SEI_Models_thickness_Chen2020_0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4e9becb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pybamm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#print(pybamm.__version__)\n",
    "param=pybamm.ParameterValues(\"Chen2020\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb74580b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ambient temperature [K]': 298.15,\n",
       " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
       " 'Bulk solvent concentration [mol.m-3]': 2636.0,\n",
       " 'Cation transference number': 0.2594,\n",
       " 'Cell cooling surface area [m2]': 0.00531,\n",
       " 'Cell thermal expansion coefficient [m.K-1]': 1.1e-06,\n",
       " 'Cell volume [m3]': 2.42e-05,\n",
       " 'Contact resistance [Ohm]': 0,\n",
       " 'Current function [A]': 5.0,\n",
       " 'EC diffusivity [m2.s-1]': 2e-18,\n",
       " 'EC initial concentration in electrolyte [mol.m-3]': 4541.0,\n",
       " 'Electrode height [m]': 0.065,\n",
       " 'Electrode width [m]': 1.58,\n",
       " 'Electrolyte conductivity [S.m-1]': <function electrolyte_conductivity_Nyman2008 at 0x7fb7e366f6d0>,\n",
       " 'Electrolyte diffusivity [m2.s-1]': <function electrolyte_diffusivity_Nyman2008 at 0x7fb7e366f640>,\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]': 1000.0,\n",
       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
       " 'Initial inner SEI thickness [m]': 1e-13,\n",
       " 'Initial outer SEI thickness [m]': 1e-13,\n",
       " 'Initial temperature [K]': 298.15,\n",
       " 'Inner SEI electron conductivity [S.m-1]': 8.95e-14,\n",
       " 'Inner SEI lithium interstitial diffusivity [m2.s-1]': 1e-20,\n",
       " 'Inner SEI open-circuit potential [V]': 0.1,\n",
       " 'Inner SEI partial molar volume [m3.mol-1]': 9.585e-05,\n",
       " 'Inner SEI reaction proportion': 0.5,\n",
       " 'Lithium interstitial reference concentration [mol.m-3]': 15.0,\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 current collector conductivity [S.m-1]': 58411000.0,\n",
       " 'Negative current collector density [kg.m-3]': 8960.0,\n",
       " 'Negative current collector specific heat capacity [J.kg-1.K-1]': 385.0,\n",
       " 'Negative current collector thermal conductivity [W.m-1.K-1]': 401.0,\n",
       " 'Negative current collector thickness [m]': 1.2e-05,\n",
       " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n",
       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Negative electrode OCP [V]': <function graphite_LGM50_ocp_Chen2020 at 0x7fb7e366f2e0>,\n",
       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
       " 'Negative electrode active material volume fraction': 0.75,\n",
       " 'Negative electrode charge transfer coefficient': 0.5,\n",
       " 'Negative electrode conductivity [S.m-1]': 215.0,\n",
       " 'Negative electrode density [kg.m-3]': 1657.0,\n",
       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
       " 'Negative electrode double-layer capacity [F.m-2]': 0.2,\n",
       " 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7fb7e366f490>,\n",
       " 'Negative electrode porosity': 0.25,\n",
       " 'Negative electrode reaction-driven LAM factor [m3.mol-1]': 0.0,\n",
       " 'Negative electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n",
       " 'Negative electrode thermal conductivity [W.m-1.K-1]': 1.7,\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",
       " 'Outer SEI open-circuit potential [V]': 0.8,\n",
       " 'Outer SEI partial molar volume [m3.mol-1]': 9.585e-05,\n",
       " 'Outer SEI solvent diffusivity [m2.s-1]': 2.5000000000000002e-22,\n",
       " 'Positive current collector conductivity [S.m-1]': 36914000.0,\n",
       " 'Positive current collector density [kg.m-3]': 2700.0,\n",
       " 'Positive current collector specific heat capacity [J.kg-1.K-1]': 897.0,\n",
       " 'Positive current collector thermal conductivity [W.m-1.K-1]': 237.0,\n",
       " 'Positive current collector thickness [m]': 1.6e-05,\n",
       " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n",
       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Positive electrode OCP [V]': <function nmc_LGM50_ocp_Chen2020 at 0x7fb7e366f520>,\n",
       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
       " 'Positive electrode active material volume fraction': 0.665,\n",
       " 'Positive electrode charge transfer coefficient': 0.5,\n",
       " 'Positive electrode conductivity [S.m-1]': 0.18,\n",
       " 'Positive electrode density [kg.m-3]': 3262.0,\n",
       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
       " 'Positive electrode double-layer capacity [F.m-2]': 0.2,\n",
       " 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7fb7e366f5b0>,\n",
       " 'Positive electrode porosity': 0.335,\n",
       " 'Positive electrode reaction-driven LAM factor [m3.mol-1]': 0.0,\n",
       " 'Positive electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n",
       " 'Positive electrode thermal conductivity [W.m-1.K-1]': 2.1,\n",
       " 'Positive electrode thickness [m]': 7.56e-05,\n",
       " 'Positive particle radius [m]': 5.22e-06,\n",
       " 'Ratio of lithium moles to SEI moles': 2.0,\n",
       " 'Reference temperature [K]': 298.15,\n",
       " 'SEI growth activation energy [J.mol-1]': 0.0,\n",
       " 'SEI kinetic rate constant [m.s-1]': 1e-12,\n",
       " 'SEI open-circuit potential [V]': 0.4,\n",
       " 'SEI reaction exchange current density [A.m-2]': 1.5e-07,\n",
       " 'SEI resistivity [Ohm.m]': 200000.0,\n",
       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Separator density [kg.m-3]': 397.0,\n",
       " 'Separator porosity': 0.47,\n",
       " 'Separator specific heat capacity [J.kg-1.K-1]': 700.0,\n",
       " 'Separator thermal conductivity [W.m-1.K-1]': 0.16,\n",
       " 'Separator thickness [m]': 1.2e-05,\n",
       " 'Thermodynamic factor': 1.0,\n",
       " 'Total heat transfer coefficient [W.m-2.K-1]': 10.0,\n",
       " 'Upper voltage cut-off [V]': 4.2,\n",
       " 'citations': ['Chen2020']}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param=pybamm.ParameterValues(\"Chen2020\")\n",
    "param['Initial inner SEI thickness [m]']=1e-13\n",
    "param['Initial outer SEI thickness [m]']=1e-13\n",
    "#param['Inner SEI open-circuit potential [V]']=0.25\n",
    "param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5f1a08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_pts = {\n",
    "    \"x_n\": 5,  # negative electrode\n",
    "    \"x_s\": 5,  # separator \n",
    "    \"x_p\": 5,  # positive electrode\n",
    "    \"r_n\": 10,  # negative particle\n",
    "    \"r_p\": 20,  # positive particle\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c404a73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Time [s]',\n",
       " 'Time [min]',\n",
       " 'Time [h]',\n",
       " 'x [m]',\n",
       " 'x_n [m]',\n",
       " 'x_s [m]',\n",
       " 'x_p [m]',\n",
       " 'r_n [m]',\n",
       " 'r_p [m]',\n",
       " 'Current variable [A]',\n",
       " 'Total current density [A.m-2]',\n",
       " 'Current [A]',\n",
       " 'C-rate',\n",
       " 'Discharge capacity [A.h]',\n",
       " 'Throughput capacity [A.h]',\n",
       " 'Discharge energy [W.h]',\n",
       " 'Throughput energy [W.h]',\n",
       " 'Porosity',\n",
       " 'Negative electrode porosity',\n",
       " 'X-averaged negative electrode porosity',\n",
       " 'Separator porosity',\n",
       " 'X-averaged separator porosity',\n",
       " 'Positive electrode porosity',\n",
       " 'X-averaged positive electrode porosity',\n",
       " 'Porosity change',\n",
       " 'Negative electrode porosity change [s-1]',\n",
       " 'X-averaged negative electrode porosity change [s-1]',\n",
       " 'Separator porosity change [s-1]',\n",
       " 'X-averaged separator porosity change [s-1]',\n",
       " 'Positive electrode porosity change [s-1]',\n",
       " 'X-averaged positive electrode porosity change [s-1]',\n",
