846746, step=0.06093991658846746)…" ] }, "metadata": {}, @@ -289,17 +276,17 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "xns = [f\"Average x{i}_n\" for i in range(6)] # negative electrode reactions: x0_n, x1_n, ..., x5_n\n", - "xps = [f\"Average x{i}_p\" for i in range(4)] # positive electrode reactions: x0_p, x1_p, ..., x3_p\n", + "xns = [f\"Average x_n_{i}\" for i in range(6)] # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5\n", + "xps = [f\"Average x_p_{i}\" for i in range(4)] # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3\n", "sim.plot(\n", " [\n", " xns,\n", @@ -322,22 +309,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -387,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -396,12 +383,14 @@ "text": [ "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", "[2] Daniel R Baker and Mark W Verbrugge. Multi-species, multi-reaction model for porous intercalation electrodes: part i. model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode. Journal of The Electrochemical Society, 165(16):A3952, 2018.\n", - "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "[8] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", + "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[6] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[8] Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao, and Wentian Gu. Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide. Journal of The Electrochemical Society, 164(11):E3243, 2017.\n", + "[9] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[10] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", "\n" ] } diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index c9beb8fe7e..cc47801167 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -146,11 +146,17 @@ def get_fundamental_variables(self): # Standard potential variables variables.update(self._get_standard_potential_variables(U)) - # Calculate the stoichiometry from the potential + # Standard fractional occupancy variables (these are indexed by reaction number) + variables.update(self._get_standard_fractional_occupancy_variables(U)) + variables.update( + self._get_standard_differential_fractional_occupancy_variables(U) + ) + + # Calculate the (total) stoichiometry from the potential x = self.phase_param.x(U) dxdU = self.phase_param.dxdU(U) - # Standard stoichiometry and concentration variables (size-independent) + # Standard (total) stoichiometry and concentration variables (size-independent) c_s = x * c_max variables.update(self._get_standard_concentration_variables(c_s)) variables.update(self._get_standard_differential_stoichiometry_variables(dxdU)) @@ -488,6 +494,58 @@ def _get_standard_potential_distribution_variables(self, U): } return variables + def _get_standard_fractional_occupancy_variables(self, U): + options = self.options + domain = self.domain + subscript = domain[0] + variables = {} + # Loop over all reactions + N = int(getattr(options, domain)["number of MSMR reactions"]) + for i in range(N): + x = self.phase_param.x_j(U, i) + x_surf = pybamm.surf(x) + x_surf_av = pybamm.x_average(x_surf) + x_xav = pybamm.x_average(x) + x_rav = pybamm.r_average(x) + x_av = pybamm.r_average(x_xav) + variables.update( + { + f"x_{subscript}_{i}": x, + f"X-averaged x_{subscript}_{i}": x_xav, + f"R-averaged x_{subscript}_{i}": x_rav, + f"Average x_{subscript}_{i}": x_av, + f"Surface x_{subscript}_{i}": x_surf, + f"X-averaged surface x_{subscript}_{i}": x_surf_av, + } + ) + return variables + + def _get_standard_differential_fractional_occupancy_variables(self, U): + options = self.options + domain = self.domain + subscript = domain[0] + variables = {} + # Loop over all reactions + N = int(getattr(options, domain)["number of MSMR reactions"]) + for i in range(N): + dxdU = self.phase_param.dxdU_j(U, i) + dxdU_surf = pybamm.surf(dxdU) + dxdU_surf_av = pybamm.x_average(dxdU_surf) + dxdU_xav = pybamm.x_average(dxdU) + dxdU_rav = pybamm.r_average(dxdU) + dxdU_av = pybamm.r_average(dxdU_xav) + variables.update( + { + f"dxdU_{subscript}_{i}": dxdU, + f"X-averaged dxdU_{subscript}_{i}": dxdU_xav, + f"R-averaged dxdU_{subscript}_{i}": dxdU_rav, + f"Average dxdU_{subscript}_{i}": dxdU_av, + f"Surface dxdU_{subscript}_{i}": dxdU_surf, + f"X-averaged surface dxdU_{subscript}_{i}": dxdU_surf_av, + } + ) + return variables + def _get_standard_differential_stoichiometry_variables(self, dxdU): domain, Domain = self.domain_Domain phase_name = self.phase_name diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index e09ac457b9..3104c7cd7c 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -361,5 +361,9 @@ def test_well_posed_current_sigmoid_ocp(self): self.check_well_posedness(options) def test_well_posed_msmr(self): - options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + } self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py index b28bebbd49..315896b29f 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py @@ -29,23 +29,6 @@ def test_default_parameters(self): ) os.chdir(cwd) - def test_set_msmr_variables(self): - with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): - pybamm.lithium_ion.BaseModel().set_msmr_reaction_variables(None) - - options = { - "open-circuit potential": "MSMR", - "particle": "MSMR", - } - model = pybamm.lithium_ion.SPM(options) - parameter_values = pybamm.ParameterValues("MSMR_Example") - model.set_msmr_reaction_variables(parameter_values) - xn_2 = model.variables["x2_n"] - # For SPM, xn_2 will be a broadcast of the reaction formula, whose child should - # be the parameter "Xj_n_2" - self.assertIsInstance(xn_2.children[0].children[0], pybamm.Parameter) - self.assertEqual(xn_2.children[0].children[0].name, "Xj_n_2") - if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py index f8c2124079..7c3da8ba03 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py @@ -48,6 +48,7 @@ def test_well_posed_msmr_with_psd(self): "open-circuit potential": "MSMR", "particle": "MSMR", "particle size": "distribution", + "number of MSMR reactions": ("6", "4"), } self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index 8ef5e9c5e6..a19bf4ae4a 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -158,7 +158,11 @@ def test_error(self): class TestElectrodeSOHMSMR(TestCase): def test_known_solution(self): - options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + } param = pybamm.LithiumIonParameters(options=options) parameter_values = pybamm.ParameterValues("MSMR_Example") @@ -195,7 +199,11 @@ def test_known_solution(self): esoh_solver._check_esoh_feasible(inputs) def test_known_solution_cell_capacity(self): - options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + } param = pybamm.LithiumIonParameters(options) parameter_values = pybamm.ParameterValues("MSMR_Example") @@ -360,7 +368,11 @@ def test_min_max_ocp(self): class TestGetInitialOCPMSMR(TestCase): def test_get_initial_ocp(self): - options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + } param = pybamm.LithiumIonParameters(options) parameter_values = pybamm.ParameterValues("MSMR_Example") Un, Up = pybamm.lithium_ion.get_initial_ocps( @@ -377,7 +389,11 @@ def test_get_initial_ocp(self): self.assertAlmostEqual(Up - Un, 4) def test_min_max_ocp(self): - options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + } param = pybamm.LithiumIonParameters(options) parameter_values = pybamm.ParameterValues("MSMR_Example") diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index 8b7b498453..208c9858f7 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -112,6 +112,7 @@ def test_msmr(self): options = { "open-circuit potential": "MSMR", "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), } model = pybamm.lithium_ion.MPM(options) model.check_well_posedness() diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index d6406ca05f..e77b3fe135 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -120,7 +120,11 @@ def test_set_initial_stoichiometries(self): self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100)) def test_set_initial_ocps(self): - options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + } param_100 = pybamm.ParameterValues("MSMR_Example") param_100.set_initial_ocps(1, inplace=True, options=options) param_0 = param_100.set_initial_ocps(0, inplace=False, options=options) diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index 64d5de3456..990d4eb167 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -204,7 +204,11 @@ def test_solve_with_initial_soc(self): self.assertEqual(sim._built_initial_soc, 0.5) # test with MSMR - options = {"open-circuit potential": "MSMR", "particle": "MSMR"} + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + } model = pybamm.lithium_ion.SPM(options) param = pybamm.ParameterValues("MSMR_Example") sim = pybamm.Simulation(model, parameter_values=param) From 899067f32b9bcd92bba2f71864a1e0d658f7ec55 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 18 Jul 2023 16:56:27 +0100 Subject: [PATCH 026/154] add MSMR model class --- docs/source/api/models/lithium_ion/msmr.rst | 7 +++ .../examples/notebooks/models/MSMR.ipynb | 56 ++++++++----------- examples/scripts/MSMR.py | 12 +--- .../lithium_ion/__init__.py | 1 + .../full_battery_models/lithium_ion/msmr.py | 39 +++++++++++++ 5 files changed, 73 insertions(+), 42 deletions(-) create mode 100644 docs/source/api/models/lithium_ion/msmr.rst create mode 100644 pybamm/models/full_battery_models/lithium_ion/msmr.py diff --git a/docs/source/api/models/lithium_ion/msmr.rst b/docs/source/api/models/lithium_ion/msmr.rst new file mode 100644 index 0000000000..89ac143e2e --- /dev/null +++ b/docs/source/api/models/lithium_ion/msmr.rst @@ -0,0 +1,7 @@ +Multi-Species Multi-Reaction (MSMR) Model +========================================= + +.. autoclass:: pybamm.lithium_ion.MSMR + :members: + +.. footbibliography:: diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 1dc3178d16..3f16a2e902 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -106,7 +106,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next we load in the model. We choose to use the DFN along with our MSMR model for the open-circuit potential and solid phase (particle) transport" + "Next we load in the model and specify the number of reactions in each electrode" ] }, { @@ -115,15 +115,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = pybamm.lithium_ion.DFN(\n", - " {\n", - " \"open-circuit potential\": \"MSMR\",\n", - " \"particle\": \"MSMR\",\n", - " \"number of MSMR reactions\": (\"6\", \"4\"),\n", - " }\n", - ")\n", - "\n", - "parameter_values = pybamm.ParameterValues(\"MSMR_Example\")" + "model = pybamm.lithium_ion.MSMR({\"number of MSMR reactions\": (\"6\", \"4\")})" ] }, { @@ -136,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -153,24 +145,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "At t = 275.534 and h = 8.78023e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 275.531 and h = 7.24097e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 275.616 and h = 2.99777e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 275.7 and h = 2.12769e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -188,7 +180,7 @@ " ],\n", " period=\"10 seconds\",\n", ")\n", - "sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment)\n", + "sim = pybamm.Simulation(model, experiment=experiment)\n", "sim.solve()" ] }, @@ -202,18 +194,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fcc2a79e22aa429bbab4fe2506d4724c", + "model_id": "21de049b5a8a40e18d9f40118ca2e8a6", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.093991658846746, step=0.06093991658846746)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.0940492544338145, step=0.06094049254433814…" ] }, "metadata": {}, @@ -222,10 +214,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -256,18 +248,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "575d8fe02ded48a48dd1831c728e5766", + "model_id": "665dd9d40ffd4664954ce188fd744061", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.093991658846746, step=0.06093991658846746)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.0940492544338145, step=0.06094049254433814…" ] }, "metadata": {}, @@ -276,10 +268,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -309,22 +301,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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HROj8eJZhWHRZr+YhMuKLDRs2YNSoUbj66sv/na1YsQJqtdrxSkhIGKAIg2eoJH/19nh0NOQsi7179/a5FYWQcEKTWS9xYp0jCYy2GZCh6K233sL58+ehkogxKTXJpT8282q01A+tx9V8ioqKgkAgQFVVlVN7VVUVDAbDZe81Go14//33ce+993r8dRYuXIimpibHq7S01K+4+dbS0uLIe8gaYpPZaE6I+ReTwZ544gnU1dXxHBEh/qHJrJes1gjHymxJScmgeNRGSH+1tbXh2WefBQBcn5UKESdw6hdwHDrar+AjtCFLJBJh3Lhx2LFjh6PNarVix44dmDBhwmXv/eCDD2AymXDnnXd6/HXEYjFUKpXTK5zl5+fDZrPBwHHQDcKTvzy5Q6tFukiMuro6LFy4kO9wCPELTWa91G5UQSYSOsoQ0VYDMpSsXbsWlZWV0MqlGJ+c6NIfmzkJxqahtcoVCnJzc7F+/Xps2rQJhYWFeOCBB2A0GjFnzhwAwN133+12wrJhwwbceuutiIwcelUnurcYDLVV2W4cw2DJxX3Wb731Fvbu3ctzRIT4buh9HfWTsVECTiSGQa1EU3sHjh07hokTJ/IdFiFB19TUhJUrVwIAbhyZDk7g/F2YE4nR0jiKj9CGvNmzZ6OmpgZLly5FZWUlcnJysG3bNkdSWElJCVjW+e/r5MmT2L17N7788ks+QuZdd/LXUKlk4M44mQy3qlT4uLkZc+fOxb59+yAQCDzfSEiIocms1xioo+NgUCtxsrKG9s2SIeMvf/kL6uvrEa2UY1xinEt/bMa1qL7gWqKLDIz58+dj/vz5bvt27drl0paRkTGkjzV1rMyKh+5kFgAe0UXjm9ZW5OXlYd26dZg3bx7fIRHiNdpm4AOJkspzkaGltrYWL7/8MgBgRnYGWNb5VC+RVIbG2hF8hEaI15qbm3Hy5EkAQ6+SQW+RHIcFUToAwKJFi1wSCQkJB7Qy6wOBSIcYVc/BCTabDQxDR3aSweuFF15Aa2sr4jQqjIp3zZA3pE1DdWn/305kCg5CEQOWsSEc1gZFTCffIZAAOnToEAAghuMQOQSTv3r7X40GW5qaUNDUhMcffxybNm3iOyRCvEI/xf0kEff8765ODfQqBRgAdXV1/SqBQ0i4Ki8vx9/+9jcAwMxRGWB7fXGTKtWoq0zt11jxsUDyntchPBdeTzSiMxcBSOE7DBIgQ7W+bF8EF5PBbi85j3feeQf33XcfJk+ezHdYhPQbbTPopwiupwRXW7MSQk6ASIW9liZtNSCD2bPPPouOjg4kRWmRadC59OuSp8HS6TlpJD4WSHv/obCbyJLBhyazrkZLpfi1WgMAmDt3Ljo76WkECR80me0nZVOx4393GEUQyxWIUfdsNSBkMCoqKsL69esBALNGZbhsp5Fro1BbnuRxHKGYxfCdq8BYLcEIkxCvDMVjbPvjYZ0OGoEAx44dw6uvvsp3OIT0G01m+0lywXk1SaWLpyQwMug9/fTT6OrqQro+CsN1rrVIIxOmwWrx/DaSoSyDoOJcMEIkxCtNTU04ffo0AGDkEK9k0JtGIMAjF5PBli1bhrKyMp4jIqR/aDLbT1x9BWTKni3GEkVPRQNamSWD0fHjx/GPf/wDgH1Vtjd1dAyqL8R7HEeu4hC1dU2gwyPEJ3l5eQCAOKEQWkr+cnGbWo0ciRStra145JFH+A6HkH6hyawXNIqeR6QMG+nYZlBQUACr1cpXWIQExdKlS2Gz2ZAdp0dChMalX6WfCtg8V/HI6MoH294a+AAJ8YFjvyytyrrFXkwGYwFs3rzZ6ZhkQkIVTWa9oLQ1Ov53p0mDSIUMHMuira0N587RI1QyeBw8eBAfffQRGAAzs11XZbWxw1B9wXMFD20kB/W2N4MQISG+oeQvz0ZIJLhdowUAzJs3DyaTieeICLk8msx6Qd5c6vjfrU0KCFgW0SoFANo3SwaXxYsXAwDGDotzbKe5lEx7LRh4XpVNr91JSV8kpFDyV/88FBWFSIEAJ0+exOrVq/kOh5DLosmsF6QXChz/u9MkgFwbRftmyaDz/fffY9u2bWAZBjeOTHPpj0pMQ125a4mu3vR6AeTfvh+MEAnxSUNDA86ePQsAyKLJ7GUpBQI8posGACxfvhznz5/nOSJC+kaTWS8ITx3EpZWJlJGxjn2ztDJLBgObzYZFixYBAK5OTkDUxVrKlxLJr+nXWClFHwcyNEL81p38lSAUQiPwXBt5qLtZpcJVUina29vxpz/9ie9wCOkTTWa9wHYYodIKHf8tkuqpPBcZVL788kt8//334FgW07NcT/XSp2SjvlLrcZy4WEC6f1swQiTEZ937ZWlVtn8YhsFivQEcGHz88cf4/PPP+Q6JELdoMusltaRnI7wNPRUNTp48CbPZzFdYhPjt0lXZianDoJFJXS/ifuZ5IAZIOvzPAEdHiP+6J7PZVMmg39LEYtyltX+BffDBB9He3s5zRIS4osmslxSddY7/bWpXQS2VQCLk0NXVhZMnT/IYGSH++c9//oODBw9CzAlwXeZwl/6Y9CvQVKPyOE5STBfEBT8GI0RC/ELJX76ZGxUFPcehqKgIL7zwAt/hEOKCJrNekjf0lOBqbZRBwHGUBEbCnsVicVQwmJyeDIVE7NTPMCy6LFd5HIdlGcTv3RCUGAnxR11dnaOEIm0z8I6cZfHnaHsy2MqVKx1JdISECjr+pJ86humBfEBSfASIvxoAYLWwUEbpYVApUVzbQPtmSdh67733UFhYCKlIiCnpKS79sZlXo67SNRmstxRDG0TfHOmz3zImAwfHKlEr6YQNAC7+31A3PlOKyXwHQfzSvSqbKBRCRclfXpuhUGKiTIYf29rw4IMPYuvWrWAYz+X5CBkINJntpz1jRPjZfwHu3FEIkllYOu0nfsk1sbQyS8Ka2WzGsmXLAADTMoZDKhI69bMCDh3tYz2OIxCyiP12XZ/9FTdfhQXZh/wLlicxig6+QyB+oi0G/ulOBvtl8Tl88cUX+Pjjj3HbbbfxHRYhAGibQb/tUdcAHAfG0gWNpuePjRNHU3kuEtY2btyIc+fOQSkRY1LaMJf+uMyJMDa5SQbrJTWyAVzZGbd9pquz8aeR4TmRJYMDnfzlvySRCL/TRgAAFixYAKPRyHNEhNiFxGR27dq1SEpKgkQiwfjx47Fv377LXr9mzRpkZGRAKpUiISEBDz/8MDo6grty0sZ0AskJAAC1sM3RbrVEOFZmi4uL0dLSEtQ4CAmk9vZ2LF++HABw/YjhEHPOD2sEQhFam0Z5HEcoZmH46lW3fYxGjSVTKmGjJ5KER90rs9k0mfXL7yMjEctxKC0txbPPPst3OIQACIHJ7ObNm5Gbm4tly5YhLy8PY8aMwYwZM1BdXe32+vfeew9PPPEEli1bhsLCQmzYsAGbN2/Gk08+GfRYm4bZv5HKO3piazcqIReLoLqYMFNQUOD2XkJC0WuvvYby8nJoZFL8LCXRpT8u81q0G8Vu7nSWrqqEoK7Cbd/uX6WimGv0N1RCfFZTU+M4wWoEleXyi5Rl8aReDwB4+eWXceLECZ4jIiQEJrOrV6/G/fffjzlz5iArKwvr1q2DTCbDxo0b3V7/448/YtKkSfjtb3+LpKQk3Hjjjbj99ts9ruYGQmm0/Y9LVtPzKNXYJAEnEtHhCSTsNDc3Y8WKFQCAG7PSwPVKihFKpGisG+FxHIlMAN32v7nts4zOwCv6w/4HS4gfuldlk4QiKCn5y2/XKZSYKpejs7MT8+bNg80WHomcZPDidTJrNptx8OBBTJ8+3dHGsiymT5+OPXv2uL1n4sSJOHjwoGPyWlRUhM8//xw33XST2+tNJhOam5udXr46rrVvLxAXXbr3j4E6Op6SwEjYWbNmDerq6qBTyjEuKc6lPyZ9KsztQjd3OkuXFINtrnPtYFmsu64rEKES4hdK/gq8J6P1EDMMvvnmG2zevJnvcMgQx+tktra2FhaLBfqLjyy66fV6VFZWur3nt7/9LZ555hlcc801EAqFGD58OKZOndrnNoMVK1ZArVY7XgkJCT7H+5PMHhNXdgZiac+3e4nSQCuzJKzU1dXh5ZdfBgDMGJkOAev8ViBRKNFQleZxHLmKQ8Tn7vfKNkwfi2+l5/0PlhA/UfJX4MWLRPh9RCQAIDc316+FIkL8xfs2A2/t2rULzz//PF577TXk5eVhy5Yt2Lp1qyOJpbeFCxeiqanJ8SotLfX5177ANYGNsv/watQ97QKRzlHRgFZmSTh48cUX0dzcjFiNCqMTYlz6o1OmodPsuXJfmq0QbIdrRjMjk+GFMSUBiZUQf1HyV3D8LiICiUIhKioq8NRTT/EdDhnCeJ3MRkVFQSAQoKqqyqm9qqoKBoPB7T1LlizBXXfdhfvuuw+jRo3Cbbfdhueffx4rVqyA1Wp1uV4sFkOlUjm9/NGRbI9LxfR8C+3q1CBapQADe6JBX8lrhISCiooKvPqqfTV1ZnY62F6Fz2VqLeoqkj2Oo9IKodn+utu+sz/PRhHX4H+whPipqqoKpaWlYACMkHhOZiT9J2ZZLL74ZPWvf/0rLeYQ3vA6mRWJRBg3bhx27NjhaLNardixYwcmTJjg9p62tjawvR6JCi5u6B+ITeh1MfZTkOTG8p6YmpUQcxwiFDIAtDpLQttzzz2H9vZ2DIvUYERMtEt/1LBpsHR5TpJJaz8A1mxyaWejIrAi5XhAYiX9522Jw8bGRsybNw8xMTEQi8VIT0/H559/PkDRDpzuVdlkkQhylpK/Au0auQI3KpSwWCyYO3cuJYMRXvC+zSA3Nxfr16/Hpk2bUFhYiAceeABGoxFz5swBANx9991YuHCh4/qbb74Zr7/+Ot5//32cO3cOX331FZYsWYKbb77ZMakNpqIoCwBAWnnK0dZhFEEsV9DhCSTkFRcX48033wQAzBqV4XIcpTIyGjVlriW6etNGclB9ucFt3/6bhqOJpROzBpK3JQ7NZjNuuOEGFBcX48MPP8TJkyexfv16xMW5JgKGO0r+Cr4/R0dDxrLYvXs33nnnHb7DIUMQ78fZzp49GzU1NVi6dCkqKyuRk5ODbdu2OZLCSkpKnFZiFy9ebD9Wb/FilJWVQafT4eabb8Zzzz03IPEeUTVhEgDR6QPAmJ872lW6OBhUShwrq6LJLAlZTz/9NDo7O5Gmj0JqdJRLvyZuGmpKPX/HTW34HozV4tLODIvHX2KPBCRW0n+XljgEgHXr1mHr1q3YuHEjnnjiCZfrN27ciPr6evz4448QCu0VK5KSkgYy5AHjSP6i+rJBEyMU4oGISLxcW4PHHnsMt9xyC7RaLd9hkSGE95VZAJg/fz7Onz8Pk8mEvXv3Yvz48Y6+Xbt24e2333b8N8dxWLZsGc6cOYP29naUlJRg7dq10Gg0AxLrfkk5IBBA0FANuarnu4BEYYBBQ0lgJHSdOHHCsWoyKzvDpV+tj0NNaazHcaKiOSi/eddt35czdDAzrpNcEjy+lDj85JNPMGHCBMybNw96vR7Z2dl4/vnnYbH0/XcXyDKHA4kqGQyMuyIikCISoaamBosXL+Y7HDLEhMRkNpy0MmYwF7O/NfKeN36GjYJBZZ/MFhQUuE1GI4RPy5Ytg9VqxchYPRIjNS79Kt1UAJ7PnE0t2+a23ZqVivWR9EVuoPlS4rCoqAgffvghLBYLPv/8cyxZsgQvv/zyZY8nDWSZw4FSUVGB8vJyMAAyaTIbVCKGwdKL/wZff/11x/YOQgYCTWZ90JpgfzyrtPVka3eaNNAp5RCwLFpbWx1HJxISCg4dOoR///vfYADMyE536Y+IS0ZNmd71xl4MBhayH/7jtu/f14n8DZMMEKvViujoaLz55psYN24cZs+ejUWLFmHdunV93hPIMocDpXtClSISQc7Sx12wXS2T4+dKFWw2G+bOnUuLOmTA0E+3DyoN9g9teVNPHc3WJgUELItopb3aAe2bJaFkyZIlAICcxFjEalzL00k1k/s1Tspp9xNZ89XZ2KI85baPBJcvJQ5jYmKQnp7ulDQ7YsQIVFZWwmw2u70n0GUOBwLVlx14j0dHQ8Gy2LdvH9566y2+wyFDBE1mfXBaa3+zF5f1lB/qNAkg10bR4Qkk5Pz444/YunUrWIbBjSNdV2V1wzJQV+6aDNZbfCwgOfilawfD4K2J7YEIlfjAlxKHkyZNwpkzZ5xWzk6dOoWYmBiIRINnhb17v2wWTWYHjI7j8GCU/f1k4cKFqK2t5TkiMhTQZNYHh5T2c+hFp/LAXPInqIyMpWNtSUix2WyOo56vSoqH7uKTg0tx0kmeB2KAYUf+5barZUoOdtGxtbzytsThAw88gPr6eixYsACnTp3C1q1b8fzzz2PevHl8/RaConsySyuzA+t2jRYZYjHq6+vdVtMgJNBoMuuDfGEFGKkUbIcRKo3Q0S6S6h2TWVqZJaHg66+/xrfffgsBy+KGkWku/frho9BQpfE4zrCYLoiP7Xbt4Di8Oq4mAJESf8yePRurVq3C0qVLkZOTg/z8fJcShxUVFY7rExISsH37duzfvx+jR4/GQw89hAULFgyqiUd5eTkqKyvBAsikslwDirskGWzDhg19VtUgJFB4rzMbjmwMYE2KBVN4FmqJCU0XvxPYmAjHZPbEiRMwm82D6pEdCS82mw2LFi0CAEwcngiNTOp6Efszj+OwLIPEfW+77au9IQf5onw/oiSBMn/+fMyfP99t365du1zaJkyYgJ9++inIUfGne1V2uEgMKSV/DbixUhluU6nxn+YmzJ07F/v37wfH0ZSDBAf9hPuoKV4DAFB29uwHMrWroZVJIeY4dHV14fTp0zxFRwjw3//+F/v374eIE+C6Eaku/THp49BUq/Q4TrKhHcIzh1zaGakEq7JpewEJTVRfln+P6HRQsQLk5+fj9ddf5zscMojRZNZHF3T2PzpZ/TlHW2uDDKxAAINaAYC2GhD+WCwWRwWDyWlJUErETv0Mw6LLcqXHcQQcg7jv33TbVzJzNIq4Brd9hPCNjrHlXwTH4U86ezLY4sWL+6x7TIi/aDLro0JtGwBAUtxzdKfVwkKlM1ASGOHd+++/j2PHjkEq5DAlY7hLf2zm1Wipd00G6224rhlc6UmXdkatwgtpVIqLhCabzUbJXyHiN2oNsiUSNDc347HHHuM7HDJI0WTWR/tl9pqO3LmjEAh7/hjlmlgqz0V41dnZiWXLlgEApmYMh0wkdOpnBRza26/wOA4nYhGz4zW3fYWzMlEtaPU/WEKCoKysDNXV1RAAyBCLPV5PgkdwMRmMAfDuu+/i22+/5TskMgjRZNZHxVwjmAgtGKsFGk3PHyMn1tHKLOHV22+/jbNnz0IhFuGatCSX/tjMiWhr8rxala6tgaC6xKWd0UXhpWEFgQiVkKDoXpVNFYshoeQv3mVLpPhftQYAMG/ePHR2dvIbEBl06KfcD53D7KfrqIVtjjarNRIGlX0yW1RUhNZWWr0iA6ejowPPPPMMAOD6EakQC52zhwVCEVqbR3scRywVQPfl39z27b8pCS2syf9gCQkSSv4KPQt0OmgFAhQUFOCVV17hOxwyyNBk1g91sfZEL3lHtaOt3aiEQiJ2JNwcP37c7b2EBMO6detw4cIFqKUS/Gx4okt/XOZkdLR6LheXJr8AQUO1SzuTEIs1MbR9hoQ2R/IX1ZcNGRqBAI/odACAp556ChcuXOA5IjKY0GTWD+d1NgCArKanBJexUQJOJIJBZZ/o0lYDMlBaW1vx/PPPAwBuHJkGoUDg1C+USNBUn+VxHKmcQ9S2V9327ZxhgJmx+B8sIUFyafIXrcyGlltVaoyVSGE0GpGbm8t3OGQQocmsH46qmwEA4rN5l7QyUOniYFCr7NdQEhgZIK+88gpqamoQpZDhyqR4l/6Y9CkwtQnd3OksXXgGbGuTa0daMl6POuLaTkgIKS0tRW1tLThQ8leoYRkGS/R6sAA++OADfPnll3yHRAYJmsz64SdJOcAw4MqLIJb2rIJJVTGOWrO0MksGQkNDA1566SUAwIzsdAh6Jb2I5Qo0VGd6HEeu4hDxhfu9sv+droCN8T9WQoLp0uQvMSV/hZxMiQR3aLUA7KfWmUy0/574j37S/dDEdoCJtSeBadQ97QJhFGJoZZYMoJdeeglNTU2IUSsxJiHWpV8/fCo6TQI3dzpLtx4FY2p3abeMzsA/NYUBiZWQYKL6sqFvfmQUogQCnD59GqtWreI7HDII0GTWT+2J9g3tKrbF0dbVpYX+4p7Zqqoq1NTU8BIbGRoqKysd2cEzszPAMs7Lp1KVBnWVrgcn9KbSCqH5Yp3bvn9OoSVZEh7o5K/QpxQI8Hh0NADgueeeQ3FxMb8BkbBHk1k/VcXY3zDlreWOtrZmJcRCDpFyGQDaakCCa8WKFWhra0NChAZZsdEu/brkabB09mNV1rgXjKXLpd00fhQ+U5wJSKyEBBMlf4WPnytVGC+Tob29HQsWLOA7HBLmaDLrp6II+4e/tLLnyM8OowgShZIOTyBBV1JSgnXr7KupN43KANNrVVYREYWasmEex4mI4qD8aqNrB8PgrQltru2EhKDi4mLU19eDA5AuouSvUMYwDBZF68ExDD755BN89tlnfIdEwhhNZv2Up6oDAIhOH3BqV0bFOSaztG+WBMszzzwDs9mM4dGRSNNHufRHxE+DzeL5xzy19lswNptLe8uUHHwrPR+QWAkJtu4tBuliMUSU/BXyUsVi3HMxGeyhhx5Ce7vrfn1C+oN+2v10SFQJCIUQNFRDruo5bUmiMNDKLAmqU6dO4e233wYAzMrOcOlXR8eg+oJria7edHoBFLvec+3gOPxtXK2/YRIyYHq2GEh5joT01x8jo2DgOJw7dw4rVqzgOxwSpmgy6yczYwGGxQEANPKeYvIMG+k0mbW5WfUixB/Lli2DxWLBiJhoJEVpXfpV+qnoTy2t4aXb3LbXXZ+DQ6IKf8MkZMB0r8xSJYPwIWdZPHExGeyFF17A6dOnPdxBiCuazAZAS4J9IqG0NTrazCYNdAo5BCyDlpYWlJSU8BQdGYwOHz6M999/HwAwMzvdpV8bOwzVFwwex4mJYSH78WOXdkYiwepRpX7HSchAoeSv8HWDQolrZHKYzWY8+OCDtPhDvEaT2QAo19tPVZI39UxYjY1ycAIWOiUdnkACb8mSJQCAnIQYxGnVLv0y7bVg4HlVNvnkR27by2aMxmlhnX9BEjKAioqK0NjYCCHDIJVO/gorDMPgSb0eQobB9u3bsWXLFr5DImGGJrMBcFLbAQAQlxU42jrNHBQRUYihJDASYD/99BM+/fRTMAxw40jXVdmohFTUles8jhMfC0jyvnZpZxRyvJBBj/pIeOneYpApFkPEUF3kcJMkEuG+iAgAwJ/+9Ce0trbyHBEJJzSZDYAD8moAgOhUHphL/kQVEXEwqCgJjATWokWLAABXJcUj+uLhHJcSKiZ7HoQBhh3+p9uuM7NGokLQ4raPkFDVvcUgS0xbDMLV/RGRiBcKceHCBSxfvpzvcEgY4TxfQgAgXqTF4T76TghrwSjkYFuNUGmEaKrvBACIpNEwaGhllgTOjh078M0330DAMpielebSH52chYZK12Sw3obFdEG880eXdkarwYspwTu2VspJkaNIhILhAITPvrhEiPgOgXhAyV/hT8KyeDJaj7llF7B69Wrcc889yMrK4jssEgZoMttPD9U3YOtl+i3JcWCPnoJaYkLTxQVvGyIdK7MnTpxAZ2cnhELhAERLBiObzeZYlZ2QMgwRF0+YuxQrmuBxHIYFEve97bbvyE3paGDz/IqzL9O0WXjmxF5o2k56vjjU6K/jOwJyGVar1TGZzaLJbFibqlBgmkKBna2tmDdvHr755huXw2AI6Y22GfRTbHMV4mT6PvsbYlUAAEVnT9KMqV0FrVwKMSeA2WymkiPEL5999hn27t0LoYDFdSOGu/Qb0nLQWO2aDNZbiqEDwjOHXNoZfTRejg/OE4Spmiz85dBX0LQ1BGV8MrSdPXsWTU1NEFHy16DwZHQ0JAyDXbt24V//+hff4ZAwQJNZL4wSu56w1K1UZ//mKG8452hraZSB4zjoad8s8ZPVasXixYsBAJPTkqGS9lp9YhhYbeM9jsMKGMTuftNt30+zEtHGdvoda2/J8ji8cHw3BDaL54sJ8cGlyV9CWsULe3FCEX4fGQkAeOSRR9DU1MRzRCTU0WTWCyO6+u4r0NozLyXFRxxtNgsLVVQMnQRG/Pbvf/8bR44cgUTIYWpGikt/bMaVaK6TexwnNboFwhLXx/zMsHi8Yjji5g7/cCyHF+uaIDNRZnKwrV27FklJSZBIJBg/fjz27dvX57Vvv/02GIZxeknC+PE81ZcdfH6njUCSUITKykosW7aM73BIiKPJrBcyW+r77NsrtZ+UxJ07Ck7Y88cq08RQeS7il66uLixduhQAMCU9BTKxczISw7Iwm6/0OA4nYmH45jW3fTtu1KGLsfofbC/3K0cgs+J4wMclzjZv3ozc3FwsW7YMeXl5GDNmDGbMmIHq6uo+71GpVKioqHC8zp8/P4ARBxZNZgcfEctisd6+te/VV1/F4cN9pWATQpNZr4yoPtNnX6WgFYwuCoylC2pNzx8rJ46mlVnil02bNuH06dOQi0WYnJ7s0h+XOQGtDZ7Pok/T1oGrcjNhSU/GG5GB/6KVJI/D/Ue/Cvi4xNXq1atx//33Y86cOcjKysK6desgk8mwcePGPu9hGAYGg8Hx0uv7zgkIZVarFXl59qTFkVSWa1CZKJdjhlIJq9WKuXPnwmoN/BduMjjQZNYLWmMdoiWRffabkuwfBmphm6PNatU6JrNnz56F0WgMbpBkUDGZTHj66acBANdlDodE6FyARCAUwtg6xuM4IokA0V+96rbvv9crYAvCNsMnjRYILebAD0ycmM1mHDx4ENOnT3e0sSyL6dOnY8+ePX3e19raimHDhiEhIQG//OUvUVBQ0Oe1oez06dNoaWmBmGEwnJK/Bp0/66IhY1n8+OOP2LRpE9/hkBBFk1kvpUv6PlmpLsa+Z1He0fNor61VBaVEDLlYBJvNhsLC4NXwJIPPG2+8gdLSUqilEkwcPsylPzZjEtpbPK9GpSnLIKivcmm3jM7APzWB/zc5XTsSE871vWeTBE5tbS0sFovLyqper0dlZaXbezIyMrBx40b897//xbvvvgur1YqJEyfiwoULff46JpMJzc3NTq9Q0J38NUIsAUfJX4OOQSjE3IvJYI8//jjq6/ve7keGLprMeikdfdeJPRdpz9aW1fSU4GprEkMoltC+WeI1o9GI5557DgAwPSsVQk7g1C8US9DcOMrjOBI5B90X7ldl/3Vt4N8CRKwIjxSH5yrfUDFhwgTcfffdyMnJwZQpU7BlyxbodDq88cYbfd6zYsUKqNVqxyshIWEAI+6b4+QvCa3KDlZ3aSOQKhKhtrbWUWubkEvRZNZLGe1tffYdUdnLh4iL8i9pZaCKjqN9s8Rrf/3rX1FdXY1IuQxXJ7tOHGLSJ8Nk9HwIR7roLNjWRpd281XZ+EQZ+NrHd6oyEF9fEvBxiXtRUVEQCASoqnJeea+qqoLBYOjXGEKhEGPHjsWZM33nBSxcuBBNTU2OV2lpqV9xB0r3ZDZb4nnfOAlPQobBEr393/Ibb7yB/fv38xwRCTU0mfVSekN5n337pOWAQACu7AzE0p5VNKnS4DgJjCazpD8aGxvx4osvAgBuzE6DgHX+URVJZWisHeFxHLmKQ8QXa107GAYbJ3YEJNZLRYi1+P3x7wI+LumbSCTCuHHjsGPHDkeb1WrFjh07MGGC5xPhAMBiseDo0aOIiYnp8xqxWAyVSuX04pvFYsGhQ/YDQKiSweB2lUyGm1Uq2Gw2zJ07FxYL1a0mPWgy66WkmrMQse7PaW9lzGDi7R8GmksOYmI5HWI0tM2A9N+qVavQ2NgIvUqBsQlxLv2GtGkwd3g+jTrNVgi2wzXpsG3SGHwjKw5EqE7mcXrITS0BH5dcXm5uLtavX49NmzahsLAQDzzwAIxGI+bMmQMAuPvuu7Fw4ULH9c888wy+/PJLFBUVIS8vD3feeSfOnz+P++67j6/fgk9OnTqF1tZWSBkGKSL378tk8HhUFw0Fy+LAgQNYv3493+GQEEKTWS9x1i6kyPtevTAm2Deqq5ie5IiuLo3jFLCKigrU1dW5vZcQAKiursaaNWsAADOzM8CyzkktUqUK9ZWpHsdRaYXQbH/dtUMgwOtXB/5Y2eGKePxPwQ7PF5KAmz17NlatWoWlS5ciJycH+fn52LZtmyMprKSkBBUVFY7rGxoacP/992PEiBG46aab0NzcjB9//BFZWVl8/RZ84kj+kkggoOSvQU/HcXgoyn4S55NPPomamhqeIyKhgiazPkjn+n68VmWwJyHIjT0fHMZmJSRCDlq5fU8XbTUgl7NixQoYjUbEa9XIjnOt/alLnoauToGbO52ltR8Aaza5tDdNy8FecVlAYr3Uw+0MHVnLo/nz5+P8+fMwmUzYu3cvxo/vOd54165dePvttx3//Ze//MVxbWVlJbZu3YqxY8fyELV/HMlfVF92yPh/Gi0yxWI0NDTgz3/+M9/hkBBBk1kfpHf1/YF9OsJeV1Na2XNkqMkohEShon2zxKMLFy7g9dftq6mzRmWA6bXaJNdEoLbC9eCE3rSRHFRfbnDtEArxSo77ck3+uFqdjilnfgj4uIRcTk/yF01mhwqOYbD0YjLY3//+d/zwA73vEJrM+iTNTWZ4t0NK+xYC0ekDTu0qXRyV5yIeLV++HCaTCSm6CKTro1z6IxOnwdrl+cc2tfEHMFbXL101N+TgmNC13qw/GDDIrQ78BJmQy6Hkr6ErRyrF/6jtiSlz585FV1cXzxERvoXEZHbt2rVISkqCRCLB+PHjsW/f5YutNzY2Yt68eYiJiYFYLEZ6ejo+//zzAYoWSK8512dfvqgSjFQCQUM15KqeBB2x3EDluchlnTlzBhs22FdTZ2W7rsqqovSouZDocZyoaA7KHe+4tDNSCV4eWRyQWC81SzsSI8vp3zQZWCdOnEBbWxukDIMkSv4acnKjdFALBDhy5AjWrnVTsYUMKbxPZjdv3ozc3FwsW7YMeXl5GDNmDGbMmIHq6mq315vNZtxwww0oLi7Ghx9+iJMnT2L9+vWIi3PN+A6WqJYqRIg1bvsssMGaZI9FI+9ZGWPYSMfK7LFjx2Cz2YIeJwkvTz31FCwWCzINOiTrIlz61bFTYevHubPDK75y215642gUcYFN/BKyQjxUTBNZMvB6Dkug5K+hSMtxeDjKfiLnkiVLnBIcydDD+2R29erVuP/++zFnzhxkZWVh3bp1kMlk2Lhxo9vrN27ciPr6enz88ceYNGkSkpKSMGXKFIwZ4/l8+kBKk0T32dcUrwEAKG09EwezSQOdUgGWYdDU1HTZYyPJ0HPs2DG89957AICZozJc+jWGBNSUxnocR28QQP79hy7tjFKBFzNO+R9oL7erRiCODkggPOiuZEBbDIauX6vVGCWRoKWlBY8++ijf4RAe8TqZNZvNOHjwIKZPn+5oY1kW06dPx549e9ze88knn2DChAmYN28e9Ho9srOz8fzzz/dZQDlY54mnMX2/gV7Q2f9Y5U09H/KtDXJwnAA6pRwAbTUgzpYsWQKbzYbR8QbEa9Uu/YrIKQA8rz6lFH3itv3MzCxUClr9DdOJUqjA709S8gXhR/fKLE1mhy72YjIYA+C9997Dzp07+Q6J8ITXyWxtbS0sFoujFmI3vV6Pykr3CSVFRUX48MMPYbFY8Pnnn2PJkiV4+eWX8eyzz7q9Pljniaeb+j49qVBrP/JWcuG4o62rUwBFRBQlgREX+/btw8cffwyGAWZkp7v0R8anoLa87ycB3eJiGEj3ue4dZyK0eDGlMCCxXupeyTCo2wJfr5YQT7q6upCfnw+AKhkMdSMlEvw/jQYAMG/ePJjNZn4DIrzgfZuBt6xWK6Kjo/Hmm29i3LhxmD17NhYtWoR169a5vT5Y54mnNfWdEb5PZu8TnToA5pI/YUVEHCWBEReLFy8GAIwbFu84XONSYtVkz4MwQFLB+267js5MRQPb7leMvemlUbjzOK2CEH4UFhaivb0dcpbFMCElfw11D0XpECEQoLCw0HHgDBlaeJ3MRkVFQSAQoKrKeWJYVVUFg8Hg9p6YmBikp6dDIOgpGj9ixAhUVla6/UYWrPPEh9ecBcu4/+M7zzWCiYwAY2qHSiN0tIuk0Y7JLK3MEsBezP6rr76CgGVwQ1aaS78uKRP1FZEex0mMsUJ85DuXdkYfjVUJgf/iNA8REHf1/XSCkGDqOSxBDJaSv4Y8tUCAR3X2p1dPP/10wBatSPjgdTIrEokwbtw47NjRcwSm1WrFjh07MGHCBLf3TJo0CWfOnIHVanW0nTp1CjExMRANYHkWqbkNCTL3E24AMCfZ+zSSng98my3SMZktLCyk2nhDnM1mw6JFiwAA45MTEamQuVwjEE/yOA7DAIkHXUtxAcC+WYloYzv9C7SXVEUCfln4TUDHJMQbPclfUp4jIaHilyoVrpBK0dbWhocffpjvcMgA432bQW5uLtavX49NmzahsLAQDzzwAIxGI+bMmQMAuPvuu7Fw4ULH9Q888ADq6+uxYMECnDp1Clu3bsXzzz+PefPmDXjs6SJtn311MfZEL0VnraOto02FCLkMQoEAJpMJZ86cCXqMJHR98cUX+PHHHyEUsJielerSb0gdjcZq12Sw3pJizBCd3O/SziTE4RVD4J8A/KkdYG1WzxcSEiSU/EV6YxgGS/V6CAB89NFH2L59O98hkQHE+2R29uzZWLVqFZYuXYqcnBzk5+dj27ZtjqSwkpISp/pxCQkJ2L59O/bv34/Ro0fjoYcewoIFC/DEE08MeOxp1r7/+Ip19jqysvqeAxZaG6XgOCEMagUA2jc7lFmtVseq7KTUJKikvT6UGQZW5mcex2EFDOJ/fMtt384ZepiZvo9e9sUV6lQ6tpbwqrOz05H8RZNZcql0sQR3au2LTPPnz0dHB22FGip4n8wC9n9058+fh8lkwt69ezF+/HhH365du/D22287XT9hwgT89NNP6OjowNmzZ/Hkk0867aEdKOnGvst8HVE1AQDExYcdbTYrC5UuhioaEHz00UfIz8+HmOMwLXO4S39s+jg01yo8jjNcb4SwuMC1IzUJr0cdCUSoTnLr6gI+JiHeOH78OEwmExQsi0Sh0PMNZEiZFxkFHcfhzJkzeOmll/gOhwwQzvMlpC+pDWWAa/I5AGCvpBy/Z1kIzxWAG86iy2x/LCvXxMCgoooGQ1lXVxeWLFkCAJiSkQy52HmvN8OyMJuv8jgOJ2QRs+t1t31bp6thYwJ7MMf12iyMydsW0DEJ8dalWwyClfx19I6r8WFM6B8Gck1zDG548xDfYYQUhUCAP+ui8WhFOZ5//nnceeedSE5O5jssEmQ0mfVDQl0xpJpUtFtcH2W0sCYw8TFASRk0Gha11fbJrECko/JcQ9y7776LkydPQiYS4tp01zfZuMyfobbCc2JLamQDuPIil3bryDRs0rpZrfWDgBHgoQu0x5vwz5H8JQ7eFoOvo6tRKKz1fCHPCiNrMWHKWCi+pQntpWYplfigSYa9bW146KGH8Omnn/IdEgmyfk1mfTk1K1AlsEIZa7NiuDwGx5rPue03JkZBVlIGFWdELcQAAIslwjGZPXPmDNrb2yGVUkbuUGEymfDUU08BAK7LHA5Jr8ekAo5DW+tYj+MIxSz0X73qtu+DaYGv6nGrJgspRVsDPu5QcuSI99s+srKywHG05nCpYCd/MTIZ9knKgjJ2MDx79QW8kKeArSWwJ/yFM4ZhsCRaj9vOF+Ozzz7DJ598gltuuYXvsEgQ9etdUqPRgPHicQ7DMDh16hRSUlJ8DixcpAsU6Gt9tTJGjBQAivYqAIkAgPZWJZQSMWQiIdrMnSgsLMQVV1wxUOESnr311ls4f/48VBIxJqUmufTHZk5CTZnY4zhp6ioI6ipc2jvHZeEj5clAhOogEYgx92xeQMccinJycsAwDGw2W7+uZ1l2yLyP9pfZbMbhw/Y8hGBNZrvSEmFB+DyFKOIakHfrWIz9h2tFk6EsRSzGPVot3qqvx0MPPYTp06dDJnMtf0gGh35/5f/www8RERHh8TqbzYabbrrJr6DCSVpn39niZ7RmpACQVZ8GhPbJrLFZDJFUhhi1Emdr6nH06FGazA4RbW1tjmOXp2elQcg5Jy1yIjFaGkd5HEciEyB6m/tV2XeuCXzt4juU6Yg+Q6uygbB3717odDqP19lsNmRnZw9AROGloKAAZrMZKpZFQpCSv6qGhd9TxRfj8/HeiOFgCs/yHUpI+WNkFLY2N+P8+fN4/vnn+zz2noS/fk1mhw0bhmuvvRaRkZ5PIgKAlJQUCIdIlmlaa9/Z3QeVdbgRgLjoEJBxPQCAAQO1zn6s7dmaeto3O4T87W9/Q2VlJbRyKa5OTnDpj824FtUXPG8RSJOWgG12/XfXMWE0tsuOByTWbmqRCr87sTugYw5VU6ZMQWpqKjQXz5H35Nprr6UtSL04Tv6SSLx6WuiNk9Hhd5iNBTasm8HggVMCwBLYcnzhTMayWBitx0PlZXjxxRdx1113ISMjg++wSBD0qzTXuXPn+j2RBeyJTQkJrh/Wg1F6tWsCTrcjokowUgkEFecgkfWswkmUBsSo7d/+qTzX0NDU1ISVK1cCAGaMTAcncP7RE0llaKjL9DiOTMkh8vO/unawLN74WUtAYr3UfeIEqNqbAj7uULRz585+T2QB4PPPP0dMTEzwAgpD3clf2UGsL7tHXR20sYNpp7QYVTPpKV9v1ysUmCyXo7OzE/Pnz+/3Nh8SXvyqM3vhwgWnY2WHIq2xDpFi9yeBWWCDNSkeAKBW9fwAsVwUHZwwxKxevRoNDQ2IVilwRWKcS78hbSo62z0/zUhnT4Jtd030aJkyBj9IAnseuUGqw+3HdwZ0TOLqhx9+gMlk4juMsHDpymwwMGoV8kWVQRl7IDyVfRJMdBTfYYQUhmGwKFoPEcPg66+/xgcffMB3SCQI/JrMZmVlobi4OEChhK90aXSffU3x9uNI1UxPRYjOTq2jokFZWRkaGhqCGyDhVW1tLVavXg0AmJmdDpZ1fjwqVapQX5nmcRyFWgjtF6+5dnAcXr2iJiCxXmoutBB30Qk6wTZr1iyUlYVP9jxfTCaToyJEdpDKcpnTwvuJYh3bhi9vDe/fQzAkikS472LOz8MPP4yWlsA/xSL88msyS8v1dmnoO/u8NNq+vUDeWu5oa2tWQCIUQiOz74ej1dnBbeXKlWhtbUWcVoVRcQaXfl3KNHR1ej7BLr0zH4zZdXJZd31OwFeTUhTxuKWQVmUHAr2P9s+xY8fQ2dkJNcsiLkg5GRWJ8qCMO5DWRx6FabznRNKh5r6ISCQIhSgvL8fTTz/NdzgkwELiONtwl97R3mdfocYIAJCWFzraTG1CSFUa2mowBJSVlWHt2rUAgJnZGS5JK3JNBGrLPZ9Oo44QQr39TZd2RizG6tGB3V4AAAs6BBDYKJGEhI5L68sGK/mrQDc4tnu8eE0dGGnw9hWHIwnL4sloPQBgzZo19Lk7yPg1mX3yySf7Va5rsEtr6ntVbJ+8CgAgOn3QqV0VFUdJYEPAs88+i46ODiRHaZFpcC3JFJl4Haxdnn8M05t/BGNxzbIunzEGp7m+K2r4YoxqOK47/X1AxyR9e+ONN6DX6/kOI+T1TGaDV+FhtzJ898te6qioGidvGc13GCFnikKB6xUKWCwWzJs3j56KDCJ+TWYXLlzoVXbuYDW8+iwEjPvHxCWCRrBREWCb66BQ91RCE8n0tDI7yBUVFeGtt94CAMwc5boqq4rSo+aC5/1tkToOih2bXNoZuRwvZga+uPufGr0/8Y/47re//S3k8vB/vB1sjmNsJZ4PFfEFGxWJ08LAfjHk0/Lkw0BKIt9hhJwnovWQMAy+++47vPvuu3yHQwKkX5PZ3NxcGI3Gfg+6cOFC1NfX+xxUuBF3dSBB5roXsptpmL1PLe9ZWWPYSKeVWfqGOPg89dRT6OrqQoZBh+E619J26phpsNk8Py4dXrUDjJt/H+dmjkSZILATz8maEbjy/EHPFxKv/epXv/LqaPA77rgD1dX9LxO1du1aJCUlQSKRYPz48di3b1+/7nv//ffBMAxuvfXWfv9aA62jo8PxBCtYK7Ntaa5VRsKZibHg3ZvkQJC2ZISrOKEQf4y0V3x49NFH0djYyG9AJCD6NZl95ZVX0NbW1u9B165dO+T+gaSJNH321cbaV11Ulp6qBaYONaKVcrAMg8bGRpSXl/d1OwlDBQUFjm/9M7PTXfo1hgTUXPBcQzRaL4Diu3+7tDMaNV5MDeyxtSzDYkFFSUDHJD3++9//oqamBs3NzR5fTU1N+PTTT9Ha6lqGzZ3NmzcjNzcXy5YtQ15eHsaMGYMZM2Z4nAwXFxfj0UcfxeTJkwPxWwyao0ePoqurC1qBALFcvw+u9EpZ/ODbY/qJ8jQap1Pt2d7+LyICySIRqqursWTJEr7DIQHQr8mszWZDeno6IiIi+vXyZhV3sEi39P3t91yUvRavrPG8o621UQ6O4xCltE90ad/s4LJ06VLYbDaMijMgIULj0q+ImgKgH6uy5z512144MwO1bGB/zmZpspBRWej5QuKT7vdRrVbr8eXt++jq1atx//33Y86cOcjKysK6desgk8mwcePGPu+xWCy444478PTTTyMlJSUQv8WgceyXFQcv+euIru9E3nD29NhzYLQavsMIKSKGweKLyWCvvfYa8vLyeI6I+KtfX3H//ve/ez3wUEtoSGvru27dEVUTJgKQlh4DonMAAJZOAVSR0TColKhubsWxY8cwc+bMgQmWBNWBAwewZcsWMABmuFmVjUwYjtqyvmsTd4uNYSD911aXdkYXhZeGFQQiVAeO5TC/mPZuB9POnd6XOouL8/zo22w24+DBg1i4cKGjjWVZTJ8+HXv27OnzvmeeeQbR0dG499578f33nhP+TCaT0+EO3myZ8NellQyCZZfiQtDG5lOZoBk/3joGE/5O24cuNUEuxyylEl+0tGDu3Ln48ccfwbJU4Clc9Wsye8899wQ7jrCXXl8CqNz37ZdU4I8sC+GZQ2ANd8Fqte9/lEfEIUatxJELFZQENogsXrwYAHDFsDjH4RiXEismw9iPp8dJhe5PqsmblYwW9pBfMfb2a3UW4s9+FtAxibMpU6YEZdza2lpYLBaXBQS9Xo8TJ064vWf37t3YsGED8vPz+/3rrFixgrf6nD3JX0E6+SvWgApBbVDGDgVr9Idx9ZgMCA4HdmtSuHtcF41vjUbs3bsXGzduxH333cd3SMRH9DUkQOLrSiDjZG77WlgTmIRYMOYOqLQ93x+EEp1jskPbDAaH77//Htu3bwfLMLhxpOuqbHRyFuorPZezS4i1QZLvupLHxMVgdeyRgMTaTcpJ8YdT/UsWIuGvpaUFd911F9avX4+oqP4ffbpw4UI0NTU5XqWlga9v7E57e7vjy36wJrOtqX0n8A4GNgZYc30HEKT9xuFKLxRi/sVksCeeeAJ1dYOnmsVQQ5PZAGFgQ+plKhoYE+w/MGpxzwlONluEYzJ7/PhxWCxUpD6c2Ww2PPnkkwCA8SkJiFS4frlhRRM8jsMwwLBD/3Db9/2MWJiYwP47uUueiqjW/mfNk9ASFRUFgUCAqqoqp/aqqioYDK7vSWfPnkVxcTFuvvlmcBwHjuPwzjvv4JNPPgHHcTh79qzbX0csFkOlUjm9BsKRI0dgsVgQKRDAEKTJWEmcKCjjhpK94jKU3kzJYL3dodUiTSRGXV2d01YdEl5oMhtAaYK+a0VWxNjfLJXmGkdbe5sakXIZhAIWHR0dfX6IkPCwfft27N69G5yAxfQRaS79MWk5aKxWexwnKaYTosK9Lu1MciLW6gO7KqsRqTHnBB2QEM5EIhHGjRuHHTt2ONqsVit27NiBCRNcvzxlZmbi6NGjyM/Pd7xuueUWTJs2Dfn5+UhI8Fz7eCB175fNCuLJX/mR/asaEe6eSi8AEzu4V6G9JWQYLLm4Reett97C3r2u770k9NFkNoDSOl1PaOp2WmsGAEjrzznajA1SCEVC6FX21VnaNxu+bDYbFi1aBACYOHwY1DLnx6EMw6LLOt7jOKyAQfxP7jPQt90YAQsCW4/4XnE8FB10SEK4y83Nxfr167Fp0yYUFhbigQcegNFoxJw5cwAAd999t2PVSSKRIDs72+ml0WigVCqRnZ0NkSi0Vim7J7PZwUr+Yll8Jx+cyV+9tbAmfPxL15MIh7orZTL8UqWCzWbD3Llz6SlpGKLJbAClN/e93+aA0r4iKzmX72iz2RiodLG0b3YQ2LJlC/Ly8iDmBLguc7hLf2zm1Wip93zKU0p0G4RFrquvthHDsSEisF929NIo3H7c+wx7EjjV1dX4/vvv8f3333t1QEJvs2fPxqpVq7B06VLk5OQgPz8f27ZtcySFlZSUoKKiIlBhD6igJ38lxqGO7X8d9XD3T00hjJNz+A4j5Dyii4aSZZGXl4d169bxHQ7xktcbkIxGI1auXIkdO3aguroaVqvVqb+oqChgwYWb9OozgMH9PrKjwiowUim44gII01l0mi7WnlXHOCaztDIbniwWi6Pw9uT0ZCh6HbfJCjh0tHveqyYQsoj9zv2b6EfXBf7Uoz8ykRB3dXi+kARcy8VyQO+//75jFUggEGD27NlYu3Yt1GrP21F6mz9/PubPn++2b9euXZe99+233/b61xsIbW1tKCiwl6EbKQ7OZLZpuA5AeE70ffX8+Ao8f0gOW+vQqwnflyiOw4IoHZ6trsKiRYvw61//esiVGA1nXk9m77vvPnz77be46667EBMTE7Q9TOFI3d4IvTQFVe2uJV5sDGBNigNTeAZqNYvaavtkViDWIYZWZsPaP//5TxQWFkIqEmJKumvx+bgR16CmzPMHcWpkA7iyMy7tXWNH4N8q9yWWfJUkj8WttCrLm/vuuw+HDh3CZ5995tjXumfPHixYsAB/+MMf8P777/McYWjIz8+H1WpFlECA6CAlf52LEQRl3FB2WliHI7degVHvUhWTS83WaLClqRHHm5rw+OOPY9OmTXyHRPrJ63eHL774Alu3bsWkSZOCEU/YSxNHuZ3MAkBjghraQkDNGVEL++qdpUvrWJk9ffo0Ojo6IAliYXASWGazGU899RQAYFrGcEhFQqd+oUSC5oZsj+MIxSwMX6912/ePyVa37f6Y1ykGZ+17jzcJrs8++wzbt2/HNddc42ibMWMG1q9fT4enXOLSLQbBWjg5GDE094w/n3AI72WkgDk5dJ+m9iZgGCzRG/DbkvN45513cN9994X8Uc/Ezus9s91HLRL30iHss+9CtH0FQN7W80irvUUFlUQMmUgIq9XaZ5FzEpo2bNiAc+fOQSkRY1LaMJf+mPSpMLV5TqhJU1dBUFvm0m762Sh8IQ9slYsRyiTMOPldQMck3omMjHS7lUCtVkOr1fIQUWgK+slfHIfvpCXBGTvEWWDD+pkCgE69cjJGKsWvL/5szp07F52dnTxHRPrD63/Fy5cvx9KlS9HWNnQ2zHsjo73vP5fjGvv+JFn1aUdbW4sYYpmcksDCUHt7O5YvXw4AuH5EKsS9HoNKFCo0VLkenNCbWCZA9LZXXTsYBm9OCPyetgWtZjABropAvLN48WLk5uaisrLS0VZZWYnHHnvMsf+a9KzMZksCv2ccAJCSgDZ26E5WvpadQ/WscXyHEXIe1kVDIxDg2LFjePVVN+/NJOR4vc3g5ZdfxtmzZ6HX65GUlASh0HklMi8vL2DBhaOM+jKgj6T1n2QVmA1AfCYPyLrB0a7SxcGgUqKopp6SwMLI2rVrUVFRAa1Mip+luNbmjE6ZhupSzz9i6dISsG4qYbRem4PvJYH9cnOlOg2T8nd4vpAE1euvv44zZ84gMTERiYmJAOwVB8RiMWpqavDGG284rh2q76mtra0oLCwEELyV2frkSAADc5JZqHoq+xRe3xcFW83gPc7XWxqBALlROiytqsSyZcswe/ZsxMXF8R0WuQyvJ7O33nprEMIYPJJqzkKsSoHJYnLpKxM0g42KBKpLIL2KQ7vRvmdRooyhldkw09zcjJUrVwIAbhiZBk7gnEQi10ahtjzZ4zgyBYfIbX9z7eA4vHpl4D9cFtBxjSGB3kc9607+iuY46IKU/HU2JijDhpVa1oivbx2F69fTZPZSv1Kr8VFTIw63tuKRRx6hpMwQ5/U7xLJly/p13b/+9S/ccsstkMs919YcTAQ2C1LlsShoPue235RkgLC2DhqVFe0XnyCzgkhHRQNamQ0Pa9asQV1dHXRKOcYNc/3GHpkwDdWlnnfxpAtOgjW6JqDUX5eDQ6L8QITqMFWThZxD2wI6JvGNN++jRqNxyL2PAsGvLwsA+zQNQRs7nLwRdRSTr8qGaD99/nRjLyaD/e/5YmzevBn3338/rr/+er7DIn0I2s7vP/zhDy5nhQ8VmQJFn321sfYPJSV6JjCdZo1jZba0tBSNjY1BjY/4p66uDqtWrQIAzMhOh6BXAoU6OhbVF+I9jqNQC6H94jWXdkYsxl9GB/ZEIpZh8VC5+y9YJHQN5fdRR/JXkOrLMhIJ9kiGxslf/fHCtfVgqJKOkyyJBLdr7AmZ8+bNg8nk+sSVhIagTWZttqGbYJJh7rvkUVGUvUC6vKXnTdTYpIRUJIRaan8j6S4STkLTiy++iJaWFsRqVBgd7/qcUhk91V5Y2IP0zkNgzK6HFpTfOBonhYF95HeTJgtpVScDOiYJvqH8PhrsSgaW1ESYGTq2tNtRUTVO3TKa7zBCzkNRUYjkOJw8eRKrV6/mOxzSB6rJEQSZTX0fS3lE1QQAkJYXOtrMHRxkai1tNQgDFRUVjuzWmdnpYHvVvoyIS0ZtmcHjOJpIIdTb3nRpZ+RyvJQZ2LqPQlaIecX0b4qEj5aWFpw8af/yFazJbM0w709ZG+yeSTkMJjmR7zBCilIgwGNROgD2ak7nz5/nOSLiDk1mgyC9+gwYuF+Z2ysuAwQCiE4dwKWXKKPiKAksDDz33HNob2/HsEgNRsREu/RL1Nf2a5y0ph/AWF1Xhc7NHIkLXJPfcV7qf9QjEF8/NGtpkvB06NAh2Gw2GDgOUUFK/jplCPxhJOHOxFjwj5/LATrZ08nNKhWukkrR3t6OP/3pT3yHQ9ygyWwQyE0tSJC5X51rYzvBJMSAbW2EUt1T1kwk09PKbIgrLi7Gm2/aV1Nnjcp0OZFIl5SJ+opIj+NE6jgov3Y9JpHRqPFiamC3Akg5Kf5wen9AxyQk2AYi+esndU3Qxg5nnyhPo/H6K/gOI6QwDIPFegM4hsHHH3+Mzz//nO+QSC80mQ2STHHfp6S1JkQBANSynmLdDCKdVmaH8l65UPX000+js7MTafoopEa7TloF4v4d8Zxa+bXb9uOzMlDLBvaQhDsUqYhqGZoJRCR8BXu/LKNU4ICoPChjDwZPX3EOjFbDdxghJU0sxl0Xk8EefPBBtLe38xwRuVTQJrPDhg1zOVBhKMm09P2YptJgP95Uaal3tJk61IhWKsAwQH19vdPJQIR/hYWFeOeddwAAs7IzXPoNqWPQWO15D57eIID8+w9c2pnoKLw0LLAr8iqREnNO7A7omGRgDdX30WBPZjvTEvuTozlklQmasefWVL7DCDlzo6Kg5zgUFRXhhRde4DsccgmvJ7M7d+7ss+/SU2uOHTuGhATXU5GGiszWvusXnoqwl/eQNxQ72lobZRAJhYhS2Et30b7Z0LJs2TJYrVaMjNUjMVLj3MkwsOJn/RonpegTt+0HZiajlTH7GaWz34kToWoP7P5bEhj0Ptq35uZmnDp1CkDwynJVJvRdPpHYrTbkwzLG9Yv7UCZnWfw52p4rsXLlSpw9e5bniEg3ryezM2fOxGOPPYbOzp5H5LW1tbj55pvxxBNPBDS4cJZZ03dNz4MKe9klSWlPCS5LlwAqnd6x1YD2zYaOQ4cO4YMPPgADewWD3uIyrkJzneei9nGxgHSf614rJiEWf4k9EohQHXSSCPz2xLcBHZMEDr2P9q37+N4YjkNEkJK/CvWdni8iWHN9BzAEnwxczgyFEhNlMphMJjz44IO0JTBEeP1OsXPnTtx999346quv8N577+HcuXO49957kZGRgfz8/CCEGBoKIq7HyAv7+n29rrkSkbFjUGdyXaE9KqwCI5OBO3sIbOzdsFrsPwwyTSxi1EocvVBJK7MhZPHixQCAnMRYxGhUTn2sQICOjnGeB2GApKPuj0PcdaMBZqbvcm6++IMgGlJzfkDH9JcNDMrjZmKHdRwaLWK+w/HKOMGV6N+O6P4Zqu+j/dGd/JUdxOSvH1S0jas/9orLUHLzFUjc0v/PvsGuOxnsl8Xn8MUXX+Djjz/GbbfdxndYQ57Xk9mJEyciPz8ff/zjH3HFFVfAarVi+fLlePzxx12yuweTl2snYAMrBGPt/zf6TKkeP7iZzNoYwJIcB7bgNNRaDg219jGFkmhamQ0xP/zwAz7//HOwDIMZI11XZWMzJ6K2XOpxnGExXRDv/N61IzUJr+kCuyqbIDPgV8f7fozNBxvLYaP+SSw/m8l3KD55akxEQCezQ/V9tD+698tmBSv5K0KL40KqZNBfT6Ufw9/jY2G7QAlz3ZJEIvxOG4E36uuwYMEC3HjjjUPyyOlQ4lMC2KlTp3DgwAHEx8eDu3gyRltbW6BjCymNXRzaIkd6dU8m+n480xhnTxZSi3oyIm3WCEd5roKCAlitVAeRTzabDYsWLQIAXJUcjyil85uVQChCa/MYj+OwLIPEvX932/fZdHXAE1HmWhQQevGlayB8ZHgEy8+F50Q2WIbi+2h/dE9msyWevyT6oiPN81HTpEcrY8aWWzyXHBxqfh8ZiVhOiNLSUjz77LN8hzPkeT2ZXblyJSZMmIAbbrgBx44dw759+3Do0CGMHj0ae/bsCUaMIeOCxLvN8JltrX2PFW2fwSjMPSsE7W0qRMrl4FgW7e3tKCoK7ElQxDtff/01vv32WwhYFjdkpbn0x2ZORkeryOM4KQYjhGfzXdqt2el4RxvYo4vTFIm46eSugI7pr6KEX+HRIs+T/qFkKL+PXk5jYyPOnDkDIHiVDMoTgjNJHsz+pS5E65SxfIcRUqQsiyf19mSwl19+GSdOnOA5oqHN68nsK6+8go8//hivvvoqJBIJsrOzsW/fPvzqV7/C1KlTgxBi6DhqSfLq+hF1pX32FWjs9URltT3ZkK2NEgjFYuhV9kxb2mrAH5vNhieffBIAMHH4MGhkzh+AQokUTXVZHsfhRCzidr7mtu9f0wT+B9rLgnYbWFvorOh3qRIx+/wv+Q4j5Azl99HL6U7+ihMKoREE/ucDAI7qOoIy7mD37NUXwCipCsSlrlMoMVUuR2dnJ+bNm0fJYDzyejJ79OhRzJo1y6lNKBTipZdewpdffulTEGvXrkVSUhIkEgnGjx+Pffv6t9n8/fffB8MwuPXWW336db31vTHGq+sTa89Bxsnc9v0kqwAASM4d7mm0MVBH07G2oeC///0vDhw4ABEnwHUjhrv0x6RPhbndc5ZvmqYWggrXyhbmq7LxX8XpgMTaLUc1HFPO/BDQMf31omguasyUDd1bMN5HBwNHfdkgleQCgO8VtPfTF0VcA/JuG8F3GCHnyWg9xAyDb775Bps3b+Y7nCHL68lsVFRUn31TpkzxOoDNmzcjNzcXy5YtQ15eHsaMGYMZM2aguvry2d3FxcV49NFHMXnyZK9/TV99XRcJG9v/nDkGNmTIY932VQhawOiiICgphFDc89cgVRkoCYxnFovFUcFgcloylBLnzHuJQomGKtdtB72JpQJEf/WqawfDYMOkwK8OLWhsDviY/iiN/znevJDIdxghKdDvo4NFsI+xZWL0OM81BmXsoeDFuHxYs+gwhUvFi0T4fYR9T3Fubi6am0PrfXio4P0429WrV+P+++/HnDlzkJWVhXXr1kEmk2Hjxo193mOxWHDHHXfg6aefRkpKyoDFauwSwKxxXaW7nEzG/cosAJiSDGBsNmg0PRlAAqHOkQRGK7P8eP/991FQUACpkMPUDNd/X9Ep09Bp9vylJl16HoIG1y9lxsk52CktDkSoDpM0mbjy/MGAjukPm0iB31dSuRrinWCf/GUc7t3TNeLMAhtev9EGBGkLSLj6XUQEEoVCVFRU4KmnnuI7nCGJ18ms2WzGwYMHMX36dEcby7KYPn36ZZMgnnnmGURHR+Pee+8diDCdVMu8+1Y6wtT3ClxtjH2iqxb0JIpZurSOldlTp07BZDL5ECXxVWdnJ5YuXQoAmJo5HFKR8yNymSYCdRXJHseRKTlEfuFmVZbj8LeragMSazcGDBZUXgjomP7aqb8Hha19f5EjpLeGhgZH0muwJrMl8Z4TNsnlfSs9j4qf96O29hAiZlks1usBAH/9619pIYoHvE5ma2trYbFYoL/4j6CbXq9HZaX7ota7d+/Ghg0bsH79+n79GiaTCc3NzU4vf5xmkry6PqOxos++oigLAEDe1vN7bWtWQC2VQCrkYLFYcPLkSZ/iJL75+9//jqKiIijEIlyTluTSH5U4DZYuz6sS6SgE2+5azaJueg4Oivr+N+GLG7VZGFFxPKBj+qNTnYQHiybwHcaQ403uwZYtW3DllVdCo9FALpcjJycH//jHPwYwWlfdWwwShEKog7TydzjKGJRxh5qlI46DMUTzHUZIuUauwI0KJSwWC+bOnUvJYAOM920G3mhpacFdd92F9evXX3bP2aVWrFgBtVrtePl7znleR5xX16dVnQbXxz7bI6pGAIC06pSjrd0ohkShpCQwHnR0dOCZZ54BAFw/IhXiXkdpKqP0qCnz/O9HpRVC84VrBQNGKsGqUSWBCfYijuHwYElofeHZIPk/GC1h9dYS9rzNPYiIiMCiRYuwZ88eHDlyBHPmzMGcOXOwffv2AY68R7C3GIBh8K08tJ5ghKsmtgNbb6UtG739OToaMpbF7t278c477/AdzpDC6ydOVFQUBAIBqqqqnNqrqqpgMBhcrj979iyKi4tx8803g+M4cByHd955B5988gk4jsPZs2dd7lm4cCGampocr9LSvstl9cfORp1X1wstZgyXu58A7xOXAxwH8WnnvY4qXRwlgfHg9ddfR1lZGTQyCSYMd01c0sROhc3q+UcmvW0f2C6zS/v5WaNxlqsPSKzdfqkZgWG1oVOPuFl/NVaedz0pjQSXt7kHU6dOxW233YYRI0Zg+PDhWLBgAUaPHo3du3cPcOQ9HMlfQapkwAyLQy1LK7OB8ra2AO0TR/MdRkiJEQrxwMVksMceewwNDa4ngJLg4HUyKxKJMG7cOOzYscPRZrVasWPHDkyY4PqYMjMzE0ePHkV+fr7jdcstt2DatGnIz893u+oqFouhUqmcXv4oaJHDKvXuNJRMzv2v2cZ2gkmIhaC2DDJFzyqgRGGgldkB1tLSghUrVgAAbshKA9frMafGEI+aUveVKS4VEcVB+eUGl3ZGq8HKtMCuoIoFYjxw9lBAx/SHDQye6vgt32EMOb7mHnSz2WzYsWMHTp48iWuvvbbP6wK9Zau3YK/MNg2nx+KB9vzEajB0jKuTuyIikCISoaamxlEVhwQf788Cc3NzsX79emzatAmFhYV44IEHYDQaMWfOHADA3XffjYULFwKAo7j4pS+NRgOlUons7GyIRAOzub9Z7d3K04hOS599LYn2ibFa2XMNw0bRyuwAe+WVV1BTU4MohRxXJrked6mInArA87mzaTU7wbjZK5X3i7SArwr9P2UG9E2hUzPzQvxN2FJFE4aB5kvuAQA0NTVBoVBAJBLh5z//OV599VXccMMNfV4f6C1bl6qrq0NxcTEAICtIk9lzMZSBH2gnhbU4+kvPh8cMJSKGwdKLP4uvv/6644kDCS7eJ7OzZ8/GqlWrsHTpUuTk5CA/Px/btm1zvDGXlJSgoiKwCTP+KhN6zma/VEZzTZ99FXp7trzK1uRoM5s0iFHZJ7Pnz5+nunVBVl9fj1WrVgEAZmSnQcA6/1hEJgxHbbnnSVq0XgD5t++7dqQMw0txh13b/aAQynHfyR8DOqY/bAIxFtTcwncYxAtKpRL5+fnYv38/nnvuOeTm5mLXrl19Xh/oLVuX6v7ATxQKoQpS8tfBCHofDYbnEw/D5qaE4VB2tUyOnytVsNlsmDt3LqzW0DmVcbDq/wkAQTR//nzMnz/fbd/l3lwB4O233w58QB4UWhIw0ovrM6tOg4nRwgbXFbvTESakAZC3XABg/4ZrbJRDJhZBJRWjud2EgoICt9suSGC89NJLaGpqQoxaiTEJrlsJxIrJMLoWJnAx/Nynbtv/PUuBLiawb2b3SJOhaSsM6Jj+OBw7G3mnlXyHMSR5m3vQjWVZpKbaSw3m5OSgsLAQK1as6PM4XbFYDLFY7LbPX91bDLKDlfzFcdgtC9zkm/ToYqxYP1OA359mAZq0OTweHY1vja3Yt28f3nrrLfz+97/nO6RBjfeV2XC0x9j3B4Q7io5mJMjc33NAYa85KinrKa3UaeagiNAhRm3fa0v7ZoOnsrISf/3rXwEAM7MzwDLOWwmik0egvjLC4zhxMQyk+7a6tLddk4MPVYHdKxsh1uLuE98FdEx/WCVazCudxncYQ5a3uQd9sVqtvNW1DvbJX7bhiWhlXJMySWB8LTuH6llUe/ZSOo7DgxerLi1cuBC1tYGtL06c0WTWB9/UR8LGePdHlyF2PyEq4KrAKOQQnT6IS+dRiohYGFQKALRvNpief/55tLW1ITFCg6xY160ErGii50EYIKnAdXsBIxZj1c/63mLiq/uFMZCZ+rFUPEC+jroLZR3BWbEj/eNN7gFg3//61VdfoaioCIWFhXj55Zfxj3/8A3feeScv8Qc7+as+yfMXUuKfZdknwUT3r2TmUHG7RosMsRj19fV44okn+A5nUKPJrA8aOjl0qpO8umeExX3ykI0BLElxYI3NUGp6TpsSSaNh0NhXZmkyGxznz5/HG2+8AQCYNSoDTK9VWUNaDhqr1R7HGRZjgfiI60ppyU1jcExY5eYO38VKozH7+DcBHdMfXapEPHzuar7DGPK8zT0wGo2YO3cuRo4ciUmTJuGjjz7Cu+++i/vuu2/AY6+pqUFJib3+claQynKdoZKoQVfHtmHbba7Js0MZd0ky2IYNG/pVXYT4JiT2zIajenkqDI39r++Z2dp3vbmGeBUijwFqqRnNDfYJlQ2RjiSwo0ePwmazuUy2iH+eeeYZmM1mpEZHIk3fa0WBYWC1eZ6kMSyQsP9t1/bICDybHvg9rQ9AA6EldB6Xvie/G8Zq+k4cCrzJPXj22Wfx7LPPDkBUnnVvMUgSiqAIUvLXT9q6oIxLnG2IOIYpE0ZDsucI36GEjLFSGW5TqfGf5ibMnTsX+/fvB8fR1CvQ6FPIR0VsklfXj6jue+JborNPUpVdPXtqTO0q6FUKMLCX3unrJB/im5MnT2LTpk0A7KuyvcVlXIXmOoXHcVIMJohO57m07/9FChrYdv8DvcRwRTxuLtwZ0DH90R6VjWXFI/gOg4S5YG8xYKRS7BWXBWVs4mrlpBowMhnfYYSUR3Q6qAUC5Ofn4/XXX+c7nEGJJrM+Omz27ljbqJYqRPW1b1Zt3/8oazjvaGtpkEEsFiNSYS9ITUlggbVs2TJYLBZkxUZjWKTWqY8VCNBh8pzMIOAYxO5+w6WdSU7E6pjAr0w82CGAwNZ3zeKB9hfcCZuNnhYQ/3SvzAarkkFXWmLAq4mQvh0X1uDYrdl8hxFSIjgOCy4mgy1evPiy9Z+Jb2gy66Pdzd4Xh8+Uur/nJ7m98L3kfM+E1WZlodLFwKCmJLBAO3z4MDZv3gzAXsGgt9iMCTA2Sj2OM1zXAmGJa6WC/87UBPzDc7QqBdef/j6gY/qj3nAN3rzgeuQvId4K9sps9TD/Tn0k3nsuMR9I964e+2D3G7UG2RIJmpub8dhjj/EdzqBDk1kf/dSohE3o3TF+I2xCt+3VrBGMPhrCs/kQcD0rXXJNDJXnCoIlS5YAAHISYhGrcf6gEwiFMLaO8TgGJ2Jh+OY1l/ausSPwrua4mzv8s6ApdM6Ut4HBYuP/8h0GGQSqqqpw4cIFMABGSIJTEeOEPnSeZgwVXYwVb87iAJamGN0EF5PBGADvvvsuvv32W75DGlToX5qPLDYWbZo0r+7JbG/ps68jSQ/G0gW1ticBQiDS0bG2AfbTTz/h008/BcswmJHt+vcXmzEZ7S2eP1TTtLXgqs47NzIMNkzpDFSoDhM1Gbi6eH/Ax/VVafwv8HkNleAh/uveYpAsEkHOBif56wd1YCuKkP6h2rOusiVS/K9aAwCYN28eOjsD/3kxVNFk1g+VkuFeXT+itqTPvupY+2NttbAnachiiUDMxclsQUEBHYkXAIsWLQIAXJkUB53SOcFLKJagucHzOeNiqQDRX77q0t56bQ52SIsDEmc3BgwWVJUHdEx/2AQi5Nb8gu8wyCAR9OQvjRpHRDSZ5cuS7BNg9Dq+wwgpC3Q6aAUCFBQU4JVXXuE7nEGDJrN+OGVL8Or6+LrzUPSxNeFsZBcAQNHR88bb3qpEpEIGjmVhNBpRXFzsc6wE2LFjB7755hsIWBbTs1xXZWPSp8DUJvI4TrqsFIKGXtUlhEL85crAf2jeoM1CVnlBwMf1VUHsb3CgiY6tJYER7OQvU5p379EksBrYdnzxS++SpQc7jUCAR3T2Cf5TTz2FCxcu8BzR4ECTWT8caPeuEjcDGzJksW778pX2OrSymjOONmOzGFK5AtF0EpjfbDabY1V2QkoiIuTOpWPEcgUaalyTwXqTKjhEbnNdla2akYOjosCWT+MYDg+WngromP6wiZWYf+F6vsMgg0j3ymywDksoT6QSUXzbGHkM7RNH8x1GSLlVpcZYiRRGoxG5ubl8hzMo0GTWDzsbvH98MoJ1nyV/UFwOCIUQFx1ytDFgoNLFOfbNUhKY7z799FPs3bsXIoEA141w3R6iHz4VnSbPhazTBafAGpud2hilAiuzzgYs1m6/1IxAUk3gx/XV7ug7UNwenEkHGXoqKipQXl4OFkBmkFZmj+lMQRmXeOfZSVVg5N4lTA9mLMNgiV4PFsAHH3yAL7/8ku+Qwh5NZv1Q1CaBRa736p7MdveF9E2MBUiKA1d2BmJpTyKERGlw7JullVnfWK1WLF68GABwTVoSVFLnD06pSoO6Ss/7nxVqDhFfrHVpPzUrC2WCZjd3+E4iEOOBs4c8XzhALHI9HiqeyHcYZBDp3mKQIhJBHqSs92+VdFhCKDjN1eHwbZ7zEYaSTIkEd2jtNc7nz58Pk4m+ePmDzlTzU5MyDRHG/u+VzGwoB/ooYdqcEAHV6WJo1EDVxTkvK4iilVk/bd68GUePHoVEyGFqRopLvy55GqpLPGdSp3ceBmPucGpjdFF4MSXwpbhuV6ZDf2ZrwMf11afau9BQR28XJHCCnvwVo8d5zrdjbBNkBuiECthsNoBh7P8/BNV0taKsLTwS3FYk5OO9EalgCs94vniImB8ZhS9aWnD69GmsWrXKsRWOeI8+nfxUKkpGBHb3+/rh1achSk6C2Wp26SvTc1ABULEtqIJ9r1enWQODyj6ZPXnyJMxmM0Qiz0lKxK6zsxNLly4FAEzNSIFM7Pxnp4iIQk3ZMI/jqCOEUP/3TZf2/TcloYnJD0is3ZRCBe498UNAx/SHWZOCPxfl8B0GGWSCPZk1Do8B4NtkdmGHAJMLvglsQEFQq4jGbXF6NJqb+A7FIwtseG2GDfNOc0BXF9/hhASlQIDHdTo8XlGB5557DnfccQeSkpL4Diss0TYDPxV0xXt1PWftQqrcfRJYobYNACBv7Xk01tqsgEYmgUTIoaurCydPup44Rfq2adMmnDlzBnKxCNekuZ5IExE/DTaL5x+D9OYfwVic34CZYfFYExP41fLfSZOgbm8M+Li+2iS5CyYrvVWQwLHZbI5tBsGazJbE+/alnwGD0eWFAY4mOKJaq/EkE8l3GP32rfQ8yn5+Bd9hhJSfK1UYL5Ohvb0dCxYs4DucsEWfUH76scW7igYAMIJzX9rooKwGACCt7Mlg72wXQqGNdKzO0r7Z/jOZTHjmmWcAANdnDodE6PwgQh0di+oLnr+MROo4KHZscmnfPiMKZiawpwvpJBG4o3BXQMf0h1GXg+eKPVd5IMQb5eXlqKystCd/BamSwaGoVp/uS1bEhdSXSU9mndiFG7Qj+Q6j35ZmFoCJ8/5zc7BiGAaLovXgGAaffPIJPvvsM75DCks0mfXTtw0a2BjvTq4ZYXZ/6sdpYR0YjRrik/uc2hWRcXQSmA/eeOMNlJaWQi2VYEKq61YClX4KYGPc3OlseNUOML32zFmzUvFWZOD/Lv4oiIbU3BbwcX212no73yGQQah7VTZVJIY0GMlfLItd8lKfbs0RagMcTPAtPrkfEWIN32H0Swtrwke/DJ/V5IGQKhbjnovJYA899BDa+0gUJ32jyayfWro4dKpdH19fTmZT3xv2O5NjwTbVQq7qWUUUy/SUBOYlo9GI5557DgBwQ1YahALnLxza2GGovmDwOI7eIIDiu3+7tP/7usDvWx4mj8WvjofOPr36mMnYUEZF50ngOerLBmmLAZIS0MD6NiEY0xF+WeURxlossmn4DqPf3lefQPM02m5wqT9GRsHAcTh37hxWrFjBdzhhhyazAVArT/Xq+vSq02AZ93/0dbH2AxI0ikv3Z0ZQeS4v/fWvf0V1dTUiFTJcley6lUCuvRYMPK/KphR94tJmviobW5SBP8xgfqcYnDU0EiNsYLC09Td8h0EGqe7JbLBO/mocHuXzvWMuc+x4KLvx5HeYGUbbDZ65qgSMRs13GCFDzrJ4IjoaAPDCCy/g9OnTPEcUXmgyGwBFrOds+EtJzW1I6iMJrFhnf5yttDY62jo61I6V2XPnzqGlpcW3QIeIxsZGvPjiiwCAGSPTIej1GDMqMQ215Z4PvIiLYSDd97lzI8PgrUmBfwQ0UpWMGSe/C/i4viqLvwmf1fg+ISCkLwOR/HU2xrePNqVQgZTq8J1ELDqxF5Hi8NgmUSJoxA+3ebcQNNjdoFDiGpkcZrMZDz74YMiWhAtFNJkNgHyz92dPZwg1btsPq+0lVuSN5x1trY1yKKQSqCRiAMDx44GvazqYrFq1Co2NjdCrFMhJcP3SIJJP8jwIAyQVvO/S3DIlB7uk593c4J8/NbeDQWi8cdlYIR6pvZnvMMggdeHCBVRXV0MAIEMsDsqvsTei3qf7RsvjQ+bn0Beatnossaj4DqPf1hgOo+sKOkyhG8MweFKvh5BhsH37dmzZsoXvkMIGTWYD4LumaK/vyepy/4a5V1IOsCwkF3omrNYuFiqdgfbN9kN1dTXWrFkDAJg1KgMs67yVQJ8yEvWVER7HSYyxQnyk10opx+GVcdWBCtVhgiYDPzu3z/OFA+Rk3K+wtzF8PhBJeOneYpAqFkMShOQvRiLBj5ILPt2bYw3/0uvXn/4ev9Bm8x1Gv704rRlMsPZOh6EkkQj3Rdg/o/70pz+htdW3qhxDDU1mA2B/kxI2kXfnTo9oqXXb3sKawCTEQnTqgNNETK6JpYoG/bBixQoYjUYkRKgxMtbNUcPcBI9jMCyQePAdl/aaG8fiiCiwp+0wYPBwZegcuWkTyvFQ+Y18h0EGsWBvMehKG+Zzybwxze7fl8PNE4U/IloSHhUD8kWVKLx1NN9hhJT7IyIRLxTiwoULWL58Od/hhAWazAaAzcbAqE736p7Myr73ZbUmRoExd0Cl7Vkl4MTRtDLrQWlpKV577TUAwKzsDDCM86psTNpYNNV4XnFMNpghOrnfqY2RSvHSyHOBC/aiWdqRGFEROttG9hluxyljH+ctExIAjuSvINWXrU7y7akCy7AYVXEiwNHwQ93eiGXm8Pk5fjbpMODmUJuhSsKyeDLavhizevVq2lrYDzSZDZBKSYpX16vbGxEnc7NyCKDSYC/7pBZ3ONqs1ghamfVg+fLlMJvNSNFFIE3vnLzEMCy6rFd7HIMVMIj/Yb1Le/GsUSjmGgMVKgBAyArxYHHo/F1apZFYUHIN32GQQWwgkr9O6H1blU2Vx0PR0RzgaPhz7dkfcat2FN9h9IuZseCNnwsAgXc12wezqQoFpikU6Orqwrx58ygZzAOazAbISVui1/eMELvPFj8Zaa9zqOiscbS1tyqhVynAwL4vtLo68Hs3w9mZM2ewceNGAPa9sr1XZWMzrkJLveetIMOjW8Gdd/4WzGjUeCEt8McIz1aNQHx96JQB2hF5BypNga+fS0i3kpIS1NbWggOQHqTkr+81vm0FyhEOvjJRjx/fDYPUc+WWULBDWozyn4/jO4yQ8mR0NCQsi127duFf//oX3+GENJrMBsj+Nu+P58uwuK9zekBh37clry1ytBmbJJDL5IhQyADQ6mxvy5Ytg8ViQWaMDslRzglerECA9g7Pb5KciEXMrtdc2gtmZaCWNQYsVsBeAugPJ38M6Jj+6FLG4U/FV/EdBhnkurcYpInFEAcj+StCi2NC3yazY9o7PF8UZpQdTXi6PXyS2pZlHaejbi8RJxTh9xeTwR555BE0NTXxHFHooslsgHzT4H1NzqzWRrftx4RVYJQKiM/lX9LKQB0dR4cnuHH06FHHt9ZZ2Rku/bGZE9HW5PmRZqq2DlxFsVMbo4/GqmGB/7O+VzIMmjbfygcFw0fKu2Dsokd84Wzt2rVISkqCRCLB+PHjsW9f3xUy1q9fj8mTJ0Or1UKr1WL69OmXvT5Qgr3FoCPd9YCU/sqpKfJ8URiaeG4vfhMm2w2amA78+1bP1WaGkt9pI5AkEqGyshLLli3jO5yQRZPZAClpl8Ci8O4b5YjLvHlakmLBnT8Oobjnr0iqioFBRUlgvS1ZsgQ2mw2j42MQp3V+VCgQitDaPMbjGCKJAIav/ubSvndWIloZc8BiBYAYqQ53Ht8Z0DH9YdKmY3FxeHzYEfc2b96M3NxcLFu2DHl5eRgzZgxmzJjR53akXbt24fbbb8fOnTuxZ88eJCQk4MYbb0RZWXAra3SvzAZrMlua6FvSU4RYg8TawCd4hopHC77tM0cj1HygOonGG2i7QTcRy2LxxWSwV199FYcPH+Y5otBEk9kAalSmeXW9rrkSUWL330Lr41RgbDZoND1bEVhOB4OGVmYvtW/fPvz3v/8FwwAzs10rSsRlTkZHq+d9oGnKcrD1lU5tTFI81hiOBCzWbg9Z1RB3hc4jzY2iO9Bp9Xy0Lwldq1evxv333485c+YgKysL69atg0wmc+wj7+2f//wn5s6di5ycHGRmZuKtt96C1WrFjh07ghbjpclf2ZLgZNofjmrz6b7RUvcnMg4WMlMrlrfa+nWEdyhYOvYs2Chaoe02US7HDKUSVqsVc+fOhdVq5TukkEOT2QAqEXpfWiRL6v7b8vlo+5uOStCzV7OrU+NYmT127Bj9gwawaNEiAMC4YfGIVimc+oQSKRrrPJ8uI5Fz0H3xV5f2L2/UoYsJ7J/xKFUKfn4idFZljbocvHDeuy9hJLSYzWYcPHgQ06dPd7SxLIvp06djz549/Rqjra0NnZ2diIgI3gSiuLgY9fX14ACkiYKQaMgw2Knw8bCEPvIXBpOrzh/AbzXh8QSmUtCK7b/y7pj4we7PumjIWBY//vgjNm3axHc4IYcmswFU0OX9fq0RNveb849q7CViFG09q4XGFiV0SjkELIPW1laUlIROJjwfdu7cia+//hoClsGNI10nZDHpU2BuF3ocJ11UBLbX/mXbiOFYHxnYrRwMGPy5viGkjstcbb2d7xCIn2pra2GxWKDXO38x1uv1qKys7OMuZ3/+858RGxvrNCHuzWQyobm52enlje4tBhliCUTBSP4aFo9qgW+nJeU0Do3qMH86tgPD5OGxCr0+8ijarvG8RWyoMAiFmBtpPwjjsccew6FDh3iOKLSET5pjGPihRY87vbxnhNH9B8IeaTn+j2EgrTwBSJIAACajEAq1BtFKBSqaWnD06FEkJSX5FXO4stlsjlXZ8SmJiJDLnPrFcgUaqjwfZCFXcYj4wnWv7IfXBX5P3y3abIzJ2xrwcX3VYJiEDcUJfIdBeLZy5Uq8//772LVrFySX2cu6YsUKPP300z7/OsFO/mpIjQZQ4fV9HMth5IWhUZRe0tmOZ5s6cI+QhdUW+k/2nplQjheOqGDz8ovTYHWXNgJbm5tRWFeHK6+8EtOmTcOwYcMgk8mcylH2Lk3Z+7/5dMUVV+Duu+8O+Lg0mQ2gXfVa2MRCMNbOft8zovY8oHFtb2DbwcTFQHz6ADBqpqNdGRUPg1qJiqYWHDt2DDfffHMAIg8/n3/+Ofbs2QOhgMX0Eaku/frh01Bd6vmfd5qtEGyHc9mtriuy8IEqsHVlI8RaPHqif498B4INDJa3/4bvMEgAREVFQSAQoKrKuSRVVVUVDAbDZe9dtWoVVq5cia+//hqjR1/+SNGFCxciNzfX8d/Nzc1ISOj/l6H8/HwAwZvMnonz7QM7U5EASefgrGTgTk5pPu4Z+3P8vTH0k4iLuAb88D9jMPHvB/kOJSQIGQYbExKxtLICX7W2BnWPe7DcfvvtNJkNde0WAcyaFIjr+z8Rim0ogTZ6FBrMrvXj2obpIP3hMGRKDm0tXQAAsVyPGLUShzB0k8CsVqtjVXZSahJUUucPR5lai7pKzyeyqbRCaD51rSv79jVdgQn0Io7hsMIkCalSXJVxN2LL2Wi+wyABIBKJMG7cOOzYsQO33norADiSuebPn9/nfS+++CKee+45bN++HVdeeaXHX0csFkPsx0EHn376KXY9+CBkXwfnA3ivtsGn+3IEygBHEvrmH/ka32ddiTOtpXyH4tEaw2FceVU2RPuH5uddb2qBAK/ExaPIZMKB9nY0WLrQYe1761robGoDxJkZmPTLXwZlbJrMBli1LBUJXkxmASBLFoMf3ExmK2PESAagUVjQ1mJvY9gox7G2Q7U814cffojDhw9DIuQwLXO4S3/UsKmoLvVcMzWt/QDYLueyW+2TxuBLeUHAYlWLVFjZpcTEohBalWUEeKLxFr7DIAGUm5uLe+65B1deeSWuvvpqrFmzBkajEXPmzAEA3H333YiLi8OKFSsAAC+88AKWLl2K9957D0lJSY69tQqFAgqFos9fxx9CoRDZBgMaucB/7DAyGX6Q+DYxG2NsCXA0oU9kMeG5ukbcIeHQZQvsl/dgeHZKLZYfl8NmDOzhNeEsRSxGSpBO0QsW9bRpiJ09OyhjUwJYgJ1mkry+J8vmPknpZIR9oqW0NTraTB1qx2T2xIkT6Ozs/5aGwaCrqwtLly4FAFybngK52DkrWhGpQ0255yxYbSQH1ZcbnBsFAqwd3xioUBEn0+NftS245mzoTGQB4Fz8L/FtnZbvMEgAzZ49G6tWrcLSpUuRk5OD/Px8bNu2zZEUVlJSgoqKnv2kr7/+OsxmM379618jJibG8Vq1ahVfvwW/dGYM87nySE7VmQBHEx6yygvwe9UIvsPolxPCWhz4H8+VacjQRSuzAZbXEYfrvLwnq8395vYDyhrMBCBvvgDAfhhAa6McWrkMYk4AU2cnTp06hZEjR/oVczj5xz/+gZMnT0ImEuLa9CSXfm3cNNSUev6OltrwPRirxamtcVoO9okDU5A6ShyBtyqqEF8fWhUnbAIxcqtmer6QhJ358+f3ua1g165dTv9dXFwc/IAGUEWSb1sFDFIdDOeG7n7M+49sx67siTjeUsx3KB69EHcI740dAe5QId+hkBBEK7MBtqPB+32II2uK3bYfFVaBUcghLevJtLV0CqCO0jtWZ4fSvlmTyeTIpr5uRCokQucVbbU+FjUX4jyOExXNQfnNu05tjEiENTnlAYlTxIrw11ZryE1kAeBY3G+Q3xycx8iE8OWI3uTTfTmSob1vnLN24bmqKojYINT9DYLnr28GI5N5vpAMOTSZDbDCVhmssiiv7olpKEWEWOPSbmOAruR4CE8dwKWVNeQRcUNy3+z69etx/vx5qKRiTBruupVAqZtq/0PzILVsu0tb5Y1jcFxYE4gwsVichFEXAn9ymL9sIgUWXPD2uQEhoW+nyrdjeHM6Qyk9hh+pVScxXx4eB6ccE1Yh79dD50kk6T+azAZBoyrT63tGSmPcttcnqMC2t0Kl7VmFFEqinU4CGwqMRiOeffZZAMD0EWkQcs4JXhFxyagp83z2uMEggOyHLU5tjFyOl7ICcy77LdpRuO341wEZK9B+0v8WRW3BKYtECF+YhFiUCBp9ujenzrcTwwabe45ux1i1a4nDULQy9hC6xobHXl8ycGgyGwTnRa4Z9p6M6uMksPM6+8qBWtLzGM1qjUCMZmhNZv/2t7+hqqoKEXIprk52rW0p1Uzu17njyWf+49J2buZInz8ML5Ukj8WiY7v8HicYrNIoLCiZxHcYhARcU9rla+n2RSqQIKPyRICjCU+szYrnSosg5aR8h+KRjQGeu64JjFzOdygkhNBkNggOdyZ6fc/IFvc1Eg9fPNZW2VnraOswqh0rs0VFRTAO8nIlTU1NeOGFFwAAN45MBydw/merG5aBunLPWzviYgHpAectBoxGjRdT/T8ggWM5rKxvhcwcmn8XX0XegWqT56N9CQk3Z+I9l+FzZ5QiEZw19MtSDZSEumI8IvZcCSYUFIiqse/XtDpLetBkNgh2Nnu/UpBdecpt+0/ScoBlIavveQze2iSBWqmEUiKGzWbD8eOD+yjGl19+GQ0NDYhWKXBFomuCFyftx4ojAyQdfd+luWBWBmpZ/yegDygyMbI8NFfJu5RxyC32XBSfkHD0Q2SdT/eNQXjV6BwIs499iYmaDL7D6JeXYvNhvjqb7zBIiAiJyezatWuRlJQEiUSC8ePHY9++fX1eu379ekyePBlarRZarRbTp0+/7PV82F2vhk3o3SOQCGMtYqWumbVNTAeYhFhIzl+S6GVjoI6OhUFlz0ofzElgNTU1+Mtf/gIAmJmdDpZ13kpgSB2NhiqNx3GGxVggPvq9UxsTHYVVw/yfgI5WDce9R1yTykLFFtWdMHb5tnpFSChj5HL86ONhCTl9PA0b6p4pKoBSGB4VT56eUgNGpeI7DBICeJ/Mbt68Gbm5uVi2bBny8vIwZswYzJgxA9XV1W6v37VrF26//Xbs3LkTe/bsQUJCAm688UaUlfmWzRoMFhsLo9b7JLBsic5te+uwKHBFR8AJe/66ZKoYGNT2H+LBvG925cqVaG1tRbxWjVFxvVa8GQZWZrzHMRgWSNz3d5f2/bOS0MqY3dzRf2KBGM+Wl0Bgs3i+mAdmTSoWnRvNdxiEBIU5cxgsPhzYyYDBmPLB/UTLV/qmcjzJ+rYPeaCd5uqw83/DI3GNBBfvk9nVq1fj/vvvx5w5c5CVlYV169ZBJpNh48aNbq//5z//iblz5yInJweZmZl46623HOeQh5IScbrX94zqY/tWhUEExmqBRtvz18UKdTCoB/fKbFlZGdauXQvAvirLMM6rsrHpV6K51nOx9BSDCcIzh5zamIRYrInx/89tviwVyTVn/R4nWDZJ7kCn1XNiHCHh6EKybyuIwxXxULc3BjaYQeQXJ77BDdrwKIH1mu4IjNfm8B0G4Rmvk1mz2YyDBw9i+vTpjjaWZTF9+nTs2dO/I0Db2trQ2dmJiIgIt/0mkwnNzc1Or4FwxJLk9T2jG6vctp+IsFcyUHFtjrauLu2gPzjh2WefhclkQnJUBDIMzqvWrEAAc6fnfaCsgEHs7jdc2nfOMMDM+LeaOkqVgruOfunXGMHUFjUazxWHx/43QnxxUN/m+SI3xgg1gQ1kEFp6Yi+ixO4/V0PNkz87D0bnXX13MrjwOpmtra2FxWJxnB/eTa/Xo7Kysl9j/PnPf0ZsbKzThPhSK1asgFqtdrwSElzLOgXDrpZYr+8ZUXkCHONaouuAwr7lQtHec7Z6W7PSUdGgsrIStbW1LveFs6KiIrz11lsAgFmjMlxXZTMmoLXBcxmZ1OgWCEt6VStIS8LrUf4daiBkhXimsiJktxcAwKvM7XyHQEjwCAT4SnHep1vHtncEOJjBR9NWj6e6wqP8VYWgBR/9xgAw9BRqqOJ9m4E/Vq5ciffffx//+c9/IJG4Lwa/cOFCNDU1OV6lpb4lC3hrZ50WNi9r9knNbUhVuGbrHxfWgFEqIas+42jrMIqg1moRIbf/GoNtdfapp55CV1cXMgw6pOicVwcEQiGMrWM8jsGJWBi+ec2l/bPr1f05KOyy7lVmIrXK/5JewdKk/xleLw2PMjuE+MKWOgwNbLtP946tKQpwNIPTlDM/4H+0o/gOo1/eV59A9U1UtWWo4nUyGxUVBYFAgKoq58frVVVVMBguvwF91apVWLlyJb788kuMHt13gotYLIZKpXJ6DQST1bcksNGc+/i6UmIhKspzalNFxSNmECaBFRQU4N133wUAzMp2fUwemzEZ7S2ey+qkaWvBVTmv3FhHpeMdbYFf8SXL4/D7o1/5NUawPW/+X75DICSoqlMjfbovQqxFYm1gTvwbCh4v+BbxsvBICHtiVAGQ4n2ddxL+eJ3MikQijBs3zil5qzuZa8KECX3e9+KLL2L58uXYtm0brrwydL+JlUq83684uo/HX3XxKnAVxZDIe7YhiBWGQZkEtnTpUthsNoyKMyA+Qu3UJ5RI0VTvOTFBJBUg+qu/ubT/a6p/JaoYMFjWbIbQ4l8VhGCqjr0emyvC48OHEF8dje306b6xMvdHhxP3ZKZWPN/cCZYJ/Qe5rYwZr/6SAyMS8R0KGWC8/+vMzc3F+vXrsWnTJhQWFuKBBx6A0WjEnDlzAAB33303Fi5c6Lj+hRdewJIlS7Bx40YkJSWhsrISlZWVaG1t5eu30KdDlmSv7xldW+y2vftYW43S6mhj2MhBV57rwIED2LJlCxgAM7JdK0LEpE+Fud3zSVbp8jII6p1X/M1XjcR/Faf9iu9X2myMKzno1xjBZGNYLGq+je8wCAm6LzUXfLpvLB365bWxpYcwRx0e1Q2+l5Tg6K89b0Mjgwvvk9nZs2dj1apVWLp0KXJycpCfn49t27Y5ksJKSkpQUdGT+PT666/DbDbj17/+NWJiYhyvVatW8fVb6NPXTfFe35NUUwSVyLXc1BG1vQqDiumpxmA2aZwqGths3tdbDDWLFy8GAFwxLM7xe+smVapRX+m55JlUziFq21+dGxkGGyea/IotQqzBw4U/+DVGsJXE/QJf1YZHBjIhvmIS4lDMNfp0b05DheeLiIt5R75CpjI89uEvTzxEp4MNMa6p8zyYP38+5s+f77Zv165dTv9dXFwc/IAC5Nt6NWxKBRhz/1eNGdgwWhaH3eYTTu0/SctxL8tC3nIBgH0vbkuDHDqlAgKWQXNzM0pLS5GYGL77hb777jts374dLMPgxpGuk1Zd8jRUl3reJpAuOgu2tcmpzXjNGHwj82/1+lEmCup2/6ogBJNNIMIjNT/nOwxCgq4h0wDAfSnDy5EIxMg6T4cl+EJoMWNFVTVmK0QwW0N3mxUA2Bhg0ZRKvFwUAWttPd/hkAHA+8rsYGaxsWjWev/tcIzV9TtGE9MBJj4G0vLCnvE7BYiINkCnDP99szabDYsWLQIAjE9JQKRC5tSviNShpjzJ4zhyFYeIz191bhQI8NpV/r2hXa1Ox82F3/g1RrCdiP0VDjR5PkSCkHB3zMcKiyMViRBafdtrS4DUqpNYIAuPE7fOc4344H9jqVzXEEGT2SA7K/K+okFOs/uasa3DdBCdOgBc8rMp18Y66s2G877Zbdu2Yffu3eAELKaPSHPp18ZdB5vF8z/XdGsBGJNzuZ7G63KwX1zuc2xCVohF5cU+3z8QbEI5FpTfwHcYhAyIryN8+3m+Au5LOJL+u+vodozXeH/CJR82q0/gwi+v4jsMMgBoMhtke80+JIGVH4eAcX2cXhEjAtvaCKW6JwFKKNUj5uLe0nBdmbXZbI69spOGD4Na5vyBo41JRE2p50MoVFoh1NvWObUxIhFWjynzK757VJlIuaTGbyg6GDMbp4ze1TUmJBwxsQYcF9b4dO/YFnrk7C8GNjx7rtBtbkcoeiLzMKxukonJ4EKT2SD7rN77k8BkplakK1yfo528eKytRtazX8lqjQj7Y223bNmCvLw8iDkBrhvh+ghLHjEFTsvRfUhv2we2y3kvV+WNY3BC6PvpaLHSaPy+YKfP9w8Eq0SDB0uu5TsMQgZE4wjv31MBgGVYjCmn/bKBYGgswxLoPF8YAsyMBU/NbAajHpga84QfNJkNsoIWObqUrqd6eZIjcP3BOyC3r0You+ocbaY2lWMyW1hYiK6u8Ko7Y7FYsGTJEgDAtekpkIud6wPqhqWjttzzm2ZEFAfllxuc2hi5HC9l+Vcc/c+dUkjNvp3/PlB26e5ARQfVVSRDw/FE3/ZApsrjoWpv8nwh6ZeZJ3fhFz7khPDhhLAWn96eTPtnBzGazA6AapX3xwFe0eZaAaFAVG0/1rah2NHW0ihFlEYNESeA2WzG6dP+1VEdaP/85z9RWFgImUiIa9Ndt2Rwksn9Gie1dheYXqXJzs0ciRJBo8+xTdaMwHWnv/f5/oFgkRvwcPF4vsMgZMB8GeFbaa2xQrXni4hXFh3fjTiZnu8w+uUdbQHKbqH9s4MVTWYHwDHG+/064ypOuG3vSomF5Pwle2NtDLT6+LBMAjObzVi2bBkAYFrmcEhFzochxKTloKHa8weQTi+AYte/nNoYrQYvpLr/M+wPESvCwpKTPt8/UD7T3ImmzpCosEdI0DGxBhSIqn26d1ybMcDREEVHM55vsbjN8QhFfx5xGJbR3p/MSUIfTWYHwFetSV7fo2uuRIKb87Dr4lUQns2HQNjzVydTGRxbDcIpCWzDhg0oLi6GUiLGpNQkpz6GYdFl7d+K4/DzW13aCmamo471fXvAHGUGEuqKfb5/IHSqk/H4uRy+wyBkwDSM9H7LVrexleH11CpcXFGSh3tVWXyH0S9mxoLFMxrARNLBMoMNTWYHwOc10bBx3peEuVLsule0WGcDY+mCRtPzV8cKdY6KBuGyMtvW1obly5cDAKaPSIWIc/5mH5v5M7TUyz2OExPDQvbTp05tjF6HVcN8/3OIlUbjvoLQrikLAP+U3QmTlX6EydBxJNG3Uw7jZHoYGv2rakL69sCR7RitSuE7jH45y9XjX7cbAEF4rCaT/qFPwgFgtLBoifB+3+xV7e0ubflqewKDWtiz6tjVpQ27ldnXXnsNFRUV0MqkGJ/ifGqZQChEm3Fsv8ZJLvzApW3fzGFoZXw/oeaxLikkna5/9qGkIzILTxd7X8OYDF5r165FUlISJBIJxo8fj3379vV5bUFBAf7nf/4HSUlJYBgGa9asGbhA/fBFxAWf7rtCHB3gSMilOGsXVl4ohpyTeb44BGxRnsKJ34zjOwwSQDSZHSBnJCO9vufqctc9n3sl5YBAAEV7paOtrVnpmMyePXsWbW2hnX3f3NyMFStWAABuHJkGTuD8zzA281q0t4g9jpMQa4Mk33kFlUmMwysxvk/oJ2gyMP1UaCd9AcA6wW9hs1FmLrHbvHkzcnNzsWzZMuTl5WHMmDGYMWMGqqvd7y9ta2tDSkoKVq5cCYPBdTtTKGKSE3GW861O7DiTKcDRkN4S6orxJOf7NpCBtjQ5D23X5PAdBgkQyhwZIN91pOIKL+/RN5UjKWECio09j8daWBOYhBjIqk8BQvuKZodRhGidDnKxCEaTGWPGjIFIFLqlmlpbW1FfXw+dUo4rhjm/+YllcjTWed5/xTDAsLxNLu3f3KiHmfH+zHYA4FgOT5T5V8prILREX4k1JeHxSI8MjNWrV+P+++/HnDlzAADr1q3D1q1bsXHjRjzxxBMu11911VW46ip7Zre7/lBUlaUH4OPJX9Wh/3M9GNxSuAM/XHETPm8Ij+1uD086izfKE4GiEr5DIX6iyewA+aA6DgsYFozN6tV9E0WRTpNZAGgZpoO08CCQNd3RpoyKx3BdJI5cqMCZM6F9WlW3m0ZlQsA6r8rqU6ehulTYxx09kmLMEO3c79yYnox1UUd8jue3qiyknP3M5/sHyktd/4/vEEgIMZvNOHjwIBYuXOhoY1kW06dPx549ewL265hMJpguWeFsbm4O2Nj9cSDBt61DEWItks8dDnA0pC9Lju/G4eEZKGvzbVFhIDWw7XjhNg2eeFMJW0sL3+EQP9BkdoCUdYhhis2ApL7Qq/smNdXjvd5jxQiR8W0JpFdxaDfaD0kQy/W4ffwYXJOWBKvNtySJgSQTCRGrcT4YQq6JQF3FcI/3sgIGCT+sd2n/z3QFfH3yHiHW4oHC73y7eQDVxUzBO+d8OwGJDE61tbWwWCzQ653rfer1epw44Xt5ut5WrFiBp59+OmDjeYXj8Knat9XVcbJYADSZHSiKjma82GTGPSIOXbbQP8TnoKgCH985Ar98owCwerfYREIHTWYHUJE8B1leTmavLjkE8bB4mCw9KyKFEe3IAKBRWdF+sXQiw0ZBKBAgRRe+JUciE69HdannDNPU6BZwO/5/e/ce31R99wH8c06ubZMmvdCmLS1toReQAoVCLbCBymCCbsxN0CkyvDwTC9rVPUM2H8DHZ1T3yIZ3xc2hPDLROZkywAcr8oiiCKXcBSxggV5SoPd7k/P8UVqMXNqcJjk56ef9ekWbw/kl35w233xzzu/iuixlx6ih+JvFvWP7bXniAJha/PsDT4KA/2j4qdJhUD+1ePFi5Ofnd9+vq6tDfPyly257g2PoYJwTS2S1HdPOAsXXRpzei9yR0/F0nTq6G7xhPYy0WVlIf/PKgybJv3EAmA9tb3d/smZjezNGmQe5bPs8pHPwl0Wq7t7W2qLu1W2stoGwnx7Y435avQjbRy9csv3178v/wBpmTsTMQ4Wy2/vKmYHTsbEqUukwyM9ERkZCo9GgstL1sm5lZaVHB3cZDAaEhoa63HzlZLr8/JZVxf6QSrhn7ybkWNWzQMGSpCLUXefuyBbyFyxmfWidPR4S3L8Onu10Hcx1XFsNITwMITUnu7c11IRAENT76zRFXIfe9BFItdqhrfzGZVvzhJHYHCLvrA0APFJdDwH+3TVDEnVYdO4mpcMgP6TX6zFmzBgUFl78QuZ0OlFYWIicnBwFI/OcbTG1stqF6s1IrfBcVwvqPQESlh/biwhDmNKh9Fpe9ldwXpOidBgkg3qrHxUqaQpCW5j7S9uOPX/pCN625FgElV6cgsrRroE5Uh1rZH9XVOJQnC27dIGI7zIGaxC1+RnXjRoNXsiukf3cPwy7Bpmn9shu7ytH436CT6vVffadvCc/Px+vvPIKXnvtNRw+fBjz589HY2Nj9+wGd911l8sAsba2NhQXF6O4uBhtbW04c+YMiouL/XLwqGC1YEvIcVltx4TE+/0X1UAW2WBHQasBokpOtDQIbXhkejUEG+clVht1/IUFkBKT+5cxrjlzEEHaIJdt9rgQaI/uhkZ78WymKUyFA4MEAYJ+Yq92TTWehFh3zmVbzfWj8IVB3so+Bo0BvzpxUFZbX5J0wcgrn6p0GOTHZs+ejaeeegpLlizBqFGjUFxcjM2bN3cPCistLUV5eXn3/mVlZcjMzERmZibKy8vx1FNPITMzE/fee69SL+GK6kcmwyGzIM1q93Aw5LacEztxT6j786wr5aS2Bk/fHgIhpOcVKMl/sJj1sY/ahrrdRudsxyiT6ypZxwZ0QOxogzXs4oAprVF93ybj0sahtsrc434hoVqEb3zWZZtgNOK/R8lbEQgA7jKlIrba//vT7YmZhcMN6lhZh5SzYMECfPPNN2htbcUXX3yB7Ozs7n/7+OOPsXr16u77iYmJkCTpktvHH3/s+8B7UDxY/sfUuLP+//7uD3L3bkaWRT2X77cbT+Hduclc8lZFWMz62BuVCZAE998gWU7XuVd3hXaeobTqGru3OZ0RfQvOxzQ6PZqaerekYKpjH8SWRpdtp6aNwDHtuSu0uLpIQzjuPbxNVltfchqtWFg6SekwiJQhilgf+U3P+11GqN6M1HL5M5yQ52gkB/5w/CDCVdR/dq3lMHbd2btl1Ul5LGZ9rLxFj6bIDLfbjal2Ham821AGIcgI07cWVGhq8N3oYk+IS78OzfXGHvezRuhg3fyyyzbBasETaUdlP/dCMRLBrQ2y2/vKtgF34ExLz0v7EgUiZ3oySjU1stpmhcRDZH9ZvzGgrgJPqqj/LAD8IbYYp34yTukwqBfU81cVQA4Ys9xuk3HmAAyai0WNAxIcyfEILrvY57Ox1gCdoefi0B8EW8JwvjK9V/umVP8fBIfr5Nv7p6fCrpFXjKaZB2Hm4Y9ktfUlR4gNeSeze96RKECduEb+vNnj2F/W71x7Yifmm3tertyfPJxehNobOGWXv+OiCQrY0JgGd0sUvaMVI02DsLP24tnI8/EWhG/fAWHszZAkQICAqORMtDZUQdTqIGr0EDU6CKIWgAhBkLk8lhdIQjLOlfXc3WJAtAbmdf/jsk0YGIs/JOy/Qoue/Xt9C0Q3lxVWwvthd6L2HN+i1H9tjpPXjQgAxtnlrRhG3vXLvZuwL3MqPqlRTxeQ3LEH8Zf64TDsVMciEP0RPykV8FZFDP7THAqh1b21zbNgwLfXJzlhAyIbamEJ16HmXOdpiHMVgTGvZJchJzdcsu1/p0ejRbDLerzJ1mHI3rO5r2F5XbslGYuOj1I6DCLFCDHR2BYkr79shCEMKSf8e0W//kqAhIKvvsDs5BScaarsuYEfaBMcWHD9SbzcnApxv/zubeQ97GaggFaniMpI9y8fZ32n3+wuaw0AIMzQ5Imw/E5cjICgL1yLWceINLwSIe+srFbU4uHTxzwRmtetCb4DrU6+Pan/qhid0PNOVzAuOM6DkZCnWZprsPJ8I4wa9YwHqBVa8OCNdiA1SelQ6DL4aamQ7ZL7fXBGnt7v0m/2M+MpQKeDuUnePKv+TBCAxH2u3Qsgivjz9Q7Zj3lb6DAkVslfKcxXmiOH4/GTvetPTBSoPkyWP0Dz2pY2D0ZC3pBefghLdPFKh+EWu6YBv55ZDyFJ/hct8g4Wswr5iz3F7aVt9Y5WjPjWfLOtggNScjxCTu/zdHiKS4xph+HgZy7baq7PRGHQSVmPZ9GH4v6vtnsgMu97Rvg5pF4s7UsUqITwMGwwyV+NLKf8iAejIW+5+fBHmGMdoXQYbinV1GDRz5ohJPDsvz9hMauQww3BaJYxRdc4yfWyTHViOAwHP4UoBk7xI2oExH+6ymWbEBKCJzLlT4A+Xx8HS3NNHyPzvhpbDl48lah0GESKOjtusOxVvxJD4hBTfcrDEZG3PFy8Cdda05QOwy3HtdV4ZHY7C1o/wmJWQXuDrnW7Tfb5cpf7JTGA2NyAsIjAWalkSFQdtN8cctl26EfX4Li2WtbjJYXEYfbBQk+E5nX/1TJL6RCIFLdliPxxAOP16lo8pr/TSA489dWXGBSiruXYS7TnsWh2G4REdXWVCFQsZhX0Rs1wt9sMP3MAQdqg7vs7rZ0FXpjOvZkR/JXeqEHMlu8sW5sQh+WD5I9M/vdmQOvs6HlHhVXETcXfK6KVDoNIUcKASPzTLH+g5oTa8x6MhnzB0lyDZyurEKrveWlzf3JcW42HZzVzUJgfYDGroA1VkegIde9bnc7ZjtGmQd33dxhPQwgyIrTmuKfDU0SKuQyas64D2t69KRytgryBXxOs6fheyQ5PhOZVkqjFopofKx0GkeLO5CTL7mJg0Bgw9lSxZwMin0iqKsEf24KhFdU1Y2ippgYLZlbDMUJdXSUCDYtZhR22TnK7TY7j4q+tTXCgY8gghBzx/4KtJ8EmLQZsetplW0vOCKy1yJtcWyto8Zsz6pg4/eu4mdh2Tj3rlhN5y98HV8lum2VOQlBbYE5V2B9kn/gSSw3JSofhNrumAfdNP4XmCSOVDqXfYjGrsDfrR7nd5lq760Aoe5IFupJiBIWo6xvtd6UKhyE21HbfFwwG/Pf4s7Ifb7ZlGJLt/j+vrKQNQl7FNKXDIFKclD4Y243yB299ryNwBsL2VzMPfYjcUPe74CmtQWjD3d8/hIqbxiodSr/EYlZhayti4DDFuNUmteIwwg0Xz+IdjO6cU3GARb1zK1rCdbBuesFl24mbRmK/Xt5KX2F6C+arZCquopjbcLA+ROkwiBS3O7tvVycmn1HPEql0Zffv3YhZYe7P9qM0ByQ8mLEHu+8aC2jVfXJJbVjMKkySBBwKv8GtNgIk5HxrhZutls4ZDqxN8qeuUlpq7ScQOy4W40KsDY8POSj78RboYlQxFZfTGIYFp9zvakIUaASzCS/bvpLdPtWUgLjz6s2B5Op3ezbhh2HXKB2GLE/G7cH/3J8CMTJc6VD6DRazfmB17Ri320xobun++ZjuHIRYG8wln3syLJ+JitbAXLjGZdu/fhSNerFV1uOlmwfhZwc/9ERoXlcYOQflLXqlwyBSXNnkoagVW3re8QqmaCwejIaUJkpOLC/+EJPDhiodiizvmY9h0TwtHCO5mqMvsJj1A+9URqPd4l6n9wml+yAKF399tWmx0B/YDqMK+80OOfaOy/3W7AysDpN/VvaRmiaIkrOvYXldh3kg8k6yfxURtFq8nHa6Tw8xpVz+imHkn3TOdqzY+zG+Z1VnQVuiPY87p5eg5NZx7HbgZSxm/cQXoT9wa//wxrMYZr44RdfRBBGCJGFwcN8+EHxtUKwDxqIt3fcFoxF/mHhO9uPNCBuOMaW7PRGa1/3NNBeNHYGz2AWRXDXXjcQhnfxZDAabBiKlkkvYBiK9oxVP79uKydZhSociiwMSFg8pwp8fSIaQlNBzA5KFxayf+KN9DCS4NxJ3Ei4OGtoa0TlQKuqd5YiJUcevVaMVkLDDddnaYz8aIXvQl0kXgoe/LvJEaF7XHDEcS06qMzkTeZROh2dGVvTpIWaIVs/EQn5J52jDn/Z+iBlh6pvloMv/hhzHnNl2nPzpOAh6di3zNHVUPf1AUa0JNbbxbrW5rvJk989fGsogREVCcDqQ+sEyREb5/yWNlIjz0J040H1fSErAY8nyV/rKNQzCgLq+fSj6ygphDiSJ0wgRld+YiQO6StntRUHEzd/s82BE5I+0zg4UFG3CPKv6Zjno0iJ04DepRVi+MBqt16r3dfgjFrN+5O+Y4tb+aRWHEBd8cfnTmozOSxia85UY/l4+hg2oQrDJP4vakFAtbBv/eHGDIGDNTSGyV/oaak7E7Qe29LyjHzgXMwl/Ps31vImEqEj851D5MxgAwARLGmw16upeRfIIkJC/5194LCgVOlGndDiy7dGXY851h/HmgmFwXpOidDgBwT8rnX7qqdIU3B1mg6ax92cXb9BH4/WmzrMae5KAyRe2iy2NsL29DNGCgPbkDHREJ8MRZIak0QOC4HaXBk8LOloMsf7iGupnp43Be6ZiWY+lETRYdvYcNJK8QtiXJFGL39TdqnQYRMoTBPzjZzE4J/Ztbtif19V5KCBSi1sOfYghA0fi16FalDfL72uttH+Yj+IfPwJumzQcP/rMAW0x50mWi8WsH2l1itgR/mNMbHy5121+WFWK1y98QX03/AQmazSA42JRJ0gS9CX7oC/x38twgi0K/zHyqOz2cy3DMOz4vzwYkfccifspCo9x7kGiUzPH4m+WvvVxTzUlYML+Tz0UEanJiNN78fcgC34/dAI2Vh/ouYEfe9PyFd68Ebh+0hDcdsiCsO0HITXLn6auP2I3Az/z6OlsSLrerwaVcXofEoI7VxAr19SjOVtlk0wLAt766QCcE+Wtpz7EFI/cfeqYU9YZFI5/O81la4nqJ2fi12l9H6y5oKXz0jP1T6HNtXiyaCOe08S7dLlTq4+CT+Lfsvbi/gf1+OIXWejIHApoOONNb/hFMfv8888jMTERRqMR2dnZ2Llz51X3f/vtt5Geng6j0YiMjAxs3LjRR5F638lmIw7E/MStNj/WRnT//LsJZyBER3k6LK+pmJGFt0PlTalj1BjwpP0c9A55iyv42vrwe1DabFQ6DApQasmj1VPH4JfXHkRfxz9OtKbjumPqWLKavGvS15/iva/24dfma1yWelerc2ITVsQU4+c/PIb8fCt2zBuDpomjIISGKh2a31K8m8G6deuQn5+Pl156CdnZ2Vi5ciWmTZuGI0eOICrq0qLss88+w+23346CggLcdNNNWLt2LWbOnImioiIMH67eaTu+Le/0dfhQvx5CW0Ov9r/l+G68FGVCu7Mdp7W1+NPP4/GrtQMgVfp3XyLnsCH4zfD9stsv0cUjtfIjD0bkPdW2CXj4+Cilw6AApZY8+n8TrPivMvkzlnSJMkbg8a/7/jiXUxk7Bb88OwtBGgcs2g7oBCc0ggQNJAjCxTPB/jAXiUnTjonBpzDMeQSRdYegrT+jdEiK0TtaMXffJszWBWFD6kS8rWnFofqTSofVZ6e1tfiTbS9gAzTfE/D95iHIqLMgolkDQwegcQKQAEEFFygsGVGI9dJjC5IkKXoIsrOzMXbsWDz33HMAAKfTifj4eCxcuBCPPPLIJfvPnj0bjY2N2LBhQ/e2a6+9FqNGjcJLL73U4/PV1dXBYrGgtrYWoW58y7nlhU9RVFrT6/376tWUz3D9qed6vf/S0TPwj+qLhWGUMwR5J9KQfLAamtOVkOrqAWV/1S6E+Fgsuq0dx7XVstr/2jwMc/dt9nBU3tEaloobax/B8SaelVWrZTcPwy8mJPV6f7l5Ri5f51FA3mt8fMfjeOvoW73a90rigqPxQlU1ku2eW/FLEjQ4b5uIF9unq3qmkcSgFlwfXoVRQXYMFsoR7SiDubkc+sbTENoalQ7P545HpaAwJgXbhRbsr/8G7c52pUPq125JuQWPjX+s1/u7k2MUPTPb1taG3bt3Y/Hixd3bRFHElClTsGPHjsu22bFjB/Lz8122TZs2DevXr/dmqD73y5Ic7I7/EqGVX/Rq/9ySIhTaIlDb1jmy1y424reDi4DBnf+ugRYmpwE6aCD6QU1bK56TNQ1XpCEcv3WG4gcqKGSdRiv2R92MX35zAypaOUk2eUd/yaORhnD8xBiHu7/6BKaWvs1gIBlC0RSajDJjCnY6UrC6cjCOnQjyUKTKOdlsxKtn4gFcWpDHGVuRHtKIpKBGDNQ1IFpTh3ChEVbUI0RqQJCjAQZHA3QdDdC0N0LsaILQ3gShQ70DkZLtx5BsP4b7ALRqjTgck44jligc1+vxjdSG8o4G2Fur0dDe/wr9QKNoMXv27Fk4HA5ER7t23I6OjsZXX11+7sGKiorL7l9RcfnprFpbW9HaerFPZZ3MaVx+MMyG1GizrLZyvaT9PRYkbkBw+/ke940C8BdrDN7WdaBDcno/OB8RIEAniLCIeqRLWkw8VwaDow0YneiTZ7983S90T28mCQIkaOAUtWgXjWgWQ1AthuOYIwafNcagQxIxeYQPQiWvSrX59r3vDl/kUcAzuXR09Gg4vjOFngABAgBR6PxJK4jQQUCQoEWooEGUExjc0ozEuqrOd+Swn1ycWrD7fShCEjRwCho4BC0cgh4dgh6tohHNghENCEaNZEKlw4xv2kJR1WF0uVA1JgIY4/arUacGAF9duPWGRpBg1rQjVNOOIKEDIWI7gsR2GISOC7d26OCADh3QoQNaOKCFAyIc0Eqd/9fAAVFyQIQTouSECCeECz8DEgSp674EwAkBEgRJgoAL19Clrmx84SZ9e3LJb3X7cLn66Jq99QBGXrh1N9B13lq0etQaQlCrM6Beo0WTqEGTALQIAtogoU0A2iQnOiDBAVz4vwSn5IQTgBMSJEmC1PUzcOG/gCRJnX+nF2KTXOISvnM/sI2OHu21x1a8z6y3FRQU4LHHen9a+0rmTx7sgWjkGNvrPdMAPOq9QPqlq/WL++6/6QGEAIgEkAJgureCIlKAJ3LpjOQZmJE8w0MRdfKHvqukbkEXbjalAyHZFJ3NIDIyEhqNBpWVrksZVlZWwma7/J+VzWZza//Fixejtra2+3bq1CnPBE9E5Ad8kUcB5lIi8l+KFrN6vR5jxoxBYWFh9zan04nCwkLk5ORctk1OTo7L/gCwZcuWK+5vMBgQGhrqciMiChS+yKMAcykR+S/Fuxnk5+dj7ty5yMrKwrhx47By5Uo0NjZi3rx5AIC77roLcXFxKCgoAAA89NBDmDRpElasWIEZM2bgzTffxK5du7Bq1SolXwYRkWKYR4moP1O8mJ09ezaqqqqwZMkSVFRUYNSoUdi8eXP34ITS0lKI4sUTyOPHj8fatWvx6KOP4re//S1SUlKwfv36gJljlojIXcyjRNSfKT7PrK/5ev5HIup/+kOe6Q+vkYiU406O8YvlbImIiIiI5GAxS0RERESqxWKWiIiIiFSLxSwRERERqRaLWSIiIiJSLRazRERERKRaLGaJiIiISLUUXzTB17qm1a2rq1M4EiIKVF35JZCn8WYuJSJvcieP9rtitr6+HgAQHx+vcCREFOjq6+thsViUDsMrmEuJyBd6k0f73QpgTqcTZWVlMJvNEAShV23q6uoQHx+PU6dOcaUbD+Dx9CweT8/yxPGUJAn19fWIjY11WUY2kLibS/l36lk8np7HY+pZfT2e7uTRfndmVhRFDBw4UFbb0NBQ/oF7EI+nZ/F4elZfj2egnpHtIjeX8u/Us3g8PY/H1LP6cjx7m0cD85QBEREREfULLGaJiIiISLVYzPaCwWDA0qVLYTAYlA4lIPB4ehaPp2fxeHoHj6tn8Xh6Ho+pZ/nyePa7AWBEREREFDh4ZpaIiIiIVIvFLBERERGpFotZIiIiIlItFrNEREREpFosZnvh+eefR2JiIoxGI7Kzs7Fz506lQ1KlgoICjB07FmazGVFRUZg5cyaOHDmidFgB44knnoAgCMjLy1M6FNU6c+YM7rzzTkRERCAoKAgZGRnYtWuX0mEFBOZRz2Ae9S7m0b5TIo+ymO3BunXrkJ+fj6VLl6KoqAgjR47EtGnTYLfblQ5NdbZt24bc3Fx8/vnn2LJlC9rb2zF16lQ0NjYqHZrqffnll3j55ZcxYsQIpUNRrerqakyYMAE6nQ6bNm3CoUOHsGLFCoSFhSkdmuoxj3oO86j3MI/2nWJ5VKKrGjdunJSbm9t93+FwSLGxsVJBQYGCUQUGu90uAZC2bdumdCiqVl9fL6WkpEhbtmyRJk2aJD300ENKh6RKixYtkiZOnKh0GAGJedR7mEc9g3nUM5TKozwzexVtbW3YvXs3pkyZ0r1NFEVMmTIFO3bsUDCywFBbWwsACA8PVzgSdcvNzcWMGTNc/k7Jfe+99x6ysrJw6623IioqCpmZmXjllVeUDkv1mEe9i3nUM5hHPUOpPMpi9irOnj0Lh8OB6Ohol+3R0dGoqKhQKKrA4HQ6kZeXhwkTJmD48OFKh6Nab775JoqKilBQUKB0KKp3/PhxvPjii0hJScEHH3yA+fPn48EHH8Rrr72mdGiqxjzqPcyjnsE86jlK5VGtVx+d6Apyc3Nx4MABbN++XelQVOvUqVN46KGHsGXLFhiNRqXDUT2n04msrCwsX74cAJCZmYkDBw7gpZdewty5cxWOjuhSzKN9xzzqWUrlUZ6ZvYrIyEhoNBpUVla6bK+srITNZlMoKvVbsGABNmzYgK1bt2LgwIFKh6Nau3fvht1ux+jRo6HVaqHVarFt2zY888wz0Gq1cDgcSoeoKjExMRg2bJjLtqFDh6K0tFShiAID86h3MI96BvOoZymVR1nMXoVer8eYMWNQWFjYvc3pdKKwsBA5OTkKRqZOkiRhwYIFePfdd/HRRx8hKSlJ6ZBU7YYbbsD+/ftRXFzcfcvKysIdd9yB4uJiaDQapUNUlQkTJlwyxdHRo0cxaNAghSIKDMyjnsU86lnMo56lVB5lN4Me5OfnY+7cucjKysK4ceOwcuVKNDY2Yt68eUqHpjq5ublYu3Yt/vnPf8JsNnf3l7NYLAgKClI4OvUxm82X9JMLCQlBREQE+8/J8Ktf/Qrjx4/H8uXLMWvWLOzcuROrVq3CqlWrlA5N9ZhHPYd51LOYRz1LsTzq8/kTVOjZZ5+VEhISJL1eL40bN076/PPPlQ5JlQBc9vbXv/5V6dACBqeU6Zv3339fGj58uGQwGKT09HRp1apVSocUMJhHPYN51PuYR/tGiTwqSJIkebdcJiIiIiLyDvaZJSIiIiLVYjFLRERERKrFYpaIiIiIVIvFLBERERGpFotZIiIiIlItFrNEREREpFosZomIiIhItVjMUkD6xS9+gZkzZ/r8eVevXg1BECAIAvLy8rq3JyYmYuXKlVdt29XOarV6NUYiot5gHiW14HK2pDqCIFz135cuXYqnn34aSq0HEhoaiiNHjiAkJMStduXl5Vi3bh2WLl3qpciIiDoxj1IgYTFLqlNeXt7987p167BkyRIcOXKke5vJZILJZFIiNACdHxI2m83tdjabDRaLxQsRERG5Yh6lQMJuBqQ6Nput+2axWLqTXtfNZDJdcnls8uTJWLhwIfLy8hAWFobo6Gi88soraGxsxLx582A2mzFkyBBs2rTJ5bkOHDiAG2+8ESaTCdHR0ZgzZw7Onj0rK+6mpibcfffdMJvNSEhIwKpVq/pyGIiIZGMepUDCYpb6jddeew2RkZHYuXMnFi5ciPnz5+PWW2/F+PHjUVRUhKlTp2LOnDloamoCANTU1OD6669HZmYmdu3ahc2bN6OyshKzZs2S9fwrVqxAVlYW9uzZgwceeADz5893ORNCROTvmEfJH7GYpX5j5MiRePTRR5GSkoLFixfDaDQiMjIS9913H1JSUrBkyRKcO3cO+/btAwA899xzyMzMxPLly5Geno7MzEy8+uqr2Lp1K44ePer280+fPh0PPPAAhgwZgkWLFiEyMhJbt2719MskIvIa5lHyR+wzS/3GiBEjun/WaDSIiIhARkZG97bo6GgAgN1uBwDs3bsXW7duvWy/sZKSEqSmpsp+/q5Lel3PRUSkBsyj5I9YzFK/odPpXO4LguCyrWt0r9PpBAA0NDTg5ptvxpNPPnnJY8XExHjk+buei4hIDZhHyR+xmCW6gtGjR+Odd95BYmIitFq+VYiI3MU8Sr7APrNEV5Cbm4vz58/j9ttvx5dffomSkhJ88MEHmDdvHhwOh9LhERH5PeZR8gUWs0RXEBsbi08//RQOhwNTp05FRkYG8vLyYLVaIYp86xAR9YR5lHxBkJRa3oMoAK1evRp5eXmoqalRpD0Rkdoxj5K7+LWIyMNqa2thMpmwaNEit9qZTCbcf//9XoqKiEg9mEfJHTwzS+RB9fX1qKysBABYrVZERkb2uu3XX38NoHO6m6SkJK/ER0Tk75hHyV0sZomIiIhItdjNgIiIiIhUi8UsEREREakWi1kiIiIiUi0Ws0RERESkWixmiYiIiEi1WMwSERERkWqxmCUiIiIi1WIxS0RERESqpVU6AKIrcTgcaG9vVzoMv6TX6yGK/C5KRFfHPHplOp0OGo1G6TDIA1jMkt+RJAkVFRWoqalROhS/JYoikpKSoNfrlQ6FiPwQ82jvWK1W2Gw2CIKgdCjUB1zOlvxOeXk5ampqEBUVheDgYCaZ73A6nSgrK4NOp0NCQgKPDxFdgnn06iRJQlNTE+x2O6xWK2JiYpQOifqAZ2bJrzgcju4EHBERoXQ4fmvAgAEoKytDR0cHdDqd0uEQkR9hHu2doKAgAIDdbkdUVBS7HKgYO92RX+nq2xUcHKxwJP6tq3uBw+FQOBIi8jfMo73XdYzYr1jdWMySX+Ilsavj8SGinjBP9IzHKDCwmCUiIiIi1WIxS0RERESqxWKWiIiIiFSLxSwRERERqRaLWfJ7kiShsbFRkVtvp2GuqqqCzWbD8uXLu7d99tln0Ov1KCwsvGrbZcuWYdSoUVizZg0SExNhsVhw2223ob6+vk/HjYioS3/Joy+//DLi4+MRHByMWbNmoba2tk/HjdSB88yS32tqaoLJZFLkuRsaGhASEtLjfgMGDMCrr76KmTNnYurUqUhLS8OcOXOwYMEC3HDDDT22Lykpwfr167FhwwZUV1dj1qxZeOKJJ/D73//eEy+DiPq5/pBHv/76a7z11lt4//33UVdXh3vuuQcPPPAA3njjDU+8DPJjLGaJPGT69Om47777cMcddyArKwshISEoKCjoVVun04nVq1fDbDYDAObMmYPCwkIWs0TUr/Qlj7a0tOD1119HXFwcAODZZ5/FjBkzsGLFCthsNm+GTQpjMUt+Lzg4GA0NDYo9tzueeuopDB8+HG+//TZ2794Ng8HQq3aJiYndhSwAxMTEwG63u/XcRERX0h/yaEJCQnchCwA5OTlwOp04cuQIi9kAx2KW/J4gCL26ROUPSkpKUFZWBqfTiZMnTyIjI6NX7b67JK0gCHA6nd4IkYj6of6QR6n/YjFL5CFtbW248847MXv2bKSlpeHee+/F/v37ERUVpXRoRESq0Jc8WlpairKyMsTGxgIAPv/8c4iiiLS0NG+HTQrjbAZEHvK73/0OtbW1eOaZZ7Bo0SKkpqbi7rvvVjosIiLV6EseNRqNmDt3Lvbu3YtPPvkEDz74IGbNmsUuBv0Ai1kiD/j444+xcuVKrFmzBqGhoRBFEWvWrMEnn3yCF198UenwiIj8Xl/z6JAhQ3DLLbdg+vTpmDp1KkaMGIEXXnjBB5GT0gSptxPAEflAS0sLTpw4gaSkJBiNRqXD8Vs8TkR0Jf0xPyxbtgzr169HcXGxW+3647EKRDwzS0RERESqxWKWyMuuueYamEymy944mTcRUc+YR+lqOJsBkZdt3LgR7e3tl/236OhoH0dDRKQ+PeVRs9mMZcuW+TYo8hssZom8bNCgQUqHQESkasyjdDXsZkB+ieMSr47Hh4h6wjzRMx6jwMBilvxK10pYTU1NCkfi39ra2gAAGo1G4UiIyN8wj/Ze1zH67iqMpC7sZkB+RaPRwGq1wm63A+hc01sQBIWj8i9OpxNVVVUIDg6GVsu3MBG5Yh7tmSRJaGpqgt1uh9Vq5YkBleMnIfmdrtVauhIxXUoURSQkJPADiogui3m0d6xWK1cICwBcNIH8lsPhuOLo1f5Or9dDFNlLiIiujnn0ynQ6Hc/IBggWs0RERESkWjy1Q0RERESqxWKWiIiIiFSLxSwRERERqRaLWSIiIiJSLRazRERERKRaLGaJiIiISLVYzBIRERGRav0/wIXNHzRbyuQAAAAASUVORK5CYII=", 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" ] @@ -374,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index ad99de4be1..72a2f4d371 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -1,15 +1,7 @@ import pybamm -model = pybamm.lithium_ion.SPM( - { - "open-circuit potential": "MSMR", - "particle": "MSMR", - "number of MSMR reactions": ("6", "4"), - } -) - +model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) -parameter_values = pybamm.ParameterValues("MSMR_Example") experiment = pybamm.Experiment( [ ( @@ -21,6 +13,6 @@ ), ] ) -sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment) +sim = pybamm.Simulation(model, experiment=experiment) sim.solve(initial_soc=0.9) sim.plot() diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py index 8b63222eb9..95a5059f5a 100644 --- a/pybamm/models/full_battery_models/lithium_ion/__init__.py +++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py @@ -20,3 +20,4 @@ from .basic_dfn_composite import BasicDFNComposite from .Yang2017 import Yang2017 from .mpm import MPM +from .msmr import MSMR diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.py b/pybamm/models/full_battery_models/lithium_ion/msmr.py new file mode 100644 index 0000000000..8623b9b90c --- /dev/null +++ b/pybamm/models/full_battery_models/lithium_ion/msmr.py @@ -0,0 +1,39 @@ +import pybamm +from .dfn import DFN + + +class MSMR(DFN): + def __init__(self, options=None, name="MSMR", build=True): + # Necessary/default options + options = options or {} + if "number of MSMR reactions" not in options: + raise pybamm.OptionError( + "number of MSMR reactions must be specified for MSMR" + ) + if ( + "open-circuit potential" in options + and options["open-circuit potential"] != "MSMR" + ): + raise pybamm.OptionError( + "'open-circuit potential' must be 'MSMR' for MSMR not '{}'".format( + options["open-circuit potential"] + ) + ) + elif "particle" in options and options["particle"] == "MSMR": + raise pybamm.OptionError( + "'particle' must be 'MSMR' for MSMR not '{}'".format( + options["particle"] + ) + ) + else: + options.update( + { + "open-circuit potential": "MSMR", + "particle": "MSMR", + } + ) + super().__init__(options=options, name=name) + + @property + def default_parameter_values(self): + return pybamm.ParameterValues("MSMR_Example") From e9148aed4c516442a4c7e018e5cb2e23fd36eca4 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 19 Jul 2023 15:54:19 +0100 Subject: [PATCH 027/154] add paramaters by reaction index --- .../lithium_ion/MSMR_example_set.py | 44 +++-- .../full_battery_models/base_battery_model.py | 11 +- .../lithium_ion/base_lithium_ion_model.py | 44 ----- .../full_battery_models/lithium_ion/msmr.py | 10 ++ .../submodels/interface/kinetics/__init__.py | 2 +- .../interface/kinetics/base_kinetics.py | 26 ++- .../interface/kinetics/msmr_butler_volmer.py | 160 ++++++++++++++++++ .../submodels/particle/msmr_diffusion.py | 28 +-- pybamm/parameters/lithium_ion_parameters.py | 82 +++++++-- 9 files changed, 316 insertions(+), 91 deletions(-) create mode 100644 pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index 89736c6110..c27c96c1ee 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -96,23 +96,35 @@ def get_parameter_values(): # negative electrode "Number of reactions in negative electrode": 6, "U0_n_0": 0.08843, - "Xj_n_0": 0.43336, + "X_n_0": 0.43336, "w_n_0": 0.08611, + "a_n_0": 0.5, + "j0_ref_n_0": 2.7, "U0_n_1": 0.12799, - "Xj_n_1": 0.23963, + "X_n_1": 0.23963, "w_n_1": 0.08009, + "a_n_1": 0.5, + "j0_ref_n_1": 2.7, "U0_n_2": 0.14331, - "Xj_n_2": 0.15018, + "X_n_2": 0.15018, "w_n_2": 0.72469, + "a_n_2": 0.5, + "j0_ref_n_2": 2.7, "U0_n_3": 0.16984, - "Xj_n_3": 0.05462, + "X_n_3": 0.05462, "w_n_3": 2.53277, + "a_n_3": 0.5, + "j0_ref_n_3": 2.7, "U0_n_4": 0.21446, - "Xj_n_4": 0.06744, + "X_n_4": 0.06744, "w_n_4": 0.09470, + "a_n_4": 0.5, + "j0_ref_n_4": 2.7, "U0_n_5": 0.36325, - "Xj_n_5": 0.05476, + "X_n_5": 0.05476, "w_n_5": 5.97354, + "a_n_5": 0.5, + "j0_ref_n_5": 2.7, "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, "Negative electrode diffusivity [m2.s-1]": 3.3e-14, @@ -121,22 +133,30 @@ def get_parameter_values(): "Negative particle radius [m]": 5.86e-06, "Negative electrode Bruggeman coefficient (electrolyte)": 1.5, "Negative electrode Bruggeman coefficient (electrode)": 0, - "Negative electrode exchange-current density [A.m-2]" "": 2.7, "Negative electrode OCP entropic change [V.K-1]": 0.0, + "Negative electrode exchange-current density [A.m-2]" "": 2.7, # positive electrode "Number of reactions in positive electrode": 4, "U0_p_0": 3.62274, - "Xj_p_0": 0.13442, + "X_p_0": 0.13442, "w_p_0": 0.96710, + "a_p_0": 0.5, + "j0_ref_p_0": 5, "U0_p_1": 3.72645, - "Xj_p_1": 0.32460, + "X_p_1": 0.32460, "w_p_1": 1.39712, + "a_p_1": 0.5, + "j0_ref_p_1": 5, "U0_p_2": 3.90575, - "Xj_p_2": 0.21118, + "X_p_2": 0.21118, "w_p_2": 3.50500, + "a_p_2": 0.5, + "j0_ref_p_2": 5, "U0_p_3": 4.22955, - "Xj_p_3": 0.32980, + "X_p_3": 0.32980, "w_p_3": 5.52757, + "a_p_3": 0.5, + "j0_ref_p_3": 5, "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, "Positive electrode diffusivity [m2.s-1]": 4e-15, @@ -145,8 +165,8 @@ def get_parameter_values(): "Positive particle radius [m]": 5.22e-06, "Positive electrode Bruggeman coefficient (electrolyte)": 1.5, "Positive electrode Bruggeman coefficient (electrode)": 0, - "Positive electrode exchange-current density [A.m-2]" "": 5, "Positive electrode OCP entropic change [V.K-1]": 0.0, + "Positive electrode exchange-current density [A.m-2]" "": 5, # separator "Separator porosity": 0.47, "Separator Bruggeman coefficient (electrolyte)": 1.5, diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 1f4958f426..812c4a61da 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -55,10 +55,10 @@ class BatteryModelOptions(pybamm.FuzzyDict): "surface form" cannot be 'false'. * "intercalation kinetics" : str Model for intercalation kinetics. Can be "symmetric Butler-Volmer" - (default), "asymmetric Butler-Volmer", "linear", "Marcus", or - "Marcus-Hush-Chidsey" (which uses the asymptotic form from Zeng 2014). - A 2-tuple can be provided for different behaviour in negative and - positive electrodes. + (default), "asymmetric Butler-Volmer", "linear", "Marcus", + "Marcus-Hush-Chidsey" (which uses the asymptotic form from Zeng 2014), + or "MSMR" (which uses the form from Baker 2018). A 2-tuple can be + provided for different behaviour in negative and positive electrodes. * "interface utilisation": str Can be "full" (default), "constant", or "current-driven". * "lithium plating" : str @@ -216,6 +216,7 @@ def __init__(self, extra_options): "linear", "Marcus", "Marcus-Hush-Chidsey", + "MSMR", ], "interface utilisation": ["full", "constant", "current-driven"], "lithium plating": [ @@ -992,6 +993,8 @@ def get_intercalation_kinetics(self, domain): return pybamm.kinetics.Marcus elif options["intercalation kinetics"] == "Marcus-Hush-Chidsey": return pybamm.kinetics.MarcusHushChidsey + elif options["intercalation kinetics"] == "MSMR": + return pybamm.kinetics.MSMRButlerVolmer def get_inverse_intercalation_kinetics(self): if self.options["intercalation kinetics"] == "symmetric Butler-Volmer": diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index 1b691afb2f..41e4670cf7 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -413,47 +413,3 @@ def set_convection_submodel(self): self.submodels[ "through-cell convection" ] = pybamm.convection.through_cell.NoConvection(self.param, self.options) - - def set_msmr_reaction_variables(self, parameter_values): - """ - Set variables for the individual MSMR reactions in the negative and - positive electrodes. - - Parameters - ---------- - parameter_values : :class:`pybamm.ParameterValues` - The parameter values to use for the model. - """ - if self.options["open-circuit potential"] != "MSMR": - raise pybamm.OptionError( - "'open-circuit potential' must be 'MSMR' to add MSMR reaction variables" - ) - - for Domain in ["Negative", "Positive"]: - domain = Domain.lower() - suffix = domain[0] - U = self.variables[f"{Domain} particle potential [V]"] - T = self.variables[f"{Domain} electrode temperature [K]"] - N = parameter_values[f"Number of reactions in {domain} electrode"] - f = pybamm.constants.F / (pybamm.constants.R * T) - for i in range(N): - U0 = pybamm.Parameter(f"U0_{suffix}_{i}") - w = pybamm.Parameter(f"w_{suffix}_{i}") - Xj = pybamm.Parameter(f"Xj_{suffix}_{i}") - - x = Xj / (1 + pybamm.exp(f * (U - U0) / w)) - x_surf = pybamm.surf(x) - x_surf_av = pybamm.x_average(x_surf) - x_xav = pybamm.x_average(x) - x_rav = pybamm.r_average(x) - x_av = pybamm.r_average(x_xav) - self.variables.update( - { - f"x{i}_{suffix}": x, - f"X-averaged x{i}_{suffix}": x_xav, - f"R-averaged x{i}_{suffix}": x_rav, - f"Average x{i}_{suffix}": x_av, - f"Surface x{i}_{suffix}": x_surf, - f"X-averaged surface x{i}_{suffix}": x_surf_av, - } - ) diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.py b/pybamm/models/full_battery_models/lithium_ion/msmr.py index 8623b9b90c..59eaac2643 100644 --- a/pybamm/models/full_battery_models/lithium_ion/msmr.py +++ b/pybamm/models/full_battery_models/lithium_ion/msmr.py @@ -25,11 +25,21 @@ def __init__(self, options=None, name="MSMR", build=True): options["particle"] ) ) + # elif ( + # "intercalation kinetics" in options + # and options["intercalation kinetics"] == "MSMR" + # ): + # raise pybamm.OptionError( + # "'intercalation kinetics' must be 'MSMR' for MSMR not '{}'".format( + # options["intercalation kinetics"] + # ) + # ) else: options.update( { "open-circuit potential": "MSMR", "particle": "MSMR", + # "intercalation kinetics": "MSMR", } ) super().__init__(options=options, name=name) diff --git a/pybamm/models/submodels/interface/kinetics/__init__.py b/pybamm/models/submodels/interface/kinetics/__init__.py index c8b8552574..d99ec56783 100644 --- a/pybamm/models/submodels/interface/kinetics/__init__.py +++ b/pybamm/models/submodels/interface/kinetics/__init__.py @@ -5,7 +5,7 @@ from .marcus import Marcus, MarcusHushChidsey from .tafel import ForwardTafel # , BackwardTafel from .no_reaction import NoReaction - +from .msmr_butler_volmer import MSMRButlerVolmer from .diffusion_limited import DiffusionLimited from .inverse_kinetics.inverse_butler_volmer import ( InverseButlerVolmer, diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py index ccfefe6bfe..4806b0134d 100644 --- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py +++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py @@ -77,8 +77,19 @@ def get_coupled_variables(self, variables): ): delta_phi = pybamm.PrimaryBroadcast(delta_phi, [f"{domain} particle size"]) - # Get exchange-current density + # Get exchange-current density. For MSMR models we calculate the exchange + # current density for each reaction, then sum these to give a total exchange + # current density. Note: this is only used for the "exchange current density" + # variables. For the interfacial current density variables, we sum the + # interfacial currents from each reaction. + if self.options["intercalation kinetics"] == "MSMR": + N = int(domain_options["number of MSMR reactions"]) + for i in range(N): + variables.update( + self._get_exchange_current_density_by_reaction(variables, i) + ) j0 = self._get_exchange_current_density(variables) + # Get open-circuit potential variables and reaction overpotential if ( domain_options["particle size"] == "distribution" @@ -155,7 +166,18 @@ def get_coupled_variables(self, variables): # Update j, except in the "distributed SEI resistance" model, where j will be # found by solving an algebraic equation. # (In the "distributed SEI resistance" model, we have already defined j) - j = self._get_kinetics(j0, ne, eta_r, T, u) + # For MSMR model we calculate the total current density by summing the current + # densities from each reaction + if self.options["intercalation kinetics"] == "MSMR": + d = domain[0] + j = 0 + for i in range(N): + j0 = variables[f"j0_{d}_{i} [A.m-2]"] + j_j = self._get_kinetics_by_reaction(j0, ne, eta_r, T, u, i) + variables.update(self._get_standard_icd_by_reaction_variables(j_j, i)) + j += j_j + else: + j = self._get_kinetics(j0, ne, eta_r, T, u) if j.domain == [f"{domain} particle size"]: # If j depends on particle size, get size-dependent "distribution" diff --git a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py new file mode 100644 index 0000000000..f819ad2aa2 --- /dev/null +++ b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py @@ -0,0 +1,160 @@ +# +# Bulter volmer class for the MSMR formulation +# + +import pybamm +from .base_kinetics import BaseKinetics + + +class MSMRButlerVolmer(BaseKinetics): + """ + Submodel which implements the forward Butler-Volmer equation in the MSMR + formulation in which the interfacial current density is summed over all + reactions. + + Parameters + ---------- + param : parameter class + model parameters + domain : str + The domain to implement the model, either: 'Negative' or 'Positive'. + reaction : str + The name of the reaction being implemented + options: dict + A dictionary of options to be passed to the model. + See :class:`pybamm.BaseBatteryModel` + phase : str, optional + Phase of the particle (default is "primary") + """ + + def __init__(self, param, domain, reaction, options, phase="primary"): + super().__init__(param, domain, reaction, options, phase) + + def _get_exchange_current_density_by_reaction(self, variables, index): + """ " + A private function to obtain the exchange current density for each reaction + in the MSMR formulation. + + Parameters + ---------- + variables: dict + The variables in the full model. + + Returns + ------- + j0 : :class: `pybamm.Symbol` + The exchange current density. + """ + phase_param = self.phase_param + domain, Domain = self.domain_Domain + phase_name = self.phase_name + + c_e = variables[f"{Domain} electrolyte concentration [mol.m-3]"] + T = variables[f"{Domain} electrode temperature [K]"] + + if self.reaction == "lithium-ion main": + # For "particle-size distribution" submodels, take distribution version + # of c_s_surf that depends on particle size. + domain_options = getattr(self.options, domain) + if domain_options["particle size"] == "distribution": + c_s_surf = variables[ + f"{Domain} {phase_name}particle surface " + "concentration distribution [mol.m-3]" + ] + # If all variables were broadcast (in "x"), take only the orphans, + # then re-broadcast c_e + if ( + isinstance(c_s_surf, pybamm.Broadcast) + and isinstance(c_e, pybamm.Broadcast) + and isinstance(T, pybamm.Broadcast) + ): + c_s_surf = c_s_surf.orphans[0] + c_e = c_e.orphans[0] + T = T.orphans[0] + + # as c_e must now be a scalar, re-broadcast to + # "current collector" + c_e = pybamm.PrimaryBroadcast(c_e, ["current collector"]) + # broadcast c_e, T onto "particle size" + c_e = pybamm.PrimaryBroadcast(c_e, [f"{domain} particle size"]) + T = pybamm.PrimaryBroadcast(T, [f"{domain} particle size"]) + + else: + c_s_surf = variables[ + f"{Domain} {phase_name}particle surface concentration [mol.m-3]" + ] + # If all variables were broadcast, take only the orphans + if ( + isinstance(c_s_surf, pybamm.Broadcast) + and isinstance(c_e, pybamm.Broadcast) + and isinstance(T, pybamm.Broadcast) + ): + c_s_surf = c_s_surf.orphans[0] + c_e = c_e.orphans[0] + T = T.orphans[0] + + j0 = phase_param.j0_j(c_e, c_s_surf, T, index) + + # Size average. For j0 variables that depend on particle size, see + # "_get_standard_size_distribution_exchange_current_variables" + if j0.domain in [["negative particle size"], ["positive particle size"]]: + j0 = pybamm.size_average(j0) + # Average, and broadcast if necessary + j0_av = pybamm.x_average(j0) + + # X-average, and broadcast if necessary + if j0.domain == []: + j0 = pybamm.FullBroadcast( + j0, f"{domain} electrode", "current collector" + ) + elif j0.domain == ["current collector"]: + j0 = pybamm.PrimaryBroadcast(j0, f"{domain} electrode") + + d = domain[0] + variables = { + f"j0_{d}_{index} [A.m-2]": j0, + f"X-averaged j0_{d}_{index} [A.m-2]": j0_av, + } + + return variables + + def _get_exchange_current_density(self, variables): + options = self.options + domain = self.domain + d = domain[0] + j0 = 0 + # Loop over all reactions + N = int(getattr(options, domain)["number of MSMR reactions"]) + for i in range(N): + j0 += variables[f"j0_{d}_{i} [A.m-2]"] + return j0 + + def _get_kinetics_by_reaction(self, j0, ne, eta_r, T, u, index): + alpha = self.phase_param.alpha_bv_j(index) + Feta_RT = self.param.F * eta_r / (self.param.R * T) + arg_ox = ne * alpha * Feta_RT + arg_red = -ne * (1 - alpha) * Feta_RT + return u * j0 * (pybamm.exp(arg_ox) - pybamm.exp(arg_red)) + + def _get_standard_icd_by_reaction_variables(self, j, index): + domain = self.domain + j.print_name = f"j_{domain[0]}" + + # Size average. For j variables that depend on particle size, see + # "_get_standard_size_distribution_interfacial_current_variables" + if j.domain in [["negative particle size"], ["positive particle size"]]: + j = pybamm.size_average(j) + # Average, and broadcast if necessary + j_av = pybamm.x_average(j) + if j.domain == []: + j = pybamm.FullBroadcast(j, f"{domain} electrode", "current collector") + elif j.domain == ["current collector"]: + j = pybamm.PrimaryBroadcast(j, f"{domain} electrode") + + d = domain[0] + variables = { + f"j_{d}_{index} [A.m-2]": j, + f"X-averaged j_{d}_{index} [A.m-2]": j_av, + } + + return variables diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index cc47801167..63ebbd4e41 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -497,7 +497,7 @@ def _get_standard_potential_distribution_variables(self, U): def _get_standard_fractional_occupancy_variables(self, U): options = self.options domain = self.domain - subscript = domain[0] + d = domain[0] variables = {} # Loop over all reactions N = int(getattr(options, domain)["number of MSMR reactions"]) @@ -510,12 +510,12 @@ def _get_standard_fractional_occupancy_variables(self, U): x_av = pybamm.r_average(x_xav) variables.update( { - f"x_{subscript}_{i}": x, - f"X-averaged x_{subscript}_{i}": x_xav, - f"R-averaged x_{subscript}_{i}": x_rav, - f"Average x_{subscript}_{i}": x_av, - f"Surface x_{subscript}_{i}": x_surf, - f"X-averaged surface x_{subscript}_{i}": x_surf_av, + f"x_{d}_{i}": x, + f"X-averaged x_{d}_{i}": x_xav, + f"R-averaged x_{d}_{i}": x_rav, + f"Average x_{d}_{i}": x_av, + f"Surface x_{d}_{i}": x_surf, + f"X-averaged surface x_{d}_{i}": x_surf_av, } ) return variables @@ -523,7 +523,7 @@ def _get_standard_fractional_occupancy_variables(self, U): def _get_standard_differential_fractional_occupancy_variables(self, U): options = self.options domain = self.domain - subscript = domain[0] + d = domain[0] variables = {} # Loop over all reactions N = int(getattr(options, domain)["number of MSMR reactions"]) @@ -536,12 +536,12 @@ def _get_standard_differential_fractional_occupancy_variables(self, U): dxdU_av = pybamm.r_average(dxdU_xav) variables.update( { - f"dxdU_{subscript}_{i}": dxdU, - f"X-averaged dxdU_{subscript}_{i}": dxdU_xav, - f"R-averaged dxdU_{subscript}_{i}": dxdU_rav, - f"Average dxdU_{subscript}_{i}": dxdU_av, - f"Surface dxdU_{subscript}_{i}": dxdU_surf, - f"X-averaged surface dxdU_{subscript}_{i}": dxdU_surf_av, + f"dxdU_{d}_{i}": dxdU, + f"X-averaged dxdU_{d}_{i}": dxdU_xav, + f"R-averaged dxdU_{d}_{i}": dxdU_rav, + f"Average dxdU_{d}_{i}": dxdU_av, + f"Surface dxdU_{d}_{i}": dxdU_surf, + f"X-averaged surface dxdU_{d}_{i}": dxdU_surf_av, } ) return variables diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 6bf7469702..0c9f4ae72b 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -607,33 +607,87 @@ def dUdT(self, sto): inputs, ) + def X_j(self, index): + "Available host sites indexed by reaction j" + domain = self.domain + d = domain[0] + Xj = pybamm.Parameter(f"X_{d}_{index}") + return Xj + + def U0_j(self, index): + "Equilibrium potential indexed by reaction j" + domain = self.domain + d = domain[0] + U0j = pybamm.Parameter(f"U0_{d}_{index}") + return U0j + + def w_j(self, index): + "Order parameter indexed by reaction j" + domain = self.domain + d = domain[0] + wj = pybamm.Parameter(f"w_{d}_{index}") + return wj + + def alpha_bv_j(self, index): + "Dimensional Butler-Volmer exchange-current density indexed by reaction j" + domain = self.domain + d = domain[0] + alpha_bv_j = pybamm.Parameter(f"a_{d}_{index}") + return alpha_bv_j + def x_j(self, U, index): "Fractional occupancy of site j as a function of potential" - domain = self.domain - subscript = domain[0] T = self.main_param.T_ref f = self.main_param.F / (self.main_param.R * T) - U0 = pybamm.Parameter(f"U0_{subscript}_{index}") - w = pybamm.Parameter(f"w_{subscript}_{index}") - Xj = pybamm.Parameter(f"Xj_{subscript}_{index}") + U0j = self.U0_j(index) + wj = self.w_j(index) + Xj = self.X_j(index) # Equation 5, Baker et al 2018 - xj = Xj / (1 + pybamm.exp(f * (U - U0) / w)) + xj = Xj / (1 + pybamm.exp(f * (U - U0j) / wj)) return xj def dxdU_j(self, U, index): - "Derivative of fractional occupancy of site j as a function of potential" - domain = self.domain - subscript = domain[0] + "Derivative of fractional occupancy of site j as a function of potential [V-1]" T = self.main_param.T_ref f = self.main_param.F / (self.main_param.R * T) - U0 = pybamm.Parameter(f"U0_{subscript}_{index}") - w = pybamm.Parameter(f"w_{subscript}_{index}") - Xj = pybamm.Parameter(f"Xj_{subscript}_{index}") - e = pybamm.exp(f * (U - U0) / w) + U0j = self.U0_j(index) + wj = self.w_j(index) + Xj = self.X_j(index) + e = pybamm.exp(f * (U - U0j) / wj) # Equation 25, Baker et al 2018 - dxjdU = -(f / w) * (Xj * e) / (1 + e) ** 2 + dxjdU = -(f / wj) * (Xj * e) / (1 + e) ** 2 return dxjdU + def j0_j(self, c_e, c_s_j_surf, T, index): + "Exchange-current density index by reaction j [A.m-2]" + tol = pybamm.settings.tolerances["j0__c_e"] + c_e = pybamm.maximum(c_e, tol) + c_e_ref = self.main_param.c_e_init + tol = pybamm.settings.tolerances["j0__c_s"] + c_s_j_surf = pybamm.maximum( + pybamm.minimum(c_s_j_surf, (1 - tol) * self.c_max), tol * self.c_max + ) + c_max = self.c_max + + domain = self.domain + d = domain[0] + wj = self.w_j(index) + Xj = self.X_j(index) + aj = self.alpha_bv_j(index) + xj = c_s_j_surf / c_max + + j0_ref_j = pybamm.FunctionParameter( + f"j0_ref_{d}_{index}", {"Temperature [K]": T} + ) + + j0_j = ( + j0_ref_j + * xj ** (wj * aj) + * (Xj - xj) ** (wj * (1 - aj)) + * (c_e / c_e_ref) ** (1 - aj) + ) + return j0_j + def x(self, U): "Stoichiometry as a function of potential (for use with MSMR models)" N = int(self.options["number of MSMR reactions"]) From 986d1715231aec834d57afb2821c0d9716d1a853 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 19 Jul 2023 17:03:56 +0100 Subject: [PATCH 028/154] debugging current density --- examples/scripts/MSMR.py | 41 ++++- .../full_battery_models/lithium_ion/msmr.py | 20 +-- pybamm/parameters/lithium_ion_parameters.py | 3 - test.ipynb | 161 ++++++++++++++++++ 4 files changed, 206 insertions(+), 19 deletions(-) create mode 100644 test.ipynb diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index 72a2f4d371..84ba9698ca 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -1,18 +1,47 @@ import pybamm -model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) +pybamm.set_logging_level("DEBUG") +model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) +param = model.param +for i in range(6): + xj = model.variables[f"Average x_n_{i}"] + Xj = model.param.n.prim.X_j(i) + model.variables[f"Xj - xj n_{i}"] = Xj - xj +for i in range(4): + xj = model.variables[f"Average x_p_{i}"] + Xj = model.param.p.prim.X_j(i) + model.variables[f"Xj - xj p_{i}"] = Xj - xj experiment = pybamm.Experiment( [ ( "Discharge at 1C until 3V", - "Rest for 1 hour", - "Charge at C/2 until 4.1 V", - "Hold at 4.1 V until 10 mA", - "Rest for 1 hour", + # "Rest for 1 hour", + # "Charge at C/2 until 4.1 V", + # "Hold at 4.1 V until 10 mA", + # "Rest for 1 hour", ), ] ) sim = pybamm.Simulation(model, experiment=experiment) sim.solve(initial_soc=0.9) -sim.plot() +xns = [ + f"Average x_n_{i}" for i in range(6) +] # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5 +Xxns = [f"Xj - xj n_{i}" for i in range(6)] +xps = [ + f"Average x_p_{i}" for i in range(4) +] # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3 +Xxps = [f"Xj - xj p_{i}" for i in range(4)] +sim.plot( + [ + xns, + Xxns, + xps, + Xxps, + "Current [A]", + "Negative electrode interfacial current density [A.m-2]", + "Positive electrode interfacial current density [A.m-2]", + "Voltage [V]", + ] +) diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.py b/pybamm/models/full_battery_models/lithium_ion/msmr.py index 59eaac2643..3ca07c4ef8 100644 --- a/pybamm/models/full_battery_models/lithium_ion/msmr.py +++ b/pybamm/models/full_battery_models/lithium_ion/msmr.py @@ -25,21 +25,21 @@ def __init__(self, options=None, name="MSMR", build=True): options["particle"] ) ) - # elif ( - # "intercalation kinetics" in options - # and options["intercalation kinetics"] == "MSMR" - # ): - # raise pybamm.OptionError( - # "'intercalation kinetics' must be 'MSMR' for MSMR not '{}'".format( - # options["intercalation kinetics"] - # ) - # ) + elif ( + "intercalation kinetics" in options + and options["intercalation kinetics"] == "MSMR" + ): + raise pybamm.OptionError( + "'intercalation kinetics' must be 'MSMR' for MSMR not '{}'".format( + options["intercalation kinetics"] + ) + ) else: options.update( { "open-circuit potential": "MSMR", "particle": "MSMR", - # "intercalation kinetics": "MSMR", + "intercalation kinetics": "MSMR", } ) super().__init__(options=options, name=name) diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 0c9f4ae72b..4ac43cd5fe 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -664,9 +664,6 @@ def j0_j(self, c_e, c_s_j_surf, T, index): c_e = pybamm.maximum(c_e, tol) c_e_ref = self.main_param.c_e_init tol = pybamm.settings.tolerances["j0__c_s"] - c_s_j_surf = pybamm.maximum( - pybamm.minimum(c_s_j_surf, (1 - tol) * self.c_max), tol * self.c_max - ) c_max = self.c_max domain = self.domain diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000000..f7d4b1c840 --- /dev/null +++ b/test.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pybamm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.lithium_ion.MSMR({\"number of MSMR reactions\": (\"6\", \"4\")})\n", + "param = model.param\n", + "param_n = param.n.prim\n", + "param_p = param.p.prim\n", + "pv = pybamm.ParameterValues(\"MSMR_Example\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "U_n = pybamm.linspace(0, 1.5, 1000)\n", + "U_p = pybamm.linspace(2.5, 4.5, 1000)\n", + "c_n_max = param_n.c_max\n", + "c_p_max = param_p.c_max\n", + "c_e = param.c_e_init\n", + "T = param.T_init" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 2, figsize=(10, 5))\n", + "\n", + "x = param_n.x(U_n)\n", + "xj_sum = 0\n", + "color = [\"r\", \"g\", \"b\", \"c\", \"m\", \"y\"]\n", + "for i in range(6):\n", + " wj = param_n.w_j(i)\n", + " aj = param_n.alpha_bv_j(i)\n", + " Xj = param_n.X_j(i)\n", + " xj = param_n.x_j(U_n, i)\n", + " xj_sum += xj\n", + " j0j = xj ** (wj * aj) * (Xj - xj) ** (wj * (1 - aj))\n", + " c_s = xj * c_n_max\n", + " j0j_param = param_n.j0_j(c_e, c_s, T, i)\n", + " ax[0, 0].plot(pv.evaluate(x), pv.evaluate(j0j), color=color[i], label=f\"j0_{i}\")\n", + " ax[0, 0].plot(\n", + " pv.evaluate(x), pv.evaluate(j0j_param), \"--\", color=color[i], label=f\"j0_{i}\"\n", + " )\n", + " ax[1, 0].plot(pv.evaluate(x), pv.evaluate(xj), color=color[i], label=f\"xj_{i}\")\n", + " ax[1, 0].plot(\n", + " pv.evaluate(x),\n", + " np.ones_like(pv.evaluate(x)) * pv.evaluate(Xj),\n", + " \"--\",\n", + " color=color[i],\n", + " label=f\"Xj\",\n", + " )\n", + "ax[1, 0].plot(pv.evaluate(x), pv.evaluate(xj_sum), color=color[i], label=f\"xj_sum\")\n", + "ax[0, 0].legend()\n", + "ax[1, 0].legend()\n", + "\n", + "x = param_p.x(U_p)\n", + "xj_sum = 0\n", + "for i in range(4):\n", + " wj = param_p.w_j(i)\n", + " aj = param_p.alpha_bv_j(i)\n", + " Xj = param_p.X_j(i)\n", + " xj = param_p.x_j(U_p, i)\n", + " xj_sum += xj\n", + " j0j = xj ** (wj * aj) * (Xj - xj) ** (wj * (1 - aj))\n", + " c_s = xj * c_p_max\n", + " j0j_param = param_p.j0_j(c_e, c_s, T, i) \n", + " ax[0, 1].plot(pv.evaluate(x), pv.evaluate(j0j), color=color[i], label=f\"j0_{i}\")\n", + " ax[0, 1].plot(\n", + " pv.evaluate(x), pv.evaluate(j0j_param), \"--\", color=color[i], label=f\"j0_{i}\"\n", + " )\n", + " ax[1, 1].plot(pv.evaluate(x), pv.evaluate(xj), color=color[i], label=f\"xj_{i}\")\n", + " ax[1, 1].plot(\n", + " pv.evaluate(x),\n", + " np.ones_like(pv.evaluate(x)) * pv.evaluate(Xj),\n", + " \"--\",\n", + " color=color[i],\n", + " label=f\"Xj\",\n", + " )\n", + "ax[1, 1].plot(pv.evaluate(x), pv.evaluate(xj_sum), color=color[i], label=f\"xj_sum\")\n", + "ax[0, 1].legend()\n", + "ax[1, 1].legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "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": "3.9.16" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 10bec4584545496c6e8162e755386022a758a0e4 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 20 Jul 2023 16:54:40 +0100 Subject: [PATCH 029/154] fix exchange current parameters --- examples/scripts/MSMR.py | 69 +++++--- .../lithium_ion/MSMR_example_set.py | 16 +- .../full_battery_models/base_battery_model.py | 46 ++--- pybamm/parameters/lithium_ion_parameters.py | 6 +- test.ipynb | 161 ------------------ .../test_base_battery_model.py | 18 +- .../base_lithium_ion_tests.py | 2 + .../test_lithium_ion/test_msmr.py | 22 +++ tests/unit/test_simulation.py | 7 +- 9 files changed, 130 insertions(+), 217 deletions(-) delete mode 100644 test.ipynb create mode 100644 tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py index 84ba9698ca..4ceb4fc9f4 100644 --- a/examples/scripts/MSMR.py +++ b/examples/scripts/MSMR.py @@ -1,47 +1,68 @@ import pybamm -pybamm.set_logging_level("DEBUG") +pybamm.set_logging_level("INFO") -model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) -param = model.param -for i in range(6): - xj = model.variables[f"Average x_n_{i}"] - Xj = model.param.n.prim.X_j(i) - model.variables[f"Xj - xj n_{i}"] = Xj - xj -for i in range(4): - xj = model.variables[f"Average x_p_{i}"] - Xj = model.param.p.prim.X_j(i) - model.variables[f"Xj - xj p_{i}"] = Xj - xj +# Use the MSMR model, with 6 negative electrode reactions and 4 positive electrode +# reactions +msmr_model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) + +# We can also use a SPM with MSMR thermodynamics, transport and kinetics by changing +# model options. Note we need to se the "surface form" to "algebraic" or "differential" +# to use the MSMR, since we cannot explicitly invert the kinetics +spm_msmr_model = pybamm.lithium_ion.SPM( + { + "number of MSMR reactions": ("6", "4"), + "open-circuit potential": "MSMR", + "particle": "MSMR", + "intercalation kinetics": "MSMR", + "surface form": "algebraic", + }, + name="Single Particle MSMR", +) + +# Load in the example MSMR parameter set +parameter_values = pybamm.ParameterValues("MSMR_Example") + +# Define an experiment experiment = pybamm.Experiment( [ ( - "Discharge at 1C until 3V", - # "Rest for 1 hour", - # "Charge at C/2 until 4.1 V", - # "Hold at 4.1 V until 10 mA", - # "Rest for 1 hour", + "Discharge at 1C for 1 hour or until 3 V", + "Rest for 1 hour", + "Charge at C/3 until 4.2 V", + "Hold at 4.2 V until 10 mA", + "Rest for 1 hour", ), ] ) -sim = pybamm.Simulation(model, experiment=experiment) -sim.solve(initial_soc=0.9) + +# Loop over the models, creating and solving a simulation +sols = [] +for model in [msmr_model, spm_msmr_model]: + sim = pybamm.Simulation( + model, parameter_values=parameter_values, experiment=experiment + ) + sol = sim.solve(initial_soc=0.9) + sols.append(sol) + +# Plot the fractional occupancy x_j of the individual MSMR reactions, along with some +# other variables of interest xns = [ f"Average x_n_{i}" for i in range(6) ] # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5 -Xxns = [f"Xj - xj n_{i}" for i in range(6)] xps = [ f"Average x_p_{i}" for i in range(4) ] # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3 -Xxps = [f"Xj - xj p_{i}" for i in range(4)] -sim.plot( +pybamm.dynamic_plot( + sols, [ xns, - Xxns, xps, - Xxps, "Current [A]", "Negative electrode interfacial current density [A.m-2]", "Positive electrode interfacial current density [A.m-2]", + "Negative particle surface concentration [mol.m-3]", + "Positive particle surface concentration [mol.m-3]", "Voltage [V]", - ] + ], ) diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index c27c96c1ee..475ed307e0 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -81,7 +81,15 @@ def get_parameter_values(): Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050. - and references therein. + and references therein. Verbrugge et al. (2017) does not provide kinetic parameters + so we set the reference exchange current density to 5 A.m-2 for the positive + electrode reactions and 2.7 A.m-2 for the negative electrode reactions, which are + the values used in the Chen et al. (2020) paper. We also assume that the + exchange-current density is symmetric. Note: the 4th reaction in the positive + electrode gave unphysical results so we set the reference exchange current density + and symmetry factor to 1e6 and 1, respectively. The parameter values are intended + to serve as an example set to use with the MSMR model and do not claim to match any + experimental cycling data. """ return { # cell @@ -134,7 +142,6 @@ def get_parameter_values(): "Negative electrode Bruggeman coefficient (electrolyte)": 1.5, "Negative electrode Bruggeman coefficient (electrode)": 0, "Negative electrode OCP entropic change [V.K-1]": 0.0, - "Negative electrode exchange-current density [A.m-2]" "": 2.7, # positive electrode "Number of reactions in positive electrode": 4, "U0_p_0": 3.62274, @@ -155,8 +162,8 @@ def get_parameter_values(): "U0_p_3": 4.22955, "X_p_3": 0.32980, "w_p_3": 5.52757, - "a_p_3": 0.5, - "j0_ref_p_3": 5, + "a_p_3": 1, + "j0_ref_p_3": 1e6, "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, "Positive electrode diffusivity [m2.s-1]": 4e-15, @@ -166,7 +173,6 @@ def get_parameter_values(): "Positive electrode Bruggeman coefficient (electrolyte)": 1.5, "Positive electrode Bruggeman coefficient (electrode)": 0, "Positive electrode OCP entropic change [V.K-1]": 0.0, - "Positive electrode exchange-current density [A.m-2]" "": 5, # separator "Separator porosity": 0.47, "Separator Bruggeman coefficient (electrolyte)": 1.5, diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 812c4a61da..967fc36f12 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -72,10 +72,13 @@ class BatteryModelOptions(pybamm.FuzzyDict): "stress-driven", "reaction-driven", or "stress and reaction-driven". A 2-tuple can be provided for different behaviour in negative and positive electrodes. - * "number of MSMR reactions" : int + * "number of MSMR reactions" : str Sets the number of reactions to use in the MSMR model in each electrode. A 2-tuple can be provided to give a different number of reactions in - the negative and positive electrodes. Default is "none". + the negative and positive electrodes. Default is "none". Can be any + 2-tuple of strings of integers. For example, set to ("6", "4") for a + negative electrode with 6 reactions and a positive electrode with 4 + reactions. * "open-circuit potential" : str Sets the model for the open circuit potential. Can be "single" (default), "current sigmoid", or "MSMR". If "MSMR" then the "particle" @@ -232,7 +235,19 @@ def __init__(self, extra_options): "reaction-driven", "stress and reaction-driven", ], - "number of MSMR reactions": ["none", "1", "2", "3", "4", "5", "6"], + "number of MSMR reactions": [ + "none", + "1", + "2", + "3", + "4", + "5", + "6", + "7", + "8", + "9", + "10", + ], "open-circuit potential": ["single", "current sigmoid", "MSMR"], "operating mode": [ "current", @@ -382,23 +397,16 @@ def __init__(self, extra_options): ) ) - # IF "open-circuit potential" is "MSMR" then "particle" must be "MSMR" too - # and vice-versa - if ( - options["open-circuit potential"] == "MSMR" - and options["particle"] != "MSMR" - ): - raise pybamm.OptionError( - "If 'open-circuit potential' is 'MSMR' then 'particle' must be 'MSMR' " - "too" - ) - if ( - options["particle"] == "MSMR" - and options["open-circuit potential"] != "MSMR" - ): + # If any of "open-circuit potential", "particle" or "intercalation kinetics" is + # "MSMR" then all of them must be "MSMR". + msmr_check_list = [ + options[opt] == "MSMR" + for opt in ["open-circuit potential", "particle", "intercalation kinetics"] + ] + if any(msmr_check_list) and not all(msmr_check_list): raise pybamm.OptionError( - "If 'particle' is 'MSMR' then 'open-circuit potential' must be 'MSMR' " - "too" + "If any of 'open-circuit potential', 'particle' or " + "'intercalation kinetics' is 'MSMR' then all of them must be 'MSMR'" ) # If "SEI film resistance" is "distributed" then "total interfacial current diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 4ac43cd5fe..971889f855 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -665,6 +665,9 @@ def j0_j(self, c_e, c_s_j_surf, T, index): c_e_ref = self.main_param.c_e_init tol = pybamm.settings.tolerances["j0__c_s"] c_max = self.c_max + c_s_j_surf = pybamm.maximum( + pybamm.minimum(c_s_j_surf, (1 - tol) * c_max), tol * c_max + ) domain = self.domain d = domain[0] @@ -677,10 +680,11 @@ def j0_j(self, c_e, c_s_j_surf, T, index): f"j0_ref_{d}_{index}", {"Temperature [K]": T} ) + # Use tolerances to avoid division by zero in the Jacobian j0_j = ( j0_ref_j * xj ** (wj * aj) - * (Xj - xj) ** (wj * (1 - aj)) + * (pybamm.maximum(Xj - xj, tol)) ** (wj * (1 - aj)) * (c_e / c_e_ref) ** (1 - aj) ) return j0_j diff --git a/test.ipynb b/test.ipynb deleted file mode 100644 index f7d4b1c840..0000000000 --- a/test.ipynb +++ /dev/null @@ -1,161 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pybamm\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model = pybamm.lithium_ion.MSMR({\"number of MSMR reactions\": (\"6\", \"4\")})\n", - "param = model.param\n", - "param_n = param.n.prim\n", - "param_p = param.p.prim\n", - "pv = pybamm.ParameterValues(\"MSMR_Example\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "U_n = pybamm.linspace(0, 1.5, 1000)\n", - "U_p = pybamm.linspace(2.5, 4.5, 1000)\n", - "c_n_max = param_n.c_max\n", - "c_p_max = param_p.c_max\n", - "c_e = param.c_e_init\n", - "T = param.T_init" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(2, 2, figsize=(10, 5))\n", - "\n", - "x = param_n.x(U_n)\n", - "xj_sum = 0\n", - "color = [\"r\", \"g\", \"b\", \"c\", \"m\", \"y\"]\n", - "for i in range(6):\n", - " wj = param_n.w_j(i)\n", - " aj = param_n.alpha_bv_j(i)\n", - " Xj = param_n.X_j(i)\n", - " xj = param_n.x_j(U_n, i)\n", - " xj_sum += xj\n", - " j0j = xj ** (wj * aj) * (Xj - xj) ** (wj * (1 - aj))\n", - " c_s = xj * c_n_max\n", - " j0j_param = param_n.j0_j(c_e, c_s, T, i)\n", - " ax[0, 0].plot(pv.evaluate(x), pv.evaluate(j0j), color=color[i], label=f\"j0_{i}\")\n", - " ax[0, 0].plot(\n", - " pv.evaluate(x), pv.evaluate(j0j_param), \"--\", color=color[i], label=f\"j0_{i}\"\n", - " )\n", - " ax[1, 0].plot(pv.evaluate(x), pv.evaluate(xj), color=color[i], label=f\"xj_{i}\")\n", - " ax[1, 0].plot(\n", - " pv.evaluate(x),\n", - " np.ones_like(pv.evaluate(x)) * pv.evaluate(Xj),\n", - " \"--\",\n", - " color=color[i],\n", - " label=f\"Xj\",\n", - " )\n", - "ax[1, 0].plot(pv.evaluate(x), pv.evaluate(xj_sum), color=color[i], label=f\"xj_sum\")\n", - "ax[0, 0].legend()\n", - "ax[1, 0].legend()\n", - "\n", - "x = param_p.x(U_p)\n", - "xj_sum = 0\n", - "for i in range(4):\n", - " wj = param_p.w_j(i)\n", - " aj = param_p.alpha_bv_j(i)\n", - " Xj = param_p.X_j(i)\n", - " xj = param_p.x_j(U_p, i)\n", - " xj_sum += xj\n", - " j0j = xj ** (wj * aj) * (Xj - xj) ** (wj * (1 - aj))\n", - " c_s = xj * c_p_max\n", - " j0j_param = param_p.j0_j(c_e, c_s, T, i) \n", - " ax[0, 1].plot(pv.evaluate(x), pv.evaluate(j0j), color=color[i], label=f\"j0_{i}\")\n", - " ax[0, 1].plot(\n", - " pv.evaluate(x), pv.evaluate(j0j_param), \"--\", color=color[i], label=f\"j0_{i}\"\n", - " )\n", - " ax[1, 1].plot(pv.evaluate(x), pv.evaluate(xj), color=color[i], label=f\"xj_{i}\")\n", - " ax[1, 1].plot(\n", - " pv.evaluate(x),\n", - " np.ones_like(pv.evaluate(x)) * pv.evaluate(Xj),\n", - " \"--\",\n", - " color=color[i],\n", - " label=f\"Xj\",\n", - " )\n", - "ax[1, 1].plot(pv.evaluate(x), pv.evaluate(xj_sum), color=color[i], label=f\"xj_sum\")\n", - "ax[0, 1].legend()\n", - "ax[1, 1].legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "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": "3.9.16" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 0e8d4b22f7..3a2bc67f71 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -24,11 +24,12 @@ 'dimensionality': 0 (possible: [0, 1, 2]) 'electrolyte conductivity': 'default' (possible: ['default', 'full', 'leading order', 'composite', 'integrated']) 'hydrolysis': 'false' (possible: ['false', 'true']) -'intercalation kinetics': 'symmetric Butler-Volmer' (possible: ['symmetric Butler-Volmer', 'asymmetric Butler-Volmer', 'linear', 'Marcus', 'Marcus-Hush-Chidsey']) +'intercalation kinetics': 'symmetric Butler-Volmer' (possible: ['symmetric Butler-Volmer', 'asymmetric Butler-Volmer', 'linear', 'Marcus', 'Marcus-Hush-Chidsey', 'MSMR']) 'interface utilisation': 'full' (possible: ['full', 'constant', 'current-driven']) 'lithium plating': 'none' (possible: ['none', 'reversible', 'partially reversible', 'irreversible']) 'lithium plating porosity change': 'false' (possible: ['false', 'true']) 'loss of active material': 'stress-driven' (possible: ['none', 'stress-driven', 'reaction-driven', 'stress and reaction-driven']) +'number of MSMR reactions': 'none' (possible: ['none', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']) 'open-circuit potential': 'single' (possible: ['single', 'current sigmoid', 'MSMR']) 'operating mode': 'current' (possible: ['current', 'voltage', 'power', 'differential power', 'explicit power', 'resistance', 'differential resistance', 'explicit resistance', 'CCCV']) 'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile', 'MSMR']) @@ -371,6 +372,20 @@ def test_options(self): pybamm.BaseBatteryModel({"open-circuit potential": "MSMR"}) with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): pybamm.BaseBatteryModel({"particle": "MSMR"}) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel({"intercalation kinetics": "MSMR"}) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel( + {"open-circuit potential": "MSMR", "particle": "MSMR"} + ) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel( + {"open-circuit potential": "MSMR", "intercalation kinetics": "MSMR"} + ) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel( + {"particle": "MSMR", "intercalation kinetics": "MSMR"} + ) def test_build_twice(self): model = pybamm.lithium_ion.SPM() # need to pick a model to set vars and build @@ -420,6 +435,7 @@ def test_print_options(self): with io.StringIO() as buffer, redirect_stdout(buffer): BatteryModelOptions(OPTIONS_DICT).print_options() output = buffer.getvalue() + self.assertEqual(output, PRINT_OPTIONS_OUTPUT) def test_option_phases(self): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 3104c7cd7c..2426a6a816 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -365,5 +365,7 @@ def test_well_posed_msmr(self): "open-circuit potential": "MSMR", "particle": "MSMR", "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + "surface form": "differential", } self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py new file mode 100644 index 0000000000..96369fbac2 --- /dev/null +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py @@ -0,0 +1,22 @@ +# +# Tests for the lithium-ion MSMR model +# +from tests import TestCase +import pybamm +import unittest + + +class TestMSMR(TestCase): + def test_well_posed(self): + model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) + model.check_well_posedness() + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + pybamm.settings.debug_mode = True + unittest.main() diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index 990d4eb167..d0926e5c94 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -204,12 +204,7 @@ def test_solve_with_initial_soc(self): self.assertEqual(sim._built_initial_soc, 0.5) # test with MSMR - options = { - "open-circuit potential": "MSMR", - "particle": "MSMR", - "number of MSMR reactions": ("6", "4"), - } - model = pybamm.lithium_ion.SPM(options) + model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")}) param = pybamm.ParameterValues("MSMR_Example") sim = pybamm.Simulation(model, parameter_values=param) sim.build(initial_soc=0.5) From 2c14fb2c0114f23e22679f6812395b04fdea1af1 Mon Sep 17 00:00:00 2001 From: Ferran Brosa Planella Date: Fri, 21 Jul 2023 12:42:09 +0100 Subject: [PATCH 030/154] #3101 always save final cycle --- pybamm/simulation.