       " 'Negative electrode interface utilisation variable',\n",
       " 'X-averaged negative electrode interface utilisation variable',\n",
       " 'Negative electrode interface utilisation',\n",
       " 'X-averaged negative electrode interface utilisation',\n",
       " 'Positive electrode interface utilisation variable',\n",
       " 'X-averaged positive electrode interface utilisation variable',\n",
       " 'Positive electrode interface utilisation',\n",
       " 'X-averaged positive electrode interface utilisation',\n",
       " 'Negative particle crack length [m]',\n",
       " 'X-averaged negative particle crack length [m]',\n",
       " 'Negative particle cracking rate [m.s-1]',\n",
       " 'X-averaged negative particle cracking rate [m.s-1]',\n",
       " 'Positive particle crack length [m]',\n",
       " 'X-averaged positive particle crack length [m]',\n",
       " 'Positive particle cracking rate [m.s-1]',\n",
       " 'X-averaged positive particle cracking rate [m.s-1]',\n",
       " 'Negative electrode active material volume fraction',\n",
       " 'X-averaged negative electrode active material volume fraction',\n",
       " 'Negative electrode capacity [A.h]',\n",
       " 'Negative particle radius',\n",
       " 'Negative particle radius [m]',\n",
       " 'X-averaged negative particle radius [m]',\n",
       " 'Negative electrode surface area to volume ratio [m-1]',\n",
       " 'X-averaged negative electrode surface area to volume ratio [m-1]',\n",
       " 'Negative electrode active material volume fraction change [s-1]',\n",
       " 'X-averaged negative electrode active material volume fraction change [s-1]',\n",
       " 'Loss of lithium due to loss of active material in negative electrode [mol]',\n",
       " 'Positive electrode active material volume fraction',\n",
       " 'X-averaged positive electrode active material volume fraction',\n",
       " 'Positive electrode capacity [A.h]',\n",
       " 'Positive particle radius',\n",
       " 'Positive particle radius [m]',\n",
       " 'X-averaged positive particle radius [m]',\n",
       " 'Positive electrode surface area to volume ratio [m-1]',\n",
       " 'X-averaged positive electrode surface area to volume ratio [m-1]',\n",
       " 'Positive electrode active material volume fraction change [s-1]',\n",
       " 'X-averaged positive electrode active material volume fraction change [s-1]',\n",
       " 'Loss of lithium due to loss of active material in positive electrode [mol]',\n",
       " 'Separator pressure [Pa]',\n",
       " 'X-averaged separator pressure [Pa]',\n",
       " 'negative electrode transverse volume-averaged velocity [m.s-1]',\n",
       " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-1]',\n",
       " 'separator transverse volume-averaged velocity [m.s-1]',\n",
       " 'X-averaged separator transverse volume-averaged velocity [m.s-1]',\n",
       " 'positive electrode transverse volume-averaged velocity [m.s-1]',\n",
       " 'X-averaged positive electrode transverse volume-averaged velocity [m.s-1]',\n",
       " 'Transverse volume-averaged velocity [m.s-1]',\n",
       " 'negative electrode transverse volume-averaged acceleration [m.s-2]',\n",
       " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n",
       " 'separator transverse volume-averaged acceleration [m.s-2]',\n",
       " 'X-averaged separator transverse volume-averaged acceleration [m.s-2]',\n",
       " 'positive electrode transverse volume-averaged acceleration [m.s-2]',\n",
       " 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]',\n",
       " 'Transverse volume-averaged acceleration [m.s-2]',\n",
       " 'Negative electrode volume-averaged velocity [m.s-1]',\n",
       " 'Negative electrode volume-averaged acceleration [m.s-2]',\n",
       " 'X-averaged negative electrode volume-averaged acceleration [m.s-2]',\n",
       " 'Negative electrode pressure [Pa]',\n",
       " 'X-averaged negative electrode pressure [Pa]',\n",
       " 'Positive electrode volume-averaged velocity [m.s-1]',\n",
       " 'Positive electrode volume-averaged acceleration [m.s-2]',\n",
       " 'X-averaged positive electrode volume-averaged acceleration [m.s-2]',\n",
       " 'Positive electrode pressure [Pa]',\n",
       " 'X-averaged positive electrode pressure [Pa]',\n",
       " 'Negative particle stoichiometry',\n",
       " 'Negative particle concentration',\n",
       " 'Negative particle concentration [mol.m-3]',\n",
       " 'X-averaged negative particle concentration',\n",
       " 'X-averaged negative particle concentration [mol.m-3]',\n",
       " 'R-averaged negative particle concentration',\n",
       " 'R-averaged negative particle concentration [mol.m-3]',\n",
       " 'Average negative particle concentration',\n",
       " 'Average negative particle concentration [mol.m-3]',\n",
       " 'Negative particle surface stoichiometry',\n",
       " 'Negative particle surface concentration',\n",
       " 'Negative particle surface concentration [mol.m-3]',\n",
       " 'X-averaged negative particle surface concentration',\n",
       " 'X-averaged negative particle surface concentration [mol.m-3]',\n",
       " 'Negative electrode extent of lithiation',\n",
       " 'X-averaged negative electrode extent of lithiation',\n",
       " 'Minimum negative particle concentration',\n",
       " 'Maximum negative particle concentration',\n",
       " 'Minimum negative particle concentration [mol.m-3]',\n",
       " 'Maximum negative particle concentration [mol.m-3]',\n",
       " 'Minimum negative particle surface concentration',\n",
       " 'Maximum negative particle surface concentration',\n",
       " 'Minimum negative particle surface concentration [mol.m-3]',\n",
       " 'Maximum negative particle surface concentration [mol.m-3]',\n",
       " 'Positive particle stoichiometry',\n",
       " 'Positive particle concentration',\n",
       " 'Positive particle concentration [mol.m-3]',\n",
       " 'X-averaged positive particle concentration',\n",
       " 'X-averaged positive particle concentration [mol.m-3]',\n",
       " 'R-averaged positive particle concentration',\n",
       " 'R-averaged positive particle concentration [mol.m-3]',\n",
       " 'Average positive particle concentration',\n",
       " 'Average positive particle concentration [mol.m-3]',\n",
       " 'Positive particle surface stoichiometry',\n",
       " 'Positive particle surface concentration',\n",
       " 'Positive particle surface concentration [mol.m-3]',\n",
       " 'X-averaged positive particle surface concentration',\n",
       " 'X-averaged positive particle surface concentration [mol.m-3]',\n",
       " 'Positive electrode extent of lithiation',\n",
       " 'X-averaged positive electrode extent of lithiation',\n",
       " 'Minimum positive particle concentration',\n",
       " 'Maximum positive particle concentration',\n",
       " 'Minimum positive particle concentration [mol.m-3]',\n",
       " 'Maximum positive particle concentration [mol.m-3]',\n",
       " 'Minimum positive particle surface concentration',\n",
       " 'Maximum positive particle surface concentration',\n",
       " 'Minimum positive particle surface concentration [mol.m-3]',\n",
       " 'Maximum positive particle surface concentration [mol.m-3]',\n",
       " 'Negative electrode potential [V]',\n",
       " 'X-averaged negative electrode potential [V]',\n",
       " 'Negative electrode ohmic losses [V]',\n",
       " 'X-averaged negative electrode ohmic losses [V]',\n",
       " 'Gradient of negative electrode potential [V.m-1]',\n",
       " 'Positive electrode potential [V]',\n",
       " 'X-averaged positive electrode potential [V]',\n",
       " 'Positive electrode ohmic losses [V]',\n",
       " 'X-averaged positive electrode ohmic losses [V]',\n",
       " 'Gradient of positive electrode potential [V.m-1]',\n",
       " 'Porosity times concentration [mol.m-3]',\n",
       " 'Negative electrode porosity times concentration [mol.m-3]',\n",
       " 'Separator porosity times concentration [mol.m-3]',\n",
       " 'Positive electrode porosity times concentration [mol.m-3]',\n",
       " 'Total lithium in electrolyte [mol]',\n",
       " 'Electrolyte potential [V]',\n",
       " 'X-averaged electrolyte potential [V]',\n",
       " 'X-averaged electrolyte overpotential [V]',\n",
       " 'Gradient of electrolyte potential [V.m-1]',\n",
       " 'Negative electrolyte potential [V]',\n",
       " 'X-averaged negative electrolyte potential [V]',\n",
       " 'Gradient of negative electrolyte potential [V.m-1]',\n",
       " 'Separator electrolyte potential [V]',\n",
       " 'X-averaged separator electrolyte potential [V]',\n",
       " 'Gradient of separator electrolyte potential [V.m-1]',\n",
       " 'Positive electrolyte potential [V]',\n",
       " 'X-averaged positive electrolyte potential [V]',\n",
       " 'Gradient of positive electrolyte potential [V.m-1]',\n",
       " 'Ambient temperature [K]',\n",
       " 'Cell temperature [K]',\n",
       " 'Negative current collector temperature [K]',\n",
       " 'Positive current collector temperature [K]',\n",
       " 'X-averaged cell temperature [K]',\n",
       " 'Volume-averaged cell temperature [K]',\n",
       " 'Negative electrode temperature [K]',\n",
       " 'X-averaged negative electrode temperature [K]',\n",
       " 'Separator temperature [K]',\n",
       " 'X-averaged separator temperature [K]',\n",
       " 'Positive electrode temperature [K]',\n",
       " 'X-averaged positive electrode temperature [K]',\n",
       " 'Ambient temperature [C]',\n",
       " 'Cell temperature [C]',\n",
       " 'Negative current collector temperature [C]',\n",
       " 'Positive current collector temperature [C]',\n",
       " 'X-averaged cell temperature [C]',\n",
       " 'Volume-averaged cell temperature [C]',\n",
       " 'Negative electrode temperature [C]',\n",
       " 'X-averaged negative electrode temperature [C]',\n",
       " 'Separator temperature [C]',\n",
       " 'X-averaged separator temperature [C]',\n",
       " 'Positive electrode temperature [C]',\n",
       " 'X-averaged positive electrode temperature [C]',\n",
       " 'Negative current collector potential [V]',\n",
       " 'Inner SEI thickness [m]',\n",
       " 'Outer SEI thickness [m]',\n",
       " 'X-averaged inner SEI thickness [m]',\n",
       " 'X-averaged outer SEI thickness [m]',\n",
       " 'SEI [m]',\n",
       " 'Total SEI thickness [m]',\n",
       " 'X-averaged SEI thickness [m]',\n",
       " 'X-averaged total SEI thickness [m]',\n",
       " 'X-averaged negative electrode resistance [Ohm.m2]',\n",
       " 'Inner SEI interfacial current density [A.m-2]',\n",
       " 'X-averaged inner SEI interfacial current density [A.m-2]',\n",
       " 'Outer SEI interfacial current density [A.m-2]',\n",
       " 'X-averaged outer SEI interfacial current density [A.m-2]',\n",
       " 'SEI interfacial current density [A.m-2]',\n",
       " 'X-averaged SEI interfacial current density [A.m-2]',\n",
       " 'Inner SEI on cracks thickness [m]',\n",
       " 'Outer SEI on cracks thickness [m]',\n",
       " 'X-averaged inner SEI on cracks thickness [m]',\n",
       " 'X-averaged outer SEI on cracks thickness [m]',\n",
       " 'SEI on cracks [m]',\n",
       " 'Total SEI on cracks thickness [m]',\n",
       " 'X-averaged SEI on cracks thickness [m]',\n",
       " 'X-averaged total SEI on cracks thickness [m]',\n",
       " 'Inner SEI on cracks interfacial current density [A.m-2]',\n",
       " 'X-averaged inner SEI on cracks interfacial current density [A.m-2]',\n",
       " 'Outer SEI on cracks interfacial current density [A.m-2]',\n",
       " 'X-averaged outer SEI on cracks interfacial current density [A.m-2]',\n",
       " 'SEI on cracks interfacial current density [A.m-2]',\n",
       " 'X-averaged SEI on cracks interfacial current density [A.m-2]',\n",
       " 'Lithium plating concentration [mol.m-3]',\n",
       " 'X-averaged lithium plating concentration [mol.m-3]',\n",
       " 'Dead lithium concentration [mol.m-3]',\n",
       " 'X-averaged dead lithium concentration [mol.m-3]',\n",
       " 'Lithium plating thickness [m]',\n",
       " 'X-averaged lithium plating thickness [m]',\n",
       " 'Dead lithium thickness [m]',\n",
       " 'X-averaged dead lithium thickness [m]',\n",
       " 'Loss of lithium to lithium plating [mol]',\n",
       " 'Loss of capacity to lithium plating [A.h]',\n",
       " 'Negative electrode lithium plating reaction overpotential [V]',\n",
       " 'X-averaged negative electrode lithium plating reaction overpotential [V]',\n",
       " 'Lithium plating interfacial current density [A.m-2]',\n",
       " 'X-averaged lithium plating interfacial current density [A.m-2]',\n",
       " 'Negative crack surface to volume ratio [m-1]',\n",
       " 'Negative electrode roughness ratio',\n",
       " 'X-averaged negative electrode roughness ratio',\n",
       " 'Positive crack surface to volume ratio [m-1]',\n",
       " 'Positive electrode roughness ratio',\n",
       " 'X-averaged positive electrode roughness ratio',\n",
       " 'Electrolyte transport efficiency',\n",
       " 'Negative electrolyte transport efficiency',\n",
       " 'X-averaged negative electrolyte transport efficiency',\n",
       " 'Separator electrolyte transport efficiency',\n",
       " 'X-averaged separator electrolyte transport efficiency',\n",
       " 'Positive electrolyte transport efficiency',\n",
       " 'X-averaged positive electrolyte transport efficiency',\n",
       " 'Electrode transport efficiency',\n",
       " 'Negative electrode transport efficiency',\n",
       " 'X-averaged negative electrode transport efficiency',\n",
       " 'Separator electrode transport efficiency',\n",
       " 'X-averaged separator electrode transport efficiency',\n",
       " 'Positive electrode transport efficiency',\n",
       " 'X-averaged positive electrode transport efficiency',\n",
       " 'Separator volume-averaged velocity [m.s-1]',\n",
       " 'Separator volume-averaged acceleration [m.s-2]',\n",
       " 'X-averaged separator volume-averaged acceleration [m.s-2]',\n",
       " 'Volume-averaged velocity [m.s-1]',\n",
       " 'Volume-averaged acceleration [m.s-1]',\n",
       " 'X-averaged volume-averaged acceleration [m.s-1]',\n",
       " 'Pressure [Pa]',\n",
       " 'Negative electrode stoichiometry',\n",
       " 'Negative electrode volume-averaged concentration',\n",
       " 'Negative electrode volume-averaged concentration [mol.m-3]',\n",
       " 'Total lithium in primary phase in negative electrode [mol]',\n",
       " 'Positive electrode stoichiometry',\n",
       " 'Positive electrode volume-averaged concentration',\n",
       " 'Positive electrode volume-averaged concentration [mol.m-3]',\n",
       " 'Total lithium in primary phase in positive electrode [mol]',\n",
       " 'Negative electrode effective conductivity',\n",
       " 'Negative electrode current density [A.m-2]',\n",
       " 'Positive electrode effective conductivity',\n",
       " 'Positive electrode current density [A.m-2]',\n",
       " 'Electrode current density [A.m-2]',\n",
       " 'Positive current collector potential [V]',\n",
       " 'Local voltage [V]',\n",
       " 'Terminal voltage [V]',\n",
       " 'Voltage [V]',\n",
       " 'Contact overpotential [V]',\n",
       " 'Electrolyte concentration concatenation [mol.m-3]',\n",
       " 'Negative electrolyte concentration [mol.m-3]',\n",
       " 'X-averaged negative electrolyte concentration [mol.m-3]',\n",
       " 'Separator electrolyte concentration [mol.m-3]',\n",
       " 'X-averaged separator electrolyte concentration [mol.m-3]',\n",
       " 'Positive electrolyte concentration [mol.m-3]',\n",
       " 'X-averaged positive electrolyte concentration [mol.m-3]',\n",
       " 'Negative electrolyte concentration [Molar]',\n",
       " 'X-averaged negative electrolyte concentration [Molar]',\n",
       " 'Separator electrolyte concentration [Molar]',\n",
       " 'X-averaged separator electrolyte concentration [Molar]',\n",