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 0315bd8144..bef6bb87a6 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -673,6 +673,8 @@ def solve( save_this_cycle = ( # always save cycle 1 cycle_num == 1 + # always save last cycle + or cycle_num == num_cycles # None: save all cycles or save_at_cycles is None # list: save all cycles in the list From 28ac4e48010beb6eb27f49146d9973f97ff6e18b Mon Sep 17 00:00:00 2001 From: Ferran Brosa Planella Date: Fri, 21 Jul 2023 12:42:35 +0100 Subject: [PATCH 031/154] #3101 add test --- .../test_simulation_with_experiment.py | 36 +++++++++++++++++-- 1 file changed, 33 insertions(+), 3 deletions(-) diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index 460d82c8c6..19b4f37791 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -382,9 +382,9 @@ def test_save_at_cycles(self): solver=pybamm.CasadiSolver("fast with events"), save_at_cycles=[3, 4, 5, 9] ) # Note offset by 1 (0th cycle is cycle 1) - for cycle_num in [1, 5, 6, 7, 9]: + for cycle_num in [1, 5, 6, 7]: self.assertIsNone(sol.cycles[cycle_num]) - for cycle_num in [0, 2, 3, 4, 8]: + for cycle_num in [0, 2, 3, 4, 8, 9]: # first & last cycle always saved self.assertIsNotNone(sol.cycles[cycle_num]) # Summary variables are not None self.assertIsNotNone(sol.summary_variables["Capacity [A.h]"]) @@ -605,7 +605,7 @@ def test_padding_rest_model(self): pybamm.lithium_ion.SPM, ) - def test_run_time_stamped_experiment(self): + def test_run_start_time_experiment(self): model = pybamm.lithium_ion.SPM() # Test experiment is cut short if next_start_time is early @@ -640,6 +640,36 @@ def test_run_time_stamped_experiment(self): sol = sim.solve(calc_esoh=False) self.assertEqual(sol["Time [s]"].entries[-1], 10800) + def test_starting_solution(self): + model = pybamm.lithium_ion.SPM() + + experiment = pybamm.Experiment( + [ + pybamm.step.string("Discharge at C/2 for 10 minutes"), + pybamm.step.string("Rest for 5 minutes"), + pybamm.step.string("Rest for 5 minutes"), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + solution = sim.solve(save_at_cycles=[1]) + + # test that the last state is correct (i.e. final cycle is saved) + self.assertEqual(solution.last_state.t[-1], 1200) + + experiment = pybamm.Experiment( + [ + pybamm.step.string("Discharge at C/2 for 20 minutes"), + pybamm.step.string("Rest for 20 minutes"), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + new_solution = sim.solve(calc_esoh=False, starting_solution=solution) + + # test that the final time is correct (i.e. starting solution correctly set) + self.assertEqual(new_solution["Time [s]"].entries[-1], 3600) + if __name__ == "__main__": print("Add -v for more debug output") From 516e0260baa1067afdf6295a674bedc2e268f69e Mon Sep 17 00:00:00 2001 From: Ferran Brosa Planella Date: Fri, 21 Jul 2023 13:53:39 +0100 Subject: [PATCH 032/154] #3101 take initial_start_time from starting_solution if available --- pybamm/simulation.py | 22 ++++++++++++++++++++-- pybamm/solvers/solution.py | 12 ++++++++++++ 2 files changed, 32 insertions(+), 2 deletions(-) diff --git a/pybamm/simulation.py b/pybamm/simulation.py index bef6bb87a6..5034543630 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -638,6 +638,21 @@ def solve( starting_solution.all_first_states.copy() ) + # set simulation initial_start_time + if starting_solution is None: + initial_start_time = self.experiment.initial_start_time + else: + initial_start_time = starting_solution.initial_start_time + + if ( + initial_start_time is None + and self.experiment.initial_start_time is not None + ): + raise ValueError( + "When using experiments with `start_time`, the starting_solution " + "must have a `start_time` too." + ) + cycle_offset = len(starting_solution_cycles) all_cycle_solutions = starting_solution_cycles all_summary_variables = starting_solution_summary_variables @@ -702,7 +717,7 @@ def solve( ( op_conds.end_time - ( - self.experiment.initial_start_time + initial_start_time + timedelta(seconds=float(start_time)) ) ).total_seconds(), @@ -757,7 +772,7 @@ def solve( rest_time = ( op_conds.next_start_time - ( - self.experiment.initial_start_time + initial_start_time + timedelta(seconds=float(step_solution.t[-1])) ) ).total_seconds() @@ -895,6 +910,9 @@ def solve( callbacks.on_experiment_end(logs) + # record initial_start_time of the solution + self.solution.initial_start_time = initial_start_time + return self.solution def step( diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 411341f887..9f51bd6c26 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -131,6 +131,9 @@ def __init__( # Initialize empty summary variables self._summary_variables = None + # Initialise initial start time + self.initial_start_time = None + # Solution now uses CasADi pybamm.citations.register("Andersson2019") @@ -421,6 +424,15 @@ def cycles(self, cycles): def summary_variables(self): return self._summary_variables + @property + def initial_start_time(self): + return self._initial_start_time + + @initial_start_time.setter + def initial_start_time(self, value): + """Updates the reason for termination""" + self._initial_start_time = value + def set_summary_variables(self, all_summary_variables): summary_variables = {var: [] for var in all_summary_variables[0]} for sum_vars in all_summary_variables: From e549a4ea48f161801d208846a05ae4c09cbd1973 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 21 Jul 2023 14:36:29 +0100 Subject: [PATCH 033/154] fix PSD --- .../examples/notebooks/models/MSMR.ipynb | 266 ++++++++++++++---- .../interface/kinetics/base_kinetics.py | 13 +- .../interface/kinetics/msmr_butler_volmer.py | 62 ++-- .../test_lithium_ion/test_dfn.py | 1 + .../test_lithium_ion/test_electrode_soh.py | 4 + .../test_lithium_ion/test_mpm.py | 1 + .../test_parameters/test_parameter_values.py | 1 + 7 files changed, 256 insertions(+), 92 deletions(-) diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 3f16a2e902..1e55ef3270 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -8,6 +8,29 @@ "# Multi-Species Multi-Reaction model" ] }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pybamm -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -15,7 +38,7 @@ "source": [ "## Model Equations\n", "\n", - "Here we briefly outline the models used for the open-circuit potential and solid phase transport used in the MSMR model, as described in Baker and Verbrugge (2018). The remaining physics is modelled differently depending on which options are selected. By default, the rest of the battery model is as described in Maquis et al. (2019)." + "Here we briefly outline the models used for the open-circuit potential, kinetics, and solid phase transport used in the MSMR model, as described in Baker and Verbrugge (2018). The remaining physics is modelled differently depending on which options are selected. By default, the rest of the battery model is as described in Maquis et al. (2019). In the following we give equations for a single electrode." ] }, { @@ -23,7 +46,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Thermodynamics\n", + "### Thermodynamics\n", "The MSMR model is developed by assuming that all electrochemical reactions at the electrode/electrolyte interface in a lithium insertion cell can be expressed in the form \n", "$$ \\text{Li}^{+} + \\text{e}^{-} + \\text{H}_{j} \\rightleftharpoons (\\text{Li--H})_{j}.$$\n", "For each species $j$, a vacant host site $\\text{H}_{j}$ can accommodate one lithium leading to a filled host site $(\\text{Li--H})_{j}$. The OCV for this reaction is written as\n", @@ -45,7 +68,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Solid phase transport\n", + "### Kinetics\n", + "The kinetics of the insertion reaction are given as\n", + "$$i_j = i_{0,j}[e^{(1-\\alpha_j)f\\eta} - e^{-\\alpha_jf\\eta}], \\qquad i = \\sum_j i_j,$$\n", + "where $i_j$ is the interfacial current associated with reaction $j$, $\\alpha_j$ is the symmetry factor, $\\eta$ is the overpotential, given by \n", + "$$ \\eta = \\phi_s - \\phi_e - U(x),$$\n", + "where $\\phi_s$ and $\\phi_e$ are the solid phase and electrolyte potentials, respectively, and $i_{0,j}$ is the exchange current density of reaction $j$, given by\n", + "$$i_{0,j} = i_{0,j}^{ref}(x_j)^{\\omega_j\\alpha_j}(X_j-x_j)^{\\omega_j(1-\\alpha_j)}(c_e/c_e^{ref})^{1-\\alpha_j},$$\n", + "where $c_e$ is the electrolyte concentration." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solid phase transport\n", "Within the MSMR framework, the flux within the particles is expressed in terms of gradient of the chemical potential\n", "$$N = -c_{\\text{T}}x\\frac{D}{RT}\\nabla \\mu + x(N+N_{\\text{H}}),$$\n", "where $N$ is the flux of lithiated sites, $N_{\\text{H}}$ is the flux of unlithiated sites, $c_{\\text{T}}$ is the total concentration of lithiated and delithiated sites, and $D$ is a diffusion coefficient. Ignoring volumetric expansion during lithiation, the total flux of sites vanishes\n", @@ -56,8 +94,8 @@ "A mass balance in the solid phase then gives\n", "$$\\frac{\\partial x}{\\partial t} = -\\nabla\\cdot\\left(x(1-x)fD\\frac{\\text{d}U}{\\text{d}x}\\nabla x\\right),$$\n", "which, for a radially symmetric spherical particle, must be solved subject to the boundary conditions\n", - "$$N\\big\\vert_{r=0} = 0, \\quad N\\big\\vert_{r=R} = \\frac{j}{F},$$\n", - "where $j$ is the interfacial current density and $R$ is the particle radius. This must be supplemented with a suitable initial condition for the electrode state of charge.\n", + "$$N\\big\\vert_{r=0} = 0, \\quad N\\big\\vert_{r=R} = \\frac{i}{F},$$\n", + "where $R$ is the particle radius. This must be supplemented with a suitable initial condition for the electrode state of charge.\n", "\n", "Solution of this problem requires evaluate of the function $U(x)$ and the derivative $\\text{d}U/\\text{d}x$, but these functions cannot be explicitly integrated. This problem can be avoided by replacing the dependent variable $x$ with a new dependent variable $U$ subject to the transformation \n", "$$x = \\sum_j \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]}.$$\n", @@ -72,33 +110,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Example solving MSMR using PyBaMM\n", - "Below we show how to set up and solve a CCCV experiment using the MSMR model in PyBaMM. We use an example parameter set based on an Gr vs NMC cell similar to the LG M50.\n", + "## Parameterization of the MSMR model\n", + "The behaviour of MSMR model is characterised by the parameters $X_j$, $U^0_j$, $\\omega_j$, $\\alpha_j$, and $i_{0,j}^{ref}$. Let's take a look at their values in the example parameter set provided in PyBaMM. The thermodynamic parameter values are taken from Verbrugge et al. (2017) and correspond to a graphite negative electrode and NMC positive electrode. The remaining value are based on a parameterization of the LG M50 cell, from Chen et al. (2020).\n", "\n", - "We begin by importing pybamm, numpy and matplotlib" + "We first load in the MSMR model and specify the number of reactions in each electrode" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ - "%pip install pybamm -q # install PyBaMM if it is not installed\n", - "import pybamm\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" + "model = pybamm.lithium_ion.MSMR({\"number of MSMR reactions\": (\"6\", \"4\")})" ] }, { @@ -106,16 +130,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next we load in the model and specify the number of reactions in each electrode" + "Then we can inspect the parameter values" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "negative electrode:\n", + "X_n_0 = 0.43336, U0_n_0 = 0.08843, w_n_0 = 0.08611, a_n_0 = 0.5 j0_ref_n_0 = 2.7\n", + "X_n_1 = 0.23963, U0_n_1 = 0.12799, w_n_1 = 0.08009, a_n_1 = 0.5 j0_ref_n_1 = 2.7\n", + "X_n_2 = 0.15018, U0_n_2 = 0.14331, w_n_2 = 0.72469, a_n_2 = 0.5 j0_ref_n_2 = 2.7\n", + "X_n_3 = 0.05462, U0_n_3 = 0.16984, w_n_3 = 2.53277, a_n_3 = 0.5 j0_ref_n_3 = 2.7\n", + "X_n_4 = 0.06744, U0_n_4 = 0.21446, w_n_4 = 0.0947, a_n_4 = 0.5 j0_ref_n_4 = 2.7\n", + "X_n_5 = 0.05476, U0_n_5 = 0.36325, w_n_5 = 5.97354, a_n_5 = 0.5 j0_ref_n_5 = 2.7\n", + "positive electrode:\n", + "X_p_0 = 0.13442, U0_p_0 = 3.62274, w_p_0 = 0.9671, a_p_0 = 0.5 j0_ref_p_0 = 5\n", + "X_p_1 = 0.3246, U0_p_1 = 3.72645, w_p_1 = 1.39712, a_p_1 = 0.5 j0_ref_p_1 = 5\n", + "X_p_2 = 0.21118, U0_p_2 = 3.90575, w_p_2 = 3.505, a_p_2 = 0.5 j0_ref_p_2 = 5\n", + "X_p_3 = 0.3298, U0_p_3 = 4.22955, w_p_3 = 5.52757, a_p_3 = 1 j0_ref_p_3 = 1000000.0\n" + ] + } + ], "source": [ - "model = pybamm.lithium_ion.MSMR({\"number of MSMR reactions\": (\"6\", \"4\")})" + "parameter_values = model.default_parameter_values\n", + "\n", + "# Loop over domains\n", + "for domain in [\"negative\", \"positive\"]:\n", + " print(f\"{domain} electrode:\")\n", + " d = domain[0]\n", + " # Loop over reactions\n", + " N = int(parameter_values[\"Number of reactions in \" + domain + \" electrode\"])\n", + " for i in range(N):\n", + " print(\n", + " f\"X_{d}_{i} = {parameter_values[f'X_{d}_{i}']}, \"\n", + " f\"U0_{d}_{i} = {parameter_values[f'U0_{d}_{i}']}, \"\n", + " f\"w_{d}_{i} = {parameter_values[f'w_{d}_{i}']}, \"\n", + " f\"a_{d}_{i} = {parameter_values[f'a_{d}_{i}']} \"\n", + " f\"j0_ref_{d}_{i} = {parameter_values[f'j0_ref_{d}_{i}']}\"\n", + " )" ] }, { @@ -123,16 +181,115 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the MSMR model, the individual reactions are given variables names `x_k_j` where `k` can be `n` or `p` to denote the negative or positive electrode, and `j` is the reaction index. E.g. the variable for the second reaction in the negative electrode can be accessed as" + "We can plot the functional form of the open-circuit potential $U$, fractional occupancies $x_j$, and exchange current densities $i_{0,j}$ as a function of stoichiometry $x$" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "xn_2 = model.variables[\"x_n_2\"]" + "# get symbolic parameters\n", + "param = model.param\n", + "param_n = param.n.prim\n", + "param_p = param.p.prim\n", + "\n", + "# set up ranges for plotting\n", + "U_n = pybamm.linspace(0.05, 1.1, 1000)\n", + "U_p = pybamm.linspace(2.8, 4.4, 1000)\n", + "\n", + "# get maximum concentrations, reference electrolyte concentration and temperature\n", + "c_n_max = param_n.c_max\n", + "c_p_max = param_p.c_max\n", + "c_e = param.c_e_init\n", + "T = param.T_init\n", + "\n", + "# set up figure\n", + "fig, ax = plt.subplots(3, 2, figsize=(10, 10))\n", + "colors = [\"r\", \"g\", \"b\", \"c\", \"m\", \"y\"]\n", + "\n", + "# sto vs potential\n", + "x_n = param_n.x(U_n)\n", + "x_p = param_p.x(U_p)\n", + "ax[0, 0].plot(parameter_values.evaluate(x_n), parameter_values.evaluate(U_n), \"k-\")\n", + "ax[0, 1].plot(parameter_values.evaluate(x_p), parameter_values.evaluate(U_p), \"k-\")\n", + "ax[0, 0].set_xlabel(\"x_n\")\n", + "ax[0, 0].set_ylabel(\"U_n [V]\")\n", + "ax[0, 1].set_xlabel(\"x_p\")\n", + "ax[0, 1].set_ylabel(\"U_p [V]\")\n", + "\n", + "# fractional occupancy vs potential\n", + "for i in range(6):\n", + " xj = param_n.x_j(U_n, i)\n", + " ax[1, 0].plot(\n", + " parameter_values.evaluate(x_n),\n", + " parameter_values.evaluate(xj),\n", + " color=colors[i],\n", + " label=f\"x_n_{i}\",\n", + " )\n", + "ax[1, 0].set_xlabel(\"x_n\")\n", + "ax[1, 0].set_ylabel(\"x_n_j\")\n", + "ax[1, 0].legend()\n", + "for i in range(4):\n", + " xj = param_p.x_j(U_p, i)\n", + " ax[1, 1].plot(\n", + " parameter_values.evaluate(x_p),\n", + " parameter_values.evaluate(xj),\n", + " color=colors[i],\n", + " label=f\"x_p_{i}\",\n", + " )\n", + "ax[1, 1].set_xlabel(\"x_p\")\n", + "ax[1, 1].set_ylabel(\"x_p_j\")\n", + "ax[1, 1].legend()\n", + "\n", + "# exchange current density vs potential\n", + "# note: when solving pybamm sets uses a tolerances on the arguments of the exchange\n", + "# current density functions to avoid numerical issues when the surface stoichiometry\n", + "# is close to 0 or 1. This means that the exchange current density functions are not\n", + "# evaluated at exactly x = 0 or x = 1. For plotting we set this tolerance to 0.\n", + "pybamm.settings.tolerances[\"j0__c_s\"] = 0\n", + "for i in range(6):\n", + " xj = param_n.x_j(U_n, i)\n", + " j0 = param_n.j0_j(c_e, xj * c_n_max, T, i)\n", + " ax[2, 0].plot(\n", + " parameter_values.evaluate(x_n),\n", + " parameter_values.evaluate(j0),\n", + " color=colors[i],\n", + " label=f\"j0_n_{i}\",\n", + " )\n", + "ax[2, 0].set_xlabel(\"x_n\")\n", + "ax[2, 0].set_ylabel(\"j0_n_j [A.m-2]\")\n", + "ax[2, 0].legend()\n", + "for i in range(4):\n", + " xj = param_p.x_j(U_p, i)\n", + " j0 = param_p.j0_j(c_e, xj * c_p_max, T, i)\n", + " ax[2, 1].plot(\n", + " parameter_values.evaluate(x_p),\n", + " parameter_values.evaluate(j0),\n", + " color=colors[i],\n", + " label=f\"j0_p_{i}\",\n", + " )\n", + "ax[2, 1].set_ylim([0, 0.5])\n", + "ax[2, 1].set_xlabel(\"x_p\")\n", + "ax[2, 1].set_ylabel(\"j0_p_j [A.m-2]\")\n", + "ax[2, 1].legend()\n", + "\n", + "plt.tight_layout()\n", + "\n", + "# reset tolerances for simulations\n", + "pybamm.settings.tolerances[\"j0__c_s\"] = 1e-8" ] }, { @@ -140,29 +297,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next we define our experiment, before creating and solving a simulation" + "## Example solving MSMR using PyBaMM\n", + "Below we show how to set up and solve a CCCV experiment using the MSMR model in PyBaMM. We already created the model in the previous section, so we can go ahead and define our experiment, before creating and solving a simulation" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "At t = 275.616 and h = 2.99777e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 275.7 and h = 2.12769e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 275.452 and h = 5.2064e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 275.447 and h = 1.38705e-09, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -194,18 +352,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21de049b5a8a40e18d9f40118ca2e8a6", + "model_id": "89a93c1d13174601a3e215cabc5c3470", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.0940492544338145, step=0.06094049254433814…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.091312743854075, step=0.06091312743854074)…" ] }, "metadata": {}, @@ -214,10 +372,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -248,18 +406,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "665dd9d40ffd4664954ce188fd744061", + "model_id": "b73b1cbaee524062a200c192572a240f", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.0940492544338145, step=0.06094049254433814…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.091312743854075, step=0.06091312743854074)…" ] }, "metadata": {}, @@ -268,10 +426,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -301,22 +459,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -366,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, "metadata": {}, "outputs": [ { diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py index 4806b0134d..51df02f9a4 100644 --- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py +++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py @@ -84,11 +84,15 @@ def get_coupled_variables(self, variables): # interfacial currents from each reaction. if self.options["intercalation kinetics"] == "MSMR": N = int(domain_options["number of MSMR reactions"]) + j0 = 0 for i in range(N): + j0_j = self._get_exchange_current_density_by_reaction(variables, i) variables.update( - self._get_exchange_current_density_by_reaction(variables, i) + self._get_standard_exchange_current_by_reaction_variables(j0_j, i) ) - j0 = self._get_exchange_current_density(variables) + j0 += j0_j + else: + j0 = self._get_exchange_current_density(variables) # Get open-circuit potential variables and reaction overpotential if ( @@ -169,11 +173,10 @@ def get_coupled_variables(self, variables): # For MSMR model we calculate the total current density by summing the current # densities from each reaction if self.options["intercalation kinetics"] == "MSMR": - d = domain[0] j = 0 for i in range(N): - j0 = variables[f"j0_{d}_{i} [A.m-2]"] - j_j = self._get_kinetics_by_reaction(j0, ne, eta_r, T, u, i) + j0_j = self._get_exchange_current_density_by_reaction(variables, i) + j_j = self._get_kinetics_by_reaction(j0_j, ne, eta_r, T, u, i) variables.update(self._get_standard_icd_by_reaction_variables(j_j, i)) j += j_j else: diff --git a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py index f819ad2aa2..5550404c09 100644 --- a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py +++ b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py @@ -95,46 +95,42 @@ def _get_exchange_current_density_by_reaction(self, variables, index): j0 = phase_param.j0_j(c_e, c_s_surf, T, index) - # Size average. For j0 variables that depend on particle size, see - # "_get_standard_size_distribution_exchange_current_variables" - if j0.domain in [["negative particle size"], ["positive particle size"]]: - j0 = pybamm.size_average(j0) - # Average, and broadcast if necessary - j0_av = pybamm.x_average(j0) - - # X-average, and broadcast if necessary - if j0.domain == []: - j0 = pybamm.FullBroadcast( - j0, f"{domain} electrode", "current collector" - ) - elif j0.domain == ["current collector"]: - j0 = pybamm.PrimaryBroadcast(j0, f"{domain} electrode") - - d = domain[0] - variables = { - f"j0_{d}_{index} [A.m-2]": j0, - f"X-averaged j0_{d}_{index} [A.m-2]": j0_av, - } - - return variables + return j0 - def _get_exchange_current_density(self, variables): - options = self.options + def _get_standard_exchange_current_by_reaction_variables(self, j0, index): domain = self.domain + # Size average. For j0 variables that depend on particle size, see + # "_get_standard_size_distribution_exchange_current_variables" + if j0.domain in [["negative particle size"], ["positive particle size"]]: + j0 = pybamm.size_average(j0) + # Average, and broadcast if necessary + j0_av = pybamm.x_average(j0) + + # X-average, and broadcast if necessary + if j0.domain == []: + j0 = pybamm.FullBroadcast(j0, f"{domain} electrode", "current collector") + elif j0.domain == ["current collector"]: + j0 = pybamm.PrimaryBroadcast(j0, f"{domain} electrode") + d = domain[0] - j0 = 0 - # Loop over all reactions - N = int(getattr(options, domain)["number of MSMR reactions"]) - for i in range(N): - j0 += variables[f"j0_{d}_{i} [A.m-2]"] - return j0 + variables = { + f"j0_{d}_{index} [A.m-2]": j0, + f"X-averaged j0_{d}_{index} [A.m-2]": j0_av, + } + + return variables def _get_kinetics_by_reaction(self, j0, ne, eta_r, T, u, index): alpha = self.phase_param.alpha_bv_j(index) Feta_RT = self.param.F * eta_r / (self.param.R * T) - arg_ox = ne * alpha * Feta_RT - arg_red = -ne * (1 - alpha) * Feta_RT - return u * j0 * (pybamm.exp(arg_ox) - pybamm.exp(arg_red)) + return ( + u + * j0 + * ( + pybamm.exp(ne * (1 - alpha) * Feta_RT) + - pybamm.exp(-ne * alpha * Feta_RT) + ) + ) def _get_standard_icd_by_reaction_variables(self, j, index): domain = self.domain diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py index 7c3da8ba03..d7e95247e0 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py @@ -49,6 +49,7 @@ def test_well_posed_msmr_with_psd(self): "particle": "MSMR", "particle size": "distribution", "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", } self.check_well_posedness(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index a19bf4ae4a..013c0e42f1 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -162,6 +162,7 @@ def test_known_solution(self): "open-circuit potential": "MSMR", "particle": "MSMR", "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", } param = pybamm.LithiumIonParameters(options=options) parameter_values = pybamm.ParameterValues("MSMR_Example") @@ -203,6 +204,7 @@ def test_known_solution_cell_capacity(self): "open-circuit potential": "MSMR", "particle": "MSMR", "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", } param = pybamm.LithiumIonParameters(options) parameter_values = pybamm.ParameterValues("MSMR_Example") @@ -372,6 +374,7 @@ def test_get_initial_ocp(self): "open-circuit potential": "MSMR", "particle": "MSMR", "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", } param = pybamm.LithiumIonParameters(options) parameter_values = pybamm.ParameterValues("MSMR_Example") @@ -393,6 +396,7 @@ def test_min_max_ocp(self): "open-circuit potential": "MSMR", "particle": "MSMR", "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", } param = pybamm.LithiumIonParameters(options) parameter_values = pybamm.ParameterValues("MSMR_Example") diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py index 208c9858f7..ce2f461783 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py @@ -113,6 +113,7 @@ def test_msmr(self): "open-circuit potential": "MSMR", "particle": "MSMR", "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", } model = pybamm.lithium_ion.MPM(options) model.check_well_posedness() diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py index e77b3fe135..c6a4831e86 100644 --- a/tests/unit/test_parameters/test_parameter_values.py +++ b/tests/unit/test_parameters/test_parameter_values.py @@ -124,6 +124,7 @@ def test_set_initial_ocps(self): "open-circuit potential": "MSMR", "particle": "MSMR", "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", } param_100 = pybamm.ParameterValues("MSMR_Example") param_100.set_initial_ocps(1, inplace=True, options=options) From 78d277601aa2d5a8458d3bcff1d342cb4e5327db Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 21 Jul 2023 15:39:31 +0100 Subject: [PATCH 034/154] esoh coverage --- .../test_lithium_ion/test_electrode_soh.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py index 013c0e42f1..2709b3c98d 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py @@ -187,9 +187,6 @@ def test_known_solution(self): self.assertAlmostEqual(sol["Q_Li"], Q_Li, places=5) # Solve with split esoh and check outputs - esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( - parameter_values, param, options=options - ) ics = esoh_solver._set_up_solve(inputs) sol_split = esoh_solver._solve_split(inputs, ics) for key in sol: @@ -228,6 +225,22 @@ def test_known_solution_cell_capacity(self): self.assertAlmostEqual(sol["Up(y_0) - Un(x_0)"], Vmin, places=5) self.assertAlmostEqual(sol["Q"], Q, places=5) + def test_error(self): + options = { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "number of MSMR reactions": ("6", "4"), + "intercalation kinetics": "MSMR", + } + param = pybamm.LithiumIonParameters(options) + parameter_values = pybamm.ParameterValues("MSMR_Example") + + esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver( + parameter_values, param, known_value="cell capacity", options=options + ) + with self.assertRaisesRegex(ValueError, "solve_for must be "): + esoh_solver._get_electrode_soh_sims_split() + class TestElectrodeSOHHalfCell(TestCase): def test_known_solution(self): From 3ca2a8a1f0f7dd650c59881ab91e5bce8ce77c63 Mon Sep 17 00:00:00 2001 From: Ferran Brosa Planella Date: Mon, 24 Jul 2023 15:30:43 +0100 Subject: [PATCH 035/154] #3101 start implementing padding beginning starting_solution --- pybamm/simulation.py | 90 ++++++++++++++----- .../test_simulation_with_experiment.py | 38 ++++++++ 2 files changed, 104 insertions(+), 24 deletions(-) diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 5034543630..0b10267564 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -665,6 +665,45 @@ def solve( idx = 0 num_cycles = len(self.experiment.cycle_lengths) feasible = True # simulation will stop if experiment is infeasible + + # Add initial padding rest if current time is earlier than first start time + # This could be the case when using a starting solution + # TODO: not running, think better how to proceed! + if starting_solution is not None: + op_conds = self.experiment.operating_conditions_steps[0] + if op_conds.start_time is not None: + rest_time = ( + op_conds.start_time + - ( + initial_start_time + + timedelta(seconds=float(current_solution.t[-1])) + ) + ).total_seconds() + if rest_time > pybamm.settings.step_start_offset: + # logs["step operating conditions"] = "Initial rest for padding" + # callbacks.on_step_start(logs) + + kwargs["inputs"] = { + **user_inputs, + "Ambient temperature [K]": ( + op_conds.temperature or self._original_temperature + ), + "start time": current_solution.t[-1], + } + steps = current_solution.cycles[-1].steps + step_solution = steps[-1] + + step_solution_with_rest = self.run_padding_rest( + kwargs, rest_time, step_solution + ) + step_solution += step_solution_with_rest + + cycle_solution, _, _ = pybamm.make_cycle_solution( + steps, esoh_solver=esoh_solver, save_this_cycle=True + ) + current_solution.cycles[-1] = cycle_solution + current_solution.plot() + for cycle_num, cycle_length in enumerate( # tqdm is the progress bar. tqdm.tqdm( @@ -777,36 +816,20 @@ def solve( ) ).total_seconds() if rest_time > pybamm.settings.step_start_offset: - start_time = step_solution.t[-1] - # Let me know if you have a better name - op_conds_str = "Rest for padding" - model = self.op_conds_to_built_models[op_conds_str] - solver = self.op_conds_to_built_solvers[op_conds_str] - logs["step number"] = (step_num, cycle_length) - logs["step operating conditions"] = op_conds_str + logs["step operating conditions"] = "Rest for padding" callbacks.on_step_start(logs) - ambient_temp = ( - op_conds.temperature or self._original_temperature - ) kwargs["inputs"] = { **user_inputs, - "Ambient temperature [K]": ambient_temp, - "start time": start_time, + "Ambient temperature [K]": ( + op_conds.temperature or self._original_temperature + ), + "start time": step_solution.t[-1], } - # Make sure we take at least 2 timesteps - # The period is hardcoded to 10 minutes, the user can - # always override it by adding a rest step - npts = max(int(round(rest_time / 600)) + 1, 2) - - step_solution_with_rest = solver.step( - step_solution, - model, - rest_time, - npts=npts, - save=False, - **kwargs, + + step_solution_with_rest = self.run_padding_rest( + kwargs, rest_time, step_solution ) step_solution += step_solution_with_rest @@ -915,6 +938,25 @@ def solve( return self.solution + def run_padding_rest(self, kwargs, rest_time, step_solution): + model = self.op_conds_to_built_models["Rest for padding"] + solver = self.op_conds_to_built_solvers["Rest for padding"] + + # Make sure we take at least 2 timesteps. The period is hardcoded to 10 + # minutes,the user can always override it by adding a rest step + npts = max(int(round(rest_time / 600)) + 1, 2) + + step_solution_with_rest = solver.step( + step_solution, + model, + rest_time, + npts=npts, + save=False, + **kwargs, + ) + + return step_solution_with_rest + def step( self, dt, solver=None, npts=2, save=True, starting_solution=None, **kwargs ): diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index 19b4f37791..bd58770feb 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -670,6 +670,44 @@ def test_starting_solution(self): # test that the final time is correct (i.e. starting solution correctly set) self.assertEqual(new_solution["Time [s]"].entries[-1], 3600) + def test_experiment_start_time_starting_solution(self): + model = pybamm.lithium_ion.SPM() + + experiment = pybamm.Experiment( + [ + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 8, 0, 0), + ), + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 8, 20, 0), + ), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + solution = sim.solve() + + experiment = pybamm.Experiment( + [ + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 9, 0, 0), + ), + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 9, 20, 0), + ), + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + new_solution = sim.solve(calc_esoh=False, starting_solution=solution) + + # test that the final time is correct (i.e. starting solution correctly set) + self.assertEqual(new_solution["Time [s]"].entries[-1], 3600) + if __name__ == "__main__": print("Add -v for more debug output") From 31614b01d1c9ae489fe3cb97878146d5440b63ed Mon Sep 17 00:00:00 2001 From: Ferran Brosa Planella Date: Thu, 27 Jul 2023 10:15:02 +0100 Subject: [PATCH 036/154] #3101 fix starting_solution and start_time case --- pybamm/simulation.py | 14 +++++++++----- .../test_simulation_with_experiment.py | 4 ++-- 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/pybamm/simulation.py b/pybamm/simulation.py index 0b10267564..86769028cd 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -668,7 +668,6 @@ def solve( # Add initial padding rest if current time is earlier than first start time # This could be the case when using a starting solution - # TODO: not running, think better how to proceed! if starting_solution is not None: op_conds = self.experiment.operating_conditions_steps[0] if op_conds.start_time is not None: @@ -691,18 +690,23 @@ def solve( "start time": current_solution.t[-1], } steps = current_solution.cycles[-1].steps - step_solution = steps[-1] + step_solution = current_solution.cycles[-1].steps[-1] step_solution_with_rest = self.run_padding_rest( kwargs, rest_time, step_solution ) - step_solution += step_solution_with_rest + steps[-1] = step_solution + step_solution_with_rest cycle_solution, _, _ = pybamm.make_cycle_solution( steps, esoh_solver=esoh_solver, save_this_cycle=True ) - current_solution.cycles[-1] = cycle_solution - current_solution.plot() + old_cycles = current_solution.cycles.copy() + old_cycles[-1] = cycle_solution + current_solution += step_solution_with_rest + current_solution.cycles = old_cycles + + # Update _solution + self._solution = current_solution for cycle_num, cycle_length in enumerate( # tqdm is the progress bar. diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index bd58770feb..8816566bfb 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -703,10 +703,10 @@ def test_experiment_start_time_starting_solution(self): ) sim = pybamm.Simulation(model, experiment=experiment) - new_solution = sim.solve(calc_esoh=False, starting_solution=solution) + new_solution = sim.solve(starting_solution=solution) # test that the final time is correct (i.e. starting solution correctly set) - self.assertEqual(new_solution["Time [s]"].entries[-1], 3600) + self.assertEqual(new_solution["Time [s]"].entries[-1], 5400) if __name__ == "__main__": From 8893136eb061b6a7488998189687d34fc0cec21a Mon Sep 17 00:00:00 2001 From: Ferran Brosa Planella Date: Thu, 27 Jul 2023 10:48:29 +0100 Subject: [PATCH 037/154] #3101 update CHANGELOG --- CHANGELOG.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1897b4c94f..930bcbecaa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,9 +7,11 @@ ## Bug fixes - Fix to simulate c_rate steps with drive cycles ([#3186](https://github.com/pybamm-team/PyBaMM/pull/3186)) -- Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019. -- Error generated when invalid parameter values are passed. -- Thevenin() model is now constructed with standard variables: `Time [s], Time [min], Time [h]` ([#3143](https://github.com/pybamm-team/PyBaMM/pull/3143)) +- Always save last cycle in experiment, to fix issues with `starting_solution` and `last_state` ([#3177](https://github.com/pybamm-team/PyBaMM/pull/3177)) +- Fix simulations with `starting_solution` to work with `start_time` experiments ([#3177](https://github.com/pybamm-team/PyBaMM/pull/3177)) +- Error generated when invalid parameter values are passed ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132)) +- Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019 ([#3096](https://github.com/pybamm-team/PyBaMM/pull/3096)) +- Thevenin() model is now constructed with standard variables: `Time [s]`, `Time [min]`, `Time [h]` ([#3143](https://github.com/pybamm-team/PyBaMM/pull/3143)) ## Breaking changes From efc08b64cb5ddd5a32c390a63801674277aabbc4 Mon Sep 17 00:00:00 2001 From: Ferran Brosa Planella Date: Thu, 27 Jul 2023 12:00:53 +0100 Subject: [PATCH 038/154] #3101 improve coverage --- .../test_simulation_with_experiment.py | 21 +++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index 8816566bfb..1b60e35c8f 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -673,6 +673,27 @@ def test_starting_solution(self): def test_experiment_start_time_starting_solution(self): model = pybamm.lithium_ion.SPM() + # Test error raised if starting_solution does not have start_time + experiment = pybamm.Experiment( + [pybamm.step.string("Discharge at C/2 for 10 minutes")] + ) + sim = pybamm.Simulation(model, experiment=experiment) + solution = sim.solve() + + experiment = pybamm.Experiment( + [ + pybamm.step.string( + "Discharge at C/2 for 10 minutes", + start_time=datetime(1, 1, 1, 9, 0, 0), + ) + ] + ) + + sim = pybamm.Simulation(model, experiment=experiment) + with self.assertRaisesRegex(ValueError, "experiments with `start_time`"): + sim.solve(starting_solution=solution) + + # Test starting_solution works well with start_time experiment = pybamm.Experiment( [ pybamm.step.string( From fc0d71ea0002818ba3d65f1121433c77b47ce689 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 28 Jul 2023 16:44:36 +0100 Subject: [PATCH 039/154] update notebook --- .../examples/notebooks/models/MSMR.ipynb | 55 +++++++++---------- 1 file changed, 26 insertions(+), 29 deletions(-) diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 1e55ef3270..0ef9596dcd 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -18,7 +18,7 @@ "output_type": "stream", "text": [ "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -286,10 +286,7 @@ "ax[2, 1].set_ylabel(\"j0_p_j [A.m-2]\")\n", "ax[2, 1].legend()\n", "\n", - "plt.tight_layout()\n", - "\n", - "# reset tolerances for simulations\n", - "pybamm.settings.tolerances[\"j0__c_s\"] = 1e-8" + "plt.tight_layout()" ] }, { @@ -303,24 +300,24 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "At t = 275.452 and h = 5.2064e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 275.447 and h = 1.38705e-09, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 274.914 and h = 6.23205e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 274.889 and h = 6.85471e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -352,18 +349,18 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "89a93c1d13174601a3e215cabc5c3470", + "model_id": "b8335b7e2d4f4ffb85e1c6b3c5134b1a", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.091312743854075, step=0.06091312743854074)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.09113762530448, step=0.0609113762530448), …" ] }, "metadata": {}, @@ -372,10 +369,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -406,18 +403,18 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b73b1cbaee524062a200c192572a240f", + "model_id": "e324ec2ac34e471280ec259ede8e0b4c", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.091312743854075, step=0.06091312743854074)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.09113762530448, step=0.0609113762530448), …" ] }, "metadata": {}, @@ -426,10 +423,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -459,22 +456,22 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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HQafwrB8rUV012GGRfnrjjTcwatQoXH755Re9bvny5dBqta5XfHz8IEUYOOeX5QonOYYoaEUiHDx4EGvXrhU6HEIGjCazfuKkRtextrQyS8LRp59+il9++QUSToRrRyR79JuSs1BXoRcgsvBkMBggEolQWVnp1l5ZWQmz2XzRe1tbW7Fx40b85S9/8fnnzJs3D42Nja5XSUnJgOIWWltbG44ePQpg6FcyuJCe4/CgwfnkZOHChSgvLxc4IkIGhiazfrLbI1yT2ZKSEjQ2NgocESGDx2azuSoYXJWaBLVM6n4BwwDsBAEiC18SiQTjxo3Dtm3bXG12ux3btm3DhAkX/7f497//DYvFgj/96U8+/xypVAqNRuP2CmUHDhyAzWZDpEgEYxgkf13oFq0Wo2QyNDc34+GHHxY6HEIGhCazfmpvVUMhEUMrd36SP3LkiMARETJ43n//fRw9ehRyMYfJ6Z71Y2PSxqGxRi1AZOEtJycH69evx9tvv438/Hzcc889aG1txaxZswAAt99+O+bNm+dx3xtvvIGbb74ZkZGeRxAPdd1bDLLCJPnrQuy5ZDAGwHvvvYft27cLHRIh/RZ+H0cHqLVBBk4igVmrRmN7Bw4dOoSJEycKHRYhAdfZ2YnFixcDAKZkJEMucT/HnmFZWDsvFSK0sDdz5kxUV1dj0aJFqKioQHZ2NrZs2eJKCisuLgbLuq9dFBQU4Mcff8TXX38tRMiC665kMNTry17MSJkMv9fp8H5DA+bMmYO8vDxIJBKhwyLEbzSZ9RsDrTEOZq0aBRXVlARGwsaGDRtw+vRpqGVSTEpN9OiPSb8CtRWKwQ+MAADmzp2LuXPneu3bsWOHR1t6ejocDkeAowpe56/MhrP7DFH4qrkZ+fn5WLNmDR555BGhQyLEb7TNoB9kajOiKQmMhJH29nYsXboUAHDdiGRIL9hjKOI4tLeOFSI0QvzW2tqK/Px8AOFVlssbrUiEh6OMAIAlS5aEfGIfCU+0MtsPIkmUKwns8OHDcDgcYbnnioSPV155BWVlZdAp5LhieIJHf0zGlag+K/Vyp3dyFQeJhAHLOBAKa4MSplPoEAiP8vLyYLfbESXiYOTEvm8Y4n6j0eDDxgbktrXhwQcfxIcffih0SIT4hSazfXR+0nZXpw4mtQoMA9TW1qKiogLR0dHCBUdIADU3N2P58uUAgBsyU8GJRG79nESKpoZRfRorOppF8r43IDn+C+9xBpIxYz4Az4Q3Eppoi4E7hmGwyGTCfxcW4qOPPsJXX32FadOmCR0WIX1G2wz6KILrOe2mrUkNMSeCQeU8+pH2zZKhbM2aNaipqUGUWolxibEe/TEZ18DS6nt1y2QSIf2jB0JuIkuGnvOPsSVOaVIZ/qR31oeeO3cuOjo6BI6IkL6jyWwfqRsLXf/d0SqBTKWmwxPIkFdXV4eVK1cCAKaNTIPogox4qUKJhuoMn+OwLIPUvDfAWi0BiZMQf4TjMbZ9MSfSgCgRh5MnT+K5554TOhxC+owms30kK3VffVUbYmHW9OybJWQoevbZZ9HU1IQYnQaj4z230phSJsPa4Xu3UrKpBZKCvYEIkRC/NDc349ixYwDCuyyXNyqRCI8anclgy5Ytw5kzZwSOiJC+oclsH3F15VCoe35py1QmWpklQ1p5eTlefPFFAMCNWWlgL0hylGt0qK1I8TmORCZCzNfPByRGQvyVl5cHh8MBM8chKgxP/vJlulqN8QoFOjo6cN999wkdDiF9QpNZP+hUNtd/M6zBVZ7ryJEjsNvtQoVFSEA8/fTTaG9vx7BIHUZEGz36oxInw9Yp8nKnu3RlCUS1dPY7CQ7dWwxoVdY7hmGw0GiCmGHwxRdf4PPPPxc6JEJ8osmsH9SOBtd/Wy06RKoU4FgW7e3tOH36tHCBEcKzwsJCvPbaawCA6aPSPUrPqSIMqClL9DmOUsMhcvMLgQiRkH6hSga+DZdKcce5ZLD77rsPbW1tAkdEyMXRZNYPyqZS13+3NighYlmYNCoAtG+WDC1LlixBZ2cnUo2RSDEaPPoj4qbAbvP99pFmPwS2vSUQIRLSL65jbKU0mb2Yv0UaEM1xKCoqwrJly4QOh5CLosmsH+TnJYF1WjmoIgxuhycQMhQcO3YM77zzDgDgxlHpHv1aYzSqSuN8jqONEEP35Tre4yOkv5qamlBQUACAKhn4omBZzDOaADgTQbu/boQEI5rM+kF8IhfnP21VRcRSEhgZchYtWgS73Y6RMSYMi9R79GtMUwCH7xPv0hp/BGPrCkSIhPTL/v37AQDRHIdISv7y6TqVClcplejs7MTcuXPhcITCeX0kHNFk1g9sews0+p7i8BK5kVZmyZCyf/9+/Pvf/wYDYFpWmke/PmYYqkpNPscxGDmot70TgAgJ6T+qL+sfhmEw32iChGHwzTff4N///rfQIRHiFU1m/aSV9RR9dzARrooGBQUFsFioIDwJbQsWLAAAZCfEIEan8ehX6K8GA9+rsilnt/AeGyEDRZNZ/yVIJLgrIgIA8OCDD6K5uVngiAjxRJNZP6k7a1z/bWnTQiuXQSbmYLPZaE8RCWk7d+7E5s2bwTIMbhjpuSobNSwNtWVRPseJjmah2PlJIEIkZEC6k7+yZHKBIwktd0VEIl4sRllZGZYsWSJ0OIR4oMmsnxR1PSeiNDcoIOI41+os7ZslocrhcGD+/PkAgMuS4hClVnpcw8kn9WmspIKPeI2NED40NDTgxIkTAGhl1l8ylsXj55LB1qxZQ9vqSNChHfB91DHMBOQBsqJDQNx4AIDDxkJtMMOsVeNMTT39gJOQ9c033+C7776DiGVxfWaqR78pOQv1lZ7JYBeKj3FAtuOb3i9IS8L+KwwoV1gBR3ceWfAnlYzPkOMqoYMgA5KbmwsAiBWLoRP5PuyDuLtGpcJ1KhW2tbRgzpw52LFjh0f9aUKEQpPZPto1RoIrPgO4M4cgSmJh63Se+KXUxVBFAxLSHA4HHn/8cQDAxORh0Cm8PIIVTfA5DsMAw/a/22t/85SxuGf8UViZkn7HKpRoVYfQIZAB6t5iMJLqy/bbY0YTdra24vvvv8e7776L2267TeiQCAFA2wz67GdNDcBxYGxd0Ol6vmyclCoakND22Wef4ZdffoGEE+HaEcke/dFpl6CxWu1znMToTkjyf/ba5xiRjDnj82FlbF77CQk0Sv4auFixGPdEOg9Refjhh9HQ0CBsQIScExST2bVr1yIxMREymQzjx4/Hnj17Lnr9mjVrkJ6eDrlcjvj4eDz44IPo6AjsykkrawWS4gEAWnHP0X52mx7RGucv+qKiIjQ1NQU0DkL4ZLPZXBUMrkpNglomdetnGBadtst9jsOKGMTtet1rHyOT4dmbrOhgqOYsEQ5NZvlxR0QEhkskqKqqwsKFC4UOhxAAQTCZ/eCDD5CTk4PFixcjNzcXY8aMwbRp01BVVeX1+vfeew+PPfYYFi9ejPz8fLzxxhv44IMPXI9JA6lxmLM8iaqj0tXW3qqGQiqBRu6cBBw5ciTgcRDCl40bN+LIkSOQizlMTh/u0R+TcTla6hQ+xxluaoX4jPcnE8duHo19kvIBx0pIf9XX1+P06dMAaDI7UBKGwYJzyWAvv/yyay8yIUISfDK7evVq3H333Zg1axYyMzOxbt06KBQKbNiwwev1P/30EyZNmoQ//OEPSExMxA033IBbb73V52ouH0qNzqQBefVJV1trowycRAqzhvbNktDS2dmJRYsWAQAmZyRDLhG79bMiDh3tY32Ow4lZxOx4xWsfkxiPpxIPDDxYQgage79svFgMLSV/DdgVSiVuUqtht9sxe/Zs2O12oUMiYU7QyazVasW+ffswdepUVxvLspg6dSp27drl9Z6JEydi3759rsnr6dOnsXnzZtx0001er7dYLGhqanJ79dcRfSsAQHrq/E+iDLTGWFd5Lto3S0LFm2++idOnT0MlleDK1ESP/tiMiWht9F2PMyWyHlzZaa99H96khYX2yRKB0RYD/j1iNELJsti9e3evi0+EDBZBJ7M1NTWw2WwwmdyPxzSZTKioqPB6zx/+8AcsXboUV155JcRiMZKTkzF58uRetxksX74cWq3W9YqPj+93vD8rnDFxZachlfd8updrzJQERkJKR0cHli5dCgC4bkQKpBecU89JJGhpHOVzHIlMBNPWl7z2Wa4YhQ+0xwYeLCEDRJUM+GfkxJh7LhnsscceQ21trcARkXAm+DYDf+3YsQPLli1z7dX5+OOPsWnTJjz55JNer583bx4aGxtdr5KS/pcFKuUawRoiAQA6bU87y0UhWus8+vPQoUNwOIK/biYJb6+88grOnj0LnUKGCckJHv0x6VejvVXq5U53qepyiGq97IflOLw4sZGPUAkZMFqZDYw/6vVIk0hRW1uLefPmCR0OCWOCTmYNBgNEIhEqKyvd2isrK2E2m73es3DhQtx222246667MGrUKPz2t7/FsmXLsHz5cq/7dqRSKTQajdtrICyJzrg0TM92ha4uHYwaFRg4V5t7S14jJBg0Nzdj2bJlAIDrM1PBXbCHUCyTo6Em0+c4MiWHqC3eV2VrpmZjr7Rs4MESMkC1tbUoLCwEAGTSZJZXHMNg4bknq6+//jp2794tcEQkXAk6mZVIJBg3bhy2bdvmarPb7di2bRsmTPBepL2trQ0s6x626Nwv48FYEa2OcR7zqWzt+UXd1qSGhBMhUuXsoyQwEszWrFmDmpoaGFRKXJoY59EfnTYZ1g7f56mkSU6Dba7zaGcUCjwzqpCPUIkf/C1x2NDQgDlz5iA6OhpSqRRpaWnYvHnzIEU7eLq3GCSIxdBQ8hfvxikUuFmjgcPhwOzZs2Gz0R55MvgE32aQk5OD9evX4+2330Z+fj7uuecetLa2YtasWQCA22+/3e3xxYwZM/DKK69g48aNOHPmDLZu3YqFCxdixowZrkltIJ0xOH9Q5RXHXW0drRLIVGqYtSoAtG+WBK+6ujqsXLkSADAtKw2iCz4YylRq1FWm+RxHqeEQ8eU/vPadvikLRVzDgGMlfedviUOr1Yrrr78ehYWF+PDDD1FQUID169cjNjZ2kCMPvO4tBlm0KhswD0UZoWFZ5ObmYt26dUKHQ8KQ4MfZzpw5E9XV1Vi0aBEqKiqQnZ2NLVu2uJLCiouL3VZiFyxYAIZhsGDBApw9exZRUVGYMWMGnn766UGJ96CmEZMASAv2AGN/5WpXG2IRrdXg8NlKWpklQevZZ59FU1MTorVqjImP9ug3Dp+CqhLfHwrTHEfBdrR6tDOREViWfJSXWEnfnV/iEADWrVuHTZs2YcOGDXjsscc8rt+wYQPq6urw008/QSx2lmRLTEwczJAHjSv5iyazARPJcbjfEIUnqyoxf/583HLLLR6J3YQEkuArswAwd+5cFBUVwWKxYPfu3Rg/fryrb8eOHXjrrbdc/89xHBYvXoyTJ0+ivb0dxcXFWLt2LXQ63aDEuldWBohEYBtroNT0fBaQqaiiAQluFRUVePHFFwEAN45KB8swbv0KXQRqy5N8jqPRi6H90ntd2dybktHIBvY0PuKuPyUOP//8c0yYMAFz5syByWRCVlYWli1bdtFHxHyWORxMlPw1OP5Xp8NIqQyNjY145JFHhA6HhJmgmMyGkhbGCubcipZO2fPGz7AGV63ZI0eOUBFpEnSefvpptLe3IyFCh8xoo0e/IWEKbF2+V2VT238B22X1aGfiY7A69iAvsZK+60+Jw9OnT+PDDz+EzWbD5s2bsXDhQqxatQpPPfVUr38On2UOB0t1dTWKi4sBAJlUliugROeSwRgA77zzDn744QehQyJhhCaz/dAS76ytp3bUu9qsFi0iVQqIWBatra2u7FlCgkFRURFeffVVAMD0UelgLliVVRtMqD7re3KiN4ih+foNr33fTjPTAQkhwm63w2g04rXXXsO4ceMwc+ZMzJ8//6L7HfksczhYurcYJEkkUFHyV8CNlstxi1YHAJg9ezY6OzuFDYiEDZrM9kOFWQIAUDYWu9paG5QQsSxMGmcSGO2bJcFkyZIl6OzsRKoxEqkmg0e/LnoyHHbfbwcpdd+DsXtOWB3pw7HOQKuyQuhPicPo6GikpaW5Jc2OGDECFRUVsFo9V90B/sscDobuLQa0Kjt4HoyKgk4kwuHDh/HSS95L9xHCN5rM9sMJvfPNXnq2J9Gl08pBFWGgfbMk6BQUFODtt98G4NwreyGdKQ7VpTE+x4kyclB/+67Xvo+vU8DBeO0iAdafEoeTJk3CyZMn3bZDHT9+HNHR0ZBIJAGPebBQJYPBpxOJ8JAhCgCwePFinD17VuCISDigyWw/5KprAADSgl/AnPcVVEXEwqxxTmZpZZYEi0WLFsFut2NkjAnDIvUe/aqoawD4nokml37ptb1r7Ag6tlZg/pY4vOeee1BXV4f7778fx48fx6ZNm7Bs2TLMmTNHqL9CQFAlA2H8VqtFtkyOlpYWPPTQQ0KHQ8IATWb74YC4AoxcDsbSDo1O7GqXyI2I1tHKLAkeeXl5+Ne//gUGzrqyF4qITULNWd8ldKKjWSh++tRr31tX0T5Zoc2cORMrV67EokWLkJ2djby8PI8Sh+XlPccOx8fH46uvvsLevXsxevRo3Hfffbj//vu9lvEKVRUVFSgtLQUDYARNZgcVey4ZjIWzBvI333wjdEhkiBO8zmwocjCAPTEGTP4paGUWNJ77TOBApGtltqCgAFardUg9siOhZ8GCBQCA7IQYxOg89zjKtVejrc33OEkFH3ttb580Bl8rjwwoRsKPuXPnYu7cuV77duzY4dE2YcIE/PzzzwGOSjjnJ38pWVq3GWwjZDLcqtPjnw31mDNnDg4ePAipVCp0WGSIop/wfmqM0wEA1J01rjZLuwY6hQwyMYeuri4UFBQIFB0hwE8//YRNmzaBZRjcMNJzVTYqMR215ZE+x4mLAWS5Wz07RCKsHd/AQ6SE8I+2GAjvPoMBkSIRjh8/jlWrVgkdDhnCaDLbT6VRzi+dou6Mq625QQERx7lWZ2mrARGKw+HA/PnzAQCXJcYhSq30uIaTTfI9EAMMO/BPr10N12Zjj5SSO0hwouQv4alFIvw9ylnT+qmnnqKSlSRgaDLbT/l657NZWWFPOSKHjYXGEO2qaEBJYEQo27Ztw44dOyBiWUzNTPXoN6eMRn2lzuc4w6K7ID3yk0c7I5Fg9RiayJLg5VqZpbJcgpqh0eAyuRzt7e144IEHhA6HDFE0me2nPQpnTUfuzCFw4p4vo0IfTeW5iKAcDgcef/xxAMDE5ATolXL3CxgGDuYKn+OwLIOE3W967SubNgbHxDVe+wgRWllZGcrKysACyKCVWUExDIMFJjM4MPjss8+wadMmoUMiQxBNZvupiGsAE6EHY7dBq+v5MnISo+tYW1qZJUL4/PPPsXfvXohFIlw7IsWjPyb1EjTWqHyOk2Ruh/hUnkc7o1Li2RGn+AiVkIDoXpUdLpFAQclfgkuVSnGb3lkW8N5770V7e7vAEZGhhn7KB6AzMRoAoBX3pIPbbXrXymxhYSGam5sFiY2EJ5vN5qpgcHVaItQy9+xhhmFh7brM5zgijkHsD6957Ts1fSTOipoGHiwhAdI9maX9ssFjtsEAE8fhzJkzWLFihdDhkCGGJrMDUBvtTKpRdfQcI9neqoZSKoHm3CTiyBEqW0QGz8aNG3H48GHIxByuSU/26I/JGI+WeoXPcVKimsCVeFbjYPQ6PDM8n5dYCQkU1zG2NJkNGkqWxWNGZzLYM888g5MnTwocERlKaDI7AEVRDgCAvOqEq621UQaxVEb7Zsmg6+zsxOLFiwEAU9KToZCI3fpZEYeO9rE+x+EkLMzbXvbad/CmNNSz9IiQBC+Hw3FeJQO5j6vJYLpBpcYkhRIWiwX33nsvHA6H0CGRIYImswNwSOt81Co7lXteKwNNVAxVNCCD7s0338SpU6egkkpwZWqiR39sxkS0NvpeqUrTV0NUVezRzpiNWBVH388kuJWVlaGyshIiAOlUpD+oMAyD+SYTxAyDLVu24JNPPhE6JDJE0GR2AH6WlQEMA1H5GUgVIle7XEMVDcjg6ujowNKlSwEA141IgVTsfrgfJ5GgpXGUz3GkchGMW1702rdregLa2M6BB0tIAHWvyiZLpJBT8lfQSZRI8JeICADAAw88gNbWVoEjIkMB/aQPQCPbASbWmQR2/kmhLBdFK7NkUL3yyis4e/YstHIZrkhO8OiPSb8a7a2+V6nSFCVgGz1LbjGJ8XjRdNDLHYQEl+7JLJ38FbzujohErFiMkpISPPnkk0KHQ4YAmswOUHu8AQCgYXqyu7u6dDBpVGAAVFdXo6qqSqDoSDhobm7G8uXLAQA3jEyFWCRy6xfL5GioyfQ5jlzFIXLLS177vrohEl2MfeDBEhJgdIxt8JOzLB4/lwy2atUq5OdTUikZGJrMDlBljPMNU9la5mpra1JBynGIUDmzxmmrAQmkF154AdXV1TColLg0Mc6jPzptMqwdnJc73aVxJ8C2epbccoxIxuuR9D1Mgt/5yV80mQ1uU1RqTFGq0NXVhTlz5lAyGBkQmswO0Gl9FwBAXn7M1dbRKoFMpaHDE0jA1dXVYeXKlQCAaVmpEF2wR1CmUqO+0vM42wuptBwiNv/Da99H11JGOAkNpaWlqK6uBgdK/goF84xGSBkG27dvx8aNG4UOh4QwmswOUJ66DgAgPf6LW7smKhZmDSWBkcB67rnn0NjYiGitGmPiYzz6jcOnoNPqe1U2tesgGGuHR3vX2BH4l+aYlzsICT6u5C+pFDJK/gp6cRIJ/hoZCQB46KGH0NREh7GQ/qGf9gHaJy0HI5GAbayBUtMzaZAqzTDraGWWBE5FRQVefNFZeeDGrHSwDOPWr9DqUVue5HMcbYQYui2veu175yraJ0tCR099WdpiECr+rI/AMLEY5eXlrjrZhPiLJrMDZGVscAxzrojplDZXO8NGulZmjxw5ArudJgWEX8uWLUNbWxsSInTIjDF69BuGTYGtS+TlTnepzbvA2Lo82tsnjsYW5SleYiVkMFDyV+iRsCwWmMwAgJdeegkHD1LVFOI/mszyoCnOWTNP7ah3tVktWkSplRCxLFpaWlBUVCRUeGQIKioqwquvOldTp49KB3PBqqwqMgrVZz1LdF0owsBB/c1bnh0si1fHN/MRKiGDwi35S0qT2VAySanEDSo1bDYbZs+eTYs/xG80meVBmcm5vUDZ2HNqUku9CiKRCEa1EgDtmyX8Wrp0KaxWK1KMkUg1GTz69bFT4LD7/vFOrd4OxksWcdOUbPwkK+ElVkIGQ1FREWprayn5K0Q9ZjRCwbLYuXMn3nnnHaHDISGGJrM8OK53Js7ISo+62ro6RVBFGKiiAeFdQUEB3n77bQDOVdkLaU0xqC6N9TmO0SSC8jsvGcRiMV7IrhhwnIQMpu4tBmlSKSSU/BVyzGIx7jmXDPbII4+gvr7exx2E9KCfeB7sVToPRZAc/wXMeV9RlT6GjrUlvFu8eDFsNhsyY4wYFqn36FdHTQYcjOeNFxhetMlre/X12TgkoYM+SGih+rKh73Z9BJIlElRXV2P+/PlCh0NCiO+aPQQAECvR4UAvfQXiGjBqFdDcAo1OjMY65/n1EoWJjrUlvDpw4AA++OADAM4KBheKiE1E9VkTfE1lo6NZKN7/j0c7I5fhuZFn+AjVKzErRrY6ETpWAiB0iqQnQCJ0CMSHnsks1UUOVWKGwUKTCXeWlGDdunX485//jEsvvVTosEgIoMlsH91b34jNF+m3JcaAPXQcOlkHGuHMIHc4Il2T2WPHjsFqtUIioV+KpP8WLFgAAMiOj0GMTuPRL9ddjbY236uyScc+9NpefONoFHK5AwuyF5doU/BMYQHMp7YFZPyAMl0rdATkIhwOh2ubAZXlCm2XK5T4L7UGXzQ3Yfbs2di1axdEIt9VWUh4o20GfRTXWIFYhanX/vpY58RC1Vnrauto00CvkEPKcejq6sLx48cDHicZunbt2oUvvvgCLMNgWpbnqV6GhFTUlnkmg10oPsYB2X7PCSWj0eCZtAJeYr3QaE0y1h39GeaGswEZn4S3M2fOoL6+HmKGQQolf4W8vxuNULEs9u7di/Xr1wsdDgkBNJn1Q5a094lCcZRzNUxRd9rV1tIgh4gTw6xVAaB9s6T/HA4HHn/8cQDApYlxiFKrPK4RK670OQ7DAMPy/um17/j0DFSxrQML1IsoWQReOHUEcmsb72MTAvRsMUiXSiFhfD+ZIMEtiuNwr8H5+/bxxx9HdXW1wBGRYEeTWT9ketaVdzmibQEAyAp79sY67Cy0RkoCIwO3bds27NixAyKWxfWZnquypuQs1Fd6JoNdaFh0FyRHd3m0s4YIPJt01MsdA8OAwVMWGQwtlFAWaGvXrkViYiJkMhnGjx+PPXv29HrtW2+9BYZh3F6yEH48373FIJPqyw4Zt+r0yJBKUV9fj0cffVTocEiQo8msHzKa63rt+1lZBgDgzhwCJ+75siq0ZirPRQbE4XC4MnsnJCdAr/SS4CK6wuc4LMsgbvcbXvv235iMRqZjQHF689/6LEw8/TPv4xJ3H3zwAXJycrB48WLk5uZizJgxmDZtGqqqev8QodFoUF5e7nqF8sEudIzt0MMxDBadOxnszTffxM6dOwWOiAQzmsz6IaOq96M9q9hWMKYoMHYbtLqeL6tIEkUrs2RAPv/8c+zZswdikQjXjUjx6I9OuwSN1Z7JYBdKMrdDcsrzqEgmxoxVcfx/0DJII5Bz9EfexyWeVq9ejbvvvhuzZs1CZmYm1q1bB4VCgQ0bNvR6D8MwMJvNrpfJ1HtOQDA7P/mLynINLdlyOX6n1QIAZs+eja6uizweJWGNJrN+iGitQZQsotd+S4Lzl4FW3LPv0GaLgFnjnMyePn0aLS0tgQ2SDCl2u91VweCq1ESoZe7JLQzDost2mc9xRByD2B9e89q3c1osOhj+f0nkMBFQdzTyPi5xZ7VasW/fPkydOtXVxrIspk6dil27PLeUdGtpacGwYcMQHx+P3/zmNzhy5MhghMu7U6dOobGxERJK/hqScgxR0LAiHDx4EGvXrhU6HBKkaDLrp3SZsde+mhgFAEDVXulqa29RQyWTuiYhR4/yvy+RDF0bN27E4cOHIRNzmJyR7NEfk3E5muuUPsdJjmoCV+JZqYBJjMdLZs/V2oHK1iRjRv63vI9LPNXU1MBms3msrJpMJlRUeD/JLT09HRs2bMBnn32Gd999F3a7HRMnTkRpaWmvf47FYkFTU5PbKxicn/wlpuSvISeC4/BglDMZbOHChSgvLxc4IhKMaDLrpzSIe+07HWkDACiqTrjaWpukEMvkdHgC8VtnZycWL14MAJicPhwKifv3HisSob39Ep/jcBIW0dte9tr31Q2RsPF8eAEDBo9R9nFQmzBhAm6//XZkZ2fjmmuuwccff4yoqCi8+uqrvd6zfPlyaLVa1ys+Pn4QI+4d1Zcd+m7R6jBKJkNzczMefvhhocMhQYgms35Kb++9vNBBTQMAQHp6v6uNAQNtVKxrqwHtmyV99dZbb+HkyZNQSiW4KjXJoz8mYxLaGn3/Ak/TV0NUVezR7shIxuuR/H8/ztBnYWQZfZ8PFoPBAJFIhMrKSrf2yspKmM3mPo0hFosxduxYnDx5stdr5s2bh8bGRterpKRkQHHzpXtlNpMms0OW6NzJYAyA9957D9u3bxc6JBJkaDLrp9T6sl779kjLAI6DqPwMZIqeE0tkaqpoQPzT0dGBpUuXAgCuG5ECqdj9sD6RWIKWptE+x5HIRYj6+h9e+z65lv9jP+UiGe47td/3hYQ3EokE48aNw7ZtPQdh2O12bNu2DRMmTOjTGDabDYcOHUJ0dHSv10ilUmg0GreX0Ox2e8/KLJXlGtKyZHL8XqcDAMyZMwdWq1XYgEhQocmsn5KqT0HMet9q0MZ2golz/jLQanoe3bJiqmhA/LNu3TqUlpZCK5dhQnKCR39M+pXoaPF9NHKashSies/yTF3ZI7BRe4yXWM93pyoVpsbeP/CRwMjJycH69evx9ttvIz8/H/fccw9aW1sxa9YsAMDtt9+OefPmua5funQpvv76a5w+fRq5ubn405/+hKKiItx1111C/RX65eTJk2huboaUYZBMyV9D3n2GKESIRMjPz8eaNWuEDocEEZrM+omzdyFZGdNrf0tCJABAwzS72rqsWpjOnQJWWVlJp5mQi2ppacGyZcsAANdnpkJ8wbnkYpkMTfUjfY4jV3IwbHnJa98/r+J3nyzgPOnrzqM7eB+X+DZz5kysXLkSixYtQnZ2NvLy8rBlyxZXUlhxcbFb4kx9fT3uvvtujBgxAjfddBOamprw008/ITMzU6i/Qr90bzHIkErBUfLXkKcVifBwlDMJe8mSJUGz1YUIjyaz/ZDG9f54rcLkXC1TtfScQd/apIaU4xCpdFY7oNVZcjEvvPACqqurYVApcFlSnEd/dNo1sLT1nojYLVV8EmyLZ2ksyxWjsEnV+97I/prDGqCw8n8cLumbuXPnoqioCBaLBbt378b48eNdfTt27MBbb73l+v/nn3/edW1FRQU2bdqEsWPHChD1wHRPZqm+bPj4jUaDS+RytLW14cEHHxQ6HBIkaDLbD2ldtl77Tuid+3jkZfmuNkubGHKNjrYaEJ/q6+vx3HPPAQBuGJkGEev+IypVqlBfleFzHKWGQ+SXXvbKMgxev6L3JMb+SlHF4+ajlJRBBldPJQP+93+T4MQwDBaZTBAB+Oijj/DVV18JHRIJAjSZ7YfUloZe+3LVNQAAyYl9bu0aQyyV5yI+Pffcc2hsbES0Vo3sBM/tLKbkyei0iLzc6S7NfhiMpd2jveXqbHwn5//Y0gfbHBA5ev+QRwjfbDYbcnNzAdDKbLhJk8rwJ70egPOJREcH/0dxk9ASFJPZtWvXIjExETKZDOPHj8eePXsuen1DQwPmzJmD6OhoSKVSpKWlYfPmzYMULZBWfabXvgPiCjAKBdimWqi0PRnoEoWJVmbJRVVWVuKFF14AAEzLSgN7wR5AuUaH2grPgxMupNGLod3ipV4ox2HtpTW8xHq+y7VpuPrUT7yPS8jFHD9+HC0tLZAzDJIkvpMhydAyJ9KAKI7DyZMnXU+zSPgSfDL7wQcfICcnB4sXL0Zubi7GjBmDadOmoarKMwMbcB7deP3116OwsBAffvghCgoKsH79esTGxg5azIbmSuglWq99DgawJTpX1LTKniNCGTbSVZ7r8OHDcDj4T8AhoW3ZsmVoa2tDfIQOI2NMHv1RSVNg6+zDqmzbHrBdnmVrGqaMwT4Jv6fnMGCQU13p+0JCeNa9xSBDKqPkrzCkEonw6LlksGXLluHMmd4XmcjQJ/hkdvXq1bj77rsxa9YsZGZmYt26dVAoFNiwYYPX6zds2IC6ujp8+umnmDRpEhITE3HNNddgzJgxgxp3mtxzstGtIU4HANDY6l1tlg4totRKiFgGzc3NKC72LGJPwldxcTHWrVsHAJg+Kh3MBb+cVREGVJ8d5nMcvUEM9ddveLQzEglWjznr5Y6BuVE/EiPP0rYZMvgo+YtMV6sxXqFAR0cH7rvvPqHDIQISdDJrtVqxb98+TJ061dXGsiymTp2KXbt2eb3n888/x4QJEzBnzhyYTCZkZWVh2bJlsNm879cL1HniqWzvb6ClRudERNHYM2FtaVBCJBLBqHaW6KJ9s+R8S5cuhdVqRbIxEqnGSI9+fdy1cNh8/7im1n4Hxsuqf8UNY3BMzO8WAzErxn2FtGWGCIOOsSUMw2Ch0QQxw+CLL77A559/LnRIRCCCTmZrampgs9lctRC7mUwmVFRUeL3n9OnT+PDDD2Gz2bB582YsXLgQq1atwlNPPeX1+kCdJ552kQ3nR3XO8kSykp5f9LZOETSRRto3SzwcP37cVTZpepbnqqzWGIPqUt/baAxGDqrt//RoZxQKPJfJ/yO4mZoRiKujJwxk8J2f/EXH2Ia34VIp7jiXDHbfffehrY3/ai0k+Am+zcBfdrsdRqMRr732GsaNG4eZM2di/vz5rke0FwrUeeJpjb3vE9ytcE7EJSdywYp6JiZKfQxVNCAeFi9eDJvNhhHRRiQa9B79GtNk52ZsH1JKv/TaXnhjFopFDQOM0p1KrMT/O+796QkhgXbs2DG0tbVR8hcBAPwt0oBojkNRUZHrwBkSXgSdzBoMBohEIlRWuk8MKysrYTabvd4THR2NtLQ0iM47FWnEiBGoqKjwelZzoM4TT646CZbx/uU7K2oCa4gEY+2AVtcTp1hOK7PE3YEDB7Bx40YAwI1ZaR79+phhqCrtfX92N7OZheKnTz3aGY0Gz6UcH3CcF/qzPAn61lrexyWkL7q3GGTKZBBR8lfYU7As5hmd75PPPfccjh/n/z2PBDdBJ7MSiQTjxo3Dtm3bXG12ux3btm3DhAkTvN4zadIknDx5Ena73dV2/PhxREdHQzKIn9Blne1IUHifcANAR5KzTyPt2Y7gcPRUNMjPz0dnZ2dggyRBb+HChQCA7PhoxOo9K2Qo9FeDge9f1sNPfOK1/dhNGagStQwsyAtEySLwp/wdvI5JiD8o+Ytc6DqVClcplbBarZg7dy5VDAozgm8zyMnJwfr16/H2228jPz8f99xzD1pbWzFr1iwAwO2334558+a5rr/nnntQV1eH+++/H8ePH8emTZuwbNkyzJkzZ9BjT5V4PhLuVhOtBACorT1JN+2tGugUckg5ETo7O3HixImAx0iC188//4z//Oc/YBjnaV8XMiSkorYsyuc4cTGAbN/XHu2sIQIrE4/yEuv5ZrNRkFtpXxoRDk1myYUYhsF8owkShsHWrVvx4YcfCh0SGUSCT2ZnzpyJlStXYtGiRcjOzkZeXh62bNniSgorLi5GeXlPbcz4+Hh89dVX2Lt3L0aPHo377rsP999/Px577LFBjz3V3vuX74zBWV1BXteTeNPaIIdYIqF9swQAMH/+fADAZYlxMGpUHv1ixZW+B2GAYQff99qVe2MyGhl+T8ZJUsbit0e/5XVMQvzR1dWFvLw8AFTJgLhLkEhwV0QEAODBBx9Ec3OzwBGRwcL5viTw5s6di7lz53rt27Fjh0fbhAkT8PPPPwc4Kt9S23ov83VA04BJAGRn8oBhzi0TDgcDTVQMzBo1imobcPjwYcycOXNwgiVBZdu2bfj2228hYllMzUz16DcNz0J9Ze8r/92GRdsg3f6jRzsTY8bqOP4/LN1v4ejYWiKo/Px8tLe3Q8myGCam5C/i7q6ISPynqQklZ89iyZIlWLlypdAhkUEQFJPZUJVWVwqovfftkZZhNseBKzwCLpVFl9W5x1ehMVMSWJhzOByuVdkJwxMQoVR4XsRd4XMchgUS9rzpte+nabHoYPitKztGk4zrDmzndUxC/NW9xSBTKvU48pkv+bdejo2xwV927tJWI2asOwTQ/lAXGcvicaMJ95wtxZo1a3DnnXciKytL6LBIgNFkdgDiaosg16Wg3eb5KLeN7QQTFw0UlkCvY1Fd5ZzMiqRRtM0gzP3nP//B7t27IRaJcO2IZI/+6LRLUF/tu+rGcLMF4m/3e7QzifF4ycT/99YDDfwcOELIQJxfySBQvjXVIp/nQ0YCIV9Xg6umXgLd1n1ChxJUrlGpcJ1KhW0tLZgzZw527NjhUb+bDC19msz259QsvkpgBTPWYUeKMgaHmk577W9JiISysAQargXVcL7x2rr0rsns6dOn0draCqVSOWgxE2HZ7XYsWLAAAHBVaiI0cvdfyAzDost2mc9xWBGDmB9f9dr39Q0GdDHlXvv66xrdCFy6/ytexww3Bw8e9PuezMxMcBytOZyve2U2SyYPyPiMTIZdstKAjB0IS8aewZpfdHDUNwgdSlB5zGjCztZWfP/993j33Xdx2223CR0SCaA+vUvqdDq/PtUwDIPjx49j+PDh/Q4sVKSKlOhtDazcLEEKAGVbBYBEAEBbswZqmRQqqQQtFiuOHj2Kyy7zPXkhQ8MHH3yAQ4cOQSbmMDnd8+cjJv0y1Fb6/nCTYmyGeFuBR7sjfThej+B3VZZlWNxfVsjrmOEoOzsbDMP0uWQQy7Jh8z7aV52dnThw4ACAwFUysKUkwMp4X6AIRmdFTfjp5jGY8Catzp4vVizG3yINWFNTjYcffhgzZsyATqcTOiwSIH3+yP/hhx8i4lyW4MU4HA7cdNNNAwoqlKR29p4MczzCghQAiqoTgCQRANDeLIVEroBZq8bJqlocPnyYJrNhorOzE4sWLQIATE4fDoXUPXmFFYnQ3jHO5zichIX525e99n16nbIvh4X55b90I5F6ehO/g4ap3bt3IyrKd7k1h8NB+/y8OHr0KDo6OqBiWSSIxQH5M6qHedZ7DnbPmw/g8jHpEB3w/IAbzu6MiMBnTY04U1WFhQsX4qWXXhI6JBIgfZrMDhs2DFdffTUiIyP7NOjw4cMhDtAbTbBJben9FKR9ymrcBEB6MhfIvN7VrjXGIfrcZJb2zYaPt99+GydPnoRSKsGVqUke/TEZk1BT5nu1KVVfC66yyKPdNjod72vzeYm1m4SVYO4Z/x+PE0/XXHMNUlJS+rw6dPXVV0MuD8yj9FB1fn3ZQCV/HTfbfV8UhNZc14GHjnBAV5fQoQQNCcNggdGEv5SW4OWXX8asWbNwySWXCB0WCYA+1Zk9c+ZMnyeygDNLPz4+vt9BhZK0qt4fRx0WV4FRKiGqKoZc2fO5QaaiigbhpqOjA0uXLgUAXJeRDJnY/XOkSCxBS9Non+NIZCIYt3pfXXj/Gv7LRs/UZCC6voT3ccPR9u3b/XrMuXnzZkRHRwcuoBDUnfw1Uhq45K/d2uBP/PJmt/QsSmbQRO1CE5RKTFerYbfbMXv2bLfTQ8nQMaDffqWlpWH/jaFvrYVB6n37hYMBbEmxAACdpufrxIgiqaJBmHn11VdRUlICrVyGCSnDPPpjM65CR4vvmpmp6rMQ1VV6tFsvz8LnKn5PlFOLVfh/BT/xOibxtHPnTlgsFqHDCAmBPvmLUSmxV3I2IGMPhifSjoCJpQ9AF3okyggFy2L37t3YsGGD0OGQABjQZDYzMxOFhYU8hRK60uS974Grj3VWdVCj0dXWadXBpHFOZisqKlBTE5orAaRvWlpasGzZMgDA9ZmpEItEbv1imQwNtZk+x5EpOUR96WVVlmHwxkR+T/oCgDvlidC11fE+LnE3ffp0nD0buhOowWK1WgOe/NWZmsD7nvPB1Mxa8MlvDEKHEXRMYjHmRjq/Lo899hhqa3vfHkhC04Ams33Nyh3qUiHtta80yvnOqGzuKfXS0qSCTMwhQuncD0dbDYa2F198EVVVVYhUKXBZUpxHf3TaZFjbfe8xT5OcAtvS4NHeetUYbJcX8hBpD4M0Arfl7+B1TOIdvY/2zZEjR2C1WqFhWcQHKCejMqGXU3BCyHvafLRelS10GEHnj3o90iRS1NbWYt68eUKHQ3jG/ya7MJTW0d5r32F9CwBAfrYnMaezXQyFLoL2zYaB+vp6PPfccwCAaSPTIGLdf+SkShXqK9N8jqPUcIj4cq1nh0iEly/lf/X0b5wRcmsb7+MS0l+uk79ksoAVwM83WgMy7mBbNr4cjIrql59PzDBYaDIBAF5//XXs3r1b4IgInwY0mX388cf7VK5rqEtt6L1A/W65s09y4hfgvPdfdWQsomnf7JC3cuVKNDQ0wKxVIzs+xqPflDwFnVbfRUXSHEfBdrR6tDdcm4290jJeYu2WoIjG747SsbWD5dVXX4Xp3C9Z0ruewxICl/z1o9ZzP3ooOiGuxYGbfW9dCjfjFArcrNHA4XBg9uzZsNl6L61JQsuAJrPz5s2jIsQAkqtOQcSIvPZViFrAmKLAtjRCre15NCZRGGlldoirrKzEmjVrAAA3ZqWBZd1XkxRaPWrLfRfE1+jF0H75imeHWIw1Y/idyALAvV0yiO2dvI9LvPvDH/5ApwD2gauSQaCSv/Q6HBVXB2RsISyPz4PDy8Es4e6hKCM0LIvc3FysW7dO6HAIT/o0mc3JyUFrq+eqUG/mzZuHurrwSRyR2CxIUJh77bcMc666aBU9EwQGkTBrnclhhw8fpn1zQ9Dy5cvR1taG+AgtRsZ4rrwZhk2Brcv7h6Dzpbb/ArbL8/Fn1Q3ZvP/yHaFOxLSC73kdkzj97ne/8+to8D/+8Y+oqqrq8/Vr165FYmIiZDIZxo8fjz179vTpvo0bN4JhGNx88819/rMGm8VicR0HHKiyXJZUz/3socwGB16bzgIs7SY8XyTH4X6DM2l7/vz5qKwcGqvx4a5P3+UvvPAC2tr6vn9u7dq1aGho6G9MISlNouu1rypaAQDQ2HoyKC0dOkSplGAZBk1NTSgpoVqeQ0lxcTFeecW5mjo9K91jj5860ojqsgSf4+gjOWi+fsOjnZHLsTLzDD/BnueBZgsY0AerQPjss89QXV2NpqYmn6/Gxkb85z//QUtLS5/G/uCDD5CTk4PFixcjNzcXY8aMwbRp03xOhgsLC/Hwww/jqquu4uOvGDCHDh1CZ2cntCyL2AAlf5XFKwIyrpC2yQtRNd33qYLh5n91OoyUytDY2IhHHnlE6HAID/o0mXU4HEhLS0NERESfXv6s4g4VqbbeExJOG5wnsijqe05tammQQ8xxiFI7Hy/SVoOh5cknn4TVakVyVARSTZ6lcnSxU+Cw+f7xS6n/AYzdc19X8Y2jUMg18BGqy3hdGiaeoaSIQOl+H9Xr9T5f/r6Prl69GnfffTdmzZqFzMxMrFu3DgqF4qI1NW02G/74xz9iyZIlGD48uB9Hn7/FIFDJX4ejhmat3yeyjoOJonJd5xOdSwZjALzzzjv44YcfhA6JDFCfjrN98803/R443BIa0tp6f3x4SN2IqwHISg4DJucJLbYuEdQGE6K1GlQ2teDQoUO46aabBilaEkgnTpxw/cxMH+W5Kqs1xaK61DMZ7EIGIwf1v971aGfUajyTxv8Z7A9U0eO2QNq+3f+kutjYWJ/XWK1W7Nu3z63cEMuymDp1Knbt2tXrfUuXLoXRaMRf/vKXPv0yt1gsboc7+LNlYqB6DksI3PG+36uHZq3fGrYV39w8Ctetp3rm5xstl+MWrQ7/bmzA7NmzkZubC3GAVv1J4PVpMnvHHXcEOo6Ql1pbDGi99+2WncUcjgN3Yj/YmDtgtzkf4yr1MTBrVUAJrcwOJYsXL4bNZsOIaCMSDZ7VPjRRk1F91vfqUsrZLV7bj08fgSo2d8Bxnu96/Uhk5X7J65jE3TXXXBOQcWtqamCz2TwWEEwmE44dO+b1nh9//BFvvPEG8vLy+vznLF++HEuWLBlIqP3WvTIbqEoGjMmIQm7o5nm8ajiEqy7PgmQP/Z4534NRUdja0ozDhw/jpZdeQk5OjtAhkX6ineE8ia0rgZLzvueqg+kCEmLAdlmh0/ck/IhlRkSfSwKj8lxDw8GDB/H+++8DcFYwuFBEbBKqz/p+amE2i6DY+YlHOxOhx7NJRwce6HlEjAj3lhzndUwSvJqbm3Hbbbdh/fr1MBj6/vh53rx5aGxsdL0Ga59/R0eH6/0xM0CT2baUoX8E7LNX1YMJYFmzUKQTifDQuWSwxYsX00l8IYwmszxh4ECKovc3xJb4SACARtJz7KjD3nNwQn5+Prq6ugIbJAm4hQsXAgDGxEcjVu+5VC/XXt2ncYaf+Nhr+8GbUtHI8nt07c26TCRVn+J1TDJ4DAYDRCKRR1Z2ZWUlzGbPKiunTp1CYWEhZsyYAY7jwHEc3nnnHXz++efgOA6nTnn/XpBKpdBoNG6vwXDw4EF0dXVBLxIhhuvTw0S/lcb1forjUHFQUonjvx4tdBhB57daLbJlcrS0tOChhx4SOhzSTzSZ5VGaqPds2HKzcy+O2tKTXdzeqoFeKYeEE8FqteLEiRMBj5EEzs8//4zPP/8cDOM87etCUYkZqC2P9DlObAwg2/e1RztjNmJVLL8r+FKRFPec2s/rmGRwSSQSjBs3Dtu2bXO12e12bNu2DRMmTPC4PiMjA4cOHUJeXp7r9etf/xpTpkxBXl4e4uPjBzN8nwYj+euAITxOu1s6/ACYxOD69xUaey4ZjIWzKsj5P0ckdNBklkdp1t4LzRfonYkT8trTrraWRhnEEilMGjo8YShYsGABAODSYXEwalQe/SLpJN+DMEDioY1eu36+MQFtLL+HGdyqToepkf+DF8jgysnJwfr16/H2228jPz8f99xzD1pbWzFr1iwAwO233+5KEJPJZMjKynJ76XQ6qNVqZGVlQSKRCPlX8eBK/gpQfVkwDHaowqM0ooWx4d0ZKiBAHwpC1QiZDLfq9ACAOXPmuCU6ktBAk1kepTX1ni36i8q5Iis7fd4qmIOB1kjH2g4F3377LbZt2wYRy+D6kake/ebUbDRU9ZIheJ5h0TZID3lmljPD4vCC+SAvsXZTi1W4q2Anr2MS/1RVVeGHH37ADz/84NcBCReaOXMmVq5ciUWLFiE7Oxt5eXnYsmWLKymsuLgY5eW9H7sdzHoqGQQo+SsuBlVs+JST/Ex1Ag3XXSJ0GEHnPoMBkSIRCgoKsHr1aqHDIX7yewNSa2srVqxYgW3btqGqqgp2u92t//Tp073cOfSlVp0Cor1PWI6Kq8GolOCKj0GSKYK1w1k7VK4x07G2Ic7hcGD+/PkAgCuGJyBC6b7dhGFY2B2X+xyHYYH4vW957fvmhih0MRUDjvV8d8oToW3jN5mM9E1zczNmz56NjRs3us6HF4lEmDlzJtauXQut1vcHnwvNnTsXc+fO9dq3Y8eOi9771ltv+f3nDYb29nYcOXIEQOAqGTQlGwGEV1m6pWPP4PlftHA0NAodStBQi0T4e5QRj1WU48knn8Stt96KxMREocMifeT3ZPauu+7Cd999h9tuuw3R0dEB28MUitQdjYiRp6Ks3fsKiy0pFuyh49BpgapzOTwicRStzIa4L774Aj///DPEIhbXjUjx6I/JuBy1FZ7bDi403GyB5FsvJbfSkvBaJL/fG5FSPf6U/x2vY5K+u+uuu7B//3588cUXrn2tu3btwv3334+//vWv2LjR+1aTcHPgwAHYbDZEikQwBSj5qygmMOMGs1KuCbt+m40r3vxF6FCCygyNBh81NmBvezseeOABfPrpp0KHRPrI75/iL7/8Eps2bcKkSX3Y/xeG0qSGXiez9bEaRB4CNKIWVMFZ/NvWpXetzJ46dQptbW1QKIbesYpDld1ud+2VvTI1CRq5++qRiOPQ3ub7kR4rYhDz46te+z67TgUHz58Z/8qZobAe4HdQ0mdffPEFvvrqK1x55ZWutmnTpmH9+vW48cYbBYwsuJy/xSBQCyd5kX07Mnioed6Uh/dGp0N0kP8DWEIVwzBYYDLjv4sK8dlnn2HTpk341a9+JXRYpA/83jPbfdQi8S7NIeq1rzjK+WasbO3Zu9barIZaJoVSKoHD4UB+fn7AYyT8+de//oWDBw9CJuYwJd3zSNCYjKvQ1uT78WiKsRniYs9fKrZR6finjt/viViFCbcc/ZbXMYl/IiMjvW4l0Gq10Ov1AkQUnM6vZBAQIhG+U4ZH8teFHAzw4nUWIEAr3qEqVSrFbeeSwe699160t7cLHBHpC78ns08++SQWLVqEtrbwKGXir/T25l77juicKwDyyp4C9R0tEkiVKtfqLG01CB1dXV1YtGgRAOCatOFQSN2zwMUyOZrqs3yOw0lYmL992Wvf+9fwn6M5x66G2M5vVQTinwULFiAnJwcVFT37oCsqKvD3v//dVauYBD75C4lxaGT4rdscSnbJSlEyg5LBLjTbYICJ43DmzBmsWLFC6HBIH/j9kWzVqlU4deoUTCYTEhMTPc4yzs3l95jNUJNeWwqovff9LC/HnwBIT/wCjOp5lKiJclY0OFVVS0lgIeTtt9/GiRMnoJRKcFVakkd/dNpkVJX4Pus7VV8DrrLIo916eRY+V3s/jrS/UlUJ+NXhHbyOSfz3yiuv4OTJk0hISEBCQgIAZ8UBqVSK6upqvPpqz5aTcH1PbW1txdGjzgTFQCV/NQw3AAjvU5+eSDuCDTFmOMr4TTANZUqWxaNGI3LKyvDMM8/gtttuQ0qKZz4ECR5+T2ZvvvnmAIQxdCTUnIFCn462Ls+V6ypRCxiTEaLKcihUHNpanCd+yVRmmDW0MhtKLBaL65z6azOSIRO7/yjJVBrUVXoenHAhiVwE49Z/eHYwDN6YyP+K0X3tAOuw+76QBBS9j/p24MAB2O12GEQiGDnfHwr743Q0VadsZi349DfD8ZtXaDJ7vmkqNSYqFPiprQ333nsvNm/eTAnvQczvyezixYv7dN3777+PX//611AqlX4HFcoYOJCujMH+xpNe+zuGGSGtrIJWbUPbubwDhjVQea4Q8+qrr6KkpARauQwTk4d59BuHT0FVSe/7p7ulKc9CVOdZFqj1yjHYLuf3eyFbk4zJB7bzOibpH3/eR1tbW8PufRTo2WIQqFVZANgbUR+wsUPJP3X5mHpVNpQ/5AkdStDoTgb7TeEZbNmyBZ988gl+97vfCR0W6UXAPpb+9a9/9TgrPFyks71XI6iOdvZpHD31/TotOpi1ztJNZWVlqKurC2yAZEBaW1vx9NNPAwCmZqZAzLlPWpV6A2rKPbcdXEiu5GDY8qJnh0iEly/j/3vg/oYm3sckgRXO76M9yV/ywPwBYjF2ykoDM3YIWja+HIwq/D40XUyiRII/650J7w888ABaW8PncI1QE7DJrMPhCNTQQW9ER++Ph08ZnFsLlE09GbQtjSrIxGLoFc43bVqdDW4vvvgiqqqqEKlU4PIkz3POI+OnwN7l+0crVXwSbItn0fKGKdnYK+X3iNkrdRm4tGgfr2OSwAvn99FAJ385UhLQwXQFZOxQdEJciwM3ZwodRtD5f5GRiOE4lJSU4MknnxQ6HNIL2jAUAOkNve89OqBxPtaSlfWUW+q0iKDUG6iiQQhoaGjAs88+CwCYlpUGEev+I6Q1RqOqNM7nOEoNh8gvPffKMhIJ1mTzO5FlwOC+ClqBIqGjpaXFVaYwUJPZmiQqMXmh5fF5cGQkCx1GUJGzLB4/dyz0qlWrqHxmkKLJbACkVp4Ax3jfjrxXWgZwHCQFe3H+XnJ1ZIzrJDBamQ1eK1euRENDA0waFbLjYzz6NaYp6MsJB2n2Q2AsnvULK68fg6Pial5i7XajfiRGlNOxtSR05OXlweFwwMRxiApQHdSTJkqEvJANDrw6jQFEvvf7h5NrVWpMUarQ1dWFOXPmhPUTk2BFk9kAkNgsSFJ6TnQAwMLYgGGxYNtboNb1ZOhK5CZamQ1yVVVVWLNmDQBg+qh0sKz7pDUiNglVpSaf42j0Yui+XOfRzsjleC7zDC+xduMYDnOK+S3vRUigdW8xyAxg8tduHeUmePOtohBVN1Lt2QvNMxohZRhs376djpsOQjSZDZARYs/Tfbo1xzsfb2nlVlebg4lwq2hAn/yCz/Lly9Ha2or4CC1GxnhOWuW6q8CgD6uyrbvB2Dz36hVOH4UiroGPUF1u1o3AsJrTvI5JSKC5KhlIAzOZZRQK7Kbkr14tzioAYzQIHUZQiZNI8NfISADAQw89hKYmSqgNJgGbzA4bNszjQIVwktFp67Wv3OT8uqi7al1tljYtjGoVWIZBY2MjSkvpjTaYlJSU4OWXnad0Tc9K96g3GJWYjtoy32/+EQYO6q0bPNoZjQbPpRz3ckf/SUVS/O3Ufl7HJIMrXN9HA32MbVdqPGygBYPe1LJt2Pob33v/w82f9REYJhajvLy8z+X1yODwezK7fXvvdSrPP7Xm8OHDiI/3zPQOFxnNtb32FUQ4qx0o6gtdbS0NCkgkYkSpnaVRaN9scHnyySdhtVoxPCoCqSbPSSsnvbJP46RWbwfjZdU9/6YMVIlaBhzn+W5Vp8PUyG8yGeEHvY/2rqmpCQUFBQACN5mtGtb7kzPi9JrhMCzjRwkdRlCRsCwWmMwAgJdeegkHDx4UOCLSze/J7I033oi///3v6OzsOdu9pqYGM2bMwGOPPcZrcKEsvbL3VbZ9SmeCj6yoZ2+s3cZCbTBTElgQOnHiBDZscK6mTh/luSprThmD+irfvxyNJhGU33nutWINkXhu2BF+gj1HLVbhroKdvI5J+EPvo73bv38/HA4HzByHyAAlfx0zUkmuvnjmqjow8sDtWw5Fk5RK3KBSw2azYfbs2bDbKZEwGPj9TrF9+3bcfvvt2Lp1K9577z2cOXMGf/nLX5Ceno68vLwAhBgcjkZci8zSPX2+XtPeiFhFGs62eRY8PyquBqNWQ3wqD6JhLGydzh8GpS7GuW+2pJySwILIE088AZvNhozoKCQZLijnwzCwY3yfxkk+8x+v7ftuGo5mlt/tAHfIE6FtC74KBrXR1+BrZiJqbAqE0rbwcaJLMYnH8cL1fbQvAr3FAAB2asPzIAp/HRZX4thvLkH6xr7/7gsHjxmN+LGtFTt37sQ777yDO++8U+iQwp7fk9mJEyciLy8Pf/vb33DJJZfAbrfjySefxCOPPDKkzy1eXTsB61kxGHun74vPyZBGep3MAkBXYgxEhwqg07GorXZOZjmpkY61DTKHDh3C+++/D8C5V/ZCMemXoq5S5XOc2GgG8vc3ebQzsdFYHcPvo6oIqR635X/H65h82BJ3P/52sm8T/2DzxJgIXiez4fo+2heBPiyB0WlxUEKT2b56MvEA3h0+DDhdJHQoQcMsFuOeyEisqq7GI488gt/85jfQ6/VChxXW+pUAdvz4cfzyyy+Ii4sDx3EoKChAW1sb37EFlbpOMdoj/DsdZYSt9y9vfaxz0qoR99Qatdv1rsns0aNH0dVFj8KEtnDhQjgcDoyOi0as3n0rASsSwWK51PcgDDDsyPteu76/McZZro1H/09shsIaXMcu7or/fyE7kQ2UcHwf7YvuldmsAE1mLamU2OQPK2PD27+SA2H+IetCt+sjkCyRoLq6GvPnzxc6nLDn92R2xYoVmDBhAq6//nocPnwYe/bswf79+zF69Gjs2rUrEDEGjRJ5hl/Xj2hp6LWv2Oh8Y1BZqlxt7S1qRCgVEItEsFgsOHXqVL/iJPzYvXs3PvvsMzAMcGNWmkd/TPoEtDb4Pjd+WHQXZAd/8OxIHoaXjfyuysYqTPjfo70nFwmhNvoa/OHkNUKHEVTC+X30YhobG3H8uDPfYGSAynKVxysDMu5Qtkl1EnXXjxM6jKAiZhgsPHcy2Lp161xPFIgw/J7MvvDCC/j000/x0ksvQSaTISsrC3v27MHvfvc7TJ48OQAhBo/D9mF+XZ9eU9j7WNpmAIC8+qSrraVRBqlMDrPW+dia9s0Ka8GCBQCAccPiYNS4byUQicVobRnjcwxWxCDh5ze89m2+Xsd7eaB77BqIbVbfFw4SuzwCt1bdDkcfTkULJ+H8Pnoxubm5AIAYjoM+QMlfR4yWgIw71D2RfRJMBD1KP9/lCiX+S62Bw+HA7NmzYbPx+5SN9J3fk9lDhw5h+vTpbm1isRjPPfccvv76634FsXbtWiQmJkImk2H8+PHYs6dvm803btwIhmFw88039+vP9dcPLd5P9eqNqbEMEVLvP/w/K8oBALLTPYk/DBhojLEwa2jfrNC2b9+Ob775BiKWwQ0jUz36Y9KvQnuz1Oc4ycYWiE97rr7aR6biLT2/FQySVXGYcSy4VmXf09+D462+V6/DTSDeR4eCni0Ggfue+UFdHrCxh7IKUQu+/+1wocMIOn83GqFiWezduxevv/660OGELb8nswZD74Xhr7nG/0eJH3zwAXJycrB48WLk5uZizJgxmDZtGqqqqi56X2FhIR5++GFcddVVfv+Z/bW1NhIO1r/VghFy78eb1rCtYMxGcKUnIJH3nIMtV/eU56KVWWE4HA7XHqjxwxMQoVS49YtlMjTVj/Q5DidhEb3jZa9971/L/6rTvR0isI7gKRPTYJ6ABad9f53CEd/vo0NFoI+xZYwGnOLoGNv+esl4AF2X+Jc7MtRFcRzuPffzPG/ePFRXVwscUXgS/Djb1atX4+6778asWbOQmZmJdevWQaFQuOp6emOz2fDHP/4RS5YswfDhg/dJsbVLBKs22a970h29n97Tkeic6Oo1PW0sF0UVDQS2adMm7Nq1C2IRi6kjUjz6o9Mmw9Lm+1SmVH0NuPJCj3brZVn4THWCj1BdRmuG47oTXvblCsTBipHT/CehwyAhxnWMbYAms20p/j1dI55WTWkGI5EIHUZQuVWnR4ZUivr6+rCvEy0UQSezVqsV+/btw9SpU11tLMti6tSpF02CWLp0KYxGI/7yl78MRphuqpSek5uLGdHee1Z5VbTzUZpG1Oxq6+rSuSazJ0+eRHt7u9d7SWDY7XbXquyklERoLigYLlWqUF/lWaLrQlKFCKYtL3h2MAzemNTBS6znu78xuKoXHI79X3xbS/vrSN/V19e7kl4DtTJbGkcHAAzUPkk5Tv0mW+gwggrHMFh07mSwDRs2YOdOOrBmsAk6ma2pqYHNZoPJ5P4o3mQyoaKiwus9P/74I9544w2sX7++T3+GxWJBU1OT22sgTjCJfl2fUVfSa9+pSGfpLWVrz5GjrY0qqGVSKCRi2O125Ofn9ytO0j///ve/cfDgQcjEHKZkeK7Cm5KnoNMi8nKnuzR5MdjGGo/2lquzsV1eyEeoLhN06bi8cC+vYw6EXR6Jv5VM9X0h4ZU/uQcff/wxLr30Uuh0OiiVSmRnZ+P//u//BjFaT93JX3FiMXQi3z9j/XEgikqf8eGJ5INghlGJs/Nly+X4ndZZvnH27NlUWnOQCb7NwB/Nzc247bbbsH79+ovuOTvf8uXLodVqXa+BnnOe2xHr1/XDqk9DwSm89uWp6wEA8vICV5ulTQKFRkf7ZgXQ1dWFRYsWAQCuSRsOpdT9UZpCo0Ntue9tLQo1h8jNL3p2cBxevJT//VT3V3n/4CeUTZF34myH7+Q4wh9/cw8iIiIwf/587Nq1CwcPHsSsWbMwa9YsfPXVV4MceY9AH5YAAN8rSwM2djjpYLqwcYbvI7zDTY4hClqRCAcPHsTatWuFDiesCDqZNRgMEIlEqKx0P42lsrISZrPZ4/pTp06hsLAQM2bMAMdx4DgO77zzDj7//HNwHOe1Luu8efPQ2NjoepWU9L5S2hffNUT5dT0DB9KV3vdp/SItA8RiSE6416dTR8XSvlkBvPPOOzh+/DiUUgmuSkvy6DckToGtqw+rskw+2PYWj/bqG8YiT8LvxPN6/UiMPBs8H3is+lQ8fHqs0GGEHX9zDyZPnozf/va3GDFiBJKTk3H//fdj9OjR+PHHHwc58h6uSgYBqi/LxMegQuT5c0n65yN1ARqvu0ToMIJKBMfhgXMLbQsXLkR5OVXOGCyCTmYlEgnGjRuHbdu2udrsdju2bduGCRMmeFyfkZGBQ4cOIS8vz/X69a9/jSlTpiAvL8/rqqtUKoVGo3F7DcShZiXs8gi/7slgvK/MWhkbkBgLUX0VlJqe7HapwoRorTNOmswODovFgiVLlgAArs1IhkzsXm1AHWlE9VnfdYY1ejF0mz0rGDBKJZ7JOsNPsOeIGBHmlhzndcyBell8Byz2kHrgE/L6m3vQzeFwYNu2bSgoKMDVV1/d63V8b9m6UKArGTQle68sQ/pvySVFYHS0Qnu+W7Q6jJLJ0NzcjIcffljocMKG4L91cnJysH79erz99tvIz8/HPffcg9bWVsyaNQsAcPvtt2PevHkA4Coufv5Lp9NBrVYjKysLkkHKsGzWeJ4GdTEjLL0n/DTFOSfGOmVPsWWGNdDBCYPstddeQ3FxMTRyKSYme05a9TGT4ejDJC21bS/YLs9DC47/10gUixr4CNXl17pMDK8OnlPiGswTsKaY6lAOtv7kHgDO07ZUKhUkEgl+9atf4aWXXsL111/f6/V8b9k6X21tLc6ccX7YC9RktigmMIcwhLNSrhG7futZhzucic6dDMYAeO+997B9e3DV/h6qBJ/Mzpw5EytXrsSiRYuQnZ2NvLw8bNmyxfXGXFxcHHRL9aUS/35hZ9SX9dpXZnK+waod9a42S4fWtc3g7NmzqK+v93ov4UdrayueeuopAMD1makQc+5bCbSmWFSV+t4rHWHgoPnas2g2E2PG8iR+V9glrASzTx/gdcyBcIDB4vaZQodB/KBWq5GXl4e9e/fi6aefRk5ODnbs2NHr9Xxv2Tpfd/JXvFgMbYCSv3Ijm31fRPz2vCkPtjG+K7yEkyyZHDN1OgDAnDlzYLUGz6mMQ5Xgk1kAmDt3LoqKimCxWLB7926MHz/e1bdjxw689dZbvd771ltv4dNPPw18kOc5YvdvRSKl6iS4Xg5bOBbhLL2lbCx2tbU0KCGTSKBTOEt30VaDwHrppZdQVVWFSKUClyd5/ttqoiYD8H0ca0rNd2AcnsfTbplhRgvD75vZTE0GzA3Bk8xSEvdf+KzSKHQYYcnf3INuLMsiJSUF2dnZeOihh3DLLbdg+fLlvV7P95at8wW6viw4Dt8p+Jt8kx4OBnj+ug5A7Lv2dji53xCFCJEI+fn5WLNmjdDhDHlBMZkNNT+3RPt1vdhmRYrS+8reXqUz21hWetTVZusUQR1pdG01oMls4DQ0NOCZZ54BANyQlQoR6/4jERGbhKpS35M0o0kE1Y73PNo7x2XijQh+//2UnAJ3H/e9F3KwOERSPFA9Q+gwwpa/uQe9sdvtsFgsgQjRp+7kr4BVMkiKQzMrzN8tHOyRnkXRDEr8PJ9WJMJDUc6E8SVLlvD6JIN4oslsP2yri4CD8e9Ll8F5X8U4wdWC0WogPpGL84dURcS6ksBo32zgrFq1Cg0NDTBpVBgb7/mBQ669Ggzje1V2eNEmz0aOwz+u5r+u5R3KZOhba3kft78OxMxEbqNK6DDCmj+5B4Bz/+vWrVtx+vRp5OfnY9WqVfi///s//OlPwpzaFuiyXA2JkQEZl/RYknYYjJf30HD2G40Wl8jlaGtrw4MPPih0OEMaTWb7obGTQ5cm0a97Mjp7L6DclRgDtqMVGl3PYxqxLIpWZgOsqqoKzz//PADgxqx0sKz7pDVqWDpqy33/EoyJZqD4+T8e7dU3jMUuGb9bAfQSLW7P/57XMQfCLtNjTskUocMIe/7mHrS2tmL27NkYOXIkJk2ahI8++gjvvvsu7rrrrkGPvaamBkVFRQCAzACV5ToZ4/sDKRmYFsaKf/+aTv07H8swWGg0QQTgo48+ErSO81BHk9l+qlX5eaxtY+/F8mvjnMleOllP1QOHI9JtZdbhZS8mGZgVK1agtbUVcXotsmI9y/Zw8kl9Gifp6L882hi1CstHnh5wjBe6WxILpSV4Elm+jfoTHZAQJPzJPXjqqadw4sQJtLe3o66uDj/99BNmzhQmga97i0GiWAJ1gJK/9ugpiXYw/EtzDE1TqPbs+dJlMvxR75zkz507Fx0d/B9nTmgy229n2ES/rk+vPAGmlySiIoNzoqrq7Dn+tL1NA6NaCZZh0NDQgLKy3isiEP+Vlpbi5Zed9WCnj0r32EpgThmN+kqdz3ESYuyQHtjh0X70vzJRyjXyEapLtDwKM48GT5mXLnUc7j9zudBhkBAX6C0GjEyGn3h+QkJ6t/SyYjBa/pIDh4K5kQZEcRxOnjyJ5557TuhwhiSazPZTntW/vUFKSzPiFd4ziw9rnSttirqeovqt9XJIZTIY1EoAtG+Wb08++SQsFguGR0UgzXTB0cgMAwdzhc8xGBZI2PeOZ3uMGc8k8P/vdQ/0kNiCJ4llo/oOtPbhRDRCLibQk1lbSoLzgBoyKIpFDfj5t/7VYh/qVCIRHo1yJhIvW7bMVVOZ8Icms/30fZP/ZYgypN5PDvtZXgYwDKSFPXVDHQ4GmqgYmDV0rC3fTp48iTfeeAMAMD3Lc1U2Jm0cGmt8JzQlma2QFOz1aN9+Uwza2E5+gj1nuCoOv84PnlXZjshMLDyTKXQYZAhwHWMboMls9TA6oWqwrTZT7dkLTVerMV6hQEdHB+677z6hwxlyaDLbTz83aOAQK/26Z4TN+zaDerYdTGw0xGeOgJP0/JMotNGIPnd4Aq3M8ueJJ56AzWZDhjkKSVHuHzAYlkVn56U+x2BFDOJ2rvdod2Qk4xXDQd5i7XZfhwgiR/CsLr3A/gkOByXVkIGprKxESUkJGAAjZIHZe11gDp6fm3BBtWc9MeeSwcQMgy+++AKff/650CENKTSZ7SeHg0Gbzr9j/EY01/Xa1zYsCozdBp2u559EJI5ynQRGK7P8OHz4MN57z1kP9sZRnisHMelXoLle4XOcZGMLuKKjHu0fTJWC7zneaM1wXHfiB34HHYAG80S8UpIodBhkCOhelU2SSKBkA7Nl5Wdtje+LCO/2SM+i6NdUe/Z8w6VS3HEuGey+++5DWxv/pRvDFU1mB6BS5uextlWneh/L7FyV0HCtrjabTe9amT169ChsNlphGKiFCxfC4XBgdJwZcXr3x48ijkN7q+83X07CInrHyx7tlvGj8LH6OG+xdruvqdX3RYPEAQZPtP+v0GGQIaJ7MhuoklyMWo19EkqeFcrilENgEqj27Pn+FmlANMehqKgIy5YtEzqcIYMmswNQ4Ejw6/rIlmoYZd7rlp6IdO6xVLVXuNpamzSIUCogFrHo6OjAqVO9T4aJb3v27MGnn34KhgGmZXkmKMRkXIm2Zt+POlP1teDKC90bWRavTGzhKdIeE3XpGH/Gc1+uUMpib8SndGwt4Umgj7G1psXz/qSE9F0b24mNv9YJHUZQUbAs5hmdpSCfe+45HD/O/wJIOKLJ7AD80u7fsbYAkC7zPhHYr3Ke6KSoOulq62iRQK5Ww0RJYLyYP38+AGDcsDjX17QbJ5GiuSHL5xgSmQjGrS95tDdcNxY/yfg9rpABg/sqy31fOEgcrBgP1/1a6DDIEBLoSgblw+hkOqF9pC5A49RxQocRVK5TqXCVUgmr1Yq5c+dSHXke0GR2ALbVRfl9T4aD89q+X1oORiaD5OQ+t3aNIda1b5aSwPpv+/bt+OabbyBiGdww0nOvc0z61eholfgcJ011FqK6Src2RibDqjFneYu12w36TIwsC54PMAWxv8OuesoMJ/woLy9HWVkZGAAZAZrMHokKnlJ24eyJS86A0euEDiNoMAyD+UYTJAyDrVu34sMPPxQ6pJBHk9kBKGyXwab0PDnqYka0eT+9yQYH7Imx4CqLIFP2THhlKrNr3yytzPaPw+FwrcqOH56ACKV7gpdErkBDzQif48iVHAxbXvRoL5k2GgVifpNMOIbDvcUFvI45EA6JEveW3SB0GGQI6d4vO1wigZINzK+iH9UVvi8iAXdW1IQff5ssdBhBJUEiwV0Rzmo6Dz74IJqbg+dkx1DkfZmQ9FmjJg0RrZW+LzxnRE0h0MviVmO8DrpjgF5jR/m5nB9GZKCV2QHavHkzdu3aBbGIxdQRnscQm1OnoKrE949Cqvgk2Bb3U70YrQYrMvifdN6sG4FhpzfxPm5/7Tb9ASdOyIUOgwwhga4vyxgNOCGu7de9sQoTTGI1HEBQPwKu7WpFSVtoTNhfMB3A+EsyweV6VoEJV3dFROI/TU0oOXsWS5cupdPBBoAmswNUIk5EBPpeNimurhhqQyaaOz2ThUqMIugAqNGEcjj3elktOtdk9sSJE+jo6IAsQG/+Q5Hdbnetyk5KSYRG7v61k6s1qKvwnOBeSKXlEPnlPzzaj9yUgSo2l59gz5GJpLjnFL9jDoRdbsC9RVcKHQYZYrr3y2YG6P2sLTUWQEO/7n3UKsWUI9/yGk8gNCr0uDlhGGosvZd9DCbPTWnG40ekcFho+wcAyFgWjxtNuOdsKdasWYM777wTI0eOFDqskETbDAboSFec3/dkKLwnjh3VO5djlc09+y9bG5TQyKRQSMSw2+3Iz8/vX6Bh6sMPP8SBAwcgE3OYkuH5mCsq6Vp0dfqub5lqOwTG0u7WxpiNeDYAx9b+QZ0GY2PwJH5tibwN1VYqfk74FehKBiVx/T+EYUzZMR4jCRxtWz0W2UInyW2/pBzHfzNG6DCCyjUqFa5TqdDV1YXZs2cH9ZOAYEaT2QHa1WL2+54Mxvvj2p8VzgmMvKxnwtpp5aCONNLhCf3Q1dWFhQsXAgCuThsOpdQ9wUupj0RNeaLPcbQRYmi3vObR/tP0eN6PrdVI1PjzsZ28jjkQXZoEPHTmEqHDIENMWVkZKioqwALICFCN2TxD/+ozJyiiEdEaOgctTDnxI2boRwkdRp8tHX4ASB4mdBhB5TGjCTKGwffff493331X6HBCEk1mB+jbugg4GP9OrsnoaPfaflbUBNYQCcnxvcB5tRFVETG0b7Yf/u///g/Hjx+HQiLG1WmJHv2R8VNg7/L9I5Da8jPYLqt74/AEvGjm/9jaP0sToG1v4H3c/npPeTvabYE5mYmEr+5V2WSJFPJAJH+xLHaoSvt1a7bUwHMwgfdo/s5ea5gHGwtjw5u/kgEBSvoLRbFiMf4W6fy+e/jhh9HQ0CBsQCGIvpsGqLVLhE6dnyeB1fVexqkjyQy2pQFqbc9jXYncCDPVmvWLxWLBE088AQC4dkQKZBecEa6JMqO6NN7nOBEGDuqtb3q0b75BDxv4fRxklBnwx/wdvI45EO2RI7G40HeVB0L8Fej6skxCLGrY/q3MjrHy+7RlMGjbG7DYGjoJml8qT6H6Rqo9e747IyKQJJGgqqrK9USR9B1NZnlQo/Cv5Mjw6pOQirzv56qJUQIAtIqeN1QHIhGto5VZf6xfvx7FxcXQyKWY5OWRltY8GY4+HA2UWr0dzAV7mOyj0vCW/ghvsXabzUZA1ul91V4Ia9k/9ulrRIi/Al3JoCHZ/xrg3cbU9G9FV2hXn/oJN4fQdoNFowrAGENvFTxQJAyDBedOBnv55ZeRmxs8ScChgCazPDjFJvp1PWfvQqoyxvtYBhsAQGPrKSljade4VmZLS0vpEYQPra2teOqppwAAU0ekQsy5PybXRyegutT36W1RJhGU3230aH9vMv+P3ROVsbj56Hbex+2vRtMV+EdJotBhkCHI4XAEfGX2dEz/fkaVnAIplcFT39lfjx79AWZ5/yfyg6mWbcPm38YKHUZQmaBUYrpaDbvdjtmzZ8NutwsdUsigySwP8iz+/0COEHnPQM3T1AMAFPWFrrbmBgWUcjl0Cucb/5Ej/K8KDiX/+Mc/UFlZiQilHJcneW4lUEZeDbdNyb1ILvKs82q5YhQ+V53gI0w391s5iBw23sftryctM4UOgQxRpaWlqKqqgghAurT/FQcuZk9kfb/uy1LFBdXPob9UHU1Y0hE6lUfejDiC9klU3eB8j0QZoWBZ7N69Gxs2bBA6nJBBk1kefNdo9PueDIvVa/teaRnAcZCV9ExYHTYWmqho1+osbTXoXUNDA5555hkAwLSRaeBE7t/ikfHJqDnr+98rOpqB4uf/uDeyLF6d4FkfeKBGa4Zj6vG+1yoOtIrY6/FhhX8n2xHSV91bDFKkUsgCkATESKX4UVbSr3uzHYGZXA+miad/xv+E0HaDpRPLwahDp7xYoJnEYsw9lwz22GOPoba2fwd/hBuazPJgX5MKDol/P4wjGr2fGtbBdAHDYsGd2A9W1LN6qNRFU3muPli9ejXq6+th0qgwNsFzxVyqvqpP4yTle56V3TQ5u9+/JC/mwYbgOcbQwYjwaMPNQodBhrBAbzGwpSTAwvRvdXVMc2gcPuDLw0e+Q6wiND6QnuLqsPd3GUKHEVT+qNcjVSJFbW0t5s2bJ3Q4IYEmszxwOBi0atP8uie18gREvZT0ao6PANtlhU7f0y+SGBFN5bkuqrq6Gs8//zwA4MasNLCs+1YCY9II1JVH+BwnPsYBWd4Fp/+IxVhzSd+PLe6rq3UjcGnRPt7H7a/TcTfju1q90GGQIcyV/BWg+rI1ibp+3ceAweiyoXEojcLSgqWtzr9TKHguOg+20elChxE0xAyDhSbnh5HXX38du3fvFjii4EeTWZ6Uy/wrzyXrbEdSL0lgZWbnnieNpMPVZrPp3VZm6ZQQTytWrEBLSwvi9FpkxXoeZsFKJvocg2GAYfs9i1ZX3ZCNw2J+J7Msw+KBskJexxwIByfHAxU3Ch0GGcLOT/4K1DG2+eb+rcomqWKDqsbzQF1euBe/14XGdgMHA6ye2g5GIvF9cZi4VKHAbzQaOBwOzJ49GzZb6O7lHgw0meVJgSPB73syxTqv7cf0zvJMakvP5Km9RQ2jRgWGAerq6lBeHjzHnQaD0tJSrF27FgAwfVQ6GMZ9RcKcmo2GKq3PcRKjOyHJ/9mtjVEo8FzmGf6CPec3upFIDaLM6QPR/4NDzUqhwyBDWHFxMWpqasAhcMlfO3VV/bovWzz0nkg8eORbJPRyfHqw2Sstw4mbs4UOI6g8FGWEmmWRm5uLdevWCR1OUKPJLE/2tHlfZb2YEZ3eP2ntUTnfjOU1p1xtrU1SKJQqGFTOyQbtm3X31FNPwWKxYHhUBNJMF9QuZBjYHeN9jsGKGMTtet2j/cz0LBRxDTxF6iQTSTH7dB6vYw6EQ6rF3JIpQodBhrjuLQapUimkgUj+0mmRJ6no171jOiw8RyM8ubUNTzZ3gmVC41f9Ejrq1o2B43C/wVlqbf78+ais5H+r21ARGt/hIWBbnf9HCWY0VXttP8HVgtFpITtvssOAgdYYR/tmvTh58iTeeOMNAMD0LM9V2dj0y9BU63vFcbipFeIz7h8SGJ0Wz6bwv3p6myoN5obeT4IbbN8Z/4jSjtDP5A5na9euRWJiImQyGcaPH489e/b0eu369etx1VVXQa/XQ6/XY+rUqRe9ni+BTv7qSPN9ql9vsmuKeIwkeFxSnIvbtFlCh9EnFsaG138lBUR0hHa3mTodMqVSNDY24pFHHhE6nKBFk1menO2QokvtX73ZERXHe92g35kUA66kABJZzw+1TG2migZePPHEE+jq6kKGOQpJUe4JXqxIhA6L72MTOTGLmB2veLQfvim938di9iZCqsNf8r/ndcyBsClNeLDwCqHDIAPwwQcfICcnB4sXL0Zubi7GjBmDadOmoarK+yP3HTt24NZbb8X27duxa9cuxMfH44YbbsDZs4H9gBXoyezZYYp+3aeRqJFUdZLnaILHvYe+QZIyNA4o+Fp5GuU3XSJ0GEFDxDBYZDKDAfDOO+/ghx+Cp4xjMKHJLI8aVSl+Xa+0NCNB6X0/U22Ms9SX7rxtnqzIQCuzFzh8+DDee+89AMCNozyzYWPSJ6C1wfeZ5cmR9eDKTru1MSYjnovn/+s8mzNDaQmeclxf6G5DfScndBhkAFavXo27774bs2bNQmZmJtatWweFQtFr0fV//vOfmD17NrKzs5GRkYHXX38ddrsd27ZtC1iMDofjvGNsff9M9sfBqP4dBz1GEQcGQzepVtrVgacbWnutoBNsFo3MBxMbGnt9B8NouRy3aHUAgNmzZ6Ozs/PiN4QhmszyqEic5Pc9IyTekw4Ko5xvrFpRz6Sn06pzrcwePXqUshsBLFy4EA6HA6PjzIjTuyd4icRitLb4Pl1GLGVh/matR/vP0xPQxvL7ppGiisctRwI3YfBXpzYJj5zJFjoMMgBWqxX79u3D1KlTXW0sy2Lq1KnYtWtXn8Zoa2tDZ2cnIiJ8l67rr8LCQtTV1YEDkBqIrHWGwbeq/tWBHmMPjUneQIwqPYg/azKFDqNPGpkO/OvmwH0vhqIHo6KgE4lw+PBhvPTSS0KHE3RoMsujg53+79fK7PLefkDbCABQtpS52lob1YhUKsGJWLS3t+P06dPebw4Te/fuxaeffgqGAaZledb5jUm/Cu3NvveBpmorIapxf7zKJMbhBfNB3mLt9veWrqA6LvNdxZ9gsdPbQCirqamBzWaDyeReJN9kMqGiom/JUI8++ihiYmLcJsQXslgsaGpqcnv5o3uLQbpUBkkgkr8SYlEh6t8Jfdm95C8MNfcc2oo0lf+Vd4Twb00BGq73vUUsXOhEIjx0Lhls8eLF2Llzp8ARBRd6tsijnc0m3OnnPSObauBt2+xuWRn+n0gEeUUBoHBuX7B2cFDpI2DSqHC2vgmHDx9GamrqgOMOVfPnzwcAjBsWB9O5o367iaUyNNX7XoWQKUQwbvH8lPv1DVHoYvqXFd2b6/UjMTH3S17HHIj2yCwsLaSTd8LdihUrsHHjRuzYsQOyi+xlXb58OZYsWdLvP6d7i0Gg9ss2phgB+P8zK2JEGHV2aByW4IvYZsXTNXW4VcGhy97LSkoQWTD2FNbmGeCorhE6lKDwW60Wnzc1Ym9LC6666ipcffXVGD58OBQKhUfi84V89Q+WcePG4Y477uB9XJrM8uj7Oh0ccikYW99LvIyoOA4mWgfHBfu1mlkLmPhoSAv2AGN/5WpXG2IRrdW4JrO//e1veYs/lOzYsQNbt26FiGVwfabnhD467RpUlfp+lJkqKwLb5H72tWNEMtZH8rtXVi1W4dFTB3gdc6DWsn+AwxEcb3Ck/wwGA0QikUfZnsrKSpjNnoeHnG/lypVYsWIFvvnmG4wePfqi186bNw85OTmu/29qakJ8fN+fRuXl5QEI3GT2VGz/tgqkqeKgsPBfRzpYZZQfxV/H3IS1TcGfRFwlasGm/x6Jm9bRZBYAWIbBy3HxWFpZgf80NeG7777Dd999J3RYfrn11ltpMhvsLHYWHboUyGuP9PkedUcj4hUZKG7zPAShJSEKysL9UGo4tDY5P0VLFCaYNc7ksHBNAnM4HK5V2fFJCYhUuWcwS5Uq1Ff7XnFUqDlEfum5KvvRtfwmpzBgsBSRMDUe5XXcgWgyjcc/ihKFDoPwQCKRYNy4cdi2bRtuvvlmAHAlc82dO7fX+5599lk8/fTT+Oqrr3DppZf6/HOkUimkAzjo4D//+Q923HsvFN8EZs/4zxH1/bpvDOf7MJWh5q5DX2PHqEk40hT8k/i39Edw9TVjofpuv9ChBAUly+KZ6BjMiTRgb3sb6rps6HDYL3pPsKQ2SjMyMClAC3A0meVZpTwFiej7ZBYARkojvU5my6LFSAWgU3Wh9dz2NAaRiNZpAIRvea4vv/wSP/30EzgRi+syPStImJIno6rE9ypNGnsMbLv7HruuSzLxL80x3mKVc3IsZaMxtWAHb2PyYZn1f4UOgfAoJycHd9xxBy699FJcfvnlWLNmDVpbWzFr1iwAwO23347Y2FgsX74cAPDMM89g0aJFeO+995CYmOjaW6tSqaBSqQISo1gsRpbZjAaO/187jFyGnbL+JX9lt/Fbei8UcPYuLCsvw/9qpLD48SRRKIsuL8bzB3Vw1DcIHUrQSJBIkBBix/9qJ09GzP/8T0DGpswPnh2H/6eXjOzy/qnqWEQHAEBtb3C1WTp0MJ/bH3r8+HFYLMH/RsQnu93uWpW9MiURWrn7I0u5RofaimSf46h1Yui+fNmj/a0r+dtHFiWLwD+bgBuDbCJbFXMdNpZT2ZuhZObMmVi5ciUWLVqE7Oxs5OXlYcuWLa6ksOLiYrcjsF955RVYrVbccsstiI6Odr1Wrlwp1F9hQLrShsHK9C+xckzVKd8XDUHDq07gXoXv98pgUMo1Ytst/lcLIuGDVmZ59ktHDG7w857MxhqvHyv2KKswA4CyoQiADgDQ3KCAVimHXCJGu7UTx44dw5gxvstPDRUfffQR8vLyIOU4TMnwfCOOSuzbqmyqdT9Yq/sHgfZJY/C10r9V9d5oJGqsr21DctVxXsbji4NhsaApPPdZD3Vz587tdVvBjh073P6/sLAw8AENoorE/m0ViJJFIO5MHr/BhJDbDn2Nb8dMRm5j8B8Ysc5wCBMmjYF8Z3DlHpDgQCuzPPu61uj3PSMrjnk9O7tAXANGq4GstGevpb2LhTYq2rU6G077Zru6urBw4UIAwDXpSVBK3R+xqCIMqC5L9DmOLlIM7ZbX3BtFIrw8voGXODmGw/MWRdBNZAGgOPa/8HUN1W8kQ8shk7Vf92XLw/sJBeuw46mSU5BzgTnEgm+LJp4Fowu/Pc7EN5rM8qywXeb3sbYKSwuSlDFe+zqTYiE5/gtYtifrXKmPgVnr3NcWTvtm3333XRQUFEAhEePqNM9HTvq4a+Gw+f6WTm3cCcbu/kiycUo2dkv5OcozR5mGywv38jIWnxwiCf5ec5PQYRDCu2+1pf26b0zwV6cKuPjaIjwk9X97nBCKuAZsvyU0tkaQwUWT2QCoU/tfu3Ok2PtJYLVxKjDWDmj0PTtCOGkUorXOJLBwWZm1WCx44oknAADXZiRDJha79WuNMagu9f0hIjKKg/qbt93aGIkEz2eX9XKHf6bqR+K2Q1t4GYtvx2J+hz0NGqHDIIRXTIwZhVxDv+4dW8fPz32om3n4a0zUeR4HHoxejjqI9knhs7WO9A1NZgPgjGi43/dkWb0vEZyOciaHaaUdrja7PdJ1rG24rMy+/vrrKCoqgkYmxaSURI9+tXEy0IeaqSkV33i0VVw/BkfFAz8BKEZuxJL8nwY8TiA4xErcX3a90GEQwrumtP5tFZCKpBhRHh6HJfTFkjNHoRYHppIF3xZNPAtGrxM6DBJEaDIbAPuscX7fM7LB+8k1eeeOtVVbeyZb7S1q12S2uLgYjY2N/YgydLS1teGpp54CAEzNTIWYc0/wiohNRPVZk7db3ZjMIih/+LdbG6NQYGXmwGstsgyLZS0OaNqD89/iF/NMHG8NjX1xhPjjVFz/8phHquIhtvVvr+1QZG44i3miix+yESyKuAZsvSVR6DBIEKHJbAB80+D/G0JG+TFwrOeb8i5ZKcBxUNSedrW1NsqgUalcZamOHOEnAz9Y/eMf/0BFRQUilHJcnuR54pBcezUYb2cCX2D46c892gqnZ6Gon48oz/dn7UiMK9434HECwS7T476Sq4UOg5CA2GXo52EJDH24u9CM/G8xVT9S6DD65DXDYbRcM1boMEiQCIrJ7Nq1a5GYmAiZTIbx48djz549vV67fv16XHXVVdDr9dDr9Zg6depFrxdCbqMadrl/GeMSmwXpKs+JWgfTBSTEQlp4fjkSBlpjrGt1dijvm21sbMSKFSsAADeMTAMncv+WjRqWjtpyg89xYqMZyPdsdmtjtBo8k1Iw4BjT1cMw++DWAY8TKDui/ojyjtAqrk1IXzAKBX6QFffr3rEtwfkURWiLCvYgQuo9hyPYPD7+DJgo3+//ZOgTfDL7wQcfICcnB4sXL0Zubi7GjBmDadOmoaqqyuv1O3bswK233ort27dj165diI+Pxw033ICzZ/nJROdLo3aE3/dkibzvV2pOiABXeARiac8/l1wTExb7ZletWoX6+noYNSpckuCZ4MXJJ/kehAESj2z0aM6fnoEadmCn/3Ash6eraoL2caVNacaDhZcLHQYhAdGZkYgu5uJHefYmu4z2y3qjb63FEza10GH0SYWoBZ/8T3iXVyNOgk9mV69ejbvvvhuzZs1CZmYm1q1bB4VCgQ0bNni9/p///Cdmz56N7OxsZGRk4PXXX3edQx5MzohT/b5ndHuH1/YysxiMwwGdtuefi+UMiB7ik9nq6mo8//zzAIAbs9LcypMBgDllNOordT7HSYi2Q3rwe7c2JsqA54YNfHvGX1UZSK8I3l+K/9H/CY2ddDYKGZpKk/qXsJSojIG+tZbnaIaOKSd+xM36UUKH0SfvafNRe+OlQodBBCboZNZqtWLfvn2YOnWqq41lWUydOhW7du3q0xhtbW3o7OxERIT3x/oWiwVNTU1ur8GwvyvB73uyaou8tudHtAMANFyLq62rS+e2zcDhcPQjyuC2YsUKtLS0IE6vxajYC/YhMwzszHifYzAskPDL2x7tv0xPQjM7sKOA09XDcNehrwc0RiB1apPw6OlsocMgJGD2m9r7dV+2JJLnSIaeR4/+gBi5/4cACeGxMcfAxPtX350MLYJOZmtqamCz2Vznh3czmUyoqPCe3X+hRx99FDExMW4T4vMtX74cWq3W9YqP99yXGgjfNPj/6COp6pTX0ih7Fc4tF6q2nq9Ja6MKJrUKDAPU1taisrKy/8EGodLSUqxduxaAc1WWYdxXZWPSL0VTje9HYUlmKyTHf3FrY+Jj8HzMwQHFJ2JEWFJTD84evFXX31X8CRa74A9fCAkMlsVWdT/3y3YM7INsOFB1NOGpNqZPybVCa2Q78MZvVYDI91HmZGgK6d90K1aswMaNG/HJJ59AJpN5vWbevHlobGx0vUpKSgYltl31WthlOr/uYeDASKXnp8sT4loweh3klT3JSpY2CTQRkTColACGXhLYU089BYvFgiRDBNLNUW59rEgEi8X3YyVWxCBu53qP9h03mGFlbF7u6Ls/aUdiZFnwbu9ojxyJpYX+H95BSMhIHtbvPe/ZNYX8xjJEXVa4F7fpQmO7wRblKZz57TihwyACEXQyazAYIBKJPFYVKysrYTZfvLzVypUrsWLFCnz99dcYPXp0r9dJpVJoNBq312Bp1Plf4mS0w3vWeWdiNKQn3Es/aQyxQzIJ7NSpU3jjjTcAANNHpXtZlZ2A1gbfZXWSjS3gio66N6Yk4uWoga3KxipMmHPk2wGNEWjrRLfC0YdDJAgJVVVp/cti10m0SKo6xXM0Q9f9B7cixUulnWA0PyUP9pH+56uQ0CfoZFYikWDcuHFuyVvdyVwTJkzo9b5nn30WTz75JLZs2YJLLw3ejd9nJP1IAmtp8NpeG6uCqOYs5KqeZB6JwgSzZuiV53riiSfQ1dWFdHMUhke574XmJBK0NGf7HIOTsIje8bJH+xdTtX05KOyiFnRwkFvbBjZIADUZL8MLxf6fQkdIKDkc078tPtnKGDAYejkGgSKxWbCspt5rHfRg08XYsezGNjBKpdChkEEm+DaDnJwcrF+/Hm+//Tby8/Nxzz33oLW1FbNmzQIA3H777Zg3b57r+meeeQYLFy7Ehg0bkJiYiIqKClRUVKClpaW3P0Iwv1gT/b5nVLn3uqfdx9rq1Oc9HmcNiNYNrZXZI0eO4J///CcAYHqW51nh0elXo6PFd83UFH0tuPJCtzZ7Vhre0Q+sgsE0/UhceapvyYlCeaZzptAhEBJwW3X9K8eY3SX4r72QM6L8KGarQmPb0kFJJX6cGRqxEv4I/lM9c+ZMrFy5EosWLUJ2djby8vKwZcsWV1JYcXExysvLXde/8sorsFqtuOWWWxAdHe16rVy5Uqi/Qq+21Mf4fU9Eaw3iFZ5bLA6eO9ZW4+gp9G1t17pWZo8cOQK7vX/1FoPJwoUL4XA4MCrOjLgIrVufRK5AQ63v+r0SmQjmrf/waH9/ysCSA9RiFR47kTugMQKtJmYy/lnu//cdIaGEiYvBKa6uX/eObRhaybKD5c8Hv8JYbYrQYfTJC6YDaJ5Mp4OFk6B4bjB37lzMnTvXa9+OHTvc/r+wsDDwAfEkt1EFe0QU2LZqv+4bIzWgpM29msNuWRn+KhJB2VQCwLkXt6VBAYNaBY5l0dbWhjNnziA5OZmv8Afd3r178cknn4ABMG1kmke/OXUKqkrEPsdJVZeBrXP/+lkvG4nPVAM77WuuNB6G5qO+LxSIAwwWNv9O6DAICbj6jGgA3g/WuRgJK0FW8dA+/jtQRA4bni45hVsiFGjrCt5tVt0eHX8ar5yIhuNsue+LScgTfGV2qKvR9Z6c1psxVs+9YC2MFUx8DGTnnVpj6xJBb4qGSeMs5xXq+2YXLFgAALhkWE9iWzeFRofaCt+rAjIlh6gvX3RvZBhsmDiwUjyZ6kT8/nDwHlkLAKVxN+HLajrakQx9R/uZj5SpiofERmW5+iu+tgiPiuOEDqNPathWvH6LGuCCYs2OBBhNZgPsuMj/xzLZtaVe25uHGSAp2Ivzk/uVuqFxrO13332Hr7/+GizD4AYvq7KGxOtg6/S9TSBNchrsBUl0rVeOwbeKwn7HxjIsFtbWg3UE7zYOByvGI7X/JXQYhAyKbyL7Vof8QmNZBc+RhJ/fHf0GU/SZQofRJ18pTiP/fy4ROgwyCGgyG2A7OxL9vie1ogAKzvNNt9wsBtveAo2+51E7JzO6nQQWihwOB+bPnw8AGD88HpEq97+7JsqM6rO+l2KUGg4RX16wV1YkwsuX9W9vXbdbdCORdTa4v7YnYm/Grnqt7wsJCXGM2YjD4v7te72kudH3RcSnJQV7YZB6P3Uz2DwxLBfWy7OEDoMEGE1mA+zT6mg4/DxBReSwYZTK81FOfkQHAEAr63lMZrdHhvzK7JYtW7Bz505wIhZTMz3LmWnNU+Dow0lWaY6jYDvci6g3XJuNvdKyfseml2hxX/7Oft8/GBxiBe4vnyZ0GIQMisbM/j3mZsBgbBntl+WDvrUWSzpDY5XbwQCPXVMOxkhbsIYymswGWHmHBFa9//VmL3FIPdr2Ks8da9tZ62prb1Ej+txk9vjx47BYQms/mN1ud63KTkpJhFbufpJbRGwSqkovfoAGAGj0Ymi/fMWtjZFIsHpM/8r3dHtQZIK2vWFAYwTafvP/Ir8lNH6xEDJQ+Qn9+7U1XBULbVs9z9GEr6tP/YTf6/3PCRFCKdeI/5sZRftnhzCazA6Csyr/H3GMbfSsgFAgrgGj1UBZf8bV1toog0Gng0zMoaurCwUFA8vYH2wff/wx9u/fDynH4doMz0oMMu3VfTobPK1tD9guq1tb+Q1jcExc0+/YRmuG4+aj23xfKCCHVIv7Sq4ROgxCBs3Xkf170nKJWM9zJOShQ9sw3MtTxGD0ueoEjt1C+2eHKprMDoJf7P6vzI4pOwqO8fwU2ZkUC1nh+cexMtAaY12rs6G01cBms2HhwoUAgKvTkqCUuh+GYByeibrySJ/j6A1iqL9+w62NUSrx7IjT/Y6NZVjMr64O+pOCvjf+AaUdnqv4hAxFTLQJhyT+l+QCgLHt7TxHQ2Sd7VhR0wAx67tkYjBYnJgLyxWjhA6DBABNZgfBloYEv+9RWFqQofZMeqqNVYE7fRCcuOefTq6JCckksHfffRfHjh2DQiLGNelJ7p0MA4ab1KdxUmu/A+Nwn3SeuXEkSrn+J3v8t24kMoN8f51NacQDhb0f+0zIUFM/sv+rgOMqTvIYCek2ovwo7lN6VqAJRg4G+PvVJWBifG9dI6GFJrODYHudDnaZzu/7LmNVHm1njA4wdht0+p5/OpYzhFwSmMViweLFiwEAUzKSIRO7f7KPSRuHxmq1t1vdRJlEUG3/p1sbo9fhmZRj/Y5NJ9Hi/mM/9fv+wbJZfxvqO2kPGAkfh/1fFwAAmOVRiKkv5jcY4nLHwS0YrwuNCW2FqAUv/68KjJSeaA0lNJkdBA4Hg9oI/4/Wu6zJs6TUAW0DAEDD9WTtd3bqQ25l9vXXX0dRURE0MikmpSS69bEiESzWy/o0TnLxZo+2w9PTUMv2/4Sae8XRQZ8o0qVJwCNnsoUOgwSRtWvXIjExETKZDOPHj8eePXt6vfbIkSP47//+byQmJoJhGKxZs2bwAh2ALyO91+D2ZazMxHMk5HwMHFh2+gh0ktAoD7hdXohdf6DtBkMJTWYHyWGR/0Wmx5UeAse6r7ztlpUBHAdVe0/R8NZGFaI1zslsUVERmpqaBhZsgLW1teGpp54CAFyXmQIJ534YQmzGJLQ2yH2OEx3NQLHrc7c2xmTEqoT+r06PUA/DLUe+6ff9g2Wj6ja023wfIkHCwwcffICcnBwsXrwYubm5GDNmDKZNm4aqKu/7S9va2jB8+HCsWLECZnNoPHJlEuNwgqv1faEXl3o5VZHwy9hYjifsOqHD6LPV5jzUTrtU6DAIT+gZ5SDZ2jIcU/y8R2FpwRj1eOxrPOFq6z7WVlF1AhAPAwBY28UwGE3QyKVoardg7NixkAbxI5TW1lZUVFRAr5RjfJL7c0OxVIbmxr6Ve0nK/7dH2+7pCWhh8vod27z6lqA+6QsAOiIysPjMCKHDIEFk9erVuPvuuzFr1iwAwLp167Bp0yZs2LABjz32mMf1l112GS67zPn0w1t/MKrKjAbQv5O/xlUV8hoL8e66Ez/gfy/5Ff5VHxpPCB8cexhvlaaCPXLC98UkqNFkdpB8VmXC03IZmK4Ov+6b5JBi3wVtzQmRkB3LBUZMdbWpDbFIjorE/uIynD7d/yz+wTQ9Kx2cyP3hQHT6NagqkfRyR4+EGDtkO7a7tTHD4rDGfLCXO3z7lT4LY3M9ty0Em/XiP8LmoIcqxMlqtWLfvn2YN2+eq41lWUydOhW7du3i7c+xWCxudawH+wnQL/FW3xd5oZdoMfxMaOQSDAV/P/QtcjMuwcmWEqFD8amD6cLC6U1YXhkJe03/Vv1JcKDJ7CBptbFojhwDTeVuv+6bWF2MFy9YZC0zi5H2XRHkl3Job3U+PpMoTPjfy0ZjYvIw2B3BXU4KAOQSMWJ0Gvc2tQZ1lRk+72VYIGHfOx7tX0+LQhfTv5UbBadAzqn9/bp3MLUYx2FVkWc9XhK+ampqYLPZYDK57ws1mUw4dqz/iZAXWr58OZYsWcLbeH7hOGzSFvXr1kuUsWAQGiuFQ4Gssx3PVtXgVrUUFlvwH+JzQlyLDX9Ixp2vNAGdnUKHQ/qJJrOD6KhkFK6Af5PZzLIj0KVnocHaU2bqaGQ70gDoNHa0n8sDYxAJsUiEpKjQOC/bm6ika1FV4nsfaJLZAsm3e93aHCOSsT6y/7+w7lYMh7Ex+Fdln+v6vdAhkDA1b9485OT8//buPDyq+t4f+PucWTOZJBOyTRISEiCEnSQEImALCqKCtlxboYpIcWkLAUGuLcV6AbsQ2yvPD7eq0J9FfcojtbZUKWIxAkVFWUIQBAOG1WwTAlkmMdvMuX+EBEeWzJzMzDln8n49z2jm5HxnPjnMfOYz53yXpV336+vrkZJy5fSBgeAe3B8OnbwrTqNZnwRdRlUJfh43Fb9t9N+XqUDaGl6KIbOzkLdhv9KhkEy8VhlE/27yffEEARJywz3nVvw0vAoAECnVdm1rbtbGKNJriYxNQHVZWrf76fQCkj98+Yrtf7vZfJW9vZNiseP+I+pe6QsAahIn4tXyZKXDIJWJjY2FTqdDVVWVx/aqqiq/Du4ymUyIjIz0uAXLmcE22W1H16j/cncomnXk35gSPUzpMLy2JrEYVdO9m0WH1IfFbBC9WZUISef7wKzcNs8BSaX6CxCibQivv5yknRfDIYja/eeMSrwZkrv7+AfG1cNw1nPJ3rbRQ/FmpPxlfB9rNcGo8sthEgSsdP5A6TBIhYxGI0aPHo3CwstfyNxuNwoLCzFuXGgsqrEzSd4CKBEGKwZXHPNzNOStJ499jKSweKXD8NqjIw6hdYx2CnC6TLvVjwY1tOtRHytjvlnHlX3FWvsnIazsaNd9t0tEVFxSj+JTSkzKADi+6v4MksEkwr79Oc+NgoBXviNvYAgA3GDLxM0ndstuHyxlfW/HlupYpcMglVq6dCnWr1+PV199FceOHcP8+fPR2NjYNbvB/fff7zFArLW1FcXFxSguLkZrayvKyspQXFyML79U3ypZQmQktltOyWqbE56i+tlJQlnk13X4Q0PbVZdmV6N2wY2lN5VBSJe5OgcphsVskH1u9G7aqW/KqCpBlNHzkl51shWGkn0QvvEvaIlO7Gl4ijCGT4QAodv9BkVWQne+zGNb43eyUBh2Wtbz6gQdlpWrf1UgSTTg5zV3Kh0GqdisWbPw9NNPY8WKFcjKykJxcTG2bdvWNSjs7NmzqKio6Nq/vLwc2dnZyM7ORkVFBZ5++mlkZ2fjoYceUupPuCZn1gC0C/IK0jGu7vMKBdaoc4ew2JqpdBhec+ic+PVd7RCibUqHQj5gMRtkW5y+v6kFSMj5Vr/Z0jgXxOZGRNouLwOrN2rnck6nxEE5uFhl63a/sHA94t59xnOjXo9nx5yX/dw/tA3FwCr53ROC5UTyDOy5qO0+0RR4CxcuxJkzZ9DS0oJPP/0UeXl5Xb/buXMnNmzY0HU/LS0NkiRdcdu5c2fwA+/GoQHyP6Zya8q634kCbu5n2/Bdm3bmxj5sdODPcxK45K2GsJgNsreqEiCZInxuN7rd8wxDUWTHUrc28+V5a10ubc1kIOr0aG3L635HAIMMJyA6az22VU/NxkFjxdUbdCPCYMXCL/w3B2egSAYLFlfcqnQYRMoQBPwjVt6UXBEGKwaXH+1+Rwo4ARJWl+xFYlic0qF4bWt4KbbfPwTQ8FiU3oT/SkHW4hZRHTPW53a5NZ5rku8zlUMwGhHRenm5yq8bgje62B+Sh3zXq2VrrVEG9Nn6vMc2ITwcfxgmrx8dAPzU3A+2pguy2wfLwcSZOOa0KB0GkSKkwQNwRl8rq+1oawp0ksu/AZFsUU0X8XR9+xVLtKvZutgjOPKj0UqHQV5gMauA/aLv/WYHlx+F1RDedb9VcMGd3hcWx/GubU0NJhjDtFH4mK2RqKsZ7tW+mS37IbR6rpx2Yvow2R9y/cKTcO+R92W1DSa32YZFZycqHQaRYk4Nl3+1aUwb+8uqzcivDuExi+9TVCrp1/0OouJ7vp+AouDSzlekEPLGhQxM87GNTnIhKzwFH9ZenoS6LjUaEUf2AZmXC5749By0fl0Dnd4MUWcEBB0EQQC8GGAVVGIazpd1//LrE6tH5FvrPbYJ8bEoSP9c9lMvbTHA4Fb/TOo742aj7AT7bFHvtS1Z/hKjedXyuidQYM0+/B4O5UzDuxe1s8TwkqFFWN+Qg8gdRUqHQtfAYlYB/7lgQ1t8PxjqfUu2uS4dPvzG/TMJIkZtPwVzjh7Nl5a1PV8RWt8gB1Z/AOFby/N+eEc/NIiHZD1eXtQg3Fys/rOyrnA7Hj3tXX9iolAk2OOxM0xeQRptjMKgU9oplnqbVUd24URmFr50amNBC0kAFuR9jv/fNAKmT7k0shqxm4FCSqNu8LnNmAvlHvcPRTcAAPpEhma/sAS7COuuTR7bpMz+eDZeXiErCiJ+XqmN0c3/tM1BXRu/a1LvVTW6n+y2Y8P7QoDU/Y6kCEtrI/5fZaVH1zm1axVcWDDpJFyjBisdCl0Fi1mFbGsZ4XOboeWfw6K/3Cd2j6UcEAREutU/kEmOAcf/fsW2v9xigiSzx8R/2YYhs1L9qwG12vpj+alRSodBpKgP0htlt72hpd2PkVAgpFWXYrUU49Uc42rRILZg4W3lcA8dqHQo9C0sZhXy54pUSHqzT2307nZkWy+vTFIjNkHomwTrhVJ/h6e4lCQJ5qLtHtsav5uFtyNOyHq8cL0Fi47v9UdoAfd62H1o8WJpX6JQJUTb8LZV/mpkN1Sof/5oAm468SHmR3k3EFgtasQmPHJnNaTM/kqHQt/AT0yF1LXpURPve1eDsS6dx31nejzCSg/4KyxVEESg3/4NntvMZvzv2CrZj/mTsHTEOKt7GFngNcWOxG9Pa2e1HKJAOD92oOxVv1Itieh7Qf0r+1GHnxVvxZToYUqH4ROH2IjF37/AglZFWMwq6D/CGJ/bjP3WfLPnkgzQnzoMY5juGi20p7+9Gcbj+z22nZw+EkcN8orRvhY77vv8A3+EFnDP4F5IcvtREIWI9zO+lt12nCnWj5FQoAmQ8Lsj/0FmhPw+0kqo1DmxaEYNuxyoBItZBa2vyoTkY3+hIeVHEWGwdt0/HNMIQZIQYwuNwQ56g4jknS94bBMSE/DbgfKn4vrvNhOMrpaehhZwF+0T8PJXqd3vSBTCxNgYbI443v2O1zChoc6P0VAwWFob8fzZU4gxRSsdik8cYiPy73RwUJgKsJhV0DGnBU1xvg300UkujA6/XPDsDu8YnR/llj8fo5pk9KmBvvykx7Z/35mEBlFeMTo2ahCmHN/tj9ACSoKAJ7++W+kwiBRXNn4AXDJnIjCIBuSdOejniCgY7LVf4TmnBLNOW3Nr14hNePj2s2i5wfdB3eQ/LGYVttc8wec2N3xjoG6lzgkhORER57U/4MFk0SFh21qPbe05Q7E+Rt68fjpBh19Ulne/owqUJ9+GzVXxSodBpLi/DTgvu+3oyP6wtMqfBYGUNeKrz/CUYIcoaKs0cQqtmDepBLW3cOlbpWjrFROC/nTe95Gc46o8Zy9wDkhA2LGP/BWSYjJNpyDWfeODTK/HM5PkfzD9wDYUmZVH/RBZYEmiAb+48D2lwyBSnJTZH7vN8gdvTWwPnbEDvdXkE7vxi3DtDYJtF9z4Se4hlN49FhA47iHYWMwq7KOLUWiOGepTm/6OE0gIuzzI4UyyEfryk7BGaXeSfWuUHjFbn/XYVjEtB5+a5C1yEGGwYmHJJ/4ILeCOJ9+Fjy5GKR0GkeKK8mJ61H5i+Rfd70SqN/vwe3jIps3L9ssHFqHwoSwIYb5NvUk9w2JWBfZaJvrcZpw5sevnopiOlcBiw5v9FlOwZbYcgNByeQSzEBeLXw+R/8GUb0pFdKP6+xFLxnAsKp+qdBhEihMirFiXKP89P9CagpSa0/4LiBS1+OC/8INobRa0L8cexvM/TYaQZFc6lF6DxawKvFDt+2pP45suX37fZT0H6PWwOU/7MargiYnTI/K99R7bdnw/FTVik6zHG2hNwazP3/dHaAH3acK9ON4YpnQYRIqrmDgEF0X5U3JN1tn8FwypwoqD72JatLYWVei0K+wMFt3XjOZxI5UOpVdgMasCn9ZGojEuy6c2485+1tVJvk5oBvqnwHri4wBEF3gZZe9CkC6PXm7PHoI/xn0m+/F+6WyF3q3+5SzdllgsOnOj0mEQKU+vx0uDv+p+v+u4pfJk9zuRpoiSG6sPvofbNLaoQqdKnRP3TzqKz+4bA8GkrVkatIbFrErsMt3k0/62pgsYHpHedf/8gBgYj32KMKu2+s0mJwGWjzdf3mAwYO1N8s7IAsCt0cOQd2pfzwMLgn/1mYvqVoPSYRAprnbSKNmLogBAengyMiuP+TEiUgud5MJTB/+N6Ro9QwsAv005iKfz7ZCGDFA6lJDFYlYl1pQPhyT6Vth8F5cvT3+e1LH04wDzOb/GFUiiKCD9wAaPbefuyMZemYO+LHoLHjsp/4xuMLXa+uOxk9lKh0GkPIMBz42q6NFDTNdpa7J98o1OcmH1wW2YpdE+tADwqakMs79/Dp/fOxaCxaJ0OCGHxaxKlDaFwWH3bSDYdx2nu35+P7pjPtW4vxcgMVEb04IMSHDCWHL5LKrQNwmrBslf6WtBWDrstfIK4WD7k2kuWtx8+xFV3J6Nw0aH7PaiIOLOs0f8GBGpkSi58UTRv7AocjgEH1fOVIt2wY0n+xXh8fwINEzK5hRefsRPUxV5o32ST/sPqTjaNUVXieE8hORECG4XMt95HOlJbap+n4RZ9Uh6b43Htr99r4/slb4GR/TD7CPb/RFawDXE5+IPZzKUDoNIcUJ8z2YtAYBxUYOQdFH+3LSkLT85tBV/MKYhTKfdqa9O6Gvw4LjDWPfIQLSN9m1qTro6bXWwDHHPf5WOhX2SoHN6v2rVTeYkvPF1x0IDNcOS0aesAqKzFukblyA1Nhktg8bAZbFB0ukhqejbbNjJI9BdqOq6Xzc5B5ui5HUR0Ak6rKqp1cSgLwkCVrXMVjoMIuUJAv7xw0TUiD3r63pvvdNPAZFW3FayC+n2IXgsNgmnG7WxyuPVvG85hfenAneMH4xZew0w7T0CSPKWcu7tWMyqSJtbwMfR38N3nC953eaWC1V441KNWtSvHVO+8Tvd+TJYzqv/srsY2wf/M1r+SOT7o4Zi2MF/+TGiwDnb90689WWC0mEQKe7cjDHYGFXUo8cYaE3Bdw5rcxYX6pnMymPYdCEc/zt8Ev52Ud6S52qxxfolttwMjJ2QirlfxCN+9zFIDfyS5gt2M1CZlWVjIem9v3wy+kwR+pg6Bj9sjj4N6LX3/eTtH6agUifvjTvQmoL8w4V+jigwJKMVC6ruVDoMIsU1TMrGY5k9K2QBYGGzCAE8k9VbWVobsbLoX/gTEpAenqx0OD2211SG/FEH8cACCbsezEFL3gjAwBlvvKGKYvaFF15AWloazGYz8vLysHfv3uvu/+abb2Lw4MEwm80YMWIEtm7dGqRIA+9kkxnHE7/n9f46yYXbLCkAAIfOCecEbY32rLktF69Fyxv0ZdaZ8FT1BZjatbHy2fvxD+DzhnClw6AQpZU8enHqaPz0hs8h9bDX0wTbYEw+sds/QZGm5Z3ah78f3Ycnwgd7LPWuVQ1iC16I/wxzbj6GhY+GY+dDOWi4KRtCH87acS2Kn8bbtGkTli5dipdeegl5eXlYu3Ytbr31VpSUlCA+Pv6K/T/++GPcc889KCgowB133IGNGzdixowZKCoqwvDh2p2H7puWO6bgLd1mCK5Wr/afUXESGy+dzP1l3im8cCYF0mkNTNGVkY6fZx2V3XylPgWZlR/4MaDAccZlY8HJPKXDoBCllTz6nwk2/Lb8UI8fJ94cg9+UBmYavuqkmzG/ZhYsOhcide0wim4IkCAI6Ph/QJ5VnjDRhRst5zBcKkFcw1Ho6zWQ9wNE727HrCP/xl06I7YNuhGbjBIO1ZcqHVaPOXTOjkWE4gAhDxjXko7s+mjENelhbgd0bkArFyeiRsQhKUCPLUiSsr2N8/LyMGbMGDz//PMAALfbjZSUFCxatAi//OUvr9h/1qxZaGxsxJYtW7q23XDDDcjKysJLL3Xf17S+vh5RUVGoq6tDZGSk13He9cePUHS21uv9e2pzxjZknXvN6/3njLoJxZfeuMmuSPzqcH/EFZ2F5KhWZYdyIcmO/7kX+MJwXlb7n1uH4v7D2/wcVWC0R/TFXS0r8Fm9VelQSKZVdw7Fjyekd7/jJXLzjFzBzqOAvL/xN3t+g78e/6tX+15LUlg8Xjxfh/6OEz16nG+SBBEX7RPwctt0vPxVqt8eN9iSzS2Y3KcGOZZKDBAqYHdVILK5DEZnGYTW3tcH81xMGt5PHoKPxDYUO8+gxSVvthzyj7sy7sKT45/0en9fcoyiZ2ZbW1tx4MABLF++vGubKIqYMmUK9uzZc9U2e/bswdKlSz223Xrrrdi8eXMgQw26eaen4NPYPTBe9C5hP1LbgAdFARIklOnqsSCrGMgCdNAj0m2GQdJBTV/f6sVaNAu+zz4Qb47B4+1WTNZAISsZLDhpvx0/KZuG0qaw7hsQydBb8miMKRr/FdYXDxz7EBHNdT16LMkUiabI/ig3D8Re1yC8WjUAx09p/z1a1mzCa+VJeO0q57/splYMtTYi3exEX6MTCTonYgQnolCPcMkJi8sJo6sRhnYn9O2NENuaILQ1QtBIN66rSak5jXk1pzEPQJvOiBJ7Jr6w2VFqMuGs1IbydieqWi6ioa33FfqhRtFi9vz583C5XEhI8BzdnZCQgC++uPrcg5WVlVfdv7Ky8qr7t7S0oKXl8rex+vp6WbHeMtSOQQkRstrKtcH0DOYJb8Pg6n551zEA1sSn4SN3Q+ADCyIRAvSCiGjRhCGSDuNrymF0tQI53p8lk+96Q0sEQOi46CgJItyCHu2iES1iOOp00TjltuPDphQ4XXqMsXX8+5B2DbIH973vi2DkUcA/uTQnIQcuyeWxTbg0Bb4odPykhwCDICJM0CNS0CHeDQxobkZavaPjHTl0xuWL/d94D0qCDm5BB5egh0swol0wokU042vBDCcsqJWsqHJF4ExrJBxtnoNsc2KAHJ//Gm1qBFBy6eYNnSAhQtcGq64N4UI7LLo2hAntMAttMAntMKIdBrTDKLRDDxcMaIcOLujggl5yQbz0syi5IXb93w0BbohSxzVyQeq8LwG41K1DkiDg0jV0qTMbX7pJna8ACYDQ8RjAt65CemZvPYBhl27ApWaGjluLzoBaUzjqDGY49Xo0iiK+FgU0Q0ALJLQKQBsktEsS2tFxc0sSXHDDDcANCZIkQer8Gbj0X0CSpI7X6aXYJI+4hG/dD205CYF7lyneZzbQCgoK8OST3p/Wvpb5k5RaU3mC13veculG/tNd/7jO3+vQkRfDANgA9AMwKWBREQWfP3Lp9P7TMb3/dD9F1EFNfVhJm8wA7JdupE2KzmYQGxsLnU6Hqqoqj+1VVVWw26/+srLb7T7tv3z5ctTV1XXdzp3rvR3kiSj0BCOPAsylRKReihazRqMRo0ePRmHh5XlC3W43CgsLMW7cuKu2GTdunMf+ALB9+/Zr7m8ymRAZGelxIyIKFcHIowBzKRGpl+LdDJYuXYq5c+ciNzcXY8eOxdq1a9HY2Ih58+YBAO6//34kJyejoKAAALB48WJMnDgRa9aswfTp0/HGG29g//79WLdunZJ/BhGRYphHiag3U7yYnTVrFqqrq7FixQpUVlYiKysL27Zt6xqccPbsWYji5RPI48ePx8aNG/HEE0/g8ccfR0ZGBjZv3hwyc8wSEfmKeZSIejPF55kNtmDP/0hEvU9vyDO94W8kIuX4kmNUsZwtEREREZEcLGaJiIiISLNYzBIRERGRZrGYJSIiIiLNYjFLRERERJrFYpaIiIiINIvFLBERERFpluKLJgRb57S69fX1CkdCRKGqM7+E8jTezKVEFEi+5NFeV8w2NDQAAFJSUhSOhIhCXUNDA6KiopQOIyCYS4koGLzJo71uBTC3243y8nJERERAEASv2tTX1yMlJQXnzp3jSjd+wOPpXzye/tfTYypJEhoaGpCUlOSxjGwo8TWX8nXqfzym/sXj6V/BzKO97sysKIro27evrLaRkZF8gfsRj6d/8Xj6X0+Oaaieke0kN5fydep/PKb+xePpX8HIo6F5yoCIiIiIegUWs0RERESkWSxmvWAymbBy5UqYTCalQwkJPJ7+xePpfzym/sdj6n88pv7F4+lfwTyevW4AGBERERGFDp6ZJSIiIiLNYjFLRERERJrFYpaIiIiINIvFLBERERFpFotZL7zwwgtIS0uD2WxGXl4e9u7dq3RImlRQUIAxY8YgIiIC8fHxmDFjBkpKSpQOK2Q89dRTEAQBS5YsUToUzSorK8N9992HmJgYhIWFYcSIEdi/f7/SYYUE5lH/YB4NLObRnlMij7KY7camTZuwdOlSrFy5EkVFRRg1ahRuvfVWOBwOpUPTnF27diE/Px+ffPIJtm/fjra2NkydOhWNjY1Kh6Z5+/btw8svv4yRI0cqHYpmXbx4ERMmTIDBYMC7776Lo0ePYs2aNYiOjlY6NM1jHvUf5tHAYR7tOcXyqETXNXbsWCk/P7/rvsvlkpKSkqSCggIFowoNDodDAiDt2rVL6VA0raGhQcrIyJC2b98uTZw4UVq8eLHSIWnSsmXLpBtvvFHpMEIS82jgMI/6B/OofyiVR3lm9jpaW1tx4MABTJkypWubKIqYMmUK9uzZo2BkoaGurg4A0KdPH4Uj0bb8/HxMnz7d43VKvnv77beRm5uLu+++G/Hx8cjOzsb69euVDkvzmEcDi3nUP5hH/UOpPMpi9jrOnz8Pl8uFhIQEj+0JCQmorKxUKKrQ4Ha7sWTJEkyYMAHDhw9XOhzNeuONN1BUVISCggKlQ9G8kydP4sUXX0RGRgbee+89zJ8/H4888gheffVVpUPTNObRwGEe9Q/mUf9RKo/qA/roRNeQn5+PI0eO4MMPP1Q6FM06d+4cFi9ejO3bt8NsNisdjua53W7k5uZi9erVAIDs7GwcOXIEL730EubOnatwdERXYh7tOeZR/1Iqj/LM7HXExsZCp9OhqqrKY3tVVRXsdrtCUWnfwoULsWXLFuzYsQN9+/ZVOhzNOnDgABwOB3JycqDX66HX67Fr1y48++yz0Ov1cLlcSoeoKYmJiRg6dKjHtiFDhuDs2bMKRRQamEcDg3nUP5hH/UupPMpi9jqMRiNGjx6NwsLCrm1utxuFhYUYN26cgpFpkyRJWLhwIf7xj3/ggw8+QHp6utIhadrkyZNx+PBhFBcXd91yc3Mxe/ZsFBcXQ6fTKR2ipkyYMOGKKY6OHz+Ofv36KRRRaGAe9S/mUf9iHvUvpfIouxl0Y+nSpZg7dy5yc3MxduxYrF27Fo2NjZg3b57SoWlOfn4+Nm7ciH/+85+IiIjo6i8XFRWFsLAwhaPTnoiIiCv6yYWHhyMmJob952R49NFHMX78eKxevRozZ87E3r17sW7dOqxbt07p0DSPedR/mEf9i3nUvxTLo0GfP0GDnnvuOSk1NVUyGo3S2LFjpU8++UTpkDQJwFVvf/7zn5UOLWRwSpmeeeedd6Thw4dLJpNJGjx4sLRu3TqlQwoZzKP+wTwaeMyjPaNEHhUkSZICWy4TEREREQUG+8wSERERkWaxmCUiIiIizWIxS0RERESaxWKWiIiIiDSLxSwRERERaRaLWSIiIiLSLBazRERERKRZLGYpJP34xz/GjBkzgv68GzZsgCAIEAQBS5Ys6dqelpaGtWvXXrdtZzubzRbQGImIvME8SlrB5WxJcwRBuO7vV65ciWeeeQZKrQcSGRmJkpIShIeH+9SuoqICmzZtwsqVKwMUGRFRB+ZRCiUsZklzKioqun7etGkTVqxYgZKSkq5tVqsVVqtVidAAdHxI2O12n9vZ7XZERUUFICIiIk/MoxRK2M2ANMdut3fdoqKiupJe581qtV5xeWzSpElYtGgRlixZgujoaCQkJGD9+vVobGzEvHnzEBERgYEDB+Ldd9/1eK4jR47g9ttvh9VqRUJCAubMmYPz58/LirupqQkPPPAAIiIikJqainXr1vXkMBARycY8SqGExSz1Gq+++ipiY2Oxd+9eLFq0CPPnz8fdd9+N8ePHo6ioCFOnTsWcOXPQ1NQEAKitrcXNN9+M7Oxs7N+/H9u2bUNVVRVmzpwp6/nXrFmD3NxcHDx4EAsWLMD8+fM9zoQQEakd8yipEYtZ6jVGjRqFJ554AhkZGVi+fDnMZjNiY2Px8MMPIyMjAytWrEBNTQ0+++wzAMDzzz+P7OxsrF69GoMHD0Z2djZeeeUV7NixA8ePH/f5+adNm4YFCxZg4MCBWLZsGWJjY7Fjxw5//5lERAHDPEpqxD6z1GuMHDmy62edToeYmBiMGDGia1tCQgIAwOFwAAAOHTqEHTt2XLXfWGlpKQYNGiT7+Tsv6XU+FxGRFjCPkhqxmKVew2AweNwXBMFjW+foXrfbDQBwOp2488478fvf//6Kx0pMTPTL83c+FxGRFjCPkhqxmCW6hpycHLz11ltIS0uDXs+3ChGRr5hHKRjYZ5boGvLz83HhwgXcc8892LdvH0pLS/Hee+9h3rx5cLlcSodHRKR6zKMUDCxmia4hKSkJH330EVwuF6ZOnYoRI0ZgyZIlsNlsEEW+dYiIusM8SsEgSEot70EUgjZs2IAlS5agtrZWkfZERFrHPEq+4tciIj+rq6uD1WrFsmXLfGpntVrxs5/9LEBRERFpB/Mo+YJnZon8qKGhAVVVVQAAm82G2NhYr9t++eWXADqmu0lPTw9IfEREasc8Sr5iMUtEREREmsVuBkRERESkWSxmiYiIiEizWMwSERERkWaxmCUiIiIizWIxS0RERESaxWKWiIiIiDSLxSwRERERaRaLWSIiIiLSLL3SARBdi8vlQltbm9JhqJLRaIQo8rsoEV0f8+i1GQwG6HQ6pcMgP2AxS6ojSRIqKytRW1urdCiqJYoi0tPTYTQalQ6FiFSIedQ7NpsNdrsdgiAoHQr1AJezJdWpqKhAbW0t4uPjYbFYmGS+xe12o7y8HAaDAampqTw+RHQF5tHrkyQJTU1NcDgcsNlsSExMVDok6gGemSVVcblcXQk4JiZG6XBUKy4uDuXl5Whvb4fBYFA6HCJSEeZR74SFhQEAHA4H4uPj2eVAw9jpjlSls2+XxWJROBJ16+xe4HK5FI6EiNSGedR7nceI/Yq1jcUsqRIviV0fjw8RdYd5ons8RqGBxSwRERERaRaLWSIiIiLSLBazRERERKRZLGaJiIiISLNYzJLqSZKExsZGRW7eTsNcXV0Nu92O1atXd237+OOPYTQaUVhYeN22q1atQlZWFl5//XWkpaUhKioKP/rRj9DQ0NCj40ZE1Km35NGXX34ZKSkpsFgsmDlzJurq6np03EgbOM8sqV5TUxOsVqsiz+10OhEeHt7tfnFxcXjllVcwY8YMTJ06FZmZmZgzZw4WLlyIyZMnd9u+tLQUmzdvxpYtW3Dx4kXMnDkTTz31FH73u9/5488gol6uN+TRL7/8En/961/xzjvvoL6+Hg8++CAWLFiAv/zlL/74M0jFWMwS+cm0adPw8MMPY/bs2cjNzUV4eDgKCgq8aut2u7FhwwZEREQAAObMmYPCwkIWs0TUq/QkjzY3N+O1115DcnIyAOC5557D9OnTsWbNGtjt9kCGTQpjMUuqZ7FY4HQ6FXtuXzz99NMYPnw43nzzTRw4cAAmk8mrdmlpaV2FLAAkJibC4XD49NxERNfSG/JoampqVyELAOPGjYPb7UZJSQmL2RDHYpZUTxAEry5RqUFpaSnKy8vhdrtx+vRpjBgxwqt2316SVhAEuN3uQIRIRL1Qb8ij1HuxmCXyk9bWVtx3332YNWsWMjMz8dBDD+Hw4cOIj49XOjQiIk3oSR49e/YsysvLkZSUBAD45JNPIIoiMjMzAx02KYyzGRD5ya9+9SvU1dXh2WefxbJlyzBo0CA88MADSodFRKQZPcmjZrMZc+fOxaFDh7B792488sgjmDlzJrsY9AIsZon8YOfOnVi7di1ef/11REZGQhRFvP7669i9ezdefPFFpcMjIlK9nubRgQMH4q677sK0adMwdepUjBw5En/84x+DEDkpTZC8nQCOKAiam5tx6tQppKenw2w2Kx2OavE4EdG19Mb8sGrVKmzevBnFxcU+teuNxyoU8cwsEREREWkWi1miABs2bBisVutVb5zMm4ioe8yjdD2czYAowLZu3Yq2trar/i4hISHI0RARaU93eTQiIgKrVq0KblCkGixmiQKsX79+SodARKRpzKN0PexmQKrEcYnXx+NDRN1hnugej1FoYDFLqtK5ElZTU5PCkahba2srAECn0ykcCRGpDfOo9zqP0bdXYSRtYTcDUhWdTgebzQaHwwGgY01vQRAUjkpd3G43qqurYbFYoNfzLUxEnphHuydJEpqamuBwOGCz2XhiQOP4SUiq07laS2cipiuJoojU1FR+QBHRVTGPesdms3GFsBDARRNItVwu1zVHr/Z2RqMRosheQkR0fcyj12YwGHhGNkSwmCUiIiIizeKpHSIiIiLSLBazRERERKRZLGaJiIiISLNYzBIRERGRZrGYJSIiIiLNYjFLRERERJrFYpaIiIiINOv/AEpvyU8DIwOIAAAAAElFTkSuQmCC", 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" ] @@ -524,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "metadata": {}, "outputs": [ { From b7901c6f724f506f714cefb2e4466aa2714f8bbf Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Mon, 7 Aug 2023 21:07:48 -0400 Subject: [PATCH 040/154] Initial try at new release system --- .github/release_checklist.md | 14 ------- .github/release_reminder.md | 4 ++ .github/release_workflow.md | 56 ++++++++++++++++++++++++++ .github/workflows/create_release.yml | 50 ----------------------- .github/workflows/publish_pypi.yml | 13 +++--- .github/workflows/release_reminder.yml | 21 ++++++++++ .github/workflows/update_version.yml | 26 +++++++----- 7 files changed, 103 insertions(+), 81 deletions(-) delete mode 100644 .github/release_checklist.md create mode 100644 .github/release_reminder.md create mode 100644 .github/release_workflow.md delete mode 100644 .github/workflows/create_release.yml create mode 100644 .github/workflows/release_reminder.yml diff --git a/.github/release_checklist.md b/.github/release_checklist.md deleted file mode 100644 index f461d471e0..0000000000 --- a/.github/release_checklist.md +++ /dev/null @@ -1,14 +0,0 @@ -- Run `scripts/update_version.py` to - - - Increment version number in - - `pybamm/version.py` - - `docs/conf.py` - - `CITATION.cff` - - `vcpkg.json` - - `docs/source/_static/versions.json`, and check if any links fail - - - Update baseline of registries in `vcpkg-configuration.json` as the latest commit id from [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) - - Update `CHANGELOG.md` with a summary of the release - -- Update jax and jaxlib to latest version in `pybamm.util` and fix any bugs that arise -- If building wheels on Windows gives a `vcpkg` related error - revert the baseline of default-registry to a stable commit in `vcpkg-configuration.json` diff --git a/.github/release_reminder.md b/.github/release_reminder.md new file mode 100644 index 0000000000..58d917ba8e --- /dev/null +++ b/.github/release_reminder.md @@ -0,0 +1,4 @@ +--- +title: Create {{ date | date('YY.MM') }} release +--- +Quarterly reminder to create a non-pre-release before the end of this month. See [Release Workflow](./release_workflow.md) for the process. diff --git a/.github/release_workflow.md b/.github/release_workflow.md new file mode 100644 index 0000000000..3e0f869354 --- /dev/null +++ b/.github/release_workflow.md @@ -0,0 +1,56 @@ +# Release workflow + +This file contains the workflow required to make a `PyBaMM` release on GitHub and PyPI by the maintainers. + +## rc1 releases (automated) + +1. The `update_version.yml` workflow will run on every 1st of January, May and September, creating 2 PRs - + + 1. Incrementing the version to `YY.MMrc1` by running `scripts/update_version.py` in the following files - + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/source/_static/versions.json` + + 2. A PR from `develop` to `main` + + The version PR should be merged into `develop`, and then the devlop-to-main PR should be merged into `main`. + +2. Once the tests pass, create a new GitHub *pre-release* with the same tag (`YY.MMrc1`) and a description copied from `CHANGELOG.md`. + +3. This release will automatically trigger `publish_pypi.yml` and create a *pre-release* on PyPI. + +## rcX and actual releases (manual) + +Once satisfied with the release candidate (or if a new release candidate is required after the release of `rc1`) - + +1. Run `update_version.yml` manually, leaving the `release_type` field blank ("") for an actual release or with a release candidate version number (`rc2`, `rc3`, ...) for another pre-release. + +2. This will create the same 2 PRs mentioned in the previous section - + + 1. Incrementing the version to `YY.MMrcX` or `YY.MM` by running `scripts/update_version.py` in the following files - + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/source/_static/versions.json` + + 2. A PR from `develop` to `main` + + The version PR should be merged into `develop`, and then the devlop-to-main PR should be merged into `main`. + +3. Once the tests pass, create a new GitHub *release* or *pre-release* with the same tag and a description copied from `CHANGELOG.md`. + +4. This release will automatically trigger `publish_pypi.yml` and create a *release* or a *pre-release* on PyPI. + + +## Other checks + +Some other important things to check throughout the release process - + +- Update baseline of registries in `vcpkg-configuration.json` as the latest commit id from [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) +- Update `CHANGELOG.md` with a summary of the release +- Update jax and jaxlib to latest version in `pybamm.util` and fix any bugs that arise +- If building wheels on Windows gives a `vcpkg` related error - revert the baseline of default-registry to a stable commit in `vcpkg-configuration.json` +- Make sure the URLs in `docs/source/_static/versions.json` are valid diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml deleted file mode 100644 index 5a8673fd99..0000000000 --- a/.github/workflows/create_release.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Create GitHub release - -on: - push: - branches: main - workflow_dispatch: - -jobs: - create-release: - # This workflow is only of value to PyBaMM and would always be skipped in forks - if: github.repository_owner == 'pybamm-team' - runs-on: ubuntu-latest - permissions: - contents: write - strategy: - matrix: - python-version: [3.8] - - steps: - - uses: actions/checkout@v3 - - - name: Get current date - run: | - echo "VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV - echo "TODAY=$(date +'%d')" >> $GITHUB_ENV - - - name: Fail the job if date < 20 - if: env.TODAY < 20 - uses: actions/github-script@v5 - with: - script: core.setFailed('This workflow should be triggered only at the end of the month, or else it will create a release for the wrong month.') - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Install dependencies - run: | - pip install wheel - pip install --editable . - - - name: Get Changelog - run: python -c "from scripts.update_version import get_changelog; get_changelog()" - - - name: Create release - uses: softprops/action-gh-release@v1 - with: - tag_name: ${{ env.VERSION }} - body_path: CHANGELOG.md diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index 6d89da1387..1d58d805a3 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -1,8 +1,8 @@ name: Build and publish package to PyPI on: - push: - branches: main + release: + types: [published] workflow_dispatch: inputs: target: @@ -22,7 +22,7 @@ jobs: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 with: - python-version: 3.8 + python-version: 3.11 - name: Install cibuildwheel run: python -m pip install cibuildwheel==2.12.3 @@ -77,7 +77,7 @@ jobs: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 with: - python-version: 3.8 + python-version: 3.11 - name: Install cibuildwheel run: python -m pip install cibuildwheel==2.12.3 @@ -130,15 +130,12 @@ jobs: build_sdist: name: Build sdist runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8] steps: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 with: - python-version: ${{ matrix.python-version }} + python-version: 3.11 - name: Install dependencies run: pip install wheel diff --git a/.github/workflows/release_reminder.yml b/.github/workflows/release_reminder.yml new file mode 100644 index 0000000000..6e27fc78fc --- /dev/null +++ b/.github/workflows/release_reminder.yml @@ -0,0 +1,21 @@ +name: Create a release reminder + +on: + schedule: + # Run at 10 am UTC on 28th every 4th month from January through December + - cron: 0 10 28 1-12/4 * + +permissions: + contents: read + issues: write + +jobs: + remind: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: JasonEtco/create-an-issue@v2 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + filename: .github/release_reminder.md diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index 7f05b6edfb..2258bec911 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -2,31 +2,39 @@ name: Update version on: workflow_dispatch: + inputs: + release_type: + description: 'Release type. Leave blank for an actual release or "rc2", "rc3", ..., for release candidates."' + default: "" + schedule: + # Run at 10 am UTC on 1st every 4th month from January through December + - cron: 0 10 1 1-12/4 * jobs: update-version: # This workflow is only of value to PyBaMM and would always be skipped in forks - if: github.repository_owner == 'pybamm-team' + # if: github.repository_owner == 'pybamm-team' runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8] steps: - uses: actions/checkout@v3 - - - name: Set up Python ${{ matrix.python-version }} + - name: Set up Python 3.11 uses: actions/setup-python@v4 with: - python-version: ${{ matrix.python-version }} + python-version: 3.11 - name: Install dependencies run: | pip install wheel pip install --editable . - - name: Get current date - run: echo "VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV + - name: Get current date for the first release candidate + if: github.event_name == 'schedule' + run: echo "VERSION=$(date +'v%y.%-m')rc1" >> $GITHUB_ENV + + - name: Get current date for a manual release + if: github.event_name == 'workflow_dispatch' + run: echo "VERSION=$(date +'v%y.%-m')${{ github.event.inputs.release_type }}" >> $GITHUB_ENV - name: Update version run: python scripts/update_version.py From 2c63ed67d72b26a3f78cf9ca32df7b963e75f3b9 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Mon, 7 Aug 2023 22:51:19 -0400 Subject: [PATCH 041/154] Logic for rc2, rc3, ... --- .github/release_workflow.md | 45 ++++++++++++++++++++-------- .github/workflows/publish_pypi.yml | 10 +++---- .github/workflows/update_version.yml | 18 ++++++++--- 3 files changed, 51 insertions(+), 22 deletions(-) diff --git a/.github/release_workflow.md b/.github/release_workflow.md index 3e0f869354..01637cd69a 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -2,11 +2,11 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub and PyPI by the maintainers. -## rc1 releases (automated) +## rc0 releases (automated) 1. The `update_version.yml` workflow will run on every 1st of January, May and September, creating 2 PRs - - 1. Incrementing the version to `YY.MMrc1` by running `scripts/update_version.py` in the following files - + 1. Incrementing the version to `YY.MMrc0` by running `scripts/update_version.py` in the following files - - `pybamm/version.py` - `docs/conf.py` - `CITATION.cff` @@ -17,32 +17,51 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an The version PR should be merged into `develop`, and then the devlop-to-main PR should be merged into `main`. -2. Once the tests pass, create a new GitHub *pre-release* with the same tag (`YY.MMrc1`) and a description copied from `CHANGELOG.md`. +2. Once the tests pass, create a new GitHub *pre-release* with the same tag (`YY.MMrc0`) and a description copied from `CHANGELOG.md`. 3. This release will automatically trigger `publish_pypi.yml` and create a *pre-release* on PyPI. -## rcX and actual releases (manual) +## rcX releases (manual) -Once satisfied with the release candidate (or if a new release candidate is required after the release of `rc1`) - +If a new release candidate is required after the release of `rc0` - -1. Run `update_version.yml` manually, leaving the `release_type` field blank ("") for an actual release or with a release candidate version number (`rc2`, `rc3`, ...) for another pre-release. +1. Fix a bug in `main` (no new features should be added to `main` once `rc0` is released) as well as `develop` individually. -2. This will create the same 2 PRs mentioned in the previous section - +2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...). - 1. Incrementing the version to `YY.MMrcX` or `YY.MM` by running `scripts/update_version.py` in the following files - +3. This will create a PR incrementing the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files - + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/source/_static/versions.json` + + The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something that we don't want (`develop` will have new features). + +4. Once the tests pass, create a new GitHub *pre-release* with the same tag and a description copied from `CHANGELOG.md`. + +5. This release will automatically trigger `publish_pypi.yml` and create a *pre-release* on PyPI. + +6. Manually merge `main` back to `develop` if substantial conflicts arise. + +## Actual release (manual) + +Once satisfied with the release candidates - + +1. Run `update_version.yml` manually, leaving the `append_to_tag` field blank ("") for an actual release. + +2. This will create a PR incrementing the version to `YY.MM` by running `scripts/update_version.py` in the following files - - `pybamm/version.py` - `docs/conf.py` - `CITATION.cff` - `vcpkg.json` - `docs/source/_static/versions.json` - 2. A PR from `develop` to `main` - - The version PR should be merged into `develop`, and then the devlop-to-main PR should be merged into `main`. + The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something that we don't want (`develop` will have new features). -3. Once the tests pass, create a new GitHub *release* or *pre-release* with the same tag and a description copied from `CHANGELOG.md`. +3. Once the tests pass, create a new GitHub *release* with the same tag and a description copied from `CHANGELOG.md`. -4. This release will automatically trigger `publish_pypi.yml` and create a *release* or a *pre-release* on PyPI. +4. This release will automatically trigger `publish_pypi.yml` and create a *release* on PyPI. ## Other checks diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index 1d58d805a3..f6db257b98 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -2,17 +2,17 @@ name: Build and publish package to PyPI on: release: - types: [published] + types: [published] workflow_dispatch: inputs: target: description: 'Deployment target. Can be "pypi" or "testpypi"' default: "pypi" debug_enabled: - type: boolean - description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)' - required: false - default: false + type: boolean + description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)' + required: false + default: false jobs: build_windows_wheels: diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index 2258bec911..e77412083f 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -3,8 +3,8 @@ name: Update version on: workflow_dispatch: inputs: - release_type: - description: 'Release type. Leave blank for an actual release or "rc2", "rc3", ..., for release candidates."' + append_to_tag: + description: 'Leave blank for an actual release or "rc1", "rc2", ..., for release candidates."' default: "" schedule: # Run at 10 am UTC on 1st every 4th month from January through December @@ -18,6 +18,15 @@ jobs: steps: - uses: actions/checkout@v3 + if: github.event_name == 'schedule' + + # the version should be updated in main for manual releases as features will + # be frozen once the scheduled release (rc0) is created + - uses: actions/checkout@v3 + if: github.event_name == 'workflow_dispatch' + with: + ref: 'main' + - name: Set up Python 3.11 uses: actions/setup-python@v4 with: @@ -30,11 +39,11 @@ jobs: - name: Get current date for the first release candidate if: github.event_name == 'schedule' - run: echo "VERSION=$(date +'v%y.%-m')rc1" >> $GITHUB_ENV + run: echo "VERSION=$(date +'v%y.%-m')rc0" >> $GITHUB_ENV - name: Get current date for a manual release if: github.event_name == 'workflow_dispatch' - run: echo "VERSION=$(date +'v%y.%-m')${{ github.event.inputs.release_type }}" >> $GITHUB_ENV + run: echo "VERSION=$(date +'v%y.%-m')${{ github.event.inputs.append_to_tag }}" >> $GITHUB_ENV - name: Update version run: python scripts/update_version.py @@ -52,6 +61,7 @@ jobs: - [ ] Check the [release checklist](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_checklist.md) - name: Make a PR from develop to main + if: github.event_name == 'schedule' # features are frozen once a release candidate is out uses: repo-sync/pull-request@v2 with: destination_branch: "main" From 0e32e7fd3e9746e370ec4858f797957440c6cb9e Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Mon, 7 Aug 2023 23:26:26 -0400 Subject: [PATCH 042/154] Don't edit CHANGELOG automatically --- scripts/update_version.py | 49 +-------------------------------------- 1 file changed, 1 insertion(+), 48 deletions(-) diff --git a/scripts/update_version.py b/scripts/update_version.py index 4a5f60d8d8..3008b381f3 100644 --- a/scripts/update_version.py +++ b/scripts/update_version.py @@ -5,9 +5,7 @@ import json import os import re -from datetime import date, datetime -from dateutil.relativedelta import relativedelta import pybamm @@ -16,11 +14,7 @@ def update_version(): """ Opens file and updates the version number """ - current_year = datetime.now().strftime("%y") - current_month = datetime.now().month - - release_version = f"{current_year}.{current_month}" - last_day_of_month = date.today() + relativedelta(day=31) + release_version = os.getenv("VERSION") # pybamm/version.py with open(os.path.join(pybamm.root_dir(), "pybamm", "version.py"), "r+") as file: @@ -83,47 +77,6 @@ def update_version(): file.seek(0) file.write(replace_commit_id) - changelog_line1 = "# [Unreleased](https://github.com/pybamm-team/PyBaMM/)\n" - changelog_line2 = f"# [v{release_version}](https://github.com/pybamm-team/PyBaMM/tree/v{release_version}) - {last_day_of_month}\n\n" # noqa: E501 - - # CHANGELOG.md - with open(os.path.join(pybamm.root_dir(), "CHANGELOG.md"), "r+") as file: - output_list = file.readlines() - output_list[0] = changelog_line1 - output_list.insert(2, changelog_line2) - file.truncate(0) - file.seek(0) - file.writelines(output_list) - - -def get_changelog(): - """ - Opens CHANGELOG.md and overrides the changelog with the latest version. - Used in GitHub workflow to create the changelog for the GitHub release. - """ - # This month - now = datetime.now() - current_year = now.strftime("%y") - current_month = now.month - - # Previous month - previous_date = datetime.now() + relativedelta(months=-1) - previous_year = previous_date.strftime("%y") - previous_month = previous_date.month - - current_version = re.escape(f"# [v{current_year}.{current_month}]") - previous_version = re.escape(f"# [v{previous_year}.{previous_month}]") - - # Open CHANGELOG.md and keep the relevant lines - with open(os.path.join(pybamm.root_dir(), "CHANGELOG.md"), "r+") as file: - output = file.read() - re_changelog = f"{current_version}.*?(##.*)(?={previous_version})" - release_changelog = re.findall(re_changelog, output, re.DOTALL)[0] - print(release_changelog) - file.truncate(0) - file.seek(0) - file.write(release_changelog) - if __name__ == "__main__": update_version() From cb0415c7c84e1898fc841ba0471d62f04bb27f44 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Mon, 7 Aug 2023 23:45:27 -0400 Subject: [PATCH 043/154] Fix versioning --- scripts/update_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/update_version.py b/scripts/update_version.py index 3008b381f3..70e2e2a1af 100644 --- a/scripts/update_version.py +++ b/scripts/update_version.py @@ -14,7 +14,7 @@ def update_version(): """ Opens file and updates the version number """ - release_version = os.getenv("VERSION") + release_version = os.getenv("VERSION")[1:] # pybamm/version.py with open(os.path.join(pybamm.root_dir(), "pybamm", "version.py"), "r+") as file: From 246ca6b9cf11a09d2e7b7310721040f125d929b5 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Mon, 7 Aug 2023 23:52:38 -0400 Subject: [PATCH 044/154] Fix release_workflow --- .github/release_workflow.md | 40 ++++++++++++++++++------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/.github/release_workflow.md b/.github/release_workflow.md index 01637cd69a..343ca63836 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -17,9 +17,9 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an The version PR should be merged into `develop`, and then the devlop-to-main PR should be merged into `main`. -2. Once the tests pass, create a new GitHub *pre-release* with the same tag (`YY.MMrc0`) and a description copied from `CHANGELOG.md`. +2. Once the tests pass, create a new GitHub _pre-release_ with the same tag (`YY.MMrc0`) from the `main` branch and a description copied from `CHANGELOG.md`. -3. This release will automatically trigger `publish_pypi.yml` and create a *pre-release* on PyPI. +3. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI. ## rcX releases (manual) @@ -30,19 +30,20 @@ If a new release candidate is required after the release of `rc0` - 2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...). 3. This will create a PR incrementing the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files - - - `pybamm/version.py` - - `docs/conf.py` - - `CITATION.cff` - - `vcpkg.json` - - `docs/source/_static/versions.json` - The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something that we don't want (`develop` will have new features). +- `pybamm/version.py` +- `docs/conf.py` +- `CITATION.cff` +- `vcpkg.json` +- `docs/source/_static/versions.json` + +The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something that we don't want (`develop` will have new features). -4. Once the tests pass, create a new GitHub *pre-release* with the same tag and a description copied from `CHANGELOG.md`. +4. Once the tests pass, create a new GitHub _pre-release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. -5. This release will automatically trigger `publish_pypi.yml` and create a *pre-release* on PyPI. +5. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI. -6. Manually merge `main` back to `develop` if substantial conflicts arise. +6. Manually merge `main` back to `develop` if any conflicts arise. ## Actual release (manual) @@ -50,19 +51,18 @@ Once satisfied with the release candidates - 1. Run `update_version.yml` manually, leaving the `append_to_tag` field blank ("") for an actual release. -2. This will create a PR incrementing the version to `YY.MM` by running `scripts/update_version.py` in the following files - - - `pybamm/version.py` - - `docs/conf.py` - - `CITATION.cff` - - `vcpkg.json` - - `docs/source/_static/versions.json` +2. This will create a PR incrementing the version to `YY.MM` by running `scripts/update_version.py` in the following files - + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/source/_static/versions.json` The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something that we don't want (`develop` will have new features). -3. Once the tests pass, create a new GitHub *release* with the same tag and a description copied from `CHANGELOG.md`. - -4. This release will automatically trigger `publish_pypi.yml` and create a *release* on PyPI. +1. Once the tests pass, create a new GitHub _release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. +2. This release will automatically trigger `publish_pypi.yml` and create a _release_ on PyPI. ## Other checks From 405dc745e8778c35270fd914352bd0b1a6578d9f Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Tue, 8 Aug 2023 00:16:56 -0400 Subject: [PATCH 045/154] Format markdown --- .github/release_workflow.md | 39 +++++++++++++++++++------------------ 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/.github/release_workflow.md b/.github/release_workflow.md index 343ca63836..d3a42d154e 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -15,7 +15,7 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an 2. A PR from `develop` to `main` - The version PR should be merged into `develop`, and then the devlop-to-main PR should be merged into `main`. + The version PR should be merged into `develop`, and then the develop-to-main PR should be merged into `main`. 2. Once the tests pass, create a new GitHub _pre-release_ with the same tag (`YY.MMrc0`) from the `main` branch and a description copied from `CHANGELOG.md`. @@ -25,19 +25,19 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an If a new release candidate is required after the release of `rc0` - -1. Fix a bug in `main` (no new features should be added to `main` once `rc0` is released) as well as `develop` individually. +1. Fix a bug in `main` (no new features should be added to `main` once `rc0` is released) and `develop` individually. 2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...). 3. This will create a PR incrementing the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files - -- `pybamm/version.py` -- `docs/conf.py` -- `CITATION.cff` -- `vcpkg.json` -- `docs/source/_static/versions.json` + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/source/_static/versions.json` -The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something that we don't want (`develop` will have new features). + The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something we don't want (`develop` will have new features). 4. Once the tests pass, create a new GitHub _pre-release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. @@ -51,25 +51,26 @@ Once satisfied with the release candidates - 1. Run `update_version.yml` manually, leaving the `append_to_tag` field blank ("") for an actual release. -2. This will create a PR incrementing the version to `YY.MM` by running `scripts/update_version.py` in the following files - - - `pybamm/version.py` - - `docs/conf.py` - - `CITATION.cff` - - `vcpkg.json` - - `docs/source/_static/versions.json` +2. This will create a PR incrementing the version to `YY.MM` by running `scripts/update_version.py` in the following files - - The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something that we don't want (`develop` will have new features). + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/source/_static/versions.json` -1. Once the tests pass, create a new GitHub _release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. + The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something we don't want (`develop` will have new features). -2. This release will automatically trigger `publish_pypi.yml` and create a _release_ on PyPI. +3. Once the tests pass, create a new GitHub _release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. + +4. This release will automatically trigger `publish_pypi.yml` and create a _release_ on PyPI. ## Other checks -Some other important things to check throughout the release process - +Some other essential things to check throughout the release process - - Update baseline of registries in `vcpkg-configuration.json` as the latest commit id from [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) - Update `CHANGELOG.md` with a summary of the release -- Update jax and jaxlib to latest version in `pybamm.util` and fix any bugs that arise +- Update jax and jaxlib to the latest version in `pybamm.util` and fix any bugs that arise - If building wheels on Windows gives a `vcpkg` related error - revert the baseline of default-registry to a stable commit in `vcpkg-configuration.json` - Make sure the URLs in `docs/source/_static/versions.json` are valid From db34b6432f703bf30a4cbe16b79c60eee4202401 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Tue, 8 Aug 2023 00:26:39 -0400 Subject: [PATCH 046/154] Apply suggestions from code review --- .github/workflows/publish_pypi.yml | 6 +++--- .github/workflows/update_version.yml | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index f6db257b98..c7ea6771c2 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -22,7 +22,7 @@ jobs: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 with: - python-version: 3.11 + python-version: 3.8 - name: Install cibuildwheel run: python -m pip install cibuildwheel==2.12.3 @@ -77,7 +77,7 @@ jobs: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 with: - python-version: 3.11 + python-version: 3.8 - name: Install cibuildwheel run: python -m pip install cibuildwheel==2.12.3 @@ -135,7 +135,7 @@ jobs: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 with: - python-version: 3.11 + python-version: 3.8 - name: Install dependencies run: pip install wheel diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index e77412083f..82e371fb22 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -13,7 +13,7 @@ on: jobs: update-version: # This workflow is only of value to PyBaMM and would always be skipped in forks - # if: github.repository_owner == 'pybamm-team' + if: github.repository_owner == 'pybamm-team' runs-on: ubuntu-latest steps: @@ -30,7 +30,7 @@ jobs: - name: Set up Python 3.11 uses: actions/setup-python@v4 with: - python-version: 3.11 + python-version: 3.8 - name: Install dependencies run: | From 38313a141ad25bb2370ceb057e209368adebac53 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Tue, 8 Aug 2023 16:31:36 -0400 Subject: [PATCH 047/154] Update version.json file path --- scripts/update_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/update_version.py b/scripts/update_version.py index 70e2e2a1af..e2d0f73fb5 100644 --- a/scripts/update_version.py +++ b/scripts/update_version.py @@ -36,7 +36,7 @@ def update_version(): # docs/source/_static/versions.json for readthedocs build with open( - os.path.join(pybamm.root_dir(), "docs", "source", "_static", "versions.json"), + os.path.join(pybamm.root_dir(), "docs", "_static", "versions.json"), "r+", ) as file: output = file.read() From 9efd58bf7d3cd2339e2c42401aaf138f8864285d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 10 Aug 2023 07:15:38 +0000 Subject: [PATCH 048/154] style: pre-commit fixes --- tests/unit/test_experiments/test_simulation_with_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index 1b60e35c8f..13531281de 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -678,7 +678,7 @@ def test_experiment_start_time_starting_solution(self): [pybamm.step.string("Discharge at C/2 for 10 minutes")] ) sim = pybamm.Simulation(model, experiment=experiment) - solution = sim.solve() + solution = sim.solve() experiment = pybamm.Experiment( [ From d73a1ebf3ef108ba9c58868362d2ad83c7c782c0 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 10 Aug 2023 09:06:46 +0000 Subject: [PATCH 049/154] style: pre-commit fixes --- .../6-a-simple-SEI-model.ipynb | 5 + .../examples/notebooks/models/MSMR.ipynb | 1 - .../notebooks/models/jelly-roll-model.ipynb | 13 + .../models/loss_of_active_materials.ipynb | 1 + .../parameterization/parameterization.ipynb | 3632 +++++++++-------- 5 files changed, 1837 insertions(+), 1815 deletions(-) diff --git a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb index 54101815af..ac34142fab 100644 --- a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb +++ b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb @@ -199,6 +199,7 @@ "V_hat = pybamm.Parameter(\"Partial molar volume [m3.mol-1]\")\n", "c_inf = pybamm.Parameter(\"Bulk electrolyte solvent concentration [mol.m-3]\")\n", "\n", + "\n", "def D(cc):\n", " return pybamm.FunctionParameter(\"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc})" ] @@ -485,9 +486,11 @@ " {\"SEI layer\": {xi: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(1)}}}\n", ")\n", "\n", + "\n", "def Diffusivity(cc):\n", " return cc * 10**(-12)\n", "\n", + "\n", "# parameter values (not physically based, for example only!)\n", "param = pybamm.ParameterValues(\n", " {\n", @@ -565,6 +568,7 @@ "L_0_eval = param.evaluate(L_0)\n", "xi = np.linspace(0, 1, 100) # dimensionless space\n", "\n", + "\n", "def plot(t):\n", " _, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n", " ax1.plot(solution.t, L_out(solution.t) * 1e6)\n", @@ -581,6 +585,7 @@ " plt.tight_layout()\n", " plt.show()\n", " \n", + "\n", "import ipywidgets as widgets\n", "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=solution.t[-1],step=0.1,value=0));" ] diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 0ef9596dcd..11698cd93d 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -27,7 +27,6 @@ "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", - "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb index f03c328abf..933d27aa78 100644 --- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb +++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb @@ -280,23 +280,36 @@ "outputs": [], "source": [ "# define spiral \n", + "\n", + "\n", "def spiral_pos_inner(t):\n", " return r0 - eps * delta + eps * t / (2 * pi)\n", + "\n", + "\n", "def spiral_pos_outer(t):\n", " return r0 + eps * delta + eps * t / (2 * pi)\n", "\n", + "\n", "def spiral_neg_inner(t):\n", " return r0 - eps * delta + eps / 2 + eps * t / (2 * pi)\n", + "\n", + "\n", "def spiral_neg_outer(t):\n", " return r0 + eps * delta + eps / 2 + eps * t / (2 * pi)\n", "\n", + "\n", "def spiral_am1_inner(t):\n", " return r0 + eps * delta + eps * t / (2 * pi)\n", + "\n", + "\n", "def spiral_am1_outer(t):\n", " return r0 - eps * delta + eps / 2 + eps * t / (2 * pi)\n", "\n", + "\n", "def spiral_am2_inner(t):\n", " return r0 + eps * delta + eps / 2 + eps * t / (2 * pi)\n", + "\n", + "\n", "def spiral_am2_outer(t):\n", " return r0 - eps * delta + eps + eps * t / (2 * pi)" ] diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb index f1e81796a1..7eae36e725 100644 --- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb +++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb @@ -312,6 +312,7 @@ "def current_LAM(i, T):\n", " return -1e-10 * (abs(i) + 1e3 * abs(i) ** 0.5)\n", "\n", + "\n", "model = pybamm.lithium_ion.DFN(\n", " options=\n", " {\n", diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb index b7315a62e4..5c4c71348d 100644 --- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb @@ -1,1817 +1,1821 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameterisation\n", - "\n", - "In this notebook, we show how to find which parameters are needed in a model and define them.\n", - "\n", - "For other notebooks about parameterization, see:\n", - "\n", - "- The API documentation of [Parameters](https://docs.pybamm.org/en/latest/source/api/parameters/index.html)\n", - "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb) can be found at `pybamm/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n", - "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding your own parameter sets (using a dictionary)\n", - "\n", - "We will be using the model defined and explained in more detail in [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example notebook. We begin by importing the required libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install pybamm[plot,cite] -q # install PyBaMM if it is not installed\n", - "import pybamm\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the model\n", - "\n", - "We define all the parameters and variables using `pybamm.Parameter` and `pybamm.Variable` respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n", - "\n", - "R = pybamm.Parameter(\"Particle radius [m]\")\n", - "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n", - "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n", - "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n", - "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we define our model equations, boundary and initial conditions. We also add the variables required using the dictionary `model.variables`" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = pybamm.BaseModel()\n", - "\n", - "# governing equations\n", - "N = -D * pybamm.grad(c) # flux\n", - "dcdt = -pybamm.div(N)\n", - "model.rhs = {c: dcdt} \n", - "\n", - "# boundary conditions \n", - "lbc = pybamm.Scalar(0)\n", - "rbc = -j\n", - "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n", - "\n", - "# initial conditions \n", - "model.initial_conditions = {c: c0}\n", - "\n", - "model.variables = {\n", - " \"Concentration [mol.m-3]\": c,\n", - " \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n", - " \"Flux [mol.m-2.s-1]\": N,\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also define the geometry, since there are parameters in the geometry too" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", - "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters required" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To know what parameters are required by the model and geometry, we can do" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial concentration [mol.m-3] (Parameter)\n", - "Interfacial current density [A.m-2] (InputParameter)\n", - "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n", - "\n", - "Particle radius [m] (Parameter)\n" - ] - } - ], - "source": [ - "model.print_parameter_info()\n", - "geometry.print_parameter_info()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tells us that we need to provide parameter values for the initial concentration and Faraday constant, an `InputParameter` at solve time for the interfacial current density, and diffusivity as a function of concentration. Since the electrolyte concentration does not appear anywhere in the model, there is no need to provide a value for it." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding the parameters\n", - "\n", - "Now we can proceed to the step where we add the `parameter` values using a dictionary. We set up a dictionary with parameter names as the dictionary keys and their respective values as the dictionary values." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def D_fun(c):\n", - " return 3.9 #* pybamm.exp(-c)\n", - "\n", - "values = {\n", - " \"Particle radius [m]\": 2,\n", - " \"Diffusion coefficient [m2.s-1]\": D_fun,\n", - " \"Initial concentration [mol.m-3]\": 2.5,\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can pass this dictionary in `pybamm.ParameterValues` class which accepts a dictionary of parameter names and values. We can then print `param` to check if it was initialised." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Diffusion coefficient [m2.s-1]': ,\n", - " 'Initial concentration [mol.m-3]': 2.5,\n", - " 'Particle radius [m]': 2}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param = pybamm.ParameterValues(values)\n", - "\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Updating the parameter values\n", - "\n", - "The parameter values or `param` can be further updated by using the `update` function of `ParameterValues` class. The `update` function takes a dictionary with keys being the parameters to be updated and their respective values being the updated values. Here we update the `\"Particle radius [m]\"` parameter's value. Additionally, a function can also be passed as a `parameter`'s value which we will see ahead, and a new `parameter` can also be added by passing `check_already_exists=False` in the `update` function." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Diffusion coefficient [m2.s-1]': ,\n", - " 'Initial concentration [mol.m-3]': 1.5,\n", - " 'Particle radius [m]': 2}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param.update({\"Initial concentration [mol.m-3]\": 1.5})\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solving the model " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters in a model\n", - "\n", - "The `parameter` function of the `BaseModel` class can be used to obtain the parameters of a model." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[Parameter(-0x7c3ebfeae2200290, Initial concentration [mol.m-3], children=[], domains={}),\n", - " InputParameter(-0x4a08933302b1e44e, Interfacial current density [A.m-2], children=[], domains={}),\n", - " FunctionParameter(0x66f7cbc27c44053b, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parameters = model.parameters\n", - "parameters" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As explained in the [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example, we first process both the `model` and the `geometry`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "param.process_model(model)\n", - "param.process_geometry(geometry)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now set up our mesh, choose a spatial method, and discretise our model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", - "var_pts = {r: 20}\n", - "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n", - "\n", - "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n", - "disc = pybamm.Discretisation(mesh, spatial_methods)\n", - "disc.process_model(model);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We choose a solver and times at which we want the solution returned, and solve the model. Here we give a value for the current density `j`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# solve\n", - "solver = pybamm.ScipySolver()\n", - "t = np.linspace(0, 3600, 600)\n", - "solution = solver.solve(model, t, inputs={\"Interfacial current density [A.m-2]\": 1.4})\n", - "\n", - "# post-process, so that the solution can be called at any time t or space r\n", - "# (using interpolation)\n", - "c = solution[\"Concentration [mol.m-3]\"]\n", - "c_surf = solution[\"Surface concentration [mol.m-3]\"]\n", - "\n", - "# plot\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", - "\n", - "ax1.plot(solution.t, c_surf(solution.t))\n", - "ax1.set_xlabel(\"Time [s]\")\n", - "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n", - "\n", - "rsol = mesh[\"negative particle\"].nodes # radial position\n", - "time = 1000 # time in seconds\n", - "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n", - "ax2.set_xlabel(\"Particle radius [microns]\")\n", - "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n", - "ax2.legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using pre-defined models in `PyBaMM`\n", - "\n", - "In the next few steps, we will be showing the same workflow with the Single Particle Model (`SPM`). We will also see how you can pass a function as a `parameter`'s value and how to plot such `parameter functions`.\n", - "\n", - "We start by initializing our model" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "spm = pybamm.lithium_ion.SPM()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finding the parameters in a model\n", - "\n", - "We can print the `parameters` of a model by using the `get_parameters_info` function." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n", - "Initial concentration in electrolyte [mol.m-3] (Parameter)\n", - "Separator thickness [m] (Parameter)\n", - "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n", - "Negative electrode thickness [m] (Parameter)\n", - "Electrode height [m] (Parameter)\n", - "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n", - "Number of cells connected in series to make a battery (Parameter)\n", - "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n", - "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n", - "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n", - "Lower voltage cut-off [V] (Parameter)\n", - "Nominal cell capacity [A.h] (Parameter)\n", - "Typical electrolyte concentration [mol.m-3] (Parameter)\n", - "Upper voltage cut-off [V] (Parameter)\n", - "Positive electrode electrons in reaction (Parameter)\n", - "Negative electrode electrons in reaction (Parameter)\n", - "Initial temperature [K] (Parameter)\n", - "Reference temperature [K] (Parameter)\n", - "Positive electrode thickness [m] (Parameter)\n", - "Number of electrodes connected in parallel to make a cell (Parameter)\n", - "Electrode width [m] (Parameter)\n", - "Separator Bruggeman coefficient (electrolyte) (Parameter)\n", - "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", - "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n", - "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n", - "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n", - "Ambient temperature [K] (FunctionParameter with input(s) 'Time [s]')\n", - "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n", - "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", - "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n", - "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n", - "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", - "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n", - "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n", - "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "\n" - ] - } - ], - "source": [ - "spm.print_parameter_info()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that there are no `InputParameter` objects in the default SPM. Also, note that if a `FunctionParameter` is expected, it is ok to provide a scalar (parameter) instead. However, if a `Parameter` is expected, you cannot provide a function instead." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another way to view what parameters are needed is to print the default parameter values. This can also be used to get some good defaults (but care must be taken when combining parameters across datasets and chemistries)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Negative electrode thickness [m]': 0.0001,\n", - " 'Separator thickness [m]': 2.5e-05,\n", - " 'Positive electrode thickness [m]': 0.0001,\n", - " 'Electrode height [m]': 0.137,\n", - " 'Electrode width [m]': 0.207,\n", - " 'Nominal cell capacity [A.h]': 0.680616,\n", - " 'Current function [A]': 0.680616,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n", - " 'Negative electrode diffusivity [m2.s-1]': ,\n", - " 'Negative electrode OCP [V]': ,\n", - " 'Negative electrode porosity': 0.3,\n", - " 'Negative electrode active material volume fraction': 0.6,\n", - " 'Negative particle radius [m]': 1e-05,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", - " 'Negative electrode OCP entropic change [V.K-1]': ,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", - " 'Positive electrode diffusivity [m2.s-1]': ,\n", - " 'Positive electrode OCP [V]': ,\n", - " 'Positive electrode porosity': 0.3,\n", - " 'Positive electrode active material volume fraction': 0.5,\n", - " 'Positive particle radius [m]': 1e-05,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode OCP entropic change [V.K-1]': ,\n", - " 'Separator porosity': 1.0,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Ambient temperature [K]': 298.15,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Lower voltage cut-off [V]': 3.105,\n", - " 'Upper voltage cut-off [V]': 4.1,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565,\n", - " 'Initial temperature [K]': 298.15}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now define a dictionary of values for `ParameterValues` as before (here, a subset of the `Chen2020` parameters)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Ambient temperature [K]': 298.15,\n", - " 'Current function [A]': 5.0,\n", - " 'Electrode height [m]': 0.065,\n", - " 'Electrode width [m]': 1.58,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", - " 'Initial temperature [K]': 298.15,\n", - " 'Lower voltage cut-off [V]': 2.5,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n", - " ([array([0. , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n", - " 0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n", - " 0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n", - " 0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n", - " 0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n", - " 0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n", - " 0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n", - " 0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n", - " 0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n", - " 0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n", - " 0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n", - " 0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n", - " 0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n", - " 0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n", - " 0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n", - " 0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n", - " 0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n", - " 0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n", - " 0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n", - " 0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n", - " 0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n", - " 0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n", - " 0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n", - " 0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n", - " 0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n", - " 0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n", - " 0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n", - " 0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n", - " 0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n", - " 0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n", - " 0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n", - " 0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n", - " 0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n", - " 0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361 ,\n", - " 0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n", - " 0.67557767, 0.67928044, 0.68298322, 0.686686 , 0.69038878,\n", - " 0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n", - " 0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n", - " 0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n", - " 0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n", - " 0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n", - " 0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n", - " 0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n", - " 0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n", - " 0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n", - " 0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n", - " 0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n", - " 0.89774404, 0.9014468 , 1. ])],\n", - " array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n", - " 0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n", - " 0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n", - " 0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n", - " 0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n", - " 0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n", - " 0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n", - " 0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n", - " 0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n", - " 0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n", - " 0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n", - " 0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n", - " 0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n", - " 0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n", - " 0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n", - " 0.160027 , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n", - " 0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n", - " 0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n", - " 0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n", - " 0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n", - " 0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n", - " 0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n", - " 0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n", - " 0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n", - " 0.13315202, 0.132713 , 0.1330184 , 0.13278936, 0.13225491,\n", - " 0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n", - " 0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n", - " 0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n", - " 0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n", - " 0.13011713, 0.129869 , 0.12992626, 0.12942998, 0.12796026,\n", - " 0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n", - " 0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n", - " 0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n", - " 0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n", - " 0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n", - " 0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n", - " 0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n", - " 0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n", - " 0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n", - " 0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n", - " 0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n", - " 0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n", - " 0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n", - " 0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n", - " 0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n", - " 0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n", - " 0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n", - " 0.08709427, 0.08503284, 0.07601531]))),\n", - " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Negative electrode active material volume fraction': 0.75,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", - " 'Negative electrode porosity': 0.25,\n", - " 'Negative electrode thickness [m]': 8.52e-05,\n", - " 'Negative particle radius [m]': 5.86e-06,\n", - " 'Nominal cell capacity [A.h]': 5.0,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n", - " ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n", - " 0.27703505, 0.27975753, 0.28248 , 0.28520247, 0.28792495,\n", - " 0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n", - " 0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n", - " 0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n", - " 0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n", - " 0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n", - " 0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n", - " 0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n", - " 0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n", - " 0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n", - " 0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n", - " 0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n", - " 0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n", - " 0.453996 , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n", - " 0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n", - " 0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n", - " 0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n", - " 0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n", - " 0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n", - " 0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656 ,\n", - " 0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n", - " 0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n", - " 0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n", - " 0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n", - " 0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n", - " 0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n", - " 0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n", - " 0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n", - " 0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n", - " 0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n", - " 0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n", - " 0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n", - " 0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n", - " 0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n", - " 0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n", - " 0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n", - " 0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n", - " 0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n", - " 0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n", - " 0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n", - " 0.82152961, 0.82425208, 0.82697453, 0.829697 , 0.83241946,\n", - " 0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n", - " 0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n", - " 0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n", - " 0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n", - " 0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n", - " 0.90320364, 0.90592613, 1. ])],\n", - " array([4.4 , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n", - " 4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n", - " 4.213294 , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n", - " 4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n", - " 4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n", - " 4.1768146 , 4.1768146 , 4.1752872 , 4.173111 , 4.1726718 ,\n", - " 4.1710877 , 4.1702285 , 4.168797 , 4.1669831 , 4.1655135 ,\n", - " 4.1634517 , 4.1598248 , 4.1571712 , 4.154079 , 4.1504135 ,\n", - " 4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n", - " 4.1272392 , 4.1228104 , 4.1186109 , 4.114182 , 4.1096005 ,\n", - " 4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n", - " 4.0818448 , 4.077683 , 4.0733309 , 4.0690737 , 4.0647216 ,\n", - " 4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n", - " 4.041986 , 4.0384736 , 4.035171 , 4.0320406 , 4.0289288 ,\n", - " 4.02597 , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n", - " 4.0122066 , 4.009954 , 4.0075679 , 4.0050669 , 4.0023184 ,\n", - " 3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n", - " 3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n", - " 3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n", - " 3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n", - " 3.9192028 , 3.9166257 , 3.9117961 , 3.90815 , 3.9038739 ,\n", - " 3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n", - " 3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n", - " 3.8581741 , 3.854146 , 3.8499846 , 3.8450022 , 3.8422534 ,\n", - " 3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164 ,\n", - " 3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n", - " 3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n", - " 3.788383 , 3.7855389 , 3.7838206 , 3.78111 , 3.7794874 ,\n", - " 3.7769294 , 3.773608 , 3.7695992 , 3.7690265 , 3.7662776 ,\n", - " 3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n", - " 3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n", - " 3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n", - " 3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861 ,\n", - " 3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n", - " 3.7099447 , 3.7071004 , 3.7045615 , 3.703588 , 3.70208 ,\n", - " 3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n", - " 3.6890991 , 3.686522 , 3.6849759 , 3.6821697 , 3.6808143 ,\n", - " 3.6786573 , 3.6761947 , 3.674763 , 3.6712887 , 3.6697233 ,\n", - " 3.6678908 , 3.6652565 , 3.6630611 , 3.660274 , 3.6583652 ,\n", - " 3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n", - " 3.6383405 , 3.635076 , 3.633549 , 3.6322317 , 3.6306856 ,\n", - " 3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n", - " 3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n", - " 3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n", - " 3.5976417 , 3.5951984 , 3.593843 , 3.5916286 , 3.5894907 ,\n", - " 3.587429 , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n", - " 3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n", - " 3.5684922 , 3.5672133 , 3.52302167]))),\n", - " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Positive electrode active material volume fraction': 0.665,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode porosity': 0.335,\n", - " 'Positive electrode thickness [m]': 7.56e-05,\n", - " 'Positive particle radius [m]': 5.22e-06,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Separator porosity': 0.47,\n", - " 'Separator thickness [m]': 1.2e-05,\n", - " 'Typical current [A]': 5.0,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Upper voltage cut-off [V]': 4.4}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T):\n", - " D_ref = 3.9 * 10 ** (-14)\n", - " E_D_s = 42770\n", - " arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))\n", - " return D_ref * arrhenius\n", - "\n", - "neg_ocp = np.array([[0. , 1.81772748],\n", - " [0.03129623, 1.0828807 ],\n", - " [0.03499902, 0.99593794],\n", - " [0.0387018 , 0.90023398],\n", - " [0.04240458, 0.79649431],\n", - " [0.04610736, 0.73354429],\n", - " [0.04981015, 0.66664314],\n", - " [0.05351292, 0.64137149],\n", - " [0.05721568, 0.59813869],\n", - " [0.06091845, 0.5670836 ],\n", - " [0.06462122, 0.54746181],\n", - " [0.06832399, 0.53068399],\n", - " [0.07202675, 0.51304734],\n", - " [0.07572951, 0.49394092],\n", - " [0.07943227, 0.47926274],\n", - " [0.08313503, 0.46065259],\n", - " [0.08683779, 0.45992726],\n", - " [0.09054054, 0.43801501],\n", - " [0.09424331, 0.42438665],\n", - " [0.09794607, 0.41150269],\n", - " [0.10164883, 0.40033659],\n", - " [0.10535158, 0.38957134],\n", - " [0.10905434, 0.37756538],\n", - " [0.1127571 , 0.36292541],\n", - " [0.11645985, 0.34357086],\n", - " [0.12016261, 0.3406314 ],\n", - " [0.12386536, 0.32299468],\n", - " [0.12756811, 0.31379458],\n", - " [0.13127086, 0.30795386],\n", - " [0.13497362, 0.29207319],\n", - " [0.13867638, 0.28697687],\n", - " [0.14237913, 0.27405477],\n", - " [0.14608189, 0.2670497 ],\n", - " [0.14978465, 0.25857493],\n", - " [0.15348741, 0.25265783],\n", - " [0.15719018, 0.24826777],\n", - " [0.16089294, 0.2414345 ],\n", - " [0.1645957 , 0.23362778],\n", - " [0.16829847, 0.22956218],\n", - " [0.17200122, 0.22370236],\n", - " [0.17570399, 0.22181271],\n", - " [0.17940674, 0.22089651],\n", - " [0.1831095 , 0.2194268 ],\n", - " [0.18681229, 0.21830064],\n", - " [0.19051504, 0.21845333],\n", - " [0.1942178 , 0.21753715],\n", - " [0.19792056, 0.21719357],\n", - " [0.20162334, 0.21635373],\n", - " [0.2053261 , 0.21667822],\n", - " [0.20902886, 0.21738444],\n", - " [0.21273164, 0.21469313],\n", - " [0.2164344 , 0.21541846],\n", - " [0.22013716, 0.21465495],\n", - " [0.22383993, 0.2135479 ],\n", - " [0.2275427 , 0.21392964],\n", - " [0.23124547, 0.21074206],\n", - " [0.23494825, 0.20873788],\n", - " [0.23865101, 0.20465319],\n", - " [0.24235377, 0.20205732],\n", - " [0.24605653, 0.19774358],\n", - " [0.2497593 , 0.19444147],\n", - " [0.25346208, 0.19190285],\n", - " [0.25716486, 0.18850531],\n", - " [0.26086762, 0.18581399],\n", - " [0.26457039, 0.18327537],\n", - " [0.26827314, 0.18157659],\n", - " [0.2719759 , 0.17814088],\n", - " [0.27567867, 0.17529686],\n", - " [0.27938144, 0.1719375 ],\n", - " [0.28308421, 0.16934161],\n", - " [0.28678698, 0.16756649],\n", - " [0.29048974, 0.16609676],\n", - " [0.29419251, 0.16414985],\n", - " [0.29789529, 0.16260378],\n", - " [0.30159806, 0.16224113],\n", - " [0.30530083, 0.160027 ],\n", - " [0.30900361, 0.15827096],\n", - " [0.31270637, 0.1588054 ],\n", - " [0.31640913, 0.15552238],\n", - " [0.32011189, 0.15580869],\n", - " [0.32381466, 0.15220118],\n", - " [0.32751744, 0.1511132 ],\n", - " [0.33122021, 0.14987253],\n", - " [0.33492297, 0.14874637],\n", - " [0.33862575, 0.14678037],\n", - " [0.34232853, 0.14620776],\n", - " [0.34603131, 0.14555879],\n", - " [0.34973408, 0.14389819],\n", - " [0.35343685, 0.14359279],\n", - " [0.35713963, 0.14242846],\n", - " [0.36084241, 0.14038612],\n", - " [0.36454517, 0.13882096],\n", - " [0.36824795, 0.13954628],\n", - " [0.37195071, 0.13946992],\n", - " [0.37565348, 0.13780934],\n", - " [0.37935626, 0.13973714],\n", - " [0.38305904, 0.13698858],\n", - " [0.38676182, 0.13523254],\n", - " [0.3904646 , 0.13441178],\n", - " [0.39416737, 0.1352898 ],\n", - " [0.39787015, 0.13507985],\n", - " [0.40157291, 0.13647321],\n", - " [0.40527567, 0.13601512],\n", - " [0.40897844, 0.13435452],\n", - " [0.41268121, 0.1334765 ],\n", - " [0.41638398, 0.1348317 ],\n", - " [0.42008676, 0.13275118],\n", - " [0.42378953, 0.13286571],\n", - " [0.4274923 , 0.13263667],\n", - " [0.43119506, 0.13456447],\n", - " [0.43489784, 0.13471718],\n", - " [0.43860061, 0.13395369],\n", - " [0.44230338, 0.13448814],\n", - " [0.44600615, 0.1334765 ],\n", - " [0.44970893, 0.13298023],\n", - " [0.45341168, 0.13259849],\n", - " [0.45711444, 0.13338107],\n", - " [0.46081719, 0.13309476],\n", - " [0.46451994, 0.13275118],\n", - " [0.46822269, 0.13443087],\n", - " [0.47192545, 0.13315202],\n", - " [0.47562821, 0.132713 ],\n", - " [0.47933098, 0.1330184 ],\n", - " [0.48303375, 0.13278936],\n", - " [0.48673651, 0.13225491],\n", - " [0.49043926, 0.13317111],\n", - " [0.49414203, 0.13263667],\n", - " [0.49784482, 0.13187316],\n", - " [0.50154759, 0.13265574],\n", - " [0.50525036, 0.13250305],\n", - " [0.50895311, 0.13324745],\n", - " [0.51265586, 0.13204496],\n", - " [0.51635861, 0.13242669],\n", - " [0.52006139, 0.13233127],\n", - " [0.52376415, 0.13198769],\n", - " [0.52746692, 0.13254122],\n", - " [0.53116969, 0.13145325],\n", - " [0.53487245, 0.13298023],\n", - " [0.53857521, 0.13168229],\n", - " [0.54227797, 0.1313578 ],\n", - " [0.54598074, 0.13235036],\n", - " [0.5496835 , 0.13120511],\n", - " [0.55338627, 0.13089971],\n", - " [0.55708902, 0.13109058],\n", - " [0.56079178, 0.13082336],\n", - " [0.56449454, 0.13011713],\n", - " [0.5681973 , 0.129869 ],\n", - " [0.57190006, 0.12992626],\n", - " [0.57560282, 0.12942998],\n", - " [0.57930558, 0.12796026],\n", - " [0.58300835, 0.12862831],\n", - " [0.58671112, 0.12656689],\n", - " [0.59041389, 0.12734947],\n", - " [0.59411664, 0.12509716],\n", - " [0.59781941, 0.12110791],\n", - " [0.60152218, 0.11839751],\n", - " [0.60522496, 0.11244226],\n", - " [0.60892772, 0.11307214],\n", - " [0.61263048, 0.1092165 ],\n", - " [0.61633325, 0.10683058],\n", - " [0.62003603, 0.10433014],\n", - " [0.6237388 , 0.10530359],\n", - " [0.62744156, 0.10056993],\n", - " [0.63114433, 0.09950104],\n", - " [0.63484711, 0.09854668],\n", - " [0.63854988, 0.09921473],\n", - " [0.64225265, 0.09541635],\n", - " [0.64595543, 0.09980643],\n", - " [0.64965823, 0.0986612 ],\n", - " [0.653361 , 0.09560722],\n", - " [0.65706377, 0.09755413],\n", - " [0.66076656, 0.09612258],\n", - " [0.66446934, 0.09430929],\n", - " [0.66817212, 0.09661885],\n", - " [0.67187489, 0.09366032],\n", - " [0.67557767, 0.09522548],\n", - " [0.67928044, 0.09535909],\n", - " [0.68298322, 0.09316404],\n", - " [0.686686 , 0.09450016],\n", - " [0.69038878, 0.0930877 ],\n", - " [0.69409156, 0.09343126],\n", - " [0.69779433, 0.0932404 ],\n", - " [0.70149709, 0.09350762],\n", - " [0.70519988, 0.09339309],\n", - " [0.70890264, 0.09291591],\n", - " [0.7126054 , 0.09303043],\n", - " [0.71630818, 0.0926296 ],\n", - " [0.72001095, 0.0932404 ],\n", - " [0.72371371, 0.09261052],\n", - " [0.72741648, 0.09249599],\n", - " [0.73111925, 0.09240055],\n", - " [0.73482204, 0.09253416],\n", - " [0.7385248 , 0.09209515],\n", - " [0.74222757, 0.09234329],\n", - " [0.74593034, 0.09366032],\n", - " [0.74963312, 0.09333583],\n", - " [0.75333589, 0.09322131],\n", - " [0.75703868, 0.09264868],\n", - " [0.76074146, 0.09253416],\n", - " [0.76444422, 0.09243873],\n", - " [0.76814698, 0.09230512],\n", - " [0.77184976, 0.09310678],\n", - " [0.77555253, 0.09165615],\n", - " [0.77925531, 0.09159888],\n", - " [0.78295807, 0.09207606],\n", - " [0.78666085, 0.09175158],\n", - " [0.79036364, 0.09177067],\n", - " [0.79406641, 0.09236237],\n", - " [0.79776918, 0.09241964],\n", - " [0.80147197, 0.09320222],\n", - " [0.80517474, 0.09199972],\n", - " [0.80887751, 0.09167523],\n", - " [0.81258028, 0.09322131],\n", - " [0.81628304, 0.09190428],\n", - " [0.81998581, 0.09167523],\n", - " [0.82368858, 0.09285865],\n", - " [0.82739136, 0.09180884],\n", - " [0.83109411, 0.09150345],\n", - " [0.83479688, 0.09186611],\n", - " [0.83849965, 0.0920188 ],\n", - " [0.84220242, 0.09320222],\n", - " [0.84590519, 0.09131257],\n", - " [0.84960797, 0.09117896],\n", - " [0.85331075, 0.09133166],\n", - " [0.85701353, 0.09089265],\n", - " [0.86071631, 0.09058725],\n", - " [0.86441907, 0.09051091],\n", - " [0.86812186, 0.09033912],\n", - " [0.87182464, 0.09041547],\n", - " [0.87552742, 0.0911217 ],\n", - " [0.87923019, 0.0894611 ],\n", - " [0.88293296, 0.08999555],\n", - " [0.88663573, 0.08921297],\n", - " [0.89033849, 0.08881213],\n", - " [0.89404126, 0.08797229],\n", - " [0.89774404, 0.08709427],\n", - " [0.9014468 , 0.08503284],\n", - " [1. , 0.07601531]])\n", - "\n", - "pos_ocp = np.array([[0.24879728, 4.4 ],\n", - " [0.26614516, 4.2935653 ],\n", - " [0.26886763, 4.2768621 ],\n", - " [0.27159011, 4.2647018 ],\n", - " [0.27431258, 4.2540312 ],\n", - " [0.27703505, 4.2449446 ],\n", - " [0.27975753, 4.2364879 ],\n", - " [0.28248 , 4.2302647 ],\n", - " [0.28520247, 4.2225528 ],\n", - " [0.28792495, 4.2182574 ],\n", - " [0.29064743, 4.213294 ],\n", - " [0.29336992, 4.2090373 ],\n", - " [0.29609239, 4.2051239 ],\n", - " [0.29881487, 4.2012677 ],\n", - " [0.30153735, 4.1981564 ],\n", - " [0.30425983, 4.1955218 ],\n", - " [0.30698231, 4.1931167 ],\n", - " [0.30970478, 4.1889744 ],\n", - " [0.31242725, 4.1881533 ],\n", - 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" [0.88686877, 3.5801177 ],\n", - " [0.88959124, 3.5778842 ],\n", - " [0.89231371, 3.5763381 ],\n", - " [0.8950362 , 3.5737801 ],\n", - " [0.89775868, 3.5721002 ],\n", - " [0.90048116, 3.5702102 ],\n", - " [0.90320364, 3.5684922 ],\n", - " [0.90592613, 3.5672133 ],\n", - " [1. , 3.52302167]])\n", - "\n", - "from pybamm import exp, constants\n", - "\n", - "\n", - "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n", - " m_ref = 6.48e-7 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", - " E_r = 35000\n", - " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", - "\n", - " return (\n", - " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n", - " )\n", - "\n", - "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n", - " m_ref = 3.42e-6 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", - " E_r = 17800\n", - " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", - "\n", - " return (\n", - " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n", - " )\n", - "\n", - "\n", - "values = {\n", - " 'Negative electrode thickness [m]': 8.52e-05,\n", - " 'Separator thickness [m]': 1.2e-05,\n", - " 'Positive electrode thickness [m]': 7.56e-05,\n", - " 'Electrode height [m]': 0.065,\n", - " 'Electrode width [m]': 1.58,\n", - " 'Nominal cell capacity [A.h]': 5.0,\n", - " 'Typical current [A]': 5.0,\n", - " 'Current function [A]': 5.0,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", - " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\n", - " 'Negative electrode porosity': 0.25,\n", - " 'Negative electrode active material volume fraction': 0.75,\n", - " 'Negative particle radius [m]': 5.86e-06,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n", - " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", - " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\n", - " 'Positive electrode porosity': 0.335,\n", - " 'Positive electrode active material volume fraction': 0.665,\n", - " 'Positive particle radius [m]': 5.22e-06,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n", - " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Separator porosity': 0.47,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Ambient temperature [K]': 298.15,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Lower voltage cut-off [V]': 2.5,\n", - " 'Upper voltage cut-off [V]': 4.4,\n", - " \"Initial concentration in electrolyte [mol.m-3]\": 1000,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", - " 'Initial temperature [K]': 298.15\n", - "}\n", - "param = pybamm.ParameterValues(values)\n", - "param" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we would have got the same result by doing" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Negative electrode thickness [m]': 8.52e-05,\n", - " 'Separator thickness [m]': 1.2e-05,\n", - " 'Positive electrode thickness [m]': 7.56e-05,\n", - " 'Electrode height [m]': 0.065,\n", - " 'Electrode width [m]': 1.58,\n", - " 'Nominal cell capacity [A.h]': 5.0,\n", - " 'Current function [A]': 5.0,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", - " 'Negative electrode OCP [V]': ,\n", - " 'Negative electrode porosity': 0.25,\n", - " 'Negative electrode active material volume fraction': 0.75,\n", - " 'Negative particle radius [m]': 5.86e-06,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", - " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", - " 'Positive electrode OCP [V]': ,\n", - " 'Positive electrode porosity': 0.335,\n", - " 'Positive electrode active material volume fraction': 0.665,\n", - " 'Positive particle radius [m]': 5.22e-06,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", - " 'Separator porosity': 0.47,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Ambient temperature [K]': 298.15,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Lower voltage cut-off [V]': 2.5,\n", - " 'Upper voltage cut-off [V]': 4.2,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", - " 'Initial temperature [K]': 298.15}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param_same = pybamm.ParameterValues(\"Chen2020\")\n", - "{k: v for k,v in param_same.items() if k in spm._parameter_info}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Updating a specific parameter" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once a parameter set has been defined (either via a dictionary or a pre-built set), single parameters can be updated" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a constant value:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current function [A]\t5.0\n" - ] - }, - { - "data": { - "text/plain": [ - "4.0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "param.search(\"Current function [A]\")\n", - "\n", - "param.update({\"Current function [A]\": 4.0})\n", - "\n", - "param[\"Current function [A]\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using a function:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def curren_func(time):\n", - " return 1 + pybamm.sin(2 * np.pi * time / 60)\n", - "\n", - "param.update({\"Current function [A]\": curren_func})\n", - "\n", - "param[\"Current function [A]\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting parameter functions\n", - "\n", - "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting \"Current function \\[A]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "currentfunc = param[\"Current function [A]\"]\n", - "time = pybamm.linspace(0, 120, 60)\n", - "evaluated = param.evaluate(currentfunc(time))\n", - "evaluated = pybamm.Array(evaluated)\n", - "pybamm.plot(time, evaluated)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Taking another such example:\n", - "\n", - "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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GvBV7MQy4NaI13yVdzU09W2leFakVJ8MwjNp8IDo6moiICGbPnm3bFhoaytChQ0lOTq7RPsLCwkhISGDy5MlnfP/ll19m8uTJ5OTk0KRJkxrt02w24+3tTUFBAV5eXjX6jIiIXBpHi0p5+sstfJqZDUCb5h5Mv6U7V3X2s3Nl4mhq+v1dq3tYSktLycjIYMKECZW2x8fHs2LFihrtw2q1UlhYiI+PT7Vt3n77be68886zhpWSkhJKSkpsP5vN5hr9fhERuXQMw+Dz9QeZ9sUWjhSV4uQE9/QN4W/Xd6axm26blPNXq7MnPz8fi8VCQEBApe0BAQHk5ubWaB8zZsygqKiIYcOGnfH9VatWsWnTJt5+++2z7ic5OZmpU6fWrHAREbnkcgpO8cSnm/h+Wx4AXQI8efa27vRu29zOlUl9cF5x9/TrjoZh1OhaZEpKClOmTOGzzz7D39//jG3efvttwsPD6dOnz1n3NXHiRJKSkmw/m81mgoKCalC9iIhcTFarQcrqLJLTtnGipBxXFyceHtCJB6/pgFsjraosF0etAouvry8uLi5VRlPy8vKqjLqcLjU1lTFjxrBw4UIGDhx4xjYnT57ko48+Ytq0aeesxWQyYTKZal68iIhcdHvyi5iwaAMr9xwFoHfbigngOgdoAji5uGoVfd3c3IiMjCQ9Pb3S9vT0dPr27Vvt51JSUhg9ejQLFiw466PKH3/8MSUlJdx99921KUtERC6zcouVOUt/5YaXl7Fyz1E8XF2YfGM3PhnbV2FFLolaXxJKSkpixIgRREVFERsby9y5c8nKymLs2LFAxaWa7Oxs5s+fD1SElZEjRzJz5kxiYmJsozMeHh54e3tX2vfbb7/N0KFDadFCC16JiDiqbblm/vHJBjYcKADgyo6+JN/anSCfxnauTOqzWgeWhIQEjhw5wrRp08jJySE8PJy0tDSCg4MByMnJqTQny5w5cygvLycxMZHExETb9lGjRjFv3jzbzzt27OC///0v33777QUcjoiIXCql5VZe/2EXs37cRZnFwNO9EU8O7sYdUW00p4pccrWeh8VRaR4WEZFLZ/3+4zy2aAPbcgsBiOsWwD+HhhPg5W7nyqSuuyTzsIiISMNSXGbhpfQdvLl8N1YDWjRxY8pNYdzYI1CjKnJZKbCIiMgZrd57lH98soE9+UUA3NyrFU8NCcOniZudK5OGSIFFREQqKSop54Ul23nv54r1fwK8TDwztDsDu519+gqRS0mBRUREbH7alc9jizZw4NgpABKignh8cCjeHq52rkwaOgUWERGhsLiM6WnbSFlV8ZRn62YeJN+qxQrFcSiwiIg0cEt3HGbiog0cLCgG4O6YtkwYFEpTk74ixHHobBQRaaAKTpXxzy+3sDDjAABtfRrz7G3d6dvB186ViVSlwCIi0gD9Z9shJi7eyCFzCU5OMLpvO/5+fRcau+lrQRyTzkwRkQak4GQZ077cwqK1FaMqIb5NeP72HlzRzsfOlYmcnQKLiEgD8d2WQzz+6UbyCitGVe67MoSkuC54uLnYuzSRc1JgERGp5wpOljH1i80szswGoL1fE164vSeRwc3tXJlIzSmwiIjUY38cVXF2gvv6tycprjPurhpVkbpFgUVEpB7SqIrUNwosIiL1zPdbK54A0qiK1CcKLCIi9UTBqTKmffG/J4Da+zbhhTs0qiL1gwKLiEg98MP2PCYs2mCbV+W+K0N4NL6LRlWk3lBgERGpw8zFZTzz5VZS1+wHKuZVefGOHkQGa14VqV8UWERE6qjlOw/z2CcVawA5OcE9fUP4+/WaV0XqJwUWEZE6pqiknOlpW/lwZcXKym19GvPC7T2Ibt/CzpWJXDoKLCIidcjPvx7h75+s58CxUwCMiAlmwqCuNNHKylLP6QwXEakDTpVaeH7JNt79aS8ArZt58PztPejXUSsrS8OgwCIi4uDWZh3jbx+vZ3d+EQB3XhHEpMGheLq72rkykctHgUVExEGVlFuY+d1O3lj6K1YDArxMPHtbDwZ08bd3aSKXnQKLiIgD2nywgEc/Xs+23EIAbundmilDwvBurFEVaZgUWEREHEi5xcrsH39l5vc7KbcatGjixjO3hHNDeKC9SxOxKwUWEREHsSvvBI8uXM/6/ccBuD4sgGdu6Y5vU5N9CxNxAAosIiJ2ZrUavLtiL89/s42Sciue7o2YdnMYQ3u1xsnJyd7liTgEBRYRETs6cOwkf1u4nl92HwWgfydfnr+9B4HeHnauTMSxKLCIiNiBYRgszDjAtC+2cKKkHA9XFyYNDmV4dFuNqoicgQKLiMhldriwhImLN/Ld1kMARAY3Z8YdPWnn28TOlYk4LgUWEZHL6JtNuUz6dCNHikpxc3EmKb4z9/dvj4uzRlVEzkaBRUTkMjAXlzH18y0sWnsAgK4tPXkpoRehgV52rkykblBgERG5xFbsyudvC9dzsKAYZycYe3UHxg3shKmRi71LE6kzFFhERC6R4jILLyzZztv/3QNAW5/G/GtYT6La+di5MpG6R4FFROQS2JRdwCOp69iZdwKAu/q05YnBoTQx6c+uyPlwPp8PzZo1i5CQENzd3YmMjGT58uXVtl28eDFxcXH4+fnh5eVFbGwsS5YsqdLu+PHjJCYmEhgYiLu7O6GhoaSlpZ1PeSIidmOxGrz+wy5umfUTO/NO4NvUxDujo0i+tbvCisgFqPW/ntTUVMaPH8+sWbPo168fc+bMYdCgQWzZsoW2bdtWab9s2TLi4uKYPn06zZo1491332XIkCGsXLmS3r17A1BaWkpcXBz+/v588skntGnThv379+Pp6XnhRygicplkHTnJIx+vI2PfMQBuCGvJ9Fu749PEzc6VidR9ToZhGLX5QHR0NBEREcyePdu2LTQ0lKFDh5KcnFyjfYSFhZGQkMDkyZMBeOONN3jhhRfYtm0brq7ntxKp2WzG29ubgoICvLx0172IXD6GYfDxmv1M+2ILRaUWmpoaMfWmMG6N0NT6IudS0+/vWl0SKi0tJSMjg/j4+Erb4+PjWbFiRY32YbVaKSwsxMfnfzedff7558TGxpKYmEhAQADh4eFMnz4di8VS7X5KSkowm82VXiIil1v+iRLun5/BY4s2UlRqoU+ID1+P689tkW0UVkQuolpdEsrPz8disRAQEFBpe0BAALm5uTXax4wZMygqKmLYsGG2bbt37+Y///kPw4cPJy0tjZ07d5KYmEh5ebltFOZ0ycnJTJ06tTbli4hcVN9vPcRjizaQf6JiEri/Xd+ZMVdqEjiRS+G87gA7/f81GIZRo/8nkZKSwpQpU/jss8/w9/e3bbdarfj7+zN37lxcXFyIjIzk4MGDvPDCC9UGlokTJ5KUlGT72Ww2ExQUdD6HIyJSKydLy3n6y62krMoCoEuAJy/fqUngRC6lWgUWX19fXFxcqoym5OXlVRl1OV1qaipjxoxh4cKFDBw4sNJ7gYGBuLq64uLyv0mUQkNDyc3NpbS0FDe3qjesmUwmTCZTbcoXEblg6/Yf55HUdezJLwLgvitD+Nv1XXB31SRwIpdSre5hcXNzIzIykvT09Erb09PT6du3b7WfS0lJYfTo0SxYsIDBgwdXeb9fv37s2rULq9Vq27Zjxw4CAwPPGFZERC63couVmd/t5LbZK9iTX0SgtzsL7ovmiRu7KayIXAa1noclKSmJt956i3feeYetW7fyyCOPkJWVxdixY4GKSzUjR460tU9JSWHkyJHMmDGDmJgYcnNzyc3NpaCgwNbmwQcf5MiRI4wbN44dO3bw1VdfMX36dBITEy/CIYqIXJh9R4q4Y87PvPTdDixWgyE9W/HNuKvo29HX3qWJNBi1voclISGBI0eOMG3aNHJycggPDyctLY3g4GAAcnJyyMrKsrWfM2cO5eXlJCYmVgogo0aNYt68eQAEBQXx7bff8sgjj9CjRw9at27NuHHjeOyxxy7w8EREzp9hGCxcc4CpX2ymqNSCp6kRTw8NZ2jv1vYuTaTBqfU8LI5K87CIyMV0rKiUiYs38s3minv2+oT48K9hPWnTvLGdKxOpX2r6/a15okVETrN852Ee/Xg9eYUluLo4kRTXhQeu0uPKIvakwCIi8pviMgvPf7Odd36qWF25g18TZt7Zm/DW3nauTEQUWEREgO25hYz7KJNtuYUAjIgJ5vH/C8XDTU8AiTgCBRYRadAMw2Deir0kf72N0nIrLZq48fztPbgu9OxzS4nI5aXAIiINVl5hMX9fuIGlOw4DMKCLH8/f3hM/T01KKeJoFFhEpEH6fush/v7JBo4WlWJq5MykwaGMiAnWgoUiDkqBRUQalFOlFqanbeX9X/YB0LWlJ6/c1ZvOAZ52rkxEzkaBRUQajK05Zv6aksnOvBNAxTpAf7+hC6ZGurFWxNEpsIhIvWe1Gry7Yi/Pfb2NUosVP08TM+7oyVWd/exdmojUkAKLiNRrhwtL+NvC9bYbaweG+vPcbT1o0VQ31orUJQosIlJv/bAtj79/sp78ExU31j5xYzfujm6rG2tF6iAFFhGpd4rLLDz3zTbe/WkvoBtrReoDBRYRqVd2HirkLyn/m7H2nn7teOyGrri76sZakbpMgUVE6gXDMFiwKounv9xCcVnFjLUv3tGTAV397V2aiFwECiwiUucdP1nKY4s2sGTzIQD6d/JlxrCe+Hu627kyEblYFFhEpE77+dcjPJK6jlxzMa4uTjx2Q1fu7ReCs7NurBWpTxRYRKROKrdYmfn9Tl77YReGAe19m/DKXb0Jb+1t79JE5BJQYBGROufAsZOM+2gdGfuOAXBHZBum3BRGE5P+pInUV/rXLSJ1ylcbcpiweAOFxeV4mhrxzK3dualnK3uXJSKXmAKLiNQJp0otTPtyMymr9gPQu20zXrmzN0E+je1cmYhcDgosIuLwtuWa+cuCikULnZzgoWs6MH5gZ1xdnO1dmohcJgosIuKwDMPgg5VZ/PPLLZSUW/H3NPFSQi/6dfS1d2kicpkpsIiIQyo4WcY/Fq23za0yoIsfL97RU4sWijRQCiwi4nDW7D3KuI/WkX38lOZWERFAgUVEHIjFavDG0l/5V/oOLFaD4BaNefWu3vRo08zepYmInSmwiIhDyCss5pHUdfy06wgAN/dqxT+HhuPp7mrnykTEESiwiIjdLd1xmEc/Xkf+iVI8XF2YdnMYt0e2wclJl4BEpIICi4jYTZnFyoxvd/DG0l8B6NrSk9f+1JuO/p52rkxEHI0Ci4jYxYFjJ/lrSiZrs44DcHdMW54Y3A13Vxf7FiYiDkmBRUQuuyWbc/n7wvWYf5te/7nbe/B/3QPtXZaIODAFFhG5bErKLSSnbWPeir0A9Axqxmt3aXp9ETk3BRYRuSz25hfxcMpaNmWbAbi/fwh/v74rbo00vb6InJsCi4hccl+sP8jExRs5UVJO88auzBjWk2u7Bti7LBGpQxRYROSSKS6zMPWLLaSsygKgTzsfZt7Vi0BvDztXJiJ1jQKLiFwSvx4+QeKHa9mWW4iTEyRe05HxAzvRSCssi8h5OK+/HLNmzSIkJAR3d3ciIyNZvnx5tW0XL15MXFwcfn5+eHl5ERsby5IlSyq1mTdvHk5OTlVexcXF51OeiNjZvzOzGfLqf9mWW4hvUzfm39uHv13fRWFFRM5brf96pKamMn78eCZNmkRmZib9+/dn0KBBZGVlnbH9smXLiIuLIy0tjYyMDAYMGMCQIUPIzMys1M7Ly4ucnJxKL3d39/M7KhGxi+IyCxMWbWB86jpOllqIbd+CtL/2p38nP3uXJiJ1nJNhGEZtPhAdHU1ERASzZ8+2bQsNDWXo0KEkJyfXaB9hYWEkJCQwefJkoGKEZfz48Rw/frw2pVRiNpvx9vamoKAALy+v896PiJyfXXkneHjB/y4B/fXaTvz1uk64aIVlETmLmn5/12qEpbS0lIyMDOLj4yttj4+PZ8WKFTXah9VqpbCwEB8fn0rbT5w4QXBwMG3atOHGG2+sMgJzupKSEsxmc6WXiNjHvzOzuem13y8BmfhwTDSPxHVWWBGRi6ZWgSU/Px+LxUJAQOXHEQMCAsjNza3RPmbMmEFRURHDhg2zbevatSvz5s3j888/JyUlBXd3d/r168fOnTur3U9ycjLe3t62V1BQUG0ORUQugjNeAhp3JX07+tq7NBGpZ87rKaHTV1A1DKNGq6qmpKQwZcoUPvvsM/z9/W3bY2JiiImJsf3cr18/IiIiePXVV3nllVfOuK+JEyeSlJRk+9lsNiu0iFxGuw+f4KEPdQlIRC6PWgUWX19fXFxcqoym5OXlVRl1OV1qaipjxoxh4cKFDBw48KxtnZ2dueKKK846wmIymTCZTDUvXkQums/XH2Tiog0UlVrwberGywm9ubKTRlVE5NKp1SUhNzc3IiMjSU9Pr7Q9PT2dvn37Vvu5lJQURo8ezYIFCxg8ePA5f49hGKxbt47AQC2GJuJIisssTPp0I39NyaSo1EJ0iA9pf+2vsCIil1ytLwklJSUxYsQIoqKiiI2NZe7cuWRlZTF27Fig4lJNdnY28+fPByrCysiRI5k5cyYxMTG20RkPDw+8vb0BmDp1KjExMXTq1Amz2cwrr7zCunXreP311y/WcYrIBdp3pIiHPlzL5oNmTQQnIpddrQNLQkICR44cYdq0aeTk5BAeHk5aWhrBwcEA5OTkVJqTZc6cOZSXl5OYmEhiYqJt+6hRo5g3bx4Ax48f54EHHiA3Nxdvb2969+7NsmXL6NOnzwUenohcDN9syuXvn6ynsLgcnyZuvJTQi6s7a24VEbl8aj0Pi6PSPCwiF19puZXnvtnG2//dA0BUcHNe/VNvrQUkIhdNTb+/tZaQiJzRweOneHjBWtZmHQfggava8/fru+CqS0AiYgcKLCJSxY/b83gkdR3HTpbh5d6IF+/oSXxYS3uXJSINmAKLiNhYrAYzv9vBqz/swjCge2tvZg2PIMinsb1LE5EGToFFRADIP1HCuI8y+WnXEQDujmnLE4O74e7qYufKREQUWEQEWL33KA8vWMshcwkeri48e1t3bu7V2t5liYjYKLCINGCGYfDW8j08+802LFaDjv5NmT08gk4BnvYuTUSkEgUWkQbKXFzG3xeuZ8nmQwDc1LMVybd2p4lJfxZExPHoL5NIA7TloJkHP8xg35GTuLo4MfnGbtwdE1yjRUxFROxBgUWkgVm4Zj9P/HsTJeVWWjfz4PXhEfQKambvskREzkqBRaSBKC6zMPWLzaSs2g/A1Z39eDmhF82buNm5MhGRc1NgEWkA9h89yYMfZrApu2LhwkcGdubhAR1xdtYlIBGpGxRYROq5H7blMT51HQWnymje2JWZd/bmKi1cKCJ1jAKLSD31+6y1r/xnFwC9gprx+vAIWjfTwoUiUvcosIjUQ0eLShn3USbLd+YDMCImmCduDMXUSLPWikjdpMAiUs+s33+chz5cS/bxU7i7OpN8a3du6d3G3mWJiFwQBRaResIwDD5avZ+nPttMqcVKuxaNeWNEJF1betm7NBGRC6bAIlIPFJdZmPzZJj5ecwCAuG4BzBjWEy93VztXJiJycSiwiNRx+4+eZOwHGWw+aMbZCf52fRfGXtVBjyyLSL2iwCJSh/24PY9xH1U8suzTxI1X7+pNv46+9i5LROSiU2ARqYOsVoPXftjFS9/twDCgZ1AzZg+PoJUeWRaRekqBRaSOKThVxqMfr+O7rXkA/Cm6LU8N6aZHlkWkXlNgEalDtuWaGft+BnuPnMStkTP/HBrOsKgge5clInLJKbCI1BGfrctmwqKNnCqz0LqZB2/cHUn3Nt72LktE5LJQYBFxcGUWK89+vY23/7sHgP6dfHnlzt5aZVlEGhQFFhEHdriwhIcXrGXlnqMAPHRNBx6N74KLHlkWkQZGgUXEQWVmHePBD9aSay6miZsLM4b15IbwQHuXJSJiFwosIg7oo1VZTP5tiv32fk2YOyKSjv6e9i5LRMRuFFhEHEhJuYUpn28hZVUWAPG/TbHvqSn2RaSBU2ARcRC5BcU8+GEGmVnHcXKCR+M689A1HTXFvogICiwiDmH13qM8+MFa8k+U4O3hysw7e3FNF397lyUi4jAUWETsyDAM3v9lH9O+2EK51aBrS0/mjoiibYvG9i5NRMShKLCI2ElxmYUn/72JhRkHALixRyDP396Dxm76Zykicjr9ZRSxg5yCU4x9P4P1BwpwdoLHbujKA1e1x8lJ96uIiJyJAovIZbZy9xESF6wl/0QpzRq78updvenfyc/eZYmIODQFFpHLxDAMPvhlH1N1v4qISK05n8+HZs2aRUhICO7u7kRGRrJ8+fJq2y5evJi4uDj8/Pzw8vIiNjaWJUuWVNv+o48+wsnJiaFDh55PaSIOqaTcwoRFG3nys82UWw1u7BHI4of6KqyIiNRQrQNLamoq48ePZ9KkSWRmZtK/f38GDRpEVlbWGdsvW7aMuLg40tLSyMjIYMCAAQwZMoTMzMwqbfft28ff/vY3+vfvX/sjEXFQh8zFJMz5hdQ1+3F2ggmDuvLqXb11c62ISC04GYZh1OYD0dHRREREMHv2bNu20NBQhg4dSnJyco32ERYWRkJCApMnT7Zts1gsXH311dxzzz0sX76c48eP8+9//7vGdZnNZry9vSkoKMDLy6vGnxO5lDL2HWPsBxkcLqyYX+XVu3pzVWfdryIi8ruafn/XaoSltLSUjIwM4uPjK22Pj49nxYoVNdqH1WqlsLAQHx+fStunTZuGn58fY8aMqU1JIg4rdXUWd839hcOFJXQJ8OTzh/sprIiInKdajUnn5+djsVgICAiotD0gIIDc3Nwa7WPGjBkUFRUxbNgw27affvqJt99+m3Xr1tW4lpKSEkpKSmw/m83mGn9W5FIqs1iZ9sUW3v9lHwA3hLVkxrCeNDHpEpCIyPk6r5tuT58rwjCMGs0fkZKSwpQpU0hNTcXfv2La8cLCQu6++27efPNNfH19a1xDcnIy3t7etldQUFDtDkLkEsg/UcLwt1bawsqjcZ2ZNTxCYUVE5ALV6q+or68vLi4uVUZT8vLyqoy6nC41NZUxY8awcOFCBg4caNv+66+/snfvXoYMGWLbZrVaK4pr1Ijt27fToUOHKvubOHEiSUlJtp/NZrNCi9jVpuwC/vx+BtnHT9HU1IiXEnoR1+3s/y5ERKRmahVY3NzciIyMJD09nVtuucW2PT09nZtvvrnaz6WkpHDvvfeSkpLC4MGDK73XtWtXNm7cWGnbE088QWFhITNnzqw2hJhMJkwmU23KF7lkvlh/kL9/sp7iMivtWjTmrVFRdPT3tHdZIiL1Rq3HqZOSkhgxYgRRUVHExsYyd+5csrKyGDt2LFAx8pGdnc38+fOBirAycuRIZs6cSUxMjG10xsPDA29vb9zd3QkPD6/0O5o1awZQZbuIo7FYDWZ8u51ZP/4KwFWd/Xj1zt54N3a1c2UiIvVLrQNLQkICR44cYdq0aeTk5BAeHk5aWhrBwcEA5OTkVJqTZc6cOZSXl5OYmEhiYqJt+6hRo5g3b96FH4GInRQWlzHuo3X8Z1seAH++qj3/uKErLs5aD0hE5GKr9TwsjkrzsMjltCe/iPvnr2FX3glMjZx57rYeDO3d2t5liYjUOTX9/tajCyK1tGzHYR5esBZzcTkBXibmjoiiZ1Aze5clIlKvKbCI1JBhGLz93z1MT9uK1YDebZsx5+5I/L3c7V2aiEi9p8AiUgMl5RYmfbqJTzIOAHB7ZBueuSUcUyMXO1cmItIwKLCInENeYTFj389gbdZxnJ1g0uBu3NuvXY0mSxQRkYtDgUXkLDZlF3D//DXkFBTj5d6I1/4UofWARETsQIFFpBpfbcjh0YXrKC6z0t6vCW+NjKK9X1N7lyUi0iApsIicxmo1ePn7nbzy/U4Aru7sxyt39cbbQ5PBiYjYiwKLyB+cLC3n0Y/X8/WmihmZ7+8fwoRBoZoMTkTEzhRYRH6TffwU97+3hi05ZtxcnHnmlnDuiNKCmiIijkCBRQTI2HeMP7+/hvwTpfg2dWPOiEgig33sXZaIiPxGgUUavEUZB5i4eCOlFiuhgV68NSqK1s087F2WiIj8gQKLNFgWq8HzS7YxZ+luAK4PC+ClhF40dtM/CxERR6O/zNIgnSgpZ/xHmXy3tWKl5YcHdCQprjPOurlWRMQhKbBIg7P/6Enue28N2w8V4tbImRdu78HNvbTSsoiII1NgkQZlzd6j/Pn9DI4UleLnaWLuiEh6t21u77JEROQcFFikwVi89gATFlXcXNvtt5trW+nmWhGROkGBReo9q9XgxW+3M+vHXwHdXCsiUhfpL7bUaydLy3kkdR1LNh8CIHFABx6N66Kba0VE6hgFFqm3cguKGfPeajYfrJi59tnbunNrRBt7lyUiIudBgUXqpQ0HjnPfe2vIKyyhRZOKmWuj2mnmWhGRukqBReqdrzfm8MjH6ygus9I5oClvj7qCIJ/G9i5LREQugAKL1BuGYTDrx195Ycl2AAZ08eOVu3rj6e5q58pERORCKbBIvVBSbmHi4o0sXpsNwD392vHE4G646OZaEZF6QYFF6ryjRaX8+f01rN57DBdnJ6bcFMaImGB7lyUiIheRAovUabvyTjDmvdXsO3IST1MjXh8ewVWd/exdloiIXGQKLFJn/bQrnwc/yMBcXE6b5h68O/oKOgV42rssERG5BBRYpE5KXZ3FpE83UW41iGjbjLkjo/BtarJ3WSIicokosEidYrUaPLdkG3OW7gbgpp6teP72Hri7uti5MhERuZQUWKTOOFVq4ZHUdXyzOReAcdd1YvzATjg56UkgEZH6ToFF6oS8wmLuf28N6w8U4ObizHO3d+eW3ppmX0SkoVBgEYe3PbeQe+etJvv4KZo3dmXOiCj6hGiafRGRhkSBRRza0h2HSfxwLSdKymnv24R3Rl9BO98m9i5LREQuMwUWcVgfrtzH5M82Y7EaRIf4MGdEJM0au9m7LBERsQMFFnE4VqvBc99sY86yiieBbo1ozbO39sCtkbOdKxMREXtRYBGHUlxW8STQ15sqngR6ZGBn/npdRz0JJCLSwCmwiMPIP1HCfe+tYd3+43oSSEREKjmvMfZZs2YREhKCu7s7kZGRLF++vNq2ixcvJi4uDj8/P7y8vIiNjWXJkiVV2kRFRdGsWTOaNGlCr169eP/998+nNKmjduWd4JZZP7Fu/3GaNXbl/TF9FFZERMSm1oElNTWV8ePHM2nSJDIzM+nfvz+DBg0iKyvrjO2XLVtGXFwcaWlpZGRkMGDAAIYMGUJmZqatjY+PD5MmTeLnn39mw4YN3HPPPdxzzz1Vgo3UT7/sPsKts35i/9FTBLdozOIH+xLdvoW9yxIREQfiZBiGUZsPREdHExERwezZs23bQkNDGTp0KMnJyTXaR1hYGAkJCUyePLnaNhEREQwePJinn366Rvs0m814e3tTUFCAl5dXjT4j9vdp5gH+8ckGyiwVawK9OTKKFloTSESkwajp93etRlhKS0vJyMggPj6+0vb4+HhWrFhRo31YrVYKCwvx8TnzxF+GYfD999+zfft2rrrqqmr3U1JSgtlsrvSSusMwDGZ+t5NHUtdTZjEY3D2QBffHKKyIiMgZ1eqm2/z8fCwWCwEBAZW2BwQEkJubW6N9zJgxg6KiIoYNG1Zpe0FBAa1bt6akpAQXFxdmzZpFXFxctftJTk5m6tSptSlfHERpuZXHP93IJxkHAPjz1e157PquODvrSSARETmz83pK6PRHTA3DqNFjpykpKUyZMoXPPvsMf3//Su95enqybt06Tpw4wffff09SUhLt27fnmmuuOeO+Jk6cSFJSku1ns9lMUFBQ7Q9GLitzcRkPfbCW/+7Kx9kJpt0czt0xwfYuS0REHFytAouvry8uLi5VRlPy8vKqjLqcLjU1lTFjxrBw4UIGDhxY5X1nZ2c6duwIQK9evdi6dSvJycnVBhaTyYTJpMsHdcnB46e4593VbD9USGM3F17/UwQDuvqf+4MiItLg1eoeFjc3NyIjI0lPT6+0PT09nb59+1b7uZSUFEaPHs2CBQsYPHhwjX6XYRiUlJTUpjxxYJsPFnDLrJ/YfqgQP08TH/85VmFFRERqrNaXhJKSkhgxYgRRUVHExsYyd+5csrKyGDt2LFBxqSY7O5v58+cDFWFl5MiRzJw5k5iYGNvojIeHB97e3kDF/ShRUVF06NCB0tJS0tLSmD9/fqUnkaTuWrrjMA99kEFRqYXOAU15954+tG7mYe+yRESkDql1YElISODIkSNMmzaNnJwcwsPDSUtLIzi44j6EnJycSnOyzJkzh/LychITE0lMTLRtHzVqFPPmzQOgqKiIhx56iAMHDuDh4UHXrl354IMPSEhIuMDDE3v7ePV+Jn66EYvVILZ9C94YEYm3h6u9yxIRkTqm1vOwOCrNw+JYDMPgpe928sr3OwG4tXdrnr1NCxiKiEhlNf3+1lpCctGd/tjyX67tSFJcZy1gKCIi502BRS6qwuIyHvpwLct35uPi7MQ/h4ZzV5+29i5LRETqOAUWuWgOmYsZ/e5qtuaYKx5bHh7BgC56EkhERC6cAotcFDsOFTL6nVUcLCjGt6mJd0dfQfc23vYuS0RE6gkFFrlgv+w+wgPz12AuLqe9XxPeu6cPQT6N7V2WiIjUIwosckG+WH+QRz9eT6nFSlRwc94cGUXzJm72LktEROoZBRY5b28t380/v9oKwA1hLXn5zl64u7rYuSoREamPFFik1qxWg39+tZV3ftoDwOi+7Xjyxm64aLVlERG5RBRYpFaKyyw8unA9X23IAeDx/+vK/f3ba44VERG5pBRYpMYKTpXxwPw1rNxzFFcXJ168oyc392pt77JERKQBUGCRGskpOMWod1ax49AJPE2NmDMikr4dfe1dloiINBAKLHJO23MLGf3uKnIKivH3NDHvnj50a6X1mkRE5PJRYJGzWrn7CPf/NsdKR/+mzLvnCto01xwrIiJyeSmwSLW+3pjDuNR1lJZbiQxuztujomjWWHOsiIjI5afAImf0/s97mfz5ZgwD4roF8OpdvTXHioiI2I0Ci1RiGAYzvt3Baz/sAuBP0W15+uZwzbEiIiJ2pcAiNuUWK49/upGP1xwAICmuM3+5tqPmWBEREbtTYBEATpVaeHjBWr7floezE0y/pTt39mlr77JEREQABRYBjhWVMua91azNOo6pkTOv/SmCuG4B9i5LRETERoGlgTt4/BQj31nFrrwTeHu48vaoKKLa+di7LBERkUoUWBqwHYcKGfn2KnLNxQR6u/PevX3oHOBp77JERESqUGBpoNbsPcqY99ZQcKqMjv5NmX9vH1o187B3WSIiImekwNIAfb/1EA99uJaScisRbZvxzugrNCGciIg4NAWWBmbhmv1MWLwRi9VgQBc/Zg2PxMNNE8KJiIhjU2BpQOYs/ZXkr7cBcGtEa567rQeuLs52rkpEROTcFFgaAKvV4NlvtjF32W4A/nxVeyYM6qoJ4UREpM5QYKnnyixWJizayKK1FbPXPv5/XXngqg52rkpERKR2FFjqsT/OXuvi7MRzt/Xg9sg29i5LRESk1hRY6qmCk2XcN381q/cew9TImdf/FMFAzV4rIiJ1lAJLPXTIXMyod1axLbcQT/dGvDP6Cq7Q7LUiIlKHKbDUM3vzixjxzkr2Hz2Fv6eJ9+7tQ2igl73LEhERuSAKLPXIloNmRr6zivwTJQS3aMz790bTtkVje5clIiJywRRY6olVe44y5r3VFBaXExroxXv3XoG/p7u9yxIREbkoFFjqgT9Otd+nnQ9vjorC28PV3mWJiIhcNAosddy/M7N5dOF6LFaDgaH+vPanCNxdNdW+iIjUL+c1L/usWbMICQnB3d2dyMhIli9fXm3bxYsXExcXh5+fH15eXsTGxrJkyZJKbd5880369+9P8+bNad68OQMHDmTVqlXnU1qDMu+nPYxPXYfFanBr79bMvjtSYUVEROqlWgeW1NRUxo8fz6RJk8jMzKR///4MGjSIrKysM7ZftmwZcXFxpKWlkZGRwYABAxgyZAiZmZm2Nj/++CN33XUXP/zwAz///DNt27YlPj6e7Ozs8z+yeswwDGZ+t5MpX2wBYHTfdrx4R0+tCyQiIvWWk2EYRm0+EB0dTUREBLNnz7ZtCw0NZejQoSQnJ9doH2FhYSQkJDB58uQzvm+xWGjevDmvvfYaI0eOrNE+zWYz3t7eFBQU4OVVfx/jtVoNnv5qC+/+tBeARwZ25q/XddS6QCIiUifV9Pu7VvewlJaWkpGRwYQJEyptj4+PZ8WKFTXah9VqpbCwEB+f6icyO3nyJGVlZWdtU1JSQklJie1ns9lco99fl5VbrPxj0QYWr60YeZp6Uxij+razb1EiIiKXQa2uIeTn52OxWAgIqDzFe0BAALm5uTXax4wZMygqKmLYsGHVtpkwYQKtW7dm4MCB1bZJTk7G29vb9goKCqrZQdRRxWUWHvxwLYvXZuPi7MRLCT0VVkREpME4r5seTr/8YBhGjS5JpKSkMGXKFFJTU/H39z9jm+eff56UlBQWL16Mu3v184hMnDiRgoIC22v//v21O4g65ERJOffOW036lkO4NXJmzt2R3NJbixiKiEjDUatLQr6+vri4uFQZTcnLy6sy6nK61NRUxowZw8KFC6sdOXnxxReZPn063333HT169Djr/kwmEyaTqTbl10nHikoZ/e4q1h8ooImbC2+NuoLYDi3sXZaIiMhlVasRFjc3NyIjI0lPT6+0PT09nb59+1b7uZSUFEaPHs2CBQsYPHjwGdu88MILPP3003zzzTdERUXVpqx665C5mIS5P7P+QAHNG7uS8kCMwoqIiDRItZ44LikpiREjRhAVFUVsbCxz584lKyuLsWPHAhWXarKzs5k/fz5QEVZGjhzJzJkziYmJsY3OeHh44O3tDVRcBnryySdZsGAB7dq1s7Vp2rQpTZs2vSgHWtfsP3qS4W+tJOvoSQK8THwwJppOAZ72LktERMQuan0PS0JCAi+//DLTpk2jV69eLFu2jLS0NIKDgwHIycmpNCfLnDlzKC8vJzExkcDAQNtr3LhxtjazZs2itLSU22+/vVKbF1988SIcYt2z81Aht7+xgqyjJ2nr05hPxvZVWBERkQat1vOwOKr6Mg/LxgMFjHxnJcdOltE5oCkfjInG30uLGIqISP10SeZhkUtr1Z6j3DtvNSdKyukZ1Ix5o6+geRM3e5clIiJidwosDuLH7Xn8+f0MSsqtxLT34a1RV9DUpP88IiIioMDiENI25jDuo0zKLAbXdvVn1nCtuCwiIvJHCix29knGAf7xyXqsBtzYI5CXEnppEUMREZHTKLDY0Xsr9vLU55sBSIgKYvqt3XFx1iKGIiIip1NgsZPXf9jFC0u2A3BvvxCevDFUKy6LiIhUQ4HlMjMMgxe/3c7rP/wKwF+v7cgjcZ0VVkRERM5CgeUyMgyDqV9sYd6KvQBMHNSVP1/dwb5FiYiI1AEKLJeJxWrw+OKNpK6pWFX66ZvDGBHbzr5FiYiI1BEKLJdBmcXKox+v5/P1B3F2gudv78ntkW3sXZaIiEidocByiZWUW/jLgky+3XKIRs5OzLyzN4N7BNq7LBERkTpFgeUSOlVqYewHGSzdcRi3Rs7MHh7BdaEB9i5LRESkzlFguUROlJRz33ur+WX3UTxcXXhrVBT9OvrauywREZE6SYHlEig4Vcbod1eRmXUcT1Mj3rnnCq5o52PvskREROosBZaL7FhRKSPeWcmmbDPNGrsy/94+9GjTzN5liYiI1GkKLBfR4cIS7n5rJdsPFdKiiRsf3BdNaKCXvcsSERGp8xRYLpLcgmL+9NYv7D5chL+niQX3R9PR39PeZYmIiNQLCiwXwf6jJxn+1kqyjp6kdTMPPrwvmna+TexdloiISL2hwHKB9uYX8ac3f+FgQTFtfRqz4P5o2jRvbO+yRERE6hUFlguwK+8Ew9/6hUPmEtr7NmHB/TG09Ha3d1kiIiL1jgLLedqeW8jwt34h/0QpnQOa8sF90fh7KqyIiIhcCgos52FTdgEj3l7JsZNldAv04oP7ovFp4mbvskREROotBZZaWr//OCPeXom5uJyebbx5794+NGussCIiInIpKbDUQsa+Y4x+ZxWFJeVEtG3GvHv74OXuau+yRERE6j0Flhpavfcoo99ZRVGphT4hPrwz+gqamtR9IiIil4O+cWvg51+PcO+81Zwqs9C3QwveGhVFYzd1nYiIyOWib91zWL7zMPfPX0NxmZX+nXx5c2QU7q4u9i5LRESkQXG2dwGOrKiknHEfraO4zMq1Xf0VVkREROxEgeUsmpga8cbdkQzt1Yo37o5UWBEREbETXRI6hz4hPvQJ8bF3GSIiIg2aRlhERETE4SmwiIiIiMNTYBERERGHp8AiIiIiDk+BRURERBzeeQWWWbNmERISgru7O5GRkSxfvrzatosXLyYuLg4/Pz+8vLyIjY1lyZIlldps3ryZ2267jXbt2uHk5MTLL798PmWJiIhIPVXrwJKamsr48eOZNGkSmZmZ9O/fn0GDBpGVlXXG9suWLSMuLo60tDQyMjIYMGAAQ4YMITMz09bm5MmTtG/fnmeffZaWLVue/9GIiIhIveRkGIZRmw9ER0cTERHB7NmzbdtCQ0MZOnQoycnJNdpHWFgYCQkJTJ48ucp77dq1Y/z48YwfP742ZWE2m/H29qagoAAvL69afVZERETso6bf37UaYSktLSUjI4P4+PhK2+Pj41mxYkWN9mG1WiksLMTH58ImYyspKcFsNld6iYiISP1Uq8CSn5+PxWIhICCg0vaAgAByc3NrtI8ZM2ZQVFTEsGHDavOrq0hOTsbb29v2CgoKuqD9iYiIiOM6r5tunZycKv1sGEaVbWeSkpLClClTSE1Nxd/f/3x+tc3EiRMpKCiwvfbv339B+xMRERHHVau1hHx9fXFxcakympKXl1dl1OV0qampjBkzhoULFzJw4MDaV3oak8mEyWS64P2IiIiI46vVCIubmxuRkZGkp6dX2p6enk7fvn2r/VxKSgqjR49mwYIFDB48+PwqFRERkQar1qs1JyUlMWLECKKiooiNjWXu3LlkZWUxduxYoOJSTXZ2NvPnzwcqwsrIkSOZOXMmMTExttEZDw8PvL29gYqbebds2WL739nZ2axbt46mTZvSsWPHGtX1+8NOuvlWRESk7vj9e/ucDy0b5+H11183goODDTc3NyMiIsJYunSp7b1Ro0YZV199te3nq6++2gCqvEaNGmVrs2fPnjO2+eN+zmX//v1n3Ideeumll1566eX4r/3795/1e77W87A4KqvVysGDB/H09KzRDcA1ZTabCQoKYv/+/Zrf5RzUV7Wj/qo59VXNqa9qTn1Vc5eyrwzDoLCwkFatWuHsXP2dKrW+JOSonJ2dadOmzSXbv5eXl07oGlJf1Y76q+bUVzWnvqo59VXNXaq++v0WkbPR4ociIiLi8BRYRERExOEpsJyDyWTiqaee0pwvNaC+qh31V82pr2pOfVVz6quac4S+qjc33YqIiEj9pREWERERcXgKLCIiIuLwFFhERETE4SmwiIiIiMNrEIFl9uzZ9OjRwzbhTWxsLF9//bXtfcMwmDJlCq1atcLDw4NrrrmGzZs3V9pHSUkJf/nLX/D19aVJkybcdNNNHDhwoFKbY8eOMWLECLy9vfH29mbEiBEcP378chziRXOuvho9ejROTk6VXjExMZX20VD66nTJyck4OTkxfvx42zadW2d2pr7SuVVhypQpVfqhZcuWtvd1Tv3PufpK51Rl2dnZ3H333bRo0YLGjRvTq1cvMjIybO87/LlV48V66rDPP//c+Oqrr4zt27cb27dvNx5//HHD1dXV2LRpk2EYhvHss88anp6exqJFi4yNGzcaCQkJRmBgoGE2m237GDt2rNG6dWsjPT3dWLt2rTFgwACjZ8+eRnl5ua3NDTfcYISHhxsrVqwwVqxYYYSHhxs33njjZT/eC3Guvho1apRxww03GDk5ObbXkSNHKu2jofTVH61atcpo166d0aNHD2PcuHG27Tq3qqqur3RuVXjqqaeMsLCwSv2Ql5dne1/n1P+cq690Tv3P0aNHjeDgYGP06NHGypUrjT179hjfffedsWvXLlsbRz+3GkRgOZPmzZsbb731lmG1Wo2WLVsazz77rO294uJiw9vb23jjjTcMwzCM48ePG66ursZHH31ka5OdnW04Ozsb33zzjWEYhrFlyxYDMH755Rdbm59//tkAjG3btl2mo7o0fu8rw6j4A3DzzTdX27Yh9lVhYaHRqVMnIz093bj66qttX8I6t6qqrq8MQ+fW75566imjZ8+eZ3xP51RlZ+srw9A59UePPfaYceWVV1b7fl04txrEJaE/slgsfPTRRxQVFREbG8uePXvIzc0lPj7e1sZkMnH11VezYsUKADIyMigrK6vUplWrVoSHh9va/Pzzz3h7exMdHW1rExMTg7e3t61NXXN6X/3uxx9/xN/fn86dO3P//feTl5dne68h9lViYiKDBw9m4MCBlbbr3Kqqur76nc6tCjt37qRVq1aEhIRw5513snv3bkDn1JlU11e/0zlV4fPPPycqKoo77rgDf39/evfuzZtvvml7vy6cW/Vm8cNz2bhxI7GxsRQXF9O0aVM+/fRTunXrZuvAgICASu0DAgLYt28fALm5ubi5udG8efMqbXJzc21t/P39q/xef39/W5u6orq+Ahg0aBB33HEHwcHB7NmzhyeffJJrr72WjIwMTCZTg+urjz76iLVr17J69eoq7/1+LDq3Kpytr0Dn1u+io6OZP38+nTt35tChQ/zzn/+kb9++bN68WefUac7WVy1atNA59Qe7d+9m9uzZJCUl8fjjj7Nq1Sr++te/YjKZGDlyZJ04txpMYOnSpQvr1q3j+PHjLFq0iFGjRrF06VLb+05OTpXaG4ZRZdvpTm9zpvY12Y+jqa6vunXrRkJCgq1deHg4UVFRBAcH89VXX3HrrbdWu8/62Ff79+9n3LhxfPvtt7i7u1fbTudWzfpK51aFQYMG2f539+7diY2NpUOHDrz33nu2G0Z1TlU4W18lJSXpnPoDq9VKVFQU06dPB6B3795s3ryZ2bNnM3LkSFs7Rz63GswlITc3Nzp27EhUVBTJycn07NmTmTNn2u4oPz355eXl2ZJmy5YtKS0t5dixY2dtc+jQoSq/9/Dhw1USq6Orrq/OJDAwkODgYHbu3Ak0rL7KyMggLy+PyMhIGjVqRKNGjVi6dCmvvPIKjRo1sh2Lzq1z95XFYqnymYZ8bv1RkyZN6N69Ozt37tTfq3P4Y1+dSUM+pwIDA20j5b8LDQ0lKysLoE6cWw0msJzOMAxKSkoICQmhZcuWpKen294rLS1l6dKl9O3bF4DIyEhcXV0rtcnJyWHTpk22NrGxsRQUFLBq1Spbm5UrV1JQUGBrU1f93ldncuTIEfbv309gYCDQsPrquuuuY+PGjaxbt872ioqKYvjw4axbt4727dvr3PrNufrKxcWlymca8rn1RyUlJWzdupXAwED9vTqHP/bVmTTkc6pfv35s37690rYdO3YQHBwMUDfOrQu6ZbeOmDhxorFs2TJjz549xoYNG4zHH3/ccHZ2Nr799lvDMCoe5fL29jYWL15sbNy40bjrrrvO+ChXmzZtjO+++85Yu3atce21157xUa4ePXoYP//8s/Hzzz8b3bt3r3OPvp2trwoLC41HH33UWLFihbFnzx7jhx9+MGJjY43WrVs3yL46k9OffNG5Vb0/9pXOrf959NFHjR9//NHYvXu38csvvxg33nij4enpaezdu9cwDJ1Tf3S2vtI5VdmqVauMRo0aGc8884yxc+dO48MPPzQaN25sfPDBB7Y2jn5uNYjAcu+99xrBwcGGm5ub4efnZ1x33XW2sGIYFY9zPfXUU0bLli0Nk8lkXHXVVcbGjRsr7ePUqVPGww8/bPj4+BgeHh7GjTfeaGRlZVVqc+TIEWP48OGGp6en4enpaQwfPtw4duzY5TjEi+ZsfXXy5EkjPj7e8PPzM1xdXY22bdsao0aNqtIPDaWvzuT0wKJzq3p/7CudW//z+9wXrq6uRqtWrYxbb73V2Lx5s+19nVP/c7a+0jlV1RdffGGEh4cbJpPJ6Nq1qzF37txK7zv6ueVkGIZxYWM0IiIiIpdWg72HRUREROoOBRYRERFxeAosIiIi4vAUWERERMThKbCIiIiIw1NgEREREYenwCIiIiIOT4FFREREHJ4Ci4iIiDg8BRYRERFxeAosIiIi4vAUWERERMTh/T+cUZXjWGNckwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n", - "x = pybamm.linspace(3000,6000,100)\n", - "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n", - "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n", - "evaluated = pybamm.Array(evaluated)\n", - "pybamm.plot(x, evaluated)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulating and solving the model\n", - "\n", - "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: PrimaryBroadcast(0x55db2b43f3b99d37, broadcast, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1] - ((0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1])'], domains={'primary': ['positive electrode'], 'secondary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Subtraction(0x6e57fdf0f90fdbb0, -, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]', '(0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Addition(-0x524f51ed4f620efd, +, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))', 'Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x36e2f7f52718fd84, *, children=['0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction', 'arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Arcsinh(-0x70bcbae05a17171a, function (arcsinh), children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Division(0x2d06e4ce68936693, /, children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m])', '2.0 * Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x533055788c0787e9, *, children=['2.0', 'Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: FunctionParameter(0x79a3a2c645f54668, Positive electrode exchange-current density [A.m-2], children=['maximum(Initial concentration in electrolyte [mol.m-3], 1e-08)', 'maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]), 0.99999999 * Maximum concentration in positive electrode [mol.m-3]), 1e-08 * Maximum concentration in positive electrode [mol.m-3])', 'Maximum concentration in positive electrode [mol.m-3]', 'broadcast(Ambient temperature [K])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Maximum(-0x10b1c06354524acc, maximum, children=['Initial concentration in electrolyte [mol.m-3]', '1e-08'], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Parameter(-0x23a8868071606836, Initial concentration in electrolyte [mol.m-3], children=[], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:58\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n\u001b[1;32m 59\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: 'Initial concentration in electrolyte [mol.m-3]'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m sim \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mSimulation(spm, parameter_values\u001b[39m=\u001b[39mparam)\n\u001b[1;32m 2\u001b[0m t_eval \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m sim\u001b[39m.\u001b[39;49msolve(t_eval\u001b[39m=\u001b[39;49mt_eval)\n\u001b[1;32m 4\u001b[0m sim\u001b[39m.\u001b[39mplot()\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:559\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 556\u001b[0m logs \u001b[39m=\u001b[39m {}\n\u001b[1;32m 558\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moperating_mode \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mwithout experiment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdrive cycle\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuild(check_model\u001b[39m=\u001b[39;49mcheck_model, initial_soc\u001b[39m=\u001b[39;49minitial_soc)\n\u001b[1;32m 560\u001b[0m \u001b[39mif\u001b[39;00m save_at_cycles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 561\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 562\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39msave_at_cycles\u001b[39m\u001b[39m'\u001b[39m\u001b[39m option can only be used if simulating an \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 563\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExperiment \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 564\u001b[0m )\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:449\u001b[0m, in \u001b[0;36mSimulation.build\u001b[0;34m(self, check_model, initial_soc)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_built_model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m 448\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 449\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_parameters()\n\u001b[1;32m 450\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mMesh(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_geometry, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_submesh_types, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_pts)\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disc \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mDiscretisation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_spatial_methods)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:399\u001b[0m, in \u001b[0;36mSimulation.set_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unprocessed_model\n\u001b[1;32m 398\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 399\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parameter_values\u001b[39m.\u001b[39;49mprocess_model(\n\u001b[1;32m 400\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unprocessed_model, inplace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m 401\u001b[0m )\n\u001b[1;32m 402\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameter_values\u001b[39m.\u001b[39mprocess_geometry(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeometry)\n\u001b[1;32m 403\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:465\u001b[0m, in \u001b[0;36mParameterValues.process_model\u001b[0;34m(self, unprocessed_model, inplace)\u001b[0m\n\u001b[1;32m 462\u001b[0m new_initial_conditions[new_variable] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(equation)\n\u001b[1;32m 463\u001b[0m model\u001b[39m.\u001b[39minitial_conditions \u001b[39m=\u001b[39m new_initial_conditions\n\u001b[0;32m--> 465\u001b[0m model\u001b[39m.\u001b[39mboundary_conditions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_boundary_conditions(unprocessed_model)\n\u001b[1;32m 467\u001b[0m new_variables \u001b[39m=\u001b[39m {}\n\u001b[1;32m 468\u001b[0m \u001b[39mfor\u001b[39;00m variable, equation \u001b[39min\u001b[39;00m unprocessed_model\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mitems():\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:541\u001b[0m, in \u001b[0;36mParameterValues.process_boundary_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 539\u001b[0m sides \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnegative tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mpositive tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mno tab\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 540\u001b[0m \u001b[39mfor\u001b[39;00m variable, bcs \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39mboundary_conditions\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 541\u001b[0m processed_variable \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(variable)\n\u001b[1;32m 542\u001b[0m new_boundary_conditions[processed_variable] \u001b[39m=\u001b[39m {}\n\u001b[1;32m 543\u001b[0m \u001b[39mfor\u001b[39;00m side \u001b[39min\u001b[39;00m sides:\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:731\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[39m# Unary operators\u001b[39;00m\n\u001b[1;32m 730\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mUnaryOperator):\n\u001b[0;32m--> 731\u001b[0m new_child \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mchild)\n\u001b[1;32m 732\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_unary_new_copy(new_child)\n\u001b[1;32m 733\u001b[0m \u001b[39m# ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:657\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 655\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(new_child))\n\u001b[1;32m 656\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child))\n\u001b[1;32m 659\u001b[0m \u001b[39m# Create Function or Interpolant or Scalar object\u001b[39;00m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(function_name, \u001b[39mtuple\u001b[39m):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:620\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"See :meth:`ParameterValues.process_symbol()`.\"\"\"\u001b[39;00m\n\u001b[1;32m 619\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mParameter):\n\u001b[0;32m--> 620\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m[symbol\u001b[39m.\u001b[39;49mname]\n\u001b[1;32m 621\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, numbers\u001b[39m.\u001b[39mNumber):\n\u001b[1;32m 622\u001b[0m \u001b[39m# Check not NaN (parameter in csv file but no value given)\u001b[39;00m\n\u001b[1;32m 623\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(value):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:139\u001b[0m, in \u001b[0;36mParameterValues.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m--> 139\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dict_items[key]\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:73\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39min\u001b[39;00m k \u001b[39mand\u001b[39;00m k\u001b[39m.\u001b[39mendswith(\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 70\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m 71\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Use the dimensional version \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m )\n\u001b[0;32m---> 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Best matches are \u001b[39m\u001b[39m{\u001b[39;00mbest_matches\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mKeyError\u001b[0m: \"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"" - ] - } - ], - "source": [ - "sim = pybamm.Simulation(spm, parameter_values=param)\n", - "t_eval = np.arange(0, 3600, 1)\n", - "sim.solve(t_eval=t_eval)\n", - "sim.plot()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "The relevant papers for this notebook are:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "\n" - ] - } - ], - "source": [ - "pybamm.print_citations()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pybamm", - "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": "3.9.16" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - }, - "vscode": { - "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" - } - } + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameterisation\n", + "\n", + "In this notebook, we show how to find which parameters are needed in a model and define them.\n", + "\n", + "For other notebooks about parameterization, see:\n", + "\n", + "- The API documentation of [Parameters](https://docs.pybamm.org/en/latest/source/api/parameters/index.html)\n", + "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb) can be found at `pybamm/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n", + "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding your own parameter sets (using a dictionary)\n", + "\n", + "We will be using the model defined and explained in more detail in [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example notebook. We begin by importing the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pybamm[plot,cite] -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the model\n", + "\n", + "We define all the parameters and variables using `pybamm.Parameter` and `pybamm.Variable` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n", + "\n", + "R = pybamm.Parameter(\"Particle radius [m]\")\n", + "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n", + "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n", + "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n", + "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define our model equations, boundary and initial conditions. We also add the variables required using the dictionary `model.variables`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = pybamm.BaseModel()\n", + "\n", + "# governing equations\n", + "N = -D * pybamm.grad(c) # flux\n", + "dcdt = -pybamm.div(N)\n", + "model.rhs = {c: dcdt} \n", + "\n", + "# boundary conditions \n", + "lbc = pybamm.Scalar(0)\n", + "rbc = -j\n", + "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n", + "\n", + "# initial conditions \n", + "model.initial_conditions = {c: c0}\n", + "\n", + "model.variables = {\n", + " \"Concentration [mol.m-3]\": c,\n", + " \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n", + " \"Flux [mol.m-2.s-1]\": N,\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also define the geometry, since there are parameters in the geometry too" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n", + "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters required" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To know what parameters are required by the model and geometry, we can do" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial concentration [mol.m-3] (Parameter)\n", + "Interfacial current density [A.m-2] (InputParameter)\n", + "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n", + "\n", + "Particle radius [m] (Parameter)\n" + ] + } + ], + "source": [ + "model.print_parameter_info()\n", + "geometry.print_parameter_info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that we need to provide parameter values for the initial concentration and Faraday constant, an `InputParameter` at solve time for the interfacial current density, and diffusivity as a function of concentration. Since the electrolyte concentration does not appear anywhere in the model, there is no need to provide a value for it." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding the parameters\n", + "\n", + "Now we can proceed to the step where we add the `parameter` values using a dictionary. We set up a dictionary with parameter names as the dictionary keys and their respective values as the dictionary values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def D_fun(c):\n", + " return 3.9 #* pybamm.exp(-c)\n", + "\n", + "\n", + "values = {\n", + " \"Particle radius [m]\": 2,\n", + " \"Diffusion coefficient [m2.s-1]\": D_fun,\n", + " \"Initial concentration [mol.m-3]\": 2.5,\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can pass this dictionary in `pybamm.ParameterValues` class which accepts a dictionary of parameter names and values. We can then print `param` to check if it was initialised." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Diffusion coefficient [m2.s-1]': ,\n", + " 'Initial concentration [mol.m-3]': 2.5,\n", + " 'Particle radius [m]': 2}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param = pybamm.ParameterValues(values)\n", + "\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Updating the parameter values\n", + "\n", + "The parameter values or `param` can be further updated by using the `update` function of `ParameterValues` class. The `update` function takes a dictionary with keys being the parameters to be updated and their respective values being the updated values. Here we update the `\"Particle radius [m]\"` parameter's value. Additionally, a function can also be passed as a `parameter`'s value which we will see ahead, and a new `parameter` can also be added by passing `check_already_exists=False` in the `update` function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Diffusion coefficient [m2.s-1]': ,\n", + " 'Initial concentration [mol.m-3]': 1.5,\n", + " 'Particle radius [m]': 2}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param.update({\"Initial concentration [mol.m-3]\": 1.5})\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving the model " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters in a model\n", + "\n", + "The `parameter` function of the `BaseModel` class can be used to obtain the parameters of a model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Parameter(-0x7c3ebfeae2200290, Initial concentration [mol.m-3], children=[], domains={}),\n", + " InputParameter(-0x4a08933302b1e44e, Interfacial current density [A.m-2], children=[], domains={}),\n", + " FunctionParameter(0x66f7cbc27c44053b, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parameters = model.parameters\n", + "parameters" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As explained in the [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example, we first process both the `model` and the `geometry`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "param.process_model(model)\n", + "param.process_geometry(geometry)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now set up our mesh, choose a spatial method, and discretise our model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n", + "var_pts = {r: 20}\n", + "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n", + "\n", + "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n", + "disc = pybamm.Discretisation(mesh, spatial_methods)\n", + "disc.process_model(model);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We choose a solver and times at which we want the solution returned, and solve the model. Here we give a value for the current density `j`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# solve\n", + "solver = pybamm.ScipySolver()\n", + "t = np.linspace(0, 3600, 600)\n", + "solution = solver.solve(model, t, inputs={\"Interfacial current density [A.m-2]\": 1.4})\n", + "\n", + "# post-process, so that the solution can be called at any time t or space r\n", + "# (using interpolation)\n", + "c = solution[\"Concentration [mol.m-3]\"]\n", + "c_surf = solution[\"Surface concentration [mol.m-3]\"]\n", + "\n", + "# plot\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", + "\n", + "ax1.plot(solution.t, c_surf(solution.t))\n", + "ax1.set_xlabel(\"Time [s]\")\n", + "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n", + "\n", + "rsol = mesh[\"negative particle\"].nodes # radial position\n", + "time = 1000 # time in seconds\n", + "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n", + "ax2.set_xlabel(\"Particle radius [microns]\")\n", + "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n", + "ax2.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using pre-defined models in `PyBaMM`\n", + "\n", + "In the next few steps, we will be showing the same workflow with the Single Particle Model (`SPM`). We will also see how you can pass a function as a `parameter`'s value and how to plot such `parameter functions`.\n", + "\n", + "We start by initializing our model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "spm = pybamm.lithium_ion.SPM()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding the parameters in a model\n", + "\n", + "We can print the `parameters` of a model by using the `get_parameters_info` function." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n", + "Initial concentration in electrolyte [mol.m-3] (Parameter)\n", + "Separator thickness [m] (Parameter)\n", + "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n", + "Negative electrode thickness [m] (Parameter)\n", + "Electrode height [m] (Parameter)\n", + "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n", + "Number of cells connected in series to make a battery (Parameter)\n", + "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n", + "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n", + "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n", + "Lower voltage cut-off [V] (Parameter)\n", + "Nominal cell capacity [A.h] (Parameter)\n", + "Typical electrolyte concentration [mol.m-3] (Parameter)\n", + "Upper voltage cut-off [V] (Parameter)\n", + "Positive electrode electrons in reaction (Parameter)\n", + "Negative electrode electrons in reaction (Parameter)\n", + "Initial temperature [K] (Parameter)\n", + "Reference temperature [K] (Parameter)\n", + "Positive electrode thickness [m] (Parameter)\n", + "Number of electrodes connected in parallel to make a cell (Parameter)\n", + "Electrode width [m] (Parameter)\n", + "Separator Bruggeman coefficient (electrolyte) (Parameter)\n", + "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", + "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n", + "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n", + "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n", + "Ambient temperature [K] (FunctionParameter with input(s) 'Time [s]')\n", + "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n", + "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", + "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n", + "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n", + "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", + "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n", + "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n", + "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "\n" + ] + } + ], + "source": [ + "spm.print_parameter_info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that there are no `InputParameter` objects in the default SPM. Also, note that if a `FunctionParameter` is expected, it is ok to provide a scalar (parameter) instead. However, if a `Parameter` is expected, you cannot provide a function instead." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way to view what parameters are needed is to print the default parameter values. This can also be used to get some good defaults (but care must be taken when combining parameters across datasets and chemistries)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Negative electrode thickness [m]': 0.0001,\n", + " 'Separator thickness [m]': 2.5e-05,\n", + " 'Positive electrode thickness [m]': 0.0001,\n", + " 'Electrode height [m]': 0.137,\n", + " 'Electrode width [m]': 0.207,\n", + " 'Nominal cell capacity [A.h]': 0.680616,\n", + " 'Current function [A]': 0.680616,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n", + " 'Negative electrode diffusivity [m2.s-1]': ,\n", + " 'Negative electrode OCP [V]': ,\n", + " 'Negative electrode porosity': 0.3,\n", + " 'Negative electrode active material volume fraction': 0.6,\n", + " 'Negative particle radius [m]': 1e-05,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Negative electrode electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode OCP entropic change [V.K-1]': ,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", + " 'Positive electrode diffusivity [m2.s-1]': ,\n", + " 'Positive electrode OCP [V]': ,\n", + " 'Positive electrode porosity': 0.3,\n", + " 'Positive electrode active material volume fraction': 0.5,\n", + " 'Positive particle radius [m]': 1e-05,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Positive electrode electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode OCP entropic change [V.K-1]': ,\n", + " 'Separator porosity': 1.0,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", + " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'Ambient temperature [K]': 298.15,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Lower voltage cut-off [V]': 3.105,\n", + " 'Upper voltage cut-off [V]': 4.1,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565,\n", + " 'Initial temperature [K]': 298.15}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now define a dictionary of values for `ParameterValues` as before (here, a subset of the `Chen2020` parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Ambient temperature [K]': 298.15,\n", + " 'Current function [A]': 5.0,\n", + " 'Electrode height [m]': 0.065,\n", + " 'Electrode width [m]': 1.58,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", + " 'Initial temperature [K]': 298.15,\n", + " 'Lower voltage cut-off [V]': 2.5,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n", + " ([array([0. , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n", + " 0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n", + " 0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n", + " 0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n", + " 0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n", + " 0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n", + " 0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n", + " 0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n", + " 0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n", + " 0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n", + " 0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n", + " 0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n", + " 0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n", + " 0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n", + " 0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n", + " 0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n", + " 0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n", + " 0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n", + " 0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n", + " 0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n", + " 0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n", + " 0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n", + " 0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n", + " 0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n", + " 0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n", + " 0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n", + " 0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n", + " 0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n", + " 0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n", + " 0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n", + " 0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n", + " 0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n", + " 0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n", + " 0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361 ,\n", + " 0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n", + " 0.67557767, 0.67928044, 0.68298322, 0.686686 , 0.69038878,\n", + " 0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n", + " 0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n", + " 0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n", + " 0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n", + " 0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n", + " 0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n", + " 0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n", + " 0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n", + " 0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n", + " 0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n", + " 0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n", + " 0.89774404, 0.9014468 , 1. ])],\n", + " array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n", + " 0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n", + " 0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n", + " 0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n", + " 0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n", + " 0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n", + " 0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n", + " 0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n", + " 0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n", + " 0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n", + " 0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n", + " 0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n", + " 0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n", + " 0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n", + " 0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n", + " 0.160027 , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n", + " 0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n", + " 0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n", + " 0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n", + " 0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n", + " 0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n", + " 0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n", + " 0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n", + " 0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n", + " 0.13315202, 0.132713 , 0.1330184 , 0.13278936, 0.13225491,\n", + " 0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n", + " 0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n", + " 0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n", + " 0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n", + " 0.13011713, 0.129869 , 0.12992626, 0.12942998, 0.12796026,\n", + " 0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n", + " 0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n", + " 0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n", + " 0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n", + " 0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n", + " 0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n", + " 0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n", + " 0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n", + " 0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n", + " 0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n", + " 0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n", + " 0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n", + " 0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n", + " 0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n", + " 0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n", + " 0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n", + " 0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n", + " 0.08709427, 0.08503284, 0.07601531]))),\n", + " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Negative electrode active material volume fraction': 0.75,\n", + " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative electrode electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode porosity': 0.25,\n", + " 'Negative electrode thickness [m]': 8.52e-05,\n", + " 'Negative particle radius [m]': 5.86e-06,\n", + " 'Nominal cell capacity [A.h]': 5.0,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n", + " ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n", + " 0.27703505, 0.27975753, 0.28248 , 0.28520247, 0.28792495,\n", + " 0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n", + " 0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n", + " 0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n", + " 0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n", + " 0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n", + " 0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n", + " 0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n", + " 0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n", + " 0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n", + " 0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n", + " 0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n", + " 0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n", + " 0.453996 , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n", + " 0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n", + " 0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n", + " 0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n", + " 0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n", + " 0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n", + " 0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656 ,\n", + " 0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n", + " 0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n", + " 0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n", + " 0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n", + " 0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n", + " 0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n", + " 0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n", + " 0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n", + " 0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n", + " 0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n", + " 0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n", + " 0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n", + " 0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n", + " 0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n", + " 0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n", + " 0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n", + " 0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n", + " 0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n", + " 0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n", + " 0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n", + " 0.82152961, 0.82425208, 0.82697453, 0.829697 , 0.83241946,\n", + " 0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n", + " 0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n", + " 0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n", + " 0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n", + " 0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n", + " 0.90320364, 0.90592613, 1. ])],\n", + " array([4.4 , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n", + " 4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n", + " 4.213294 , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n", + " 4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n", + " 4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n", + " 4.1768146 , 4.1768146 , 4.1752872 , 4.173111 , 4.1726718 ,\n", + " 4.1710877 , 4.1702285 , 4.168797 , 4.1669831 , 4.1655135 ,\n", + " 4.1634517 , 4.1598248 , 4.1571712 , 4.154079 , 4.1504135 ,\n", + " 4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n", + " 4.1272392 , 4.1228104 , 4.1186109 , 4.114182 , 4.1096005 ,\n", + " 4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n", + " 4.0818448 , 4.077683 , 4.0733309 , 4.0690737 , 4.0647216 ,\n", + " 4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n", + " 4.041986 , 4.0384736 , 4.035171 , 4.0320406 , 4.0289288 ,\n", + " 4.02597 , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n", + " 4.0122066 , 4.009954 , 4.0075679 , 4.0050669 , 4.0023184 ,\n", + " 3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n", + " 3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n", + " 3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n", + " 3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n", + " 3.9192028 , 3.9166257 , 3.9117961 , 3.90815 , 3.9038739 ,\n", + " 3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n", + " 3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n", + " 3.8581741 , 3.854146 , 3.8499846 , 3.8450022 , 3.8422534 ,\n", + " 3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164 ,\n", + " 3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n", + " 3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n", + " 3.788383 , 3.7855389 , 3.7838206 , 3.78111 , 3.7794874 ,\n", + " 3.7769294 , 3.773608 , 3.7695992 , 3.7690265 , 3.7662776 ,\n", + " 3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n", + " 3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n", + " 3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n", + " 3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861 ,\n", + " 3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n", + " 3.7099447 , 3.7071004 , 3.7045615 , 3.703588 , 3.70208 ,\n", + " 3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n", + " 3.6890991 , 3.686522 , 3.6849759 , 3.6821697 , 3.6808143 ,\n", + " 3.6786573 , 3.6761947 , 3.674763 , 3.6712887 , 3.6697233 ,\n", + " 3.6678908 , 3.6652565 , 3.6630611 , 3.660274 , 3.6583652 ,\n", + " 3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n", + " 3.6383405 , 3.635076 , 3.633549 , 3.6322317 , 3.6306856 ,\n", + " 3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n", + " 3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n", + " 3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n", + " 3.5976417 , 3.5951984 , 3.593843 , 3.5916286 , 3.5894907 ,\n", + " 3.587429 , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n", + " 3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n", + " 3.5684922 , 3.5672133 , 3.52302167]))),\n", + " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Positive electrode active material volume fraction': 0.665,\n", + " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive electrode electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode porosity': 0.335,\n", + " 'Positive electrode thickness [m]': 7.56e-05,\n", + " 'Positive particle radius [m]': 5.22e-06,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Separator porosity': 0.47,\n", + " 'Separator thickness [m]': 1.2e-05,\n", + " 'Typical current [A]': 5.0,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", + " 'Upper voltage cut-off [V]': 4.4}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T):\n", + " D_ref = 3.9 * 10 ** (-14)\n", + " E_D_s = 42770\n", + " arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))\n", + " return D_ref * arrhenius\n", + "\n", + "\n", + "neg_ocp = np.array([[0. , 1.81772748],\n", + " [0.03129623, 1.0828807 ],\n", + " [0.03499902, 0.99593794],\n", + " [0.0387018 , 0.90023398],\n", + " [0.04240458, 0.79649431],\n", + " [0.04610736, 0.73354429],\n", + " [0.04981015, 0.66664314],\n", + " [0.05351292, 0.64137149],\n", + " [0.05721568, 0.59813869],\n", + " [0.06091845, 0.5670836 ],\n", + " [0.06462122, 0.54746181],\n", + " [0.06832399, 0.53068399],\n", + " [0.07202675, 0.51304734],\n", + " [0.07572951, 0.49394092],\n", + " [0.07943227, 0.47926274],\n", + " [0.08313503, 0.46065259],\n", + " [0.08683779, 0.45992726],\n", + " [0.09054054, 0.43801501],\n", + " [0.09424331, 0.42438665],\n", + " [0.09794607, 0.41150269],\n", + " [0.10164883, 0.40033659],\n", + " [0.10535158, 0.38957134],\n", + " [0.10905434, 0.37756538],\n", + " [0.1127571 , 0.36292541],\n", + " [0.11645985, 0.34357086],\n", + " [0.12016261, 0.3406314 ],\n", + " [0.12386536, 0.32299468],\n", + " [0.12756811, 0.31379458],\n", + " [0.13127086, 0.30795386],\n", + " [0.13497362, 0.29207319],\n", + " [0.13867638, 0.28697687],\n", + " [0.14237913, 0.27405477],\n", + " [0.14608189, 0.2670497 ],\n", + " [0.14978465, 0.25857493],\n", + " [0.15348741, 0.25265783],\n", + " [0.15719018, 0.24826777],\n", + " [0.16089294, 0.2414345 ],\n", + " [0.1645957 , 0.23362778],\n", + " [0.16829847, 0.22956218],\n", + " [0.17200122, 0.22370236],\n", + " [0.17570399, 0.22181271],\n", + " [0.17940674, 0.22089651],\n", + " [0.1831095 , 0.2194268 ],\n", + " [0.18681229, 0.21830064],\n", + " [0.19051504, 0.21845333],\n", + " [0.1942178 , 0.21753715],\n", + " [0.19792056, 0.21719357],\n", + " [0.20162334, 0.21635373],\n", + " [0.2053261 , 0.21667822],\n", + " [0.20902886, 0.21738444],\n", + " [0.21273164, 0.21469313],\n", + " [0.2164344 , 0.21541846],\n", + " [0.22013716, 0.21465495],\n", + " [0.22383993, 0.2135479 ],\n", + " [0.2275427 , 0.21392964],\n", + " [0.23124547, 0.21074206],\n", + " [0.23494825, 0.20873788],\n", + " [0.23865101, 0.20465319],\n", + " [0.24235377, 0.20205732],\n", + " [0.24605653, 0.19774358],\n", + " [0.2497593 , 0.19444147],\n", + " [0.25346208, 0.19190285],\n", + " [0.25716486, 0.18850531],\n", + " [0.26086762, 0.18581399],\n", + " [0.26457039, 0.18327537],\n", + " [0.26827314, 0.18157659],\n", + " [0.2719759 , 0.17814088],\n", + " [0.27567867, 0.17529686],\n", + " [0.27938144, 0.1719375 ],\n", + " [0.28308421, 0.16934161],\n", + " [0.28678698, 0.16756649],\n", + " [0.29048974, 0.16609676],\n", + " [0.29419251, 0.16414985],\n", + " [0.29789529, 0.16260378],\n", + " [0.30159806, 0.16224113],\n", + " [0.30530083, 0.160027 ],\n", + " [0.30900361, 0.15827096],\n", + " [0.31270637, 0.1588054 ],\n", + " [0.31640913, 0.15552238],\n", + " [0.32011189, 0.15580869],\n", + " [0.32381466, 0.15220118],\n", + " [0.32751744, 0.1511132 ],\n", + " [0.33122021, 0.14987253],\n", + " [0.33492297, 0.14874637],\n", + " [0.33862575, 0.14678037],\n", + " [0.34232853, 0.14620776],\n", + " [0.34603131, 0.14555879],\n", + " [0.34973408, 0.14389819],\n", + " [0.35343685, 0.14359279],\n", + " [0.35713963, 0.14242846],\n", + " [0.36084241, 0.14038612],\n", + " [0.36454517, 0.13882096],\n", + " [0.36824795, 0.13954628],\n", + " [0.37195071, 0.13946992],\n", + " [0.37565348, 0.13780934],\n", + " [0.37935626, 0.13973714],\n", + " [0.38305904, 0.13698858],\n", + " [0.38676182, 0.13523254],\n", + " [0.3904646 , 0.13441178],\n", + " [0.39416737, 0.1352898 ],\n", + " [0.39787015, 0.13507985],\n", + " [0.40157291, 0.13647321],\n", + " [0.40527567, 0.13601512],\n", + " [0.40897844, 0.13435452],\n", + " [0.41268121, 0.1334765 ],\n", + " [0.41638398, 0.1348317 ],\n", + " [0.42008676, 0.13275118],\n", + " [0.42378953, 0.13286571],\n", + " [0.4274923 , 0.13263667],\n", + " [0.43119506, 0.13456447],\n", + " [0.43489784, 0.13471718],\n", + " [0.43860061, 0.13395369],\n", + " [0.44230338, 0.13448814],\n", + " [0.44600615, 0.1334765 ],\n", + " [0.44970893, 0.13298023],\n", + " [0.45341168, 0.13259849],\n", + " [0.45711444, 0.13338107],\n", + " [0.46081719, 0.13309476],\n", + " [0.46451994, 0.13275118],\n", + " [0.46822269, 0.13443087],\n", + " [0.47192545, 0.13315202],\n", + " [0.47562821, 0.132713 ],\n", + " [0.47933098, 0.1330184 ],\n", + " [0.48303375, 0.13278936],\n", + " [0.48673651, 0.13225491],\n", + " [0.49043926, 0.13317111],\n", + " [0.49414203, 0.13263667],\n", + " [0.49784482, 0.13187316],\n", + " [0.50154759, 0.13265574],\n", + " [0.50525036, 0.13250305],\n", + " [0.50895311, 0.13324745],\n", + " [0.51265586, 0.13204496],\n", + " [0.51635861, 0.13242669],\n", + " [0.52006139, 0.13233127],\n", + " [0.52376415, 0.13198769],\n", + " [0.52746692, 0.13254122],\n", + " [0.53116969, 0.13145325],\n", + " [0.53487245, 0.13298023],\n", + " [0.53857521, 0.13168229],\n", + " [0.54227797, 0.1313578 ],\n", + " [0.54598074, 0.13235036],\n", + " [0.5496835 , 0.13120511],\n", + " [0.55338627, 0.13089971],\n", + " [0.55708902, 0.13109058],\n", + " [0.56079178, 0.13082336],\n", + " [0.56449454, 0.13011713],\n", + " [0.5681973 , 0.129869 ],\n", + " [0.57190006, 0.12992626],\n", + " [0.57560282, 0.12942998],\n", + " [0.57930558, 0.12796026],\n", + " [0.58300835, 0.12862831],\n", + " [0.58671112, 0.12656689],\n", + " [0.59041389, 0.12734947],\n", + " [0.59411664, 0.12509716],\n", + " [0.59781941, 0.12110791],\n", + " [0.60152218, 0.11839751],\n", + " [0.60522496, 0.11244226],\n", + " [0.60892772, 0.11307214],\n", + " [0.61263048, 0.1092165 ],\n", + " [0.61633325, 0.10683058],\n", + " [0.62003603, 0.10433014],\n", + " [0.6237388 , 0.10530359],\n", + " [0.62744156, 0.10056993],\n", + " [0.63114433, 0.09950104],\n", + " [0.63484711, 0.09854668],\n", + " [0.63854988, 0.09921473],\n", + " [0.64225265, 0.09541635],\n", + " [0.64595543, 0.09980643],\n", + " [0.64965823, 0.0986612 ],\n", + " [0.653361 , 0.09560722],\n", + " [0.65706377, 0.09755413],\n", + " [0.66076656, 0.09612258],\n", + " [0.66446934, 0.09430929],\n", + " [0.66817212, 0.09661885],\n", + " [0.67187489, 0.09366032],\n", + " [0.67557767, 0.09522548],\n", + " [0.67928044, 0.09535909],\n", + " [0.68298322, 0.09316404],\n", + " [0.686686 , 0.09450016],\n", + " [0.69038878, 0.0930877 ],\n", + " [0.69409156, 0.09343126],\n", + " [0.69779433, 0.0932404 ],\n", + " [0.70149709, 0.09350762],\n", + " [0.70519988, 0.09339309],\n", + " [0.70890264, 0.09291591],\n", + " [0.7126054 , 0.09303043],\n", + " [0.71630818, 0.0926296 ],\n", + " [0.72001095, 0.0932404 ],\n", + " [0.72371371, 0.09261052],\n", + " [0.72741648, 0.09249599],\n", + " [0.73111925, 0.09240055],\n", + " [0.73482204, 0.09253416],\n", + " [0.7385248 , 0.09209515],\n", + " [0.74222757, 0.09234329],\n", + " [0.74593034, 0.09366032],\n", + " [0.74963312, 0.09333583],\n", + " [0.75333589, 0.09322131],\n", + " [0.75703868, 0.09264868],\n", + " [0.76074146, 0.09253416],\n", + " [0.76444422, 0.09243873],\n", + " [0.76814698, 0.09230512],\n", + " [0.77184976, 0.09310678],\n", + " [0.77555253, 0.09165615],\n", + " [0.77925531, 0.09159888],\n", + " [0.78295807, 0.09207606],\n", + " [0.78666085, 0.09175158],\n", + " [0.79036364, 0.09177067],\n", + " [0.79406641, 0.09236237],\n", + " [0.79776918, 0.09241964],\n", + " [0.80147197, 0.09320222],\n", + " [0.80517474, 0.09199972],\n", + " [0.80887751, 0.09167523],\n", + " [0.81258028, 0.09322131],\n", + " [0.81628304, 0.09190428],\n", + " [0.81998581, 0.09167523],\n", + " [0.82368858, 0.09285865],\n", + " [0.82739136, 0.09180884],\n", + " [0.83109411, 0.09150345],\n", + " [0.83479688, 0.09186611],\n", + " [0.83849965, 0.0920188 ],\n", + " [0.84220242, 0.09320222],\n", + " [0.84590519, 0.09131257],\n", + " [0.84960797, 0.09117896],\n", + " [0.85331075, 0.09133166],\n", + " [0.85701353, 0.09089265],\n", + " [0.86071631, 0.09058725],\n", + " [0.86441907, 0.09051091],\n", + " [0.86812186, 0.09033912],\n", + " [0.87182464, 0.09041547],\n", + " [0.87552742, 0.0911217 ],\n", + " [0.87923019, 0.0894611 ],\n", + " [0.88293296, 0.08999555],\n", + " [0.88663573, 0.08921297],\n", + " [0.89033849, 0.08881213],\n", + " [0.89404126, 0.08797229],\n", + " [0.89774404, 0.08709427],\n", + " [0.9014468 , 0.08503284],\n", + " [1. , 0.07601531]])\n", + "\n", + "pos_ocp = np.array([[0.24879728, 4.4 ],\n", + " [0.26614516, 4.2935653 ],\n", + " [0.26886763, 4.2768621 ],\n", + " [0.27159011, 4.2647018 ],\n", + " [0.27431258, 4.2540312 ],\n", + " [0.27703505, 4.2449446 ],\n", + " [0.27975753, 4.2364879 ],\n", + " [0.28248 , 4.2302647 ],\n", + " [0.28520247, 4.2225528 ],\n", + " [0.28792495, 4.2182574 ],\n", + " [0.29064743, 4.213294 ],\n", + " [0.29336992, 4.2090373 ],\n", + " [0.29609239, 4.2051239 ],\n", + " [0.29881487, 4.2012677 ],\n", + " [0.30153735, 4.1981564 ],\n", + " [0.30425983, 4.1955218 ],\n", + " [0.30698231, 4.1931167 ],\n", + " [0.30970478, 4.1889744 ],\n", + " [0.31242725, 4.1881533 ],\n", + " [0.31514973, 4.1865883 ],\n", + " [0.3178722 , 4.1850228 ],\n", + " [0.32059466, 4.1832285 ],\n", + " [0.32331714, 4.1808805 ],\n", + " [0.32603962, 4.1805749 ],\n", + " [0.32876209, 4.1789522 ],\n", + " [0.33148456, 4.1768146 ],\n", + " [0.33420703, 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+ " [0.89775868, 3.5721002 ],\n", + " [0.90048116, 3.5702102 ],\n", + " [0.90320364, 3.5684922 ],\n", + " [0.90592613, 3.5672133 ],\n", + " [1. , 3.52302167]])\n", + "\n", + "from pybamm import exp, constants\n", + "\n", + "\n", + "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n", + " m_ref = 6.48e-7 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", + " E_r = 35000\n", + " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", + "\n", + " return (\n", + " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n", + " )\n", + "\n", + "\n", + "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n", + " m_ref = 3.42e-6 # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n", + " E_r = 17800\n", + " arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n", + "\n", + " return (\n", + " m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n", + " )\n", + "\n", + "\n", + "values = {\n", + " 'Negative electrode thickness [m]': 8.52e-05,\n", + " 'Separator thickness [m]': 1.2e-05,\n", + " 'Positive electrode thickness [m]': 7.56e-05,\n", + " 'Electrode height [m]': 0.065,\n", + " 'Electrode width [m]': 1.58,\n", + " 'Nominal cell capacity [A.h]': 5.0,\n", + " 'Typical current [A]': 5.0,\n", + " 'Current function [A]': 5.0,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", + " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\n", + " 'Negative electrode porosity': 0.25,\n", + " 'Negative electrode active material volume fraction': 0.75,\n", + " 'Negative particle radius [m]': 5.86e-06,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Negative electrode electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n", + " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", + " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\n", + " 'Positive electrode porosity': 0.335,\n", + " 'Positive electrode active material volume fraction': 0.665,\n", + " 'Positive particle radius [m]': 5.22e-06,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Positive electrode electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n", + " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Separator porosity': 0.47,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'Ambient temperature [K]': 298.15,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Lower voltage cut-off [V]': 2.5,\n", + " 'Upper voltage cut-off [V]': 4.4,\n", + " \"Initial concentration in electrolyte [mol.m-3]\": 1000,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", + " 'Initial temperature [K]': 298.15\n", + "}\n", + "param = pybamm.ParameterValues(values)\n", + "param" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we would have got the same result by doing" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Negative electrode thickness [m]': 8.52e-05,\n", + " 'Separator thickness [m]': 1.2e-05,\n", + " 'Positive electrode thickness [m]': 7.56e-05,\n", + " 'Electrode height [m]': 0.065,\n", + " 'Electrode width [m]': 1.58,\n", + " 'Nominal cell capacity [A.h]': 5.0,\n", + " 'Current function [A]': 5.0,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", + " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative electrode OCP [V]': ,\n", + " 'Negative electrode porosity': 0.25,\n", + " 'Negative electrode active material volume fraction': 0.75,\n", + " 'Negative particle radius [m]': 5.86e-06,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n", + " 'Negative electrode electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", + " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive electrode OCP [V]': ,\n", + " 'Positive electrode porosity': 0.335,\n", + " 'Positive electrode active material volume fraction': 0.665,\n", + " 'Positive particle radius [m]': 5.22e-06,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n", + " 'Positive electrode electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", + " 'Separator porosity': 0.47,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", + " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'Ambient temperature [K]': 298.15,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Lower voltage cut-off [V]': 2.5,\n", + " 'Upper voltage cut-off [V]': 4.2,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", + " 'Initial temperature [K]': 298.15}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_same = pybamm.ParameterValues(\"Chen2020\")\n", + "{k: v for k,v in param_same.items() if k in spm._parameter_info}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Updating a specific parameter" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once a parameter set has been defined (either via a dictionary or a pre-built set), single parameters can be updated" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a constant value:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current function [A]\t5.0\n" + ] }, - "nbformat": 4, - "nbformat_minor": 2 + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param.search(\"Current function [A]\")\n", + "\n", + "param.update({\"Current function [A]\": 4.0})\n", + "\n", + "param[\"Current function [A]\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def curren_func(time):\n", + " return 1 + pybamm.sin(2 * np.pi * time / 60)\n", + "\n", + "\n", + "param.update({\"Current function [A]\": curren_func})\n", + "\n", + "param[\"Current function [A]\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting parameter functions\n", + "\n", + "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting \"Current function \\[A]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "currentfunc = param[\"Current function [A]\"]\n", + "time = pybamm.linspace(0, 120, 60)\n", + "evaluated = param.evaluate(currentfunc(time))\n", + "evaluated = pybamm.Array(evaluated)\n", + "pybamm.plot(time, evaluated)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking another such example:\n", + "\n", + "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n", + "x = pybamm.linspace(3000,6000,100)\n", + "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n", + "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n", + "evaluated = pybamm.Array(evaluated)\n", + "pybamm.plot(x, evaluated)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating and solving the model\n", + "\n", + "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: PrimaryBroadcast(0x55db2b43f3b99d37, broadcast, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1] - ((0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1])'], domains={'primary': ['positive electrode'], 'secondary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Subtraction(0x6e57fdf0f90fdbb0, -, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]', '(0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Addition(-0x524f51ed4f620efd, +, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))', 'Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x36e2f7f52718fd84, *, children=['0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction', 'arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Arcsinh(-0x70bcbae05a17171a, function (arcsinh), children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Division(0x2d06e4ce68936693, /, children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m])', '2.0 * Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x533055788c0787e9, *, children=['2.0', 'Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: FunctionParameter(0x79a3a2c645f54668, Positive electrode exchange-current density [A.m-2], children=['maximum(Initial concentration in electrolyte [mol.m-3], 1e-08)', 'maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]), 0.99999999 * Maximum concentration in positive electrode [mol.m-3]), 1e-08 * Maximum concentration in positive electrode [mol.m-3])', 'Maximum concentration in positive electrode [mol.m-3]', 'broadcast(Ambient temperature [K])'], domains={'primary': ['current collector']})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Maximum(-0x10b1c06354524acc, maximum, children=['Initial concentration in electrolyte [mol.m-3]', '1e-08'], domains={})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: Parameter(-0x23a8868071606836, Initial concentration in electrolyte [mol.m-3], children=[], domains={})", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:58\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n\u001b[1;32m 59\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", + "\u001b[0;31mKeyError\u001b[0m: 'Initial concentration in electrolyte [mol.m-3]'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m sim \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mSimulation(spm, parameter_values\u001b[39m=\u001b[39mparam)\n\u001b[1;32m 2\u001b[0m t_eval \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m sim\u001b[39m.