       " 'Positive electrolyte concentration [Molar]',\n",
       " 'X-averaged positive electrolyte concentration [Molar]',\n",
       " 'Electrolyte concentration [mol.m-3]',\n",
       " 'X-averaged electrolyte concentration [mol.m-3]',\n",
       " 'Electrolyte concentration [Molar]',\n",
       " 'X-averaged electrolyte concentration [Molar]',\n",
       " 'Electrolyte current density [A.m-2]',\n",
       " 'X-averaged concentration overpotential [V]',\n",
       " 'X-averaged electrolyte ohmic losses [V]',\n",
       " 'Negative electrode surface potential difference [V]',\n",
       " 'Negative electrode surface potential difference at separator interface [V]',\n",
       " 'X-averaged negative electrode surface potential difference [V]',\n",
       " 'Positive electrode surface potential difference [V]',\n",
       " 'Positive electrode surface potential difference at separator interface [V]',\n",
       " 'X-averaged positive electrode surface potential difference [V]',\n",
       " 'Ohmic heating [W.m-3]',\n",
       " 'X-averaged Ohmic heating [W.m-3]',\n",
       " 'Volume-averaged Ohmic heating [W.m-3]',\n",
       " 'Irreversible electrochemical heating [W.m-3]',\n",
       " 'X-averaged irreversible electrochemical heating [W.m-3]',\n",
       " 'Volume-averaged irreversible electrochemical heating [W.m-3]',\n",
       " 'Reversible heating [W.m-3]',\n",
       " 'X-averaged reversible heating [W.m-3]',\n",
       " 'Volume-averaged reversible heating [W.m-3]',\n",
       " 'Total heating [W.m-3]',\n",
       " 'X-averaged total heating [W.m-3]',\n",
       " 'Volume-averaged total heating [W.m-3]',\n",
       " 'Current collector current density [A.m-2]',\n",
       " 'Inner SEI concentration [mol.m-3]',\n",
       " 'X-averaged inner SEI concentration [mol.m-3]',\n",
       " 'Outer SEI concentration [mol.m-3]',\n",
       " 'X-averaged outer SEI concentration [mol.m-3]',\n",
       " 'SEI concentration [mol.m-3]',\n",
       " 'X-averaged SEI concentration [mol.m-3]',\n",
       " 'Loss of lithium to SEI [mol]',\n",
       " 'Loss of capacity to SEI [A.h]',\n",
       " 'X-averaged negative electrode SEI interfacial current density [A.m-2]',\n",
       " 'Negative electrode SEI interfacial current density [A.m-2]',\n",
       " 'Positive electrode SEI interfacial current density [A.m-2]',\n",
       " 'X-averaged positive electrode SEI volumetric interfacial current density [A.m-2]',\n",
       " 'Positive electrode SEI volumetric interfacial current density [A.m-3]',\n",
       " 'Negative electrode SEI volumetric interfacial current density [A.m-3]',\n",
       " 'X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]',\n",
       " 'Inner SEI on cracks concentration [mol.m-3]',\n",
       " 'X-averaged inner SEI on cracks concentration [mol.m-3]',\n",
       " 'Outer SEI on cracks concentration [mol.m-3]',\n",
       " 'X-averaged outer SEI on cracks concentration [mol.m-3]',\n",
       " 'SEI on cracks concentration [mol.m-3]',\n",
       " 'X-averaged SEI on cracks concentration [mol.m-3]',\n",
       " 'Loss of lithium to SEI on cracks [mol]',\n",
       " 'Loss of capacity to SEI on cracks [A.h]',\n",
       " 'X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]',\n",
       " 'Negative electrode SEI on cracks interfacial current density [A.m-2]',\n",
       " 'Positive electrode SEI on cracks interfacial current density [A.m-2]',\n",
       " 'X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-2]',\n",
       " 'Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
       " 'Negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
       " 'X-averaged negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
       " 'Negative electrode lithium plating interfacial current density [A.m-2]',\n",
       " 'X-averaged negative electrode lithium plating interfacial current density [A.m-2]',\n",
       " 'Lithium plating volumetric interfacial current density [A.m-3]',\n",
       " 'X-averaged lithium plating volumetric interfacial current density [A.m-3]',\n",
       " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n",
       " 'X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
       " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n",
       " 'Positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
       " 'Negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
       " 'X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
       " 'Negative electrode open-circuit potential [V]',\n",
       " 'X-averaged negative electrode open-circuit potential [V]',\n",
       " 'Negative electrode bulk open-circuit potential [V]',\n",
       " 'Negative particle concentration overpotential [V]',\n",
       " 'Negative electrode entropic change [V.K-1]',\n",
       " 'X-averaged negative electrode entropic change [V.K-1]',\n",
       " 'Positive electrode open-circuit potential [V]',\n",
       " 'X-averaged positive electrode open-circuit potential [V]',\n",
       " 'Positive electrode bulk open-circuit potential [V]',\n",
       " 'Positive particle concentration overpotential [V]',\n",
       " 'Positive electrode entropic change [V.K-1]',\n",
       " 'X-averaged positive electrode entropic change [V.K-1]',\n",
       " 'Negative electrode interfacial current density [A.m-2]',\n",
       " 'X-averaged negative electrode interfacial current density [A.m-2]',\n",
       " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n",
       " 'X-averaged negative electrode total volumetric interfacial current density [A.m-3]',\n",
       " 'Negative electrode exchange current density [A.m-2]',\n",
       " 'X-averaged negative electrode exchange current density [A.m-2]',\n",
       " 'Negative electrode reaction overpotential [V]',\n",
       " 'X-averaged negative electrode reaction overpotential [V]',\n",
       " 'Negative electrode volumetric interfacial current density [A.m-3]',\n",
       " 'X-averaged negative electrode volumetric interfacial current density [A.m-3]',\n",
       " 'SEI film overpotential [V]',\n",
       " 'X-averaged SEI film overpotential [V]',\n",
       " 'Positive electrode interfacial current density [A.m-2]',\n",
       " 'X-averaged positive electrode interfacial current density [A.m-2]',\n",
       " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n",
       " 'X-averaged positive electrode total volumetric interfacial current density [A.m-3]',\n",
       " 'Positive electrode exchange current density [A.m-2]',\n",
       " 'X-averaged positive electrode exchange current density [A.m-2]',\n",
       " 'Positive electrode reaction overpotential [V]',\n",
       " 'X-averaged positive electrode reaction overpotential [V]',\n",
       " 'Positive electrode volumetric interfacial current density [A.m-3]',\n",
       " 'X-averaged positive electrode volumetric interfacial current density [A.m-3]',\n",
       " 'Negative particle rhs [mol.m-3.s-1]',\n",
       " 'Negative particle bc [mol.m-2]',\n",
       " 'Negative particle effective diffusivity [m2.s-1]',\n",
       " 'X-averaged negative particle effective diffusivity [m2.s-1]',\n",
       " 'Negative particle flux [mol.m-2.s-1]',\n",
       " 'Positive particle rhs [mol.m-3.s-1]',\n",
       " 'Positive particle bc [mol.m-2]',\n",
       " 'Positive particle effective diffusivity [m2.s-1]',\n",
       " 'X-averaged positive particle effective diffusivity [m2.s-1]',\n",
       " 'Positive particle flux [mol.m-2.s-1]',\n",
       " 'Electrolyte flux [mol.m-2.s-1]',\n",
       " 'Electrolyte diffusion flux [mol.m-2.s-1]',\n",
       " 'Electrolyte migration flux [mol.m-2.s-1]',\n",
       " 'Electrolyte convection flux [mol.m-2.s-1]',\n",
       " 'Sum of negative electrode electrolyte reaction source terms [A.m-3]',\n",
       " 'Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]',\n",
       " 'Sum of negative electrode volumetric interfacial current densities [A.m-3]',\n",
       " 'Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]',\n",
       " 'Sum of positive electrode electrolyte reaction source terms [A.m-3]',\n",