\u001b[39;49msolve(t_eval\u001b[39m=\u001b[39;49mt_eval)\n\u001b[1;32m 4\u001b[0m sim\u001b[39m.\u001b[39mplot()\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:559\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 556\u001b[0m logs \u001b[39m=\u001b[39m {}\n\u001b[1;32m 558\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moperating_mode \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mwithout experiment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdrive cycle\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuild(check_model\u001b[39m=\u001b[39;49mcheck_model, initial_soc\u001b[39m=\u001b[39;49minitial_soc)\n\u001b[1;32m 560\u001b[0m \u001b[39mif\u001b[39;00m save_at_cycles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 561\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 562\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39msave_at_cycles\u001b[39m\u001b[39m'\u001b[39m\u001b[39m option can only be used if simulating an \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 563\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExperiment \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 564\u001b[0m )\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:449\u001b[0m, in \u001b[0;36mSimulation.build\u001b[0;34m(self, check_model, initial_soc)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_built_model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m 448\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 449\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_parameters()\n\u001b[1;32m 450\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mMesh(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_geometry, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_submesh_types, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_pts)\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disc \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mDiscretisation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_spatial_methods)\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:399\u001b[0m, in \u001b[0;36mSimulation.set_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unprocessed_model\n\u001b[1;32m 398\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 399\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parameter_values\u001b[39m.\u001b[39;49mprocess_model(\n\u001b[1;32m 400\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unprocessed_model, inplace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m 401\u001b[0m )\n\u001b[1;32m 402\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameter_values\u001b[39m.\u001b[39mprocess_geometry(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeometry)\n\u001b[1;32m 403\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:465\u001b[0m, in \u001b[0;36mParameterValues.process_model\u001b[0;34m(self, unprocessed_model, inplace)\u001b[0m\n\u001b[1;32m 462\u001b[0m new_initial_conditions[new_variable] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(equation)\n\u001b[1;32m 463\u001b[0m model\u001b[39m.\u001b[39minitial_conditions \u001b[39m=\u001b[39m new_initial_conditions\n\u001b[0;32m--> 465\u001b[0m model\u001b[39m.\u001b[39mboundary_conditions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_boundary_conditions(unprocessed_model)\n\u001b[1;32m 467\u001b[0m new_variables \u001b[39m=\u001b[39m {}\n\u001b[1;32m 468\u001b[0m \u001b[39mfor\u001b[39;00m variable, equation \u001b[39min\u001b[39;00m unprocessed_model\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mitems():\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:541\u001b[0m, in \u001b[0;36mParameterValues.process_boundary_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 539\u001b[0m sides \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnegative tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mpositive tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mno tab\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 540\u001b[0m \u001b[39mfor\u001b[39;00m variable, bcs \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39mboundary_conditions\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 541\u001b[0m processed_variable \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(variable)\n\u001b[1;32m 542\u001b[0m new_boundary_conditions[processed_variable] \u001b[39m=\u001b[39m {}\n\u001b[1;32m 543\u001b[0m \u001b[39mfor\u001b[39;00m side \u001b[39min\u001b[39;00m sides:\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:731\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[39m# Unary operators\u001b[39;00m\n\u001b[1;32m 730\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mUnaryOperator):\n\u001b[0;32m--> 731\u001b[0m new_child \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mchild)\n\u001b[1;32m 732\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_unary_new_copy(new_child)\n\u001b[1;32m 733\u001b[0m \u001b[39m# ensure domain remains the same\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:657\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 655\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(new_child))\n\u001b[1;32m 656\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child))\n\u001b[1;32m 659\u001b[0m \u001b[39m# Create Function or Interpolant or Scalar object\u001b[39;00m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(function_name, \u001b[39mtuple\u001b[39m):\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:620\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"See :meth:`ParameterValues.process_symbol()`.\"\"\"\u001b[39;00m\n\u001b[1;32m 619\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mParameter):\n\u001b[0;32m--> 620\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m[symbol\u001b[39m.\u001b[39;49mname]\n\u001b[1;32m 621\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, numbers\u001b[39m.\u001b[39mNumber):\n\u001b[1;32m 622\u001b[0m \u001b[39m# Check not NaN (parameter in csv file but no value given)\u001b[39;00m\n\u001b[1;32m 623\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(value):\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:139\u001b[0m, in \u001b[0;36mParameterValues.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m--> 139\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dict_items[key]\n", + "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:73\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39min\u001b[39;00m k \u001b[39mand\u001b[39;00m k\u001b[39m.\u001b[39mendswith(\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 70\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m 71\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Use the dimensional version \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m )\n\u001b[0;32m---> 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Best matches are \u001b[39m\u001b[39m{\u001b[39;00mbest_matches\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", + "\u001b[0;31mKeyError\u001b[0m: \"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"" + ] + } + ], + "source": [ + "sim = pybamm.Simulation(spm, parameter_values=param)\n", + "t_eval = np.arange(0, 3600, 1)\n", + "sim.solve(t_eval=t_eval)\n", + "sim.plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "The relevant papers for this notebook are:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "\n" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pybamm", + "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": "3.9.16" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 } From 32b73e5eae5471c4e86514dc15f0a2b5de4ba751 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 10 Aug 2023 10:22:01 +0100 Subject: [PATCH 050/154] fix tests --- pybamm/models/submodels/particle/msmr_diffusion.py | 3 ++- .../test_full_battery_models/test_base_battery_model.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index 63ebbd4e41..09fa5d86f7 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -256,7 +256,8 @@ def get_coupled_variables(self, variables): # Note: diffusivity is given as a function of concentration here, # not stoichiometry c_max = self.phase_param.c_max - D_eff = self._get_effective_diffusivity(x * c_max, T) + current = variables["Total current density [A.m-2]"] + D_eff = self._get_effective_diffusivity(x * c_max, T, current) f = self.param.F / (self.param.R * T) N_s = c_max * x * (1 - x) * f * D_eff * pybamm.grad(U) variables.update( diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index bf9cd31a12..1914cd9dd7 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -30,7 +30,7 @@ 'interface utilisation': 'full' (possible: ['full', 'constant', 'current-driven']) 'lithium plating': 'none' (possible: ['none', 'reversible', 'partially reversible', 'irreversible']) 'lithium plating porosity change': 'false' (possible: ['false', 'true']) -'loss of active material': 'stress-driven' (possible: ['none', 'stress-driven', 'reaction-driven', 'stress and reaction-driven']) +'loss of active material': 'stress-driven' (possible: ['none', 'stress-driven', 'reaction-driven', 'current-driven', 'stress and reaction-driven']) 'number of MSMR reactions': 'none' (possible: ['none', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']) 'open-circuit potential': 'single' (possible: ['single', 'current sigmoid', 'MSMR']) 'operating mode': 'current' (possible: ['current', 'voltage', 'power', 'differential power', 'explicit power', 'resistance', 'differential resistance', 'explicit resistance', 'CCCV']) From d8dace34dfcc4179110e723c3dc356d95b32745b Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 11 Aug 2023 15:54:25 +0100 Subject: [PATCH 051/154] debug half cell --- pybamm/models/submodels/interface/kinetics/base_kinetics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py index 29020b0fd1..c6cdc94ec3 100644 --- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py +++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py @@ -83,7 +83,7 @@ def get_coupled_variables(self, variables): # current density. Note: this is only used for the "exchange current density" # variables. For the interfacial current density variables, we sum the # interfacial currents from each reaction. - if self.options["intercalation kinetics"] == "MSMR": + if domain_options["intercalation kinetics"] == "MSMR": N = int(domain_options["number of MSMR reactions"]) j0 = 0 for i in range(N): @@ -187,7 +187,7 @@ def get_coupled_variables(self, variables): # (In the "distributed SEI resistance" model, we have already defined j) # For MSMR model we calculate the total current density by summing the current # densities from each reaction - if self.options["intercalation kinetics"] == "MSMR": + if domain_options["intercalation kinetics"] == "MSMR": j = 0 for i in range(N): j0_j = self._get_exchange_current_density_by_reaction(variables, i) From 42a499358214549349b2ed84d6ab8f494efb4a2a Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 22:36:52 -0400 Subject: [PATCH 052/154] Use branching + Martin's suggestion --- .github/release_reminder.md | 9 ++++- .github/release_workflow.md | 50 +++++++++++------------- .github/workflows/release_reminder.yml | 4 +- .github/workflows/update_version.yml | 53 ++++++++++++++------------ 4 files changed, 60 insertions(+), 56 deletions(-) diff --git a/.github/release_reminder.md b/.github/release_reminder.md index 58d917ba8e..2515166837 100644 --- a/.github/release_reminder.md +++ b/.github/release_reminder.md @@ -1,4 +1,9 @@ --- -title: Create {{ date | date('YY.MM') }} release +title: Create {{ date | date('YY.MM') }} (final or rc0) release --- -Quarterly reminder to create a non-pre-release before the end of this month. See [Release Workflow](./release_workflow.md) for the process. +Quarterly reminder to create a - + +1. pre-release if the month has just started. +2. non-pre-release if the month is about to end (**before the end of the month**). + +See [Release Workflow](./release_workflow.md) for more information. diff --git a/.github/release_workflow.md b/.github/release_workflow.md index d3a42d154e..5d75e3f57e 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -4,20 +4,17 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an ## rc0 releases (automated) -1. The `update_version.yml` workflow will run on every 1st of January, May and September, creating 2 PRs - +1. The `update_version.yml` workflow will run on every 1st of January, May and September, updating incrementing the version to `YY.MMrc0` by running `scripts/update_version.py` in the following files - - 1. Incrementing the version to `YY.MMrc0` by running `scripts/update_version.py` in the following files - - - `pybamm/version.py` - - `docs/conf.py` - - `CITATION.cff` - - `vcpkg.json` - - `docs/source/_static/versions.json` - - 2. A PR from `develop` to `main` + - `pybamm/version.py` + - `docs/conf.py` + - `CITATION.cff` + - `vcpkg.json` + - `docs/_static/versions.json` - The version PR should be merged into `develop`, and then the develop-to-main PR should be merged into `main`. + These changes will be automatically pushed to a new branch `YY.MM`. -2. Once the tests pass, create a new GitHub _pre-release_ with the same tag (`YY.MMrc0`) from the `main` branch and a description copied from `CHANGELOG.md`. +2. Create a new GitHub _pre-release_ with the tag `YY.MMrc0` from the `YY.MM` branch and a description copied from `CHANGELOG.md`. 3. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI. @@ -25,52 +22,51 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an If a new release candidate is required after the release of `rc0` - -1. Fix a bug in `main` (no new features should be added to `main` once `rc0` is released) and `develop` individually. +1. Fix a bug in `YY.MM` (no new features should be added to `YY.MM` once `rc0` is released) and `develop` individually. 2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...). -3. This will create a PR incrementing the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files - +3. This will increment the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files - - `pybamm/version.py` - `docs/conf.py` - `CITATION.cff` - `vcpkg.json` - - `docs/source/_static/versions.json` + - `docs/_static/versions.json` - The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something we don't want (`develop` will have new features). + These changes will be automatically pushed to the existing branch `YY.MM`. -4. Once the tests pass, create a new GitHub _pre-release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. +4. Create a new GitHub _pre-release_ with the same tag (`YY.MMrcX`) from the `YY.MM` branch and a description copied from `CHANGELOG.md`. 5. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI. -6. Manually merge `main` back to `develop` if any conflicts arise. - ## Actual release (manual) Once satisfied with the release candidates - 1. Run `update_version.yml` manually, leaving the `append_to_tag` field blank ("") for an actual release. -2. This will create a PR incrementing the version to `YY.MM` by running `scripts/update_version.py` in the following files - +2. This will increment the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files - - `pybamm/version.py` - `docs/conf.py` - `CITATION.cff` - `vcpkg.json` - - `docs/source/_static/versions.json` + - `docs/_static/versions.json` + + These changes will be automatically pushed to the existing branch `YY.MM`. - The version PR should be merged into `main`, because merging it into `develop` would require merging `develop` into `main`, something we don't want (`develop` will have new features). +3. Next, a PR from `YY.MM` to `main` will be generated that should be merged once all the tests pass. -3. Once the tests pass, create a new GitHub _release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. +4. Create a new GitHub _release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`. -4. This release will automatically trigger `publish_pypi.yml` and create a _release_ on PyPI. +5. This release will automatically trigger `publish_pypi.yml` and create a _release_ on PyPI. ## Other checks Some other essential things to check throughout the release process - -- Update baseline of registries in `vcpkg-configuration.json` as the latest commit id from [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) +- If updating our custom vcpkg registory entries [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) or [pybamm-team/casadi-vcpkg-registry](https://github.com/pybamm-team/casadi-vcpkg-registry) (used to build Windows wheels), make sure to update the baseline of the registories in vcpkg-configuration.json to the latest commit id. - Update `CHANGELOG.md` with a summary of the release -- Update jax and jaxlib to the latest version in `pybamm.util` and fix any bugs that arise -- If building wheels on Windows gives a `vcpkg` related error - revert the baseline of default-registry to a stable commit in `vcpkg-configuration.json` -- Make sure the URLs in `docs/source/_static/versions.json` are valid +- Update jax and jaxlib to the latest version in `pybamm.util` and `setup.py`, fixing any bugs that arise +- Make sure the URLs in `docs/_static/versions.json` are valid diff --git a/.github/workflows/release_reminder.yml b/.github/workflows/release_reminder.yml index 6e27fc78fc..ac2f1afa29 100644 --- a/.github/workflows/release_reminder.yml +++ b/.github/workflows/release_reminder.yml @@ -2,8 +2,8 @@ name: Create a release reminder on: schedule: - # Run at 10 am UTC on 28th every 4th month from January through December - - cron: 0 10 28 1-12/4 * + # Run at 10 am UTC on 1st and 28th every 4th month from January through December + - cron: 0 10 1-28/27 1-12/4 * permissions: contents: read diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index 82e371fb22..29115bf490 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -17,17 +17,29 @@ jobs: runs-on: ubuntu-latest steps: + - name: Get current date for the first release candidate + if: github.event_name == 'schedule' + run: | + echo "VERSION=$(date +'v%y.%-m')rc0" >> $GITHUB_ENV + echo "NON_RC_VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV + + - name: Get current date for a manual release + if: github.event_name == 'workflow_dispatch' + run: | + echo "VERSION=$(date +'v%y.%-m')${{ github.event.inputs.append_to_tag }}" >> $GITHUB_ENV + echo "NON_RC_VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV + - uses: actions/checkout@v3 if: github.event_name == 'schedule' + with: + ref: 'develop' - # the version should be updated in main for manual releases as features will - # be frozen once the scheduled release (rc0) is created - uses: actions/checkout@v3 if: github.event_name == 'workflow_dispatch' with: - ref: 'main' + ref: '${{ env.NON_RC_VERSION }}' - - name: Set up Python 3.11 + - name: Set up Python uses: actions/setup-python@v4 with: python-version: 3.8 @@ -37,34 +49,25 @@ jobs: pip install wheel pip install --editable . - - name: Get current date for the first release candidate - if: github.event_name == 'schedule' - run: echo "VERSION=$(date +'v%y.%-m')rc0" >> $GITHUB_ENV - - - name: Get current date for a manual release - if: github.event_name == 'workflow_dispatch' - run: echo "VERSION=$(date +'v%y.%-m')${{ github.event.inputs.append_to_tag }}" >> $GITHUB_ENV - - name: Update version run: python scripts/update_version.py - - name: Create Pull Request - id: version_pr - uses: peter-evans/create-pull-request@v3 + - uses: EndBug/add-and-commit@v9 + if: github.event_name == 'schedule' + with: + message: 'Bump to ${{ env.VERSION }}' + new_branch: '${{ env.NON_RC_VERSION }}' + + - uses: EndBug/add-and-commit@v9 + if: github.event_name == 'workflow_dispatch' with: - delete-branch: true - branch-suffix: short-commit-hash - commit-message: Update version to ${{ env.VERSION }} - title: Update to ${{ env.VERSION }} - body: | - - [x] Update to ${{ env.VERSION }} - - [ ] Check the [release checklist](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_checklist.md) + message: 'Bump to ${{ env.VERSION }}' - - name: Make a PR from develop to main - if: github.event_name == 'schedule' # features are frozen once a release candidate is out + - name: Make a PR from ${{ env.VERSION }} to main + if: github.event_name == 'workflow_dispatch' && github.event.inputs.append_to_tag == "" uses: repo-sync/pull-request@v2 with: destination_branch: "main" pr_title: "Make release ${{ env.VERSION }}" - pr_body: "**DO NOT MERGE UNTIL #${{ steps.version_pr.outputs.pull-request-number }} IS MERGED.**

Make release ${{ env.VERSION }}" + pr_body: "**Check the [release workflow](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_workflow.md)" github_token: ${{ secrets.GITHUB_TOKEN }} From 07566dc78d0b305b95556258258b7dd2ff77f6eb Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 22:41:40 -0400 Subject: [PATCH 053/154] Fix syntax --- .github/workflows/update_version.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index 29115bf490..7153003ab9 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -64,7 +64,7 @@ jobs: message: 'Bump to ${{ env.VERSION }}' - name: Make a PR from ${{ env.VERSION }} to main - if: github.event_name == 'workflow_dispatch' && github.event.inputs.append_to_tag == "" + if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, "rc") uses: repo-sync/pull-request@v2 with: destination_branch: "main" From ffbf836c8a163c7e62f6e5acdde619fcb18ac364 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 22:43:00 -0400 Subject: [PATCH 054/154] Just rc --- .github/workflows/update_version.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index 7153003ab9..f0eed7e0c8 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -64,7 +64,7 @@ jobs: message: 'Bump to ${{ env.VERSION }}' - name: Make a PR from ${{ env.VERSION }} to main - if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, "rc") + if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, rc) uses: repo-sync/pull-request@v2 with: destination_branch: "main" From 26c1eb0e5b5bc275861f8d5b4e876e8e00ae11a6 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 22:43:49 -0400 Subject: [PATCH 055/154] Single quotes --- .github/workflows/update_version.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index f0eed7e0c8..f1ea98afe4 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -64,7 +64,7 @@ jobs: message: 'Bump to ${{ env.VERSION }}' - name: Make a PR from ${{ env.VERSION }} to main - if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, rc) + if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc') uses: repo-sync/pull-request@v2 with: destination_branch: "main" From 10c7d7175043daf9e66a303f418d4df3167c17bf Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 22:44:32 -0400 Subject: [PATCH 056/154] Run on fork --- .github/workflows/update_version.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index f1ea98afe4..ff83c465ee 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -13,7 +13,7 @@ on: jobs: update-version: # This workflow is only of value to PyBaMM and would always be skipped in forks - if: github.repository_owner == 'pybamm-team' + # if: github.repository_owner == 'pybamm-team' runs-on: ubuntu-latest steps: From 3bdd128fc9c9a43b822fbd17f39833f53f3cb3f1 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 22:50:47 -0400 Subject: [PATCH 057/154] Install all --- .github/workflows/update_version.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index ff83c465ee..a5c4d23e63 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -47,7 +47,7 @@ jobs: - name: Install dependencies run: | pip install wheel - pip install --editable . + pip install --editable ".[all]" - name: Update version run: python scripts/update_version.py @@ -63,7 +63,7 @@ jobs: with: message: 'Bump to ${{ env.VERSION }}' - - name: Make a PR from ${{ env.VERSION }} to main + - name: Make a PR from ${{ env.NON_RC_VERSION }} to main if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc') uses: repo-sync/pull-request@v2 with: From c88e74446572667fc054a34e7c83bc34d1f312b5 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 23:01:09 -0400 Subject: [PATCH 058/154] PR from new branch to main --- .github/workflows/update_version.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index a5c4d23e63..3d2c20f31a 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -67,7 +67,8 @@ jobs: if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc') uses: repo-sync/pull-request@v2 with: + source_branch: '${{ env.NON_RC_VERSION }}' destination_branch: "main" - pr_title: "Make release ${{ env.VERSION }}" + pr_title: "Make release ${{ env.NON_RC_VERSION }}" pr_body: "**Check the [release workflow](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_workflow.md)" github_token: ${{ secrets.GITHUB_TOKEN }} From 84c16b2c20137ef9b471d3fbbcb6d0846f0c7172 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 23:27:38 -0400 Subject: [PATCH 059/154] Update CHANGELOG programmatically --- scripts/update_version.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/scripts/update_version.py b/scripts/update_version.py index e2d0f73fb5..f2782020a7 100644 --- a/scripts/update_version.py +++ b/scripts/update_version.py @@ -5,6 +5,8 @@ import json import os import re +from datetime import date +from dateutil.relativedelta import relativedelta import pybamm @@ -15,6 +17,8 @@ def update_version(): Opens file and updates the version number """ release_version = os.getenv("VERSION")[1:] + last_day_of_month = date.today() + relativedelta(day=31) + # pybamm/version.py with open(os.path.join(pybamm.root_dir(), "pybamm", "version.py"), "r+") as file: @@ -77,6 +81,21 @@ def update_version(): file.seek(0) file.write(replace_commit_id) + changelog_line1 = "# [Unreleased](https://github.com/pybamm-team/PyBaMM/)\n" + changelog_line2 = f"# [v{release_version}](https://github.com/pybamm-team/PyBaMM/tree/v{release_version}) - {last_day_of_month}\n\n" # noqa: E501 + + # CHANGELOG.md + with open(os.path.join(pybamm.root_dir(), "CHANGELOG.md"), "r+") as file: + output_list = file.readlines() + output_list[0] = changelog_line1 + if "rc0" in release_version: + output_list.insert(2, changelog_line2) + else: + output_list[2] = changelog_line2 + file.truncate(0) + file.seek(0) + file.writelines(output_list) + if __name__ == "__main__": update_version() From 1a0b4392a3539ebcd1c2f1ed237092ef237ef69e Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 23:37:20 -0400 Subject: [PATCH 060/154] Fix PR description --- .github/workflows/update_version.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index 3d2c20f31a..addb683db1 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -70,5 +70,5 @@ jobs: source_branch: '${{ env.NON_RC_VERSION }}' destination_branch: "main" pr_title: "Make release ${{ env.NON_RC_VERSION }}" - pr_body: "**Check the [release workflow](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_workflow.md)" + pr_body: "**Check the [release workflow](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_workflow.md)**" github_token: ${{ secrets.GITHUB_TOKEN }} From afe5e449b6674e362758c49ffbcdfcba957ba50d Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sat, 19 Aug 2023 23:52:01 -0400 Subject: [PATCH 061/154] CHANGELOG is now updated automatically --- .github/release_workflow.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/release_workflow.md b/.github/release_workflow.md index 5d75e3f57e..04f0667773 100644 --- a/.github/release_workflow.md +++ b/.github/release_workflow.md @@ -11,6 +11,7 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an - `CITATION.cff` - `vcpkg.json` - `docs/_static/versions.json` + - `CHANGELOG.md` These changes will be automatically pushed to a new branch `YY.MM`. @@ -33,6 +34,7 @@ If a new release candidate is required after the release of `rc0` - - `CITATION.cff` - `vcpkg.json` - `docs/_static/versions.json` + - `CHANGELOG.md` These changes will be automatically pushed to the existing branch `YY.MM`. @@ -53,6 +55,7 @@ Once satisfied with the release candidates - - `CITATION.cff` - `vcpkg.json` - `docs/_static/versions.json` + - `CHANGELOG.md` These changes will be automatically pushed to the existing branch `YY.MM`. @@ -67,6 +70,5 @@ Once satisfied with the release candidates - Some other essential things to check throughout the release process - - If updating our custom vcpkg registory entries [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) or [pybamm-team/casadi-vcpkg-registry](https://github.com/pybamm-team/casadi-vcpkg-registry) (used to build Windows wheels), make sure to update the baseline of the registories in vcpkg-configuration.json to the latest commit id. -- Update `CHANGELOG.md` with a summary of the release - Update jax and jaxlib to the latest version in `pybamm.util` and `setup.py`, fixing any bugs that arise - Make sure the URLs in `docs/_static/versions.json` are valid From 94009b6db83937c4e97f104367b24e554b9edade Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sun, 20 Aug 2023 14:58:56 -0400 Subject: [PATCH 062/154] Simplify cron job syntax Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> --- .github/workflows/release_reminder.yml | 4 ++-- .github/workflows/update_version.yml | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/release_reminder.yml b/.github/workflows/release_reminder.yml index ac2f1afa29..204ace0b68 100644 --- a/.github/workflows/release_reminder.yml +++ b/.github/workflows/release_reminder.yml @@ -2,8 +2,8 @@ name: Create a release reminder on: schedule: - # Run at 10 am UTC on 1st and 28th every 4th month from January through December - - cron: 0 10 1-28/27 1-12/4 * + # Run at 10 am UTC on days-of-month 1 and 28 in January, May, and September. + - cron: "0 10 1,28 1,5,9 *" permissions: contents: read diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index addb683db1..0b1657af12 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -7,8 +7,8 @@ on: description: 'Leave blank for an actual release or "rc1", "rc2", ..., for release candidates."' default: "" schedule: - # Run at 10 am UTC on 1st every 4th month from January through December - - cron: 0 10 1 1-12/4 * + # Run at 10 am UTC on day-of-month 1 in January, May, and September. + - cron: "0 10 1 1,5,9 *" jobs: update-version: From 6d3697474137db7d1c7b37130d0ed4d7b9488dbe Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sun, 20 Aug 2023 14:59:32 -0400 Subject: [PATCH 063/154] Run only in pybamm-team --- .github/workflows/update_version.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index 0b1657af12..2a3a1d24ab 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -13,7 +13,7 @@ on: jobs: update-version: # This workflow is only of value to PyBaMM and would always be skipped in forks - # if: github.repository_owner == 'pybamm-team' + if: github.repository_owner == 'pybamm-team' runs-on: ubuntu-latest steps: From 3b4c5073ed7eb3c2bd3001f41956e3d0103f83c4 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Sun, 20 Aug 2023 15:00:56 -0400 Subject: [PATCH 064/154] No docs for rc versions --- scripts/update_version.py | 35 ++++++++++++++++++----------------- 1 file changed, 18 insertions(+), 17 deletions(-) diff --git a/scripts/update_version.py b/scripts/update_version.py index f2782020a7..fb9b15dd31 100644 --- a/scripts/update_version.py +++ b/scripts/update_version.py @@ -39,23 +39,24 @@ def update_version(): file.write(replace_version) # docs/source/_static/versions.json for readthedocs build - with open( - os.path.join(pybamm.root_dir(), "docs", "_static", "versions.json"), - "r+", - ) as file: - output = file.read() - json_data = json.loads(output) - json_data.insert( - 2, - { - "name": f"v{release_version}", - "version": f"{release_version}", - "url": f"https://docs.pybamm.org/en/v{release_version}/", - }, - ) - file.truncate(0) - file.seek(0) - file.write(json.dumps(json_data, indent=4)) + if "rc" not in release_version: + with open( + os.path.join(pybamm.root_dir(), "docs", "_static", "versions.json"), + "r+", + ) as file: + output = file.read() + json_data = json.loads(output) + json_data.insert( + 2, + { + "name": f"v{release_version}", + "version": f"{release_version}", + "url": f"https://docs.pybamm.org/en/v{release_version}/", + }, + ) + file.truncate(0) + file.seek(0) + file.write(json.dumps(json_data, indent=4)) # vcpkg.json with open(os.path.join(pybamm.root_dir(), "vcpkg.json"), "r+") as file: From d498a8b9cee93ba0bfa9ed71c154de288b8c8ab5 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 30 Aug 2023 10:31:26 +0100 Subject: [PATCH 065/154] #3298 heaviside shape --- pybamm/expression_tree/binary_operators.py | 7 +++++++ tests/unit/test_expression_tree/test_binary_operators.py | 6 ++++++ 2 files changed, 13 insertions(+) diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py index 6794d201af..8e209915a4 100644 --- a/pybamm/expression_tree/binary_operators.py +++ b/pybamm/expression_tree/binary_operators.py @@ -508,6 +508,13 @@ def _binary_jac(self, left_jac, right_jac): # need to worry about shape return pybamm.Scalar(0) + def _evaluate_for_shape(self): + """ + Returns the scalar 'NaN' to represent the shape of a Heaviside. + See :meth:`pybamm.Symbol.evaluate_for_shape()` + """ + return np.nan + class EqualHeaviside(_Heaviside): """A heaviside function with equality (return 1 when left = right)""" diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py index 4e4bbb80cc..6acd7c41b0 100644 --- a/tests/unit/test_expression_tree/test_binary_operators.py +++ b/tests/unit/test_expression_tree/test_binary_operators.py @@ -324,6 +324,12 @@ def test_heaviside(self): self.assertEqual(1 < b + 2, -1 < b) self.assertEqual(b + 1 > 2, b > 1) + # expression with a subtract + expr = 2 * (b < 1) - (b > 3) + self.assertEqual(expr.evaluate(y=np.array([0])), 2) + self.assertEqual(expr.evaluate(y=np.array([2])), 0) + self.assertEqual(expr.evaluate(y=np.array([4])), -1) + def test_equality(self): a = pybamm.Scalar(1) b = pybamm.StateVector(slice(0, 1)) From d1cd97748840de61094558e66a248ff0b8e21ff0 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 31 Aug 2023 11:47:30 +0100 Subject: [PATCH 066/154] relax option check --- .../examples/notebooks/models/MSMR.ipynb | 44 +++++++++---------- .../full_battery_models/base_battery_model.py | 9 +++- .../interface/kinetics/msmr_butler_volmer.py | 20 ++++----- pybamm/parameters/lithium_ion_parameters.py | 32 ++++++++------ 4 files changed, 56 insertions(+), 49 deletions(-) diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 11698cd93d..4ca02f908d 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -190,7 +190,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -209,9 +209,7 @@ "U_n = pybamm.linspace(0.05, 1.1, 1000)\n", "U_p = pybamm.linspace(2.8, 4.4, 1000)\n", "\n", - "# get maximum concentrations, reference electrolyte concentration and temperature\n", - "c_n_max = param_n.c_max\n", - "c_p_max = param_p.c_max\n", + "# get reference electrolyte concentration and temperature\n", "c_e = param.c_e_init\n", "T = param.T_init\n", "\n", @@ -254,14 +252,9 @@ "ax[1, 1].legend()\n", "\n", "# exchange current density vs potential\n", - "# note: when solving pybamm sets uses a tolerances on the arguments of the exchange\n", - "# current density functions to avoid numerical issues when the surface stoichiometry\n", - "# is close to 0 or 1. This means that the exchange current density functions are not\n", - "# evaluated at exactly x = 0 or x = 1. For plotting we set this tolerance to 0.\n", - "pybamm.settings.tolerances[\"j0__c_s\"] = 0\n", "for i in range(6):\n", " xj = param_n.x_j(U_n, i)\n", - " j0 = param_n.j0_j(c_e, xj * c_n_max, T, i)\n", + " j0 = param_n.j0_j(c_e, U_n, T, i)\n", " ax[2, 0].plot(\n", " parameter_values.evaluate(x_n),\n", " parameter_values.evaluate(j0),\n", @@ -273,7 +266,7 @@ "ax[2, 0].legend()\n", "for i in range(4):\n", " xj = param_p.x_j(U_p, i)\n", - " j0 = param_p.j0_j(c_e, xj * c_p_max, T, i)\n", + " j0 = param_p.j0_j(c_e, U_p, T, i)\n", " ax[2, 1].plot(\n", " parameter_values.evaluate(x_p),\n", " parameter_values.evaluate(j0),\n", @@ -306,14 +299,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 274.914 and h = 6.23205e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 274.889 and h = 6.85471e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 274.961 and h = 1.52718e-09, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 274.979 and h = 1.05441e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -354,12 +347,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b8335b7e2d4f4ffb85e1c6b3c5134b1a", + "model_id": "35f60c3770484dc4b96a92c8ff5bb504", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.09113762530448, step=0.0609113762530448), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106518969745211, step=0.06106518969745211)…" ] }, "metadata": {}, @@ -368,7 +361,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -408,12 +401,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e324ec2ac34e471280ec259ede8e0b4c", + "model_id": "9063a577a8df454e823a702c1e1e18dc", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.09113762530448, step=0.0609113762530448), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106518969745211, step=0.06106518969745211)…" ] }, "metadata": {}, @@ -422,7 +415,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -461,7 +454,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -470,7 +463,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -545,6 +538,13 @@ "pybamm.print_citations()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 5a46e13d85..ad6a3d6cee 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -422,11 +422,18 @@ def __init__(self, extra_options): # If any of "open-circuit potential", "particle" or "intercalation kinetics" is # "MSMR" then all of them must be "MSMR". + # Note: this check is currently performed on full cells, but is loosened for + # half-cells where you must pass a tuple of options to only set MSMR models in + # the working electrode msmr_check_list = [ options[opt] == "MSMR" for opt in ["open-circuit potential", "particle", "intercalation kinetics"] ] - if any(msmr_check_list) and not all(msmr_check_list): + if ( + options["working electrode"] == "both" + and any(msmr_check_list) + and not all(msmr_check_list) + ): raise pybamm.OptionError( "If any of 'open-circuit potential', 'particle' or " "'intercalation kinetics' is 'MSMR' then all of them must be 'MSMR'" diff --git a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py index 5550404c09..6a4b9f5023 100644 --- a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py +++ b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py @@ -47,7 +47,6 @@ def _get_exchange_current_density_by_reaction(self, variables, index): """ phase_param = self.phase_param domain, Domain = self.domain_Domain - phase_name = self.phase_name c_e = variables[f"{Domain} electrolyte concentration [mol.m-3]"] T = variables[f"{Domain} electrode temperature [K]"] @@ -57,18 +56,17 @@ def _get_exchange_current_density_by_reaction(self, variables, index): # of c_s_surf that depends on particle size. domain_options = getattr(self.options, domain) if domain_options["particle size"] == "distribution": - c_s_surf = variables[ - f"{Domain} {phase_name}particle surface " - "concentration distribution [mol.m-3]" + ocp = variables[ + f"{Domain} electrode open-circuit potential distribution [V]" ] # If all variables were broadcast (in "x"), take only the orphans, # then re-broadcast c_e if ( - isinstance(c_s_surf, pybamm.Broadcast) + isinstance(ocp, pybamm.Broadcast) and isinstance(c_e, pybamm.Broadcast) and isinstance(T, pybamm.Broadcast) ): - c_s_surf = c_s_surf.orphans[0] + ocp = ocp.orphans[0] c_e = c_e.orphans[0] T = T.orphans[0] @@ -80,20 +78,18 @@ def _get_exchange_current_density_by_reaction(self, variables, index): T = pybamm.PrimaryBroadcast(T, [f"{domain} particle size"]) else: - c_s_surf = variables[ - f"{Domain} {phase_name}particle surface concentration [mol.m-3]" - ] + ocp = variables[f"{Domain} electrode open-circuit potential [V]"] # If all variables were broadcast, take only the orphans if ( - isinstance(c_s_surf, pybamm.Broadcast) + isinstance(ocp, pybamm.Broadcast) and isinstance(c_e, pybamm.Broadcast) and isinstance(T, pybamm.Broadcast) ): - c_s_surf = c_s_surf.orphans[0] + ocp = ocp.orphans[0] c_e = c_e.orphans[0] T = T.orphans[0] - j0 = phase_param.j0_j(c_e, c_s_surf, T, index) + j0 = phase_param.j0_j(c_e, ocp, T, index) return j0 diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index d83e030f41..2f8d51ac5a 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -679,33 +679,37 @@ def dxdU_j(self, U, index): dxjdU = -(f / wj) * (Xj * e) / (1 + e) ** 2 return dxjdU - def j0_j(self, c_e, c_s_j_surf, T, index): + def j0_j(self, c_e, U, T, index): "Exchange-current density index by reaction j [A.m-2]" + domain = self.domain + d = domain[0] + tol = pybamm.settings.tolerances["j0__c_e"] c_e = pybamm.maximum(c_e, tol) c_e_ref = self.main_param.c_e_init - tol = pybamm.settings.tolerances["j0__c_s"] - c_max = self.c_max - c_s_j_surf = pybamm.maximum( - pybamm.minimum(c_s_j_surf, (1 - tol) * c_max), tol * c_max - ) + xj = self.x_j(U, index) + # xj = pybamm.maximum(pybamm.minimum(xj, (1 - tol)), tol) - domain = self.domain - d = domain[0] + f = self.main_param.F / (self.main_param.R * T) wj = self.w_j(index) - Xj = self.X_j(index) + self.X_j(index) aj = self.alpha_bv_j(index) - xj = c_s_j_surf / c_max - j0_ref_j = pybamm.FunctionParameter( f"j0_ref_{d}_{index}", {"Temperature [K]": T} ) - # Use tolerances to avoid division by zero in the Jacobian + # Equation 16, Baker et al 2018 + # j0_j = ( + # j0_ref_j + # * xj ** (wj * aj) + # * (Xj - xj) ** (wj * (1 - aj)) + # * (c_e / c_e_ref) ** (1 - aj) + # ) + # Reformulate in terms of potential to avoid singularity as x_j approaches X_j j0_j = ( j0_ref_j - * xj ** (wj * aj) - * (pybamm.maximum(Xj - xj, tol)) ** (wj * (1 - aj)) + * xj**wj + * pybamm.exp(f * (1 - aj) * (U - self.U0_j(index))) * (c_e / c_e_ref) ** (1 - aj) ) return j0_j From 28da1b4590ebd6b85afaa9a5276c26d5b05f210a Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Sun, 3 Sep 2023 22:04:48 +0100 Subject: [PATCH 067/154] fix heaviside shape --- pybamm/expression_tree/binary_operators.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py index 8e209915a4..749384e9bc 100644 --- a/pybamm/expression_tree/binary_operators.py +++ b/pybamm/expression_tree/binary_operators.py @@ -510,10 +510,14 @@ def _binary_jac(self, left_jac, right_jac): def _evaluate_for_shape(self): """ - Returns the scalar 'NaN' to represent the shape of a Heaviside. - See :meth:`pybamm.Symbol.evaluate_for_shape()` + Returns an array of NaNs of the correct shape. + See :meth:`pybamm.Symbol.evaluate_for_shape()`. """ - return np.nan + left = self.children[0].evaluate_for_shape() + right = self.children[1].evaluate_for_shape() + # _binary_evaluate will return an array of bools, so we multiply by NaN to get + # an array of NaNs + return self._binary_evaluate(left, right) * np.nan class EqualHeaviside(_Heaviside): From 7edd403134b7c6a244b9eff7da01097f2ce25e5d Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Sun, 3 Sep 2023 22:07:45 +0100 Subject: [PATCH 068/154] #3298 fix domain and shape --- pybamm/expression_tree/binary_operators.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py index 8e209915a4..749384e9bc 100644 --- a/pybamm/expression_tree/binary_operators.py +++ b/pybamm/expression_tree/binary_operators.py @@ -510,10 +510,14 @@ def _binary_jac(self, left_jac, right_jac): def _evaluate_for_shape(self): """ - Returns the scalar 'NaN' to represent the shape of a Heaviside. - See :meth:`pybamm.Symbol.evaluate_for_shape()` + Returns an array of NaNs of the correct shape. + See :meth:`pybamm.Symbol.evaluate_for_shape()`. """ - return np.nan + left = self.children[0].evaluate_for_shape() + right = self.children[1].evaluate_for_shape() + # _binary_evaluate will return an array of bools, so we multiply by NaN to get + # an array of NaNs + return self._binary_evaluate(left, right) * np.nan class EqualHeaviside(_Heaviside): From 460e540a6551dbf083393954a9457665da59b0c4 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Sun, 3 Sep 2023 22:10:29 +0100 Subject: [PATCH 069/154] #3298 changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5b42fbb69c..2ec5e39b8b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,7 @@ ## Bug fixes +- Fixed a bug with `_Heaviside._evaluate_for_shape` which meant some expressions involving heaviside function and subtractions did not work ([#3306](https://github.com/pybamm-team/PyBaMM/pull/3306)) - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042)) - Fixed a bug where the "basic" lithium-ion models gave incorrect results when using nonlinear particle diffusivity ([#3207](https://github.com/pybamm-team/PyBaMM/pull/3207)) - Particle size distributions now work with SPMe and NewmanTobias models ([#3207](https://github.com/pybamm-team/PyBaMM/pull/3207)) From 845ad84ccde1e4f73f6ab3cda74290835d236f11 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 4 Sep 2023 19:32:49 +0000 Subject: [PATCH 070/154] Bump actions/checkout from 3 to 4 Bumps [actions/checkout](https://github.com/actions/checkout) from 3 to 4. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v3...v4) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/benchmark_on_push.yml | 2 +- .github/workflows/create_release.yml | 2 +- .github/workflows/lychee_url_checker.yml | 2 +- .github/workflows/periodic_benchmarks.yml | 4 ++-- .github/workflows/publish_pypi.yml | 6 +++--- .github/workflows/run_benchmarks_over_history.yml | 4 ++-- .github/workflows/run_periodic_tests.yml | 6 +++--- .github/workflows/test_on_push.yml | 12 ++++++------ .github/workflows/update_license.yml | 4 ++-- .github/workflows/update_version.yml | 2 +- .github/workflows/work_precision_sets.yml | 2 +- 11 files changed, 23 insertions(+), 23 deletions(-) diff --git a/.github/workflows/benchmark_on_push.yml b/.github/workflows/benchmark_on_push.yml index 07747af58c..126f4d3a09 100644 --- a/.github/workflows/benchmark_on_push.yml +++ b/.github/workflows/benchmark_on_push.yml @@ -13,7 +13,7 @@ jobs: benchmarks: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python 3.8 uses: actions/setup-python@v4 with: diff --git a/.github/workflows/create_release.yml b/.github/workflows/create_release.yml index 6280387e53..8d029d3bc5 100644 --- a/.github/workflows/create_release.yml +++ b/.github/workflows/create_release.yml @@ -17,7 +17,7 @@ jobs: python-version: [3.8] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Get current date run: | diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml index a6735d2806..d727ca0784 100644 --- a/.github/workflows/lychee_url_checker.yml +++ b/.github/workflows/lychee_url_checker.yml @@ -24,7 +24,7 @@ jobs: restore-keys: cache-lychee- # check URLs with Lychee - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 # use stable version for now to avoid breaking changes - name: Lychee URL checker diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml index 239f5fa5bb..ce0ad37cd2 100644 --- a/.github/workflows/periodic_benchmarks.yml +++ b/.github/workflows/periodic_benchmarks.yml @@ -19,7 +19,7 @@ jobs: benchmarks: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python 3.8 uses: actions/setup-python@v4 with: @@ -58,7 +58,7 @@ jobs: - name: Install asv run: pip install asv - name: Checkout pybamm-bench repo - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: repository: pybamm-team/pybamm-bench token: ${{ secrets.BENCH_PAT }} diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index 6d89da1387..54dd70d5a7 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -19,7 +19,7 @@ jobs: name: Build wheels on windows-latest runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: 3.8 @@ -74,7 +74,7 @@ jobs: matrix: os: [ubuntu-latest, macos-latest] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: 3.8 @@ -135,7 +135,7 @@ jobs: python-version: [3.8] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml index 9cdcc0b2c0..6752e38800 100644 --- a/.github/workflows/run_benchmarks_over_history.yml +++ b/.github/workflows/run_benchmarks_over_history.yml @@ -22,7 +22,7 @@ jobs: benchmarks: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python 3.8 uses: actions/setup-python@v4 with: @@ -59,7 +59,7 @@ jobs: - name: Install asv run: pip install asv - name: Checkout pybamm-bench repo - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: repository: pybamm-team/pybamm-bench token: ${{ secrets.BENCH_PAT }} diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml index f70a748800..663eb5cfca 100644 --- a/.github/workflows/run_periodic_tests.yml +++ b/.github/workflows/run_periodic_tests.yml @@ -32,7 +32,7 @@ jobs: if: ${{ needs.pre_job.outputs.should_skip != 'true' }} runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Setup python uses: actions/setup-python@v4 with: @@ -53,7 +53,7 @@ jobs: python-version: ["3.8", "3.9", "3.10", "3.11"] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 with: @@ -127,7 +127,7 @@ jobs: python-version: ["3.8", "3.9", "3.10", "3.11"] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Install python & create virtualenv shell: bash run: | diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml index 2fd4c92b2e..839d53306f 100644 --- a/.github/workflows/test_on_push.yml +++ b/.github/workflows/test_on_push.yml @@ -16,7 +16,7 @@ jobs: style: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Setup Python uses: actions/setup-python@v4 with: @@ -43,7 +43,7 @@ jobs: steps: - name: Check out PyBaMM repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 # Install and cache apt packages - name: Install Linux system dependencies @@ -123,7 +123,7 @@ jobs: steps: - name: Check out PyBaMM repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 # Install and cache apt packages - name: Install Linux system dependencies @@ -186,7 +186,7 @@ jobs: steps: - name: Check out PyBaMM repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 # Install and cache apt packages - name: Install Linux system dependencies @@ -266,7 +266,7 @@ jobs: steps: - name: Check out PyBaMM repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 # Install and cache apt packages - name: Install Linux system dependencies @@ -327,7 +327,7 @@ jobs: steps: - name: Check out PyBaMM repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 # Install and cache apt packages - name: Install Linux system dependencies diff --git a/.github/workflows/update_license.yml b/.github/workflows/update_license.yml index 718b24e6b7..4712b5dd94 100644 --- a/.github/workflows/update_license.yml +++ b/.github/workflows/update_license.yml @@ -13,7 +13,7 @@ jobs: if: github.repository_owner == 'pybamm-team' runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Update year in license @@ -28,7 +28,7 @@ jobs: needs: license runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Update year in docs diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml index c205603c88..7488c40aa2 100644 --- a/.github/workflows/update_version.yml +++ b/.github/workflows/update_version.yml @@ -13,7 +13,7 @@ jobs: python-version: [3.8] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 diff --git a/.github/workflows/work_precision_sets.yml b/.github/workflows/work_precision_sets.yml index 5be4d079f8..87eb068947 100644 --- a/.github/workflows/work_precision_sets.yml +++ b/.github/workflows/work_precision_sets.yml @@ -9,7 +9,7 @@ jobs: benchmarks_on_release: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Setup python uses: actions/setup-python@v4 with: From d86e4a0efd13453bdfd6b8d2270ea8b1b0ed8df9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 5 Sep 2023 03:37:08 +0000 Subject: [PATCH 071/154] chore: update pre-commit hooks MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.284 → v0.0.287](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.284...v0.0.287) - [github.com/adamchainz/blacken-docs: 1.15.0 → 1.16.0](https://github.com/adamchainz/blacken-docs/compare/1.15.0...1.16.0) --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6a3891ec28..3ea9b14dd8 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ ci: repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.0.284" + rev: "v0.0.287" hooks: - id: ruff args: [--fix, --ignore=E741, --exclude=__init__.py] @@ -17,7 +17,7 @@ repos: args: ["--fix","--ignore=E501,E402"] - repo: https://github.com/adamchainz/blacken-docs - rev: "1.15.0" + rev: "1.16.0" hooks: - id: blacken-docs additional_dependencies: [black==22.12.0] From 559017eea419fd461939c0d065ef5f22ba4f3ca2 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 5 Sep 2023 03:37:26 +0000 Subject: [PATCH 072/154] style: pre-commit fixes --- README.md | 14 +++++++++----- benchmarks/README.md | 6 +++--- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 72d39b927d..1b54201f5b 100644 --- a/README.md +++ b/README.md @@ -54,6 +54,7 @@ The easiest way to use PyBaMM is to run a 1C constant-current discharge with a m ```python3 import pybamm + model = pybamm.lithium_ion.DFN() # Doyle-Fuller-Newman model sim = pybamm.Simulation(model) sim.solve([0, 3600]) # solve for 1 hour @@ -64,13 +65,16 @@ or simulate an experiment such as a constant-current discharge followed by a con ```python3 import pybamm + experiment = pybamm.Experiment( [ - ("Discharge at C/10 for 10 hours or until 3.3 V", - "Rest for 1 hour", - "Charge at 1 A until 4.1 V", - "Hold at 4.1 V until 50 mA", - "Rest for 1 hour") + ( + "Discharge at C/10 for 10 hours or until 3.3 V", + "Rest for 1 hour", + "Charge at 1 A until 4.1 V", + "Hold at 4.1 V until 50 mA", + "Rest for 1 hour", + ) ] * 3, ) diff --git a/benchmarks/README.md b/benchmarks/README.md index 893fdf2f5a..ac7ffbdfb3 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -63,8 +63,8 @@ Note that benchmark functions _must_ start with the prefix `time_`, for instance ```python3 def time_solve_SPM_ScipySolver(self): - solver = pb.ScipySolver() - solver.solve(self.model, [0, 3600]) + solver = pb.ScipySolver() + solver.solve(self.model, [0, 3600]) ``` In the case where some setup is necessary, but should not be timed, a `setup` function @@ -76,7 +76,7 @@ class TimeSPM: model = pb.lithium_ion.SPM() geometry = model.default_geometry - # ... + # ... self.model = model From d1975758a721bfa8f18d43d878fab1d94a228886 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Tue, 5 Sep 2023 12:44:25 +0530 Subject: [PATCH 073/154] Fix pre-commit --- pybamm/geometry/battery_geometry.py | 2 +- pybamm/models/full_battery_models/base_battery_model.py | 2 +- pybamm/models/submodels/base_submodel.py | 2 +- pybamm/parameters/base_parameters.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pybamm/geometry/battery_geometry.py b/pybamm/geometry/battery_geometry.py index 92ecaab218..8c06fbcd68 100644 --- a/pybamm/geometry/battery_geometry.py +++ b/pybamm/geometry/battery_geometry.py @@ -28,7 +28,7 @@ def battery_geometry( A geometry class for the battery """ - if options is None or type(options) == dict: + if options is None or isinstance(options, dict): options = pybamm.BatteryModelOptions(options) geo = pybamm.GeometricParameters(options) L_n = geo.n.L diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index afd1693f75..d74f886cc6 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -831,7 +831,7 @@ def options(self, extra_options): # if extra_options is a dict then process it into a BatteryModelOptions # this does not catch cases that subclass the dict type # so other submodels can pass in their own options class if needed - if extra_options is None or type(extra_options) == dict: + if extra_options is None or isinstance(extra_options, dict): options = BatteryModelOptions(extra_options) else: options = extra_options diff --git a/pybamm/models/submodels/base_submodel.py b/pybamm/models/submodels/base_submodel.py index 90a2d2bb97..51b82c8a9d 100644 --- a/pybamm/models/submodels/base_submodel.py +++ b/pybamm/models/submodels/base_submodel.py @@ -73,7 +73,7 @@ def __init__( self.external = external - if options is None or type(options) == dict: + if options is None or isinstance(options, dict): options = pybamm.BatteryModelOptions(options) self.options = options diff --git a/pybamm/parameters/base_parameters.py b/pybamm/parameters/base_parameters.py index 5d1efef9f7..ef3aef367e 100644 --- a/pybamm/parameters/base_parameters.py +++ b/pybamm/parameters/base_parameters.py @@ -62,7 +62,7 @@ def options(self): @options.setter def options(self, extra_options): - if extra_options is None or type(extra_options) == dict: + if extra_options is None or isinstance(extra_options, dict): self._options = pybamm.BatteryModelOptions(extra_options) else: self._options = extra_options From b7ee3ca0c6f592791f5e3d0229fb78cc81962162 Mon Sep 17 00:00:00 2001 From: Arjun Date: Tue, 5 Sep 2023 16:37:42 +0530 Subject: [PATCH 074/154] Pin `asv==0.5.1` to fix failing benchmarks (#3310) * Pin `asv==0.5.1` to fix failing benchmarks * Add comment for pinning `asv==0.5.1` --- .github/workflows/benchmark_on_push.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/benchmark_on_push.yml b/.github/workflows/benchmark_on_push.yml index 126f4d3a09..8be4af8741 100644 --- a/.github/workflows/benchmark_on_push.yml +++ b/.github/workflows/benchmark_on_push.yml @@ -23,8 +23,9 @@ jobs: sudo apt-get update sudo apt install gfortran gcc libopenblas-dev - name: Install python dependencies + # Pin asv==0.5.1 to fix failing benchmarks. Related to https://github.com/airspeed-velocity/asv/issues/1323 run: | - python -m pip install --upgrade pip wheel setuptools virtualenv asv wget cmake casadi numpy + python -m pip install --upgrade pip wheel setuptools virtualenv asv==0.5.1 wget cmake casadi numpy - name: Install SuiteSparse and Sundials run: python scripts/install_KLU_Sundials.py - name: Fetch base branch From f73621866f72d25b8d64cb7865137b5224c09296 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Wed, 6 Sep 2023 00:02:54 +0530 Subject: [PATCH 075/154] #3309 Fix doctests --- docs/source/user_guide/installation/windows-wsl.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/user_guide/installation/windows-wsl.rst b/docs/source/user_guide/installation/windows-wsl.rst index d08545edc0..6453c92211 100644 --- a/docs/source/user_guide/installation/windows-wsl.rst +++ b/docs/source/user_guide/installation/windows-wsl.rst @@ -22,13 +22,13 @@ Get PyBaMM's Source Code sudo apt install git-core -3. Clone the PyBaMM repository:: +3. Clone the PyBaMM repository: .. code:: bash git clone https://github.com/pybamm-team/PyBaMM.git -4. Enter the PyBaMM Directory by running:: +4. Enter the PyBaMM Directory by running: .. code:: bash From a05a4616153b0c4eb638f54f4bf3154b2026c392 Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Thu, 7 Sep 2023 10:48:03 +0000 Subject: [PATCH 076/154] #3130 use model.y_slices rather than var.y_slice --- pybamm/solvers/solution.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 411341f887..9284aa942b 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -308,7 +308,8 @@ def check_ys_are_not_too_large(self): y = y[:, -1] if np.any(y > pybamm.settings.max_y_value): for var in [*model.rhs.keys(), *model.algebraic.keys()]: - y_var = y[model.variables[var.name].y_slices[0]] + first_index = model.y_slices[var][0] + y_var = y[first_index] if np.any(y_var > pybamm.settings.max_y_value): pybamm.logger.error( f"Solution for '{var}' exceeds the maximum allowed value " From c5bdce7e7414a988ef685710cfd71b1c50427902 Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Thu, 7 Sep 2023 10:48:48 +0000 Subject: [PATCH 077/154] #3130 use a more accurate name --- pybamm/solvers/solution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 9284aa942b..3da5673e34 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -308,8 +308,8 @@ def check_ys_are_not_too_large(self): y = y[:, -1] if np.any(y > pybamm.settings.max_y_value): for var in [*model.rhs.keys(), *model.algebraic.keys()]: - first_index = model.y_slices[var][0] - y_var = y[first_index] + slice = model.y_slices[var][0] + y_var = y[slice] if np.any(y_var > pybamm.settings.max_y_value): pybamm.logger.error( f"Solution for '{var}' exceeds the maximum allowed value " From 209ad4761ee3c70ab2a3b24642015753aab38a70 Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Thu, 7 Sep 2023 11:07:58 +0000 Subject: [PATCH 078/154] #3130 fix case where model isnt discretised --- pybamm/solvers/solution.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 3da5673e34..d6f8b03f3e 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -308,8 +308,18 @@ def check_ys_are_not_too_large(self): y = y[:, -1] if np.any(y > pybamm.settings.max_y_value): for var in [*model.rhs.keys(), *model.algebraic.keys()]: - slice = model.y_slices[var][0] - y_var = y[slice] + var = model.variables[var.name] + # find the statevector corresponding to this variable + statevector = None + for node in var.pre_order(): + if isinstance(node, pybamm.StateVector): + statevector = node + if statevector is None: + raise RuntimeError( + "Cannot find statevector corresponding to variable {}" + .format(var.name) + ) + y_var = y[statevector.y_slices[0]] if np.any(y_var > pybamm.settings.max_y_value): pybamm.logger.error( f"Solution for '{var}' exceeds the maximum allowed value " From 52ab34103514b6c68205c294508d99995d829dbb Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Thu, 7 Sep 2023 12:42:08 +0000 Subject: [PATCH 079/154] #3130 no cover for branch --- pybamm/solvers/solution.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index d6f8b03f3e..65c3e0b5a7 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -314,7 +314,9 @@ def check_ys_are_not_too_large(self): for node in var.pre_order(): if isinstance(node, pybamm.StateVector): statevector = node - if statevector is None: + + # there will always be a statevector, but just in case + if statevector is None: # pragma: no cover raise RuntimeError( "Cannot find statevector corresponding to variable {}" .format(var.name) From ccd53ebb0823189d5851485707a02945432ecf7c Mon Sep 17 00:00:00 2001 From: martinjrobins Date: Thu, 7 Sep 2023 14:59:10 +0000 Subject: [PATCH 080/154] #3130 add changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2ec5e39b8b..d6927f20bd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,7 @@ ## Bug fixes +- Fixed a bug that occured in `check_ys_are_not_too_large` when trying to reference `y-slice` where the referenced variable was not a `pybamm.StateVector` ([#3313](https://github.com/pybamm-team/PyBaMM/pull/3313) - Fixed a bug with `_Heaviside._evaluate_for_shape` which meant some expressions involving heaviside function and subtractions did not work ([#3306](https://github.com/pybamm-team/PyBaMM/pull/3306)) - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042)) - Fixed a bug where the "basic" lithium-ion models gave incorrect results when using nonlinear particle diffusivity ([#3207](https://github.com/pybamm-team/PyBaMM/pull/3207)) From dd6318e23fa5bea6e9148c5e54c940dbb6110159 Mon Sep 17 00:00:00 2001 From: kratman Date: Wed, 6 Sep 2023 19:43:47 -0400 Subject: [PATCH 081/154] Move note to Linux section --- .../installation/install-from-source.rst | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst index 787778fa01..ce5d7e0ca3 100644 --- a/docs/source/user_guide/installation/install-from-source.rst +++ b/docs/source/user_guide/installation/install-from-source.rst @@ -42,17 +42,17 @@ You can install the above with Where ``X`` is the version sub-number. + .. note:: + + On Windows, you can install ``graphviz`` using the `Chocolatey `_ package manager, or + follow the instructions on the `graphviz website `_. + .. tab:: MacOS .. code:: bash brew install python openblas gcc gfortran graphviz libomp -.. note:: - - On Windows, you can install ``graphviz`` using the `Chocolatey `_ package manager, or - follow the instructions on the `graphviz website `_. - Finally, we recommend using `Nox `_. You can install it with @@ -114,7 +114,7 @@ Using Nox (recommended) nox -s dev .. note:: - It is recommended to use ``--verbose`` or ``-v`` to see outputs of all commands run. + It is recommended to use ``--verbose`` or ``-v`` to see outputs of all commands run. This creates a virtual environment ``.nox/dev`` inside the ``PyBaMM/`` directory. It comes ready with PyBaMM and some useful development tools like `pre-commit `_ and `ruff `_. @@ -131,7 +131,7 @@ You can now activate the environment with .. code:: bash - .nox\dev\Scripts\activate.bat + .nox\dev\Scripts\activate.bat and run the tests to check your installation. From 9a99dea87ca455580daf0014b4c2ee7051b9ee47 Mon Sep 17 00:00:00 2001 From: kratman Date: Sun, 10 Sep 2023 13:48:53 -0400 Subject: [PATCH 082/154] Write files to a temporary directory --- ...torial-6-managing-simulation-outputs.ipynb | 163 +++++++++--------- 1 file changed, 85 insertions(+), 78 deletions(-) diff --git a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb index bea655f2b5..f4e5a92aaf 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -9,7 +8,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -23,17 +21,10 @@ "execution_count": 1, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -50,7 +41,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -58,7 +48,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -75,7 +64,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -93,7 +81,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -108,26 +95,26 @@ { "data": { "text/plain": [ - "array([3.77047806, 3.75305182, 3.74567027, 3.74038822, 3.73581196,\n", - " 3.73153391, 3.72742393, 3.72343929, 3.71956623, 3.71580184,\n", - " 3.71214621, 3.7086004 , 3.70516561, 3.70184253, 3.69863121,\n", - " 3.69553118, 3.69254137, 3.68966018, 3.68688562, 3.68421526,\n", - " 3.68164637, 3.67917591, 3.6768006 , 3.67451688, 3.67232094,\n", - " 3.67020869, 3.66817572, 3.66621717, 3.66432762, 3.6625009 ,\n", - " 3.66072974, 3.65900536, 3.65731692, 3.65565066, 3.65398895,\n", - " 3.65230898, 3.65058135, 3.6487688 , 3.64682546, 3.64469798,\n", - " 3.64232968, 3.63966973, 3.63668796, 3.63339303, 3.62984711,\n", - " 3.62616692, 3.6225045 , 3.61901241, 3.61580868, 3.6129572 ,\n", - " 3.61046847, 3.60831405, 3.60644483, 3.60480596, 3.60334607,\n", - " 3.60202167, 3.60079822, 3.5996495 , 3.59855637, 3.59750531,\n", - " 3.59648723, 3.59549638, 3.59452954, 3.59358541, 3.59266405,\n", - " 3.59176646, 3.59089417, 3.59004885, 3.58923192, 3.58844407,\n", - " 3.58768477, 3.58695179, 3.58624057, 3.58554372, 3.58485045,\n", - " 3.58414611, 3.58341187, 3.58262441, 3.58175587, 3.58077378,\n", - " 3.57964098, 3.57831538, 3.5767492 , 3.57488745, 3.57266504,\n", - " 3.5700019 , 3.56679523, 3.56290766, 3.5581495 , 3.55225276,\n", - " 3.54483361, 3.53533853, 3.52296795, 3.50656968, 3.48449277,\n", - " 3.45439366, 3.41299182, 3.35578871, 3.27680072, 3.16842636])" + "array([3.77048098, 3.75309871, 3.74569826, 3.74040906, 3.73582978,\n", + " 3.73155017, 3.72743983, 3.72345507, 3.71958265, 3.71581858,\n", + " 3.71216287, 3.70861698, 3.7051823 , 3.70185947, 3.69864846,\n", + " 3.69554865, 3.69255894, 3.6896778 , 3.68690322, 3.68423281,\n", + " 3.68166383, 3.67919326, 3.67681781, 3.67453394, 3.67233783,\n", + " 3.6702254 , 3.66819225, 3.66623353, 3.66434383, 3.66251699,\n", + " 3.66074577, 3.65902141, 3.65733311, 3.65566717, 3.65400602,\n", + " 3.65232696, 3.6506007 , 3.64879012, 3.64684952, 3.64472566,\n", + " 3.64236191, 3.63970731, 3.63673126, 3.63344172, 3.62989992,\n", + " 3.62622171, 3.6225587 , 3.61906361, 3.61585516, 3.61299814,\n", + " 3.61050386, 3.60834443, 3.606471 , 3.60482876, 3.60336628,\n", + " 3.60203993, 3.60081505, 3.59966528, 3.59857137, 3.59751973,\n", + " 3.59650118, 3.59550993, 3.59454272, 3.59359821, 3.59267644,\n", + " 3.59177838, 3.59090556, 3.59005965, 3.58924208, 3.58845355,\n", + " 3.58769359, 3.58695999, 3.58624826, 3.58555109, 3.58485777,\n", + " 3.58415379, 3.5834204 , 3.58263444, 3.58176818, 3.58078926,\n", + " 3.57966067, 3.57834049, 3.57678113, 3.57492782, 3.57271582,\n", + " 3.57006555, 3.566875 , 3.56300793, 3.5582764 , 3.55241508,\n", + " 3.54504405, 3.53561555, 3.52333845, 3.50707266, 3.48518447,\n", + " 3.45535426, 3.41433385, 3.35766635, 3.27941791, 3.17203869])" ] }, "execution_count": 4, @@ -140,7 +127,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -192,7 +178,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -207,7 +192,7 @@ { "data": { "text/plain": [ - "array([3.72947892, 3.7086004 , 3.67810702, 3.65400557])" + "array([3.729495 , 3.70861698, 3.67812431, 3.65402263])" ] }, "execution_count": 6, @@ -220,7 +205,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -228,11 +212,17 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "In some cases simulations might take a long time to run so it is advisable to save in your computer so it can be analysed later without re-running the simulation. You can save the whole simulation doing:" + "In some cases simulations might take a long time to run, so it is advisable to save in your computer. The output can be analysed later without re-running the simulation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output files should be saved to a working directory where they can be recovered at a later time. Since this is only a tutorial, we will save the files to a temporary folder that will be discarded at the end." ] }, { @@ -241,15 +231,19 @@ "metadata": {}, "outputs": [], "source": [ - "sim.save(\"SPMe.pkl\")" + "import os\n", + "from tempfile import TemporaryDirectory\n", + "temp_folder = TemporaryDirectory()\n", + "\n", + "def temp_path_to(name):\n", + " return os.path.join(temp_folder.name, name)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "If you now check the root directory of your notebooks you will notice that a new file called `\"SPMe.pkl\"` has appeared. We can load the stored simulation doing" + " You can save the whole simulation doing:" ] }, { @@ -258,26 +252,41 @@ "metadata": {}, "outputs": [], "source": [ - "sim2 = pybamm.load(\"SPMe.pkl\")" + "sim.save(temp_path_to(\"SPMe.pkl\"))" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "which allows the same manipulation as the original simulation would allow" + "If you now check the root directory of your notebooks you will notice that a new file called `\"SPMe.pkl\"` has appeared. We can load the stored simulation doing" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [], + "source": [ + "sim2 = pybamm.load(temp_path_to(\"SPMe.pkl\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which allows the same manipulation as the original simulation would allow" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80a9bcfaa1264a6f80be4c12906f491e", + "model_id": "8f1fa742280c4e5d8f00ad80e65695ce", "version_major": 2, "version_minor": 0 }, @@ -287,6 +296,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -294,7 +313,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -303,16 +321,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "sol = sim.solution\n", - "sol.save(\"SPMe_sol.pkl\")" + "sol.save(temp_path_to(\"SPMe_sol.pkl\"))" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -321,13 +338,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fc7b0729d6c40fe9e4f5921bb12b57d", + "model_id": "cc70e659f14e47ed88a6f4753e824b56", "version_major": 2, "version_minor": 0 }, @@ -341,21 +358,20 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sol2 = pybamm.load(\"SPMe_sol.pkl\")\n", + "sol2 = pybamm.load(temp_path_to(\"SPMe_sol.pkl\"))\n", "pybamm.dynamic_plot(sol2)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -364,15 +380,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "sol.save_data(\"sol_data.pkl\", [\"Current [A]\", \"Voltage [V]\"])" + "sol.save_data(temp_path_to(\"sol_data.pkl\"), [\"Current [A]\", \"Voltage [V]\"])" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -381,18 +396,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "sol.save_data(\"sol_data.csv\", [\"Current [A]\", \"Voltage [V]\"], to_format=\"csv\")\n", + "sol.save_data(temp_path_to(\"sol_data.csv\"), [\"Current [A]\", \"Voltage [V]\"], to_format=\"csv\")\n", "# matlab needs names without spaces\n", - "sol.save_data(\"sol_data.mat\", [\"Current [A]\", \"Voltage [V]\"], to_format=\"matlab\",\n", + "sol.save_data(temp_path_to(\"sol_data.mat\"), [\"Current [A]\", \"Voltage [V]\"], to_format=\"matlab\",\n", " short_names={\"Current [A]\": \"I\", \"Voltage [V]\": \"V\"})" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -400,29 +414,22 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Before finishing we will remove the data files we saved so that we leave the directory as we found it" + "Since the data is no longer needed, we will remove the files we saved." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "os.remove(\"SPMe.pkl\")\n", - "os.remove(\"SPMe_sol.pkl\")\n", - "os.remove(\"sol_data.pkl\")\n", - "os.remove(\"sol_data.csv\")\n", - "os.remove(\"sol_data.mat\")" + "temp_folder.cleanup()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -433,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -443,7 +450,7 @@ "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n", + "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } @@ -455,7 +462,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.13 ('python39-pybamm')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -469,7 +476,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.18" }, "toc": { "base_numbering": 1, From e4b046ad7ff7c14a57e8e1e506a9e0abfc883623 Mon Sep 17 00:00:00 2001 From: kratman Date: Sun, 10 Sep 2023 14:13:46 -0400 Subject: [PATCH 083/154] Fixing some grammar --- ...torial-6-managing-simulation-outputs.ipynb | 61 +++++++++++++------ 1 file changed, 44 insertions(+), 17 deletions(-) diff --git a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb index f4e5a92aaf..4ec423db82 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb @@ -24,7 +24,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -215,14 +215,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In some cases simulations might take a long time to run, so it is advisable to save in your computer. The output can be analysed later without re-running the simulation." + "In some cases simulations might take a long time to run, so it is advisable to save files to your computer. The output can be analysed later without re-running the simulation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The output files should be saved to a working directory where they can be recovered at a later time. Since this is only a tutorial, we will save the files to a temporary folder that will be discarded at the end." + "The output files should be saved to a working directory outside the PyBaMM project. Since this is only a tutorial, we will save the files to a temporary folder that can be discarded at the end." ] }, { @@ -259,13 +259,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you now check the root directory of your notebooks you will notice that a new file called `\"SPMe.pkl\"` has appeared. We can load the stored simulation doing" + "If you now check the output directory you will notice that a new file called `\"SPMe.pkl\"` has appeared." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['SPMe.pkl']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.listdir(temp_folder.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can load the stored simulation doing" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "sim2 = pybamm.load(temp_path_to(\"SPMe.pkl\"))" @@ -280,13 +307,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f1fa742280c4e5d8f00ad80e65695ce", + "model_id": "7ea56723de29416895d03d7c5d867c26", "version_major": 2, "version_minor": 0 }, @@ -300,10 +327,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -321,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -338,13 +365,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc70e659f14e47ed88a6f4753e824b56", + "model_id": "44804d087e41415ea60b63324ccf7bff", "version_major": 2, "version_minor": 0 }, @@ -358,10 +385,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -380,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -422,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -440,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { From 1a117d2e64355ba7fa7a2efca95ed58a2e04e495 Mon Sep 17 00:00:00 2001 From: kratman Date: Sun, 10 Sep 2023 14:27:20 -0400 Subject: [PATCH 084/154] Fix a web link --- docs/source/user_guide/installation/windows.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst index 20e4129709..6ff48293bd 100644 --- a/docs/source/user_guide/installation/windows.rst +++ b/docs/source/user_guide/installation/windows.rst @@ -82,4 +82,4 @@ Installation using WSL If you want to install the optional PyBaMM solvers, you have to use the Windows Subsystem for Linux (WSL). You can find the installation -instructions `here `__. +instructions `here `__. From 9363ada508c38054f18583f56ee59ab0db1163ec Mon Sep 17 00:00:00 2001 From: kratman Date: Sun, 10 Sep 2023 22:25:35 -0400 Subject: [PATCH 085/154] Remove with clause --- noxfile.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/noxfile.py b/noxfile.py index d1f119cdf1..039742f746 100644 --- a/noxfile.py +++ b/noxfile.py @@ -152,16 +152,16 @@ def build_docs(session): """Build the documentation and load it in a browser tab, rebuilding on changes.""" envbindir = session.bin session.install("-e", ".[all,docs]") - with session.chdir("docs/"): - session.run( - "sphinx-autobuild", - "-j", - "auto", - "--open-browser", - "-qT", - ".", - f"{envbindir}/../tmp/html", - ) + session.chdir("docs") + session.run( + "sphinx-autobuild", + "-j", + "auto", + "--open-browser", + "-qT", + ".", + f"{envbindir}/../tmp/html", + ) @nox.session(name="pre-commit") From c030b883d3888881e9503a3fecf1dd03ec684923 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Mon, 11 Sep 2023 11:38:26 +0100 Subject: [PATCH 086/154] ferran msmr comments --- .../examples/notebooks/models/MSMR.ipynb | 36 +-- .../parameterization/parameterization.ipynb | 223 +++++++----------- .../full_battery_models/base_battery_model.py | 35 +-- pybamm/parameters/lithium_ion_parameters.py | 6 +- .../base_lithium_ion_tests.py | 2 + .../test_base_battery_model.py | 11 +- 6 files changed, 134 insertions(+), 179 deletions(-) diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 4ca02f908d..9e75ce24d4 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -299,14 +299,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 274.961 and h = 1.52718e-09, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 274.979 and h = 1.05441e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 275.087 and h = 1.14637e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 275.085 and h = 1.46692e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -347,12 +347,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35f60c3770484dc4b96a92c8ff5bb504", + "model_id": "64dd40f0b3d54afd95cf71432e0ab43d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106518969745211, step=0.06106518969745211)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106549815808839, step=0.06106549815808839)…" ] }, "metadata": {}, @@ -361,7 +361,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -401,12 +401,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9063a577a8df454e823a702c1e1e18dc", + "model_id": "b9cf28dee3884302ab11e22d567d9e36", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106518969745211, step=0.06106518969745211)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106549815808839, step=0.06106549815808839)…" ] }, "metadata": {}, @@ -415,7 +415,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -454,7 +454,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -463,7 +463,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -537,20 +537,6 @@ "source": [ "pybamm.print_citations()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb index 5c4c71348d..35226ed89f 100644 --- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb @@ -35,6 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "zsh:1: no matches found: pybamm[plot,cite]\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -214,7 +215,11 @@ { "data": { "text/plain": [ - "{'Diffusion coefficient [m2.s-1]': ,\n", + "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n", + " 'Diffusion coefficient [m2.s-1]': ,\n", + " 'Electron charge [C]': 1.602176634e-19,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", " 'Initial concentration [mol.m-3]': 2.5,\n", " 'Particle radius [m]': 2}" ] @@ -248,7 +253,11 @@ { "data": { "text/plain": [ - "{'Diffusion coefficient [m2.s-1]': ,\n", + "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n", + " 'Diffusion coefficient [m2.s-1]': ,\n", + " 'Electron charge [C]': 1.602176634e-19,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", " 'Initial concentration [mol.m-3]': 1.5,\n", " 'Particle radius [m]': 2}" ] @@ -291,9 +300,9 @@ { "data": { "text/plain": [ - "[Parameter(-0x7c3ebfeae2200290, Initial concentration [mol.m-3], children=[], domains={}),\n", - " InputParameter(-0x4a08933302b1e44e, Interfacial current density [A.m-2], children=[], domains={}),\n", - " FunctionParameter(0x66f7cbc27c44053b, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" + "[Parameter(-0x6a2dafa7592b0120, Initial concentration [mol.m-3], children=[], domains={}),\n", + " InputParameter(0x217db8be7d80d00, Interfacial current density [A.m-2], children=[], domains={}),\n", + " FunctionParameter(-0x1834ea6ea33ab3ac, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]" ] }, "execution_count": 9, @@ -362,7 +371,7 @@ "outputs": [ { "data": { - "image/png": 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lZfXKpeIqspxc6R2OqampsLa2LnfMy/j7+2P06NH4559/cOzYMSxZsgR79uwps4uyKtTO/0JqkKlTp2Lq1KmqDuONOFkZ4I/Jndh2TEREREQ1WgOhBto1Nka7xsayY1KpFMk5BYhMzsXd1FxEpubhbkou4jOeIutpIS7FZuJSbKZsvJoAcDDT+7dN+XnxsKGRDu82JCKqAbS0tCAWy2+mouwWY09PTwQEBMitQ3jy5El07txZFsPrlpNzcHCAlZUVAgIC4ObmBqBk7cLAwECsXr36tTE0b94czZs3x6xZszBq1Chs27aNBUJSPbYdExEREVFtJBAIZDsh93b5946NgiIxotOeIPL5nYZ3U3MRmZKL7PwixD56ithHT/HPzRTZeH1tDTg/Lxa2tTVCe3sTNDJm0ZCIqLrZ29vjypUruH//Pho0aAATExOFW4xLC4pPnjzBo0ePEBYWBi0tLbi4uAAAZsyYgW7dumH16tXw8fHBoUOHcOrUKQQFBcnmeN1ycgKBADNnzsSKFSvQrFkzNGvWDCtWrICuri5Gjx790tiePXuGefPm4d1334WDgwOSkpIQEhKCYcOGKfiTqhosEBIAth0TERERUd2gramOVo0M0aqRoeyYVCrFozyR7C7Du6l5iEzJReyjJ8grKMbV+1m4ej8LO4JLFpe3NtRGBwcTtLc3QQcHEziaN4CaGguGRERVae7cuRg7dixcXFzw7NkzxMfHw97eXqE5Su/oA4Br167h999/h52dHe7fvw8A6Ny5M/bs2YPPP/8cixcvRtOmTfHHH3+gY8eOsvdVZDm5Tz/9FM+ePcPUqVORnZ2Njh074uTJk9DXL3+pDABQV1dHZmYmPvzwQ6SlpcHMzAxDhw7F0qVLFfqMVUUglUqlqg6iPsrNzYWhoSFycnJgYGCg6nDkSKVSHApLxvKjkXiUJwIAth0TERHVEjU5x6AS/I5qjsJiCeIynuBuSh4iknMQcj8btx/moFgi/yuSsa4mPOxN0PF50dDVxgAa6tw9mYhqpoKCAsTHx8PBwQHa2tqqDqfeEggEOHDgAIYMGaLQ+3x9ffH48WMcPHiw3Ndf9f1WJsfgHYRUhkAgwBC3hujlbIH1p6Kx/RLbjomIiIio7tHSUCtZi9DKAEPcGgIo2VE5LOExrsRnIeR+Fq4nZCM7vwgBd9IQcCcNAKCrpQ53O2PZHYZtbY24AQoREZUxatQomJqaIikp6bVjL1y4gP79+0MkEmHgwIHVEJ08FgjppfS1NbF4kAuGezSC36EIXI0vaTve97ztuDvbjomIiIiojtHV0kBnRzN0djQDUHKX4e3kHITEZ+Hq86JhbkExLkRn4EJ0BgBAU12A1o2M0MHBBB3sTeBubwwDbU1VfgwiIlKx6OhoACWtxRXh4eEhW0Pxdbs3VwW2GKtIbWstKa/tuJ+rFRYPZtsxERFRTVLbcoz6iN9R7SaRSBGVnoerzwuGV+OzkP48Py4lEADOVgYlBcPnbcnm+kIVRUxE9Q1bjOu2qmoxZoFQRWprYphXUCRrOxZLpNDWVMMnPZux7ZiIiKiGqK05Rn3C76hukUqlSMjKlxULQ+5n4X5mfplxDmZ66PC8Jblbc3MWDImoyrBAWLexQFjH1PbE8G5qrqztGChJeNh2TEREpHq1PceoD/gd1X3puQW4ej+rpC35fjbupubixd+6BAKgTSMj9Ha2QC9nSzhZ6UMg4C7JRKQcpQUke3t76Oiw46+uefbsGe7fv88CYV1RFxJDth0TERHVPHUhx6jr+B3VPznPinDtQRauxmfjYkwGbj3MkXu9oZEOejpZoJezBTo1MeWGJ0RUKUVFRYiJiYGNjQ0MDQ1VHQ4pWU5ODpKTk+Ho6AhNTfn1blkgrIXqUmLItmMiIqKaoy7lGHUVvyNKyy3AmbvpOB2ZhqCYDBQUSWSv6Wqp4y1HM/R2toSXkwVbkYlIYVKpFAkJCSgqKoKNjQ3U1NRUHRIpiUQiQXJyMjQ1NdG4ceMyd5+zQFgL1cXE8F5qHhYfui3XdrxksAt6tLBQcWRERET1R13MMeoafkf0omeFYlyKzcCpyHScuZuGtNx/NzwpbUXu5VTSiuxszVZkIqqYwsJCxMfHQyKRvH4w1SpqampwcHCAlpZWmddYIKyF6mpiKJVKcTg8GV/+82/bcV9XSywe5IJGxroqjo6IiKjuq6s5Rl3C74heRiqVIiI5F6ci03A6Mv2lrcg9nS3gyVZkInoNiUSCwsJCVYdBSqalpfXSu0JZIKyF6npimFdQhA2norHthbbjaV6OmNStCduOiYiIqlBdzzHqAn5HVFEVbUXu4WQOC33uVEpEVN+xQFgL1ZfE8F5qHvwO3caV523H9qa68H/blW3HREREVaS+5Bi1Gb8jehMFRWJcjMnA6ecFwxdbkQGgja0RerMVmYioXmOBsBaqT4lhadvx8n8ikf687djbpaTt2NaEbcdERETKVJ9yjNqK3xFV1outyGfupuNmknwrso2hNvq4WMLHrSHcbI1YLCQiqidYIKyF6mNiWF7b8cc9StqOuX4KERGRctTHHKO24XdEyvaqVmQ7U10MadsQQ9wawsFMT4VREhFRVWOBsBaqz4lheW3HS952hRfbjomIiCqtPucYtQW/I6pKpa3If99MwfHbqXhWJJa91tbWCO+4NcSg1tYwbSBUYZRERFQVWCCshep7Ysi2YyIioqpR33OM2oDfEVWXp6JiBNxJw4EbD3Eh+hEkz3/z01AToHtzcwxxa4jezpbQ0WI3DxFRXcACYS3ExLBEXkERvj0djV8ulrQdCzX+3e2YbcdERESKY45R8/E7IlVIzyvA3+EpOBj2UG7NwgZCDfRraYV33BqiUxNTqKtxvUIiotqqMjmGWhXFRFQh+tqaWDTQBcdmdEWnJiYQFUvwdUAU+q4/j7P30lUdHhEREalAVFQUfHx8YGZmBgMDA3Tp0gVnz56VGzNjxgy4u7tDKBSibdu25c5z4sQJdOrUCfr6+jA3N8ewYcMQHx8ve93X1xcCgaDMw9XV9ZXxhYSEoFevXjAyMoKxsTG8vb0RFhZW2Y9NVKUs9LUx/i0HHJ72Fk7N7oZpXo5oaKSDJ6Ji7LuWhPd/voLOq05jxdFI3EnOVXW4RERUzVggpBqhuaU+dk/qhG9HucFCX4gHmfkYty0E/9sRisSsfFWHR0RERNVo4MCBKC4uxpkzZ3Dt2jW0bdsWgwYNQmpqqmyMVCrF+PHjMWLEiHLniIuLg4+PD3r27ImwsDCcOHECGRkZGDp0qGzMhg0bkJKSInskJibCxMQEw4cPf2lseXl56Nu3Lxo3bowrV64gKCgIBgYG6Nu3L4qKipT3QyCqQo4W+pjbtwUufOqFvVM8MbpjYxjqaCItV4Qfz8dhwLcX0G/9eWwOjEXy42eqDpeIiKoBW4xVhK0lL/dEVFzSdhwUj2K2HRMRESmktucYGRkZMDc3x/nz59G1a1cAJUU5AwMDnDp1Cr169ZIb7+/vj4MHD5a5g2/fvn0YNWoURCIR1NRKrokfOXIEPj4+EIlE0NTULHPugwcPYujQoYiPj4ednV258YWGhqJ9+/ZISEiAra0tAODWrVto3bo1YmJi0LRp09d+xtr+HVHdJCoW49y9Rzh44yFOR6ajUFyyE7JAAHRyMMU7bg3Rr5UVDLTL/tkhIqKagS3GVKc0EGpg4QBnHJvRFZ5NTNl2TEREVI+YmprC2dkZO3bswNOnT1FcXIwtW7bA0tIS7u7uFZ7Hw8MD6urq2LZtG8RiMXJycrBz5054e3uXWxwEgK1bt6J3794vLQ4CQIsWLWBmZoatW7eisLAQz549w9atW+Hq6vrS94lEIuTm5so9iGoaoYY6+rpaYdMH7ghZ1BurhrZCRwcTSKVAcFwmPt1/Ex5fnsLHu64j4E4aCoslqg6ZiIiUiHcQqgivHFeMVCrFkZspWP7PHaTllux23MfFEn7c7ZiIiKhcdSHHePjwIXx8fHD9+nWoqanB0tIS//zzT7lrDb7sDkIAOH/+PIYPH47MzEyIxWJ4enri6NGjMDIyKjM2JSUFtra2+P333/Hee++9Mr6IiAj4+PjI1jNs3rw5Tpw4gcaNG5c73t/fH0uXLi1zvDZ/R1R/JGXn41BYMg7ceIiY9Cey40a6mhjU2hrD3W3RxtZIdQESEZEM7yCkOksgEODtNjY4PacH/tetCTTUBAi4k4be3wTi29PRKCgSqzpEIiIiqgB/f/9yNwR58REaGgqpVIqpU6fCwsICFy5cwNWrV+Hj44NBgwYhJSWlwudLTU3FxIkTMXbsWISEhCAwMBBaWlp49913Ud718e3bt8PIyAhDhgx55bzPnj3D+PHj0aVLF1y+fBkXL16Eq6srBgwYgGfPyl+rbcGCBcjJyZE9EhMTK/w5iFStkbEuPvZyRMCsbvj7k7cw8S0HmOsL8Ti/CL9dToDP9xfh8/1F7L+WxNyciKgW4x2EKlIXru6rQnRaHvwORSA4LhMAYGeqC//BrvByslBxZERERDVDTc0xMjIykJGR8cox9vb2uHjxIry9vZGdnS0Xf7NmzTBhwgTMnz9f7j0vu4Nw8eLFOHbsGEJDQ2XHkpKSYGtri+DgYHTq1El2XCqVonnz5hg0aBDWrVv3yhi3bt2KhQsXIiUlRba2YWFhIYyNjbF161aMHDnyle8Hau53RFRRYokUl2IzsP9aEo7eSpWtV2iip4UR7W3xfsfGaGTMbh8ioupWmRxDo4piIqoSzSz18fukjvj7Zgq+/OdOyW7H20PYdkxERFTDmZmZwczM7LXj8vPzAUBWfCulpqYGiaTia57l5+dDXV1+c7PS5/+dJzAwEDExMZgwYUKF5lVTU4NAIJCLTSAQKBQfUW2mriZA12bm6NrMHJ8PEuGPkETsuvwAyTkF2HQuFlsCY9HL2RIfetrhLUczuT8vRERUM7HFmGodgUCAwc/bjiez7ZiIiKhO8fT0hLGxMcaOHYvw8HBERUVh3rx5iI+Px8CBA2XjYmJiEBYWhtTUVDx79gxhYWEICwtDYWEhAGDgwIEICQnBsmXLEB0djevXr2PcuHGws7ODm5ub3Dm3bt2Kjh07omXLlmXiOXDgAJycnGTP+/Tpg+zsbHz88ceIjIxEREQExo0bBw0NDXh5eVXRT4Wo5jJrIMTHXo44/6kXtoxxRxdHU0ikQMCdNIzZehW9vgnEtovxyC0oUnWoRET0ChVqMT58+LDCE/fp0wc6OjpvFFR9wNYS5YlOy8OSwxG4FPtv2/GSwS7o6WSp4siIiIiqX13IMUJDQ7Fo0SKEhoaiqKgIrq6u8PPzQ//+/WVjevTogcDAwDLvjY+Ph729PQBgz549WLNmDaKioqCrqwtPT0+sXr1aruCXk5MDa2trbNiwAZMmTSoz3/bt2zFu3Di5dQsDAgKwdOlS3L59G2pqanBzc8Py5cvl2pZfpS58R0SvEpOeh53BD7D/+kM8ERUDAHS11PGOW0N86GmPFlb6Ko6QiKhuqkyOUaEC4X9bPF47qUCA6OhoNGnSRKH31SdMDJVLKpXK2o5Ldzvu7WyJJYPZdkxERPULc4yaj98R1RdPRMU4cD0JO4IfIPqFHZA7OphgbGd79HGxhKY6m9qIiJSlWgqEqampsLCo2EYQ+vr6CA8PZ4HwFZgYVo0nomJsPB2NrUHxKJZIIdRQw9QejpjcvQm0NdVfPwEREVEtxxyj5uN3RPWNVCpFcFwmdgY/wMk7aRBLSn4FtTQQ4v2OdhjZwRYW+toqjpKIqParTI5Rocs1Y8eOVahd+IMPPmCyQyrRQKiBBQOccXxmV3RuagpRsQTrTkXBe915nLmbpurwiIiIiIjqHYFAgM5NzbDpA3cEfeaFT3o6wqyBFtJyRfgmIApdVp3BJ7tvIPR+Fipw/woREVWBCt1BSMrHK8dVTyqV4p9bKfjy70ik5hYAAHo7W2DJYFe2HRMRUZ3FHKPm43dEBIiKxTh+OxU7gh/g2oNs2XFnawOM9bSDT9uG0NFiBxARkSKqvMWYlI+JYfV5KirGt2eisfXCv23HH/Voiindm7LtmIiI6hzmGDUfvyMiebcf5mBn8AMcDHsIUbEEAGCgrYH3PGzxQSc72JvpqThCIqLaodoKhAEBAQgKCkL37t3Rs2dPnD9/HitXroRIJMKYMWMwbtw4hYOvr5gYVr+Y9JLdji/GlOx23NikZLfjXs7c7ZiIiOoO5hg1H78jovI9zi/E3tAk7Lz8AAlZ+bLj3ZubY3K3JvBsagqBQKDCCImIarZqKRD+9ttvGDduHFq3bo2oqChs3LgRs2bNwrvvvgupVIqdO3di165dePfdd9/oQ9Q3TAxVQyqV4uitVHzx9x25tmO/Qa5obMq2YyIiqv2YY9R8/I6IXk0ikSIw6hF+Db6PwKhHKP2N1a2xET7u4YhezhYsFBIRlaNaCoRubm4YN24cpk+fjtOnT2Pw4MFYvnw5Zs2aBQD45ptv8NdffyEoKEjxT1APMTFUraeiYmw8E4OfL8ShWCKFloYaprLtmIiI6gDmGDUfvyOiinuQ+RS/BMVjT0iirP3YyUofH3s5YkAra6irsVBIRFSqWgqEDRo0wK1bt+Dg4AAA0NLSQmhoKFq3bg0AuHfvHrp06YKMjAwFw6+fmBjWDDHpT+B/OAJBMSX/3dqa6MB/sCvbjomIqNZijlHz8TsiUlx6XgG2BsXjt+AHeFooBgA4mOnho+5NMcStIbQ01FQcIRGR6lUmx6jw36KampooLCyUPRcKhWjQoIHsuZaWFp49e6bQyZXN3t4eAoFA7jF//ny5MQkJCRg8eDD09PRgZmaG6dOny30uALh16xa6d+8OHR0dNGzYEMuWLcN/66iBgYFwd3eHtrY2mjRpgs2bN1f55yPlc7RogJ0TOuD70e1gbaiNxKxnmPBrKCZsD0FCZv7rJyAiIiIioipnoa+NBf2dcXF+T8zq3RxGupqIz3iKT/ffhNfac/j10n0UFIlVHSYRUa2lUdGBjo6OuHv3Llq0aAEAePjwIfT19WWvx8bGolGjRsqPUEHLli3DpEmTZM9fLGKKxWIMHDgQ5ubmCAoKQmZmJsaOHQupVIqNGzcCKKm29unTB15eXggJCUFUVBR8fX2hp6eHOXPmAADi4+MxYMAATJo0Cb/99hsuXryIqVOnwtzcHMOGDaveD0yVJhAIMLC1NXq0MMfGMzHYGhSH03fTcSEmAx91b4qPerDtmIiIiIioJjDS1cKM3s0woasDfr/yAD9diMfDx8+w5HAENp6JwcSuDni/Y2Poa2uqOlQiolqlwi3GBw4cgKmpKbp161bu66tWrcLTp0/xxRdfKDVARdjb22PmzJmYOXNmua8fO3YMgwYNQmJiImxsbAAAe/bsga+vL9LT02FgYIBNmzZhwYIFSEtLg1AoBFDy2TZu3IikpCQIBAJ89tlnOHz4MCIjI2VzT5kyBeHh4QgODq5QrGwtqbnKazteMsgVvV3YdkxERDUfc4yaj98RkfIUFImxNzQRmwPj8PBxSUebgbYGfLs4YFxnexjraak4QiKi6lMtaxDWBvb29hCJRCgsLIStrS2GDx+OefPmQUur5H8Kfn5+OHToEMLDw2Xvyc7OhomJCc6cOQMvLy98+OGHyMnJwaFDh2Rjbty4gXbt2iEuLg4ODg7o1q0b3NzcsGHDBtmYAwcO4L333kN+fj40NcterRKJRBCJRLLnubm5sLW1ZWJYQ0mlUhy7XbLbcUpOyW7HvZwssGQwdzsmIqKajcWnmo/fEZHyFYklOBSWjB/OxSDu0VMAgK6WOj7oZIeJbznAwkBbxRESEVW9almDsDyrVq3C48ePKzOFUs2YMQN79uzB2bNnMW3aNKxfvx5Tp06VvZ6amgpLS/m7wIyNjaGlpYXU1NSXjil9/roxxcXFL92kZeXKlTA0NJQ9bG1tK/dhqUoJBAIMaGWNU7O746MeTaGpLsDpu+novS4Q6wKiuL4JEREREVENoqmuhnfdGyFgVnd8P7odXKwNkF8oxo/n4/DWmrNYfPA2ErO4xjgR0ctUqkC4YsUKZGVlKSuWcvn7+5fZeOS/j9DQUADArFmz0L17d7Ru3RoTJ07E5s2bsXXrVmRmZsrmEwgEZc4hlUrljv93TOlNloqOedGCBQuQk5MjeyQmJiryYyAV0RNq4LN+Tjg+sxvecjRDYbEEG05Ho8+6QJy6k6bq8IiIiIiI6AXqaiXri/8z/S1s820PdztjFBZLsPPyA3itPYc5f4Yj9tETVYdJRFTjVHiTkvJUR3fytGnTMHLkyFeOsbe3L/d4p06dAAAxMTEwNTWFlZUVrly5IjcmOzsbRUVFsjsCraysZHcKlkpPTweA147R0NCAqalpubEIhULZmoZU+zQ1L9ntuLTtODHrGSbuCEVPJwssGewCO1M9VYdIRERERETPCQQCeDlZoEcLc1yOy8IP52JwIToD+68n4a8bSRjQ0hpTvZrC1cZQ1aESEdUIlSoQVgczMzOYmZm90Xtv3LgBALC2tgYAeHp6Yvny5UhJSZEdO3nyJIRCIdzd3WVjFi5ciMLCQtnahSdPnoSNjY2sEOnp6YkjR47InevkyZPw8PAod/1BqhtK2467NzfHd2dj8POFOJy5m46gmAxM6d4UU7nbMRERERFRjSIQCODZ1BSeTU0RlvgY35+NQcCdNPxzKwX/3EqBVwtzTOvpCHc7E1WHSkSkUpXapKR0N2B1ddUXRYKDg3H58mV4eXnB0NAQISEhmDVrFjw8PGQbjojFYrRt2xaWlpb46quvkJWVBV9fXwwZMgQbN24EAOTk5KBFixbo2bMnFi5ciOjoaPj6+sLPzw9z5swBAMTHx6Nly5aYPHkyJk2ahODgYEyZMgW7d+/GsGHDKhQvF6eu/WIflex2fCG6ZN3JRsY6WDLYFb2dLV7aak5ERFTVmGPUfPyOiFTrbmoufjgbi79vJkPy/LfhTk1M8EnPZujc1JS5PBHVWirbxfjJkyeQSCRyx1SV5Fy/fh1Tp07F3bt3IRKJYGdnh5EjR+LTTz+Fru6/u84mJCRg6tSpOHPmDHR0dDB69GisXbtWrv331q1b+Pjjj3H16lUYGxtjypQp8PPzk/sfRWBgIGbNmoWIiAjY2Njgs88+w5QpUyocLxPDuqG83Y69WpjD/21Xth0TEZFKMMeo+fgdEdUM9zOeYnNgLPZfT0KRuOTX4s5NTTG/vxNaNzJSbXBERG+gWguE8fHxmDZtGs6dO4eCggLZ8dKNPsRi7u5aEUwM65b8wmJsPFPSdlwklkJLQw1TujXBRz0coaOl+jtsiYio/mCOUfPxOyKqWZIfP8OP5+Pw+5UEFIpLboAZ1Noac71bwN6MF/2JqPao1gJh586dAQAzZsyApaVlmduvu3fvrlAA9RUTw7qpvLZjv0Eu6ONS9s8KERFRVWCOUfPxOyKqmZKy8/FNQBQO3HgIqRTQUBNgdMfG+KRnM5jrc8NJIqr5qrVA2KBBA1y7dg0tWrRQ6EQkj4lh3SWVSnH8edtx8gttx0sGu/IKJBERVTnmGDUfvyOimi0yJRerj9/FuXuPAAC6WuqY1LUJJnVrggbCGr/PJxHVY5XJMdQUPVn79u2RmJio6NuI6g2BQID+raxxak53TO3RFJrqApy99wje687jm5P38KyQbfhERERERDWVs7UBto/rgN8ndUSbRobILxRjw+lo9PjqLHYE30dhseT1kxAR1TIK30EYGxuLKVOm4IMPPkDLli2hqakp93rr1q2VGmBdxSvH9cd/244bGulgyWC2HRMRUdVgjlHz8Tsiqj1KNyX86sQ9xGc8BQDYmepirncLDGxlDTU15vNEVHNUa4vx5cuXMXr0aNy/f//fSQQCblKiICaG9YtUKsWJiFQsO/Jv23GPFubwZ9sxEREpGXOMmo/fEVHtUySW4I+QRKw/FY2MJyIAQKuGhpjf3wldHM1UHB0RUYlqLRC6uLjA2dkZn376abmblNjZ2SkUQH3FxLB+yi8sxvdnY/Dj+ee7HaurYXL3JpjK3Y6JiEhJmGPUfPyOiGqvp6JibA2Kx5bAWDx9vnRQ12Zm+KyfE1o2NFRxdERU31VrgVBPTw/h4eFwdHRU6EQkj4lh/Rb36AmW/Kft2G+wC7zZdkxERJVU2RwjNzdX4fcwl1EM80Ci2i/ziQgbz8Rg15UHKBKX/Eo9pK0N5ni3gK2JroqjI6L6qloLhIMHD4avry+GDRum0IlIHhNDKm07/uLvSDx8/AwA246JiKjyKptjqKmpKXSxSiAQICoqCk2aNFH4XPUV80CiuiMhMx9fB9zDobBkAICmugAfdLLDNC9HmDYQqjg6IqpvqrVA+OOPP+LLL7/E+PHj0apVqzKblLz99tsKBVBfMTGkUqVtxz+dj0ehWMK2YyIiqhRlFAj3798PExOT146VSqUYMGAAbt++zQKhApgHEtU9tx/mYPXxu7IOoQZCDUzu1gQTujpAV0tDxdERUX1RrQVCNTW1l0/GTUoqjIkh/VfcoyfwP3IH56MeAWDbMRERvZnK5hgODg4IDQ2Fqalphca3bNkSx44dg62trcLnqq+YBxLVXUHRGVh1PBK3H5Ys12CuL8TM3s3wnoctNNVf/rs0EZEyVGuBkJSDiSGVp6TtOA1f/H1H1nbcvbk5/N92hQPbjomIqAKYY9R8/I6I6jaJRIq/b6Vg7Yl7SMjKBwA0MdPDvL4t0K+lFS/+E1GVYYGwFmJiSK/yrFAs2+24tO34f92a4GMvth0TEdGrMceo+fgdEdUPhcUS7L6agG9PRyPzaSEAoI2tERYNcEYHh9cv40BEpKgaUSAMDQ1Ffn4+unXrpozp6jwmhlQR8RlPseRwhFzb8eJBLujryrZjIiIqn7JyjMzMTNy8eRNt2rSBiYkJMjIysHXrVohEIgwfPhzOzs5KjLp+YR5IVL88ERXjp/Nx+OlCHPILS5bkGtLWBgsHOMPCQFvF0RFRXVIjCoTOzs6IioriGoQVxMSQKqq8tuNuzc2xlG3HRERUDmXkGFevXoW3tzdyc3NhZGSEgIAADB8+HBoaGpBKpXj48CGCgoLQrl07JUdfPzAPJKqfHuWJsO5UFHZfTYBUWrKRyczezTC2sz3XJyQipahMjqG0v4VOnz6NuLg4ZU1HRM8JBAL0a2mFU7O7Y5qXI7TU1XA+6hH6rjuPr07cRX5hsapDJCKiOmbRokUYPnw4cnJysHDhQgwZMgS9evVCVFQUoqOjMXr0aHzxxRdVdv6oqCj4+PjAzMwMBgYG6NKlC86ePSs3ZsaMGXB3d4dQKETbtm3LnefEiRPo1KkT9PX1YW5ujmHDhiE+Pl5uzK5du9CmTRvo6urC2toa48aNQ2Zm5ivjS0hIwODBg6GnpwczMzNMnz4dhYWFlfrMRFT3mesLseKdVjj88Vtoa2uEJ6JifPlPJAZ+ewGX41799w4RUVVTWoHQxsYGdnZ2ypqOiP5DR0sdc/u2wIlZ3dC9uTkKxRJ8fzYWfb45j+O3U8DlRImISFmuXbuG2bNnQ19fHzNmzEBycjImTZoke/3jjz9GSEhIlZ1/4MCBKC4uxpkzZ3Dt2jW0bdsWgwYNQmpqqmyMVCrF+PHjMWLEiHLniIuLg4+PD3r27ImwsDCcOHECGRkZGDp0qGxMUFAQPvzwQ0yYMAERERHYu3cvQkJCMHHixJfGJhaLMXDgQDx9+hRBQUHYs2cP9u/fjzlz5ijvB0BEdVqrRob466POWD2sFYx1NRGV9gQjf7yMGXtuIC23QNXhEVE9VaEW49zc3ApPyDaJimFrCVWGVCrFyTtpWHZEvu3Yf7ALmpg3UHF0RESkSsrIMRo0aIDbt2/D3t4eAKCvr4/w8HA0adIEQMkddC1atMCzZ8+UFbZMRkYGzM3Ncf78eXTt2hUAkJeXBwMDA5w6dQq9evWSG+/v74+DBw8iLCxM7vi+ffswatQoiEQiqKmVXBM/cuQIfHx8IBKJoKmpibVr12LTpk2IjY2VvW/jxo1Ys2YNEhMTy43v2LFjGDRoEBITE2FjYwMA2LNnD3x9fZGenl6hnznzQCIq9Ti/EGtP3sOuKyVtx3pa6pjVpznbjonojVR5i7GRkRGMjY1f+SgdQ0RVTyAQoK9rSdvxJz3/bTvut/4C246JiKjSbG1t5ZaO2bNnD6ytrWXPU1JSYGZmViXnNjU1hbOzM3bs2IGnT5+iuLgYW7ZsgaWlJdzd3Ss8j4eHB9TV1bFt2zaIxWLk5ORg586d8Pb2hqamJgCgc+fOSEpKwtGjRyGVSpGWloZ9+/Zh4MCBL503ODgYLVu2lBUHAaBv374QiUS4du1aue8RiUTIzc2VexARAYCRrha+HPJv2/HTQrGs7Tg4lm3HRFR9NCoy6L9rvhBRzaCjpY453i0wtF0jLD0SgXP3HuH7s7E4cP0h/Aa7oK+rFXc7JiIihY0cORLp6emy5/8tmB0+fBgdOnSoknMLBAIEBATAx8cH+vr6UFNTg6WlJY4fPw4jI6MKz2Nvb4+TJ09i+PDhmDx5MsRiMTw9PXH06FHZmM6dO2PXrl0YMWIECgoKUFxcjLfffhsbN2586bypqamwtLSUO2ZsbAwtLS25FugXrVy5EkuXLq1w7ERU/5S2He+9lojVx+8hKu0JRv10GW+3scGigc6w5G7HRFTFlLaLMSmGrSWkbFKpFAF30rD0hbbjrs3MsPRtV7YdExHVI9WRY+Tn50NdXR1CobDC7/H3939tkSwkJATu7u4YMmQIioqKsGjRIujo6ODnn3/G4cOHERISIncnY+m85bUYp6amolu3bhgyZAhGjRqFvLw8+Pn5QUNDAwEBARAIBLhz5w569+6NWbNmoW/fvkhJScG8efPQvn17bN26tdwY//e//+HBgwc4ceKE3HEtLS3s2LEDI0eOLPMekUgEkUgke56bmwtbW1vmgURUrvLajmf2bg7fLmw7JqJXq0we+EYFwsePH2Pr1q2IjIyEQCCAi4sLxo8fD0NDQ0WnqrdYIKSq8qxQjE3nYrA5MA6FYgk01QWY1LUJpvV0hK5WhW4aJiKiWqyqcoyLFy/Cw8NDoaLgizIyMpCRkfHKMfb29rh48SK8vb2RnZ0tF3+zZs0wYcIEzJ8/X+49LysQLl68GMeOHUNoaKjsWFJSEmxtbREcHIxOnTphzJgxKCgowN69e2VjgoKC0LVrVyQnJ5cpRgKAn58fDh06hPDwcNmx7OxsmJiY4MyZM/Dy8nrtz4J5IBFVxK2kHCw+dBthiY8BAM0sGmCZT0t4NjVVbWBEVGNV+RqELwoNDUXTpk2xbt06ZGVlISMjA9988w2aNm2K69evKzodESmZjpY6Znu3wMlZ3dCjhTmKxFL8cC4Wvb8OxLFb3O2YiIjeTP/+/fHw4cM3fr+ZmRmcnJxe+dDW1kZ+fj4AyDYWKaWmpgaJRFLh85Xe5fii0uel8+Tn55c5T+mYl/3/0tPTE7dv30ZKSors2MmTJyEUChVaI5GI6HVK247XDGsNEz0tRKeXtB1P383djolI+RQuEM6aNQtvv/027t+/j7/++gsHDhxAfHw8Bg0ahJkzZ1ZBiET0JuzN9LDNtz1+HOOOhkY6SM4pwEe7ruPDX64i7tETVYdHRES1THVdYPL09ISxsTHGjh2L8PBwREVFYd68eYiPj5dbCzEmJgZhYWFITU3Fs2fPEBYWhrCwMBQWFgIoWTcxJCQEy5YtQ3R0NK5fv45x48bBzs4Obm5uAIDBgwfjr7/+wqZNmxAXF4eLFy9i+vTp6NChg2wTkgMHDsDJyUl2Xm9vb7i4uGDMmDG4ceMGTp8+jblz52LSpEm8G5CIlE5NTYD32tvizJzuGNPJDgIBcDg8GT3XnsNP5+NQJK74hRMioldRuMVYR0cHN27ckEuUAODOnTvw8PCQXfWlV2NrCVUnWdvx+TgUFrPtmIioLquqHENfXx/h4eFo0qSJ0uZ8mdDQUCxatAihoaEoKiqCq6sr/Pz80L9/f9mYHj16IDAwsMx74+PjYW9vD6Bk9+U1a9YgKioKurq68PT0xOrVq+Xy2I0bN2Lz5s2Ij4+HkZERevbsidWrV6Nhw4YAgO3bt2PcuHFyBdKEhARMnToVZ86cgY6ODkaPHo21a9dWuP2aeSARvanbD0vajm8kPAZQ0na81McVnZtWzc7yRFS7VOsahJaWlti5cye8vb3ljp84cQIffvgh0tLSFAqgvmJiSKpwP+Mplh6JwNl7jwAANobaWDzIBf1acrdjIqK6oqpyjN9//x0+Pj7Q09NT2pz1FfNAIqoMiUSKfdeSsOr4XWQ9LblrenAbGywa4AwrQ+52TFSfVesahCNGjMCECRPwxx9/IDExEUlJSdizZw8mTpyIUaNGKTodEVUjezM9/OLbHj996IFGxvJtx7FsOyYiolcYPXo0i4NERDVAeW3HR8KT0evrc/jxfCzbjonojSh8B2FhYSHmzZuHzZs3o7i4GACgqamJjz76CKtWrXrjne3qG145JlUrKBLjh3Ox2BwYK2s7nti1CT5h2zERUa2mzByjoKAAGzduxNmzZ5Genl5mkxBuUPdmmAcSkTKx7ZiISlVri3Gp/Px8xMbGQiqVwtHREbq6um8yTb3FxJBqigeZT+F/+N+2Y+vnbcf92XZMRFQrKTPHGD16NAICAvDuu+/C0tKyzP8XlixZUqn56yvmgUSkbBKJFPuuJ2HVsX/bjoe6NYTfYBcY6WqpODoiqi4qKRBS5TAxpJpEKpXiVGQ6lh6JQFL2MwDAW45m8H/bFY4WDVQcHRERKUKZOYahoSGOHj2KLl26KCk6ApgHElHVyckvwtcB97Dz8gNIpYBZAyG+HNIS/VpaqTo0IqoG1VogZKuJcjAxpJqIbcdERLWfMnMMFxcX7NmzB61bt1ZSdAQwDySiqnc9IRvz9oYj9tFTAMCg1tZY+rYrTBtwSTCiuqxaC4RsNVEOJoZUkz3IfIqlR+7gzN10AGw7JiKqTZSZYxw7dgzffvstNm/eDDs7OyVFSMwDiag6FBSJseF0NH48HwexRApTPS0s9XHFwFbWzOmJ6qhqLRCy1UQ5mBhSbXDqThr82XZMRFSrKDPHePToEd577z2cP38eurq60NTUlHs9KyurUvPXV8wDiag63Ux6jHl7b+JeWh4AoJ+rFb4Y0hLm+rybkKiuqUyOoXDPYMOGDaGvr6/o24ioFurtYom3mpnJ2o6DYjLQf8N5THirpO1YT8i2YyKiumzUqFF4+PAhVqxYUW7nCBER1XytGxnhyCdv4buzMfjhbAyOR6Ticnwm/Ae7wqetDf9uJyIAb3AHIVtNlINXjqm2Ka/t+POBLhjQim3HREQ1iTJzDF1dXQQHB6NNmzZKio4A5oFEpDoRyTn4dN9NRCTnAgB6O1tg+TutYGmgreLIiEgZKpNjqCl6Mg8PDxQUFKBJkybQ19eHiYmJ3IOI6iY7Uz384tseP3/ogUbGOkjJKcDHv1/HmK1XEZP+RNXhERFRFXBycsKzZ89UHQYRESmJq40hDn7cBXO9m0NTXYBTkeno/U0g/gxNhIL3DhFRHaPwHYS9e/dGQkICJkyYUG6rydixY5UaYF3FK8dUmxUUibHpXCw2vbDbMduOiYhqBmXmGCdPnsTSpUuxfPlytGrVqswahMxh3gzzQCKqCaLS8jBvbzjCk3IAAN2bm2PF0FZoaKSj4siI6E1V6yYlbDVRDiaGVBc8yHyKZUfu4DTbjomIagxl5hhqaiXNJv/9O10qlUIgEEAsFldq/vqKeSAR1RTFYgl+DorHNwFRKCyWoIFQAwsGOGF0h8bM54lqoWrdpIStJkRUys5UD1t92+PUnTQs/TsCiVnP8PHv19HF0RRL327J3Y6JiGq5s2fPqjoEIiKqQhrqapjSvSl6O1vi033huJ7wGIsO3MY/N1Owelhr2JroqjpEIqomCt9ByFYT5eCVY6prCorE2BwYix/O/dt2PP4tB0zv2Yxtx0RE1Yg5Rs3H74iIaiKxRIrtl+7jqxN3UVAkga6WOj7r54Qxneygpsa7CYlqg2rdpKRfv34IDg5Gr169YGFhAWNjYxgbG8PIyAjGxsaKTldhy5cvR+fOnaGrqwsjI6NyxyQkJGDw4MHQ09ODmZkZpk+fjsLCQrkxt27dQvfu3aGjo4OGDRti2bJlZRZjDQwMhLu7O7S1tdGkSRNs3ry5zLn2798PFxcXCIVCuLi44MCBA0r7rES1kbamOmb2bo5Ts7qjt7MFisRSbAmMQ6+vA/H3zWQuekxEREREVIOpqwkw4S0HHJ/RDR0cTJBfKMaSwxEY+dNl3M94qurwiKiKKXxbj6paTQoLCzF8+HB4enpi69atZV4Xi8UYOHAgzM3NERQUhMzMTIwdOxZSqRQbN24EUFJJ7dOnD7y8vBASEoKoqCj4+vpCT08Pc+bMAQDEx8djwIABmDRpEn777TdcvHgRU6dOhbm5OYYNGwYACA4OxogRI/DFF1/gnXfewYEDB/Dee+8hKCgIHTt2rL4fClEN1NhUFz+PbY/TkWnwP1LSdjzt9xvY7ZiApW+7wtFCX9UhEhFRJTk7OyMqKoprEBIR1UH2ZnrYM6kTfrvyAKuO3cXV+Cz023Aec71bYFwXB6jzbkKiOknhFmNV2759O2bOnInHjx/LHT927BgGDRqExMRE2NjYAAD27NkDX19fpKenw8DAAJs2bcKCBQuQlpYGoVAIAFi1ahU2btyIpKQkCAQCfPbZZzh8+DAiIyNlc0+ZMgXh4eEIDg4GAIwYMQK5ubk4duyYbEy/fv1gbGyM3bt3V+hzsLWE6oPStuNN52IhKpZAQ02ACV3ZdkxEVJWqI8c4ePAgcnJyMHbs2CqZv65jHkhEtUViVj7m/3UTF2MyAQDtGhthzbttuNY4UQ1V5S3GN2/ehEQiqfCkERERKC4uViiQygoODkbLli1lxUEA6Nu3L0QiEa5duyYb0717d1lxsHRMcnIy7t+/Lxvj7e0tN3ffvn0RGhqKoqKiV465dOnSS+MTiUTIzc2VexDVdaVtxwHP246LJWw7JiKqC4YMGcLiIBFRPWBroovfJnTEyqGt0ECogesJjzHg2wvYHBiLYnHFawREVPNVqEDo5uaGzMzMCk/q6emJhISENw7qTaSmpsLS0lLumLGxMbS0tJCamvrSMaXPXzemuLgYGRkZrxxTOkd5Vq5cCUNDQ9nD1tb2DT4lUe1U2na8dawHGpvoIjW3ANN+v4H3f76CmPQ8VYdHREREREQvIRAIMKpDY5yc1Q3dm5ujsFiCVcfuYtimS7iXylyeqK6oUI+fVCrF4sWLoatbsS3O/7sxyMv4+/tj6dKlrxwTEhICDw+PCs0nEJRdC0Eqlcod/++Y0juYlDGmvPOXWrBgAWbPni17npubyyIh1Tu9nC3RxdEMWwLj8MO5GFyKzUS/9Rcw4S0HfNKrGRqw7ZiISOXc3NxemdO86Pr161UcDRER1RQ2RjrYPq499l1Lwhd/30F4Ug4GbbyAT/s6YcJbDtzpmKiWq9Bv4926dcO9e/cqPKmnpyd0dHReO27atGkYOXLkK8fY29tX6JxWVla4cuWK3LHs7GwUFRXJ7vazsrIqc5dfeno6ALx2jIaGBkxNTV855r93Fb5IKBTKtTYT1VfamuqY0bsZhrZriKVH7uBUZBq2nI/DwbCH+HygCwa1tq7wL6ZERKR8Q4YMUXUIRERUQwkEAgz3sEW35uZYdOAWTkWmY/nRSJyPfoSv32sDC31tVYdIRG+oQgXCc+fOVcnJzczMYGZmppS5PD09sXz5cqSkpMDa2hoAcPLkSQiFQri7u8vGLFy4EIWFhdDS0pKNsbGxkRUiPT09ceTIEbm5T548CQ8PD2hqasrGBAQEYNasWXJjOnfurJTPQlQf2Jro4uexHjhzNw3+h+8gISsfn+y+gd1XS3Y7bmbJ3Y6JiFRhyZIlqg6BiIhqOEsDbfz0oQd2X03Esr8jcCE6A/3XX8BXw1ujp9PLb5whopqrQmsQ1gQJCQkICwtDQkICxGIxwsLCEBYWhidPngAAvL294eLigjFjxuDGjRs4ffo05s6di0mTJsl2bhk9ejSEQiF8fX1x+/ZtHDhwACtWrMDs2bNldyxNmTIFDx48wOzZsxEZGYlffvkFW7duxdy5c2WxzJgxAydPnsTq1atx9+5drF69GqdOncLMmTOr/edCVNv1dLLEyVndMKt3cwg11HApNhP9N1zAiqOReCKq3s2OiIiofNeuXcNvv/2GXbt24caNG6oOh4iIagCBQIDRHRvj70/egrO1ATKfFmL89lD4H45AQZFY1eERkYIE0lqyjaivry9+/fXXMsfPnj2LHj16ACgpIk6dOhVnzpyBjo4ORo8ejbVr18q19t66dQsff/wxrl69CmNjY0yZMgV+fn5yLY2BgYGYNWsWIiIiYGNjg88++wxTpkyRO+++ffvw+eefIy4uDk2bNsXy5csxdOjQCn+eymw9TVRXJWbly9qOAcDSQIhFA10wmG3HREQVpswcIz09HSNHjsS5c+dgZGQEqVSKnJwceHl5Yc+ePTA3N1dS1PUL80AiqmsKisRYc/wefrkYDwBwstLHt6Pc0JxdQUTVqjI5Rq0pENY1TAyJXu7FtmMA8GxiimU+bDsmIqoIZeYYI0aMQGxsLHbu3AlnZ2cAwJ07dzB27Fg4Ojpi9+7dygi53mEeSER11dl76Zi3NxwZTwoh1FDD54Nc8EHHxrzYT1RNWCCshZgYEr1aQZEYP56Pw/dnYyAqlkBDTYDxbzlgOnc7JiJ6JWXmGIaGhjh16hTat28vd/zq1avw9vbG48ePKzV/fcU8kIjqskd5IszdG47AqEcAgN7OlljzbmuY6GmpODKiuq8yOUatWYOQiOoXbU11TO/VDKdmd0cfF0sUS6T48Xwcen19DofDk8FrG0REVU8ikcg2aXuRpqYmJBKJCiIiIqKazlxfiG2+7bF4kAu01NVwKjIN/dafx8WYDFWHRkSv8EZ3EEZFReHcuXNIT08vkxz6+fkpLbi6jFeOiRRz9m46/I9E4EHmv23HS31cua4JEdF/KDPH8PHxwePHj7F7927Y2NgAAB4+fIj3338fxsbGOHDggDJCrneYBxJRfRGRnIPpu28g9tFTCATA5G5NMbtPc2hp8F4loqpQrS3GP/30Ez766COYmZnByspKbi0BgUCA69evKxRAfcXEkEhxBUVi/HQ+Dt+90HY8ros9ZvRuzrZjIqLnlJljJCYmwsfHB7dv34atrS0EAgESEhLQqlUrHDp0CI0aNVJS1PUL80Aiqk+eFYrxxT938PuVBABA60aG2DDSDQ5meiqOjKjuqdYCoZ2dHaZOnYrPPvtMoRORPCaGRG8uMSsfX/x9Byfv/Lvb8cIBzni7jQ0XQCaieq8qcoyAgADcvXsXUqkULi4u6N27t1Lmra+YBxJRfXT8dgo+238LOc+KoKuljmU+LTGsXUPm70RKVK0FQgMDA4SFhaFJkyYKnYjkMTEkqryz99Lhf/jftuNOTUywzKcl246JqF5jjlHz8TsiovoqJecZZv0RhstxWQCAwW1s8OWQljDUKbveLREprloLhBMmTED79u0xZcoUhU5E8pgYEilHadvx9+diUFDEtmMiImXnGFevXn3p2tPffPNNpeevj5gHElF9JpZIsTkwFt8EREEskaKhkQ42jGwLD3sTVYdGVOtVJsdQ+LdnR0dHLF68GJcvX0arVq3K7Gw3ffp0RackInpj2prq+KRXMwxxayhrO/7pQjwOhSVj0UC2HRMRVcaKFSvw+eefo0WLFrC0tCyz9jQREZGi1NUE+NjLEZ2bmmLGnjAkZOXjvS3BmN6rGaZ5OUJDnRuYEKmCwncQOjg4vHwygQBxcXGVDqo+4JVjoqpx9l46lh6OwP3nbccdHUzwxRC2HRNR/aHMHMPS0hKrV6+Gr6+vcoKroKioKMybNw8XL15EYWEhWrVqhS+//BJeXl6yMTNmzEBQUBBu374NZ2dnhIWFlZnnxIkTWLJkCSIiIqCtrY1u3bph7dq1cvnsrl27sGbNGkRHR8PQ0BD9+vXD2rVrYWpqWm5s4eHhWLVqFYKCgpCRkQF7e3tMmTIFM2bMqPDnYx5IRFQir6AISw5F4K8bDwEA7e2NsW5EWzQy1lVxZES1U2VyDIVL8/Hx8S99sDhIRKrm1cICx2d2w1zv5tDWVMOV+Cz033ABX/59B3kFRaoOj4ioVlFTU0OXLl2q/bwDBw5EcXExzpw5g2vXrqFt27YYNGgQUlNTZWOkUinGjx+PESNGlDtHXFwcfHx80LNnT4SFheHEiRPIyMjA0KFDZWOCgoLw4YcfYsKECYiIiMDevXsREhKCiRMnvjS2a9euwdzcHL/99hsiIiKwaNEiLFiwAN99953yfgBERPWEvrYmvhnRFutHtEUDoQZC7mej/4YL