       " 'Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]',\n",
       " 'Sum of positive electrode volumetric interfacial current densities [A.m-3]',\n",
       " 'Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]',\n",
       " 'Interfacial current density [A.m-2]',\n",
       " 'Exchange current density [A.m-2]',\n",
       " 'Sum of volumetric interfacial current densities [A.m-3]',\n",
       " 'Sum of electrolyte reaction source terms [A.m-3]',\n",
       " 'Surface open-circuit voltage [V]',\n",
       " 'Bulk open-circuit voltage [V]',\n",
       " 'Particle concentration overpotential [V]',\n",
       " 'X-averaged reaction overpotential [V]',\n",
       " 'X-averaged solid phase ohmic losses [V]',\n",
       " 'Battery open-circuit voltage [V]',\n",
       " 'Battery negative electrode bulk open-circuit potential [V]',\n",
       " 'Battery positive electrode bulk open-circuit potential [V]',\n",
       " 'Battery particle concentration overpotential [V]',\n",
       " 'Battery negative particle concentration overpotential [V]',\n",
       " 'Battery positive particle concentration overpotential [V]',\n",
       " 'X-averaged battery reaction overpotential [V]',\n",
       " 'X-averaged battery negative reaction overpotential [V]',\n",
       " 'X-averaged battery positive reaction overpotential [V]',\n",
       " 'X-averaged battery solid phase ohmic losses [V]',\n",
       " 'X-averaged battery negative solid phase ohmic losses [V]',\n",
       " 'X-averaged battery positive solid phase ohmic losses [V]',\n",
       " 'X-averaged battery electrolyte ohmic losses [V]',\n",
       " 'X-averaged battery concentration overpotential [V]',\n",
       " 'Battery voltage [V]',\n",
       " 'Change in open-circuit voltage [V]',\n",
       " 'Local ECM resistance [Ohm]',\n",
       " 'Terminal power [W]',\n",
       " 'Power [W]',\n",
       " 'Resistance [Ohm]',\n",
       " 'Total lithium in negative electrode [mol]',\n",
       " 'LAM_ne [%]',\n",
       " 'Loss of active material in negative electrode [%]',\n",
       " 'Total lithium in positive electrode [mol]',\n",
       " 'LAM_pe [%]',\n",
       " 'Loss of active material in positive electrode [%]',\n",
       " 'LLI [%]',\n",
       " 'Loss of lithium inventory [%]',\n",
       " 'Loss of lithium inventory, including electrolyte [%]',\n",
       " 'Total lithium [mol]',\n",
       " 'Total lithium in particles [mol]',\n",
       " 'Total lithium capacity [A.h]',\n",
       " 'Total lithium capacity in particles [A.h]',\n",
       " 'Total lithium lost [mol]',\n",
       " 'Total lithium lost from particles [mol]',\n",
       " 'Total lithium lost from electrolyte [mol]',\n",
       " 'Total lithium lost to side reactions [mol]',\n",
       " 'Total capacity lost to side reactions [A.h]']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cycle_number =3\n",
    "exp = pybamm.Experiment(\n",
    "    [(\"Charge at C/10 until 4.2 V\",\n",
    "       \"Rest for 10 hours\",\n",
    "     \"Discharge at C/10 until 2.5 V\",\n",
    "     \"Rest for 10 hours\")] *cycle_number\n",
    "#      +[(\"Charge at 0.3C  until 4.2 V\", \n",
    "#       \"Rest for 2 hours\", \n",
    "#       \"Discharge at 0.7C until 2.5 V\", \n",
    "#        \"Rest for 2 hours\")]* cycle_number\n",
    ")\n",
    "#print((exp))\n",
    "#print(exp.operating_conditions_cycles)\n",
    "#print(exp.convert_time_to_seconds)\n",
    "pybamm.lithium_ion.DFN().variable_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6339833",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SEIModels=[\"reaction limited\", \"solvent-diffusion limited\", \"interstitial-diffusion limited\" , \"ec reaction limited\"]\n",
    "#SEIModels=[\"reaction limited\", \"solvent-diffusion limited\", \"interstitial-diffusion limited\"]\n",
    "SEIModels=[\"reaction limited\"];\n",
    "#t_eval = np.linspace(0, 4600, 600) # time in seconds\n",
    "# maximum t val is 3600? don't know why? \n",
    "#for ii in np.arange(0.2,0.3,0.3):\n",
    "models=[];\n",
    "solutions=[]\n",
    "for index in range(len(SEIModels)):\n",
    "    models.append (pybamm.lithium_ion.SPMe(\n",
    "    { \"SEI\": SEIModels[index],\n",
    "    \"calculate discharge energy\": \"true\",  # for compatibility with older PyBaMM versions\n",
    "    }))\n",
    "    #\"x-average side reactions\": \"true\"\n",
    "     #sim = pybamm.Simulation(models[index], parameter_values=param, experiment=exp, var_pts=var_pts)\n",
    "    solver = pybamm.CasadiSolver(mode=\"safe\", atol=1e-6, rtol=1e-6)\n",
    "    #solver = pybamm.IDAKLUSolver()\n",
    "    sim = pybamm.Simulation(models[index], parameter_values=param,  var_pts=var_pts, experiment=exp,  solver=solver)\n",
    "    solutions.append(sim.solve(initial_soc=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eec08ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "578dd75b26f048ae837887c8c24b719e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='t', max=119.95470261965863, step=1.1995470261965864)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pybamm.plotting.quick_plot.QuickPlot at 0x7fb7e3569db0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pybamm.dynamic_plot(solutions, output_variables=[\"X-averaged inner SEI thickness [m]\", \n",
    "                     \"X-averaged SEI film overpotential [V]\" ,\"Electrolyte potential [V]\",\n",
    "                                                 \"X-averaged SEI film overpotential [V]\" , \n",
    "                                                 \"Discharge capacity [A.h]\",\n",
    "                                                \"Terminal voltage [V]\", \"Power [W]\",\n",
    "                                                    ], \n",
    "                    labels=SEIModels,variable_limits='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff4b0481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TTz6J0aNHo0mTJqYDhBcuXIhx48bh2WefRbt27TBixAjs27cPzZs3BwDceuut+OKLLzB79mxER0djw4YNpgNpa6tjx451/n7sBVuJYytxoq00TcN3u89i6OzfsPlYNpwcdJjQpyW2P98fU++KgLeb2OkKqPZ0mmb974yu6SOIy8rKkJqaesOPcaf6o9fr4erKo8Wvutm+d+bMGbRs2dLygymIrcSxlTjRVp9vP4V31x0DANwW3hiv39sJbZo03PFk9qSm39/X4jMjJKyoqEj2CEq5+tIY3RxbiWMrcSKt1iSmmxYizwyMwDeP9uRCRAIuRoiIyC79fjIHzy0/CODKqdCfHtC2zh8ISrXDxQgJq4/PkrEngwYNkj2CMthKHFuJq6nV4Qv5+L9v41Fh0DCscwheGdaBCxGJuBghYaWlpTe/EZkcOXJE9gjKYCtxbCXuRq3O5ZbgkYX7UKSvRK9W/pg1KhoOKpyv3YbZzGJEgeNwlXf1xGp0xc32ufPnz1toEvWxlTi2Elddq8vF5Ri/cC8uFurRLsgbn4/rwbfsWgHlFyNX37LKX5QN769nL7V3JSUlAK4/a+xVf/0gP7oxthLHVuL+2qqswoDHv9mP0xeLEeLrhkWP3gJfd75t1xoo/9ZeTdOQlpaGiooKhIaGwsFB+fUVWTlN01BSUoLs7Gz4+fmZTtVPRNbLYNTw1Hfx2HAkCz5uTljxVG9EBPFzfhqa6Ft71fgQlxrodDqEhIQgNTUVZ8+elT2OTSsuLr7u83TsmZ+fX42ffrxkyRKMGTPGghOpi63EsZW4q600TcOra5Kx4UgWXJwc8MW4HlyIWBnlFyPAlbOOhoeH86WaBrZ27Vrcc889ssewCs7OznzZikgR/9l8Eov3pEGnAz4d3QW9WgfIHon+SlNAfn6+BkDLz8+XPYoUGRkZ2qRJk7RWrVppLi4uWrNmzbR77rlH27Rpk0Xn2L9/v0W3Z47x48drADQAmpOTk9ayZUtt2rRpWmlpqbSZVOoVGBioDRw4UPvqq680g8Fg8XlUaiV737LmVppmPT+vNO1KqyV7zmotXlirtXhhrbbo91SLz2DvRH9/28QzI7bszJkz6NOnD/z8/PDhhx8iKioKFRUVWL9+PSZOnIhjx45ZbJbAwECLbas2hgwZgoULF6KiogLx8fEYP348dDod3n//fSnzqNLLYDAgKysLsbGxmDx5MlasWIGffvoJTk6W+/GgSitr2LesuZU1/bwCgLPFTnjp1yQAwMT+bTC+d0uLbp/E8WhPK/evf/0LOp0Oe/fuxciRIxEREYFOnTph6tSpdf6Ie3Pt2LHDotszl6urK4KDgxEWFoYRI0Zg4MCB2Lhxo7R5VOnVtGlTdOvWDS+99BLWrFmDX3/9FYsWLbLoLKq0soZ9y5pbWdPPq4Pn8vBr3FYYNeCB7s3w3KB2Ft0+mYeLESuWm5uL2NhYTJw4sdoDR/38/Cw/lCKSk5Oxc+dOuLi4yB5FKXfeeSeio6OxcuVK2aNYLe5b1bOmn1cl5ZWY8kMijJqGfu2aYObfonh2VSvHl2ms2MmTJ6FpGtq3by97FABXflFZs7Vr18LLywuVlZXQ6/VwcHDAnDlzpM1j7b1upH379jh06JBFt2ntraxp37LWVtb08+qD2BSk5hTD0bs1fhzdFc6O/Hu3tTPr39C8efPQuXNn+Pj4wMfHBzExMfj1119rvM/y5cvRvn17uLm5ISoqCuvWravTwPZEs7JTwJw6dUr2CDXq378/EhMTsWfPHowfPx4TJkzAyJEjpc1j7b1uRNM0i/8t0tpbWdO+Za2trOXn1c6TOVi08wwA4JFId/h68KRmKjBrMdKsWTO89957iI+Px/79+3HnnXfivvvuw+HDh6u9/c6dOzFmzBg89thjOHDgAEaMGIERI0YgOTm5Xoa3deHh4dDpdBY/6OtGrP08Lp6enmjbti2io6OxYMEC7NmzB1999ZW0eay9140cPXoUrVq1sug2rb2VNe1b1trKGn5eFZZVYNqKK8/qje3VHI4lOdJmIfOYtRgZPnw47r77boSHhyMiIgLvvPMOvLy8bnhg0uzZszFkyBBMmzYNHTp0wFtvvYVu3bpJfepcJf7+/hg8eDDmzp2L4uLi667Py8uz6DwqvUbu4OCAl156Ca+88oq0D/hTqddVmzdvRlJSksX/1q9SK9n7lrW2soafV2+tPYL0vFKE+bvjpbs7WG0rul6tX0gzGAxYunQpiouLERMTU+1tdu3ahYEDB1a5bPDgwdi1a1eNj63X61FQUFDly17NnTsXBoMBPXv2xI8//ogTJ07g6NGj+Oyzz27YvaHIfMmjNkaNGgVHR0fMnTtXyvatvZder0dmZibS09ORkJCAd999F/fddx/uuecejBs3zqKzWHurv5K5b1lzK5k/r+KOZmHZ/vPQ6YBZo7rA09XJqltRVWYvRpKSkuDl5QVXV1c8+eSTWLVqFTp27FjtbTMzMxEUFFTlsqCgIGRmZta4jZkzZ8LX19f0FRYWZu6YNqN169ZISEhA//798eyzzyIyMhJ33XUX4uLiMG/ePIvOsnz5cotur66cnJwwadIkfPDBB9X+Ta2hWXuv2NhYhISEoGXLlhgyZAi2bNmCzz77DGvWrLH42WWtvdVfydy3rLmVrJ9Xl4vL8eLKK+cTebxvK/Rs5Q/AulvRX5h7NjW9Xq+dOHFC279/v/biiy9qjRs31g4fPlztbZ2dnbXFixdXuWzu3LlaYGBgjdsoKyvT8vPzTV/nzp2z6zOwWou//rukmrGXOLYSx1bXm/h9vNbihbXagFlbtdLyStPlbCVfg52B1cXFBW3btgUAdO/eHfv27cPs2bPxv//977rbBgcHIysrq8plWVlZNX64GHDlBEOurq7mjkYNrHXr1rJHUAp7iWMrcWxV1c8HL2DtoQw4Ougwa1Q03Jz/fFaPrdRR5zdfG41G6PX6aq+LiYlBXFxclcs2btxo8WMdqH40b95c9ghKYS9xbCWOrf6UXViGV9dceXfmxH5tEB3mV+V6tlKHWYuR6dOnY/v27Thz5gySkpIwffp0bN26FWPHjgUAjBs3DtOnTzfdfvLkyYiNjcWsWbNw7NgxvP7669i/fz8mTZpUv98FWcTWrVtlj6AU9hLHVuLY6gpN0zD9xyTklVSgU6gPJt0Zft1t2EodZr1Mk52djXHjxiEjIwO+vr7o3Lkz1q9fj7vuugsAkJaWBgeHP9c3vXv3xuLFi/HKK6/gpZdeQnh4OFavXo3IyMj6/S6IiMiuLI8/j7hj2XBxdMCsB6Ph4sSzrKpMp2lWctq8GhQUFMDX1xf5+fnw8fGRPY7dSk9PR9OmTWWPoQz2EsdW4tgKOH+5BEM+/Q1F+kq8MKQ9nurXptrbsZV8or+/uZQkYenp6bJHUAp7iWMrcfbeymjU8PyKQyjSV6Jbcz/88/YbH6Rq761UwsUICbPWz8SwVuwljq3E2Xur7/acxc5Tl+Dm7IBZD3aBo8ONP0fJ3luphIsREnbt8UB0c+wljq3E2XOr1JxivLvuKABg+tAOaNXYs8bb23Mr1fCYESIisnoGo4ZR83ciIS0PvdsE4LvHesGhhmdFyDrwmBGqdytXrpQ9glLYSxxbibPXVl/8dhoJaXnwcnXCBw90FlqI2GsrFXExQsJudHI7qh57iWMrcfbY6lhmAT7ecBwAMGN4RzRr5CF0P3tspSouRkgYz2ZoHvYSx1bi7K1VeaURzy47iHKDEQPaB2JU92bC97W3VirjYoSERUREyB5BKewljq3E2VurOVtO4vCFAvh5OGPm36Kg04kfJ2JvrVTGxQgJ27Rpk+wRlMJe4thKnD21OnQ+D3O3nAQAvD0iEoE+bmbd355aqY6LESIisjplFQZMXXYQBqOGezqH4J7OobJHogbExQgJ69Onj+wRlMJe4thKnL20mrUhBSezi9DYyxVv3Ve7zzOzl1a2gIsREpaTkyN7BKWwlzi2EmcPrfam5uLLHakAgPdHRqGRp0utHsceWtkKLkZIWEpKiuwRlMJe4thKnK23KtZX4rnlB6FpwIM9mmFAh6BaP5att7IlXIwQEZHVeHfdUaTllqCpnztevaej7HHIQng6eBJmNBr5WQ9mYC9xbCXOllttO34R4xfsBQB8/3gv9GnbuE6PZ8utVMHTwVO9W7t2rewRlMJe4thKnK22yi+twAsrDgEAxse0qPNCBLDdVraIixESVlxcLHsEpbCXOLYSZ6ut3vjpMDILytCqsSdeHNqhXh7TVlvZIi5GSFjTpk1lj6AU9hLHVuJssVVsciZWHkiHgw74aFRnuLs41svj2mIrW8XFCAmLjKzde/3tFXuJYytxttbqUpEeL69KAgD88/Y26N7Cv94e29Za2TIuRkjY+vXrZY+gFPYSx1bibKmVpml4eVUyLhWXo12QN565K7xeH9+WWtk6LkaIiEiKNYkXEHs4E04OOsx6MBquTvXz8gyph4sREtarVy/ZIyiFvcSxlThbaZWZX4YZa5IBAE8PCEdkU99634attLIHXIyQsKKiItkjKIW9xLGVOFtopWkaXvjxEArKKtG5mS+e6temQbZjC63sBRcjJOzw4cOyR1AKe4ljK3G20GrJ3nPYdvwiXJwc8PGD0XB2bJhfRbbQyl5wMUJERBaTdqkEb/9yBADw/OB2aBvoLXkisgY8HTwJq6iogLOzs+wxlMFe4thKnMqtjEYND32xG3tTc9GzpT+W/PNWODroGmx7KreyFTwdPNW7DRs2yB5BKewljq3Eqdxq4c4z2JuaCw8XR3w0KrpBFyKA2q3sDRcjJKygoED2CEphL3FsJU7VViezi/BB7DEAwMvDOqB5gEeDb1PVVvaIixESFhQUJHsEpbCXOLYSp2KrSoMRzy5LhL7SiNvCG+PvPZtbZLsqtrJXXIyQsB49esgeQSnsJY6txKnYav62Uzh4Ph/ebk744IHO0Oka9uWZq1RsZa+4GCFhv/zyi+wRlMJe4thKnGqtDl/Ix+y4EwCAN+7thBBfd4ttW7VW9oyLESIiahD6SgOeXXYQFQYNgzsF4f6u/BRdqh4XIySse/fuskdQCnuJYytxKrWavekEjmUWwt/TBe/cH2Wxl2euUqmVveNihIRVVFTIHkEp7CWOrcSp0ioh7TLmbzsFAHj3/kg09nK1+AyqtCIuRsgMhw4dkj2CUthLHFuJU6FVabkBzy07CKMG3N+1KYZEhkiZQ4VWdAUXI0REVK/ejz2G0znFCPJxxevDO8kehxTA08GTsNLSUri7W+5IeNWxlzi2EmftrXaeysHfv9gDAFg04Rb0axcobRZrb2UPeDp4qnfbt2+XPYJS2EscW4mz5laFZRWYtvzKSyNjejaXuhABrLsVVcXFCAnLzc2VPYJS2EscW4mz5lZvrz2K9LxShPm74+VhHWSPY9WtqCouRkhYQECA7BGUwl7i2EqctbbafCwLP+w/B50O+PCBaHi5OskeyWpb0fV4zAgJKykpgYdHw3+4la1gL3FsJc4aW10uLsegT7fjYqEej/VthVfv6Sh7JADW2creNMgxIzNnzsQtt9wCb29vBAYGYsSIEUhJSanxPosWLYJOp6vy5ebmZs5myUqsWbNG9ghKYS9xbCXOGlu99tNhXCzUo00TT0wb3E72OCbW2IqqZ9ZiZNu2bZg4cSJ2796NjRs3oqKiAoMGDUJxcXGN9/Px8UFGRobp6+zZs3UamoiIrMOGw5n46eAFODroMOvBLnBzdpQ9EinIrBf1YmNjq/x50aJFCAwMRHx8PG6//fYb3k+n0yE4OLh2E5LV6NKli+wRlMJe4thKnDW1Mhg1fLD+yrPjT9zWGl3C/OQO9BfW1IpqVqcDWPPz8wEA/v7+Nd6uqKgILVq0QFhYGO677z4cPny4xtvr9XoUFBRU+SL5HBx4vLM52EscW4mzpla/JGXgZHYRfN2d8a/+bWSPcx1rakU1q/W/KaPRiClTpqBPnz6IjIy84e3atWuHBQsWYM2aNfjuu+9gNBrRu3dvnD9//ob3mTlzJnx9fU1fYWFhtR2T6lFCQoLsEZTCXuLYSpy1tDIYNXwWdwIA8HjfVvBxc5Y80fWspRXdXK0XIxMnTkRycjKWLl1a4+1iYmIwbtw4dOnSBXfccQdWrlyJJk2a4H//+98N7zN9+nTk5+ebvs6dO1fbMYmIqAFc+6zI+D4tZY9DiqvVG8EnTZqEtWvXYvv27WjWrJlZ93V2dkbXrl1x8uTJG97G1dUVrq6W/4RHqtk999wjewSlsJc4thJnDa1UeFYEsI5WJMasZ0Y0TcOkSZOwatUqbN68Ga1atTJ7gwaDAUlJSQgJkfMpjlR7e/fulT2CUthLHFuJs4ZWqjwrYg2tSIxZz4xMnDgRixcvxpo1a+Dt7Y3MzEwAgK+vr+nDiMaNG4emTZti5syZAIA333wTt956K9q2bYu8vDx8+OGHOHv2LB5//PF6/laooWVnZ8seQSnsJY6txMlupcqzIoD8ViTOrMXIvHnzAAD9+vWrcvnChQvxyCOPAADS0tKqHMF8+fJlPPHEE8jMzESjRo3QvXt37Ny5Ex07WscZ+kicr6+v7BGUwl7i2Eqc7FZrD11Q4lkRQH4rEsfTwZMwvV7PY3nMwF7i2EqczFaVBiMGfbIdp3OK8exdEfj3gHApc4jifiVfg5wOnuzbypUrZY+gFPYSx1biZLZaEX8ep3OK4e/pggl9zT9m0NK4X6mDixEiIrqpsgoDZv9xrMjE/m2t4lN5yXZwMULCajq5HV2PvcSxlThZrb7bfRYZ+WUI9XXD2F7NpcxgLu5X6uBihITxo7jNw17i2EqcjFaFZRWYu+XKuaGmDIxQ5sPwuF+pg4sREsb37JuHvcSxlTgZrb78LRWXSyrQpokn/tatqcW3X1vcr9TBxQgREd3QpSI9vvztNADg2UHt4OTIXxtU/7hXkbChQ4fKHkEp7CWOrcRZutV/t55CcbkBUU19MTQy2KLbrivuV+rgYoSEHTx4UPYISmEvcWwlzpKtLuSV4tvdZwEA0wa3g06ns9i26wP3K3VwMULCLly4IHsEpbCXOLYSZ8lWn2w8jvJKI25t7Y/bwhtbbLv1hfuVOrgYIWFeXl6yR1AKe4ljK3GWanX4Qj5WJJwHADw/pL1yz4oA3K9UwtPBkzCDwQBHRzXe0mcN2EscW4mzRCtN0zD2yz3YeeoShkeH4j9jujbo9hoK9yv5eDp4qnfLli2TPYJS2EscW4mzRKv1h7Ow89QluDg54PnB7Rp8ew2F+5U6uBghIiKTy8XleHVNMgDgidtaIcyfJw6jhsfFCAlr37697BGUwl7i2EpcQ7YqrzRi4uIEXCzUo22gF/59p3V/Ku/NcL9SBxcjJMzf31/2CEphL3FsJa6hWhXpK/F/3+7HzlOX4OniiM8e6qrMad9vhPuVOrgYIWE7d+6UPYJS2EscW4lriFYX8krxwLyd2JJyEa5ODpj3j+7oGKr+mwW4X6mDnwFNRGTHks7n47Gv9yG7UI/GXq74cnwPdAnzkz0W2Rm+tZeE5eTkoHFj9U58JAt7iWMrcfXZav3hTExZmojSCgMigryw4JFb0KyR7Rywyv1KPr61l+rdsWPHZI+gFPYSx1bi6qOVpmmYt/UUnvwuHqUVBtwe0QQrnuptUwsRgPuVSvgyDQk7d+6c7BGUwl7i2EpcXVuVlhvw/I+H8PPBK6dKH9urOd64t5NNfhov9yt1cDFCwtzd3WWPoBT2EsdW4urSKj2vFP/8Zj8OXyiAk4MOr9/bCf+4tUU9TmdduF+pg8eMEBHZgV2nLmHS4gRcKi5HgKcL/ju2G3q1DpA9Ftk4HjNC9W7p0qWyR1AKe4ljK3Hmtqo0GDFrQwr+/uVuXCouR6dQH/z07752sRDhfqUOvkxDwhR4Es2qsJc4thIn2krTNOw4mYN3fjmKY5mFAIBR3Zvhzfsi4e6i9snMRHG/UgcXIyQsPFztU0NbGnuJYytxNbW6VKRH8oUC7Dp1CZuOZuFkdhEAwNfdGW+NiMS90aGWGtMqcL9SBxcjJCw4OFj2CEphL3GWalVpMOJsbglOZRchu1CPnCI9LhWVo1hfCYOmwWDUoGmAhit/o9Y0XP/nPx7ryl+6q15+9W/imun6qpf/eduqj3/ldlqVx0c129U0DR4V+Sje+3uVxzcaNZy/XILLJRVVvl8PF0c82CMMkweEo5GnS13zKYf/DaqDixES9ttvv2HMmDGyx1AGe4lryFa5xeVYmXAeW1KysS/1MsoNxgbZjqUMcDmBHeXV/41fpwPCGnmgZyt/xLQOwKBOQfB2c7bwhNaD/w2qg4sRIrJJJ7MLMW/rafx86ALKK/9cgHi4OKJ1E0+E+rojwMsVTbxc4OnqBEcHHRx0OjjoAJ1OB53uyu11AKDT4Y8/QqcDdKh6/Z//rMMf//vjtrqq1/9xX1S5TFflca69/tpt4o9tpvx+EaP7dDc99tXrg3zc0KaJl90cD0K2hYsREta/f3/ZIyiFvcTVZ6tzuSWYHXcCKxPOw/jHSxlRTX1xf9emuD2iCVo39oSDg67mB7Fi0QFD+PKDIP43qA4uRkjYmTNn+EPQDOwlrj5aZReW4b9bTuH7PWdRYbiyCrmrYxD+1a8NuoT5mZ6BUB33K3FspQ6eZ4SEpaamyh5BKewlri6t8ksq8EHsMdzxwVYs2nkGFQYNfds2xuqJffDFuB7o2ryRzSxEAO5X5mArdfCZERLm7Gy/B8LVBnuJq02rsgoDFvyeivlbT6GgrBIA0CXMD88PbofebW33k1q5X4ljK3XwdPBEpBRN07AuKRPvrjuK9LxSAEC7IG88N7gdBnYItKlnQYhUx9PBU71bsWKF7BGUwl7iRFuVlhsw5YdETFycgPS8UoT4uuHjB6OxbvJtuKtjkF0sRLhfiWMrdfBlGhJWUVFx8xuRCXuJE2lVaTBi4uIEbD6WDUcHHSb2b4un7mhjd29l5X4ljq3UwcUICWvVqpXsEZTCXuJu1krTNLy8Khmbj2XDzdkBiyb0xK128EFv1eF+JY6t1MGXaUhYy5YtZY+gFPYSd7NWn2w6gR/2n4ODDvjPmG52uxABuF+Zg63UwcUICduyZYvsEZTCXuJqarV4Txo+izsBAHh7RBTu6hhkqbGsEvcrcWylDi5GiMhqbTyShVdWJwEAnh4Qjr/3ai55IiJqCFyMkLDbbrtN9ghKYS9x1bWKP3sZ/16SAKMGPNijGZ4ZyI+DB7hfmYOt1GHWYmTmzJm45ZZb4O3tjcDAQIwYMQIpKSk3vd/y5cvRvn17uLm5ISoqCuvWrav1wCRPZmam7BGUwl7i/trq1MUiPP71PpRVGNG/XRO8c3+UXbxtVwT3K3FspQ6zFiPbtm3DxIkTsXv3bmzcuBEVFRUYNGgQiouLb3ifnTt3YsyYMXjsscdw4MABjBgxAiNGjEBycnKdhyfLOnHihOwRlMJe4q5tlV1QhvEL9uJySQWim/li7thucHbkk7hXcb8Sx1bqMOutvbGxsVX+vGjRIgQGBiI+Ph633357tfeZPXs2hgwZgmnTpgEA3nrrLWzcuBFz5szB/Pnzazk2ycC/mZqHvcRdbVVYVoFHFu7D+culaBnggQWP3AIPF56B4Frcr8SxlTrq9NeN/Px8AIC/v/8Nb7Nr1y4MHDiwymWDBw/Grl27bngfvV6PgoKCKl8k30MPPSR7BKWwl7iHHnoI+koD/u/beBzJKEBjLxd8/WhPBHi5yh7N6nC/EsdW6qj1YsRoNGLKlCno06cPIiMjb3i7zMxMBAVVfSteUFBQja/lzZw5E76+vqavsLCw2o5J9Wj16tWyR1AKe4lbtWoVnl12EDtPXYKniyMWTeiJFgGesseyStyvxLGVOmq9GJk4cSKSk5OxdOnS+pwHADB9+nTk5+ebvs6dO1fv2yDzlZaWyh5BKewlRtM0HDp7EWsPZcDZUYf5D3dHZFNf2WNZLe5X4thKHbV6MXbSpElYu3Yttm/fjmbNmtV42+DgYGRlZVW5LCsrC8HBwTe8j6urK1xd+fSsteEzVOZhLzELfj+D/TlX/l700aho3BbeRPJE1o37lTi2UodZz4xomoZJkyZh1apV2Lx5s9B5/2NiYhAXF1flso0bNyImJsa8SUm69u3byx5BKex1c0cuFOC