+PtmsqpDI6p3FL6D8EWlb2WLieJ45Zio6iVll+x2fCKiZLdjC30h246JqM5TZo6xZs0aJCcnY/369coJrgIyMjJgbm6O8+fPo2vXrgCAvLw8GBgY4NSpU+jVq5fceH9/fxw8eLDMHYT79u3DqFGjIBKJoKZWck38yJEj8PHxgUgkgqamJtauXYtNmzYhNjZW9r6NGzdizZo1SExMrHDMH3/8MSIjI3HmzJkKjWceSERUVkJmPmb8cQM3Eh4DAIa7N4L/267Q47riRBVWrXcQAsCOHTvQqlUr6OjoQEdHB61bt8bOnTvfZCoioirTyFgXW8Z4YPu49rA31UV6nggz9oRh5I+XcS81T9XhERHVeHPnzsW9e/fQtGlTDB48GEOHDpV7VAVTU1M4Oztjx44dePr0KYqLi7FlyxZYWlrC3d29wvN4eHhAXV0d27Ztg1gsRk5ODnbu3Alvb2/ZGtqdO3dGUlISjh49CqlUirS0NOzbtw8DBw5UKOacnByYmLx8cX2RSITc3Fy5BxERyWtsqos/J3vik56OEAiAvdeSMGhjEG4mPVZ1aET1gsIFwm+++QYfffQRBgwYgD///BN//PEH+vXrhylTpmDdunVVESMRUaX0aGGBE7Pk244HfHsBX7DtmIjolT755BOcPXsWzZs3h6mpKQwNDeUeVUEgECAgIAA3btyAvr4+tLW1sW7dOhw/fhxGRkYVnsfe3h4nT57EwoULIRQKYWRkhKSkJOzZs0c2pnPnzti1axdGjBgBLS0tWFlZwcjICBs3bqzweYKDg/Hnn39i8uTJLx2zcuVKuZ+bra1thecnIqpPNNXVMMe7BXZP6gRrQ23EZzzF0B8uYUtgLCrR/EhEFfBGm5QsXboUH374odzxX3/9Ff7+/oiPj1dqgHUVW0uIVOO/bcfm+kIsGuAMn7ZsOyaiukGZOYa+vj727Nmj8B115fH398fSpUtfOSYkJATu7u4YMmQIioqKsGjRIujo6ODnn3/G4cOHERISAmtr6zLzltdinJqaim7dumHIkCEYNWoU8vLy4OfnBw0NDQQEBEAgEODOnTvo3bs3Zs2ahb59+yIlJQXz5s1D+/btsXXr1td+poiICHh5eWH69On4/PPPXzpOJBJBJBLJnufm5sLW1pZ5IBHRKzzOL8SCv27h2O2S9We9XSyx9r02MNDWVHFkRDVXZfJAhQuE2trauH37NhwdHeWOR0dHo1WrVigoKFAogPqKBUIi1Tp3Lx3+L+x23MHBBF/4tEQLK+52TES1mzJzDDs7O5w4cQJOTk6VjisjIwMZGRmvHGNvb4+LFy/C29sb2dnZcvE3a9YMEyZMwPz58+Xe87IC4eLFi3Hs2DGEhobKjiUlJcHW1hbBwcHo1KkTxowZg4KCAuzdu1c2JigoCF27dkVycnKZYuSL7ty5Ay8vL0ycOBHLly+vyI9AhnkgEVHFSKVS/H41AUsP30GhWAIHMz1s/sCdOTvRS1TrGoSOjo74888/yxz/448/0KxZM0WnIyJSidK243l9W0BbUw1X2XZMRFSGv78/lixZgvz8/ErPZWZmBicnp1c+tLW1Zecq3ViklJqaGiQSSYXPl5+fD3V1dbljpc9L58nPzy9zntIxr7qGXnrn4NixYxUuDhIRUcUJBAK839EOe6d4wuZ5y/GQ7y/iUNhDVYdGVOcovB3Q0qVLMWLECJw/fx5dunSBQCBAUFAQTp8+XW7hkIiophJqqONjL0f4tLXBl39H4nhEKrYGxeNweDLbjomIAHz77beIjY2FpaUl7O3tZZt7lLp+/brSz+np6QljY2OMHTsWfn5+0NHRwU8//YT4+Hi5VueYmBg8efIEqampePbsmewOQhcXF2hpaWHgwIFYt24dli1bJmsxXrhwIezs7ODm5gYAGDx4MCZNmoRNmzbJWoxnzpyJDh06wMbGBgBw4MABLFiwAHfv3gXwb3HQ29sbs2fPRmpqSeuburo6zM3Nlf7zICIioI2tEf6e3hUz9tzAhegMzNgThhsJj7FwgDO0NN5o71Ui+g+FW4wB4Nq1a1i3bh0iIyMhlUrh4uKCOXPmyJItej22lhDVPIFRj+B/OALxGU8BlLQdL/NxhZMV/4wSUe2hzBzjdWsGLlmypFLzv0xoaCgWLVqE0NBQFBUVwdXVFX5+fujfv79sTI8ePRAYGFjmvfHx8bC3twcA7NmzB2vWrEFUVBR0dXXh6emJ1atXy7VMb9y4EZs3b0Z8fDyMjIzQs2dPrF69Gg0bNgQAbN++HePGjZPdUfiytRTt7Oxw//79Cn0+5oFERG9GLJFiXUAUvjsbAwBwtzPG96PbwcpQW8WREdUM1boGISkHE0OimklULMbPF+Kx8Uw0CookUFcTYKynPWb2acYFkYmoVmCOUfPxOyIiqpxTd9Iw688w5BUUw6yBFjaOagfPpqaqDotI5ap8DcLc3Fy5f3/Vg4ioNittOz49pwf6uVpBLJHil4vx6PV1IA7cSHrlmlRERERERFT1ertY4si0t+BkpY+MJ4X4YOsV/Hg+lrk6USVUqEBobGyM9PR0AICRkRGMjY3LPEqPExHVBQ2NdLB5jDt+Hd8BDmZ6eJQnwqw/wjFiy2XcTeXFECKqu0xMTF672/CLGjdujAcPHlRhRERERGXZm+nhwNQuGOrWEGKJFCuO3sXUXdfxRFSs6tCIaqUKbVJy5swZmJiYAADOnj1bpQEREdUk3Zub4/jMrrK246v3szDw2yC2HRNRnfX48WMcO3YMhoaGFRqfmZkJsVhcxVERERGVpaOljq/fawM3O2MsOxKBY7dTEZWWhy1j3OFooa/q8IhqFYXXIExISICtrW2ZnT2lUikSExPRuHFjpQZYV3HtGaLa5+HjZ/jy7zs4drtkx0qzBkIsGuiEIW0bcrdjIqoxKptjqKkpvhtkTEwMmjRpovD76ivmgUREync9IRsf77qOlJwC6GqpY827rTGotY2qwyKqVtW6SYm6ujpSUlJgYWEhdzwzMxMWFha8glxBTAyJaq/zz3c7jivd7djeBEt9XOFszT/LRKR6zDFqPn5HRERVI+OJCNN338Cl2EwAwIS3HDC/vxM01RW/+EVUG1X5JiUvkkql5d4p8+TJE2hrc2txIqr7ujU3x7GZXTGvbwvoaKrj6v0sDNoYhKVHIpBbUKTq8IiIiIiI6iWzBkLsGN8BH/VoCgDYGhSP93+6gvTcAhVHRlTzVfgOwtmzZwMANmzYgEmTJkFXV1f2mlgsxpUrV6Curo6LFy9WTaR1DK8cE9UN5bUdLxzghHfc2HZMRKrBHKPm43dERFT1TkSkYu6f4cgTFcNcX4gf3m+H9vYmqg6LqEpVS4uxl5cXACAwMBCenp7Q0tKSvaalpQV7e3vMnTsXzZo1UyiA+oqJIVHd8t+24/b2xljm05Jtx0RU7Zhj1Hz8joiIqkfcoyf46LfruJeWBw01ARYMcMb4Lva8kE91VrWuQThu3Dhs2LCByUwlMTEkqntExWJsDYrHxtMxeFYkhrqaAGM62WG2d3PudkxE1YY5Rs3H74iIqPrkFxZjwV+3cCgsGQAwqLU1Vg9rDT2hhoojI1K+ai0QknIwMSSqux4+fobl/9zB0Vv/th0v6O+Eoe3YdkxEVY85Rs3H74iIqHpJpVL8euk+vvwnEsUSKZpZNMCmD9zhaNFA1aERKVW1FwhDQkKwd+9eJCQkoLCwUO61v/76S9Hp6iUmhkR134XoR1hySL7teOnbLeFiwz/zRFR1lJ1jSCQSxMTEID09HRKJRO61bt26VXr++oh5IBGRalx7kIWpu64jLVcEPS11rB3eBv1bWas6LCKlqdZdjPfs2YMuXbrgzp07OHDgAIqKinDnzh2cOXMGhoaGik5HRFRndW1Wstvxp/1KdjsOuZ+NQRsvwP9wBHKecbdjIqr5Ll++DEdHRzg7O6Nbt27o0aOH7FG6PjUREVFt4W5ngr8/6YqODiZ4WijGR7uuY+XRSBSLJa9/M1Edp3CBcMWKFVi3bh3+/vtvaGlpYcOGDYiMjMR7772Hxo0bV0WMRES1llBDHVN7OOL0nO4Y0MoKEimw/dJ99Pr6HPZfSwJXeSCimmzKlCnw8PDA7du3kZWVhezsbNkjKytL1eEREREpzFxfiF0TO2JytyYAgC3n4/DB1it4lCdScWREqqVwi7Genh4iIiJgb28PMzMznD17Fq1atUJkZCR69uyJlJSUqoq1TmFrCVH9dCH6EZYcjkDco5K2Yw+7kt2O2XZMRMqizBxDT08P4eHhcHR0VFJ0BDAPJCKqKY7dSsHcveF4WiiGpYEQmz5wR7vGxqoOi+iNVWuLsYmJCfLy8gAADRs2xO3btwEAjx8/Rn5+vqLTERHVK12bmeP4jG74rJ8TdDTVEfqAbcdEVHN17NgRMTExqg6DiIioSvRvZY1D096Co0UDpOWKMHLLZRy88VDVYRGphML7enft2hUBAQFo1aoV3nvvPcyYMQNnzpxBQEAAevXqVRUxEhHVKVoaavioR1P4tLXB8n8i8c+tFGy/dB9/30zG/P7OGOrWEGpq3O2YiFTvk08+wZw5c5CamopWrVpBU1NT7vXWrVurKDIiIiLlcLRogEMfd8HsP8NwIiINM/8IQ0z6E8zu05w5OdUrCrcYZ2VloaCgADY2NpBIJFi7di2CgoLg6OiIxYsXw9iYt+NWBFtLiKhUUHQG/A7fZtsxESmFMnMMNbWyzSYCgQBSqRQCgQBisbhS89dXzAOJiGoeiUSKtSfv4YdzsQCAAa2s8PXwttDRUldxZEQVV5kcQ6ECYXFxMXbt2oW+ffvCyspK4UDpX0wMiehFhcUS/HIxHt+ejkZ+oRhqAuBDT3vM6tMchjqar5+AiOg5ZeYYDx48eOXrdnZ2lZq/vmIeSERUc+2/loQFf91CoViCVg0N8dOHHrAy1FZ1WEQVUm0FQgDQ1dVFZGQkE8JKYmJIROVJyXmGL/+JxD83SzZ8MmugxbZjIlIIc4yaj98REVHNFnI/C5N3XkPW00JYGgjx84ft0aqRoarDInqtat2kpGPHjrhx44aibyMiogqwNtTB96Pb4bcJHdHUXA8ZTwoxd2843tsSjDvJuaoOj4jqodjYWHzyySfo3bs3+vTpg+nTpyM2NlbVYREREVWZ9vYmOPRxFzS3LNm8ZPiWSzh2K0XVYRFVKYULhFOnTsWcOXPw3XffITg4GDdv3pR7VJXly5ejc+fO0NXVhZGRUbljBAJBmcfmzZvlxty6dQvdu3eHjo4OGjZsiGXLluG/N1EGBgbC3d0d2traaNKkSZk5AGD//v1wcXGBUCiEi4sLDhw4oLTPSkT0VjMzHJvRDfP7O0FXi7sdE5FqnDhxAi4uLrh69Spat26Nli1b4sqVK3B1dUVAQICqwyMiIqoytia62P9RZ/RoYY6CIgk+2nUd352JLlM/IKorFG4xVtVi1UuWLIGRkRGSkpKwdetWPH78uNw4tm3bhn79+smOGRoaQkdHB0DJrZbNmzeHl5cXFi1ahKioKPj6+mLJkiWYM2cOACA+Ph4tW7bEpEmTMHnyZFy8eBFTp07F7t27MWzYMABAcHAwunbtii+++ALvvPMODhw4AD8/PwQFBaFjx44V+jxsLSGiimLbMREpQpk5hpubG/r27YtVq1bJHZ8/fz5OnjyJ69evV2r++op5IBFR7VEslmDF0bv45WI8AGBIWxusGtYa2prcvIRqnmpdg1DVi1Vv374dM2fOfGmB8MCBAxgyZEi57920aRMWLFiAtLQ0CIVCAMCqVauwceNGJCUlQSAQ4LPPPsPhw4cRGRkpe9+UKVMQHh6O4OBgAMCIESOQm5uLY8eOycb069cPxsbG2L17d7nnFolEEIlEsue5ubmwtbVlYkhEFXYxJgN+h24j9vlux+52xljm4wpXG66HQkT/UmbxSVtbG7du3UKzZs3kjkdFRaF169YoKCio1Pz1FQuERES1z64rD7DkUASKJVK0a2yELWM8YK4vVHVYRHKqdQ3CBw8eoGHDhrCzs5N7NGzY8LXFw+owbdo0mJmZoX379ti8eTMkEonsteDgYHTv3l1WHASAvn37Ijk5Gffv35eN8fb2lpuzb9++CA0NRVFR0SvHXLp06aVxrVy5EoaGhrKHra1tZT8qEdUzXRxL2o4XPG87vvYgG4M3BmHJodtsOyaiKmFubo6wsLAyx8PCwmBhYVH9AREREanI+x3tsGN8Bxhoa+B6wmMM+f4i7qZyjXCqOxQuEHp5eSErK6vM8ZycHHh5eSklqDf1xRdfYO/evTh16hRGjhyJOXPmYMWKFbLXU1NTYWlpKfee0uepqamvHFNcXIyMjIxXjimdozwLFixATk6O7JGYmPjmH5SI6i0tDTVM7t4Up+d0x6DW1pBIgV+DH6Dn2nPYG5oIiYRrohCR8kyaNAn/+9//sHr1aly4cAFBQUFYtWoVJk+ejP/973+qDo+IiKhadXY0w8GPu8DBTA8PHz/DsB8u4XRkmqrDIlIKhQuEpWsN/ldmZib09PQUmsvf37/cjUVefISGhlZ4vs8//xyenp5o27Yt5syZg2XLluGrr76SG/Pf2Es7rF88/qZjyvu5lBIKhTAwMJB7EBG9KWtDHXw3uh12TewIR4sGyHxaiHn7buLdzZdw+2GOqsMjojpi8eLF8PPzw8aNG9G9e3d069YN3333Hfz9/bFo0SJVh0dERFTtmpg3wIGpneHZxBRPC8WYuCMUP1+I4+YlVOtpVHTg0KFDAZQUxnx9feXadMViMW7evInOnTsrdPJp06Zh5MiRrxxjb2+v0Jwv6tSpE3Jzc5GWlgZLS0tYWVmVucsvPT0dwL93Er5sjIaGBkxNTV855r93FRIRVbUujmY4Or0rtl2Mx4bT0bie8BhvfxeEDzrZYU6fFjDU1VR1iERUiwkEAsyaNQuzZs1CXl4eAEBfX1/FUREREamWka4WdkzoAL9Dt7H7aiK+/CcSMelPsMynJbQ0FL4Pi6hGqHCB0NCwZBF8qVQKfX192c7AAKClpYVOnTph0qRJCp3czMwMZmZmCr1HETdu3IC2tjaMjIwAAJ6enli4cCEKCwuhpaUFADh58iRsbGxkhUhPT08cOXJEbp6TJ0/Cw8MDmpqasjEBAQGYNWuW3BhFC6RERMpQ2nb8dlsbLP8nEn/fTMGO4Af452YKPuvvhHfbNeJux0RUaSwMEhER/UtTXQ0r3mkFRwt9LP/nDvaEJOJ+5lNset8dxnpaqg6PSGEVLhBu27YNQMkdfXPnzlW4nbiyEhISkJWVhYSEBIjFYtmC2Y6OjmjQoAGOHDmC1NRUeHp6QkdHB2fPnsWiRYvwv//9T3a34+jRo7F06VL4+vpi4cKFiI6OxooVK+Dn5ydrD54yZQq+++47zJ49G5MmTUJwcDC2bt0qtzvxjBkz0K1bN6xevRo+Pj44dOgQTp06haCgoGr9mRARvai07Xh0hwz4HY5ATPoTfLrvJvZcTcAyn5Zo2ZC7HRPR67Vr1w6nT5+GsbEx3NzcXrmEyvXr16sxMiIioppFIBBgwlsOcDDTxfTdYbgcl4V3friIn8e2h6NFA1WHR6QQgbSWNMr7+vri119/LXP87Nmz6NGjB44fP44FCxYgJiYGEokETZo0wcSJE/Hxxx9DQ+PfOuitW7fw8ccf4+rVqzA2NsaUKVPkCoQAEBgYiFmzZiEiIgI2Njb47LPPMGXKFLnz7tu3D59//jni4uLQtGlTLF++XNaGXRGV2XqaiOh1Cosl2H4pHutPRSO/UAw1Adh2TFRPVDbHWLp0KebNmwddXV3ZetEvs2TJksqEWm8xDyQiqnvupeZh/PYQPHz8DPraGtj0vjvealZ1HZNE5alMjqFwgTAtLQ1z587F6dOnkZ6eXmYhTrFYrFAA9RUTQyKqDqk5BVh+NBJHwpMBAKZ6Wmw7JqrjmGPUfPyOiIjqpownIkzeeQ3XHmRDXU0A/7ddMaaTnarDonqkWguE/fv3R0JCAqZNmwZra+syV5V9fHwUCqC+YmJIRNXpUmwG/A6VtB0DgFtjI3zBtmOiOkmZOUaTJk0QEhIi26it1OPHj9GuXTvExcVVav76inkgEVHdVVAkxoK/buHAjYcAAN/O9vh8oDM01Ll5CVW9ai0Q6uvr48KFC2jbtq1CJyJ5TAyJqLoViSXYfvE+1p+KwtPnbcfvd7TDXG+2HRPVJcrMMdTU1JCamgoLCwu542lpabC1tUVhYWGl5q+vmAcSEdVtUqkUP5yLxVcn7gEAujU3x3ej3WCgzZybqlZlcgyFS9i2trZl2oqJiKjm01RXw6RuTXB6Tg+83cYGEimw8/IDeH19Dn+GJEIi4d/tRFTi8OHDOHz4MADgxIkTsueHDx/GgQMH8MUXX8DBwaHKzh8VFQUfHx+YmZnBwMAAXbp0wdmzZ+XGzJgxA+7u7hAKhS+9cH3ixAl06tQJ+vr6MDc3x7BhwxAfHy83ZteuXWjTpg10dXVhbW2NcePGITMzs0JxZmZmolGjRhAIBHj8+PGbfFQiIqqDBAIBPvZyxKb320FbUw3nox5h6A+XkJCZr+rQiF5K4TsIT548ia+//hpbtmyBvb19FYVV9/HKMRGp2qXYDCw5FIFoth0T1SnKyDHU1EquIQsEgjIXhjU1NWFvb4+vv/4agwYNqnS85WnWrBmaN2+OlStXQkdHB+vXr8f27dsRGxsLKysrAMD06dPRokULXLlyBTdv3kRYWJjcHHFxcXBxccHs2bMxYcIE5OTkYNasWcjNzcWNGzcAAEFBQejevTvWrVuHwYMH4+HDh5gyZQqaNWuGAwcOvDbOIUOGoLCwEMeOHUN2djaMjIwq9PmYBxIR1R+3H+Zgwq8hSMsVwVhXE1vGeKCDg4mqw6I6qlpbjI2NjZGfn4/i4mLo6upCU1P+FtmsrCyFAqivmBgSUU3w37ZjgQD4gG3HRLWaMnMMBwcHhISEwMys+nZhzMjIgLm5Oc6fP4+uXbsCAPLy8mBgYIBTp06hV69ecuP9/f1x8ODBMgXCffv2YdSoURCJRLKC55EjR+Dj4wORSARNTU2sXbsWmzZtQmxsrOx9GzduxJo1a5CYmPjKODdt2oQ//vgDfn5+6NWrFwuERET0Umm5BZj4ayhuPcyBproAX73bBkPcGqo6LKqDKpNjaCh6svXr1yv6FiIiqqFK247fbmuD5f9E4nB4MnZefoB/bqVgfj8nvOvO3Y6J6rP/tuNWB1NTUzg7O2PHjh1o164dhEIhtmzZAktLS7i7u1d4Hg8PD6irq2Pbtm3w9fXFkydPsHPnTnh7e8sucHfu3BmLFi3C0aNH0b9/f6Snp2Pfvn0YOHDgK+e+c+cOli1bhitXrlRooxaRSASRSCR7npubW+HPQUREtZ+lgTb+nOyJOXvDcPRWKmb+EYZHeSJM6tZE1aERyShcIBw7dmxVxEFERCpkaaCNb0e5YVSHxvA7dBvR6U/w6f6b+P1qAr4cwrZjovrs6dOnCAwMREJCQplNSaZPn6708wkEAgQEBMDHxwf6+vpQU1ODpaUljh8/XuE79ADA3t4eJ0+exPDhwzF58mSIxWJ4enri6NGjsjGdO3fGrl27MGLECBQUFKC4uBhvv/02Nm7c+NJ5RSIRRo0aha+++gqNGzeuUIFw5cqVWLp0aYVjJyKiukdHSx3fjWqHLw0i8cvFeCw/Gom03AIsHODMC/JUI7zRPtuxsbH4/PPPMWrUKKSnpwMAjh8/joiICKUGR0RE1cuzqSmOzuiKRQOcoaeljrDExxj8XRA+P3gLj/O5WylRfXPjxg04Ojpi1KhRmDZtGr788kvMnDkTCxcuVLirxN/fHwKB4JWP0NBQSKVSTJ06FRYWFrhw4QKuXr0KHx8fDBo0CCkpKRU+X2pqKiZOnIixY8ciJCQEgYGB0NLSwrvvvitbV/HOnTuYPn06/Pz8cO3aNRw/fhzx8fGYMmXKS+ddsGABnJ2d8cEHH1Q4lgULFiAnJ0f2eF37MhER1U1qagIsHuSMhQOcAAA/B8Vj5h9hKCyWqDgyojdYgzAwMBD9+/dHly5dcP78eURGRqJJkyZYs2YNrl69in379lVVrHUK154hopouLbdA1nYMACZ6WvisXwsMd7flVU6iGkyZOUaPHj3QvHlzbNq0CUZGRggPD4empiY++OADzJgxA0OHDq3wXBkZGcjIyHjlGHt7e1y8eBHe3t7Izs6Wi79Zs2aYMGEC5s+fL/eel61BuHjxYhw7dgyhoaGyY0lJSbC1tUVwcDA6deqEMWPGoKCgAHv37pWNCQoKQteuXZGcnAxra+syMbZt2xa3bt2CQFDy96BUKoVEIoG6ujoWLVpUoTsFmQcSEdFf15Pw6b6bKJZI8ZajGTaPcUcDocJNnkRyqnUNwvnz5+PLL7/E7Nmzoa+vLzvu5eWFDRs2KDodERHVUOW1HX+2/xZ2X03EFz4t0aoR246J6rqwsDBs2bIF6urqUFdXh0gkkl0YHjt2rEIFQjMzswptdpKfnw/g352US6mpqUEiqfgdFvn5+VBXV5c7Vvq8dJ78/HxoaGiUO+Zl19D379+PZ8+eyZ6HhIRg/PjxuHDhApo2bVrh+IiIqH4b2q4RTBsI8dFv1xAUk4GRPwZjm28HmOsLVR0a1VMKtxjfunUL77zzTpnj5ubmyMzMVEpQRERUc5S2HX8+8N+247e/D8KiA2w7JqrrNDU1ZXfKWVpaIiEhAQBgaGgo+3dl8/T0hLGxMcaOHYvw8HBERUVh3rx5iI+Pl9s8JCYmBmFhYUhNTcWzZ88QFhaGsLAw2TqJAwcOREhICJYtW4bo6Ghcv34d48aNg52dHdzc3AAAgwcPxl9//YVNmzYhLi4OFy9exPTp09GhQwfY2NgAAA4cOAAnJyfZeZs2bYqWLVvKHg4ODgAAZ2dnWFhYVMnPhIiI6qbuzc2x53+dYKqnhdsPczFs0yXEZzxVdVhUTylcIDQyMip3/ZcbN26gYUNu001EVBdpqqthYtcmODO3B3za2kAqBXZdSYDX2nPYczUBEolCq1UQUS3h5uYma9H18vKCn58fdu3ahZkzZ6JVq1ZVck4zMzMcP34cT548Qc+ePeHh4YGgoCAcOnQIbdq0kY2bOHEi3NzcsGXLFkRFRcHNzQ1ubm5ITi5ZFqFnz574/fffcfDgQbi5uaFfv34QCoU4fvw4dHR0AAC+vr745ptv8N1336Fly5YYPnw4WrRogb/++kt2npycHNy7d69KPisREVHrRkbY/1FnNDbRRUJWPt7ddAk3kx6rOiyqhxReg/DTTz9FcHAw9u7di+bNm+P69etIS0vDhx9+iA8//BBLliypqljrFK49Q0S1WXBsJpYcvo2otCcAgLa2Rmw7JqohlJljhIaGIi8vD15eXnj06BHGjh2LoKAgODo6Ytu2bXIFO6o45oFERPRf6XkFGLctBBHJudDVUsemD9zRvbm5qsOiWqYyOYbCBcKioiL4+vpiz549kEql0NDQgFgsxujRo7F9+/Yya71Q+ZgYElFtVySW4NdL97H+VDSeiIohEACjOzTGvL4tYKSrperwiOotZeUYUqkUCQkJsLCwkN1xR8rBPJCIiMrzRFSMKTtL1iTUUBNgzbutMbRdI1WHRbVItRYIS8XFxeH69euQSCRwc3NDs2bN3mSaeouJIRHVFWm5BVhxNBKHwkra+ox1NfFZPye858HdjolUQVk5hkQigba2NiIiIpjnKRnzQCIiepnCYgnm7g3H4fCS3HpBfyf8r1sT2ZrARK+ikgIhVQ4TQyKqay7HZcLv0L9tx21sjfCFjytaNzJSbWBE9YwycwxXV1ds3boVnTp1UlJ0BDAPJCKiV5NIpFhxNBI/B8UDACa85YBFA5x58Z1eqzI5hsKblLz77rtYtWpVmeNfffUVhg8fruh0RERUR3RqYop/ppfsdtxAqIHwxMfw+f4iFh64heyn3O2YqDZas2YN5s2bh9u3b6s6FCIionpDTU2Azwe5YNEAZwDA1qB4zPgjDKJisYojo7pM4TsIzc3NcebMmTI71926dQu9e/dGWlqaUgOsq3jlmIjqsvTnbccHX2g7/rSfE0aw7ZioyikzxzA2NkZ+fj6Ki4uhpaVVZi3CrKysSs1fXzEPJCKiijp44yHm7g1HsUSKLo6m2PyBO/S1NVUdFtVQlckxNBQ92ZMnT6ClVXbxeU1NTeTm5io6HRER1UEWBtpYP9INIzs0xpJDEbiXlocFf93CnpBEth0T1SLr1q3jmkdEREQqNMStIUz0tPDRb9dwMSYTI7Zcxvbx7WGhr63q0KiOUfgOwvbt22Pw4MHw8/OTO+7v748jR47g2rVrSg2wruKVYyKqL4rEEuwIfoB1AVGy3Y5HdWiMed4tYKzH3Y6JlI05Rs3H74iIiBR1KykH47ZfRcaTQtia6GDH+I5wMNNTdVhUw1TrJiWHDx/GsGHDMHr0aPTs2RMAcPr0aezevRt79+7FkCFDFAqgvmJiSET1TXpuAVYeu4sDNx4CAIye73bMtmMi5VJmjqGuro6UlBRYWFjIHc/MzISFhQXEYq6F9CaYBxIR0Zt4kPkUH/5yFQ8y82Gip4Vtvu3RxtZI1WFRDVKtm5S8/fbbOHjwIGJiYjB16lTMmTMHSUlJOHXqFIuDRET0UhYG2lg3oi3++F8ntLDUx+P8Iiz46xbe+eEiwhMfqzo8IirHy64ji0SicpecISIioqpjZ6qHfVM6o1VDQ2Q9LcTIHy/j3L10VYdFdYTCdxCScvDKMRHVZ+W1HY9s3xif9mXbMVFlKSPH+PbbbwEAs2bNwhdffIEGDRrIXhOLxTh//jzu37+PGzduKCXm+oZ5IBERVcYTUTE++u0aLkRnQENNgNXDWmOYeyNVh0U1QLW2GJcqLCxEeno6JBKJ3PHGjRu/yXT1DhNDIqLy244/7euEEe1toc62Y6I3oowcw8HBAQDw4MEDNGrUCOrq6rLXtLS0YG9vj2XLlqFjx45Kibm+YR5IRESVVVgswaf7wnEwLBkAML+/EyZ3a8LNxeq5ai0QRkdHY/z48bh06ZLccalUCoFAwLVoKoiJIRHRv67EZWLJ4QjcTc0DALRuZIgvfFpyTRWiN6DMHMPLywt//fUXjI2NlRQdAcwDiYhIOSQSKVYdv4sfz8cBAMZ1scfigS5c37seq9YCYZcuXaChoYH58+fD2tq6THW6TZs2CgVQXzExJCKSV/xC23GerO3YFvP6OsGEbcdEFcYco+bjd0RERMr084U4fPlPJABgYGtrfPNeGwg11F/zLqqLqrVAqKenh2vXrsHJyUmhE5E8JoZEROVLzyvAqqN38dcLbcfz+rbAyPaN2XZMVAHKzDHEYjG2b9+O06dPl7u0zJkzZyo1f33FPJCIiJTtUNhDzN0bjiKxFJ2bmmLLGHfoa2uqOiyqZtW6i7GLiwsyMjIUfRsREVGFWOhr45sRbfHnZE84WZXsdrzowG2888NFhHG3Y6JqNWPGDMyYMQNisRgtW7ZEmzZt5B5ERERUM/i0bYhtvh2gp6WOS7GZGLHlMjKeiFQdFtUiCt9BeObMGXz++edYsWIFWrVqBU1N+Yo0r4JWDK8cExG9HtuOiRSnzBzDzMwMO3bswIABA5QUHQHMA4mIqOrcfpgD320hyHgiQlNzPeya2AlWhtqqDouqSbW2GKupldx0+N+1B7lJiWKYGBIRVRzbjokqTpk5ho2NDc6dO4fmzZsrKToCmAcSEVHVis94ivd/uozknAI0NtHFrokdYWuiq+qwqBpUa4EwMDDwla93795doQDqKyaGRESKuxqfBb9Dt2W7HbdqaIgvhrREW+52TCSjzBzj66+/RlxcHL777rsyF4fpzTEPJCKiqpaUnY/RP11BQlY+bAy1sWtSJziY6ak6LKpi1VogJOVgYkhE9GaKxRLsvPwA35z8t+14hIctPu3HtmMiQLk5xjvvvIOzZ8/CxMQErq6uZZaW+euvvyo1f33FPJCIiKpDak4B3v/5MmIfPYW5vhC7JnZEc0t9VYdFVajaC4SPHz/G1q1bERkZCYFAABcXF4wfPx6GhoaKTlVvMTEkIqqc9LwCrDp2F39dL2k7NtQpaTse1YFtx1S/KTPHGDdu3Ctf37ZtW6Xmr6+YBxIRUXXJeCLCBz9fwd3UPBjramLnhI5o2ZC1m7qqWguEoaGh6Nu3L3R0dNChQwdIpVKEhobi2bNnOHnyJNq1a6dQAPUVE0MiIuUIuZ+FxQfl246X+bjCrbGxiiMjUg3mGDUfvyMiIqpOj/MLMfaXqwhPyoG+tga2j+sAdzvmynVRtRYIu3btCkdHR/z000/Q0NAAABQXF2PixImIi4vD+fPnFQqgvmJiSESkPMViCX67/ABfP287Bkp2O2bbMdVHys4xiouLce7cOcTGxmL06NHQ19dHcnIyDAwM0KBBAyVEXP8wDyQiouqWV1CE8dtDEHI/G7pa6tg6tj08m5qqOixSsmotEOro6ODGjRtwcnKSO37nzh14eHggPz9foQDqKyaGRETK9yhPhFXH7mL/9SQAbDum+kmZOcaDBw/Qr18/JCQkQCQSISoqCk2aNMHMmTNRUFCAzZs3Kynq+oV5IBERqUJ+YTH+t+MagmIyINRQw48feqB7c3NVh0VKVJkcQ03RkxkYGCAhIaHM8cTEROjrc7FLIiJSHXN9Ib5+rw32TfGEs7UBcp4V4fODt+HzfRBuJGSrOjyiWmfGjBnw8PBAdnY2dHR0ZMffeecdnD59WoWRERERkaJ0tTTw81gP9HKygKhYgkm/huJkRKqqw6IaQuEC4YgRIzBhwgT88ccfSExMRFJSEvbs2YOJEydi1KhRVREjERGRQjzsTXBkWhf4D3aBvrYGbj/MxTs/XMJn+24i84lI1eER1RpBQUH4/PPPoaUl36pvZ2eHhw8fqigqIiIielPamurY9IE7BrSyQqFYgo92Xcfh8GRVh0U1gMIFwrVr12Lo0KH48MMPYW9vDzs7O/j6+uLdd9/F6tWrqyJG3L9/HxMmTICDgwN0dHTQtGlTLFmyBIWFhXLjEhISMHjwYOjp6cHMzAzTp08vM+bWrVvo3r07dHR00LBhQyxbtgz/7bIODAyEu7s7tLW10aRJk3LbZ/bv3w8XFxcIhUK4uLjgwIEDyv/gRET0xjTU1eDbxQFn5vTAsHaNAAB/hCai59eB2Hn5AcQShVbYIKqXJBIJxGJxmeNJSUnsHCEiIqqltDTU8O1INwx1awixRIoZe27gz9BEVYdFKqZwgVBLSwsbNmxAdnY2wsLCcOPGDWRlZWHdunUQCoVVESPu3r0LiUSCLVu2ICIiAuvWrcPmzZuxcOFC2RixWIyBAwfi6dOnCAoKwp49e7B//37MmTNHNiY3Nxd9+vSBjY0NQkJCsHHjRqxduxbffPONbEx8fDwGDBiArl274saNG1i4cCGmT5+O/fv3y8YEBwdjxIgRGDNmDMLDwzFmzBi89957uHLlSpV8fiIienPltR0vft52fJ1tx0Sv1KdPH6xfv172XCAQ4MmTJ1iyZAkGDBigusCIiIioUjTU1bB2eBuM6tAYUinw6b6b2BF8X9VhkQopvElJTk4OxGIxTExM5I5nZWVBQ0Oj2hZa/uqrr7Bp0ybExcUBAI4dO4ZBgwYhMTERNjY2AIA9e/bA19cX6enpMDAwwKZNm7BgwQKkpaXJipmrVq3Cxo0bkZSUBIFAgM8++wyHDx9GZGSk7FxTpkxBeHg4goODAZS0Wefm5uLYsWOyMf369YOxsTF2795dofi5ODURUfUrFkuw60oC1p68h7yCkt2O3/NohM/6OcG0QdVc5CKqbsrMMZKTk+Hl5QV1dXVER0fDw8MD0dHRMDMzw/nz52FhYaGkqOsX5oFERFRTSKVSfPF3JH65GA8AWNDfCZO7N1VxVPSmqnWTkpEjR2LPnj1ljv/5558YOXKkotO9sZycHLkiZXBwMFq2bCkrDgJA3759IRKJcO3aNdmY7t27y93p2LdvXyQnJ+P+/fuyMd7e3nLn6tu3L0JDQ1FUVPTKMZcuXXppvCKRCLm5uXIPIiKqXhrqahjb2R5n5vTAu+4lbcd/hibBa+057Ay+z7Zjov+wsbFBWFgY5s2bh8mTJ8PNzQ2rVq3CjRs3WBwkIiKqAwQCARYPcsY0L0cAwMpjd7H+VFSZpdio7lO4QHjlyhV4eXmVOd6jR49qa7GNjY3Fxo0bMWXKFNmx1NRUWFpayo0zNjaGlpYWUlNTXzqm9PnrxhQXFyMjI+OVY0rnKM/KlSthaGgoe9ja2irykYmISInM9YVYO7wN9n/kCRdrA+QWFGPxoQi2HROVQ0dHB+PGjcN3332HH374ARMnTpTb0ZiIiIhqN4FAgLl9W2Be3xYAgPWnorHq+F0WCesZhQuEIpEIxcXFZY4XFRXh2bNnCs3l7+8PgUDwykdoaKjce5KTk9GvXz8MHz4cEydOlHtNIBCUOYdUKpU7/t8xpf/BK2NMeecvtWDBAuTk5MgeiYlcAJSISNXc7UxweFoXLH3bVbbb8dAfLuHTfeHc7ZgIJRc4f/nllzLHf/nllyrbnI6IiIhU42MvRywe5AIA2BIYB//DEZCww6beULhA2L59e/z4449ljm/evBnu7u4KzTVt2jRERka+8tGyZUvZ+NJ1cDw9PcvEYGVlVeYOvuzsbBQVFcnu9itvTHp6OgC8doyGhgZMTU1fOea/dxW+SCgUwsDAQO5BRESqx7ZjopfbsmULnJycyhx3dXXF5s2bVRARERERVaUJbzlg+TstIRAAvwY/wPy/bjIfric0FH3D8uXL0bt3b4SHh6NXr14AgNOnTyMkJAQnT55UaC4zMzOYmZlVaOzDhw/h5eUFd3d3bNu2DWpq8rVNT09PLF++HCkpKbC2tgYAnDx5EkKhUFa49PT0xMKFC1FYWAgtLS3ZGBsbG9jb28vGHDlyRG7ukydPwsPDA5qamrIxAQEBmDVrltyYzp07K/T5iYio5ihtOx7VwRaLD0bgTkouFh+KwB+hiVjm0xLtGhurOkSiapeamirLq15kbm6OlJQUFUREREREVe39jnbQ0VTH3L3h+DM0CQVFEnz9Xhtoqit8jxnVIgp/u126dEFwcDBsbW3x559/4siRI3B0dMTNmzfRtWvXqogRycnJ6NGjB2xtbbF27Vo8evQIqampcnfxeXt7w8XFBWPGjMGNGzdw+vRpzJ07F5MmTZLdrTd69GgIhUL4+vri9u3bOHDgAFasWIHZs2fL2oOnTJmCBw8eYPbs2YiMjMQvv/yCrVu3Yu7cubJzzZgxAydPnsTq1atx9+5drF69GqdOncLMmTOr5PMTEVH1Ydsx0b9sbW1x8eLFMscvXrwotzGcskVFRcHHxwdmZmYwMDBAly5dcPbsWbkxM2bMgLu7O4RCIdq2bVvuPCdOnECnTp2gr68Pc3NzDBs2DPHx8XJjdu3ahTZt2kBXVxfW1tYYN24cMjMzXxvj9u3b0bp1a2hra8PKygrTpk17489LRERU0wxt1wgbR7WDhpoAh8OT8fGu6xAVi1UdFlUlaS2wbds2KYByHy968OCBdODAgVIdHR2piYmJdNq0adKCggK5MTdv3pR27dpVKhQKpVZWVlJ/f3+pRCKRG3Pu3Dmpm5ubVEtLS2pvby/dtGlTmZj27t0rbdGihVRTU1Pq5OQk3b9/v0KfKScnRwpAmpOTo9D7iIio+jzKK5DO+TNMavfZ31K7z/6WtlpyXLrjUry0WCx5/ZuJVESZOcaqVaukpqam0l9++UV6//596f3796Vbt26VmpqaSlesWKGEaMvn6OgoHTBggDQ8PFwaFRUlnTp1qlRXV1eakpIiG/PJJ59Iv/vuO+mYMWOkbdq0KTNHbGysVCgUShcsWCCNiYmRXrt2TdqtWzdp27ZtZWMuXLggVVNTk27YsEEaFxcnvXDhgtTV1VU6ZMiQV8b39ddfS21sbKS7du2SxsTESG/fvi09fPhwhT8f80AiIqotTt1JlTZbdFRq99nf0g+3XpHmi4pVHRK9QmVyDIFUym1pVCE3NxeGhobIycnheoRERDXctQdZsrZjAHC1McAyn5Zwt2PbMdU8yswxpFIp5s+fj2+//RaFhYUAAG1tbXz22Wfw8/NTRrhlZGRkwNzcHOfPn5d1p+Tl5cHAwACnTp2SLXFTyt/fHwcPHkRYWJjc8X379mHUqFEQiUSypWmOHDkCHx8fiEQiaGpqYu3atdi0aRNiY2Nl79u4cSPWrFnz0g3lsrOz0bBhQxw5cqRMLBXFPJCIiGqToOgMTNoRimdFYnRqYoKfx7ZHA6HCK9ZRNahMjsEGciIiotdwtzPBkU/ewjKfkrbjiORcDNt0CfP2hiODbcdUhwkEAqxevRqPHj3C5cuXER4ejqysrCorDgKAqakpnJ2dsWPHDjx9+hTFxcXYsmULLC0tFdoQz8PDA+rq6ti2bRvEYjFycnKwc+dOeHt7y9aV7ty5M5KSknD06FFIpVKkpaVh3759GDhw4EvnDQgIgEQiwcOHD+Hs7IxGjRrhvffee2lBEQBEIhFyc3PlHkRERLXFW83M8Ov4Dmgg1MDluCyM2XoFOc+KVB0WKRkLhERERBWgribAh572ODu3B4Y/3+1477Uk9Fx7Dju42zHVcQ0aNED79u3RsmVLCIXCKj2XQCBAQEAAbty4AX19fWhra2PdunU4fvw4jIyMKjyPvb09Tp48iYULF0IoFMLIyAhJSUnYs2ePbEznzp2xa9cujBgxAlpaWrCysoKRkRE2btz40nnj4uIgkUiwYsUKrF+/Hvv27UNWVhb69Okju8vyv1auXAlDQ0PZw9bWtsKfg4iIqCbo4GCC3yZ2hIG2Bm4kPMbony4j62n5/9+j2okFQiIiIgWYNRDiq+FtsP+jznCxNkBuQTH8DkXg7e+CcO1BtqrDI1Kqp0+fYvHixejcuTMcHR3RpEkTuYci/P39IRAIXvkIDQ2FVCrF1KlTYWFhgQsXLuDq1avw8fHBoEGDFNo5OTU1FRMnTsTYsWMREhKCwMBAaGlp4d1330XpCjt37tzB9OnT4efnh2vXruH48eOIj4/HlClTXjqvRCJBUVERvv32W/Tt2xedOnXC7t27ER0dXWYjlVILFixATk6O7PGquw2JiIhqqra2RtjzP0+Y6mkhIjkXI38MRnpegarDIiV546bxmJgYxMbGolu3btDR0YFUKpXtBExERFTXudsZ48gnb+H3Kw/w1Yl7srbj4e6N8Fl/J5g1qNq7rIiqw8SJExEYGIgxY8bA2tq6UrnetGnTMHLkyFeOsbe3x5kzZ/D3338jOztbtnbODz/8gICAAPz666+YP39+hc73/fffw8DAAGvWrJEd++2332Bra4srV66gU6dOWLlyJbp06YJ58+YBAFq3bg09PT107doVX375JaytrcvMW3rMxcVFdszc3BxmZmZISEgoNxahUFjld14SERFVBxcbA/wxuRPe//kKotKeYOSWy9gzuRMs9LVVHRpVksIFwszMTIwYMQJnzpyBQCBAdHQ0mjRpgokTJ8LIyAhff/11VcRJRERU46irCTDG0x79W1ljzfG7+DM0CXuvJeFERCrm9m2B9zvaQV2NF8+o9jp27Bj++ecfdOnSpdJzmZmZwczM7LXj8vPzAUC2sUgpNTU1SCSSCp8vPz8f6urqcsdKn5fOk5+fDw0NjXLHvGwfv9Kfxb1799CoUclyA1lZWcjIyICdnV2F4yMiIqqtHC308edkT4z+6QriMp5i9E9XsOd/nXiBvJZTuMV41qxZ0NDQQEJCAnR1dWXHR4wYgePHjys1OCIiov+3d+dhNab/H8Dfpz1aRNpos5WUpFCWiZAsI18zluGLbD/GGAxmBl9LmLGMZWYsYxfGkjFlmyGZIURSqYgWS4QKkUpRquf3h+mMo0Wn7dQ579d1netynnOf53zu+zzncXc/z+e+6wJ9LXX88OnbtOM2Jv+mHX+8PhgR95/LOjyiCtPT00PDhg1r9DNdXFygp6eHMWPGIDo6GgkJCfj666+RmJgosXjI7du3ERUVhdTUVLx69QpRUVGIiooSzwPYv39/hIWFYcmSJbh16xauXr2KsWPHwtzcHA4ODgCAjz/+GP7+/ti0aRPu3r2LixcvYtq0aejYsSNMTEwAAIcPH4a1tbX4c1u1agVPT09Mnz4dly5dQkxMDMaMGQNra2v06NGjBluKiIhIdswb1cf+iZ1gpKOB209eYuS2UM5JWMdJPUAYGBiIlStXiq+YFmnZsiXu379fZYERERHVNY7mejg2tSuWeraBjoYKbqZk4pNNIZjN1Y6pjlq6dCkWLlwovquvJujr6yMgIAAvX76Em5sbnJycEBwcjKNHj8Le3l5cbsKECXBwcMCWLVuQkJAABwcHODg4IDk5GQDg5uaG/fv348iRI3BwcICHhwfU1dUREBAATU1NAICXlxfWrl2LDRs2wNbWFkOGDIGVlRX8/f3Fn5ORkYH4+HiJGPfs2YNOnTqhf//+cHV1haqqKgICAsSrIxMRESkC80b1ceD/nGGgrY74x1kYuT0UL3I4SFhXiYTS8idKoa2tjatXr6Jly5bQ1tZGdHQ0mjVrhrCwMHh4eODZs2fVFatcyczMhK6uLjIyMsTz6xARkfx49jIXK/9JOwYAbQ0VzHa3wshOZlBR5hphVH2qso/h4OCAO3fuQBAEWFhYFBsAu3r1aqX2r6jYDyQiInly+8lLDN96GWkvc2HbRAf7xjtDtx4vmslCZfoYUs9B+NFHH2HPnj1YunQpAEAkEqGwsBCrVq1iWgUREdE/Gv2TdjysgxkWHo3BjeRMLDp2A75hD7DUsw2cLGo2bZOoIgYNGiTrEIiIiKiWa2Gghf0TO+GzrZcR8ygTo3eG4tcJnaCjwUHCukTqOwhv3ryJ7t27w9HREWfOnMHAgQNx48YNPH/+HBcvXkTz5s2rK1a5wivHRESKo6BQwP4rSVgVEIfM1/kAgE/aN8WcvtZorM3JnKlqsY9R+/E7IiIieRSbkokR2y4jPecN2ps1wJ7xnaClLvV9aVQJleljSJ3jZGNjg2vXrqFjx47o3bs3srOzMXjwYERGRnJwkIiIqATKSiKMcjbH2dndMczJFADgd/Uh3NYEYdfFROQXlH9lViJZiIiIwN69e7Fv3z5ERkbKOhwiIiKqhVob62DvhE7Q1VTF1aQXGOtzBdm5+bIOi8pJ6jsIqWrwyjERkeK6mpSOhUdjEPMoE8DbzhTTjqmqVGUf48mTJxg+fDiCgoLQoEEDCIKAjIwM9OjRA76+vmjcuHEVRa1Y2A8kIiJ5dv1hBkZsv4ys1/lwbtYQPl4doammLOuwFEKN3kHo4+ODQ4cOFdt+6NAh7N69W9rdERERKZz2Zno4+kVXLB1kC11NVcSmZOLTzSGY9Vs0nmZxtWOqPb788ktkZmaKp5NJT09HTEwMMjMzMW3aNFmHR0RERLWQXVNd7BnXEVrqKrh89zkm7AnD6zcFsg6LPkDqAcIVK1ZAX1+/2HYDAwMsW7asSoIiIiKSd0Vpx2dmuTLtmGqtgIAAbNq0Ca1btxZvs7GxwcaNG3Hy5EkZRkZERES1mYOZHnaP64D6asq4ePsZJu4J5yBhLSf1AOH9+/dhaWlZbLu5uTmSkpKqJCgiIiJF0UhLHSs/bQv/KZ1h20QHWa/z4X38JgasD0bYveeyDo8UXGFhIVRVi69AqKqqisJCDmITERFR6RzNG8JnbEdoqirjwq00fL43Arn5HCSsraQeIDQwMMC1a9eKbY+OjkajRo2qJCgiIiJFU5R2/N0/acdxqVkYsjkEM3+LYtoxyYybmxumT5+O5ORk8bZHjx7hq6++Qs+ePWUYGREREdUFHS0bYqdXB2ioKuFs/FN8sS8Sefm8yFgbST1AOHz4cEybNg1nz55FQUEBCgoKcObMGUyfPh3Dhw+vjhiJiIgUgrKSCP/9Z7Xj4R3eph37X30Et9VB8GHaMcnAhg0bkJWVBQsLCzRv3hwtWrSApaUlsrKysH79elmHR0RERHWAS/NG2D66A9RVlPBX7GNMOxCJN+zX1jpSr2Kcl5eHUaNG4dChQ1BRUQHwNv1k9OjR2Lx5M9TU1KolUHnD1euIiOhDIpPSsfDoDVx/lAEAsDbSxtJBtujA1Y6pDNXRxzh9+jTi4uIgCAJsbGzQq1evKtmvomI/kIiIFNG5hKeYuDsceQWF6N/WGD8PawcVZanvW6MyVKaPIfUAYZGEhARER0dDU1MTdnZ2MDc3r8huFBY7hkREVB4FhQJ8w5LwQ0A8Ml69AQAMbt8Ec/u2RmNtdRlHR7UR+xi1H78jIiJSVGfiHmPSrxF4UyDAs50J1g5tB2UlkazDkhuV6WNUeKi2VatWGDJkCAYMGMDBQSIiomqirCTCyE5v044/62gKkYhpx1T9zpw5AxsbG2RmZhZ7LSMjA23atMGFCxdkEBkRERHVZW7Whtg4oj1UlEQ4GpWMr3+PRkFhhe5boypWoTsIHz58iGPHjiEpKQl5eXkSr61du7bKgpNnvHJMREQVEfXgBRYejcG1h/+mHS/xtEVHS6Yd01tV0ccYOHAgevToga+++qrE19etW4ezZ8/i8OHDlQlVYbEfSEREiu7k9RRMPRCJgkIBQ52aYsXgtlDinYSVVqMpxn///TcGDhwIS0tLxMfHw9bWFvfu3YMgCGjfvj3OnDkjVQCKih1DIiKqqKK041Wn4vEi55+0Y4cmmNPPGgbaGjKOjmStKvoY5ubmCAgIQOvWrUt8PS4uDu7u7khKSqpMqAqL/UAiIiLgeHQypvtGolAARnQyw/eDbCEScZCwMmo0xXju3LmYNWsWYmJioKGhAT8/Pzx48ACurq4YMmSItLsjIiIiKRWlHZ+Z9U7aceQj9Fx9DjuDmXZMlff48WOoqqqW+rqKigqePn1agxERERGRvPnY/u0chCIRsD80CYuO3UAFl8mgKiD1AGFsbCzGjBkD4G3n8NWrV9DS0sKSJUuwcuXKKg+QiIiIStawvhqWD26Lw1O6oG1TXWTl5mPJHzcxYH0wriQ+l3V4VIc1adIE169fL/X1a9euwdjYuAYjIiIiInk0yKEJVn1qD5EI2BNyH0v/iOUgoYxIPUBYv3595ObmAgBMTExw584d8WtpaWlVFxkRERGVSzvTBjg8pQuW/ccODeqpIi41C0O3hOCrg1F4kvVa1uFRHdSvXz8sXLgQr18XP35evXqFRYsWYcCAATKIjIiIiOTNp45NsWKwHQBg58VELD8Zx0FCGVCR9g3Ozs64ePEibGxs0L9/f8yaNQvXr1+Hv78/nJ2dqyNGIiIi+gBlJRFGdDJDX1sj/HAqHr5hSTgc+Qh/3XyMGb1bYYyLOVSUpb4uSApq/vz58Pf3R6tWrTB16lRYWVlBJBIhNjYWGzduREFBAf73v//JOkwiIiKSE8M6mOFNgYD5R2Kw9fxdqCiJ8HUfK85JWIOkXqTk7t27ePnyJdq2bYucnBzMnj0bwcHBaNGiBX788UeYm5tXV6xyhZNTExFRdYp+8AIL3lvtePHANujUrJGMI6PqVlV9jPv37+Pzzz/HqVOnxFfxRSIR+vTpg19++QUWFhZVFLHiYT+QiIioZLsv3cOiYzcAANN6tsTM3q1kHFHdUu2rGK9btw7/93//Bw0NDSQlJcHU1JSjuJXEjiEREVW3gkIBB8Me4IdTceLVjv/j0ARz+1rDQIerHcurqu5jpKen4/bt2xAEAS1btoSenl4VRKnY2A8kIiIq3Y7gRCz94yYAYFbvVviyZ0sZR1R3VPsAoYqKCpKTk2FgYABlZWWkpKTAwMCgwgETO4ZERFRz0rPzxGnHggBoqavgK6Ydyy32MWo/fkdERERl23LuDpafjAMAfOthjc+7N5dxRHVDZfoY5fqrwMTEBH5+frh//z4EQcDDhw+RlJRU4oOIiIhqF736alg+2A5HpnSBfVNdvMzNx9I/bqL/umCE3n0m6/CIiIiIiCRMcm2Or/tYAQBWBsRhR3CijCOSf+W6g3Dr1q348ssvkZ+fX2oZQRAgEolQUFBQpQHKK145JiIiWSgsFHAw/AFWBvybdjyonQnm9WvNtGM5wT5G7cfviIiIqHx++isBP/11CwCwZog9PnFsKuOIardqTzEGgKysLNy/fx9t27bFX3/9hUaNSp7k3N7eXqoAFBU7hkREJEvp2XlYFRiPA1f+TTue0aslxnS2gCrTjus09jFqP35HRERE5SMIAr7/MxbbgxOhrCTC5v86oreNoazDqrVqZIAQAAoKCvDrr7+iT58+MDY2ljpQ+hc7hkREVBtce/gCC47EIPqf1Y6tDLWxxJOrHddl7GPUfvyOiIiIyk8QBHz9+zX8HvEQaipK2D22I1yas69akmqfg7CIsrIyJk+ejNevX0v1IURERFQ7tW3aAIendMHywXZoUE8V8Y+zMGzrZczwjcSTTP5/T0RERESyJRKJsGKwHdxtDJGXX4iJe8Jx/Z+L21R1pM4hsrOzw927d6sjFiIiIpIBJSURPutohrOzumNEJzOIRMCRqGS4rTmH7Rfu4k1BoaxDJCIiIiIFpqKshHWfOcClWSO8zM3HGJ8ruP3kpazDkitSDxB+//33mD17Nv744w+kpKQgMzNT4kFERER1k159NSz7jx2OfvHvasff/RmL/usu4DJXOyYiIiIiGdJQVcbW0Y6wa6KL59l5GL0jFI9evJJ1WHJDqjkIAUBJ6d8xRZFIJP43VzGWDueeISKi2qywUMBv/6x2nP7Pasee/6x2bMjVjms19jFqP35HREREFffsZS6GbgnBnafZaNa4Pg5NckEjLXVZh1Ur1NgiJQBw7ty5Ml93dXWVKgBFxY4hERHVBenZeVgdGI/9/6x2XF9NGV/1bsXVjmsx9jFqP35HRERElZP84hU+3XQJyRmvYddEF/sndoK2hqqsw5K5Gh0gpKrBjiEREdUl1x6+wIKjNxD94AUAoJWhFhYPtOUKcrUQ+xi1H78jIiKiyrvz9CWGbg7Bs+w8ODdriF1jO0JDVVnWYclUja1iDADnz58v81Ed7t27h/Hjx8PS0hKamppo3rw5Fi1ahLy8PIlyIpGo2GPz5s0SZa5fvw5XV1doamqiSZMmWLJkCd4fIz137hwcHR2hoaGBZs2aFdsHAPj5+cHGxgbq6uqwsbHB4cOHq77iREREtUTbpg1w+PPOWDHYDnr1VJHw+CU+23YZ0w5E4jFXOyYiIiKiGta8sRZ2j+sILXUVXL77HFP3RyKfi+tVmIq0b+jevXuxbe/ORVgdcxDGxcWhsLAQW7ZsQYsWLRATE4OJEyciOzsbq1evlijr4+MDDw8P8XNdXV3xvzMzM9G7d2/06NEDYWFhSEhIgJeXF+rXr49Zs2YBABITE9GvXz9MnDgRe/fuxcWLFzFlyhQ0btwYn3zyCQAgJCQEw4YNw9KlS/Gf//wHhw8fxtChQxEcHIxOnTpVef2JiIhqAyUlEYZ3NIOHrRFWnXqbdnwsOhl/xz7GjF6t4NWFacdEREREVHNsm+hi+xgnjN55BX/FPsa3ftex6tO2UFISffjNJEHqXnx6errE48mTJwgICECHDh0QGBhYHTHCw8MDPj4+cHd3R7NmzTBw4EDMnj0b/v7+xco2aNAARkZG4oempqb4tX379uH169fYtWsXbG1tMXjwYMybNw9r164V30W4efNmmJmZ4aeffkLr1q0xYcIEjBs3TmIg8qeffkLv3r0xd+5cWFtbY+7cuejZsyd++umnaqk/ERFRbdKgnhq+L1rt2LQBsvMK8P2JWPT7+QJC7nC1Y6q8hIQEeHp6Ql9fHzo6OujSpQvOnj0rUWb69OlwdHSEuro62rVrV+J+Tp06BWdnZ2hra4sv9iYmJkqU2bdvH+zt7VGvXj0YGxtj7NixePas7OM4LCwMPXv2RIMGDaCnpwd3d3dERUVVpspERERUQc7NGmHjiPZQVhLB7+pDfH8itlimKH2Y1AOEurq6Eg99fX307t0bP/zwA7755pvqiLFEGRkZaNiwYbHtU6dOhb6+Pjp06IDNmzejsPDf20tDQkLg6uoKdfV/V7fp06cPkpOTce/ePXEZd3d3iX326dMH4eHhePPmTZllLl26VGq8ubm5yMzMlHgQERHVZUVpxys/eZt2fOsJ046pavTv3x/5+fk4c+YMIiIi0K5dOwwYMACpqaniMoIgYNy4cRg2bFiJ+7h79y48PT3h5uaGqKgonDp1CmlpaRg8eLC4THBwMEaPHo3x48fjxo0bOHToEMLCwjBhwoRSY8vKykKfPn1gZmaG0NBQBAcHQ0dHB3369BH3FYmIiKhm9bYxxKpP2wIAdgQnYuPZ2zKOqO6psjygxo0bIz4+vqp2V6Y7d+5g/fr1mDx5ssT2pUuX4tChQ/jrr78wfPhwzJo1C8uWLRO/npqaCkNDQ4n3FD0v6nCWViY/Px9paWlllnm30/q+5cuXSwysmpqaSllrIiKi2kdJSYRhHcxwdnZ3/NfZDCIRcCw6GW6rg7Dt/F284TwwJKW0tDTcvn0bc+bMQdu2bdGyZUusWLECOTk5uHHjhrjcunXr8MUXX6BZs2Yl7ufq1asoKCjAd999h+bNm6N9+/aYPXs2oqOjxQN5ly9fhoWFBaZNmwZLS0t07doVkyZNQnh4eKnxxcfHIz09HUuWLIGVlRXatGmDRYsW4cmTJ0hKSqraxiAiIqJyG9y+KRYOsAEArA5MwK+X78s4orpF6gHCa9euSTyio6MREBCAzz//HPb29lLty9vbu8SFRd59vN9BS05OhoeHB4YMGVLs6u78+fPh4uKCdu3aYdasWViyZAlWrVolUebd+RIBiG87fXd7Rcu8v+1dc+fORUZGhvjx4MGDUssSERHVNQ3qqeG7QXY49kVXtHsv7fjSnTRZh0d1SKNGjdC6dWvs2bMH2dnZyM/Px5YtW2BoaAhHR8dy78fJyQnKysrw8fFBQUEBMjIy8Ouvv8Ld3R2qqqoAgM6dO+Phw4c4ceIEBEHA48eP8fvvv6N///6l7tfKygr6+vrYsWMH8vLy8OrVK+zYsQNt2rSBubl5ie9hJgkREVHNGNfVEtPcWgAAFh6NwdGoRzKOqO6QepGSdu3aQSQSFcvndnZ2xs6dO6Xa19SpUzF8+PAyy1hYWIj/nZycjB49esDFxQVbt2794P6dnZ2RmZmJx48fw9DQEEZGRsXu8nvy5AmAf+8kLK2MiooKGjVqVGaZ9+8qfJe6urpEajMREZE8smuqC//PO+NQxAOsDIjHrScvMWJbKD62N8H/+rWGka6GrEOkWk4kEuH06dPw9PSEtrY2lJSUYGhoiICAADRo0KDc+7GwsEBgYCCGDBmCSZMmoaCgAC4uLjhx4oS4TOfOnbFv3z4MGzYMr1+/Rn5+PgYOHIj169eXul9tbW0EBQXB09MTS5cuBQC0atUKp06dgopKyV3r5cuXY/HixeWOnYiIiCruq96t8OLVG+wJuY9Zv0VDR1MVPawMZB1WrSf1HYSJiYm4e/cuEhMTkZiYiPv37yMnJweXLl2CtbW1VPvS19eHtbV1mQ8Njbd/SDx69Ajdu3dH+/bt4ePjAyWlD4ceGRkJDQ0NcWfSxcUF58+fR15enrhMYGAgTExMxAORLi4uOH36tMR+AgMD4eTkJL7aXFqZzp07S1V/IiIieVSUdnxmlqs47fh4dDJ6rgnC1vN3mHasoMqbOSIIAqZMmQIDAwNcuHABV65cgaenJwYMGICUlJRyf15qaiomTJiAMWPGICwsDOfOnYOamho+/fRT8YXumzdvYtq0aVi4cCEiIiIQEBCAxMTEYtPYvOvVq1cYN24cunTpgsuXL+PixYto06YN+vXrh1evXpX4HmaSEBER1RyRSATvj9vAs50J8gsFfL43AuH3nss6rFpPJNSBpV2Sk5Ph6uoKMzMz7NmzB8rKyuLXjIyMAADHjx9HamoqXFxcoKmpibNnz2LWrFnw8vLCzz//DODtwiZWVlZwc3PDvHnzcOvWLXh5eWHhwoWYNWsWgLcDoLa2tpg0aRImTpyIkJAQTJ48GQcOHMAnn3wCALh06RI++ugjfP/99/D09MTRo0cxf/58BAcHo1OnTuWqU2ZmJnR1dZGRkQEdHZ2qbC4iIqJa5frDDCw4GoOoBy8AAC0NtLDYsw06N9eXbWByqrb2MdLS0sTzOZfGwsICFy9ehLu7O9LT0yXib9myJcaPH485c+ZIvMfb2xtHjhwptorwggULcPLkSYnpah4+fAhTU1OEhITA2dkZo0aNwuvXr3Ho0CFxmeDgYHTr1g3JyckwNjYuFuOOHTswb948pKSkiC9Y5+XlQU9PDzt27PhgdgxQe78jIiIiefKmoBD/tyccZ+OfQltDBQf/zwU2JvL9/25l+hjlTjEODQ3F8+fP0bdvX/G2PXv2YNGiRcjOzsagQYOwfv36akmjDQwMxO3bt3H79m00bdpU4rWi8U1VVVX88ssvmDlzJgoLC9GsWTMsWbIEX3zxhbisrq4uTp8+jS+++AJOTk7Q09PDzJkzMXPmTHEZS0tLnDhxAl999RU2btwIExMTrFu3Tjw4CLxNR/H19cX8+fOxYMECNG/eHAcPHiz34CAREZEiKUo7/j3iIVYExDHtWEHp6+tDX//Dg8I5OTkAUCxbRElJCYWF5b/7NCcnR+KiMgDx86L95OTkFEsLLipT2jX0nJwcKCkpScw9XfRcmviIiIioeqkqK+GXkY4YvTMUYffSMXrnFfw+2QUW+vVlHVqtVO47CPv27Yvu3bvj22+/BQBcv34d7du3h5eXF1q3bo1Vq1Zh0qRJ8Pb2rs545QavHBMRkSJ6kZOHNYEJ2Bt6H4IA1FdTxvReLTG2iyVUlaWe+YRKUNf7GGlpabC2toarqysWLlwITU1NbNu2DT///DPCwsLEi+Ldvn0bL1++xObNm3H27FkcPHgQAGBjYwM1NTWcOXMGvXr1gre3Nz777DNkZWVh3rx5iIuLQ2xsLDQ1NbFr1y5MnDgR69atQ58+fZCSkoIZM2ZASUkJoaGhAIDDhw9j7ty5iIuLAwDExcWhXbt2GDduHL788ksUFhZixYoVOH78OGJjY0u86/B9df07IiIiqksyXr3B8K2XEZuSiaZ6mvD7vDMMdeTzAnVl+hjl7olHRUWhZ8+e4ue+vr7o1KkTtm3bhpkzZ2LdunX47bffpPpwIiIiUiwN6qlh6SBbHJ/aFQ5mb1c7XnYiDn1/voBLt7naMb290zAgIAAvX76Em5sbnJycEBwcjKNHj4oHBwFgwoQJcHBwwJYtW5CQkAAHBwc4ODggOTkZAODm5ob9+/fjyJEjcHBwgIeHB9TV1REQEABNTU0AgJeXF9auXYsNGzbA1tYWQ4YMgZWVFfz9/cWfk5GRgfj4ePFza2trHD9+HNeuXYOLi4s4HTkgIKBcg4NERERUs3Q1VbF7XAeYN6qHh+mvMGpHKF7k5H34jQqm3HcQamho4NatWzA1NQUAdO3aFR4eHpg/fz4A4N69e7Czs0NWVlb1RStHeOWYiIgUXWGhgN+vPsSKk3F4nv22kzagrTHm97dh2nElsI9R+/E7IiIiqnkPnufg082X8DgzFw5mDbBvQifUUyv3zHt1Qo3cQWhoaIjExEQAbydivnr1KlxcXMSvZ2VliVf5JSIiIvoQJSURhjqZ4uys7hjtYg4lEfDHtRS4rQnClnN3kJfP+dyIiIiIqGqYNqyHX8d3QoN6qohMeoFJv0YgN79A1mHVGuUeIPTw8MCcOXNw4cIFzJ07F/Xq1UO3bt3Er1+7dg3NmzevliCJiIhIfunWU8UST1scm9oV7c0aICevAMtPxqHfOqYdExEREVHVaWWoDR+vDqinpowLt9Iw82A0CgrLlVgr98o9QPjdd99BWVkZrq6u2LZtG7Zt2wY1NTXx6zt37oS7u3u1BElERETyz7aJLn6f3Bk/fNoWjeqr4faTlxixPRRT919FasZrWYdHRERERHLAwUwPW0Y5QlVZhD+vp2D+keso5+x7cq3ccxAWycjIgJaWFpSVlSW2P3/+HFpaWhKDhlQ6zj1DRERUuoycN1hzOh57L99HoQDUU1PG9J5vVztWU+Fqx2VhH6P243dEREQkeyeup2Dq/qsoFIDPuzfHtx7Wsg6p0mpkDsIiurq6xQYHAaBhw4YcHCQiIqIqUVracd+fz+Mi046JiIiIqJL62Rlj2X/sAACbgu5g6/k7Mo5ItngJnoiIiGqtorTjVf+kHd95mo2R20Pxxf6rSMl4JevwiIiIiKgOG97RDHP6vr1zcNmJOPhFPJRxRLLDAUIiIiKq1ZSURBjiZIozs7pjzD+rHf95LQU915zDZq52TERERESVMNm1OSZ91AwA8K3fNYXNVuEAIREREdUJuvVUsdjTFse/7ApHcz3k5BVgBdOOiYiIiKiSvvWwxsf2JsgvFDD51wjEpWbKOqQaxwFCIiIiqlPamOji0CQXph0TERERUZVQUhJh9ZC26GjZEFm5+fDaGaZw/UoOEBIREVGdI047ns20YyIiIiKqPHUVZWwb5YQWBlpIzXyNsT5hyHz9RtZh1RgOEBIREVGdpatZetpx8C2mHRMRERFR+enWU8WusR3QWFsdcalZmLL3qsJceOYAIREREdV5RWnHq4fYi9OO/7sjFF/sY9oxEREREZVfU7168PHqgHpqygi+nYY5/tcgCIKsw6p2HCAkIiIiuaCkJMKnjk1xZnZ3eHW2eJt2fP1t2vGmIKYdExEREVH52DbRxcaR7aGsJIL/1Uf48a9bsg6p2nGAkIiIiOSKrqYqvAe2wfEvu8Lpn7TjlQFx8GDaMRERERGVUw8rA3w/yBYAsO7vWzgYliTjiKoXBwiJiIhILrUx0cWhyS5YM8Qe+lpquPtO2nHyC6YdExEREVHZhnc0w5duLQAA8w7HICj+iYwjqj4cICQiIiK5JRKJ8IljU/w9i2nHRERERCS9mb1bYbBDExQUCvhi31XEPMqQdUjVggOEREREJPeK0o7/+LIbnMz18OrNv2nHF249lXV4RERERFRLiUQirPikLbq0aITsvAKM2xWGR3KYjcIBQiIiIlIYNiY6xdKOR+24gin7Iph2TEREREQlUlNRwqb/OsLaSBtPsnLhtfMKMnLeyDqsKsUBQiIiIlIoJaUdn7ieip5rzuGXoNtMOyYiIiKiYnQ0VOEztgOMdDRw68lL/N+v4cjNL5B1WFWGA4RERESkkEpKO/4hIB4eP53H+QSmHRMRERGRJGNdTfiM7QAtdRWEJj7H14euobBQkHVYVYIDhERERKTQiqUdp2Vj9M4r+HxvhFzOL0NEREREFdfaWAeb/tseKkoiHItOxqrAeFmHVCU4QEhEREQKr6S045Mxqei15hw2nr0tV+kjRERERFQ53Vo2xopP2gIANgXdwd7L92UcUeVxgJCIiIjoH++mHXeweJt2vOpUPPr+dIFpx0REREQk9qljU8zs3QoAsPBoDP6OfSzjiCqHA4RERERE77Ex0cFvk1ywdqg99LXUmXZMRERERMV86dYCw5xMUSgAU/dHIvrBC1mHVGEcICQiIiIqgUgkwuD2TXFmtivGdmHaMRERERFJEolE+O4/tvioVWO8elOA8bvDkPQsR9ZhVQgHCImIiIjKoKOhikUft8Gf0yTTjj1+uoBzTDsmIiIiUmiqykr4ZWR72BjrIO1lHrx8riA9O0/WYUmNA4RERERE5dDaWDLtODEtG2N2XsHkX5l2TERERKTItNRV4DO2A5o00MTdtGxM3BOO12/qVrYJBwiJiIiIyun9tGNlJRECbqSi55ogph0TERERKTBDHQ34jO0AbQ0VhN9Px6zfolFYKMg6rHLjACERERGRlIrSjv/4sis6WOjh9ZtCph0TERERKbhWhtrYOsoJaspK+PN6CpadiJV1SOXGAUIiIiKiCipKO/5xGNOOiYiIiAhwad4Iq4a0BQBsD06Ez8VEGUdUPhwgJCIiIqoEkUiE/zi8TTse18WSacdERERECs6zXRN842EFAFjyx00ExKTKOKIP4wAhERERURXQ0VDFwo9t8Oe0ruho0VAi7Tgo/omswyMiIiKiGvS5a3P819kMggBM941ExP10WYdUJg4QEhEREVUhayMdHJzkjJ+GtUNj7bdpx14+YZj0azgepufIOjwiIiIiqgEikQjeH7dBT2sD5OYXYsLuMCSmZcs6rFJxgJCIiIioiolEIgxyaIIzs1wxvuvbtONTNx6j19pz2HDmFtOOPyAhIQGenp7Q19eHjo4OunTpgrNnz0qUmT59OhwdHaGuro527dqVuJ9Tp07B2dkZ2traaNy4MT755BMkJkrOA7Rx40a0bt0ampqasLKywp49ez4YX1JSEj7++GPUr18f+vr6mDZtGvLy8ipcXyIiIpJPKspKWD/CAW2b6iI95w28fK7g2ctcWYdVIg4QEhEREVUTbQ1VLBjwT9qx5du049WBCejz43nEPMqQdXi1Vv/+/ZGfn48zZ84gIiIC7dq1w4ABA5Ca+u/8PYIgYNy4cRg2bFiJ+7h79y48PT3h5uaGqKgonDp1CmlpaRg8eLC4zKZNmzB37lx4e3vjxo0bWLx4Mb744gscP3681NgKCgrQv39/ZGdnIzg4GL6+vvDz88OsWbOqrgGIiIhIbtRTU8GOMR1g2lAT95/lYPzucLzKq30Xi0WCIAiyDkIRZWZmQldXFxkZGdDR0ZF1OERERFTNBEHA0ahkfH8iFq/zCvD3bFcYaGtU+efU9T5GWloaGjdujPPnz6Nbt24AgKysLOjo6OCvv/5Cz549Jcp7e3vjyJEjiIqKktj++++/47PPPkNubi6UlN5eEz9+/Dg8PT2Rm5sLVVVVdO7cGV26dMGqVavE75sxYwbCw8MRHBxcYnwnT57EgAED8ODBA5iYmAAAfH194eXlhSdPnpSrzev6d0RERETSu/P0JT7ZdAn11VRwYKIzzBrVq/LPqEwfg3cQEhEREdWAd9OOt41xqpbBQXnQqFEjtG7dGnv27EF2djby8/OxZcsWGBoawtHRsdz7cXJygrKyMnx8fFBQUICMjAz8+uuvcHd3h6qqKgAgNzcXGhqS34OmpiauXLmCN2/elLjfkJAQ2NraigcHAaBPnz7Izc1FREREie/Jzc1FZmamxIOIiIgUS/PGWtg9tiP8p3SulsHByqozA4QDBw6EmZkZNDQ0YGxsjFGjRiE5OVmiTHnmg7l+/TpcXV2hqamJJk2aYMmSJXj/Jspz587B0dERGhoaaNasGTZv3lwsHj8/P9jY2EBdXR02NjY4fPhw1VeaiIiI5I62hiqcmzWSdRi1lkgkwunTpxEZGQltbW1oaGjgxx9/REBAABo0aFDu/VhYWCAwMBDz5s2Duro6GjRogIcPH8LX11dcpk+fPti+fTsiIiIgCALCw8Oxc+dOvHnzBmlpaSXuNzU1FYaGhhLb9PT0oKamJpEC/a7ly5dDV1dX/DA1NS13PYiIiEh+2Js2gKFO7bxIXGcGCHv06IHffvsN8fHx8PPzw507d/Dpp5+KXy/PfDCZmZno3bs3TExMEBYWhvXr12P16tVYu3atuExiYiL69euHbt26ITIyEvPmzcO0adPg5+cnLhMSEoJhw4Zh1KhRiI6OxqhRozB06FCEhobWTGMQERER1THe3t4QiURlPsLDwyEIAqZMmQIDAwNcuHABV65cgaenJwYMGICUlJRyf15qaiomTJiAMWPGICwsDOfOnYOamho+/fRT8cXhBQsWoG/fvnB2doaqqio8PT3h5eUFAFBWVi513yKRqNg2QRBK3A4Ac+fORUZGhvjx4MGDcteDiIiIqCbU2TkIjx07hkGDBonnkCnPfDBFE1E/fvwY6urqAIAVK1Zg/fr1ePjwIUQiEb799lscO3YMsbGx4s+aPHkyoqOjERISAgAYNmwYMjMzcfLkSXEZDw8P6Onp4cCBAyXGm5ubi9zcf1eqyczMhKmpKeeeISIioipVW+e3S0tLK/WuvCIWFha4ePEi3N3dkZ6eLhF/y5YtMX78eMyZM0fiPaXNQbhgwQKcPHkS4eHh4m0PHz6EqakpQkJC4OzsLN7+5s0bPH78GMbGxti6dSu+/fZbvHjxQjx34bsWLlyIo0ePIjo6WrwtPT0dDRs2xJkzZ9CjR48PtkVt/Y6IiIioblO4OQifP3+Offv2oXPnzuI5ZMozH0xISAhcXV3Fg4NFZZKTk3Hv3j1xGXd3d4nP69OnD8LDw8Vz0ZRW5tKlS6XGzNQSIiIiUmT6+vqwtrYu86GhoYGcnBwAKDY4p6SkhMLCwnJ/Xk5OTrG7AIuev78fVVVVNG3aFMrKyvD19cWAAQNKHBwEABcXF8TExEjczRgYGAh1dXWp5kgkIiIiqk3q1ADht99+i/r166NRo0ZISkrC0aNHxa+VZz6YksoUPf9Qmfz8fPFV79LKlDbvDMDUEiIiIqLycHFxgZ6eHsaMGYPo6GgkJCTg66+/RmJiIvr37y8ud/v2bURFRSE1NRWvXr1CVFQUoqKixPNP9+/fH2FhYViyZAlu3bqFq1evYuzYsTA3N4eDgwMAICEhAXv37sWtW7dw5coVDB8+HDExMVi2bJn4cw4fPgxra2vxc3d3d9jY2GDUqFGIjIzE33//jdmzZ2PixIm8G5CIiIjqLJkOEJZ3LpoiX3/9NSIjIxEYGAhlZWWMHj1aYoGR8swH836ZovdXRZnS5p0BAHV1dejo6Eg8iIiIiEiSvr4+AgIC8PLlS7i5ucHJyQnBwcE4evQo7O3txeUmTJgABwcHbNmyBQkJCXBwcICDg4N4ETs3Nzfs378fR44cgYODAzw8PKCuro6AgABoamoCeDuH9Zo1a2Bvb4/evXvj9evXuHTpEiwsLMSfk5GRgfj4ePFzZWVl/Pnnn9DQ0ECXLl0wdOhQDBo0CKtXr66ZBiIiIiKqBiqy/PCpU6di+PDhZZZ5t4Omr68PfX19tGrVCq1bt4apqSkuX74MFxcXGBkZFVskJD09HW/evBHf7WdkZFTsLr8nT54AwAfLqKiooFGjRmWWef+uQiIiIiKSnpOTE06dOlVmmaCgoA/uZ/jw4WX2NVu3bo3IyMgy9+Hl5SVeuKSImZkZ/vjjjw9+PhEREVFdIdMBwqIBv4oouquvaOEPFxcXfP/990hJSYGxsTGA4vPBuLi4YN68ecjLy4Oampq4jImJiXgg0sXFBcePH5f4rMDAQDg5OYnnO3RxccHp06fx1VdfSZTp3LlzhepCREREREREREQkK3ViDsIrV65gw4YNiIqKwv3793H27FmMGDECzZs3h4uLC4DyzQczYsQIqKurw8vLCzExMTh8+DCWLVuGmTNnitODJ0+ejPv372PmzJmIjY3Fzp07sWPHDsyePVscz/Tp0xEYGIiVK1ciLi4OK1euxF9//YUZM2bUeNsQERERERERERFVRp0YINTU1IS/vz969uwJKysrjBs3Dra2tjh37px4ReLyzAejq6uL06dP4+HDh3BycsKUKVMwc+ZMzJw5U1zG0tISJ06cQFBQENq1a4elS5di3bp1+OSTT8RlOnfuDF9fX/j4+KBt27bYtWsXDh48iE6dOtVcoxAREREREREREVUBkfDuKh9UYzIzM6Grq4uMjAwuWEJERERVhn2M2o/fEREREVWHyvQx6sQdhERERERERERERFQ9ZLpIiSIrunEzMzNTxpEQERGRPCnqWzBJpPZiP5CIiIiqQ2X6gRwglJGsrCwAgKmpqYwjISIiInmUlZUFXV1dWYdBJWA/kIiIiKpTRfqBnINQRgoLC5GcnAxtbW3xCspVLTMzE6ampnjw4AHntwHb411sC0lsD0lsD0lsD0lsD0m1sT0EQUBWVhZMTEygpMTZZGojafuBtfE4q0msP+uvyPUH2Aasv2LXH2AbSFP/yvQDeQehjCgpKaFp06Y18lk6OjoK+SMqDdvjX2wLSWwPSWwPSWwPSWwPSbWtPXjnYO1W0X5gbTvOahrrz/orcv0BtgHrr9j1B9gG5a1/RfuBvKxMRERERERERESkwDhASEREREREREREpMA4QCjH1NXVsWjRIqirq8s6lFqB7fEvtoUktocktocktocktocktgfVBEU/zlh/1l+R6w+wDVh/xa4/wDaoqfpzkRIiIiIiIiIiIiIFxjsIiYiIiIiIiIiIFBgHCImIiIiIiIiIiBQYBwiJiIiIiIiIiIgUGAcIiYiIiIiIiIiIFBgHCOXUL7/8AktLS2hoaMDR0REXLlyQdUhVztvbGyKRSOJhZGQkfl0QBHh7e8PExASampro3r07bty4IbGP3NxcfPnll9DX10f9+vUxcOBAPHz4sKarUiHnz5/Hxx9/DBMTE4hEIhw5ckTi9aqqf3p6OkaNGgVdXV3o6upi1KhRePHiRTXXTnofag8vL69ix4uzs7NEGXlpj+XLl6NDhw7Q1taGgYEBBg0ahPj4eIkyinR8lKc9FOn42LRpE9q2bQsdHR3o6OjAxcUFJ0+eFL+uSMcG8OH2UKRjg2qOtP20c+fOwdHRERoaGmjWrBk2b95crIyfnx9sbGygrq4OGxsbHD58uLrCrzRp6u/v74/evXujcePG4t/oqVOnJMrs2rWr2O9UJBLh9evX1V2VCpOmDYKCgkqsX1xcnEQ5eT0GSjoPi0QitGnTRlymLh0DH+qzlkSezgHS1l/ezgHS1l8ef//StoE8nQPK83dJSWrqHMABQjl08OBBzJgxA//73/8QGRmJbt26oW/fvkhKSpJ1aFWuTZs2SElJET+uX78ufu2HH37A2rVrsWHDBoSFhcHIyAi9e/dGVlaWuMyMGTNw+PBh+Pr6Ijg4GC9fvsSAAQNQUFAgi+pIJTs7G/b29tiwYUOJr1dV/UeMGIGoqCgEBAQgICAAUVFRGDVqVLXXT1ofag8A8PDwkDheTpw4IfG6vLTHuXPn8MUXX+Dy5cs4ffo08vPz4e7ujuzsbHEZRTo+ytMegOIcH02bNsWKFSsQHh6O8PBwuLm5wdPTUzwIqEjHBvDh9gAU59igmiFtPy0xMRH9+vVDt27dEBkZiXnz5mHatGnw8/MTlwkJCcGwYcMwatQoREdHY9SoURg6dChCQ0NrqlrlJm39z58/j969e+PEiROIiIhAjx498PHHHyMyMlKinI6OjsTvNCUlBRoaGjVRJalVtK8eHx8vUb+WLVuKX5PnY+Dnn3+WqPeDBw/QsGFDDBkyRKJcXTkGytNnfZe8nQOkrb+8nQOkrX8Refn9A9K3gTydA8r7d8m7avQcIJDc6dixozB58mSJbdbW1sKcOXNkFFH1WLRokWBvb1/ia4WFhYKRkZGwYsUK8bbXr18Lurq6wubNmwVBEIQXL14Iqqqqgq+vr7jMo0ePBCUlJSEgIKBaY69qAITDhw+Ln1dV/W/evCkAEC5fviwuExISIgAQ4uLiqrlWFfd+ewiCIIwZM0bw9PQs9T3y3B5PnjwRAAjnzp0TBIHHx/vtIQiKfXwIgiDo6ekJ27dvV/hjo0hRewgCjw2qetL207755hvB2tpaYtukSZMEZ2dn8fOhQ4cKHh4eEmX69OkjDB8+vIqirjpV0U+1sbERFi9eLH7u4+Mj6OrqVlWI1U7aNjh79qwAQEhPTy91n4p0DBw+fFgQiUTCvXv3xNvq2jFQpKQ+6/vk7RzwrvLUvyR1/RxQpDz1l7ff//sqcgzI0zmgpL9L3leT5wDeQShn8vLyEBERAXd3d4nt7u7uuHTpkoyiqj63bt2CiYkJLC0tMXz4cNy9exfA21H21NRUiXZQV1eHq6uruB0iIiLw5s0biTImJiawtbWt821VVfUPCQmBrq4uOnXqJC7j7OwMXV3dOtlGQUFBMDAwQKtWrTBx4kQ8efJE/Jo8t0dGRgYAoGHDhgB4fLzfHkUU8fgoKCiAr68vsrOz4eLiovDHxvvtUUQRjw2qHhXpp4WEhBQr36dPH4SHh+PNmzdllqltx1dV9FMLCwuRlZVV7Bz+8uVLmJubo2nTphgwYECxu4tqi8q0gYODA4yNjdGzZ0+cPXtW4jVFOgZ27NiBXr16wdzcXGJ7XTkGpCVP54CqUNfPARUlD7//qiJP54DS/i55V02eAzhAKGfS0tJQUFAAQ0NDie2GhoZITU2VUVTVo1OnTtizZw9OnTqFbdu2ITU1FZ07d8azZ8/EdS2rHVJTU6GmpgY9Pb1Sy9RVVVX/1NRUGBgYFNu/gYFBnWujvn37Yt++fThz5gzWrFmDsLAwuLm5ITc3F4D8tocgCJg5cya6du0KW1tbAIp9fJTUHoDiHR/Xr1+HlpYW1NXVMXnyZBw+fBg2NjYKe2yU1h6A4h0bVL0q0k9LTU0tsXx+fj7S0tLKLFPbjq+q6KeuWbMG2dnZGDp0qHibtbU1du3ahWPHjuHAgQPQ0NBAly5dcOvWrSqNvypUpA2MjY2xdetW+Pn5wd/fH1ZWVujZsyfOnz8vLqMox0BKSgpOnjyJCRMmSGyvS8eAtOTpHFAV6vo5QFry9PuvCvJ0Dijt75L31eQ5QEWq0lRniEQiieeCIBTbVtf17dtX/G87Ozu4uLigefPm2L17t3gC+Yq0gzy1VVXUv6TydbGNhg0bJv63ra0tnJycYG5ujj///BODBw8u9X11vT2mTp2Ka9euITg4uNhrinh8lNYeinZ8WFlZISoqCi9evICfnx/GjBmDc+fOiV9XtGOjtPawsbFRuGODaoa0v7GSyr+/vS71/Soa64EDB+Dt7Y2jR49KDLo7OztLLB7UpUsXtG/fHuvXr8e6deuqLvAqJE0bWFlZwcrKSvzcxcUFDx48wOrVq/HRRx9VaJ+yVtFYd+3ahQYNGmDQoEES2+viMSANeTsHVJQ8nQPKSx5//5UhT+eAsv5Oe19NnQN4B6Gc0dfXh7KycrGR4idPnhQbUZY39evXh52dHW7duiVezbisdjAyMkJeXh7S09NLLVNXVVX9jYyM8Pjx42L7f/r0aZ1vI2NjY5ibm4uvKslje3z55Zc4duwYzp49i6ZNm4q3K+rxUVp7lETejw81NTW0aNECTk5OWL58Oezt7fHzzz8r7LFRWnuURN6PDapeFemnGRkZlVheRUUFjRo1KrNMbTu+KtNPPXjwIMaPH4/ffvsNvXr1KrOskpISOnToUCvvHKmqvrqzs7NE/RThGBAEATt37sSoUaOgpqZWZtnafAxIS57OAZUhL+eAqlBXf/+VJU/nAGn+LqnJcwAHCOWMmpoaHB0dcfr0aYntp0+fRufOnWUUVc3Izc1FbGwsjI2NYWlpCSMjI4l2yMvLw7lz58Tt4OjoCFVVVYkyKSkpiImJqfNtVVX1d3FxQUZGBq5cuSIuExoaioyMjDrfRs+ePcODBw9gbGwMQL7aQxAETJ06Ff7+/jhz5gwsLS0lXle04+ND7VESeT4+SiIIAnJzcxXu2ChNUXuURNGODapaFemnubi4FCsfGBgIJycnqKqqllmmth1fFe2nHjhwAF5eXti/fz/69+//wc8RBAFRUVHi32ltUlV99cjISIn6yfsxALxd/fP27dsYP378Bz+nNh8D0pKnc0BFydM5oCrU1d9/ZcnDOaAif5fU6DlAqiVNqE7w9fUVVFVVhR07dgg3b94UZsyYIdSvX19ilR95MGvWLCEoKEi4e/eucPnyZWHAgAGCtra2uJ4rVqwQdHV1BX9/f+H69evCZ599JhgbGwuZmZnifUyePFlo2rSp8NdffwlXr14V3NzcBHt7eyE/P19W1Sq3rKwsITIyUoiMjBQACGvXrhUiIyOF+/fvC4JQdfX38PAQ2rZtK4SEhAghISGCnZ2dMGDAgBqv74eU1R5ZWVnCrFmzhEuXLgmJiYnC2bNnBRcXF6FJkyZy2R6ff/65oKurKwQFBQkpKSniR05OjriMIh0fH2oPRTs+5s6dK5w/f15ITEwUrl27JsybN09QUlISAgMDBUFQrGNDEMpuD0U7NqhmfKifNmfOHGHUqFHi8nfv3hXq1asnfPXVV8LNmzeFHTt2CKqqqsLvv/8uLnPx4kVBWVlZWLFihRAbGyusWLFCUFFRkVg5u7aQtv779+8XVFRUhI0bN0qcw1+8eCEu4+3tLQQEBAh37twRIiMjhbFjxwoqKipCaGhojdevPKRtgx9//FE4fPiwkJCQIMTExAhz5swRAAh+fn7iMvJ8DBT573//K3Tq1KnEfdalY+BDfXh5PwdIW395OwdIW395+/0LgvRtUEQezgHl+TtNlucADhDKqY0bNwrm5uaCmpqa0L59+zKXza6rhg0bJhgbGwuqqqqCiYmJMHjwYOHGjRvi1wsLC4VFixYJRkZGgrq6uvDRRx8J169fl9jHq1evhKlTpwoNGzYUNDU1hQEDBghJSUk1XZUKKVry/v3HmDFjBEGouvo/e/ZMGDlypKCtrS1oa2sLI0eOFNLT02uoluVXVnvk5OQI7u7uQuPGjQVVVVXBzMxMGDNmTLG6ykt7lNQOAAQfHx9xGUU6Pj7UHop2fIwbN078/0Pjxo2Fnj17igcHBUGxjg1BKLs9FO3YoJpTVj9tzJgxgqurq0T5oKAgwcHBQVBTUxMsLCyETZs2FdvnoUOHBCsrK0FVVVWwtraW+OOxtpGm/q6urmX2dwRBEGbMmCGYmZmJf8fu7u7CpUuXarBG0pOmDVauXCk0b95c0NDQEPT09ISuXbsKf/75Z7F9yusxIAiC8OLFC0FTU1PYunVrifurS8fAh/rw8n4OkLb+8nYOkLb+8vj7r8hvQF7OAeX5O02W5wDRP0ESERERERERERGRAuIchERERERERERERAqMA4REREREREREREQKjAOERERERERERERECowDhERERERERERERAqMA4REREREREREREQKjAOERERERERERERECowDhERERERERERERAqMA4REREREREREREQKjAOERETVyNvbG+3atavxzw0KCoJIJIJIJMKgQYPK9R5vb2/xe3766adqjY+IiIioKolEIhw5cqRcZWXVPyvNrl270KBBA/Hz6o5v165d4j7fjBkzKr2vd2OvrSwsLMR1fvHihazDIQV3/vx5fPzxxzAxMZHq3PUuQRCwevVqtGrVCurq6jA1NcWyZcsqFRcHCImIKqiok1Haw8vLC7Nnz8bff/8tsxjj4+Oxa9eucpWdPXs2UlJS0LRp0+oNioiIiOSel5eXuE+kqqqKZs2aYfbs2cjOzq7UfksbPEtJSUHfvn0rte/aoib6jzo6OkhJScHSpUsrtZ9hw4YhISGhiqKqPmFhYfDz85N1GEQAgOzsbNjb22PDhg0V3sf06dOxfft2rF69GnFxcTh+/Dg6duxYqbhUKvVuIiIFlpKSIv73wYMHsXDhQsTHx4u3aWpqQktLC1paWrIIDwBgYGBQ7qu6RbEqKytXb1BERESkEDw8PODj44M3b97gwoULmDBhArKzs7Fp0yap9yUIAgoKCkp93cjIqDKhVlpeXh7U1NSqZF810X8UiURV0maamprQ1NSs8Purst3K0rhxYzRs2LDaP4eoPPr27VvmBY28vDzMnz8f+/btw4sXL2Bra4uVK1eie/fuAIDY2Fhs2rQJMTExsLKyqrK4eAchEVEFGRkZiR+6urrijta7296/yu3l5YVBgwZh2bJlMDQ0RIMGDbB48WLk5+fj66+/RsOGDdG0aVPs3LlT4rMePXqEYcOGQU9PD40aNYKnpyfu3bsndcy///477OzsoKmpiUaNGqFXr16VvpJPREREVBJ1dXUYGRnB1NQUI0aMwMiRI8WpdHv37oWTkxO0tbVhZGSEESNG4MmTJ+L3Fk2XcurUKTg5OUFdXR2//vorFi9ejOjoaPHdiUWZEu+n6T18+BDDhw9Hw4YNUb9+fTg5OSE0NLTUWH18fNC6dWtoaGjA2toav/zyS5l16969O6ZOnYqZM2dCX18fvXv3BgCsXbsWdnZ2qF+/PkxNTTFlyhS8fPlS4r27du2CmZkZ6tWrh//85z949uyZxOvv9x+7d+9eLBV40KBB8PLyEj//5Zdf0LJlS2hoaMDQ0BCffvppmfGXxMLCAt999x1Gjx4NLS0tmJub4+jRo3j69Ck8PT2hpaUFOzs7hIeHS9Tl/YvRx44dg5OTEzQ0NKCvr4/BgwcX+wwvLy/o6upi4sSJAAA/Pz+0adMG6urqsLCwwJo1a4rFtmzZMowbNw7a2towMzPD1q1bxa/n5eVh6tSpMDY2hoaGBiwsLLB8+XKp24CoNhg7diwuXrwIX19fXLt2DUOGDIGHhwdu3boFADh+/DiaNWuGP/74A5aWlrCwsMCECRPw/PnzSn0uBwiJiGrYmTNnkJycjPPnz2Pt2rXw9vbGgAEDoKenh9DQUEyePBmTJ0/GgwcPAAA5OTno0aMHtLS0cP78eQQHB0NLSwseHh7Iy8sr9+empKTgs88+w7hx4xAbG4ugoCAMHjwYgiBUV1WJiIiIxDQ1NfHmzRsAbwd0li5diujoaBw5cgSJiYkSA15FvvnmGyxfvhyxsbFwd3fHrFmz0KZNG6SkpCAlJQXDhg0r9p6XL1/C1dUVycnJOHbsGKKjo/HNN9+gsLCwxLi2bduG//3vf/j+++8RGxuLZcuWYcGCBdi9e3eZ9dm9ezdUVFRw8eJFbNmyBQCgpKSEdevWISYmBrt378aZM2fwzTffiN8TGhqKcePGYcqUKYiKikKPHj3w3XfflbcJSxQeHo5p06ZhyZIliI+PR0BAAD766KMK7evHH39Ely5dEBkZif79+2PUqFEYPXo0/vvf/+Lq1ato0aIFRo8eXWr/8c8//8TgwYPRv39/REZG4u+//4aTk5NEmVWrVsHW1hYRERFYsGABIiIiMHToUAwfPhzXr1+Ht7c3FixYUGyanDVr1sDJyQmRkZGYMmUKPv/8c8TFxQEA1q1bh2PHjuG3335DfHw89u7dCwsLiwq1AZEs3blzBwcOHMChQ4fQrVs3NG/eHLNnz0bXrl3h4+MDALh79y7u37+PQ4cOYc+ePdi1axciIiIqdGHgXUwxJiKqYQ0bNsS6deugpKQEKysr/PDDD8jJycG8efMAAHPnzsWKFStw8eJFDB8+HL6+vlBSUsL27dshEokAvL3K3aBBAwQFBcHd3b1cn5uSkoL8/HwMHjwY5ubmAAA7O7vqqSQRERHRO65cuYL9+/ejZ8+eAIBx48aJX2vWrBnWrVuHjh074uXLlxLptUuWLBHfnQe8Tb9VUVEpMz12//79ePr0KcLCwsRppS1atCi1/NKlS7FmzRrxnW6Wlpa4efMmtmzZgjFjxpT6vhYtWuCHH36Q2PbunX6WlpZYunQpPv/8c/EdiT///DP69OmDOXPmAABatWqFS5cuISAgoNTP+ZCkpCTUr18fAwYMgLa2NszNzeHg4FChffXr1w+TJk0CACxcuBCbNm1Chw4dMGTIEADAt99+CxcXFzx+/LjE7+D777/H8OHDsXjxYvE2e3t7iTJubm6YPXu2+PnIkSPRs2dPLFiwAMDbNrl58yZWrVolMWjcr18/TJkyRRzHjz/+iKCgIFhbWyMpKQktW7ZE165dIRKJxH1dorrm6tWrEAQBrVq1ktiem5uLRo0aAQAKCwuRm5uLPXv2iMvt2LEDjo6OiI+Pr3DaMe8gJCKqYW3atIGS0r+nX0NDQ4mBOmVlZTRq1EicZhMREYHbt29DW1tbPCdNw4YN8fr1a9y5c6fcn2tvb4+ePXvCzs4OQ4YMwbZt25Cenl51FSMiIiJ6xx9//AEtLS1oaGjAxcUFH330EdavXw8AiIyMhKenJ8zNzaGtrS2eWyspKUliH+/ffVYeUVFRcHBwKNecc0+fPsWDBw8wfvx4cT9LS0sL33333Qf7WSXFdvbsWfTu3RtNmjSBtrY2Ro8ejWfPnomndImNjYWLi4vEe95/Lq3evXvD3NwczZo1w6hRo7Bv3z7k5ORUaF9t27YV/9vQ0BCA5AXlom3vpoO/KyoqSjwIXJr32y02NhZdunSR2NalSxfcunVLYt7Jd2MrmtqnKA4vLy9ERUXBysoK06ZNQ2BgYJkxENVWhYWFUFZWRkREBKKiosSP2NhY/PzzzwAAY2NjqKioSAwitm7dGkDxc6g0OEBIRFTDVFVVJZ4Xre73/raiNJjCwkI4OjpK/AcRFRWFhIQEjBgxotyfq6ysjNOnT+PkyZOwsbHB+vXrYWVlhcTExMpXioiIiOg9PXr0QFRUFOLj4/H69Wv4+/vDwMAA2dnZcHd3h5aWFvbu3YuwsDAcPnwYAIpNn1K/fn2pP1eaRTOK+lvbtm2T6GfFxMTg8uXLZb73/dju37+Pfv36wdbWFn5+foiIiMDGjRsBQJxaXZGpXZSUlIq9r2h/AKCtrY2rV6/iwIEDMDY2xsKFC2Fvb48XL15I/Vnv9kmLMldK2lZaunZ52v79dhMEQbzfd7eVFVtRLEVxtG/fHomJiVi6dClevXqFoUOHVjrdkkgWHBwcUFBQgCdPnqBFixYSj6K7drt06YL8/HyJixhFq4lX5u5ZDhASEdVy7du3x61bt2BgYFDsPwldXV2p9iUSidClSxcsXrwYkZGRUFNTE3fIiYiIiKpS/fr10aJFC5ibm0sM7sTFxSEtLQ0rVqxAt27dYG1tXeodae9TU1MrczVj4O2dZlFRUeWasN/Q0BBNmjTB3bt3i/WzLC0tyxVTkfDwcOTn52PNmjVwdnZGq1atkJycLFHGxsam2MDjhwYiGzdujJSUFPHzgoICxMTESJRRUVFBr1698MMPP+DatWu4d+8ezpw5I1X8VaFt27b4+++/pXqPjY0NgoODJbZdunQJrVq1grKycrn3o6Ojg2HDhmHbtm04ePAg/Pz8Kr1oA1F1ePnypfhiBAAkJiYiKioKSUlJaNWqFUaOHInRo0fD398fiYmJCAsLw8qVK3HixAkAQK9evdC+fXuMGzcOkZGRiIiIwKRJk9C7d+9iqcnS4AAhEVEtN3LkSOjr68PT0xMXLlxAYmIizp07h+nTp+Phw4fl3k9oaCiWLVuG8PBwJCUlwd/fH0+fPhXfjk5ERERUE8zMzKCmpob169fj7t27OHbsGJYuXVqu91pYWIj/mE5LS0Nubm6xMp999hmMjIwwaNAgXLx4EXfv3oWfnx9CQkJK3Ke3tzeWL1+On3/+GQkJCbh+/Tp8fHywdu1aqerVvHlz5Ofni+v166+/YvPmzRJlpk2bhoCAAPzwww9ISEjAhg0bPjj/oJubG/7880/8+eefiIuLw5QpUyTuDvzjjz+wbt06REVF4f79+9izZw8KCwsrPA9ZZSxatAgHDhzAokWLEBsbi+vXrxebp/F9s2bNwt9//42lS5ciISEBu3fvxoYNGyTmKfyQH3/8Eb6+voiLi0NCQgIOHToEIyOjYissE9UG4eHhcHBwEM8VOnPmTDg4OGDhwoUA3s43P3r0aMyaNQtWVlYYOHAgQkNDYWpqCuDtXcXHjx+Hvr4+PvroI/Tv3x+tW7eGr69vpeLiACERUS1Xr149nD9/HmZmZhg8eDBat26NcePG4dWrV9DR0Sn3fnR0dHD+/Hn069cPrVq1wvz587FmzRr07du3GqMnIiIiktS4cWPs2rULhw4dgo2NDVasWIHVq1eX672ffPIJPDw80KNHDzRu3BgHDhwoVkZNTQ2BgYEwMDBAv379YGdnhxUrVpR6N9qECROwfft27Nq1C3Z2dnB1dcWuXbukvoOwXbt2WLt2LVauXAlbW1vs27cPy5cvlyjj7OyM7du3Y/369WjXrh0CAwMxf/78Mvc7btw4jBkzBqNHj4arqyssLS3Ro0cP8esNGjSAv78/3Nzc0Lp1a2zevBkHDhxAmzZtpIq/KnTv3h2HDh3CsWPH0K5dO7i5uSE0NLTM97Rv3x6//fYbfH19YWtri4ULF2LJkiUlrmpdGi0tLaxcuRJOTk7o0KED7t27hxMnTkjM+01UW3Tv3h2CIBR7FK3craqqisWLFyMxMRF5eXlISUmBv7+/xHygJiYm8PPzQ1ZWFlJTU+Hj41OueVfLIhIqMgkCERHVakFBQejRowfS09OlvnJqYWGBGTNmSKzCR0RERETyYdeuXZgxY0aF5iisyyrTPyZSBBxOJyKSY02bNsVnn31WrrLLli2DlpZWpVa+IiIiIqLaLyMjA1paWvj2229lHUqNaNOmDbNmiD6AdxASEcmhV69e4dGjRwDeplwUrXhVlufPn4sncm7cuLHUC6AQERERUe2XlZWFx48fA3ibnqyvry/jiKrf/fv3xSs/N2vWjKnHRCXgACEREREREREREZEC47A5ERERERERERGRAuMAIRERERERERERkQLjACEREREREREREZEC4wAhERERERERERGRAuMAIRERERERERERkQLjACEREREREREREZEC4wAhERERERERERGRAuMAIRERERERERERkQL7f1xuDMB9WlTcAAAAAElFTkSuQmCC", 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", 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" ] @@ -440,48 +449,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n", - "Initial concentration in electrolyte [mol.m-3] (Parameter)\n", - "Separator thickness [m] (Parameter)\n", - "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n", - "Negative electrode thickness [m] (Parameter)\n", - "Electrode height [m] (Parameter)\n", - "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n", - "Number of cells connected in series to make a battery (Parameter)\n", "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n", - "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n", - "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n", + "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n", "Lower voltage cut-off [V] (Parameter)\n", - "Nominal cell capacity [A.h] (Parameter)\n", - "Typical electrolyte concentration [mol.m-3] (Parameter)\n", - "Upper voltage cut-off [V] (Parameter)\n", - "Positive electrode electrons in reaction (Parameter)\n", - "Negative electrode electrons in reaction (Parameter)\n", - "Initial temperature [K] (Parameter)\n", - "Reference temperature [K] (Parameter)\n", - "Positive electrode thickness [m] (Parameter)\n", - "Number of electrodes connected in parallel to make a cell (Parameter)\n", + "Faraday constant [C.mol-1] (Parameter)\n", + "Ideal gas constant [J.K-1.mol-1] (Parameter)\n", "Electrode width [m] (Parameter)\n", + "Positive electrode thickness [m] (Parameter)\n", "Separator Bruggeman coefficient (electrolyte) (Parameter)\n", - "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", - "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n", + "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n", + "Upper voltage cut-off [V] (Parameter)\n", + "Number of electrodes connected in parallel to make a cell (Parameter)\n", + "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n", + "Nominal cell capacity [A.h] (Parameter)\n", + "Reference temperature [K] (Parameter)\n", + "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n", + "Separator thickness [m] (Parameter)\n", + "Initial concentration in electrolyte [mol.m-3] (Parameter)\n", + "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n", + "Electrode height [m] (Parameter)\n", + "Number of cells connected in series to make a battery (Parameter)\n", + "Negative electrode thickness [m] (Parameter)\n", + "Ambient temperature [K] (FunctionParameter with input(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]')\n", + "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n", + "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n", "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n", - "Ambient temperature [K] (FunctionParameter with input(s) 'Time [s]')\n", "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n", - "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", + "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", - "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n", "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n", - "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", - "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n", + "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n", "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", + "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n", + "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", + "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n", + "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n", "\n" ] } @@ -516,7 +523,9 @@ { "data": { "text/plain": [ - "{'Negative electrode thickness [m]': 0.0001,\n", + "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Negative electrode thickness [m]': 0.0001,\n", " 'Separator thickness [m]': 2.5e-05,\n", " 'Positive electrode thickness [m]': 0.0001,\n", " 'Electrode height [m]': 0.137,\n", @@ -531,7 +540,6 @@ " 'Negative particle radius [m]': 1e-05,\n", " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", " 'Negative electrode exchange-current density [A.m-2]': ,\n", " 'Negative electrode OCP entropic change [V.K-1]': ,\n", " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", @@ -542,12 +550,10 @@ " 'Positive particle radius [m]': 1e-05,\n", " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", " 'Positive electrode exchange-current density [A.m-2]': ,\n", " 'Positive electrode OCP entropic change [V.K-1]': ,\n", " 'Separator porosity': 1.0,\n", " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", " 'Reference temperature [K]': 298.15,\n", " 'Ambient temperature [K]': 298.15,\n", @@ -556,8 +562,7 @@ " 'Lower voltage cut-off [V]': 3.105,\n", " 'Upper voltage cut-off [V]': 4.1,\n", " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565,\n", - " 'Initial temperature [K]': 298.15}" + " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}" ] }, "execution_count": 15, @@ -586,9 +591,14 @@ "data": { "text/plain": [ "{'Ambient temperature [K]': 298.15,\n", + " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n", " 'Current function [A]': 5.0,\n", " 'Electrode height [m]': 0.065,\n", " 'Electrode width [m]': 1.58,\n", + " 'Electron charge [C]': 1.602176634e-19,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", + " 'Initial concentration in electrolyte [mol.m-3]': 1000,\n", " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", " 'Initial temperature [K]': 298.15,\n", @@ -698,7 +708,7 @@ " 'Negative electrode active material volume fraction': 0.75,\n", " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", " 'Negative electrode porosity': 0.25,\n", " 'Negative electrode thickness [m]': 8.52e-05,\n", " 'Negative particle radius [m]': 5.86e-06,\n", @@ -808,7 +818,7 @@ " 'Positive electrode active material volume fraction': 0.665,\n", " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", " 'Positive electrode porosity': 0.335,\n", " 'Positive electrode thickness [m]': 7.56e-05,\n", " 'Positive particle radius [m]': 5.22e-06,\n", @@ -1400,7 +1410,9 @@ { "data": { "text/plain": [ - "{'Negative electrode thickness [m]': 8.52e-05,\n", + "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n", + " 'Faraday constant [C.mol-1]': 96485.33212,\n", + " 'Negative electrode thickness [m]': 8.52e-05,\n", " 'Separator thickness [m]': 1.2e-05,\n", " 'Positive electrode thickness [m]': 7.56e-05,\n", " 'Electrode height [m]': 0.065,\n", @@ -1415,7 +1427,6 @@ " 'Negative particle radius [m]': 5.86e-06,\n", " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", " 'Negative electrode exchange-current density [A.m-2]': ,\n", " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", @@ -1426,12 +1437,10 @@ " 'Positive particle radius [m]': 5.22e-06,\n", " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", " 'Positive electrode exchange-current density [A.m-2]': ,\n", " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", " 'Separator porosity': 0.47,\n", " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", " 'Reference temperature [K]': 298.15,\n", " 'Ambient temperature [K]': 298.15,\n", @@ -1440,8 +1449,7 @@ " 'Lower voltage cut-off [V]': 2.5,\n", " 'Upper voltage cut-off [V]': 4.2,\n", " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n", - " 'Initial temperature [K]': 298.15}" + " 'Initial concentration in positive electrode [mol.m-3]': 17038.0}" ] }, "execution_count": 17, @@ -1568,7 +1576,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1579,7 +1587,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -1612,7 +1620,7 @@ "outputs": [ { "data": { - "image/png": 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GvBV7MQy4NaI13yVdzU09W2leFakVJ8MwjNp8IDo6moiICGbPnm3bFhoaytChQ0lOTq7RPsLCwkhISGDy5MlnfP/ll19m8uTJ5OTk0KRJkxrt02w24+3tTUFBAV5eXjX6jIiIXBpHi0p5+sstfJqZDUCb5h5Mv6U7V3X2s3Nl4mhq+v1dq3tYSktLycjIYMKECZW2x8fHs2LFihrtw2q1UlhYiI+PT7Vt3n77be68886zhpWSkhJKSkpsP5vN5hr9fhERuXQMw+Dz9QeZ9sUWjhSV4uQE9/QN4W/Xd6axm26blPNXq7MnPz8fi8VCQEBApe0BAQHk5ubWaB8zZsygqKiIYcOGnfH9VatWsWnTJt5+++2z7ic5OZmpU6fWrHAREbnkcgpO8cSnm/h+Wx4AXQI8efa27vRu29zOlUl9cF5x9/TrjoZh1OhaZEpKClOmTOGzzz7D39//jG3efvttwsPD6dOnz1n3NXHiRJKSkmw/m81mgoKCalC9iIhcTFarQcrqLJLTtnGipBxXFyceHtCJB6/pgFsjraosF0etAouvry8uLi5VRlPy8vKqjLqcLjU1lTFjxrBw4UIGDhx4xjYnT57ko48+Ytq0aeesxWQyYTKZal68iIhcdHvyi5iwaAMr9xwFoHfbigngOgdoAji5uGoVfd3c3IiMjCQ9Pb3S9vT0dPr27Vvt51JSUhg9ejQLFiw466PKH3/8MSUlJdx99921KUtERC6zcouVOUt/5YaXl7Fyz1E8XF2YfGM3PhnbV2FFLolaXxJKSkpixIgRREVFERsby9y5c8nKymLs2LFAxaWa7Oxs5s+fD1SElZEjRzJz5kxiYmJsozMeHh54e3tX2vfbb7/N0KFDadFCC16JiDiqbblm/vHJBjYcKADgyo6+JN/anSCfxnauTOqzWgeWhIQEjhw5wrRp08jJySE8PJy0tDSCg4MByMnJqTQny5w5cygvLycxMZHExETb9lGjRjFv3jzbzzt27OC///0v33777QUcjoiIXCql5VZe/2EXs37cRZnFwNO9EU8O7sYdUW00p4pccrWeh8VRaR4WEZFLZ/3+4zy2aAPbcgsBiOsWwD+HhhPg5W7nyqSuuyTzsIiISMNSXGbhpfQdvLl8N1YDWjRxY8pNYdzYI1CjKnJZKbCIiMgZrd57lH98soE9+UUA3NyrFU8NCcOniZudK5OGSIFFREQqKSop54Ul23nv54r1fwK8TDwztDsDu519+gqRS0mBRUREbH7alc9jizZw4NgpABKignh8cCjeHq52rkwaOgUWERGhsLiM6WnbSFlV8ZRn62YeJN+qxQrFcSiwiIg0cEt3HGbiog0cLCgG4O6YtkwYFEpTk74ixHHobBQRaaAKTpXxzy+3sDDjAABtfRrz7G3d6dvB186ViVSlwCIi0gD9Z9shJi7eyCFzCU5OMLpvO/5+fRcau+lrQRyTzkwRkQak4GQZ077cwqK1FaMqIb5NeP72HlzRzsfOlYmcnQKLiEgD8d2WQzz+6UbyCitGVe67MoSkuC54uLnYuzSRc1JgERGp5wpOljH1i80szswGoL1fE164vSeRwc3tXJlIzSmwiIjUY38cVXF2gvv6tycprjPurhpVkbpFgUVEpB7SqIrUNwosIiL1zPdbK54A0qiK1CcKLCIi9UTBqTKmffG/J4Da+zbhhTs0qiL1gwKLiEg98MP2PCYs2mCbV+W+K0N4NL6LRlWk3lBgERGpw8zFZTzz5VZS1+wHKuZVefGOHkQGa14VqV8UWERE6qjlOw/z2CcVawA5OcE9fUP4+/WaV0XqJwUWEZE6pqiknOlpW/lwZcXKym19GvPC7T2Ibt/CzpWJXDoKLCIidcjPvx7h75+s58CxUwCMiAlmwqCuNNHKylLP6QwXEakDTpVaeH7JNt79aS8ArZt58PztPejXUSsrS8OgwCIi4uDWZh3jbx+vZ3d+EQB3XhHEpMGheLq72rkykctHgUVExEGVlFuY+d1O3lj6K1YDArxMPHtbDwZ08bd3aSKXnQKLiIgD2nywgEc/Xs+23EIAbundmilDwvBurFEVaZgUWEREHEi5xcrsH39l5vc7KbcatGjixjO3hHNDeKC9SxOxKwUWEREHsSvvBI8uXM/6/ccBuD4sgGdu6Y5vU5N9CxNxAAosIiJ2ZrUavLtiL89/s42Sciue7o2YdnMYQ3u1xsnJyd7liTgEBRYRETs6cOwkf1u4nl92HwWgfydfnr+9B4HeHnauTMSxKLCIiNiBYRgszDjAtC+2cKKkHA9XFyYNDmV4dFuNqoicgQKLiMhldriwhImLN/Ld1kMARAY3Z8YdPWnn28TOlYk4LgUWEZHL6JtNuUz6dCNHikpxc3EmKb4z9/dvj4uzRlVEzkaBRUTkMjAXlzH18y0sWnsAgK4tPXkpoRehgV52rkykblBgERG5xFbsyudvC9dzsKAYZycYe3UHxg3shKmRi71LE6kzFFhERC6R4jILLyzZztv/3QNAW5/G/GtYT6La+di5MpG6R4FFROQS2JRdwCOp69iZdwKAu/q05YnBoTQx6c+uyPlwPp8PzZo1i5CQENzd3YmMjGT58uXVtl28eDFxcXH4+fnh5eVFbGwsS5YsqdLu+PHjJCYmEhgYiLu7O6GhoaSlpZ1PeSIidmOxGrz+wy5umfUTO/NO4NvUxDujo0i+tbvCisgFqPW/ntTUVMaPH8+sWbPo168fc+bMYdCgQWzZsoW2bdtWab9s2TLi4uKYPn06zZo1491332XIkCGsXLmS3r17A1BaWkpcXBz+/v588skntGnThv379+Pp6XnhRygicplkHTnJIx+vI2PfMQBuCGvJ9Fu749PEzc6VidR9ToZhGLX5QHR0NBEREcyePdu2LTQ0lKFDh5KcnFyjfYSFhZGQkMDkyZMBeOONN3jhhRfYtm0brq7ntxKp2WzG29ubgoICvLx0172IXD6GYfDxmv1M+2ILRaUWmpoaMfWmMG6N0NT6IudS0+/vWl0SKi0tJSMjg/j4+Erb4+PjWbFiRY32YbVaKSwsxMfnfzedff7558TGxpKYmEhAQADh4eFMnz4di8VS7X5KSkowm82VXiIil1v+iRLun5/BY4s2UlRqoU+ID1+P689tkW0UVkQuolpdEsrPz8disRAQEFBpe0BAALm5uTXax4wZMygqKmLYsGG2bbt37+Y///kPw4cPJy0tjZ07d5KYmEh5ebltFOZ0ycnJTJ06tTbli4hcVN9vPcRjizaQf6JiEri/Xd+ZMVdqEjiRS+G87gA7/f81GIZRo/8nkZKSwpQpU/jss8/w9/e3bbdarfj7+zN37lxcXFyIjIzk4MGDvPDCC9UGlokTJ5KUlGT72Ww2ExQUdD6HIyJSKydLy3n6y62krMoCoEuAJy/fqUngRC6lWgUWX19fXFxcqoym5OXlVRl1OV1qaipjxoxh4cKFDBw4sNJ7gYGBuLq64uLyv0mUQkNDyc3NpbS0FDe3qjesmUwmTCZTbcoXEblg6/Yf55HUdezJLwLgvitD+Nv1XXB31SRwIpdSre5hcXNzIzIykvT09Erb09PT6du3b7WfS0lJYfTo0SxYsIDBgwdXeb9fv37s2rULq9Vq27Zjxw4CAwPPGFZERC63couVmd/t5LbZK9iTX0SgtzsL7ovmiRu7KayIXAa1noclKSmJt956i3feeYetW7fyyCOPkJWVxdixY4GKSzUjR460tU9JSWHkyJHMmDGDmJgYcnNzyc3NpaCgwNbmwQcf5MiRI4wbN44dO3bw1VdfMX36dBITEy/CIYqIXJh9R4q4Y87PvPTdDixWgyE9W/HNuKvo29HX3qWJNBi1voclISGBI0eOMG3aNHJycggPDyctLY3g4GAAcnJyyMrKsrWfM2cO5eXlJCYmVgogo0aNYt68eQAEBQXx7bff8sgjj9CjRw9at27NuHHjeOyxxy7w8EREzp9hGCxcc4CpX2ymqNSCp6kRTw8NZ2jv1vYuTaTBqfU8LI5K87CIyMV0rKiUiYs38s3minv2+oT48K9hPWnTvLGdKxOpX2r6/a15okVETrN852Ee/Xg9eYUluLo4kRTXhQeu0uPKIvakwCIi8pviMgvPf7Odd36qWF25g18TZt7Zm/DW3nauTEQUWEREgO25hYz7KJNtuYUAjIgJ5vH/C8XDTU8AiTgCBRYRadAMw2Deir0kf72N0nIrLZq48fztPbgu9OxzS4nI5aXAIiINVl5hMX9fuIGlOw4DMKCLH8/f3hM/T01KKeJoFFhEpEH6fush/v7JBo4WlWJq5MykwaGMiAnWgoUiDkqBRUQalFOlFqanbeX9X/YB0LWlJ6/c1ZvOAZ52rkxEzkaBRUQajK05Zv6aksnOvBNAxTpAf7+hC6ZGurFWxNEpsIhIvWe1Gry7Yi/Pfb2NUosVP08TM+7oyVWd/exdmojUkAKLiNRrhwtL+NvC9bYbaweG+vPcbT1o0VQ31orUJQosIlJv/bAtj79/sp78ExU31j5xYzfujm6rG2tF6iAFFhGpd4rLLDz3zTbe/WkvoBtrReoDBRYRqVd2HirkLyn/m7H2nn7teOyGrri76sZakbpMgUVE6gXDMFiwKounv9xCcVnFjLUv3tGTAV397V2aiFwECiwiUucdP1nKY4s2sGTzIQD6d/JlxrCe+Hu627kyEblYFFhEpE77+dcjPJK6jlxzMa4uTjx2Q1fu7ReCs7NurBWpTxRYRKROKrdYmfn9Tl77YReGAe19m/DKXb0Jb+1t79JE5BJQYBGROufAsZOM+2gdGfuOAXBHZBum3BRGE5P+pInUV/rXLSJ1ylcbcpiweAOFxeV4mhrxzK3dualnK3uXJSKXmAKLiNQJp0otTPtyMymr9gPQu20zXrmzN0E+je1cmYhcDgosIuLwtuWa+cuCikULnZzgoWs6MH5gZ1xdnO1dmohcJgosIuKwDMPgg5VZ/PPLLZSUW/H3NPFSQi/6dfS1d2kicpkpsIiIQyo4WcY/Fq23za0yoIsfL97RU4sWijRQCiwi4nDW7D3KuI/WkX38lOZWERFAgUVEHIjFavDG0l/5V/oOLFaD4BaNefWu3vRo08zepYmInSmwiIhDyCss5pHUdfy06wgAN/dqxT+HhuPp7mrnykTEESiwiIjdLd1xmEc/Xkf+iVI8XF2YdnMYt0e2wclJl4BEpIICi4jYTZnFyoxvd/DG0l8B6NrSk9f+1JuO/p52rkxEHI0Ci4jYxYFjJ/lrSiZrs44DcHdMW54Y3A13Vxf7FiYiDkmBRUQuuyWbc/n7wvWYf5te/7nbe/B/3QPtXZaIODAFFhG5bErKLSSnbWPeir0A9Axqxmt3aXp9ETk3BRYRuSz25hfxcMpaNmWbAbi/fwh/v74rbo00vb6InJsCi4hccl+sP8jExRs5UVJO88auzBjWk2u7Bti7LBGpQxRYROSSKS6zMPWLLaSsygKgTzsfZt7Vi0BvDztXJiJ1jQKLiFwSvx4+QeKHa9mWW4iTEyRe05HxAzvRSCssi8h5OK+/HLNmzSIkJAR3d3ciIyNZvnx5tW0XL15MXFwcfn5+eHl5ERsby5IlSyq1mTdvHk5OTlVexcXF51OeiNjZvzOzGfLqf9mWW4hvUzfm39uHv13fRWFFRM5brf96pKamMn78eCZNmkRmZib9+/dn0KBBZGVlnbH9smXLiIuLIy0tjYyMDAYMGMCQIUPIzMys1M7Ly4ucnJxKL3d39/M7KhGxi+IyCxMWbWB86jpOllqIbd+CtL/2p38nP3uXJiJ1nJNhGEZtPhAdHU1ERASzZ8+2bQsNDWXo0KEkJyfXaB9hYWEkJCQwefJkoGKEZfz48Rw/frw2pVRiNpvx9vamoKAALy+v896PiJyfXXkneHjB/y4B/fXaTvz1uk64aIVlETmLmn5/12qEpbS0lIyMDOLj4yttj4+PZ8WKFTXah9VqpbCwEB8fn0rbT5w4QXBwMG3atOHGG2+sMgJzupKSEsxmc6WXiNjHvzOzuem13y8BmfhwTDSPxHVWWBGRi6ZWgSU/Px+LxUJAQOXHEQMCAsjNza3RPmbMmEFRURHDhg2zbevatSvz5s3j888/JyUlBXd3d/r168fOnTur3U9ycjLe3t62V1BQUG0ORUQugjNeAhp3JX07+tq7NBGpZ87rKaHTV1A1DKNGq6qmpKQwZcoUPvvsM/z9/W3bY2JiiImJsf3cr18/IiIiePXVV3nllVfOuK+JEyeSlJRk+9lsNiu0iFxGuw+f4KEPdQlIRC6PWgUWX19fXFxcqoym5OXlVRl1OV1qaipjxoxh4cKFDBw48KxtnZ2dueKKK846wmIymTCZTDUvXkQums/XH2Tiog0UlVrwberGywm9ubKTRlVE5NKp1SUhNzc3IiMjSU9Pr7Q9PT2dvn37Vvu5lJQURo8ezYIFCxg8ePA5f49hGKxbt47AQC2GJuJIisssTPp0I39NyaSo1EJ0iA9pf+2vsCIil1ytLwklJSUxYsQIoqKiiI2NZe7cuWRlZTF27Fig4lJNdnY28+fPByrCysiRI5k5cyYxMTG20RkPDw+8vb0BmDp1KjExMXTq1Amz2cwrr7zCunXreP311y/WcYrIBdp3pIiHPlzL5oNmTQQnIpddrQNLQkICR44cYdq0aeTk5BAeHk5aWhrBwcEA5OTkVJqTZc6cOZSXl5OYmEhiYqJt+6hRo5g3bx4Ax48f54EHHiA3Nxdvb2969+7NsmXL6NOnzwUenohcDN9syuXvn6ynsLgcnyZuvJTQi6s7a24VEbl8aj0Pi6PSPCwiF19puZXnvtnG2//dA0BUcHNe/VNvrQUkIhdNTb+/tZaQiJzRweOneHjBWtZmHQfggava8/fru+CqS0AiYgcKLCJSxY/b83gkdR3HTpbh5d6IF+/oSXxYS3uXJSINmAKLiNhYrAYzv9vBqz/swjCge2tvZg2PIMinsb1LE5EGToFFRADIP1HCuI8y+WnXEQDujmnLE4O74e7qYufKREQUWEQEWL33KA8vWMshcwkeri48e1t3bu7V2t5liYjYKLCINGCGYfDW8j08+802LFaDjv5NmT08gk4BnvYuTUSkEgUWkQbKXFzG3xeuZ8nmQwDc1LMVybd2p4lJfxZExPHoL5NIA7TloJkHP8xg35GTuLo4MfnGbtwdE1yjRUxFROxBgUWkgVm4Zj9P/HsTJeVWWjfz4PXhEfQKambvskREzkqBRaSBKC6zMPWLzaSs2g/A1Z39eDmhF82buNm5MhGRc1NgEWkA9h89yYMfZrApu2LhwkcGdubhAR1xdtYlIBGpGxRYROq5H7blMT51HQWnymje2JWZd/bmKi1cKCJ1jAKLSD31+6y1r/xnFwC9gprx+vAIWjfTwoUiUvcosIjUQ0eLShn3USbLd+YDMCImmCduDMXUSLPWikjdpMAiUs+s33+chz5cS/bxU7i7OpN8a3du6d3G3mWJiFwQBRaResIwDD5avZ+nPttMqcVKuxaNeWNEJF1betm7NBGRC6bAIlIPFJdZmPzZJj5ecwCAuG4BzBjWEy93VztXJiJycSiwiNRx+4+eZOwHGWw+aMbZCf52fRfGXtVBjyyLSL2iwCJSh/24PY9xH1U8suzTxI1X7+pNv46+9i5LROSiU2ARqYOsVoPXftjFS9/twDCgZ1AzZg+PoJUeWRaRekqBRaSOKThVxqMfr+O7rXkA/Cm6LU8N6aZHlkWkXlNgEalDtuWaGft+BnuPnMStkTP/HBrOsKgge5clInLJKbCI1BGfrctmwqKNnCqz0LqZB2/cHUn3Nt72LktE5LJQYBFxcGUWK89+vY23/7sHgP6dfHnlzt5aZVlEGhQFFhEHdriwhIcXrGXlnqMAPHRNBx6N74KLHlkWkQZGgUXEQWVmHePBD9aSay6miZsLM4b15IbwQHuXJSJiFwosIg7oo1VZTP5tiv32fk2YOyKSjv6e9i5LRMRuFFhEHEhJuYUpn28hZVUWAPG/TbHvqSn2RaSBU2ARcRC5BcU8+GEGmVnHcXKCR+M689A1HTXFvogICiwiDmH13qM8+MFa8k+U4O3hysw7e3FNF397lyUi4jAUWETsyDAM3v9lH9O+2EK51aBrS0/mjoiibYvG9i5NRMShKLCI2ElxmYUn/72JhRkHALixRyDP396Dxm76Zykicjr9ZRSxg5yCU4x9P4P1BwpwdoLHbujKA1e1x8lJ96uIiJyJAovIZbZy9xESF6wl/0QpzRq78updvenfyc/eZYmIODQFFpHLxDAMPvhlH1N1v4qISK05n8+HZs2aRUhICO7u7kRGRrJ8+fJq2y5evJi4uDj8/Pzw8vIiNjaWJUuWVNv+o48+wsnJiaFDh55PaSIOqaTcwoRFG3nys82UWw1u7BHI4of6KqyIiNRQrQNLamoq48ePZ9KkSWRmZtK/f38GDRpEVlbWGdsvW7aMuLg40tLSyMjIYMCAAQwZMoTMzMwqbfft28ff/vY3+vfvX/sjEXFQh8zFJMz5hdQ1+3F2ggmDuvLqXb11c62ISC04GYZh1OYD0dHRREREMHv2bNu20NBQhg4dSnJyco32ERYWRkJCApMnT7Zts1gsXH311dxzzz0sX76c48eP8+9//7vGdZnNZry9vSkoKMDLy6vGnxO5lDL2HWPsBxkcLqyYX+XVu3pzVWfdryIi8ruafn/XaoSltLSUjIwM4uPjK22Pj49nxYoVNdqH1WqlsLAQHx+fStunTZuGn58fY8aMqU1JIg4rdXUWd839hcOFJXQJ8OTzh/sprIiInKdajUnn5+djsVgICAiotD0gIIDc3Nwa7WPGjBkUFRUxbNgw27affvqJt99+m3Xr1tW4lpKSEkpKSmw/m83mGn9W5FIqs1iZ9sUW3v9lHwA3hLVkxrCeNDHpEpCIyPk6r5tuT58rwjCMGs0fkZKSwpQpU0hNTcXfv2La8cLCQu6++27efPNNfH19a1xDcnIy3t7etldQUFDtDkLkEsg/UcLwt1bawsqjcZ2ZNTxCYUVE5ALV6q+or68vLi4uVUZT8vLyqoy6nC41NZUxY8awcOFCBg4caNv+66+/snfvXoYMGWLbZrVaK4pr1Ijt27fToUOHKvubOHEiSUlJtp/NZrNCi9jVpuwC/vx+BtnHT9HU1IiXEnoR1+3s/y5ERKRmahVY3NzciIyMJD09nVtuucW2PT09nZtvvrnaz6WkpHDvvfeSkpLC4MGDK73XtWtXNm7cWGnbE088QWFhITNnzqw2hJhMJkwmU23KF7lkvlh/kL9/sp7iMivtWjTmrVFRdPT3tHdZIiL1Rq3HqZOSkhgxYgRRUVHExsYyd+5csrKyGDt2LFAx8pGdnc38+fOBirAycuRIZs6cSUxMjG10xsPDA29vb9zd3QkPD6/0O5o1awZQZbuIo7FYDWZ8u51ZP/4KwFWd/Xj1zt54N3a1c2UiIvVLrQNLQkICR44cYdq0aeTk5BAeHk5aWhrBwcEA5OTkVJqTZc6cOZSXl5OYmEhiYqJt+6hRo5g3b96FH4GInRQWlzHuo3X8Z1seAH++qj3/uKErLs5aD0hE5GKr9TwsjkrzsMjltCe/iPvnr2FX3glMjZx57rYeDO3d2t5liYjUOTX9/tajCyK1tGzHYR5esBZzcTkBXibmjoiiZ1Aze5clIlKvKbCI1JBhGLz93z1MT9uK1YDebZsx5+5I/L3c7V2aiEi9p8AiUgMl5RYmfbqJTzIOAHB7ZBueuSUcUyMXO1cmItIwKLCInENeYTFj389gbdZxnJ1g0uBu3NuvXY0mSxQRkYtDgUXkLDZlF3D//DXkFBTj5d6I1/4UofWARETsQIFFpBpfbcjh0YXrKC6z0t6vCW+NjKK9X1N7lyUi0iApsIicxmo1ePn7nbzy/U4Aru7sxyt39cbbQ5PBiYjYiwKLyB+cLC3n0Y/X8/WmihmZ7+8fwoRBoZoMTkTEzhRYRH6TffwU97+3hi05ZtxcnHnmlnDuiNKCmiIijkCBRQTI2HeMP7+/hvwTpfg2dWPOiEgig33sXZaIiPxGgUUavEUZB5i4eCOlFiuhgV68NSqK1s087F2WiIj8gQKLNFgWq8HzS7YxZ+luAK4PC+ClhF40dtM/CxERR6O/zNIgnSgpZ/xHmXy3tWKl5YcHdCQprjPOurlWRMQhKbBIg7P/6Enue28N2w8V4tbImRdu78HNvbTSsoiII1NgkQZlzd6j/Pn9DI4UleLnaWLuiEh6t21u77JEROQcFFikwVi89gATFlXcXNvtt5trW+nmWhGROkGBReo9q9XgxW+3M+vHXwHdXCsiUhfpL7bUaydLy3kkdR1LNh8CIHFABx6N66Kba0VE6hgFFqm3cguKGfPeajYfrJi59tnbunNrRBt7lyUiIudBgUXqpQ0HjnPfe2vIKyyhRZOKmWuj2mnmWhGRukqBReqdrzfm8MjH6ygus9I5oClvj7qCIJ/G9i5LREQugAKL1BuGYTDrx195Ycl2AAZ08eOVu3rj6e5q58pERORCKbBIvVBSbmHi4o0sXpsNwD392vHE4G646OZaEZF6QYFF6ryjRaX8+f01rN57DBdnJ6bcFMaImGB7lyUiIheRAovUabvyTjDmvdXsO3IST1MjXh8ewVWd/exdloiIXGQKLFJn/bQrnwc/yMBcXE6b5h68O/oKOgV42rssERG5BBRYpE5KXZ3FpE83UW41iGjbjLkjo/BtarJ3WSIicokosEidYrUaPLdkG3OW7gbgpp6teP72Hri7uti5MhERuZQUWKTOOFVq4ZHUdXyzOReAcdd1YvzATjg56UkgEZH6ToFF6oS8wmLuf28N6w8U4ObizHO3d+eW3ppmX0SkoVBgEYe3PbeQe+etJvv4KZo3dmXOiCj6hGiafRGRhkSBRRza0h2HSfxwLSdKymnv24R3Rl9BO98m9i5LREQuMwUWcVgfrtzH5M82Y7EaRIf4MGdEJM0au9m7LBERsQMFFnE4VqvBc99sY86yiieBbo1ozbO39sCtkbOdKxMREXtRYBGHUlxW8STQ15sqngR6ZGBn/npdRz0JJCLSwCmwiMPIP1HCfe+tYd3+43oSSEREKjmvMfZZs2YREhKCu7s7kZGRLF++vNq2ixcvJi4uDj8/P7y8vIiNjWXJkiVV2kRFRdGsWTOaNGlCr169eP/998+nNKmjduWd4JZZP7Fu/3GaNXbl/TF9FFZERMSm1oElNTWV8ePHM2nSJDIzM+nfvz+DBg0iKyvrjO2XLVtGXFwcaWlpZGRkMGDAAIYMGUJmZqatjY+PD5MmTeLnn39mw4YN3HPPPdxzzz1Vgo3UT7/sPsKts35i/9FTBLdozOIH+xLdvoW9yxIREQfiZBiGUZsPREdHExERwezZs23bQkNDGTp0KMnJyTXaR1hYGAkJCUyePLnaNhEREQwePJinn366Rvs0m814e3tTUFCAl5dXjT4j9vdp5gH+8ckGyiwVawK9OTKKFloTSESkwajp93etRlhKS0vJyMggPj6+0vb4+HhWrFhRo31YrVYKCwvx8TnzxF+GYfD999+zfft2rrrqqmr3U1JSgtlsrvSSusMwDGZ+t5NHUtdTZjEY3D2QBffHKKyIiMgZ1eqm2/z8fCwWCwEBAZW2BwQEkJubW6N9zJgxg6KiIoYNG1Zpe0FBAa1bt6akpAQXFxdmzZpFXFxctftJTk5m6tSptSlfHERpuZXHP93IJxkHAPjz1e157PquODvrSSARETmz83pK6PRHTA3DqNFjpykpKUyZMoXPPvsMf3//Su95enqybt06Tpw4wffff09SUhLt27fnmmuuOeO+Jk6cSFJSku1ns9lMUFBQ7Q9GLitzcRkPfbCW/+7Kx9kJpt0czt0xwfYuS0REHFytAouvry8uLi5VRlPy8vKqjLqcLjU1lTFjxrBw4UIGDhxY5X1nZ2c6duwIQK9evdi6dSvJycnVBhaTyYTJpMsHdcnB46e4593VbD9USGM3F17/UwQDuvqf+4MiItLg1eoeFjc3NyIjI0lPT6+0PT09nb59+1b7uZSUFEaPHs2CBQsYPHhwjX6XYRiUlJTUpjxxYJsPFnDLrJ/YfqgQP08TH/85VmFFRERqrNaXhJKSkhgxYgRRUVHExsYyd+5csrKyGDt2LFBxqSY7O5v58+cDFWFl5MiRzJw5k5iYGNvojIeHB97e3kDF/ShRUVF06NCB0tJS0tLSmD9/fqUnkaTuWrrjMA99kEFRqYXOAU15954+tG7mYe+yRESkDql1YElISODIkSNMmzaNnJwcwsPDSUtLIzi44j6EnJycSnOyzJkzh/LychITE0lMTLRtHzVqFPPmzQOgqKiIhx56iAMHDuDh4UHXrl354IMPSEhIuMDDE3v7ePV+Jn66EYvVILZ9C94YEYm3h6u9yxIRkTqm1vOwOCrNw+JYDMPgpe928sr3OwG4tXdrnr1NCxiKiEhlNf3+1lpCctGd/tjyX67tSFJcZy1gKCIi502BRS6qwuIyHvpwLct35uPi7MQ/h4ZzV5+29i5LRETqOAUWuWgOmYsZ/e5qtuaYKx5bHh7BgC56EkhERC6cAotcFDsOFTL6nVUcLCjGt6mJd0dfQfc23vYuS0RE6gkFFrlgv+w+wgPz12AuLqe9XxPeu6cPQT6N7V2WiIjUIwosckG+WH+QRz9eT6nFSlRwc94cGUXzJm72LktEROoZBRY5b28t380/v9oKwA1hLXn5zl64u7rYuSoREamPFFik1qxWg39+tZV3ftoDwOi+7Xjyxm64aLVlERG5RBRYpFaKyyw8unA9X23IAeDx/+vK/f3ba44VERG5pBRYpMYKTpXxwPw1rNxzFFcXJ168oyc392pt77JERKQBUGCRGskpOMWod1ax49AJPE2NmDMikr4dfe1dloiINBAKLHJO23MLGf3uKnIKivH3NDHvnj50a6X1mkRE5PJRYJGzWrn7CPf/NsdKR/+mzLvnCto01xwrIiJyeSmwSLW+3pjDuNR1lJZbiQxuztujomjWWHOsiIjI5afAImf0/s97mfz5ZgwD4roF8OpdvTXHioiI2I0Ci1RiGAYzvt3Baz/sAuBP0W15+uZwzbEiIiJ2pcAiNuUWK49/upGP1xwAICmuM3+5tqPmWBEREbtTYBEATpVaeHjBWr7floezE0y/pTt39mlr77JEREQABRYBjhWVMua91azNOo6pkTOv/SmCuG4B9i5LRETERoGlgTt4/BQj31nFrrwTeHu48vaoKKLa+di7LBERkUoUWBqwHYcKGfn2KnLNxQR6u/PevX3oHOBp77JERESqUGBpoNbsPcqY99ZQcKqMjv5NmX9vH1o187B3WSIiImekwNIAfb/1EA99uJaScisRbZvxzugrNCGciIg4NAWWBmbhmv1MWLwRi9VgQBc/Zg2PxMNNE8KJiIhjU2BpQOYs/ZXkr7cBcGtEa567rQeuLs52rkpEROTcFFgaAKvV4NlvtjF32W4A/nxVeyYM6qoJ4UREpM5QYKnnyixWJizayKK1FbPXPv5/XXngqg52rkpERKR2FFjqsT/OXuvi7MRzt/Xg9sg29i5LRESk1hRY6qmCk2XcN381q/cew9TImdf/FMFAzV4rIiJ1lAJLPXTIXMyod1axLbcQT/dGvDP6Cq7Q7LUiIlKHKbDUM3vzixjxzkr2Hz2Fv6eJ9+7tQ2igl73LEhERuSAKLPXIloNmRr6zivwTJQS3aMz790bTtkVje5clIiJywRRY6olVe44y5r3VFBaXExroxXv3XoG/p7u9yxIREbkoFFjqgT9Otd+nnQ9vjorC28PV3mWJiIhcNAosddy/M7N5dOF6LFaDgaH+vPanCNxdNdW+iIjUL+c1L/usWbMICQnB3d2dyMhIli9fXm3bxYsXExcXh5+fH15eXsTGxrJkyZJKbd5880369+9P8+bNad68OQMHDmTVqlXnU1qDMu+nPYxPXYfFanBr79bMvjtSYUVEROqlWgeW1NRUxo8fz6RJk8jMzKR///4MGjSIrKysM7ZftmwZcXFxpKWlkZGRwYABAxgyZAiZmZm2Nj/++CN33XUXP/zwAz///DNt27YlPj6e7Ozs8z+yeswwDGZ+t5MpX2wBYHTfdrx4R0+tCyQiIvWWk2EYRm0+EB0dTUREBLNnz7ZtCw0NZejQoSQnJ9doH2FhYSQkJDB58uQzvm+xWGjevDmvvfYaI0eOrNE+zWYz3t7eFBQU4OVVfx/jtVoNnv5qC+/+tBeARwZ25q/XddS6QCIiUifV9Pu7VvewlJaWkpGRwYQJEyptj4+PZ8WKFTXah9VqpbCwEB+f6icyO3nyJGVlZWdtU1JSQklJie1ns9lco99fl5VbrPxj0QYWr60YeZp6Uxij+razb1EiIiKXQa2uIeTn52OxWAgIqDzFe0BAALm5uTXax4wZMygqKmLYsGHVtpkwYQKtW7dm4MCB1bZJTk7G29vb9goKCqrZQdRRxWUWHvxwLYvXZuPi7MRLCT0VVkREpME4r5seTr/8YBhGjS5JpKSkMGXKFFJTU/H39z9jm+eff56UlBQWL16Mu3v184hMnDiRgoIC22v//v21O4g65ERJOffOW036lkO4NXJmzt2R3NJbixiKiEjDUatLQr6+vri4uFQZTcnLy6sy6nK61NRUxowZw8KFC6sdOXnxxReZPn063333HT169Djr/kwmEyaTqTbl10nHikoZ/e4q1h8ooImbC2+NuoLYDi3sXZaIiMhlVasRFjc3NyIjI0lPT6+0PT09nb59+1b7uZSUFEaPHs2CBQsYPHjwGdu88MILPP3003zzzTdERUXVpqx665C5mIS5P7P+QAHNG7uS8kCMwoqIiDRItZ44LikpiREjRhAVFUVsbCxz584lKyuLsWPHAhWXarKzs5k/fz5QEVZGjhzJzJkziYmJsY3OeHh44O3tDVRcBnryySdZsGAB7dq1s7Vp2rQpTZs2vSgHWtfsP3qS4W+tJOvoSQK8THwwJppOAZ72LktERMQuan0PS0JCAi+//DLTpk2jV69eLFu2jLS0NIKDgwHIycmpNCfLnDlzKC8vJzExkcDAQNtr3LhxtjazZs2itLSU22+/vVKbF1988SIcYt2z81Aht7+xgqyjJ2nr05hPxvZVWBERkQat1vOwOKr6Mg/LxgMFjHxnJcdOltE5oCkfjInG30uLGIqISP10SeZhkUtr1Z6j3DtvNSdKyukZ1Ix5o6+geRM3e5clIiJidwosDuLH7Xn8+f0MSsqtxLT34a1RV9DUpP88IiIioMDiENI25jDuo0zKLAbXdvVn1nCtuCwiIvJHCix29knGAf7xyXqsBtzYI5CXEnppEUMREZHTKLDY0Xsr9vLU55sBSIgKYvqt3XFx1iKGIiIip1NgsZPXf9jFC0u2A3BvvxCevDFUKy6LiIhUQ4HlMjMMgxe/3c7rP/wKwF+v7cgjcZ0VVkRERM5CgeUyMgyDqV9sYd6KvQBMHNSVP1/dwb5FiYiI1AEKLJeJxWrw+OKNpK6pWFX66ZvDGBHbzr5FiYiI1BEKLJdBmcXKox+v5/P1B3F2gudv78ntkW3sXZaIiEidocByiZWUW/jLgky+3XKIRs5OzLyzN4N7BNq7LBERkTpFgeUSOlVqYewHGSzdcRi3Rs7MHh7BdaEB9i5LRESkzlFguUROlJRz33ur+WX3UTxcXXhrVBT9OvrauywREZE6SYHlEig4Vcbod1eRmXUcT1Mj3rnnCq5o52PvskREROosBZaL7FhRKSPeWcmmbDPNGrsy/94+9GjTzN5liYiI1GkKLBfR4cIS7n5rJdsPFdKiiRsf3BdNaKCXvcsSERGp8xRYLpLcgmL+9NYv7D5chL+niQX3R9PR39PeZYmIiNQLCiwXwf6jJxn+1kqyjp6kdTMPPrwvmna+TexdloiISL2hwHKB9uYX8ac3f+FgQTFtfRqz4P5o2jRvbO+yRERE6hUFlguwK+8Ew9/6hUPmEtr7NmHB/TG09Ha3d1kiIiL1jgLLedqeW8jwt34h/0QpnQOa8sF90fh7KqyIiIhcCgos52FTdgEj3l7JsZNldAv04oP7ovFp4mbvskREROotBZZaWr//OCPeXom5uJyebbx5794+NGussCIiInIpKbDUQsa+Y4x+ZxWFJeVEtG3GvHv74OXuau+yRERE6j0Flhpavfcoo99ZRVGphT4hPrwz+gqamtR9IiIil4O+cWvg51+PcO+81Zwqs9C3QwveGhVFYzd1nYiIyOWib91zWL7zMPfPX0NxmZX+nXx5c2QU7q4u9i5LRESkQXG2dwGOrKiknHEfraO4zMq1Xf0VVkREROxEgeUsmpga8cbdkQzt1Yo37o5UWBEREbETXRI6hz4hPvQJ8bF3GSIiIg2aRlhERETE4SmwiIiIiMNTYBERERGHp8AiIiIiDk+BRURERBzeeQWWWbNmERISgru7O5GRkSxfvrzatosXLyYuLg4/Pz+8vLyIjY1lyZIlldps3ryZ2267jXbt2uHk5MTLL798PmWJiIhIPVXrwJKamsr48eOZNGkSmZmZ9O/fn0GDBpGVlXXG9suWLSMuLo60tDQyMjIYMGAAQ4YMITMz09bm5MmTtG/fnmeffZaWLVue/9GIiIhIveRkGIZRmw9ER0cTERHB7NmzbdtCQ0MZOnQoycnJNdpHWFgYCQkJTJ48ucp77dq1Y/z48YwfP742ZWE2m/H29qagoAAvL69afVZERETso6bf37UaYSktLSUjI4P4+PhK2+Pj41mxYkWN9mG1WiksLMTH58ImYyspKcFsNld6iYiISP1Uq8CSn5+PxWIhICCg0vaAgAByc3NrtI8ZM2ZQVFTEsGHDavOrq0hOTsbb29v2CgoKuqD9iYiIiOM6r5tunZycKv1sGEaVbWeSkpLClClTSE1Nxd/f/3x+tc3EiRMpKCiwvfbv339B+xMRERHHVau1hHx9fXFxcakympKXl1dl1OV0qampjBkzhoULFzJw4MDaV3oak8mEyWS64P2IiIiI46vVCIubmxuRkZGkp6dX2p6enk7fvn2r/VxKSgqjR49mwYIFDB48+PwqFRERkQar1qs1JyUlMWLECKKiooiNjWXu3LlkZWUxduxYoOJSTXZ2NvPnzwcqwsrIkSOZOXMmMTExttEZDw8PvL29gYqbebds2WL739nZ2axbt46mTZvSsWPHGtX1+8NOuvlWRESk7vj9e/ucDy0b5+H11183goODDTc3NyMiIsJYunSp7b1Ro0YZV199te3nq6++2gCqvEaNGmVrs2fPnjO2+eN+zmX//v1n3Ideeumll1566eX4r/3795/1e77W87A4KqvVysGDB/H09KzRDcA1ZTabCQoKYv/+/Zrf5RzUV7Wj/qo59VXNqa9qTn1Vc5eyrwzDoLCwkFatWuHsXP2dKrW+JOSonJ2dadOmzSXbv5eXl07oGlJf1Y76q+bUVzWnvqo59VXNXaq++v0WkbPR4ociIiLi8BRYRERExOEpsJyDyWTiqaee0pwvNaC+qh31V82pr2pOfVVz6quac4S+qjc33YqIiEj9pREWERERcXgKLCIiIuLwFFhERETE4SmwiIiIiMNrEIFl9uzZ9OjRwzbhTWxsLF9//bXtfcMwmDJlCq1atcLDw4NrrrmGzZs3V9pHSUkJf/nLX/D19aVJkybcdNNNHDhwoFKbY8eOMWLECLy9vfH29mbEiBEcP378chziRXOuvho9ejROTk6VXjExMZX20VD66nTJyck4OTkxfvx42zadW2d2pr7SuVVhypQpVfqhZcuWtvd1Tv3PufpK51Rl2dnZ3H333bRo0YLGjRvTq1cvMjIybO87/LlV48V66rDPP//c+Oqrr4zt27cb27dvNx5//HHD1dXV2LRpk2EYhvHss88anp6exqJFi4yNGzcaCQkJRmBgoGE2m237GDt2rNG6dWsjPT3dWLt2rTFgwACjZ8+eRnl5ua3NDTfcYISHhxsrVqwwVqxYYYSHhxs33njjZT/eC3Guvho1apRxww03GDk5ObbXkSNHKu2jofTVH61atcpo166d0aNHD2PcuHG27Tq3qqqur3RuVXjqqaeMsLCwSv2Ql5dne1/n1P+cq690Tv3P0aNHjeDgYGP06NHGypUrjT179hjfffedsWvXLlsbRz+3GkRgOZPmzZsbb731lmG1Wo2WLVsazz77rO294uJiw9vb23jjjTcMwzCM48ePG66ursZHH31ka5OdnW04Ozsb33zzjWEYhrFlyxYDMH755Rdbm59//tkAjG3btl2mo7o0fu8rw6j4A3DzzTdX27Yh9lVhYaHRqVMnIz093bj66qttX8I6t6qqrq8MQ+fW75566imjZ8+eZ3xP51RlZ+srw9A59UePPfaYceWVV1b7fl04txrEJaE/slgsfPTRRxQVFREbG8uePXvIzc0lPj7e1sZkMnH11VezYsUKADIyMigrK6vUplWrVoSHh9va/Pzzz3h7exMdHW1rExMTg7e3t61NXXN6X/3uxx9/xN/fn86dO3P//feTl5dne68h9lViYiKDBw9m4MCBlbbr3Kqqur76nc6tCjt37qRVq1aEhIRw5513snv3bkDn1JlU11e/0zlV4fPPPycqKoo77rgDf39/evfuzZtvvml7vy6cW/Vm8cNz2bhxI7GxsRQXF9O0aVM+/fRTunXrZuvAgICASu0DAgLYt28fALm5ubi5udG8efMqbXJzc21t/P39q/xef39/W5u6orq+Ahg0aBB33HEHwcHB7NmzhyeffJJrr72WjIwMTCZTg+urjz76iLVr17J69eoq7/1+LDq3Kpytr0Dn1u+io6OZP38+nTt35tChQ/zzn/+kb9++bN68WefUac7WVy1atNA59Qe7d+9m9uzZJCUl8fjjj7Nq1Sr++te/YjKZGDlyZJ04txpMYOnSpQvr1q3j+PHjLFq0iFGjRrF06VLb+05OTpXaG4ZRZdvpTm9zpvY12Y+jqa6vunXrRkJCgq1deHg4UVFRBAcH89VXX3HrrbdWu8/62Ff79+9n3LhxfPvtt7i7u1fbTudWzfpK51aFQYMG2f539+7diY2NpUOHDrz33nu2G0Z1TlU4W18lJSXpnPoDq9VKVFQU06dPB6B3795s3ryZ2bNnM3LkSFs7Rz63GswlITc3Nzp27EhUVBTJycn07NmTmTNn2u4oPz355eXl2ZJmy5YtKS0t5dixY2dtc+jQoSq/9/Dhw1USq6Orrq/OJDAwkODgYHbu3Ak0rL7KyMggLy+PyMhIGjVqRKNGjVi6dCmvvPIKjRo1sh2Lzq1z95XFYqnymYZ8bv1RkyZN6N69Ozt37tTfq3P4Y1+dSUM+pwIDA20j5b8LDQ0lKysLoE6cWw0msJzOMAxKSkoICQmhZcuWpKen294rLS1l6dKl9O3bF4DIyEhcXV0rtcnJyWHTpk22NrGxsRQUFLBq1Spbm5UrV1JQUGBrU1f93ldncuTIEfbv309gYCDQsPrquuuuY+PGjaxbt872ioqKYvjw4axbt4727dvr3PrNufrKxcWlymca8rn1RyUlJWzdupXAwED9vTqHP/bVmTTkc6pfv35s37690rYdO3YQHBwMUDfOrQu6ZbeOmDhxorFs2TJjz549xoYNG4zHH3/ccHZ2Nr799lvDMCoe5fL29jYWL15sbNy40bjrrrvO+ChXmzZtjO+++85Yu3atce21157xUa4ePXoYP//8s/Hzzz8b3bt3r3OPvp2trwoLC41HH33UWLFihbFnzx7jhx9+MGJjY43WrVs3yL46k9OffNG5Vb0/9pXOrf959NFHjR9//NHYvXu38csvvxg33nij4enpaezdu9cwDJ1Tf3S2vtI5VdmqVauMRo0aGc8884yxc+dO48MPPzQaN25sfPDBB7Y2jn5uNYjAcu+99xrBwcGGm5ub4efnZ1x33XW2sGIYFY9zPfXUU0bLli0Nk8lkXHXVVcbGjRsr7ePUqVPGww8/bPj4+BgeHh7GjTfeaGRlZVVqc+TIEWP48OGGp6en4enpaQwfPtw4duzY5TjEi+ZsfXXy5EkjPj7e8PPzM1xdXY22bdsao0aNqtIPDaWvzuT0wKJzq3p/7CudW//z+9wXrq6uRqtWrYxbb73V2Lx5s+19nVP/c7a+0jlV1RdffGGEh4cbJpPJ6Nq1qzF37txK7zv6ueVkGIZxYWM0IiIiIpdWg72HRUREROoOBRYRERFxeAosIiIi4vAUWERERMThKbCIiIiIw1NgEREREYenwCIiIiIOT4FFREREHJ4Ci4iIiDg8BRYRERFxeAosIiIi4vAUWERERMTh/T+cUZXjWGNckwAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ "
" ] @@ -1623,7 +1631,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1656,87 +1664,28 @@ "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: PrimaryBroadcast(0x55db2b43f3b99d37, broadcast, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1] - ((0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1])'], domains={'primary': ['positive electrode'], 'secondary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Subtraction(0x6e57fdf0f90fdbb0, -, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]', '(0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Addition(-0x524f51ed4f620efd, +, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))', 'Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x36e2f7f52718fd84, *, children=['0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction', 'arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Arcsinh(-0x70bcbae05a17171a, function (arcsinh), children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Division(0x2d06e4ce68936693, /, children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m])', '2.0 * Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x533055788c0787e9, *, children=['2.0', 'Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: FunctionParameter(0x79a3a2c645f54668, Positive electrode exchange-current density [A.m-2], children=['maximum(Initial concentration in electrolyte [mol.m-3], 1e-08)', 'maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]), 0.99999999 * Maximum concentration in positive electrode [mol.m-3]), 1e-08 * Maximum concentration in positive electrode [mol.m-3])', 'Maximum concentration in positive electrode [mol.m-3]', 'broadcast(Ambient temperature [K])'], domains={'primary': ['current collector']})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Maximum(-0x10b1c06354524acc, maximum, children=['Initial concentration in electrolyte [mol.m-3]', '1e-08'], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: Parameter(-0x23a8868071606836, Initial concentration in electrolyte [mol.m-3], children=[], domains={})", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:58\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n\u001b[1;32m 59\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n", - "\u001b[0;31mKeyError\u001b[0m: 'Initial concentration in electrolyte [mol.m-3]'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m sim \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mSimulation(spm, parameter_values\u001b[39m=\u001b[39mparam)\n\u001b[1;32m 2\u001b[0m t_eval \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m sim\u001b[39m.\u001b[39;49msolve(t_eval\u001b[39m=\u001b[39;49mt_eval)\n\u001b[1;32m 4\u001b[0m sim\u001b[39m.\u001b[39mplot()\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:559\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 556\u001b[0m logs \u001b[39m=\u001b[39m {}\n\u001b[1;32m 558\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moperating_mode \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mwithout experiment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdrive cycle\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 559\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuild(check_model\u001b[39m=\u001b[39;49mcheck_model, initial_soc\u001b[39m=\u001b[39;49minitial_soc)\n\u001b[1;32m 560\u001b[0m \u001b[39mif\u001b[39;00m save_at_cycles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 561\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 562\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39msave_at_cycles\u001b[39m\u001b[39m'\u001b[39m\u001b[39m option can only be used if simulating an \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 563\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExperiment \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 564\u001b[0m )\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:449\u001b[0m, in \u001b[0;36mSimulation.build\u001b[0;34m(self, check_model, initial_soc)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_built_model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m 448\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 449\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_parameters()\n\u001b[1;32m 450\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mMesh(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_geometry, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_submesh_types, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_pts)\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disc \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mDiscretisation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_spatial_methods)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:399\u001b[0m, in \u001b[0;36mSimulation.set_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unprocessed_model\n\u001b[1;32m 398\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 399\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parameter_values\u001b[39m.\u001b[39;49mprocess_model(\n\u001b[1;32m 400\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unprocessed_model, inplace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m 401\u001b[0m )\n\u001b[1;32m 402\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameter_values\u001b[39m.\u001b[39mprocess_geometry(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeometry)\n\u001b[1;32m 403\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:465\u001b[0m, in \u001b[0;36mParameterValues.process_model\u001b[0;34m(self, unprocessed_model, inplace)\u001b[0m\n\u001b[1;32m 462\u001b[0m new_initial_conditions[new_variable] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(equation)\n\u001b[1;32m 463\u001b[0m model\u001b[39m.\u001b[39minitial_conditions \u001b[39m=\u001b[39m new_initial_conditions\n\u001b[0;32m--> 465\u001b[0m model\u001b[39m.\u001b[39mboundary_conditions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_boundary_conditions(unprocessed_model)\n\u001b[1;32m 467\u001b[0m new_variables \u001b[39m=\u001b[39m {}\n\u001b[1;32m 468\u001b[0m \u001b[39mfor\u001b[39;00m variable, equation \u001b[39min\u001b[39;00m unprocessed_model\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mitems():\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:541\u001b[0m, in \u001b[0;36mParameterValues.process_boundary_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 539\u001b[0m sides \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnegative tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mpositive tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mno tab\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 540\u001b[0m \u001b[39mfor\u001b[39;00m variable, bcs \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39mboundary_conditions\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 541\u001b[0m processed_variable \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(variable)\n\u001b[1;32m 542\u001b[0m new_boundary_conditions[processed_variable] \u001b[39m=\u001b[39m {}\n\u001b[1;32m 543\u001b[0m \u001b[39mfor\u001b[39;00m side \u001b[39min\u001b[39;00m sides:\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:731\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[39m# Unary operators\u001b[39;00m\n\u001b[1;32m 730\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mUnaryOperator):\n\u001b[0;32m--> 731\u001b[0m new_child \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mchild)\n\u001b[1;32m 732\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_unary_new_copy(new_child)\n\u001b[1;32m 733\u001b[0m \u001b[39m# ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m 749\u001b[0m \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m 751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m 725\u001b[0m new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:657\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 655\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(new_child))\n\u001b[1;32m 656\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child))\n\u001b[1;32m 659\u001b[0m \u001b[39m# Create Function or Interpolant or Scalar object\u001b[39;00m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(function_name, \u001b[39mtuple\u001b[39m):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m 720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m 721\u001b[0m \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m 723\u001b[0m new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m 724\u001b[0m \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m 610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m 612\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m 614\u001b[0m \u001b[39mreturn\u001b[39;00m processed_symbol\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:620\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"See :meth:`ParameterValues.process_symbol()`.\"\"\"\u001b[39;00m\n\u001b[1;32m 619\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mParameter):\n\u001b[0;32m--> 620\u001b[0m value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m[symbol\u001b[39m.\u001b[39;49mname]\n\u001b[1;32m 621\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, numbers\u001b[39m.\u001b[39mNumber):\n\u001b[1;32m 622\u001b[0m \u001b[39m# Check not NaN (parameter in csv file but no value given)\u001b[39;00m\n\u001b[1;32m 623\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(value):\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:139\u001b[0m, in \u001b[0;36mParameterValues.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m--> 139\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dict_items[key]\n", - "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:73\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39min\u001b[39;00m k \u001b[39mand\u001b[39;00m k\u001b[39m.\u001b[39mendswith(\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 70\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m 71\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Use the dimensional version \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m )\n\u001b[0;32m---> 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Best matches are \u001b[39m\u001b[39m{\u001b[39;00mbest_matches\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mKeyError\u001b[0m: \"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eea07489478640aab13bd2aab1fe5020", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1781,7 +1730,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -1812,7 +1761,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index ad6a3d6cee..54441e3aed 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -7,6 +7,16 @@ import warnings +def represents_positive_integer(s): + """Check if a string represents a positive integer""" + try: + val = int(s) + except ValueError: + return False + else: + return val > 0 + + class BatteryModelOptions(pybamm.FuzzyDict): """ Attributes @@ -251,19 +261,7 @@ def __init__(self, extra_options): "current-driven", "stress and reaction-driven", ], - "number of MSMR reactions": [ - "none", - "1", - "2", - "3", - "4", - "5", - "6", - "7", - "8", - "9", - "10", - ], + "number of MSMR reactions": ["none"], "open-circuit potential": ["single", "current sigmoid", "MSMR"], "operating mode": [ "current", @@ -646,7 +644,16 @@ def __init__(self, extra_options): value_list.append(val) for val in value_list: if val not in self.possible_options[option]: - if not (option == "operating mode" and callable(val)): + if option == "operating mode" and callable(val): + # "operating mode" can be a function + pass + elif ( + option == "number of MSMR reactions" + and represents_positive_integer(val) + ): + # "number of MSMR reactions" can be a positive integer + pass + else: raise pybamm.OptionError( f"\n'{val}' is not recognized in option '{option}'. " f"Possible values are {self.possible_options[option]}" diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 2f8d51ac5a..05ce9b8084 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -698,14 +698,16 @@ def j0_j(self, c_e, U, T, index): f"j0_ref_{d}_{index}", {"Temperature [K]": T} ) - # Equation 16, Baker et al 2018 + # Equation 16, Baker et al 2018. The original formulation would be implemented + # as: # j0_j = ( # j0_ref_j # * xj ** (wj * aj) # * (Xj - xj) ** (wj * (1 - aj)) # * (c_e / c_e_ref) ** (1 - aj) # ) - # Reformulate in terms of potential to avoid singularity as x_j approaches X_j + # However, we reformulate in terms of potential to avoid singularity as x_j + # approaches X_j j0_j = ( j0_ref_j * xj**wj diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 24ca2abfe9..6c787cea0b 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -313,6 +313,8 @@ def test_basic_processing_msmr(self): options = { "open-circuit potential": "MSMR", "particle": "MSMR", + "intercalation kinetics": "MSMR", + "number of MSMR reactions": ("6", "4"), } parameter_values = pybamm.ParameterValues("MSMR_Example") model = self.model(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 1914cd9dd7..60eed9d6fb 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -31,7 +31,7 @@ 'lithium plating': 'none' (possible: ['none', 'reversible', 'partially reversible', 'irreversible']) 'lithium plating porosity change': 'false' (possible: ['false', 'true']) 'loss of active material': 'stress-driven' (possible: ['none', 'stress-driven', 'reaction-driven', 'current-driven', 'stress and reaction-driven']) -'number of MSMR reactions': 'none' (possible: ['none', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10']) +'number of MSMR reactions': 'none' (possible: ['none']) 'open-circuit potential': 'single' (possible: ['single', 'current sigmoid', 'MSMR']) 'operating mode': 'current' (possible: ['current', 'voltage', 'power', 'differential power', 'explicit power', 'resistance', 'differential resistance', 'explicit resistance', 'CCCV']) 'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile', 'MSMR']) @@ -388,6 +388,15 @@ def test_options(self): pybamm.BaseBatteryModel( {"particle": "MSMR", "intercalation kinetics": "MSMR"} ) + with self.assertRaisesRegex(pybamm.OptionError, "MSMR"): + pybamm.BaseBatteryModel( + { + "open-circuit potential": "MSMR", + "particle": "MSMR", + "intercalation kinetics": "MSMR", + "number of MSMR reactions": "1.5", + } + ) def test_build_twice(self): model = pybamm.lithium_ion.SPM() # need to pick a model to set vars and build From f8a0fbce7a4232b64cbef755b5edc8777153a347 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Mon, 11 Sep 2023 15:38:50 +0100 Subject: [PATCH 087/154] update docs --- docs/source/api/models/lithium_ion/index.rst | 1 + .../models/submodels/interface/kinetics/butler_volmer.rst | 4 ++-- .../source/api/models/submodels/interface/kinetics/index.rst | 1 + .../submodels/interface/kinetics/msmr_butler_volmer.rst | 5 +++++ .../submodels/interface/open_circuit_potential/index.rst | 1 + docs/source/api/models/submodels/particle/index.rst | 1 + docs/source/examples/index.rst | 1 + pybamm/input/parameters/lithium_ion/MSMR_example_set.py | 1 + 8 files changed, 13 insertions(+), 2 deletions(-) create mode 100644 docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst diff --git a/docs/source/api/models/lithium_ion/index.rst b/docs/source/api/models/lithium_ion/index.rst index f925d2c3d4..1a72c3c662 100644 --- a/docs/source/api/models/lithium_ion/index.rst +++ b/docs/source/api/models/lithium_ion/index.rst @@ -9,5 +9,6 @@ Lithium-ion Models mpm dfn newman_tobias + msmr yang2017 electrode_soh diff --git a/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst b/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst index abf878e57b..522418a42f 100644 --- a/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst +++ b/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst @@ -1,5 +1,5 @@ -Butler Volumer -============== +Butler Volmer +============= .. autoclass:: pybamm.kinetics.SymmetricButlerVolmer :members: diff --git a/docs/source/api/models/submodels/interface/kinetics/index.rst b/docs/source/api/models/submodels/interface/kinetics/index.rst index 8def3d7fc8..efb8be4d30 100644 --- a/docs/source/api/models/submodels/interface/kinetics/index.rst +++ b/docs/source/api/models/submodels/interface/kinetics/index.rst @@ -10,5 +10,6 @@ Kinetics marcus no_reaction tafel + msmr_butler_volmer total_main_kinetics inverse_kinetics/index diff --git a/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst b/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst new file mode 100644 index 0000000000..18bea7ee7a --- /dev/null +++ b/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst @@ -0,0 +1,5 @@ +MSMR Butler Volmer +================== + +.. autoclass:: pybamm.kinetics.MSMRButlerVolmer + :members: diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst index 132e5b88a9..fc664adf2b 100644 --- a/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst +++ b/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst @@ -6,3 +6,4 @@ Open-circuit potential models base_ocp current_sigmoid_ocp single_ocp + msmr_ocp diff --git a/docs/source/api/models/submodels/particle/index.rst b/docs/source/api/models/submodels/particle/index.rst index ae020ac3fa..b17a7502e4 100644 --- a/docs/source/api/models/submodels/particle/index.rst +++ b/docs/source/api/models/submodels/particle/index.rst @@ -8,3 +8,4 @@ Particle fickian_diffusion polynomial_profile x_averaged_polynomial_profile + msmr_diffusion diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst index 4287e28927..4bab430032 100644 --- a/docs/source/examples/index.rst +++ b/docs/source/examples/index.rst @@ -59,6 +59,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries notebooks/models/lead-acid.ipynb notebooks/models/lithium-plating.ipynb notebooks/models/MPM.ipynb + notebooks/models/MSMR.ipynb notebooks/models/pouch-cell-model.ipynb notebooks/models/rate-capability.ipynb notebooks/models/SEI-on-cracks.ipynb diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index 475ed307e0..fce5c7f068 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -92,6 +92,7 @@ def get_parameter_values(): experimental cycling data. """ return { + "chemistry": "lithium_ion", # cell "Negative electrode thickness [m]": 8.52e-05, "Separator thickness [m]": 1.2e-05, From 4da2b30281c7d833ce45142a62ad91e511d8cd40 Mon Sep 17 00:00:00 2001 From: Saransh Chopra Date: Mon, 11 Sep 2023 23:17:12 +0530 Subject: [PATCH 088/154] fix pre-commit --- pybamm/geometry/battery_geometry.py | 2 +- pybamm/models/full_battery_models/base_battery_model.py | 2 +- pybamm/models/submodels/base_submodel.py | 2 +- pybamm/parameters/base_parameters.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pybamm/geometry/battery_geometry.py b/pybamm/geometry/battery_geometry.py index 8c06fbcd68..0dfe3fd256 100644 --- a/pybamm/geometry/battery_geometry.py +++ b/pybamm/geometry/battery_geometry.py @@ -28,7 +28,7 @@ def battery_geometry( A geometry class for the battery """ - if options is None or isinstance(options, dict): + if options is None or type(options) == dict: # noqa: E721 options = pybamm.BatteryModelOptions(options) geo = pybamm.GeometricParameters(options) L_n = geo.n.L diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index d74f886cc6..64ce55ebda 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -831,7 +831,7 @@ def options(self, extra_options): # if extra_options is a dict then process it into a BatteryModelOptions # this does not catch cases that subclass the dict type # so other submodels can pass in their own options class if needed - if extra_options is None or isinstance(extra_options, dict): + if extra_options is None or type(extra_options) == dict: # noqa: E721 options = BatteryModelOptions(extra_options) else: options = extra_options diff --git a/pybamm/models/submodels/base_submodel.py b/pybamm/models/submodels/base_submodel.py index 51b82c8a9d..ab095b9be2 100644 --- a/pybamm/models/submodels/base_submodel.py +++ b/pybamm/models/submodels/base_submodel.py @@ -73,7 +73,7 @@ def __init__( self.external = external - if options is None or isinstance(options, dict): + if options is None or type(options) == dict: # noqa: E721 options = pybamm.BatteryModelOptions(options) self.options = options diff --git a/pybamm/parameters/base_parameters.py b/pybamm/parameters/base_parameters.py index ef3aef367e..a7b319ec81 100644 --- a/pybamm/parameters/base_parameters.py +++ b/pybamm/parameters/base_parameters.py @@ -62,7 +62,7 @@ def options(self): @options.setter def options(self, extra_options): - if extra_options is None or isinstance(extra_options, dict): + if extra_options is None or type(extra_options) == dict: # noqa: E721 self._options = pybamm.BatteryModelOptions(extra_options) else: self._options = extra_options From 5069a0e2ebcec52170c39ba8719ac857a3d28805 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 12 Sep 2023 04:30:35 +0000 Subject: [PATCH 089/154] chore: update pre-commit hooks MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.287 → v0.0.288](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.287...v0.0.288) --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3ea9b14dd8..288f139afa 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ ci: repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.0.287" + rev: "v0.0.288" hooks: - id: ruff args: [--fix, --ignore=E741, --exclude=__init__.py] From 5d01a4a5618e45abeedd26161745a15dd48487f4 Mon Sep 17 00:00:00 2001 From: kratman Date: Tue, 12 Sep 2023 13:34:26 -0400 Subject: [PATCH 090/154] Revert tutorial changes --- ...torial-6-managing-simulation-outputs.ipynb | 190 +++++++----------- 1 file changed, 78 insertions(+), 112 deletions(-) diff --git a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb index 4ec423db82..bea655f2b5 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -21,10 +23,17 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -41,6 +50,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -48,6 +58,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -64,6 +75,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -81,6 +93,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -95,26 +108,26 @@ { "data": { "text/plain": [ - "array([3.77048098, 3.75309871, 3.74569826, 3.74040906, 3.73582978,\n", - " 3.73155017, 3.72743983, 3.72345507, 3.71958265, 3.71581858,\n", - " 3.71216287, 3.70861698, 3.7051823 , 3.70185947, 3.69864846,\n", - " 3.69554865, 3.69255894, 3.6896778 , 3.68690322, 3.68423281,\n", - " 3.68166383, 3.67919326, 3.67681781, 3.67453394, 3.67233783,\n", - " 3.6702254 , 3.66819225, 3.66623353, 3.66434383, 3.66251699,\n", - " 3.66074577, 3.65902141, 3.65733311, 3.65566717, 3.65400602,\n", - " 3.65232696, 3.6506007 , 3.64879012, 3.64684952, 3.64472566,\n", - " 3.64236191, 3.63970731, 3.63673126, 3.63344172, 3.62989992,\n", - " 3.62622171, 3.6225587 , 3.61906361, 3.61585516, 3.61299814,\n", - " 3.61050386, 3.60834443, 3.606471 , 3.60482876, 3.60336628,\n", - " 3.60203993, 3.60081505, 3.59966528, 3.59857137, 3.59751973,\n", - " 3.59650118, 3.59550993, 3.59454272, 3.59359821, 3.59267644,\n", - " 3.59177838, 3.59090556, 3.59005965, 3.58924208, 3.58845355,\n", - " 3.58769359, 3.58695999, 3.58624826, 3.58555109, 3.58485777,\n", - " 3.58415379, 3.5834204 , 3.58263444, 3.58176818, 3.58078926,\n", - " 3.57966067, 3.57834049, 3.57678113, 3.57492782, 3.57271582,\n", - " 3.57006555, 3.566875 , 3.56300793, 3.5582764 , 3.55241508,\n", - " 3.54504405, 3.53561555, 3.52333845, 3.50707266, 3.48518447,\n", - " 3.45535426, 3.41433385, 3.35766635, 3.27941791, 3.17203869])" + "array([3.77047806, 3.75305182, 3.74567027, 3.74038822, 3.73581196,\n", + " 3.73153391, 3.72742393, 3.72343929, 3.71956623, 3.71580184,\n", + " 3.71214621, 3.7086004 , 3.70516561, 3.70184253, 3.69863121,\n", + " 3.69553118, 3.69254137, 3.68966018, 3.68688562, 3.68421526,\n", + " 3.68164637, 3.67917591, 3.6768006 , 3.67451688, 3.67232094,\n", + " 3.67020869, 3.66817572, 3.66621717, 3.66432762, 3.6625009 ,\n", + " 3.66072974, 3.65900536, 3.65731692, 3.65565066, 3.65398895,\n", + " 3.65230898, 3.65058135, 3.6487688 , 3.64682546, 3.64469798,\n", + " 3.64232968, 3.63966973, 3.63668796, 3.63339303, 3.62984711,\n", + " 3.62616692, 3.6225045 , 3.61901241, 3.61580868, 3.6129572 ,\n", + " 3.61046847, 3.60831405, 3.60644483, 3.60480596, 3.60334607,\n", + " 3.60202167, 3.60079822, 3.5996495 , 3.59855637, 3.59750531,\n", + " 3.59648723, 3.59549638, 3.59452954, 3.59358541, 3.59266405,\n", + " 3.59176646, 3.59089417, 3.59004885, 3.58923192, 3.58844407,\n", + " 3.58768477, 3.58695179, 3.58624057, 3.58554372, 3.58485045,\n", + " 3.58414611, 3.58341187, 3.58262441, 3.58175587, 3.58077378,\n", + " 3.57964098, 3.57831538, 3.5767492 , 3.57488745, 3.57266504,\n", + " 3.5700019 , 3.56679523, 3.56290766, 3.5581495 , 3.55225276,\n", + " 3.54483361, 3.53533853, 3.52296795, 3.50656968, 3.48449277,\n", + " 3.45439366, 3.41299182, 3.35578871, 3.27680072, 3.16842636])" ] }, "execution_count": 4, @@ -127,6 +140,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -178,6 +192,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -192,7 +207,7 @@ { "data": { "text/plain": [ - "array([3.729495 , 3.70861698, 3.67812431, 3.65402263])" + "array([3.72947892, 3.7086004 , 3.67810702, 3.65400557])" ] }, "execution_count": 6, @@ -205,6 +220,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -212,17 +228,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "In some cases simulations might take a long time to run, so it is advisable to save files to your computer. The output can be analysed later without re-running the simulation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output files should be saved to a working directory outside the PyBaMM project. Since this is only a tutorial, we will save the files to a temporary folder that can be discarded at the end." + "In some cases simulations might take a long time to run so it is advisable to save in your computer so it can be analysed later without re-running the simulation. You can save the whole simulation doing:" ] }, { @@ -231,19 +241,15 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from tempfile import TemporaryDirectory\n", - "temp_folder = TemporaryDirectory()\n", - "\n", - "def temp_path_to(name):\n", - " return os.path.join(temp_folder.name, name)" + "sim.save(\"SPMe.pkl\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - " You can save the whole simulation doing:" + "If you now check the root directory of your notebooks you will notice that a new file called `\"SPMe.pkl\"` has appeared. We can load the stored simulation doing" ] }, { @@ -252,53 +258,11 @@ "metadata": {}, "outputs": [], "source": [ - "sim.save(temp_path_to(\"SPMe.pkl\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you now check the output directory you will notice that a new file called `\"SPMe.pkl\"` has appeared." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['SPMe.pkl']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "os.listdir(temp_folder.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can load the stored simulation doing" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "sim2 = pybamm.load(temp_path_to(\"SPMe.pkl\"))" + "sim2 = pybamm.load(\"SPMe.pkl\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -307,13 +271,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7ea56723de29416895d03d7c5d867c26", + "model_id": "80a9bcfaa1264a6f80be4c12906f491e", "version_major": 2, "version_minor": 0 }, @@ -323,16 +287,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -340,6 +294,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -348,15 +303,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "sol = sim.solution\n", - "sol.save(temp_path_to(\"SPMe_sol.pkl\"))" + "sol.save(\"SPMe_sol.pkl\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -365,13 +321,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "44804d087e41415ea60b63324ccf7bff", + "model_id": "1fc7b0729d6c40fe9e4f5921bb12b57d", "version_major": 2, "version_minor": 0 }, @@ -385,20 +341,21 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sol2 = pybamm.load(temp_path_to(\"SPMe_sol.pkl\"))\n", + "sol2 = pybamm.load(\"SPMe_sol.pkl\")\n", "pybamm.dynamic_plot(sol2)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -407,14 +364,15 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "sol.save_data(temp_path_to(\"sol_data.pkl\"), [\"Current [A]\", \"Voltage [V]\"])" + "sol.save_data(\"sol_data.pkl\", [\"Current [A]\", \"Voltage [V]\"])" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -423,17 +381,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "sol.save_data(temp_path_to(\"sol_data.csv\"), [\"Current [A]\", \"Voltage [V]\"], to_format=\"csv\")\n", + "sol.save_data(\"sol_data.csv\", [\"Current [A]\", \"Voltage [V]\"], to_format=\"csv\")\n", "# matlab needs names without spaces\n", - "sol.save_data(temp_path_to(\"sol_data.mat\"), [\"Current [A]\", \"Voltage [V]\"], to_format=\"matlab\",\n", + "sol.save_data(\"sol_data.mat\", [\"Current [A]\", \"Voltage [V]\"], to_format=\"matlab\",\n", " short_names={\"Current [A]\": \"I\", \"Voltage [V]\": \"V\"})" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -441,22 +400,29 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Since the data is no longer needed, we will remove the files we saved." + "Before finishing we will remove the data files we saved so that we leave the directory as we found it" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "temp_folder.cleanup()" + "import os\n", + "os.remove(\"SPMe.pkl\")\n", + "os.remove(\"SPMe_sol.pkl\")\n", + "os.remove(\"sol_data.pkl\")\n", + "os.remove(\"sol_data.csv\")\n", + "os.remove(\"sol_data.mat\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -467,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -477,7 +443,7 @@ "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n", "\n" ] } @@ -489,7 +455,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.9.13 ('python39-pybamm')", "language": "python", "name": "python3" }, @@ -503,7 +469,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.13" }, "toc": { "base_numbering": 1, From 096f0b33e45a6fe12289d0c9b01f2af84cd7b305 Mon Sep 17 00:00:00 2001 From: bobonice Date: Wed, 13 Sep 2023 16:49:13 +0800 Subject: [PATCH 091/154] #3323 fix electrode thickness change [m] --- pybamm/models/submodels/particle_mechanics/base_mechanics.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pybamm/models/submodels/particle_mechanics/base_mechanics.py b/pybamm/models/submodels/particle_mechanics/base_mechanics.py index f6a0e09b7d..feffbdd380 100644 --- a/pybamm/models/submodels/particle_mechanics/base_mechanics.py +++ b/pybamm/models/submodels/particle_mechanics/base_mechanics.py @@ -50,12 +50,13 @@ def _get_mechanical_results(self, variables): c_0 = domain_param.c_0 E0 = domain_param.E nu = domain_param.nu + L0 = domain_param.L sto_init = pybamm.r_average(domain_param.prim.c_init / domain_param.prim.c_max) v_change = pybamm.x_average( eps_s * domain_param.prim.t_change(sto_rav) ) - pybamm.x_average(eps_s * domain_param.prim.t_change(sto_init)) - electrode_thickness_change = self.param.n_electrodes_parallel * v_change + electrode_thickness_change = self.param.n_electrodes_parallel * v_change * L0 # Ai2019 eq [10] disp_surf = Omega * R0 / 3 * (c_s_rav - c_0) # c0 reference concentration for no deformation From c330ffa2fa8fd8ebfd5497a8d41217ced18875a4 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 13 Sep 2023 10:03:34 +0100 Subject: [PATCH 092/154] fix example notebooks --- .../examples/notebooks/models/MSMR.ipynb | 28 ++-- .../models/electrode-state-of-health.ipynb | 120 +++++++++--------- .../notebooks/solvers/speed-up-solver.ipynb | 71 ++++++----- .../submodels/particle/base_particle.py | 1 + .../submodels/particle/msmr_diffusion.py | 1 - 5 files changed, 113 insertions(+), 108 deletions(-) diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb index 9e75ce24d4..7413339f5b 100644 --- a/docs/source/examples/notebooks/models/MSMR.ipynb +++ b/docs/source/examples/notebooks/models/MSMR.ipynb @@ -17,15 +17,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "zsh:1: no matches found: pybamm[plot,cite]\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - "%pip install pybamm -q # install PyBaMM if it is not installed\n", + "%pip install pybamm[plot,cite] -q # install PyBaMM if it is not installed\n", "import pybamm\n", "import matplotlib.pyplot as plt" ] @@ -299,14 +297,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 275.087 and h = 1.14637e-10, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 275.085 and h = 1.46692e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 275.026 and h = 2.68649e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 275.028 and h = 4.19765e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -347,12 +345,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "64dd40f0b3d54afd95cf71432e0ab43d", + "model_id": "67da37f9dcb64ac696aca8772d5ffce7", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106549815808839, step=0.06106549815808839)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106530343899824, step=0.06106530343899824)…" ] }, "metadata": {}, @@ -361,7 +359,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -401,12 +399,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b9cf28dee3884302ab11e22d567d9e36", + "model_id": "0b056c49819644d6848340ae609978f2", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106549815808839, step=0.06106549815808839)…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106530343899824, step=0.06106530343899824)…" ] }, "metadata": {}, @@ -415,7 +413,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -454,7 +452,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -463,7 +461,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb index 5d78554b9a..4d32f6a40e 100644 --- a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb +++ b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb @@ -20,20 +20,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR: Invalid requirement: '#'\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "zsh:1: no matches found: pybamm[plot,cite]\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -55,18 +49,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3b55c57e62d4444fb157f8a1bbdde58c", + "model_id": "17f51c91ccd74aeb9afa13693702858e", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=2.32485835391946, step=0.0232485835391946), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=2.3248351274860397, step=0.0232483512748604)…" ] }, "metadata": {}, @@ -75,10 +69,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -146,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -165,18 +159,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "x_100 : 0.83337428922595\n", - "y_100 : 0.03354553395256055\n", - "Q : 4.968932758817601\n", - "x_0 : 0.0015118453536460735\n", - "y_0 : 0.8908948803914055\n" + "x_100 : 0.8333742766485323\n", + "y_100 : 0.03354554691532985\n", + "Q : 4.968932683689383\n", + "x_0 : 0.0015118453536460618\n", + "y_0 : 0.8908948803914054\n" ] } ], @@ -244,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -252,10 +246,10 @@ "output_type": "stream", "text": [ "x_100 : 0.833374276202919\n", - "y_100 : 0.03354554737459606\n", + "y_100 : 0.0335455473745959\n", "Q : 4.968932679279884\n", - "x_0 : 0.0015118456462390728\n", - "y_0 : 0.8908948800898482\n" + "x_0 : 0.0015118456462390713\n", + "y_0 : 0.890894880089848\n" ] } ], @@ -288,12 +282,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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AREREOD8wbm13q/DwcOcHUXPbun8YasLCwpwfnM1t6/7hrQkNDW0wNm/auicbzWkbEhLSYNvIyEiPBKmpbb/4AvjwQ2DVKguOH/dsGxoK9O8fgd69I9Czp7xSl5wMdOxoQadOrdG2LdCtG3DxIlBcDIwe3fTn3hevk4aeT1+8Thp6PgPhddLY89nS14nFYmmwLTUiNBSYORNYvlyWiJjYEfmPexm2hUmdt0yb2FFw+vRT4NVXgS1bXOciIoCpU4G0NGDECGDIELk95p1kZgL/93/AqlXA6NH+jZkoYMyeLRO7NWuA3/1O9w8cIlNwOFxrRupchgU4xo6CxJEjMnGbOlUmdaGhwHe+A6xYIStLOTnAggVyf+W7JXUAMGeO/L1qFWCuwQhkalOnApGRwKlTwKFDqqMhMqbiYqCsDIiJAW6ZuKYHJnYU0BwO4Le/BQYPBoqK5NW5f/93OWTh738Hvvvd5i3mPWOG/Hz76ivgyy99HTVRgGrdWn5DAjg7lshftPfWjBnyQ0tnTOwoYN24IRO3n/0MqKmR2+wdPQq89VbLlwRq08b1+bZ6dYtDJQoeWmlI2+qIiHxLS+wUlGEBJnYUoK5eBSZOBD75BAgPB955B1i/HrhlKbQWeeAB+dttGT8i45s5U/4uLgbc1mgkIh84cUIOcwgLA6ZPVxICEzsKONeuAdOmAXv3yv3LN28GnnrK9+O8s7KAkBB5P27rTxMZW2IiMHKkPHZboJqIfEC7Wjd+PNCunZIQlCd2S5cuRffu3REVFYVRo0ahuLj4ju1///vfo2/fvmjVqhWSk5Px3HPP3bYNEwWv+nrgwQeBPXuAuDi53NaYMf65r06d5GQLgFUp8p+A7ONYjiXyD8VlWACAUOjjjz8WERER4r333hOHDh0STz75pGjbtq0oKytrsP1HH30kIiMjxUcffSROnTolCgoKRGJionjuueeafJ82m00AEDabzVcPg3zohReEAIRo3VqIkhL/39/vfifvb9Ik/98X+VcgvrcDto87cEC+8CMjhbhxw9uHRUQNuXJFiNBQ+d46edKn/7Q3/ZvSxG7kyJFi/vz5zv+22+0iKSlJLFmypMH28+fPF5MnT/Y4l52dLcaOHdvk+wzEzp+kdevk+wEQYuVKfe7z5El5f6GhQpSX63Of5B+B+N4O2D7O4RCiRw/54l+1qsn/NhHdwV/+It9Tgwb5/J/2pn9TVoqtra3F3r17PTaxDwkJQVpaGnbu3NngbcaMGYO9e/c6SxknT55EXl4eMjIydImZ/Of6deDpp+Xxc88BDz2kz/326AGkpMjdlrT1JIl8IaD7OIuF5VgiX3PfbUIhZTtPlJeXw263Iz4+3uN8fHw8jhw50uBtHnnkEZSXl2PcuHEQQqC+vh4//vGP8fOf/7zR+6mpqUFNTY3zvysqKnzzAMinXn4ZOHcOuOce4L/+S9/7fuAB4PPP5bInP/yhvvdNxhXwfdysWcDvfy8nUNjtctVvImqemhq5dAOgdnwdAmDyhDc2b96M1157DX/4wx+wb98+/OMf/0Bubi5+9atfNXqbJUuWIDY21vmTnJysY8TUFIcOAW+/LY/feQdoYGtSv9KWPSkoACor9b1vIne69nHjxgFt28qtW3bt8s0DIDKrzZvl4quJicCwYUpDUZbYxcXFITQ0FGVlZR7ny8rKkJCQ0OBtXnnlFcydOxdPPPEEBg0ahAceeACvvfYalixZAofD0eBtFi5cCJvN5vw5e/aszx8LtcyvfiV3mPiXf5E7Hult0CBZkq2ulskdkS8EfB8XHi43TQZYjiVqKa0Mq62jpZCye4+IiMCwYcNQVFTkPOdwOFBUVITU1NQGb1NVVYWQW/5god+WD0QjG35GRkYiJibG44cCx5dfAitXyuNFi9TEYLFwsWLyvaDo47SxQNxejKj5hAiMZU6+pTStzM7OxrvvvosPPvgAhw8fxtNPP43Kyko89thjAIBHH30UCxcudLbPysrCH//4R3z88cc4deoUCgsL8corryArK8vZ+VFw+a//ku+JOXPkJAZVtMQuJweoq1MXBxlLwPdx06fLK3dHj8ofIvJeSYkcJB4dDUyerDoadZMnAODhhx/G5cuXsWjRIpSWlmLw4MHIz893DjY+c+aMx7fXl19+GRaLBS+//DLOnz+Pjh07IisrC/+l92h78omyMmDFCnn8yitqY0lNlQsWX7oEbNni2keWqCUCvo+LiQEmTQI2bJBXHF54wT/3Q2Rk2tW69HQgKkptLAAsorHr+wZVUVGB2NhY2Gw2lmUVe/NNIDtb7m60e7fqaIAnnwT+9CfgmWeApUtVR0Pe4ntb8vrvsHQp8OyzcjLFZ5/5P0AioxkyBNi/H1i+HJg3zy934c37OqhmxZKxfPCB/O2n94HXtHLs6tVyMgeRKWRlyd87dgCXL6uNhSjYnDkjk7qQECBA1tRlYkdK7N8v146LiAC+9z3V0UhTpgBWK3DhgtyrlsgUunaVVxwcDiA3V3U0RMFFW9l+zBigY0e1sXyLiR0psXy5/D17NtC+vdJQnCIjXV+4ODuWTIWzY4maR1sqKABmw2qY2JHuhAA++UQe/+AHamO5FZc9IVPSEruCArmgIxHdnc0mFyYGlG8j5o6JHeluzx45M7x1a2DaNNXReMrIkOXho0eBRnZ9IjKeIUOALl2AqirAbd09IrqDggK5PlbfvkCfPqqjcWJiR7rTrobNmBEQM8M9xMTIsXYAr9qRiVgsLMcSeSsAy7AAEztSYPVq+VsrewaaOXPkbyZ2ZCpaYrduHaeFE91NXR2QlyePA6gMCzCxI50dPQocPgyEhQXMzPDbzJ4tL2D885+yZExkChMnAm3aABcvclo40d1s2wZcuyZnwo4erToaD0zsSFfaVbDJk4G2bZWG0qj4eDlzHeDe6GQikZFyizGA5Viiu9E+HGbOBAJsS1MmdqQrLbEL1DKshrNjyZQ4zo7o7oRwvUcCrAwLMLEjHZ0/DxQXy+MAG2t6G22c3ebNwDffqIyESEcZGfLqw4EDwKlTqqMhCkyHDsn3R1QUMHWq6mhuw8SOdKNduR49GkhMVBvL3fTsCQwaBNjtQE6O6miIdNK+PXD//fKYV+2IGqa9N9LS5LpdAYaJHekmWMqwGve9Y4lMg+VYojvTrlIEYBkWYGJHOrl61bVAd7Akdlo5Nj9frttKZArah9WWLfKNS0QuFy+6xhTNnKk2lkYwsSNd5OYC9fXAvfcCvXurjqZpBg8GunUDbt4ENmxQHQ2RTnr2lG9Uux1Yv151NESBRRubM2pUwI4pYmJHutDKsNpVsGBgsbAcSybFcixRwwK8DAswsSMd3Lwpy5lA8JRhNVq869bJK45EpqBNW1+/HqitVRsLUaCorAQ+/VQeM7EjMysslGPUunYFhg5VHY13xo4F4uLkkidbt6qOhkgnI0bIlborKuRYOyKSH2Y1NcA998jhCgGKiR35nXsZ1mJRGorXQkNdX8y4WDGZRkgIkJUlj1mOJZLcy7AB/GHGxI78qr5eljGB4Bpf5859nJ0QSkMh0o9Wjl2zhi98IvdFTQO4DAswsSM/++wz4MoVoEMH17qnwUZbg/LcOWDvXtXREOlkyhSgVSvg7Fng889VR0Ok1q5dQHk50K4dMG6c6mjuiIkd+ZU2mzQrCwgLUxpKs0VFATNmyGOWY8k0WrUCpk2TxyzHktlpZdiMDCA8XG0sd8HEjvxGCFdiF6xlWI1WjmViR6biXo4lMjPty02Al2EBJnbkR/v2AWfOANHRri/+wSozU35JO3wYOHpUdTREOsnMlIPE9+2TYxGIzOjoUfkTHg5Mn646mrtiYkd+o12tmz5dVnWCWWwsMHmyPOZixWQanToBqanyWJsFRWQ22tW6SZOAmBi1sTQBEzvyG61sGWyLEjdGKyezHEumwnIsmV0QlWEBJnbkJ199BRw6JCdMZGaqjsY3Zs+WVandu4ELF1RHQ6QT7cNs40a5YDGRmVy+DOzYIY+Z2JGZaVe1Jk6Us8ONIDERGD1aHvPiBZlG375A795AXR2wYYPqaIj0lZsLOBzAkCFAcrLqaJqEiR35hTYOzShlWA3LsWQ6FgvLsWReQVaGBZjYkR9cvAjs3CmPtc8Do9AS1U2bgKtX1cZCpBvtQy03V24nQ2QG1dVAQYE8DqIPMyZ25HPal/qRI4HOndXG4mu9e8u9n+vrgbw81dEQ6SQ1VW4fc/UqsH276miI9FFUBFRVAV26AIMHq46myZjYkc8ZbTbsrbhYMZlOWBgwc6Y8ZjmWzMK9DGuxqI3FC0zsyKeuXZOT5wDjJnbaOLv164GbN5WGQqQfrRy7dq3cVobIyBwO19qNQVSGBZjYkY/l5ckyZb9+cjKdEQ0dCnTtKq/QFxaqjoZIJ9OmAZGRwIkTcgsWIiPbs0cOGG/TBpgwQXU0XmFiRz5l9DIsIK/Ia1ftuAsFmYbVCkyZIo9ZjiWj08qw06fLLzRBhIkd+Ux1tSxPAsZO7ADX41u7lpMEyUTcy7FERqa9xoOsDAswsSMf+vRToLJSzoQdPlx1NP41bpycJHjlCrBtm+poiHSSlSV/794NlJaqjYXIX06dAg4cAEJDgRkzVEfjNSZ25DNaGXbOnKCaQNQsYWGuzziWY8k0kpKAESPk5ImcHNXREPmHdrXu/vuB9u3VxtIMTOzIJ+rrXe8Fo5dhNe7LnnCSIJkGy7FkdEFchgWY2JGP7NgBlJfLfWHHj1cdjT6mTgWio4EzZ4CSEtXREOlES+wKC+XUcCIjuXoV2LJFHmtlmSDDxI58QivDZmUB4eFqY9FLq1ZywhTAxYrJRAYNArp1k7OluN4PGc369YDdLrcY6tlTdTTNwsSOWkwIz/F1ZqKVYznOjkzDYnGVqFiOJaMJ8jIswMSOfGD/fuDrr+UVrPR01dHoKzNTTqQ4eBA4flx1NEQ60cqx69bJqxtERlBb61qzS3uNByEmdtRi2tWq9HQ55sxM2rUDJk6UxyzHkmmMHw/ExgKXL8ulT4iMYMsWoKICSEiQs7+DFBM7ajGzlmE17rNjiUwhPBzIyJDHLMeSUWiv5awsICR406PgjZwCwokTrnUcg3QCUYtpQzF27ZJbCxKZApc9ISMRwrVVXhCXYQEmdtRCWhl2woSgXMfRJzp3BkaNkv0CP+PINKZPlwNMDx8GvvpKdTRELfP558DZs3I8kbYncpBiYkctopUfzbIocWO0MjTLsWQabdu6BpjyGw0FO+01PG2anAkYxJjYUbOVlcmFiYGgnhnuE1piu3EjYLOpjYVINyzHklEYpAwLMLGjFlizRpYfhw8HkpNVR6NW375A//5AXR2Ql6c6GiKdaB+C27bJrWeIgtG5c8C+fXKNxsxM1dG0GBM7ajZtfJ3Zy7Aazo4l0+nWDUhJARwOfqOh4LVunfw9ZgzQqZPaWHyAiR01S0UFUFQkj5nYSdo4u/Xr5W5LRKbAciwFOwOVYQEmdtRMeXlyke4+fYB+/VRHExiGDwe6dAFu3HAlvUSGp30Y5ufzGw0Fn4oKOTgaYGJH5uY+G9ZiURtLoLBYODuWTGjYMCApCaisBDZtUh0NkXc2bJCDow10lYKJHXmtpsY1nIZlWE9aYrd2LbfQJJOwWFiOpeBlsDIswMSOmqGoSJYbk5KCejs9vxg/Xu4fe/kysH276miIdOKe2DkcamMhaqr6eiA3Vx4zsSMz08qMs2cH9XZ6fhEe7tpaTZs1TGR4kycDVitw4YJcNoIoGGzfDly9CnToIGfEGgQ/lskrdrur2sIybMPclz0RQm0sRLqIjATS0+Uxy7EULLQy7MyZcsNzg2BiR17ZuRO4dAmIjXXtJkSetB1pTp+W2w8SmYJWytI+LIkCmfvm3gYqwwJM7MhLWhl25kxZdqTbRUe7Ll5wdiyZRmamHJvxxRfyWw1RIDt8GDhxQl5tnjZNdTQ+xcSOmkwIz2VOqHHa34fj7Mg0OnQAxo2Tx9pK/kSBSruyPGWKHB9qIEzsqMkOHABOnQKiooDp01VHE9i0IRtffAGcPKk6GiKdsBxLwcKgZViAiR15QbtaN20a0Lq12lgCXfv2wIQJ8pjlWDIN7UNyyxbg2jWloRA1qrQU2L1bHmvLGBgIEztqMi1B0RbhpTtjOZZMp3dvoH9/uT5Yfr7qaIgalpMjxxaNGCEXZDUYJnbUJKdOyRmeISGG/ILjF7Nny9/btwNlZWpjIdINy7EU6AxchgWY2FETaVedxo8H4uKUhhI0kpOB4cM9Z9UTGZ72jWb9eqC2Vm0sRLeqqgIKC+Wx9lo1GCZ21CScDds87osVE5nCyJFAp06AzQZ89pnqaIg8FRYC1dVA9+7AwIGqo/ELJnZ0V5cuAdu2yWODfsHxGy2xKyoCKirUxkKki9BQOS0cYDmWAo97GdZiURuLnzCxo7tat06WE4cOBbp1Ux1NcOnfH+jbV1ak1q9XHQ2RTrRvgGvXcl89Chx2u2uNRQNfpVCe2C1duhTdu3dHVFQURo0aheLi4ju2v3btGubPn4/ExERERkaiT58+yMvL0ylac2IZtmW0WcQsx5qTKfu4tDS54OXXX8sFMIkCwe7dwOXLck/M++9XHY3fKE3sVqxYgezsbCxevBj79u1DSkoK0tPTcenSpQbb19bWYurUqTh9+jT+/ve/4+jRo3j33XfRuXNnnSM3j+vXXeNMucxJ82gJcV4eUFOjNhbSl2n7uOhoYOpUecxyLAUKrQybkWHsPTGFQiNHjhTz5893/rfdbhdJSUliyZIlDbb/4x//KO655x5RW1vb7Pu02WwCgLDZbM3+N8xkxQohACF69RLC4VAdTXCy24VISpJ/x7w81dEYVyC+t03dx/3pT/JFP3y42jiINP37y9fk//2f6ki85s37WtkVu9raWuzduxdpaWnOcyEhIUhLS8POnTsbvM3atWuRmpqK+fPnIz4+HgMHDsRrr70Gu92uV9imoy1z8sADhh1n6nchISzHmpHp+7iZM2WnsWcPcP686mjI7L76Cjh8GAgLA2bMUB2NXylL7MrLy2G32xEfH+9xPj4+HqWlpQ3e5uTJk/j73/8Ou92OvLw8vPLKK3jjjTfw6quvNno/NTU1qKio8PihpqmtBXJz5THH17WMltitWSPH75Lxmb6Pi48HRo2Sx9qAdSJVtDLsxIlyjJ2BKZ884Q2Hw4FOnTph2bJlGDZsGB5++GH84he/wDvvvNPobZYsWYLY2FjnT3Jyso4RB7eNG+USHQkJrv6ZmmfiRKBtW7l0zK5dqqOhQGW4Ps59diyRSgbfbcKdssQuLi4OoaGhKLtlr6WysjIkJCQ0eJvExET06dMHoaGhznP9+/dHaWkpahtZ4XzhwoWw2WzOn7Nnz/ruQRicVjacPVuWE6n5wsNdS3uxHGsO7OPg+hAtKgJu3FAbC5lXeblrMVYmdv4TERGBYcOGoaioyHnO4XCgqKgIqampDd5m7NixOH78OBwOh/PcsWPHkJiYiIiIiAZvExkZiZiYGI8fujuHwzWZjWVY33AfZ8elvYyPfRzkQo49e8pxHQUFqqMhs8rLkx9qKSmmWIxV6XWY7OxsvPvuu/jggw9w+PBhPP3006isrMRjjz0GAHj00UexcOFCZ/unn34a33zzDX7605/i2LFjyM3NxWuvvYb58+eregiGtWuX3Lg+JgaYNEl1NMYwfbpc2uvkSS7tZRam7+MsFpZjST0TlWEBIEzlnT/88MO4fPkyFi1ahNLSUgwePBj5+fnOwcZnzpxBiFsNMDk5GQUFBXjuuedw3333oXPnzvjpT3+KF198UdVDMCytXJiZCTRyoYC81Lo1MG2a7GNWrwbuu091RORv7OMgP0x/9zs5E6u+Xs5KJNJLdTWQny+PDbzbhDuLEOYqClVUVCA2NhY2my2wShYBRAigd2/gxAlg5UrgoYdUR2Qcy5cDjz0GDB4MlJSojsZY+N6WAu7vUF8vZ8h+8w2wZQswfrzqiMhM1q+XCxInJQHnzgXtul3evK85JJ5uc+iQTOoiIw2/3I/uZs6UE1H27wdOnVIdDZEOwsLkpX+A5VjSn3sZNkiTOm8xsaPbaGXYqVMBq1VtLEYTF+e6YKEt/kxkeNrYpjVrOHOI9COEK7EzSRkWYGJHDdASO+4N6x/aLGMmdmQa6elysO7x48CRI6qjIbPYuxe4cEFeoTDRLEAmduTh66/l2K+QENNMINKdljBv2wZcvqw0FCJ9tGkDTJ4sj1mOJb1or7X0dDm2yCSY2JEH7SrSuHFAx45KQzGsrl2BoUPlskr8jCPT0L4p8kVPejFhGRZgYke3YBlWHyzHkulkZcnfO3fKRTKJ/On0aeDzz2X5KSNDdTS6YmJHTpcvA599Jo+524R/aX/fwkLg+nW1sRDpoksXYNgwOaA9N1d1NGR069bJ3+PGAR06qI1FZ0zsyCknR5YHBw8GundXHY2xDRgg1wqsqXGtnUlkeCzHkl5MWoYFmNiRG60My6t1/mexeO4dS2QKWmK3YQNQVaU2FjKua9eAzZvlsTYEwESY2BEA4MYN2dcCHF+nFy2Bzs2Ve6QTGV5Kipw9dPMmUFSkOhoyqvx8ueNJ//6yNGIyTOwIAFBQIMuC99wDDBqkOhpzGDUKSEgAKiqATZtUR0OkA4uF5VjyPxOXYQEmdvQt9zKsSXZdUS4kxNXvsBxLpqElduvWyUG9RL5UVwfk5cljky7GysSOUFsrJ04AHF+nN+3vvWYNP+PIJCZMAGJi5JInxcWqoyGj2boVsNmATp2AkSNVR6MEEzvC5s2u98Ho0aqjMZdJk4DYWKC0FNi9W3U0RDqIiABmzJDHLMeSr2mvqawsIDRUbSyKMLEj5yK5s2eb9n2gTEQEkJkpj1mOJdPQSmRr1qiNg4xFCNdryqRlWICJnek5HK7EjmVYNdyXPRFCaShE+pgxAwgLA778Ejh+XHU0ZBQHDsgNz1u1AtLSVEejDBM7kysuBi5e9Nyjm/Q1Y4bcn/r4cfk5R2R47doB48fLY22HAKKW0sqwU6cC0dFqY1GIiZ3JaeW/jAyZXJD+rFbZDwEsx5KJsBxLvsYyLAAmdqYmBHebCBTa35+JHZmG9uG7bRtw5YraWCj4nT8P7Nkj1+uaOVN1NEoxsTOxw4eBr77ynKRGamRlyXXt9u2TQ0SIDK9HD7kaut0OrF+vOhoKdtqaXaNHA/HxamNRjImdiWlXh6ZMkctKkTodOwLjxsljVqbINFiOJV9hGdaJiZ2JcTZsYGE5lkxH23olP1/uaUjUHDduuPYeZmLHxM6szp51DUfg+yAwaMuebN0KlJcrDYVIH8OGAYmJ8oN582bV0VCw2rBBbqHUqxfQv7/qaJRjYmdS2tW6sWNNPxwhYHTvDgweLNcW5AoQZAohIXKAKcBdKKj53Muw3OyciZ1ZaeU+7SoRBQatHKsl3kSGp5Vj167lCt3kvfp6IDdXHrP8BICJnSlduSLLfQDH1wUa7fnYsAGorFQbC5EuJk+Wi8meOweUlKiOhoLNzp3yQ619e1mCIiZ2ZpSTI1cYuO8+4J57VEdD7gYOlM9JdbUcT05keFFRQHq6PGY5lryllWEzM+U2dcTEzoy4KHHgslhYjiUT0sqxXPaEvCEElzlpABM7k6msBAoK5DHH1wUmLbHLyQHq6tTGQqSLjAw5kWL/fuDMGdXRULA4elRush0R4brqS0zszGbDBlnm694dSElRHQ01RFs4/do1rgBBJtGxIzBmjDzmlHBqKu1q3eTJQJs2amMJIEzsTMa9DMtZ4YEpNNRVVeBixWQaLMeSt7QxmSzDevA6sdu0aVOj/+9///d/WxQM+VddnevLMMuwgU0rx65ZI9e1I/3MmzcPW7Vp46Qf7cN582bAZlMaCgWBS5fkjFjAtRYiAWhGYjd9+nS88MILqHMb/FNeXo6srCy89NJLPg2OfGvrVlne69iRs8IDnVZZuHAB+Oc/VUdjLjabDWlpaejduzdee+01nD9/XnVI5tCnD9C3r/wGqg0EJmpMTo6cPDFsGNCli+poAkqzrtitWrUKI0aMwJdffonc3FwMHDgQFRUV2L9/vx9CJF/RynqzZslyHwWuyEg5ex9gOVZvq1evxvnz5/H0009jxYoV6N69O2bMmIG///3vHl9oyQ9YjqWmYhm2UV4ndmPGjMH+/fsxcOBADB06FA888ACee+45bN68Gd26dfNHjOQDDodr+QwucxIctHL5qlVckF9vHTt2RHZ2Nj7//HPs3r0bvXr1wty5c5GUlITnnnsOX331leoQjUn7kM7L45RwatzNm3ImIOD6MkBOzZo8cezYMezZswddunRBWFgYjh49iqqqKl/HRj60Zw9w/jxgtQJTpqiOhppixgw5i//YMeDIEdXRmNPFixdRWFiIwsJChIaGIiMjAwcOHMCAAQPw5ptvqg7PeEaPlmNFrl0Dtm1THQ0Fqk8/lcld165ypX3y4HVi9+tf/xqpqamYOnUqDh48iOLiYpSUlOC+++7DTm0gIwUc7WrdjBlyoXcKfDExQFqaPGY5Vj91dXX45JNPMHPmTHTr1g1/+9vfsGDBAly4cAEffPABPv30U6xcuRL/+Z//qTpU4wkNBWbOlMcsx1Jj3MuwXN7hNl4ndm+99RZWr16N//mf/0FUVBQGDhyI4uJi/Mu//AsmTpzohxDJF7jbRHByL8eSPhITE/Hkk0+iW7duKC4uxp49e/DjH/8YMTExzjaTJk1C27Zt1QVpZFo5du1ajkGg2zkcruUdWIZtkEUI79455eXliIuLa/D/bdmyBRMmTPBJYP5SUVGB2NhY2Gw2j47ayI4cAfr3B8LDgcuXgdhY1RFRU5WVAYmJ8vPtzBkgOVl1RIHLV+/tv/zlL3jooYcQFaSXtoO+j6usBOLi5ErqBw7IDZSJNLt2AampsqRx+bIcr2IC3ryvvb5i11hSByDgkzqz0q72TJ7MpC7YxMe7lqZhZUofc+fODdqkzhBat3aNQeCLnm6llWG1Qch0G+48YQKcDRvctOeN5VgyDfdyLJE77TXBMmyjmNgZ3PnzQHGxHF/K90Fw0sbZbdkCXLmiNBQifWgTKIqLgYsX1cZCgePECeDQISAsDJg+XXU0AYuJncFpV+tGjwYSEpSGQs10zz1yRr/dDuTmqo6GSAeJicCoUfJYGyhPpF2tGz8eaNdObSwBjImdwbEMawwsx5LpsBxLt2IZtkmY2BnY1atyP22AiV2w056/ggKAa4GTKWiJ3aefypmyZG7ffAN89pk8zspSG0uAY2JnYDk5QH29XC2gVy/V0VBL3Hcf0L27XGyd+6OTKdx7rxyHUFPj2j6KzCsvT45HGTQI6NFDdTQBjYmdgWllO23wPQUvi8V11U4rrxMZmsXCciy5sAzbZEzsDKqqCsjPl8cswxqD9jyuW8f90ckktMQuJ0derSFzqqkB1q+Xx9prghrFxM6gCgtl2a5bN2DIENXRkC+MGSP3R796Fdi6VXU0RDoYN07OfiwvB7gXuXlt3gzcuCFnSw8bpjqagMfEzqDcy7DcI9kYQkNdX1Y5O5ZMITwcyMiQxyzHmpf23M+aBYQwbbkb/oUMqL7etfQTx9cZi/s4O+6PTqbAcXbmJoRnYkd3xcTOgD77TM4M79BBVjLIOKZMAaxWuaPInj2qoyHSwfTp8srd0aPyh8ylpAQ4d07uITx5supoggITOwPSynSzZsmdV8g4oqLk3tcAy7FkEjExwKRJ8phX7cxHe87T02UHSHfFxM5ghOBuE0bHZU/IdFiONa81a+RvlmGbjImdwezbB5w9K69ap6Wpjob8ISNDVqYOH2ZlikxC+1DfsQO4fFltLKSfM2eA/fvlhInMTNXRBA0mdgajleemTwdatVIbC/lHbKwcawewHEsmkZws121yOIDcXNXRkF60WYBjxwJxcWpjCSJM7AxG+6BnGdbYtNnOTOzINFiONR+WYZuFiZ2BHDsGfPmlnDChLf1ExjR7tlyfsLhYzpAlMjxtK6mCArn6OhmbzSYXJgaY2HmJiZ2BaIPpJ02Si7WTcSUkAKmp8lj7UktkaIMHA126yP0SN25UHQ35W0GB3DuxXz+gTx/V0QQVJnYGwjKsubAcS6ZisbAcayYswzYbEzuDuHAB2LVLHmsVCzI2LYHfvFnuH0tkeFrntnatnEhBxlRXB+TlyWMmdl5jYmcQ2pebUaOApCS1sZA+evUCBg6UW8hxoiCZwoQJQJs2QGkpt14xsm3bgGvXgI4dgdGjVUcTdJjYGQQXJTYn7flmOZZMITJSruUEsBxrZNpzO3MmEBqqNpYgxMTOAK5dc40lZmJnLto4u/x8ThQkk9DKsZw1ZExCcHxdCzGxM4DcXFmOGzCAk4fMZsgQoFs3OVGwsFB1NEQ6mDFDXsU5eBA4eVJ1NORrhw4Bp07JfWGnTlUdTVBiYmcAWhlOu3pD5mGxcHYsmUz79sD998tjbWcCMg6tDJuWJvfGJK8xsQtyN2/KMhzAMqxZac/7unXyyi2R4bEca1wsw7YYE7sg9+mnQGWlXLdz2DDV0ZAKY8cCHToAV64An32mOhoiHWRlyd9bt3KtHyO5eFFupwO4nmPyGhO7IOdehrVYlIZCioSFub7carOjiQytZ0/g3nsBux1Yv151NOQrOTny96hRcnsdahYmdkGsvt41HIFlWHPTnv/Vq+WkMiLDYznWeFiG9YmASOyWLl2K7t27IyoqCqNGjUKxdin2Lj7++GNYLBbMMemsge3bZfmtfXtg/HjV0ZBK2jjjM2eAfftUR0Pu2L/5ifbhv349UFurNhZqucpKObYIYGLXQsoTuxUrViA7OxuLFy/Gvn37kJKSgvT0dFy6dOmOtzt9+jSef/553K/NjjIhrQyblSXLcWRerVq51m3l7NjAwf7Nj0aMkOW669eBLVtUR0MtVVgI1NQA99wjy+zUbMoTu9/97nd48skn8dhjj2HAgAF45513EB0djffee6/R29jtdvzrv/4rfvnLX+Kee+7RMdrAIQSXOSFP7uVYCgzs3/woJMQ1wJ7l2ODnXoblgPEWUZrY1dbWYu/evUhLS3OeCwkJQVpaGnbu3Nno7f7zP/8TnTp1wuOPP37X+6ipqUFFRYXHjxHs3y/Lbq1aAdOmqY6GAkFmprxye+gQ8NVXqqMhPfo3wLh9XJNoJbu1azm4NJjZ7a6JEyzDtpjSxK68vBx2ux3x8fEe5+Pj41FaWtrgbbZt24Y///nPePfdd5t0H0uWLEFsbKzzJzk5ucVxBwLtat306UB0tNpYKDC0bQtMmiSPWY5VT4/+DTBuH9ckU6bIDvDsWeDzz1VHQ821axdQXg60aweMG6c6mqCnvBTrjevXr2Pu3Ll49913ERcX16TbLFy4EDabzflz9uxZP0epD+2Dm7NhyR3LscGrOf0bYNw+rkncSxYsxwYv7bnLyADCw9XGYgBKh9zHxcUhNDQUZWVlHufLysqQ0MAaNidOnMDp06eR5bZwocPhAACEhYXh6NGj6Nmzp8dtIiMjERkZ6Yfo1Tl+XG6TGBoqy29EmtmzgWeeAXbulGt9Jiaqjsi89OjfAGP2cV6ZNUt+k1m7Fli8WHU01Bzaul0sw/qE0it2ERERGDZsGIqKipznHA4HioqKkJqaelv7fv364cCBA9i/f7/zZ9asWZg0aRL2799vmhKEdjVm4kS51AmRJikJGD1aHvMChlrs33SSmSkH2+/bB5w7pzoa8tbRo/InPNw1tZ9aRPkiGdnZ2Zg3bx6GDx+OkSNH4ve//z0qKyvx2GOPAQAeffRRdO7cGUuWLEFUVBQGDhzocfu2bdsCwG3njYxlWLqTOXPkkJVVq4Af/1h1NObG/k0HnToBY8bIhT3XrpWXrCl4aFfrJk0CYmLUxmIQyhO7hx9+GJcvX8aiRYtQWlqKwYMHIz8/3zng+MyZMwgJCaqhgH5VWirLbIBr4XUidw88ALz0ErBxI3DtmpxUQWqwf9PJrFlM7IIVy7A+ZxHCXHPEKyoqEBsbC5vNhpgg/HawbBnw1FNybc4mLmBPJnTvvcCXXwIffQQ88ojqaPQR7O9tXzHl3+HIEaB/f1nOKy/nlZ9gcfmyXGTa4ZDrd3G4QaO8eV/zq2KQYRmWmkJ7fXDZEzKFfv2APn2AujqgoEB1NNRUubkyqRsyhEmdDzGxCyI2G6CNw2ZiR3ei7Uayfj1w86bSUIj04b5YMQUHlmH9goldEMnLk19I+/aVX1CJGjNsmPwCXFnp+jJAZGhacpCbC9TXq42F7q662nV1lQPGfYqJXRDRljnh1Tq6G4vFddWO5VgyhTFjgA4dgKtXgW3bVEdDd1NUBFRVAV26AIMHq47GUJjYBYnqannFDmBiR02jJXZr1/ICBplAaCgwc6Y8Zjk28LmXYS0WtbEYDBO7IFFUBNy4AXTuDAwfrjoaCgbjx8sFrMvLgR07VEdDpAP3cXbmWvAhuDgcwLp18phlWJ9jYhcktHLa7NkAl72ipggLA7TdqViOJVOYNg2IjAROnJDr/VBg2rNH7nnYpg0wYYLqaAyHKUIQsNtdV61ZhiVvuC97wgsYZHhWKzBlijxmOTZwac/N9OkyESefYmIXBHbskOs4tmvHLzfknalTgVatgK+/BvbvVx0NkQ647Eng054blmH9goldENDKaDNnyoXViZoqOtq1r7Y2q5rI0LTxB7t3yz0YKbCcOgUcOCAnu8yYoToaQ2JiF+CEcH0ga7McibzBXSjIVJKS5J6LQgA5OaqjoVtpV+vuv1/O7iKfY2IX4L74Qn7BiYoC0tNVR0PBaOZM+eX4wAE5ppzI8FiODVwsw/odE7sAp11lSU8HWrdWGwsFp3btgIkT5TGv2pEpaElDYaHcfoUCw9WrwJYt8lgrmZPPMbELcNoHMcuw1BJaOZbj7MgUBg4EuneXK7t/+qnqaEizfr1c5uHee4GePVVHY1hM7ALYyZOyFBsayi831DLaBYwdO4CyMrWxEPmdxcJybCBiGVYXTOwCmHZ1Zfx4uQUiUXN16eIaT75mjepoiHSgJQ/r1smrRKRWba28Yge4km7yCyZ2AUwrw3JRYvIFzo4lU7n/fiA2Vi4Cunu36mhoyxagogJISJDfMslvmNgFqLIyYPt2ecyr1uQLWmJXVCT7VyJDCw8HMjLkMcux6mnPQVYW98X0M/51A9S6dbJsNmwY0LWr6mjICPr1kz91dUBenupoiHSgfSvm+AO13MeAsAzrd0zsAhTLsOQP2uxqlmPJFKZPB8LCgCNHgGPHVEdjXp9/Dpw9K7fC0fbyJb9hYheAKipcM/SZ2JEvaa+nvDygpkZtLER+FxvrWsRx3TqloZiaVoadNk1uXk1+xcQuAOXnywlEvXsD/furjoaMZPhwoHNn4MYNOdaOyPBYjlWPZVhdMbELQO5lWItFbSxkLCEhLMeSyWiLgG7fDpSXq43FjM6dA/btkx9mmZmqozEFJnYBpqYGyM2VxyzDkj9oid2aNVzei0ygWzcgJQVwODhrSAWtBD5mDNCpk9pYTIKJXYDZuBG4fh1ITARGjlQdDRnRhAly/9jLl4GdO1VHQ6QDlmPVYRlWd0zsAoy228Ts2Vzqh/wjPByYOVMesxxLpqAlFQUFcv9Y0kdFhbxaATCx0xFThwBit7u+3LAMS/7kvguFEGpjIfK7oUPlrKHKSmDTJtXRmMeGDXLhzD595CKapAsmdgFk1y6544T7DH0if5g2DYiKAk6dAr74QnU0RH5msbiuGLEcqx+WYZVgYhdAtLJYZiYQEaE2FjK21q2B9HR5rJX/iQxNSy7WrZMTKci/6utdMwGZ2OmKiV2AEML1AcsyLOnBvRxLZHiTJgFWK3Dhglx+g/xr+3bg6lWgQwc5I5Z0w8QuQBw8CJw4AURGyl1wiPxt5kwgNFTu9nPqlOpoiPzMvXNlOdb/tL+x1tGQbpjYBQjtqsm0afJLJZG/degAjB8vj1mOJVPQSoLaFlfkH0K4/sYsw+qOiV2A0D5YtcVjifTAciyZSkaGvHr0xRfA6dOqozGuw4ddJahp01RHYzpM7ALA6dNASYlct07b/YZID9oXiW3bgEuXlIZC5H8dOgDjxsljXrXzH60MO2UKS1AKMLELANrVuvvvBzp2VBoKmUxyMjBsmGflhMjQWI71P5ZhlWJiFwC0Mhhnw5IK2uuO4+zIFLRkY8sW4No1paEYUmkpsHu3PGYJSgkmdopdvizLYIBrO0MiPWmJXWGh3KeYyNB69QIGDJDrrK1frzoa48nJkSWAESOApCTV0ZgSEzvFtLUyhwwBundXHQ2ZUf/+csef2lp+zpFJsBzrPyzDKsfETjGWYUk1i8U1iYKzY8kUtKRj/Xr5jYZ8o6pKXvoHWIJSiImdQtevu94DXOaEVNK+WOTmAjU1amMh8rtRo4BOnQCbDdi6VXU0xlFYCFRXy/LTwIGqozEtJnYKFRTID9GePfkeILVGjgQSE+WXjU2bVEdD5Gfua0uxHOs77mVYi0VtLCbGxE4h9zIs3wOkUkiIq3LCciyZgvs4OyHUxmIEdrscNA6wDKsYEztFamtl2Qvg+DoKDNrrcM0aOaGHyNDS0oBWrYCvv5Y7UVDL7N4tl3mIjZWLspIyTOwU2bxZDu+IjwdGj1YdDREwcaLsk8vKgF27VEdD5GfR0cDUqfKY5diW0/6GGRlAeLjaWEyOiZ0iWrlr9mxZBiNSLSICmDlTHrMcS6bAZU98R/sbsgyrHFMKBRwO11Z6LMNSIHFf9oTDjsjwZs6UA5z37AHOn1cdTfD66ivg8GEgLAyYPl11NKbHxE6B3buBixeBmBhg8mTV0RC5TJ8OREYCJ04Ahw6pjobIz9zHwmgD/8l72tU6bTwHKcXETgFtT86MDFn+IgoUViswbZo8ZjmWTIHl2JZjGTagMLHTmRDcbYICm/a6ZGJHpqAlI0VF3Cy5Oa5ccW14rq0NSEoxsdPZl1/K4QiRkcCMGaqjIbrdzJlyQk9JCXD6tOpoiPysXz+gVy+5BtWGDaqjCT65uXLgeEoK0K2b6mgITOx0p10FSUsD2rRRGwtRQzp2dC1DpU3yITIsi4Xl2JZgGTbgMLHTmTa+jnvDUiBjOZZMRUvscnKA+nq1sQST6mogP18ea39DUo6JnY7OnAH27pVlLr4HKJBpXzw++0wuJk9kaGPHAu3bA998A+zYoTqa4LFpE1BZCXTuDAwdqjoa+hYTOx1pV+vGjgU6dVIaCtEddesGDBkih87k5KiOhsjPwsKAzEx5zHJs02l/q1mzuOF5AGFipyOWYSmYsBxLpqKVUdas4ercTSGEZ2JHAYOJnU6uXAG2bpXHXOaEgoH2Ot2wAbhxQ20sRH6Xni4XFj1+HDhyRHU0gW/vXuDCBbn45aRJqqMhN0zsdLJuHWC3yxnhPXqojobo7u69F+jZE6ipcY2PJjKsNm1cWwGxHHt32t9I266GAgYTO51wUWIKNhaL6/WqDSMgMjT3cizdGcuwAYuJnQ4qK13rXnJ8HQUTLbHLyZHrtxIZmrZzwq5dQFmZ2lgC2enTwOefA6Ghcm9MCihM7HRQUCCX++nRA7jvPtXREDXd6NFyn3SbDdi8WXU0RH7WpQswbJicGJCbqzqawLVunfw9bhzQoYPaWOg2TOx04F6G5YxwCiYhIa4F5Tk7lkyB5di7Yxk2oDGx87O6Otc6YBxfR8FIe92uWSPXtSMyNO2bTGEhUFWlNpZAdO2a6/I9E7uAxMTOz7Zske+Djh2B1FTV0RB5b/JkICYGuHgRKC5WHQ2Rn913H9C1K3DzJlBUpDqawJOfL7ddGzAA6NVLdTTUACZ2fqaVr2bPluNMiYJNRIRrUX6WY8nwLBaWY++EZdiAx8TOjxwO1zIRLMNSMNNmc69axUX5yQS0cuy6dRx/4K6uDsjLk8dM7AIWEzs/2rPHtTC3tu4lUTCaMUOuQfrVV8Dhw6qjIfKz8ePl+INLlzj+wN3WrXKKfKdOwKhRqqOhRjCx8yOtbJWRAURFqY2FqCXatAHS0uQxy7FkeBER8tsMwHKsO60Mm5Ulp8xTQOIz40fcbYKMxL0cS2R4WjmW24tJQriSXJZhAxoTOz85fBg4ehQID+fC3GQMs2bJL+l79wJnz6qOhsjPpk8HwsKAL78Ejh9XHY16Bw4AX38NtGrlunxPASkgErulS5eie/fuiIqKwqhRo1B8hzEN7777Lu6//360a9cO7dq1Q1pa2h3bq6JNmpgyRQ7VIAp2nToBY8fKY+4d23RG7N9MoV07OdYO4FU7wPU3mDoViI5WGwvdkfLEbsWKFcjOzsbixYuxb98+pKSkID09HZcuXWqw/ebNm/H9738fmzZtws6dO5GcnIxp06bh/PnzOkd+ZyzDkhFpr2eWY5vGqP2babAc68IybPAQio0cOVLMnz/f+d92u10kJSWJJUuWNOn29fX1ok2bNuKDDz5oUnubzSYACJvN1qx4m+LsWSEAISwWIUpL/XY3RLo7eVK+tkNDhSgvVx2NJz3e297Su38TIjD/DkErkF/wejp3jh9qinnzvlZ6xa62thZ79+5Fmlu9PiQkBGlpadi5c2eT/o2qqirU1dWhffv2Df7/mpoaVFRUePz4m1amGjNGbqBOZBQ9egApKYDd7toqjxqmR/8GqOnjTKNHD2DQIPmC19ZvMyPtzT56ND/UgoDSxK68vBx2ux3xt7xQ4uPjUVpa2qR/48UXX0RSUpJH5+luyZIliI2Ndf4kJye3OO670RI7bRYhkZGwHNs0evRvgJo+zlRYjmUZNsgoH2PXEr/+9a/x8ccfY9WqVYhqZKG4hQsXwmazOX/O+nk63zffuPZH5vg6MiLtdV1QAFRWqo3FyJrSvwH693GmoyUz+flATY3aWFS4ccO1Zy4Tu6CgNLGLi4tDaGgoysrKPM6XlZUhISHhjrf97//+b/z617/Ghg0bcN999zXaLjIyEjExMR4//pSTI6/aDxoE9Ozp17siUmLQIFmhqq6WyR01TI/+DdC/jzOdYcOAxESZ4GzapDoa/W3YANTWAr16Af37q46GmkBpYhcREYFhw4ahSPs2AMDhcKCoqAipqamN3u7111/Hr371K+Tn52P48OF6hNpk3BuWjM5icb2+uexJ44zYv5lSSIjrSpUZy7HuZViLRW0s1CTKS7HZ2dl499138cEHH+Dw4cN4+umnUVlZicceewwA8Oijj2LhwoXO9r/5zW/wyiuv4L333kP37t1RWlqK0tJS3LhxQ9VDcKqqklfrAY6vI2PTErt16+S+4NQwI/Vvpuae2AmhNhY91dcDubnymGXYoBGmOoCHH34Yly9fxqJFi1BaWorBgwcjPz/fOeD4zJkzCHHbk+6Pf/wjamtr8eCDD3r8O4sXL8Z//Md/6Bn6bTZsAG7eBLp1AwYPVhoKkV+lpgIdOwKXLwNbtnAh+sYYqX8ztcmTgdatgfPngX37ZHnWDHbuBK5cAdq3d61OTgFPeWIHAM8++yyeffbZBv/fZm0mwrdOnz7t/4CayX1RYl6xJiMLDZWTBf/0J1mOZWLXOKP0b6YWFQWkpwP/+Ie8ameWxE4rw2Zmyu3VKCgoL8UaRX29LEsBLMOSObiPs3M4lIZC5H9mG2cnBJc5CVJM7Hxk61bg6lUgLg4YN051NET+N2UKYLXK6tSePaqjIfKzzEw5kWL/fuDMGdXR+N/Ro8Dx40BEhLxaSUGDiZ2PaGXYWbNkmYrI6CIjgYwMeczFisnw4uJc48zMcNVOu1o3eTLQpo3aWMgrTOx8QAguc0LmxGVPyFTMVI7VHiPLsEGHiZ0P7N0LnDsnJ01xEDmZSUaGrNQcOSJ/iAxNS3I2bwZsNqWh+NWlS3JGLABkZamNhbzGxM4HtDLUjBly8hSRWcTEyLF2AMuxZAJ9+gD9+snFG7VFS40oJ0eWooYNA7p0UR0NeYmJnQ+4L3NCZDbaLHAmdmQKZijHsgwb1JjYtdDRo8Dhw0B4uJw0RWQ2s2fLdRv/+U85JIHI0LRkJy/PmNuu3LwpV9sH5Jubgg4TuxbSBo1PmgTExioNhUiJ+HhgzBh5rE2kIzKs0aPltivXrgGffaY6Gt/79FOZ3HXtCtx3n+poqBmY2LUQy7BErtc/y7FkeKGhwMyZ8tiI5Vj3Miy3UApKTOxa4Px5YPduecwr1mRm2ji7zZvlQt1EhuY+zk4ItbH4ksPh2kKJH2pBi4ldC2hfbEaPBhIT1cZCpFLPnsCgQYDdLifUERna1KlyCYRTp4CDB1VH4zvFxUBZmZzuPn686miomZjYtQDLsEQuLMeSabgvWmqkcqz2WGbMkAtUUlBiYtdMV68CmzbJYyZ2RK5ybH4+UFWlNBQi/zPisifaY2EZNqgxsWum3Fygvh64916gd2/V0RCpN3gw0K2bnFBXWKg6GiI/03ZkKC4GLlxQG4svnDgBHDoEhIUB06erjoZagIldM2nLnGhXKYjMzmJhOZZMJCEBGDVKHhthYKl2tW78eKBdO7WxUIswsWuGmzeB9evlMcuwRC7a+2HdOnlFm8jQjFSOZRnWMJjYNUNhoRxD1LUrMHSo6miIAsfYsUBcHPDNN8DWraqjIfIzLQn69FPgxg21sbTEN9+4FlvWSswUtJjYNYN7GZbrNxK5hIa6LmJo7xMiwxowALjnHqCmJrgHlublybWKBg0CevRQHQ21EBM7L9XXu65Yc3wd0e20cuzq1cZau5XoNhaLMcqxLMMaChM7L23bBly5AnToANx/v+poiAJPWppc5uvsWWDvXtXREPmZlgzl5MirXsGmpsY1aFxLUimoMbHzkjbbLytLzgonIk9RUXJ9U4DlWDKBsWPlLNLycmDnTtXReG/zZjk+MDERGDZMdTTkA0zsvCCE64OKs2GJGsdlT8g0wsOBjAx5HIzlWC3mWbOAEKYERsBn0QslJcCZM0B0tNwqkIgalpkpP+++/BI4dkx1NER+ppVj16xRG4e3hPBM7MgQmNh5Qbv6MH060KqV2liIAllsLDBpkjzmVTsyvPR0+U3m2DHg6FHV0TRdSQlw7pwcFDt5supoyEeY2HlB+4BiGZbo7txnxxIZWkyM65tMMJVjtVjT0+XgWDIEJnZN9NVXrm30MjNVR0MU+GbPlqtB7NpljK00ie4oGMuxWqwswxoKE7sm0q46TJzIbfSImiIxERg9Wh4H02cdUbNoOzbs2AFcvqw2lqY4cwbYv19OmODVCkNhYtdELMMSeU9bxJvj7MjwkpOBIUPkhITcXNXR3N26dfK3tg8gGQYTuya4eFGWkwAuzE3kDe2L0KZNwLVrSkMh8r9gKsdyNqxhMbFrgrVr5ZewkSOBzp1VR0MUPHr3Bu69V27FFwwXMYhaREuSNmwAbt5UG8ud2Gzy2xbAxM6AmNg1AcuwRM3HxYrJNAYPliXZqipg40bV0TSuoACoqwP69QP69FEdDfkYE7u7sNlc708mdkTe08bZ5ecH9kUMohazWFxXwAK5HMsyrKExsbuLvDz5xaZ/f6BvX9XREAWfoUOBrl2Bykrg009VR0PkZ1qytG4d4HCojaUhdXWucRFM7AyJid1daOUj7aoDEXnHYuHsWDKRCROANm2A0lJgzx7V0dxu2zY5k6ljR9d6RGQoTOzuoLoaWL9eHrMMS9R8WmK3dq2cSEFkWJGRwIwZ8jgQy7FaGXbmTCA0VG0s5BdM7O7g00+BGzfkTNjhw1VHQxS87r8f6NABuHIF2L5ddTREfqaVOANtPz0huNuECTCxuwPtPTlnjiwnEVHzhIW5FuZnOZYMLzMTiIgAvvwS+Pxz1dG4HDoEnDol94WdOlV1NOQnTOwaYbe7rlizDEvUcu7LngihNhYiv2rb1vVN5i9/URqKB+1DLS0NaN1abSzkN0zsGrF9u9zur107YPx41dEQBb+pU4HoaLlFZUmJ6miI/GzuXPn7r3+VVwoCAcuwpsDErhFaGTYrCwgPVxoKkSG0agVMny6PA23oEZHPzZghB5ZevAgUFamORsZRXCyPtauJZEhM7BogBJc5IfIH7kJBphERATz8sDz+85/VxgIAOTny96hRQEKC2ljIr5jYNeDzz4HTp+UVhvR01dEQGUdmppxIcfAgcPy46miI/OxHP5K///534Kuv1MbCMqxpMLFrgHY1IT1djgkiIt9o1w6YOFEesxxLhpeSIr/NOBzAr3+tLg73bV9mz1YXB+mCiV0DtA8czoYl8j2WY8lUfvEL+fv//T85c0iFwkKgpga45x5gwAA1MZBumNjd4uRJ4Isv5ILcM2eqjobIeLQLBjt3yl2XiAwtNRWYNEluubJwoZoY3MuwXJTV8JjY3UK7ijBhAtC+vdpYiIyoc2c5ftt9EXwiQ/vtb4GQELn0ycaN+t633e6aOMEyrCkwsbuFltixDEvkP9psc46zI1MYNgx45hl5/MwzciNyvezaBZSXywGuY8fqd7+kDBM7N2VlwI4d8phfbIj8R/viVFQE2GxqYyHSxa9+JZcZOXoU+Pd/1+9+tcviGRlclNUkmNi5WbtWloeGDweSk1VHQ2RcffsC/fsDdXVAXp7qaIh00LatnEBhsQDvvgt88IE+96ttI8arFabBxM4Ny7BE+tHKsZwdS6YxdSrwy1/K4x/9CCgo8O/9HT0qf8LDuSiriTCx+1ZFhWvXFyZ2RP6nvc/Wr9d3yBGRUr/4BfDQQ0BtrXwT+HMyhXa1btIkICbGf/dDAYWJ3bfWr5fvsz59gH79VEdDZHzDhwNdugA3bgTGVppEuggJAT78UC5cfPOm3ED5/ff9c18sw5oSE7tvuZdhucwPkf9ZLCzHkklFRMhtxh56SA40/bd/Ax5/XJaOfOXyZddswKws3/27FPCY2EEuyK0N4GYZlkg/WmK3dq1cbovINKKigI8/BhYvlt9y3nsPGDgQ+OgjuQVZS+Xmyn9nyBDOBjQZJnaQQxyuXweSkoARI1RHQ2Qe48fL5bXcLy4QmUZICPAf/wFs3iy3+zp7FvjBD4DBg+Ws2Zqa5v/bLMOaFhM7uMpAs2fL9xkR6SM83FUlYjmWTGv8eODAAeC//gto00Ye//CHct27H/1IrkXnzYKP1dWuGbezZvklZApcpk9j7HbX+o0swxLpT3vfrVol15EkMqXoaODnPwdOnwaWLJEzi65dk2vezZkj97gcPRp44QXgL38BSkoan05eVARUVckS7ODB+j0GCghhqgNQbedO4NIluXbkxImqoyEyn2nTgFat5OfZF18AKSmqIzKGyspKtGnTBpZvZ4PV1tairq4OYWFhiIyM9GgHAK1atULItyWLuro61NbWIjQ0FFFRUc1qW1VVBSEEoqKiEBoaCgCor69HTU0NQkJC0KpVq2a1vXnzJhwOByIjIxEWJj/C7HY7qqurvWprsVgQHR3tbFtdXQ273Y6IiAiEf7tDgzdtHQ4Hbt68CQBo3bq1s21NTQ3q6+sRHh6OiIiIu7eNjER4djYiXngB2LoVYuVKVBUWAidOIHr3blh275bPJ4A6iwXhSUmI6NoV6NoVIjERVW3bAlu2IBqAZdYswGLx6rn3xeukoefTF68T7fls6evk1uezpa+Txp7Plr5OhBCoqqqC14TJ2Gw2AUDYbDYhhBD/3/8nBCDED36gODAiE5szR74PFy9u/r9x63vbrLS/AwBx6dIl5/lXX31VABBPPPGER/vo6GgBQJw6dcp57s033xQAxCOPPOLRNi4uTgAQBw8edJ5btmyZACBmz57t0bZbt24CgCguLnae+/DDDwUAkZaW5tF2wIABAoDYtGmT89yqVasEADFmzBiPtsOHDxcARE5OjvPchg0bBACRkpLi0XbChAkCgFi5cqXz3LZt2wQA0atXL4+2GRkZAoB4//33nedKSkoEAJGUlOTR9sEHHxQAxNtvv+08d+zYMQFAxMbGerSdN2+eACBef/1157lz584JACIsLMyj7TPPPCMAiMVub4SrV686n8/aZcuEmD9fiPHjxfORkQKAeF5e6BYCELXftgMgrgJCFBQIIYRYvHixACCeeeYZj/sLCwsTAMS5c+ec515//XUBQMybN8+jbWxsrAAgjh075jz39ttvCwDiwQcf9GiblJQkAIiSkhLnuffff18AEBkZGR5te/XqJQCIbdu2Oc+tXLlSABATJkzwaJuSkiIAiA0bNjjP5eTkCABi+PDhHm3HjBkjAIhVq1Y5z23atEkAEAMGDPBom5aWJgCIDz/80HmuuLhYABDdunXzaDt79mwBQCxbtsx57uDBgwKAiIuL82j7yCOPCADizTffdJ47deqUACCio6M92j7xxBMCgHj11Ved5y5duuR8Pr3p30xdihXCNa5Hm51HRPpzL8cSUSN++EPg7beBLVuAZ5+V5/71X+XSKW+8AWRnu9o++SQwZYqSMEktixDmGtVSUVGB2NhY2Gw2nD4dg5QUOeu8vBxwuypKRDr65hugUyc55vXECTlB0Fvu7+0YE6+yr/0dLly4gISEBJZig60Ue4dyXHR09G3PpzdtWYoN3lKs3W5vcv9m6sTuzTdj8B//IScNaRMoiEiNKVPk0kO3XnhoKiZ2Ev8ORMbjzfva1KXY1avlb5ZhidTTyrH/+IfaOIiIgplpE7vTp4H9++W6ddxthUi9Bx4AQkOB7duBXbtUR0NEFJxMm9jl5srf48cDcXFqYyEioHNnYN48efzyy2pjISIKVqZN7HJy5G8uSkwUOBYtkrtRFBXJ8XZEROQd0yZ2O3fK39xGjyhwdOsGPPWUPH78ceDCBbXxEBEFG9MmdkIAQ4fKDxIiChyLFwO9eslxsOnpcmcYIiJqmoBI7JYuXYru3bsjKioKo0aNQnFx8R3b/+1vf0O/fv0QFRWFQYMGIS8vr1n3yzIsUeCJiwMKC4HERODgQWDQIDmDPVgXZlLVvxGROSlP7FasWIHs7GwsXrwY+/btQ0pKCtLT03Gpka/pO3bswPe//308/vjjKCkpwZw5czBnzhwcPHjQ6/tmYkcUmLp3l+Ps7r1XXrF74AFg+HDgz38GyspUR9d0Kvs3IjIn5QsUjxo1CiNGjMDbb78NQK7KnJycjJ/85Cd46aWXbmv/8MMPo7KyEjna7AcAo0ePxuDBg/HOO+/c9f60Rf66d7+AEycSEBLCVdm5KjtXZb/T86lyVfbqalmafeutGtTU1AMIh8USgX79gJQUgc6dq5CUBPTo0RqxsRWYMiWwFubVu38DuEAxkRF59b6+626yflRTUyNCQ0M9NukVQohHH31UzJo1q8HbJCcne2yoK4QQixYtEvfdd1+T7pMbZLsYboPs2lrn+eeff15ukP38885ztbW1rg2yr151nucG2VIgb5D9gx888e1r7VVtv3MBuDbIlv/d9E2y9aCifxNCeLVZOBEFB2/e12He542+U15eDrvdjvj4eI/z8fHxOHLkSIO3KS0tbbB9aWlpg+1rampQU1Pj/G+bzeY8vn79uvOqS3V1NQB5RaaiosLZRnx7QfP69evO81rburo6j7YOhwMAcOPGDed57epDU9pqV5Pq6+s92trtdgDyqlBT21ZVVTnPa1eT7Ha7R9v6+vpG2zocDo+2dXV1zsejnb9x44bXbYUQHm1ra2udf1Pt/PXr1+/Ytqamxnne/f9XVFQ4r/xoz7l7Wy0ura12ZU1r681z31jb5j73d2vrzXPfUFtvnvumtG3Oc99Y26Y89yEh8rn/t3+rxg9/WIGSEqC4+Dp++1v5/4cNq8D58xUoLXU9F6rp0b8Bjfdx7n8/Igpu2vu5Sf2bHxPMuzp//rwAIHbs2OFx/oUXXhAjR45s8Dbh4eHir3/9q8e5pUuXik6dOjXYXrsawx/+8MccP2fPnvVNB9VCevRvQrCP4w9/zPTTlP5N6RW7uLg4hIaGouyW0dBlZWVISEho8DYJCQletV+4cCGy3XYUv3btGrp164YzZ84gNja2hY8gMFRUVCA5ORlnz541zJgaPqbgEEiPSQiB69evIykpSWkcGj36N4B9XLDiYwoOgfKYvOnflCZ2ERERGDZsGIqKijBnzhwAslxTVFSEZ599tsHbpKamoqioCAsWLHCeKywsRGpqaoPtIyMjPQa5a2JjYw3zwtPExMTwMQUBPib/CaRERo/+DWAfF+z4mIJDIDympvZvShM7AMjOzsa8efMwfPhwjBw5Er///e9RWVmJxx57DADw6KOPonPnzliyZAkA4Kc//SkmTJiAN954A5mZmfj444+xZ88eLFu2TOXDICK6Dfs3ItKb8sTu4YcfxuXLl7Fo0SKUlpZi8ODByM/Pdw4gPnPmjHOQOwCMGTMGf/3rX/Hyyy/j5z//OXr37o3Vq1dj4MCBqh4CEVGD2L8Rke7uOgrPYKqrq8XixYtFdXW16lB8ho8pOPAxkR6M+JzwMQUHPqbAoHyBYiIiIiLyDeVbihERERGRbzCxIyIiIjIIJnZEREREBmHIxG7p0qXo3r07oqKiMGrUKBQXF9+x/d/+9jf069cPUVFRGDRoEPLy8nSKtOm8eUzLly+HxWLx+HHfWD4QbN26FVlZWUhKSoLFYsHq1avvepvNmzdj6NChiIyMRK9evbB8+XK/x+kNbx/T5s2bb3ueLBbLHbeP0tOSJUswYsQItGnTBp06dcKcOXNw9OjRu94uGN5PwY59XGD3cezfAr9/A4zbxxkusVuxYgWys7OxePFi7Nu3DykpKUhPT8elS5cabL9jxw58//vfx+OPP46SkhLMmTMHc+bMwcGDB3WOvHHePiZALqZ48eJF58/XX3+tY8R3V1lZiZSUFCxdurRJ7U+dOoXMzExMmjQJ+/fvx4IFC/DEE0+goKDAz5E2nbePSXP06FGP56pTp05+itA7W7Zswfz587Fr1y4UFhairq4O06ZNc+4p25BgeD8FO/ZxUiD3cezfXAK1fwMM3MepnpbrayNHjhTz5893/rfdbhdJSUliyZIlDbb/7ne/KzIzMz3OjRo1Sjz11FN+jdMb3j6m999/X8TGxuoUXcsBEKtWrbpjm5/97Gfi3nvv9Tj38MMPi/T0dD9G1nxNeUybNm0SAMTVq1d1iamlLl26JACILVu2NNomGN5PwY59XHD1cezfruoSky8YpY8z1BW72tpa7N27F2lpac5zISEhSEtLw86dOxu8zc6dOz3aA0B6enqj7fXWnMcEADdu3EC3bt2QnJyM2bNn49ChQ3qE6zeB/jy1xODBg5GYmIipU6di+/btqsNplM1mAwC0b9++0TZGfp4CAfs4FyP1cYH+HLVEsPRvgHH6OEMlduXl5bDb7c5V3TXx8fGN1vVLS0u9aq+35jymvn374r333sOaNWvw4YcfwuFwYMyYMTh37pweIftFY89TRUUFbt68qSiqlklMTMQ777yDTz75BJ988gmSk5MxceJE7Nu3T3Vot3E4HFiwYAHGjh17x10QAv39FOzYx0lG6+PYv6lnpD5O+ZZi5Hupqakem4aPGTMG/fv3x//+7//iV7/6lcLIyF3fvn3Rt29f53+PGTMGJ06cwJtvvom//OUvCiO73fz583Hw4EFs27ZNdShE7OOCQDD1b4Cx+jhDXbGLi4tDaGgoysrKPM6XlZUhISGhwdskJCR41V5vzXlMtwoPD8eQIUNw/Phxf4Soi8aep5iYGLRq1UpRVL43cuTIgHuenn32WeTk5GDTpk3o0qXLHdsG+vsp2LGPa1iw93Hs39QyWh9nqMQuIiICw4YNQ1FRkfOcw+FAUVGRx7c7d6mpqR7tAaCwsLDR9nprzmO6ld1ux4EDB5CYmOivMP0u0J8nX9m/f3/APE9CCDz77LNYtWoVNm7ciB49etz1NmZ5nlRhH9ewYO/jAv058pVA6t8AA/dxqmdv+NrHH38sIiMjxfLly8WXX34pfvSjH4m2bduK0tJSIYQQc+fOFS+99JKz/fbt20VYWJj47//+b3H48GGxePFiER4eLg4cOKDqIdzG28f0y1/+UhQUFIgTJ06IvXv3iu9973siKipKHDp0SNVDuM3169dFSUmJKCkpEQDE7373O1FSUiK+/vprIYQQL730kpg7d66z/cmTJ0V0dLR44YUXxOHDh8XSpUtFaGioyM/PV/UQbuPtY3rzzTfF6tWrxVdffSUOHDggfvrTn4qQkBDx6aefqnoIHp5++mkRGxsrNm/eLC5evOj8qaqqcrYJxvdTsGMfF/h9HPu3wO/fhDBuH2e4xE4IIf7nf/5HdO3aVURERIiRI0eKXbt2Of/fhAkTxLx58zzar1y5UvTp00dERESIe++9V+Tm5uoc8d1585gWLFjgbBsfHy8yMjLEvn37FETdOG0q/K0/2uOYN2+emDBhwm23GTx4sIiIiBD33HOPeP/993WP+068fUy/+c1vRM+ePUVUVJRo3769mDhxoti4caOa4BvQ0GMB4PF3D9b3U7BjHxfYfRz7t8Dv34Qwbh9nEUII/14TJCIiIiI9GGqMHREREZGZMbEjIiIiMggmdkREREQGwcSOiIiIyCCY2BEREREZBBM7IiIiIoNgYkdERERkEEzsiIiIiAyCiR0FlB/+8IeYM2eO7ve7fPlyWCwWWCwWLFiwwHm+e/fu+P3vf3/H22q3a9u2rV9jJKLgxz6O/C1MdQBkHhaL5Y7/f/HixXjrrbegajOUmJgYHD16FK1bt/bqdhcvXsSKFSuwePFiP0VGRMGAfRwFAiZ2pJuLFy86j1esWIFFixbh6NGjznNWqxVWq1VFaABkp5yQkOD17RISEhAbG+uHiIgomLCPo0DAUizpJiEhwfkTGxvr7GS0H6vVeluZYuLEifjJT36CBQsWoF27doiPj8e7776LyspKPPbYY2jTpg169eqF9evXe9zXwYMHMWPGDFitVsTHx2Pu3LkoLy9vVtxVVVX4t3/7N7Rp0wZdu3bFsmXLWvJnICKDYh9HgYCJHQW8Dz74AHFxcSguLsZPfvITPP3003jooYcwZswY7Nu3D9OmTcPcuXNRVVUFALh27RomT56MIUOGYM+ePcjPz0dZWRm++93vNuv+33jjDQwfPhwlJSV45pln8PTTT3t8Cyciagn2ceRLTOwo4KWkpODll19G7969sXDhQkRFRSEuLg5PPvkkevfujUWLFuHKlSv44osvAABvv/02hgwZgtdeew39+vXDkCFD8N5772HTpk04duyY1/efkZGBZ555Br169cKLL76IuLg4bNq0ydcPk4hMin0c+RLH2FHAu++++5zHoaGh6NChAwYNGuQ8Fx8fDwC4dOkSAODzzz/Hpk2bGhzLcuLECfTp06fZ96+VVrT7IiJqKfZx5EtM7CjghYeHe/y3xWLxOKfNRHM4HACAGzduICsrC7/5zW9u+7cSExN9cv/afRERtRT7OPIlJnZkOEOHDsUnn3yC7t27IyyML3EiMhb2cXQnHGNHhjN//nx88803+P73v49//vOfOHHiBAoKCvDYY4/BbrerDo+IqEXYx9GdMLEjw0lKSsL27dtht9sxbdo0DBo0CAsWLEDbtm0REsKXPBEFN/ZxdCcWoWoJbKIAsnz5cixYsADXrl1TcnsiIn9iH2ceTO2JvmWz2WC1WvHiiy96dTur1Yof//jHfoqKiMg32MeZA6/YEQG4fv06ysrKAABt27ZFXFxck297/PhxAHKZgh49evglPiKilmAfZx5M7IiIiIgMgqVYIiIiIoNgYkdERERkEEzsiIiIiAyCiR0RERGRQTCxIyIiIjIIJnZEREREBsHEjoiIiMggmNgRERERGQQTOyIiIiKD+P8BoPTRfnkYdJcAAAAASUVORK5CYII=", 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" ] @@ -350,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -406,12 +400,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -466,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -487,7 +481,17 @@ }, { "data": { - "image/png": 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", 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gQdoXYE5OMGVKWi9FW3L9+nVSUlIynR/2xIkTme5jaj7ZyMhIw/XPP/+cwYMHU7ZsWRwcHLCzs2P+/Pk89dRTJnOR6SFEbrhzB9q314qF1arBypXGI3Ld3aFyZe1iil6vfZYzKzamL0LeugX37sGZM9rFnNTejuaGWRcrJl9aCyFEYSQFRSEewvbtMHCgtj16tNaz41HodNoIDA8PqFAh6/iUFG1ItSW9H6OitEVjEhO1b77/+8+ynDw9sx52nXrx9gY7WeIp1+zfv5+WLVsarqf2uOnbty+LFi3iypUrXLx40XB/+fLl2bBhAyNGjODTTz+lbNmyfP311wQHBz/U48fFxWFvb5/hdnt7e1zSVZ7jzFQg7OzscHV1fajYu3fvYmp2Dp1Oh5ub20PF3rt3D72ZlZPSD2/KTmx8fDwpKSk5Euvm5obu/n9pCQkJJCcn50isq6srdvc/tImJiSQlJeVIrIuLi+G9kp3YpKQkEhMTTcY6OzvjcH+Oh+zEJicnG/Uke5CTk5NhXqzsxKakpBAfH28y1tHREaf73XqyE6vX680unGQyNi6O1HdV6mfLwcHB0ONYKcXdu3eN7gd45ZU4li0DcKBGDWe+/x5q1NBizX1GC5LPP/+c3bt3s27dOgICAvjjjz944403KFOmDK1atcp0n3Hjxhn1fEydHkKIh5WcDD17wpEj2nnVhg3ayJLssrPTztV8faFuXdNxd+8aFxgz6/F4+bI22ib1XPLQIdPHc3ExXXBMvb1MGZBBEEIIUcCoQiY6OloBKjo62tqpiHzq1CmlvL2VAqW6dFEqJcXaGWXt7l2l/vlHqX37lNqwQamFC5X66COl3npLqd69lQoOVqpOHaX8/JRydNSeW3YudnZK+foqVaOGUs88o1TPnkoNG6bUlClKzZ+v1Nq1Su3apdTZs0rFxmYj8djYtAfJYsfY2FgFKEDFmomVNsByqa+VqUvbtm2N4t3c3EzGNm/e3CjWx8fHZGz9+vWNYgMCAkzGVqtWzSi2WrVqJmMDAgKMYuvXr28y1sfHxyi2efPmJmPd3NyMYtu2bWv2dUuva9euZmPTv5f79u1rNjYqKsoQGxISYjb2/PnzhtiRI0eajT127JghNiwszGzs3r17DbHTpk0zG7t161ZD7OzZs83Grl+/3hC7cOFCs7ErVqwwxK5YscJs7MKFCw2x69evNxs7e/ZsQ+zWrVvNxk6bNs0Qu3fvXrOxYWFhhthjx46ZjR05cqQh9vz582nvQdIaZLf7t4WEhBhio6KizB73iSf6qvh4LTZ9Wwq201YmJCQoe3t7tXr1aqPb+/Tpo55//vlM9/H391effPKJ0W0TJ05UNWvWVEopdffuXeXo6Gj0/lJKqYEDB6rg4GCLc5O/K+JRvfmm9hF2cVFq925rZ6NJSVHq6lWlDh1Sav16pb76SqmJE5UaOFCp555TqmZNpYoXz975oo+PUrVqKdW2rVKDBikVFqbUvHnauenhw0pdu6aUXp+LT0rOK4UQIkdJD0UhsuHGDWjXTusd2LAhfPtt/uiZ5+oK5cppl6wopU38ndWw6/SLz+j1afP1WMLc4jNGPSI9oNSjPXUhhBBZqF/f9nsOOTk5Ua9ePcLDww2LUun1esLDwxkyZEim+wQFBREeHs7w4cMNt6WfTzYpKYmkpCRDD9xU9vb2ZnskC5GTPv9cuwB89x00amTdfFLZ2aWdj9WubTouPj5jD8fMrickaFP2XL+u9cQ0xdnZuFejqV6PMjWPEEJYn6zyLISFEhO1CbL/+EMrzO3ZA6Wk2kVSknZymNWiM+kXn7GUG3HEoa2wV7dyLEVKuZsceu3hEUedOrIaX05Kfa0uX76c6WslQ54zj5UhzzLk2SpDnu/PFRh39Sq4u2cY8rxv310GDoRjx+IALfbMmauUKpUx9u7du8TExFCmTBmbaiuXL19O3759+eqrr2jYsCGzZs1ixYoVnDhxAl9fX/r06YOfnx9Tp04FYOfOnTRv3pwPP/yQdu3asWzZMj744AMOHjxI9erVAWjRogXXr19n9uzZBAQEsG3bNl5//XVmzpzJ66+/blFe8ndFPKwNG+D557UvZj/8EMaMsXZGuUMp7Ut5U4vJpBYdr12z/Jje3qbndEwtRPr4PPDFv6zyLIQQOUp6KAphAaW0Sev/+AOKFNFOAKWYqHF01FYBLF0661hzi89k2vsxypl2Slt29K9TzqScMnd0Z2A99vZQubKzyfkeU88jheXc3d1Nnkg/GJedY1oqfREwJ2PTFy1zMtYlG90mshObnVW4sxPr5ORkKFJZK9bR0dFQrMvJWAcHB0NxMSdj7e3tLX4PZyfWzs7u4WKdnQ1LNLt7e0O656HXw6xZOsaPdychAby9nXnttfU0aQIBAd4ZnrNOp8Pd3d1sodtaevTowbVr15g4cSKRkZHUrl2bTZs2GRZeuXjxolFvwyZNmrB06VLeeecdxo8fT6VKlVizZo2hmAiwbNkyxo0bR69evbh58yYBAQFMmTKF1157Lc+fnyhcDh+GHj20z+jAgdqc3AWVTqcV93x8zK8en5CgrVRtbkGZS5e0L6dv3tQuR4+aPl7qAomphUb/0s406b+e4sVBt9sZvwDt9sz+xDs7O7P+frtq6d9TIYQobKSHohAWeP99mDAB7O21YuJDrm0hsiklRVuJ0JKh16mLz2QtBpA2wBLSXgqRv128CP36wdat2vW2beGbbyz7Qkw+/5aT10pk16VL2tDmS5fgmWfgl1+MV3QWpimlnRuaWkwm9RIVZfkxixXLeiXrEiUyn+ZIPv9CiMJMeigKkYVly7RiIsDs2VJMzEv29mnfaFerlnV8fLw2XMZc0fHyZfjrr9zPXQghrEUpWLIE3nhD+6LFzQ1mzoTBg7WeQkII64mNhQ4dtKJX1aqwapUUE7NDp9OGO3t7Q40apuMSEyEy0vTw6tTtu3e1AuWtW3DsmOnjOThoo3EeLDQWK5bzz1EIIfILKSgKYcbOnVrvDoDQUJARUHksKUn7rxigVy+zZ9xJSUksW7bkfmgvk0MiY2LAyyvHMxVCCOtJ11beeK4Xrw91ZOVK7a7GjbUFxCpVSg1NYsmSrNtKIUTOS0mBl16CQ4e0Hm8bNkDRotbOqmBycspkQcIHziuVgyPR0ZkXG//9N4mjR5dw6xbExPQiOdmRf/+Ff/+1ytMRQgibJEOehTDh3DltOMr169CxI/z4o9ZjTuQhmTzbqgyvlYlFWbC3N15m0cxCK9jZGU9SlJ3Yu3e1LleZ0em07lcPE3vvnjZ5lSnp30PZiY2P1/5rzIlYN7e0LmUJCWBmoZVsxbq6po3dSkzU/snKiVgXl7SGMjuxSUlavCnOzmnzAmYnNjlZey1McXJK+6IiO7EpKeZXmHJ01OKzG6vXa++17MbGxWmrVQHVS17lfJQ72DswbpIzY8eCg73SPhvcbyvvx8Zevaq1lQ4Oacs8Ky02JiYGLxtblMVWyd8VYakRI2DWLK3527pVK/iLPPSQ55W3bsUSG+ueaW/Hf/6JYft2+fwLIQopVchER0crQEVHR1s7FWHDbt5U6vHHlQKl6tVTKjbW2hkVUrGx2i8BsvwlxMbGKkABKtZMrLQBljO8Vqm/gwcvbdsa7+DmlnkcKNW8uXGsj4/p2Pr1jWMDAkzHVqtmHFutmunYgADj2Pr1Tcf6+BjHNm9uOtbNzTi2bVvTsQ/+2e3a1Xxs+vdy377mY6Oi0mJDQszHnj+fFjtypPnYY8fSYsPCzMfu3ZsWO22a+ditW9NiZ882H7t+fVrswoXmY1esSItdscJ87MKFabHr15uPnT07LXbrVvOx06alxe7daz42LCwt9tgx87EjR6bFnj9vNvZq15C02Kgow+2xaO0k97cVaO+tVPfb3ej7MdJWZk3+rghLzJmTeTMl8pCcVwohRI7KZGpZIQq3xETo2hVOnICyZWHdOrNfYAohhBA2p2RJa2cghEj1yy/w5pva9gcfQLdu1s1HCCGEyAky5FmIdJSCV16BBQu0ERE7dkDNmtbOqhCTIc9WJUOeZchztmNlyLMmD4Y8JyXomf7ePT78EJz1cVxDG8bM1avaeyyTYcwgQ55zg/xdEeb89Rc0baqdxgwYAF9/LYsjWY2cVwohRI6SRVmESOejj7Riop0dLF8uxUQhAO2E25Juutnpypud2PRFwJyMTV+0zMnY9EXWnIx1dk4r+uRkrJNTWkHLWrGOjpYvc5qdWAeHtOJiTsba21v+Hs5OrJ2dRbGnTkHv3nbs3avFdu4C/Hj/zsw+rzpd5sc1F2uu0C2EsMiVK9C+vVa7atkSvvxSiolCCCEKDhnyLMR9K1fCuHHa9mefQdu21s1HCCGESE8prSBRuzbs3autWL90KSxebO3MhBAPiouDDh20VYEff1xb3M/S71iEEEKI/KDQ9lCMi4vDPpMle+3t7XFJ12MkzsywPDs7O1zT9VrJTuzdu3cxNdpcp9Phlq6XTXZi7927h97MsLz03fWzExsfH0+Kmd4K2Yl1c3NDd//r2YSEBJLNDMvLTqyrqyt294flJSYmkmRmqN2DsTt2JNG7t3ZfSAj065c2ItPFxcXwXsnquOljk5KSSDQzLM/Z2RmH+71hshObnJxMgplheU5OTjje772TndiUlBTizQzLc3R0xOn+mXB2YvV6PffMDOEzGxsXh7thMw4HBwec7/e6Ukpx9/4QvtT7sSDW3OdUCCFs1ZUr2nDJTZu06888AwsXgr8/IM2aTZBzSzm3TI29dy+RF19M4sABKF5c+9LayUnOLR+U5+eWD5xXAhadL5qLlfNKIUShZq3VYKwldSUuU5e2D6xa6ubmZjK2+QOrlvr4+JiMrf/AqqUBAQEmY6s9sGpptWrVTMYGPLBqaf369U3G+jywamnz5s1Nxro9sGpp27Ztzb5u6XXt2tVsbPqV0vr27Ws2NirdqqUhISFmY8+nW7V05MiRZmOPpVu1dNiwMLOxe9OtWjpt2jSzsVvTrVo6e/Zss7Hr061aunDhQrOxK9ItB7hixQqzsQvTrVq6fv16s7Gz061aunXrVrOx09KtWrp3716zsWHpVi09duyY2diR6VYtPX/+vNF99qC63r/YgwoJSVu1NCoqyuxx+6ZbtTT9Sn2pF1mNL2uycqEQtmHVKqW8vbWFSZ2dlZo1S6mUlHQBSUnasrErVmjbZiQlJakVK1aoFStWqCQzsfL5t5ycW6aRc0tNUJCcWyple+eWD55XQs6dW0pbKYQojAptD0UhAKKjteFiwjalAKusnYQQQlhJdLS2Mux332nX69SB77+HatUeCHRwsHjZWAcHB7rJErNC5JqvvoJdu6ydhciMnFcKIUTOKrSrPF82sWqpDEvJPLYgDktJSbGjXTvYsiWRUqWS2LYN/PwyxsqwFI1Vhjw/wNyQZ0tjY2JiKCMrl1pEVi4UwnoiIqBvX7h4UVurZexYCAvLuznY5PNvOTm3lHPL1NgtW7Rzy5SURCZMSGLMmMxj5dxSUxDOLeW8UghRmBXagqI0+oWbUvDaazBvnraY5fbtWs8PYWOSk2H1am27c2ezK7AmJyez+n5s586dDSfJD5I2wHLyWgmR9+Lj4Z13YOZM7W9VhQrw7bfQtKmZnaSttCp5rQTAsWPQpAncuaN9GbBwoazobHOkrRRCiBwlQ55FoTRjhlZMtLODH36QYqLNSkiA7t217dhYsyd+CQkJdL8fGxsba/LETwghbNWRI/Dyy1phAmDQIO3vVZEiWewobaUQVhUZCe3aacXE5s21c0wpJtogaSuFECJH2Vk7ASHy2urVMHq0tj1zJnToYN18hBBCFG4pKfDRR9CggVZMLFkS1q3TihJZFhOFEFZ19y48/7w2PUHlyvDTT3k3NYEQQghhTVYvKM6ZM4fAwEBcXFxo1KgRe/fuNRs/a9YsqlSpgqurK/7+/owYMcLsfBtCpLdvH/TqpQ0je+MNGDrU2hkJIYQozM6fhxYttDkSk5KgY0c4elS+7BIiP9DroU8f7fzS2xs2bNB+CiGEEIWBVQuKy5cvJzQ0lLCwMA4ePEitWrUIDg4mKioq0/ilS5cyduxYwsLCOH78ON988w3Lly9n/PjxeZy5yI8uXtS+Qb53D9q0gVmzZDiKEEII61BKm2OtZk3480/w8IBvvtF60Zcsae3shBCWGDcOfvxR65G4Zg1UrGjtjIQQQoi8Y9WC4syZMxk0aBD9+/enWrVqzJ07Fzc3NxYsWJBp/M6dO2natCkvvfQSgYGBPPvss/Ts2TPLXo1CxMRoc9tERkKNGrB8udlpU4QQQohcc+0avPACDBigTeP15JPa/IkDBsgXXULkF/Pnw7Rp2vY330CzZtbNRwghhMhrVisoJiYmcuDAAVq1apWWjJ0drVq1YteuXZnu06RJEw4cOGAoIJ47d46NGzfStm1bk4+TkJBATEyM0UUULsnJ0KOHNi9VqVKwfr3MSSWEEMI61q+H6tW13kyOjvDhhxARoa3mLITIH377DV5/XdsOC9MWUxJCCCEKG6v10bp+/TopKSn4+voa3e7r68uJEycy3eell17i+vXrPPnkkyilSE5O5rXXXjM75Hnq1Km8++67OZq7yD+U0uZJ3LQJXF3h55+hXDlrZyWEEKKwiY2Ft97SFloBeOIJ+P57qF3bqmkJIbLp77+ha1dtMaVevbSCohBCCFEY5atBnxEREXzwwQd88cUXNGrUiDNnzjBs2DAmT57MhAkTMt1n3LhxhIaGGq7HxMTg7++fVykLK5s1C778UhtCtnQp1K9v7YxEtjg5aZOMpW6bDXVi4f1YJ1leUQhhQ3btgt694exZ7XpoKEyZAi4uOfQA0lYKkSeiorQpdKKjtakKvvlGpinIV6StFEKIHKVTSilrPHBiYiJubm6sWrWKTp06GW7v27cvt2/fZu3atRn2adasGY0bN2b69OmG277//nsGDx5MbGwsdnZZj+COiYnBy8uL6MuX8fT0zBhgb298hh8XZ/pgdnZat7eHib17V+s+lxmdDtzcHi723j1tyTlT3N0fLjY+XvsqNidi3dzSzr4SErQxyTkR6+qqvc4AiYlsWJNEjx6ggA+mwLBhpmNJSjJ9XBcX7X2R3dikJC3eFGfntIkcsxObnKy9FqY4OWnj6LIbm5Ki/e5McXRMO/nKTqxer73XciLWwUF7LUD7TNy9m+3YmJgYvMqUITo6OvM2QBgY2kt5rYR4KImJ8N57MHWq1rz5+8OiRfD009bOLGvy+becnFsWjnPLe9GJtGmVxL79UKE8bN0KPj6Zx8q5ZeE5t5TzSiFEoaasqGHDhmrIkCGG6ykpKcrPz09NnTo10/i6deuq0aNHG922dOlS5erqqpKTky16zOjoaAWoaO3PQMZL27bGO7i5ZR4HSjVvbhzr42M6tn5949iAANOx1aoZx1arZjo2IMA4tn5907E+PsaxzZubjnVzM45t29Z07INvo65dzcfGxqbF9u1rPjYqKi02JMR87PnzhtDI3iPNxx47lnbcsDDzsXv3psVOm2Y+duvWtNjZs83Hrl+fFrtwofnYFSvSYlesMB+7cGFa7Pr15mNnz06L3brVfOy0aWmxe/eajw0LS4s9dsx87MiRabHnz5uPDQlJi42KMh/bt29abGys4fZoUICKjo5W+c3s2bNVQECAcnZ2Vg0bNlR79uwxG//JJ5+oypUrKxcXF1W2bFk1fPhwde/ePYsfz9Be5sPXSghr+/tvperWTWuSXn5ZqVu3rJ2V5R7l81+sWLFsXby9vdWFCxdy4VnkDTm3TKeAnlumpCi1trKcWyql5Nwy1f1zy/x8XimEEI/KqkOeQ0ND6du3L/Xr16dhw4bMmjWLuLg4+vfvD0CfPn3w8/Nj6tSpAHTo0IGZM2dSp04dw5DnCRMm0KFDB+xTv7kThd5//8HPP8Hr1k5E5KlkYPP97WDy2XwOFli+fDmhoaHMnTuXRo0aMWvWLIKDgzl58iQlS5bMEL906VLGjh3LggULaNKkCadOnaJfv37odDpmzpxphWcgROGg18Ps2TBmjNbZxtsb5s6Fbt1y8UGTk2Hz/RYwODit11Gmoclsvh8bHByMg5nYh3X79m1mzZqFl5dXlrFKKUJCQkgx1/tMCCt75x0odsraWYi8VNDPK4UQIidYbchzqtmzZzN9+nQiIyOpXbs2n332GY0aNQKgRYsWBAYGsmjRIkA7CZ4yZQrfffcdly5dokSJEnTo0IEpU6ZQtGhRix5PhqUU7GEpd+LsaNYM/j6SSK2qSfz2G2T6/4wMS8kYa4vDUuLiIHXhpqtXtV+miWEpcXFxeNyPjb16FXcTsfl1aEqjRo1o0KABs2fPBkCv1+Pv78+bb77J2LFjM8QPGTKE48ePEx4ebrjtrbfeYs+ePfz5558WPaYMeRQie/77D/r311aABa22t2ABlCmTyw8cFwceHtp2bKzx3+QMoXF43I+NjY3F3UTso3z+7ezsiIyMzPTLjswUKVKEI0eOUCGfLnUt55YF+9xy4WI7BgwARxJZNC+Jl14yHSvnltj2ueWD55Xu7iaHPGc4rzQRm1/PK4UQIidYvaCY1+Qf5IIrORk6dYING6BkSdizBwIDrZ2VeCQ29k+ytTzMnLNLly4lJCSEX3/9lYYNG3Lu3DnatWtH7969GT9+vEWPmx9fKyGsZdkyeP11uH1bqyt8/LF2PU8WbJC20qrktSq4fv9d+2IgOVnrpTh5srUzEo9E2kohhMhR0ntbFBihoVox0cUF1q2TYqIoOK5fv05KSgq+qd+q3+fr68uJEycy3eell17i+vXrPPnkkyilSE5O5rXXXjNbTExISCAhXa+DmJiYnHkCQhRgt25BSIhWUARo0AC++w6qVLFuXkKIR3PiBHTpohUTX3xRW2BJCCGEEGmkoCgKhM8/1y6g/SN3f9S8EIVWREQEH3zwAV988YVhztlhw4YxefJkJkyYkOk+U6dO5d13383jTIXIv377Dfr1g0uXtBGJ77wDb7+dNtpPaE6fPs3WrVuJiopC/8Bw2IkTJ1opKyFMu3YN2rXTehw3aQILF+ZRb2MhhBAiH5GCosj3NmyA4cO17alToWtXq6YjRI7z8fHB3t6eq1evGt1+9epVSpUqlek+EyZMoHfv3rzyyisA1KhRg7i4OAYPHszbb7+NXeo8T+mMGzeO0NBQw/WYmBj8/f1z8JkIUTDcuwdjx8Jnn2nXK1WSL7NMmT9/Pq+//jo+Pj6UKlUKXbqqjE6nk4KisDnx8doUOufOQYUKsGaN8RSYQgghhNBIQVHka0eOaMNQ9HoYOFBbVVOIgsbJyYl69eoRHh5umENRr9cTHh7OkCFDMt3n7t27GYqG9vcndTc1da6zszPOqZONCyEydeAAvPyyNhwStHkSp083OxVXofb+++8zZcoUxsgfaJEPKAUDBsDOnVC0qPaldYkS1s5KCCGEsE1SUBT51uXL0L69Nqfy00/Dl1/KcBRRcIWGhtK3b1/q169Pw4YNmTVrFnFxcfTv3x+APn364Ofnx9SpUwHo0KEDM2fOpE6dOoYhzxMmTKBDhw6GwqIQwnLJyfDRRzBpkrZdqpS2gnObNtbOzLbdunWLbt26WTsNISwSFgY//KAt5vvjj/D449bOSAghhLBdUlAU+VJcHHToAP/9p53srVolc1YVSE5OMHt22rbZUCdm3491yiI2P+rRowfXrl1j4sSJREZGUrt2bTZt2mRYqOXixYtGPRLfeecddDod77zzDpcuXaJEiRJ06NCBKVOmWOspCJFvnTkDffrArl3a9S5dYO5c8PGxbl4GNtxWduvWjV9//ZXXXnst1x9LiEfx7bdpqzjPm6d9WS0KGBtuK4UQIj/SKVNj3wqomJgYvLy8iI6OxtPT09rpiIeQkgIvvKCt5OzjA3v2aHPcCGEJaQMsJ6+VKOyUgq+/hhEjtC+yPD21/0Vffrng94h/lM//Z6mTSwJxcXHMnDmTdu3aUaNGDRwf+PZv6NChOZKvNUlbmf9t2watW0NSEowbBx98YO2MRH4hn38hRGEmPRRFvjNqlFZMdHaGtWulmCiEECLnRUbCoEGwfr12vUULWLQIAgKsmVX+8Mknnxhd9/DwYNu2bWzbts3odp1OVyAKiiJ/O3UKOnfWiondusH771s7IyGEECJ/kIKiyFe+/BJS/09ZtAiaNLFqOiK3paTA9u3adrNmYGbuv5SUFLbfj23WrJnMEyiEeGhr1mjFxOvXtVFxU6fC8OGQyeLotsHG2srz58/n+DGFyA3Xr0O7dnDrFjRuDIsX2/DnXDw6G2srhRAiv5M/mSLf2LwZ3nxT2548WVvdWRRw8fHQsqV2iY/PIjSeli1b0rJlS+KziBVCiMzExGgrvHburBUaataE/fshNNTGiwyFqK2cM2cOgYGBuLi40KhRI/bu3Ws2fuXKlTz++OO4uLhQo0YNNm7cmCHm+PHjPP/883h5eeHu7k6DBg24ePFibj0FYSMSErTP+pkzEBiojXpxdbV2ViJXFaK2Uggh8oItnx4LYXD0qDYMJSUF+vaFt9+2dkZCCCEKku3boVYtWLhQmx9xzBjYuxdq1LB2ZgXT2rVr+fbbb7O1z/LlywkNDSUsLIyDBw9Sq1YtgoODiYqKyjR+586d9OzZk4EDB3Lo0CE6depEp06dOHbsmCHm7NmzPPnkkzz++ONERETw119/MWHCBFxcXB7p+QnbphS88gr8+Sd4ecGGDVCypLWzEkIIIfIXWZRF2LzISGjUCC5ehObN4ddfs1yYTRQUcXHg4aFtx8aCu7uZ0Dg87sfGxsbibiJW2gDLyWslCoOEBAgLg2nTtCJDYKC22muzZtbOLBvyYVv5+OOPc/r0aVJSUizep1GjRjRo0MCw8qper8ff358333yTsWPHZojv0aMHcXFxrE+dCBNo3LgxtWvXZu7cuQC8+OKLODo68t133z30c5G2Mv95912YNAkcHOCXX6BVK2tnJPJEPmwrhRDClkkPRWHT7t6F55/XiomVK8NPP0kxUQghRM44dkz7wuqjj7RiYv/+cORIPism5lMnTpzIVjExMTGRAwcO0Cpd5cfOzo5WrVqxa9euTPfZtWuXUTxAcHCwIV6v17NhwwYqV65McHAwJUuWpFGjRqxZs8ZsLgkJCcTExBhdRP6xZIlWTARtbm4pJgohhBAPRwqKwmbp9dC7N+zbB8WLa8NRvL2tnZUQQoj8Tq+HGTOgXj2tgOjjo31htWABSAeTvHH79m1DT0NLXL9+nZSUFHx9fY1u9/X1JTIyMtN9IiMjzcZHRUURGxvLhx9+yHPPPcevv/5K586deeGFFzKsSJ3e1KlT8fLyMlz8/f0tfh7CurZv1+ZJBRg9Whv2LIQQQoiHIwVFYbPGjk3rkbhmDVSsaO2MhBBC5Hf//APPPAMjR0JiIrRvr83T27mztTMrHMLDw3nppZcoXbo0YWFhVs1Fr9cD0LFjR0aMGEHt2rUZO3Ys7du3NwyJzsy4ceOIjo42XP7999+8Slk8gjNntM95YiJ06aKt3i6EEEKIhycFRWGT5s+H6dO17QUL4MknrZuPEEKI/E0p+O47beXmiAht6qx582DdOihVytrZFWz//vsv7733HuXLl+fZZ59Fp9OxevVqkz0LM+Pj44O9vT1Xr141uv3q1auUMvELLFWqlNl4Hx8fHBwcqFatmlFM1apVza7y7OzsjKenp9FF2LZ796BdO7hxAxo21OZJtemV24UQQoh8wMHaCQjxoN9+g9df17bDwqBXL+vmI6zI0VFbKSF122yoI9PuxzpmESuEKFxu3IDXXoNVq7TrQUFaQaHA9Hy3wbYyKSmJNWvW8PXXX7N9+3aee+45pk+fTs+ePXn77bczFPGy4uTkRL169QgPD6dTp06A1sMwPDycIUOGZLpPUFAQ4eHhDB8+3HDbli1bCAoKMhyzQYMGnDx50mi/U6dOERAQkK38hG3bvRtOndKmN1i7FtzcrJ2RsAobbCuFECI/k4KisCl//w1du0JKilZItPJoKGFtTk4wapSFoU6MsjBWCFF4bNqkLbYSGamt6DppEowZo20XGDbYVvr5+fH444/z8ssvs2zZMooVKwZAz549H/qYoaGh9O3bl/r169OwYUNmzZpFXFwc/fv3B6BPnz74+fkx9f5Y1mHDhtG8eXNmzJhBu3btWLZsGfv372fevHmGY44aNYoePXrw1FNP0bJlSzZt2sTPP/9MRETEwz95YXNu3NB+Pv649Egu1GywrRRCiPysIJ1Oi3zu6lVtOEp0tDbE+ZtvQKezdlZCCCHyo7g47f/GL7/Urj/+OHz/vbYQi8h9ycnJ6HQ6dDod9vb2OXLMHj16cO3aNSZOnEhkZCS1a9dm06ZNhoVXLl68iF26caxNmjRh6dKlvPPOO4wfP55KlSqxZs0aqlevbojp3Lkzc+fOZerUqQwdOpQqVarw448/8qTMtVKg3Lql/bxf1xZCCCFEDpCCorAJ9+5Bx45w4QI89hisXg3OztbOSlhdSgocPKht160LZv4pTUlJ4eD92Lp16+bYP7BCiPxnzx7o3RtOn9auDx0KH34Irq7WzSvX2GBbefnyZX788Ue++eYbhg0bRps2bXj55ZfRPeI3hUOGDDE5xDmzXoXdunWjW7duZo85YMAABqQu/SsKJCkoCsAm20ohhMjPpKAorE6vh759tX8AixWDjRu1OW6EID5emz0dIDZWW0XBZGg8De/HxsbG4m4mVghRMCUlwZQp8P772v+Nfn6waBG0amXtzHKZDbaVLi4u9OrVi169enH27FkWLlzI0KFDSU5OZsqUKfTr14+nn35a/kkXeeL2be1n0aLWzEJYnQ22lUIIkZ/J+mbC6t55B1au1OZGXr0aKle2dkZCCCHym5MnoWlTePddrZjYsyccPVoIion5wGOPPcb777/PP//8w4YNG0hISKB9+/aGocpC5DbpoSiEEELkPOmhKKxq4UK4P3c6X38NzZtbNx8hhBD5i1LaPIkjR2rTZxQtCl98oRUUhW2xs7OjTZs2tGnThmvXrvHdd99ZOyVRSEhBUQghhMh5UlAUVvP77zB4sLb9zjvQp4918xFCCJG/XL4MAwbA5s3a9VattC+qypa1bl4iayVKlCA0NNTaaYhCQgqKQgghRM6TIc/CKk6cgC5dIDkZevTQhqgJIYQQllq5EmrU0IqJLi7w2WfathQTrc/b25vr169bHF+uXDn++eefXMxIFHZSUBRCCCFynvRQFHnu/Hlo0UKbIDsoSJsw305K20IIISxw+za8+SZ8/712vW5dbbtqVaumJdK5ffs2v/zyC15eXhbF37hxg5SUlFzOShRmUlAUQgghcp4UFEWeun0batWCO3egeHFYu1brWSKEEEJkZetW6NsX/v1X+yJq/HiYMAGcnKydmXhQ3759rZ2CEAZSUBRCCCFynhQURZ7R69OKiQDPPQclSlg3J2HjHB0hLCxt22yoI2H3Yx2ziBVC5C/x8fD22zBzpnb9scfgu++0Xu4Cm2sr9Xp9rhxXiIehlPaFNmiLNolCzMbaSiGEyO90SillzQTmzJnD9OnTiYyMpFatWnz++ec0bNjQZPzt27d5++23+emnn7h58yYBAQHMmjWLtm3bWvR4MTExeHl5ER0djaenZ049DWGBp56C7dvTrvfqlTZkTYi8Im2A5eS1Erbg8GF4+WX43/+064MHw4wZ4OFh1bQKPPn8W05eK9t25w6k/lri4sDNzbr5iIJFPv9CiMLMqjPXLV++nNDQUMLCwjh48CC1atUiODiYqKioTOMTExNp3bo1Fy5cYNWqVZw8eZL58+fj5+eXx5mL7Bo8OK2YmDrP1c2b1stHCCGEbUtJgY8+goYNtWJiyZLw88/w1VdSTBRCWC51uLOTE7i6WjcXIYQQoiCx6pDnmTNnMmjQIPr37w/A3Llz2bBhAwsWLGDs2LEZ4hcsWMDNmzfZuXOnoet5YGBgXqYsHsKnn8L8+dp248Ywdix06gQ3blg1LZEf6PVw/Li2XbWq2dV79Ho9x+/HVq1aFTtZ6UeIfOvcOW2uxD//1K536gTz5sk0GSZJWymESennT9TprJuLsDJpK4UQIkdZrWVMTEzkwIEDtGrVKi0ZOztatWrFrl27Mt1n3bp1BAUF8cYbb+Dr60v16tX54IMPZGVAG7Z5M4wYoW2XLav1UixeXLsuPRRFlu7dg+rVtcu9e1mE3qN69epUr16de1nECiFsk1KwYIE23+6ff0KRIrBwIfz0kxQTzZK2UgiTZEEWYSBtpRBC5Cir9VC8fv06KSkp+Pr6Gt3u6+vLiRMnMt3n3Llz/P777/Tq1YuNGzdy5swZQkJCSEpKMkya+6CEhAQSEhIM12NiYnLuSQizTp+GDh20fxDd3eHIEXBwAG9v7X4pKAohhEgVFaVNj7F2rXa9WTNYvBjKl7duXkKI/E0KikIIIUTuyFd9t/V6PSVLlmTevHnUq1ePHj168PbbbzN37lyT+0ydOhUvLy/Dxd/fPw8zLrxiYqB+fUhKAnt72L07rZCY+vPWLW3kgRBCiMLt55+hRg2tmOjoqM2duHWrFBMLgubNm/Ptt99KDx9hNVJQFEIIIXKH1Xoo+vj4YG9vz9WrV41uv3r1KqVKlcp0n9KlS+Po6Ii9vb3htqpVqxIZGUliYiJOTk4Z9hk3bhyhoaGG6zExMVJUzGV6PdSurRUVAX78URtZkCq1oKgU3L6ddl0IYd6cOXOYPn06kZGR1KpVi88//5yGDRuajL99+zZvv/02P/30Ezdv3iQgIIBZs2bRtm3bPMxaCNNiYyE0NG2e3erV4fvvtSHPomCoU6cOI0eO5M0336R79+4MHDiQxo0bWzstUYikFhSLFrVqGkLkKqUUycnJMhWYEOKR2dvb4+DggM6CiYetVlB0cnKiXr16hIeH06lTJ0DrgRgeHs6QIUMy3adp06YsXboUvV5vmBj31KlTlC5dOtNiIoCzszPOzs658hxE5lq3hvPnte2pU6FjR+P7nZy0FTpjY7Vhz1JQFCJry5cvJzQ0lLlz59KoUSNmzZpFcHAwJ0+epGTJkhniExMTad26NSVLlmTVqlX4+fnxzz//UFT+oxI2YudO6N1bW4BFp9MKi++/Dy4u1s5M5KRZs2bx8ccfs27dOhYvXsxTTz1FxYoVGTBgAL17984w9Y0QOe32be2n9FAUBVViYiJXrlzh7t271k5FCFFAuLm5ma2zpbLqKs+hoaH07duX+vXr07BhQ2bNmkVcXJxh1ec+ffrg5+fH1KlTAXj99deZPXs2w4YN48033+T06dN88MEHDB061JpPQ6Tz5pvw++/adq9e2orOmfH2TisoCiGyNnPmTAYNGmRoH+fOncuGDRtYsGABYzP5oC1YsICbN2+yc+dOHB0dAQgMDMzLlIXIVGIivPsufPih1qO9XDltrsQWLaydmcgtDg4OvPDCC7zwwgtERUUxb948JkyYwPjx42nbti1Dhw7l6aeftnaaooCSIc+iINPr9Zw/fx57e3vKlCmDk5OTRb2KhBAiM0opEhMTuXbtGufPn6dSpUpmV7m3akGxR48eXLt2jYkTJxIZGUnt2rXZtGmT4dvqixcvGiXv7+/P5s2bGTFiBDVr1sTPz49hw4YxZswYaz0Fkc6XX8Ls2dp2vXrasDVTvL3h4kW4cSNvchMiP0tMTOTAgQOMGzfOcJudnR2tWrVi165dme6zbt06goKCeOONN1i7di0lSpTgpZdeYsyYMUbTRqQni1iJ3Hb8OLz8Mhw8qF3v0wc++wy8vKybl8gbe/fuZeHChSxbtoySJUvSr18/Ll26RPv27QkJCeHjjz+2doqiAJKCoijIEhMT0ev1+Pv74+bmZu10hBAFgKurK46Ojvzzzz8kJibiYmb4kFULigBDhgwxOcQ5IiIiw21BQUHs3r07l7MS2fX77/DGG9p26dLaUDZzihfXfkoPRWGWoyOMHJm2bTbUkZH3Yx2ziM1vrl+/TkpKSoahgb6+vpw4cSLTfc6dO8fvv/9Or1692LhxI2fOnCEkJISkpCTCwsIy3Wfq1Km8++67OZ6/EErBF19oH+f4eO1Lpa++gq5drZ1ZAWHDbWVUVBTfffcdCxcu5PTp03To0IEffviB4OBgQy+afv368dxzz0lBUeQKKSgKAxtuKx+VuR5EQgiRXZa2KVYvKIr87/x5aNNG+4fRzQ0OH9bmSTQndd5EKSgKs5ycYPp0C0OdmG5hbGGg1+spWbIk8+bNw97ennr16nHp0iWmT59usqAoi1iJ3HD1KgwYABs3ateffRYWLoQyZaybV4Fiw21l2bJleeyxxxgwYAD9+vWjRIkSGWJq1qxJgwYN8iwnUbhIQVEY2HBbKYQQ+ZF8lSEeSWws1K2rzYllbw87dkAm60NkIAVFISzn4+ODvb09V69eNbr96tWrlCpVKtN9SpcuTeXKlY2GN1etWpXIyEgSExMz3cfZ2RlPT0+jixCP4uefoUYNrZjo7Ayffgq//CLFxMIkPDyc48ePM2rUqEyLiQCenp5s3bo1jzMThYUUFIUoXHQ6HWvWrLEodtKkSdSuXdtsTIsWLRg+fPgj55WXLly4gE6n4/Dhw9ZO5ZFERESg0+m4nbq6lrA5UlAUD02v14qJqZ/vZcsgi/bYILWgKHMoCrP0erhwQbvo9VmE6rlw4QIXLlxAn0VsfuPk5ES9evUIDw833KbX6wkPDycoKCjTfZo2bcqZM2eMXotTp05ZtFqXEI8qLg5eew2efx6uXYOaNeHAARg6FGRUVi6w4bYyLCws038EYmJiZCEWkSekoCgMbLitLGyuXbvG66+/Trly5XB2dqZUqVIEBwezY8cOQ0x2CoPpXblyhTZt2uRYrj/99BOTJ0/OseM9rEWLFlG0aFGLYv39/bly5QrVq1fP3aREoSdDnsVDa9MGTp/Wtt99N3tzYckcisIi9+5B+fLadmwsuLubCb1H+fuxsbGxuJuJzY9CQ0Pp27cv9evXp2HDhsyaNYu4uDjDqs99+vTBz8+PqVOnAvD6668ze/Zshg0bxptvvsnp06f54IMPGDp0qDWfhigE9u+HXr3g1CnQ6eCtt+D997UeiiKX2HBbuW3btkx7RcfHx7N9+/ZcfWwhlEr74tvC/8NFQWbDbWVh06VLFxITE1m8eDEVKlTg6tWrhIeHcyMHepuYGr3zsLxTe8LkE4mJiTg5OeX46yBEZqSfgHgoI0bAr79q2z16wMSJ2dtfhjwLkT09evTg448/ZuLEidSuXZvDhw+zadMmw0ItFy9e5MqVK4Z4f39/Nm/ezL59+6hZsyZDhw5l2LBhjB071lpPQRRwKSnwwQcQFKQVE8uWhd9+06arkmJi4fPXX3/x119/oZTi77//Nlz/66+/OHToEN988w1+fn7WTlMUcPfuadPygPRQFMJW3L59m+3bt/PRRx/RsmVLAgICaNiwIePGjeP5558HIDAwEIDOnTuj0+kM1wG+/PJLHnvsMZycnKhSpQrfffed0fEf7Nn433//0bNnT7y9vXF3d6d+/frs2bPHaJ/vvvuOwMBAvLy8ePHFF7lz547hvgeHPN+6dYs+ffpQrFgx3NzcaNOmDadTe9mQ1pNw/fr1VKlSBTc3N7p27crdu3dZvHgxgYGBFCtWjKFDh5KSkmLYLyEhgZEjR+Ln54e7uzuNGjUyLFIbERFB//79iY6ORqfTodPpmDRpkuG1mjx5Mn369MHT05PBgwdnOuT5f//7H+3bt8fT05MiRYrQrFkzzp49a/L3dOzYMdq0aYOHhwe+vr707t2b69evG70uQ4cOZfTo0Xh7e1OqVClDTgAvvfQSPXr0MDpmUlISPj4+fPvtt4DWE3jq1KmUL18eV1dXatWqxapVq0zmBPDjjz/yxBNP4OzsTGBgIDNmzDC6P/X16NmzJ+7u7vj5+TFnzhyjmNu3b/PKK69QokQJPD09efrppzly5IjZxxUmqEImOjpaASo6OtraqeRb8+crpX3nq1SdOg93jDVrtP0bNcrZ3EQBExub9maLjc0iNFYBClCxZmKlDbCcvFbCUufOKdW0adrHtXt3pW7etHZWhYgNtpU6nU7Z2dkpOzs7pdPpMlzc3NzUN99881DHtjXSVtqu//7TPhb29krp9dbORlidDbaVj+revXvq77//Vvfu3TPcptdrTy+vL5Z+xpKSkpSHh4caPny4io+PzzQmKipKAWrhwoXqypUrKioqSiml1E8//aQcHR3VnDlz1MmTJ9WMGTOUvb29+v333w37Amr16tVKKaXu3LmjKlSooJo1a6a2b9+uTp8+rZYvX6527typlFIqLCxMeXh4qBdeeEEdPXpU/fHHH6pUqVJq/PjxhuM1b95cDRs2zHD9+eefV1WrVlV//PGHOnz4sAoODlYVK1ZUiYmJSimlFi5cqBwdHVXr1q3VwYMH1bZt21Tx4sXVs88+q7p3767+97//qZ9//lk5OTmpZcuWGY77yiuvqCZNmqg//vhDnTlzRk2fPl05OzurU6dOqYSEBDVr1izl6emprly5oq5cuaLu3LmjlFIqICBAeXp6qo8//lidOXNGnTlzRp0/f14B6tChQ0oppf777z/l7e2tXnjhBbVv3z518uRJtWDBAnXixIlMX/9bt26pEiVKqHHjxqnjx4+rgwcPqtatW6uWLVsavS6enp5q0qRJ6tSpU2rx4sVKp9OpX3/9VSml1Pr165Wrq6shT6WU+vnnn5Wrq6uKiYlRSin1/vvvq8cff1xt2rRJnT17Vi1cuFA5OzuriIgIpZRSW7duVYC6deuWUkqp/fv3Kzs7O/Xee++pkydPqoULFypXV1e1cOFCw2MEBASoIkWKqKlTp6qTJ0+qzz77TNnb2xvyUkqpVq1aqQ4dOqh9+/apU6dOqbfeeksVL15c3bhxI9PXozDKrG3JjBQURbZs26aUTqf9HS5VSqmEhIc7zvbt2jEqVszZ/EQBUwBP/PITea1EVvR6pb79VqkiRbSPaZEiSi1eLP+45zkbbCsvXLigzp8/r3Q6ndq3b5+6cOGC4XL58mWVnJz8UMe1RdJW2q6jR7WPhY+PtTMRNsEG28pHldk//emfZl5esnhJjaxatUoVK1ZMubi4qCZNmqhx48apI0eOGMWkLwymatKkiRo0aJDRbd26dVNt27bNdL+vvvpKFSlSxGShKCwsTLm5uRkKXEopNWrUKNUoXa+X9AXFU6dOKUDt2LHDcP/169eVq6urWrFihVJKKygC6syZM4aYV199Vbm5uRkV14KDg9Wrr76qlFLqn3/+Ufb29urSpUtG+T3zzDNq3LhxhuN6eXlleA4BAQGqU6dORrc9WFAcN26cKl++vKHomZXJkyerZ5991ui2f//9VwHq5MmThtflySefNIpp0KCBGjNmjFJKKxz7+Piob7/91nB/z549VY8ePZRSSsXHxys3NzdDcTfVwIEDVc+ePZVSGQuKL730kmrdurVR/KhRo1S1atWMXo/nnnvOKKZHjx6qTZs2Simltm/frjw9PTMUsx977DH11VdfZfHKFB6WFhRlyLOw2D//wLPPan8yXF3h0CF42LUdZMizEELkb7duwYsvQp8+cOcOPPkk/PWXdl2ns3Z2wtoCAgIIDAxEr9dTv359AgICDJfSpUsbrUAvRG6RBVmEsE1dunTh8uXLrFu3jueee46IiAjq1q3LokWLzO53/PhxmjZtanRb06ZNOX78eKbxhw8fpk6dOmbnQQwMDKRIkSKG66VLlyYqKsrk4zs4ONCoUSPDbcWLF6dKlSpGObi5ufHYY48Zrvv6+hIYGIiHh4fRbamPc/ToUVJSUqhcuTIeHh6Gy7Zt28wOS05Vv359s/cfPnyYZs2a4ejomOWxAI4cOcLWrVuNcnn88ccBjPKpWbOm0X7pXzsHBwe6d+/OkiVLAIiLi2Pt2rX06tULgDNnznD37l1at25t9Djffvutyeds6vd/+vRpo+HjDy5aGRQUZPj9HDlyhNjYWIoXL270uOfPn7fotRbGZFEWYZG7d6FOHUhI0Fbo/OMPeJR5XlPb9Fu3tEXWZNVPIYTIP37/Hfr2hf/+AwcHmDQJxo4FqREJgHXr1tGmTRscHR1Zt26d2djU+bKEyA1SUBSFkZubtuaMNR43O1xcXGjdujWtW7dmwoQJvPLKK4SFhdGvX78cy8nV1TXLmAeLbDqd7pFX9s7smOYeJzY2Fnt7ew4cOJDhC7f0RUhTslo0yJLXIb3Y2Fg6dOjARx99lOG+0qVLG7azeu169epF8+bNiYqKYsuWLbi6uvLcc88ZHgNgw4YNGeZUds7FybdjY2MpXbq0YX7K9CxdRVukkYKiyJJeD3Xrpp2ULV0KWXwJkqXUgqJSEB0tJ3pCCJEfJCTAO+/AjBla+12pEixZAg0aWDszYUs6depEZGQkJUuWpFOnTibjdDqdUY8CIXKaFBRFYaTTmV3A2mZVq1bNaDEVR0fHDH8jqlatyo4dO+jbt6/hth07dlCtWrVMj1mzZk2+/vprbt68mSOrNVetWpXk5GT27NlDkyZNALhx4wYnT540mYMl6tSpQ0pKClFRUTRr1izTGCcnp4f+m1mzZk0WL15MUlKSRb0U69aty48//khgYCAODg9fMmrSpAn+/v4sX76cX375hW7duhkev1q1ajg7O3Px4kWaN29u0fFSf//p7dixg8qVKxsVYnfv3m0Us3v3bqpWrWp4bpGRkTg4OBgt9iMejhQURZY6dICTJ7XtCRO0VZ0flZMTeHho357duCEnesIEBwcICUnbNhvqQMj92Ef5wyeEyNz//ge9ekHqIniDB8PMmfnzn5YCx8bayvS9Ex61l4cQj+L2be2ndDoRgM21lYXVjRs36NatGwMGDKBmzZoUKVKE/fv3M23aNDp27GiICwwMJDw8nKZNm+Ls7EyxYsUYNWoU3bt3p06dOrRq1Yqff/6Zn376id9++y3Tx+rZsycffPABnTp1YurUqZQuXZpDhw5RpkyZDMNiLVGpUiU6duzIoEGD+OqrryhSpAhjx47Fz8/PKPfsqly5Mr169aJPnz7MmDGDOnXqcO3aNcLDw6lZsybt2rUjMDCQ2NhYwsPDqVWrFm5ubrhZ2C10yJAhfP7557z44ouMGzcOLy8vdu/eTcOGDalSpUqG+DfeeIP58+fTs2dPwyrOZ86cYdmyZXz99dfZmrbkpZdeYu7cuZw6dYqtW7cabi9SpAgjR45kxIgR6PV6nnzySaKjo9mxYweenp5GReNUb731Fg0aNGDy5Mn06NGDXbt2MXv2bL744gujuB07djBt2jQ6derEli1bWLlyJRs2bACgVatWBAUF0alTJ6ZNm0blypW5fPkyGzZsoHPnzlkOHxfGpHUUZo0eDRs3atsvvADvvZdzx/b21gqKMo+iMMnZGebMsTDUmTkWxgohLKcUzJ6t/T2IjwcfH/jmG5CRqjZE2kohMiU9FIURaSttgoeHB40aNeKTTz7h7NmzJCUl4e/vz6BBgxg/frwhbsaMGYSGhjJ//nz8/Py4cOECnTp14tNPP+Xjjz9m2LBhlC9fnoULF9KiRYtMH8vJyYlff/2Vt956i7Zt25KcnEy1atUe6Xe7cOFChg0bRvv27UlMTOSpp55i48aNFs9PaO6477//Pm+99RaXLl3Cx8eHxo0b0759e0Dr7ffaa6/Ro0cPbty4QVhYGJMmTbLo2MWLF+f3339n1KhRNG/eHHt7e2rXrp1hPsJUZcqUYceOHYwZM4Znn32WhIQEAgICeO6557DL5lxlvXr1YsqUKQQEBGR4vMmTJ1OiRAmmTp3KuXPnKFq0KHXr1jV6H6RXt25dVqxYwcSJE5k8eTKlS5fmvffeyzBM/q233mL//v28++67eHp6MnPmTIKDgwFtdMTGjRt5++236d+/P9euXaNUqVI89dRT+Pr6Zuu5CdAppZS1k8hLMTExeHl5ER0djaenp7XTsWmLF0PqZ7NGDW2y/ZxUpw4cPgy//AL3p1IQItdJG2A5ea3ElSvQvz9s3qxdf+45WLjw0ebQFflDTn3+hw4dSsWKFRk6dKjR7bNnz+bMmTPMmjXrETO1PmkrbdewYfDZZzBuHHzwgbWzEQWRtT//8fHxnD9/nvLly+Pi4pLnjy+ErQkMDGT48OEMHz7c2qnka5a2LbIUhsjUjh0wYIC2XaIE7N2b84+ROo3FjRs5f2xRQCgF165plyy++1BKce3aNa5du0Yh+55EiFyxZo32ZdLmzeDiovVS3LhRiok2yYbbyh9//DHTHhBNmjRh1apVuf74onCTHorCiA23lUIIkR/JkGeRwcWL8Mwz2mIsLi5aL8Lc+MKreHHtpwx5FibdvQslS2rbsbFmJ2u7e/cuJe/HxsbGZrnamRAic7GxMGIEfP21dr12bW3hlUeYa1zkNhtuK2/cuIGXl1eG2z09Pbl+/XquPrYQUlAURmy4rRRCiPxIeigKI/Hx2orOCQlgZwd//AFlyuTOY6X2UJSCohBC2Ia9e7XpKL7+WlslcvRo2L1bioni4VWsWJFNmzZluP2XX36hQoUK2T7enDlzCAwMxMXFhUaNGrE3iyEUK1eu5PHHH8fFxYUaNWqwMXVi6Ey89tpr6HS6AjEMW2ikoCiEEIXLhQsXZLhzHsp2QTH9yjwP+uqrrx4pGWFdej3Uq5c2BHnxYmjQIPceTwqKoqDr27cvf/zxh7XTECJLyckweTI0aQJnzkDZshAeDh99pM1hL8TDCg0NZfTo0YSFhbFt2za2bdvGxIkTGTt2LCNGjMjWsZYvX05oaChhYWEcPHiQWrVqERwcTFRUVKbxO3fupGfPngwcOJBDhw7RqVMnOnXqxLFjxzLErl69mt27d1Mmt75FFVYhBUUhhBAi92S7oPjcc88xatQokpKSDLddv36dDh06MHbs2BxNTuStzp3h77+17XHj4OWXc/fxUoc8yxyKoqCKjo6mVatWVKpUiQ8++IBLly5ZOyUhMjh3Dpo3h4kTISUFXnxRW4SrZUtrZyYKggEDBjBjxgy++eYbWrZsScuWLfn+++/58ssvGTRoULaONXPmTAYNGkT//v2pVq0ac+fOxc3NjQULFmQa/+mnnxrOW6tWrcrkyZOpW7cus2fPNoq7dOkSb775JkuWLHnkVTqFbUktKBYtatU0hBBCiALpoXoorl69mgYNGvD333+zYcMGqlevTkxMDIcPH86FFEVeGDcO1q3Ttp9/Pm9WwpMeiqKgW7NmDZcuXeL1119n+fLlBAYG0qZNG1atWmX0pYwQ1qCU1hO9dm3YuRM8PeG772DpUunNI3LW66+/zn///cfVq1eJiYnh3Llz9OnTJ1vHSExM5MCBA7Rq1cpwm52dHa1atWLXrl2Z7rNr1y6jeIDg4GCjeL1eT+/evRk1ahRPPPFEtnIStu/2be2ntGlCCCFEzst2QbFJkyYcPnyY6tWrU7duXTp37syIESOIiIggICAgN3IUuez77+HDD7XtatVg9eq8eVwpKIrCoESJEoSGhnLkyBH27NlDxYoV6d27N2XKlGHEiBGcPn3a2imKQujmTejeHfr1gzt34Mkn4cgRrWe6Tmft7ERBVaJECTw8PB5q3+vXr5OSkoKvr6/R7b6+vkRGRma6T2RkZJbxH330EQ4ODgwdOtTiXBISEoiJiTG6CNuTkAD37mnbUlAUQgghct5DLcpy6tQp9u/fT9myZXFwcODkyZPcvXs3p3MTeWDPHujbV9suXhwOHNAWY8kLUlAUhcmVK1fYsmULW7Zswd7enrZt23L06FGqVavGJ598Yu30RCESHg41a8KqVeDgAFOmQEQEBAZaOzNRUK1atYru3bvTuHFj6tata3SxpgMHDvDpp5+yaNEidNmopE+dOhUvLy/Dxd/fPxezFA8rdbizTqf1wBZCCCFEzsp26ejDDz8kKCiI1q1bc+zYMfbu3cuhQ4eoWbOmySEnwjZdvgwtWmiLsTg7w+HD4OKSd48vcyiKLDk4aBXvvn21bbOhDvTt25e+ffvikEVsXklKSuLHH3+kffv2BAQEsHLlSoYPH87ly5dZvHgxv/32GytWrOC9996zdqqiEEhIgJEjoVUruHQJKleGXbtg/Hiwt7d2duKR2HBb+dlnn9G/f398fX05dOgQDRs2pHjx4pw7d442bdpYfBwfHx/s7e25evWq0e1Xr16lVKlSme5TqlQps/Hbt28nKiqKcuXK4eDggIODA//88w9vvfUWgWYq7OPGjSM6Otpw+ffffy1+HiLvpJ8/Ma++LBc2zobbSiGEyJdUNpUqVUpt3LjR6LbExEQ1cuRI5eTklN3D5bno6GgFqOjoaGunYlX37ilVooRSoJSdnVJ//pn3OVy5oj2+TqdUSkreP74onPKyDShevLgqVqyYCgkJUYcOHco05tatWyowMDDXc3kY0l4WHEePKlWzptbmglKvvqpUbKy1sxK2LKc+/1WqVFFLly5VSinl4eGhzp49q5RSasKECeqNN97I1rEaNmyohgwZYriekpKi/Pz81NSpUzON7969u2rfvr3RbUFBQerVV19VSil1/fp1dfToUaNLmTJl1JgxY9SJEycszkvaStu0Y4fW3lWoYO1MREFm7c//vXv31N9//63u3btnlce3toULFyovL68cO9758+cVYPK8Pa+PY4mwsDBVsmRJBajVq1fn+uNZ09atWxWgbt26ZfE+zZs3V8OGDTMbExAQoD755JOHzuvB37eleWb1uHn5PnqQpW1Ltr9uOXr0KD4+Pka3OTo6Mn36dNq3b/8otU2Rhxo2hGvXtO2vv4amTfM+h9T5bJSC6GiZ30YUPJ988gndunXDxUzX36JFi3L+/Pk8zEoUJno9zJ4No0drPRR9fOCbb7TFt4TICxcvXqRJkyYAuLq6cufOHQB69+5N48aNM6y4bE5oaCh9+/alfv36NGzYkFmzZhEXF0f//v0B6NOnD35+fkydOhWAYcOG0bx5c2bMmEG7du1YtmwZ+/fvZ968eQAUL16c4qnDJe5zdHSkVKlSVKlS5ZGfu7Cu1B6Kcn4phG2KjIxkypQpbNiwgUuXLlGyZElq167N8OHDeeaZZ6ydnsX69evH7du3WbNmjeE2f39/rly5kqFuktOOHz/Ou+++y+rVq2ncuDHFpMGzCU2aNOHKlSt4eXkBsGjRIoYPH87t1JXCLJRX76NHke2Corkn07x580dKRuSNLl3g6FFte+RIuH8enuecncHdHeLitGHP0v6JDJSC1PlZ3dzMrhahlDLM5erm5pat+bByS+/eva2dgijELl/W2vdff9Wut22rFRNNjA4V+ZkNt5WlSpXi5s2bBAQEUK5cOXbv3k2tWrU4f/48SqlsHatHjx5cu3aNiRMnEhkZSe3atdm0aZNh4ZWLFy9il25sa5MmTVi6dCnvvPMO48ePp1KlSqxZs4bq1avn6HMUtin9kGchAJtuKwubCxcu0LRpU4oWLcr06dOpUaMGSUlJbN68mTfeeIMTJ05YO8VHYm9vb3I6jpx09uxZADp27GjyPZqYmIiTk1Ou5yLSODk55cjvP6/eR49CZhQpZCZOhJ9+0rbbtIHp062bT2rHAFmYRWTq7l3w8NAuWSz8dPfuXTw8PPDw8JBFokSht3q1tvDKr79qc+POng3r10sxscCy4bby6aefZt26dQD079+fESNG0Lp1a3r06EHnzp2zfbwhQ4bwzz//kJCQwJ49e2jUqJHhvoiICBYtWmQU361bN06ePElCQgLHjh2jbdu2Zo9/4cIFhg8fnu28hO1J7QgiX1gLAxtuKwubkJAQdDode/fupUuXLlSuXJknnniC0NBQdu/ebYibOXMmNWrUwN3dHX9/f0JCQoiNjTV77J9//pkGDRrg4uKCj4+P0d8anU5n1JMQtNFCD/7tSJWSksLAgQMpX748rq6uVKlShU8//dRw/6RJk1i8eDFr165Fp9Oh0+mIiIjgwoUL6HQ6Dh8+bIjdtm0bDRs2xNnZmdKlSzN27FiSk5MN97do0YKhQ4cyevRovL29KVWqFJMmTTL5PCdNmkSHDh0AsLOzMxQU+/XrR6dOnZgyZQplypQx9Lg/evQoTz/9NK6urhQvXpzBgwcbvZap+33wwQf4+vpStGhR3nvvPZKTkxk1ahTe3t6ULVuWhQsXmn399Xo906ZNo2LFijg7O1OuXDmmTJkCaOcEQ4YMMYq/du0aTk5OhIeHA5CQkMCYMWPw9/fH2dmZihUr8s0332T6WDdu3KBnz574+fnh5uZGjRo1+OGHHzLEJScnM2TIELy8vPDx8WHChAlmv9S8ffs2r7zyCiVKlMDT05Onn36aI0eOmH3e6UVERKDT6bh9+zYRERH079+f6Ohow3sk/e/17t27DBgwgCJFilCuXDnDKAogw/to0aJFFH3gW7I1a9YYFZMnTZpE7dq1WbBgAeXKlcPDw4OQkBBSUlKYNm0apUqVomTJkobfyaOSGWYLkR9+gMmTte0qVbR/Lq3N2xsuXpSCohBC5ITYWBg+XOuJCFC7NixZAtWqWTMrUZjNmzcPvV4PwBtvvEHx4sXZuXMnzz//PK+++qqVsxMFmQx5FoVdXFycyfvs7e2NpuQxF2tnZ4erq6vZWHd3d4vzunnzJps2bWLKlCmZ7pe+YGJnZ8dnn31G+fLlOXfuHCEhIYwePZovvvgi02Nv2LCBzp078/bbb/Ptt9+SmJjIxo0bLc7tQXq9nrJly7Jy5UrD36/BgwdTunRpunfvzsiRIzl+/DgxMTGGQpu3tzeXL182Os6lS5do27Yt/fr149tvv+XEiRMMGjQIFxcXo+LS4sWLCQ0NZc+ePezatYt+/frRtGlTWrdunSG3kSNHEhgYSP/+/bly5YrRfeHh4Xh6erJlyxZA+50FBwcTFBTEvn37iIqK4pVXXmHIkCFGxdTff/+dsmXL8scff7Bjxw4GDhzIzp07eeqpp9izZw/Lly/n1VdfpXXr1pQtWzbT12zcuHHMnz+fTz75hCeffJIrV64YepymPuaMGTNwdnYG4Pvvv8fPz4+nn34a0KYv2bVrF5999plhRMP169czfaz4+Hjq1avHmDFj8PT0ZMOGDfTu3ZvHHnuMhg0bGr2uAwcOZO/evezfv5/BgwdTrlw5Bg0alOlxu3XrhqurK7/88gteXl589dVXPPPMM5w6dQpvb+9M9zGlSZMmzJo1i4kTJ3Ly5EkAPDw8DPfPmDGDyZMnM378eFatWsXrr79O8+bNH2nqlbNnz/LLL7+wadMmzp49S9euXTl37hyVK1dm27Zt7Ny5kwEDBtCqVSujL2YfSu5P52hbrD1xrrXs26ctvgJKeXsrFRdn7Yw0Tz+t5bRkibUzETYpNjZtFYksVpCIjY1VgAJUrJnYwtoGPAx5rfKX3buVeuyxtMWuRo9WKiHB2lmJPCFtpVXJa2WbRozQPhKjR1s7E2EzCmBbaW7hhNT8M7u0bdvWKNbNzc1kbPPmzY1ifXx8MsRkx549exSgfvrpp2w/35UrV6rixYsbrj+4KEtQUJDq1auXyf3JZOESLy8vtXDhQqWUZYtgvPHGG6pLly6G63379lUdO3Y0innwOOPHj1dVqlRRer3eEDNnzhzl4eGhUu6vTtq8eXP15JNPGh2nQYMGasyYMSZzWb16dYbXv2/fvsrX11clpDsJnDdvnipWrJjRe3nDhg3Kzs5ORUZGGvYLCAgw5KOUtrBas2bNDNeTk5OVu7u7+uGHHzLNJyYmRjk7O6v58+dnev+9e/dUsWLF1PLlyw231axZU02aNEkppdTJkycVoLZs2ZLp/pYsdtKuXTv11ltvGa43b95cVa1a1ei1HzNmjKpatarhevrFUbZv3648PT1VfHy80XEfe+wx9dVXX2X6mFktymJq8aCAgAD18ssvG67r9XpVsmRJ9eWXX2Z63MyO8+B7ICwsTLm5uamYmBjDbcHBwSowMDDD79bUonZKWb4oi00MeZ4zZw6BgYG4uLjQqFEj9u7da9F+y5YtQ6fT0alTp9xNMJ+LjISnntIm53dygoMHtWlDbEHqkOcbN6ybhxBC5FfJyfDee9riWmfPgr8//P47fPSR1uYLYW23bt3i448/ZuDAgQwcOJAZM2ZwU4YmiFwmPRSFsE0qG/Pn/vbbbzzzzDP4+flRpEgRevfuzY0bN0wOQz98+HCOL+gyZ84c6tWrR4kSJfDw8GDevHlcvHgxW8c4fvw4QUFBRkNTmzZtSmxsLP/995/htpo1axrtV7p0aaKiorKdc40aNYzmTTx+/Di1atUy6hHatGlT9Hq9odccwBNPPGE0F7Gvry81atQwXLe3t6d48eImczp+/DgJCQkmfwcuLi707t2bBQsWAHDw4EGOHTtGv379AO33Z29vb/HaHCkpKUyePJkaNWrg7e2Nh4cHmzdvzvD7ady4sdFrHxQUxOnTp0lJSclwzCNHjhAbG0vx4sUN0x54eHhw/vx5w5yVOSn971yn01GqVKmH+p2nFxgYSJEiRQzXfX19qVatWobf7aM+DtjAkOfly5cTGhrK3LlzadSoEbNmzSI4OJiTJ09SsmRJk/tduHCBkSNH0qxZszzMNv9JTNSGvN27p807vGULBARYO6s0qT2G5f8KIWxX3LVr2MfHZ7jd3skJl3TDUuLM/FGyc3DANd0QgezE3r1+HXV/yOSDdHZ2uKVbLCw7sfdu3kSfbu6aB7mn+xuUndj427dJSUzMkVg3Hx909//4J8TEkPzA7+HWLejVC/YfgBR8ePFFO774AtzsY4iLyvg7S+Xq7Y2dg3YKkBgbS5KZ+aGyE+tStCj2909gsxObdPcuiWbmRHL29MTh/tCs7MQmx8eTEBNjMtbJwwPH+9+wZSc2JTGReDMr9Tm6ueF0fzhLdmL1ycncM/MH0WRsXByp/yLERUWBuzsOLi44e3oCoPR67t4fLpR+qJq52BP/u2Myj+z4448/eP755/H09KR+/foAfPbZZ7z33nv8/PPPPPXUUznyOEI8SAqKorAzN9egvb290XVzhYX0RQjQ/g9/FJUqVUKn02W58MqFCxdo3749r7/+OlOmTMHb25s///yTgQMHkpiYiFsmPWTSD83OjE6ny1DQTEpKMhm/bNkyRo4cyYwZMwgKCqJIkSJMnz6dPXv2mH2ch+Xo6JghX72J81pzsjMEPavHz05OWb3+oA17rl27Nv/99x8LFy7k6aefJuB+gcKS/dObPn06n376KbNmzTLMtTl8+HASzZxbZyU2NpbSpUsTERGR4b4H5y/MCdl5fe3s7Cx6/z7q7zE7rF5QnDlzJoMGDaL//aWG586dy4YNG1iwYAFjx47NdJ+UlBR69erFu+++y/bt27O9/HZh0rgxXL2qbc+dq/VUtCVSUBTC9rlXrEhmpyX7SpSgQfoTUF/fTOMADnt5UTtdW32vVCl8THxD/bebG9XSFT1ulipF2Uy+QQQ44+xMxXRFtstly1IxISHT2P/s7XFLVxQ87+9PNRMFr+s6He7p/sierFCB2tHRmcbGgTaA6r6jlSvT4Nq1TGN5IPZQ9eoEXbpkMjTu6lVDAXJf3bo8+cA3o+7AtvvbCz/6m/6jqwKwrUZTmh87ZvK4/23fTtknnwRgZ8uWtNi/32TsmTVrqNixoxbbvj0ttm0zGfv3okVU69tXi+3RgxZm5i06/Mkn1L6/+MXOfv1ovnKlydh9YWE0uD/H0J6hQ3nSxOTcALtGjCBo5kxtv/HjCfrkE5Oxfw4cyJNffw3AoQ8/pMG775qM3datG81XrADg6BdfUHvECJOxEW3b0mLDBgBO/vAD1e5/855pbPPmtLh/0npuwwYqmhl1EVG/Pi327QPg8u7dlM3kS1X3ChW0fKtXp/nRowDcOHkSn0wm0nSvUAF34M/HHuPJM2cArSjv7uvL4yazyJ433niD7t278+WXXxr+gU1JSSEkJIQ33niDo/dzFCKnSUFRFHbZKSrlVmxmvL29CQ4OZs6cOQwdOjTD8W7fvk3RokU5cOAAer2eGTNmGIqaK+7/HTalZs2ahIeHG2oLDypRooTRfIOnT582u+jOjh07aNKkCSEhIYbbHuyl5uTklGlPt/SqVq3Kjz/+iFLK0FNux44dFClSxORchDmpatWqLFq0iLi4OMPrvWPHDuzs7B5prr4HVapUCVdXV8LDw3nllVcyjalRowb169dn/vz5LF26lNmzZxvdp9fr2bZtG61atcry8Xbs2EHHjh15+eWXAW3Oy1OnTlHtgXOeBwvAu3fvplKlShkK6wB169YlMjISBwcHAgMDs8zBEpa8RyxRokQJ7ty5Y/R7TL/wjzVYdchzYmIiBw4cMHqz2NnZ0apVK3bt2mVyv/fee4+SJUsycODAvEgz33rxRTh0SNsePhwGD7ZqOpmSgqIQQuSM+wv9CfHQkpIgLCxnj3nmzBneeusto5N2e3t7QkNDOXO/iClEbkgtKOZChxIhxCOaM2cOKSkpNGzYkB9//JHTp09z/PhxPvvsM4KCggCoWLEiSUlJfP7555w7d47vvvuOuXPnmj1uWFgYP/zwA2FhYRw/fpyjR4/y0UcfGe5/+umnmT17NocOHWL//v289tprGXpupVepUiX279/P5s2bOXXqFBMmTGDf/S/2UgUGBvLXX39x8uRJrl+/nmmPsZCQEP7991/efPNNTpw4wdq1awkLCyM0NDRDD9Dc0KtXL1xcXOjbty/Hjh1j69atvPnmm/Tu3RtfX98cexwXFxfGjBnD6NGj+fbbbzl79iy7d+/OsErzK6+8wocffohSymgV7sDAQPr27cuAAQNYs2YN58+fJyIiwmQhuVKlSmzZsoWdO3dy/PhxXn31Va6m9qZK5+LFi4SGhnLy5El++OEHPv/8c4YNG5bpMVu1akVQUBCdOnXi119/5cKFC+zcuZO3336b/Wa+gDcnMDCQ2NhYwsPDuX79+kOvHN+oUSPc3NwYP348Z8+eZenSpSZXKM8rVu2heP36dVJSUjK8iX19fU12gf7zzz/55ptvLK7EJiQkkJCut0qMmeFMBcl778Hy5dp269ZgpoOGVckcisIse3vo2jVt22yoPV3vx2b2bVNBMGfOHKZPn05kZCS1atXi888/N1rBzJRly5bRs2dPOnbsyJo1a7L9uHFnzmCfbh6OVDUenKDv6lVMrRFYxcH4z41rZKTJ2PIPxHpHRhJnokt+mQdOwsr895/JWO8HYsv/+y9xJoYxPzjgosq5cyZjH1Tj1CnizA15Trdd59gxs7Hph2g3OHiQuAeGPHfvDhHb4LNPYUCVtNjGO3ZkiE2vTLoh5U22biXOzIlNhfSx69ebja2S7j/3JsuXm42tkT520SLi0n1D/aA694fjAjT67DPiPvjAZGyDdLENPviAOBOjHQAapVtlr87YscSl64HwoCbpYmuEhBD30kumY9MNw6rSsydxbdpYFFuhXTviMjkRziy2TOPGabHx8TgPGQJAwuzZ4OJC43SrdxavUsUQGx8fT6f7sfH3Yxu4uHDxIvToAbt3+/AlVxkw4A4sqGgyF0vVrVuX48ePZ+gBkTqfkxC5JbVTvPRQFAZyXmkzKlSowMGDB5kyZQpvvfUWV65coUSJEtSrV48vv/wSgFq1ajFz5kw++ugjxo0bx1NPPcXUqVPp06ePyeO2aNGClStXMnnyZD788EM8PT2NptaYMWMG/fv3p1mzZpQpU4ZPP/2UAwcOmDzeq6++yqFDh+jRowc6nY6ePXsSEhLCL7/8YogZNGgQERER1K9fn9jYWLZu3ZqhZ5ufnx8bN25k1KhR1KpVC29vbwYOHMg777zzkK9g9ri5ubF582aGDRtGgwYNcHNzo0uXLsy8P6IjJ02YMAEHBwcmTpzI5cuXKV26NK+99ppRTM+ePRk+fDg9e/Y0Wm0c4Msvv2T8+PGEhIRw48YNypUrx/jx4zN9rHfeeYdz584RHByMm5sbgwcPplOnTkQ/MKqoT58+3Lt3j4YNG2Jvb8+wYcMYbKK3lU6nY+PGjbz99tv079+fa9euUapUKZ566qmHLr42adKE1157jR49enDjxg3CwsKMVve2lLe3N99//z2jRo1i/vz5PPPMM0yaNMnkc8kLOpWdWVFz2OXLl/Hz82Pnzp2GbyIARo8ezbZt2zJ0Tb1z5w41a9bkiy++oM39k/N+/fpx+/Ztk/8kT5o0iXczGcIUHR2NZ7p/OgqSVaugWzdtu2JFOHkS8uCLj4eydi106gSNGsHu3dbORhQGMTExeHl55bs2YPny5fTp08dovtmVK1daNN/sk08+SYUKFfD29s5WQTG/vlaFyRNPwN9/a/PjWjAyRIhM/fILvPyyNlrAywsWLYKnn86Zz//y5csZPXo0b775Jo0bNwa0oUZz5szhww8/pGrVqobYByejzy+krbRNRYpAbCycPq2dDwuRG6z9+Y+Pj+f8+fOUL18+Q2FGCFt14cIFHnvsMfbt20fdunWtnY7IhKVti1V7KPr4+GBvb5+hW+rVq1cpVapUhvizZ89y4cIFOqQb15U6kaSDgwMnT57kscceM9pn3LhxhIaGGq7HxMTg7++fk0/Dphw+rA11Bm2Ix6FDtltMBBnyLISlZL5ZkZnUhQHzYPodUQAlJ2tDnFM7fNarBytWQIUKkFMDOnr27AloXxZndl/qBPk6nS5H5hcSArTh+6nrUUgPRSGEsA1JSUncuHGDd955h8aNG0sxsQCwakHRycmJevXqER4eTqf7k5Dr9XrCw8MZcn84TnqPP/54hsm733nnHe7cucOnn36aaaHQ2dkZZ2fnXMnf1kRFQdOmkJICTk5w8CCkG6Flk6SgKETWUuebHTdunOG27M43u3379rxIVeShO3fSij5+ftbNReQ/V65Az56QusZOSAjMnAk5fcp0/vz5nD2gEBZI//2ZzKEohBC2YceOHbRs2ZLKlSuzatUqa6cjcoDVV3kODQ2lb9++1K9fn4YNGzJr1izi4uIMvXD69OmDn58fU6dOxcXFherVqxvtn7p094O3FzbJyVC7Nty9CzqdNnypfHlrZ5W11DkUb90Cvd62e1MKK4iLS6uKx8aCmVXl4uLi8LgfGxsb+8gr0NmSvJhvFgrvnLP5Veri0F5e2tA+UYg9ZFsJsXh4uDN/ftrohpwWEBCQOwcWwozUBVk8PbOcKk8UJnJeKYRVtWjRAivOuCdygdULij169ODatWtMnDiRyMhIateuzaZNmwz/OF+8eDFPVj7K74KCtN4GALNnw9NPWzcfS6UOQ9HrITpahqUIkRPu3LlD7969mT9/Pj7pFvXIytSpUzOdc1bYJhnuLLJLr4cPP0y7/sQT8OOP8MB6Kbni77//5uLFiyQ+sAjR888/n/sPLgqd1IKinFcKIYQQucfqBUWAIUOGZDrEGSAiIsLsvtZeJtsWvPwypK5gHhKiXfILZ2fty8G4OG3Ys5z4CZFRXsw3C4Vvztn8TgqKIjuuXdPOF379Ne22rVuhRIncfdxz587RuXNnjh49apgvEbRVFAGZN1HkitSCogx3FkIIIXKPdP3L56ZOhSVLtO2WLWHOHOvm8zBShz3fuGHdPISwVennm02VOt9sUFBQhvjU+WYPHz5suDz//PO0bNmSw4cPmywSOjs74+npaXQRtksKisJSf/4JdepoxcT0C/W5ueX+Yw8bNozy5csTFRWFm5sb//vf//jjjz+oX79+ll8aC/GwpIeiEEIIkftsooeieDhr18L48dp2hQrw22/WzedheXvDxYuyMIsQ5sh8s+JBUlAUWVEKZsyAsWO1BduqVIFvv4VGjfIuh127dvH777/j4+ODnZ0ddnZ2PPnkk0ydOpWhQ4dy6NChvEtGFBqpi7JIQVEIIYTIPVJQzKeOHYMuXbRtT084dCj/LmgiKz0LkTWZb1Y8SAqKwpybN6FfP/j5Z+16z54wb562cFteSklJocj9VYN8fHy4fPkyVapUISAggJMnT+ZtMqLQkB6KQgghRO6TgmI+dPMmNG6s9TZwdNTmT8zPIxOloCiEZWS+WZGeFBSFKfv2Qbdu8M8/4OQEn30GgwdrxcS4uLzNpXr16hw5coTy5cvTqFEjpk2bhpOTE/PmzaNChQp5m4woNKSgKIQQQuQ+6c6SzyQnQ82a2j8EOp3W86BSJWtn9WhkDkVhkr09tG2rXeztswi1p23btrRt2xb7LGKFKAikoCgM7reVqm1bvpxnT9OmWjGxQgXYtQtefTWtZ2Jet5XvvPOOYVGo9957j/Pnz9OsWTM2btzIZ599luuPLwonKSiKTMl5ZYGzaNEiw7Q+OeHChQvodDoOHz5sE8exxKRJk/D19UWn07FmzZpcf7zc1q9fPzp16mS43qJFC4YPH261fHJCXr4f8pr0UMxnmjWDS5e07U8/heBg6+aTE6SHojDJxQU2bLAw1IUNFsYKkd/du5f2JYwUFAUuLsT8sIFXXoGV9xdq79wZFi4EL68HQ/O2rQxOd6JSsWJFTpw4wc2bNylWrJhhpWchcpoUFEWm5LzSpkRGRjJlyhQ2bNjApUuXKFmyJLVr12b48OE888wz1k7PYv369eP27dtGxTx/f3+uXLmCj49Prj728ePHeffdd1m9ejWNGzemmDR6NunB90NERAQtW7bk1q1bOVoQtwYpKOYj/fvD7t3a9qBB8Oab1s0np0hBUQghsif1iyU3t4wFI1H4HDkCXbvCmTPg4ADTp8OwYXk/X2JmoqOjSUlJwTv1jz3g7e3NzZs3cXBwkNXkRa5ILSjm8//ThCiwLly4QNOmTSlatCjTp0+nRo0aJCUlsXnzZt544w1OnDhh7RQfib29PaVKlcr1xzl79iwAHTt2NPklXWJiIk5OTrmeizAtr94P1iBDnvOJGTMgdQq0Zs20idULCikoCiFE9qQf7mwLRSNhHUrB119r8yqfOQP+/rB9OwwfbjvvixdffJFly5ZluH3FihW8+OKLVshIFAbSQ1EI2xYSEoJOp2Pv3r106dKFypUr88QTTxAaGsru1B40wMyZM6lRowbu7u74+/sTEhJCbGys2WP//PPPNGjQABcXF3x8fOjcubPhvsyGBRctWtTkXOMpKSkMHDiQ8uXL4+rqSpUqVfj0008N90+aNInFixezdu1adDodOp2OiIiITIe4btu2jYYNG+Ls7Ezp0qUZO3YsycnJhvtbtGjB0KFDGT16NN7e3pQqVYpJkyaZfJ6TJk2iQ4cOANjZ2RkKiqlDhqdMmUKZMmWoUqUKAEePHuXpp5/G1dWV4sWLM3jwYKPXMnW/Dz74AF9fX4oWLcp7771HcnIyo0aNwtvbm7Jly7Jw4UKzr79er2fatGlUrFgRZ2dnypUrx5QpUwz3//vvv3Tv3p2iRYvi7e1Nx44duXDhgtljZsXc7/y7776jfv36FClShFKlSvHSSy8RFRVluD8iIgKdTseGDRuoWbMmLi4uNG7cmGPHjhlibty4Qc+ePfHz88PNzY0aNWrwww8/WPy8078fLly4QMuWLQEMozX69evHt99+S/HixUlISDA6bqdOnejdu/cjvT65SQqK+cDGjTBqlLYdEABZrL2Q78gcisKkuDhwd9cuWawkEBcXh7u7O+7u7sTl9aoDQuQxmT9RxMVB377aiAW7+Dju2bvzzw13GtewrbZyz549hhPn9Fq0aMGePXty/fFF4XT7tvZTCorCSGE7r4yLM32Jj7c89t69rGOz4ebNm2zatIk33ngDd3f3DPenHwJqZ2fHZ599xv/+9z8WL17M77//zujRo00ee8OGDXTu3Jm2bdty6NAhwsPDadiwYbbyS0+v11O2bFlWrlzJ33//zcSJExk/fjwrVqwAYOTIkXTv3p3nnnuOK1eucOXKFZo0aZLhOJcuXaJt27Y0aNCAI0eO8OWXX/LNN9/w/vvvG8UtXrwYd3d39uzZw7Rp03jvvffYsmVLprmNHDnSUNxLfexU4eHhnDx5ki1btrB+/Xri4uIIDg6mWLFi7Nu3j5UrV/Lbb79lWOzx999/5/Lly/zxxx/MnDmTsLAw2rdvT7FixdizZw+vvfYar776Kv+lnohmYty4cXz44YdMmDCBv//+m6VLl+Lr6wtAUlISwcHBFClShO3bt7Njxw48PDx47rnnSExMtOA3klFWv/OkpCQmT57MkSNHWLNmDRcuXKBfv34ZjjNq1ChmzJjBvn37KFGiBB06dCApKQmA+Ph46tWrx4YNGzh27BiDBw+md+/e7N2716LnnZ6/vz8//vgjACdPnuTKlSt8+umndOvWjZSUFNatW2eIjYqKYsOGDQwYMOChXps8oQqZ6OhoBajo6Ghrp2KRv/9WysFBKVCqSBGlbt2ydkY5748/tOdXqZK1MxE2JzZWe3OAtm02NFYBClCxZmLzWxtgTfJa2a6pU7WPRZ8+1s5EWMP//qdUtWrae8DOTqmP37XdttLNzU399ddfGW7/66+/lKur6yMd21ZIW2l7vLy0j8OJE9bORNiUAnheee/ePfX333+re/fuZbwz9blmdmnb1jjWzc10bPPmxrE+PhljsmHPnj0KUD/99FP2nqxSauXKlap48eKG6wsXLlReXl6G60FBQapXr14m9wfU6tWrjW7z8vJSCxcuVEopdf78eQWoQ4cOmTzGG2+8obp06WK43rdvX9WxY0ejmAePM378eFWlShWl1+sNMXPmzFEeHh4qJSVFKaVU8+bN1ZNPPml0nAYNGqgxY8aYzGX16tXqwZJO3759la+vr0pISDDcNm/ePFWsWDGj9/KGDRuUnZ2dioyMNOwXEBBgyEcppapUqaKaNWtmuJ6cnKzc3d3VDz/8kGk+MTExytnZWc2fPz/T+7/77rsMr0NCQoJydXVVmzdvNuSR/vVs3ry5GjZsmMnXIKvf+YP27dunAHXnzh2llFJbt25VgFq2bJkh5saNG8rV1VUtX77c5HHatWun3nrrLaVU1s/7wfdD6mPeeqC48/rrr6s2bdoYrs+YMUNVqFDB6PXKK2bblnSkh6INu30bGjXSVnZ2cIA9ewrmXDAy5FkIIbJHeigWXt9/Dw0awN9/Q+nS8Pvv8NZb1s7KtIYNGzIvk3la5s6dS7169ayQkSjoUlIgOlrblh6KQtgepZTFsb/99hvPPPMMfn5+FClShN69e3Pjxg3u3r2bafzhw4dzfEGXOXPmUK9ePUqUKIGHhwfz5s3j4sWL2TrG8ePHCQoKMprnsGnTpsTGxhr19qtZs6bRfqVLlzYanmupGjVqGM2bePz4cWrVqmXUI7Rp06bo9XpOnjxpuO2JJ57Azi6tROTr60uNGjUM1+3t7SlevLjJnI4fP05CQoLJ38GRI0c4c+YMRYoUwcPDAw8PD7y9vYmPjzfMB5ldWf3ODxw4QIcOHShXrhxFihShefPmABl+h0FBQYZtb29vqlSpwvHjxwFt6PvkyZOpUaMG3t7eeHh4sHnzZsMxsnrelho0aBC//vorl+5Plr5o0SL69etn04vYyaIsNio5GWrVgjt3tHmQ1q6FqlWtnVXuSB3yfOsW6PVgJ2VuIYQwSwqKhc+9e9pCK/Pna9efeQaWLAFfX8CGR+O9//77tGrViiNHjhhOtMPDw9m3bx+//vqrlbMTBVFqMRGkoCgKOXNzDdrbG183V7R68J+zR5zvrlKlSuh0uiwXXrlw4QLt27fn9ddfZ8qUKXh7e/Pnn38ycOBAEhMTcXNzy7CPq6ur2WPqdLoMBc3UYa2ZWbZsGSNHjmTGjBkEBQVRpEgRpk+fnmtTdjg6OmbIV6/XZ/s4mQ0lf9jHz05OWb3+sbGx1KtXjyVLlmS4r0SJEtnMNuvHTB3qHRwczJIlSyhRogQXL14kODg4W0Osp0+fzqeffsqsWbMMc3oOHz7ccIysnrel6tSpQ61atfj222959tln+d///mfzq81L6cZGtWwJqUXz6dOhbVvr5pObUk/29Hrjk0AhhBCZk4Ji4XL6NAQFacVEnQ7CwmDz5vvFRBvXtGlTdu3ahb+/PytWrODnn3+mYsWK/PXXXzRr1sza6YkCKHVBFnd3eOD/YCEKl9T5IjO7uLhYHvtgsSSzmGzw9vYmODiYOXPmZDo/5e37k6AeOHAAvV7PjBkzaNy4MZUrV+by5ctmj12zZk3Cw8NN3l+iRAmjuQZPnz5tsrcjwI4dO2jSpAkhISHUqVOHihUrZuhJ5+TkREpKitm8qlatyq5du4yKmTt27KBIkSKUzYOTuapVq3LkyBGj13vHjh3Y2dkZFm3JCZUqVcLV1dXk76Bu3bqcPn2akiVLUrFiRaOLl5fXQz2mud/5iRMnuHHjBh9++CHNmjXj8ccfN9m7Mv1iQLdu3eLUqVNUvd+ja8eOHXTs2JGXX36ZWrVqUaFCBU6dOmXx835Qau/RzN43r7zyCosWLWLhwoW0atUKf39/i45pLVJQtEGDB8Off2rb/fvb9lCmnODsnPZ3SIY9CyFE1u6PhJCCYiGwahXUqwdHjkCJElohcdKkjJ1LbFnt2rVZsmQJ//vf/9i/fz8LFiygUqVK1k5LFFCpBcWCOE2QEAXFnDlzSElJoWHDhvz444+cPn2a48eP89lnnxmGnlasWJGkpCQ+//xzzp07x3fffcfcuXPNHjcsLIwffviBsLAwjh8/ztGjR/noo48M9z/99NPMnj2bQ4cOsX//fl577bUMPfDSq1SpEvv372fz5s2cOnWKCRMmsG/fPqOYwMBA/vrrL06ePMn169cz7fEYEhLCv//+y5tvvsmJEydYu3YtYWFhhIaGGg0xzi29evXCxcWFvn37cuzYMbZu3cqbb75J7969M1045GG5uLgwZswYRo8ezbfffsvZs2fZvXs333zzjSEPHx8fOnbsyPbt2zl//jwREREMHTrU7EIv5pj7nZcrVw4nJyfDe2jdunVMnjw50+O89957hIeHc+zYMfr164ePjw+dOnUCtPfBli1b2LlzJ8ePH+fVV1/l6tWrFj/vBwUEBKDT6Vi/fj3Xrl0zWm37pZde4r///mP+/Pm2vRjLfVJQtDGffpo2nCkoCBYssG4+eUXmURRCCMskJkLqOYwUFAuuxERtiHO3btr0J08+CYcOQevW1s5MCNuWWlCU4c5C2K4KFSpw8OBBWrZsyVtvvUX16tVp3bo14eHhfPnllwDUqlWLmTNn8tFHH1G9enWWLFnC1KlTzR63RYsWrFy5knXr1lG7dm2efvppo5V4Z8yYgb+/P82aNeOll15i5MiRmQ6dTvXqq6/ywgsv0KNHDxo1asSNGzcICQkxihk0aBBVqlShfv36lChRgh07dmQ4jp+fHxs3bmTv3r3UqlWL1157jYEDB/LOO+9k52V7aG5ubmzevJmbN2/SoEEDunbtyjPPPMPs2bNz/LEmTJjAW2+9xcSJE6latSo9evQw9Ap0c3Pjjz/+oFy5crzwwgtUrVqVgQMHEh8fj6en50M9nrnfeYkSJVi0aBErV66kWrVqfPjhh3z88ceZHufDDz9k2LBh1KtXj8jISH7++WdDT8J33nmHunXrEhwcTIsWLShVqpSh2GjJ836Qn58f7777LmPHjsXX19dotW0vLy+6dOmCh4dHhsewRTqVnVlRC4CYmBi8vLyIjo5+6Ddtbtm8Gdq00ZbKKlsWzp/XFmMpDOrUgcOH4Zdf4LnnrJ2NsBn37mkfCtDeHGbmp7h37x5t7sf+8ssvJueysOU2wNbIa2Wb/vkHAgPByQni47UhsKJguXABevSA1P+BRo+G9983M3xT2kqrktfKtqxcCd27Q7Nm8Mcf1s5G2JQC2FbGx8dz/vx5ypcvj8uDw5iFEFmKiIigZcuW3Lp1i6I20rX9mWee4YknnuCzzz6zWg6Wti2FpFxl+06ehA4dtGKih4c2tKmwFBNBeigKE1xdISLCwlBXIiyMFSI/Sz9/ohQTC57166FPH62XVbFi8O230L59FjtJWymEgfRQFCZJWymEsGG3bt0iIiKCiIgIvvjiC2unY5FCVLKyXTEx0LAhJCVpRcRdu9IKbIWFFBSFEMIysiBLwZSUBO+8A9OmadcbNoQVKyAgwLp5CZHfSEFRCCFEflSnTh1u3brFRx99lKOL5eQmKShamV4PtWtrRUWAH3+E6tWtmpJVSEFRCCEsIwXFgufSJXjxxbQF2YYOhenTtWHtQojskYKiEEIIS7Vo0QJbmQXwwoUL1k4h26SgaGXPPKPNlQgwdSo8/7x187GW4sW1nzduWDcPYWPi4rTJ4kCbVCx1OfBMQ+MIvB974cIF3M3ECpGfSUGxYNmyBV56Ca5fhyJFtMXYunbN5kFsrK184YUXLI796aefcvzxReEmBUVhko21lUIIkd9JQdGKQkLSpvHo3RvGjrVqOlYlPRSFSdevZyPU8lgh8ispKBYMKSnw3nswebI2f3Lt2tpiEhUrPuQBbait9PLyytXjC2FOakHRRubWF7bGhtpKIYTI76SgaCVffAFffqltN2igTbpemElBUQghLCMFxfzv6lXo1QvCw7XrgwfDrFlmFxzNVxYuXGjtFEQhJj0URWFkK0M2hRAFg6Vtil0u5yEyER4OQ4Zo22XKwM6d1s3HFqQOeZaCohBCmJdaUPTzs24e4uH88QfUqaOdC7i5wXffwVdfFZxiYm6bM2cOgYGBuLi40KhRI/bu3Ws2fuXKlTz++OO4uLhQo0YNNm7caLgvKSmJMWPGUKNGDdzd3SlTpgx9+vTh8uXLuf00RC66fVv7KQVFURg4OjoCcPfuXStnIoQoSFLblNQ2xhTpoZjHzp6FNm204U3u7nDkiLayc2GX2kNR5lAUQgjTkpPhyhVtW3oo5i96vbaC89tva9vVqmlDnKtVs3ZmOa9OnTrodDqLYg8ePGjxcZcvX05oaChz586lUaNGzJo1i+DgYE6ePEnJkiUzxO/cuZOePXsydepU2rdvz9KlS+nUqRMHDx6kevXq3L17l4MHDzJhwgRq1arFrVu3GDZsGM8//zz79++3OC9hW6SHoihM7O3tKVq0KFFRUQC4ublZ3P4KIcSDlFLcvXuXqKgoihYtir29vdl4KWXlodhYqF8fkpLA3l7rmejjY+2sbIMMeRZCiKxdvarNvWdvD76+1s5GWOrGDejTB1I7x/XurU17UlDn+O/UqVOuHHfmzJkMGjSI/v37AzB37lw2bNjAggULGJvJRNSffvopzz33HKNGjQJg8uTJbNmyhdmzZzN37ly8vLzYsmWL0T6zZ8+mYcOGXLx4kXLlyuXK8xC5SwqKorApVaoUgKGoKIQQj6po0aKGtsUcKSjmEb1em3A9dRjGypVQs6Y1M7ItqQXFW7e018pOBuMLIUQGqcOdy5TRiorC9u3eDd27w7//gosLfP45DBwIBbkDSVhYWI4fMzExkQMHDjBu3DjDbXZ2drRq1Ypdu3Zlus+uXbsIDQ01ui04OJg1a9aYfJzo6Gh0Oh1FZUWPfEmvlyHPovDR6XSULl2akiVLkpSUZO10hBD5nKOjY5Y9E1NJQTGPPPecNtwZ4P33oXNn6+Zja1ILino9REfLSaC4z85O69abum021I7692PtpCItCihZkCX/UAo+/RRGjdKGqleqpH2ZWKtWLjyYjbeVt2/fZtWqVZw9e5ZRo0bh7e3NwYMH8fX1xc/CyUCvX79OSkoKvg90zfX19eXEiROZ7hMZGZlpfGRkZKbx8fHxjBkzhp49e+Lp6Wkyl4SEBBISEgzXY2JiLHoOIvfduaOdS4KcS4pM2Hhb+ajs7e0tLgIIIUROkIJiHhg2DFJH1Lz4ojZ/kjDm7KwN/YqL04Y9y0mgALRVCvbtszDUlX0WxgqRX0lBMX+4fRsGDIDVq7Xr3brB11+DmRrVo7HhtvKvv/6iVatWeHl5ceHCBQYNGoS3tzc//fQTFy9e5Ntvv82zXMxJSkqie/fuKKX48ssvzcZOnTqVd999N48yE9mROtzZ2VnrESyEERtuK4UQIj/KH1+35GPz5sFnn2nbdevCDz9YNx9bJvMoCiGEeZcuaT+loGi7Dh6EevW0YqKjozbEefnyXCwm2rjQ0FD69evH6dOncUlX4Wnbti1//PGHxcfx8fHB3t6eq1evGt1+9epVk3P8lCpVyqL41GLiP//8w5YtW8z2TgQYN24c0dHRhsu///5r8fMQuUvmTxRCCCHyjk0UFOfMmUNgYCAuLi40atSIvXv3moydP38+zZo1o1ixYhQrVoxWrVqZjbemiAh47TVtu1QpMDHFj7hPCopCCGGe9FC0XUrB3LnQpAmcOwcBAbBjBwwZUrDnS8zKvn37ePXVVzPc7ufnZ3LocWacnJyoV68e4eHhhtv0ej3h4eEEBQVluk9QUJBRPMCWLVuM4lOLiadPn+a3336jePHiWebi7OyMp6en0UXYBikoCiGEEHnH6gXF5cuXExoaSlhYGAcPHqRWrVoEBwebXKUqIiKCnj17snXrVnbt2oW/vz/PPvssl1K7bdiIf/6B4GDtHwxXVzhyBJycrJ2VbUs9h79xw7p5CBty9y4EBmqXu3ezCL1LYGAggYGB3M0iVoj8SgqKtunOHejVC15/HRISoEMHOHQIGjTIowRsuK10dnbOdI7BU6dOUaJEiWwdKzQ0lPnz57N48WKOHz/O66+/TlxcnGHV5z59+hgt2jJs2DA2bdrEjBkzOHHiBJMmTWL//v0MGTIE0IqJXbt2Zf/+/SxZsoSUlBQiIyOJjIwkMTHxEZ61sBZZkEWYZcNtpRBC5EdWLyjOnDmTQYMG0b9/f6pVq8bcuXNxc3NjwYIFmcYvWbKEkJAQateuzeOPP87XX39t+IbaVty9C3XqQGKiNt/vn39CyZLWzsr2SQ9FkYFSWnX+n3+0bbOhin/++Yd//vkHlUVsflVQe3MLy0lB0fYcO6YVDn/4QVt5e/p0WLs2jwsaNtxWPv/887z33nuGlUd1Oh0XL15kzJgxdOnSJVvH6tGjBx9//DETJ06kdu3aHD58mE2bNhkWXrl48SJXrlwxxDdp0oSlS5cyb948atWqxapVq1izZg3Vq1cH4NKlS6xbt47//vuP2rVrU7p0acNl586dOfQKiLwkPRSFWTbcVgohRH5k1UVZEhMTOXDggNG3yXZ2drRq1YpdFo4Pvnv3LklJSXinVqMekNcr8en1WjEx9YTmhx+0uRNF1qSgKIRpqb25586dS6NGjZg1axbBwcGcPHmSkpl8Y5Ham7tJkya4uLjw0Ucf8eyzz/K///3P4lVVhW3R62UORVuzeLHWK/HePfDz0+ZKbNrU2lnZlhkzZtC1a1dKlizJvXv3aN68OZGRkQQFBTFlypRsH2/IkCGGHoYPioiIyHBbt27d6NatW6bxgYGBUigoYKSgKIQQQuQdqxYUr1+/TkpKiuGb5VS+vr6cOHHComOMGTOGMmXK0KpVq0zvz+uV+Nq1g1OntO1Jk6B79zx76HwvdcizFBSFyCh9b26AuXPnsmHDBhYsWMDYsWMzxC9ZssTo+tdff83/27vz+Kaq9H/gn3RfQltKoQuUUqDsm0ApFaQoHYs7UrUgyiKrgsoAA9SvQHErm4KOHRz9QYFxQRlEZwBxsFAGoaxSrWwDSAWxCwhtui/J+f1xm7ShSZuWJDdpPu/XK6/e3Dy595wUniZPzjl3+/btSEtLw8SJE63SZjKvGzekke8KBRAcLHdrHFtpKfDii4B2MsX99wMffww0cQavQ/D19cXevXtx6NAh/PjjjyguLsbAgQONvm8juhMsKBIREVmPrAXFO7VixQps3boV6enpelcOrCsxMRHz5s3T3VepVAgNDbVIe+bPB/bskbafeAJYtswip2mxtCMUuYYikT5rjOYGrD+im5pGO905KEi6ejDJ4/x54MkngawsaVmTpCTglVek6c5k3LBhwzCMwzfJwrQFRT8/WZtBRETkEGRdQzEgIADOzs7Iy8vT25+Xl4egoKAGn7tmzRqsWLEC//nPf9CvXz+jcda6Et+GDcA770jb/foB27ZZ5DQtGqc8ExnW0GhuU6+S2thobkAa0e3r66u7WerLF2oerp8ov88/BwYPloqJgYHA3r3AkiUsJhqyb98+9OrVy+AXE4WFhejduzcOHjwoQ8uoJeMIRSIiIuuRtaDo5uaGQYMG6V1QRXuBlejoaKPPW7VqFV5//XXs2bMHgwcPtkZTG/T998D06dJ2u3bA8ePytsdesaBIZBna0dw7duwwOpobkEZ0FxYW6m5Xr161YiupMSwoyqeiApg9Gxg3DiguBmJipKs433ef3C2zXevWrcP06dMNfpHr6+uLmTNn4h3tN7FEZsKCIhERkfXIPuV53rx5mDRpEgYPHowhQ4Zg3bp1KCkp0a0TNnHiRLRv3x7JyckAgJUrV2Lp0qX49NNP0alTJ93oHKVSCaVSafX2X7kCxMZKFwrz8JA+YLi5Wb0ZLQLXUKR6FAqgV6/a7QZDFehVE6toJNbemGM093fffdfgaG5AGtHt7u5+x+0ly9AWFHlNHev65RdpPeSTJ6X7r7wCLF8OuMj+DqoOG8yVP/74I1auXGn08fvvvx9r1qyx2PnJMRUUSD9ZUCSDbDBXEhHZM9nfDickJOD69etYunQpcnNzMWDAAOzZs0c3te/KlStwcqodSLl+/XpUVlbiiSee0DvOsmXLkJSUZM2mo7RUuoJzRYW0jtJ//wuEhFi1CS0K11Ckery8gNOnTQz1wmkTY+1N3dHcY8aMAVA7mtvY1U4BaTT3m2++iW+//dYmRnPTneEIRev7+mtg0iSgsFD6G/Xxx8ADD8jdKgNsMFfm5eXBtYHFPl1cXHD9+nWLt4McC0coUoNsMFcSEdkz2QuKADBnzhyjH4rT09P17mdnZ1u+QSbQaKR1lLTFry1bgMhIedtk77QFxVu3pNfXSdYJ+US2xd5Hc9OdY0HReqqqgMWLa9dGjo6W1k/ksqKma9++PX7++Wd07drV4OM//fQTgnm5cjIzFhSJiIishyWbZnrsMeDsWWn7lVeACRPkbU9LoC0oajQALy5LpC8hIQFr1qzB0qVLMWDAAGRmZtYbzZ2Tk6OLrzuaOzg4WHfjFEP7xYKidVy9Kq2RqC0mzpsHHDjAYmJTPfjgg1iyZAnKy8vrPVZWVoZly5bh4YcflqFl1FIJwYIiERGRNSmEEELuRliTSqWCr68vCgsLm33F58REYMUKaXvMGGDHDvO1z9EplUBJCXDxItCli9ytIdmVltYO/T1+XJqqYjS0FJE1scePH4eXkVhz5ABHwdfKdggBeHsDZWXMj5a0Zw/wzDPS7ANfXyA1FXj8cblbZQIbzJV5eXkYOHAgnJ2dMWfOHHTv3h0AcO7cOaSkpECtVuOHH36od/V6e8RcaRuKi4FWraTtoiLpPSWRHhvMlURE9swmpjzbky1baouJvXsD27fL256Wxt9fKijevMkPzASpinLmTO12g6ECZ2piHex7EnIAt25JxUSAF2WxhOpqICkJePNN6f7AgcC2bUDnzrI2y3Q2mCsDAwNx+PBhPP/880hMTNSdS6FQIC4uDikpKS2imEi2Qzs60cVF+gKGqB4bzJVERPaMBcUmyMgAapYrQ0AAcOIE1/kzN39/aboZr/RMRFTr2jXpZ0AA4OEhb1tampwc4OmnAe2SzS+8ALz9Nl9ncwgLC8Pu3btx69YtXLx4EUIIREREoDXno5IF1J3uzIvyEhERWR4Liib67Tfgvvuk9f08PIBTp/hhwxLatJF+sqBIRFSL6ydaxv79wPjxQF6eND3yo4+AcePkblXL07p1a93UQSJLKSiQfrJeTUREZB0cX2eC8nJp+lN5uTQicd8+fqizFO2FWbRXzyYiIhYUzU2jAd54A4iNlYqJfftKsw5YTCSyX7wgCxERkXVxhGIjNBpgyBDg+nXpfmoqEB0tb5taMm1BkSMUiYhqsaBoPtevA88+C3z7rXR/yhTg/fcbXJufiOwAC4pERETWxYJiI554AsjKkrYXLgQmTpS3PS0dC4pERPWxoGgehw4BCQnSmpSenkBKSu3ayERk31hQJCIisi4WFBuwZAmwY4e0/eCDwMqV8rbHEXANRdKjUABhYbXbDYYqEFYTq+Bq7NTCsKB4Z4SQLrSyeDGgVgPdu0tXce7bV+6WmQlzJZGuoOjnJ2szyJYxVxIRmRULikZ89pm0vhIA9OgB/Pvf8rbHUXANRdLj5QVkZ5sY6oVsE2OJ7A0Lis136xYweTLwr39J98eNAz78EGjVStZmmRdzJRFHKFLjmCuJiMyKBUUDjh8HnnlG2vb3B06elC7GQpbHKc9ERPWxoNg8J04ATz4pfX50cwPefReYObPRgSlEZIdYUCQiIrIulsluk5sLxMRIF2NxdwdOneJC7dbEgiIRkT6VSroBQPv28rbFXgghrY84bJhUTOzcGcjIAGbNYjGRqKViQZGIiMi6WFCso7ISGDAAKCuTPnB89x3QsaPcrXIs2jUUOeWZAEj/GSMjpVtZWSOhZYiMjERkZCTKGoklsifXrkk//fwApVLWptgFlUqa1jxnjvR3/fHHpZkGAwfK3TILYq4kQkGB9JMFRTKKuZKIyKw45bmOqCggL0/a/vBDYPhwedvjiLQjFG/dkkaJcqq5g9NopDmL2u0GQzU4UROraSSWyJ5opztzdGLjfvxRmuJ84QLg4gKsXg28/LIDjEpkriTiCEVqHHMlEZFZsVxT46mngMxMafvPfwamTZO1OQ5LW1DUaGqn+BEROTKun9g4IYANG4ChQ6ViYmgocPAgMHeuAxQTiQgAC4pERETWxoIigOXLgW3bpO377wfeeUfe9jgyd3fA21va5jqKREQsKDampES6ivO0aUB5OfDgg9L6x0OHyt0yIrImbUHRz0/WZhARETkMhy8ofvEFkJQkbUdEAN98I2tzCLWjFLmOIhERC4oNOXtWWq5kyxZpiYy33gL+/e/a9XiJyDGUlQEVFdI2RygSERFZh0MXFDMzgaeflrb9/IAffuCafbaAV3omIqrFgqJhn3wirat/+jQQFATs2wckJvLvOJEj0o5OdHICWrWSty1ERESOwmHfdl+/DgwbBqjVgJubVEzk1TNtAwuKRES1tFd5ZkFRUl4OzJwJPPOMNN151CjpC8KYGLlbRkRyqTvdmV8qEBERWYfDXuV52DCgtFRarP2bb4DwcLlbRFraqWosKBIAICCgCaGmxxLZC45QrHXxonQV58xM6e/3kiXA0qWAs7PcLbMBzJXkwAoKpJ+c7kyNYq4kIjIbhy0o5uVJP1NSgPvuk7ctpI9rKJKOt7c0nNikUG9cNzGWyF6UldXmQkcvKG7fDjz3HKBSSZ8HP/lEupAagbmSHB6v8EwmYa4kIjIrh54UMGcO8PzzcreCbscpz0REEu10Z29vwNdX3rbIpbISmDsXeOIJqZg4fLg0QpHFRCLSYkGRiIjI+hy2oDhiBPDXv8rdCjKEBUUiIknd6c4KhbxtkcOvvwL33AO8+650f+FC6eIr7dvL2y4isi1111AkIiIi63DYguLXX8vdAjKGayiSTlkZMHKkdCsrayS0DCNHjsTIkSNR1kgskb1w5PUTd+4E7roLOHZMGnX0r38BK1cCrq5yt8wGMVeSg+MIRTIJcyURkVk57BqKvAKc7eIaiqSj0QAHDtRuNxiqwYGaWE0jsUT2whELitXVwKuvSsVDAIiMBL74AujUSdZm2TbmSnJwLCiSSZgriYjMymELimS7OOWZiEjiaAXFa9eA8eOBgwel+y++CKxZA7i5ydsuIrJtLCgSERFZHwuKZHM45ZmISOJIBcW9e4EJE6QLcLZqBWzYADz5pNytIiJ7UFAg/WRBkYiIyHo48ZdsTt0RipxhQESOzBEKimo1kJQExMVJxcT+/YGTJ1lMJCLTcYQiERGR9dlEQTElJQWdOnWCh4cHoqKicOzYsQbjt23bhh49esDDwwN9+/bF7t27rdRSsgbtm0GNBlCp5G0LkS1hrnQ8Lb2gmJ8PjB4NLF8OCAFMnw5kZAAREXK3jGyVufOgEAJLly5FcHAwPD09ERsbiwsXLliyC2QBLCgSERFZn+xTnj///HPMmzcPH3zwAaKiorBu3TrExcXh/PnzaNeuXb34w4cPY/z48UhOTsbDDz+MTz/9FGPGjMEPP/yAPn36yNADMjcPD8DLCygtlUYp+vnJ3SIyByGAqirpggtVVbW32+/X3adWAffUPH/3bqDc2fjzSkpqz/X664BCYTiutFSW7t8x5krHU1kJ5OVJ2+3by9sWS/jvf4Fx44CcHCnnf/AB8OyzcreKbJkl8uCqVavw3nvvYfPmzQgPD8eSJUsQFxeHM2fOwMPDw9pdpGbSFhT5npGIiMh6FEIIIWcDoqKiEBkZiffffx+AdBWt0NBQvPjii1i8eHG9+ISEBJSUlGDnzp26fUOHDsWAAQPwwQcfNHo+lUoFX19f/H7xInxatar3uLObGzzqvBspyc83eiwnFxd4aufnNjG29MYNCCPzeRVOTvAKCGhWbNnNm9BUVxtth3edN9xNiS0vKIC6stIssV4BAVDUXGa7QqVCdXl5vZgBA4Dfc4D0owGIHNJwrJanvz+cXKQaeWVxMaoaqBw1JdbDzw/ONVcEaEpsVWkpKouLjca6+/jApebDyu2xanVtMay6GlB4+EA4e6CqCigrKkd5oUqvWKaNq64G1M5KaJy9UF0NVJSUo7JIpVeE096qqoBqJyWqFV6oqgIqyyqhLimod0ztdqXwQoVQSvcrKoHyAqnop65fuCtXe6FMrZT2VVTDpeomNEYyTRW8UAUlAECBaniidvFML5QgG70BAJ1wGiq0QSV8ah7VwAs3dLECJSirifXEaaiNxAoUoQxdUVhYCB8fH9gLa+dKoDZf2ttr1VJkZwPh4dIFScrLpSJ5S6DRAKtXA//3f1L+6NkT+Oc/gV695G6ZHSspAbR/h/PzAW/vBkJLdMW3/Px8eBuJtcX//+bOg0IIhISEYP78+ViwYAEAoLCwEIGBgdi0aRPGjRtnUrts8bVyNN7e0heGFy8CXbrI3RqyWQ6SK4mIrEXWEYqVlZU4efIkEhMTdfucnJwQGxuLjIwMg8/JyMjAvHnz9PbFxcXhq6++atK5vbt2haE/C8fbtkVk3cJgYKDBOADI9PXFAO0q0ADKgoIQYKQ+e8bLC73qDKG6GRSEDmq1wdiL7u7oWqdw9nuHDuhaUWEw9jdnZ3jVKQpeDg1FLyMFrxsKBbzrFCbPd+6MAYWFBmNLAGlIWY2sbt0Qef26wVjcFnuqTx9EX7tmNLQkL09XgDw+cCCGX7pUL0Y72Wjs/DNo3a0nAGDUl8PwdMHPRo/7/OiDKAscDgCI2Xkvpvxxwmjs7JFfoSj0MQBA9LcP4/n8A0ZjX47ehBvhkwAAg9MT8OffjU8bfanvWmR3movqamDoyclYmr/NaOzTPsuQ7p2Eqirg8cKX8GHVBqOxT+LP+CfeAQA8gVewDWuNxk7GVGzG/wMAPIgV2IXlRmNn40n8DV8AAGLwN6Tjz0Zj/4IHsQa7AACD8RmOY7LR2CTEYDnSAQC9sAunMcZo7BoMRpL3cbi6AmHiCDIL7zEYdx2dkerVB1uGZMHVFWhVfh7bDxqrQHTGDt8uyJhxEa6ugHP5Dbz2TiAAQAXA12hrbJO1cmVFRQUq6uQaVc2aAwMHAs7Od9ABahbtnwF3d2kqcEtx4YI0OhEAnnlGGpnYwGc6MoW3t/4w7QZDvVFiYqwtsUQevHz5MnJzcxEbG6t73NfXF1FRUcjIyDBaUDSWK+fM4RXJ5SBE7ewDTnmmBjlAriQisiZZC4o3btyAWq1GYGCg3v7AwECcO3fO4HNyc3MNxufm5hqMN/amj+zDwe+BG99L2wMbif1mD/BrzXbvRmLT04EzNdudG4k9nAGcqPmsEtJI7E9ZwIEsaTu8kdhCFZBT88/R+HjOWs7OgKsr4KYGUGU8LqANMDRCig2/CiDbeGyXzsCLD0mxvqcA7Dce268fsGUB4OICVHwP4G/GYwcPAs78Qzpu/n4AMxqIHQwUH5e2f/setXOcDejcGdhf08YbZwE0MKKpbQCwapW0XZIP1NRj7ZI1ciUAJCcnY/ny+gVoAzV/sqKiIumKxy2Juzvw/vvA1KktZ+QlWZYl8qD2p7ly5T/+0Xg/yHJatQJ87e0bQyIiIjsm+xqKlmbsTV/JxYtwNjDlue/tXy3n5cHYd1PdXfRfPs/cXKOx4bfF+ufmosTINOYQJ/1r5YT89pvRWP/bYsOvXkWJkWnMnrfd7/7LL0Zjb9f3f/9DSUNTnuts3/Xzzw3G1p2iHfnDDygxMI35yhVg505gnlsAFDVdVJUfwgp1/Vjth9FZHv5wqnmZ1eX7sbK6VO/xuiZ51sZqyndidXX9UZ3a543z9MN4F+m+pvxzvF1VqnfMuttP+vhhgqdUSFNUb8Inmvfh4iLdd3WtLQq6uACv+vrgzVbSNqrew+mqtwzGuboCn/r7wLXmF1hd/hZKVPWndmklK5Vw9dLGLkaJ6gWjsS/WiVVXvoCSgqeNxiZ4ecFNWRMbPx4lyx4wGnt/ndjOnR5CyWN5RmPv9vLSbYcMHYqSPOOxQ+usZ9Wme/cGYyPrxHoFBOhiS4qKgK5djT7PkSUmJuqN5lGpVAgNDcV999X8OyWrc3IC+vQB6qyYYfecnYFHHpGmOhPZI2O5culSaR1okseIERxNT0REZE2yfkQMCAiAs7Mz8m4rCuTl5SEoKMjgc4KCgpoUb+xNn3fbtvA2YZ0LbwOLfJsjtm5RzZyxnk341NmUWI8mrHLdlFh3Hx+4G/g99GwH9Bx8+16fmpsplDU3OWO9am7mjXXx8NCtvWjOWGc3N5P/DTcl1snFpfmx5eVAfLy0vX273ic1hZOT/tqd5eWIr4ndvn073I3Equ3w0541ciUAuLu7w93dvd7+HTsALgtEZMMayJX1Q/Vzpb1ceMQSeVD7My8vD8HBwXoxAwYMMNoWY7ly/nzmSiKb5gC5kojImpwaD7EcNzc3DBo0CGlpabp9Go0GaWlpiI6ONvic6OhovXgA2Lt3r9F4d3d3+Pj46N2IyE6o1dLlnXfvlrYbDFVj9+7d2L17N9SNxNoba+RKIrJjDpArLZEHw8PDERQUpBejUqlw9OhR5kqilsgBciURkTXJPolt3rx5mDRpEgYPHowhQ4Zg3bp1KCkpwZQpUwAAEydORPv27ZGcnAwAePnllxETE4O3334bDz30ELZu3YoTJ07gww8/lLMbREQWxVxJRI7O3HlQoVBg7ty5eOONNxAREYHw8HAsWbIEISEhGDNmjFzdJCIiIrILshcUExIScP36dSxduhS5ubkYMGAA9uzZo1sg+8qVK3Cqs07g3XffjU8//RSvvvoqXnnlFUREROCrr75Cnz595OoCEZHFMVcSkaOzRB5cuHAhSkpKMGPGDBQUFGD48OHYs2cPpzcSERERNUIhhBByN8KaVCoVfH19UVhYyOnPRLaupARQ1qxZWVwMeHs3EFoCZU1scXExvI3EMgeYjq8VkZ1grpQVXysiO8FcSURkVrKuoUhERERERERERET2hQVFIiIiIiIiIiIiMpnsayham3aGt0qlkrklRNSokpLabZWqwSvyldSJValURq/Ip/2/72CrPTQL8yWRnWCulBVzJZGdYK4kIjIrhysoFhUVAQBCQ0NlbgkRNUlISBNCG48tKiqCr6/vnbSoxWO+JLJDzJVWx1xJZIeYK4mI7pjDXZRFo9Hg999/R6tWraBQKORuTj0qlQqhoaG4evWq3S7syz7YBvbBMCEEioqKEBISonc1UKqP+dLy7L0P9t5+gH0whrnSdMyVlsc+2AZ77wNzJRGReTncCEUnJyd06NBB7mY0ysfHxy7/UNfFPtgG9qE+foNsGuZL67H3Pth7+wH2wRDmStMwV1oP+2Ab7L0PzJVERObBr1GIiIiIiIiIiIjIZCwoEhERERERERERkclYULQx7u7uWLZsGdzd3eVuSrOxD7aBfaCWriX8+7D3Pth7+wH2gVq+lvDvg32wDfbeB3tvPxGRrXG4i7IQERERERERERFR83GEIhEREREREREREZmMBUUiIiIiIiIiIiIyGQuKREREREREREREZDIWFC0gJSUFnTp1goeHB6KionDs2LEG47dt24YePXrAw8MDffv2xe7du3WPVVVVYdGiRejbty+8vb0REhKCiRMn4vfff9c7RqdOnaBQKPRuK1askL39ADB58uR6bRs9erRezM2bNzFhwgT4+PjAz88PU6dORXFxcbPab4k+3N5+7W316tW6GHP+Dprah9OnTyM+Pl7XhnXr1jXrmOXl5Zg9ezbatGkDpVKJ+Ph45OXl2UwfkpOTERkZiVatWqFdu3YYM2YMzp8/rxczcuTIer+HWbNmNbsPZDn2nivN3QeA+dLS7WeurMVcaT+YK5kr+d7Scn1gviQiugOCzGrr1q3Czc1NbNy4UZw+fVpMnz5d+Pn5iby8PIPxhw4dEs7OzmLVqlXizJkz4tVXXxWurq4iKytLCCFEQUGBiI2NFZ9//rk4d+6cyMjIEEOGDBGDBg3SO05YWJh47bXXRE5Oju5WXFwse/uFEGLSpEli9OjRem27efOm3nFGjx4t+vfvL44cOSIOHjwounbtKsaPH9/k9luqD3XbnpOTIzZu3CgUCoW4dOmSLsZcv4Pm9OHYsWNiwYIF4rPPPhNBQUFi7dq1zTrmrFmzRGhoqEhLSxMnTpwQQ4cOFXfffbfN9CEuLk6kpqaKn3/+WWRmZooHH3xQdOzYUe91jomJEdOnT9f7PRQWFjarD2Q59p4rLdEHIZgvLd1+5krmSnvDXMlcyfeWlu0D8yURUfOxoGhmQ4YMEbNnz9bdV6vVIiQkRCQnJxuMf+qpp8RDDz2kty8qKkrMnDnT6DmOHTsmAIhff/1Vty8sLMzgH8mmskT7J02aJB577DGj5zxz5owAII4fP67b98033wiFQiGuXbtmE3243WOPPSbuu+8+vX3m+h0I0fQ+mNKOxo5ZUFAgXF1dxbZt23QxZ8+eFQBERkaGTfThdvn5+QKAOHDggG5fTEyMePnll5vcXrIue8+VQjBfGuvD7SyZL5krmStbOuZKCXPlnWO+ZL4kIjI3Tnk2o8rKSpw8eRKxsbG6fU5OToiNjUVGRobB52RkZOjFA0BcXJzReAAoLCyEQqGAn5+f3v4VK1agTZs2uOuuu7B69WpUV1fbTPvT09PRrl07dO/eHc8//zz++OMPvWP4+flh8ODBun2xsbFwcnLC0aNHbaYPWnl5edi1axemTp1a77E7/R00tw/mOObJkydRVVWlF9OjRw907Nixyee1RB8MKSwsBAD4+/vr7f/kk08QEBCAPn36IDExEaWlpWY7J905e8+Vlu4D86Xl2m+OYzJXkrUwVzbcB+ZKy/bBHMdkviQiatlc5G5AS3Ljxg2o1WoEBgbq7Q8MDMS5c+cMPic3N9dgfG5ursH48vJyLFq0COPHj4ePj49u/0svvYSBAwfC398fhw8fRmJiInJycvDOO+/I3v7Ro0dj7NixCA8Px6VLl/DKK6/ggQceQEZGBpydnZGbm4t27drpHcPFxQX+/v5GXwdr96GuzZs3o1WrVhg7dqzefnP8DprbB3McMzc3F25ubvU+UDT0WtzJ+e6URqPB3LlzMWzYMPTp00e3/+mnn0ZYWBhCQkLw008/YdGiRTh//jy+/PJLs5yX7py950pL9oH50rJ/s8xxTOZKshbmSuN9YK7ke0tD57tTzJdERE3DgqIdqaqqwlNPPQUhBNavX6/32Lx583Tb/fr1g5ubG2bOnInk5GS4u7tbu6l6xo0bp9vu27cv+vXrhy5duiA9PR2jRo2SsWXNs3HjRkyYMAEeHh56+235d9ASzZ49Gz///DO+//57vf0zZszQbfft2xfBwcEYNWoULl26hC5duli7mSQDe82VAPOlrfweWhLmSjKGudJ2MFfaBuZLIqKm4ZRnMwoICICzs3O9K5fl5eUhKCjI4HOCgoJMite+6fv111+xd+9evW+RDYmKikJ1dTWys7Ntov11de7cGQEBAbh48aLuGPn5+Xox1dXVuHnzZoPHkaMPBw8exPnz5zFt2rRG29Kc3wHQvD6Y45hBQUGorKxEQUHBHZ/XEn2oa86cOdi5cyf279+PDh06NBgbFRUFALp/byQ/e8+Vlu5DXcyX5m2/OY7JXEnWwlxpWjzAXNkY5svGMV8SETUdC4pm5ObmhkGDBiEtLU23T6PRIC0tDdHR0QafEx0drRcPAHv37tWL177pu3DhAr777ju0adOm0bZkZmbCycmp3nQPOdp/u99++w1//PEHgoODdccoKCjAyZMndTH79u2DRqPR/cG2lT5s2LABgwYNQv/+/RttS3N+B83tgzmOOWjQILi6uurFnD9/HleuXGnyeS3RBwAQQmDOnDnYsWMH9u3bh/Dw8Eafk5mZCQC6f28kP3vPlZbsw+2YL83bfnMck7mSrIW50ngfbsdcaf4+mOOYzJdERC2cnFeEaYm2bt0q3N3dxaZNm8SZM2fEjBkzhJ+fn8jNzRVCCPHss8+KxYsX6+IPHTokXFxcxJo1a8TZs2fFsmXLhKurq8jKyhJCCFFZWSkeffRR0aFDB5GZmSlycnJ0t4qKCiGEEIcPHxZr164VmZmZ4tKlS+Ljjz8Wbdu2FRMnTpS9/UVFRWLBggUiIyNDXL58WXz33Xdi4MCBIiIiQpSXl+uOM3r0aHHXXXeJo0ePiu+//15ERESI8ePHN7n9luiDVmFhofDy8hLr16+vd05z/g6a04eKigpx6tQpcerUKREcHCwWLFggTp06JS5cuGDyMYUQYtasWaJjx45i37594sSJEyI6OlpER0fbTB+ef/554evrK9LT0/X+L5SWlgohhLh48aJ47bXXxIkTJ8Tly5fF119/LTp37ixGjBjRrD6Q5dh7rrREH5gvLf83i7mSudLeMFcyV/K9pWX7wHxJRNR8LChawF//+lfRsWNH4ebmJoYMGSKOHDmieywmJkZMmjRJL/6LL74Q3bp1E25ubqJ3795i165duscuX74sABi87d+/XwghxMmTJ0VUVJTw9fUVHh4eomfPnuKtt97Se1MlV/tLS0vF/fffL9q2bStcXV1FWFiYmD59ut4bDSGE+OOPP8T48eOFUqkUPj4+YsqUKaKoqKhZ7Td3H7T+/ve/C09PT1FQUFDvMXP/DpraB2P/TmJiYkw+phBClJWViRdeeEG0bt1aeHl5iccff1zk5OTYTB+M/V9ITU0VQghx5coVMWLECOHv7y/c3d1F165dxV/+8hdRWFjY7D6Q5dh7rjR3H5gvLf83i7kyVQjBXGlvmCuZK/ne0nJ9YL4kImo+hRBC3OkoRyIiIiIiIiIiInIMXEORiIiIiIiIiIiITMaCIhEREREREREREZmMBUUiIiIiIiIiIiIyGQuKREREREREREREZDIWFImIiIiIiIiIiMhkLCgSERERERERERGRyVhQJCIiIiIiIiIiIpOxoEhEREREREREREQmY0GRmiw7OxsKhQKZmZkmP2fy5MkYM2ZMgzEjR47E3Llz76htCoUCX331FQDT22nKeese15qSkpKgUCigUCiwbt26OzrWpk2b4OfnZ7XzETk65krrYa4ksl/MldbDXElERObEgmILlJubixdffBGdO3eGu7s7QkND8cgjjyAtLU3upllVaGgocnJy0KdPHwBAeno6FAoFCgoKmnysnJwcPPDAA2ZuoWl69+6NnJwczJgxo95jycnJcHZ2xurVq81yrgULFiAnJwcdOnQwy/GIbBlzpYS5sumYK8mRMFdKmCubjrmSiKhlY0GxhcnOzsagQYOwb98+rF69GllZWdizZw/uvfdezJ49W+7mWZWzszOCgoLg4uJyx8cKCgqCu7u7GVrVdC4uLggKCoKXl1e9xzZu3IiFCxdi48aNZjmXUqlEUFAQnJ2dzXI8IlvFXFmLubLpmCvJUTBX1mKubDrmSiKilo0FxRbmhRdegEKhwLFjxxAfH49u3bqhd+/emDdvHo4cOQIAeO655/Dwww/rPa+qqgrt2rXDhg0bAAAajQarVq1C165d4e7ujo4dO+LNN980eE61Wo2pU6ciPDwcnp6e6N69O959912DscuXL0fbtm3h4+ODWbNmobKy0mhfKioqsGDBArRv3x7e3t6IiopCenq6ya9F3akp2dnZuPfeewEArVu3hkKhwOTJk3WxGo0GCxcuhL+/P4KCgpCUlKR3rLpTUwx9I52ZmQmFQoHs7GwAtdNAdu7cie7du8PLywtPPPEESktLsXnzZnTq1AmtW7fGSy+9BLVabXKf6jpw4ADKysrw2muvQaVS4fDhwyY979tvv0XPnj2hVCoxevRo5OTkNOv8RPaMubIWc6VhzJVEzJV1MVcaxlxJROS47vwrNrIZN2/exJ49e/Dmm2/C29u73uPadU6mTZuGESNGICcnB8HBwQCAnTt3orS0FAkJCQCAxMREfPTRR1i7di2GDx+OnJwcnDt3zuB5NRoNOnTogG3btqFNmzY4fPgwZsyYgeDgYDz11FO6uLS0NHh4eCA9PR3Z2dmYMmUK2rRpY/QN5Zw5c3DmzBls3boVISEh2LFjB0aPHo2srCxEREQ06bUJDQ3F9u3bER8fj/Pnz8PHxweenp66xzdv3ox58+bh6NGjyMjIwOTJkzFs2DD86U9/atJ56iotLcV7772HrVu3oqioCGPHjsXjjz8OPz8/7N69G7/88gvi4+MxbNgw3eveFBs2bMD48ePh6uqK8ePHY8OGDbj77rsbbdOaNWvwj3/8A05OTnjmmWewYMECfPLJJ83tJpHdYa40jrmytk3MleTomCuNY66sbRNzJRGRAxPUYhw9elQAEF9++WWjsb169RIrV67U3X/kkUfE5MmThRBCqFQq4e7uLj766CODz718+bIAIE6dOmX0+LNnzxbx8fG6+5MmTRL+/v6ipKREt2/9+vVCqVQKtVothBAiJiZGvPzyy0IIIX799Vfh7Owsrl27pnfcUaNGicTERKPnBSB27NhhsJ379+8XAMStW7f0nhMTEyOGDx+uty8yMlIsWrTI4HENHefUqVMCgLh8+bIQQojU1FQBQFy8eFEXM3PmTOHl5SWKiop0++Li4sTMmTON9mfZsmWif//+9fYXFhYKT09PkZmZqTu/UqnUO/btDLUpJSVFBAYG1osNCwsTa9euNXosInvGXMlcyVxJ1DjmSuZK5koiImoIpzy3IEIIk2OnTZuG1NRUAEBeXh6++eYbPPfccwCAs2fPoqKiAqNGjTL5eCkpKRg0aBDatm0LpVKJDz/8EFeuXNGL6d+/v956LdHR0SguLsbVq1frHS8rKwtqtRrdunWDUqnU3Q4cOIBLly6Z3C5T9evXT+9+cHAw8vPz7+iYXl5e6NKli+5+YGAgOnXqBKVSqbevOef57LPP0KVLF/Tv3x8AMGDAAISFheHzzz9vUpvM0U8ie8Nc2XzMlUSOg7my+ZgriYjIEXDKcwsSEREBhUJhdApJXRMnTsTixYuRkZGBw4cPIzw8HPfccw8A6E3ZMMXWrVuxYMECvP3224iOjkarVq2wevVqHD16tFn9AIDi4mI4Ozvj5MmT9RZyrvvGyVxcXV317isUCmg0GoOxTk5SHb7uG+2qqiqTjtmU8zRkw4YNOH36tN7C4BqNBhs3bsTUqVONPs/Q+ZvygYGoJWCubD7mSiLHwVzZfMyVRETkCFhQbEH8/f0RFxeHlJQUvPTSS/XWuykoKNCtd9OmTRuMGTMGqampyMjIwJQpU3RxERER8PT0RFpaGqZNm9boeQ8dOoS7774bL7zwgm6foW97f/zxR5SVleneWB45cgRKpRKhoaH1Yu+66y6o1Wrk5+fr3pDeKTc3NwBo9mLVWm3btgUA5OTkoHXr1gCkxbOtJSsrCydOnEB6ejr8/f11+2/evImRI0fi3Llz6NGjh9XaQ2RvmCsbxlxJRABzZWOYK4mIyNFxynMLk5KSArVajSFDhmD79u24cOECzp49i/feew/R0dF6sdOmTcPmzZtx9uxZTJo0Sbffw8MDixYtwsKFC7FlyxZcunQJR44c0V2p73YRERE4ceIEvv32W/zvf//DkiVLcPz48XpxlZWVmDp1Ks6cOYPdu3dj2bJlmDNnju6b2bq6deuGCRMmYOLEifjyyy9x+fJlHDt2DMnJydi1a1ezXpuwsDAoFArs3LkT169fR3FxcbOO07VrV4SGhiIpKQkXLlzArl278PbbbzfrWM2xYcMGDBkyBCNGjECfPn10txEjRiAyMlL3e3r//febNL2IyJEwVxrHXElEWsyVxjFXEhGRo2NBsYXp3LkzfvjhB9x7772YP38++vTpgz/96U9IS0vD+vXr9WJjY2MRHByMuLg4hISE6D22ZMkSzJ8/H0uXLkXPnj2RkJBgdE2UmTNnYuzYsUhISEBUVBT++OMPvW+VtUaNGoWIiAiMGDECCQkJePTRR5GUlGS0L6mpqZg4cSLmz5+P7t27Y8yYMTh+/Dg6duzY9BcGQPv27bF8+XIsXrwYgYGBmDNnTrOO4+rqis8++wznzp1Dv379sHLlSrzxxhvNOlZTVVZW4uOPP0Z8fLzBx+Pj47FlyxZUVVXhxo0bFlkXiKglYK40jrmSiLSYK41jriQiIkenEFzowmEVFxejffv2SE1NxdixY+VuDhmQlJSEr776yqpTXwCgU6dOmDt3LubOnWvV8xLZIuZK28dcSSQ/5krbx1xJRETmxBGKDkij0SA/Px+vv/46/Pz88Oijj8rdJGpAVlYWlEol/va3v1n8XG+99RaUSmW9KykSOSLmSvvCXEkkD+ZK+8JcSURE5sIRig4oOzsb4eHh6NChAzZt2sT1UGzYzZs3cfPmTQDSot2+vr4t6nxEtoy50n4wVxLJh7nSfjBXEhGRObGgSERERERERERERCbjlGciIiIiIiIiIiIyGQuKREREREREREREZDIWFImIiIiIiIiIiMhkLCgSERERERERERGRyVhQJCIiIiIiIiIiIpOxoEhEREREREREREQmY0GRiIiIiIiIiIiITMaCIhEREREREREREZmMBUUiIiIiIiIiIiIy2f8HJtdiLXcjq7QAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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7uyd5TmRkJJGRkdbvg4ODMzxPkc1FR8O8eZjNZuYBFkdH+vTpg7Oz8wOfunw5dOumP+AKkZkepn0EaSNFGj1C+5hW8+fDuHEQFKTb1JiYdH8JIYRIP7HtI0B09+7MW7AAIFVtZGQk9O4NS5ak/uUcHWHoUPj444fOWAghhHgk2apD8WFMnjyZDz/80Og0RHYSFQVvvYUjMBQIA7p162b3ZFApmDQJPvhAf1+7NhQtmvqXlCvMxoqOhlTUPMmRpI0UafIQ7ePD+P576NMn5cednPTonMQ3V9ek3zs6pmtquVJubiOFSLXY9hEgqn173ordflAbeesWtG0Lv/+u26tmzfSuQkMhLEzfEm5HRennmc0wZQp07AhVq2b4TyeEEEIkka06FAsWLMj169dt7rt+/Tre3t4pjr4ZNWoUgwcPtn4fHBxM0bT09AjxAFFR8OabEFdQd/BgfYInH2Kzj+Bg8PExOotH8zDtI0gbKbKe336DN97Q2/36wZAhSTsJnbLV2Uv2lxPaSCGyohMn4MUX4dw5/Te2ciU0bWr/OTExehmCPn30zJhBg2DHDrk4LYQQIvNlq1PyOnXqsHnzZpv7fv75Z+rUqZPic1xdXXF1dc3o1EQudfcuvPqqXjPRwQG++AL69zc6K5EbPUz7CNJGiqzl2DFo00ZfqHn5Zd2mysUZIURO9Msv0K6dXtahZEnYuBHKl3/w85ycwMtLX7xev15fhFmzRp+PCiGEEJnJwcgXDwkJ4eDBgxw8eBCA8+fPc/DgQS5cuADokTNvxA1TAPr27cu5c+cYPnw4J06c4Msvv2TFihW8++67RqQvcrkbN6BuXd2Z6OkJP/wgnYki/Uj7KHKbK1egRQu4dw/q1IHvvpPORCFEzrRiBTRvrjsT69WDvXtT15mYUNGiMGyY3h46VBehEkIIITKToR2KBw4coGrVqlSNXfhj8ODBVK1alTFjxgBw9epV64dngJIlS7Jp0yZ+/vlnKleuzLRp0/j6669p1qyZIfmL3G3wYD1VpUgRve5Ny5ZGZyRyEmkfRW5y/76e9nfhApQpAxs2gJ2Z+iKTbd9udAZC5Bw3b+qlcsxm6NJFj1T093+4fQ0frs9DAwNhxoz0zFIIIYR4MJNSShmdRGYKDg7Gx8eHoKAgvL29jU5HZEWhoXrIIZAHXXQgJCSEPHnyWEN27IBGjfR6Nfv3Q/XqhmQq0om0C/HkWAi7UtE+plV0NLz0Evz0E+TPD3/+CaVKpU+64tGtWAEdOwYD0i5I+yjsStA+hl6/jmeBAkDSNrJXL/jmG6hSBQ4cePSR2N9+C1276pc+dQoKFXq0/Ym0kXZBCJGbGTpCUYjsKDoaBgzQ2/36SWeiEEI8LKV0YYGffgIPD72GmHQmZh1//BFfIEcI8ej27tWdiQCzZ6fPsg6dO0Pt2hASAu+//+j7E0IIIVIrWxVlESJTuLrCxo2YzWZWAsrR0aZoxeef68IBfn7w0UfGpSnST0iI0RkIkU08oH1Mq7FjYeFC/aF6xQqoWTPdMhWP6MwZXSAnMlKvbfnjj0ZnJEQWF9s+Arh6e7Mxbju2jTSb4y9IBwTodbjTg4ODnu5cp45uTwcMkIvdQgghMod0KAqRmJMTvPgijkDiZREvX4Zx4/T2lCmQN28m5yYyxKefGp2BENmEnfYxrebPhwkT9PacOXoNRZE13L6t1wW+dQtq1NAjqgoXNjorIbK42PYR9AesFxM1at98A3/9BT4+8Mkn6fvSzzyj12NcuhQGDdKVn02m9H0NIYQQIjGZ8ixEGgwdqkez1amjry6L7O/sWZg1y+gshMhdNm3SS0YAjB4NvXsbm4+IFxkJbdvC6dNQrBj88AM8whKZQgh0J/2oUXp7/HiIXV4xXU2erItZ/f47rFqV/vsXQgghEpMRikIkFh0NS5cSYzazVCmUkxNdunRh1y5nli/XU0tmz9ZfRfY3bJh+y4UQqZBC++js7JzqXRw4AB066Ol/AQHw4YcZmK9IE6WgRw/YtQu8vWHzZihYEIKDjc5MiGwgtn0EiO7QgaUrVgDQpUsXPvjAmTt3oGJF6N8/Y16+aFEYMULPpBk2TBe7cnfPmNcSQgghQKo8G52OyIqSqWJ6504I9erl4fhxeOst+OILQzMU6eTXX6FxY3BwCMZikXYBpI0UD/CIVZ7PndMjvG/cgBde0MuNpaEvUmSw0aP12sBOTnrNxCZN9P3SLmhyHIRdKVR53rUrhOeey4NSsHMnPPdcxqUQFgZly8KlSzBxIrz3Xsa9ltCkXRBC5GYyxkqIVJg9G44fB3//+DW/RPYWE6PXGQLo1cvQVITIFW7dgubNdWdilSp6Sp50JmYdCxbEFxqbOze+M1EI8WgGD9ajf7t0ydjORAAPj/j1GSdNgitXMvb1hBBC5G7SoShEKkyapL9OmQKPPWZoKiKdzJ8PR46Ar2/8ukZCiIwRFgatWsWvy7d5M3h5GZ2ViLNtG/Tpo7ffe09PexZCpI/9+/XAxalTM+f1OnXSRVpCQ2WEohBCiIwlHYpCpEJ4uL6qLIVYcoa7d/XUPtDrt/n6GpuPEDmZ2axH5uzZoy/IbNkChQoZnZWIc+wYvPqqHrX92msyCl+IjDBhQua1eyYTzJihtxct0uvWCiGEEBkh1xZlCQ0NxdHRMcn9jo6OuLm52cSlxMHBAfcEqx2nJTYsLIyUlq80mUx4eHg8VGx4eDgWiyXFPBKuc5WW2IiICMxmc7rEenh4YDKZAIiMjCQmJiZdYt3d3XGIrZQSFRVFtJ1KG3ZjQ0NJvBqYoyPMmQPR0fb36+bmZv29io6OJioqKsVYV1dXnJyc0hwbExNDZGRkirEuLi7WAglpiTWbzURERKQY6+zsjIuLS5pjLRYL4eHh6RLr5OSEq6srAEopwsLCHir2gw90xcVy5aBrV+z+LEKIh6cUDBwI69aBqyts2KD/7kTWcO0atGwJQUFQr56e9iwFxx5MziHlHDLZ2ATnjwnfzwoV9PrbD9pvep5DVqigLxAsXw4DB7ry++9OmExyDpke55CJyTmkECJXU7lMUFCQAlK8tWzZ0ibew8MjxdgGDRrYxPr5+aUYW6NGDZvY4sWLpxhbvnx5m9jy5cunGFu8eHGb2Bo1aqQY6+fnZxPboEGDFGM9PDxsYlu2bGn3uCXUrl07u7EhISHW2ICAALuxN27csMb279/fbuz58+etsUOHDrUbe/ToUWvs2LFjbX92/RlYqdhtQA0ZonOeMmWK3f1u377dut9Zs2bZjd24caM1dsGCBXZjV6xYYY1dsWKF3dgFCxZYYzdu3Gg3dtasWdbY7du3242dMmWKNXbfvn12Y8eOHWuNPXr0qN3YoUOHWmPPnz9vN7Z///7W2Bs3btiNDQgIsMaGhITYjW3Tpo0CVFBQkMrt4tpIORYiWSEhSdrHhG16YlOm6HCTSamVKzMxT/FAoaFK1ayp35/SpZW6eTPl2JzSLiT+fw+osmXLpvr5cg4ZT84htYS/U8mdPwJq2zZjzyFhhVq+XMfKOaQm55BCCJE+cu0IRSHSYsQIozMQQojsZdkyGD5cb0+fDu3aGZuPiBc3DX3/fsiXT69p6edndFaZ4+mnn+aXX36xfh83ykuIjFK7ttEZ6La4dWujsxBCCJHTmJRKYR5EDhUcHIyPjw9XzpzBO5kV4R1dXHBLUHUj9MaNFPfl4OSEe4LF19ISG3brFiqFqSImBwc8EpzZpyU2/M4dLHamdOTJn/+hYiPu3cNsZ+pFWmI9/PwwxU4ViQwOJsbOVIG0xLr7+uIQ+8EgKiSEaDvTE+zGxsRwdd5mPvzQzGrg7aHeTJzcHicnpwfu1+2xx3CMnXoRHRZGVEhIirGu3t44xU6NSktsTEQEkcHBKca6eHriHDuFKS2x5qgoIu7dSzHW2cMDF0/PNMdaYmIIv3MnXWKd3Nxw9fYGQFkshN26labYX36Bzl3AyRF+/x1KldKxoRERFChenKCgILxjn5NbxbWRcixEsmJiYO1azGYzawHl6Ejbtm2TdMps3w7NmkF0NLz7ru5QFFnH4MHw2Wfg4qILsjz7rP34nNIujBs3jnXr1nHw4MGHer71HPLKlWSPg0x5Tj4210x5jonBccMGNm+GDstb4u65mc8+c6Fbt1f1OWQmTnmOExYG1aq5cumSExMmwMiRMuUZSNcpz6GhoRQoUCDbt49CCPFQjBweaQTrtJ0E0xJsbommqygPj+TjQKlE01WUn1/KsYmmq6jixVOOTTRdRZUvn3JsoukqqkaNlGMTTVdRDRqkHJtouopq2TLl2MS/Ru3a2Y9NOD0uIMB+bILpKqp/f/uxCaarqKFD7ccmmK6ixo61H7tvX3xs3Py9lG4JpquoWbPsxyaYrqIWLLAfm2DKs1qxwn5sgukqauNG+7EJpquo7dvtxyaYrqL27bMfm2C6ijp61H5sgukq6vx5+7EJpquoGzfsxyaYrpJwmmZytyCZrmKVU6Y2CuMcPqyUj4/+82rXTimz2eiMREIJ/zUtW5a65+SUdmHs2LHKw8NDFSpUSJUsWVJ17txZ/ffff6l+vvU4XLmi/68kvoWH2z4huZi4W1jYw8eGhqYcGxr68LFhYfbzeNjY8PD0i7VY4mMjItIvNmFDFRn50LEXT4Qof48Q5UGIWvRliFIxManfb8LYqCj7sdHRqY5dtiTaemp/KTDa/n6jouL3G52G2JgY+7GRkQ8XazanX2xERHysxZJusUHXr+eI9lEIIR6GLL0thBBCiHRx6VJ8kY/69WHJEinykZVs3AjvvKO3J07URRtyk9q1a7Nw4UK2bNnCnDlzOH/+PPXr1+f+/fvJxkdGRhIcHGxzA6BwYfD0THp79VXbHeTPn3ycpye0aGEbW6JEyrHPPWcbW758yrE1a9rG1qyZcmz58raxzz2XcmyJEraxLVqkHJtg1gqgj0tKsbEzFKy6drUfm3Ck2Jtv2o9NOIth8GD7sRcuxMe+/7792OPH42MnTbJ57PGnPLkR5kkonrzR3xP+/js+duZM+/vdtSs+dt48+7Fbt8bHLl1qN7ajy1rq1NGHbs0ba+3vd+nS+P1u3Wo/dt68+Nhdu+zHzpwZH/v33/ZjJ02Kjz1+3H7s++/Hx164YD928OD42Fu37Me++WZ8bFiY/dg+fRBCiNwq9y4cc+UKJDcsPXHVPjvTmJN8SgoMTH3ssWN6gEByYqdnWO3fn/rY334DO1NQbPz4Y+pjV6/Wiy6lxpIlsHBhyo8nmF7D3Lkwe3bqYqdPhylTUo5NMB2IiRNh3LjUxb73HgwbBsDNm1CjSgz1gzbQ6TUL4c0U6tw52latqqeWDBwI/funvN8EU53o0we6dUs5NnYqBaAXs2rfPnWxbduCnenRxE79APScw9TG1q9vPzZ2WgsA1aqlPrZcudTHFitmPzbhtEo/v1TH3gz1oLJ3CEHB8OVsCAhIFBsaCuvXp7wvIYRmZ8pzUJDuTLx0Sf/Zr1tn2yQKY/39t+5AtFigZ08YNcrojDJfiwSdeJUqVaJ27doUL16cFStW0LNnzyTxkydP5sMPP8zMFEUOEQOsBfjll/hzSIOYTLo/r1Yt/THhbcMyEUIIkdPk2jUUZZ0LkZw+fWDp/FBC0VfM8wBhQEhIiM0aPyJ76dtX911Xrar75xNfN5B2IZ4cC2FXaKh1RFHC9tHZOQ8tWsCvv0LBgrBnDxQvbmimIoGLF3VhiKtXoUkTXYQl4bWcB8nJ7ULNmjVp0qQJkydPTvJYZGSkzRpywcHBFC1alKAU1lDE0dG2F93Ouog4ONhe3ExLbFiY/QvNCS/GpiU2PNz+heaE50FpiY2IsH9ROi2xHh7xF9MjI/VFjvSIdXePv/gfFaUXgE1DbFQU1K8Wyt7AAgCEnjuHZ+xCzdZzyAft180t/gQlOlrHp8TVNf7CaSpjAwJg6eIYnqsVybZtScckAPpCc1zjEBOjj1tKEsaazfq9S4mzc/xF7LTEWiz6dy09Yp2c4i/QK2U72vURYoNDQ/GRNRSFELlU7h2hKEQif/0FX38N7g8OFdnIoUMwf77enjkzaWeiEOLRWCzQo4fuTPT01J1V0pmYdQQHw4sv6s7EChVg1aq0dSbmZCEhIZw9e5auXbsm+7irq6u1MIONPHlsO8FSkpYLkWmJTdgJmJ6x7mk4A0pLbFqGKqcl1tXVdgZHesW6uNjO4EhF7Mwv4WhggvuTez/Tsl9n59T/oaYydtIkWLXKie37nFj+A3Tq9IAnODnZzgyxx9Ex9b/DaYl1cMiYWJMp/WJTO4NLCCFyIFnZSAj0xce339ZfO3YwOhuRXpSCQYN0h0eHDnpWtxAifY0bp5fdcnLSq2NUrWp0RiJOdLRu+44c0SNHN20CHx+jszLO0KFD2blzJ4GBgfzxxx+0bdsWR0dHOj2wZ0WIlF27BhMmGJ3FgxUpEr/UwfDh9gfoCSGEEKkhIxSFQH8Y/vNPfQHyo4+AFUZnJNLDmjWwY4ce8GBv+U2RVGhoKI7JDOd0dHTELcEIklA70/QcHBxwTzCKJS2xYWFhpLQih8lkwiPBqJu0xIaHh2OxM00v4dIGaYmNiIjAbGeUQlpiPTw8MMXORYuMjCTGzjS9tMS6u7vjEDtNLyoqimg7U+9SjA0NJfE4jenT9c8yfz40bBhFaGjK+3Vzc7P+XkVHRxNlZ5qeq6urdd2xtMTGxMTYTFNNzMXFBefY0TxpiTWbzUTYmabn7OyMS+zoo7TEWiwWwu1M00tLrJOTk3VEncWiePPNGLZudcbDQ7FyZQT58lmsM2sTxiqlCLPTu2DvZ8lOLl26RKdOnbh9+zb+/v48++yz7NmzB39/f6NTE9nYe+/B/ftQvxrw9wPDDTVkiG6rL1yATz+FMWOMzkgIIUS2ZmiNaQMEBQUpQAUFBRmdisgigoOVKlRIKVBq0iSlVEiI/gaUByhAhYSEGJ2mSKPwcKVKlNBv5ejR9mOlXYgXdyxSurVs2dIm3sPDI8XYBg0a2MT6+fmlGFujRg2b2OLFi6cYW758eZvY8uXLpxhbvHhxm9gaNWqkGOvn52cT26BBgxRjPTw8bGJbtmxp97gl1K5dO7uxCdubgIAAu7E3btywxvbv399u7Pnz562xQ4cOtRt79OhRa+zYsWPjf+7YtjFh+wg71YQJOnbKlCl297t9+3brfmfNmmU3duPGjdbYBQsW2I1dsWKFNXbFihV2YxcsWGCN3bhxo93YWbNmWWO3b99uN3bKlCnW2H379tmNHTt2rDX26NGjdmOHDh1qjT1//rzd2P79+1tjR4++H/tWmRW0ShIbEBBgjQ0JCbG73zZt2iiQNlL+V4jE9u2zNolq76/x548h168n26ZnBcuX6zQ9PJS6eNHobLI/aReEELmZTHkWud6kSXptqSeegHffNTobkV6mT9eF14sUgREjjM5GiJzrpZfg/feNzkIktHIlTJjgGfvdIOAHA7MRImdSCt55R2937aqrKGcHHTpAvXp6yvPIkUZnI4QQIjuTKs8iVzt9Wi9SHxUFGzZAq1akWMVUqjxnH1euwJNP6rdy6VLo3Nl+vLQL8eKOxZUzZ/D28kryuKOLC26PPWb9PvTGjRT35eDkhLuv70PFht26hUphurHJwQEPP7+Hig2/cweLnWnBefLnf6jYiHv3MNuZkpuWWA8/P0yx040jg4OJsTPdNC2x7r6+OMROC44KCSHazhTXFGNDQ8kTW7k0rn28feMOvv55U7Vft8cewzF2+m50WBhRISEpxrp6e+MUO70+LbExERFEBgenGOvi6Ylz7DT4tMSao6KIuHcvxVhnDw9cYv93pCXWEhND+J076RLr5ObG3/9606iRLs7ar1cYUycmf9yc3NxwjW3vlMVC2K1bKe43NCKCAsWL5/o2Uv5XiIS+/VZ3JObJA6dOQWGf+PPH0OvX8SygKz5nxXPIAwegZk29vWePrgIvHo60C0KIXM3oIZKZTYali4ReeklP+2jeXCmLJfbOqCilFixQ0V9/rRbOn68WLFigoqKiDM1TpM0bb+j3tU6dBO+rHdIuxLMeiwRTW21uiaY8Kw+P5ONAqURTnpWfX8qxiaY8q+LFU45NNOVZlS+fcmyiKc+qRo2UYxNNeVYNGqQcm2jKs2rZMuXYxP9q27WzH5twelxAgP3YBFOeVf/+9mMTTHlWQ4faj00w5VmNHZvk8WhQC0EtABX1xx/xsVOm2N9vginPatYs+7EJpjyrBQvsxyaY8qxWrLAfm2DKs9q40X5sginPavt2+7EJpjzbzINM7pZgyrM6etR+bIIpz+r8ebuxZ7qMsf6ZtW4eoWJwSDk+wZTnhEt9JHcLkinPSin5XyHi3b+vVOHC+k9k0qTYO2PPH9WCBSoqNFQtWLAgS59Dxp0rPfts6s6VRPKkXRBC5GZSlEXkWps3w8aNujLpjBkQW9cAnJ2hWzecgAAD8xMPZ98+WLxYb8+cmeB9FUKkG5v20UlOJbKCO+TlxU39uHUPqleH7+YE41gy5aJCQoiHN3myng1RqlSC5XJizx8BnIFusdtZ1cSJenmE33+HtWvhlVeMzkgIIUR2I1OeRa4UFQUVK+opKkOHwtSpRmck0oNSULeunr4TEAALF6buedIuxLMeiytXkj8Wjo66bHYcO5WbcXCABJWb0xQbFqbf0OSYTJCgcnOaYsPDwU7lZhJOS0tLbEQE2KncnKZYD4/4nvDISLAz7TpNse7u+jiDbgTtVHlOHBsTHk2HDrD1J8jnC7/+CqVLx8a6uenfi9TsN2FsdLSOT4mra3xnZVpiY2L0sUiJi4v+4J/WWLNZv3cpcXbW8WmNtVj079ojxEZGwgut3fhttyPFiuk2sFBBpf82UuLkpI8b6L8fO7HBoaH4FCiQaW2kb4LlD1LDZDLx999/U7x48QzKSJP/FQLg/HkoV07/3a1bB23aGJ3Rwxs9Gj76SLfn//4b39SI1JN2QQiRmxk+rGD27NlMnTqVa9euUblyZb744gtq2VnVeMaMGcyZM4cLFy7g5+dHu3btmDx5Mm4JP+AK8QCzZunOxPz59cmUjZgY2LoVs9nMVkA5OtKsWTOcZBROlvfdd/qDtKenHj2QExjWRubJY9sJZi8uLftMrYSdgOkZm7DTMj1j03J80xLr6hrf6ZOesS4uqf7kqJxd6P+WC2t/gjyuMfz03lZK/mdm838J2seH2C/OzvGddekZ6+SU+lGTaYl1dEz973BaYh0cHilWKejVF37bDd7esGkTFCoEYEr9fk0PiLXXAZ4B7t27x4wZM/Dx8XlgrFKK/v37Y87kHEXuNXy47kxs3Bhat07wQOz5I0BM48Zs3bYNIEufQw4fDvPnw5kzMGcODBxodEZCCCGyFSPnWy9fvly5uLio//3vf+rff/9VvXv3Vo899pi6fv16svFLly5Vrq6uaunSper8+fNq69atqlChQurdd99N9WvKOhfixg2lfHz0ujFff51MQIK1pDxAASok4ZpmIksKCVGqSJFE6xmlUlZtF6SNFFnBhAn678rBQakflkv7mNWMGaPfEicnpX76KWNeI7PbBZPJlGI7lxxPT0919uzZDMxIk/ZR/PZbfHt4+HCiBxOcP4Zcv67IJm3k3Lk6bV9fpe7cMTqb7EfaBSFEbuZgVEcmwPTp0+nduzfdu3enfPnyfPXVV3h4ePC///0v2fg//viDevXq0blzZ0qUKMELL7xAp06d2LdvXyZnLrKz0aMhKAiqVbMudSNygE8+gcuXoWTJBOsZZXPSRgqjLVwYP4p71ix46SVD0xGJLFoE48fr7a++gqZNjc0nvVgsFvInqIz+IPfv36dUbPVxITKKxQKDBunt3r310jk5QY8e8PTTcOeOXldRCCGESC3DOhSjoqL466+/aNKkSXwyDg40adKEP//8M9nn1K1bl7/++sv64fjcuXNs3ryZli1bZkrOIvs7dEhP7QBdiCVuKS+Rvf33X/w6mJ9+mrYZpVmVtJHCaD/9pD80A4wcCf36GZuPsLV9e/z789570LOnsfkIkdMtXgx//62XFojryM8JnJziz6G++ALOnTM2HyGEENmHYQt63Lp1C7PZTIECBWzuL1CgACdOnEj2OZ07d+bWrVs8++yzKKWIiYmhb9++vPfeeym+TmRkJJEJFlwPDg5Onx9AZDtK6SvLFgt06AD16xudkUgvw4fr+geNGkHbtkZnkz6kjRRG+ucfePVVvSRYly4yaiWrOX5ct3XR0fDaazBhgtEZZazTp0+zfft2bty4gSVRoaQxY8YYlJXITUJCYNQovT16tF6DOydp3lyPcP75Z/1zfv+90RkJIYTIDgyd8pxWO3bsYNKkSXz55Zf8/fffrFmzhk2bNjHBzpn05MmT8fHxsd6KFi2aiRmLrGTtWtixQ49emzLF6GxEevntN1ixQtcqmDEjvuBtbiRtpEgP//0HLVvqD9DPPw//+198wWdhvOvX9fsTFAT16sGCBTn7/Zk/fz7lypVjzJgxrFq1irVr11pv69atMzo9kUt88glcuwZPPAFvv210NunPZNIzPEwmfU6VwkQIIYQQwoZhIxT9/PxwdHTk+vXrNvdfv36dggULJvuc0aNH07VrV3r16gVAxYoVCQ0NpU+fPrz//vs4JHNGPWrUKAYPHmz9Pjg4WD4w50IRETB0qN4eNgyKFzc2H5E+zOb4ioR9+kClSsbmk56kjRRGuHNHj1S5dk2vD7ZmTeqLNouMFxamq8oGBkLp0rBuXc5Y4sGejz76iIkTJzJixAijUxG51H//6c420FODXV2NzSejVKoE3bvri0iDB8Mff+Tui7RCCCEezLBr2i4uLlSvXp1t27ZZ77NYLGzbto06deok+5ywsLAkH4gdYxfBU0ol+xxXV1e8vb1tbiL3+ewzOH8eihQB+UyScyxYAAcPgo9PzlrPCKSNFJkvIgLatIETJ+Dxx2HzZv23JbIGsxlefx327YN8+fT74+dndFYZ7+7du7Rv397oNEQuNnKkbh8bNoSXXzY6m4w1YQJ4eMCePbBqldHZCCGEyOoMG6EIMHjwYAICAqhRowa1atVixowZhIaG0r17dwDeeOMNihQpwuTJkwFo1aoV06dPp2rVqtSuXZszZ84wevRoWrVqZf3QLERiV6/CpEl6+5NPIE+eBzzBxQVmzcJsNvMpYHF0xEWG6GQ5QUG6EAHAuHHg729oOhlC2kiRWSwW3Vn1+++6E/HHH3WnYhLSPhpm+HC9dIeLix6ZWKaM0Rlljvbt2/PTTz/Rt29fo1MRudAff8Dy5Xqk3mefPWDEXmz7CODi6cmsuO1s1EYWLqzbmnHj9AX41q1z7ohMIYQQj87QDsWOHTty8+ZNxowZw7Vr16hSpQpbtmyxFiG4cOGCzWibDz74AJPJxAcffMDly5fx9/enVatWTJTV4oUdI0fqtcCeeQY6d07FE5ydYcAAHAEpapp1ffQR3LwJZcvCgAFGZ5MxpI0UmUEpPb1t9er4zqoKFVIIlvbREF9+CdOn6+1Fi+DZZ43NJ6N9/vnn1u3SpUszevRo9uzZQ8WKFXF2draJfeeddzI7PZFLWCzxy6r06AFVqjzgCbHtI4AzMCCbnpwMHQpz5+qZPbNmwZAhRmckhBAiqzKplObB5VDBwcH4+PgQFBQkU/tygb17dUci6GliNWsam49IH6dPw9NP6wqnmzdDixaPtj9pF+LJsch9pk+P/8C4bJmuGiyyjk2b9Cghi0VX27ZTtD3DZHa7ULJkyVTFmUwmzp07l8HZxJP2MXdZuFCvKejlpc87Yq/l5Qr/+x/07AmPPQZnzuhlFkTypF0QQuRmho5QFCIjWSzxlfi6d09DZ6LZDLt2YTab2QXg6Ej9+vVlymgWMmSI7kxs0eLROxOFyM2+/z6+M3Hq1FR0Jkr7mKn++Qc6dtT/z3r2hFGjjM4oc5w/f97oFEQud/9+/N/b6NGp7EyMbR8BzHXrsuuPPwCyZRsZEAAzZ8Lhw3pdxRkzjM5ICCFEViQjFEWOlfDK8qlTkEJh3KRCQ8HTE4A8QBgQEhJCngcuvigyw08/QbNm4OQER47AU089+j6lXYgnxyL32LkTXngBoqL0xZeZM1NR0VPax0xz6RLUrg1XrkCTJno0dqLZvplG2gVNjkPuMWoUfPyxrqZ+9Ggq1xFM0D6GXr+OZ2wvZHZtI3/+Wf+PcHKCY8dyz7qtaSXtghAiNzOsyrMQGSk4WK+dCPrKcqo7E0WWFh0N776rt996K306E4XIjf79V1crjYqCV15JRbEBkamCg+HFF3Vn4tNP62qrRnUmZlXr169n8eLFRqchcqCzZ+PXLJ0+PfcWJWnaVM8CiYnRBVqEEEKIxKRDUeRIH30E16/rq6lxC2qL7O+rr/RVcj8/GDPG6GyEyJ6uXNEfEu/dg3r14NtvIZvNxsvRoqOhQwc91bBgQb2Goo+P0VllPSNGjLBWvBciPQ0dqi+2NG0KL71kdDbGmjoVHBx0hfnY2dxCCCGElXQoihzn1Kn4tV4++0xXLRXZ3+3bMHas3p4wAfLmNTYfIbKj4GDdmXjxoq6Qvn49uLsbnZWIo5Qefb11K3h4wA8/QPHiRmeVNZ04cQKz2Wx0GiKH2bZNV7p3dJSR26BHSPfqpbeHDNHruQohhBBxpENR5DiDB8cX7HjxRaOzEell7Fi4excqVYLevY3ORojsJyoKXn1Vj3wrUAB+/FEqd2Y1U6fCvHm6E+O776BGDaMzyrru3bvHrFmzjE5D5CAxMTBokN7u3193pgkYP14vDbl/PyxfbnQ2QgghshLpUBQ5yo8/6ulhTk76yrLIGY4e1dOdQY8+lemZQqSNUnqUyS+/QJ48usBHyZJGZyUSWrkyfp2yzz6DNm2MzSer2rZtG507d6ZQoUKMjRu2/hA+/vhjTCYTg+J6kESuN2+ePt/w9YVx44zOJusoUCB+XfJRoyAiwth8hBBCZB3SoShyjMjI+PUSBw7U0/lE9qeULsRiNuviEY0aGZ2RENnPBx/AkiW6M37VKqhWzeiMREJ//gldu+rtt9+WtX8Tu3jxIuPHj6dkyZK88MILmEwm1q5dy7Vr1x5qf/v372fu3LlUqlQpnTMV2dWdO7qIH+gReb6+xuaT1bz7Ljz+OFy4ADNnGp2NEEKIrMLJ6ASESC+ffQanT+srqY9UsMPZGaZMISYmhvGAxckJZymvaZgfftCjqlxd9XRAIUTafPUVTJqkt+fPh+bNH2Fn0j6mu7NnoXVrfVGsVSsZXR8nOjqadevW8fXXX7Nr1y6aN2/O1KlT6dSpE++//z7ly5d/qP2GhITQpUsX5s+fz0cffZTOWYvsavRo3alYoQK8+eZD7iS2fQRw9vBgStx2DmgjPTxg4kQICND/T3r0AH9/o7MSQghhNJNSShmdRGYKDg7Gx8eHoKAgvL29jU5HpJNLl/SIxLAwWLw4fqSHyN4iI/UaRmfP6mk2cZ0i6U3ahXhyLHKW9ev1yF6LBT78UKqjZzV37kDdunDypB41unOnXqssqzGiXcifPz9PPfUUr7/+Ou3btydvbCUuZ2dnDh069NAdigEBAfj6+vLZZ5/RsGFDqlSpwoy4Sm4PIO1jznTokP77s1hg+3Zo2NDojLImiwVq1oS//4YBA0CWMNWkXRBC5GYy5VnkCEOG6M7EZ5+F1183OhuRXj7/XHcmFiqkOxSFEKm3Zw906qQ/BPbqFT+dT2QNkZG6s/fkSShaFDZuzJqdiUaJiYnBZDJhMplwTKeFc5cvX87ff//N5MmTUxUfGRlJcHCwzU3kLErpZQYsFujQQToT7XFwgE8/1dtffQUnThibjxBCCONJh6LI9n79FVas0Cc6X3yhq2M+ErMZ9u/HvGcP+/fsYf/+/ZjN5nTJVaTe9eswYYLenjwZvLyMzUeI7OT0aT19NjwcWraEOXPSoW0EaR/TiVK6Wv3Onbpt27RJXzgR8a5cuUKfPn1YtmwZBQsW5NVXX2Xt2rWYHvIX+eLFiwwcOJClS5fi5uaWqudMnjwZHx8f661o0aIP9doi61q2DHbt0lN64zrLHlps+8j+/Zijoti/f3+OayMbNdL/W8zm+CJSQgghci+Z8iyytehoqFIFjh1Lx+kXoaHWYSJ5gDD0mkt58uRJh52L1OrVC775Rk+v2bNHdxhnFGkX4smxyP6uX9fTaM+dgxo19BS+dBv5Ju1juhg3Tk9Bd3LSFbebNjU6I/uMbhfOnj3LggULWLRoEZcvX6ZTp05069aN559/PtWjF9etW0fbtm1t4s1mMyaTCQcHByIjI5PsKzIyksjISOv3wcHBFC1aVNrHHCIkRC+Xc+UKfPQRvP/+I+4wQfsYev06ngUKxL5OzmojT5zQa02azTJFHIxvH4UQwkgyQlFka7Nm6c5EPz9dlU/kDH/9Bf/7n96eOTNjOxOFyElCQ+Gll3RnYsmSMo02K1q8WHcmgp42mNU7E7OCJ554go8++oj//vuPTZs2ERkZyUsvvUSB2A6b1GjcuDFHjhzh4MGD1luNGjXo0qULBw8eTLZj0tXVFW9vb5ubyDk++kh3JpYqpZfOEanz1FPQt6/eHjJETxcXQgiRO0mVZ5FtXb0KY8fq7cmTwdfX2HxE+lAKBg7UX7t0gTp1jM5IiOwhJkavAXbgAOTLB1u26Kr3IuvYvl2Pvga9LmzPnsbmk904ODjQokULWrRowc2bN1myZEmqn+vl5UWFChVs7suTJw/58uVLcr/I+U6fhunT9faMGZDKWfAi1tixsGSJLtCydKkUQxRCiNxKxv2IbGvECLh/X0+J7dHD6GxEelmxAnbv1usZffyx0dkIkT0oBf366emzbm56ZOKTTxqdlUjo+HFo21Yv1dGxox4dJR6ev78/gwcPNjoNkU0NGqT/Flu00KO6Rdr4+8N77+nt997ThRGFEELkPtKhKLKl33/XV0ZNJpg9W6bE5hRhYTBsmN4eORIef9zYfITILj76CL7+WreFy5fDM88YnZFI6Pp1XRwnKEivb7lwofzfssfX15dbt26lOr5YsWL8999/aX6dHTt2MGPGjDQ/T2RvGzfqiy/Oznp0YroUrMqFBg6EYsXg0iV9HIUQQuQ+MuVZZDsxMboAC+jpYjVrGpuPSD+ffgoXL+oT1KFDjc5GiOxhwQIYM0Zvz5oFbdoYm4+wFRYGrVtDYCA88QSsXy/TKx/k3r17/Pjjj/j4+KQq/vbt2zmqkq7IOBERuiMMYPBgGcn9KNzc9JJDXbrorz17yjIbQgiR20iHosh2vvwSDh+GvHn1CYzIGS5ejJ/iPHUquLsbm48Q2cGWLdC7t94eOVJPexZZh8Wi1xbbt0+v87t5sy4iJh4sICDA6BREDjR1qi5aVaQIfPCB0dlkf6+9pkcn7t+v11X86iujMxJCCJGZpENRZCvXr8Po0Xp78uQM+mDm7Axjx2I2mxkJmB0dcXZ2zoAXEgmNHAnh4VC/PrRvb3Q2QmR9f/0F7dqB2Qyvvw6TJmXCi0r7mCbDh8OaNeDiAuvWyWio1LJI2ViRAQID49vJadPA0zOdXyC2fQRw9vBgbNx2Dm4jHRz0sXzuOZg/H955B8qXNzorIYQQmcWklFJGJ5GZgoOD8fHxISgoCG9vb6PTEWnUrRssWgTVq8PeveDoaHRGIj388QfUq6fXMTpwAKpVy9zXl3YhnhyL7OHcOV0B/cYNaNIENm3SnVYi65gzB/r319tLl0Lnzsbm8yikXdDkOGRvr7wCa9dCw4bw66+ydmJ6iju2LVvq/0e5ibQLQojcTJYEF9nG7t26M9Fk0tOepTMxZ7BY4tcz6tEj8zsThchubt2C5s11Z2LlyrB6tXQmZjWbN8Nbb+ntCROyd2eiEDnB1q26w8vREb74QjoT09snn4CTk277fvnF6GyEEEJkFpnyLLKFxIVYatXKwBezWOD4cSwWC8cBHBwoV64cDlKSM0MsXqxHJXp5wcSJRmcjRNYWFgatWsHp07p40ebNkKkDIqR9fKCDB6FjR32ouneH9983OiMhcrfISD0VF+Dtt6FChQx6odj2EcBStizHT54EyBVtZJkyekT255/DkCHw999y4V8IIXID6VAU2cKcOXDoUCYVYgkPhwoVcABqAWFASEgIefLkyeAXzn3u34dRo/T26NFSHVAIe8xmPdJtzx547DH48UcoXDiTk5D20a5Ll+DFFyEkBBo3hrlzZSSUEEabMQNOndLnGOPGZeALxbaPAOHXr1Mhdju3tJFjxuiLxIcP66/duxudkRBCiIyWsy+XiRwhYSGWSZOkQmZOMmkSXLsGpUvHjx4QQiSllB5Zs349uLrChg2y8H1Wc/8+vPQSXLmi35tVq3SNBiGEcS5d0ssOAEyZAj4+xuaTk+XLF185+4MPIDTU2HyEEEJkPOlQFFmaUtC3LwQF6UIsvXsbnZFIL+fOwfTpenvaNN1JIoRI3qRJeqS2yQTffquroYusIyZGT3M+dEiPgtq8WY8iFY+uQYMGLF68mPDwcKNTEdmMUvDuu7pjq25deP11ozPK+d56C0qW1BdWpk0zOhshhBAZzfApz7Nnz2bq1Klcu3aNypUr88UXX1DLzgJ59+7d4/3332fNmjXcuXOH4sWLM2PGDFq2bJmJWYvMMn06rFunCw7MmyfrseQkQ4dCVBQ0barXhBPJkzZSLFwYP+pj5kxo187QdEQicaNHf/wR3N3hhx+geHGjs8o5qlatytChQ3n77bfp0KEDPXv25JlnnjE6LZENzJunRwo7OsKsWZDDlzHMElxd4eOP9QWWKVP0QIBChYzOKndQShETE4PZbDY6FSFENufo6IiTkxOmVKzbY2iH4vfff8/gwYP56quvqF27NjNmzKBZs2acPHmS/PnzJ4mPioqiadOm5M+fn1WrVlGkSBH+++8/HpNhADnSrl0wYoTenjlTqv/mJL/+Gl9t8bPPZI2xlEgbKX78EXr10tvDh+uOK5G1TJsGX32l27Fly6BmTaMzyllmzJjBp59+yoYNG1i0aBHPPfccpUuXpkePHnTt2pUCsviuSMaBA/FLqUyaBFWrGptPbtK+vT6327NHL1n09ddGZ5TzRUVFcfXqVcLCwoxORQiRQ3h4eFCoUCFcXFzsxpmUUiqTckqidu3a1KxZk1mzZgFgsVgoWrQob7/9NiNHjkwS/9VXXzF16lROnDiB80MuTBQcHIyPjw9BQUF4Z2ppTJEW16/rk7+rV6FLF1iyJBM7nUJDwdMTgDxI0YH0FhOjO4ePHNFTY774wuiMsm67IG1k7rZ/PzRqpJuk11+HRYuywAgbaR9trF4dP2L0s89g0CBD08kwWalduHHjBvPmzWPixImYzWZatmzJO++8w/PPP5/hr52VjoNI2Z07+jzjv/+gdWs90yVTziETtI+h16/jGdvZnRvbyD/+gHr19HE/eBAqVTI6o4xjdLtgsVg4ffo0jo6O+Pv74+LikqpRRUIIkRylFFFRUdy8eROz2UyZMmVwsPMBxLARilFRUfz111+MiivxCjg4ONCkSRP+/PPPZJ+zYcMG6tSpw4ABA1i/fj3+/v507tyZESNG4ChzYXMMsxk6ddKdieXLx4/8EDnD11/rzsS8eTO42mI2J21k7nb2rK4WHBqqlwX45pss0JkobOzZE78m21tvwcCBxuaTG+zbt48FCxawfPly8ufPT7du3bh8+TIvvfQS/fv359NPPzU6RWEwiwW6dtWdiaVK6Qsxcg6Z+erW1SMVV66EYcNg61ajM8q5oqKirBecPTw8jE5HCJEDuLu74+zszH///UdUVBRubm4pxhrWoXjr1i3MZnOSqSoFChTgxIkTyT7n3Llz/Prrr3Tp0oXNmzdz5swZ+vfvT3R0NGPHjk32OZGRkURGRlq/Dw4OTr8fQmSIsWNh+3bIk0evfRN7sTfzODvD0KGYzWbeBsyOjg892kvYuns3fi248eN1RUCRPGkjc68bN6B5c7h5U4/UXr1aryObJUj7COiiUq1bQ0SEruw8Y4Z0WmSUGzdusGTJEhYsWMDp06dp1aoVy5Yto1mzZtZRON26daN58+bSoSj4+GNdFMnVVZ9DZuqKH7HtI4CzhwdD47ZzYRsJ+r1Ytw5++gm2bNH/10TGsTeCSAgh0iq1bYrhRVnSwmKxkD9/fubNm4ejoyPVq1fn8uXLTJ06NcUPy5MnT+bDDz/M5EzFw9q0CSZO1Ntffw3lyhmQhIsLTJ2KI/CxAS+fk334Idy+rUee9u1rdDY5j7SR2V9ICLRsCWfOQIkS+oOxl5fRWSUg7SN37uj3KK7Dd9kyKRiWkR5//HGeeOIJevToQbdu3fD3908SU6lSJWrK4pW53rZtes0+gNmzDVg3MbZ9BHABpsZu51alSul1f6dP1/2sTZqAU7b65CmEEOJBDLuU4efnh6OjI9evX7e5//r16xQsWDDZ5xQqVIgnn3zSZupeuXLluHbtGlFRUck+Z9SoUQQFBVlvFy9eTL8fQqSrW7fgjTf09ltvwWuvGZuPSF/Hj+sTfNBrjclJpX3SRuY+UVHw6qvw11/g56eniKXwVguDREbCK6/AyZNQtChs3GjAKPpcZtu2bRw/fpxhw4Yl25kI4O3tzfbt2zM5M5GVXL0KnTvrKc/dukGPHkZnJEDPSvH1hX//hQULjM5G5BYmk4l169alKnbcuHFUqVLFbkzDhg0ZlM0WSQ4MDMRkMnHw4EGjU3kkO3bswGQyce/ePaNTESkwrEPRxcWF6tWrs23bNut9FouFbdu2UadOnWSfU69ePc6cOYPFYrHed+rUKbvVZ1xdXfH29ra5iaxp1Cg98qNSJTB01pLFAoGBWM6dI/DcOQIDA21+58TDGTxYF2Rp1QpeeMHobLI+aSNzF4tFfwD+6Sfw8NCjtZ980uiskpGL20eloHdv2LlTjxrduBEKFzY6q5xv7NixyX6QCA4OzpRCLCJ7GDJELxdRqZK+eGnIEgSx7SOBgVhiYggMDMxVbWRy8uaFMWP09ujRcP++sfmIrOPmzZv069ePYsWK4erqSsGCBWnWrBm7d++2xqSlYzChq1ev0qJFi3TLdc2aNUyYMCHd9vewFi5cyGOpXMehaNGiXL16lQoVKmRsUiLXM3SxhcGDBzN//nwWLVrE8ePH6devH6GhoXTv3h2AN954w6YgQb9+/bhz5w4DBw7k1KlTbNq0iUmTJjFgwACjfgSRTvbt00UHAL78Uq99Y5jwcChZEocnnuDpJ56gZMmShIeHG5hQ9rd5s14/x9kZpk0zOpvsQ9rI3GPECFi6VI/cXb0aatUyOqMU5OL28cMPYckSPb151aqcXbU0K9m5c2eyI6wjIiLYtWuXARmJrGbHDr30gMmkR8EZVpcitn2kZEnC79yhZMmSuaqNTEm/flC6NFy/DlOmGJ2NyCpeffVV/vnnHxYtWsSpU6fYsGEDDRs25Pbt24+874IFC+Kajh8mfX198cpS68/YFxUVhaOjIwULFsRJpoSJDGZoh2LHjh359NNPGTNmDFWqVOHgwYNs2bLFWoTgwoULXL161RpftGhRtm7dyv79+6lUqRLvvPMOAwcOZOTIkUb9CCIdmM0wYIAe/fHGG1CvntEZifQUFQXvvqu3Bw6EMmWMzSc7kTYyd5g+PX5U9jffyML1WdHixbpDEWDOHBllnRkOHz7M4cOHUUpx7Ngx6/eHDx/mn3/+4ZtvvqFIkSJGpykMFh2tzyFBr81crZqx+YikXFzgk0/09rRpcOmSsfkI4927d49du3bxySef0KhRI4oXL06tWrUYNWoUrVu3BqBEiRIAtG3bFpPJZP0eYM6cOTzxxBO4uLhQtmxZlixZYrP/xCMbL126RKdOnfD19SVPnjzUqFGDvXv32jxnyZIllChRAh8fH1577TXuJxhOm3jK8927d3njjTfImzcvHh4etGjRgtOnT1sfjxtJuHHjRsqWLYuHhwft2rUjLCyMRYsWUaJECfLmzcs777yD2Wy2Pi8yMpKhQ4dSpEgR8uTJQ+3atdmxYwegp/52796doKAgTCYTJpOJcePGWY/VhAkTeOONN/D29qZPnz7JTnn+999/eemll/D29sbLy4v69etz9uzZFN+no0eP0qJFCzw9PSlQoABdu3bl1q1bNsflnXfeYfjw4fj6+lKwYEFrTgCdO3emY8eONvuMjo7Gz8+PxYsXA3r21eTJkylZsiTu7u5UrlyZVatWpZgTwOrVq3n66adxdXWlRIkSTEs0WiXueHTq1Ik8efJQpEgRZsetuxXr3r179OrVC39/f7y9vXn++ec5dOiQ3dcVKVC5TFBQkAJUUFCQ0amIWHPnKgVKeXsrde2a0dkopUJCdEKgPEABKiQkxOissq3p0/XhzJ9fqXv3jM4medIuxJNjkbmWLrU2N+qTT4zOJhVyYfu4fbtSzs76xx4xwuhsjGFEu2AymZSDg4NycHBQJpMpyc3Dw0N98803mZaPUtI+ZkXTpum/zXz5lLp92+BkErSPIdevK3JJG5kaFotSzz6rD09AgNHZpC+j24Xw8HB17NgxFR4ebr3PYtG/jpl9s1hSl3N0dLTy9PRUgwYNUhEREcnG3LhxQwFqwYIF6urVq+rGjRtKKaXWrFmjnJ2d1ezZs9XJkyfVtGnTlKOjo/r111+tzwXU2rVrlVJK3b9/X5UqVUrVr19f7dq1S50+fVp9//336o8//lBKKTV27Fjl6empXnnlFXXkyBH122+/qYIFC6r33nvPur8GDRqogQMHWr9v3bq1KleunPrtt9/UwYMHVbNmzVTp0qVVVFSUUkqpBQsWKGdnZ9W0aVP1999/q507d6p8+fKpF154QXXo0EH9+++/6ocfflAuLi5q+fLl1v326tVL1a1bV/3222/qzJkzaurUqcrV1VWdOnVKRUZGqhkzZihvb2919epVdfXqVXX//n2llFLFixdX3t7e6tNPP1VnzpxRZ86cUefPn1eA+ueff5RSSl26dEn5+vqqV155Re3fv1+dPHlS/e9//1MnTpxI9vjfvXtX+fv7q1GjRqnjx4+rv//+WzVt2lQ1atTI5rh4e3urcePGqVOnTqlFixYpk8mkfvrpJ6WUUhs3blTu7u7WPJVS6ocfflDu7u4qODhYKaXURx99pJ566im1ZcsWdfbsWbVgwQLl6uqqduzYoZRSavv27QpQd+/eVUopdeDAAeXg4KDGjx+vTp48qRYsWKDc3d3VggULrK9RvHhx5eXlpSZPnqxOnjypPv/8c+Xo6GjNSymlmjRpolq1aqX279+vTp06pYYMGaLy5cunbhv+jyTrSK5tSY50KApD3bqllK+vPsGYMcPobGLlwg/MGeXGDaV8fPTh/Ppro7NJmbQL8eRYZJ6tW5VyctJ/HwMHpv5E3FC5rH08dkypxx7TP3KHDkqZzUZnZAwj2oXAwEB1/vx5ZTKZ1P79+1VgYKD1duXKFRUTE5NpucSR9jFruXJFKS8v/fc5f77R2SjpUHyAvXv14TGZlPr7b6OzST9GtwvJfehP8KuYqbe0/KqvWrVK5c2bV7m5uam6deuqUaNGqUOHDtnEJOwYjFO3bl3Vu3dvm/vat2+vWrZsmezz5s6dq7y8vFLsKBo7dqzy8PCwdnAppdSwYcNU7dq1rd8n7FA8deqUAtTu3butj9+6dUu5u7urFStWKKV0hyKgzpw5Y4158803lYeHh03nWrNmzdSbb76plFLqv//+U46Ojury5cs2+TVu3FiNGjXKul8fH58kP0Px4sXVyy+/bHNf4g7FUaNGqZIlS1o7PR9kwoQJ6oUXXrC57+LFiwpQJ0+etB6XZ5991iamZs2aakTs1dfo6Gjl5+enFi9ebH28U6dOqmPHjkoppSIiIpSHh4e1czdOz549VadOnZRSSTsUO3furJo2bWoTP2zYMFW+fHmb49G8eXObmI4dO6oWLVoopZTatWuX8vb2TtKZ/cQTT6i5c+c+4MjkHqntUDR0yrMQ77+vC7FUrBg/ZUXkHKNHQ1AQVK2qqy4KIbT9+3W14JgY6NRJT3s2pIiASNGNG/Dii3DvHtSpAwsXgoOcNWWa4sWLU6JECSwWCzVq1KB48eLWW6FChWyq2YvcadgwXeSjVi2p6pwd1Kql/98pBUOH6q8i93r11Ve5cuUKGzZsoHnz5uzYsYNq1aqxcOFCu887fvw49RKtj1WvXj2OHz+ebPzBgwepWrUqvr6+Ke6zRIkSNmskFipUiBs3bqT4+k5OTtSuXdt6X758+ShbtqxNDh4eHjzxxBPW7wsUKECJEiXw9PS0uS/udY4cOYLZbObJJ5/E09PTetu5c6fdaclxatSoYffxgwcPUr9+fZydnR+4L4BDhw6xfft2m1yeeuopAJt8KiVaUDrhsXNycqJDhw4sXboUgNDQUNavX0+XLl0AOHPmDGFhYTRt2tTmdRYvXpziz5zS+3/69Gmb6eOJC1jWqVPH+v4cOnSIkJAQ8uXLZ/O658+fT9WxFrZklU5hmAMHYN48vT1rli5GIHKOQ4dg/ny9PXOmLmQghIDTp6FlSwgNhSZNpKMqKwoPh9at4fx5KFUK1q8Hd3ejs8o9NmzYQIsWLXB2dmbDhg12Y+PW20qNOXPmMGfOHAIDAwF4+umnGTNmTLpWAxWZ47ffdCErk0lXdZY2NHuYNAnWrIFff9UF+1580eiMciYPDwgJMeZ108LNzY2mTZvStGlTRo8eTa9evRg7dizd0nEUgnsq/nkn7mQzmUyPXJ09uX3ae52QkBAcHR3566+/klwwS9gJmZI8efLYfTw1xyGhkJAQWrVqxSdxC6AmUKhQIev2g45dly5daNCgATdu3ODnn3/G3d2d5rGLhYfE/pJu2rQpyZrI6VlUJ7GQkBAKFSpkXZ8yodRW0RbxpAtHGMJigbfe0lcnu3SB554zOiORnpSCQYP0+9yhA9Svb3RGQmQNV69Cs2Zw6xZUr64/WLm4GJ2VSMhiga5dYe9eyJtXf+j19zc6q9zl5Zdf5tq1a+TPn5+XX345xTiTyWQzIuFBHn/8cT7++GPKlCmDUopFixbRpk0b/vnnH55++ul0yFxkhpgYfQ4J0KcPPGBgjshCSpTQBfqmTNEjTJs1kwEFGcFkggf0L2VJ5cuXtymm4uzsnKSNL1euHLt37yYgIMB63+7duylfvnyy+6xUqRJff/01d+7csTtKMbXKlStHTEwMe/fupW7dugDcvn2bkydPpphDalStWhWz2cyNGzeon8IHJxcXlzT9z0uoUqVKLFq0iOjo6FSNUqxWrRqrV6+mRIkSj1Qpum7duhQtWpTvv/+eH3/8kfbt21tfv3z58ri6unLhwgUaNGiQqv3Fvf8J7d69myeffNKmI3bPnj02MXv27KFcuXLWn+3atWs4OTnZFPsRD0eacGGIhQv1hzUvL5g61ehsEnFygv79MZvN9AJiHB0fqSHNjdasgR07wM1NnzQKIfT0/xYt9Ki30qV1R1WCGTbZQy5oH0eMgNWrdUfvunVQtqzRGeU+CUc3POookYRatWpl8/3EiROZM2cOe/bskQ7FbGT2bDhyBHx9YeJEo7NJILZ9BHByc6N/3HYOayMf1ahR8M03cPy4nsnSr5/RGYnMdvv2bdq3b0+PHj2oVKkSXl5eHDhwgClTptCmTRtrXIkSJdi2bRv16tXD1dWVvHnzMmzYMDp06EDVqlVp0qQJP/zwA2vWrOGXX35J9rU6derEpEmTePnll5k8eTKFChXin3/+oXDhwkmmxaZGmTJlaNOmDb1792bu3Ll4eXkxcuRIihQpYpN7Wj355JN06dKFN954g2nTplG1alVu3rzJtm3bqFSpEi+++CIlSpQgJCSEbdu2UblyZTw8PPBI5bDQt956iy+++ILXXnuNUaNG4ePjw549e6hVqxZlkznRGTBgAPPnz6dTp07WKs5nzpxh+fLlfP3112ladqRz58589dVXnDp1iu3bt1vv9/LyYujQobz77rtYLBaeffZZgoKC2L17N97e3jadxnGGDBlCzZo1mTBhAh07duTPP/9k1qxZfPnllzZxu3fvZsqUKbz88sv8/PPPrFy5kk2bNgHQpEkT6tSpw8svv8yUKVN48sknuXLlCps2baJt27YPnD4uEsmcJR2zDqMXzhVK3b2rlL+/Xrx32jSjsxHpLTxcqRIl9Ps7erTR2aSOtAvx5FhkjPBwpRo21H8XBQoodfas0RmJ5MyZE7+4/NKlRmeTdeTEdiEmJkYtW7ZMubi4qH///TdVz8mJxyG7uXZNKW9v/Tcqa+dnX198od9Df3+lsvufk9HtQmoLJ2QlERERauTIkapatWrKx8dHeXh4qLJly6oPPvhAhYWFWeM2bNigSpcurZycnFTx4sWt93/55ZeqVKlSytnZWT355JM2RT+USlrMJTAwUL366qvK29tbeXh4qBo1aqi9e/cqpXRRlsqVK9s8/7PPPrN5vcRVnu/cuaO6du2qfHx8lLu7u2rWrJk6deqU9fHkiqck9zoBAQGqTZs21u+joqLUmDFjVIkSJZSzs7MqVKiQatu2rTp8+LA1pm/fvipfvnwKUGPHjlVK6SIkn332mc2+ExdlUUqpQ4cOqRdeeEF5eHgoLy8vVb9+fXXWzgnpqVOnVNu2bdVjjz2m3N3d1VNPPaUGDRqkLLFVBBMfF6WUatOmjQpIVMr92LFjClDFixe3PjeOxWJRM2bMUGXLllXOzs7K399fNWvWTO3cuVMplbQoi1K6oE/58uWVs7OzKlasmJo6darNPosXL64+/PBD1b59e+Xh4aEKFiyoZs6caRMTHBys3n77bVW4cGHl7OysihYtqrp06aIuXLiQ4vHIbVLbtpiUyl1L4gYHB+Pj40NQUBDe3t5Gp5MrvfsuzJgB5crpdfZSuTasyCYmTdLFdooUgZMns8eUC2kX4smxSH9mM7z2GqxapUck7typCxWJrOXHH+Gll/SU5wkT4IMPjM4o6zCyXXjnnXcoXbo077zzjs39s2bN4syZM8yYMSNN+zty5Ah16tQhIiICT09PvvvuO1q2bJlsbGRkJJGRkdbvg4ODKVq0qLSPBureXc9yqVED9uyR9Zmzq+hoqFABTp3SIxYnTTI6o4dn9HlTREQE58+fp2TJkri5uWX66wuR1ZQoUYJBgwYxaNAgo1PJ1lLbtsgSxiJTHTsGX3yht2fOzKKdiUrBzZuoGze4eeMGN2/eJJf1uz+0K1fiTwo/+SR7dCYKkZHi1hNdtUq3d+vWZfPOxBzaPh46pNd7tVh0Rfr33zc6IxFn9erVSSo6gl6XadWqVWneX9myZTl48CB79+6lX79+BAQEcOzYsWRjJ0+ejI+Pj/VWtGjRNL+eSD979ujORNDF/LJcZ2Js+8jNmyiLhZs3b+aYNjK9OTvHL4nz2Wdw4YKx+QghhHg40qEoMo1S8M47erTOyy9D06ZGZ5SCsDDInx9TgQKUKFCA/PnzExYWZnRW2cKoUbpybZ060Lmz0dkIYbyPP9YffAGWLIHnnzc2n0eWA9vHS5d0pdGQEP3+zJ2rF7QXWcPt27fx8fFJcr+3tze3bt1K8/5cXFwoXbo01atXZ/LkyVSuXJmZM2cmGztq1CiCgoKst4sXL6b59UT6MJvjC7F07w61axubT7Ji20fy5yfs1i3y58+fI9rIjNK6NTRoABERchFHCCGyK+lQFJlm7VrYtg1cXWHaNKOzEelt3z5YvFhvz5wpH8iFWLgQ3ntPb8+YAR07GpmNSM79+3qa8+XLUL58fDEWkXWULl2aLVu2JLn/xx9/pFSpUo+8f4vFYjOtOSFXV1e8vb1tbsIY33wDf/0FPj4webLR2Yj0YDLBp5/q7W+/hQMHjM1HCJEzBAYGynTnTJTmDsWElXkSmzt37iMlI3Ku8HAYPFhvDx8O6fAZQGQhSsHAgXo7IABq1jQ2HyMFBATw22+/GZ2GMNjmzdCrl94eMSL+70NkHTExupP30CEoUAA2bYLHHjM6K5HY4MGDGT58OGPHjmXnzp3s3LmTMWPGMHLkSN5999007WvUqFH89ttvBAYGcuTIEUaNGsWOHTvo0qVLBmUv0sOdO/EXZz78UP+9ipyhRg14/XW9PWSIPp8UQgiRfaS5Q7F58+YMGzaM6Oho6323bt2iVatWjBw5Ml2TEznH1Knw339QtCjIr0nO8913em2jPHmy98La6SEoKIgmTZpQpkwZJk2axOXLl41OSWSyvXuhfXs9Re+NN2Q0TVakFLz9ti7E4u4OP/wAJUoYnZVITo8ePZg2bRrffPMNjRo1olGjRnz77bfMmTOH3r17p2lfN27c4I033qBs2bI0btyY/fv3s3XrVppm2TVYBMDo0XD7ti7iMWCA0dmI9DZxIri5wW+/wYYNRmcjhBAiLR5qhOLatWupWbMmx44dY9OmTVSoUIHg4GAOHjyYASmK7O6//+I/UH/6KXh4GJuPSF+hoXoEFug1cAoXNjYfo61bt47Lly/Tr18/vv/+e0qUKEGLFi1YtWqVzYUYkTOdOqXX4wsLg+bN4euvZfp/VjRtGnz1lX5vvvsud4+qzg769evHpUuXuH79OsHBwZw7d4433ngjzfv55ptvCAwMJDIykhs3bvDLL79IZ2IWd/Cg/lsFXdTPycnQdEQGKFYM4gYbDx+uK0ALIYTIHtLcoVi3bl0OHjxIhQoVqFatGm3btuXdd99lx44dFC9ePCNyFNnc0KF6weWGDfWoHZGzfPKJXn+sZMn4E8Lczt/fn8GDB3Po0CH27t1L6dKl6dq1K4ULF+bdd9/l9OnTRqcoMsC1a9CsmR5JU6MGrFyZRSvZ53KrV8OwYXp72jRdJExkD/7+/nh6ehqdhsgkSulCLBaLXp6gYUOjMxIZZeRIXc/m1Kn4DmQhhBBZ30MVZTl16hQHDhzg8ccfx8nJiZMnT0oFM5Gs7dth1SpwcIDPP5eROjnNf//p6eygv7q5GZtPVnP16lV+/vlnfv75ZxwdHWnZsiVHjhyhfPnyfPbZZ0anJ9JRcDC0bAmBgfDEE3o9Pun3yHr27Ilfr2vAAJA1u7OHVatW0aFDB5555hmqVatmcxM519KlsHu3ntkSV7xD5Eze3np9TNBf790zNB0hhBCplOYOxY8//pg6derQtGlTjh49yr59+/jnn3+oVKkSf/75Z0bkKLKpmJj4QgT9+kHFisbmk2pOThAQgPn11+n0+usEBATgJHNskjV8ePzo01deMTqbrCE6OprVq1fz0ksvUbx4cVauXMmgQYO4cuUKixYt4pdffmHFihWMHz/e6FRFOomKgldfhX/+AX9/2LpVj7TIkbJx+3juHLRurdusF1/UlbflIlfW9/nnn9O9e3cKFCjAP//8Q61atciXLx/nzp2jRYsWRqcnMsj9+/ocA+CDD+Dxx43NJ1Vi20cCAnBycyMgICBbtZFG69ULypXTo/xz+3rcQgiRbag0KliwoNq8ebPNfVFRUWro0KHKxcUlrbvLdEFBQQpQQUFBRqeS482erRQo5eur1O3bRmcj0tvOnfr9dXBQ6uBBo7N5NOnZLuTLl0/lzZtX9e/fX/3zzz/Jxty9e1eVKFHikV8rI0gbmTZms1Kvv67/FvLkUWr/fqMzEsm5c0epp57S71PVqkrdv290RtmLke1C2bJl1XfffaeUUsrT01OdPXtWKaXU6NGj1YABAzI1F2kfM8+wYfrvtXRppSIijM5GZJaNG/X77uKi1LlzRmeTOka3C+Hh4erYsWMqPDzckNc32oIFC5SPj0+67e/8+fMKSPEcPrP3kxpjx45V+fPnV4Bau3Zthr+ekbZv364Adffu3VQ/p0GDBmrgwIF2Y4oXL64+++yzh84r8fud2jwf9LqZ+XuUWGrbljSPUDxy5EiSK8LOzs5MnTqVn3766VH6NkUOcueOrsoHMH48+Poam49IX2Zz/FTB3r2hcmVD08lSPvvsM65cucLs2bOpUqVKsjGPPfYY58+fz9zERIYYNQq+/VYPTFm1Sq+dKLKWqCg9gvrECT3KaeNGmY6enVy4cIG6desC4O7uzv379wHo2rUry5YtMzI1kUFOntQjiEF/dXU1MhuRmVq2hMaNdbv93ntGZyMy2rVr13j77bcpVaoUrq6uFC1alFatWrFt2zajU0uTbt268XKiBZmLFi3K1atXqVChQoa+9vHjx/nwww+ZO3cuV69elZH7WUTdunW5evUqPj4+ACxcuJDHHnsszfvJrN+jR5HmDkU/P78UH2vQoMEjJSNyjjFjdKdixYrw5ptGZ5NGSkFoKCokhNCQEEJDQ1FKGZ1VlrJggZ7e6eMDEyYYnU3W0rVrV9xkMclc4fPPYcoUvf3117qqc46XzdpHpfQ0uh07wMtLr22Z2yvRZzcFCxbkzp07ABQrVow9e/YAcP78+Sz9uycejlJ6uZzoaL00wYsvGp1RGsS2j4SGoiwWQkNDs3wbmdWYTHq9TJMJli+HvXuNzkhklMDAQKpXr86vv/7K1KlTOXLkCFu2bKFRo0YMGDDA6PQemaOjIwULFszwJQ/Onj0LQJs2bShYsCCuyVyBiYqKytAcRFIuLi4ULFgQ0yOurZNZv0eP4qGKsghhz5EjMGeO3p45U4/cyVbCwsDTE5OXF/m9vPD09JSiQwkEBcH77+vtsWP1mnFC5DYrVsSP0p00SS+blStks/Zx/HhYsgQcHXXV7UqVjM5IpNXzzz/Phg0bAOjevTvvvvsuTZs2pWPHjrRt29bg7ER627BBr0Pr4hI/SjHbiG0f8fQk7NYtPD09s3wbmRVVqRL/P3XwYN1PK3Ke/v37YzKZ2LdvH6+++ipPPvkkTz/9NIMHD7ZeOAKYPn06FStWJE+ePBQtWpT+/fsTEhJid98//PADNWvWxM3NDT8/P5v/FSaTiXXr1tnEP/bYYyxcuDDZfZnNZnr27EnJkiVxd3enbNmyzJw50/r4uHHjWLRoEevXr8dkMmEymdixYweBgYGYTCYOHjxojd25cye1atXC1dWVQoUKMXLkSGJiYqyPN2zYkHfeeYfhw4fj6+tLwYIFGTduXIo/57hx42jVqhUADg4O1s6ruBGTEydOpHDhwpQtWxbQM02ff/553N3dyZcvH3369LE5lnHPmzRpEgUKFOCxxx5j/PjxxMTEMGzYMHx9fXn88cdZsGCB3eNvsViYMmUKpUuXxtXVlWLFijFx4kRA/09/6623bOJv3ryJi4uLdWRqZGQkI0aMoGjRori6ulK6dGm++eabZF/r9u3bdOrUiSJFiuDh4UHFihWTnb0QExPDW2+9hY+PD35+fowePdruxZ579+7Rq1cv/P398fb25vnnn+fQoUN2f+6EduzYgclk4t69e+zYsYPu3bsTFBRk/R1J+L6GhYXRo0cPvLy8KFasGPPmzbM+lvj3KLmRjuvWrbPpuBw3bhxVqlThf//7H8WKFcPT05P+/ftjNpuZMmUKBQsWJH/+/Nb35FFlt64ekcXFXVm2WHSRgkaNjM5IpLePPoIbN6BsWV0lVYjcZscO6NpVt3cDBsDIkUZnJJKzZAnEna99+SU0a2ZoOuIhzZs3D4vFAsCAAQPIly8ff/zxB61bt+bNbDcFQtgTEQHvvqu3hwyB0qWNzUcY56OP9IW7P/6A1auhXTujM8qeQkNDU3zM0dHRZkaNvVgHBwfc3d3txubJkyfVed25c4ctW7YwceLEZJ+XsMPEwcGBzz//nJIlS3Lu3Dn69+/P8OHD+fLLL5Pd96ZNm2jbti3vv/8+ixcvJioqis2bN6c6t8QsFguPP/44K1eutP7/6dOnD4UKFaJDhw4MHTqU48ePExwcbO1o8/X15cqVKzb7uXz5Mi1btqRbt24sXryYEydO0Lt3b9zc3Gw6lxYtWsTgwYPZu3cvf/75J926daNevXo0bdo0SW5Dhw6lRIkSdO/enatXr9o8tm3bNry9vfn5558B/Z41a9aMOnXqsH//fm7cuEGvXr146623bDpTf/31Vx5//HF+++03du/eTc+ePfnjjz947rnn2Lt3L99//z1vvvkmTZs25fEUqmWNGjWK+fPn89lnn/Hss89y9epVTpw4AWB9zWnTpllHU3777bcUKVKE559/HoA33niDP//8k88//5zKlStz/vx5bt26lexrRUREUL16dUaMGIG3tzebNm2ia9euPPHEE9SqVcvmuPbs2ZN9+/Zx4MAB+vTpQ7Fixejdu3ey+23fvj3u7u78+OOP+Pj4MHfuXBo3bsypU6fwTeNabnXr1mXGjBmMGTOGkydPAuCZYO2dadOmMWHCBN577z1WrVpFv379aNCggbUj+GGcPXuWH3/8kS1btnD27FnatWvHuXPnePLJJ9m5cyd//PEHPXr0oEmTJtSuXfuhXwdIe1GW7M7ohXNzulWr9GLKbm5KnT9vdDYPKSRE/xCgPEABKiQkxOissoRTp5RydtaHZ9Mmo7NJP9IuxJNjYd/hw0r5+Oi/gVdeUSomxuiMMlk2aR+3b49vq0aMMDqb7E/aBU2OQ8aaMEH/zRYpkk0LJyVoH0OuX1dk4TYyOxgzRh/OUqWUiow0OpuUGd0u2CucEPc7mNytZcuWNrEeHh4pxjZo0MAm1s/PL0lMWuzdu1cBas2aNWn+eVeuXKny5ctn/T5xUZY6deqoLl26pPh8kilc4uPjoxYsWKCUSl0RjAEDBqhXX33V+n1AQIBq06aNTUzi/bz33nuqbNmyymKxWGNmz56tPD09ldlsVkrp4iHPPvuszX5q1qypRtg5kVm7dm2S4x8QEKAKFCigIhP84cybN0/lzZvXpj3atGmTcnBwUNeuXbM+r3jx4tZ8lNKF0erXr2/9PiYmRuXJk0ctW7Ys2XyCg4OVq6urmj9/frKPh4eHq7x586rvv//eel+lSpXUuHHjlFJKnTx5UgHq559/Tvb5qSl28uKLL6ohQ4ZYv2/QoIEqV66czbEfMWKEKleunPX7hMVRdu3apby9vVVEoopgTzzxhJo7d26yr/mgoiwpFQ8qXry4ev31163fWywWlT9/fjVnzpxk95vcfhL/DowdO1Z5eHio4OBg633NmjVTJUqUSPLeTp48OdmfR6kMLMoiRErCw/UVZYBhw6BECUPTERlgyBC9rlGLFnrhbCFykwsX9DqJQUHw7LO6GIujo9FZicROnIC2bXVb1aGDnpIusre7d+/y6aef0rNnT3r27Mm0adOs6yqKnOHChfi/1U8/lcJJQn+WKFgQzp2D2bONzkakJ5WGeey//PILjRs3pkiRInh5edG1a1du376d4lICBw8epHHjxumVKgCzZ8+mevXq+Pv74+npybx587hw4UKa9nH8+HHq1KljMzW1Xr16hISEcOnSJet9lRKtzVKoUCFu3LiR5pwrVqyIi4uLzetXrlzZZkRovXr1sFgs1lFzAE8//TQODvFdRAUKFKBixYrW7x0dHcmXL1+KOR0/fpzIyMgU3wM3Nze6du3K//73PwD+/vtvjh49Srdu3QD9/jk6Oqa6NofZbGbChAlUrFgRX19fPD092bp1a5L355lnnrE59nXq1OH06dOYzeYk+zx06BAhISHky5fPunSFp6cn58+ft65ZmZ4Svucmk4mCBQs+1HueUIkSJfDy8rJ+X6BAAcqXL5/kvX3U1wGZ8izS0bRp8N9/uormiBFGZyPS208/wQ8/6DUxp083OhshMtedO7oz8coVKF9er/OVYPaPyCJu3NAXO+7dgzp1YOFCcJBLp9nab7/9RuvWrfH29qZGbBn1zz//nPHjx/PDDz/w3HPPGZyhSA9Dh+oL0w0aQMeORmcjsgJPT134r3dv/TUgANI40zDXs7fWoGOiK6L2OhYcEv0jDQwMfKS8ypQpg8lksk6DTUlgYCAvvfQS/fr1Y+LEifj6+vL777/Ts2dPoqKi8PDwSPIc9wecnJlMpiQdmtHR0SnGL1++nKFDhzJt2jTq1KmDl5cXU6dOZW8GVQxydnZOkm/csh9pkZYp6A96/bTk9KDjD3rac5UqVbh06RILFizg+eefp3jx4ql+fkJTp05l5syZzJgxw7rW5qBBgx6pEE1ISAiFChVix44dSR57mErND5KW4+vg4JCq399HfR/TQk6zRbq4fBkmT9bbn3wCD9mGiSwqOjp+XaMBA+Cpp4zNR4jMFB4OrVvD8eNQpAhs2QJ58xqdlUgsPBzatIHz56FUKVi/Xjp9c4IBAwbQoUMHzp8/z5o1a1izZg3nzp3jtddeyxGVQIVel3blSt35//nnusKvEADdu0OFCnD3rl5XUaRNnjx5UrwlXD/xQbGJO3mSi0kLX19fmjVrxuzZs5Ndj/HevXsA/PXXX1gsFqZNm8YzzzzDk08+mWRtwsQqVapkLe6RHH9/f5v1Bk+fPm23cNLu3bupW7cu/fv3p2rVqpQuXTrJKDUXF5dkR7olVK5cOf7880+bzqDdu3fj5eWV4lqE6alcuXIcOnTI5njv3r0bBweHR1qrL7EyZcrg7u5u9z2oWLEiNWrUYP78+Xz33Xf06NHD5jGLxcLOnTtT9Xq7d++mTZs2vP7661SuXJlSpUpx6tSpJHGJO4D37NlDmTJlknSsA1SrVo1r167h5ORE6dKlbW5+fn6pyiux1PyOpIa/vz/379+3eR8TFv4xgnQoinQxcqQublevHnTqZHQ2Ir199RUcOwZ+frqysxC5RUyMbtN27wYfH92ZWLSo0VmJxCwWXShnzx7d2bt5s1SgzynOnDnDkCFDbE76HR0dGTx4MGfOnDEwM5EeYmJ0MT+Avn2lEruw5eiop8ADzJoF8iefc8yePRuz2UytWrVYvXo1p0+f5vjx43z++efUqVMHgNKlSxMdHc0XX3zBuXPnWLJkCV999ZXd/Y4dO5Zly5YxduxYjh8/zpEjR/jkk0+sjz///PPMmjWLf/75hwMHDtC3b98kI7cSKlOmDAcOHGDr1q2cOnWK0aNHs3//fpuYEiVKcPjwYU6ePMmtW7eSHTHWv39/Ll68yNtvv82JEydYv349Y8eOZfDgwUlGgGaELl264ObmRkBAAEePHmX79u28/fbbdO3alQIFCqTb67i5uTFixAiGDx/O4sWLOXv2LHv27ElSpblXr158/PHHKKVsqnCXKFGCgIAAevTowbp16zh//jw7duxgxYoVyb5emTJl+Pnnn/njjz84fvw4b775JtevX08Sd+HCBQYPHszJkydZtmwZX3zxBQPj/vkk0qRJE+rUqcPLL7/MTz/9RGBgIH/88Qfvv/8+Bw4ceKjjUqJECUJCQti2bRu3bt2y24ltT+3atfHw8OC9997j7NmzfPfddylWKM8s0qEoHtmff+q1xEwmmDkzB1xZdnSEdu0wt21Lm7ZtadeuXbJXL3KL27fjOxEnTJCRWSL3iKvivH49uLrqac4VKhidlcGyaPs4cqSuBOriAuvW6Sr0ImeoVq0ax48fT3J/3HpQInubPx8OH9bnFuPHG53NI4ptH2nXDkcXF9q1a5dl2sjsrFkzfYuO1m29yBlKlSrF33//TaNGjRgyZAgVKlSgadOmbNu2jTlz5gBQuXJlpk+fzieffEKFChVYunQpk+OmxKWgYcOGrFy5kg0bNlClShWef/559u3bZ3182rRpFC1alPr169O5c2eGDh2a7NTpOG+++SavvPIKHTt2pHbt2ty+fZv+/fvbxPTu3ZuyZctSo0YN/P392b17d5L9FClShM2bN7Nv3z4qV65M37596dmzJx988EFaDttD8/DwYOvWrdy5c4eaNWvSrl07GjduzKxZs9L9tUaPHs2QIUMYM2YM5cqVo2PHjkmm1Hfq1AknJyc6deqUZLTsnDlzaNeuHf379+epp56id+/eKVYh/+CDD6hWrRrNmjWjYcOGFCxYkJdffjlJ3BtvvEF4eDi1atViwIABDBw4kD59+iS7T5PJxObNm3nuuefo3r07Tz75JK+99hr//fffQ3e+1q1bl759+9KxY0f8/f2ZMmXKQ+3H19eXb7/9ls2bN1OxYkWWLVtmUyXcEHZLtmSSWbNmqeLFiytXV1dVq1YttXfv3lQ9b9myZQpIUlXJHqMrceU0ZrNSNWvqKmw9ehidjcgIAwbo97dSpZxb0TYrtwuZ2T4qlbWPRWYbN07/7js4KPUQhQhFJpkzx1pYVX37rdHZ5ExGtgvLly9XxYoVU1OnTlW7du1Su3btUlOnTlUlSpRQy5cvV4cOHbLeMpq0j+nr9m2lfH313+6sWUZnI7KyI0f0/2JQ6vffjc7GltHtQmorsQqRlZw/f145ODiov/76y+hURApS27YYXpTl+++/Z/DgwXz11VfUrl2bGTNm0KxZM06ePEn+/PlTfF5gYCBDhw6lfv36mZitSGzJEti/H7y8YOJEo7MR6e3oUT3dGWDGDKlom9mkfTTOvHkQd8Fv9mxdNVhkPT/+qEeRgh7d1KWLsfmI9Ncpdh2V4cOHJ/tY3AL7JpMpXdYnEpln7Fhd8KpiRXjzTaOzEVlZhQrQowd8/TUMGaJnR2X7GVFC5ELR0dHcvn2bDz74gGeeeYZq1aoZnZJ4RIZPeZ4+fTq9e/eme/fulC9fnq+++goPDw9rKfHkmM1munTpwocffkipUqUyMVuR0P378VMPRo+GggWNzUekL6V0IRazGV55BRo1Mjqj3EfaR2OsXw/9+unt0aP1ul4i6zl0CDp00OsndusGmTRrSGSy8+fP272dO3fO+lVkH0ePQuysRmbOBCfDhziIrG7CBF30ce9e+P57o7MRQjyM3bt3U6hQIfbv3//A9TBF9mDov++oqCj++usvRo0aZb3PwcGBJk2a8Oeff6b4vPHjx5M/f3569uzJrl277L5GZGQkkZGR1u+Dg4MfPXEBwKRJcO0alC4N77xjdDbpKDQUPD0ByAOEocvHp7WCWXb3ww/wyy967bipU43OJvfJjPYRpI1MbPdueO013UnVqxd8+KHRGWUxWaR9vHQJXnwRQkLg+edh7lwZrZJTFS9e3OgURDpTShdiMZvh1Vdz0AXLBO1j6PXreMautZUbzyEzQsGCMGIEjBmjBzS8/DIkWnpNCJHFNWzY0KbStcj+DO1QvHXrFmazOcnilgUKFODEiRPJPuf333/nm2++SXV57MmTJ/OhfCJMd2fPwvTpenv6dN3pJHKOyEgYPFhvDx4MMtAt82VG+wjSRib077/w0ksQEQGtWunRM9JJlfXcv6/fp8uXoVy5+GIsImc7duwYFy5cICoqyub+1q1bG5SReFhr1sCvv+rOoLgKvkKkxuDBeime//6DL76AYcOMzkgIIXK3bDXB4P79+3Tt2pX58+fj5+eXqueMGjWKwXE9I+jRN0WLFs2oFHONwYMhKgpeeEF/sBM5y8yZutO4UCFIMEBOZGEP0z6CtJFxLl6E5s3h3j2oUweWL5cpeFlRTAx07KinO+fPD5s3w2OPGZ2VyEjnzp2jbdu2HDlyxLpeIugqjICsm5jNhIfD0KF6e9gwKFHC0HRENpMnj16zvXv3+K9pOOURQgiRzgz9uOTn54ejoyPXr1+3uf/69esUTGZBvrNnzxIYGEirVq2s91ksFgCcnJw4efIkTzzxhM1zXF1dcZXhc+lq9WrYsEF/2P7sMxnBk9NcuwYffaS3J0/WBXdE5suM9hGkjQS4e1d3Jl66pEe8bdwIHh5GZyUSU0ovr/Hjj+DurpdlkM6InG/gwIGULFmSbdu2UbJkSfbt28ft27cZMmQIn8rwtmxn3DgIDITHH9fTV4VIq65d9YXvgwd1Ma7PPzc6IyGEyL0MLcri4uJC9erV2bZtm/U+i8XCtm3bqFOnTpL4p556iiNHjnDw4EHrrXXr1jRq1IiDBw/mylE1me3uXXjrLb09ciSUL29sPiL9vf++nlJYs6Y+aRPGkPYxc4SHQ+vWcOwYFCkCW7aAr6/RWYnkTJ8ePw196VKoVcvojERm+PPPPxk/fjx+fn44ODjg4ODAs88+y+TJk3knRy3gnPMdOBA/xfnLL/VoMyHSytEx/vdozhw4dcrYfIQQIjczfELX4MGDCQgIoEaNGtSqVYsZM2YQGhpK9+7dAXjjjTcoUqQIkydPxs3NjQoVKtg8/7HYuU6J7xcZY+hQPYLtqaekomZO9PffsGCB3p45ExwMrwOfu0n7mLFiYqBTJ/j9d/Dx0Z2JxYoZnZVIzurV8WtlffoptG1rbD4i85jNZrxih8r7+flx5coVypYtS/HixTl58mSq9zN58mTWrFnDiRMncHd3p27dunzyySeULVs2o1IXCURHQ8+euuBVp056nVohHlbjxrow16ZNeqTr2rVGZySEELmT4R2KHTt25ObNm4wZM4Zr165RpUoVtmzZYi1EcOHCBRykVyNL2LYN/vc/PTrk66+lEEtOE1d1USno0kWvIyeMJe1jxlEK3n4b1q/XbdmGDSD9rlnT3r3w+uv6PRswAN591+iMRGaqUKEChw4domTJktSuXZspU6bg4uLCvHnzKJWGimE7d+5kwIAB1KxZk5iYGN577z1eeOEFjh07JhV4M8GUKXD4MOTLpy9YCvGopk7VFwLXrYOdO6FBA6MzEkKI3Mekclnd7uDgYHx8fAgKCsLb29vodLKN0FCoWBHOn9cf6GbNMjqjDBQRAa++itli4VWliHZ0ZPXq1bi5uRmdWYb6/nt47TW9dtzJk3p9o9xC2oV4ueVYTJ4M772nL5CsWgWvvGJ0RtlEJreP589D7dpw86YejbJunRTLMYKR7cLWrVsJDQ3llVde4cyZM7z00kucOnWKfPny8f333/P8888/1H5v3rxJ/vz52blzJ88991yqnpNb2sf0dvw4VKmii/ktXQqdOxudUQaJbR8BIpYu5dUuXQByxTmkUfr101Wfa9TQF5+MuMZqdLsQERHB+fPnKVmyZK78PVu4cCGDBg3i3r176bK/wMBASpYsyT///EOVKlUM309qjBs3jjlz5nDjxg3Wrl3Lyy+/nKGvl9G6devGvXv3WLduHQANGzakSpUqzJgxw9C8HkVm/j6kl9S2LXJaLlJlzBj9wa5oUf1BPEdzc4NNm3AE1hmdSyYJC4Phw/X2yJG5qzNR5D5LlujORNAjZaQzMQ0ysX28exdattSdiVWrSuXt3KpZs2bW7dKlS3PixAnu3LlD3rx5rZWeH0ZQUBAAvnYWTY2MjCQyMtL6fXBw8EO/Xm5lNuupzlFR+qJAp05GZ5SBYttHADdgU+y2yDgffqg7qQ8cgGXL9AwbkX1cu3aNiRMnsmnTJi5fvkz+/PmpUqUKgwYNonHjxkanl2qJO8AAihYtytWrV/HL4DLkx48f58MPP2Tt2rU888wz5M2bN0NfTzycxL8PO3bsoFGjRty9e9e6RFV2JXPlxAPt3w9xFwTmzpWqvznRp5/ChQt6/bihQ43ORoiM8/PP0KOH3h42TE97FllPVJQe6HPihL7AsXEjeHoanZUwQlBQEHfu3LG5z9fXl7t37z50B5/FYmHQoEHUq1fP7hqzkydPxsfHx3qT4lZp9+WX8Oef+twxrqiSEOklf34YNUpvjxqlC62J7CEwMJDq1avz66+/MnXqVI4cOcKWLVto1KgRAwYMMDq9R+bo6EjBggVxyuAroWfPngWgTZs2FCxYENdk1iSLiorK0BzEg2XW74MRpENR2BUVFb+Idpcu0KKF0RmJ9HbxInz8sd6eOhXc3Y3NR4iMcvCg7qSKidHT++N+70XWohT07g3bt+tOiE2boHBho7MSRnnttddYvnx5kvtXrFjBa6+99lD7HDBgAEePHk12vwmNGjWKoKAg6+3ixYsP9Xq5VWBgfGfPlCl6losQ6W3QIP27dfGirM+ZnfTv3x+TycS+fft49dVXefLJJ3n66acZPHgwe/bsscZNnz6dihUrkidPHooWLUr//v0JCQmxu+8ffviBmjVr4ubmhp+fH20TVHIzmUw2IwlBFzFcuHBhsvsym8307NmTkiVL4u7uTtmyZZmZ4Bdt3LhxLFq0iPXr12MymTCZTOzYsYPAwEBMJhMHDx60xu7cuZNatWrh6upKoUKFGDlyJDExMdbHGzZsyDvvvMPw4cPx9fWlYMGCjBs3LsWfc9y4cbSKrXDl4OBgHbXfrVs3Xn75ZSZOnEjhwoWtxceOHDnC888/j7u7O/ny5aNPnz42xzLueZMmTaJAgQI89thjjB8/npiYGIYNG4avry+PP/44C+IqeKbAYrEwZcoUSpcujaurK8WKFWPixInWxy9evEiHDh147LHH8PX1pU2bNgQGBtrd54PYe8+XLFlCjRo18PLyomDBgnTu3JkbN25YH9+xYwcmk4lNmzZRqVIl3NzceOaZZzh69Kg15vbt23Tq1IkiRYrg4eFBxYoVWbZsWap/7oS/D4GBgTRq1AjAOtuiW7duLF68mHz58tnMjAB4+eWX6dq16yMdn4wkHYrCrkmT4MgR8POLH6WY44WGQp48qDx58PfwIE+ePISGhhqdVYYZOVJf0a1fH9q3NzobITLGf//p6bP370PDhrBwoVQxfyiZ0D5OmACLF4OjI6xcCZUqpevuRTazd+9e64l3Qg0bNmTv3r1p3t9bb73Fxo0b2b59O48/YH0PV1dXvL29bW4idSwW6NVLNxnPPQd9+hidUSaIbR/Jk4fQGzfIkydPjj+HzArc3fXnFdBfE/QTiNDQlG8REamPTTz0M7mYNLhz5w5btmxhwIAByRbFSjgF1MHBgc8//5x///2XRYsW8euvvzI8bp2mZGzatIm2bdvSsmVL/vnnH7Zt20atWrXSlF9CFouFxx9/nJUrV3Ls2DHGjBnDe++9x4oVKwAYOnQoHTp0oHnz5ly9epWrV69St27dJPu5fPkyLVu2pGbNmhw6dIg5c+bwzTff8NFHH9nELVq0iDx58rB3716mTJnC+PHj+fnnn5PNbejQodbOvbjXjrNt2zZOnjzJzz//zMaNGwkNDaVZs2bkzZuX/fv3s3LlSn755Rfeeustm33++uuvXLlyhd9++43p06czduxYXnrpJfLmzcvevXvp27cvb775JpcuXUrxmI0aNYqPP/6Y0aNHc+zYMb777jtrQcno6GiaNWuGl5cXu3btYvfu3Xh6etK8efOHHkn5oPc8OjqaCRMmcOjQIdatW0dgYCDdunVLsp9hw4Yxbdo09u/fj7+/P61atSI6OhrQ6wlWr16dTZs2cfToUfr06UPXrl3Zt29fqn7uhIoWLcrq1asBOHnyJFevXmXmzJm0b98es9nMhg0brLE3btxg06ZN9IibXpUVqVwmKChIASooKMjoVLK8/fuVcnRUCpRavtzobDJRSIj+oUF5gAJUSEiI0VlliD/+0D+qyaTUX38ZnY1xpF2IlxOPxZ07SpUvr3/XK1RQ6u5dozPKxjK4fVyyxLp7NXduuu1WPCIj2wUPDw91+PDhJPcfPnxYubu7p3o/FotFDRgwQBUuXFidOnXqoXLJie1jRvniC/137O6u1EMe7uwnQfsYcv26IoefQ2YlZrNS1avrw9+vX+a+ttHtQnh4uDp27JgKDw9P+mDcP9Tkbi1b2sZ6eKQc26CBbayfX9KYNNi7d68C1Jo1a9L2wyqlVq5cqfLly2f9fsGCBcrHx8f6fZ06dVSXLl1SfD6g1q5da3Ofj4+PWrBggVJKqfPnzytA/fPPPynuY8CAAerVV1+1fh8QEKDatGljE5N4P++9954qW7asslgs1pjZs2crT09PZTablVJKNWjQQD377LM2+6lZs6YaMWJEirmsXbtWJe7SCQgIUAUKFFCRkZHW++bNm6fy5s1r0x5t2rRJOTg4qGvXrlmfV7x4cWs+SilVtmxZVb9+fev3MTExKk+ePGrZsmXJ5hMcHKxcXV3V/Pnzk318yZIlSY5DZGSkcnd3V1u3brXmkfB4NmjQQA0cODDFY/Cg9zyx/fv3K0Ddv39fKaXU9u3bFaCWJ+jwuH37tnJ3d1fff/99ivt58cUX1ZAhQ5RSD/65E/8+xL3m3UQfSvr166datGhh/X7atGmqVKlSNscrs9htWxKQ8RkiWRER8MYbejHtjh31TeQsFgu8847e7tEDqlUzNh8hMkJkpC66cuyYnja7eTNk87WPc6ydO+PXtxw+PJeMaBIPVKtWLebNm5fk/q+++orq1aunej8DBgzg22+/5bvvvsPLy4tr165x7do1wmXRtXR36lR8obepU6FMGWPzETmfgwNMm6a3583TlcVF1qWUSnXsL7/8QuPGjSlSpAheXl507dqV27dvExYWlmz8wYMH072gy+zZs6levTr+/v54enoyb948Lly4kKZ9HD9+nDp16tgUE6tXrx4hISE2o/0qJZqWUahQIZvpualVsWJFXFxcbF6/cuXKNiNC69Wrh8Vi4eTJk9b7nn76aRwSTOEpUKAAFStWtH7v6OhIvnz5Uszp+PHjREZGpvgeHDp0iDNnzuDl5YWnpyeenp74+voSERFhXQ8yrR70nv/111+0atWKYsWK4eXlRYMGDQCSvId16tSxbvv6+lK2bFmOxzYmZrOZCRMmULFiRXx9ffH09GTr1q3WfTzo506t3r1789NPP3H58mVAVzHv1q3bIxWhy2g5b1VIkS5Gj9b/jAsUgNmzjc5GZIQlS3RVPC8vSLCshRA5hlK6g2rHDv17vnmzrOGVVZ08CW3bQnS0Xnph8mSjMxJZxUcffUSTJk04dOiQ9UR927Zt7N+/n59++inV+5kzZw6gp0ontGDBgmSnPomHExOjL0iHh0OTJtCvn9EZidyiQQNo0wbWr9cd2j/8YHRGWYC9tQYdHW2/t9dplXiNmEdc765MmTKYTCZOnDhhNy4wMJCXXnqJfv36MXHiRHx9ffn999/p2bMnUVFReHh4JHmO+wMWgzeZTEk6NOOmtSZn+fLlDB06lGnTplGnTh28vLyYOnXqQy25kRrOzs5J8rVYLGneT3JTyR/29dOS04OOf0hICNWrV2fp0qVJHvP3909jtg9+zbip3s2aNWPp0qX4+/tz4cIFmjVrlqYp1lOnTmXmzJnMmDHDuqbnoEGDrPt40M+dWlWrVqVy5cosXryYF154gX///ZdNmzaly74zioxQFEn8/nv8Vb758yFfPmPzEenv/n29diLozuNklncQItv74AP47jtwcoJVq6ByZaMzEsm5eVOvb3n3LtSpA4sWyfqWIl69evX4888/KVq0KCtWrOCHH36gdOnSHD58mPr166d6P0qpZG/SmZi+PvkE9u4FHx/43//kb1lkrk8+0f/zN26EX381OpssIHZNz2Rvbm6pj03cWZJcTBr4+vrSrFkzZs+enewao/fu3QP0yDKLxcK0adN45plnePLJJ7ly5YrdfVeqVIlt27al+Li/v7/NWoOnT59OcbQjwO7du6lbty79+/enatWqlC5dOslIOhcXF8xms928ypUrx59//mnTmbl79268vLweuJ5veihXrhyHDh2yOd67d+/GwcHBWrQlPZQpUwZ3d/cU34Nq1apx+vRp8ufPT+nSpW1uPj4+D/Wa9t7zEydOcPv2bT7++GPq16/PU089leLoyoTFgO7evcupU6coV64coI9VmzZteP3116lcuTKlSpXi1KlTqf65E4sbPZrc702vXr1YuHAhCxYsoEmTJhTN4qMh5N+8sBESAgEBemRP9+4QWzhK5DCTJ8O1a1C6dPy0ZyFyknnz4hdpnzcPXnjB2HxE8sLDoXVrOHcOSpXSI0uk0rxIrEqVKixdupR///2XAwcO8L///Y8yMo82y/nnH4grSPrFFzIiXGS+smWhb1+9PWSIXt5HZE2zZ8/GbDZTq1YtVq9ezenTpzl+/Diff/65depp6dKliY6O5osvvuDcuXMsWbKEr776yu5+x44dy7Jlyxg7dizHjx/nyJEjfPLJJ9bHn3/+eWbNmsU///zDgQMH6Nu3b5IReAmVKVOGAwcOsHXrVk6dOsXo0aPZv3+/TUyJEiU4fPgwJ0+e5NatW8mOeOzfvz8XL17k7bff5sSJE6xfv56xY8cyePBgmynGGaVLly64ubkREBDA0aNH2b59O2+//TZdu3ZNtnDIw3Jzc2PEiBEMHz6cxYsXc/bsWfbs2cM333xjzcPPz482bdqwa9cuzp8/z44dO3jnnXfsFnqxx957XqxYMVxcXKy/Qxs2bGDChAnJ7mf8+PFs27aNo0eP0q1bN/z8/Hj55ZcB/Xvw888/88cff3D8+HHefPNNrl+/nuqfO7HixYtjMpnYuHEjN2/etKm23blzZy5dusT8+fOzdjGWWNKhKGwMH64/2BUtCp99ZnQ2IiOcOxc/AnXaNHB1NTYfIdLb5s3Qv7/eHjtWXxwRWY/FoqdG7tkDefPq9+0hZ7sIIQwWt/Z2TIxet/b1143OSORWY8eCtzccPKiX9xFZU6lSpfj7779p1KgRQ4YMoUKFCjRt2pRt27ZZl6ioXLky06dP55NPPqFChQosXbqUyQ9YE6Vhw4asXLmSDRs2UKVKFZ5//nmbSrzTpk2jaNGi1K9fn86dOzN06NBkp07HefPNN3nllVfo2LEjtWvX5vbt2/SPO8mM1bt3b8qWLUuNGjXw9/dn9+7dSfZTpEgRNm/ezL59+6hcuTJ9+/alZ8+efPDBB2k5bA/Nw8ODrVu3cufOHWrWrEm7du1o3Lgxs2bNSvfXGj16NEOGDGHMmDGUK1eOjh07WkcFenh48Ntvv1GsWDFeeeUVypUrR8+ePYmIiMDb2/uhXs/ee+7v78/ChQtZuXIl5cuX5+OPP+bTTz9Ndj8ff/wxAwcOpHr16ly7do0ffvjBOpLwgw8+oFq1ajRr1oyGDRtSsGBBa2djan7uxIoUKcKHH37IyJEjKVCggE21bR8fH1599VU8PT2TvEZWZFJpWRU1BwgODsbHx4egoKCH/qXNqX7+OX4Uz88/67VvcqXwcGjRArPFQguliHJ05Mcff0y3tRGM9uqrsGYNNG0KW7dCFl7jNdNIuxAvux+LAwegYUMIDYVu3fSUO/kdT0fp2D4OH64LNjg76/85sWtkiywou7cL6UWOQ8ri/p7z54ejR3PpxYHY9hEgfM0aWrzyCkCOOofMLqZMgREjoEgRXSTITn/RIzO6XYiIiOD8+fOULFkSt8TTmIUQD7Rjxw4aNWrE3bt3eSyLVG5s3LgxTz/9NJ9//rlhOaS2bZGiLALQ6/AGBOjtAQNycWci6Pl2O3bgCKR+uffsYft23Zno6KhHoEpHi8hJTp3Sn+VCQ3WH+bx58jue7tKpfZw7V3c+gO70lc5EIbKvX36BuAEf8+fn0s5EsLaPAO7oD6nCGO+8A19+Cf/9B9On6zWVhRAiq7t79y47duxgx44dfPnll0ankyoy5VlYp51dvQrlyukFjUXOExMDgwbp7X794OmnDU1HiHR15YoeYX3rFlSvDqtX65FvIuvZskVfuAL48EOZGilEdnb5MnTurNfe7t1br4kqhNHc3ODjj/X2xx/rdcOFECKrq1q1Kt26deOTTz5J12I5GUlGKAqmTNFTX93dYcWKNBfqEtnE11/D4cN6rbK4RdOFyAnu3YPmzfVIhNKl9Vp8Xl5GZyWSs307tG8PZrMeFT96tNEZCSEeVkwMvPaartReuTLMnGl0RkLE69hRz8bZt0+vqzh3rtEZCSGyooYNG5JVVgEMDAw0OoU0kw7FXO733+OnAXzxBVSoYGw+WUJoKJQogVKKEkCYyURgYCB5snFP69278e/zhx9CvnzG5iNEeomrEnzkCBQsCD/9pNfwEhnkIdvHixdh2DD4/nv9faNGMiVdpOyV2LXnUmPNmjUZmImw5/339XmklxesXCkV2uPaR4DQf/+lROxUkOx+DpldmUx6uvOzz+qL6m+/LZ9zhBAivUmHYi52+zZ06qRHinTuDNmgKnnmuXULE3ALCDM6l3Qwfrx+v8uX19OdhcgJYmJ0G7Zrl67ouGULlCxpdFa5QBrax/Bwvbba5Ml628EB+vTRS2vEFs4TIgkfHx+jUxAPsGGDnuECeh3UMmWMzSfLuHUrweYtO4EiM9Srp4sRrl6tL2r9+KPRGQkhRM4iHYq5lFK6AuqlS/Dkk/DVVzJSJKc6cQJmzdLbM2aAk/zVi1QIvXkTx4iIJPc7urjglqACWuiNGynuw8HJCXdf34eKDbt1C2WxJBtrcnDAPZ8f/frB+vXg43yLlYsslC4EoTeSxnr4+Vm/D79zB0tMTIp55EkwvDEtsRH37mGOikqXWA8/P0wOeonjyOBgYpJ5Hx4m1t3XF4fYBiAqJITosJS7A1OMDQ0l8Tibi4FRRJnzEBwM926EcP9OGCEheir6N9/AxUtgAp6vDVM+f4zqtXRPYnRYGFEhISnm4OrtjVNsVbm0xMZERBAZHJxirIunJ86xJT/TEmuOiiLi3r0UY509PHDx9ExzrCUmhvA7d9Il1snNDdfYKqPKYiHMTodGWmIj7PxeZYQFCxZk6uuJtAkMjC/k98470K6doekI8f/27ju+yXL9H/gnTXdLF6WTMmUoS2QJCqhUEBREKyJ6RBCQo6AgIOucUlCRLUN6UPmyHIhwGP4EBKFSRPYWKSAgLRztoEJbkk6S+/fH3SYtNGla2jwZn/frlVefJHeeXH0aLp5czz3Mmj1bFsB37JCjGHr2VDqimmErQzaJyDFYnFOEk8nOzhYARHZ2ttKhKGr+fCEAITw8hDh1SulobIxGIw8OILwBAUBoNBqlo6qy3r3lr9O3r9KR2C7mBSPDsSj+N3Dn7UidOmXaa0y0E4A46e9fpu11lcpk27Pe3mXaXlOrTba96OEh4uLkXRcXIS64ephse02tLrPfs97eJtteV6nKtD3p72+yreaO/z6P1Kljsq24o+2ByEizbTXp6Ya2+xo3Ntv2elKSuHZNiKNHhdjWoKXZtuNj9okRI4QYOlSIFbXbm207ossWMWCAEM89J8SSoO7ltinJj23xmeHhCehjdr8nFy40/G6JAwaYbXskLs54HIYNM9v2wLvvGo/vu++abbtv2DDj363kg2TiljhggPHzsHCh2bZ7+vQxfs5Wrzbftnt3Q9uLW7aYb9u+vfHfxb595uNt2dLQ9npSkvnj0Lixoa0mPd1s213h4YI5kv9XCCFEfr4Q7YvTR8eOQhQUKB2RDSl1/qhJTxeA/Z9DOoqxY+WfplUrIW7frt59K50Xbt++LZKSkkRmZqYi709EjikzM1MkJSWJ2xUkTfZVckK//AJMniy3Fy2SE2mTY9q+XQ7vcHMDFixQOhqi6qHTyblAASA+HnAZC8B0R0KH9vTTwJErcntpBW3/uxFIKd6+v4K2+w8AScXbrSto6wI5b6WfH+CXAcB0hz8ii7Rt2xYqC4dNnDhxooajoRJCyHnojh2TC7ytX8+pC8g+xMYCq1fL+ZZXrwaGDVM6ouqjVqsREBCAjOJRIN7e3hbnTyKiOwkhkJubi4yMDAQEBECtVpttrxJCCCvFZhNycnLg7++P7Oxs+BUP9XEmKSlAhw5yRb6XXgLWruVQ57totUDx8DIfyDnCNBqN3U2oXVgItG4NXLgATJgAzJundES2y9nzQmklx+KvS5fgV85SyUoPed65E3h1sAtyEYx//xv44IOKh0dbc8izEHKob2qqLHyWdFdxDwwxbOdnZeF2QSH0ekCvl+2KiuStsBAQXsEouu2CvDxAeyMH2ux85OUBubnA1avyolBuXvFxQjDUaheEhwP+njnw886Hjw/g7S1vXl6Ap2fxz8AgeHq7ws0NUBVpoNbnQq2W8xoWj5o2xKjyDoJK7QpXV0Ct08ANuXB1BTx0Wrw4upH8/SHzY9bfN+AfFAig4qHUngEBULtzyLO9DXnW5ucjtH59q+XIGSVXDCwQFxdXg5GU5ez/VyxcCIwbJ88bt24F+vRROiIbU+r8UZueDt/QUAD2eQ7piD7+GBg/HggPB37/3fCnume2kBeEEEhLS0OWmf93iIgqIyAgAGFhYRVeoGBB0YlotXJy4tOngbZt5UIGPL8ph4MUFEtO/ENC5IkT57g3zZnzwp1s+VgcOgQ88YRc3OP11+WqjUpdELl+XfZ0OHMGuHhRzimWkiJ/mql7VZuwMKB3b/mFPjoaKFXjrVkOkh+pcmw5L1iTMx+HrVuBfv3kBYcFC+T5Bd2BBUWbVlAgFyf84w8gLg6YPr169mtLeUGn06GoqEjRGIjI/rm5uVXYM7EEhzw7Cb1eTqB9+rQsMG3ZwmKiSS4uQPv20Ov1aAugwMUFLiXdd+zE9evGIaEzZ7KYSPbvwgXgmWdkMbFPH+suJHXrFrB3r7ydPi2LiGlp5l8THCyHAqpUZW8lvQFLegaW/HR3l1MTuLkZtz09ZZ4u6XHo4wMEBcmiaps2xl6FVuUA+ZHsT1ZWFv773//i8uXLeO+99xAUFIQTJ04gNDQUkZGRSofn8H79FRg0SBYTR4wA3n1X6YhsVHF+BGSv+/Yl28yRNsHDQy7Q8uKLctTOG28AERFKR1W91Gq1xUUAIqLqwIKik/jwQ2DjRvkldfNmoF49pSOyYV5ewNGjcAHwi9KxVFFsLJCdLXuiDh2qdDRE9yYtDXjqKeDvv4GOHeW8XW5uNfd+hYWyN+Tu3UBCAnD4sByWfKdGjYBWrYD77wcaNgTq1wcaNJD51cur5uJTlAPkR7Ivv/76K6Kjo+Hv74/k5GSMGDECQUFB2LRpE65evYovvvhC6RAdWno60Lev7Hn9xBNy3lpOlWNCcX4EAC8AR4u3yXa88ALQpQtw4IA8V16xQumIiIjsGwuKTmDTJtm1H5C9erp0UTYeqlmnTwPLl8vtxYtlDygie6XVyi+zycnAfffJYXc10bv69m0gMRFYt05efLlzGqJGjYAePeQctK1aAS1aAOVMMUlE1WzcuHEYMmQI5s6di1ql/tH16dMHL7/8soKROb68PKB/fzl3a5MmwIYNNXsxh6imqVRyyH7nzsCqVcA773BxSiKie8GCooM7fRp49VW5PXasnHeMHJcQ8u+s1wMDBgBduyodEVHV6XTAK6/IFUWDg+WK5XXqVM++Cwpkz8c//pAXXdavB0qvGRMSIguIJbcGDarnfYmoco4ePYrPPvvsrscjIyORVtHcA1RlQgDDh8ve2oGB8mJOqXWziOzWww/LYc/r18tFC3/8kb1uiYiqigVFB1ZYCLz8slwZ9MknucqvxXJzgQcegF4IPCAE8l1ckJSUBO/ilT5t2aZNspeVpyf/3mT/JkwAvvtOznv03Xeyh2JVaLVymN6PP8rVl1NTgZs3725Xu7YcDjVoEPDoo+zdWy47zo9knzw8PJBTzkrcv//+O+pU1xUGust//wusXQu4uspe202bKh2RHSjOjwCQe+wYHiieQ5E50vbMni3nk9+9G9ixQy5yRkRElWcTswTHx8ejQYMG8PT0RKdOnXDkyBGTbZcvX46uXbsiMDAQgYGBiI6ONtvemc2dCyQlyZ4233wjTwrJAkIAKSlwuXoV165dQ0pKCuxhMfT8fFmAAYD33pPzuZH9c9b8+MknwKJFcvuLL6o2VUNBgdxP48bApElyPsSkJGMx0d1dznc4eLDs/ZiaKqeF6N6dxUST7DQ/kv3q168f3n//fcPKpSqVClevXsWkSZMQExOjcHSOKTsbGDNGbk+dCjz+uLLx2I3i/IiUFAi9HikpKcyRNqphQzncGZDnzrdvKxsPEZG9Uryg+O2332LcuHGIi4vDiRMn0KZNG/Tq1QsZpceelZKYmIhBgwZhz549OHjwIKKiotCzZ0/8+eefVo7ctv3+u1yIBQAWLpQ9b8ixffyxnGcuMlIWT8j+OWt+/P57OXQfAGbNkkOTKuP2bTk3UtOm8gtDerqcAzE+XvZG+O03ucBLfr787rdmjVz0hXODEdmeBQsWQKPRICQkBHl5eejevTvuu+8+1KpVCzNnzlQ6PIf0r3/JCyxNmgBTpigdDVHNmDpVDuNPSgJWrlQ6GiIi+6QSCl8269SpEzp06IClS5cCAPR6PaKiovD2229j8uTJFb5ep9MhMDAQS5cuxeDBgytsn5OTA39/f2RnZ8PPz++e47dFQsg5v/bsAXr2lF35OTdIJWi1gK8vAMAHQC4AjUYDn5pYCaKa/PWXLJ5otcBXX8l558hytpoXrJ0fAeWPxfHjQLducuTY8OHA559XLn8dPy7njT13Tt6PiJArOb7+uuyRSPfIDvMj3Tul8wIA7N+/H6dPn4ZGo8FDDz2E6Ohoq8dgC8ehph05IueYE0JegOnRQ+mI7Eip/KhNT4dvaCgA5khbtmSJ7I0bEgJculS1xdacIS8QEZmi6CDYwsJCHD9+HFNKXf50cXFBdHQ0Dh48aNE+cnNzUVRUhCATM0UXFBSgoKDAcL+8eXgczRdfyGKilxewbBmLic5g6lR5Hvvww3LeTLJ/1siPgG3lyKtXgWeekcXEnj2B//zH8vwlhCw+vvOOnD+2dm3Zs+att2QuJCL79sgjj+CRRx5ROgyHdvs28MYbMp+++iqLieT4/vlPOTXKpUvAnDnG0V1ERGQZRYc8Z2ZmQqfTIbT4Cl6J0NBQi1fumzRpEiIiIkxeqZ41axb8/f0Nt6ioqHuO25ZlZgLjx8vtuDg5zI8c25EjcsgmACxezAKyo7BGfgRsJ0dmZwNPPy1XXm7VCtiwwfIhyFot8Npr8otBYSHQrx9w8aLMhSwmEtmnn376CQ888EC5Fzmys7PRokUL7Nu3r1L7/Pnnn9G3b19ERERApVJhy5Yt1RStY1i8GDh9Wg4DXbBA6WiIap67u5xzHpCf+f/9T9l4iIjsjeJzKN6L2bNnY926ddi8eTM8PT3LbTNlyhRkZ2cbbteuXbNylNY1YYKcG6xVK2DcOKWjoZomhHHi9NdeAzp2VDYesh2W5EfANnJkUREwYICc2zA8HNi2DbB01NCFC0CnTsCXX8qFVObMkSs3BgbWaMhEVMMWLVqEESNGlDuE0N/fHyNHjsTHH39cqX1qtVq0adMG8fHx1RWmw0hJAaZNk9vz5gFcQJucRf/+QNeucl7lf/1L6WiIiOyLokOeg4ODoVarkZ6eXubx9PR0hIWFmX3t/PnzMXv2bOzevRutW7c22c7DwwMeHh7VEq+t++kn2VNNpZJD/7jAQBWpVMADD0AvBJoLgXwXF6hstNvf2rXAoUOAjw/w0UdKR0PVyRr5EVA+RwohhyXv2iU/x1u3ApZ2kty8Wa7QrNEAYWHAunVyhWaqQXaUH8m+nT59GnPmzDH5fM+ePTF//vxK7bN3797o3bv3vYbmcIQARo+W00106wYMHap0RHaqOD8CgMrFBQ+UbDNH2jSVSvZO7NhRThs1Zgzw0ENKR0VEZB8U7aHo7u6Odu3aISEhwfCYXq9HQkICOnfubPJ1c+fOxQcffIAdO3agffv21gjV5t28KYf7AcCbb8q59KiKvL2Bs2fhkpSE4+fO4ezZs/D29lY6qrvcugVMnCi3p06Vi0+Q43CW/DhnDvB//we4uMiCoKUn8UuWADExspj42GPAyZMsJlqFneRHsn/p6elwM3Nl1NXVFdevX6/RGAoKCpCTk1Pm5ohWrpQXc9zcgE8/5dQpVVacH3H2LLyDg3H27FnmSDvRoYNxDvLx42WRnYiIKqb4kOdx48Zh+fLlWLNmDc6dO4c333wTWq0WQ4svjw4ePLjMogRz5sxBbGwsVq5ciQYNGiAtLQ1paWnQaDRK/QqKy8gAHn9czhkWEcGeas7iww/l6s6NGxvnzSTH4uj58dtv5cIpgJy765lnKn6NXi8L6WPGyBP+N9+UvRsr6LRJRHYmMjISv/32m8nnf/31V4SHh9doDLYyx2xN+vxzYMQIuT11KnD//crGQ6SUjz4CPDyAxERZYCciooopXlAcOHAg5s+fj2nTpuHBBx/EqVOnsGPHDsNCBFevXkVqaqqh/bJly1BYWIgXXngB4eHhhltlh704ij//lL1yTp8GQkKAH34A/P2Vjopq2oULwMKFcnvxYnkCRI7HkfPj/v1y3k8AGDtWDrerSGGhXHl03jx5/6OPgPh4wFXRyTuIqCb06dMHsbGxyM/Pv+u5vLw8xMXF4RlLrkLcA1uYY7YmLVoEjBwpL868/bZxDkUiZ1S/vjwfAYD33pPzOxMRkXkqIZyrU3dOTg78/f2RnZ1d7kTf9uTKFaBHD/mzbl0gIQFo2lTpqBxAbi7QoQP0QqBD8RxhR48etZkhK0IATz0F/PijXBWXV1HvnSPlhXtljWNx6ZKcluHvv4FnnwU2bpQLqpiTnS2HOCckyALiihVy/kSyMhvPj1QzlMiR6enpeOihh6BWqzF69Gg0a9YMAHD+/HnEx8dDp9PhxIkThgsslaVSqbB582b079/f4tc40v8VH31kXIBi4kRg9mwOdb5nxfkRAHL37kWH4nk4mCPtR3Y2cN99QGamvGD51lsVv8aR8gIRUWWxX4edOn8eiI6WPRQbNZJfshs0UDoqByEEkJQEFwDnAeQCsKW6+3ffyWKiu7vsXUBkT/7+G+jTR/5s3x74+uuKi4lpabKIfvo04OsrC5A9e1onXrqDjedHchyhoaE4cOAA3nzzTUyZMsXwOVOpVOjVqxfi4+OrXEx0ZkIAsbHAzJny/owZ8j6LidWgOD8CgNDrkVSyzRxpN/z95b+JUaOAuDjglVc48ouIyBwWFO3QgQNA//7A9etyrpvdu7kgh7PIywPefVduv/eevIpKZC8KCoDnnpPzvdavD3z/vVzZ2ZyUFHnx5NIlIDRUTuvQtq114iUiZdWvXx/bt2/HzZs3cenSJQgh0KRJEwQGBlZpfxqNBpcuXTLcv3LlCk6dOoWgoCDUq1evusK2WZmZcp7E5cvl/blz5bkEERmNGCEXfrtwAZg1S/beJSKi8ik+hyJZLjUVGDIEeOQRWUxs2xbYu5fFRGcydy6QnAxERRkXsyCyB3o9MHQosG+fvNq/bVvFC6n8/jvQtassJjZoIOddZDGRyPkEBgaiQ4cO6NixY5WLiQBw7NgxtG3bFm2LE8m4cePQtm1bTHPwyQOzsuT8iA0bGouJS5eymEhUHjc341zNixbJC5tERFQ+FhTtQGEhMH8+0KwZsGaNfGzIEOCnn4A6dRQNjawoOdl4lXTBgop7dhHZkmnTgG++kfMfbtwItGhhvv3p07KYeO0a0Lw58MsvckVzIqKqeuyxxyCEuOu2evVqpUOrERqNnCuxUSPggw/k/bZtgZ075ZBOIirfM88Ajz0mR1ZMnap0NEREtosFRRum1wP/7/8BrVrJq8i3bgEdOwKHDgGrVgEBAUpHSNY0bhyQnw88/jjwwgtKR0NkuVWrjPN1ffaZXEzKnEOH5Il8Rob88vvzz0BkZI2HSURk94QAjh2T06M0bCgXXrl5E3jgAeC//5XPcQ5aIvNUKnnxXqUC1q4Fjh5VOiIiItvEORRtUFoasHKlHJaSnCwfCwkB5syRq5q6sAzsdH78Edi8WS5e8cknnDyd7Mfu3cAbb8jtqVOB11833/6nn4B+/QCtVk7vsHUrL54QEVXk8mW5yNXatXLutxKNG8tFJl56qeIFsIjI6KGHgFdfBb74Ahg/Xk4zxfNvIqKyWFC0ETodsGeP7L2zZQtw+7Z8PCBAfhmfOpWrjFmNSgXUrw+9EIgSAvkuLlApeAZRWAi8847cfvvtioeKEtmKs2eBmBiZzwYNkkPuzNm5Uy44lZ8PPPmkLKJzaL+NsbH8SOTMrlyRvQ43bCjbg8rTU16YeeUVoHdvOSccWUFxfgQAlYsL6pdsM0farQ8/BNavl/M/b9kiF5YjIiIjFhQVVFgIJCYCmzbJ/6TS043Pde4MjBwJDBgAeHsrFaGT8vYGkpPhAuC80rEAWLxY9jYICQGmT1c6GiLLpKUBffoAOTnAo4/KYc/meld//70cyl9YCPTtK78ge3hYL16ykI3lRyJnc/myLCKWDF8u4eIip5N45RVZ9PDzUy5Gp1WcHwHAG0ByyTAjsltRUbJ34syZwMSJwNNPA+7uSkdFRGQ7WFC0sqwsICEB+O47+QU6K8v4XEAA8PLLspDYurVCAZJN+esv4P335fbcueylSvZBq5VFwatXgSZN5AUTc8XBTZuAgQNlT8aYGDlkjyfsRESyx/bPPwM//CBvpYczu7gA3bvLi8/PPQeEhSkXJ5GjmjRJTkN16RLw6afGUUNERMSCYo27fVsOQ/nxRzmc7/BhudhKiZAQeRL4/PNyEQJ+iabSJk6UqzI+/LCcx4XI1ul0sofMsWNA7drA9u3ypynr1gH/+Id83UsvAV9+KVeCJiJyVn/8YSwg7tkD5OYan1Or5fnigAFyiojQUKWiJHIOtWrJi/v//Kecj/TVV4HAQKWjIiKyDfzaVgNSUmQB8ccf5YIEpXshAkDz5sBTT8kiYpcunCTb5uTlAd26Qa/XoxuAAhcX/Pzzz/Dy8rJqGPv2yQnWVSpg6VIuxkP2YcIE2QPbw0P+vO8+022//BIYMkReZBk8WC5GxXxo42wkPxI5krw8ueBDSRHx4sWyz0dEyPPG3r2B6GguVGWzivMjAOTt3IluvXoBAHOkAxg2DFiyBEhKksOf589XOiIiItvAgmI1+N//ZM/DxERZRPz997LPBwbKE8CePeVCA8VzNJOt0uuBY8fgAuAkgFwA+tLdSq1Ap5MLsADAiBFAu3ZWfXuiKlm6FFi0SG6vWSNXaTZlxQr52RYCGD5cLkjForkdsIH8SGTPhJDTQRw6JG8HDwInT8r5Y0u4usr8WVJEbN2aq8vaheL8CAD627dxrGSbOdLuuboC8+bJORQ/+QQYNQpo2FDpqIiIlMeCYiXl58uTv8OH5e3IETnPXWlqtRyi2rOnvHXowF43VDmffQacPi2L0TNnKh0NUcW2bgXGjJHbH30k50Q0Zdky4K235Pabb7IHLhE5tsxMOe3NDz8AP/0EpKbe3aZuXVk8fOopubgK50wmsi0lPYR37wYmTwa+/VbpiIiIlMeCYgWEkBNg79wpb4mJckRDaWo10LKlXJm5Z0/giSd4IkhVl5kJ/PvfcvvDD4HgYGXjIarIiROygKjXy2FBkyebbrt4MTB2rNweOxb4+GP2vCEix5OUJFdi/uEHeQFaCONzrq7Agw/Ki88lt0aNmAuJbJlKJYc6t20LrF8vz2E6d1Y6KiIiZbGgaEZ8vOzenpJS9vGwMDlFSseO8vbQQ4CPjzIxkuP517+Amzfll42RI5WOhsi8a9eAZ56RiwY8+aTsfWjqS/H8+cB778ntiROB2bP5BZqIHM+mTXLRlNIjXVu3Bvr0AXr1kueO3t7KxUdEVdOmDTB0qJzzefx4YP9+pSMiIlIWC4omlP7i6+4OdO0qTwJ79QJateKXYKoZx44By5fL7U8+4VB5sm05OXI+odRU2Ut7wwbAza38tjNnGnvexsbKlRKZR4nI0ezeDQwaJIuJTzwht596Sg5pJiL798EHwLp1cgqsjRvl6DQiImfFgmI54uONxcTYWGDSJPZApJqn1wOjR8thUf/4B/Doo0pHRGRaUZHsgXPmjOy1vW2b6ake3n8fiIszbsfGWi9OIiJrOXQI6N9fLrASEyPnWOOFQSLHEhEhvyfOmCG/I3bvrnRERETKYUHxDitWyKIOAEydKr/8khMKDoYQAsEAcq3UjeqLL+Q8S76+wNy5VnlLoioRQq5w+OOPctje1q1AvXrlt50501hMnD1bnnyTnVMgPxLZujNn5JBmrVZO//D11ywmOqVSE18HcxJsh/Xee8DnnwN//GEcWURE5IxYUCxl7VpgxAi5/e67ckEMckI+PsD161ABSKmwcfXIzjYWWuLigPBwK70xURXMnStPoFUq4JtvgHbtym83Z45xmPOcOXLeRLJzCuRHIlt3+bIc9njzplxgZdMmwMND6ajI6orzIwD4ALhevE2Ox8dHDn0ePpydAIjIubkoHYCt2LgRGDxY9rx5801gwQLO70XWM306kJEBNG8OvPOO0tEQmbZ+vXEV50WLgH79ym+3YIGx3cyZLCYSkWP66y/ZIzEtTc6xvX27HGlARI5tyBC52FJ2ttKREBEphwVFyIUwBg0CdDq5ctfSpSwmkvX89ptcgAUAliyRiwAR2aIDB+SFF0AWvk0VvxcvBiZMkNszZsjpI4iIHNGwYcCVK0DjxsDOnUBgoNIREZE1qNVyEU8iImfm9EOehQDeflsuMNC/vxzG58Iyq3PLywN694ZOr0dvIVCoVuOHH36Al5dXtb+VELIoo9MBzz8vezkQ2aLLl4FnnwUKCmSvxI8/Lr9dfDwwdqzcjo0Fpk2zWohkDVbMj0S2budOYMcOubr9tm2crsTpFedHAMjbtAm9n38eAJgjHdiTT8q5U7dvVzoSIiJlOH1Bce1auSqfj4/8IswJtAl6PbB3L9QA9gPIBaDX66tl10LIgkxurrzt2AHs2QN4esohokS26O+/5QlzZibQvr3Mm+XlysWLjcXEKVNk70RyMDWYH4nsiU4nF2YA5CJVzZopGw/ZgOL8CAD627ext2SbOdKhrV0LBAQoHQURkTKctqCovX4d+X/nY+J7tQGo8d4YDfxdc5Gf5Q7PUv8raDMyTO7DxdUVXkFBVWqbm5kJYeIEQ+XiAu9SK8NVpm3ejRvQ375tMg6fkJAqtc3PyoKusLBa2noHB0NV3A20ICcHt/Pzq6WtV1AQXFzlR7pQo0FRbm6l2ur18uJy/t9a3Llg7Y8/yvNEzQ0NtFm5yM2VbUtuublAfj6guR0Abb47cnOBAk0uCjUaw3O5uUBunnGfBfCDDp4AgMnjc1HHWwOtiY+Qh58fXD1l29v5+SjIyTH5u7n7+sLN27vSbXWFhcjPyjLZ1s3bG+7FE0NVpq3+9m3k3bhRLW1dPT3h4ecHABB6PXIzM6ulbb6Zz5WzKygAnnsO+P13uZLz99/LCzB3mjvXuLDQpEly3kROHUFEjmrVKrmyc0CA7I1NRM6J5zpE5NSEk8nOzhYARDYgYjFDAEI0xGWRBw8hAHGkTp0y7TWyU1m5t5P+/mXaXlepTLY96+1dpu01tdpk24seHmXaXvTwMNn2mlpdpu1Zb2+Tba+rVGXanvT3N9lWc8dH40idOibbijvaHoiMNNtWk55uaLuvcWOzba8nJRnaJrZoabbtqrh94v/+T4glS4RYG9nebNu+jbaI5s2FqFdPiA9du5ts5w0IAALQCECICehjdr/dsdBw9y0MMB+DS5zw8xPi8ceF2PPaMLNtD7z7rvH4vvuu2bb7hg0z/t3i4sy2TRwwwPh5WLjQbNs9ffoYP2erV5tv27278fO7ZYv5tu3bG/9d7NtnPt6WLQ1tryclmT8OjRsb2mrS08223RUeLgCI7Oxs4ewMOTI7W+j1Qrz8sjxMfn5CnDlT/mvef994OKdNE0Kvt27MZEUazV35UaPRKB0V1bDSecGZlRyHP//MFmFh8p/CggVKR0U2o1R+1KSnF58/Mkc6OuZHInJmTttD8SrqYh7kWJX5mABPFCgckfPYvx+4pQdu3ADCs8y37dkT+LMQ0GiAublAdzNtp88AUoq351YQw+U/gPPF20UWxNymjVy1MfgCANMd3TDgBWBYP8DLC7i9BMA+021jY4H/N11u/zLcgiCIrGzaNDmUx9UV2LgRaNmy7PNCyDYffijvf/gh8K9/WT9OIiJrWrJErurcqJEc7kxERETkjFRCCKF0EPHx8Zg3bx7S0tLQpk0bfPLJJ+jYsaPJ9hs2bEBsbCySk5PRpEkTzJkzB3369LHovXJycuDv74++vdPx/Q8h6NqlENs3ZRm6q6vdOeS5vLb5WVm4XVCI/Hzg1i0gJwfIzpa3mzeBHF0IbtyQRcIbf2XhxvVCZGbC8FheqRGluQhGyQLj7siBK0wPNzXV1stTDrv08pI/PT0Bd/8g+NRyhY8P4OOmgY97Lry9jW28vGC4XyskCL5+rvD2BjxUGrirco3PCy1qNW4kjwHkHGEajQY+Pj4VDqX2DAiAuniZ5qJcOeTZlNLDmCvTlkOeq3/IszY/H6H16yM7Oxt+xa+xFdbMj4AxR8bHZ2PUKHksVqwAXn+9bDsh5NDmefPk/XnzjCs7kwPTauXVFdydH8lxleQFW8yRlVXZnFpayXHw9MxGfr4fNmwAXnihhgMm+1EqP2rT0+EbGgqAOdLROVJ+JCKqLMULit9++y0GDx6MTz/9FJ06dcKiRYuwYcMGXLhwASGlClolDhw4gG7dumHWrFl45plnsHbtWsyZMwcnTpxAyzu7z5SjJOkD2XBx8cOJE7L3mbPQ6WQR8O+/ZSHQUAS8YbxfUijMyir7MztbroZdVWo1ULs2EBQkfwYGyrmH/PwAf3/5088PqFVL3nx9ZSHwzp9eXjW8Eje/MDsdWz0ZtHZ+BIzHQq3Ohk7nh6lT5XyIJfR64LvvgNmzgSNH5GOLF8vVyskJMD86JVvNkZVV2Zx6p9LnkF26+OGXXzh/GpXCgqJTcpT8SERUFYoXFDt16oQOHTpg6dKlAORKaFFRUXj77bcxefLku9oPHDgQWq0WW7duNTz28MMP48EHH8Snn35a4fuVPhkcOdIPFrxEcfri4cEZGfKWni4LgoWFZW8FBXKBEK227E2jMRYNs7Jkz6J7oVIZi4AlhcGgIHkLDASCg4E6deTPku3ateVr7OLEW6sFQkIgAIQIgVyVChkZGTwZdGC2ejJo7fwIlM2RL73kh6+/lgX8ggLgq69kT8QLF2RbT0859G/EiHv+VcleMD86JVvNkZVV2Zx6p9L58eBBPzz8cA0HTPalOD8CgPbKFYQ0bAgAzJEOzlHyIxFRVSg6h2JhYSGOHz+OKVOmGB5zcXFBdHQ0Dh48WO5rDh48iHHjxpV5rFevXtiyZUu57QsKClBQYJwfMad4+Kebmxy6O1yhuevKK+rpdGWHEpfcbtyQz1UnX9+yxcCSnwEBxpu/v/FnyS0gQL62RnsIKs3HB9BqoQJwXelYyGlZIz8CpnNkcLAsGL7xhryosXMn8Ndfsk1AgJw37J13DN+dyFkwP5KdqkpONZUfn38eLCbS3YrzIyB7cGuLt4mIiByVogXFzMxM6HQ6hBYPCSgRGhqK8+fPl/uatLS0ctunpaWV237WrFmYMWPGXY8XFcnFBuxJYCAQGiq/wNeuLYf+ursbb25ucg5Ab295TlNyKykelu5F6Oam9G9DROZYIz8CpnNkZiawenXZxyIjgXfflUXGWrUs+z2IiGxBVXKqqfwYF1cjIRIRERHZFYdf5XnKlClleuzk5OQgKioKU6capjlRzJ3Df0sPJS59CwqSw4aL1/ogIqo2pnLktGmyh2KJBg2AmBjmISJyHqbyY4MGysVEREREZCsULSgGBwdDrVYjPT29zOPp6ekICwsr9zVhYWGVau/h4QEPD4+7Hp80SRbviO6Snw/ExECn1yNGCBSp1di4cSM8S1dXiGqYNfIjYDpHjh/PHEnlYH4kO1WVnGoqPxKVqzg/AkD+118j5pVXAIA5koiIHJaiM+G5u7ujXbt2SEhIMDym1+uRkJCAzp07l/uazp07l2kPALt27TLZnqjSdDpg+3aod+zArp07sX37duiqexJLogowP5JNYn4kO1WVnEpUKcX5Edu3Q1dYiO3btzNHEhGRQ1N8yPO4cePw2muvoX379ujYsSMWLVoErVaLoUOHAgAGDx6MyMhIzJo1CwAwZswYdO/eHQsWLMDTTz+NdevW4dixY/j888+V/DWIiKod8yMRUfWpKKcSERERkeUULygOHDgQ169fx7Rp05CWloYHH3wQO3bsMEyaffXqVbiUWlK4S5cuWLt2Lf79739j6tSpaNKkCbZs2YKWLVsq9SsQEdUI5kcioupTUU4lIiIiIsuphBBC6SCsKScnB/7+/sjOzoYfJwij8mi1hhV7fADkAtBoNPDx8VE0LKo5zAtGPBZkFvOjU2JekHgcyKxS+VGbng7f4kI1c6RjY14gImem6ByKREREREREREREZF9YUCQiIiIiIiIiIiKLKT6HorWVjPDOyclROBKyWVqtYbNkPoCcnByu0ufASvKBk80AUS7mSDKL+dEpMUdKzI9kVqn8qL11y7DNHOnYmB+JyJk5XUHxVvF/8FFRUQpHQvYkIiJC6RDICm7dugV/f3+lw1AUcyRVFvOj83D2HMn8SBa77z7DJnOkc3D2/EhEzsnpFmXR6/X466+/UKtWLahUKqXDsVhOTg6ioqJw7do1u5rwl3FbF+OuGiEEbt26hYiIiDKrJjsj5kjrsceYAcZtbbYQN3OkxPxoXYzbuhh31TA/EpEzc7oeii4uLqhbt67SYVSZn5+fXf0nX4JxWxfjrjxeVZaYI63PHmMGGLe1KR03cyTzo1IYt3Ux7spjfiQiZ8XLKERERERERERERGQxFhSJiIiIiIiIiIjIYiwo2gkPDw/ExcXBw8ND6VAqhXFbF+MmZ2WPnyF7jBlg3NZmr3GT7bDXzxDjti7GTUREleV0i7IQERERERERERFR1bGHIhEREREREREREVmMBUUiIiIiIiIiIiKyGAuKREREREREREREZDEWFG3ArFmz0KFDB9SqVQshISHo378/Lly4YPY1q1evhkqlKnPz9PS0UsTS9OnT74qhefPmZl+zYcMGNG/eHJ6enmjVqhW2b99upWiNGjRocFfcKpUKo0aNKre9Usf6559/Rt++fREREQGVSoUtW7aUeV4IgWnTpiE8PBxeXl6Ijo7GxYsXK9xvfHw8GjRoAE9PT3Tq1AlHjhyxWtxFRUWYNGkSWrVqBR8fH0RERGDw4MH466+/zO6zKp81chzMkdbFHFlzOZL5kaob86N1MT/yHJKIiIxYULQBe/fuxahRo3Do0CHs2rULRUVF6NmzJ7RardnX+fn5ITU11XBLSUmxUsRGLVq0KBPDL7/8YrLtgQMHMGjQIAwbNgwnT55E//790b9/f/z2229WjBg4evRomZh37doFABgwYIDJ1yhxrLVaLdq0aYP4+Phyn587dy6WLFmCTz/9FIcPH4aPjw969eqF/Px8k/v89ttvMW7cOMTFxeHEiRNo06YNevXqhYyMDKvEnZubixMnTiA2NhYnTpzApk2bcOHCBfTr16/C/Vbms0aOhTmSObI89pgjmR+pujE/Mj+Wxx7zY0VxM0cSEdkgQTYnIyNDABB79+412WbVqlXC39/fekGVIy4uTrRp08bi9i+++KJ4+umnyzzWqVMnMXLkyGqOrHLGjBkjGjduLPR6fbnP28KxBiA2b95suK/X60VYWJiYN2+e4bGsrCzh4eEhvvnmG5P76dixoxg1apThvk6nExEREWLWrFlWibs8R44cEQBESkqKyTaV/ayRY2OOtC7myJrJkcyPVBOYH62L+ZHnkEREzow9FG1QdnY2ACAoKMhsO41Gg/r16yMqKgrPPvsszp49a43wyrh48SIiIiLQqFEjvPLKK7h69arJtgcPHkR0dHSZx3r16oWDBw/WdJgmFRYW4quvvsLrr78OlUplsp0tHOvSrly5grS0tDLH09/fH506dTJ5PAsLC3H8+PEyr3FxcUF0dLSif4Ps7GyoVCoEBASYbVeZzxo5NuZI62GOVDZHMj9SZTE/Wg/zI88hiYicHQuKNkav12Ps2LF45JFH0LJlS5PtmjVrhpUrV+K7777DV199Bb1ejy5duuB///uf1WLt1KkTVq9ejR07dmDZsmW4cuUKunbtilu3bpXbPi0tDaGhoWUeCw0NRVpamjXCLdeWLVuQlZWFIUOGmGxjC8f6TiXHrDLHMzMzEzqdzqb+Bvn5+Zg0aRIGDRoEPz8/k+0q+1kjx8UcaV3Mkcr9DZgfqbKYH62L+ZHnkEREzs5V6QCorFGjRuG3336rcG6Pzp07o3Pnzob7Xbp0wf3334/PPvsMH3zwQU2HCQDo3bu3Ybt169bo1KkT6tevj/Xr12PYsGFWieFerVixAr1790ZERITJNrZwrB1RUVERXnzxRQghsGzZMrNtHeGzRtWDOdK6mCOVwfxIVcH8aF3Mj8phjiQisg3soWhDRo8eja1bt2LPnj2oW7dupV7r5uaGtm3b4tKlSzUUXcUCAgLQtGlTkzGEhYUhPT29zGPp6ekICwuzRnh3SUlJwe7duzF8+PBKvc4WjnXJMavM8QwODoZarbaJv0HJiWBKSgp27dpl9spyeSr6rJFjYo60LubIil9TE5gfqSqYH62L+bHi19QU5kgiItvBgqINEEJg9OjR2Lx5M3766Sc0bNiw0vvQ6XQ4c+YMwsPDayBCy2g0Gly+fNlkDJ07d0ZCQkKZx3bt2lXmyq01rVq1CiEhIXj66acr9TpbONYNGzZEWFhYmeOZk5ODw4cPmzye7u7uaNeuXZnX6PV6JCQkWPVvUHIiePHiRezevRu1a9eu9D4q+qyRY2GOZI6sLHvNkcyPVFnMj8yPlWWv+RFgjiQisjlKrghD0ptvvin8/f1FYmKiSE1NNdxyc3MNbV599VUxefJkw/0ZM2aInTt3isuXL4vjx4+Ll156SXh6eoqzZ89aLe7x48eLxMREceXKFbF//34RHR0tgoODRUZGRrkx79+/X7i6uor58+eLc+fOibi4OOHm5ibOnDljtZhL6HQ6Ua9ePTFp0qS7nrOVY33r1i1x8uRJcfLkSQFAfPzxx+LkyZOGlexmz54tAgICxHfffSd+/fVX8eyzz4qGDRuKvLw8wz6eeOIJ8cknnxjur1u3Tnh4eIjVq1eLpKQk8cYbb4iAgACRlpZmlbgLCwtFv379RN26dcWpU6fKfN4LCgpMxl3RZ40cG3Mkc2R57DFHMj9SdWN+ZH4sjz3mx4riZo4kIrI9LCjaAADl3latWmVo0717d/Haa68Z7o8dO1bUq1dPuLu7i9DQUNGnTx9x4sQJq8Y9cOBAER4eLtzd3UVkZKQYOHCguHTpksmYhRBi/fr1omnTpsLd3V20aNFCbNu2zaoxl9i5c6cAIC5cuHDXc7ZyrPfs2VPu56IkNr1eL2JjY0VoaKjw8PAQPXr0uOv3qV+/voiLiyvz2CeffGL4fTp27CgOHTpktbivXLli8vO+Z88ek3FX9Fkjx8YcaX3MkTWTI5kfqboxP1of8yPPIYmISFIJIUQVOzcSERERERERERGRk+EcikRERERERERERGQxFhSJiIiIiIiIiIjIYiwoEhERERERERERkcVYUCQiIiIiIiIiIiKLsaBIREREREREREREFmNBkYiIiIiIiIiIiCzGgiIRERERERERERFZjAVFIiIiIiIiIiIishgLilRlycnJUKlUOHXqlMWvGTJkCPr372+2zWOPPYaxY8feU2wqlQpbtmwBYHmclrxv6f1a0/Tp06FSqaBSqbBo0aJ72tfq1asREBBgtfcjclbMkdbDHElkX5gfrYf5kYiIagoLig4sLS0Nb7/9Nho1agQPDw9ERUWhb9++SEhIUDo0q4qKikJqaipatmwJAEhMTIRKpUJWVlal95WamorevXtXc4SWadGiBVJTU/HGG2/c9dysWbOgVqsxb968anmvCRMmIDU1FXXr1q2W/RHZIuZIiTmy8pgjydExP0rMj5XH/EhE5DxYUHRQycnJaNeuHX766SfMmzcPZ86cwY4dO/D4449j1KhRSodnVWq1GmFhYXB1db3nfYWFhcHDw6Maoqo8V1dXhIWFwdvb+67nVq5ciYkTJ2LlypXV8l6+vr4ICwuDWq2ulv0R2RrmSCPmyMpjjiRHxvxoxPxYecyPRETOgwVFB/XWW29BpVLhyJEjiImJQdOmTdGiRQuMGzcOhw4dAgC8/vrreOaZZ8q8rqioCCEhIVixYgUAQK/XY+7cubjvvvvg4eGBevXqYebMmeW+p06nw7Bhw9CwYUN4eXmhWbNmWLx4cbltZ8yYgTp16sDPzw///Oc/UVhYaPJ3KSgowIQJExAZGQkfHx906tQJiYmJFh+L0sNVkpOT8fjjjwMAAgMDoVKpMGTIEENbvV6PiRMnIigoCGFhYZg+fXqZfZUerlLeVepTp05BpVIhOTkZgHFoyNatW9GsWTN4e3vjhRdeQG5uLtasWYMGDRogMDAQ77zzDnQ6ncW/U2l79+5FXl4e3n//feTk5ODAgQMWvW7nzp24//774evri6eeegqpqalVen8ie8QcacQcWT7mSHJWzI9GzI/lY34kIiIAuPfLbWRzbty4gR07dmDmzJnw8fG56/mSuU+GDx+Obt26ITU1FeHh4QCArVu3Ijc3FwMHDgQATJkyBcuXL8fChQvx6KOPIjU1FefPny/3ffV6PerWrYsNGzagdu3aOHDgAN544w2Eh4fjxRdfNLRLSEiAp6cnEhMTkZycjKFDh6J27domTzJHjx6NpKQkrFu3DhEREdi8eTOeeuopnDlzBk2aNKnUsYmKisLGjRsRExODCxcuwM/PD15eXobn16xZg3HjxuHw4cM4ePAghgwZgkceeQRPPvlkpd6ntNzcXCxZsgTr1q3DrVu38Pzzz+O5555DQEAAtm/fjj/++AMxMTF45JFHDMe9MlasWIFBgwbBzc0NgwYNwooVK9ClS5cKY5o/fz6+/PJLuLi44B//+AcmTJiAr7/+uqq/JpHdYI40jTnSGBNzJDkj5kfTmB+NMTE/EhERAECQwzl8+LAAIDZt2lRh2wceeEDMmTPHcL9v375iyJAhQgghcnJyhIeHh1i+fHm5r71y5YoAIE6ePGly/6NGjRIxMTGG+6+99poICgoSWq3W8NiyZcuEr6+v0Ol0QgghunfvLsaMGSOEECIlJUWo1Wrx559/ltlvjx49xJQpU0y+LwCxefPmcuPcs2ePACBu3rxZ5jXdu3cXjz76aJnHOnToICZNmlTufsvbz8mTJwUAceXKFSGEEKtWrRIAxKVLlwxtRo4cKby9vcWtW7cMj/Xq1UuMHDnS5O8TFxcn2rRpc9fj2dnZwsvLS5w6dcrw/r6+vmX2fafyYoqPjxehoaF3ta1fv75YuHChyX0R2SPmSOZI5kii8jE/Mj8yPxIRkaU45NkBCSEsbjt8+HCsWrUKAJCeno4ffvgBr7/+OgDg3LlzKCgoQI8ePSzeX3x8PNq1a4c6derA19cXn3/+Oa5evVqmTZs2bcrM4dK5c2doNBpcu3btrv2dOXMGOp0OTZs2ha+vr+G2d+9eXL582eK4LNW6desy98PDw5GRkXFP+/T29kbjxo0N90NDQ9GgQQP4+vqWeawq7/PNN9+gcePGaNOmDQDgwQcfRP369fHtt99WKqbq+D2J7AVzZNUxRxI5NubHqmN+JCIiZ8Mhzw6oSZMmUKlUJoeVlDZ48GBMnjwZBw8exIEDB9CwYUN07doVAMoM47DEunXrMGHCBCxYsACdO3dGrVq1MG/ePBw+fLhKvwcAaDQaqNVqHD9+/K7JnUufTFUXNze3MvdVKhX0en25bV1cZD2+9Ml3UVGRRfuszPuYs2LFCpw9e7bMZOF6vR4rV67EsGHDTL6uvPevzJcIInvGHFl1zJFEjo35seqYH4mIyNmwoOiAgoKC0KtXL8THx+Odd965aw6crKwswxw4tWvXRv/+/bFq1SocPHgQQ4cONbRr0qQJvLy8kJCQgOHDh1f4vvv370eXLl3w1ltvGR4r7wrw6dOnkZeXZzjZPHToEHx9fREVFXVX27Zt20Kn0yEjI8Nwknqv3N3dAaDKE1iXqFOnDgAgNTUVgYGBAOSE2tZy5swZHDt2DImJiQgKCjI8fuPGDTz22GM4f/48mjdvbrV4iOwFc6R5zJFEzov50TzmRyIiIiMOeXZQ8fHx0Ol06NixIzZu3IiLFy/i3LlzWLJkCTp37lym7fDhw7FmzRqcO3cOr732muFxT09PTJo0CRMnTsQXX3yBy5cv49ChQ4bV++7UpEkTHDt2DDt37sTvv/+O2NhYHD169K52hYWFGDZsGJKSkrB9+3bExcVh9OjRhqu1pTVt2hSvvPIKBg8ejE2bNuHKlSs4cuQIZs2ahW3btlXp2NSvXx8qlQpbt27F9evXodFoqrSf++67D1FRUZg+fTouXryIbdu2YcGCBVXaV1WsWLECHTt2RLdu3dCyZUvDrVu3bujQoYPh77R06dJKDTkicgbMkaYxRxI5N+ZH05gfiYiIjFhQdFCNGjXCiRMn8Pjjj2P8+PFo2bIlnnzySSQkJGDZsmVl2kZHRyM8PBy9evVCREREmediY2Mxfvx4TJs2Dffffz8GDhxocp6UkSNH4vnnn8fAgQPRqVMn/P3332WuNJfo0aMHmjRpgm7dumHgwIHo168fpk+fbvJ3WbVqFQYPHozx48ejWbNm6N+/P44ePYp69epV/sAAiIyMxIwZMzB58mSEhoZi9OjRVdqPm5sbvvnmG5w/fx6tW7fGnDlz8OGHH1ZpX5VVWFiIr776CjExMeU+HxMTgy+++AJFRUXIzMyskbmCiOwZc6RpzJFEzo350TTmRyIiIiOV4KQXTk+j0SAyMhKrVq3C888/r3Q4VI7p06djy5YtVh0OAwANGjTA2LFjMXbsWKu+L5EtYY60fcyRRMpgfrR9zI9ERFRT2EPRien1emRkZOCDDz5AQEAA+vXrp3RIZMaZM2fg6+uL//znPzX+Xh999BF8fX3vWl2RyJkwR9oX5kgi62F+tC/Mj0REVBPYQ9GJJScno2HDhqhbty5Wr17NOVJs2I0bN3Djxg0AciJvf39/h3o/IlvEHGk/mCOJrIv50X4wPxIRUU1hQZGIiIiIiIiIiIgsxiHPREREREREREREZDEWFImIiIiIiIiIiMhiLCgSERERERERERGRxVhQJCIiIiIiIiIiIouxoEhEREREREREREQWY0GRiIiIiIiIiIiILMaCIhEREREREREREVmMBUUiIiIiIiIiIiKyGAuKREREREREREREZLH/D8BQdK+ElC/vAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -557,14 +561,16 @@ } ], "source": [ + "# Skip the MSMR example parameter set since we need to set up the ESOH solver differently\n", + "all_parameter_sets.remove(\"MSMR_Example\")\n", + "# Loop over all parameter sets and solve the ESOH problem\n", "for parameter_set in all_parameter_sets:\n", " print(parameter_set)\n", " try:\n", " sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)\n", " fig, axes = plot_sweep(sweep, sol_init_QLi, sol_init_Q, parameter_set)\n", " except ValueError:\n", - " pass\n", - " # print(\"success\")" + " pass" ] }, { @@ -579,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -588,26 +594,26 @@ "text": [ "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[3] Daniel R Baker and Mark W Verbrugge. Multi-species, multi-reaction model for porous intercalation electrodes: part i. model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode. Journal of The Electrochemical Society, 165(16):A3952, 2018.\n", + "[4] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", "[5] Madeleine Ecker, Stefan Käbitz, Izaro Laresgoiti, and Dirk Uwe Sauer. Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: II. Model Validation. Journal of The Electrochemical Society, 162(9):A1849–A1857, 2015. doi:10.1149/2.0541509jes.\n", "[6] Madeleine Ecker, Thi Kim Dung Tran, Philipp Dechent, Stefan Käbitz, Alexander Warnecke, and Dirk Uwe Sauer. Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: I. Determination of Parameters. Journal of the Electrochemical Society, 162(9):A1836–A1848, 2015. doi:10.1149/2.0551509jes.\n", "[7] Alastair Hales, Laura Bravo Diaz, Mohamed Waseem Marzook, Yan Zhao, Yatish Patel, and Gregory Offer. The cell cooling coefficient: a standard to define heat rejection from lithium-ion batteries. Journal of The Electrochemical Society, 166(12):A2383, 2019.\n", "[8] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", "[9] Gi-Heon Kim, Kandler Smith, Kyu-Jin Lee, Shriram Santhanagopalan, and Ahmad Pesaran. Multi-domain modeling of lithium-ion batteries encompassing multi-physics in varied length scales. Journal of the Electrochemical Society, 158(8):A955–A969, 2011. doi:10.1149/1.3597614.\n", - "[10] Michael J. Lain, James Brandon, and Emma Kendrick. Design strategies for high power vs. high energy lithium ion cells. Batteries, 5(4):64, 2019. doi:10.3390/batteries5040064.\n", - "[11] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", - "[12] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[13] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n", - "[14] Andreas Nyman, Mårten Behm, and Göran Lindbergh. Electrochemical characterisation and modelling of the mass transport phenomena in lipf6–ec–emc electrolyte. Electrochimica Acta, 53(22):6356–6365, 2008.\n", - "[15] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", - "[16] Kieran O'Regan, Ferran Brosa Planella, W. Dhammika Widanage, and Emma Kendrick. Thermal-electrochemical parameters of a high energy lithium-ion cylindrical battery. Electrochimica Acta, 425:140700, 2022. doi:10.1016/j.electacta.2022.140700.\n", - "[17] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[18] Eric Prada, D. Di Domenico, Y. Creff, J. Bernard, Valérie Sauvant-Moynot, and François Huet. A simplified electrochemical and thermal aging model of LiFePO4-graphite Li-ion batteries: power and capacity fade simulations. Journal of The Electrochemical Society, 160(4):A616, 2013. doi:10.1149/2.053304jes.\n", - "[19] P Ramadass, Bala Haran, Parthasarathy M Gomadam, Ralph White, and Branko N Popov. Development of first principles capacity fade model for li-ion cells. Journal of the Electrochemical Society, 151(2):A196, 2004. doi:10.1149/1.1634273.\n", - "[20] Giles Richardson, Ivan Korotkin, Rahifa Ranom, Michael Castle, and Jamie M. Foster. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: systematic derivation and validation. Electrochimica Acta, 339:135862, 2020. doi:10.1016/j.electacta.2020.135862.\n", - "[21] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[22] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[10] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n", + "[11] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[12] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n", + "[13] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", + "[14] Kieran O'Regan, Ferran Brosa Planella, W. Dhammika Widanage, and Emma Kendrick. Thermal-electrochemical parameters of a high energy lithium-ion cylindrical battery. Electrochimica Acta, 425:140700, 2022. doi:10.1016/j.electacta.2022.140700.\n", + "[15] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[16] Eric Prada, D. Di Domenico, Y. Creff, J. Bernard, Valérie Sauvant-Moynot, and François Huet. A simplified electrochemical and thermal aging model of LiFePO4-graphite Li-ion batteries: power and capacity fade simulations. Journal of The Electrochemical Society, 160(4):A616, 2013. doi:10.1149/2.053304jes.\n", + "[17] P Ramadass, Bala Haran, Parthasarathy M Gomadam, Ralph White, and Branko N Popov. Development of first principles capacity fade model for li-ion cells. Journal of the Electrochemical Society, 151(2):A196, 2004. doi:10.1149/1.1634273.\n", + "[18] Giles Richardson, Ivan Korotkin, Rahifa Ranom, Michael Castle, and Jamie M. Foster. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: systematic derivation and validation. Electrochimica Acta, 339:135862, 2020. doi:10.1016/j.electacta.2020.135862.\n", + "[19] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[20] Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao, and Wentian Gu. Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide. Journal of The Electrochemical Society, 164(11):E3243, 2017.\n", + "[21] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[22] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n", "[23] Yan Zhao, Yatish Patel, Teng Zhang, and Gregory J Offer. Modeling the effects of thermal gradients induced by tab and surface cooling on lithium ion cell performance. Journal of The Electrochemical Society, 165(13):A3169, 2018.\n", "\n" ] @@ -620,7 +626,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -651,7 +657,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb index 905850a7fd..258c37c885 100644 --- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb +++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb @@ -23,6 +23,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "zsh:1: no matches found: pybamm[plot,cite]\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } @@ -114,8 +115,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Safe: 153.951 ms\n", - "Fast: 88.029 ms\n" + "Safe: 125.714 ms\n", + "Fast: 77.698 ms\n" ] }, { @@ -160,17 +161,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 506.167 and h = 8.15178e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 506.167, , mxstep steps taken before reaching tout.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Safe: 403.072 ms\n", + "Safe: 7.791 s\n", "Solving fast mode, error occured: Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:1401:\n", "Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:330:\n", - ".../casadi/interfaces/sundials/idas_interface.cpp:564: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n" + ".../casadi/interfaces/sundials/idas_interface.cpp:604: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n" ] }, { @@ -203,7 +204,7 @@ "try:\n", " sim.solve([0,4500], solver=fast_solver, inputs={\"Crate\": 1})\n", "except pybamm.SolverError as e:\n", - " print(\"Solving fast mode, error occured:\", e.args[0])" + " print(\"Solving fast mode, error occurred:\", e.args[0])" ] }, { @@ -221,7 +222,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99244556077647bcaf7b21b9b1c40acb", + "model_id": "d84d30bf7d8d4df1a330e8c9a69267a1", "version_major": 2, "version_minor": 0 }, @@ -345,12 +346,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "With dt_max=10, took 669.420 ms (integration time: 590.350 ms)\n", - "With dt_max=20, took 686.120 ms (integration time: 599.137 ms)\n", - "With dt_max=100, took 369.348 ms (integration time: 319.797 ms)\n", - "With dt_max=1000, took 91.474 ms (integration time: 57.455 ms)\n", - "With dt_max=3700, took 57.838 ms (integration time: 37.095 ms)\n", - "With 'fast' mode, took 49.520 ms (integration time: 37.400 ms)\n" + "With dt_max=10, took 610.021 ms (integration time: 534.636 ms)\n", + "With dt_max=20, took 686.939 ms (integration time: 536.861 ms)\n", + "With dt_max=100, took 338.657 ms (integration time: 291.815 ms)\n", + "With dt_max=1000, took 83.867 ms (integration time: 51.518 ms)\n", + "With dt_max=3700, took 52.384 ms (integration time: 32.960 ms)\n", + "With 'fast' mode, took 44.846 ms (integration time: 32.949 ms)\n" ] } ], @@ -395,20 +396,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "With dt_max=10, took 588.138 ms (integration time: 489.722 ms)\n", - "With dt_max=20, took 581.809 ms (integration time: 484.621 ms)\n", - "With dt_max=100, took 329.091 ms (integration time: 272.181 ms)\n", - "With dt_max=1000, took 113.543 ms (integration time: 69.477 ms)\n", - "With dt_max=3600, took 939.024 ms (integration time: 36.933 ms)\n" + "With dt_max=10, took 541.980 ms (integration time: 447.827 ms)\n", + "With dt_max=20, took 518.415 ms (integration time: 428.332 ms)\n", + "With dt_max=100, took 300.344 ms (integration time: 245.695 ms)\n", + "With dt_max=1000, took 101.787 ms (integration time: 60.608 ms)\n", + "With dt_max=3600, took 516.396 ms (integration time: 32.718 ms)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "At t = 460.712 and h = 1.83699e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 460.712, , mxstep steps taken before reaching tout.\n", - "At t = 460.712, , mxstep steps taken before reaching tout.\n" + "At t = 460.712 and h = 4.16966e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 460.712 and h = 5.11965e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 460.712 and h = 8.91111e-13, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] } ], @@ -461,7 +462,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Took 828.886 ms\n" + "Took 813.671 ms\n" ] } ], @@ -524,7 +525,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Took 709.638 ms\n" + "Took 629.273 ms\n" ] }, { @@ -571,14 +572,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 1262.29 and h = 1.11482e-19, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 1262.29 and h = 1.0534e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Took 699.760 ms\n" + "Took 539.358 ms\n" ] }, { @@ -625,7 +626,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Took 309.979 ms\n" + "Took 289.618 ms\n" ] }, { @@ -809,7 +810,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -854,16 +855,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Exact: 171.144 us\n", - "Smooth, k=5: 161.840 us\n", - "Smooth, k=10: 137.627 us\n", - "Smooth, k=100: 178.807 us\n" + "Exact: 172.240 us\n", + "Smooth, k=5: 161.790 us\n", + "Smooth, k=10: 150.367 us\n", + "Smooth, k=100: 193.054 us\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a7ef8a815d03434bb3f1f4283d50018b", + "model_id": "13ea3acf77af43019375fb4f53395b28", "version_major": 2, "version_minor": 0 }, @@ -986,7 +987,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pybamm", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -1000,7 +1001,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.9.16" }, "toc": { "base_numbering": 1, @@ -1017,7 +1018,7 @@ }, "vscode": { "interpreter": { - "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c" + "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c" } } }, diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py index db7cef275c..ad751c3911 100644 --- a/pybamm/models/submodels/particle/base_particle.py +++ b/pybamm/models/submodels/particle/base_particle.py @@ -131,6 +131,7 @@ def _get_standard_concentration_variables( / c_scale, f"Maximum {domain} {phase_name}particle concentration": pybamm.max(c_s) / c_scale, + f"Minimum {domain} {phase_name}particle " "surface concentration": pybamm.min(c_s_surf) / c_scale, f"Maximum {domain} {phase_name}particle " "surface concentration": pybamm.max(c_s_surf) / c_scale, diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py index 09fa5d86f7..65ab913e97 100644 --- a/pybamm/models/submodels/particle/msmr_diffusion.py +++ b/pybamm/models/submodels/particle/msmr_diffusion.py @@ -393,7 +393,6 @@ def _get_standard_potential_variables(self, U): f"Minimum {domain} {phase_name}particle potential [V]" "": pybamm.min(U), f"Maximum {domain} {phase_name}particle potential [V]" "": pybamm.max(U), f"Minimum {domain} {phase_name}particle " - f"Minimum {domain} {phase_name}particle " "surface potential [V]": pybamm.min(U_surf), f"Maximum {domain} {phase_name}particle " "surface potential [V]": pybamm.max(U_surf), From 464f8eb0ad3a6ea161dd823faf4e68c3e3d6974f Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 13 Sep 2023 10:32:03 +0100 Subject: [PATCH 093/154] fix a bug in the thermal boundary condition of the 1+1D pouch model --- .../thermal/pouch_cell/pouch_cell_1D_current_collectors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py index 05caeaa6cb..d9e41f4843 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py @@ -131,7 +131,7 @@ def set_boundary_conditions(self, variables): bottom_cooling_coefficient = ( param.n.h_tab * neg_tab_area * neg_tab_bottom_bool + param.p.h_tab * pos_tab_area * pos_tab_bottom_bool - + 10 * non_tab_bottom_area + + param.h_edge(L_y / 2, 0) * non_tab_bottom_area ) / total_area # just use left and right for clarity From c3d6355425b232ff4f9f028c18e569fa1ae49727 Mon Sep 17 00:00:00 2001 From: bobonice Date: Wed, 13 Sep 2023 18:12:11 +0800 Subject: [PATCH 094/154] #3323 regenerating figures in the tutorial with new code --- ...ating_mechanical_models_Enertech_DFN.ipynb | 26 ++++++++++++------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb index d3d5159a66..bc04f92fbc 100644 --- a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb +++ b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -64,14 +64,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.5 C\n", + "0.5 C\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "1 C\n", "2 C\n" ] @@ -125,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -155,12 +161,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -240,12 +246,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -296,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "metadata": {}, "outputs": [ { From a26d8abddf07c1496042f416f96a7f3256131378 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 13 Sep 2023 11:32:29 +0100 Subject: [PATCH 095/154] update pouch models --- .../current_collector/potential_pair.py | 26 ++++++------------- .../pouch_cell_1D_current_collectors.py | 2 +- .../pouch_cell_2D_current_collectors.py | 4 ++- pybamm/parameters/lithium_ion_parameters.py | 1 + pybamm/parameters/thermal_parameters.py | 10 +++++++ 5 files changed, 23 insertions(+), 20 deletions(-) diff --git a/pybamm/models/submodels/current_collector/potential_pair.py b/pybamm/models/submodels/current_collector/potential_pair.py index 9ca0d99c6c..f2fd3aee83 100644 --- a/pybamm/models/submodels/current_collector/potential_pair.py +++ b/pybamm/models/submodels/current_collector/potential_pair.py @@ -80,12 +80,13 @@ def set_boundary_conditions(self, variables): param = self.param applied_current_density = variables["Total current density [A.m-2]"] - cc_area = self._get_effective_current_collector_area() + total_current = applied_current_density * param.A_cc - # cc_area appears here due to choice of non-dimensionalisation - pos_tab_bc = ( - -applied_current_density * cc_area / (param.p.sigma_cc * param.p.L_cc) - ) + # In the 1+1D model, the behaviour is averaged over the y-direction, so the + # effective tab area is the cell width multiplied by the current collector + # thickness + positive_tab_area = param.L_y * param.p.L_cc + pos_tab_bc = -total_current / (param.p.sigma_cc * positive_tab_area) # Boundary condition needs to be on the variables that go into the Laplacian, # even though phi_s_cp isn't a pybamm.Variable object @@ -100,10 +101,6 @@ def set_boundary_conditions(self, variables): }, } - def _get_effective_current_collector_area(self): - """In the 1+1D models the current collector effectively has surface area l_z""" - return self.param.L_z - class PotentialPair2plus1D(BasePotentialPair): """Base class for a 2+1D potential pair model""" @@ -117,21 +114,18 @@ def set_boundary_conditions(self, variables): param = self.param applied_current_density = variables["Total current density [A.m-2]"] - cc_area = self._get_effective_current_collector_area() + total_current = applied_current_density * param.A_cc # Note: we divide by the *numerical* tab area so that the correct total # current is applied. That is, numerically integrating the current density # around the boundary gives the applied current exactly. - positive_tab_area = pybamm.BoundaryIntegral( pybamm.PrimaryBroadcast(param.p.L_cc, "current collector"), region="positive tab", ) # cc_area appears here due to choice of non-dimensionalisation - pos_tab_bc = ( - -applied_current_density * cc_area / (param.p.sigma_cc * positive_tab_area) - ) + pos_tab_bc = -total_current / (param.p.sigma_cc * positive_tab_area) # Boundary condition needs to be on the variables that go into the Laplacian, # even though phi_s_cp isn't a pybamm.Variable object @@ -160,7 +154,3 @@ def set_boundary_conditions(self, variables): "positive tab": (pos_tab_bc, "Neumann"), }, } - - def _get_effective_current_collector_area(self): - """Return the area of the current collector.""" - return self.param.L_y * self.param.L_z diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py index d9e41f4843..2d93ea4a9e 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py @@ -82,7 +82,7 @@ def set_rhs(self, variables): self.rhs = { T_av: ( - pybamm.laplacian(T_av) + pybamm.div(self.param.lambda_eff(T_av) * pybamm.grad(T_av)) + Q_av + total_cooling_coefficient * (T_av - T_amb) ) diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py index adaf5d25a0..151b8c6938 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py @@ -82,9 +82,11 @@ def set_rhs(self, variables): # correct mass matrix when discretised. The first argument is the source term # and the second argument is the variable governed by the equation that the # source term appears in. + # Note: not correct if lambda_eff is a function of T_av - need to implement div + # in 2D rather than doing laplacian directly self.rhs = { T_av: ( - pybamm.laplacian(T_av) + self.param.lambda_eff(T_av) * pybamm.laplacian(T_av) + pybamm.source(Q_av, T_av) + pybamm.source(yz_surface_cooling_coefficient * (T_av - T_amb), T_av) + pybamm.source( diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index a8558d55dc..0fe8c58167 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -63,6 +63,7 @@ def _set_parameters(self): self.h_edge = self.therm.h_edge self.h_total = self.therm.h_total self.rho_c_p_eff = self.therm.rho_c_p_eff + self.lambda_eff = self.therm.lambda_eff # Macroscale geometry self.L_x = self.geo.L_x diff --git a/pybamm/parameters/thermal_parameters.py b/pybamm/parameters/thermal_parameters.py index 9cfd09691f..ea1dd12065 100644 --- a/pybamm/parameters/thermal_parameters.py +++ b/pybamm/parameters/thermal_parameters.py @@ -72,6 +72,16 @@ def rho_c_p_eff(self, T): + self.p.rho_c_p_cc(T) * self.geo.p.L_cc ) / self.geo.L + def lambda_eff(self, T): + """Effective thermal conductivity [W.m-1.K-1]""" + return ( + self.n.lambda_cc(T) * self.geo.n.L_cc + + self.n.lambda_(T) * self.geo.n.L + + self.s.lambda_(T) * self.geo.s.L + + self.p.lambda_(T) * self.geo.p.L + + self.p.lambda_cc(T) * self.geo.p.L_cc + ) / self.geo.L + class DomainThermalParameters(BaseParameters): def __init__(self, domain, main_param): From 9be159595ed2a9c9cb43007ce73999cbda194ac9 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 13 Sep 2023 13:58:46 +0100 Subject: [PATCH 096/154] fix missing conductivity in bc --- .../notebooks/models/pouch-cell-model.ipynb | 19 ++++++++++++++----- .../pouch_cell_1D_current_collectors.py | 15 +++++++++------ .../pouch_cell_2D_current_collectors.py | 6 ++++-- 3 files changed, 27 insertions(+), 13 deletions(-) diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb index 99f1d06cee..d5d291d5b4 100644 --- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb +++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb @@ -77,7 +77,16 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:835: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n", + " options = BatteryModelOptions(extra_options)\n" + ] + } + ], "source": [ "cc_model = pybamm.current_collector.EffectiveResistance({\"dimensionality\": 1})\n", "dfn_av = pybamm.lithium_ion.DFN({\"thermal\": \"lumped\"}, name=\"Average DFN\")\n", @@ -566,7 +575,7 @@ "outputs": [ { "data": { - "image/png": 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V8/3332P58uUYMWJEtS3/ThAEQdQcu3fvBgCceuqpxuPefq9cZVKeLIryZlz89re/RW5uLr777jvMnj0bv/vd7wJlIpEIZs2ahe+//x7Tp0/H8ccfj0ceeQRdunRR4q1v2R5E9fLII48oA+3ly5fjpptuUvbJIkFlwTmv1mWridqBft3vvPNOrF27VmzXXntt4JzmzZvjjjvuwJQpU5SJ6L364rVbF6BnnAiCCOWbb77BwYMH0bVrVwDAl19+CQAio2ft2rVo27YtmjRpUmM+xktOTg46duyIl19+Gffccw9efvllcM7psS2CIIhjhFatWgEA1q1bh969eweOr1u3TilXmbRv3x6MMWzatCm0zCmnnAIA2LhxI84666zA8Y0bNxpXvGzatCkuvvhijB49GkeOHMGFF16IAwcOGG0cf/zxuOaaa3DNNdfg4YcfximnnILnnnsODz74IE455RTlr++6bdlH4uhp0KABCgsLa8RuVXHTTTfhiiuuEO9HjhyJ4cOH47LLLhP7TI/EHC0bN26sE4+P1ka8Pij3izvvvBMTJkwITIfiZfDLq1uPGzcON9xwQ+CPpl6mjVz2uuuuq0zXA9e9WbNmOPnkk2OeN3HiRDzzzDN45plnlP3NmzdHenp61M9pwP8c3LRpE7p161YBz6sHyughCCIUb4ldbz6BL774As2aNUNWVhZ++uknfPjhh7jkkktq0sVyMXLkSKxbtw5ffvkl5s6di/bt26Nnz5417RZBEARRDfTr1w/t2rXDI488EpifwbZtTJs2DdnZ2ejXr1+l287IyMDAgQPx9NNPi2xYmX379mHAgAHIyMjAX//618DxN998E1u2bMFVV11lrP93v/sdli5dimuvvTbuLNXjjjsOrVq1Ev6MGDECW7ZswVtvvRUo+9e//hVNmzbFBRdcEFfdRGwYY2jYsGG1b1WZ+ZKRkYGTTz5ZbKmpqWjRooWyr7LnUl2yZAm++uorDB8+vFLrPVYw9YukpCQ0bNgQycnJxrLyo6+JiYlo2LAhUlJS4ipbWRzNdW/UqBHuv/9+TJ06VRHFLcvCiBEjMGfOHOzatStwXmFhIUpLS3HGGWegc+fO+Otf/2qc62ffvn3l9qkqIKGHIIhQjj/+eABAbm4uACejx8vmuf3222FZFiZMmFBT7pUbL3tnypQpWLt2LWXzEARBHENEIhH89a9/xYIFCzBs2DDk5ubiwIEDyM3NxbBhw7BgwQL85S9/qbLHeZ9++mmUlZXhzDPPxOuvv44tW7Zg48aNmDlzJvr06YOGDRvi73//O/773/9izJgx+PLLL7Ft2za88MILuO6663D55Zcr2RIygwYNwo8//oiHHnrIePzvf/87xo4di/feew/ffvst1q9fj0mTJmH9+vVi8uURI0bg0ksvxahRo/DCCy9g27Zt+PLLL3HjjTfizTffxD/+8Q9lIumysjLlMYm1a9eGZgQRdZfCwkJxfQFg69atWLt2baU+ghWvjaKiIuTl5eGHH37AZ599hkceeQSXXHIJLr74YuNjOkT9oCqu+5gxY9CkSZPAQi1Tp05F69at0atXL/zzn//Ehg0bsGXLFrz44ovo1q0bCgsLwRjDrFmz8PXXX6Nfv35455138N133+HLL7/E1KlTa88fwat8XS+CIOo05557LmeM8TvvvJOnp6fzvn378osvvphHIhH+r3/9i3POeVlZGb/lllt406ZNeZMmTXiPHj1iLkv+3Xff8YsuuohnZGTwzMxM/uSTT4pjAPgzzzzD27Vrx5s2bcofeeSRmH5GW15d5qyzzuIAOAC+ZcuW0HK0vDpBEETtozKWpn399dd5u3btxO8CADw7O7tKl1b32LVrFx83bhxv27YtT0pK4scffzwfOnSoWAadc86XLVvGBw4cyNPS0nhSUhLv0qUL/8tf/sJLS0uVuhBlqetffvlFWV79s88+47/97W95dnY2T05O5k2bNuXnnHNOYCn1kpIS/thjj/EuXbrwpKQknpaWxgcOHMg/+ugjpdysWbOU9vO2k0466ajbiKhdfPDBB8ZrPWrUqJjnxru8ejw2Ro0aJfYnJCTw5s2b8/79+/MXX3zRuMQ1UT+I97rHs7y6fnzu3LkcgLK8Ouec79u3j9999928ffv2PCkpibds2ZL379+fz5s3j9u2Lcpt3ryZX3vttTwrK4snJSXxtm3b8quuuop/9tlnoX5U5/LqjPM6MpsQQRA1Ql5eHm644Qa8//77OHLkCJKSktCzZ0889NBD+PWvfw0AWLhwIe677z4sWbIEDRs2xBdffIFTTjkFjRo1MtZZWlqK0047DVdccQXuvvtuFBcXY8uWLcjJyQHgpDNffvnlmDVrFrZt24YePXpg/fr1OOmkk0L9vO6667B06dKYqy4888wzGDduHM4880ysXLkytNwDDzyABx98ED/++COaNWsWo5UIgiCI6uDIkSPYunUrsrOzA48KlIeysjIsX74cu3fvRqtWrdCvXz+amJ8gCIKoUirrd1g80GTMBEFEJTMzE2+99RYWLFiAIUOGYOXKlTjjjDOUMomJiThw4AA2bdqEnj17onv37lHrXLlyJQ4cOIApU6bAsiykpKQIkcfj7rvvRqNGjXDqqafitNNOw1dffRVV6AGcORZ++uknJCQkID093Vjm5ptvxs033xxax5EjR1BYWFgvlx8lCIIgHCKRCM4999yadoMgCIIgqgSao4cgiLjYtGkTGGPo0KFD4Nj555+Pm266CWPGjEGrVq1wxx13oKSkJLSu77//Hm3btlUmaNNp2bKleB3vyhQ7d+5E8+bN0bdv35hlw3juuefQvHlzPPbYYxWugyAIgiAIgiAIoqagjB6CIOJi06ZNaNOmjbJMosztt9+O22+/HTt37sRFF12EU089NXQZxdatW2P79u3gnFfa6g933XUXfvvb3wJA6CNj8TB8+HCceuqp4n1dWDqeIAiCIAiCIAjCg4QegiDiYtOmTejYsaPx2KeffgrOObp164bGjRsjMTFRmevAE3xmz54NADjzzDPRuHFjPPzww7jrrrsCc/RUhM6dO6Nz584VPt+jdevWaN269VHXQxAEQRAEQRAEURPQo1sEQcTFRx99hIULFxqP7d+/H7/73e+Qnp6ODh064Oyzz8bVV18tjn///fc4++yzxfuEhAQsWLAAK1asQKtWrdChQwexhDtBEARBEARBEARRcWjVLYIgqhRvha0vvvgCiYmJNe0OQRAEUYfxVixp165d6KPEBEEQBFEbOXz4MLZt21Ytq25RRg9BEFVKQkICNmzYQCIPQRAEcdR4v0toZUSCIAiirlFcXAwAyhQXVQXN0UMQBEEQBEHUCSKRCNLT07F3714AzqqMlTWpP0EQBEFUFbZt48cff0SDBg2QkFD1MgwJPQRBEARBEESdITMzEwCE2EMQBEEQdQHLstCmTZtq+QMFzdFDEARBEARB1DnKyspQUlJS024QBEEQRFwkJSXBsqpn9hwSegiCIAiCIAiCIAiCIOoJNBkzQRAEQRAEQRAEQRBEPYGEHoIgCIIgCIIgCIIgiHoCCT0EQRAEQRAEQRAEQRD1BBJ6CIIgCIIgCIIgCIIg6gkk9BAEQRAEQRAEQRAEQdQTSOghCIIgCIIgCIIgCIKoJ5DQQxAEQRAEQRAEQRAEUU8goYcgCIIgCIIgCIIgCKKeQEJPDG666SYwxjBjxoxKqe/pp59Gu3btkJKSgl69emHVqlXGcpxzXHjhhWCMYf78+ZViu67H8sADD6Bjx45o2LAhjjvuOPTv3x8rV66scH0yFMv8CturL7GUlJRg0qRJ6Nq1Kxo2bIisrCxce+212LVr11FE4FPd16W+xfPGG29gwIABaNq0KRhjWLt2bYXr0nnttdfQsWNHpKSkoGvXrnjnnXdCy1bG5+h1110HxpiyDRo0qML1ycS6Lueee27A9k033VQptgmCIAiCIIjaAQk9UZg3bx4++eQTZGVlVUp9//73vzFx4kT88Y9/xGeffYbTTz8dAwcOxN69ewNlZ8yYAcZYpdgF6kcsp5xyCv72t7/hq6++wkcffYR27dphwIAB+PHHH4+qXorl6KgvsRw6dAifffYZ7r//fnz22Wd44403sHnzZgwdOvSo6gVq5rrUt3gOHjyIvn374tFHHz3qumRWrFiBq666CqNHj8bnn3+OYcOGYdiwYVi3bl2gbGV+jg4aNAi7d+8W28svv3zUdcZ7XW644QbF9vTp04/aNkEQBEEQBFGL4ISR77//nh9//PF83bp1vG3btvyJJ55Qju/YsYP/5je/4U2aNOHHHXccHzp0KN+6dWvUOs8880w+btw48b6srIxnZWXxadOmKeU+//xzfvzxx/Pdu3dzAHzevHkUi4H9+/dzAPz999+nWCiWKoll1apVHADfvn17nY+F8/oRz9atWzkA/vnnnweO/fLLL3z06NG8WbNmvHHjxvy8887ja9eujVrfFVdcwQcPHqzs69WrF7/xxhuVfbE+R8vDqFGj+CWXXBK1TFVdl1/96lf8tttuq7DvBEEQBEEQRO2HMnoM2LaNa665BnfeeSe6dOkSOF5SUoKBAweicePGWL58OT7++GM0atQIgwYNQnFxsbHO4uJirFmzBv379xf7LMtC//79kZubK/YdOnQIV199NZ5++mlkZmZSLCEUFxfj+eefR5MmTXD66adTLBRLpccCAPv37wdjDOnp6XU+lvoYj85vfvMb7N27F++++y7WrFmD7t274/zzz0d+fn7oObm5uUosADBw4EAlllifoxVh6dKlaNGiBTp06ICxY8fi559/Fseq8roAwJw5c9CsWTOceuqpmDx5Mg4dOlQpMREEQRAEQRC1g4SadqA28uijjyIhIQG33nqr8fi///1v2LaNf/zjH+KRhFmzZiE9PR1Lly7FgAEDAuf89NNPKCsrQ8uWLZX9LVu2xKZNm8T722+/HWeddRYuueQSisXAggULMGLECBw6dAitWrXCokWL0KxZM4qFYqm0WDyOHDmCSZMm4aqrrkJaWlqdjqU+xqPz0UcfYdWqVdi7dy+Sk5MBAH/5y18wf/58/Oc//8GYMWOM5+Xl5RljycvLE+9jfY6Wl0GDBuGyyy5DdnY2vv32W9xzzz248MILkZubi0gkUqXX5eqrr0bbtm2RlZWFL7/8EpMmTcLmzZvxxhtvVEpsBEEQBEEQRM1zzGf0zJkzB40aNRLbhx9+iCeffBKzZ88OnVfiiy++wDfffIPGjRuL8zIyMnDkyBF8++23WL58uVLnnDlz4vLlzTffxJIlSyo8yWd9jmX58uUAgPPOOw9r167FihUrMGjQIFxxxRVi/gmKhWKpjFgAJ6PiiiuuAOcczz77rNhfW2Opb/FEiyWML774AoWFhWjatKly7tatW/Htt99ix44dyv5HHnkkLl/WrFkT83O0vLGMGDECQ4cORdeuXTFs2DAsWLAAq1evxtKlS0UsVXFdAGDMmDEYOHAgunbtipEjR+Kf//wn5s2bh2+//bbcsREEQRAEQRC1k2M+o2fo0KHo1auXeP/aa69h7969aNOmjdhXVlaGP/zhD5gxYwa2bduGwsJC5OTkGL9cN2/eHElJScqKMC1btkRycjIikQj27NmjlN+zZ494rGHJkiX49ttvxWMVHsOHD0e/fv3EIOBYjOX4448HADRs2BAnn3wyTj75ZPTu3Rvt27fHCy+8gMmTJ1MsFEulxOKJItu3b8eSJUtE9guAWhtLfYsnLJZoFBYWolWrVsa609PTkZ6ersSSkZEBAMjMzIway/Lly2N+jh5tLCeeeCKaNWuGb775Bueff36VXRcTnm/ffPMNTjrppKixEARBEARBEHWEmp4kqLbx008/8a+++krZsrKy+KRJk/imTZs455w///zz/LjjjuP79+8vV91nnnkmHz9+vHhfVlbGjz/+eDFR5u7duwO2AfAnn3ySf/fdd8d0LGGceOKJ/I9//CPFQrFUSizFxcV82LBhvEuXLnzv3r2B43UplvoYD+fhkzG/9957PBKJxJywWOeKK67gF198sbKvT58+YjLmeD5Hj5adO3dyxhj/73//yzmvuuti4qOPPuIA+BdffFEx5wmCIAiCIIhaBwk9caCvsHLw4EHevn17fu655/Jly5bx7777jn/wwQf8lltu4Tt37gyt55VXXuHJycl89uzZfMOGDXzMmDE8PT2d5+XlhZ6DSl55p67GUlhYyCdPnsxzc3P5tm3b+Keffsqvv/56npyczNetW0exUCxHHUtxcTEfOnQoP+GEE/jatWv57t27xVZUVFSnYqmP8fz888/8888/52+//TYHwF955RX++eef8927d3POObdtm/ft25effvrp/H//+x/funUr//jjj/k999zDV69eHVrvxx9/zBMSEvhf/vIXvnHjRv7HP/6RJyYm8q+++ir0nKNZdevAgQP8jjvu4Lm5uXzr1q38/fff5927d+ft27fnR44c4ZxX3XX55ptv+EMPPcQ//fRTvnXrVv7f//6Xn3jiifycc86pUCwEQRAEQRBE7YSEnjgwfanfvXs3v/baa3mzZs14cnIyP/HEE/kNN9wQ8y+wTz31FG/Tpg1PSkriZ555Jv/kk0+ilq9qoYfzuhHL4cOH+aWXXsqzsrJ4UlISb9WqFR86dChftWoVxUKxVEosXqaIafvggw/qVCz1MZ5Zs2YZY/EyxzjnvKCggN9yyy08KyuLJyYm8tatW/ORI0fyHTt2RK371Vdf5aeccgpPSkriXbp04W+//XbU8kcj9Bw6dIgPGDCAN2/enCcmJvK2bdvyG264ISCSVcV12bFjBz/nnHN4RkYGT05O5ieffDK/8847y505RBAEQRAEQdRuGOecV9lzYQRBEARBEARRBZSVlaGkpKSm3SAIgiCIuEhKSoJlVc96WMf8ZMwEQRAEQRBE3YFzjry8POzbt6+mXSEIgiCIuLEsC9nZ2UhKSqpyW5TRQxAEQRAEQdQZdu/ejX379qFFixZo0KABGGM17RJBEARBRMW2bezatQuJiYlo06ZNlf/uoowegiAIgiAIok5QVlYmRJ6mTZvWtDsEQRAEETfNmzfHrl27UFpaisTExCq1VT0PiBEEQRAEQRDEUeLNydOgQYMa9oQgCIIgyof3yFZZWVmV2yKhhyAIgiAIgqhT0ONaBEEQRF2jOn93kdBDEARBEARBEARBEARRTyChp5IpKirCAw88gKKiopp2pVKoT/FQLLUTiqV2QrHUTupTLARxLDFt2jT07NkTjRs3RosWLTBs2DBs3rxZKXPkyBGMGzcOTZs2RaNGjTB8+HDs2bNHKbNjxw4MHjwYDRo0QIsWLXDnnXeitLS0OkMh6jE//PADfvvb36Jp06ZITU1F165d8emnn4rjnHNMmTIFrVq1QmpqKvr3748tW7YodeTn52PkyJFIS0tDeno6Ro8ejcLCwuoOhahnLFu2DEOGDEFWVhYYY5g/f36gTGX1zy+//BL9+vVDSkoKWrdujenTp1dlaFUGCT2VTFFRER588MF68yW8PsVDsdROKJbaCcVSO6lPsRDEscSHH36IcePG4ZNPPsGiRYtQUlKCAQMG4ODBg6LM7bffjrfeeguvvfYaPvzwQ+zatQuXXXaZOF5WVobBgwejuLgYK1aswEsvvYTZs2djypQpNRESUc/45ZdfcPbZZyMxMRHvvvsuNmzYgL/+9a847rjjRJnp06dj5syZeO6557By5Uo0bNgQAwcOxJEjR0SZkSNHYv369Vi0aBEWLFiAZcuWYcyYMTURElGPOHjwIE4//XQ8/fTToWUqo38WFBRgwIABaNu2LdasWYPHHnsMDzzwAJ5//vkqja9K4ESlsn//fg6A79+/v6ZdqRTqUzwUS+2EYqmdUCy1k/oUC0FUhMOHD/MNGzbww4cP17QrR8XevXs5AP7hhx9yzjnft28fT0xM5K+99poos3HjRg6A5+bmcs45f+edd7hlWTwvL0+UefbZZ3laWhovKioy2ikqKuLjxo3jmZmZPDk5mbdp04Y/8sgjVRgZUVeZNGkS79u3b+hx27Z5ZmYmf+yxx8S+ffv28eTkZP7yyy9zzjnfsGEDB8BXr14tyrz77rucMcZ/+OGH0Hr/+Mc/8tatW/OkpCTeqlUrfsstt1RSVER9BACfN2+esq+y+uczzzzDjzvuOOUzddKkSbxDhw6h/uTn5/Orr76aN2vWjKekpPCTTz6Zv/jii8ay1fk7jJZXJwiCIAiCIOosnHMcOnSo2u02aNCgwhNr7t+/HwCQkZEBAFizZg1KSkrQv39/UaZjx45o06YNcnNz0bt3b+Tm5qJr165o2bKlKDNw4ECMHTsW69evR7du3QJ2Zs6ciTfffBOvvvoq2rRpg507d2Lnzp0V8pmoGJxzlB4urhHbCalJcffRN998EwMHDsRvfvMbfPjhhzj++ONx880344YbbgAAbN26FXl5eUofbdKkCXr16oXc3FyMGDECubm5SE9PR48ePUSZ/v37w7IsrFy5EpdeemnA7uuvv44nnngCr7zyCrp06YK8vDx88cUXRxk5ES+cc6Cs+j8/AQCRin+G6lRW/8zNzcU555wjVscCnM/ZRx99FL/88ouS4eZx//33Y8OGDXj33XfRrFkzfPPNNzh8+HClxHU0kNBzlBw5cgTFxf6Hd0FBgfKzrlOf4qFYaicUS+2EYqmdVHUsSUlJSElJqZK6CaKqOHToEBo1Sq92u4WF+9CwYcNyn2fbNiZMmICzzz4bp556KgAgLy8PSUlJSE9PV8q2bNkSeXl5oows8njHvWMmduzYgfbt26Nv375gjKFt27bl9pc4OkoPF+Pv3W6rEds3fv4kEhskx1X2u+++w7PPPouJEyfinnvuwerVq3HrrbciKSkJo0aNEn3M1AflPtqiRQvleEJCAjIyMqL20czMTPTv3x+JiYlo06YNzjzzzPKGSlSUskOwX20Ru1wVYF2xF0go/2eoicrqn3l5ecjOzg7U4R0zCT07duxAt27dhIDUrl27ow+oEiCh5yg4cuQIUhulA2XBuRJat25d/Q5VIfUpHoqldkKx1E4oltpJVcWSmZmJrVu3kthDEFXIuHHjsG7dOnz00UdVbuu6667DBRdcgA4dOmDQoEG4+OKLMWDAgCq3S9Q9bNtGjx498MgjjwAAunXrhnXr1uG5557DqFGjqszub37zG8yYMQMnnngiBg0ahIsuughDhgxBQgINU4m6wdixYzF8+HB89tlnGDBgAIYNG4azzjqrpt0ioedoKC4udkSeNhcAkURnJ7Pgz3HNpH3uT5Ge5u1j/vGo++SfUjnx03vteee/T3BfRsABOO8jYp9fhW/RKWe5ZSwmvRbludjHpDpEXYZj8e7z6jfVy/TyUhlj+Tj3hR1DtPKh+1jseiGXj10vDMe8F+XxA9DKRatDOqafB7Do5aPWL/tl9pExFvXaQY9JbmOpzkB/YU7fUupVjiFwzNRP9Dih+MgD5cP7LY9h01AX1GMwxmmoV5QxHTPcb9LngMkPRItT998YCzeWi3bMj9n1Tak3jvoZN1wf6RhTP/ucj1atfvDANTDXEdxnLA87tA7fRzt4rnuexTgY0+uwxT7LeEwE4P+0GAoOlKFN989RXFxMQg9Rp2jQoAEKC/fViN3yMn78eDEB6AknnCD2Z2Zmori4GPv27VOyevbs2YPMzExRZtWqVUp93qpcXhmd7t27Y+vWrXj33Xfx/vvv44orrkD//v3xn//8p9y+ExUjITUJN37+ZI3ZjpdWrVqhc+fOyr5OnTrh9ddfB+D3sT179qBVq1aizJ49e3DGGWeIMnv37lXqKC0tRX5+fmgfbd26NTZv3oz3338fixYtws0334zHHnsMH374IRITE+P2n6ggkQZOZk0N2a4sKqt/ZmZmBlY7jPU5e+GFF2L79u145513sGjRIpx//vkYN24c/vKXv1RKbBWFhJ7KwEoALEnoYeUReuTyFRV6LMmW55RpsMsN+1wrTBZzKib0WOqYIWDzaIUeK2TgLmI6in0VKh+6j8WswyTc+HWYRJRgee9FefwAtHLxCj1x+Wgqb6pf9svsY3mFHrWNTX1fKleJQk/gejK1z0er13l/9EKPOc4KCD2GckC40GMSTKpU6FH8qC6hxyDSwFxvsI6KCT2WsbxB6GHlFXq8n/69q1wwixbhJOoujLEKPUJVnXDOccstt2DevHlYunRp4NGAnJwcJCYmYvHixRg+fDgAYPPmzdixYwf69OkDAOjTpw+mTp2KvXv3iscPFi1ahLS0tMAAXSYtLQ1XXnklrrzySlx++eUYNGgQ8vPzxfxARNXCGIv78ama5Oyzz8bmzZuVfV9//bV43C87OxuZmZlYvHixGDgXFBRg5cqVGDt2LACnj+7btw9r1qxBTk4OAGDJkiWwbRu9evUKtZ2amoohQ4ZgyJAhGDduHDp27IivvvoK3bt3r4JICRnGWKU9PlWTVFb/7NOnD+69916UlJQIoXHRokXo0KGD8bEtj+bNm2PUqFEYNWoU+vXrhzvvvJOEHoIgCIIgCIKoz4wbNw5z587Ff//7XzRu3FjMB9GkSROkpqaiSZMmGD16NCZOnIiMjAykpaXhlltuQZ8+fdC7d28AwIABA9C5c2dcc801mD59OvLy8nDfffdh3LhxSE42CwmPP/44WrVqhW7dusGyLLz22mvIzMwMzAVEELfffjvOOussPPLII7jiiiuwatUqPP/882JZacYYJkyYgD/96U9o3749srOzcf/99yMrKwvDhg0D4GQADRo0CDfccAOee+45lJSUYPz48RgxYgSysrKMdmfPno2ysjL06tULDRo0wL/+9S+kpqbSfFKEQmFhIb755hvxfuvWrVi7di0yMjLQpk2bSuufV199NR588EGMHj0akyZNwrp16/Dkk0/iiSeeCPVtypQpyMnJQZcuXVBUVIQFCxagU6dOVdoe8UBCD0EQBEEQBEFUIc8++ywA4Nxzz1X2z5o1C9dddx0A4IknnoBlWRg+fDiKioowcOBAPPPMM6JsJBLBggULMHbsWPTp0wcNGzbEqFGj8NBDD4Xabdy4MaZPn44tW7YgEomgZ8+eeOedd2BRFh+h0bNnT8ybNw+TJ0/GQw89hOzsbMyYMQMjR44UZe666y4cPHgQY8aMwb59+9C3b18sXLhQedx3zpw5GD9+PM4//3zRn2fOnBlqNz09HX/+858xceJElJWVoWvXrnjrrbfQtGnTKo2XqFt8+umnOO+888T7iRMnAgBGjRqF2bNnA6ic/tmkSRO89957GDduHHJyctCsWTNMmTIFY8aMCfUtKSkJkydPxrZt25Camop+/frhlVdeqeQWKD+Mc85r2om6SkFBAZo0aQK0uxCIuM/A1sJHt+KZo4ce3apA+dB9LGYd9OiW997sIz265R8Pj5Me3aJHtyry6FbYHD0WCg6UIr39p9i/fz/S0tJAELWRI0eOYOvWrcjOzqa5pAiCIIg6RXX+DiM5nyAIgiAIgiAIgiAIop5AQg9BEARBEARBEARBEEQ9gYQegiAIgiAIgiAIgiCIegIJPQRBEARBEARBEARBEPUEEnoIgiAIgiAIgiAIgiDqCST0EARBEARBEARBEARB1BNI6CEIgiAIgiAIgiAIgqgnkNBDEARBEARBEARBEARRTyChhyAIgiAIgiAIgiAIop5AQg9BEARBEARBEARBEEQ9gYQegiAIgiAIgqgm/vznP4MxhgkTJij7jxw5gnHjxqFp06Zo1KgRhg8fjj179ihlduzYgcGDB6NBgwZo0aIF7rzzTpSWllaj90R9paysDPfffz+ys7ORmpqKk046CQ8//DA456IM5xxTpkxBq1atkJqaiv79+2PLli1KPfn5+Rg5ciTS0tKQnp6O0aNHo7CwsLrDIYhjHhJ6CIIgCIIgCKIaWL16Nf7+97/jtNNOCxy7/fbb8dZbb+G1117Dhx9+iF27duGyyy4Tx8vKyjB48GAUFxdjxYoVeOmllzB79mxMmTKlOkMg6imPPvoonn32Wfztb3/Dxo0b8eijj2L69Ol46qmnRJnp06dj5syZeO6557By5Uo0bNgQAwcOxJEjR0SZkSNHYv369Vi0aBEWLFiAZcuWYcyYMTUREkEc05DQQxAEQRAEQRBVTGFhIUaOHIn/+7//w3HHHacc279/P1544QU8/vjj+PWvf42cnBzMmjULK1aswCeffAIAeO+997Bhwwb861//whlnnIELL7wQDz/8MJ5++mkUFxcbbRYXF2P8+PFo1aoVUlJS0LZtW0ybNq3KYyXqHitWrMAll1yCwYMHo127drj88ssxYMAArFq1CoCTzTNjxgzcd999uOSSS3Daaafhn//8J3bt2oX58+cDADZu3IiFCxfiH//4B3r16oW+ffviqaeewiuvvIJdu3YZ7XLO8cADD6BNmzZITk5GVlYWbr311uoKmyDqLST0EARBEARBEHUWzjkOHyyq9k1+pCUexo0bh8GDB6N///6BY2vWrEFJSYlyrGPHjmjTpg1yc3MBALm5uejatStatmwpygwcOBAFBQVYv3690ebMmTPx5ptv4tVXX8XmzZsxZ84ctGvXrlx+E0cH5xz2kcM1spWnj5511llYvHgxvv76awDAF198gY8++ggXXnghAGDr1q3Iy8tT+miTJk3Qq1cvpY+mp6ejR48eokz//v1hWRZWrlxptPv666/jiSeewN///nds2bIF8+fPR9euXcvdzgRBqCTUtAMEQRAEQRAEUVGOHCrGxS0mVLvdBXtnILVhclxlX3nlFXz22WdYvXq18XheXh6SkpKQnp6u7G/ZsiXy8vJEGVnk8Y57x0zs2LED7du3R9++fcEYQ9u2bePyl6g8eNERbLs6KO5VB+3mvg+WkhpX2bvvvhsFBQXo2LEjIpEIysrKMHXqVIwcORKA38dMfVDuoy1atFCOJyQkICMjI2ofzczMRP/+/ZGYmIg2bdrgzDPPLFecBEEEIaGnMrBLAcac18yCnygl73N/euW8Moz5x6Puk39K5cRP77XnlPOCM2cDAA5u2OeGIM6T9nr7pOpF7YwHTHLJHa4dK88+r359n82k4wieF6wj/n1hxxCtfOg+FrteyOVj1wvDMe9FefwAtHLR6pCO6ecBLHr5qPXLfpl9ZIxFvXbQY5LbWKoz0F+Y23cRdgyBY6Z+oscJxUceKB/eb3kMm4a6oB6DMU5DvaKM6VjwfvPisExtxgBEi1P33xgLN5aLdsyP2fVNqTeO+hk3XB/pGPNjFmX0+sED18BcR3CfsTzs0Dp8H+3gue55FuNgTK/DFvss4zERgP/TslFwoAwEQVQ+O3fuxG233YZFixYhJSWlWm1fd911uOCCC9ChQwcMGjQIF198MQYMGFCtPhB1g1dffRVz5szB3Llz0aVLF6xduxYTJkxAVlYWRo0aVWV2f/Ob32DGjBk48cQTMWjQIFx00UUYMmQIEhJomEoQRwPdQUdBUlISMjMzkbdjUU27EpVS7WdRTTlCEARB1GoyMzORlJRU024QRLlIaZCEBXtn1IjdeFizZg327t2L7t27i31lZWVYtmwZ/va3v6GoqAiZmZkoLi7Gvn37lKyePXv2IDMzE4Bzf3rzpcjHvWMmunfvjq1bt+Ldd9/F+++/jyuuuAL9+/fHf/7zn/KEShwFLDkF7ea+X2O24+XOO+/E3XffjREjRgAAunbtiu3bt2PatGkYNWqU6GN79uxBq1atxHl79uzBGWecAcDph3v37lXqLS0tRX5+fmgfbd26NTZv3oz3338fixYtws0334zHHnsMH374IRITE8sTLkEQEiT0HAUpKSnYunVr6AR4BEEQBFGXSEpKqvaMA4I4WhhjcT9CVROcf/75+Oqrr5R9119/PTp27IhJkyYhEokgJycHiYmJWLx4MYYPHw4A2Lx5M3bs2IE+ffoAAPr06YOpU6di79694vGYRYsWIS0tDZ07dw61n5aWhiuvvBJXXnklLr/8cgwaNAj5+fnIyMiooogJGcZY3I9P1SSHDh2CZanTt0YiEdi2kxmanZ2NzMxMLF68WAg7BQUFWLlyJcaOHQvA6aP79u3DmjVrkJOTAwBYsmQJbNtGr169Qm2npqZiyJAhGDJkCMaNG4eOHTviq6++UsRRgiDKBwk9R0lKSgp9KSYIgiAIgiCMNG7cGKeeeqqyr2HDhmjatKnY36RJE4wePRoTJ05ERkYG0tLScMstt6BPnz7o3bs3AGDAgAHo3LkzrrnmGkyfPh15eXm47777MG7cOCQnm4Wuxx9/HK1atUK3bt1gWRZee+01ZGZmBuYCIoghQ4Zg6tSpaNOmDbp06YLPP/8cjz/+OH73u98BcASrCRMm4E9/+hPat2+P7Oxs3H///cjKysKwYcMAAJ06dcKgQYNwww034LnnnkNJSQnGjx+PESNGICsry2h39uzZKCsrQ69evdCgQQP861//QmpqKs0nRRBHCQk9BEEQBEEQBFHDPPHEE7AsC8OHD0dRUREGDhyIZ555RhyPRCJYsGABxo4diz59+qBhw4YYNWoUHnroodA6GzdujOnTp2PLli2IRCLo2bMn3nnnnUDmBkE89dRTuP/++3HzzTdj7969yMrKwo033ogpU6aIMnfddRcOHjyIMWPGYN++fejbty8WLlyo/NF7zpw5GD9+PM4//3zRn2fOnBlqNz09HX/+858xceJElJWVoWvXrnjrrbfQtGnTKo2XIOo7jJd3bUiCIAiCIAiCqAGOHDmCrVu3Ijs7mzKqCYIgiDpFdf4OIzmfIAiCIAiCIAiCIAiinkBCD0EQBEEQBEEQBEEQRD2BhB6CIAiCIAiCIAiCIIh6Agk9BEEQBEEQBEEQBEEQ9QQSegiCIAiCIAiCIAiCIOoJJPQQBEEQBEEQdQpaNJYgCIKoa1Tn7y4SegiCIAiCIIg6QWJiIgDg0KFDNewJQRAEQZSP4uJiAEAkEqlyWwlVboEgCIIgCIIgKoFIJIL09HTs3bsXANCgQQMwxmrYK4IgCIKIjm3b+PHHH9GgQQMkJFS9DENCD0EQBEEQBFFnyMzMBAAh9hAEQRBEXcCyLLRp06Za/kDBOD3kTBAEQRAEQdQxysrKUFJSUtNuEARBEERcJCUlwbKqZ/YcEnoIgiAIgiAIgiAIgiDqCTQZM0EQBEEQBEEQBEEQRD2BhB6CIAiCIAiCIAiCIIh6Agk9BEEQBEEQBEEQBEEQ9QQSegiCIAiCIAiCIAiCIOoJJPQQBEEQBEEQBEEQBEHUE0joIQiCIAiCIAiCIAiCqCeQ0EMQBEEQBEEQBEEQBFFPIKGHIAiCIAiCIAiCIAiinkBCD0EQBEEQBEEQBEEQRD2hVgo9y5Ytw5AhQ5CVlQXGGObPny+OlZSUYNKkSejatSsaNmyIrKwsXHvttdi1a5dSR35+PkaOHIm0tDSkp6dj9OjRKCwsVMp8+eWX6NevH1JSUtC6dWtMnz69OsIjCIIgCIIgCIIgCIKoEmql0HPw4EGcfvrpePrppwPHDh06hM8++wz3338/PvvsM7zxxhvYvHkzhg4dqpQbOXIk1q9fj0WLFmHBggVYtmwZxowZI44XFBRgwIABaNu2LdasWYPHHnsMDzzwAJ5//vkqj48gCIIgCIIgCIIgCKIqYJxzXtNORIMxhnnz5mHYsGGhZVavXo0zzzwT27dvR5s2bbBx40Z07twZq1evRo8ePQAACxcuxEUXXYTvv/8eWVlZePbZZ3HvvfciLy8PSUlJAIC7774b8+fPx6ZNm6ojNIIgCIIgCIIgCIIgiEqlVmb0lJf9+/eDMYb09HQAQG5uLtLT04XIAwD9+/eHZVlYuXKlKHPOOecIkQcABg4ciM2bN+OXX36pVv8JgiAIgiAIgiAIgiAqg4SaduBoOXLkCCZNmoSrrroKaWlpAIC8vDy0aNFCKZeQkICMjAzk5eWJMtnZ2UqZli1bimPHHXdcwFZRURGKiorEe9u2kZ+fj6ZNm4IxVqlxEQRBEERVwznHgQMHkJWVBcuqF3/7Ieo5tm1j165daNy4MX33IgiCIOoU1fm9q04LPSUlJbjiiivAOcezzz5b5famTZuGBx98sMrtEARBEER1snPnTpxwwgk17QZBxGTXrl1o3bp1TbtBEARBEBWmOr531VmhxxN5tm/fjiVLlohsHgDIzMzE3r17lfKlpaXIz89HZmamKLNnzx6ljPfeK6MzefJkTJw4Ubzfv38/2rRpg507dyr2CYIgCKIuUFBQgNatW6Nx48Y17QpBxIXXV+m7F0EQBFHXqM7vXXVS6PFEni1btuCDDz5A06ZNleN9+vTBvn37sGbNGuTk5AAAlixZAtu20atXL1Hm3nvvRUlJCRITEwEAixYtQocOHYyPbQFAcnIykpOTA/vT0tLoywZBEARRZ6FHYIi6gtdX6bsXQRAEUVepju9dtfKB/MLCQqxduxZr164FAGzduhVr167Fjh07UFJSgssvvxyffvop5syZg7KyMuTl5SEvLw/FxcUAgE6dOmHQoEG44YYbsGrVKnz88ccYP348RowYgaysLADA1VdfjaSkJIwePRrr16/Hv//9bzz55JNKxg5BEARBEARBEARBEMcehwsPYOm9o/HRhEFYeu9oHC48UNMuxU2tXF596dKlOO+88wL7R40ahQceeCAwibLHBx98gHPPPRcAkJ+fj/Hjx+Ott96CZVkYPnw4Zs6ciUaNGonyX375JcaNG4fVq1ejWbNmuOWWWzBp0qS4/SwoKECTJk2wf/9++qsSQRAEUeeg32NEXYP6LEEQBFEdLJs4FD07LkdSo2Kxr7gwCas39cM5j79ZoTqr83dYrRR66gr0ZYMgCIKoy9DvMaKuQX2WIAiCqGqWTRyKs3IWI39nBjYWX4r2w2/Altf/D52S5iGjdT5WrDm/QmJPdf4Oq5Nz9NQ27nr4GSQkJYNxDhvOsmngNjgA7v1bBtjgAHc2mwNwH81jYABjYHCf12PuIWbBecvALQYLDLZtw+ZAclKiU5VtgzFnP8DBOfft2nBee8fgvIdXt+uDs7QbB4PlPszHYDEGzgGLWSi1y2BFEpAYiYBzODEwwOY2YHNwcHAO2Jy7bcAB9z2YW5wxp17LrdddTo4BgGWJ2DkYGBiKS0uRlJSMxEgEZbYN5sbh2fJitbljy4nb2ccYg839Zx+9NrWY286cARZz9nOvvRlKSm00SEkEsyxwuwyM++3HRdtyWBywbQ4wDtv2fLERiViAFxvngOVeW/jXUfgDx58yuwwNkxPdlrDBbccOAHBui/YW9r1jbl+yLFErmNSHuGfDaTkwFoHlHEQZL0OD5ERRh3PtbDDuX0t4bQsObnv+ONeXeW3HGPzHS5nbvlL/YY4vjDHwMhupyYlum9kAZ+CwRTw293zhwi+bA8x2+pByDQEwS47Pec/AxH4GC0AZkpISwbkt/Icbn3KP2l6sED4wC6I9OSwwJre1JY6Du30aAGMWIsxGQmKiUzd3r6EToHPPuPei6DfgTltw263HvQPce9/7MLDcdvQ2/17iiFhAYmKC2j9s1y7nvg/uZ4PT/t7HAHf7jOXWywM2/PcclhVBWVkZkhMZEhIi4r4Xn2uuHcB27hHuffLYKLMBizmGLe86WZbTpyLMvWbwbbr2AaDMLkVqsgXLioDzMnC3zQD3s1T0U+7uZyJubtuIWHDuQS8W5nzUWZYlPm+Z9zlkeZ9/ZUhJdo5794AN9zOP6+3rtbt7TVGGBFE3A4MXo9eeXFxjxhx7zAIYSpCU7NxX3Ia4R5yua/v3iXePuJ9D4DYYK4Pl+g7AscfdtnZuV7cfcYBZsCzn/rEjLUEQBEEQBEE4HC48gJ4dlyN/ZwaSR3+KtEcGo3DPEPxqykwUH/4z8p/sgJ4dPsLhwgNIbVR7F7MgoacS+PuctwErAZKMImHYx71/9GMsylvm77Ai7uuQupn8RrLnDWaVyZ/0iaC0Y95bK9l9rdn06hZ1+gNm1SfvuPSTcfW9/jOSrLnHpbbTnZDag3Mh4PjnK2/U155vLAIrIt8SPPCaAWA8uD+BlyJBD0GywWQ70vsEK4IEyxdM/AQ7rl0ZLrnj9J0ElEJoK4Hr6ItrQlB0fyYmJCAiN4cQj6TYRHOKISwAwEIpLCk+PVRfXJMrYUhKTPCFEs6lljX0F0jHOUeElUlhsGCksjjqDaoZQ1JSomgfEQNXX+tt4F/nMnGN/Fhkk0w77pCakgjGLFdwVGMNJE8GbNrSbWKBefeVK14yi0k++6Jeg9QkrxiEOikJol4DeKKS35FsWLBd4YMrdXrN6l9YV5BwXEOD1ESpi0g2pNfytfXanMF22lbcc55N35QFX5jw2joSARo1SFDjEq99u0w0qyvYAWCsFAylXrNJ1w6u4OPb8+NnSE620SDFEm0qRFffiCS6QGlnxopgoUzEB8aEyKUIlszfwIBGDWykJLmxef9yPSYurrUcP2MHYVncbzfLa3PPhifyeKKP15VsEARBEARBEA4rp01Avy7F2LC5B07/f2fgtJ4F+HnV9SjttxVJqQ2wsWgY+jZ+EcunTcC5U1+oaXdDIaGnMkhuDFiJlVxpcOCuHGIVnUc7UiGTQEIFpu72hAYrvPJoNlmiJkrFe6IFWJLNqHFp50USwuQg8d5UXQQAiwTL6q/1cyPgSNLuwljueoPUBMZhxXFN9PqSLI4EQzcQPkZxIEG5nEGhMSjCeDaBSMRccdg5wt8Yn1Lm8zlSElkgFuWaRIkzMYFFvRBhh1KS/cyqwDnR6mNAYsSSKjYUDtmVksxgMf9cpa9Fue0YAxISzL7KBU19vkEK1L5naC7TdYlYHAlRrqfsu9xezCpDg1Rb9UV6Y/JRxGiVIRKBK2aFlHf7jGwzIWKjQUqxL5xoQflikV+v0yQcSYk2LMtWbcn2JdGHSSJyUmIJUlOK/LiYeo85vtjKe69McnIiGJNsisb06jfYZByFB0tAEARBEARBOCQc/AEAcNapixFJLoNdYmFLXiec7a7UffKlo4F1L4pytRUSemolcSsTFUBJ+SmHyZDzjoa4FA1TlpS8Pw6f4irKREEl0UM71eSN+Iu51kTxCEYc3rDNH+xFc9c7HlfThey3uTkOYTckSAY/USr8upgrdR7xMfsXNd4oDRLaBtyPU9fBZK+ZnIgmEU08CxPsvB1O1g4zuiySpQz1W4FK/RpiCTYiY8aQbBdIpIPfhywL4X1UOs8UMwfgPiGlu2t0UtzKzFwsIKLo9xHncJNRtP3+ecq1Za6PbiN4Aohq37/nFMHI85c5j9HpHwhKea7ejaLNGRcCjCyw+DY00ck9xwbcDiJdf+WGdB+xld5z0Y+ZJEqpjeVkufn7RBYRmJw/RxAEQRAEcUzz07Zt6N5xFQAgklyGQz83xIbke3H2jNtEmW/mvYDM9kBpw+Nrys24IKGnVlIOEaPC9bPgrqgmj8aXkHOj2mTaSLWCvpQrHitwJJpYI8MNTaocVyyodQatRrcnBvcx7IUJJ1EzS6LYFXUHlIfoA0XxeIqpbhbDXpSqY3UfKyRWz/0wu9GmpxeHTPUyuPOtmM+N1u5a3kbwWIgoxVyVQBePRFtHEYh4OT9mvD4lkodMQlGU9haVINg/ZaHKGKfFRH/3hBPu1qtk3EB7rQRq9Ax663tPfiZ4rSgLLUzqd8x7MMoXjISAxJ13zHdC9Uv6QPAfa2OIeAqVFAyXHrdzDvuv5Z+qDQbPO9MF8B5DY0GFkSAIgiAI4phk5fNPonPRVCQ3LQIAlBZFcPjCFeh18smiTPHhQ+iUPB/FB5LRa/KMGvI0PkjoOeao6Jf68mb06MO48tr1/voclnsSpwDEww6b6i2D/mibPBY2jYuVKmOEKNch129Lx+USsaI01ce0nzrunMBR86RMCS3m+qLJE1IpHsUuNwhAgQFsOFFt2ubsHE9UMAkysYQwYdPQgN4kz3aIVyKDSDvsz7OkeChbC/XJdgfs3DDNCgcCYoxi0yC++BYNfnrF3bmcmY2g2Oh2WtPdr00ZZaw8IIR4woptBzLNRLaX539Yu8qBaild0Xxx1RD1kPKBIAspegH9p/Pav6e5oiH5Jm1VWNIeJ/PFI7dqkUYoWlmxqdjzzIr7i0lpTwRBEARBEMcuH936a/TKWQ2rkY2yIgt7d2Yg86SfgP+chw+LhuHkS0fjm3kvoFPyfH/VrVo8ETNAQk8tp6oze8pjsgJCTVRJIZZN7y/b0ewehRKgFAgvFE04iVW1KWuBQbXIoAsrzPBKPioN4nj4tEkmkcgrKyZhNVqNHhfn0tgymJNh8Nf5x7NpFHRCjZW/18ndxbKC3YeFlFXMlmfcqwlDzJ2cObS/mA54CSDGdg0XauQ3soAUK0ZFx9B2Rm1vSYBjgDGLyFSBt98T2IziJJfKSL77ooQFzuT8GU0AYea4HR2DB+9F5kgmIjtGKsCkSrgkrHC5Q0rByvPd+O4oF1Q2KgXLvcQfJU4pdUmaeNrbxZ12kK6F0mpcvmlciYdzZQL1QEZQWAoaQRAEQRBEPWfd2/Nw4s7R6NPbyeIpyEvDznaP47Trr8KyiUPRs+Ny9G30IrDuRWS2B4oPJFd4afXqhoSeWk9lfwmPQzwyHipPVk4M2SCuaqJl9Jh8Chu1x7LJA4VijfPDquLaYDG0nGTDgpPRwwIlwo2LiA0qT1SRJkr1pswlIJj1Esx4CVYW8IGrMSunmrpHObp8YAAvGTFl9Mjl/blNdOGkHDa5b5MBgTl6lCq1xlUFIq1SyRFvrC+X123KeoCsF3l9UvQZSUxRrq1JVNO0CrGgFvc3Gf1aKHqDrHLqZcLEOFkU4razOJR0fymPS2r3ufyp4C0N7x8M5lxxrS0cA7ZSN1OMceO5iiikqFZ26H3D5Kwkd9l2Mb+PlsbEwdxFwKTzZbcUI36cfl/nfl/yVtuztQtJEARBEARxDPDRY/ciJ/1pJKWVgHNg+4bjkTV5DU5zM3XOefxNHC48gOXTJiDh4A8obXg8ek2eUeszeTxI6Kn11JasnoraZ/4Wswq9QMhzJzHPq7hNU03xWBRNJg+MQ86Xx7sMpqyckFWb5MGjIWEgmk1lgSSm2g8jzHc1w8B8ji7WWPCFhXjbNOxA3Da1jJ7weBBX4pnRtiZeeBk90fw0OWLbkJ4aNPRJXRRSfPAzekL1KpMYYRBqAreMJoQpMeiiSjyinUkcinKjKJku8mzM8mNXpthkUcwVZoQZzoSYEvjEkeN1bSruac76Ey17eUZynFyavJwpwqLsr5KN5RbgXJ6EW240dVJlp6waPBfZTkzSfvxCYqUub4l3yughCIIgCOIYorSkBJ/f+Sv0yvkSVoSjrNjCF593Qc8nPwmUTW3UuFYvoR4NEnrqBJX9RTyO+gJF4s3o0SWE8vy1WCtbbpGnIu2k/NndkF0TX83enKbxtJL3B3xPqzHZ5OCByMRQLYpKE2ZbngdIzkfQNb1QbS/EJteKBLKG3PI2C/YIU0+JJ7MmbptaRo/x2jAExYgoF1D33ZvGxX/vWwnopVKgJlEqaMV7x5RzZIGGQc3okfuIJ9wAUFYYkzN65DYJaApe4zLfpui3chaKiD1E7JFeGB+lM/Qpy4tHyq5Rspa4JJJI2U1CG/b2MSiyqS+CBIUZJsfJAQu2mFxKvmecFa2c+9OGJqJKfqgfCKrYpKx05fUhBthSqhSHk22jPxpoc66s0iay7JQbyL/DRSaWqMTtKNwRjSijhyAIgiCIY4WV/zcTp/E/ovuZxQCAn3dkoKDPS+h53a9r2LPKh4SeOkHU4Xj1mIzbpixhAOGzyISh/Ck+DrNyoYpmP/nlTatihZf2URIyQryT/nCv1GPeH5R+mP8y7hD1esNsGjE1rRhMhmf1WIYDFo9tU4yH49AIw+JSbDJvoB+DCo5zTXPExJyjR/PRI1pGj2wr8LgX/IF+tPmI5DeySCWXEU0vlw1ZWSuaPwGT8i1tulGkay7EG7lOV6wRvrkii0kk8qpTbLqqFFNLKMhCm6jXsgDLP89fspyJ985bvz5PEHLiVO3IS7WLOKW+4q3aBsZEvUwSbvzl2tU4vEnAldiE0MXgZSExKdPHy+hhYJTRQxAEQRDEMcHS+2/C2e3/BSvB+UPY2k9PwxnTl6FFYmJNu1YlkNBTayjPl+1yjPQrw1xcNsOGtuUdRWvpClGJV4aJ12Z4Ro+HBTWieIQTXfqC9l5vIVWu8geaARnLcEn0Xaa6Le4PSIGQVgxpWn+/H4Fej8gekpzxMnoqYtOEXI8Si2yT+7GGVWValSveLiTPk+Nl0+hz9JjElth+BC+sly0jZ/Moc+ZI/uh2dPFEuMNUS0q/cnd4K2zJ5b1MG85UsUieM8fLFgLUa8J0QUXDi4PpOxmHrmXolXhtyMQ5vvgG119HJAreId50PHKMgO1kuwQaxn/N5A4Gv50tJc1GseQk1DAeiMMRbGxftPFuVu987gg0TFNSmdfA+rJk8GPx9CmRSeRmDnEANmX0EARBEARRjzlceABb/ngO+nb/2smgLmP45NOz0e/J/9W0a1UKCT21hvJ82S5vloyEaX3nuM6Ld74caPUr6kD5zo2pZ5mG09pJcWtiTqFoGT2BAbFmNWT8qYhAxkF+yHlmD2PjhayXVzIHYtUX0rTq4zlBIS+sXsbj6LVhlzMKUduVmZdQN9osByJGg3DDGAu1yfTCEvFk9ISdz5gvoJj6axihGT3uyXrWjlxWaVtTO4Q4IWf0KJfb0yqY3p7eTy8LxW+HsPaQM3ocIYwLI0xR5yQbTIsdACwLlhBaXIFIKResy2tDmzu29Hbw6pDbTm1XC5bUgbzHzJSPUOWDhoNz7oqATKxM5t+b+gpdTH3NAMuodhIEQRAEQdR9Ppx2N7o3egFdcg4BAHZtaQn74pfR75peNexZ1UNCT62ivEP5SrARdzX6bBQVqqR8xaOWizGkLXfzOG0aK6MnzBPTkuVyzaYrxqTjMLy2lFIGu4a/4Ot2Tdg8ik+GQXt5beolGJxsitD5j45C3YqW5GG7zkS1WYHbiGsv5HbknMNGyGTaUcbTscba/lw1Wp3uMe42rt4OVhRFLzSjBxAZSsK2d9wtZMsXWjsuZ/OAq9fXuCS7ZlNpTy9GWOC8TCkrKtAEvYB4ZUGoWlwSa0TdnItpa7wYnOptJ9uFAfI6al5mjmUQOb0X8kTggXi4LD55x533NrdFho0yl5Ar5igTN/uFRHnfjiv4SBNPKzaFCMXBbRsEQRAEQRD1jZW3no2ze611snhKGFZ/3gtnzVhc025VGyT01CriTS84mr/AGjJe4iHqaNw0tC9H8WjljFXpOw32OQvsil0PYGn7oukB8j4bPFCGoeJz/sRTJtbkt2G+y6JU4HRdvNAKcG7KWPHbP0x+UxZMCj89WCCYNBEgapwxuoG+8hQQo6vrNvX6o8zRE+0ZMjtskqe4bBoEANlMyH1kc/cXgCQ4eMhPB7EQm3o/CPQXg019jh5FaJIcD9TtLU+uiByqMCOLKbJ5bkP6TcdFIo4FTXgK1G/BsvxpzJX4PNFIljbFdWewbV94kQUgJzFSenyMKbWDIQLL8gUtX+xxZRnvWSz4Yo4nvnLurvLF4c7N48bL3E2Tz4SoSxk9BEEQBEHUI/bn7caeGb9Gj947AAB2KcPH6y/BuTPm1LBn1QsJPbWOeIb9R5PRUwFzFa6AmY/HazNOcSa0wgrEpmedcITPy6MlFRjN6n8rN7WISfeKOvTSBvQ6cqIFQ3DgK0/JocQVrV7pmDfMjIWSqRSiLcZjMz5rwTI2D8yH62Y3BO2H2Y1lK7BEuc3BLBbwJeoTkwZhI16bzH3PpQvtXfdYWUti1S1TlpBuV86McgvoU7tEy2oTp0YpIMdla53WApOyUCS/Q24U5X5kgFBAwPzHq5gcu5PSIy+WxS3uCjbqDeOsum77drw+LHx2JlEWvsr3ji1ewWtUJ7PKu6vKpImVvUg9MY9rNpnWl50Jl+HVx6VrKeL36vPX/aKMHoIgCIIg6gtL7h+LHs3/g5NOcx7V2rGxFdglr+Dca3vUsGfVDwk9tY5oQ9pKEnfiNReXSVMFuhRSQZtGPSueIX8cI84QwjJwolXHoQ5SvZdeXWEiUIzkh4AN5Q0zN4/pvW4nbLAdWIZZrlAKKihjBNHbLeyJr3hsxnMpTW0ZLc6jwdQn5Llk9GOO0Sg9lwM8VlKFQRBTVt0yFdeVPt8cADejRzsxWjsqopxBnIrnVjW2vRabOXOHK525PB9LXFGz/E6lZC1pgTM4go4lrWPuCywcXEkF4mDwl04HZ86kyl51nklvmjN5Hh3lNQBuwZvHx8nG8W8GLhQjDoh5eXzfObfFJM/iGlnwlC4pCq9eV2iijB6CIAiCIOoQhwsPYOW0CUg4+ANKGx6PXpNnILVRY3xzTwec0+V7MAaUFUXwybr+OOevb9S0uzUGCT21lioQdY7KpOlgeYfgFTg9rhMM+6M8JhMNPaPHJFkZxRTDoDeWnsDhP8Wi1x+WsxVLHIrHpjIfiVdXOQbQPDQtI7hCmPyzojbj0QWDORCOkBE2ua9ysvYyHn8CNr1sGu4MokX3M9QfW+EzXH03a0c+4ttUM6bkuWFMmqccp2lCZnFMeeH/9GzJGT0WC++zAaM6JiHKvXZc7HAcUbKZpOO+COP7KN5afn1iL1fj8+OWHsiynRWp1KwZLs73r2cwJcrP6OHCN291NPX+lhqYcyBii2wcOZtHvJaNqC0GeQl6Rxjj0jxVHBxcqUvEThk9BEEQBEHUEZZNHIqeHZejX5disa947uv4uaABsk/dD8ARedanPoVz/jqqptysFdTKP+UtW7YMQ4YMQVZWFhhjmD9/vnKcc44pU6agVatWSE1NRf/+/bFlyxalTH5+PkaOHIm0tDSkp6dj9OjRKCwsVMp8+eWX6NevH1JSUtC6dWtMnz69qkMrBzzKVhl1l8Nk6Dnx+BdlEFHuEKOdYDi5AlqZBT9xwDR9R6hOEOOyePXJdcuPhIXVrxOtmVjIFnZMqddJEnAmapY33R7zzueGzeyPHHeFbEaJLapNg8jBNVuyIeEPwjHaZBBz1sjilRhTe/V7L2UfbCCY5WLoBUy17YmLnk2lPbzBvbvpDSpse+3jnsikDXq9TI1TjzvEax+pDfS284xwgx8iRssXPZjBN924Erc0UTWXG4a5/ZZxWO6mtIEFWBaDs5Iac+fz8Z3kbkEmXxn3AtiusKLfp05JJuoX9iyAWQxeRo+wKTeQVJcvPvm1e/Fx7glirjW3HuZ2FsYsrf0q8EFJEARBEARRzSybOBRn5SzGgfxG+Oib0dhz+idY8cmZSEguQ3rWfnAO/LgtAwf6f4VuI45tkQeopULPwYMHcfrpp+Ppp582Hp8+fTpmzpyJ5557DitXrkTDhg0xcOBAHDlyRJQZOXIk1q9fj0WLFmHBggVYtmwZxowZI44XFBRgwIABaNu2LdasWYPHHnsMDzzwAJ5//vkqjy+caMPZeKWAeO2Uw6R+TsyTZQxdrEIhxts28Yw6w7FhFlJM+xQTmj2u7fbGm/LY09aOxyJWM0WTvpS4JNFBH8gL4UAfTHv2OJQBbDThKaxMuW3GiC3Mpiws6OJBQNCQhYUo18BoUxesvDJaIErclrTF01cNAlhAJNNscr0h9Xi5306KAGQS3aT3XiaPEMuktglgaAO97UKFKFuK0WYBm/J11u1zKX7vKSXHHeYf5MxZkpwz2O7mvHZtuBk9nHPYujIoNZK4G5h3lMGCK9i4MpDwkbn3ohQf58x5bXOA2b5Yo3xSqK2rtLuIDUIk8mP2/fdsOEIQk65nPJ8+BEEQBEEQNcfhwgPo2XE58ndmIP22zeh1x5+BNy5Gn16rYCXasMsYSo8koMmtG9C0dduadrdWUCsf3brwwgtx4YUXGo9xzjFjxgzcd999uOSSSwAA//znP9GyZUvMnz8fI0aMwMaNG7Fw4UKsXr0aPXo4Ey899dRTuOiii/CXv/wFWVlZmDNnDoqLi/Hiiy8iKSkJXbp0wdq1a/H4448rglD1UR5loqJij0EeiPc7PvOG6PGcLJczLMset82KnOSeGKrIRKeiC8jrE+SG1RGmY7GQ14oNw3uvbDSb+ms500XUqQ2WmX6S8p65L83XJCy+CtuMUXfofjnOkO4jaQCCcvU0Q/vIophSr65aeQ5A9990rxkuBfN/skDjaj5IJ4tdllqn6fFD74Wpn8mPiDEY2kLzR38rdaWAaKrcJ8zva0z3xdh+gDZ/snSS5pBmS5nA2s2yYaIWFnqeb8K5uJyXGbJvnOPq3EBcal/XKJj0XpU0lWsN+HPt+Ja1j02ncks4603Y7JezKKOHIAiCIIhazsppE9CvSzE25l2KZkveR4t1Y9HypH0AgEP5DfDp9l/jnG4LsHzaBJw79YWadbaWUCszeqKxdetW5OXloX///mJfkyZN0KtXL+Tm5gIAcnNzkZ6eLkQeAOjfvz8sy8LKlStFmXPOOQdJSUmizMCBA7F582b88ssvRttFRUUoKChQtqNCydtn8W/wzinvpg9LpD95x2MzGEDIJhNlWZxYW4VOChutxkfYYzvqcCt80A2tnJJl4G62tIXZUuoO2cL8C9tEVg+XBufe5l5mi2kZNlo5Llk02YjlS0VsxoozdD+XBvnluL3KM+41ZcFAsqncZlrccqwBpcNkS9tE5okng5g6ifeMoOF2F+0TEovN3T6q7eNSo4feqvBt6W2gxGNoPz/TRd6Ycp6pPZRYmKtxeDY9UdHN2hEFbAZuuxk9cLJ5xGZbTpYNbLeFpY17mTFu5g3zH9UCAMYiftAivchxTNTG4WQR2fAzery7lNlgSh5e8Br5PsitwlRR0zXrZPRwcNt2spRsry0oo4cgCIIgiNpPwsEfAADWTxtx8p5rkNF6H7jtPKqV+LvtOOWayUo5og4KPXl5eQCAli1bKvtbtmwpjuXl5aFFixbK8YSEBGRkZChlTHXINnSmTZuGJk2aiK1169ZHHxAA81ClqpHsxaMSGH0LLSwRImfEOk0UisdelIrK2ayqmKHulwe1UQUMQ3lIr+Xxd5ibyoAewUfKOMyCRDwSmHeeLDh5A3h5oBs2X45sSZ6hJB7b0WxGm6OnPDHq4gkPi81km8ffXYzCDYsiYBjsmG8vc+QmkYy5KgaTO4rbWYRoIgknsmijCy+mWCzJqEkIk9sr8BiWHdynaxAMhkf3PE1aFsOk1aTk+0ieh4jJzea+92L16/E2Lk5m3gYn28ZiziTplmU72S7cm4HK38S8N8y9A1wxhwmFyRb1csmWmBfI9cWCO0Gyxd05gCw3S8cCVyMVj2Tpd503j48Xl9MH1Yb35gWyLObMO+SKf8xigdXTCIIgCIIgaht2mTOmPKv3CiSklOJwfgOs/PEuZN6zEykNGuCbeU4WT2nD42vSzVpFnRN6apLJkydj//79Ytu5c2cl1q7LAtUBC76NNWqO6wSZGF0s7DSTfzGdDKko7mZlocXlMaTJYjzSk/xTzujRPZffWzAmZYDJFRr8lEUik0Ck1+1NXmwxLcMmqk11wtlom9EmU23Gk9FjijFsCwgImi3LMthm8XcXYUsRFJjRrjF5T44xYDTohRKfLNh4io3bsGIQ79mTbMqCjS68mAQqkcGj7fOECtFeUv2yHzEzemAQ4FxxSrbJwQBL85dBmYvHdBN5/nHOtPj8k7nYfAGyjMMVeGyYZrTm0vw8nPmbEHS4Fcweku25vthw58pxM4uce0xqAN0mAGeqZ1u68/w5fHxBz20AN5NI2LO9OY8gZRHF/QFJ1BHiWYxC58iRIxg3bhyaNm2KRo0aYfjw4dizZ49SZseOHRg8eDAaNGiAFi1a4M4770RpaalSZunSpejevTuSk5Nx8sknY/bs2crxWIttEARBEITOB3f9Fn16rgDgfJ/ZvLYtyn7zBc6+/Y8AgOLDh9ApeT6KDySj1+QZNehp7aLOCT2ZmZkAEPgCsmfPHnEsMzMTe/fuVY6XlpYiPz9fKWOqQ7ahk5ycjLS0NGWrPGRJoLrQbEZVKky+RZM15DJxuGA61Tj+CJNQFFnBfEpMvIGUNLjSpIyw/zx345WgvPEw4Ik+fl3+MM5/Le8T/4WMz2L5ICZh1Td9wM0NLSvUnmCDltsmj20z1hg0ms1Apo5mzzY+IuTFWE6brqjgTHxrzhQSNmFo14AVv41j9in38R1/8A53aXAEMnr0OPU+qcdjEohE1pJcr2xH80POJvIM6jHIwp8nUMliDTgPrCLGOMAkHUZ9rMlvczDmZuzoMTmViIweTy9jQMQ9LrJ1PGfcrB0vo8apgolNiDuQ67V9RxV7fnaPxTiY5X6SKAqZO2cP8zJ3AAZLbIBsk0nxcfgNBseO5W7MyR6yIgCzmPj8IuoPsRajMHH77bfjrbfewmuvvYYPP/wQu3btwmWXXSaOl5WVYfDgwSguLsaKFSvw0ksvYfbs2ZgyZYoos3XrVgwePBjnnXce1q5diwkTJuD3v/89/ve//4kysRbbIAiCIAiPHzasx7f3dsA5Z8yDlcDBy5z9zTIOYO1z0/DDl2vx4YO3YN+THZDROh+rN/dFaqPGNet0LaJWTsYcjezsbGRmZmLx4sU444wzADgraK1cuRJjx44FAPTp0wf79u3DmjVrkJOTAwBYsmQJbNtGr169RJl7770XJSUlSExMBAAsWrQIHTp0wHHHHVf9gQGo3r+sRlEJynUgls9xHDcWYSEjYFNhfZ9h4BJXPZZ7hBlLhbdA7IGSF0o0q/F46O8PtykfUepg0iA+pP5YIgfnpslbuajfdMkC2SsGn8INimJmwroxU5c7D5wSxW6sKUvCBCjHXsijMDHiVE0y47HQ68W8pb/VGrw4TbjaSXjFURCTOFvq6WH2lPZ2ber9hLv/6HWIui0mhE2v/3vXgTG1rPJaCdTomeyBeMUBJLgyDJMncGbS9WBwZRpVkPMnrlcnVVaaRvpAkC+D5alo2o3rZfP4j2X55/h93PdDsen9wz3fPDHMF7eI+kM8i1Ho7N+/Hy+88ALmzp2LX//61wCAWbNmoVOnTvjkk0/Qu3dvvPfee9iwYQPef/99tGzZEmeccQYefvhhTJo0CQ888ACSkpLw3HPPITs7G3/9618BAJ06dcJHH32EJ554AgMHDgQQfbENgiAIgvD45Na+6Hb6V0jo4mSObttwPBpd+yY2P3cXenZcjr6NXgTWvYjM9kDxgWSsWHM+znn8zRr2unZRKzN6CgsLsXbtWqxduxaA81eitWvXYseOHWCMYcKECfjTn/6EN998E1999RWuvfZaZGVlYdiwYQCcLxeDBg3CDTfcgFWrVuHjjz/G+PHjMWLECPEl5+qrr0ZSUhJGjx6N9evX49///jeefPJJTJw4sQYiDht2VyVK3oTvRtTyseqR6/NiijFqDjwWIdVr9MeUTaLvM5wYcMPklz6fkJrRY3pQSawGZHI1ivfy5k+rov4XyODx7OsrBxmQe5QoLWU5yHZN8+bo2TVydoXvu9oiwjXtcnqPxBht8tg2w9qNhx3wbIZkskTL5gFii11CsNA2p04eyFTy7AmburuBwXbwfgjzyXYH7HIGDaSMnrjiDG1cM9zNSvIuolK3aV4e/WMBhvvFE2y0TiuqsW0wrZMwQJmbKBCj165W2Oerd/8Gg3WusX9/i/1uHxfHOVc/vb34xD3q1+/dM3rjMDdbyXlra/e3lG0k2k5ZuF3z2isk2RQ3iv/DEYicSmMJm0TdIp7FKHTWrFmDkpISZZGLjh07ok2bNsoiF127dlXmNhw4cCAKCgqwfv16UUauwyvj1UEQBEEQsfjl+x+w9f6T0KPX50hILUXpkQg++uoynPSnr9HylI6OmHP1DixfPwK5q/ph+foRwMjtJPIYqJUZPZ9++inOO+888d4TX0aNGoXZs2fjrrvuwsGDBzFmzBjs27cPffv2xcKFC5GSkiLOmTNnDsaPH4/zzz8flmVh+PDhmDlzpjjepEkTvPfeexg3bhxycnLQrFkzTJkypWJLq+szlMZ/YsXKVIomFEUMMdYfkqYQildZDC3RPNKF/xfoivhR3pFLWBz+UCo8Ui79Gzyi5wwwKMMx56/4IX6ERsq5r6HF0Rf0GCxmkBbDBuCy855pacCpF+BQbwUvmUCeY6g8No1ZGnoxZijHnDl49NtSKRdSoSfGxHWbiYG995OFZkkwqbxqENpjePHd4P78OEwVSeSXBl+ElsLVnbFi9q6tyNAyZBGFflwxSZiDer94wpnSBp4N0a4WuFgS3NDXTH3AK2lzcMu3A3h9xn9sydjPmJvRI9tU7DG3zVRhBoB4fEupWTjs7nfvY291MC9Ov0O7tUsXSiT8GMVeSZhS6pBx7Mttxyr0u4uorcSzGIXpnKSkJKSnpyv79UUuYi1gEVamoKAAhw8fRmpqaoViKioqQlFRkXh/1CueEgRBELWSZY/ej+4Nn0ObTocAOJk6GxpPx6+m/V4pl9qoMS2hHge1Uug599xzlS+3OowxPPTQQ3jooYdCy2RkZGDu3LlR7Zx22mlYvnx5hf2sfKrzC3dUVSekfLyCir6elEbAZGDYh8AoP6bNKJJAzBB53O/k6qK1RkDU0MrJMpgdKBtFqJIHtHFqgHptStaMVigQmzQI1Q3oEhdTDzgv3XGtzXxtymRT1zk8m8EhtIppLCts2o7YYzDp+MLV2MJEAx3lTpAEMOdncOgv/NQawGg74KVfvz4eF9eRc0VEUYxyVQjzhAIA6iNmYY1rUCv1zCC5CrNgIh003ZqmfbogxG0w90aR7fjHVaOensIAcKYFwuzQe9frF84LWzqoNxYXNYpHyiTxhYFrHwh2yDUGLOlm5NzWxCPt04g7j5KFZZ85x30fjTY5YFlcekspPXWBu+++G48++mjUMhs3bqwmb6qXadOm4cEHH6xpNwiCIIgq4qdtW1H28jk464R8sAhQWhTBqi/7od8TbyOnpp2rw9RKoefYJdoX7soWgaLUZ9SATOWjSRlRyhjDZMoPUS6aqhFqg5t3x3Gufoo8y4a63ywHhbqv2ZAFlWDOU/ChDGFTHuAZDMRlk6n25RfRtDU/ycAg7mjv9QGohaOwGWIjpk0to8dYVzzKWDTbmngRM6PHUIkQF0Ks6GJIIEtJyugJCC1hYiALCjWAKgqZbIo6mckPgy3dIZM4FKXTKpkulqySSZk4pn7E5PpVYQbuqld6VoxXlx+jBTBb+xhSFRbx6KZWlxD+RHta0uNkmlQl9SEvu4ZzN1+I6Y3Gha3g9WVunF7mk/9Yll9XsI0oo6du8Ic//AHXXXdd1DInnnhiXItR6GRmZqK4uBj79u1Tsnr0RS5WrVqlnKcvYBG2yEVaWlqFs3kAZ8VT+bH6goICtG7dusL1EQRBELWH3OdmICd5CiJtnZmWf9qegX09/o5+119Uw57VfUjoqXVU15fuGGJMYFe8GT2B4bwZY/3agZC/gMeuTB7lRXdDrzeYXRMsE20QH49JLpXjITb9wZpfv2ghZkgyiOKbyaaeL2B6EoRpl5IjOFbUe1FYhoWX0VMRmzpx29QyekL1hDDlKIptEYvbmP77YEZPQDjRxtwcWnaNdjbXzpEFGgY1oyfQRyQsuU250zZymwghT0cShYQwY8roMWgJol7vtWXYb+hTlhePlF2j2FNScFRjlrTPm6xanKY9/qXU557HxWEO2FK7Kwademw49mR3FJO+uqTYlB8ZE32IATb36wYQyGxljMG2uWOT+ef5WTyeeqPf4fL14dJkzNxdYp2o7TRv3hzNmzePWS6exSh0cnJykJiYiMWLF2P48OEAgM2bN2PHjh3o06ePqHfq1KnYu3eveDRs0aJFSEtLQ+fOnUWZd955R6l70aJFoo6KkpycjOTk5KOqgyAIgqhdFB8+jLWT+qNHzpewEp3veTs3tULrKRvR0l0oiTg6auVkzMc2PMpWmTBpi+KGUj4e5AFGFJ/jCTP0dKZtIZXErZn5dVkI1s4M70zhmNAH/PoWtt8Cgz7pqjinHF1BrdO/4eX3xvlzZDtSs3KDwqSfK+pjkm0e26YSW4xuH5dNdwBsSVtg2XDdVjluNaUeC+6q2CzcpuSjjq3MAx68N/3MFm2z/NeW5W/CB/hLl+u1e1M9eXC9gPtatme5NmHwx7IMZbVQuK3a80UVzbT7gnntarl3AmNin2XJx91r4Iou3BVCHD3DE0/8SY91mOSr1676BfRj8+9KC4CsWHL4k4zLHcr75GDSUu0W85dNlzc/eEj7ncetGOMBUdCZeJz7yo9rWtiUbgzhP3PblFmU0VPPiGcxih9++AEdO3YUGTpNmjTB6NGjMXHiRHzwwQdYs2YNrr/+evTp0we9e/cGAAwYMACdO3fGNddcgy+++AL/+9//cN9992HcuHFChLnpppvw3Xff4a677sKmTZvwzDPP4NVXX8Xtt98u/Iu22AZBEARxbLDkgdtw6O/tkNN7LaxEG/t3p+GTPXcg++FvkEAiT6VBGT21imr8wl1uU2H5ENEqDCkfr+3QcnpeRUjBWC4H6gvL6PHLWVDHibIHYedJyQIxrAfPUf66r9k0xafv0uvmcEQXebxrvHIhl9Pf70ek1+ON5eVHcvQ5espj00RcNrkfa1hV+hw+0WwGfJD1RK9NtYwepSpm2BfND80WY8EsGk9845I/uh1dXOLSPq6VEwW8HdwTLfzywiZTxSIuVWZJZZlUuTGjRw1HvBD9hQOQ5qUx3oDw25CJc7w4HWccjcV/rEmpjEttLHbbTrZLoGGk15ai80iCnHezcO1MV2yS4xFHuDNHj2hkKI+refPzqJNge8u/M+hZPH42lNsjmds/mXvQzQSzadmtekesxShKSkqwefNmHDp0SOx74oknRNmioiIMHDgQzzzzjDgeiUSwYMECjB07Fn369EHDhg0xatQoZa7E7OxsvP3227j99tvx5JNP4oQTTsA//vEPsbQ6EHuxDYIgCKL+UlpSgk13d8c53b9zMplLgS8+Pw2n//kD9E1OiV0BUS4YjzbrMRGVgoICNGnSBMi+CLAqoj5WRNjxRi8VTcaSczriNwlEypH/xdTXLBK/LXmUyhJD3Izlu6XajKsO530kkhCjFDdWl8CASMggVhaB9HMj4EhJUMsG4cFjzLEZJhCYhCfvfYLFkRCJ3YryAN0jwdK7HlfKB+wy/7yESHSLLPDCITFEjg4V2dyRflICiymghHWNxAQWtYEs6WS5WFKSk6kRaitKnUmJ4femmPeHBfcnJQKWFQmcaboO8kvmxWkqJol6TI+TAcmJan8HC7Gt+RthHAnaR6VuU6lH+FCKBsnwhRpDe5jitQBErDIkJLhih5TtJ7eD4r/7OmLZSE0pUwVXUc7N1GE8ECMDR2JCGSIR2++bchsywGJBPwAgKbEYKSklUvxc+ul9DsjKkucHkJRUjIjlTfrMxXGvDS1JkWKS/QOFxWjV9RPs378faWlpIIjajvfdi/osQRBE3WHD/97GcatvRosTfwIA2KUWPtpyBc57+NhaPas6f4dRRk+toiLCT0WR/kwfd/mwsuX0O97ioeV0XwwFY9ow575Ey+hx/jhuGIQjXADyao7msSFPQKrXLx28AtE12rCjNjf7JOxG0TjMyzqrNk0JF5xJWTchNs0Go9tiUWza6rjebNN4INymbFtKBvHj5By2ctWkaqOITpZRPPNrkLMzdEc4h/NIFAu2gxUWIxCe0ePVK9v2jjPfpq11XF/E0DJzZDHGtCS7ZlNpT0jXWP6bhNZEVojIyj2bXkaPq6woZbiTZSPsCv+djB5nQmWu1AlwRziRNRfphZgg2xQPlzJrvH3ufWVz23kMC1AEGy92S57VHFAeu1LnEnKLiYmnbdUm95dit2mOHoIgCIIgKoHDhQewctoEJBz8AaUNj0evyTOQ2qgxVt/aC6edvgkJJ5aC28C361uj2diFOO/adjXtcr2GhJ5aRbxfuCtDENJGBUdlUq8khn/x2gzVlvSdBvvldMlDH4ubBsGBAbzhgHxeWJ2ePW/OjfJqDgzRdTot4cLfz8xxCbRBr3KIM22OEPcErb5AG/HYNo3ORhFqYtkUc8SEVA0g8PhMeTXIsBWwosZoOGjbAEQSmtmLcJuqaBVoj5D7yOZOVpjuHiD1Heb3A+UeYPoE0ob+YrDJlTg1oUmLQRGITDFKvuliimye2wAS/MeamFTAMvkr6rWcZcjd2plWhmsyI4dXH3MmyHb7vPL5wBxRhymBiNrBEIFllSlx6xdQFrw4566P8mNZrgDknsvcrB59djHmPlpmxUp7IwiCIAiCiMGyiUPRs+Ny9OtSLPYVz/0PjlhA996lAIAj+1Kx9vCNOHva1Jpy85iChJ5aRWUIONVtNqYUUXF7cYk8USqsQFymPJ8wWUkfI5qWiebwM1liuRSmdYTijTNDCsvZSUrdhsQIwB9Yx2p2HlDRggQST0IEhfhtxkYvY3MgwoNldAEhmt1YtgLzrNgczGIBX0Kzh1x7QfEsPpvMfa/NwwsAEE/KhdRtMQQmZBZ16nblcm4BPRFEFhDFiboYFEVT8Faf8rNO/NeWK57I/ZlxhD5OqotSTlqOU6HXBziAMhGXo256j+RxANxysmuYdsMwC2DurNKy+ONrMtyZaFmqSz7OxcVyDDrvPLGoTFtpy8/u8R/jkmNj2mvu2uTimnFhnYvjTLqLuToTOEEQBEEQRLlYNnEozspZjPydGdiYdynaD78BO5+7Ed17fgkrwfnj14E9jcFGfIqzs06oaXePGUjoqVVUZ0ZPOc2GmjSdbJIXymnPKxewG6ezFWwi06DXtE/3iEMVMwB/JSjTeUYBxuBPtGh5lIGuV5/JZpiowJWBodlRZsVuWmOSRFhXiMOmXmfcNqPEeTSY9BNlEl6Yu22oWQ7wWEkVmiAWyHYxFffuH4PYBbgZPUZnzbYUUc4gThmrCQhhUDJ65BMVe3JMrjAhd2hZYDEiazNCFfIPKPcGk9pTqp9Z3hxP0jV1G5YripWtaq6cwea2uAZM1OfVIU0IrU8OzZ319vxsHP9m4Nx/7RyDsqQ95zY48wUef1U2JnwUITMm2oQyegiCIAiCqCiHCw+gZ8flyN+ZgfTbNqPFiuU4PPcy5PTeJSZctksjSBmzBamNGte0u8cU9A2vVsJibDVh0mQ3Hv+i+FvuMGOdIJ3oP1FRLri2marS3wurXPVCr8u0yeNwk8vRorRgOEHz05bqtb3NIDoElu32Ns2mI1iwkC3YhrbmQ4VsxmjDMJvxxCkbEsuChzepsBWw6Wa4eNkYin+ScCEG4N4WmNw63Kh8PT17jk2zPW+Jcb1B5Xbmhsbl0k8OeAs0gdvO5iWAyD7I1RiJdl+b7HLdrnOivB+SfwFBSY9b3JvOf5y7m/vadjduSz7Yzvw1zmNY3L22fgEmHAnGypgFbwl27+PTm9dI/qlvYDac1be4u0+9OOp6bmqDimXfvfmBmFOPbXMpFkjxuL5QRg9BEARBEBVk5bQJSGpUjI3Fl+Kzu/qj/a7haNfFEXl2bMrCRxuHIyGlDCunTahpV485KKOnVhJNoagioSemSU+WiOckuZyN0KFzucOMV7lhUeqI60zxOkzeCoMbykSTv+RWDRsDR7MVzRfdB+ELM1xJabCuHDM4zmJ6ZrBpqCo0s8YQVLyXMt44TfUKASeKMeN1lLqb8VE0r22Z+l68Zv550QwH+gkLiZObfdUOi8eGwm4XxZ5sn/k/TX0smlGT8KYTyCASnz9q+8rtEKjD0MaeBKdkQ7n/MC0QBgAWYLlpS0xU6jvgvfNP9SVAzsvMYjAcsclfPYz54g0D5IweJ4tJDY6DK/1MblHnET5f3bSkxnLq9KcJZ8wXdxhl9BAEQRAEUUESDv4AAMguexvH98kD4Hwn+eTTc9D3iXeR9OVaYN3rohxRfZDQU6uoIhGnUkyWV/LwMAwiKhRmvJLG0diIT3sKFVhCBsze3+N1UUFKNgjYCHsfOBilgGGsCw7AkjJdRBVxamOx5ucxCSfe64ra1OuKZVO0KYe7YlIUIcXgQ3mEPC7tFNklmlARqDeashfFsCcUCRNyxo1bn2yTG3xQzLvnMcn/0MJS4EIUdM+3dL9M55vfGucI4vIL5pVxrqpYAYxJLklCjnBXvq7Mr4uBKYFy7SeT37lZMJ4tCzzYHnDEFc+OK0eBsQgYvHl8uH/9mN/mvr+ug5wDESejx/mfu20bvDLqI1uyAOaLRJ6w5dXFvJ9yozDK6CEIgiAIomIcOXwYLZK3AgCO7+CIPL/80ATft5mGvk+MAgB8M+8FZLYHShseX2N+HquQ0FOrKGfGSrWarMSMngqFGe9J7onR1JM4TIaNxWNmLYQUUP+iH92mXmVUezFsGt8z9ZguVuhl1TqY9DrcO5PtitqMsTv8mBRnWPaQYdwes6eFtaO8zzQ+D2T0qAkiMVO0Am0qCUrMEEhYnaK9rfBYlFOkY0oZTeAximl6G+g2mPZesqe+9zN6WKzzgeCcUCzkgGZLmavHApjl9Xgtu4YpP6Tdzk1pw3aEIejtwtUVzZicYeQa9e4yb3Jlz77uo3uOPFm0l/EjTnELW1KQzrXyz7FCVVCCIAiCIAgzSx99AH2aPYGTTndW1LJLLOR+dzXOefDvaOaWKT58CJ2S56P4QDJ6TZ5RY74eq5DQU+uI50t3JX8xj6s6U6F4ToxjWZy4KPcwv0J2lIyQkOPxzOESTRKTX0di1Hk0V1qPQc8EUWyGDFwDdYrxrjm3xyScyO91ASEem3odJpvGY15GTxQ7FRnj6u2oHBOqh9nPcEdC9mt2zTuYPKYPMWrYxcNjkapWMsAU81ydkFnOLPHOjSaUymKffA3N19Pfa7pHmbQfUDN6mLeD+RMa620gJsPmkqhkW+B2qSuIcc2A9EMSa7yMHgsWxATIXFQMgLmr4XmZNgxMrCbmZPQ4ZSXBRmsNkdUktQcgT+osNap7zObesvJc9BVvlS77aGcnJwiCIAjimOHQ/v1Yd98gnN3jK1iJTrZ14Y+N0Kh5ITqmvoMPH7wFJ186Gt/MewGdkucjo3U+Vqw5H+fQRMzVDgk9tY5ow2e5zNGKPdKwKp7v+ZWZ0RN2aiAkw8gqLuTRZ1jd4WdGG5yaCEusiTWe11upUrWvaIeZb1PAzYPn8KDkATMPHjacxqHVX86MnrDDseLkmh35nGgCVCxM/opHiuK6p6SXSnlzpw24xfz2Z4HG1fyQhA9jRg/M19uQxKL89IQtJYtEes2ZWqXpGgQEKmaw5a0mpfujZzAJx6RrzOFnsIh2MPdZSy5j2W62i5Mno7SVLh4q1dvg3BY2xWeDtzy6tzF/ny/sWK4A44p30ieDJ7B6j7F5DSVnIfk2uTalEHOzh5jScIyx0FX4CIIgCIIgZJZMuAw57T5CTp+DAIBD+an4fMe5OGf6f7Bs4lD07LgcfRu9CKx7EZntgeIDyY7I8/ibNez5sQkJPbWW6vj2rY/qylE29gkuMdb/jkq8NqNUVM5mND3xEU0rUga92uAvTBqTbXhLsEcTSKI6G6V5Q7OSuMGmPnCNVqk0uo1XfjsqmwYX4sGSB8GqSef1Ud5iPPAi+sDZKJyZHDPviJrRIzeuIpIY7MniREBK1YzICUpc36epRPq1jdW8or5gAorznsk2GWBx5V7khphl+94PR5BigDRRMSThxg+Zw5YCZTYD15YlFyW57IMv4IjaGQvGJdtj3nxD7mTM3oeHBThLf3knSt5xr02kiZT1CIRN5l80xsW1DQhzjMO2SekhCIIgCCKcPd98i6T/9sU5PQucJdNLLKz94gycMe09nJOcCgA45/E3cbjwAJZPm4CEgz+gtOHx6DV5BmXy1CAk9NRaTJJDVdlgZpOK+Ypm9ESpNFZWjzHkaFKMh6R+mFIIoiAPnMJc0ZIWxIA6muv6MXlzxB9zhkFUkamC+pY3gDYlkQRi0IUSbfCp2wzVMSpoM5YYE82mzYM6WCCjyCBIxFqEyGjTu4W4Kg0YfTOIT8G+o175qI9laSlL3FRGEj684xFTRo/3WjrPKHTA1SN0tSSonih1hD1GFxDk5D7gVsikLB24uo2sVOm3uuhvTFplSrEv3efefkkzY5abPaOpYUp5efZt3Qd96S9ZfJKOife+YagNKs/jw8GUXs2l+aeYuhqX0obcqVZk8nhtzMCseKVTgiAIgiCONRbfeR16n/gWUlodAQAUH0zC1qx/oOfjwwNlUxs1xrlTX6huF4kQ6uS6qmVlZbj//vuRnZ2N1NRUnHTSSXj44YfBpbkGOOeYMmUKWrVqhdTUVPTv3x9btmxR6snPz8fIkSORlpaG9PR0jB49GoWFhdUdThRkOaCa6tZVCFEkdAhvKqwdj+EDY/LIwz9mHH+YbJmkk1humPyypKEfAw9pjOB/MKgYQUuyx3Jxr1bL3eT/TPs8mzyKzWgSnLdZpo35mzLodTdvBSTZD6WLGC6PXE95bcpiUGi3DLEp6jNsluVuhmOxpizhcBIwlM31xcvosTT7luWM35ml+SF1e9992SGE3goM3vnOAJ9ZwU2OU4+Vqx1Q2eSupXcz8fSPpbmq38re7S2FZBSj4PjibXqMAMAsBs6czWswr+1NHyMMkh+aGCPXzuRO4zrG4T1K6X4GMOUU5XpzxoL3shDF1btEuWO477RTh/PektOYJKNy23DpP3E9pJvFs+b74/4j1cs5wG2nUvWhNIIgCIIgCCD3uZn44cF2OLfba0hpcgSlhyPY9PmJSPl9PjoPDoo8RO2jTmb0PProo3j22Wfx0ksvoUuXLvj0009x/fXXo0mTJrj11lsBANOnT8fMmTPx0ksvITs7G/fffz8GDhyIDRs2ICUlBQAwcuRI7N69G4sWLUJJSQmuv/56jBkzBnPnzq3J8Fyq+su3IdUlqsmwwVK0tBwWclw6N1Ali3JMt8dC9hlODLhviseGMz2yyVb4XhZ4YcaUnaMOqNWaw6pT7IUUCjuXA2JZbOOV05tdEk6895KcGjjX5Ajn/uC4QjZDzotl05TRI+rjMD5iFdAbzdUbHXLa1s/oUa6zJ1gZFqCTlycXr4TAxXyxxBADd19wk0+emGM4WXmszaSLaufosXAAXpKMsuy757d+7bgbe0gsAZuu38I12zYtmKX4z0PidP4xdZTwzybnGnPoK8sJH9wqmfY55Te3LJPJr7h6XErkcUzaWqfmhmuhN5TstecY949yT3zyHXS0H+9iUEYPQRAEQRAOpaWl2DSpO3p2/xYszdm3Y1MWki5/BV1G59Ssc0S5qJNCz4oVK3DJJZdg8ODBAIB27drh5ZdfxqpVqwA4g60ZM2bgvvvuwyWXXAIA+Oc//4mWLVti/vz5GDFiBDZu3IiFCxdi9erV6NGjBwDgqaeewkUXXYS//OUvyMrKqpngBCZZoDIJEUNCTUZRFaL6GO082aYsDnnHLMOpcbZHuSZgkQZIcdhS93L1pTZoZzA3qxyt30JBe2YPQtIiYuDVZTHDVYml9cnjVlE2OODUJ99l7j9qnPHbNLVZoFiIzYrO0VOuRYg0Uco4R4/sXzR/THtlEUUuK8fmZvQYTotqj2sagWxDlw9EjgqT4oljMmfI57kVh91l8qpd3jUVWgSzwJkqknC5Xv21FBO3uf9RIolCDP66VcY43EwbxaZ04zL3TGVi59B13WWHPRXL6eAiuQccjFlSh3bKyZmqXmJO0I4XhxIgzK0tz3UUJuATBEEQBHGssWr2C2i3+wF0yskHANhlDJ98dQH6TZ9Xw54RFaFOPrp11llnYfHixfj6668BAF988QU++ugjXHjhhQCArVu3Ii8vD/379xfnNGnSBL169UJubi4AIDc3F+np6ULkAYD+/fvDsiysXLmyGqOpKXjIVt7yHE4mjB1HHS7MsJnsGMcf5fU7XrhwTd0b/p/3wIXpPG+fMUTJY70FnS3cphKrSZvS6tf3ebYCNrm6iUdptED0TA31IRIELguHU4/NKmaTB+wFrzoLs2lDeSSIa7b040q7RoHrbzjAbXfjXI3FPSZs2XJZqS4mhyAHY3LQ9992bep1B+wFqwhm0BgaN1CE+/XB0L6Ku1I9ynXVkfqX3o+8a8ZtG8xtWLluvZ2VOLnTN5giInuSKpc2Fb9+W9QTfFaPOwIMl+5KWXzx7k1x0AaYDabcfc5BxrkTm83Bue22gfwJIfvGxM+gIMnc46pcp8zb494gzBWWnP8DFREEQRAEcQzx8w/fY9NdndCNTUDTtvngNvD95kzsOX05iTx1mDqZ0XP33XejoKAAHTt2RCQSQVlZGaZOnYqRI0cCAPLy8gAALVu2VM5r2bKlOJaXl4cWLVooxxMSEpCRkSHK6BQVFaGoqEi8LygoqLSYwhHD2UquN0p9RpMmuSKeOg37eMgxPcWCm06vvnbwhoTmrAt/gMTgzunBgrWZ9AO9vuB+Y26HUhuDOyA1SLWxroJRfGLh58qDfmWpakN5OZNEPmbxitvUCbseAZuWIctDr8u0M8q411iHdA0CGT2mGE0+8fCrrselZNW4NuWMnpi2PJN6nEy75aT3St92BT/9MbSoE0YLo4Yyxvtcb1fL6URih3/vRYvbEad8+UxIIN4qXNopqiZkAczW2sTrmL5gpJl0sBmQINu0pFAVT6Rrx4QoxbmbKWRqF0PsMlyIV15drk3pAjFpcn1mvHAEQRAEQRwLLLp7DM7t9DLSz3D+wFX4YyNsaXAHejx4Zw17RhwtdVLoefXVVzFnzhzMnTsXXbp0wdq1azFhwgRkZWVh1KhRVWZ32rRpePDBB6usfofq+tIdI4UhsCvev/rGM7Lziuk+6CkHphNN9R1Nm6mjTG48wgP7dH0lTC8w7dcXKzL97V6fG0S3wREysJbq0cvLr7m2L2RMqQgIJpthV1CsZO2Wt1nwCpuePtGf4AvTBQLnuQcUm7a6glbY9Qi0Y5TuZGxbaUEnzv1hdsBZ6aLrjyh5oop8khisc7XtvYwT0W+0OXq8NgiEoYs+up4UdoszKSPHLWfKEJIfvTKYFAJR4JihT8mrjDsNa6v25AuhtbE8JY8j3kj3t/b4l1Ifc7KHxEcT404ijtIXvcqderx7mVm+qOO0LVd99NKPRF1SbRxiBThPlPIFKdVNxpx+zeA1EqRJp+E7Ky6m1KvE9eHgtif2cGdSZoIgCIIg6i2HCw9gpbbs+f4f8pD//GU4r9t3YJbzPWHHplZoc/869EhKqWmXiUqgTgo9d955J+6++26MGDECANC1a1ds374d06ZNw6hRo5CZmQkA2LNnD1q1aiXO27NnD8444wwAQGZmJvbu3avUW1paivz8fHG+zuTJkzFx4kTxvqCgAK1bt67M0BBdUKlMEShGXYERbVj5WMJOyNOBYVk98gg1bGQeXlm8J4SWZ8bXelYPD1j0xoVh5iW9JOZP+Z1ZgpJ2xWgjk4Zh8iXquZJpx5w6utbrsAwHjBk9JntePDHGnqHajGzTW+EqelXxa5iaCX2ROCEqGGzqworetpwDLBI4qtSt2/TnBVIFOWO8mk+irSVEV9LjMglEzFCnSeTRRRxpvnNZOAoToeS5j3zb/np4hqZSxD7AE0+c+zYsQVJerUvY9ZZFE6dwxah47EvKNBIiqqy8ifOZcr9aWhsKmyJ4LolUrgAU6FvuY3vy82ruD+b5yvybytvnLOfurgNmmpWcIAiCIIh6wbKJQ9Gz43L061Is9pW++hrSbYbmOaUAgF9+aILN1nU4++FHaspNogqok0LPoUOHYFmqgBCJRGDbzp+As7OzkZmZicWLFwthp6CgACtXrsTYsWMBAH369MG+ffuwZs0a5OQ4M4gvWbIEtm2jV69eRrvJyclITk6uoqiAyhVyKttUmKIgD2ZMoo4pbcN0nGnvQ8yFV1RBwjN6TGWjyV3RNJewzBRLOhY8X87sqBi6XQ5nXBpu0yGux3A0G95hkYghVa5n9MRlM0rgcdl0M3qi2bRMXTZGg4ths5Q14mkInOvT8xqqZ0FpklnmPiLXrSyJLtn3V99y3xsEH5NNk0giAvR2yJk7zLcjz6ck7lzpDZPOV4Qb+XEsQ7zCPNf0Pm9uHtk/rRLvWsqCoRDfOHeXZpcfa1IVTNl/zgELtpPtIjcMlw248cgfWZIwxcGUZc+9f5mrCgcyogBnjh7RyFAfV/PEGqXPMv8xLOZkkzH3bhDxcIjHvbiXVSTNL2RTRg9BEARB1EuWTRyKs3IWI39nBjbmXYrU7DNw6sE7kJzmTEVSVhzB6k39cda013E2Pcpd76iTQs+QIUMwdepUtGnTBl26dMHnn3+Oxx9/HL/73e8AOF+yJ0yYgD/96U9o3769WF49KysLw4YNAwB06tQJgwYNwg033IDnnnsOJSUlGD9+PEaMGFGDK27F84W7km7C8ny3N40K1YNRKjWMpAPFDCP8WMpJqL2jax+m/VT3a4PDGJ6E1Wuq2+y1uuJP1IrLccjLCoiraUMvr7ktwuplPI6Z36NofiaitSngZEzETFYIsRGr6ynihbzfmy8nSnlln2fPzXQx2tTFE8UelPlyYoVr1Filt0wraFpu3tunz0WkvTW+MWb0SMaVrBrFD28eIi72h30syRk9jqbB3fKSaiWdp895JF5bFpjlP+qlLnUvqU5yfLJNQwaRd0dbUpByCBazYFll4gRfOPJyhaBeN+ZMCu1oWQwWsyEy7ryY3YaylHl5fOMWZfQQBEEQRL3jcOEB9Oy4HPk7M9Do5nXgk6/E6cf/EwlpJQCAshILZUUWut/3Es3XV0+pk0LPU089hfvvvx8333wz9u7di6ysLNx4442YMmWKKHPXXXfh4MGDGDNmDPbt24e+ffti4cKFSEnxnzmcM2cOxo8fj/PPPx+WZWH48OGYOXNmTYTkUp03GYc5bSNKeaN/8v6w4xqBYnrdwcFYHJVUCvL8OWaLYZJMeLYPIA0AQ46FZtZEsalmGJjrNWHzGDZZvK0bjIhre4Rmw6SsmxCboSZCkNvUZNOWDoTaDGn0uPQhadwt4tTn6NHtheAJNXLuh6jVS8IwdCB5xSkwQ6ZQTLUq5K0bFDc0rryCGZPKewKLZfBDjzPEBSVLSEssUpYZ15vIChG6uGeTe5lOLHB9OediSh2viFO9k9HDPcFEM23J8/DItpkq+Cn9w4tHajwOiIwbm9siw0bJBuJSnIpIxaTXfu/xThX5S8z2s3m8mL0oKaOHIAiCIOodK6dNQL8uxfh+fTOc9GJ79Dv7IACgqCAJa7/ujpImndG3/YtYPm0Czp36Qg17S1QFjHM9gZyIl4KCAjRp0gTIvgiwEitQQwXFCtOSN3HjnVdO2ywSkpIRqx4L0uQj5TiPOW1a7iZyR++WyWa0cxwikQTjEWYs7b9PYByRiLrPKxdt4uYIOJITzPWG7fNIYDxqmLJdxSbjSIjErp8ZCiRETEIQV8+RT3NfJFhAJBJdttLP8UiM0j7hNjmSElhMYcFYFwMSE6KrPeIu0sokJTFYBnVFb0tT1YmJlnssWFDuT0GbgMWCF1TpsyZRx4vTcJ7sr8n35CQgYmpbw40S6HvaR6USlxyvImiUokEyD/gU8FOq1HufYJUhIcHPwJHtmVZI886NMBsNUssgHtNSynj7VCXMqy8xoRSRiK1cN6/TWEySAjXfExNKkJrqP0OvZPKINrHhw2G5j3clJRUh4r72+z0XZfR71hOSDhwsRauuudi/fz/S0tJAELUd77sX9VmCIIhwVtxyDnr1WSMWY+BlwOZ1J6PtH/6HRi0y8cOXa5G57mzkruqHvjMW1rS7xwzV+TusTmb01F8qKPxUi9mYz1HFW5H/5+ao5/Io1cWUJypMSG5RzH0WEJhU1isnD8tM3oVl+xxtb+Ahr+XshYBN06A54BAPnBfNNoOTTWGyGdWedqC8Nm0ORPQMGMkmtNcmm7FsyTI5g5OpwQ2PwkTVZZnqh+KyITXGtNqVNy+PkmEiCRemu5cZOrVSjffFQKuXMz+jRwtDFU4MRqO1g8gekuN0X1tgbhaKVDUHuBVbIGVeB3RvUK8PcABl4mPGyerxxBwOgFtOdo2XKSPHZLl3dUBoch0TYijzm86rhnupWaaVsVDm2kTgmCfoqP2Xaa+dIPwJqF0fRDoYlxx1LdjyJxRBEARBEHWZQ/v34/N7hqBnj7UAnO8NJYcT8NmBCTjr0QdFuW/mvYDM9kBpw+NryFOiqiGhp1YRb3JVJQtC8ZgNfQYk1smG83iUY0o5HmK3AjbjJDAYDtmnYyPoqveURbSBqDwWrkiksTAlLFgsSgtx5UdA4WIs+iNq8imK7RDdTswNG+Y0D+6K22bISUebw6jb0gfeYXGG4nWCsCwrFnypi1XGPhqm5nm7DEKMqT/KgpG8T9ez4rlV5Tl65CJ6Zk1w/h+u3EyegBHPxxL3VCH5AJfuAzlgqX7GmPtIGFNsAhxcUax8ocQ5zJQJsr1qmSdKyRNCM3fuHxFnBBYrE3bkVbe4ouZ58/L4bcC5De6quI7/rk3GANjavcFEm+gLGxAEQRAEUTdZMa4vup+xDr3Pdubh4TZwKL8BUm7YjrNSG4hyxYcPoVPyfBQfSEavyTNqyFuiqiGhp1ZSyUJOlZiM9Xf0eKowpCtUto1yYsqq0TN4TBk93sDalJlhKq/Xb7ITdk48xzy78sBdzizQx/gxM2s8bAYeOi7kShx6RlGFbSJ4XWTCbNo8KIJEW9krmuCm+6LYVEQFd3UnzWaYIGFqg0Csnhgm9y1NyOBSJV62iiJimGwbjWnxaY0rX0M5o8difgaO0WSUxmVSPKJuuajkSCCDSq5Wu/dkgcq/N/1CIhNKCph5Exu7u9XsGq8OrrabHjPzBD+nUs+sPv9QoCk4wCNlTr4c93fKc/Wo0SsNpMwL5Alj3F2tzG09Y102ZfQQBEEQRJ3m05dfxgnf3oUzz8oHY4BdyrDuq1NxoCQDZ/X8EPlPdsDGomE4+dLR+GbeC+iUPB8ZrfOxYs35OKdR45p2n6gi6E95tRIeZastJqOdEKe/HM5Ii7OQ00znH4W9OGBRNr2MCd0Lr6yc3SNvegaFXm/MaKOELNen+GEYjMsT7HJ50+0xry5u2Mz2wzKbuG4rzGZI24Vdl3ji9DbZkGiD8CY123SFHEsM8DX/OMDtEJu2H3NonO4LFmrTj1WM4+PYOFONMmmDVqdsT0x87L1nSjVmPJt6exoCFnY9mxbApMeWlE2uQ7suSrziI4bDSS9zNgYOi/mbHDssZ0Uqxpx5l/zsGtWJYA4Xd7JrHGviHmfwsoiYdDrzM28sANxyjzPRn9S7SGo37dPG6V9ciDuOp77vzI2FMUu9tpTRU+/Iz8/HyJEjkZaWhvT0dIwePRqFhYVRzzly5AjGjRuHpk2bolGjRhg+fDj27NmjlNmxYwcGDx6MBg0aoEWLFrjzzjtRWlqqlFm6dCm6d++O5ORknHzyyZg9e7ZyfNq0aejZsycaN26MFi1aYNiwYdi8eXOlxE0QBHGs8ePWHVhzS0+cVnQjmp/oiDxFBclYU3gfuj32Cc6Z8Q5WrDkfjTMK0bf9i8hcdzb6tn8RjY876Ig8j79Z0yEQVUiFMnrefLP8neKCCy5AampqRcwdQ4QOk2qZybCT4qxMKcZNO8tXXyW2my7SmP6ObvxrvDTYlI9x6ae+3zuFI7zeqJFFHVmHy2QWDwpMJt/NdUYX00yxe68ralOvK8ymXsbL2BA2o3QxXbyJ5Ydik/uv5Ywe0yNPimlpZ1gM3k4GX5jhgD/fCyShiBlijBKM4Wkm5TSvTuUgg58h4tq1ZL8CRqK7Iy0E5e+TXwh7jkdCwHD3e47qcXP5uko3F/MUH8mEHrfYY/sZPU6c2onClpS149ZiSY/wMc9vybSaVecFw4GIIxA5/3O3bYO9wnlky28TX/hibpzcddNrM29lMTVDCYwyeuojI0eOxO7du7Fo0SKUlJTg+uuvx5gxYzB37tzQc26//Xa8/fbbeO2119CkSROMHz8el112GT7++GMAQFlZGQYPHozMzEysWLECu3fvxrXXXovExEQ88sgjAICtW7di8ODBuOmmmzBnzhwsXrwYv//979GqVSsMHDgQAPDhhx9i3Lhx6NmzJ0pLS3HPPfdgwIAB2LBhAxo2bFj1jUMQBFEPKC0txfo7e6Bzt2+Q0cf5nX/w54ZYf+Qa9Lnzr+gtlT3n8TdxuPAAlk+bgISDP6C04fHoNXkGZfIcA1Ro1a3yPtPPGMOWLVtw4oknltdUrebYWnXLCk7KESxktmdcdSuOc63EkNF5rHrYUa+6xQJHzO+9fQmMO6sQaYN6vR5dm4m16lY0EhkPLLcczVfvfYRxJMZoHhZ44ZAU8fcxwwA07PxEadWtctuMs33U8zkSExhimAztXkmJIeqMZkcvkpwIWGH3pqlKry0ZkJQQcm/qooVWKlledctUQ4igZkWAhIh/MEzsMj1elpwcXNI89PE0xSb3V1ELE4GYdJ8I26VITZEmOfYOWYbztdcJCWVIiHgZQVwtY7Ll/rSsMjRIKRPijy6Q+OZF+gy8VbicVbec/frHphCMmOOPXG9CpASpKSW+b2LVLd9vb1Jm4YpbX1LSEViWrWayeX6KSbIcUUpezetAYQlanfYJrWBUT9i4cSM6d+6M1atXo0ePHgCAhQsX4qKLLsL333+PrKyswDn79+9H8+bNMXfuXFx++eUAgE2bNqFTp07Izc1F79698e677+Liiy/Grl270LJlSwDAc889h0mTJuHHH39EUlISJk2ahLfffhvr1q0TdY8YMQL79u3DwoXmFV1+/PFHtGjRAh9++CHOOeecuGKkVbcIgjiWWT7zcZxy4Ak0y84H4Dym9dlXvZEz9W0kJCXXsHdELKrzd1iFc7bz8vJg23ZcW4MGDWJXSLiwOLaaMKlLFfFulWGzPM4fHbGeeAn727dsOSyTxvtpS1ssjqYXmHz3MlDkjCJn4AflURz9cSBhU0q3MLVPmG05E6bcNqPUGc2mF6dXUeCxn2j7Y7Sr9yLs0TPhvOVszqS4rj1L2qSYQ/sd92OJ9rib3Ghcti/Fqmxa48mPlimP0bmv5cf6gGA2TuBPBga7enmTTcW+eOSN+edI10HuA0o4Uhswbwecerh4XJQ5c07ZDDYYbO5uthurbTmrqMlOQXWQc/cBLVc4cf51Ho8C3Me6vEdT3TaxPTmIw7XHnDht/y7ljkdahGq7+T7AWZEs9DOTw+bcicXmsG0O2/baArCPdnZyolaRm5uL9PR0IfIAQP/+/WFZFlauXGk8Z82aNSgpKUH//v3Fvo4dO6JNmzbIzc0V9Xbt2lWIPAAwcOBAFBQUYP369aKMXIdXxqvDxP79+wEAGRkZoWWKiopQUFCgbARBEMcan89/A+smdkHv9Clolp0PbgMHf26ArzNeRK/H3ieRhwhQoUe3Ro0aVa7HsH7729/SX13ixvSluwrEHflBi3i+54u1w8s7KAjxPa4wuWlnjIqOrq3CRJRotfKQMmH1MO14hYdZMUINPeweUJZ99wbverHQoJj0Lw8eNpymD1l1m1rVocTTzvIBHhZbvAaiFQtxRoyduXTXSMcC5yoiiNbvmeaWdkgsoKQF6IldAJRH2ASazG/MsDGcJ/ffwATMunikizumegwG9HuEuc86WXoGD9feG+p32p5Lba7OJyXblxbZArNsWKJxg42jfFZw2abtCDVuBxdnu+9l7UvsE5lGzhw9YO4y8NK01EpmleSTOlm0Z1OLEczNHmJqG7jzDxH1h7y8PLRo0ULZl5CQgIyMDOTl5YWek5SUhPT0dGV/y5YtxTl5eXmKyOMd945FK1NQUIDDhw8Hvjfato0JEybg7LPPxqmnnhoa07Rp0/Dggw+GHicIgqjPFB06jO0PdkPXrjvBXA1//+40bG96F7rdcju61Kx7RC2mQkLPrFmzylX+2WefrYiZY5jq+OJtGtXFKh+tUAV8jsvmUVVQbsJkozAxRymjDchNMpU+Ho+2BHuYPaWyKDl5oQISN9jUxYRolUqj23hFKu+0Ctk01BUPXpaQwaTz+ii7jyyk+HWy6MJFiM2oEqonFoUqgyz0ESaTUCPXq7zVRBM5U8YkouiPMsVtVzOvr7glRBMmu8gAiyv3IZc6VMAW828PR9dgYulxIZDI9l3xw5YCZzZzBRPDlZGrkgQcv59b4Nw2+uYJT17/cQQzd06fCADYfkVS2pQ3Fw8X2UXeNfD989pSFW/crCPvNOavjseYMw8RUXV8/PHH2LZtG8rKysS+a6+9ttz13H333Xj00Uejltm4cWO5661Jxo0bh3Xr1uGjjz6KWm7y5MmYOHGieF9QUIDWrVtXtXsEQRA1zvt/nIScxv8PJ52+HwBglzF8umkgev/pP8igP9QQMTjq5dUPHz4Mzrl4PGv79u2YN28eOnfujAEDBhy1g8cm1ZHVY/rzfxRYrAJh2TdR/I4ZZqyMnrAKKt5WeiaB0S2TF9rANpYkJm+e+GM6J5rAFM1I1BZghgdC9MG291qryMnQMPeFWIJVRW1GI5pNmwd1sEBGkUGQiPV702hTDMb1XArVqLzSVfQ+pl75aBNJ60uFcfk4N/dPAIFpvoztECLYcEgCjWRHFmtEBFIFpvl+9DKBc6QKlf1RPpJUTYyBMVs5wJSj0n4m3ZsWFyIRAv7AbxtjVpENpjjIFdvMDcAXAP3j/oXhil/ePn9dN8+4r0555fRJl72MHsdnx3HPvwpP90bE5KqrrkJeXh66deuGSMSZR4tV8Iv5H/7wB1x33XVRy5x44onIzMzE3r17lf2lpaXIz89HZmam8bzMzEwUFxdj3759SlbPnj17xDmZmZlYtWqVcp63KpdcRl+pa8+ePUhLSwtk84wfPx4LFizAsmXLcMIJJ0SNKzk5GcnJ9EgCQRD1j8OFB7BSmyQ5tVFjLJvxGNr9/BzO7ZAHZgF2GbA/Lx2Hzn8dfa7pHbtigkAlCD2XXHIJLrvsMtx0003Yt28fevXqhcTERPz00094/PHHMXbs2Mrw8xilKpXakLpDTYYNqSvioz6qK4+9eGxW9C/UTBs2xRcd80a1IWqNSTzyintzG5c/0ug2o0lkssgUOBZH0zKmR+JXHE3uCxNI4mvj8h2Qlx43nhKnXRNSUkjApjGjR7IZ1vWZvtegFZmEIeb9E1J3tOvJY3TysHtBPP1jiiVWR+Zq3QFfNJ997YNp/Yv7GXQxbx49Er+Af6rvmDelTgKYI6toipKSVWQyx7xcIjsoYiq1MF/4c3dZXhoTU1udSxk8ch6dLFip+7x/uWbYzyQSWUFV+nvm2OaLL77Ahg0bKqWu5s2bo3nz5jHL9enTB/v27cOaNWuQk5MDAFiyZAls20avXr2M5+Tk5CAxMRGLFy/G8OHDAQCbN2/Gjh070KdPH1Hv1KlTsXfvXvFo2KJFi5CWlobOnTuLMu+8845S96JFi0QdgNOXb7nlFsybNw9Lly5FdnZ2OVuCIAiifrBs4lD07Lgc/boUi33Fc1/Hj/kNcVbrfWDuU7h7tzVFwRnT0eGaETXkKVFXOeq/5X322Wfo168fAOA///kPWrZsie3bt+Of//wnZs6cedQOHrtU9ZdvLm3xmOQhmzetsOlYHHUFJheRyoT6E+bL0T6CoP0FPmTT/wO0rIAYXnsw6K3IlY1LP1V7UipMefU6QExoa5s2rk66K6xJSo3aClIZ+XJqweq9JdSeySbMVyLaAX3yYHmzOdzJaIMbEF0cES4Z7HqT4ppiimVTrdKdAFiaedjkklevmJDX1jbXpueP7JcSpyEWvWvpogwHxEVU4nFt6+0ibmezJiX8UXySq7FtMK2TMK+/2XL7qxtTAg20oLRpvriB6tlrnk2vDzDOA/k18iv5fuXKe79xGOeiTs5tQMsE8lbm8rOh/P+CXnuFJGvezamXZAzMYuCG+InK4cwzz8TmzZur1WanTp0waNAg3HDDDVi1ahU+/vhjjB8/HiNGjBArbv3www/o2LGjyNBp0qQJRo8ejYkTJ+KDDz7AmjVrcP3116NPnz7o3dv5y/GAAQPQuXNnXHPNNfjiiy/wv//9D/fddx/GjRsnsm1uuukmfPfdd7jrrruwadMmPPPMM3j11Vdx++23C//GjRuHf/3rX5g7dy4aN26MvLw85OXl4fDhw9XaTgRBEDXJsolDcVbOYhzIb4SPvhmNPad/gpUfnYFIko2MNvucR6vLGD7ZcDFa3bMDHS4ikYcoP0ed0XPo0CE0btwYAPDee+/hsssug2VZ6N27N7Zv337UDh67eF++q0rwCUkDCTVZsbyTmOWNNqXRvSkFpEoI/fu8saQPl/6F8loe/OmZG/JPv2WDNs1ecOVHvHh1WWGD7WhZLlz6yUxZAG47aHXog3vTZY5mMzwjQioWYrOic/REW4RI6ZJy5onILmGB5bSVdo3mj/5Ku030thN2GAt/DMsQo+iXWneXbchCmjxXjsWkeCxzuxvtSRXrzav449YtX1PHpgUu5pZR55jxKjH54ohEABePYcmxMnH3yvMtC3+Zm9EjtYj+6B2X/gUgZf94+9UGYfLnDAdgQYiaDNxZrUsE7zjFpQvlXwse6L9cFqakOtQEPAYxPxBcm1X2mUqsXbsWp59+Ojp06IDk5GR3dTQWeASqspkzZw7Gjx+P888/H5ZlYfjw4cof3kpKSrB582YcOnRI7HviiSdE2aKiIgwcOBDPPPOMOB6JRLBgwQKMHTsWffr0QcOGDTFq1Cg89NBDokx2djbefvtt3H777XjyySdxwgkn4B//+AcGDhwoynhzNp577rmKz7NmzYr5aBpBEER94HDhAfTsuBz5OzOQfttmFD88CSnvDECvvs6KgtwG7FIL+eeswtnXdKphb4m6DOM82rAmNqeddhp+//vf49JLL8Wpp56KhQsXok+fPlizZg0GDx4cuspDfaCgoABNmjQBsi8CrMQK1BDPF+yQkVOFJ1aIdV7YaN9CcAQrOxS2jwEsEtOksQ4rKfp5oT4wwIphM+TcSETXPrmpdsc9aV8iAyKWWtAopmj7EsCRFJBbudx64eda/sDbRNjVSmAcCSHNE+gdmliSqMUoD9uNPjPf10jE7FFMm1EuZZhgBXAkRpiySpNSxNDNfbEGSIiE3QeSCQPJSQBjlvm6G5wVT10xICHiO2s+32w1OQmwQu4xkwDkVRMxXBP92pkeowKAlCRz35NvZeWecf2IRIJ9L/QcZX8JGqYYyun+ue8tqVxCgo1IxLun3PuZ8UCseh0JVhlSU0rVfi3e8MA+v06OpMQyRCzVJiz5vvZTteT6ExOLkZJSIvVFQ7aR2Odl+jjvk5OLYDHbn6BZEXrkeN19rr8FB4qRdfpK7N+/n1bHrGTC/tjVtm3bavakfuF996I+SxBEXWTpvaPRr8sr+GDNZWiXuArtun4vfu/v/LoVth/pgb6nvYXl60fg3Kkv1KyzRKVTnb/DjvrRrSlTpuCOO+5Au3bt0KtXL/Es9nvvvYdu3bodtYOE+dGCo4eFbGEmw8rrQzPTvhj2vUkx9HqNYcfyobyEn+vUzNwFj1ngtdxMHFDn24DfjHpzmry3lI2F/ifX4a8WZPLbvC/UJvMzYJRNCoRxuNkF/qZfAXkALk8dYx2FzbD4AgN13aal1mtJm/BXOu4RTfo2tq0lbW5Gj6jX3S98geqPqIvLcWlXijFF5GHMqc8S9bKALcsKxh8zTvdWNN2CTI9J/sl8n+R9wn35t0yMjzLlPpHrtyy1s7idSI7bZJszv8/Kjyhxzoz3p3yNREV655QuorJLCYRJ7emeyy3wwJ3AxCNUsJiT0RP1c817lEt9nEstoX7a8MAHEBN9FZ5tokp4+umn0aRJE7Rt2xZt27ZFWloarUJKEP+fvTOPk6K4+/+nemZ2dpdld1kEllNQUUAREBBQQSJENMZbEyNRNEYfDSCIMcjPxDOCxsSoT1Ty+HgmokmeqDFeCYICKiCCRJRDVAREFlCOPYA9puv3R3d1V1VX98wue+/37avd6e6q+n6runvo+sy3qgiijRMr/RIAMLr/P9HneEfksastLFszAb1v/wxHXXozACBesa0JvSRaA4cs9Fx00UXYsmULPvjgA7zxxhve8XHjxuH3v//9oRbfBjF1yRviRTx8fgrPjdD0UZspn6l8rm0GG8ZqZ2KzNvDAntj8qUb8/8ScOUwSOYDaXSHTjEb63DXBmXnUuirDVgw10lvF1Gr6XDmmeW28ysmd1pByIf2FKEPYYodgM6Iunk1usKnNH2OcD8h0+0RcTGPbinlpbEdUMM1Zw231mOyrsOnXS7vemp/e3Du2qKM6R4+Yl8dUT62/r1ZbCGuGx0tpv5C5hkxzECmPtaFtmeGYd12VNrPderqb3Kba3ESyTcZdQcNTncRHbjSqXiNbaQfnHPPmYoI7H5MjuGoz3VjSjRh44sQ3iftZegi4cnPwQHsCzGln2/dXrYT+b4b0nSF8cdvPcYuD0/LqDcb8+fOVVaw6dOiAf//7303nEEEQBNFk7N1egs9vPgYjhy8HAMRzq1FVnoWVq09E6uLtOHnOCwCAz150onhq2nVvMl+J1sEhz9EDOEtq6st2nnjiifVRdBsk6qW7PgWfNGUFOmZh6fXjuv8hWqLcw9XLkvsoGVVZ6b5mkiE0vcEbAEw7rv9m7roa4a8sCOnikFksYtoxw30h+o8Gm+Z6BLuBUa3lnQuYZspBUxl6B97i6W2qERERjhnKMN2mIqIlymYmtsJsM61hHR2MeZ8DeUwihyQw+KOvgglNNuV5geRhYca6aj4xBEUu71bS68WUXeUmUpKG3ggSNpyl5nSbWnJ5GJ3zl0m2VZEVmn9cO+ZPmM4N32t+neThfE57Ws6N66XjSgMJsVdOI4tUiHHNDvMqLNvx7PuGPcf8NnUe9uCQQ+5fR84AsYy8V2fmfTGJNQW94V7MOUoRPQ2HbdsoKyvz5jEsLS1FdXV1E3tFEARBNCY1NTV4c9qPcdLRC9H7+AoAzjvYwb3ZwA8+womdfEGn6sB+9E++hKqyJEbMeqCJPCZaC3WK6Pnoo49g23b6hC6ffPIJampq6mIqlG3btuHHP/4xOnbsiJycHAwcOBAffPCBd55zjltvvRVdu3ZFTk4Oxo8fj40bNypl7N69GxMnTkR+fj4KCwtx1VVXoby8vF79rB0szdaIpgImw3rD8k//pgIM+ZQk4md36bP3c3xdKlJbeGBP/v1dPa4O/9AFkyi9gEt/9fKDv/fr9mr/izvXPss2lKiXgE2poxyqpgRt6fYCdpjmQyY2Iy6pblMKUPDtSBEhYStwsbT3vdk24JchKsVtEelhtqVHEMl1YFZIm7gJvYgcqZzQiB5DfWEq1yQ86Y6JcqV2FO2q11OP6BH+yuXJw7i8pja0SbB892lwo1FsG4GIHvGVEfhacQUWDuYPTZMqz031tAHYNrjtRxHZwg8uovvcZ1iuH9wBWQzQ5CjvmRZPNiBHKHHYbkSPV1+bw/bsA5y70TyK+8wbpufYUL+JvLb0vrnkdrRh2zZsiuhpMKZNm4ZTTjkFs2fPxuzZszF69GhlBSqCIAiidTP/unOw97e98d2T/4l2nSpgp4C92/Px/orhyC48iP2Pj8SiO6Zi20erseiOqdj74DEo6rkbKzacgpy89k3tPtHCqdNkzLFYDCUlJejUqVNG6fPz87F69WocccQRtXbQxJ49ezBkyBB85zvfwXXXXYdOnTph48aNOPLII3HkkUcCAO69917MmTMHTz/9NPr06YNf/epXWLNmDdauXYvs7GwAwJlnnont27fjj3/8I6qrq3HllVdi+PDhmDdvXkZ+NM5kzIY84tfmOiHy1cI2A4BYiCyYrpxYBr4aFAUGgCXq0ERu5+oQJ2MO1zi48VycAbFYeD75r5wmBo5kPLyazLVpIp6mmnqrCxsxxiMnOFbSa47FY3qUgVp2QCdxd+KWO8FxOnsGm4mQuMNomxxZ8fDJmENMeQcT8UDohIKlZRYfs7IYLENoi6kt9VSJhJjE2ZzQFHjBGJCVACzpRgirk3yOuXnjcWZK5qcPWVkrmXAnH5dOBNIZdmKMI659Vco2laga2Q9Wg9wkAMbVKC3DNZB3LAAxK4V4HPCXSueKv95UPFpdYpaNnOyU9wwq/rgyLfOGncqmORLxFGIx2783lfbn/n2ptVuWOxmz8FOdlJl75fvGuJcuK6sKMct2y+QB/yxJVZOPl5VXoevAZTSxbQPx8ccf46233gIAnHbaaTj22GOb2KOWD03GTBBEc+ffs+/C6MPuR6JdlfPjjw1s/rQHkuc8hh7DxgBwllgf3m8JsvKqvHxVZUms2HAKxtz/clO5TjQwjflvWJ2EHsuycM011yA3Nzej9I888gjWrl1bb0LPzTffjHfffRdLliwxnueco1u3brjxxhvx85//HACwb98+dOnSBU899RQuueQSrFu3DgMGDMCKFSswbNgwAMAbb7yB733ve/jqq6/QrVu3tH7Uv9CToapRL0IPYB5rEZEv7apbpvMMgVW3onqiismoVbeifK+70MNicWNQh3zMdC7BeGDVLT2tKX/cFXrCvIq6Q+KMgxlshuUVxBj3xJOQKxay4670pdx60V8fIns8BsQso4zhHAvTVVj6VbdMZQHqqluhNkMaIFHLVbd8oQewDM+m6OyHrWLFLHXVLd2CEGZMeKtuGe49L49J6NHEt0DxzHDc3cnOcofGGfLoXxOy3/EYRyymJDfu6KILWDXaJaV9+G0KIHidpbISMRsxyw3xkcr0k3BjmXGrBtnZvnCiCkscFtPFH1EIRzJhw4qljPV0yuKKE2LlrES8CtnJGveYf9yzaanHvBW2ACSTB2F5Q7JkMUjksRWRVvhQWlaN7rTqFtGCIKGHIIjmyvJ5z6Pjf25H7+O+Aos5/xbv352DbYffj35nXx5If6C8DMvnTEe8Yhtq2nXHiFkPUCRPK6cx/w2r0xw9Y8aMwYYNGzJOP2rUKOTk5NTFlJGXX34ZEyZMwMUXX4xFixahe/fu+NnPfoarr74aALBp0yaUlJRg/PjxXp6CggKMGDECS5cuxSWXXIKlS5eisLDQE3kAYPz48bAsC8uXL8f5559fb/5mTqaaW20EmogyaiPxhYpC8nFTgYZ8phl2Tfm4OXu4rRB7tSAsCiYdHGq15I5kujIzadlQPyL0iLD86QQkBa4lCDQ7Q1hkj5KKh58LlBtyS6Wrp35eLNIUlV+XuTO93p7NgKjBjI+KKhIE7XBp7hpmSChfs6DNoKBjshGwqV1beTcgYOj3tknQycCoXE9h0ySy6MKW06484JP4K8ox3UacSyIPuH8v6nWQhRcAYBYsS0TSsGD9mJitSwyKEuUx2Jx7U/d4WSwvm3+NvT/M/RuDZUkCkSTkiBqqE3Nz10dnaJezNB6kivkRP0y7GM4k1RxWurA3giAIgiAiee/J/8Ux39yKIZ3KEBvkRP2W7sjDuh0n4qR7/ol+Ifly8trTEupEg1Enoeftt9+uZzdqxxdffIFHH30UM2bMwP/7f/8PK1aswPXXX4+srCxMmjQJJSUlAIAuXboo+bp06eKdKykpQefOnZXz8XgcRUVFXhqdyspKVFZWevulpaX1WS3Uj4DTUGYjVZfaFATjmtnBbmi0unGoPoRgQxVmhKf6MVN/Vp+MWeS1tXQ6mUhWAZsGO/ppNRYgeM6kp4TZELss0mNlahfVXlhnXO3vBklTT5Ntkc3m6iTQXh0VASHaZjpbcqebAc6cLobIt8gAPE2QUuppUC4CkyhzSWSU/YlU1qTzBjFNzFvDmP/Zs8fgzdejVSM86E+kiWgHbz4fuZ7uZwsMnHPFP+bWO1RYk216ZTFljhu/Dq4AJIle3HLmr1GiatyPlmX7X1FMsuc6ZomGYNJ9x0W93KdAi9Zx/p9ybWoGAU/QUYeu+TWVr7cI1PWEMKmeUMQjuCt9EbVhyZIlGD16NN59912cfPLJGefbvn07ioqKkEwmG9A7giAIorH4esPn2PL7H2P4yWtgdXcjePbkYF3ZRRg+81GcVKvREwRRv9TLqluNjW3bGDZsGGbPng0AGDJkCD7++GPMnTsXkyZNajC7c+bMwR133NFg5TduRE8tzYZ+UaXLbMjHI84p6XiI3TrYzBBT9E26yAjhkR6oxNzy0mkJmbSsLpA4v9pH5w+zY6qTyabuAAeUFYbS2VRsh+h2YglupVSDApWpxKgFXITarA88m4pwpMdNZGBTiBCGOZ4UQ6E2Q+5Rk7ImmTQploEIG9kWU49FDdFSDUm7WkSPSKJH1uj7gaFOwl4GNzK3If1Lx70/esSXKN+voxj6x7zTomG5oljZXrs5pxls23ZGrMpui3qJIV9g2mc4ET0s5dlhkhrHNTWPu8PJPHGK2+BuRI/cPo7oZGvPBvOEJtNQQyKa119/HfF4HK+++mqthJ7LLrsMn3/+OS688EL89re/bUAPCYIgiIZk55at+PiuKzByyGp0GXMQgPO+8fnaw3HUnf/BifqEhATRBLTIN7yuXbtiwIAByrH+/ftjy5YtAOAt9b5jxw4lzY4dO7xzxcXF2Llzp3K+pqYGu3fvDiwVL5g1axb27dvnbVu3bq2X+gRhabamMBkWN3IIvnpL4/CQ7GESScO1j15LHnHMNExE98IGkIK/upW+0pZenl5mWLNI/bRAfrlceYUreYUtnXS2LDgb5+nbXLcXapOFbNq5sDqa2lCxaTAaalNs0pLs6fBscnmlKx70z0pjz91C6+l+4KE2DXm0y6OvLua1rcGgsuKVa1+sbiVW+gI3+MANgp3Jpn5NZBvuJux4q3zZ6o0g/DE+OFr9vXpqTtlgSHEGW2w2wFPwVvFybEgrY7kbwMG47W/QngDGwSwLDL7oJyLabO7UxVuhTF6pzObgSMFbFcu9n0QFGXM3iLaULXMwJmw6D5Ej5rkrd9nMW7nLW9XLa2uK6KkNd9xxB2pqanDaaachlUrhzjvvzDjvm2++iU2bNuGnP/1pA3pIEARBHAoHysvw9i1X4Z3pZ+DtW67CgfIy71x1VRVWTR2KDm8PwKnfWYZk4UHUVMaw+j9DUD72Yxwzey1iJPIQzYQWGdFz8sknB+YI+vTTT3H44YcDAPr06YPi4mIsWLAAgwcPBuAMs1q+fDmuu+46AM68QXv37sXKlSsxdOhQAMDChQth2zZGjBhhtJtMJhsp5NrULRY0kNCT1qTJblQmJXOEzZDzovcX6Bkeos1a5jQpoaEtweFF9Yg0YdPr+t2zNGW657jhhM2AGA/JLB3W7dbqSgoxiUl5OWAefhe0HWkzrIhAhTO7ogGbhkxpo3ki2tPkh3g0vLY2RfRoS2Ir1RMfoqKztHsqaNOQTxc+tLK5nEY6Yaqf/Ph7tljQbiY+2FwN6AnMNyQdU/9yxYhyfU3ikWSbK5X3h0V5z6YuQIl73QKYFFrEvIIZuDxkyqA0cdsGdydhVPQuyWfmVs6bOJkxgFuuUMO9854VIfp47S7bZH5Ej+ums2Ia89tPu2e8nDRHT6247bbb8Nhjj+Guu+5CYWFhpGgzf/58jBo1Cnl5eXjkkUewevVq3HjjjejXL2y2BoIgCKIpESthjT5WWglr3gtYvH40yq3DceJhL2HQqN0AnPeLDR8fhR6T/46hVx7VVC4TRCgt8g3vhhtuwLJlyzB79mx89tlnmDdvHv7nf/4HkydPBuB0tqZPn45f//rXePnll7FmzRpcfvnl6NatG8477zwATgTQGWecgauvvhrvv/8+3n33XUyZMgWXXHJJRituNTwsZKsvTOEOmZoMS1gHf8Miejw7maBnrHs7hUWK6MdMHoiTeofatIlgBKal08s0CSbyJiIFwvyUbXmftciLqKtnSZvc3053vU11DdhkIZuhxLB2lMsMtLFeT4MtYUQ+FvWlaLTJzRE9ciMyy2BTiuZR66lFBbkf9GtojKapxaMp37NKpIy8L/ngRfW497g+T49RQ9Ntao8mVyqq+iBH94iIHs8/+HU2iXecwYvq8ebogSN8iJNc2kRUjx/x4tbVliN5pDtZcpBLNXeEXqZE14jnRpQgXz8lysbmALPdiB7A9honGBfHpYvt19+1J00IzsFhi2gkrz7qPUMRPbWnpqYGP//5z5FKpSLT/fznP0deXh6WLVuGZ599FuPHj8dPfvKTRvKSIAiCqA2LZ5yDk4YuQNnuPLzz2VXYMWgZ3vnsKhysyMJJQxfg9CFPoEOv3eA2UPFNLj5m9+LYOf9BQQ8SeYjmSYuM6Bk+fDhefPFFzJo1C3feeSf69OmDBx54ABMnTvTS/OIXv0BFRQWuueYa7N27F6eccgreeOMNZGdne2meffZZTJkyBePGjYNlWbjwwgvx0EMPNUWVDBi7TS71IfgYyog0qcsSh2CnTjYzLYSlKTQaXSrSrYd5w9MkSCdDmY5x7ZxeK45oUSLMdz0SI6q1dB9UPSc8p8l2wCaXPus2MigzrU29ngZ3TW2b7u5RxDfNqGJTUtxEvzzgj3RT+Of0QrWjLOiDaWJlbz+k4bgWbqaLispnwz1Tpzl6ItLr9WPSQRH1EphIO+xekQUoeY4ePRLNZEuU6Ub0MGFfMa757JXliDM2t2EZfAaHswS6SM/kiZddVRBMWiUreGea7jk/hXOzecPV3JNyWzHvsFOeRRNF1hoRGfxf//VfGaV/6aWXcO211+IHP/gB5syZ05CuEQRBEHXgQHkZhvdbgt1bi1A4bQNOzcnF0qefxXHZ/4f2ncu9f2t3fHkYygfdjaN//GMMblKPCSI9jHNT94fIhNLSUhQUFAB9vgdYdRmPmWmX35CkzhNoinzBTks0MTdrVOKQ+rCY8XDavCwrA99CbFoGmxmUYcXiyhFT6aYJmxOMOxO3ah3AsM6zIAGOZNx8Pl3eOOPGpg1LL47FwJFI+MfCmljvxDo2ARar/VdGwgJiMabaM3WWTXnTyNGmW4mBIxFzJtMNm5TZT2uyGZUp/FQyAViWFRhWFCUOifPxuOHZlI+ECCPJLMBisYDwoQolQSwGxOMhtqQdk+iTnUT6CZkNTRizOOKG62lqH1X4SSE3x/bOK/eQtG+6txKxFGIWPJHH+GwrQ+eckKUY48jNrnKFNB5+DRSxxiGZSMGy5Pl7uPKw+cO1hGlHIEokqpCTXQOAuxM3i3Ny+3C/TPhpsrMrlXl85HOAmscfrcVRWl6J7oPex759+5Cfnw+i/jjzzDNx2GGHYfHixVi9ejVyc3MxfPhwfPTRR03tWotGvHvRPUsQRH3x9i1XYfSxz+Odz65CTt8xyF1+J44e/CViWU7kZqrKQizLxpJPLqHl0IlDojH/Dau1WrBkyRIAwLvvvlvvzhBAcABKQ+lw2piLdJscnlCrLcJ82qSZ1L1+28r/TT3YwQ0TRYRlPU2Y2CIPhzKVo++HbWJCZtmubt94THJMHhiiz22r73ujRSJbw+yPSKkMROH+EBZ9CFLUFQ0rP+CV+0G2odgJ24y1CvGBBTdh05swWJ7cl4uhSEGbvv9crY8kZsg2IdsNNK5vP2CLa9FEok5SuXLZgc9SGwTaS7t4+tAsLuUNa0OY6ueuJmVp9hlc/Yb7fwM+wEnktyVXNmZxMHBY4LCYI7BYDGCWLQ2D0u4wJoZIuYOzuL8x7kT0MLd87j2kQnDh/vcA44Dl+mABzkLyztAvrojymj2p4cRx5xpy9zKoT5CYq8eyGKwYU9qWVt1qOP7v//4P559/Pt5880106NABu3fvptW2CIIgmiHxim0AgCEFz2NI9RXof+LniGWlsH9PDj5Ycwp2DVmspCOIlkCth27VdVlRojaYO9ANaiOtyfCOfYYF1CFLugT13042zBMoc21f94IByvLqDH7nXUYXL2IhZcq2ws5zjlCpVtgPy6eXmW7fzxy6E3lUFjJ0I7W9ihnLeRyBwDfZ1qGMWOGBD36hgXJlcSLEpnq9tERcu7+4nocp94HexpldSyhD26LuHy+N0pi1sKuXzbVnRdSPwdeYGQNnXHkOuXRDBWxJx4UIxr1KqRNmC9FLLFcufGBwJkBm+nAvyWdfwFF9iDELnNtG34TwJOrmaFiuTzHAm71bKGlM8tPNK3aUiaqlNOpwLHceIU/g4/73E2M0R0898dFHH2HJkiXIysrCSSedhGOPPRbt2rXDBRdc4KXp2rUrunbt2oReEgRBEDqLHn0MfTusBwC061QBADiwL4l1O0/D0F8+jxGxOBbdMRXFfYGadt2b0lWCqBW1EnrkZUWvv/563Hnnnbj11lsbyrc2TFgcQ0PZYBn06tIlMMkaQGTQWNpqhpWZroC6t1XY4Jl0XqRdgUg7J2+ZtKzRp4jOdKR9w+XW7obQlndW3zJ7nK7OusglLw+v5M/g8mXU1kwVL2Rbnn1DG6YTgIx3mNcZV+SCgFFPrDHZNXtp9klXKqS+OpfPS4JEoIgwEUwWTyLuMdG2Yj4YXazxrqssgGpiqGxHPhG8vjzQ7lFfSaomxnyxxqubJquJ40x6Ni13+BRXHZLbSdNZfLvM9ufiEZ7o4pM0xIoxLjWIFcwjNaw3AJLJTw5X0nFwra2ZZ0NEBIk2sfTxd0StefDBB3HDDTcgPz8fsVgMe/bswcCBA/H00097K38SBEEQzYs3pv8Ex3dYiJN6fwurr/MixW1gw8dH4ug7lmN4MgcAUHVgP/onX0JVWRIjZj3QhB4TRO2oVcz2bbfdhr59++Kuu+5C3759SeRpcGQ5oJHK1lUIv1cSscnrMmUiY0jn9aWWAmnS+R5wNMRmJvjdP12UiM4lDVEJOW/y0A45H1UzOX3U7FqRrc7M5VvaZvSBQwqlUK9PVJuZbAhfvKEyLNw3Y1u4O9x4UitT2izmzFFiWeY06fAGxTBpc/0RQ32Mm7zKluERUFpLa4yw62nJF1RrXJamnt7wrZD2C3sWPIFRS68c08vzbtqQ9uTS/WwQTphbAVnu8fxK97CYxqm5CZmh0uLZdBY+Z4Gsnl0u3Qt60dwvnyH4WSTkzN3grPhlKWFMvp9iZS6AufE5XHr+1UYV1rz/K1UUeTm47WzBtiEy4YknnsCqVatQWVmJu+++G/fccw/27NmDb7/9Fl988QXOPPNMjB49Gu+9915Tu0oQBEFIzL/3N9g1pwfGD/8LuvTdBStho+Lbdtiwpg/AgE6Fe/DePb/Ato9WY9EdU7H3wWNQ1HM3Vmw4BTl57ZvafYLImFoP3RLLiv7xj39sCH8Ij4Z++Q6MOUljMkr2COsshBWYScQSj/AnypdDaTce8jnauqkLGZVP7vvKgo+IWpDTh5UlOtVyH1bv34bZD5uHRu1amm36ncZgCVEtb+wQuyfk9tALMkUfeefUII2AIzY3K9miHx0WyGBFyN9KFkO0EOcctptKv5bgMEfRML04rt7+jAXrKIkM4P4wIL1w41w8kMSlMHHSkMczLaJ3bESKUJKryr1jbHb9ONfKtm3IQS+ej9yQVirTmfYmrKI88puLc2c+HeW49hAzbRykUx6Xrqn/THP3/0wuiEvPG3OWQocWCcS0htEGnjmju+QbSSrfu++08CfnejC33eoqjLdtfvvb32Ljxo0AnCXqV6xYgQcffBBDhgzB4MGDcc8996Bnz574+c9/TmIPQRBEM+CVX96Cgfyv+E6/7d7CIqkqCx9umoATb/8bBjCGxTPOwfB+S3BK3hPAx0+guC9QVZbEeyvHYcz9LzdxDQiidtCqW4dAw6y6lUEahmCPMWPCV/aJMumsuhUpO4SfCywNFVInfYclwnvxkbBarroFOO3CYcXiRsEkXERxOm4JBsQ0k5mUE4e/6pZOeGs7j2w8TTWNMQzMWU0oHrEQWlhHHABiRptcThLMxpz2sWIGwSKNvsgAb5Wm0HTM0NaMIytumC9HKicqeidq1a0wQQoAkgkWXK6amfMovgBI6KtuRYgtYP7jmJVgzoS6zJA2ov7KqlsR10GujrCZneV8BQXbPcSeW76lrboVej8w1U+wFHKyxfTE0rArpuUz+JKwUojH1TAh0zMsH2cWELNs5CRrvHlwlPopl0gbggWOrISNeEyb+4bJ6V1/pK9xi3FkxWuQnaxyE3LPtl9fP+zJW53MPZZMHkTMkmxqZSgCk7BpcZSVVaPr8ctoBaM6cPDgQaxZswajR4/Gueeei82bN2PNmjU4ePAgDj/8cBxxxBFYsmQJXnjhBRx77LHo3bt3U7vcKqBVtwiCqA2vX3shxg55C4l2lV73ad+O9vh03xiMuO0v3oIGggPlZVg+ZzriFdtQ0647Rsx6gCJ5iHqjMf8Nq3VED9GYZCJq1JWQ3+FDTdZWD4zyXSsr0EMMC9WI8uNQ2oohTKyI+rWbSZvnmtZHD5bnHxPBCMGpUH1fTGKN19Vj0a2hn/N+3Yc50oOH5AubY0X2Vc5vsmkzc9kwRd0w3066GCuGQN/Vm3TXts3ROVzUQ78Nmfo3DK+eUn4hl4uIHqZl8GwaKiBuf7Ul1XrpLyHgTsSSsKlH9HiBJxERRNp8vcHrx7RrD7meALOhTEDOhc/aM+AdMtzQyjn5tBQ5Y3OAcVuxx7RkpnA5JiJdGJPqxj1H9brKn52hmNxPr+NVFt61U0UnrtpUvfU/c6n+HODchjrLk2qcczHfkJgoWq+E/u2i++0Y8lYf4879Q9SN7OxsDB8+HCeffDIGDRqEv/zlL7BtG+vXr8fq1auxePFiLFy4EJdffjn27NmDvLw8lJaWNrXbBEEQrZ5UKoXXf3EThrR/Cd89ZYck8ORha7uf4vjpv8bIkBe+nLz2tIQ60So4ZKHnpJNOwhtvvEG/qjQIeu+lPgkpM9SkSXbIpMywPFoPEQj2hI3jKuq7LaLLE7NqmHNwpQsnz9kaNZxFjzwItiwzfPJtpvM63VXw/ODBg1HamtNF5Mp+lAPyedOS9ZnaNGTJzKaYCycir+nfeHm58xAzygFVCGNq4JupjqZjygigoNOKP0wNrvOWAI+wZdoN9O9ZsN5iX6+TmHMoULah0RTRLo2mIH/9yOUzywIsTVRkapSN4qJkk7uCjSKycgZdIWRSPn+HSdeAq+fdE8x7Jn3xhnNHLXK+E0QBTJrM3P+mUK4ds5RvnKCYo64CFvjKdIeSCc+gDO3yMzHL94HRZMyHzO9+9zuMHTsWX3zxBa699loMGjQIPXv2xKpVq9CtWzd89dVX+Oqrr/Dxxx83tasEQRCtmlQqhSXXjcOQ/utxxpAy713i4L5sfLx1JEbMfhVFTesiQTQahyz0LFu2DAcPHgwIPaWlpbj77rtx7733HqqJNkZjvXQbevkZp49CLyskX2DJHBZMG6UApE+YIWrX1uxt8KgeKFKbgXQ25G5e8Ld3NQpAPx7mUfBcWHqTTS/SR8snd0J9iYkbffDycym9m9cY0ZOxTXM90trUInqiZNPIpcIN9pl0wFt9CiK6hgUzyJkUNcPXDAIRPYAaMiPlt6Vy9YieMN1Ur1tghFlo46r2xOrephXNdJPK9DgBpS9cHAIALj8oKTu4/LswqPlhWf4xR6/RjZqXE+dwo4dkB5SymXTnc8U0s1Qhick3JAtG1yhz93B4kVBeRI/UZkq9GYNtO54wC5qIw+F/E3HoS7EJcQzcqZoY0sVtrQGJWjN48GCsXLkS1157LUaOHOlFScXjcTzxxBMAgB49eqBHjx5N6SZBEESLJmpIVU1NDV67/r8w5sjXMHqsHzm5a2sRdvechgHX/Rwjmspxgmgi6iz0XHTRRRg2bBgYY9i5cyc6d+6snK+oqMBvf/tbEnpqTdRLd32KQGnKCvRow9KnE3ZCJBAvmdbDlg+FhVWEF5ZphtD0em7xy7jqJQ9YtAHEIvxVAgUMx8I8iZSgRP/RFEUR8jnMBxNK/zFwhgfT6XmlExZPb1MLXIhEr5/pNpUjetLWsxZ9XaNQwnxRIXReIJPIIfQADmkqq2BCk01/qFlERI+hDJHGKNQYbpZAVBTzN7ndw+wq2ACkOZ4CIp/4y/S/TLLNQ6+5d2fKWhvXI26YomrJNhX7zIkiEj469qVIGCHWaJFGgJgInCtOhtWT6Qe8C8klkcoVl5hbD+nh9FcrY5KIxX2b3n3hHmOu4OOepIie+uHII4/E/PnzsWPHDixbtgxVVVUYNWoUiTsEQRD1gJgkefSxVd6xqnkvYNHHI7G/IoFhR36Is07Z7Z8rz8Ka0stw4syHUNwUDhNEM6DOQk+vXr3wyiuvgHOOQYMGoWPHjhg0aBAGDRqEwYMHY8OGDejatWt9+toGaMQX7lqbClMx5N5i6BpHEbZlRSmTiJ7IwupAdESP380ydAxr4YUp+ED8/s6lfbWsaDElE3tqKe4+j7Lp7psVr8g7QO2SKn1iL6JH9y0Tmyb0u8YrU7YpRfSE2TSusJWmsb1rKUWNiIiJQESPqXgWrAOzzFqTXLa3JLrwwxOJtIgew7XSVy9LJzx5B0TkjuyzOCbdR7KPik1V61CHY0l5obWJHO0lDnh15FLsipRItKNckCNqOI5wxuDPzeNnFPeqUk8OWLC9EB//HhMF+wKQp/MwXxCymGrDbzvuXgtuFtvcKCIxRJIzeegW89rBko5Zlu+Tk0Zex8+1CXcYGePeMdGgNkX01CtdunTBueee29RuEARBtBoWzzgHJw1dgN1bi7Cu5Hz0vfBqbPjLXAzt+FecPGKx8u6xfVNnlB59IwZcMwUnNp3LBNEsqOvSTbj//vvxzjvvIB6PY9myZXjsscdw8skn47PPPsNtt92GP//5z/jNb35Tn762AXgGW32Z4pmZ80yGdV6ln/eNmQ35AslYcF/0rtJXxORsnfE6alAfDub9F7QsPqcTCJhhE8ctafPPB20aC62lTRFFYLYpdfh1VUp0hg2GTXVRyuQR59LYTFf1sDIt5tTVYv5nfavrY6aUIT5b8KJrojbZf0/8smXfDRErIWWJqCVm+Zupviabej2VXTeRV67clpbBF0s9bkmih2yUq6OJFGFHJDVfK/dZcO1bllpnsSlfKeJrxR0bxeAOieLBiyzyi5uUWXANMe+aWsIPxvxnR9zYzK+GDTHUjSuVFM+02BNleXWDv3oas5jbppJ9i3t++OU6Qo1tc2deIE01YxBzAKnfKIwx14YFiyJ6Wh27d+/GxIkTkZ+fj8LCQlx11VUoLy+PzHPw4EFMnjwZHTt2RF5eHi688ELs2LFDSbNlyxacddZZyM3NRefOnXHTTTehpqZGSfP222/jhBNOQDKZxFFHHYWnnnpKOf/oo4/i+OOPR35+PvLz8zFq1Ci8/vrr9VJvgiBaHwfKyzC83xLs3lqEwmkbMHzabKy+/3YMbv8S2nXa7/2o9M3WDtjS5Qn0uGUTBlw8pandJohmwSHP0VNRUYFEwllanH7FOlQa+YW7VuZCRBs1LiHifJRdvWweki6ykHpBFm50C1EWhZgRlkh0wcJshmVNZzMdJps2T2OThZcdskCBV5behfaEDJbeptlgyHGpLH3GFZHF5k60RaTN2ja6sG1qWI60ET1yPWW/wiJ6AKjzDxn84FwSUJj6REbZ1N1Udt2EsjDjle3aFIEgTBxzd5hWmLISlymiRzMrooBkXzm4FMEUrIMo13ydHce5PFxJsejXU/juHLWd+WsMyhiHNv8Q1HozRUlUczvtIdUH4hpx2NwGOJeii6T83I1Ak9tWurimZ4i7lXEEH9v/fuMczG1IiuhpfUycOBHbt2/H/PnzUV1djSuvvBLXXHMN5s2bF5rnhhtuwKuvvoq//e1vKCgowJQpU3DBBRfg3XffBeBMcHrWWWehuLgY7733HrZv347LL78ciUQCs2fPBgBs2rQJZ511Fq699lo8++yzWLBgAX7605+ia9eumDBhAgBnrqJ77rkHffv2BeccTz/9NM4991x8+OGHOPbYYxu+cQiCaFEsnzMdo4+twup1o3HkHYPR/cidmDCuGoDzb3f5t+3QvlMF1pVOwNhxP2xibwmiecE4ra1aZ0pLS1FQUAD0+R5gJepQQh3FCtOSNxkj8tXSNouFxH+lK8eCNPlILWwzp01r3URu793SbabL4xCLxUPOSB1nQ+4444jF1GMind5scv4YOJJxc7lhxwRxxiOrqUYkSTYZRzyWvny9ww4AcRE94R3hwTxyNubni8VYpM0wASKRYfuo+Tmy4iytsGAsjwGJeLjapehDWrKsLAbL0OMOiB+GchMJyz0XTCjb1IvPygIsFguk1x0ItFVIPRlUfwNzGQNIZgGxTFbd0oqPMY54IpDEtyPXVxE0apCb5Gr9tM+KaWk/bqUQj/sSrly/wAppUpkxZiM3JwVxj6v14544Iz8DorxEvAbxmK3UTzyQlry0u3a9shLVyE5WSW2vlq/vA9wbupWVVYmY+9lvS+6lUS+NPySsrKIaXQcuxb59+2j1zFbAunXrMGDAAKxYsQLDhg0DALzxxhv43ve+h6+++grdunUL5Nm3bx86deqEefPm4aKLLgIArF+/Hv3798fSpUsxcuRIvP766/j+97+Pr7/+Gl26dAEAzJ07FzNnzsSuXbuQlZWFmTNn4tVXX1VWF7vkkkuwd+9evPHGG6E+FxUV4b777sNVV12VUR3FuxfdswTR+nn3ulMwcvSHqKpIIKudL/Bs/qwncs/6b9jJTij++GQsfX80Tnkg/HuGIJoLjflvWJ2HbhENActwawqTmeiBGfrKuGHTs/KIqjZc++jRIaYoFFNLmDwQeW1pM40Q4lr6DEcPpa2pXJbsg4joMdp0K2CMdhHHnfgK979gGcY6s/Q2jZdP2g+zE2bTdiNPRAIubV7xTN0yuX0UH9zIFmGL21yxY1qdyoQsSOnle5tUL1u3q6VXgjTCHkP9RnUfRa+t3IbktrrZXK23vNWmnjrCplcHW64jC1xL70KHCVWyTe6f5WDgzPlrc7G59U055Tp+2O6QqODG3ESMOwKKN9STO//zIm0YvGFknu82c8uXy7QdezwFO2APyupa6jA6/8FRbMLJK+rttJ9bnv6fbV6FjGiZLF26FIWFhZ7IAwDjx4+HZVlYvny5Mc/KlStRXV2N8ePHe8f69euHXr16YenSpV65AwcO9EQeAJgwYQJKS0vxySefeGnkMkQaUYZOKpXC888/j4qKCowaNSq0TpWVlSgtLVU2giBaL6lUCv/66VnYNacnThz5HwBAVrtq2CmGvdvz8WnuQzjy9vXoOvy7+OzFxwEANe26N6XLBNEsOeShW0R9kkn3HqhXsSdjk2E20xVgyMcjzinpeIjddDbrrl+aom8itAfFI90rBmWBIWMZcj+1NjUV/cZ0d4IesAD486cY4cofJaEYcpKpTcV2lG6XxmYmd7vRZkQ9I8WXDB8vz6bsLzPMqcS8/n847mpURtMs+FGf9NiYT9wgBrtcPm/IYrTF1GP61C6ZPKpcW3VLJNEja/R9b6UpX8uQhEdDHSS73Ib0L51/s1l6fiFyevVh7mTdTLEJcHBFsbK9dnNOM3eZdN8PkVdE33hmmVI6GGKwWMqzI6+6xT1VlIsjjk3hE7edyZuFTSZsMgA2mJSXuRNTA4BlnJGcaKmUlJQEVkGNx+MoKipCSUlJaJ6srCwUFhYqx7t06eLlKSkpUUQecV6ci0pTWlqKAwcOICcnBwCwZs0ajBo1CgcPHkReXh5efPFFDBgwILROc+bMwR133JGm5gRBtHT27tqNd2b+F04e8A7Gn+YLutwGDuzLQexHq9GxqAc6userDuxH/+RLqCpLYsSsB5rEZ4JoztAbXrNED3GICnloLJOmXmq6TGl8jYzoQUjPMRObmapXQfScJgGHG46H1TwFL0jA2+Qy5BqGiUV1vQO4tskRPTqhkwdDuhTMjQyAafN9D9gz1KtWNg110a+B0WZt6ik2aUn2TNtXja4JRjkhnT0x+W9YHUUkSKhNQ5toNwwz7HtTyIRsXrnis+1G2bgXNCyyyG/sED/0ayLbcDdhx3Y/c5spDaRE9BgeSK7bVFREZ7PBkOJSVI8b0eNFL3G4EVrqBnAwbvsbtGeScTBmKWKOiGizbeZH9HiRPWLj4Eh50TjimKgcY+4Gcf1Uy45NUWlx7dyJmm3mlsd8W15bU0RPS+Dmm29WJ/A2bOvXr29qNzPimGOOwerVq7F8+XJcd911mDRpEtauXRuaftasWdi3b5+3bd26tRG9JQiioVk676/4cPJgxJ7tjzPHv4b8bo7IU3MwjlUbv4v3Vp2GnMIDKP+fUVh0x1Rs+2g1Ft0xFXsfPAZFPXdjxYZTkJPXvolrQRDND4roaZZECRUNJPSkNWmym4mgEuEvjzgven+BnuEh2qxlzkxX3/Y65ExNEzYjktjXxSJTuWFw8b+IJtTLDdPPQiNcNEedbmT6axCwaUiTqU29vHQ2vb+1qaeXINqY8Toy2aYhosdWb3VFdxAfrAiz2j0VtGnIy4O7uqjopZFOmOonP/6eLRa0m84HwBFuAlFuev2Y6S8PpJWFlECRsuijVN4vx3u2dQFKiI0WwKTQIuYV7Az98o8HJVoR0SOyeAKmdBGZ25BetBIYwF2xRkTOSRk80cdrd/XbQ0T0eDYtp3Sv/bR7xvODInpaBDfeeCOuuOKKyDRHHHEEiouLsXPnTuV4TU0Ndu/ejeLiYmO+4uJiVFVVYe/evUpUz44dO7w8xcXFeP/995V8YlUuOY2+UteOHTuQn5/vRfMAQFZWFo466igAwNChQ7FixQo8+OCD+OMf/2j0L5lMIplMRtadIIiWx5u/n4u+39yH4QNKwE52jvEUw1ebuuLggOvR79KpGO6mXTzjHAzvtwSn5D0BfPwEivsCVWVJvLdyHMbc/3KT1YEgmjOt4g3vnnvuAWMM06dP947V11KhTYfh5/B6FXlM4Q61MRmWuJb+hkX0eDYypX7aJyxSxHRMty5O6h3qsOAD0f+Uy9WJatl0D6/Jtu2e0AWmtNE1oo4cIRE9YlFw1XZIwEXtbEaUJ5ep19fW62mwJYzIx6La1WiTh0f0iAslL1Hu2ZSXBlfqpEUFiSgW7VhYRE+mj6aifQgb3Len2PSiPxybTPJBXj3KiNbOYY3KNR9kmyKih2v2vPbRi2RQonqcHVf4gHOSS5uI6lFs21Dm6DE2lDfnjW8XUJdM958bcU9yv16SLWeFL9s/79kMxsX5w7iYV39/CXrmtTMH9+f88eqjty9F9LQEOnXqhH79+kVuWVlZGDVqFPbu3YuVK1d6eRcuXAjbtjFixAhj2UOHDkUikcCCBQu8Yxs2bMCWLVu8uXNGjRqFNWvWKCLS/PnzkZ+f7w27GjVqlFKGSBM1/w7g3IOVlZW1axCCIFokVZWVeOmaSdj8//ri1KKb0OPYEjDm/Hu0ce0ROHDqcvS+dSP6XTRVyTfm/peBS7dgySeXYOn7o7Hkk0uAiZtJ5CGICFp8RM+KFSvwxz/+Eccff7xyvD6WCm1aQrtNqB/Bx1BGpEkhS2SSOI2dgM2QNFHrlZsLqkX6EJPaZ720yNLDqhHyF/DFnrDsUa1sA4hFOBTqOzNcSanzzIyZ1H2WxjtTtjrbjD4cblOvp8FdWUiC4XNaO5qNQHSNJJgE2l2+ZZVzWk2YdlRK6wlmuk35c1jDySuSmU36n2WbzCnW0vwKvT7c+DHcpiT2eXUXkSpMSx/11SEb9Sau0a6u0ZZ7zo3o8c9LjalfE684IdVJET1R9fQid9z/cWf9dOeaCvlPlgH18vzVsziHE9HD4Q9Xc0/qq8/JUVKmVeKIlkv//v1xxhln4Oqrr8bcuXNRXV2NKVOm4JJLLvFW3Nq2bRvGjRuHZ555BieeeCIKCgpw1VVXYcaMGSgqKkJ+fj6mTp2KUaNGYeTIkQCA008/HQMGDMBll12G3/zmNygpKcEvf/lLTJ482Yu2ufbaa/GHP/wBv/jFL/CTn/wECxcuxF//+le8+uqrnn+zZs3CmWeeiV69eqGsrAzz5s3D22+/jX/961+N31gEQdQLByr249Wb70PV9hJkdS3GWffchJx2uUqaks+2YMtvL8TgERvw/VNT3r9d+/fkYNPXR6DPDS+i38ToCZVz8tpj7N2PN1Q1CKLV0aKFnvLyckycOBGPPfYYfv3rX3vH9+3bh8cffxzz5s3DaaedBgB48skn0b9/fyxbtgwjR47Ev//9b6xduxZvvvkmunTpgsGDB+Ouu+7CzJkzcfvttyMrK6uJatUEL90ZmUzbncu0IEPSsN5oJuXVb3vJfW9Th58js0iaKElM3hcLAoXV4lBaO1Sw4P45efhKJmWKTnNY2SbhRO2m1sGmocxMbIJ7gRyBiYPlhLW9g5TrxVW7XLIpT6FiEqE0NyKvF4NfrrDptavYj1AmjXWUBRjps+KLbpNJETVcbddMBLsw4U22qdj3KuoclUdzcum8LIkA0jWAED2Yc99ybWidWy/uPogMcKJ3GMBElA0AWFzVnSWREpJY40oxYO6T7esp3FOUnGmRuWubuat1uVE1MdutJ5fuEfXOcN3zjov/M2FbF7PgzNMj8nAA8mgtO5Nl4YgWxbPPPospU6Zg3LhxsCwLF154IR566CHvfHV1NTZs2ID9+/d7x37/+997aSsrKzFhwgQ88sgj3vlYLIZXXnkF1113HUaNGoV27dph0qRJuPPOO700ffr0wauvvoobbrgBDz74IHr06IH//d//xYQJE7w0O3fuxOWXX47t27ejoKAAxx9/PP71r3/hu9/9bgO3CkEQDcFzl07DsaWrMar7HsS61yB1MI6NP1yIT/IH40fzHsQHL/4LVa/ejuMHfYahY/zvnG+3FeCbzlej/89ux/H0gwNBNAiM85b7ljdp0iQUFRXh97//PcaOHYvBgwfjgQcewMKFCzFu3Djs2bNHGW9++OGHY/r06bjhhhtw66234uWXX8bq1au985s2bcIRRxyBVatWYciQIQF7lZWVSnhxaWkpevbsCfT5HmAl6lCDTL/YDL3CqHWKI5Hz1UaYsSJ6y1HlWQALW3tKzmrqFSbMx9P6wAArA5tKHodYLB6MZjB81kkwjpg2ka+pz633xWPgyIqHlx9lM8vi5s59RF4GIMY44rHM0uonsmLqMdN8PabOfsICLC38KESTCNqMh6SLtMmRiDPjLWvyTz+WFY9WgDzZU0uTTABMezZN9dLbljEgKx7ybOo+aqeSWYClz3yjf2UY6hKLAXHpmhgjTwztwwAkk9LXgdRU6drWinEk4sF0pn2m7NcgJ5sr1zPMP70u8XgK8ZgcOcOVPEwqQ567xrJSyE2m/GgiJZPso1oeYCORqEEsxpWyIZXj2XKHrYpy4rEa5GZX+/Vi6rAqZ/ifKbqHI5k8CMZsL1pHaR/mPKv+94Uv/JSVV6Pr8cuwb98+5OfngyCaO6WlpSgoKKB7liCamOcunYaxRW8if+B2ZLf3+0cHy5Ko+LwIOd1LkSw4gFiW82+ZXcOwb1d7bMm/GidcfWdYsQTRqmnMf8NabETP888/j1WrVmHFihWBc/W1VKhO4y3x2QTKdlqTEYpCZgXU0eYhFVBrbIRH7Igf9MMEHJFGHDNF6siRLYAzMW1Uy0bVMN3AtrCIpBg32AzpMAfyc7VzqdsQ9TbZDbRrhjZNZWViExwBPVQRLOrh9gnI5NL8KLLBMGFITlabiB41Dws0riqShJer7GYSYSPbiBKTonUytTyu3cumkDjGIM+F4/kU1MOcLO6OJY6LSCs3k5LWtecE/cihOhY4rzFEyPg+Q4g1Eo6IYwGwpegb3Td/uXQmj+GLAd7s3QzQC+BeGJMSR+ZFWom/6nAsMceQn98XfBha8G89BEEQRBNxoGI/Rua/hU6jvgS6ngHr+Jk4YB2O9399FYYf8y6KBm333guq98excevx6Hv9n3FYx8NxWJN6ThBthxYp9GzduhXTpk3D/PnzkZ2d3Wh2Z82ahRkzZnj7XkRPvWPsVjWgHRbRyxRJ0iUIkx0iIo/SVrMuUkY6QSqadKtkhXnhTbSbQXrZhuh7pqtpKBHCQVQevavI5P6tXIaxIYId23R2Gfyl5cNsyuJEOjKxCRYUYgJ1NggS6QQg4x3mHnAmY5Z75pp95qePvsfUVgn4pCsVWuPqw7yM9iLEIe9PhGDjTwLs78tijVcD+bkwPCOB+4yZri8PtLvhVvTzK/eWNJzJqxuXk3vHLck+s2wngoirDgVENG46Z2sCEQ+WIc/Pw6S7xrswfh5ZXPWXUBdRPc63iPwdxMG1tnbbgLkyl+u4/r1FEARBEJnw6s334uyhW7Gj7Gh8Ff8xOv7mMhT3/Bajhx3w0nAb+HxNTxxx+woMzKHlzwmisWmRQs/KlSuxc+dOnHDCCd6xVCqFxYsX4w9/+AP+9a9/1ctSoTqNu8RnQ799G8pPpw5EduPDSKPmRPXSI8tNZ7Mu0glT5j6RraSVnCJMMu0vl/4eSkRPVDWj/GUIsRnRqZcLZlq0gJw/zO4h2Qwp0xNXQs5bIZ3YdAJGOrwam8rWI3o0m2H3B9OPaOlMdfSFAWYsOyOhJoNGCAhxUtkme0b0m18vXxaNJL+8Z8di2v2lRpOFihUMhrArv2Q/G1fc4wDi4vvAcC0AR2fh0jmlfbgT0SNbUoc5OjXx5hJyhTHLC81RS/UjcRi4UPW86DqT8Cr+r4cUca+9mRRVRBAEQRC1oaBqNeLtqlC9rgxDkpMQG5wC4Px7tWNLJ1R0Gocjc5/Hln39cQyJPATRJLTI5dXHjRuHNWvWYPXq1d42bNgwTJw40ftcH0uFtm64ttU2vbyFLR5em7IMaTJSOUzlpBOJosr0PzOpXPmz/p8+T41uWe486hEzcgva4MbN9J83/CKEdKKUb1PauLZkN9da1hVO1FaQ6sTdIAPDZVXrWTub0Ir0zvFom0qZ0mZzd5lw23w+k4geo1MAOOfGOtkc7hLaqi1TGznLa8uJDNdTaitwLi3RHdzEkuiyT4Akqhi2UFFJ8lU89qY2lMvicplmTcq7t1QDUjE2B+POJiruBsJ41z/gh7ATOpFV4A5WBCbOebAdXHueAMXVSCPuJpIWXPc2+f+y0069HOHFDoz14s7KXIx7oo66cLu/vLovgcErn0Ncby4X6uRkzBHQjN+/BEEQBBFkwUOPYPMtR+GkwUsAAD37b0csmQLnwL4d7VFy5F/QfdaX2PB5bwBA++KmWtyGIIgWGdHTvn17HHfcccqxdu3aoWPHjt7x+lgqtGkRL98N9WtrSBhIqMk6x51EpzfajArVyMRWbTsuHEHNk5nNG49x414medVIl2AOc20dC5wjdE7uKN+tqM62uTivUCGEaLOc+J+0MvTOfW1tyqNfQiNTdJvuTl0jejKeskSO5hACAQuZANpTBkKLkD6zwMnA9WTyZMjMqGWERdh4okTEjcqlD0LcA7Ql1S3ztTbak5QQrp+Tj7NgOZajSkjXmXv/15cN120KIUxRjpieQ1PbRN0Ygxgo5dnUrjl35VrpUnhlm75BvCXTJTvCO4uJiB5RUSe/PIeOY5MrttRhXeKjXwbzGlR6Tt1msVjQR4IgCIKQKd29D2/+4iYM6rAYYwZug3WYP1784J4cfHbgTAz42UMoyu2AIgBVBw+i25evAQOBwVf+tOkcJ4g2TosUejKhPpYKbXoa8gXc1JuN6nXXVjyJ8l0rK9BD5NLx2vhR1/byO0FqCeHLiAvEZK96FUL69UrXTw+k8I+a8waO16G6HE5Uh1jWXT8Z0Av0zqSkwRmjBLRCxQS4NtO7mr5N0yTNyhLaCH6OtOn+tW11GWn5vFEEC3Sc0yDrA0JH4NxdPltLykPKZer9IH9SRC7D+CFbsmkI2ggIIX5Z6l+jBiJ80+5pea5iZqvCh14I0w8ZxCiOoA24dkQemwOM24o9L508N5EmjFmibCZLLiLUx/S8++VwVwkRy6Cb8GZjkoQwb6gU46pN5a/0WaqnI6Ta2g2ticjcHxooBCBZXAp+uxjsec8yd21SRA9BEAQR5NVf3oohWc8gL38/zj2twjvObWDP9gIkO1SBHUhgzxs78OquxzDs8vPxwTMvonDlmxh6xpcor2iP/J6nNWENCKJt06KXV29qxPJoLXN59TS2A6djaZZXj5AljMurp/vpH3CWV8/Ujoxl7uGH4pcZi8UDZ3SL/r7fWYxbcJZXD03rf5Y9i8NZXj2tqKPZhGvTVM0QPcH7K5ZXNwo6IX74Nrl364XaMZQlllevrU0GIB5X9zOzyZGVYEYRQckbYjwRsry66XaUy0gmmLrikamOhmMMQCJueDZNH5n6KGYlGCxmRdoy7VoMiMfT+yqeAVnsy04E7z398fWOS8diFleup14v+aM8UTCzUshNqsKFpTWgsivtJGIpZ6lzQ71kUVmvZyxmIydZo0bMyA+SSO9F2PiCTlY8hVg8OPTLsSNPvKxOiJwVr0Z2skqqjK3doxyWpUb4iDokk5WwWErz0TlnaaFxchll5VXoOpCWVydaDrS8OkE0HDs3l+C9227ACd0+QLcBX3vvfJwD32wrwu4OF6DvZXcjkZ0HvvUfSC2+FGXbClHxaSdUl2YjkX8Q7Y7ehfbd9yI2Zh5Yz3ObtkIE0cyg5dXbNJmKP4eKcQBFhunDzptkkRACq3jJv0RHuRUW15EuTWaYcwaP6jVNuyiZhA21tvpv7075ZpuZ4EcXBI+JSIeATe4FFQQ7zEzb18qU/ZOCJbyhOEBIRE8GNnUbtbKpRfSY2sVUr6iGDpTB/SgXBhFdE9JI+i3u1lMIJHJEj1++QemRonng2fTL9YoPUcVYyHnjI8l937x6unU2rWimm1SmgDF8PYQOrXML9EykbCd6RxJYbDk6Rf7akMLVmPR/P7JIX/9NK0Ik5LZ2w4mn0nl6lKpZWv2Z7z/zJpGSbXGlnt61486sXExqM2XZe8Zg265YZGmRYm4Il1+2X09/XiY3isf2RSJuaxeSIAiCaFNwzvHy1OtwUrdXkdvuIM4+fb9/LsVQsuUwdLjsRRT3GgJ5uRrW81zExsxD+5U3I7/HRj9P7uGIDX2ERB6CaGJI6Gl2RL1016cIlKasQK8tLH06kSViEplAXu1neVPPMbywEH/SwSL2/I5iUAfwh3UpYkOIeflHdmY4FuaJeszQsw6xGaVbMKh+hKF0RMVHLi1VHVK+d0w6YfH0NrXAhczSSfu6aGBZ/rG0dmvR1zXac2/dtKtuhdjlHFLgWzChyaY8L5Asjhnrqj1aDCFCjemR1MvVbiBRtzC7CjacpeaETW6wCblu4q/lLQ/u7HNjm3rlylqbV1Hun5TuYbltlbowC7D8IVjOMf+B8yJzLL9sRSwCVxrN9EzKNv0Ki9W+uDRkz48aUlfS4lDnWpLrypSyxQA2J4KIe4ZZZKQmQRAE0RI5UF6G5XOmI16xDTXtumPErAeQk6eufrVp9Xp8/NubMfTI/+CsUTv9ocE2sGNrZ5QffhmOnngbelimyHwH1vNcxLp/H9j1LviBErCcYqDTyWAReQiCaBxI6GmWNMaLd1hETVR6Uxq5HJOoE9Vj1wUaLW1GzXCobaXWKzzOyNAxlNKJIIIwb7wgAe2zPFdOMOIkaDNTZF9MUTF6jIFRrDH0TMMmdpXLCERGwBzRk6lNndC7RnNEjugJi+YxLsiUprG96ydHWnAR9aJF9BjQ5x9igBcRop4RfXd/mW/ZpjwvkBLRYxB85AmVvXMRwpN3QETuMLWd9fmN/egVzaaqdZgnT9YeHFkj8W3aah25+20j24MUweVFsDhCmFCUuBCIpNK9cqUH03k23eXKmHxaFOzntaSQLk8DU5yT7XBvQuXAPQRXlHId4hyOv9p14pCH7jFlRa7gs8ndtuTu/Mzc8wPcEXwooocgCKJ1sXjGORjebwlGH1vlHaua9wIWrx+Nk+97Ea9ecylOOupddMk/gF7fO+ilsWsYtnzRHT2vfx3di47I2B6zYkCXMY3SeyEIInNI6GmWRL1419fXaAblKL3jsPTycZPfEXPleMlNigIyjOiprWAVjUnEcfaDsoncPDaAWBrT4WWHea2u+BNZaC1OM0TZlDCoRX4EhtqhFMnCrnbGET1RCpUpfZRNJkVMpLNZSwLDntz/1Tqix4XbiIzoMdkUx1iYgGLyW97R6q08bsJmyMpa8rwy4kSoRieLOFJEj0l8Y/DLlduMMRGdwhVBRRd6TF8pnHNACDwcMI2ztPT2E6qRJYQhEUEkZCI/wkYXqTgckZHF1CFYnsDkPkRCZJTrb7k3rBPJo0b0iEg65Z5lriDEOThnUIalcbccOQpJvpfcilJED0EQROth8YxzcNLQBdi9tQjrSs5H3wuvxsa/P4b+ib/jpKELUPFQV3zvtArv3wM7xbBtS1fw469H7+9PxpF1ngOUIIjmBgk9zYrGfOHmMPYco9Ib/VPiEiLOSwSShfTu07pX/+0lR9cESw+P1chExAjTE+RggmC+Q/u13ZTb5mlshokjEZUUZemzn4jkckRPmE0jEY2a1iZ3BKZIm2FKWBqMU9iLDndEAfocR574F4jo8RMq8w8Z/OAc/upTLCighNmMEp5EQn1VKy96h/vzBAnhgUuFyNqBPKQxVJCSooZEFJDaCtIQJbkhmP837L5lbjgTl4crKYb9ejLAWykOsJ1oF6amldw1imDCphBr9NxOPr8+HOIacdjcBjj3oqiU/NwVpOS2ZX5L6UMqvdzcWeqdMdv/fuMczG1omyJ6CIIgWgUHysswvN8S7N5ahMJpGzA8BSyachGGHb0GeUUHwBjQrouzelaq2sLGz47CMTPfQO+8Lk3sOUEQDQEJPc2KTF+460PgYJmbAzLsiWcY0ZORXZZBRI+poENrm+i+f3jZHOqcPSaP9L/pbJpTq1ajcoXltFgam7I4ol/egFIQjOrRPwNOZz/yNyJT510/biDKpsXSLBSXQflhNuUmUHzIMKJHTyIiXcLus8BcLvq5DCJ6jPqqYVcXiEwrlzG9baV2UOxoRiMjejiUCCJ1GJg8REk6r5ev7rpDI7mnljF9rJReDpP+MMtdnUpE0jDtvK3kkevjRNhw5T4Rf5w6ssBxwFk9jUmKlRCAlCg6qQpcivThXBua5k4C7UV9aY0looWsWq1QSBAEQTRXls+ZjtHHVmHV0oHoMGUs+h27BaefXuadt2sYrDjHqo9HYdjd83FsrX7wJQiipUFCT7OiCb5wMzaZVnUJOZ9JRE+IvdB0DddONsyChK49ZCrWcO1zhi0UsFlbovQLm0fYVPufAYdYROn6amJSNnCmnjfZNDsUcS6NTdvto0farEMjc20n0MEPKTQ0sga+UBN63UQGQwIvokcTHHSRK3D/hQlSUrm6UyJqR47oEScYnIgT3UVTPcMQU9QwYZ+JajNpUmW1QnqZgWeV+Wk9GYRpdjRRhgPglg3b5sZIGTCujADT29Ufaqa2uxMoxODFoXm+u9E13IYt+yIijjj3h5cpYpOfUo5o8lfuUlcK8zP6F5fb5lXICIIgiJZBKpXCP6ZMwWlHvwIAGH/aYrCY/49UTWUM6zcPwWHnzEbx+tNRuT8uDQ0mCKK1QkJPs6IxI3pqazITmxlG2PCIc3o6Y5Iopw+tbfR+qEnACRNruOYvg7LAkLEMOUudPE+jv5nKtliETXk4iZ6Aw5sBN2gyGNmjdPB5epuBAqRzmUqMik0WvG31ckxDsDJ99/Fsytc8KqInyg8p0iWQ3SC+6ZEt6YSrgD3vf+rJwJAkpn4WfwIRPbqNED+4tuqWsKlHBwXKFtE8ishhvlb6I8FtSP/S+Te4peeX2tCpowXL8hU0/3ngAPPW3AJgq+3GmTdBtj7fvOWu0KVH9IhniiEGy0p5vsov4uq96sQQMrdtnPNcWlCMeQ6JqCR1njG/TowiegiCIFocqVQKr//mMbRfPw/HHv0lzj5pNyxX3GExjv17c7B197EoPv9WFB07DoMALLpjKor7AjXtujet8wRBNAok9DRLmkBlT2syLI6lVoVoycPG60QVl0mXv+7tF+gkGqyavBadX52Ulg5QV9pKV25aIhLq0UReFj1CQxw3CAq6HT+wxCy2ManMMPte3aPsaSfSSXtGmxzBjAEBIfp8FJ5NuT1tDq6pFGnnNdQEh0BdXZFMzHMUsMnhLycuhLEMhB9t5W7dZOhBIRLpU7tY8vUMESDDRDBdcEsJYdBNb3EmRanAF1FMyqxmnilL4vkO2G5C7wjnTh24iOjh2hw9bnqLe0PAAmIoHMe8iB73vKiebQtJh0tpRdEcHCktcsm1I6Xzl1eXRUVHyJFXBeNunTyb8pLy0qphFNFDEATRcpj/hz+h5+dz0Oe4bZjQPQXrcP+73a5x/lEo3dkO7X/2BQbktvPOVR3Yj/7Jl1BVlsSIWQ80ttsEQTQBJPQ0SxouYqXuJk12MwkHSqdERAg8/hJPtbRZd3Rr6YZxCcIierTRFoEy0ok7UbX1ojLSRHHoQQthESvyJLhhjjoyWvprELAZYi8yc4bRPHo2768p2iOd62kipHR/vMgSz6Yh2slWb/VAG0uCRZTIptSPqfUM5OPB3YCoaKir6T6VH395riDdbjofAEccCkS56fVjpr88kDZKzFLETFNED5OeTf0GdevFLCgrUjGvYAbO1NZk2pPMuT/xsfBFiSDy2pEpwg24pYgyTMrAhXLp3kDMXXFLzN/DYXsCLOPw5gJiXhSR5KHsB0X0EARBNGtenXM/4mtexcCjNuHUPt8gNjzlnauqyMKWkiNw2PduQdEJ52PJjec6q2491A/rKs/DUedfhc9efBz9ky+hqOduvLdyHMbktW/C2hAE0ViQ0NNsaSBBxyODXl4kpsR18Flef/iQyquf9tKFF1NAiMnjsD6nHGkSVkOu/dXPh5Gue2ayzRFciUrpyKcpExxaJ1c9aRKwxH5dbabTZnSbwo7NVTHC6LbBh0z8UWxyObqHK+1jWkJbOWesg3YniL69ptTIEUXyOeNwJJN936BcrOqP1riy9irPpyPPeWM2ZPBNN2redQVI5wkRQh1jkrBq0IO5fF3dxmPy8l+i/UQ5nqv+DrfhztEj+y6HNYlMUh73Wlue4Mc9H4QYzBj37Htuixsp5oo17j4zXSTJW6d9xITL/rxAzH3iuFsP5sYLBe4NThE9BEEQjcmB8jIsnzMd8YptqGnXHSNmPYAcg/Cy7P/m45u/P4DvjF6GCb0Ogh3un6s+GMP+fTnYap2L46+fi2Ok0OEx97+MxTPOwfB+S3BK3hPAx0+guC9QVZZ0RJ77X26MahIE0QwgoafZ0tBRPYYyIk0aYwLqZidgMyRNwGbagmqRPsSk9jlT4UXudJvmhQnrewfEj5ByTdgAYuk68qZ9ZriSUv+VGTOp+8xLnbntOtuMPhxuU6+nwV2TMJWpsCR29EgJ5byrcHFDWi7fsso5swBrun5CZAnYlD+HNZysFOr10D8bBDN96F3o9eHGj0oZ6kHpfhF2vQgXg4+GMligDdw5c7SJlZU6aPWCBVhuRI/vg1pp1bQQOjlS3I/y0X2Wbfn1c//nRfT44panyBjK88UgIfhxX/hyVSd/Difm+SgLSDQhJ0EQROMgBJjRx1Z5x6rmvYDF60djzP0v4515LyD77dk4rEMZBh+5E4mz/XR2imHr5l5InvgzdBt/LbKtOIpC7Iy5/2UcKC/DEk1QokgegmhbkNDT7GiCl+6MTKbtzmVakCFZVLxLrQtLU2Y0ct/b1OHnSDOciwUt69EscrkxzWZouWHnIqoZKlhI0SBeXYwdV0NWMWQkyifNttpNrb1Nz3aGNr3ypEiX0GXWdZEkQz8UG/I5yWZg8mDZrEHLibpeot8u2/TaVezraprJsF6RkHoE8ms24dqV29V4K5o1Kz8P94+HPXuOTuMcVSKKtHKVZ0xqd2XVLa4OrRP2uTuPj2vFySsiegAwPQxOFimZXwk3JyxmAbD9Z9QtnDNHapGXeedgTtgNAMRSbkSPHH2jXhkxxFI/KyQpXczyl3rnXnp5tJaddjwjQRAEcagsnnGOM6RqaxHWlZyPvhdejY1/fwz9Ey/gpKELsGt2D4w8fA/YWD9PqtrCrq+LsDN1PAbP+j8cEUtmbC8nrz3G3v14fVeDIIgWBAk9zY7Ab94NaMfQYwrD+zW7tp0Cg/+mIozVzESsCWuvurWb3lcO+xzmQTqrpr64qZyMSGMsVDiSTohBG/Ikt2FpATkyQPw/6HU6wSrMJjMlzqDs0ONyPXnoqTpKgm45xvZxy3XVLe+pkcQHfT4nVXDSTupikbbvRfRoiqIcNSWGVinoy5KbRBndlnZeXgLdNyonUMsMu9aKaaY9JwyeSGEFm8Vr44Dvsk0m1yc8qkeduwZgljOHjrxmldFHQIoismHDhiXEHSUdV/Ixdzk6P3onBsAGY8zNF/TVHI3DtbrJSpRTvjNnj++ssGlRRA9BEESDcqC8DMP7LcHurUUonLYBHZd/hM33X40BR25Du4IDYAwo6r0HgPPv6p6SfJR1/wn6TLwF3eO5oDWyCIKoCyT0NEsa48U7zU/txvRRiergc0Y263q+bt13G+Fz34gSjZ1KTTOLEnP0iJ6olj2UJgqLSHJX31Traepwm/Jzv6MZpteF2Q3UM0ObejmZ2gRHYMUrpWNeD4+ZHgzBuRotYRJlTERKqIaInkDusHqyiPY1+G7yR/7sRQ4hWBfT3EBhAoxnStUjDDu+US7dc7p4ptvyIrnEcRFp5WZS0opoHwY/0oY7ubld40Tz6HDJvj4cDIAFJ6KHczmCzXGCS4IM53J0DwNitqYQqioW99Q7NeZJjVqSBCDvWeX+pOAivfvFxSmihyAIokFZdvf1GDOwCps/6IKKW0/AkUfuRNa4Su88t533lU/XHYkjfv5vdCooRqcm9JcgiNYBCT3NkoxDXurBhqZQhMHSJQgTVmqpRDA9QToxx1RAOlEqnHSrZEV5YZw/JI0N0T87lKiSMELLc0/IU7BmHtETHcETJVjp3dO6RPTUxqbo9MooQoEejSLOpbEfJlgZber25YgRQ7lmTw0+6UqFNp+uPszLKPiYtF79Ho4QiuSJkcW+ItZAFYakw8Hrpx0IXl8eaHdJLwlUJHBvSe3u/OFycu+4Jdlnlu0utx5sBKVtNIHMwVaWMPdX1pLz+xM1e59huSt9uU9MQFQTkTh+JRnz08pLtavfR0yNHnIdZwz1IngSBEEQKjs2l2DhXb/FyI4v4+T+2wEAJ4xd553nHKjen8DG3aPRcfyNKN5wFr4t64b+BcVN5TJBEK0MEnqaLQ399m0oP9JklHhSW18zUUTSiTXppJe6SCfMy6V3SE2lBTSpEJNaH1OJjjikiJ46VjNUCovo1PsmmRY8Inr7/l6Y3FdXm+kEqzCbynLWerYM7Ibh9etNZTMWaTPs/mD6ES1d5P0nxm4x7bi5qJBCQhCRMFpSBpN4ElGsloZrn+UhZvJJz4TlVMQ7JeaJMtVZKsIrLwAz5ONeXTmAuPg+MLSR8j1h9MGJ6JEtqcOwXPmHud863DnrCEs8UCFfVHOWURdFyMO4uPIoiv8bygK8KCIeGENIEARB1JX5Dz+JojV/QH7OART3+hYXjy1XIotT1RZKvi5GVbczcfh5v0BOQQ8cD2DRHVNR3BeoaUeDtAiCqD/SrdDcLJkzZw6GDx+O9u3bo3PnzjjvvPOwYcMGJc3BgwcxefJkdOzYEXl5ebjwwguxY8cOJc2WLVtw1llnITc3F507d8ZNN92EmpqaxqxKE8K1rbbp5c0OOV6bsgxpMlI5TOWkE4miyvQ/M6lc+bP4z3Y3EW0gMHU6ZY9lD+UWtKUy5c30X7r2iZTBuGxT2ri/ce6n8/yWOsGaJ14iFnJZ1XrWzmbolU5jUylT2mwO2Lazmc5nEtGjVEqyyzk31snmALfdTTpuaiMOZ/JcP5HhekptBe5MUizK9+om72s+AZIIYth8EUmrs+yr8ME2102vJ4soz/NHNiC7ZHMw7mwigolxgNnOBoM9rhRsvqg8cAF9/5wJjDUf3ftN6CuOT/49GBxaJV9VyZ6UQZTH4Nw7+g0oom6EqMOk/wD5ejK/CkyyptxszsYYvDmIlJuQIAiijXKgYj/+b+odmHfRdfi/qXfgQMX+tHnK95XhrzPuxsKJY7Htl0fiO/lTMXjMehwxfDNyuzgiz/592fh2WyGqD8awd3sBiqf/B0dNegiJgh4AgKoD+9E/+RKqypIYMeuBBq4lQRBtiRYp9CxatAiTJ0/GsmXLMH/+fFRXV+P0009HRUWFl+aGG27AP//5T/ztb3/DokWL8PXXX+OCCy7wzqdSKZx11lmoqqrCe++9h6effhpPPfUUbr311qaokgGtF1jvMG1LZ1JPL29WyPEMyuKmtOJcbf2QYwRq03bc4DMDD7Gh/ydKyKQZlSgGqC1ogRk3s0XJ7ZAahdl3gyNcm9LG/M3rXMotI1XM6A+kyyk1p1xObW3KI8XC7q4wm0qZ0mYxZx4dyzKfTzdliS8gqJuTnxnrZFmiYx20FyzObVUpge6SKNMZ5cOkjnvIJtVdHmplbFgmSQKa4CJfI7BgXTyXtXbRb3zTM+IJV9oFthjALAbOnE356hA3UogfXLcWuLaGB4iL8uTvAPeUa1f4vJaPyQAAz9VJREFUyZkap8OkT0z6v39lLd957jhpM+asz8WYMzGy4iOTBCxRG1diDSw5L3krfa8y5aI4CLEz8kuEaLHs3r0bEydORH5+PgoLC3HVVVehvLw8Mk99/Tj29ttv44QTTkAymcRRRx2Fp556KtTmPffcA8YYpk+fXteqEkS98Nyl07Dxh2diVNXfcVr3RRhV9Xds/OGZeO7SaYG0n7zzIV74wQXYc183JP7eA+cPnoNTz1qB4gElYDHnh5eK3bnY+O3Z4BNWov1136LzTdvw/sdjUdRzD/Y+eAwW3TEV2z5ajUV3TMXeB49BUc/dWLHhFOTQ8ucEQdQjjLeCmRh37dqFzp07Y9GiRRgzZgz27duHTp06Yd68ebjooosAAOvXr0f//v2xdOlSjBw5Eq+//jq+//3v4+uvv0aXLl0AAHPnzsXMmTOxa9cuZGVlpbVbWlqKgoICoM/3ACtRB8/TvWCHnGdAYJbZjInKxyJcEr3KMIfCjjGAxaJdYt7/NJPpr0GoTSuNTWM+IBaTRzNyQwr1s5C44gyIxYJpoloGAOLgyFIGUKo2o/LHLW3i3xAbOnFwxEMGbQZsSsKJsBm27LPRXylfLGb2ymRTFkASEZcy3CZHIsZC2wcI3s7yJMPxWPizENW2ySyAuc+mns4UWCILW/G46qxyz0WEGSWzACvkGTN9TYh6xwzXJOo6yOeyk/58NoHzhqbzhKYYN19PpvxRynL+VKNddjCdYl8zKkSteMxGXMxADiGOaJFScrnePZtCbrbfiQ18Tclz7Hg2HQEmGU/BiomIIK6mByCGVOltlohXISenWqpT8J9nfziWc865xzmSyUpYlu3bVIQg3xd/yJ1zrLSsCt0GLce+ffuQn58fsEe0PM4880xs374df/zjH1FdXY0rr7wSw4cPx7x580LzXHfddXj11Vfx1FNPoaCgAFOmTIFlWXj33XcBOD+ODR48GMXFxbjvvvuwfft2XH755bj66qsxe/ZsAMCmTZtw3HHH4dprr8VPf/pTLFiwANOnT8err76KCRMmKPZWrFiBH/zgB8jPz8d3vvMdPPDAAxnXT7x70T1L1AfPXToNY4veRP7A7chu70+QfLAsidI1XfHWN6ch0SUPXfe9hR6d9qJz72+Q7HBAKaNyfxa27+wJdtQ56PX9GxHP7WC0tXjGORjebwmy8qq8Y1VlSazYcArG3P9yw1SQIIhmRWP+G9Yq5ujZt28fAKCoqAgAsHLlSlRXV2P8+PFemn79+qFXr16e0LN06VIMHDjQE3kAYMKECbjuuuvwySefYMiQIY1bCSPyT+4NQciv2cZTYbJDRFmZ2lZsasfDlIO01O2XajUXM9aaSamVbpiU2aSemsQipn3WrQVr4JfsjcbQxYN0tplu0/8Q2mJyP9lLq6eWxr1oZVmopU1uPhfqn8mmIkqFlGd6BCKkb9PtqIghbkRPqF8hwklwX82oXGamXUMxL5CpPSPsBeoZ8nXg21GTGlc0M5TBpDooQ6rkfK4hruXzxDfLArd8oYSDB9pZv8dFsZxzt3jp2YFYXFwTfOTPbkSPcYJjqbKiXCbNAM3B4M+1IwqQ5uRxz4vAG1EXxizIkz9zzrV71BFwZFFSaTVlfh+3hly+dsyrhxAPWaiAT7RE1q1bhzfeeAMrVqzAsGHDAAD//d//je9973v47W9/i27dugXy7Nu3D48//jjmzZuH0047DQDw5JNPon///li2bBlGjhyJf//731i7di3efPNNdOnSBYMHD8Zdd92FmTNn4vbbb0dWVhbmzp2LPn364He/+x0AoH///njnnXfw+9//XhF6ysvLMXHiRDz22GP49a9/3QitQhBmDlTsx8j8t9Bp1JdA1zNgHT8TKBiAnR8vxMG3f4Huo77E+aV/RiK/Sl1YgANVFVn4ZlcHxE76FbqNvQJHZjCz/Zj7X8aB8jIsmTMd8YptqGnXHSNmPYAxFMlDEEQD0OKFHtu2MX36dJx88sk47rjjAAAlJSXIyspCYWGhkrZLly4oKSnx0sgijzgvzpmorKxEZaWv9peWltZXNSQa86U7RAyJ6nlnVFaaOuidEyUPDx7KyI+MJYG0BIaUIDhXB6DGRjEgsDhPmAdc+8wDx8329GPe4jwh5TPDsVCbXHQLDR1mRciQvQz6xwF1xSM3r80MVzjMptSwwWtRC5t2MOIpTP5jmVw4GNqW+4IJgxAVWDCD7KzAksrSRQ5vqJFBqRFDecSuK2Too5P0iY051CimgAgW2rjOri1pCNL0QR6mtg2ITrrNqK8fWRRK2c7y4JKYZYshVsK4VID8TSRWpRLijjOpT9D3QEW4rZ0QV8V5gpTLqYls3opbntZjQ70tpOge6YHk3JmVy7sWTG1nxpgr8HJvaJoaYWdJZUv15CKtrrRxcDvddzvRkli6dCkKCws9kQcAxo8fD8uysHz5cpx//vmBPPX149jSpUuVMkQafWjW5MmTcdZZZ2H8+PEk9BBNyqs334uzh25Fyb6+WLHpu+jx/M/Q79gtyIvZ6NjpABgDsgqc6BtuA19v7Qq79znodf5M5OZ0Qa862MzJa4+xdz9evxUhCIIw0OKFnsmTJ+Pjjz/GO++80+C25syZgzvuuKOBrWQqZBwqtenNRqU3HTf0Zo2nDd1CXZ3IuNp1jX5iEXv+b/bB41yxaAOIa/6apC+W5phqNWhTgWd2VQL25AiCkPy6HT/CgMGyzPdolFZicWM/35w/Tb8zTEwIDN2RInrS2q1FXzcghLkHhagQKl5ECCucQxrhqCcM2tSjiEIjesLKgFmo0dN5ZcrHmbRJ9oz11o/ZcJaaEzZ5dD7/rwXGuORPcNiUvCvrY35FJfFManzZf1Gm89cCLO7d/s4x/2EQM/g4J30JCHA6BIip0TVSVqNNv8L+al96xI5Tb7lcLl1H8U3k++mXzT2vnXL9aCOK6GldlJSUoHPnzsqxeDyOoqKi0B+x6uvHsbA0paWlOHDgAHJycvD8889j1apVWLFiRcZ1apwf2Yi2ROnuUrw+8xac1Pk1xNtVIX9HCc7q/gvEjkgp6Q6WJ5GdV4lPPuyLgfeuQK86TdNAEATRNLTIyZgFU6ZMwSuvvIK33noLPXr08I4XFxejqqoKe/fuVdLv2LEDxcXFXhp9okGxL9LozJo1C/v27fO2rVu31mNtZJhhq2+4tmWSPl05Jr8jyvaWsBEqgOZTRtU+1HbigT1Tq8grXnlDNSSLVqAksxW5bNtwTN3Uta1MVyqwSpWhVrI9W8vjHYuou4Kpd522Hk5Ej2wv1KauRhhM6T56+1yrm7QKVGBlLy71/2v5uCltK8rxVp3ioStP6VEwch2YFX7doefj/spatmxTrLJlqKup3UwiCdMTCRekNhTtKp/jHIFVzISvcnlytFZYU4trKZfNbXf9Oe5s8qpiYoMtXULpa8WflZlJcxSzgD1v1TC3Xb3lyoRN7tsXV8cWJYj6wZ+7KDCRM+BOqCw901KZ8P46DnGbw7b1+4lBDcBh3sTc8L6D/fb2V3ATh7jfju5ScBTR0zK4+eabXVE3fFu/fn1TuxnJ1q1bMW3aNDz77LPIzs7OON+cOXNQUFDgbT179mxAL4nWyDdf78Kz196CVy4+ExuuHwj78WNw4dgn0O1YR6Rs16UMsWQKdoqhqjwLn208Gvjeh1i46QYAwP52x4CRyEMQRAujRUb0cM4xdepUvPjii3j77bfRp08f5fzQoUORSCSwYMECXHjhhQCADRs2YMuWLRg1ahQAYNSoUbj77ruxc+dO79ev+fPnIz8/HwMGDDDaTSaTSCaTDVgzQdSLd32JPhmUI3eEM4odMfkdoSUae5xS6ABHBm7qNg+tfXSdwf8crKfcPDaAkLmGA+XpycI6vExpiJC8GdrUbYX5khblmgWvd1hdMo7oka95mv6nLLKZ8Fa9ysRmLTHO/cPgrnBkbvew6BPAEReiInpMNsUxfVGltPX1jKZPxCzDM8EQiLgJrbNWHpcierxLLV1zhrCyRXQK92zpbarckbJNOKqPbytYcUsToBzFxono8d3gnlP+vDy6LV84YpIA5LcPgwhjEnnldrLE/eMON/Mn4ebqBNJeuUII4uCcAZY6XMu5H/0GVtrWrShF9LQMbrzxRlxxxRWRaY444ggUFxdj586dyvGamhrs3r079Ecs+ccxOapH/3Hs/fffV/LpP46F/YCWn5+PnJwcrFy5Ejt37sQJJ5zgnU+lUli8eDH+8Ic/oLKyErFYcLb2WbNmYcaMGd5+aWkpiT1tiAPlZViuzWmTbnWqbRu34u37/xdF3y7FCcdtQFHvb/HDU3hgbjkRVbpjfWe0v/QPyOv7XSRiWTgGQNXBg+j25WvAQGDwlT9tuAoSBEE0EC1S6Jk8eTLmzZuHf/zjH2jfvr0XNlxQUICcnBwUFBTgqquuwowZM1BUVIT8/HxMnToVo0aNwsiRIwEAp59+OgYMGIDLLrsMv/nNb1BSUoJf/vKXmDx5ciOJOSYa84Xb77RknD7093cgvBtv6E0GkoX07tO6V//tFd3nN8+dIzxJ18EOK7uuNqPyhmkloqW5th+WX96RO+FR/ij6oIuYo8d0DlHlRtgTvsszrig2uSMwhdYxXGFLC9duU+46xLkzE0zoddFEAU9EsORrpXqszD9k8MObr8fNprdxmM3QekoFcK1x/XrCWSFcRM0IH9wyLdkPWcjRBRXNMV8k8V/AhTijj8BSKwlV1FPEI8dROZrHt+3OtWP7xXHvgrrRLkqhavHylZaFJ8ugMKrflH59OEQ9OWxu+1E9gHMDi/xueyhtKE3E5M2/o11vcOeOZMz2n3vOwVwHbIroaRF06tQJnTp1Sptu1KhR2Lt3L1auXImhQ4cCABYuXAjbtjFixAhjnvr6cWzUqFF47bXXlLLnz5/vlTFu3DisWbNGOX/llVeiX79+mDlzplHkARrzRzaiuSFWqRp9rLRK1bwXsHj9aGWVqi/+sxHvPvQ4RhW+hK5HfIOCFMMPRh2EFVfnZKupiuGbXYdhf7th6Dz2MrQ/+nSUPdUL7fMPYuWUB7F36CcYdvn5+OCZF1G48k0MPeNLlFe0R37P0xqtzgRBEPVFi1xePWyp4SeffNL7xevgwYO48cYb8dxzz6GyshITJkzAI488ovyitXnzZlx33XV4++230a5dO0yaNAn33HMP4mFrT2s0/PLqYdm0t/1aIfLV0jaLhYROpCsnlqGvLPjZStShiRicpeBr0z6+kVgsnrbvHxApAMQtjphlTiv1RQPnY+DIjljqPGo/bvFAxIXJrk6McSTitWxaJmzqTWuONgr66i5Znt5EIHMi4nE03s2u8pAVj15e3eSnOJiIBzvqch65Uy8ny8piTiffZIdp+xqJhOVHfej5WbAMQTILsKxYaLniROC+ZEA8HuIr/MfWtMR6dpZ0H2jtYBIJBTHGEde+KhW/LK0cUTarQW6SIxBBFNam4isEQMxKIR4X0TDS/cqkv3Id3LJjzEZuTgriHlevOQeYbbwmDByJeAqxmG28V5w6crUeLlmJKmQnq6V6+9Kpv0y6rDL6kT5ZWVXO8upemW4kkyeCyXX37ZeVV6PrwKW0VHUr4swzz8SOHTswd+5cb3n1YcOGecurb9u2DePGjcMzzzyDE088EYCzvPprr72Gp556yvtxDADee+89AP7y6t26dfN+HLvsssvw05/+NLC8+uTJk/GTn/wECxcuxPXXX29cXl0wduxYDB48mJZXJwIsnnEOThq6ALu3FmFd1fnoe+HV2Pj3x9A/60UU9dyNdcuOQE6yGgkGdOy+B9kdK8Biapem+mAcu3Z1xMGDuWg3fha6jroEzFIFRb71H0gtvhRl2wpR8WknVJdmI5F/EO2O3oX23fciNmYeWM9zG7PqBEG0Ymh59TRkok1lZ2fj4YcfxsMPPxya5vDDDw/8+tS01FH4aRSTURE9xi6edL62Nv1OiJl0BdRdu7Rh1rNki3pLiNEg6WqfLiYqyqaRiEiPdNkyshnqrDm3Xle5GM6ctjWKUlEVTdMIokzdIwZ3VaaQ68Kibtk0cO2DHKkRWHXLZBPBe0GIKqHXUg/Fkk9xNyKFBds9yqbJTeW+NkTQiKgdW76JpMfVFCVlqmcYIphFrqrzmanf+9JJ4zLvUjImJtBifnyct2IV4EXQyEIjB8AtG7btCymqEa6MANOb0mL+RMhyu7uBQn4FLHGeu+1qw+byHeQ3sn7PMlERcZjJdsRQNXWlMJHAW7adc3A7uAoZ0bJ59tlnMWXKFIwbNw6WZeHCCy/EQw895J2vrq7Ghg0bsH//fu/Y73//ey+t/OOYIBaL4ZVXXsF1112HUaNGeT+O3XnnnV6aPn364NVXX8UNN9yABx98ED169MD//u//hoo8BBHGgfIyDO+3BLu3FiF/6jokXnsHb9/+G3TP+hKVPXNgF1sYMOqLQL6ayhiq9mdh6+6jccx1c5HseBx6pvmHh/U8F7Ex89B+5c3I77HRO85zD0ds6CMk8hAE0WJpkRE9zQWK6MmkHAvS5CO1yAfAyqpDE7k9TcscAh6exyEWixvP6NEQepo4cyN6tOiGMC/885lF9JiaIc6417RGMQNm/y03oifMVpQzcWPTclNSpcy4BcTciJ4wWyzwwSFd9FGgndzIh4QhoieT24mx6IgeQH0UZDHlUCN6nHPmhOE2ActwI7CQ9PKxsHp6tph6XcTnZBa8CDbFnuGmlQ+ZInoAddl3MG0f8CJ6VJFDtRlW37gU0SNH54g5nBR/pbKciJ4ac/28+XKkiBvpdCJR40X0KP660Tyme50BSMSrkZPjD1HwRSY5Gsefxt1bQQtAVlYlLHdOIV9wE2lsyZQcTcRRVlFDET1Ei4IielovqVQK7zz3Kg6+fj/Gn7UCW//THUVd9yG3UzmA4L9ldoqh5OvO4L3PQY8J14IVHBMa9Z8ObqeAXe+CHygByykGOp0ciP4hCII4VCiip81SR+GnUcya4lHS7UfZM8VgZFJcOhuH1obGaB1DGt2S17nSSBny6yt0mZTWyFoYTppaMyrCxpQ/qrmVDqsIYQrBNJVKqC+GTrrJ0UzVaMWOIXTJmw9Fsx9mN2M74rPNwTWVInQuIMmeHnmj7zD48xwFbHL4y4mLQBGT2qaa9G3y4Dlje3M1mkmf2kUXfox2Qx5z/eeGlNB03PQWZ06UihRFxTgyWjfSiVxhUCYLgh9Y4x3hXFoxC+AW1+bocdNb/vw2RqGJcT+iRyrPN8p8wyKyBmIdrpQWseraYVx9Vtz/qfeyPzsUhx+l5HwW9ffLE6VTRA9BELWlLhMk61RXVWPRn17BtjcXokv1evTqvBNHnvAlTs6pAc500vQctE21W5aNXXu6w+4yEr2Tz2L5ypNxygP/qpc6MSsGdBnTVG/iBEEQ9Q4JPc2KTLqzDfBPUJRZ00/PGWWMyMfTnJd7s4GeYcO2kUmUyaR0Drezqv1Cr0RhGD6bRCNo50JPRIkk0vGwvxnZEx1rOXOEyJPOdqB4rzNqyMzV3VrbDMl4qDGMgevIZJss6C9X6xeoryRYZCy2MbWegackqE8E9k3qnkm61YUxYT+wWJPRiIrNDVFuev1cYUz+6mHazRAmrJpsc0WY9MtRopa0i8oAJ/pGqqRTb3egFVMrylyhhrkpbS5F80jiW/hqZe4HboGBeZmYlFAsze7pRO4E1SIJ5za4yMfhzXfk1IH7drRhYKxW85kRBNHWyXSCZJkD5Qfw9lMvY+fit9ANn6J/383ofNQunBrnYN+3g9/n7v7eXe3x7YHeqIl3Ru/L70Ve5/7IA7Dojqno3ReoadejYSpJEATRCiChp9nS0L8pZNDLi8SUuA4+ex2wQy0vXa8zM/S+aVSkjEm4qa0spQs+UWUZj8nRHCF+cm1fX4kqXSspXVoOMG5QFjxL6if5b8BmGqEqWGq0f3p9ba6KIOmidzJtD7keQjDxbHOuiACBjr1s2mDbj7OQToq+vSai+DbVc4HhSCEw36BcrOqPrkpJtuSIHrHKVpRQFfBNNyrvBhrZcSQg0nmih1aGLghxd1UqUShXipXqLd3DNtw5emT/pZAi76CUR4hEnuDHvXtdiMFCoHEOSw3MORCzvWgcPfomiGuBi+FZ/rxAYkgZd+vhCFE8eG9wiughCCJzlAmSS9QJkk8augCLZ5yDwb/8M9567AWULl+E7llf4IThnyK3YzlOy7YQO7fG+O9A1f4E9uwpwv6qHFS2H4Qe585A1r/GI3Uggd7TFiIrJ9dPe2A/+idfQlVZEiNmPdB4lScIgmhhkNDTbMkozOYQMJQRaTKDn+sztROwGZImYDNtQVL6urWR3g+qrfxkCkIylaOLE2H93yjhSAoEMRJmU4mWQPorqQg0XmWic5lsB2xKfWamJ86gzLQ29XoaXDa1g0k4C7Wj2QhE10h9+EC7yxdfOWcWYE3XTwgZJpve55D68JBVs3QvfPHA3dfEq7RPHDd+VMrQDXv3i7DrRbj4tqIm1Ja1ERHRI0Q009A95v1PKtcCLDeix/ch2Fj+RxE1w2FzDguqz57fmi1/n0kRPa4I5NkUUqneZn59HMGP+8KXqzr5Npjnoxgu5hyuj39PCIJo7cgTJBdO24BTc3Kxa9su7Er1w5alo3Feu39j1JCFsF/qgQkdYkhcVKUWkOWIygfLk9iztwNS1QwHOo/DUT+8BTn5vZCj2Vu8fowjKj14DNZVnoejzr8Kn734OPonX0JRz914b+U4jKnlcDGCIIi2BAk9zY5GfunO2FwmAkqGhQWShfVGM3UuU5kkPXLfO/p39PCTJkksTCaLSfuhwkyITQvhokSU/2JlI1FGGMZW5VCiHsIwRUYFbIYIDJmWqRUTjIqSok8Cw4ykhLW9S5RrxVW7cnRNYNlu2axBywltVT2iRxNOvJWqdDXNZFg+pYVd6e2naLDcr7ccSSS3q/GJM2tWfh7uHw979hydxjmqDlWC0r5KEJAuCHknmdp+ou7uxFmuFSeviOgBnDnl5fFwpoger2iGGLMA2JIm6uTlzJFaxBw/jljLnLAbAIil3IgeHhB0ZJ9N3yVMEYf8s879wVWb0oNv03oMBNFqqI+5c3QqDxzEey8sQOXrD+K736vC9pWHoWbOsejQfS9ya2I4u2cN4sdUe+mtWArODIXA/tJsVJTnYv/BXKDvhehz7nS0y+6MdhnYHXP/y94wsVPyngA+fgLFfYGqsqQj8oQMEyMIgiAcSOhpdgR+825AOyxoLgzll+XaYPDfVISxmpmKNYEufob5wt0wlRJWoidiRIgGcnlR5chp07W0jfBVveRy9GNyB1IftGHsQMrnWViq9HZ1m/ooGGZKnEHZocflevLQU3WUBNXIDO+YLHy4N4YumjBxTs6v3DuaR7pYpO17ERuaoihHTYnlxBX0VbPCRBmmuKmcD0QqGdUin7BrrZhm2nPCfHGRmXzRhA+9POXZ9JUXoz117hqAWf4QLP2a6M8y8xqcI8VtxIW4o6TjSj7GuHbtY3AEIubmUy+qM0TLrwPzVCw/osf3189qSQ3HXGfFsDHTKnEEQbQ86jJ3jiBVU4NV/16Gjf/3HJJ7NyOHVaG4cDc6dixF1wHbMTphg09w0h536qfGMvbvy0FuwQF8u60A8TGz0WHIuWif1QGHIjONuf9lHCgvwxJNvKJIHoIgiPSQ0NMsaYwX77BeXVT6qER18Dkjm4dUQK2xEVwVSyC6W6ZOpeh7yZEEprxyZAvgCzVR4kiUuBEV5WKSDEUESLryQ1uWBz6EJzEcU7SFkLaKIkwjNHojRIgIO4fSx5WFFO8YV6MlTHUMXXlKzyQZYjBH9HjptRszUkiRytV9D/jDHAFDDv6RBSbFC10FMtgNVEHVIww7AY/UyCJTneG3lSWOi2fTzaQU755zFqaSGsG2wO0aMMtwd3HJviuuyCKWEE84lyPYHENcEmo4F9E9rkcx2zmnCzbCrCQmyS3iC0BuxI9209vcloRVLkV/iUmeCYJoSA5U7MerN9+Hqu0lyOpajLPuuQk57XLTZ8yQTObOOeW3L+HTD9biPy+8iYIvX0Hv4u0o35eLog4VyO9QgYHF+3DC+OpQG8x9Yancn0BFWQ54ysKeql448rK7ESsehlWzb8EpBU9g7d4zMXbEFfVWt5y89hh79+P1Vh5BEERbgYSeZknGIS/1YIOpu2GkXWkpLC4iwu+0wUvpYi3C2qnubaVNW2J2y+CF3NFKl0+2EdZ3lzuOpsgaCJsRfoUKRNolN8VDhdlMdx9EiVKAFkHEEeggRxYScjo0udTxBcLradQU0sDCdpgqmJgurHExOaWYYOuHrUjiiQGagqiLQsb5s/WIHtWktx8q1kjRQmJfEWsMdsOjxFQDuggpJiQOPJcRyp9yb2kFysMP5XqL5dUZAGbZztA0Q+MpbaMJZM4HrixhbqoDY/5Ezb6wY7kijbucvGzTNcbEN4eyMheX0khijucXc30Sn92yDN9bBEHUL89dOg3Hlq7GqO57EOteg9TBODb+cCE+yR+MH8178JDLl+fOyfvZx+iy/issf2kZ9nzMgERPjDisDKMGLcQ3dx6JHgX70eeoSsSPDxd0AKByfxbKy/JQUdMZ1VUc/LAh6DzuMmQvPA/lu9ujcNoGZOXkorObniZIJgiCaH6Q0NNsaei378ieZkj6dN34+nEjs5O1FYAyQ4kWMHwO9YIjMIwlTKaSxY5YSJowe/Jxm4fPsSP7rR8PE4j0zrUJ0c2Uj4TvaXZNZacRq8w+ZIa8nLXuC3BoHVwessMYC7cZ8giJw6ERPcKGpphI3XulbF2ECq2mHtEjHZP90e9/f2WpcDtGm9qz4dnVhRLpOfJOMUeo8JqBqRE9oc+KWwjXH2w3h9ruHLZ0IM5ZQDDxU0p5mX7lOMAtcHfeHf2yO8IwB3eje5g7sRMHnF/MnWWypNTuJ1cg5OIbxBPa1DSOTVnlcdtN0gP9+XrSiekE0bppiDltZJ67dBrGFr2J/FO3I7t9pXe8sCyJTmu+wXOXTstI7DlQsR/rln6MzcvXoOzjlejOPkFuohL7D2SjT68SHH5CFdqjHLEXeqJHWRZ6H5ZC/JwqMOkloePRO5UyuQ2U783FvgNdYef3RW7vIcjuORz5fU9CbqI9TPFGix+hCZIJgiBaCoxT3HadKS0tRUFBAdDne4CVqEMJdRQynJ+b62APCJUG0vZ6rYgZbb1CzPlYyEwyeo80kDUrjb2wQi1t/EwmeVyTsbjXWUvbcRV5ACQYEHPHiUQKQlA7fjFwZHlyKw/kDdtnAOIWAhPwZiLWxBlHPELijRJkYl7TBr82omwmLMCKmVMEfJbrw4BEmtvH5CvAkYizyFs2IFa4WBYQj4UrJGEiBgAkEwCTns3APRRyvSwGxOPqPeu3hy6uqQmyEwAzPGNMSmOyGY8BMemaKL5K+Uy2s5N+9IvRXkh5sThHPBZyX7Ngev98DdrlcOUeifr6EOU594+NeMxW0nmCjGbbywdnQtF2yRrFnn6PMqG4KXW1kZVIIRaz5aTefencd/7zY0nn4vEa5OZU+76x4KTMar3dYVwMSCYrYVkpL5/aHtybDwhwv8rdz6Vlleh2/HLs27cP+fn5IIjmjnj3OtR7Vsxpk5UnzWlTnoUVGcxpkwkHKvaj5MaR6Dn6c6DrGYgdPxMoGADsW4vUR/cC29/A1iVHYtMp96Dk/RVIbv8QuShHVXUShdmlKMjbjyOP+wqJvEpU7s1BLJFCPLsGsdwqWLHMXt1T1QzcthBPprB9S2e0G3ENCgacClZwNJDsWKdV94ztVpbEig2n0ATJBEEQaaivf8MygSJ6mi1RcST1gVauaVxLIH3o7+Z1sx0+Psg/H3YuktpqlxxBAYyF/tYdPMaVE5lEAImymWKZGdOay3B+ng/T+0y+C2HjUCJ6zN75Q1SM5Uqd5DABKdSKaViMlt9YV5YmoifCbjrpW44q0W0yZhaXIpcCD5xigbR6HRWRSkQRhYgdYfYCE0JLcPmDdN6SXbNChCyTPdHe2jA6+XmQI3lkLLey/vxX0v0m3f8BX7w6yGPa5JOKdTWNBfhDpPw08ip3TnQNgyy+yH9N8pyYANkzaTkRfo4Qw5y5fTQf/Xl5hE11mJZ/j0siEPf9VidQd/LaHGCcu+3XUP/GEETzJZM5bdKJFpxzlO8rx45N2/HNVzvxzRdbULHzG1TtK0d1aRkKt6/GWZd8iX0722PxXyqRnbgF7bP3Y8DxXyK7/UGwRAI9TvoCnfdMQmJwJWIjU6G2ErnBoVbcBvaX5cCusdC+YwW++boQZQc7IHnEqeg25nxYRQNg5XTF4juvxyl9n8BnZadh7HdmHXLb0QTJBEEQLQOK6DkEGjaiJ+I8Q3gPPy2mfMz4MZAvao3q0GMMoRE9SlJDGbWO6JFsWmlsGvMBsZisfQY7cDqiNRMMiEkmQzud2n5ciujxBJyIvLLNuBW8DTIRa2KMIxEh8RrvELfAmBIsld5XQdxSo0f0PMxwUNgMi+gRSc1aIEcixiIDu/TbWe441zqixyWZBS+ixyQ+GcUh97A5oocpvpnIzoIxogchQpclXcvIa6L5K6c0RfR4u4amEwKjFePm66lpW7oWw1CNdjkG/0yOwxfCnGtpI+79+u1Hypjt+J/jVgq5OTVqmUp6rqT3y+VIJlKwYtqEypJNEQmk1yURr0JOTrVSXjC6SaoL8+viRPTYvk3mC0JyHl0gKi2rRLdBFNFDtBwO9dfQA+VlwLxeKNudh8+PeAwb//Ea8io3g6VSKK9qj+9/523kFO7HtvVdkdOuEru2FwK2hWSyBoWHlaKg2z7Y1TEc2J2LeCIFK5FCTqdyWAkbNfvjzop9cRtWXF/XMjOqK+M4eCCJgwdzYfFqVFfHsT9nAHqNOQ+JLseA5XUHcorBEu0D9RFz5wiqDuzH3gePQfsOFcDEzfU6LI0gCIKoPRTRQ7g0ZFRPSJmRUTZRftTWRym9YlM7bu7FNxiqSbmrbfJAm+FC2kk3x4/coQ0ZsBMin6nzcYTpGKam8yaBNnXE0+wrBRtDWsx6sUghVj8yCjoRtjL2y1CWJYa2heQPE0+ipG/T7ahMemuK6JHbO8of0xnpfpLLUK6hFtFjamPTbqCe2n2h37fq5L4wi4yGNlUm/+bBO4WJk0wLLJTallmWt3S4iOeJnCNIssm5eE7l2XhEbI0rhkgNLLcr50wqS7ohpcr6S7+r5fuzU4sCmFZZ7kQIyfVkFuSl+4K/wzgCTjCaR5yWHxq3hkqkFAtGBKUdkksQrYvlc6Zj9LFVWFdyPrKWzMaPTl8FKxH84u99wlYAQJdjdgbOAUB20f7AsXhuTeAY50CqOoaa6jiqKrPAkIJdE8OevQXoffRX+PyrQegz8R7ECw4HcrogO5aN7FrUJyevPRavH01z5xAEQRAKJPQ0KxrzhTtCRDK6kS7wS+6KpqmH19kxGU03VifMj/prO67thQ1J0lfR1hfnCfOIa5/tQNpw0URpZb1jHGJPIH5blJfL1i3qV4FBFzIM6ktIGfIxm6milzip1wlQBYQwP720Ir88JMgtzLaDUzXJtuTVr0xLg5sI1JFDmgBXiAosPJN8e1uqZqZeS5Pyodr0YjRcISMwLMrQuCzkM4DggmpafpurbadrEKbnJCA6mYSnMDFKvk9TNmArGghsWcDQLoz8TcT0hmB2wE95SJaXlNvaDecPGlNEI00AAyTRx/PLVtpbvksY99NxbvuilK8H+WW7Dz3n3Hv+5aFbamPaalsyv1wmfQlwO913O0G0LuIV2wAAfS+8Gl/96aaAyGOnGKwYh13DYKcsVJTloLKmHWp4ErwmhaS1HwdrcmB1OxHJDl2Qe1hXfLv5KyCnAw7rPwK5nXsAiTxUlqxE4v1J+M8rAzHwybfRLjsb7VwbVQcPYtOV3wGO/gq9fngHEt3HHFKdxtz/sjd3zil5TwAfP4Hivs7cOe+tHEdz5xAEQbRBSOhpVkS9cNe3CFSb3mxUetNxuR6GmBPvtKFbaOpwZYRJqsgkM4vYA8Q8G8HjXLFow1lBS/dI79sy7RiDqYXMNqFLCOGai1FwkgWiDHUNx4wcqcSZ0slNZ1NgcWM/35w/Tb8zTEzQhQs5oiet3QxEC6NtrVJpV92Sj0k2OYc0wjGofChty3TBJiKiJ6wMmIUaPZ1Xpnxcu4nD2t94zNTOEY0tR7rIIqO3NDnUvN7TL4sqXFJS3JMcvhhjei6c9rQAi6vPseas9yxoS6lzG0CMS+IvU24qUYIXTOPVi7nluDMEBW5OdVJlp35y7Tn8lcJkY9zzwxM/xWmK6CHaGDXtugMANv79MYy5Yx5wcAcQzwMSeUA8D+/cNQOn9H0C7274Icbe/TiSGZSZd1LwWPKIXih9ezKOHroZ733/HOwdOh7DLj8fHzzzIgpXvomhZ3yJ8or2yO95Wr3Ui+bOIQiCIGRojp5DoOHm6Enz4i06IXUiakHuqEOZrrplEm8actWtkB5iHefosWKq9um3Fg8cky0nmBs9EiGi6IIL4M/RY64Flz4Hy4kz9zYwdHT1zzKmOXrSihruTjxi1a2o8uIxIKbdPybBxhRZk26OnsBnt2NrmqPHJNSY5npJRMzRYyzH/ZuVBViGZ1MXekzDoOKxaMkvbKhZMguw9GfMINjI+S3PpixaGI0bh78ls9xV5vQselpNIIrHuDeXlUmokf1TfEA1cnOCfpjaURfF4jEbMVdp9IdXGVa409osbtUgJ9uGet18YccXa/yQG+cbkCMrkQKz9BW2uGdH/sykvPF4FXKSNd55aHP0OJ9t5b4W8/hkZR10J652I45kOzCJZI5oVFpWTXP0EC2K+pyjp6HntOFb/4HU4ktRtq0QFZ92QnVpNhL5B9Hu6F1o330vYmPmgfU895BsEARBEC2Hxpyjp65qQavi4YcfRu/evZGdnY0RI0bg/fffb2KPeMRWXzBti3DDS5+uHDmT2CJ6yxzOT++cSVmk8jKqbv22j1wbdWiW/59uWXwOdK4jypZbzHScKTbVdEqZLPoq6mVa2mdLO5bmjkiLbsOzxTO0qQYvRF5SuV288qT2EKtuWdLG9E22U8vbSC4HzBUZ3EIDdrRN+O09NbbpuouCEVqmiFpiwr4rxJnqqts0RdfoiZil2WP+Mb0NLFNarTxuG+xJfojrptTZApjltgiT6mj5dWbipoLha4VzeOIHN1QcUvSXZFM0pBMxxdw2dT8LX/X6wYnuc4a5qTeTck3hrLAlynPqxvz7x7uW4jhgWY4wYzFI9XDm4LFtDs4Z1DAxeG0mKudcE9GOzImUoogeoo2Rk9ceK9aPRlHP3dj74DFYdMdUbPtoNRbdMRV7HzwGRT13Y8WGU+pl4mLW81zExsxD+6Pz0fW0jeh13hp0PW0j2h9dQCIPQRAE0aC0+aFbf/nLXzBjxgzMnTsXI0aMwAMPPIAJEyZgw4YN6Ny5cyN705gv3NzvrWWa3uifPIAp6rwEM51n2n5IceEF1QvRffxgFIDsSZQ3Xt/sEGyaRtSZ8kadF1dR7oKa/GaGHRZIEbQeVq4+R49uM/RWjGhUY12k29TmjsAUaTNt5SPs+4Ec3j7nzqCbsGuqztPi5xXz9QTPAPp0L7oP3nw9bja9vmE29XoGBCCmCjNe2cImgzL3rzzEz2LmaxK2HLrsuzKfsHeO+0OU5IaQEoooF6ZVlIl5bRhcwUd9Qhj8ejK46TgA2M78NQZlTBQvz3Xj2JJsenMDqbkZdwQbrtXDuZa2I0yJc5aUn0silFR5Jjew9jXtXC/njmTMlsQ15yJyOCIRQbQ1GnNOG9bzXMS6fx/Y9S74gRKwnGKg08lgtYo8JgiCIIja0eaHbo0YMQLDhw/HH/7wBwCAbdvo2bMnpk6diptvvjkyb8Mvrx6WzbCcUMbE6maXxWsR/8XUz+mWVw/LayXq4Krbez+E5dXTaQ4BwQBAnBmGpGh5TPlj4MgOkVtNdmTijIPFwrWKsHqIoVu1alombOpNy/UkxnLj2jChCBOBAjJZCj4oTHFkxaOXVw8xBzAgEWehDeS1rUEAy8pyIjWMNpi2r5FIWKLrHszPgmUIklmA5V6U0BbWqsPcMuPxEF/hCwqmoVvZWQjO9aLlNzkUYxxx7atSvu7KPDly2awGuUlpGJOhPUx2LQAxK4V4HHBEHa7mF/Vjmh8AYsxGbk4K4h5XrzmHN6lzoH04EvEUYjHbfK+w4HAvQVaiCtnJaqnevowphnwpMiIDANsdulXpRv2I7G4kk9eu0rMq2S8rr0bXgUtp6BbRYqjPsPcD5WVYrs1pQ0uQEwRBEA0FLa/eSFRVVWHlypWYNWuWd8yyLIwfPx5Lly5tAo8y7XqHRdfUwUbGxdio1fw+tXChbunqWv9oxApYUSKL3vrer/uG8uSokzBCoz8iPU2fPyp9RjYNDshLSIf5oadgcKIkgquLiTKjBYooomzabkXDbMp/a4M+5Ei+H4yrbmk29TyAL6qEtm7EjcS5G5HCgsEuctRS4FvDdH21cgNOuTZs+SaSIptMUVKmehrh/r0pV9X5bIHzlF8HLZBQn6tHKtKx6YYJcVdZEeUzuFFYXGof0Y6W7US7aCKJUy4PrLqn1JkxpSzv/gAcW1LYEnf/zxhgcxu2G9GjTsQsiT2aaCQOODZFae5dyP0j8sMgft9h4OC2HLZFEG2LnLz2GHv3403tBkEQBEHUO21a6Pnmm2+QSqXQpUsX5XiXLl2wfv36QPrKykpUVlZ6+/v27XM+2NV19KCOYoU+A2+tCOtup7MZg7oGccYZUfeInpBeetr8DOB1s8mQAmAe7qN2PNXcNuPqEBX3nMkLOa8NrnUh5XOqDT2d7R4Ma6KwjqjNOPSuXdpmloSR4OUMRvUEPqcAuBE9YbZY4IO7W4voI7/fy8F4+smYjWU4FzQysSWn1fLWNaLHTljuOXPCKJnVm4w5xEbYilhhkUvytZB9F8cZD07GrNdRzwM4ET0xQ/CjpeXT68pYDSxJHBG2TL6Jugnilo14DBBih/ds6qKUJqJYzAZHjbl+zBdK5IgfcbqyqsaL6FH8tdR0uu+JuI1USrIpr73uHRNPr4jW4a7NamkCaGhp5CXl1Qih8v1OeW08uJdoQYh7tbS0tIk9IQiCIIjaIf7taoz3rjYt9NSWOXPm4I477gie2Dy/8Z0hGoS6/rZdVa9eEARBNC5lZWXOUGSCaOaUlZUBAHr27NnEnhAEQRBE3WiM9642LfQcdthhiMVi2LFjh3J8x44dKC4uDqSfNWsWZsyY4e3bto3du3ejY8eOzuSbrYDS0lL07NkTW7dupfkaQO0hQ22hQu3hQ22h0pLag3OOsrIydOvWraldIYiM6NatG7Zu3Yr27dsH3r1a0rNXX1Cd20adgbZZb6pz26gz0Hbq3ZjvXW1a6MnKysLQoUOxYMECnHfeeQAc8WbBggWYMmVKIH0ymUQymVSOFRYWNoKnjU9+fn6rfshqC7WHD7WFCrWHD7WFSktpD4rkIVoSlmWhR48ekWlayrNXn1Cd2w5tsd5U57ZDW6h3Y713tWmhBwBmzJiBSZMmYdiwYTjxxBPxwAMPoKKiAldeeWVTu0YQBEEQBEEQBEEQBFEr2rzQ88Mf/hC7du3CrbfeipKSEgwePBhvvPFGYIJmgiAIgiAIgiAIgiCI5k6bF3oAYMqUKcahWm2RZDKJ2267LTBEra1C7eFDbaFC7eFDbaFC7UEQTUNbfPaozm2HtlhvqnPboa3WuyFhnNZUJQiCIAiCIAiCIAiCaBVYTe0AQRAEQRAEQRAEQRAEUT+Q0EMQBEEQBEEQBEEQBNFKIKGHIAiCIAiCIAiCIAiilUBCD0EQBEEQBEEQBEEQRCuBhJ42ypw5czB8+HC0b98enTt3xnnnnYcNGzYoaQ4ePIjJkyejY8eOyMvLw4UXXogdO3Y0kceNxz333APGGKZPn+4da0ttsW3bNvz4xz9Gx44dkZOTg4EDB+KDDz7wznPOceutt6Jr167IycnB+PHjsXHjxib0uOFIpVL41a9+hT59+iAnJwdHHnkk7rrrLshz2Lfm9li8eDHOPvtsdOvWDYwxvPTSS8r5TOq+e/duTJw4Efn5+SgsLMRVV12F8vLyRqxF/RDVFtXV1Zg5cyYGDhyIdu3aoVu3brj88svx9ddfK2W0lrYgiObIww8/jN69eyM7OxsjRozA+++/39Qu1Rv0zta23s3a2ntYW3nXaovvVPTu1LSQ0NNGWbRoESZPnoxly5Zh/vz5qK6uxumnn46KigovzQ033IB//vOf+Nvf/oZFixbh66+/xgUXXNCEXjc8K1aswB//+Eccf/zxyvG20hZ79uzBySefjEQigddffx1r167F7373O3To0MFL85vf/AYPPfQQ5s6di+XLl6Ndu3aYMGECDh482ISeNwz33nsvHn30UfzhD3/AunXrcO+99+I3v/kN/vu//9tL05rbo6KiAoMGDcLDDz9sPJ9J3SdOnIhPPvkE8+fPxyuvvILFixfjmmuuaawq1BtRbbF//36sWrUKv/rVr7Bq1Sq88MIL2LBhA8455xwlXWtpC4JobvzlL3/BjBkzcNttt2HVqlUYNGgQJkyYgJ07dza1a/VCW39na0vvZm3xPaytvGu1xXcqendqYjhBcM537tzJAfBFixZxzjnfu3cvTyQS/G9/+5uXZt26dRwAX7p0aVO52aCUlZXxvn378vnz5/NTTz2VT5s2jXPettpi5syZ/JRTTgk9b9s2Ly4u5vfdd593bO/evTyZTPLnnnuuMVxsVM466yz+k5/8RDl2wQUX8IkTJ3LO21Z7AOAvvviit59J3deuXcsB8BUrVnhpXn/9dc4Y49u2bWs03+sbvS1MvP/++xwA37x5M+e89bYFQTQHTjzxRD558mRvP5VK8W7duvE5c+Y0oVcNR1t6Z2tr72Zt8T2sLb5rtcV3Knp3anwooocAAOzbtw8AUFRUBABYuXIlqqurMX78eC9Nv3790KtXLyxdurRJfGxoJk+ejLPOOkupM9C22uLll1/GsGHDcPHFF6Nz584YMmQIHnvsMe/8pk2bUFJSorRFQUEBRowY0eraAgBOOukkLFiwAJ9++ikA4D//+Q/eeecdnHnmmQDaXnvIZFL3pUuXorCwEMOGDfPSjB8/HpZlYfny5Y3uc2Oyb98+MMZQWFgIoG23BUE0JFVVVVi5cqXyXWRZFsaPH99qv4fb0jtbW3s3a4vvYfSuRe9UAnp3ql/iTe0A0fTYto3p06fj5JNPxnHHHQcAKCkpQVZWlvegCbp06YKSkpIm8LJhef7557Fq1SqsWLEicK4ttcUXX3yBRx99FDNmzMD/+3//DytWrMD111+PrKwsTJo0yatvly5dlHytsS0A4Oabb0ZpaSn69euHWCyGVCqFu+++GxMnTgSANtceMpnUvaSkBJ07d1bOx+NxFBUVter2OXjwIGbOnIkf/ehHyM/PB9B224IgGppvvvkGqVTK+F20fv36JvKq4WhL72xt8d2sLb6H0bsWvVMB9O7UEJDQQ2Dy5Mn4+OOP8c477zS1K03C1q1bMW3aNMyfPx/Z2dlN7U6TYts2hg0bhtmzZwMAhgwZgo8//hhz587FpEmTmti7xuevf/0rnn32WcybNw/HHnssVq9ejenTp6Nbt25tsj2I9FRXV+MHP/gBOOd49NFHm9odgiBaGW3lna2tvpu1xfcwetci6N2pYaChW22cKVOm4JVXXsFbb72FHj16eMeLi4tRVVWFvXv3Kul37NiB4uLiRvayYVm5ciV27tyJE044AfF4HPF4HIsWLcJDDz2EeDyOLl26tJm26Nq1KwYMGKAc69+/P7Zs2QIAXn31VS1aY1sAwE033YSbb74Zl1xyCQYOHIjLLrsMN9xwA+bMmQOg7bWHTCZ1Ly4uDkyGWlNTg927d7fK9hEvKps3b8b8+fO9X6SAttcWBNFYHHbYYYjFYm3ie7gtvbO11XeztvgeRu9abfudit6dGg4SetoonHNMmTIFL774IhYuXIg+ffoo54cOHYpEIoEFCxZ4xzZs2IAtW7Zg1KhRje1ugzJu3DisWbMGq1ev9rZhw4Zh4sSJ3ue20hYnn3xyYMnWTz/9FIcffjgAoE+fPiguLlbaorS0FMuXL291bQE4KwJYlvo1GYvFYNs2gLbXHjKZ1H3UqFHYu3cvVq5c6aVZuHAhbNvGiBEjGt3nhkS8qGzcuBFvvvkmOnbsqJxvS21BEI1JVlYWhg4dqnwX2baNBQsWtJrv4bb4ztZW383a4nsYvWu13XcqendqYJp2Lmiiqbjuuut4QUEBf/vtt/n27du9bf/+/V6aa6+9lvfq1YsvXLiQf/DBB3zUqFF81KhRTeh14yGv7MB522mL999/n8fjcX733XfzjRs38meffZbn5ubyP//5z16ae+65hxcWFvJ//OMf/KOPPuLnnnsu79OnDz9w4EATet4wTJo0iXfv3p2/8sorfNOmTfyFF17ghx12GP/FL37hpWnN7VFWVsY//PBD/uGHH3IA/P777+cffvihtxpCJnU/44wz+JAhQ/jy5cv5O++8w/v27ct/9KMfNVWV6kxUW1RVVfFzzjmH9+jRg69evVr5Tq2srPTKaC1tQRDNjeeff54nk0n+1FNP8bVr1/JrrrmGFxYW8pKSkqZ2rV6gdzaHtvBu1hbfw9rKu1ZbfKeid6emhYSeNgoA4/bkk096aQ4cOMB/9rOf8Q4dOvDc3Fx+/vnn8+3btzed042I/jLRltrin//8Jz/uuON4Mpnk/fr14//zP/+jnLdtm//qV7/iXbp04clkko8bN45v2LChibxtWEpLS/m0adN4r169eHZ2Nj/iiCP4LbfcovwD1Jrb46233jJ+T0yaNIlznlndv/32W/6jH/2I5+Xl8fz8fH7llVfysrKyJqjNoRHVFps2bQr9Tn3rrbe8MlpLWxBEc+S///u/ea9evXhWVhY/8cQT+bJly5rapXqD3tkc2sq7WVt7D2sr71pt8Z2K3p2aFsY55/UfJ0QQBEEQBEEQBEEQBEE0NjRHD0EQBEEQBEEQBEEQRCuBhB6CIAiCIAiCIAiCIIhWAgk9BEEQBEEQBEEQBEEQrQQSegiCIAiCIAiCIAiCIFoJJPQQBEEQBEEQBEEQBEG0EkjoIQiCIAiCIAiCIAiCaCWQ0EMQBEEQBEEQBEEQBNFKIKGHIAiCIAiCIAiCIAiilUBCD0EQBEEQBEEQBEEQRCuBhB6CIOoVzjkA4Pbbb1f2CYIgCIIgiPqH3r0IgtBhnL4JCIKoRx555BHE43Fs3LgRsVgMZ555Jk499dSmdosgCIIgCKJVQu9eBEHoUEQPQRD1ys9+9jPs27cPDz30EM4+++yMXjTGjh0LxhgYY1i9enXDO6lxxRVXePZfeumlRrdPEARBEARRV+jdiyAIHRJ6CIKoV+bOnYuCggJcf/31+Oc//4klS5ZklO/qq6/G9u3bcdxxxzWwh0EefPBBbN++vdHtEgRBEARBHCr07kUQhE68qR0gCKJ18V//9V9gjOH222/H7bffnvE48dzcXBQXFzewd2YKCgpQUFDQJLYJgiAIgiAOBXr3IghChyJ6CIKoFbNnz/ZCbeXtgQceAAAwxgD4EwKK/doyduxYTJ06FdOnT0eHDh3QpUsXPPbYY6ioqMCVV16J9u3b46ijjsLrr79eL/kIgiAIgiCaI/TuRRBEbSGhhyCIWjF16lRs377d266++mocfvjhuOiii+rd1tNPP43DDjsM77//PqZOnYrrrrsOF198MU466SSsWrUKp59+Oi677DLs37+/XvIRBEEQBEE0N+jdiyCI2kKrbhEEUWd+9atf4U9/+hPefvtt9O7du87ljB07FoMHD/Z+mRLHUqmUN848lUqhoKAAF1xwAZ555hkAQElJCbp27YqlS5di5MiRh5QPcH4Be/HFF3HeeefVuS4EQRAEQRANBb17EQSRCRTRQxBEnbj11lvr5UUjiuOPP977HIvF0LFjRwwcONA71qVLFwDAzp076yUfQRAEQRBEc4XevQiCyBQSegiCqDW33XYbnnnmmQZ90QCARCKh7DPGlGNiDLpt2/WSjyAIgiAIojlC714EQdQGEnoIgqgVt912G55++ukGf9EgCIIgCIIg6N2LIIjaQ8urEwSRMb/+9a/x6KOP4uWXX0Z2djZKSkoAAB06dEAymWxi7wiCIAiCIFoX9O5FEERdIKGHIIiM4JzjvvvuQ2lpKUaNGqWce//99zF8+PAm8owgCIIgCKL1Qe9eBEHUFRJ6CILICMYY9u3b12j23n777cCxL7/8MnBMXziwrvkIgiAIgiCaE/TuRRBEXaE5egiCaBY88sgjyMvLw5o1axrd9rXXXou8vLxGt0sQBEEQBNFU0LsXQbReGCdplSCIJmbbtm04cOAAAKBXr17IyspqVPs7d+5EaWkpAKBr165o165do9onCIIgCIJoTOjdiyBaNyT0EARBEARBEARBEARBtBJo6BZBEARBEARBEARBEEQrgYQegiAIgiAIgiAIgiCIVgIJPQRBEARBEARBEARBEK0EEnoIgiAIgiAIgiAIgiBaCST0EARBEARBEARBEARBtBJI6CEIgiAIgiAIgiAIgmglkNBDEARBEARBEARBEATRSiChhyAIgiAIgiAIgiAIopVAQg9BEARBEARBEARBEEQrgYQegiAIgiAIgiAIgiCIVgIJPQRBEARBEARBEARBEK0EEnoIgiAIgiAIgiAIgiBaCST0EARBEARBEARBEARBtBJI6CEIgiAIgiAIgiAIgmglkNBDEARBEARBEARBEATRSiChhyAIgiAIgiAIgiAIopVAQg9BEARBEARBEARBEEQrgYQegiAIgiAIgiAIgiCIVgIJPQRBEARBEARBEARBEK0EEnoIgiAIgiAIgiAIgiBaCST0EARBEARBEARBEARBtBJI6CEIgiAIgiAIgiAIgmglkNBDEARBEARBEARBEATRSiChhyAIgiAIgiAIgiAIopVAQg9BEARBEARBEARBEEQrgYQegiAIgiAIgiAIgiCIVgIJPQRBEARBEARBEARBEK0EEnoIgiAIgiAIgiAIgiBaCST0EARBEARBEARBEARBtBJI6CEIgiAIgiAIgiAIgmglkNBDEARBEARBEARBEATRSiChhyAIgiAIgiAIgiAIopVAQg9BEARBEARBEARBEEQrgYQegiAIgiAIgiAIgiCIVgIJPQRBEARBEARBEARBEK0EEnoIgiAIgiAIgiAIgiBaCST0EARBEARBEARBEARBtBJI6CEIgiAIgiAIgiAIgmglkNBDEARBEARBEARBEATRSiChhyAIgiAIgiAIgiAIopVAQg9BEARBEARBEARBEEQrgYQegiAIgiAIgiAIgiCIVgIJPQRBEARBEARBEARBEK2EZi30fPvtt+jcuTO+/PLLtGlvvvlmTJ06teGdIgiCIAiCaKWke/d6++23wRjD3r17AQBvvPEGBg8eDNu2G89JgiAIgiAiadZCz913341zzz0XvXv3Tpv25z//OZ5++ml88cUXDe8YQRAEQRBEK6Q2714AcMYZZyCRSODZZ59tWMcIgiAIgsiYeFM7EMb+/fvx+OOP41//+ldG6Q877DBMmDABjz76KO67774G9o4giOZAKpVCdXV1U7tBEC2SRCKBWCzW1G4QzYjavnsJrrjiCjz00EO47LLLGsgzgiCaA/TeRRCHRlZWFiyrcWJtmq3Q89prryGZTGLkyJHesU8++QQzZ87E4sWLwTnH4MGD8dRTT+HII48EAJx99tm45ZZbSOghiFYO5xwlJSXe0AGCIOpGYWEhiouLwRhraleIZoDp3eu1117D9OnTsXXrVowcORKTJk0K5Dv77LMxZcoUfP755947GUEQrQd67yKI+sGyLPTp0wdZWVkNbqvZCj1LlizB0KFDvf1t27ZhzJgxGDt2LBYuXIj8/Hy8++67qKmp8dKceOKJ+Oqrr/Dll19mHHJMEETLQ7xsdO7cGbm5udRJJYhawjnH/v37sXPnTgBA165dm9gjojmgv3tt3boVF1xwASZPnoxrrrkGH3zwAW688cZAvl69eqFLly5YsmQJCT0E0Qqh9y6COHRs28bXX3+N7du3o1evXg3+HDVboWfz5s3o1q2bt//www+joKAAzz//PBKJBADg6KOPVvKI9Js3byahhyBaKalUynvZ6NixY1O7QxAtlpycHADAzp070blzZxrGRQTevR599FEceeSR+N3vfgcAOOaYY7BmzRrce++9gbzdunXD5s2bG81XgiAaB3rvIoj6o1OnTvj6669RU1PjaRoNRbOdjPnAgQPIzs729levXo3Ro0dHNoh4ad2/f3+D+0cQRNMgxobn5uY2sScE0fIRzxHNuUAAwXevdevWYcSIEUqaUaNGGfPm5OTQ+xdBtELovYsg6g8xZCuVSjW4rWYr9Bx22GHYs2ePty9EnCh2794NwFHKCIJo3VDYMEEcOvQcETL6u1dt2L17N71/EUQrhv69IIhDpzGfo2Yr9AwZMgRr16719o8//ngsWbIk8lfHjz/+GIlEAscee2xjuEgQBEEQBNFq0N+9+vfvj/fff19Js2zZskC+gwcP4vPPP8eQIUMa3EeCIAiCINLTbIWeCRMm4JNPPvF+WZoyZQpKS0txySWX4IMPPsDGjRvxpz/9CRs2bPDyLFmyBKNHj84o+ocgCKKxWbx4Mc4++2x069YNjDG89NJLTWLjiiuuAGMMjDEkEgl06dIF3/3ud/HEE0/Atu1696k1kWnb9e7d20snth49egTO653m6dOnY+zYscqx0tJS3HLLLejXrx+ys7NRXFyM8ePH44UXXgDn3Ev32Wef4corr0SPHj2QTCbRp08f/OhHP8IHH3zQMI1BtDr0d69rr70WGzduxE033YQNGzZg3rx5eOqppwL5li1bhmQyGTqsiyAIoqmgd6+WDb131Z1mK/QMHDgQJ5xwAv76178CADp27IiFCxeivLwcp556KoYOHYrHHntMmbPn+eefx9VXX91ULhMEQURSUVGBQYMG4eGHH6513rFjxxo7WHW1ccYZZ2D79u348ssv8frrr+M73/kOpk2bhu9///vKaoZEkEzb7s4778T27du97cMPP1TKyc7OxsyZMyNt7d27FyeddBKeeeYZzJo1C6tWrcLixYvxwx/+EL/4xS+wb98+AMAHH3yAoUOH4tNPP8Uf//hHrF27Fi+++CL69etnXCWJIEzo7169evXC3//+d7z00ksYNGgQ5s6di9mzZwfyPffcc5g4cSLN4UEQRLOD3r1aPvTeVUd4M+aVV17h/fv356lUKm3a1157jffv359XV1c3gmcEQTQVBw4c4GvXruUHDhxoalcOCQD8xRdfzDj9qaeeyp988sl6sTFp0iR+7rnnBo4vWLCAA+CPPfZYrey0JTJtu8MPP5z//ve/Dy3n8MMP59dffz3Pysrir776qnd82rRp/NRTT/X2r7vuOt6uXTu+bdu2QBllZWW8urqa27bNjz32WD506FDjv5d79uwJ9aO1PE9E/VGbdy/OOd+1axcvKiriX3zxRQN7RhBEU9Ca/p2gd6+WB7131Z1mu7w6AJx11lnYuHEjtm3bhp49e0amraiowJNPPol4vFlXiSCIeoZz3mQrveTm5raqyQlPO+00DBo0CC+88AJ++tOfNokPFRUVANS2raqqQnV1NeLxOJLJZCBtTk4OLMsJUK2urkZVVRVisZiyepApbX1Sl7br06cPrr32WsyaNQtnnHFGwC/btvH8889j4sSJypLXgry8PADAhx9+iE8++QTz5s0z1q2wsLD2FSLaLLV59wKAL7/8Eo888gj69OnTCN4RBNEcoHev+qOp370a872rurq63pYUp/eu9DTboVuC6dOnZ/SicdFFFwWWACUIovWzf/9+5OXlNcnWGpcS7tevH7788ssmsy/a9ptvvvGO3XfffcjLy8OUKVOUtJ07d0ZeXh62bNniHXv44YeRl5eHq666Sknbu3dv5OXlYd26dQ3mu952M2fOVO6Xhx56KJDnl7/8JTZt2oRnn302cO6bb77Bnj170K9fv0i7Gzdu9OwTRH2Q6bsXAAwbNgw//OEPG9gjgiCaE/TuVb805btXY753ZTIMrjbQe1c0zV7oIQiCaIvMnj1b+cdqyZIluPbaa5Vj8j+09QXnvFX9UtaY6G130003YfXq1d52+eWXB/J06tQJP//5z3HrrbeiqqoqUF6mdgmCIAiCODTo3atlQe9d0dA4J4IgWjS5ubkoLy9vMtsNxbXXXosf/OAH3v7EiRNx4YUX4oILLvCOmcJKD5V169Y16RAMcS3ltr3pppswffr0wNDcnTt3AoCy0uLkyZNx9dVXIxaLKWnFLz4NuSqj3naHHXYYjjrqqLT5ZsyYgUceeQSPPPKIcrxTp04oLCzE+vXrI/MfffTRAID169fT8tYEQRBEg0PvXvVLU757NeZ71xVXXFGfrtN7VxpI6CEIokXDGEO7du2a2o16p6ioCEVFRd5+Tk4OOnfunNE/YHVl4cKFWLNmDW644YYGs5EO07XMyspCVlZWRmkTiYRx/HdD3yOH0nZ5eXn41a9+hdtvvx3nnHOOd9yyLFxyySX405/+hNtuuy3wclleXo7s7GwMHjwYAwYMwO9+9zv88Ic/DIwX37t3b7MZL04QBEG0fOjdq/5o6nevxnzvqq/5eQB678oEGrpFEATRSJSXl3vhpACwadMmrF69ul7DgDO1UVlZiZKSEmzbtg2rVq3C7Nmzce655+L73/++MdSV8GmItrvmmmtQUFCAefPmKcfvvvtu9OzZEyNGjMAzzzyDtWvXYuPGjXjiiScwZMgQlJeXgzGGJ598Ep9++ilGjx6N1157DV988QU++ugj3H333Tj33HPro9oEQRAE0eKgd6+WD7131Q2K6CEIgmgkPvjgA3znO9/x9mfMmAEAmDRpUr1NUJepjTfeeANdu3ZFPB5Hhw4dMGjQIDz00EOYNGlSg6xK1ZpoiLZLJBK46667cOmllyrHi4qKsGzZMtxzzz349a9/jc2bN6NDhw4YOHAg7rvvPhQUFAAATjzxRHzwwQe4++67cfXVV+Obb75B165dcdJJJ+GBBx441CoTBEEQRIuE3r1aPvTeVTcYbymzCREEQQA4ePAgNm3ahD59+ijLOBIEUXvoeSIIgiCioH8nCKL+aMzniaRDgiAIgiAIgiAIgiCIVgIJPQRBEARBEARBEARBEK0EEnoIgiAIgiAIgiAIgiBaCST0EARBEARBEARBEARBtBJI6CEIgiAIgiAIgiAIgmglkNBDEESLhBYMJIhDh54jgiAIIhPo3wuCOHQa8zkioYcgiBZFIpEAAOzfv7+JPSGIlo94jsRzRRAEQRAy9N5FEPVHVVUVACAWizW4rXiDWyAIgqhHYrEYCgsLsXPnTgBAbm4uGGNN7BVBtCw459i/fz927tyJwsLCRnnhIAiCIFoe9N5FEPWDbdvYtWsXcnNzEY83vAxDQg9BEC2O4uJiAPBeOgiCqBuFhYXe80QQBEEQJui9iyDqB8uy0KtXr0YRSxmnAZcEQbRQUqkUqqurm9oNgmiRJBIJiuQhCIIgMobeuwji0MjKyoJlNc7sOST0EARBEARBEARBEARBtBJoMuZ6YvHixTj77LPRrVs3MMbw0ksvNai93r17gzEW2CZPntygdgmCIAiCIJoDjf3uBQDbtm3Dj3/8Y3Ts2BE5OTkYOHAgPvjggwa3SxAEQRC1gYSeeqKiogKDBg3Cww8/3Cj2VqxYge3bt3vb/PnzAQAXX3xxo9gnCIIgCIJoShr73WvPnj04+eSTkUgk8Prrr2Pt2rX43e9+hw4dOjSKfYIgCILIFBq61QAwxvDiiy/ivPPO845VVlbilltuwXPPPYe9e/fiuOOOw7333ouxY8fWi83p06fjlVdewcaNG2kmfIIgCIIg2hSN8e518803491338WSJUvqx2mCIAiCaCAooqeRmDJlCpYuXYrnn38eH330ES6++GKcccYZ2Lhx4yGXXVVVhT//+c/4yU9+QiIPQRAEQRAE6v/d6+WXX8awYcNw8cUXo3PnzhgyZAgee+yxevaaIAiCIA4diuhpAPRflbZs2YIjjjgCW7ZsQbdu3bx048ePx4knnojZs2cfkr2//vWvuPTSSwPlEwRBEARBtAUa490rOzsbADBjxgxcfPHFWLFiBaZNm4a5c+di0qRJ9VIPgiAIgqgPKKKnEVizZg1SqRSOPvpo5OXleduiRYvw+eefAwDWr19vnFxZ3m6++WZj+Y8//jjOPPNMEnkIgiAIgiDQMO9etm3jhBNOwOzZszFkyBBcc801uPrqqzF37tymqiZBEARBGIk3tQNtgfLycsRiMaxcuRKxWEw5l5eXBwA44ogjsG7dushyOnbsGDi2efNmvPnmm3jhhRfqz2GCIAiCIIgWTEO8e3Xt2hUDBgxQzvfv3x9///vf68lrgiAIgqgfSOhpBIYMGYJUKoWd/7+9+w6L4nrfBn7v0jsIgog0GzawRoM1iUZjjZrYC2piYi9oLFGx92hM7CW2rzG22GPH2LEDolGUYkNAEOmdnfcPX+fnCkpbdtjl/lzXXu4e5sw+sy7Ms8+eOeflS7Ro0SLPbfT19VGjRo1C73vLli2wtbVFx44dixsmERERkVYoidyrWbNmCA4OVmp7+PAhnJ2dixUrERGRqrHQoyLJyckICQkRH4eHhyMgIADlypVD9erV0a9fPwwcOBDLli1D/fr1ERMTA19fX3h4eBS5SKNQKLBlyxZ4eXlBV5f/lURERFR2qDv3Gj9+PJo2bYoFCxagZ8+euH79OjZs2IANGzao8rCIiIiKjZMxq8i5c+fw+eef52r38vLC1q1bkZWVhXnz5mH79u2IiIiAjY0NPv30U8yePRvu7u5Fes5Tp06hXbt2CA4ORvXq1Yt7CEREREQaQ4rc6+jRo5g6dSoePXoEV1dXeHt7Y+jQocU9FCIiIpVioYeIiIiIiIiISEtw1S0iIiIiIiIiIi3BQg8RERERERERkZYo0zP45uTkYNasWdixYweioqJQsWJFDBo0CNOnT4dMJsu3v0KhwIsXL2BmZlag7YmIiEoTQRCQlJSEihUrQi7ndz9U+jH3IiIiTaXOvKtMF3oWL16MtWvXYtu2bahduzZu3ryJwYMHw8LCAmPGjMm3/4sXL+Do6KiGSImIiErOs2fPUKlSJanDIMoXcy8iItJ06si7ynSh58qVK/j666/FJTZdXFzw119/4fr16wXqb2ZmBuDNf5S5uXmJxUlERFQSEhMT4ejoKJ7PiEo75l5ERKSp1Jl3lelCT9OmTbFhwwY8fPgQ1atXR2BgIC5duoTly5fnuX1GRgYyMjLEx0lJSQAAc3NzJhtERKSxeAkMaYq371XmXkREpKnUkXeV6Qvyp0yZgt69e6NGjRrQ09ND/fr1MW7cOPTr1y/P7RcuXAgLCwvxxqHDREREVFZduHABnTt3RsWKFSGTyXDw4MF8+5w7dw4NGjSAgYEBqlatiq1bt5Z4nERERGVNmS707NmzB3/++Sd27tyJ27dvY9u2bfjll1+wbdu2PLefOnUqEhISxNuzZ8/UHDERERFR6ZCSkoK6deti9erVBdo+PDwcHTt2xOeff46AgACMGzcO33//PU6ePFnCkRIREZUtZfrSrZ9++kkc1QMA7u7uePLkCRYuXAgvL69c2xsYGMDAwEDdYRIRERGVOu3bt0f79u0LvP26devg6uqKZcuWAQBq1qyJS5cu4ddff0W7du1KKkwiIqIyp0yP6ElNTc21rJmOjg4UCoVEEQF79+7FjRs3kJWVJVkMRERERKrm5+eHNm3aKLW1a9cOfn5+H+yTkZGBxMREpRsRERF9XJke0dO5c2fMnz8fTk5OqF27Nvz9/bF8+XIMGTJEknjS09PRp08f5OTk4OnTp+IcQEFBQYiNjUX9+vVhaWkpSWxERERExREVFQU7OzulNjs7OyQmJiItLQ1GRka5+ixcuBCzZ88ukXhCQ0Nx/PhxWFtbo0+fPiXyHERERFIo0yN6Vq5ciW+//RYjRoxAzZo1MXHiRPz444+YO3euJPHExcXhiy++QI0aNVCpUiWxfd26dfjiiy8wf/58sU2hUODo0aN4/vw5BEGQIlwiIiKiElWS8yMGBgZi9OjRWLVqlcr2SUREVBqU6RE9ZmZmWLFiBVasWCF1KACAihUr4tSpU7naLS0t4eLiggYNGohtISEh6Ny5MwwNDZGUlARd3Tf/lXfu3IGBgQGqVauW67I0IiIiIqlUqFAB0dHRSm3R0dEwNzfPczQPULLzI1atWhXffPMN6tSpUyL7JyIikgorARpg/vz5CA8PFyeNBoDXr1+jTp06aNCggVjkAd5MMF2jRg1s3LhRbEtKSsKtW7eQkZGh1riJiIiI3vL09ISvr69S2+nTp+Hp6SlJPB4eHti3bx9mzZolyfMTERGVFBZ6NIhMJhPvN2nSBEFBQbh48aLSNvr6+jAyMkK9evXEtgsXLqBRo0Zo3Lix0rbZ2dklGi8RERFpr+TkZAQEBCAgIADAm+XTAwIC8PTpUwBvLrsaOHCguP2wYcMQFhaGSZMm4cGDB1izZg327NmD8ePHSxE+ERGR1mKhR8O9f3nWkSNHkJSUhEaNGoltr1+/hrW1Ndzd3ZW2bdasGbp27YrQ0FC1xEpERETa4+bNm6hfvz7q168PAPD29kb9+vXh4+MDAIiMjBSLPgDg6uqKf/75B6dPn0bdunWxbNkybNq0SfKl1QVB4HyHRESkVWQCz2xFlpiYCAsLCyQkJMDc3FzqcD5KEASkpaXB2NgYABAcHIwaNWpAT08PkZGRsLa2BvBm5S9DQ0MpQyUiIjXRpPMYEaD69+ynn36KwMBAXLp0CQ0bNlRBhERERHlTZ97FET1lhEwmE4s8AODm5oa7d+9i06ZNYpEHAAYNGoSGDRviwoULUoRJREREpDaZmZlIT0/Hy5cvpQ6FiIhIZcr0qltlXe3atVG7dm3xcVpaGo4dO4akpCSYmpqK7cnJyTA0NFSa9JmIiIhI0/31118wMDBAxYoVpQ6FiIhIZTiih0RGRkYICwvD1q1bxevtAWDJkiVwcnLC1q1bpQuOiIiISMXc3Nzg4uICfX19qUMhIiJSGRZ6SImNjQ28vLyUVvg6duwYIiMjYWRkJLalp6cjPj5eggiJiIiIiIiI6ENY6KF8XblyBfv378fXX38ttu3duxf29vaYPHmyhJERERERFV16ejrWr1+P0aNHQ6FQSB0OERGRSrDQQ/nS19dHt27dlFbjOn/+PNLT05Xm8hEEAWFhYVKESERERFRourq6mDBhAlatWoW7d+9KHQ4REZFKcHZdKpKNGzdi2LBhqFSpkth27do1eHp6om3btjhx4oTS5V9EREREpY2uri5+/PFHGBoawsLCQupwiIiIVIKFHioSmUyGRo0aKbVdv34dcrkcdnZ2SkWe48eP45NPPoGNjY26wyQiIiL6qGXLlkkdAhERkUqx0EMqM2bMGHzzzTfIzMwU26Kjo9GhQwfI5XK8evUKlpaWAN5c5sURP0RERERERESqxTl6SKUcHBzg6uoqPn7x4gXq1KmDunXrikUeABgyZAiaNWsGX19fCaIkIiIi+j8KhQL+/v54/vy51KEQEREVGws9VKLq16+PoKAgXLlyRWwTBAEnT57ElStXIJf/31swODgYixYtgr+/vxShEhERURk1ePBgNGjQAOvXr5c6FCIiomLTiEu3vL29C91n+vTpKFeuXAlEQ0Xx7opdMpkMV65cga+vLzw9PcX2w4cPY+rUqbh8+TKOHDkitj958gROTk681IuIiKgImEflr127dvj777+VLj8nIiLSVDJBEASpg8iPXC6Hp6cn9PX1C7T9pUuXEBwcjMqVK5doXImJibCwsEBCQgLMzc1L9LnKgiNHjuCPP/5Ax44dMXToUABAamoqLC0tYW1tjcDAQNja2kocJRGR9uB5rGworXlUUZTUezYjIwM5OTkwNjZW2T6JiIjepc68SyNG9ADAgQMHCvwh38zMrISjoZLQuXNndO7cWantv//+g46ODvT09FC+fHmxfdGiRQgLC8PQoUPxySefqDtUIiIijcI86uMMDAykDoGIiEhlNGKOni1btsDCwqLA269fvx52dnYlGBGpS6NGjfD69WucPn1a6dKtHTt2YOPGjXjy5InYlpycjAcPHkgRJhERUanFPKpwnj17huzsbKnDICIiKjKNKPR4eXlBV7fgg4/69u0LExOTEoyI1MnQ0BBubm5KbUuWLIG3tzc+//xzsW3fvn2oWbMm+vbtq+4QiYiISi3mUQU3dOhQuLi44J9//pE6FCIioiLTiEIP8GbZ7ilTpuDhw4dSh0KlQIcOHbBs2TJYW1uLbaGhodDR0UGtWrXENkEQcOjQIaSlpUkRJhERUanAPKpgrKysoFAolFYLJSIi0jQaU+gZOXKkOGKjRYsW2Lp1K1JTU6UOi0qRuXPn4sWLFxg+fLjY5ufnh65du6JatWrIycmRMDoiIiLpMI8qmHHjxuHevXtYvHix1KEQEREVmcYUembMmIGQkBD4+vqicuXKGDVqFOzt7TF06FBcu3ZN6vColLC1tVUa5RMbGwsnJye0bt0aOjo6YvuWLVs4nw8REWkEKysrlCtXrkC3D2EeVTAVK1ZUGhlMRESkiTRiefW8JCcnY9euXdi6dSuuXLmCmjVr4rvvvoO3t7faYuCytJpBoVAgKSlJnIgyMjISlSpVgkKhwOPHj+Hs7CxxhERE0uB5TDNs27ZNvP/q1SvMmzcP7dq1g6enJ4A3o1dPnjyJGTNmYPz48QXaZ2nIo4pCne/ZpKQkpKenK636SUREVFTqPIeppdBTmKRh+fLlhd7/P//8g4EDByI+Pl6tl+cwQdZMwcHBmDhxIhISEnDhwgWxfePGjTAzM8PXX38NIyMjCSMkIlIPnsc0zzfffIPPP/8co0aNUmpftWoVzpw5g4MHDxZ6n1LlUUWhrvfsnj17MHz4cLRv3x47duwosechIqKyQ515V8GXYCgGf39/pce3b99Gdna2uJLSw4cPoaOjg4YNGxZ4n6mpqdizZw+2bNmCS5cuoUqVKvjpp59UGjdpJzc3Nxw5ckQpmc3KysK0adMQExODf/75Bx06dJAwQiIiorydPHkyz/ljvvrqK0yZMqXA+2Ee9XFVqlTB69evcefOHaSmpsLY2FjqkIiIiApMLYWef//9V7y/fPlymJmZYdu2bbCysgIAvH79GoMHD0aLFi3y3deVK1ewefNm7N27F9nZ2fj2228xd+5ctGzZssTiJ+307pw96enp+PHHH3Hq1Cm0bdtWbN+5cyfu3r2LwYMHo1q1alKESUREJLK2tsahQ4cwYcIEpfZDhw4pzVH3IcyjCqZhw4Y4c+YMWrZsWail6YmIiEoDtc/R4+DggFOnTqF27dpK7Xfv3kXbtm3x4sWLPPstWbIEW7ZswcOHD9GoUSN899136NOnD8zMzNQRdp445F37NWnSBNevX8eKFSswduxYqcMhIlIpnsc0z9atW/H999+jffv2aNKkCQDg2rVrOHHiBDZu3IhBgwbl2a805lFFwfcsERFpKnWew9S+6lZiYiJiYmJytcfExCApKemD/ZYuXYqvvvoKgYGBuHbtGn744QeNS05IswiCgIkTJ6JLly7o3bu32O7v748JEyYgOjpawuiIiKgsGjRoEC5fvgxzc3Ps378f+/fvh7m5OS5duvTBIg/APKo4BEHAxo0bcfPmTalDISIiKhC1j+gZOHAgLl68iGXLlqFx48YA3nwT9dNPP6FFixZKK0u8KysrC3p6euoMNV/8Vqls6tKlC44cOQIvLy9s3bpV6nCIiIqM57GyozTmUUUhxXt20aJFmDp1KhwdHREUFCSu4klERFQYWj2iZ926dWjfvj369u0LZ2dnODs7o2/fvvjqq6+wZs2aPPv8/vvvhVoFYt26dR8dHURUHCNHjoSnpyd+/vlnsS0hIQFxcXESRkVERGVFaGgopk+fjr59++Lly5cAgOPHj+PevXt5bs88qniGDx+O2rVrY+LEiSyIEhGRRlB7ocfY2Bhr1qzBq1ev4O/vD39/f8TFxWHNmjUwMTHJs8/48eMLlXBMmjQpz8vDiFShXbt2uHLlCqpXry62LV68GK6urti0aZOEkRERkbY7f/483N3dce3aNfz9999ITk4GAAQGBmLmzJl59inJPGr16tVwcXGBoaGhOK/dx6xYsQJubm4wMjKCo6Mjxo8fj/T09ALHJgULCwvcvn0bY8aMgUwmkzocIiKifEm2jEBkZCQiIyPRsmVLGBkZQRCED548BUFA69atC7zqQVpamipDJfooQRBw/vx5JCYmwsbGRupwiIhIi02ZMgXz5s2Dt7e30hw7X3zxBVatWpVnn5LKo3bv3g1vb2+sW7cOTZo0wYoVK9CuXTsEBwfD1tY21/Y7d+7ElClTsHnzZjRt2hQPHz7EoEGDIJPJsHz58gI9p1T09fXF+2lpaZg2bRpmzJghriBLRERUmqi90PPq1Sv07NkT//77L2QyGR49eoTKlSvju+++g5WVFZYtW5arz4e+ofqQr7/+GuXKlVNVyEQfJZPJcPHiRZw8eRJfffWV2H7o0CE8ePAAo0aN+uBoNSIiosIICgrCzp07c7Xb2toiNjY2zz4llUctX74cQ4cOxeDBgwG8ueTrn3/+webNmzFlypRc21+5cgXNmjVD3759AQAuLi7o06cPrl27Vqj4pDZ69Gj88ccf8PPzw5UrVzjKh4iISh21F3rGjx8PPT09PH36FDVr1hTbe/XqBW9vb5UUegrKxcUFT548ydU+YsQIrF69ukSek7STXC5H+/btxcc5OTmYPHkygoODAQCTJ0+WKjQiItIilpaWiIyMhKurq1K7v78/HBwc8uxTEnlUZmYmbt26halTp4ptcrkcbdq0gZ+fX559mjZtih07duD69eto3LgxwsLCcOzYMQwYMEDl8ZWkMWPG4Ny5c1iwYAGLPEREVCqpvdBz6tQpnDx5EpUqVVJqr1atWp5Fl5J048YNpckJ7969iy+//BI9evRQaxyknX7++WesXr0aw4cPF9uio6NhYWEBQ0NDCSMjIiJN1bt3b0yePBl79+6FTCaDQqHA5cuXMXHiRAwcOFBtccTGxiInJwd2dnZK7XZ2dnjw4EGeffr27YvY2Fg0b94cgiAgOzsbw4YNU1rc4H0ZGRnIyMgQHycmJqrmAIrBw8MD9+/fV1rF7Ny5c3BwcEC1atUkjIyIiOgNtU/GnJKSAmNj41ztcXFxMDAwUGss5cuXR4UKFcTb0aNHUaVKFbRq1UqtcZD20dHRwcCBA3Ht2jWlFTqGDx+OqlWr4tSpUxJGR0REmmrBggWoUaMGHB0dkZycjFq1aqFly5Zo2rQppk+fLnV4H/V2FMyaNWtw+/Zt7N+/H//88w/mzp37wT4LFy6EhYWFeHN0dFRjxB/2bpEnJSUF/fv3R+3atXHlyhUJoyIiInpD7YWeFi1aYPv27eLjt99GLVmyBJ9//rm6wxFlZmZix44dGDJkyAeH4WZkZCAxMVHpRlRQCQkJuHnzJl68ePHB4fVEREQfo6+vj40bNyI0NBRHjx7Fjh078ODBA/zvf/+Djo6O2uKwsbGBjo4OoqOjldqjo6NRoUKFPPvMmDEDAwYMwPfffw93d3d069YNCxYswMKFC6FQKPLsM3XqVCQkJIi3Z8+eqfxYiisxMREeHh6ws7NDw4YNxfZ3RyIRERGpk9ov3VqyZAlat26NmzdvIjMzE5MmTcK9e/cQFxeHy5cvqzsc0cGDBxEfH49BgwZ9cJuFCxdi9uzZ6guKtIqFhQUePXqEc+fOoXbt2mL72rVrYWRkhP79+xd4RRQiIirbnJyc4OTkJNnz6+vro2HDhvD19UXXrl0BAAqFAr6+vhg1alSefVJTUyGXK3/H+LY4JQhCnn0MDAzUPuK7sOzt7XHs2DG8ePFCKdbPPvsMFhYW+O233+Dm5iZhhERE9K7nz5+jQoUK0NXVxcuXLzFq1CgMHjxYac5VTScTPnRmLUEJCQlYtWoVAgMDkZycjAYNGmDkyJGwt7f/aL+srCzUqFEDR48eVZrIWRXatWsHfX19HDly5IPb5HWduKOjIxISEpQuzyEqqLi4OLi6uiIxMRGHDh1Cly5dpA6JiMqQxMREWFhY8DymQby9vfNsl8lkMDQ0RNWqVT+4apaq86jdu3fDy8sL69evR+PGjbFixQrs2bMHDx48gJ2dHQYOHAgHBwcsXLgQADBr1iwsX74cGzZsQJMmTRASEoLhw4ejYcOG2L17d4GeU1PesyEhIahWrRr09PTw/Plzcbn5zMxMpaXaiYhI/Zo3b474+Hjs2rULO3fuxMKFC+Hm5ob//vsv1xcSqqTOc5gkwwcsLCwwbdq0QvfT09NDenq6yuN58uQJzpw5g/379390O034Vok0i6GhIaZPn46TJ0+iU6dOYntERATs7e1L9A8NERFpHn9/f9y+fRs5OTniKJGHDx9CR0cHNWrUwJo1azBhwgRcunQJtWrVUuqr6jyqV69eiImJgY+PD6KiolCvXj2cOHFCnKD56dOnSuex6dOnQyaTYfr06YiIiED58uXRuXNnzJ8/X2UxlRZVq1ZFSEgIrl69KhZ5AGDQoEEIDw/H0qVL0bx5cwkjJCIqm2JiYnD79m1kZGTA2toaU6dORWhoKEaPHq1Vn73UPqLnzp07eQfy/7+JcnJy+mgxZcGCBXj48CE2bdqksstcZs2ahfXr1+PZs2eF2qemfKtEmkWhUMDDwwNyuRx//vkn3N3dpQ6JiLQUz2OaZ8WKFbh48SK2bNki/p8lJCTg+++/R/PmzTF06FD07dsXaWlpOHnyZK7+JZFHqZMmv2fT0tJga2uL5ORk3LhxA40aNQLw5pI2Q0NDrfqAQURUmiUkJODKlStqv1RLnecwtRd65HK5ONnx26d+d/JjPT099OrVC+vXr89zCepu3brB19cXpqamcHd3h4mJidLP8xuV8z6FQgFXV1f06dMHixYtKlRfTU42qPT677//4OnpCR0dHTx48EDpm0AiIlXieUzzODg44PTp07lG69y7dw9t27ZFREQEbt++jbZt2yI2NjZXf1XnUeqm6e/ZqKgoHDlyBN9//72Y/86aNQubN2/G/PnzMWDAAIkjJCKikqLVl24dOHAAkydPxk8//YTGjRsDAK5fv45ly5Zh5syZyM7OxpQpUzB9+nT88ssvufpbWlrim2++UVk8Z86cwdOnTzFkyBCV7ZOoOGrVqoXw8HD8999/SkWeO3fuwN3d/YOrwhERkfZLSEjAy5cvcxV6YmJixNVALS0tkZmZmWd/VedRVDgVKlTA0KFDldoOHz6MZ8+eKa2alp2djZycHE4ZQESkJtHR0di1axdcXFzw9ddfSx1Osal9RE/jxo0xd+5ctGvXTqn95MmTmDFjBq5fv46DBw9iwoQJCA0NVWdohabp3yqR5rh58yaaNGmCjh07Yt++fZzIkYhUgucxzdOvXz/4+flh2bJl+OSTTwAAN27cwMSJE9G0aVP873//w65du/DLL7/g5s2bEkeretr4nk1PTxcXZDAyMgIA7Nq1C2PHjsXUqVMxbtw4aQMkItISFy9exKpVq9CpU6dcIyh/+eUX/PTTT2jZsiXOnz9fIs+v1SN6goKC4OzsnKvd2dkZQUFBAIB69eohMjLyo/uJiYlBcHAwAMDNzQ3ly5dXfbBEpURAQAB0dHRgZmbGIg8RURm2fv16jB8/Hr1790Z2djYAQFdXF15eXvj1118BADVq1MCmTZs+uh/mUaWHoaEhevXqpdS2Z88evHz5EvHx8dIERUSkhXx9fbFnzx7o6+vnKvT06NEDx44dQ+fOnSWKTrXUPqKnfv36qFu3LjZs2CB+YM3KysLQoUMRGBgIf39/XL58Gf3790d4eHiu/ikpKRg9ejS2b98OhUIBANDR0cHAgQOxcuVKGBsbq+1YtPFbJSq97t27h/Lly4uXcyUnJyM8PJyTNRNRkfE8prmSk5MRFhYGAKhcuTJMTU0L1K805VFFUVbes1lZWThw4ABatmyJChUqAABu376NH374Ad7e3ujbt6/EERIRaR5/f38cO3YM9erVQ8eOHdX+/Oo8h6l9ev/Vq1fj6NGjqFSpEtq0aYM2bdqgUqVKOHr0KNauXQsACAsLw4gRI/Ls7+3tjfPnz+PIkSOIj49HfHw8Dh06hPPnz2PChAnqPBQitapdu7bSnD0zZ85EgwYNsGLFCumCIiIiSZiamsLDwwMeHh4FLvIAzKM0hZ6eHnr27CkWeYA3OfStW7dw9OhRCSMjItJc9evXx7Rp0yQp8qib2kf0AEBSUhL+/PNPPHz4EMCbIcN9+/aFmZlZvn1tbGywb98+fPbZZ0rt//77L3r27ImYmJiSCDlPZeVbJSp9FAoF+vTpgz179uDYsWNqXxqQiLQDz2Oa6ebNm9izZw+ePn2aa9Ll/FbNKk15VFGU5fdsbGwsNm7ciC+//FJcmj02NhbDhg1D37590b17d4kjpNJMoVAgLi4ONjY2YtuFCxfw7NkzNGrUCG5ubhJGR1R6ZGZm4sqVK2jVqpXKF8HR6hE9AGBmZoZhw4Zh+fLlWL58OX788ccCFXkAIDU1FXZ2drnabW1tkZqaqupQiUoluVyO3bt349q1a0pFHn9/f7x69UrCyIiIqCTt2rULTZs2xf3793HgwAFkZWXh3r17OHv2LCwsLPLtzzxKc9nY2GDq1KlikQcANm3ahL///hvLli1T2laC73GpFLt48SIsLS3x6aefKrWvXbsW/fv3x4kTJ8S2yMhIVKpUCR06dEBWVpa6QyUqMU+ePMHVq1eRkpLywW1ycnLg5OSEzz//HHfv3lVjdKonSaEHAP777z+cOHEChw8fVrrlx9PTEzNnzkR6errYlpaWhtmzZ8PT07MkQyYqdRo3bizeT05ORrdu3VCzZk2tXGmFiIiABQsW4Ndff8WRI0egr6+P3377DQ8ePEDPnj3h5OSUb3/mUdrl66+/xuTJkzFq1CixLTMzEy4uLujduzcncy6jbt68idu3b4uPa9WqheTkZERERCiNAqxTpw7atGkDFxcXsS0iIgIREREICgqCnp6e2D5//nwMHz4cAQEB6jgEIpX7888/4enpiR9++OGD2+jo6MDDwwO2trZ4/vy5GqNTPbWvuhUWFoZu3bohKCgIMplM/Mbh7bConJycj/ZfsWIFvvrqK1SqVAl169YFAAQGBsLQ0BAnT54s2eCJSrGoqChxnoYaNWpIHA0REZWE0NBQcW4BfX19pKSkQCaTYfz48fjiiy8we/bsj/ZnHqVdatasiUWLFim1XbhwQbys791LA/755x/o6uqiefPmMDExUXeopCbLli3DxIkT0b17d/z9998AAGtra9y7dw9Vq1ZVKt5MmzYN06ZNU+pfp04dXLp0KVeR8M8//8T9+/fxxRdfoF69egDeFIkBwMjIqOQOiEiFKlasiNq1a390mz179sDCwkLll22pm9pH9IwdOxaurq54+fIljI2Nce/ePVy4cAGNGjXCuXPn8u3v7u6OR48eYeHChahXrx7q1auHRYsW4dGjR/n+pxFps6pVq+L27ds4deqU0sScR48eFZfgJSIizWZlZYWkpCQAgIODgzi0PD4+vkCXXjGP0n6ff/45rly5glWrVkEu/79Uf8aMGfjqq69w6NAhsS0+Ph5hYWG81EuLdOjQAXp6erC0tFT6f61Zs6ZSkedDDA0N0axZM6XJagVBwJIlSzB8+HClKQP++usv2Nra5ioWEZVGP//8MyIiIjB16tSPbmdpaanxRR5AghE9fn5+OHv2LGxsbCCXyyGXy9G8eXMsXLgQY8aMgb+//wf7ZmVloUaNGjh69CiGDh2qxqiJNIO+vj6qV68uPj5+/Dg6d+6Mxo0b49KlSwU6wRMRUenVsmVLnD59Gu7u7ujRowfGjh2Ls2fP4vTp02jduvVH+zKPKht0dHRyXYanUCjQoEEDvHr1Cp9//rnYfvDgQQwePBgdO3ZUWs0rPDwclSpVYt6gAQ4ePIioqCgMGzYMwJuCzrNnz/Kci6uoZDIZOnXqhE6dOim1nz9/HsnJyTA0NBTbcnJy8Ndff6Fdu3YoX768ymIgUhVtKOIUhNoLPTk5OeLEyzY2Nnjx4gXc3Nzg7OyM4ODgj/bV09NTuqaciD4uOTkZlpaWaN68OZM1IiItsGrVKjEXmjZtGvT09HDlyhV88803mD59+kf7Mo8qu+RyOTZt2pSrPSoqCnp6ekqXfCsUCri7uyMrKwv//fcfqlSpAgB4/PgxkpKSUKVKFRgbG6stdvqw06dPo1u3bjAxMUGnTp1QqVIlAFBpkedjtm7dipEjR8LBwUFsu3btGgYMGABra2tER0dDR0dHLbEQqdKaNWuwZcsWjBo1Cl5eXlKHUyRqv3SrTp06CAwMBAA0adIES5YsweXLlzFnzhxUrlw53/4jR47E4sWLeSkKUQH06NED9+/fV5qz4eXLl5yHgYhIA2VnZ+Po0aPiBye5XI4pU6bg8OHDWLZsGaysrPLdB/MoeteUKVOQmJiodOlNVFSU+I23s7Oz2L5u3Tp4eHhg8uTJYltOTg5mzZqFTZs2ISMjQ32BEwCgdevW+PLLLzFy5EhJRs/IZDI0btxYqdCTkpKC+vXro127dkpFngEDBsDb2xvPnj1Te5xE169fx6effopJkyYVaPvnz5/j5s2bBZpaprRS+4ie6dOni0uazZkzB506dUKLFi1gbW2N3bt359v/xo0b8PX1xalTp+Du7p5rMrn9+/eXSNxEmqpChQpKj8ePH4+dO3di1qxZmDlzpkRRERFRYenq6mLYsGG4f/9+kffBPIreZ2hoqHTpTcWKFZGQkIDIyEjo6ip/VLCyskLVqlXFxy9fvsTs2bMhl8sxaNAgsX3mzJnYu3cvxowZI15SlJWVhW3btqF8+fLo1KkTR3oUQWpqKtauXYtx48ZBR0cHcrkcx48fL1Wv5Zdffokvv/xSaWn22NhY7Ny5EwqFAmPHjhXbX7x4AWNjY1haWkoQKZUlQUFBuHbtGiwsLAq0fd++feHu7o6WLVuWcGQlR+2Fnnbt2on3q1atigcPHiAuLg5WVlYFul7O0tIS33zzTUmGSKS1FAoFbG1toaenl+s6ayIiKv0aN26MgIAApZEWhcE8igpCLpcrjdIAgEWLFmHRokW5Vsj94YcfkJqaqlQUevjwIe7fvy+uygS8KQoNHToUOjo6Skt8jx8/Htu3b8fPP/+MCRMmAHhT0BgxYgQsLS3xyy+/iPu+du0aHj16BA8PD3h4eAB4k9sEBATA0NAQNWrUECegTk9PhyAIMDAwUJqUWlMpFAq0atUKN2/eRE5OjjgyoTQVed717pQBpqam2Lt3L27fvq30t2v27NnYvHkzFi5ciIkTJ0oRJpURX331Ffbu3StOIZOfOnXqoE6dOiUcVclSa6EnKysLRkZGCAgIUHrhypUrV6D+2dnZ+Pzzz9G2bdtcoxSIKH9yuRy//vorJk6cqJTAXblyBW5ubrC2tpYwOiIiys+IESPEyx8aNmyYa0TO2w+/eWEeRarwbmHB3t4e69evz7XNokWL8P3334vz+wBvChUdOnRATk6OUuElNjYWcXFxSitExcXFYdu2bdDR0cGvv/4qtm/fvh1r1qzBjBkzxPd6cnIyGjZsCOBNccfAwAAA4OPjg6VLl2LChAn45ZdfALz5HahUqRL09fVx584dcSTJli1bsG3bNnTr1k1pxMmoUaNgbW2NMWPGiDmSIAiSTOYql8sxfPhw+Pj4iMubawpDQ0N0794d3bt3V2oPDQ1FdnY26tatK7ZFRUXh7Nmz6NKli9IqskTF4eDggG+//VbqMNRKrYUePT09ODk55fomoKBUMWSZiKBU5Hn27Bk6d+4MIyMjnD17VmnVLiIiKl169+4NABgzZozYJpPJxA+fH8uxmEeRujg7O+cadebo6Ih//vkn17a//vorpk6dChsbG7HNxMQEixYtQkZGhlJRxc3NDV9++aVSrpKVlQUHBwekp6dDX19fbH87auj9tujoaABQGoEUGhqK8+fPKxVKBUHAmjVrIAiCePkZ8GaS1qVLl2LIkCHw8fEp+ItSBP7+/jAwMECtWrUAAIMHD0aPHj0KPCqhtDtz5gxCQkLg4uIitv3111/w9vbGF198AV9fX+mCozIvKioKvr6+MDIyylWk1ARqv3Rr2rRp+Pnnn/G///2vwCN53tW4cWP4+/sXecgyESlLSkqCjY0NzMzM+HtFRFTKhYeHF6s/8ygqbWxsbJSKPMCbuYDenfT5rTFjxigVOQHA2toaz58/z7Xt0qVLMXfuXKURSAYGBggMDERmZqbSymF9+vRB3bp1lUYgCYKAmTNnIjIyEra2tmL7vXv38OTJE6XL0nJyctCuXTu0adMGo0aNUslIlL/++gv9+/dH3bp1cfXqVejr60Mmk2lNkeetd+d8AgAzMzNUrVpV6YN1ZmYmxo4di27duqF169al9nI1Kp0EQcDff/8NZ2dnNGjQoMDvnzNnzmDAgAHw9PTUyEKPTHh3nKQa1K9fHyEhIcjKyoKzs3OuIce3b9/+aP89e/Zg6tSpGD9+fKGHLKtaYmIiLCwskJCQAHNzc7U9L5GqJScnIz4+XlyWUxAExMfHF2gFFyLSXDyPlT2lKY8qCr5nSWpxcXG4d+8e7OzsxJFF/v7+aNCgAczNzRETEyOOIoqNjUW5cuWKNEfQy5cvUb16dbRr1w4bNmwo8CSy2kAQBOTk5Iijro4dO4aOHTvCzs4OERERLPRQoURFRcHe3h5yuRzp6elK80d9TGhoKPr3749WrVph0aJFKolFnecwtY/o6dq1a7H6F2fIMhHlzdTUVOnbp82bN2Pq1KnYvn07vvrqKwkjIyKi9/3vf//DunXrEB4eDj8/Pzg7O2PFihVwdXXF119//dG+zKOIiqdcuXJo0aKFUpuLiwvWr1+P+Ph4pUvFevbsiYcPH2L79u344osvPrjPZ8+e4ffff0dGRgZ+//13AICtrS3u378Pe3v7kjmQUkwmkyldWufi4oLhw4ejfPnySkWen3/+GS1btkS7du0kmTeJNENKSgqaNm2KrKysAhd5AKBKlSrw8/MrwchKltpH9BTXkydPPvpzdQ5F5rdKpI0UCgWaN28OPz8/LFq0KM+h00SkHXge0zxr166Fj48Pxo0bh/nz5+Pu3buoXLkytm7dim3btuHff//9aP/SlEcVBd+zpClSU1NRqVIlvH79GmFhYXB1dQXw5su0zZs3Y+jQofDy8gIAPHr0CNWrV4dcLsejR49QuXJlKUPXCEFBQfDw8IBcLkdISIj4+hKVZlo9ogcA4uPjsW/fPoSGhuKnn35CuXLlcPv2bdjZ2eVayvF9pT0BIdJ0crkcZ8+exaZNmzBixAipwyEionesXLkSGzduRNeuXZWGkjdq1KhAyxMzjyJSD2NjY7x48QJXr15VKkI8fPgQly9fRt26dcVCT7Vq1TBp0iR8+umncHJykipkjWJlZYVx48YhLS1N6fW9d+8eatasWaTL5Yi0idp/A+7cuYPq1atj8eLF+OWXXxAfHw8A2L9/P6ZOnVqgffzvf/9Ds2bNULFiRfGbqRUrVuDQoUMlFTZRmWJoaIhRo0aJJ0mFQoGePXti//79EkdGRFS2hYeHo379+rnaDQwMkJKSUqB9MI8iUg9DQ0N89tlnSm1eXl7Yu3cvevXqpdS+ePFidOvWTemSJfqwSpUq4ddff8W6devEttjYWDRp0gSffvqpuLoaUVml9kKPt7c3Bg0ahEePHsHQ0FBs79ChAy5cuJBv/7Vr18Lb2xsdOnRAfHy8eC25paUlVqxYUVJhE5VpW7Zswd69ezFgwACeOImIJOTq6oqAgIBc7SdOnEDNmjXz7c88ikhaNWvWxLfffouWLVtKHYrWCQgIEOcaK1++vNThUCkxePBgNG3aFGfPnpU6FLVSe6Hnxo0b+PHHH3O1Ozg4ICoqKt/+b4csT5s2TWkyrkaNGiEoKEilsRLRGwMHDsSkSZOwevVq2NnZSR0OEVGZ5e3tjZEjR2L37t0QBAHXr1/H/PnzMXXqVEyaNCnf/syjiEhbtWnTBiEhIdi+fbvSqPRff/0ViYmJEkdHUrlx4wb8/PyQnZ0tdShqpfaxgQYGBnn+oj18+LBAlVdVDFkmosLR09PD4sWLldoePXqEq1evYsCAARJFRURU9nz//fcwMjLC9OnTkZqair59+6JixYr47bffxBW1PoZ5FBFpMzs7O6UvJTdv3gxvb2+sX78ed+/e5aVxZdD27dsRGhqKBg0aSB2KWql9RE+XLl0wZ84cZGVlAXizfN7Tp08xefJkfPPNN/n2L+6QZSIqvoyMDPTq1QsDBw7EL7/8InU4RERlSr9+/fDo0SMkJycjKioKz58/x3fffVegvsyjiKgscXV1RZUqVfDDDz+wyFNGNWjQAD169ICNjY3UoaiV2gs9y5YtQ3JyMmxtbZGWloZWrVqhatWqMDMzw/z58/PtX9why0RUfLq6uujWrRvs7OzQp08fqcMhIioz5s2bh/DwcABvVvWxtbUtVH9V51GrV6+Gi4sLDA0N0aRJE1y/fv2j28fHx2PkyJGwt7eHgYEBqlevjmPHjhX6eYmICqJ169YICgrCmDFjxLbQ0FBs3rwZgiBIGBlRyZIJEr3DL126hDt37iA5ORkNGjRAmzZtCtz3zz//xKxZsxAaGgoAqFixImbPnl3gb7NUJTExERYWFkhISIC5ublan5uoNEhKSoKZmZn4ODAwEB4eHpDJZBJGRUQFxfOY5qlbty7u3r2LJk2aoH///ujZs2ehv6VUVR61e/duDBw4EOvWrUOTJk2wYsUK7N27F8HBwXkWoDIzM9GsWTPY2tri559/hoODA548eQJLS0vUrVu3QM/J9ywRFUd2djZatmwJPz8/+Pj4YPbs2VKHRCXo4cOHCAwMRO3atVGrVi2pw1HrOUzthZ5nz57B0dFRJftKTU0VRwdJgckG0f+5fv06mjVrhi5duuDPP/9UWlWPiEonnsc007179/Dnn39i165deP78Ob788kv069cPXbt2hbGxcYH3U9w8qkmTJvjkk0+watUqAG8mPXV0dMTo0aMxZcqUXNuvW7cOS5cuxYMHD6Cnp1ek5+R7loiKIycnB8uXL8eSJUtw8+ZNODs7Sx0SlaDly5djwoQJ6NmzJ3bv3i11OGo9h6n90i0XFxe0atUKGzduxOvXr4u1r6IMWSaiknH//n3IZDLI5XIYGBhIHQ4RkdaqXbs2FixYgLCwMPz7779wcXHBuHHjUKFChULtpzh5VGZmJm7duqU0Ilsul6NNmzbw8/PLs8/hw4fh6emJkSNHws7ODnXq1MGCBQvEJd6JiEqajo4OfvrpJ4SHhysVeQICAqBQKCSMjEqCjY0NmjZtmuciBNpO7YWemzdvonHjxpgzZw7s7e3RtWtX7Nu3DxkZGeoOhYhUyMvLC35+fti4caN46VZ2djZPmkREJcjExARGRkbQ19cXF7pQh9jYWOTk5CitbgO8WfEmKioqzz5hYWHYt28fcnJycOzYMcyYMQPLli3DvHnzPvg8GRkZSExMVLoRERWXqampeN/f3x+ffvopOnfujKSkJAmjIlUbOHAgLl++nOcoU22n9kJP/fr1sXTpUjx9+hTHjx9H+fLl8cMPP8DOzg5DhgxRdzhEpEINGzaEpaWl+HjatGno0KEDXr58KV1QRERaJjw8HPPnz0ft2rXRqFEj+Pv7Y/bs2R8ssJQWCoUCtra22LBhAxo2bIhevXph2rRpWLdu3Qf7LFy4EBYWFuJNVZf/ExG9FRISIn5JWZjLX4lKM7UXet6SyWT4/PPPsXHjRpw5cwaurq7Ytm2bVOEQkYpFRkZi1apVOHny5AeH8RMRUeF8+umnqFq1Kvbt24fBgwfjyZMn8PX1xXfffQcLCwu1xWFjYwMdHR1ER0crtUdHR3/wEjJ7e3tUr14dOjo6YlvNmjURFRWFzMzMPPtMnToVCQkJ4u3Zs2eqOwgiIgA9evTAtWvX8Ndffyn9fSLSZJIVep4/f44lS5agXr16aNy4MUxNTbF69epC7SM9Pb2EoiOi4rK3t8f169exePFifP3111KHQ0SkFd4uFezv74+JEyfCwcGhyPsqTh6lr6+Phg0bwtfXV2xTKBTw9fWFp6dnnn2aNWuGkJAQpUt6Hz58CHt7e+jr6+fZx8DAAObm5ko3IiJV8/DwUPr7snjxYvz2229cgl2DJSQkoFKlSmjWrNkHv0zQZmov9Kxfvx6tWrWCi4sLtm/fjl69eiE0NBQXL17EsGHD8u2vUCgwd+5cODg4wNTUFGFhYQCAGTNm4I8//ijp8ImoEGrXro1JkyaJj5OSktCiRQscPXpUwqiIiDTX/Pnzi7VErCrzKG9vb2zcuBHbtm3D/fv3MXz4cKSkpGDw4MEA3syNMHXqVHH74cOHIy4uDmPHjsXDhw/xzz//YMGCBRg5cmSRj4eISNVu3bqFKVOmYNy4cbhw4YLU4VARPXnyBBEREQgODv7glwnaTFfdTzhv3jz06dMHv//+O+rWrVuk/tu2bcOSJUswdOhQsb1OnTpYsWIFvvvuu0LtLyIiApMnT8bx48eRmpqKqlWrYsuWLWjUqFGhYyOij/vll19w6dIlREZGom3btmXyjy4RUXE9f/4chw8fxtOnT3N9S7l8+fKP9lVlHtWrVy/ExMTAx8cHUVFRqFevHk6cOCFO0Pz06VPI5f/3naKjoyNOnjyJ8ePHw8PDAw4ODhg7diwmT55c4OckIippDRo0wLJly/D06VO0atVK6nCoiKpVq4br168jPj5e6lAkIRPUPB5NEARxsquiqFq1KtavX4/WrVvDzMwMgYGBqFy5Mh48eABPT89CLdn++vVr1K9fH59//jmGDx+O8uXL49GjR6hSpQqqVKmSb//ExERYWFggISGBQ4mJCiA5ORlz5sxB69at0a5dO7E9JyeH10QTSYDnMc3j6+uLLl26iLlPnTp18PjxYwiCgAYNGuDs2bMf7a/KPEoKfM8SkRQyMzMRHR3NCeGpWNR5DlP7iJ63RZ7U1NQ8v4ny8PD4aP+IiAhUrVo1V7tCoSj0sqKLFy+Go6MjtmzZIra5uroWah9EVHCmpqZYsmSJUtuhQ4cwY8YMrF27Fs2aNZMoMiIizTB16lRMnDgRs2fPhpmZGf7++2/Y2tqiX79++Oqrr/Ltr8o8ioioLBAEAUOHDsWJEyfwzz//8MoP0ghqn6MnJiYGHTt2hJmZGWrXro369esr3fJTq1YtXLx4MVf7vn37CtT/XYcPH0ajRo3Qo0cP2Nraon79+ti4ceMHt8/IyEBiYqLSjYiKThAEzJkzB0FBQZy3h4ioAO7fv4+BAwcCAHR1dZGWlgZTU1PMmTMHixcvzre/KvMoIqKyICEhAYGBgXj16hViYmKkDocK6PDhw9i3bx+ioqKkDkUSah/RM27cOCQkJODatWv47LPPcODAAURHR2PevHlYtmxZvv19fHzg5eWFiIgIKBQK7N+/H8HBwdi+fXuhPyiGhYVh7dq18Pb2xs8//4wbN25gzJgx0NfXh5eXV67tFy5ciNmzZxfqOYjow2QyGU6dOoXFixdjxowZYntSUhJMTEyU5nYgIiLAxMREHA1tb2+P0NBQ1K5dGwAQGxubb39V5lFERGWBpaUlLly4gIsXL6J9+/ZSh0MFNHfuXNy8eROHDh1Cly5dpA5H7dQ+R4+9vT0OHTqExo0bw9zcHDdv3kT16tVx+PBhLFmyBJcuXcp3HxcvXsScOXMQGBiI5ORkNGjQAD4+Pmjbtm2hYtHX10ejRo1w5coVsW3MmDG4ceMG/Pz8cm2fkZGBjIwM8XFiYiIcHR15nTiRivXo0QORkZHYtGkTatSoIXU4RFqL851onq5du6Jjx44YOnQoJk6ciEOHDmHQoEHYv38/rKyscObMmXz3oao8Sgp8zxJRafDq1Sts3rwZEyZM4BeTpdSIESMQEBCATZs2FWu1SlXS6jl6UlJSYGtrCwCwsrJCTEwMqlevDnd3d9y+fbtA+2jRogVOnz5d7Fjs7e1z/afXrFkTf//9d57bGxgYwMDAoNjPS0Qf9uzZMxw/fhzp6em55vAiIirrli9fjuTkZADA7NmzkZycjN27d6NatWr5rrj1lqryKCKiskihUKBbt264ePEiIiIisGLFCqlDojysWbNG6hAkpfbyo5ubG4KDgwEAdevWxfr16xEREYF169bB3t4+3/6VK1fGq1evcrXHx8ejcuXKhYqlWbNmYixvPXz4EM7OzoXaDxGpjqOjI+7fv4/NmzcrTc7+5MkTqHkAIhFRqVO5cmXxb6OJiQnWrVuHO3fu4O+//y5Q/qLKPIqIqCySy+X44YcfYGtri6FDh0odDlGe1F7oGTt2LCIjIwEAM2fOxPHjx+Hk5ITff/8dCxYsyLf/48ePkZOTk6s9IyMDERERhYpl/PjxuHr1KhYsWICQkBDs3LkTGzZswMiRIwu1HyJSLUdHR3GyUQCIjIyEh4cHunTpgvj4eOkCIyIqRUaMGFGgeXnepco8ioiorOrfv7/SHGnAm5E+RKWF2i/d6t+/v3i/YcOGePLkCR48eAAnJyfY2Nh8sN/hw4fF+ydPnoSFhYX4OCcnB76+vnBxcSlULJ988gkOHDiAqVOnYs6cOXB1dcWKFSvQr1+/Qu2HiErWpUuXkJaWhujoaJiZmUkdDhFRqbBjxw5MnDjxo/nTWyWRRxERlWWmpqbi/QcPHqBHjx7Yvn07VzAsBdavX4+5c+diwIABWLhwodThSELthZ53Xb58GY0aNUKDBg3y3bZr164A3qzS8/6KWHp6enBxcSnQql3v69SpEzp16lTofkSkPj169ECdOnWgUCigo6MD4M23Jjdv3kTjxo0ljo6ISBqFuZy1pPIoIiICJk2ahLt372LSpEmcA60UCAkJQUREBNLS0qQORTKSThHevn37Ag8TVigUUCgUcHJywsuXL8XHCoUCGRkZCA4OZsGGSIvVrFlTaXjspk2b0KRJE4waNUrCqIiINAPzKCKikvO///0PQ4YMwc6dO6UOhQD8/PPPuHbtGkaMGCF1KJKRdERPUSZWDQ8PL4FIiEjThIWFQSaToWrVqlKHQkQkiaSkpEL3YR5FRKR6FhYW+OOPP5TaAgMD4eHhAZlMJlFUZZeVlVWZH/UvaaGnKObMmfPRn/v4+KgpEiKS0qJFi9CzZ0+llbmCg4ORlpaGevXqSRcYEVEJCw0NxZYtWxAWFoYVK1bA1tZWXNzi3ZGPeWEeRURU8k6fPo0OHTrAy8sL69atg66uxn3sJg0n6Ttu/fr1sLOzK1SfAwcOKD3OyspCeHg4dHV1UaVKFSYoRGXIu/N7CYKAoUOH4vLly9i7dy+6d+8uYWRERCXj/PnzaN++PZo1a4YLFy5g3rx5sLW1RWBgIP744w/s27fvo/2ZRxERlbwnT55AoVAgNTUVcrmks6WUOUlJSVi/fj0qV66Mbt26ldkRVZIVekJCQmBtbS2+8QVBKNB/gr+/f662xMREDBo0CN26dVN5nESkGVJSUmBvbw8dHR2loZrZ2dn8FoWItMaUKVMwb948eHt7K61C+MUXX2DVqlX59mceRURU8r7//ntUq1YNn376KQs9avbo0SP89NNPsLW1LdNf/Kr9Xffq1Su0adMG1atXR4cOHRAZGQkA+O677zBhwoQi7dPc3ByzZ8/GjBkzVBkqEWkQU1NT7N69G3fu3EGlSpXE9lGjRqFt27a4deuWhNEREalGUFBQngUZW1tbxMbGFmmfzKOIiFSvVatWMDAwEB/Pnz8fAQEB0gVURhgYGKBPnz7o3LmzUntR5gfWZGov9IwfPx66urp4+vQpjI2NxfZevXrhxIkTRd5vQkICEhISVBEiEWmwGjVqiPeTk5OxY8cOnD59GqmpqRJGRUSkGpaWluKXZO/y9/eHg4NDkffLPIqIqOTs2rUL06dPR/PmzREVFSV1OFqtdu3a2LFjB0aOHIlff/0VX3/9NSpWrAg9PT3Y29vjm2++wY4dO7T+nKf26xlOnTqFkydPKn3jDgDVqlXDkydP8u3/+++/Kz0WBAGRkZH43//+h/bt26s0ViLSbKamprh79y727duH5s2bi+1bt25FTEwMfvjhB1hYWEgYIRFR4fTu3RuTJ0/G3r17IZPJoFAocPnyZUycOBEDBw7Mtz/zKCIi9WvXrh3atWsHT09PVKhQQepwtMrb81hAQACuX7+O69ev4+rVq3j9+nWubaOiorB//37s378fRkZGGDt2LCZPngxLS0v1B17CZIKaxzCZmZnh9u3bqFatGszMzBAYGIjKlSvj5s2baNeuHV69evXR/q6urkqP5XI5ypcvjy+++AJTp05Vul69pCUmJsLCwgIJCQkwNzdX2/MSUdFlZWWhatWqePr0KTZs2IChQ4dKHRKRZHge0zyZmZkYOXIktm7dipycHOjq6iInJwd9+/bF1q1boaOj89H+pSmPKgq+Z4lIU+Xk5EAmk4lz9iQmJkKhUGhlkaEoFAoFkpOTkZCQgMTERCQmJirdf//xq1evEBYWhtDQ0DxH7puamqJFixb4/PPP0bx5c1SqVAlPnz7FqVOnsGfPHjx48AAAUK5cOUyfPh0jRoxQutSuJKjzHKb2Qk+HDh3QsGFDzJ07F2ZmZrhz5w6cnZ3Ru3dvKBSKfFeLKE2YbBBpnqysLOzYsQPbt2/H8ePHYWhoCODNZQ8ymYxLs1OZwvOY5nr27BmCgoKQnJyM+vXro1q1alKHpBZ8zxKRNlAoFPj666/x8OFDHDx4EDVr1pQ6JJXIycnB69ev8erVK/EWFxeX63FexZykpKQiP69cLke1atXQpEkTHDt2DDo6Ojh+/Djq16+f5/aCIODIkSOYMmUK7t+/DwBwdnbGvHnz0Ldv3xKbQFurCz13795F69at0aBBA5w9exZdunTBvXv3EBcXh8uXL6NKlSrqDKdYmGwQaY8vvvgC//77L9asWYPhw4dLHQ6RWvA8RpqG71ki0gbPnj1D06ZNERMTAz8/vw8WJN4lCAJycnKQnZ2NrKwsZGdn53v72HZZWVni7f3HBf1ZQkICYmNjERMTg9jYWLx+/brYkx7r6urCwsICFhYWMDc3h7m5eZ73LS0t4eLigqpVq8LZ2Rn6+vpIT0+HsbExBEFAVFQU7OzsPvpc2dnZ2LZtG3x8fPDixQsAb+b7/Oabb9C7d2/UqVOnWMfyPq0u9ABvJvxbtWoVAgMDkZycjAYNGmDkyJGwt7fPc/vCLIu2f/9+VYWZLyYbRNohIyMDgwYNwoEDB/Dw4UM4OTkBAOLj42FsbAx9fX2JIyQqGTyPaZ5vvvkGjRs3xuTJk5XalyxZghs3bmDv3r25+pTWPKooSuI9KwgCZDKZSvalyQRByPUBLa+PCaWlraD9pGoXBAHp6elISkpCUlKSOGIhKSkJKSkpSElJQWpqKlJTU5GSkoK0tDTIZDLo6OiIN11dXRgaGsLIyAhGRkZK983MzJQ+9FpYWMDMzAy6umqfglXrKBQKZGZmIi0tDcnJyR+8paSkKD1OTU0tcJElOztb3L+RkVGB+uXk5Ej90hSYubk5rK2tYW1tjXLlyon3ra2tYWVlBUtLS6X37rtFHAMDgyL/TRYEAa9evcLDhw/h6elZ4P2kpqbi999/x6JFi5Qmaa5duzaaNGmCr776Cj169ChSTO/S+kJPYQ0ePLjA227ZsqUEI1HGBJlIu8TFxaFcuXLi4xEjRuDw4cNYuXJlnssZE2k6nsc0T/ny5XH27Fm4u7srtQcFBaFNmzaIjo7O1ae05lFFocr3rL+/P3x8fNCrVy9Uq1YNvr6+CA4ORkxMDJKSkpQKH28/dL/99/1bXu1FaZPL5cjMzERGRgYyMjKQmZmJ9PR0pKWliUWB9+9nZWUhJyen2DcN+EhABWBiYgJLS0vY2NigfPnysLGxUbr/bhsA8b30+vVrxMXFIS4uTun+u23p6enQ19fP86ajowOFQgGFQqE04iMzM1MsXLz9+dubIAi57r/9vfvQ7d1tgDeX7Lz9PXr/9/RjbYIgKP2evb2fkZGB7OxsKf8LC+1Df5fevenp6X20XU9PL9ftQ+3v/szCwkLpPfa2sKOnpyf1y1IkiYmJ2L9/Pw4ePIhDhw6J7X379sWff/6pkv2rK++SpOSbnp6OO3fu4OXLl1AoFEo/69KlS67tS3vSQUTa4d0iT3Z2Nk6ePImIiAhYWVlJGBUR0f9JTk7Oc5Shnp4eEhMT8+zDPCpvJ0+exNGjR3H06FGpQyEtpaOjAzMzs1w3ExMT8WZsbAxjY2NxzsCcnBwoFArk5OQgKysLGRkZSEtLQ1pamlLR7+0oobfzm6SnpwOAOFooIiJCykNXm7ejb0qKvr4+TE1N87yZmJgoPTY2Nv5gQeVDxRYdHR2xfeLEifDz84Onpyc2bdr00X2927+k5pMpi8zNzTFo0CAMGjQI0dHRuHbtGq5fv45GjRpJHVqhqX1Ez4kTJzBw4EDExsbmDkYmK/CQtJiYGAQHBwMA3NzcUL58eZXGWRD8JpRIu2VkZODo0aPo3r27OPRz7dq1uHLlCiZPnqzy63aJ1I3nMc3TuHFjdOrUCT4+Pkrts2bNwpEjR3Dr1q0C7ac05FFFocr3bHJyMpydnREXFwfgzWVxjRo1gq2tLSwsLCCTycS//QqFItclFHldVlHctpycHOjr68PAwAAGBgbi/bfFAGNjYxgZGSnd19PTU7rc592bXC7/4M/y2lYul+e61CGvSx9KS1tpieNDbTo6Omq7LDAzM1Oc3DYuLg6xsbHi7e38KW//fXuTyWTi+8nKygrlypUT/33/ZmVlBSMjI3GUzvu37Oxs8XjfHfmhr68vFizevsfefa+9/+/b2/uP82oDIBbF3v89+tC/b+/LZDLx9+zd37V3b4aGhmq9fP/Zs2cYNmwY1qxZA2dnZ7U9rzbZvn07YmJi0LlzZ1SvXl3qcHLR6hE9o0ePRo8ePeDj45Pv5Eh5SUlJwejRo7F9+3ZxNJCOjg4GDhyIlStXwtjYWNUhE1EZZWBggG+++UZ8nJOTgyVLluDx48do1qwZCz1EpHYzZsxA9+7dERoaii+++AIA4Ovri7/++ivP+Xnexzzq/5iamuLPP//EH3/8AR8fn1yXwxFpEn19ffESmsqVK0sdDhWBo6Mj/vnnH6W2Q4cOoX79+uL8kfRxGzZswOXLl1GpUqVSWehRJ7WP84qOjoa3t3eRijwA4O3tjfPnz+PIkSOIj49HfHw8Dh06hPPnz2PChAkqjpaI6P/o6Ohg7969+O677+Dl5SW237hxA8eOHeP8BkRU4jp37oyDBw8iJCQEI0aMwIQJE/D8+XOcOXMGXbt2zbc/8yhlX331Ffbu3csiDxGVOoGBgejVqxfq1auHkJAQqcPRCF27dkXPnj3h4eEhdSiSU/ulW0OGDEGzZs3w3XffFam/jY0N9u3bh88++0yp/d9//0XPnj0RExOjgigLhkPeiQgA2rRpA19fX8yfPx8///yz1OEQFRjPY2VPacqjioLvWSIqK8LDw9GrVy/Y2tri8OHDnItHC2j1pVurVq1Cjx49cPHiRbi7u+eakXvMmDEf7Z+amprnaCBbW1ukpqaqNFYiovxkZ2ejXr16uHXrFvr37y+2JyUlwcjIiMucElGpwjyKiEgzuLq64tKlS0hLSxOLPDk5OYiKioKDg4PE0VFpp/ay4F9//YVTp07h77//xsqVK/Hrr7+KtxUrVuTb39PTEzNnzhRnlgeAtLQ0zJ49G56eniUYORFRbrq6uvjll1/w4sULpeunp02bBjc3Nxw7dkzC6IhI2+Tk5OCXX35B48aNUaFChVwTpuZH1XnU6tWr4eLiAkNDQzRp0gTXr18vUL9du3ZBJpMV6HIzIqKySl9fHxYWFuLjuXPnok6dOjhy5IiEUZVOcXFxSue2sk7thZ5p06Zh9uzZSEhIwOPHjxEeHi7ewsLC8u3/22+/iRMstW7dGq1bt4ajoyOuXLmC3377TQ1HQESUm5GRkXg/MzMThw4dQlhYGAwMDCSMioi0zezZs7F8+XL06tULCQkJ8Pb2Rvfu3SGXyzFr1qx8+6syj9q9eze8vb0xc+ZM3L59G3Xr1kW7du3w8uXLj/Z7/PgxJk6ciBYtWhTq+YiIyrLs7GycOnUK8fHxSEhIkDqcUmfmzJkwMTHB4sWLpQ6lVFD7HD3lypXDjRs3UKVKlSLvIzU1FX/++ScePHgAAKhZsyb69eun9EFLHXidOBF9SEpKCvbv34/+/fuLS4Du3LkTz58/x/Dhw2FmZiZxhEQ8j2miKlWq4Pfff0fHjh1hZmaGgIAAse3q1avYuXNnvvtQVR7VpEkTfPLJJ1i1ahWAN8scOzo6YvTo0ZgyZUqefXJyctCyZUsMGTIEFy9eRHx8PA4ePFjg5+R7lojKsszMTBw4cAC9evUS2wRBEHPNsqxz5844evQotm3bhoEDB0odTp7UeQ5Te6Fn/PjxKF++vFZMWMpkg4gKKisrC9WrV8fjx4+xatUqjBw5UuqQiHge00AmJia4f/8+nJycYG9vj3/++QcNGjRAWFgY6tevr7ZveTMzM2FsbIx9+/YpXX7l5eUlruSVl5kzZ+LOnTs4cOAABg0alG+hJyMjAxkZGeLjxMREODo68j1LRIQ3l962b98eY8eORbdu3aQOR1KCICAqKgomJial9vygzrxL7Zdu5eTkYMmSJWjVqhVGjx4Nb29vpVt+tm3bhn/++Ud8PGnSJFhaWqJp06Z48uRJSYZORFRkMpkMs2fPFr/JfuvRo0eIjo6WMDIi0iSVKlVCZGQkgDeje06dOgUAuHHjRoEuFVVVHhUbG4ucnJxcEzvb2dkhKioqzz6XLl3CH3/8gY0bNxb4eRYuXAgLCwvx5ujoWOC+RETabuXKlTh//jyGDRuGpKQkqcORlEwmg729fakt8qib2gs9QUFBqF+/PuRyOe7evQt/f3/xFhAQkG//BQsWiEOL/fz8sGrVKixZsgQ2NjYYP358CUdPRFQ0urq6GDhwIM6fP690ecSoUaPg4uKCPXv2SBgdEWmKbt26wdfXFwAwevRozJgxA9WqVcPAgQOVisgfIlUelZSUhAEDBmDjxo2wsbEpcL+pU6ciISFBvD179qzEYiQi0jTjx4/HxIkTsWPHDk4LQErUvu7vv//+W6z+z549Q9WqVQEABw8exLfffosffvgBzZo1w2effaaCCImI1CMlJQUJCQnIysrCJ598IrbzWmsi+pBFixaJ93v16gUnJyf4+fmhWrVq6Ny5c779VZVH2djYQEdHJ9eIxOjoaFSoUCHX9qGhoXj8+LFSjAqFAsCbQnhwcHCe8zcaGBhwUnsiog/Q09PD0qVLldquXr2K58+f49tvv5UoKvXbt28ffH190aVLF7Rv317qcEoFtY/oKS5TU1O8evUKAHDq1Cl8+eWXAABDQ0OkpaVJGRoRUaGYmJjAz88Pd+7cgaurq9g+adIkDBgwAI8ePZIwOiLSBJ6envD29i5QkQdQXR6lr6+Phg0biqOLgDeFG19f3zyXaa9RowaCgoIQEBAg3rp06YLPP/8cAQEBvCSLiEgFXr9+jV69eqFHjx7Ytm2b1OGozeHDh7Fu3TrcuHFD6lBKDbWM6OnevTu2bt0Kc3NzdO/e/aPb7t+//6M///LLL/H999+jfv36ePjwITp06AAAuHfvHlxcXFQVMhGRWshkMtSqVUt8nJCQgDVr1iA1NRUDBgxAtWrVJIyOiEqj4OBgrFy5Evfv3wfwZtWs0aNHw83NLd++qsyjvL294eXlhUaNGqFx48ZYsWIFUlJSMHjwYADAwIED4eDggIULF8LQ0BB16tRR6m9paQkAudqJiKhoTE1N0bt3bxw4cKBMTc7cv39/2Nvbo23btlKHUmqopdBjYWEhXoZgYWFRrH2tXr0a06dPx7Nnz/D333/D2toaAHDr1i306dOn2LESEUnJwsICFy5cwJ49e8Rv2gHg2LFjMDIywmeffcbLuojKsL///hu9e/dGo0aNxJEzV69eRZ06dbBr1y588803H+2vyjyqV69eiImJgY+PD6KiolCvXj2cOHFCnKD56dOnkMs1bvA4EZHG0tPTw+LFi+Hj4wMTExOx/c6dO/Dw8JAwspLVtm1bFnneo7bl1efMmYOJEyfC2NhYHU+nFlyWlojUIScnB25ubggNDcWOHTvQr18/qUMiLcHzmOapUqUK+vXrhzlz5ii1z5w5Ezt27EBoaKhEkakH37NERIVz+PBhfP3115gwYQKWLFnCAryE1HkOU9tkzLNnz8awYcNUUuh5/fo1/vjjD6Uhy0OGDEG5cuWKvW8iotImNTUV7dq1w5EjR9C1a1exPSIiAtbW1jA0NJQuOCJSq8jISAwcODBXe//+/XNNyPkhzKOIiMqOe/fuAQDS0tK0clT423NZ9erVoaOjI3E0pYfaynmqGjh04cIFuLi44Pfff8fr16/x+vVrrFy5Eq6urrhw4YJKnoOIqDQxMzPD6tWrERISojQM9/vvv4eLiwtOnjwpYXREpE6fffYZLl68mKv90qVLaNGiRb79mUcREZUtU6dOxZkzZ/Dbb79pZaFn7ty5qFWrFpYsWSJ1KKWKWpdXV8Uba+TIkejVqxfWrl0rVuxycnIwYsQIjBw5EkFBQcV+DiKi0khfX1+8n5CQgP/++w8xMTGcsJmoDOnSpQsmT56MW7du4dNPPwXwZo6evXv3Yvbs2Th8+LDStu9jHkVEVPa0bt1avC8IAqZPn47evXvD3d1dwqhUw9DQEEZGRmjYsKHUoZQqapujRy6XK03K/CFxcXEf/bmRkRECAgJyrSwRHByMevXqFWpp0FmzZmH27NlKbW5ubnjw4EGB+vM6cSKSUlZWFvz8/NCyZUuxbcqUKYiKisK0adNYAKJ88TymeQo6t4JMJkNOTk6udlXmUVLge5aIqHjWr1+PYcOGoVy5cggJCYGVlZXUIRVbRkYG5HI59PT0pA7lo7Ryjh7gzTw9xV11q0GDBrh//36uBOX+/fuoW7duofdXu3ZtnDlzRnysq6vWl4SIqMj09PSUijxJSUlYtWoVUlJS0Lt3bxZ6iLSQQqEoVn9V51FERKRZevbsie3bt6Nfv35aUeQBAAMDA6lDKHXUWtXo3bs3bG1tC93vzp074v0xY8Zg7NixCAkJURqyvHr1aixatKjQ+9bV1UWFChUK3Y+IqLQxMzODr68v9uzZg3bt2ontR44cgSAI6NSpE1daINJQfn5+ePXqFTp16iS2bd++HTNnzkRKSgq6du2KlStX5pnslmQeRUREmsXKygrnz5/XigEOOTk5nID5A9R26ZaOjg4iIyOLVOiRy+WQyWT5Tuj8oWHKHzJr1iwsXboUFhYWMDQ0hKenJxYuXAgnJ6cC9efwYSIq7XJyclCjRg2EhIRg06ZN+O6776QOiUoRnsc0R/v27fHZZ59h8uTJAICgoCA0aNAAgwYNQs2aNbF06VL8+OOPmDVrVq6+JZVHSYHvWSIi1UpPT4eXlxdGjBiBVq1aSR1OgeXk5MDJyQkeHh7YunUr7OzspA4pX1p56VZx6knh4eEqjOT/NGnSBFu3boWbmxsiIyMxe/ZstGjRAnfv3oWZmVmu7TMyMpCRkSE+TkxMLJG4iIhUJSMjA99++y12796NXr16ie1PnjyBlZUVPygRaYiAgADMnTtXfLxr1y40adIEGzduBAA4Ojpi5syZeRZ6SiqPIiIizbdo0SLs2bMH586dQ1hYmNIKr6XZzZs38eLFC6SlpcHa2lrqcEodtY3o0QTx8fFwdnbG8uXL8/zWO6/JmwHwWyUiKvXeH9raqVMnXLp0CVu3bkXXrl2lC4wkxdERmsPQ0BCPHj2Co6MjAKB58+Zo3749pk2bBgB4/Pgx3N3dkZSUJGWYJY7vWSIi1UpPT0efPn0wbtw4jRrRIwgC7t+/j9DQUHTu3FnqcApEK0f0qNp///2Hp0+fIjMzU6k9r6VEC8rS0hLVq1dHSEhInj+fOnUqvL29xceJiYliwkVEVJq9W+RJSUlBWFgYEhMTUbt2bbFdEIR8V0YkImnY2dkhPDwcjo6OyMzMxO3bt5W+fEpKSirUaiMlkUcREZHmMTQ0xIEDB6QOo9BkMhlq1aqFWrVqSR1KqaRxhZ6wsDB069YNQUFBStebv/1wUpxry5OTkxEaGooBAwbk+XMDAwPO6E1EGs/ExAR3797FzZs3lVbmmjJlCu7cuYPp06ejWbNmEkZIRO/r0KEDpkyZgsWLF+PgwYMwNjZGixYtxJ/fuXMHVapUyXc/JZlHERGR5ouMjMT8+fOxbNkyfvbVYBq3/MrYsWPh6uqKly9fwtjYGPfu3cOFCxfQqFEjnDt3rlD7mjhxIs6fP4/Hjx/jypUr6NatG3R0dNCnT5+SCZ6IqJSQy+Vo3Lix+Dg1NRXr1q3DiRMnkJ6eLrbHxsYiLCxMihCJ6B1z586Frq4uWrVqhY0bN2Ljxo3Q19cXf75582a0bds23/2oMo8iIiLtolAo0K5dO6xevRrjxo2TOpwPWrduHby9vXHv3j2pQym1NG5Ej5+fH86ePQsbGxvI5XLI5XI0b94cCxcuxJgxY+Dv71/gfT1//hx9+vTBq1evUL58eTRv3hxXr15F+fLlS/AIiIhKH2NjY9y4cQMHDhxAy5YtxfatW7fip59+wtChQ7FhwwYJIyQq22xsbHDhwgUkJCTA1NQ013Kye/fuhampab77UWUeRURE2kUul2PZsmWYPHlyqS70rFmzBkFBQahTp47SNAT0fzSu0JOTkyOuiGVjY4MXL17Azc0Nzs7OCA4OLtS+du3aVRIhEhFppOrVq4tLN7/1/Plz6OjooG7dumJbeno6fHx80KVLFzRr1ozz+hCpkYWFRZ7t5cqVK1B/VeZRRESkfb788ku0bt0acnnpvPhHEATMmzcPu3fvRrdu3aQOp9TSuEJPnTp1EBgYCFdXVzRp0gRLliyBvr4+NmzYgMqVK0sdHhGRVlmxYgVmzJihNMnrmTNnsHTpUvz11194+vSp2M7JnIlKP+ZRRESUn3eLPA8fPkTFihULNGpUHWQyGbp06cLFA/JROst0HzF9+nQoFAoAwJw5cxAeHo4WLVrg2LFj+P333yWOjohI+1hbWystAVmhQgX069cPAwcOVCrstGjRAn369MHjx48liJKICoJ5FBERFdT+/fvRoEEDjB49WupQqJBkwtvlFjRYXFwcrKys1P5NcmJiIiwsLJCQkKD0IYiIqKwJCQlBtWrVoKOjg5cvX4qXkYSFhcHMzIxzn5VSPI8RIF0eVRR8zxIRqc/FixfRqlUrNGvWDKdOnYKRkZGk8Rw/fhyhoaHo378/LC0tJY2lKNR5DtO4ET15KVeunEYkJ0RE2qpy5cq4cuUKVq9erTRXyJQpU1ChQgWsX79ewuiI6GOYRxERUV5atGiBc+fO4dy5c5IXebKzszFx4kSMHj0aq1atkjQWTaAVhR4iIpKWXC6Hp6cnfvzxR7FNEAS8fPkSCoUCDRo0ENv/++8/zJkzB4GBgVKESkREREQF1LJly1wrPUpBLpdj0qRJqFu3LkaNGiV1OKUeCz1ERFQiZDIZzp07h8ePH6NRo0Zi+65duzBz5kzMmTNHafv09HR1h0hEREREBaBQKPDbb7/hyJEjkjy/XC6Hl5cX/P39NfKyLXVjoYeIiEqUs7Oz0mUhDRs2RNeuXfHtt9+KbfHx8bC2tsaXX36J1NRUKcIkIiIiog9Yv349xo0bhxEjRiApKUmtz52dnS3e56XGBcNCDxERqdXXX3+NAwcOoE+fPmLbhQsXkJqaiufPn8PY2Fhs//vvv3HmzBlkZmZKESoRERERAfDy8kKDBg0wbdo0mJiYqO15jx49irp16+Lq1atqe05toCt1AERERJ07d8aDBw8QFRUltgmCgAkTJuDJkyc4fPgwOnfuLLbz2xwiIiIi9TE2NsaNGzcgl6tvrIhCoYCPjw/+++8/7NmzB59++qnanlvTsdBTimRlZUFPT0/qMKgYBEFARkYG0tLSkJ6eLt7effyh++8/zsnJgbm5OSwsLJRulpaWSo/Nzc1LxQRpVHDZ2dlITExEfHy8eEtISFB6/G5bYmIidHR0YGhomOfNwMDggz8ryM8NDQ2hq6srafFEJpPBzc0Nbm5uYltaWhq++OIL/Pvvv/jiiy/E9vXr12PTpk0YPXo0vLy8pAi3RAmCAOD/hianpqYiJiYGGRkZkMvlSExMVJrcmoiIiEgd3i3yKBQKCIJQop9D5HI5zpw5g8WLF2Pu3Lkl9jzaiIWeUiIzMxOGhoawtLSEnZ0dbG1tP/qvnZ0dTExM+K12CREEAXFxcXj8+HGu26tXrz5YtMnIyJAkXjMzsw8WgvJqe/exlZUVTE1NJYlbk6Wnp+PZs2cFKtS8f0tOTpY6/FxkMhkMDQ1Rvnx5uLi4wNnZWelfFxcXODo6Ql9fX20xGRsbY/PmzblG8Bw5cgS3bt1SGv2TlZWFw4cP48svv4S5ubnKY3k/htOnTyM+Ph6dOnUSlxs9d+4c9u3bhwYNGmDIkCHitn379kVsbCw2btwIZ2dnAMDevXsxffp0NGnSBOPHj8fLly/x8uVLeHt7IzY2Fp06dUJOTg5evnyJ8PBwxMXFifuzsrJSekxERESkToGBgfjxxx/Rr18/jB49ukSfq1y5cli8eHGJPoc2kglvvzqkQktMTISFhQUSEhKK/cEiIiIClSpVKlQfIyOjAhWFbG1tYW1trdZhdqWdIAh49epVnoWcJ0+e4PHjx8X+MC6TyWBkZAQjIyNx1ERB7r99/Pab+7cFg7e3dx+rapWi8uXLw83NDdWrVxdHVbi5uaFy5cpq/WBf2giCgKioKAQHByM4OBgPHjwQ74eHh6O4fz5NTEzEwtu7t/fbzMzMoFAolEZ/vX/LyMgo9M8LO++NTCZDxYoV8ywCOTs7w8nJSSx6lKSoqCgcP34crVq1QuXKlQEA58+fx2effYaKFSvi+fPnHy2Cv3jxAiEhIbCxsUGtWrUAvPm/Hj9+PF6/fo2VK1eKf9NXrFgBHx8f9OnTB+vXrxf3YWJigtTUVISGhoox/P777xg7dizat2+PqVOnisWbSZMmITk5GW3atEFGRgZevnyJZ8+eFWnSaVNTU9jZ2eHBgwfQ1S3+dzWqPI8RqQPfs0RE0lu7di1GjBgBBwcHhIWFqfTzQnJyMkaMGIEePXqIl+1rC3Wew1joKQZV/ke9HUESHR2Nly9fKv2bV1thPyDo6OjAxsZGLPw4ODjA3d0dHh4e8PDwgJ2dXbHiL20EQUBsbGyeBZy3t5SUlHz3U6FCBfGD7Nubra2tWJj5WOFGHZfCZGRkKBWB3i8E5fc4Pj5eaRb79+no6MDV1VWp+PO2IFShQgWtGVGWnp6OkJAQpULO2/uJiYkf7GdiYgJra+s8izN5tb372MLCQvJLNRUKhVgAenvJYVRUlNLvy7v/pqWl5btPOzu7PEcEOTs7w9nZucRGjx09ehQTJkxAkyZNsH37drG9Q4cOuH79Oh4+fIhy5coBAObOnQsfHx8MHToUGzZsELfNq3izcuVKjBkzBj169MCePXsAvLn0rkWLFoiPj0erVq3w7Nkz3L17FxEREcjJySlU3HK5HLa2tuLNzMwM5cuXh5OTE+zt7cV2Ozs7lC9fXmmialXgh2bSNHzPEhFJT6FQYMaMGWKxR5VWr16NUaNGwcjICKGhobC3t1fp/qXEQo+GkDLZSElJybMAlNe/r169ynd/tra2YtHHw8MDdevWRc2aNWFgYKCGoymapKQkhIaGIjQ0FGFhYblG5hSkGGZvb5+rkPP25uTkBENDQzUciXQEQUBSUhJCQkIQHByMhw8fioWOhw8ffnRUk7m5udIIoLf3q1evrvIPo6rw7uic9ws6jx8//uDoHLlcrlTsqlGjhnjfzs5Oa4pd+REEATExMR8sAhV0FJyNjY1Y/KlSpQoqV64s/uvk5FTsUSrp6eni7210dDQqVKgAAErFmy1btmDRokXo3r07Fi5cKPZdsGABdHV1MWTIENjY2AAAHjx4gGvXruH58+d49OgR7ty5g3v37n10NJSZmZlYVH/3lldbuXLlJB1tyQ/NpGn4niUi0m45OTkYMGAAhg8fjhYtWkgdjkqx0KMhNCXZyMrKQkxMjFLxJzw8HEFBQbhz5w4ePXqU54dcHR0d1KhRQ6kA5OHhAQcHB7V8uFUoFIiMjBQLOe8WdUJDQxEbG5vvPipWrJhnEeftZSbaXsgpDkEQ8OLFC6Xiz9sCUHh4OBQKxQf7Ojo65nkpmIWFhdL+C3O/MNvFxsaKxZx3izofG51jYWGRq5BTo0YNVK1atVQXPEsLQRDw+vXrXCPo3v03Pj7+o/vQ1dWFs7OzUvHn7b+VK1cu9N/Z9PR0BAYGwtTUFNWqVfvosOKMjAzcv38fd+7cUbpFR0fnub2pqanS30V3d3c4OTmhfPnyarl8TVU05TxGpdfq1auxdOlSREVFoW7duli5ciUaN26c57YbN27E9u3bcffuXQBAw4YNsWDBgg9unxe+Z4mISp979+7B1dW10F/2KhQKHDp0CH/99Rd27dql9VONsNCjIbQl2UhNTcW9e/fEDzaBgYG4c+cOXr9+nef2VlZWSiN/PDw8ULt27SKN4khPT0d4eHiehZzw8PB856CxsbFR+iD4/sSxLOSUjIyMDISGhioVf97eL8gIMqnI5XK4uLiIxZyyOjpHKgkJCWLhJzw8PNfve36Tmb/7+/7+vxUrVixQcvC2gPl+QefBgwd5XsYok8lQtWrVXAVvFxcXrUhGtOU8RtLYvXs3Bg4ciHXr1qFJkyZYsWIF9u7di+DgYNja2ubavl+/fmjWrBmaNm0KQ0NDLF68GAcOHMC9e/cKPPSf71kiotJl2bJlmDJlCsaOHYtffvmlUH2zs7Ph4OCAly9fYtu2bRg4cGAJRVk6sNCjIbQ52RAEAREREXl+GMprDgqZTIZq1arl+jDk7OyMuLg48cPc+wWdiIiIj05oq6Ojo/QN//sf7rTtddcGr169ylX8CQ4ORkhISKEn/y2Md4s0ZmZmeRZzqlatyuJfKaVQKPDixYtcfysKOoLPwMAArq6uuf5GWFtb5xqp86EVqywtLcXi9dtb7dq1YWJiUhKHXCpo83mMSl6TJk3wySefYNWqVQDe/B47Ojpi9OjRmDJlSr79c3JyYGVlhVWrVhU4ued7loiodDly5Ai6dOmCZcuWYfz48ZDJZEhISEB2djbKlSsn5uiJiYlYunQpnjx5ojSfoo+PD7Kzs/Hzzz9r/UrALPRoiLKYbKSnp+f60BQYGIiYmJg8t9fV1f3oZL/Am0sg3hZx3v2QVqVKFTg6Oko+YS2phkKhyHW51/sjaD72mKNtyrbExESEhYXlWQh68uRJvn9n3qWjowM3N7dcRR11XZZampTF8xipRmZmJoyNjbFv3z507dpVbPfy8kJ8fDwOHTqU7z6SkpJga2uLvXv3olOnTnluk5GRoTTaLzExEY6OjnzPEhGVIn/88Qd69eolFmp+++03jBs3Dn369MHOnTsBAGlpaTAxMYEgCAgODkb16tWlDFkS6sy7ir82K5UphoaGqF+/PurXr6/UHh0dnevSr//++w9ZWVkAAAcHh1yXW7y9b2NjU+Y+XJVFcrlcKy51IWmYm5ujXr16qFevXq6fZWdn49mzZx8cCeTm5qZU0KlZsyZHdhEVU2xsLHJycnKt2mlnZ4cHDx4UaB+TJ09GxYoV0aZNmw9us3DhQsyePbtYsRIRUcn67rvvlB6/HT397opZRkZGmDBhAtzc3MTFMqjkcERPMfCb0I/LyspCREQE7OzsNGpyUiKisoLnMSqqFy9ewMHBAVeuXIGnp6fYPmnSJJw/fx7Xrl37aP9FixZhyZIlOHfuHDw8PD64HUf0EBFppoyMDGRmZsLMzEzqUEoNjughraCnpwcXFxepwyAiIiIVs7GxgY6OTq6V6aKjo/P9pvaXX37BokWLcObMmY8WeYA3829x5UMiIs3Dv9/S4nUURERERFQo+vr6aNiwIXx9fcU2hUIBX19fpRE+71uyZAnmzp2LEydOoFGjRuoIlYiIqMzhiB4iIiIiKjRvb294eXmhUaNGaNy4MVasWIGUlBQMHjwYADBw4EA4ODhg4cKFAIDFixfDx8cHO3fuhIuLC6KiogC8WZRB21daISIiUicWeoiIiIio0Hr16oWYmBj4+PggKioK9erVw4kTJ8QJmp8+fao0Cf/atWuRmZmJb7/9Vmk/M2fOxKxZs9QZOhERkVbjZMzFwEksiYhIk/E8RpqG71kiItJUnIxZQ7ytkSUmJkocCRERUeG9PX/xOx/SFMy9iIhIU6kz72KhpxiSkpIAAI6OjhJHQkREVHRJSUmwsLCQOgyifDH3IiIiTaeOvIuXbhWDQqHAixcvYGZmBplMpvSzxMREODo64tmzZ2VmaHFZPGagbB43j7lsHDNQNo+7LB2zIAhISkpCxYoVleZSISqtPpZ7FUVZ+n0vCL4eufE1yY2viTK+HrnxNVH29vV4+vQpZDKZWvIujugpBrlcjkqVKn10G3Nz8zL35i6LxwyUzePmMZcdZfG4y8oxcyQPaZKC5F5FUVZ+3wuKr0dufE1y42uijK9HbnxNlFlYWKjt9eDXd0REREREREREWoKFHiIiIiIiIiIiLcFCTwkxMDDAzJkzYWBgIHUoalMWjxkom8fNYy47yuJxl8VjJiqr+PuujK9HbnxNcuNrooyvR258TZRJ8XpwMmYiIiIiIiIiIi3BET1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZZgoYeIiIiIiIiISEuw0FMCVq9eDRcXFxgaGqJJkya4fv261CGpzMKFC/HJJ5/AzMwMtra26Nq1K4KDg5W2SU9Px8iRI2FtbQ1TU1N88803iI6Olihi1Vu0aBFkMhnGjRsntmnrMUdERKB///6wtraGkZER3N3dcfPmTfHngiDAx8cH9vb2MDIyQps2bfDo0SMJIy6enJwczJgxA66urjAyMkKVKlUwd+5cvDtnvTYc84ULF9C5c2dUrFgRMpkMBw8eVPp5QY4xLi4O/fr1g7m5OSwtLfHdd98hOTlZjUdROB875qysLEyePBnu7u4wMTFBxYoVMXDgQLx48UJpH5p2zET0cdqcr71LVbnb06dP0bFjRxgbG8PW1hY//fQTsrOz1XkoJaKoeZ22vR6qyPm06TypqpxQk18TdeWLd+7cQYsWLWBoaAhHR0csWbKkpA+tSNSVS6rs9RBIpXbt2iXo6+sLmzdvFu7duycMHTpUsLS0FKKjo6UOTSXatWsnbNmyRbh7964QEBAgdOjQQXBychKSk5PFbYYNGyY4OjoKvr6+ws2bN4VPP/1UaNq0qYRRq87169cFFxcXwcPDQxg7dqzYro3HHBcXJzg7OwuDBg0Srl27JoSFhQknT54UQkJCxG0WLVokWFhYCAcPHhQCAwOFLl26CK6urkJaWpqEkRfd/PnzBWtra+Ho0aNCeHi4sHfvXsHU1FT47bffxG204ZiPHTsmTJs2Tdi/f78AQDhw4IDSzwtyjF999ZVQt25d4erVq8LFixeFqlWrCn369FHzkRTcx445Pj5eaNOmjbB7927hwYMHgp+fn9C4cWOhYcOGSvvQtGMmog/T9nztXarI3bKzs4U6deoIbdq0Efz9/YVjx44JNjY2wtSpU6U4JJUpal6nba+HqnI+bTpPqion1OTXRB35YkJCgmBnZyf069dPuHv3rvDXX38JRkZGwvr169V1mAWmjlxSla8HCz0q1rhxY2HkyJHi45ycHKFixYrCwoULJYyq5Lx8+VIAIJw/f14QhDdvcj09PWHv3r3iNvfv3xcACH5+flKFqRJJSUlCtWrVhNOnTwutWrUSEwJtPebJkycLzZs3/+DPFQqFUKFCBWHp0qViW3x8vGBgYCD89ddf6ghR5Tp27CgMGTJEqa179+5Cv379BEHQzmN+/0RVkGP877//BADCjRs3xG2OHz8uyGQyISIiQm2xF1Veycr7rl+/LgAQnjx5IgiC5h8zESkra/nau4qSux07dkyQy+VCVFSUuM3atWsFc3NzISMjQ70HoCLFyeu07fVQRc6nbedJVeSE2vSalFS+uGbNGsHKykrp92by5MmCm5tbCR9R8ZRULqnK14OXbqlQZmYmbt26hTZt2ohtcrkcbdq0gZ+fn4SRlZyEhAQAQLly5QAAt27dQlZWltJrUKNGDTg5OWn8azBy5Eh07NhR6dgA7T3mw4cPo1GjRujRowdsbW1Rv359bNy4Ufx5eHg4oqKilI7bwsICTZo00djjbtq0KXx9ffHw4UMAQGBgIC5duoT27dsD0M5jfl9BjtHPzw+WlpZo1KiRuE2bNm0gl8tx7do1tcdcEhISEiCTyWBpaQmgbBwzUVlRFvO1dxUld/Pz84O7uzvs7OzEbdq1a4fExETcu3dPjdGrTnHyOm17PVSR82nbeVIVOaG2vSbvUtXx+/n5oWXLltDX1xe3adeuHYKDg/H69Ws1HU3JKEouqcrXQ7f4h0BvxcbGIicnR+mPPgDY2dnhwYMHEkVVchQKBcaNG4dmzZqhTp06AICoqCjo6+uLb+i37OzsEBUVJUGUqrFr1y7cvn0bN27cyPUzbT3msLAwrF27Ft7e3vj5559x48YNjBkzBvr6+vDy8hKPLa/3u6Ye95QpU5CYmIgaNWpAR0cHOTk5mD9/Pvr16wcAWnnM7yvIMUZFRcHW1lbp57q6uihXrpxWvA7p6emYPHky+vTpA3NzcwDaf8xEZUlZy9feVdTcLSoqKs/X6+3PNE1x8zptez1UkfNp23lSFTmhtr0m71LV8UdFRcHV1TXXPt7+zMrKqkTiL2lFzSVV+Xqw0ENFNnLkSNy9exeXLl2SOpQS9ezZM4wdOxanT5+GoaGh1OGojUKhQKNGjbBgwQIAQP369XH37l2sW7cOXl5eEkdXMvbs2YM///wTO3fuRO3atREQEIBx48ahYsWKWnvMpCwrKws9e/aEIAhYu3at1OEQEalUWcndPqas5nUfUxZzvvwwJ6SiKi25JC/dUiEbGxvo6OjkmpU/OjoaFSpUkCiqkjFq1CgcPXoU//77LypVqiS2V6hQAZmZmYiPj1faXpNfg1u3buHly5do0KABdHV1oauri/Pnz+P333+Hrq4u7OzstO6YAcDe3h61atVSaqtZsyaePn0KAOKxadP7/aeffsKUKVPQu3dvuLu7Y8CAARg/fjwWLlwIQDuP+X0FOcYKFSrg5cuXSj/Pzs5GXFycRr8Ob0/MT548wenTp8VvYADtPWaisqgs5WvvKk7uVqFChTxfr7c/0ySqyOu06fUAVJPzadt5UhU5oba9Ju9S1fFr2+9ScXNJVb4eLPSokL6+Pho2bAhfX1+xTaFQwNfXF56enhJGpjqCIGDUqFE4cOAAzp49m2toWcOGDaGnp6f0GgQHB+Pp06ca+xq0bt0aQUFBCAgIEG+NGjVCv379xPvadswA0KxZs1zLrz58+BDOzs4AAFdXV1SoUEHpuBMTE3Ht2jWNPe7U1FTI5cp/FnV0dKBQKABo5zG/ryDH6Onpifj4eNy6dUvc5uzZs1AoFGjSpInaY1aFtyfmR48e4cyZM7C2tlb6uTYeM1FZVRbytXepInfz9PREUFCQ0oeUtx9i3i8QlHaqyOu06fUAVJPzadt5UhU5oba9Ju9S1fF7enriwoULyMrKErc5ffo03NzcNO6yLVXkkip9PQo9fTN91K5duwQDAwNh69atwn///Sf88MMPgqWlpdKs/Jps+PDhgoWFhXDu3DkhMjJSvKWmporbDBs2THBychLOnj0r3Lx5U/D09BQ8PT0ljFr13l2dQRC085ivX78u6OrqCvPnzxcePXok/Pnnn4KxsbGwY8cOcZtFixYJlpaWwqFDh4Q7d+4IX3/9tcYtNf4uLy8vwcHBQVxKc//+/YKNjY0wadIkcRttOOakpCTB399f8Pf3FwAIy5cvF/z9/cVVAQpyjF999ZVQv3594dq1a8KlS5eEatWqlerlQj92zJmZmUKXLl2ESpUqCQEBAUp/295d9UDTjpmIPkzb87V3qSJ3e7uceNu2bYWAgADhxIkTQvny5TV2OfH3FTav07bXQ1U5nzadJ1WVE2rya6KOfDE+Pl6ws7MTBgwYINy9e1fYtWuXYGxsXCqXV1dHLqnK14OFnhKwcuVKwcnJSdDX1xcaN24sXL16VeqQVAZAnrctW7aI26SlpQkjRowQrKysBGNjY6Fbt25CZGSkdEGXgPcTAm095iNHjgh16tQRDAwMhBo1aggbNmxQ+rlCoRBmzJgh2NnZCQYGBkLr1q2F4OBgiaItvsTERGHs2LGCk5OTYGhoKFSuXFmYNm2a0h9obTjmf//9N8/fYy8vL0EQCnaMr169Evr06SOYmpoK5ubmwuDBg4WkpCQJjqZgPnbM4eHhH/zb9u+//4r70LRjJqKP0+Z87V2qyt0eP34stG/fXjAyMhJsbGyECRMmCFlZWWo+mpJRlLxO214PVeR82nSeVFVOqMmvibryxcDAQKF58+aCgYGB4ODgICxatEhdh1go6solVfV6yARBEAo3BoiIiIiIiIiIiEojztFDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZZgoYeIiIiIiIiISEuw0ENEREREREREpCVY6CEilRIEAQAwa9YspcdEREREJA3mZ0Rli0zgbzkRqdCaNWugq6uLR48eQUdHB+3bt0erVq2kDouIiIiozGJ+RlS2cEQPEanUiBEjkJCQgN9//x2dO3cuUBLx2WefQSaTQSaTISAgoOSDfM+gQYPE5z948KDan5+IiIioJBU2PytKbsZ8iqj0YKGHiFRq3bp1sLCwwJgxY3DkyBFcvHixQP2GDh2KyMhI1KlTp4QjzO23335DZGSk2p+XiIiISJXGjx+P7t2752ovSn5W2NyM+RRR6aErdQBEpF1+/PFHyGQyzJo1C7NmzSrwNeDGxsaoUKFCCUeXNwsLC1hYWEjy3ERERESqcv36dXTs2DFXe1Hys8LmZsyniEoPjughokJZsGCBOCz33duKFSsAADKZDMD/Tfb39nFhffbZZxg9ejTGjRsHKysr2NnZYePGjUhJScHgwYNhZmaGqlWr4vjx4yrpR0RERKSpMjMzoaenhytXrmDatGmQyWT49NNPxZ+rKj/bt28f3N3dYWRkBGtra7Rp0wYpKSnFjp+IVIuFHiIqlNGjRyMyMlK8DR06FM7Ozvj2229V/lzbtm2DjY0Nrl+/jtGjR2P48OHo0aMHmjZtitu3b6Nt27YYMGAAUlNTVdKPiIiISBPp6uri8uXLAICAgABERkbixIkTKn2OyMhI9OnTB0OGDMH9+/dx7tw5dO/enSt4EZVCLPQQUaGYmZmhQoUKqFChAlavXo1Tp07h3LlzqFSpksqfq27dupg+fTqqVauGqVOnwtDQEDY2Nhg6dCiqVasGHx8fvHr1Cnfu3FFJPyIiIiJNJJfL8eLFC1hbW6Nu3bqoUKECLC0tVfockZGRyM7ORvfu3eHi4gJ3d3eMGDECpqamKn0eIio+FnqIqEh8fHzwv//9D+fOnYOLi0uJPIeHh4d4X0dHB9bW1nB3dxfb7OzsAAAvX75UST8iIiIiTeXv74+6deuW2P7r1q2L1q1bw93dHT169MDGjRvx+vXrEns+Iio6FnqIqNBmzpyJ7du3l2iRBwD09PSUHstkMqW2t9eXKxQKlfQjIiIi0lQBAQElWujR0dHB6dOncfz4cdSqVQsrV66Em5sbwsPDS+w5iahoWOghokKZOXMmtm3bVuJFHiIiIiIquKCgINSrV69En0Mmk6FZs2aYPXs2/P39oa+vjwMHDpTocxJR4XF5dSIqsHnz5mHt2rU4fPgwDA0NERUVBQCwsrKCgYGBxNERERERlV0KhQLBwcF48eIFTExMVL7U+bVr1+Dr64u2bdvC1tYW165dQ0xMDGrWrKnS5yGi4uOIHiIqEEEQsHTpUsTExMDT0xP29vbijZMaExEREUlr3rx52Lp1KxwcHDBv3jyV79/c3BwXLlxAhw4dUL16dUyfPh3Lli1D+/btVf5cRFQ8HNFDRAUik8mQkJCgtuc7d+5crrbHjx/nant/Sc+i9iMiIiLSZP3790f//v1LbP81a9ZU+ZLtRFQyOKKHiEqFNWvWwNTUFEFBQWp/7mHDhnFpUCIiIqJ3FDY3Yz5FVHrIBH6tTUQSi4iIQFpaGgDAyckJ+vr6an3+ly9fIjExEQBgb28PExMTtT4/ERERUWlSlNyM+RRR6cFCDxERERERERGRluClW0REREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGW+H+JITjGWARwlQAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -610,7 +619,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -674,7 +683,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -727,7 +736,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py index 2d93ea4a9e..a6555170fc 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py @@ -137,19 +137,22 @@ def set_boundary_conditions(self, variables): # just use left and right for clarity # left = bottom of cell (z=0) # right = top of cell (z=L_z) + lambda_eff = param.lambda_eff(T_av) self.boundary_conditions = { T_av: { "left": ( - bottom_cooling_coefficient - * pybamm.boundary_value( - T_av - T_amb, + pybamm.boundary_value( + bottom_cooling_coefficient * (T_av - T_amb), "left", - ), + ) + / pybamm.boundary_value(lambda_eff, "left"), "Neumann", ), "right": ( - -top_cooling_coefficient - * pybamm.boundary_value(T_av - T_amb, "right"), + pybamm.boundary_value( + -top_cooling_coefficient * (T_av - T_amb), "right" + ) + / pybamm.boundary_value(lambda_eff, "right"), "Neumann", ), } diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py index 151b8c6938..eb8e1b7e49 100644 --- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py +++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py @@ -114,10 +114,12 @@ def set_boundary_conditions(self, variables): ) negative_tab_bc = pybamm.boundary_value( - -h_tab_n_corrected * (T_av - T_amb), "negative tab" + -h_tab_n_corrected * (T_av - T_amb) / self.param.n.lambda_cc(T_av), + "negative tab", ) positive_tab_bc = pybamm.boundary_value( - -h_tab_p_corrected * (T_av - T_amb), "positive tab" + -h_tab_p_corrected * (T_av - T_amb) / self.param.p.lambda_cc(T_av), + "positive tab", ) self.boundary_conditions = { From d5714cd7b26fc4943a04ce20d5c0fcf1340aba30 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 13 Sep 2023 14:10:36 +0100 Subject: [PATCH 097/154] update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index d6927f20bd..87dd894812 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,7 @@ ## Bug fixes +- Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330)) - Fixed a bug that occured in `check_ys_are_not_too_large` when trying to reference `y-slice` where the referenced variable was not a `pybamm.StateVector` ([#3313](https://github.com/pybamm-team/PyBaMM/pull/3313) - Fixed a bug with `_Heaviside._evaluate_for_shape` which meant some expressions involving heaviside function and subtractions did not work ([#3306](https://github.com/pybamm-team/PyBaMM/pull/3306)) - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042)) From 77a62c12a07f8e6e76e9139d8e02bb9d29a8c9d9 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 13 Sep 2023 14:17:26 +0100 Subject: [PATCH 098/154] update bib in docs --- .../submodels/interface/open_circuit_potential/msmr_ocp.rst | 2 ++ docs/source/api/models/submodels/particle/msmr_diffusion.rst | 2 ++ 2 files changed, 4 insertions(+) diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst index 5f58e60abc..f2106367d2 100644 --- a/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst +++ b/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst @@ -4,3 +4,5 @@ MSMR Open Circuit Potential .. autoclass:: pybamm.open_circuit_potential.MSMROpenCircuitPotential :members: + +.. footbibliography:: diff --git a/docs/source/api/models/submodels/particle/msmr_diffusion.rst b/docs/source/api/models/submodels/particle/msmr_diffusion.rst index a03bebbcf1..af7dfe2582 100644 --- a/docs/source/api/models/submodels/particle/msmr_diffusion.rst +++ b/docs/source/api/models/submodels/particle/msmr_diffusion.rst @@ -3,3 +3,5 @@ MSMR Diffusion .. autoclass:: pybamm.particle.MSMRDiffusion :members: + +.. footbibliography:: From 6fbd807c39ff07c8841820629f1dd2889413fe67 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 13 Sep 2023 13:19:44 +0000 Subject: [PATCH 099/154] docs: update README.md [skip ci] --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1b54201f5b..0a4689429e 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) -[![All Contributors](https://img.shields.io/badge/all_contributors-61-orange.svg)](#-contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-62-orange.svg)](#-contributors) @@ -263,6 +263,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Jason Siegel
Jason Siegel
💻 🤔 Tom Maull
Tom Maull
💻 ⚠️ ejfdickinson
ejfdickinson
🤔 🐛 + bobonice
bobonice
🐛 💻 From f216c7ee14cf0d645c744340d116b3ea52483b5a Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 13 Sep 2023 13:19:45 +0000 Subject: [PATCH 100/154] docs: update .all-contributorsrc [skip ci] --- .all-contributorsrc | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.all-contributorsrc b/.all-contributorsrc index eef8a5aa16..9e46a92315 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -674,6 +674,16 @@ "ideas", "bug" ] + }, + { + "login": "bobonice", + "name": "bobonice", + "avatar_url": "https://avatars.githubusercontent.com/u/22030806?v=4", + "profile": "https://github.com/bobonice", + "contributions": [ + "bug", + "code" + ] } ], "contributorsPerLine": 7, From e8b56ae5755a7b85b97acae53553c8b0ad54c773 Mon Sep 17 00:00:00 2001 From: bobonice Date: Wed, 13 Sep 2023 23:03:12 +0800 Subject: [PATCH 101/154] #3329 Update CHANGELOG.md --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d6927f20bd..2c3bafd38f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,7 @@ - Numpy functions now work with PyBaMM symbols (e.g. `np.exp(pybamm.Symbol("a"))` returns `pybamm.Exp(pybamm.Symbol("a"))`). This means that parameter functions can be specified using numpy functions instead of pybamm functions. Additionally, combining numpy arrays with pybamm objects now works (the numpy array is converted to a pybamm array) ([#3205](https://github.com/pybamm-team/PyBaMM/pull/3205)) ## Bug fixes - +- Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)). - Fixed a bug that occured in `check_ys_are_not_too_large` when trying to reference `y-slice` where the referenced variable was not a `pybamm.StateVector` ([#3313](https://github.com/pybamm-team/PyBaMM/pull/3313) - Fixed a bug with `_Heaviside._evaluate_for_shape` which meant some expressions involving heaviside function and subtractions did not work ([#3306](https://github.com/pybamm-team/PyBaMM/pull/3306)) - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042)) From d408555df9660fffcfefe17f8936570bcb30ddfe Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 13 Sep 2023 15:08:06 +0000 Subject: [PATCH 102/154] docs: update README.md [skip ci] --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1b54201f5b..e38b198bc2 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) -[![All Contributors](https://img.shields.io/badge/all_contributors-61-orange.svg)](#-contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-62-orange.svg)](#-contributors) @@ -263,6 +263,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Jason Siegel
Jason Siegel
💻 🤔 Tom Maull
Tom Maull
💻 ⚠️ ejfdickinson
ejfdickinson
🤔 🐛 + Eric G. Kratz
Eric G. Kratz
📖 🚇 From 49ce2e1b74b7a2b2bd8406ffa1f784edc2f420a9 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 13 Sep 2023 15:08:08 +0000 Subject: [PATCH 103/154] docs: update .all-contributorsrc [skip ci] --- .all-contributorsrc | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.all-contributorsrc b/.all-contributorsrc index eef8a5aa16..787aeb1f8d 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -674,6 +674,16 @@ "ideas", "bug" ] + }, + { + "login": "kratman", + "name": "Eric G. Kratz", + "avatar_url": "https://avatars.githubusercontent.com/u/10170302?v=4", + "profile": "https://github.com/kratman", + "contributions": [ + "doc", + "infra" + ] } ], "contributorsPerLine": 7, From 9669c975c1b5a9f0fe770e7c29337f1dd4c6b762 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 04:41:19 +0530 Subject: [PATCH 104/154] #3328 fix notebook download URL logic --- docs/conf.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index d0b1683aff..28f2cb1a62 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -303,6 +303,41 @@ # a conflict with the sphinx-docsearch extension for Algolia search nbsphinx_requirejs_path = "" + +# For notebook downloads (23.5 onwards), we get the version from the environment +# variable READTHEDOCS_VERSION and set it accordingly. + +# If the version is set to "latest", then we are on the develop branch, and we +# point to the notebook in the develop blob +# If we are on "stable", we point to the notebook in the relevant release tree +# for the PyBaMM version +# On a PR build, we use READTHEDOCS_GIT_COMMIT_HASH which will always point to changes +# made to a notebook, if any. +# On local builds, the version is not set, so we use "latest". + +if (os.environ.get("READTHEDOCS_VERSION") == "latest") or (os.environ.get("READTHEDOCS_VERSION") is None): # noqa: E501 + notebooks_version = "develop" + github_download_url = ( + "https://github.com/pybamm-team/PyBaMM/blob/" + notebooks_version + ) +if os.environ.get("READTHEDOCS_VERSION") == "stable": + notebooks_version = version + github_download_url = ( + "https://github.com/pybamm-team/PyBaMM/tree/v" + notebooks_version + ) +if os.environ.get("READTHEDOCS_VERSION_TYPE") == "external": + notebooks_version = os.environ.get("READTHEDOCS_GIT_COMMIT_HASH") + github_download_url = ( + "https://github.com/pybamm-team/PyBaMM/blob/" + notebooks_version + ) + +html_context.update( + { + "notebooks_version": notebooks_version, + "github_download_url": github_download_url, + } +) + nbsphinx_prolog = r""" {% set github_docname = From 3d710d880449bdf7d17e30f4a7d148b2d3c97e48 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 04:48:45 +0530 Subject: [PATCH 105/154] #3328 update jinja templating for GitHub notebook links --- docs/conf.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 28f2cb1a62..9b3c8bef9b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -344,8 +344,8 @@ 'github/pybamm-team/pybamm/blob/develop/docs/' + env.doc2path(env.docname, base=None) %} -{% set readthedocs_download_url = -'https://docs.pybamm.org/en/latest/' %} +{% set notebooks_version = env.config.html_context.notebooks_version %} +{% set github_download_url = env.config.html_context.github_download_url %} {% set doc_path = env.doc2path(env.docname, base=None) %} @@ -366,7 +366,7 @@

Alternatively, you may - download this notebook and run it offline.

From 166f1e64001dba2365616676a1567e1c97933c7b Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 05:02:42 +0530 Subject: [PATCH 106/154] #3328 point Colab badge URLs correctly --- docs/conf.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 9b3c8bef9b..ab38db679c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -331,10 +331,13 @@ "https://github.com/pybamm-team/PyBaMM/blob/" + notebooks_version ) +google_colab_url = github_download_url.replace("github.com", "githubtocolab.com") + html_context.update( { "notebooks_version": notebooks_version, "github_download_url": github_download_url, + "google_colab_url": google_colab_url, } ) @@ -346,6 +349,7 @@ {% set notebooks_version = env.config.html_context.notebooks_version %} {% set github_download_url = env.config.html_context.github_download_url %} +{% set google_colab_url = env.config.html_context.google_colab_url %} {% set doc_path = env.doc2path(env.docname, base=None) %} @@ -358,7 +362,7 @@

An interactive online version of this notebook is available, which can be accessed via - Open this notebook in Google Colab From 126552dd134209a8e8761d0ef0c9c237ef06a55f Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 05:12:03 +0530 Subject: [PATCH 107/154] #3328 ignore links in `conf.py` --- .lycheeignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.lycheeignore b/.lycheeignore index 399827d27c..610e8500e8 100644 --- a/.lycheeignore +++ b/.lycheeignore @@ -11,3 +11,7 @@ file:///home/runner/work/PyBaMM/PyBaMM/docs/source/user_guide/fundamentals/pybam # Errors in docs/source/user_guide/index.md file:///home/runner/work/PyBaMM/PyBaMM/docs/source/user_guide/api_docs + +# Ignore links in docs/conf.py +https://github.com/pybamm-team/PyBaMM/blob/ +https://github.com/pybamm-team/PyBaMM/tree/v From e525c73efa0e7aeec14f74fc2cf20d940858a090 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 18:36:06 +0530 Subject: [PATCH 108/154] #3328 fix lint --- docs/conf.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index ab38db679c..9f45ae8850 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -315,7 +315,9 @@ # made to a notebook, if any. # On local builds, the version is not set, so we use "latest". -if (os.environ.get("READTHEDOCS_VERSION") == "latest") or (os.environ.get("READTHEDOCS_VERSION") is None): # noqa: E501 +if (os.environ.get("READTHEDOCS_VERSION") == "latest") or ( + os.environ.get("READTHEDOCS_VERSION") is None +): notebooks_version = "develop" github_download_url = ( "https://github.com/pybamm-team/PyBaMM/blob/" + notebooks_version From 1e1535b0e7e6057bc046471bcb208779ea7e369d Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 18:37:22 +0530 Subject: [PATCH 109/154] Apply suggestions from code review In order to refactor URLs Co-authored-by: Saransh Chopra --- docs/conf.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index ab38db679c..025f0cedbf 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -317,21 +317,19 @@ if (os.environ.get("READTHEDOCS_VERSION") == "latest") or (os.environ.get("READTHEDOCS_VERSION") is None): # noqa: E501 notebooks_version = "develop" - github_download_url = ( - "https://github.com/pybamm-team/PyBaMM/blob/" + notebooks_version + append_to_url = f"blob/{notebooks_version}" ) if os.environ.get("READTHEDOCS_VERSION") == "stable": notebooks_version = version - github_download_url = ( - "https://github.com/pybamm-team/PyBaMM/tree/v" + notebooks_version + append_to_url = f"tree/v{notebooks_version}" ) if os.environ.get("READTHEDOCS_VERSION_TYPE") == "external": notebooks_version = os.environ.get("READTHEDOCS_GIT_COMMIT_HASH") - github_download_url = ( - "https://github.com/pybamm-team/PyBaMM/blob/" + notebooks_version + append_to_url = f"blob/{notebooks_version}" ) google_colab_url = github_download_url.replace("github.com", "githubtocolab.com") +github_download_url = f"https://github.com/pybamm-team/PyBaMM/{append_to_url}" html_context.update( { From 1064ddfa2586059080f2126b6f8ee40fbaf8fa34 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 18:39:27 +0530 Subject: [PATCH 110/154] #3328 Fix some missed syntax errors --- docs/conf.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 2a63309e96..1cb1a521ae 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -320,18 +320,17 @@ ): notebooks_version = "develop" append_to_url = f"blob/{notebooks_version}" - ) + if os.environ.get("READTHEDOCS_VERSION") == "stable": notebooks_version = version append_to_url = f"tree/v{notebooks_version}" - ) + if os.environ.get("READTHEDOCS_VERSION_TYPE") == "external": notebooks_version = os.environ.get("READTHEDOCS_GIT_COMMIT_HASH") append_to_url = f"blob/{notebooks_version}" - ) -google_colab_url = github_download_url.replace("github.com", "githubtocolab.com") github_download_url = f"https://github.com/pybamm-team/PyBaMM/{append_to_url}" +google_colab_url = github_download_url.replace("github.com", "githubtocolab.com") html_context.update( { From de737ddb894f91156f45e5996bde171888e51e3e Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 18:55:22 +0530 Subject: [PATCH 111/154] Fix some links --- .github/workflows/lychee_url_checker.yml | 1 + .lycheeignore | 4 ---- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml index d727ca0784..4282b8f83d 100644 --- a/.github/workflows/lychee_url_checker.yml +++ b/.github/workflows/lychee_url_checker.yml @@ -45,6 +45,7 @@ jobs: --accept 200,429 --exclude-path ./CHANGELOG.md --exclude-path ./scripts/update_version.py + --exclude-path docs/conf.py './**/*.rst' './**/*.md' './**/*.py' diff --git a/.lycheeignore b/.lycheeignore index 610e8500e8..399827d27c 100644 --- a/.lycheeignore +++ b/.lycheeignore @@ -11,7 +11,3 @@ file:///home/runner/work/PyBaMM/PyBaMM/docs/source/user_guide/fundamentals/pybam # Errors in docs/source/user_guide/index.md file:///home/runner/work/PyBaMM/PyBaMM/docs/source/user_guide/api_docs - -# Ignore links in docs/conf.py -https://github.com/pybamm-team/PyBaMM/blob/ -https://github.com/pybamm-team/PyBaMM/tree/v From ed95c1612e9227a081ac6177663bea6415f2a41b Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Thu, 14 Sep 2023 20:04:59 +0530 Subject: [PATCH 112/154] #3165 Add `pytest`, `pytest-xdist`, and `nbmake` as dev extras --- setup.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index df55a24325..18aacc07ff 100644 --- a/setup.py +++ b/setup.py @@ -251,9 +251,16 @@ def compile_KLU(): "tqdm", ], "dev": [ - "pre-commit", # For code style checking - "ruff", # For code style auto-formatting - "nox", # For running testing sessions + # For working with pre-commit hooks + "pre-commit", + # For code style checks: linting and auto-formatting + "ruff", + # For running testing sessions + "nox", + # For testing Jupyter notebooks + "pytest", + "pytest-xdist", + "nbmake", ], "pandas": [ "pandas>=0.24", From 3eb2b5b5b607c042331d82ae59e5a1740f66bae7 Mon Sep 17 00:00:00 2001 From: kratman Date: Thu, 14 Sep 2023 13:01:43 -0400 Subject: [PATCH 113/154] First attempt at the removal --- pybamm/util.py | 6 +++--- tests/unit/test_parameters/test_parameter_sets_class.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pybamm/util.py b/pybamm/util.py index 5f84f37e0a..94963f5789 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -18,7 +18,7 @@ from warnings import warn import numpy as np -import pkg_resources +import importlib_metadata as il import pybamm @@ -272,8 +272,8 @@ def have_jax(): def is_jax_compatible(): """Check if the available version of jax and jaxlib are compatible with PyBaMM""" return ( - pkg_resources.get_distribution("jax").version == JAX_VERSION - and pkg_resources.get_distribution("jaxlib").version == JAXLIB_VERSION + il.distribution("jax").version == JAX_VERSION + and il.distribution("jaxlib").version == JAXLIB_VERSION ) diff --git a/tests/unit/test_parameters/test_parameter_sets_class.py b/tests/unit/test_parameters/test_parameter_sets_class.py index f548fd7955..7dbc11d305 100644 --- a/tests/unit/test_parameters/test_parameter_sets_class.py +++ b/tests/unit/test_parameters/test_parameter_sets_class.py @@ -4,7 +4,7 @@ from tests import TestCase import pybamm -import pkg_resources +import importlib_metadata as il import unittest @@ -25,7 +25,7 @@ def test_all_registered(self): """Check that all parameter sets have been registered with the ``pybamm_parameter_sets`` entry point""" known_entry_points = set( - ep.name for ep in pkg_resources.iter_entry_points("pybamm_parameter_sets") + ep.name for ep in il.entry_points("pybamm_parameter_sets") ) self.assertEqual(set(pybamm.parameter_sets.keys()), known_entry_points) self.assertEqual(len(known_entry_points), len(pybamm.parameter_sets)) From 460330d94fd23c3e75afcf089c4374a02f08483e Mon Sep 17 00:00:00 2001 From: kratman Date: Thu, 14 Sep 2023 13:11:43 -0400 Subject: [PATCH 114/154] Fix the test --- tests/unit/test_parameters/test_parameter_sets_class.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/test_parameters/test_parameter_sets_class.py b/tests/unit/test_parameters/test_parameter_sets_class.py index 7dbc11d305..0413e9e52c 100644 --- a/tests/unit/test_parameters/test_parameter_sets_class.py +++ b/tests/unit/test_parameters/test_parameter_sets_class.py @@ -25,7 +25,7 @@ def test_all_registered(self): """Check that all parameter sets have been registered with the ``pybamm_parameter_sets`` entry point""" known_entry_points = set( - ep.name for ep in il.entry_points("pybamm_parameter_sets") + ep.name for ep in il.entry_points().select(group="pybamm_parameter_sets") ) self.assertEqual(set(pybamm.parameter_sets.keys()), known_entry_points) self.assertEqual(len(known_entry_points), len(pybamm.parameter_sets)) From 34aca1c81b5ac70e8f7238c31d1af662238377c5 Mon Sep 17 00:00:00 2001 From: kratman Date: Thu, 14 Sep 2023 13:46:05 -0400 Subject: [PATCH 115/154] Adjust import name --- pybamm/util.py | 2 +- tests/unit/test_parameters/test_parameter_sets_class.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pybamm/util.py b/pybamm/util.py index 94963f5789..49453beb7c 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -18,7 +18,7 @@ from warnings import warn import numpy as np -import importlib_metadata as il +import importlib.metadata as il import pybamm diff --git a/tests/unit/test_parameters/test_parameter_sets_class.py b/tests/unit/test_parameters/test_parameter_sets_class.py index 0413e9e52c..6e90881d9e 100644 --- a/tests/unit/test_parameters/test_parameter_sets_class.py +++ b/tests/unit/test_parameters/test_parameter_sets_class.py @@ -4,7 +4,7 @@ from tests import TestCase import pybamm -import importlib_metadata as il +import importlib.metadata as il import unittest From 07f7068d3b1916ad39e62c57b57188a86141578c Mon Sep 17 00:00:00 2001 From: kratman Date: Thu, 14 Sep 2023 14:00:16 -0400 Subject: [PATCH 116/154] Fix the selection --- tests/unit/test_parameters/test_parameter_sets_class.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/test_parameters/test_parameter_sets_class.py b/tests/unit/test_parameters/test_parameter_sets_class.py index 6e90881d9e..509e6adade 100644 --- a/tests/unit/test_parameters/test_parameter_sets_class.py +++ b/tests/unit/test_parameters/test_parameter_sets_class.py @@ -25,7 +25,7 @@ def test_all_registered(self): """Check that all parameter sets have been registered with the ``pybamm_parameter_sets`` entry point""" known_entry_points = set( - ep.name for ep in il.entry_points().select(group="pybamm_parameter_sets") + ep.name for ep in il.entry_points()["pybamm_parameter_sets"] ) self.assertEqual(set(pybamm.parameter_sets.keys()), known_entry_points) self.assertEqual(len(known_entry_points), len(pybamm.parameter_sets)) From a0ce8e1fab8cbfca6a36b782faf5dbbaf0423ae6 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 15 Sep 2023 09:46:44 +0000 Subject: [PATCH 117/154] style: pre-commit fixes --- .all-contributorsrc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index e5b560f8ac..005022f607 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -685,7 +685,7 @@ "code" ] }, - { + { "login": "kratman", "name": "Eric G. Kratz", "avatar_url": "https://avatars.githubusercontent.com/u/10170302?v=4", From a57abbd23d92149e57ba5dbdf8e36ad36f7b0c61 Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Sat, 16 Sep 2023 00:35:35 +0530 Subject: [PATCH 118/154] #3165 fix improper discretisation import from tests module --- .../examples/notebooks/expression_tree/expression-tree.ipynb | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb b/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb index 862357eef5..c860198501 100644 --- a/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb +++ b/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb @@ -208,7 +208,11 @@ "metadata": {}, "outputs": [], "source": [ + "# Here, we import a dummy discretisation from the PyBaMM tests directory.\n", + "import sys\n", + "sys.path.insert(0, pybamm.root_dir())\n", "from tests import get_discretisation_for_testing\n", + "\n", "disc = get_discretisation_for_testing()\n", "disc.y_slices = {c: [slice(0, 40)]}\n", "dcdt = disc.process_symbol(dcdt)\n", From c097020fd8d43603efd2274f39bc71b3882f486b Mon Sep 17 00:00:00 2001 From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com> Date: Sat, 16 Sep 2023 00:39:03 +0530 Subject: [PATCH 119/154] #3165 replace outputs for expression tree notebook --- .../expression_tree/expression_tree1.png | Bin 34168 -> 23934 bytes .../expression_tree/expression_tree2.png | Bin 50863 -> 44056 bytes .../expression_tree/expression_tree3.png | Bin 13255 -> 13082 bytes .../expression_tree/expression_tree4.png | Bin 13255 -> 13082 bytes .../expression_tree/expression_tree5.png | Bin 15368 -> 15667 bytes 5 files changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/source/examples/notebooks/expression_tree/expression_tree1.png b/docs/source/examples/notebooks/expression_tree/expression_tree1.png index 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