9X48izeCHl+/ugPu6NJU9ktXjfiWOrdRh1mJk4sSJ+O6777B48WJ4e3sjMzMTmZmZVZ4KGzduHKZPn2768+TJkxEbG4tZs2bh2LFjeP3117F//35MmjSp/r4LsoiNGzfKHkEp7FWz8kojpi5LRIVBw1D/i3j8Nn6omQjuV+LYSh1mLUbmzZuH/Px89OvXDyEhIaavH374wXSbtLQ0ZGRkmP7cu3dvLF68GJ9//jmio6OxYsUKrF69usaDXonI9n0WdwLHMgvh7+mCDsE+fBsmkR3TaZqmyR7iZgoKCuDr64v8/Hz4+PjIHsdunT17Fi1atJA9hjLY68YOpF3GyHk7YdSA/47thk4+5WwliPuVOLaST/T3N09rSMJyc3Nlj6AU9qpeabkBzy47CKMG3NclFHdHhbCVGdhKHFupg4sREnbs2DHZIyiFvar34foUnM4pRqC3K964txMAtjIHW4ljK3VwMUJEFrPr1CUs+D0VAPD+A53h5+EieSIisgY8ZoSEGQwGODo6yh5DGexVVZG+EkM+3Y7zl0sxpmcYZv6ts+k6thLHVuLYSj4eM0L1bt26dbJHUAp7VfXOL0dw/nIpmjVyx8vDOla5jq3EsZU4tlIHFyMkrKioSPYISmGvP21JycaSvVc+1uHDB6Lh5Vr15M9sJY6txLGVOrgYIWGhoaGyR1AKe12RV1KOF1YcAgA82qcVYtoEXHcbthLHVuLYSh1cjJCw6Oho2SMohb2ueO2nw8gu1KN1E088P6RdtbdhK3FsJY6t1MHFCAn79ddfZY+gFPYC1iVlYE3iBTjogFmjouHmXP3BhGwljq3EsZU6uBghogZxsVCPV1YnAwD+1a8tujZvJHkiIrJWXIyQsJ49e8oeQSn23EvTNLy8Kgm5xeVoH+yNpweE13h7e25lLrYSx1bq4GKEhJWUlMgeQSn23GtlQjo2HMmCs6MOHz/YBS5ONf+osedW5mIrcWylDi5GSFhycrLsEZRir70u5JXi9Z8PAwCmDIxAx9Cbn6jQXlvVBluJYyt1cDFCRPVG0zS88OMhFJZVokuYH/7v9tayRyIiBfB08CRMr9fD1dVV9hjKsMde3+0+i1dWJ8PVyQHrJt+GNk28hO5nj61qi63EsZV8PB081bu4uDjZIyjF3nqdvVSMd9cdBQC8MKS98EIEsL9WdcFW4thKHVyMkLD8/HzZIyjFnnoZjBqeW34QJeUG3NraH4/0bmnW/e2pVV2xlTi2UgcXIyQsMDBQ9ghKsadeC3akYt+Zy/B0ccSHD0TDwUFn1v3tqVVdsZU4tlIHFyMkjO/ZN4+99DqRVYgPN6QAAF69pyPC/D3Mfgx7aVUf2EocW6mDixEStnbtWtkjKMUeelUYjJi67CDKK43o164JRt8SVqvHsYdW9YWtxLGVOrgYIaJa+++WU0hKz4evuzPeH9kZOp15L88QEQFcjJAZunXrJnsEpdh6r+T0fPxn8wkAwJv3dUKQj1utH8vWW9UnthLHVurgYoSEGY1G2SMoxZZ76SsNmLosEZVGDXdHBePe6NA6PZ4tt6pvbCWOrdTBxQgJS0xMlD2CUmy518cbj+N4VhEae7ngrfsi6/zyjC23qm9sJY6t1MHFCBGZJf5sLj7ffhoA8O79UQjw4hkuiahueDp4ElZSUgIPD/PftmmvbLFXSXkl7p79G85cKsHIbs0w68Ho+nlcG2zVUNhKHFvJx9PBU73bsWOH7BGUYou93v/1GM5cKkGIrxtmDO9Yb49ri60aCluJYyt1cDFCwi5duiR7BKXYWq/fT+bg611nAQAfPNAZvu7O9fbYttaqIbGVOLZSBxcjJMzf31/2CEqxpV4FZRWYtvwgAODhW1vgtvAm9fr4ttSqobGVOLZSB48ZIWGlpaVwd3eXPYYybKnXtOUHsTz+PFoEeGDd07fB09WpXh/fllo1NLYSx1by8ZgRqnerV6+WPYJSbKXXpiNZWB5/Hjod8NGo6HpfiAC208oS2EocW6mDixEiuqHc4nK8uDIJAPDEba1xS0s+7U1E9Y+LERLWuXNn2SMoxRZ6vbomGTlFeoQHemHqXRENth1baGUpbCWOrdTBxQgJc3auv3dP2APVe/188AJ+OZQBRwcdPn6wC9ycHRtsW6q3siS2EsdW6uBihITFx8fLHkEpKvfKLijDq2uSAQCT+rdFVDPfBt2eyq0sja3EsZU6uBghoio0TcOLK5OQV1KByKY+mHRnW9kjEZGN41t7SVhBQQH7m0HVXsv2ncPzPx6Ci6MD1j7dFxFB3g2+TVVbycBW4thKPr61l+rd/v37ZY+gFBV7nb9cgjfXHgEAPDsowiILEUDNVrKwlTi2UgcXIyQsKytL9ghKUa2X0ajh+RWHUKSvRPcWjfD4ba0ttm3VWsnEVuLYSh1cjJAwPt1pHtV6fbPrDHaeugR3Z0fMGhUNRwedxbatWiuZ2EocW6mDx4yQsIqKCr5Vzgwq9Tp9sQh3f/YbyiqMeOu+Tng4pqVFt69SK9nYShxbyddgx4xs374dw4cPR2hoKHQ63U1Pt7t161bodLrrvjIzM83dNEm2YsUK2SMoRZVeBqOGZ5cfRFmFEX3bNsbYXi0sPoMqrawBW4ljK3WYvRgpLi5GdHQ05s6da9b9UlJSkJGRYfoKDAw0d9NE1AA+334aB9Ly4O3qhPcf6AwHC748Q0QEAGZ/4tXQoUMxdOhQszcUGBgIPz8/s+9H1qNTp06yR1CKCr2OZRbgk43HAQAzhndEUz85n3CqQitrwVbi2EodFjuAtUuXLggJCcFdd92F33//vcbb6vV6FBQUVPki+by8vGSPoBRr71VeacTUHw6i3GDEwA5BeKB7M2mzWHsra8JW4thKHQ2+GAkJCcH8+fPx448/4scff0RYWBj69euHhISEG95n5syZ8PX1NX2FhYU19JgkYM+ePbJHUIq195qz+QSOZBSgkYcz3v1bJHQ6eS/PWHsra8JW4thKHWa/TGOudu3aoV27dqY/9+7dG6dOncInn3yCb7/9ttr7TJ8+HVOnTjX9uaCggAsSonp08Fwe5m49BQB4e0QUAr3dJE9ERPaswRcj1enZsyd27Nhxw+tdXV3h6upqwYlIxODBg2WPoBRr7VVWYcDUZYkwGDXcGx2KYZ1DZI9kta2sEVuJYyt1SDnpWWJiIkJC5P8AJPMkJyfLHkEp1trro/UpOHWxGE28XfHmfdZxgJ+1trJGbCWOrdRh9jMjRUVFOHnypOnPqampSExMhL+/P5o3b47p06cjPT0d33zzDQDg008/RatWrdCpUyeUlZXhyy+/xObNm7Fhw4b6+y7IItLT02WPoBRr7LXn9CV89XsqAOD9kVHw83CRPNEV1tjKWrGVOLZSh9mLkf3796N///6mP189tmP8+PFYtGgRMjIykJaWZrq+vLwczz77LNLT0+Hh4YHOnTtj06ZNVR6D1ODp6Sl7BKVYW69ifSWeW3EQmgaM7hGGO9sHyR7JxNpaWTO2EsdW6uDp4EmY0WiEgwM/zkiUtfV64+fDWPj7GTT1c0fslNvg7WY9p8m2tlbWjK3EsZV8DXY6eLJfP/zwg+wRlGJNvc5fLsG3u84CAN79W5RVLUQA62pl7dhKHFupg4sRIjswd8spVBo19GkbgDsimsgeh4ioCi5GSNi154uhm7OWXucvl2D5/nMAgMkDIiRPUz1raaUCthLHVurgYoSENW7cWPYISrGWXtc+K9Kzlb/scaplLa1UwFbi2EodXIyQsJt9phBVZQ29VHhWBLCOVqpgK3FspQ4uRohsmArPihARcTFCwgYOHCh7BKXI7qXKsyKA/FYqYStxbKUOLkZI2PHjx2WPoBTZveZuOanMsyKyW6mErcSxlTq4GCFh155Zl25OZq+zl4qxfP95ANb/rAjAfcscbCWOrdTBxQgJ4ycpm0dmr483HkelUcPtEU2s/lkRgPuWOdhKHFupg6eDJ7IxRzMKcPdnv0HTgLX/7ovIpr6yRyIiO8XTwVO946mVzSOr10frU6BpwLCoEGUWIty3xLGVOLZSBxcjJMxoNMoeQSkyeu0/k4u4Y9lwdNBh6iDrP1bkKu5b4thKHFupg4sREtamTRvZIyjF0r00TcMH61MAAKO6N0ObJl4W3X5dcN8Sx1bi2EodXIyQsKZNm8oeQSmW7rXt+EXsTc2Fi5MDJg8Mt+i264r7lji2EsdW6uBihIRt375d9ghKsWQvo1HDh388KzLu1hYI8XW32LbrA/ctcWwljq3UwcUIkQ1Yl5yBwxcK4OXqhH/1byt7HCIis3AxQsL69esnewSlWKpXhcGIWRuunGny8dtawd/TxSLbrU/ct8SxlTi2UgcXIySMZzM0j6V6fbf7LFJzihHg6YLH+rayyDbrG/ctcWwljq3UwcUICTt9+rTsEZRiiV75JRWYHXcCADB1UAS83ZwbfJsNgfuWOLYSx1bq4GKEhDk5OckeQSmW6PXJpuPIK6lARJAXRvcIa/DtNRTuW+LYShxbqYOngydS1N7UXIz+fBc0Dfj2sZ64LbyJ7JGIiKrg6eCp3v3444+yR1BKQ/ZKzyvFpMUJ0DRgdI8w5Rci3LfEsZU4tlIHn8MiYeXl5bJHUEpD9Tp9sQiPLtqH7EI9IoK88Mo9HRpkO5bEfUscW4ljK3VwMULCWrRoIXsEpTREr92nL+H/vo1HfmkFmjVyx6IJPZU9aPVa3LfEsZU4tlIHFyMkjJ/zYJ767rUi/jymrzyECoOGrs398PnDPdDE27VetyEL9y1xbCWOrdTBY0ZI2ObNm2WPoJT66mU0avhofQqeW34QFQYNwzqHYMkTt9rMQgTgvmUOthLHVurgMyNEVqykvBLPLT+IdUmZAICJ/dvg2bvawcFBJ3kyIqL6w8UICevbt6/sEZRS117nckvwxDf7cSyzEM6OOrxzfxQeVPhcIjXhviWOrcSxlTr4Mg0Jy87Olj2CUurSa+epHNw7ZweOZRaisZcLFj9xq80uRADuW+ZgK3FspQ4uRkjY8ePHZY+glNr00jQNC39PxcNf7cXlkgpENfXFT5P64paW/g0wofXgviWOrcSxlTr4Mg2RlbhcXI5pKw5h09EsAMCILqF4b2RnuDk7Sp6MiKhh8XTwRJLpKw1YEX8eszYcR25xOVwcHfDi0PaY0KcldDoeqEpE6uLp4KnerVmzRvYISrlRL03TkJ5Xip8OXsDLq5IQM3MzXl6VjNzicoQHemHlv3rj0b6t7Gohwn1LHFuJYyt18GUaElZSUmK5bZVX4vTFYpy5VIyLhXpcKirHpWI99BVGGDUNBg0wahrwx/N6GjRcfY7vysXaNf/85+Wo9nKtym2uvRxVLr/mvtVt98+HhwYNzS+fx7cXdkK75rHKKow4c6kYJeWGKt9vkI8rnryjDf5xaws4O9rf3xEsuW+pjq3EsZU6uBghYc2aNWvQxz91sQg/xp/H9hMXcfhCAaz/BcSalTm5Iin/crXXOTno0C7YG71aBaBveABuD28CJztchFzV0PuWLWErcWylDi5GSFjHjh3r/TE1TcOOkzmYv+0Ufj95qcp1/p4uaNXYE8E+bgjwckGApyvcXRzgoNP98QXodDpcfTVDB+DqH3R//iN0qHqbay9Hlcurua/uj9tdcxn+uK0O1d/m6uX6ony4eftVudzJUYcWAZ5o7u9hl8+A3EhD7Fu2iq3EsZU6uBghYRs2bMCYMWPq7fHiz+biw/Up2H06F8CVX9gD2gdiWOcQ9GnTGIE+bvW2LRmWLNmKEfXYy5bV975ly9hKHFupg4sRsrgjFwowa0MK4o5dOSGRi6MD/t6rOR7r2wph/h6SpyMiIkvjYoSExcTE1On+py8W4ZNNJ/DzwQsAAEcHHR7o1gxPDwxHUz/3+hjRqtS1lz1hK3FsJY6t1GH2i9bbt2/H8OHDERoaCp1Oh9WrV9/0Plu3bkW3bt3g6uqKtm3bYtGiRbUYlWTLz8+v1f2yC8owfeUh3PXJdtNC5J7OIdjwzO14/4HONrkQAWrfyx6xlTi2EsdW6jB7MVJcXIzo6GjMnTtX6PapqakYNmwY+vfvj8TEREyZMgWPP/441q9fb/awJNeRI0fMur2+0oA5m0+g30dbsWTvORiMGu5sH4hfnu6LOX/vhjZNvBpoUutgbi97xlbi2EocW6nD7Jdphg4diqFDhwrffv78+WjVqhVmzZoFAOjQoQN27NiBTz75BIMHDzZ386SItEsleOr7eBy+UAAAiA7zwyvDOtj8Z6wQEZH5GvyYkV27dmHgwIFVLhs8eDCmTJlyw/vo9Xro9XrTnwsKChpqPDLDqFGjhG6XXViGsV/txrncUjTycMaM4R1xX3RTODjYzxlFAfFexFbmYCtxbKWOBj/RQWZmJoKCgqpcFhQUhIKCApSWllZ7n5kzZ8LX19f0FRZmux+drpLY2Nib3qZIX4lHF+3DudxStAzwwK+Tb8f9XZvZ3UIEEOtFV7CVOLYSx1bqsMqzLk2fPh35+fmmr3PnzskeiQAUFhbWeH15pRFPfReP5PQCNPZywdeP9kSwr9rnCqmLm/WiP7GVOLYSx1bqaPCXaYKDg5GVlVXlsqysLPj4+MDdvfp3Ubi6usLV1bWhRyMzBQcH3/A6o1HDCz8ewm8ncuDh4ogFj9yCFgGeFpzO+tTUi6piK3FsJY6t1NHgz4zExMQgLi6uymUbN27k+78V1K1btxte98H6FKw6kA4nBx3+O7YbOjfzs9xgVqqmXlQVW4ljK3FspQ6zFyNFRUVITExEYmIigCtv3U1MTERaWhqAKy+xjBs3znT7J598EqdPn8bzzz+PY8eO4b///S+WLVuGZ555pn6+A7KYdevWVXv5ot9TMX/bKQDAeyM7o1+7QEuOZbVu1Iuux1bi2EocW6nD7MXI/v370bVrV3Tt2hUAMHXqVHTt2hUzZswAAGRkZJgWJgDQqlUr/PLLL9i4cSOio6Mxa9YsfPnll3xbr4345VAG3lh75b380wa3wwPd+SmZRERkHp2mWf8HtRcUFMDX1xf5+fnw8fGRPY7dOnnyJNq2bWv6857Tl/DwV3tRbjDi4Vtb4M37Opk++Zau70U3xlbi2EocW8kn+vvbKt9NQ9aprKzM9M/HMgvw+Df7UW4wYnCnILx+Lxcif3VtL6oZW4ljK3FspQ4uRkhYUlISACA9rxTjF+xFYVklbmnZCLMf6gpHOzyPyM1c7UU3x1bi2EocW6mDixEyy+Xicoz7ag+yCvSICPLCl+NugZuzo+yxiIhIYTxmhITlFRRjwneJOJCWhxBfN/z4VG+E2ugn7taHsrIyuLnZ70nfzMFW4thKHFvJx2NGqF5pmoa3v1qBA2l58HFzwteP9uRC5Ca2bt0qewRlsJU4thLHVurgYoSEfLcnDZcvX4aLkwO+euQWRAR5yx7J6l2+fFn2CMpgK3FsJY6t1MHFCN3UmZxivPvLUeQb3TF9aHvc0tJf9khKaNy4sewRlMFW4thKHFupg4sRqpHBqOG55QdRWmGAa9N2GB/TUvZIyuBHHohjK3FsJY6t1MHFCNXoqx2nsf/sZXi5OmGA53k48C28wn7++WfZIyiDrcSxlTi2UgcXI3RDx7MK8dH64wCAV+/pAHe+hZeIiBqAk+wByDpVGIyYuiwR5QYj+rdrggd7hCHFu6vssZRy9fOb6ObYShxbiWMrdfCZEarW3C0nkZxeAF93Z7w3sjNP9U5ERA2GixG6TtL5fMzZfBIA8NaISAT5XDlp0IEDB2SOpRz2EsdW4thKHFupg4sRqqKswoBnlyei0qhhWFQIhncOkT0SERHZOJ4OnqqYue4o/rf9NBp7uWDDM3fA39PFdF1RURG8vLwkTqcW9hLHVuLYShxbycfTwZPZ9p/Jxee/nQYAzPxb5yoLEQDYtWuXjLGUxV7i2EocW4ljK3VwMUIAgJLySjy7/CA0DXigezPc1THoutvk5ORImExd7CWOrcSxlTi2UgcXIwQAmLnuGM5eKkGorxtmDO9Y7W0aNWpk4anUxl7i2EocW4ljK3XwmBHCbycu4uGv9gIAvnusF/qGV/95Dvw4bvOwlzi2EsdW4thKPh4zQkIKyirw/IpDAIBxMS1uuBABgFWrVllqLJvAXuLYShxbiWMrdXAxYufe/PkIMvLL0CLAAy8ObS97HCIiskNcjNixjUeysCL+PHQ6YNaoaHi41PzpAFFRURaazDawlzi2EsdW4thKHVyM2Knc4nJMX3nl5Zl/3tYaPVr63/Q+fO3VPOwljq3EsZU4tlIHFyN2SNM0vLI6CTlF5YgI8sIzd0UI3W/fvn0NPJltYS9xbCWOrcSxlTq4GLFDPx/KwLqkTDg56DBrVBe4OTvKHomIiOwY39prZ7IKyjDok+3IL63AlIHhmDJQ7FkRAMjPz4evr28DTmdb2EscW4ljK3FsJR/f2kvX0TQNL/54CPmlFYhq6ouJ/duadf+EhIQGmsw2sZc4thLHVuLYSh1cjNiRZfvPYUvKRbg4OWDWg9FwdjTvX39mZmYDTWab2EscW4ljK3FspQ4uRuzEudwSvPnzEQDAc4MiEBHkbfZjeHubfx97xl7i2EocW4ljK3XwmBE7YDRq+PuXu7H7dC5uadkIS/8ZA0cHndmPU1lZCSenms9FQn9iL3FsJY6txLGVfDxmhEy+3nUGu0/nwt3ZER+Niq7VQgQAli9fXs+T2Tb2EsdW4thKHFupg4sRG3fqYhHe+/UYAOClYR3QIsBT8kRERERVcTFiwyoNRjy77CD0lUbcFt4Y/+jVvE6P17Fjx3qazD6wlzi2EsdW4thKHVyM2LD/bT+NxHN58HZzwvsjO0Onq93LM1fx/frmYS9xbCWOrcSxlTq4GLFRRzMK8Omm4wCA14d3Qqife50fc9euXXV+DHvCXuLYShxbiWMrdXAxYoPKK42YuuwgKgwa7uoYhL91ayp7JCIiohviYsQGfRZ3AkczCuDv6YJ374+q88szVw0aNKheHsdesJc4thLHVuLYSh1cjNiYA2mX8d+tJwEAb4+IRBNv13p77CNHjtTbY9kD9hLHVuLYShxbqYOLERtSVmHAs8sPwqgB93UJxd1RIfX6+OfPn6/Xx7N17CWOrcSxlTi2UgcXIzbkw/UpOH2xGIHernjj3k71/vgeHh71/pi2jL3EsZU4thLHVurg6eBtxO7TlzDmi93QNGDhhFvQv12g7JGIiMjONejp4OfOnYuWLVvCzc0NvXr1wt69e29420WLFkGn01X5cnNzq81m6QaK9JV4bvlBaBowpmdYgy1ElixZ0iCPa6vYSxxbiWMrcWylDrMXIz/88AOmTp2K1157DQkJCYiOjsbgwYORnZ19w/v4+PggIyPD9HX27Nk6DU1VvfPLUZy/XIpmjdzx8jCecZCIiNRi9mLk448/xhNPPIEJEyagY8eOmD9/Pjw8PLBgwYIb3ken0yE4ONj0FRQUVKeh6U9bUrKxZG8aAOCjUdHwcm24T6iMiIhosMe2Rewljq3EsZU4tlKHWYuR8vJyxMfHY+DAgX8+gIMDBg4cWOOZ7oqKitCiRQuEhYXhvvvuw+HDh2vcjl6vR0FBQZUvul5eSTleWHEIAPBon1a4tXVAg24vMJDHoZiDvcSxlTi2EsdW6jBrMZKTkwODwXDdMxtBQUHIzMys9j7t2rXDggULsGbNGnz33XcwGo3o3bt3jW+5mjlzJnx9fU1fYWFh5oxpN17/6TCyC/Vo3cQTzw9p1+Db27FjR4Nvw5awlzi2EsdW4thKHQ3+1t6YmBiMGzcOXbp0wR133IGVK1eiSZMm+N///nfD+0yfPh35+fmmr3PnzjX0mMr5NSkDqxMvwEEHzBoVDTdnR9kjERER1YpZBxg0btwYjo6OyMrKqnJ5VlYWgoODhR7D2dkZXbt2xcmTJ294G1dXV7i61t+ZQ23NxUI9Xl6dDAD4V7+26Nq8kUW2e+edd1pkO7aCvcSxlTi2EsdW6jDrmREXFxd0794dcXFxpsuMRiPi4uIQExMj9BgGgwFJSUkICanfs4PaC03T8PKqJOQWl6N9sDeeHhBusW2fOnXKYtuyBewljq3EsZU4tlKH2S/TTJ06FV988QW+/vprHD16FE899RSKi4sxYcIEAMC4ceMwffp00+3ffPNNbNiwAadPn0ZCQgL+8Y9/4OzZs3j88cfr77uwI6sOpGPDkSw4O+rwyegucHGy3El0+ZZs87CXOLYSx1bi2EodZr8PdPTo0bh48SJmzJiBzMxMdOnSBbGxsaaDWtPS0uDg8OcvyMuXL+OJJ55AZmYmGjVqhO7du2Pnzp3o2JHnwzBXRn4pXvvpyjuRpgyMQIcQy56N1sXFxaLbUx17iWMrcWwljq3UwdPBK0LTNIxbsBe/nchBlzA/rHgyBk6O/GghIiKyXg16OniyvO/3pOG3EzlwdXLArAejpSxEli9fbvFtqoy9xLGVOLYSx1bq4GJEAWcvFePddUcBAC8MaY82TbykzFFZWSllu6piL3FsJY6txLGVOrgYsXIGo4Zpyw+hpNyAW1v745HeLaXN0rp1a2nbVhF7iWMrcWwljq3UwcWIlVuwIxV7z+TC08URHz4QDQcHnbRZmjdvLm3bKmIvcWwljq3EsZU6uBixYieyCvHhhhQAwKv3dESYv4fUebZu3Sp1+6phL3FsJY6txLGVOrgYsVIVBiOeXX4Q5ZVG9G/XBKNv4efzEBGRbeJixEr9d8spHDqfD193Z7w3sjN0Onkvz1x1++23yx5BKewljq3EsZU4tlIHFyNWKDk9H//ZfAIA8OZ9nRDk4yZ5oivS09Nlj6AU9hLHVuLYShxbqYOLEStjNGqYvjIJlUYNd0cF497oUNkjmfBzHszDXuLYShxbiWMrdXAxYmV+OngBSen58HZ1whv3RlrFyzNXXXuaf7o59hLHVuLYShxbqYOng7cilQYjBn2yHadzijFtcDtM7N9W9khERES1xtPBK+jnQxdwOqcYjTycMV7iyc1uZOXKlbJHUAp7iWMrcWwljq3UwcWIlag0GPFZ3EkAwBO3t4aXq9kfqNzg9Hq97BGUwl7i2EocW4ljK3VwMWIlfjp4Aal/PCsyLqal7HGqxbMZmoe9xLGVOLYSx1bq4GLEClx5VuTKW3n/eXsbq3xWBAAiIiJkj6AU9hLHVuLYShxbqYOLESuwJvECzlwqgb+nC8bFtJA9zg1t2rRJ9ghKYS9xbCWOrcSxlTq4GJGs0mA0neDsn7e3hqeVPitCRETUULgYkWzVgXTTsyIP32q9z4oAQJ8+fWSPoBT2EsdW4thKHFupg4sRifSVBny66cqzIk/eYf3PiuTk5MgeQSnsJY6txLGVOLZSBxcjEi3ek4b0vFIE+7hZ7TtorpWSkiJ7BKWwlzi2EsdW4thKHVyMSFKkr8SczVfOK/L0gHC4OTtKnoiIiEgOng5eks/iTuDjjcfRMsADG6feAWdH618XGo1GftaDGdhLHFuJYytxbCUfTwdvxS4Xl+OL7acBAFMHtVNiIQIAa9eulT2CUthLHFuJYytxbKUONX4L2ph5206hUF+JjiE+uCcqRPY4woqLi2WPoBT2EsdW4thKHFupg4sRC8vIL8XXO88AAKYNaQcHB53cgczQtGlT2SMohb3EsZU4thLHVurgYsTCPos7AX2lET1b+qNfRBPZ45glMjJS9ghKYS9xbCWOrcSxlTq4GLGg0xeLsGz/eQDA80PaQadT51kRAFi/fr3sEZTCXuLYShxbiWMrdXAxYkEzfz0Gg1HDgPaB6NHSX/Y4REREVoGLEQvZeSoHG49kwdFBh+l3t5c9Tq306tVL9ghKYS9xbCWOrcSxlTq4GLGASoMRb689CgAY26s52gZ6S56odoqKimSPoBT2EsdW4thKHFupg4sRC5i/7RSOZBTAx80JkweEyx6n1g4fPix7BKWwlzi2EsdW4thKHVyMNLAdJ3JMH4b3+r2dEODlKnkiIiIi68LTwTegHSdy8OR38SjSV+K+LqH4dHQX5d5Bc62Kigo4OzvLHkMZ7CWOrcSxlTi2ko+ng5ds8Z40jF+4F0X6SvRuE4APHuis9EIEADZs2CB7BKWwlzi2EsdW4thKHU6yB7A1BqOGmeuO4ssdqQCA+7s2xXsjo+DqpP6n8hYUFMgeQSnsJY6txLGVOLZSBxcj9ahYX4nJSxOx6WgWAGDqXRH4951tlX9G5KqgoCDZIyiFvcSxlTi2EsdW6uAxI/UkI78Ujy3ajyMZBXBxcsCsUdEYHh0qe6x6VVBQYLX9rRF7iWMrcWwljq3k4zEjFhR/Nhf3zvkdRzIKEODpgiVP3GpzCxEA+OWXX2SPoBT2EsdW4thKHFupgy/T1NHSvWl4dU0yKgwa2gV548vxPRDm7yF7LCIiImVwMVJL5ZVGvLX2CL7dfRYAMDQyGB+Nioanq+0m7d69u+wRlMJe4thKHFuJYyt11Oplmrlz56Jly5Zwc3NDr169sHfv3hpvv3z5crRv3x5ubm6IiorCunXrajWstTh9sQgj5+3Et7vPQqcDnhsUgf+O7WbTCxHgynv2SRx7iWMrcWwljq3UYfZi5IcffsDUqVPx2muvISEhAdHR0Rg8eDCys7Orvf3OnTsxZswYPPbYYzhw4ABGjBiBESNGIDk5uc7DW1ppuQFzNp/AsM92ICk9H34ezvhyXA9MujPcZt4xU5NDhw7JHkEp7CWOrcSxlTi2UofZi5GPP/4YTzzxBCZMmICOHTti/vz58PDwwIIFC6q9/ezZszFkyBBMmzYNHTp0wFtvvYVu3bphzpw5dR7eEi4W6vFrUgZeXpWEnu9uwkcbjqO0woDebQIQO/l2DOjAt44RERHVhVmvK5SXlyM+Ph7Tp083Xebg4ICBAwdi165d1d5n165dmDp1apXLBg8ejNWrV99wO3q9Hnq93vTnhjpxzVc7UnEutwQVBiMqDRoqjFf+v9JoxMVCPU5fLMal4vIq9wnzd8dzg9rh3uhQu3g25FojRoyQPYJS2EscW4ljK3FspQ6zFiM5OTkwGAzXnUgmKCgIx44dq/Y+mZmZ1d4+MzPzhtuZOXMm3njjDXNGq5W1hy7gQFpejbfR6YB2Qd64paU/7uoYhL5tG8PBwb4WIVdt374dgwcPlj2GMthLHFuJYytxbKUOqzzicvr06VWeTSkoKEBYWFi9b2dkt2bo06YxnBx1cHZ0gJODDk5//L+fhzPaNPFCq8aeNn9gqqjc3FzZIyiFvcSxlTi2EsdW6jDrt2zjxo3h6OiIrKysKpdnZWUhODi42vsEBwebdXsAcHV1haurqzmj1co/bm3R4NuwJQEBAbJHUAp7iWMrcWwljq3UYdYBrC4uLujevTvi4uJMlxmNRsTFxSEmJqba+8TExFS5PQBs3Ljxhrcn69W3b1/ZIyiFvcSxlTi2EsdW6jD73TRTp07FF198ga+//hpHjx7FU089heLiYkyYMAEAMG7cuCoHuE6ePBmxsbGYNWsWjh07htdffx379+/HpEmT6u+7IItYs2aN7BGUwl7i2EocW4ljK3WYfTDE6NGjcfHiRcyYMQOZmZno0qULYmNjTQeppqWlwcHhzzVO7969sXjxYrzyyit46aWXEB4ejtWrVyMyMrL+vgsiIiJSFj+1l4QdPXoUHTp0kD2GMthLHFuJYytxbCUfP7WX6t21z3jRzbGXOLYSx1bi2Eod/DdFwhISEmSPoBT2EsdW4thKHFupg4sRIiIikorHjJCwwsJCeHt7yx5DGewljq3EsZU4tpKPx4xQvdu7d6/sEZTCXuLYShxbiWMrdXAxQsKys7Nlj6AU9hLHVuLYShxbqYOLERLm6+srewSlsJc4thLHVuLYSh08ZoSE6fV6i3xmkK1gL3FsJY6txLGVfDxmhOrdypUrZY+gFPYSx1bi2EocW6nD7NPBy3D1yZuCggLJk9i3kpIS/jswA3uJYytxbCWOreS72v9mL8Io8TLN+fPnERYWJnsMIiIiqoVz586hWbNmN7xeicWI0WjEhQsX4O3tDZ1OV2+PW1BQgLCwMJw7d47HotwEW5mHvcSxlTi2EsdW4hqylaZpKCwsRGhoaI2n51fiZRoHB4caV1R15ePjw51VEFuZh73EsZU4thLHVuIaqpXIu5p4ACsRERFJxcUIERERSWXXixFXV1e89tprfB+6ALYyD3uJYytxbCWOrcRZQyslDmAlIiIi22XXz4wQERGRfFyMEBERkVRcjBAREZFUXIwQERGRVHa9GJk7dy5atmwJNzc39OrVC3v37pU9knQzZ87ELbfcAm9vbwQGBmLEiBFISUmpcpuysjJMnDgRAQEB8PLywsiRI5GVlSVpYuvx3nvvQafTYcqUKabL2OpP6enp+Mc//oGAgAC4u7sjKioK+/fvN12vaRpmzJiBkJAQuLu7Y+DAgThx4oTEieUwGAx49dVX0apVK7i7u6NNmzZ46623qny2hz232r59O4YPH47Q0FDodDqsXr26yvUibXJzczF27Fj4+PjAz88Pjz32GIqKiiz4XVhGTa0qKirwwgsvICoqCp6enggNDcW4ceNw4cKFKo9hqVZ2uxj54YcfMHXqVLz22mtISEhAdHQ0Bg8ejOzsbNmjSbVt2zZMnDgRu3fvxsaNG1FRUYFBgwahuLjYdJtnnnkGP//8M5YvX45t27bhwoUL+Nvf/iZxavn27duH//3vf+jcuXOVy9nqisuXL6NPnz5wdnbGr7/+iiNHjmDWrFlo1KiR6TYffPABPvvsM8yfPx979uyBp6cnBg8ejLKyMomTW97777+PefPmYc6cOTh69Cjef/99fPDBB/jPf/5juo09tyouLkZ0dDTmzp1b7fUibcaOHYvDhw9j48aNWLt2LbZv345//vOflvoWLKamViUlJUhISMCrr76KhIQErFy5EikpKbj33nur3M5irTQ71bNnT23ixImmPxsMBi00NFSbOXOmxKmsT3Z2tgZA27Ztm6ZpmpaXl6c5Oztry5cvN93m6NGjGgBt165dssaUqrCwUAsPD9c2btyo3XHHHdrkyZM1TWOra73wwgta3759b3i90WjUgoODtQ8//NB0WV5enubq6qotWbLEEiNajWHDhmmPPvpolcv+9re/aWPHjtU0ja2uBUBbtWqV6c8ibY4cOaIB0Pbt22e6za+//qrpdDotPT3dYrNb2l9bVWfv3r0aAO3s2bOaplm2lV0+M1JeXo74+HgMHDjQdJmDgwMGDhyIXbt2SZzM+uTn5wMA/P39AQDx8fGoqKio0q59+/Zo3ry53babOHEihg0bVqUJwFbX+umnn9CjRw+MGjUKgYGB6Nq1K7744gvT9ampqcjMzKzSytfXF7169bK7Vr1790ZcXByOHz8OADh48CB27NiBoUOHAmCrmoi02bVrF/z8/NCjRw/TbQYOHAgHBwfs2bPH4jNbk/z8fOh0Ovj5+QGwbCslPiivvuXk5MBgMCAoKKjK5UFBQTh27JikqayP0WjElClT0KdPH0RGRgIAMjMz4eLiYtpZrwoKCkJmZqaEKeVaunQpEhISsG/fvuuuY6s/nT59GvPmzcPUqVPx0ksvYd++fXj66afh4uKC8ePHm3pU99+kvbV68cUXUVBQgPbt28PR0REGgwHvvPMOxo4dCwBsVQORNpmZmQgMDKxyvZOTE/z9/e26X1lZGV544QWMGTPG9GF5lmxll4sREjNx4kQkJydjx44dskexSufOncPkyZOxceNGuLm5yR7HqhmNRvTo0QPvvvsuAKBr165ITk7G/PnzMX78eMnTWZdly5bh+++/x+LFi9GpUyckJiZiypQpCA0NZStqEBUVFXjwwQehaRrmzZsnZQa7fJmmcePGcHR0vO5dDVlZWQgODpY0lXWZNGkS1q5diy1btqBZs2amy4ODg1FeXo68vLwqt7fHdvHx8cjOzka3bt3g5OQEJycnbNu2DZ999hmcnJwQFBTEVn8ICQlBx44dq1zWoUMHpKWlAYCpB/+bBKZNm4YXX3wRDz30EKKiovDwww/jmWeewcyZMwGwVU1E2gQHB1/3RoXKykrk5ubaZb+rC5GzZ89i48aNpmdFAMu2ssvFiIuLC7p37464uDjTZUajEXFxcYiJiZE4mXyapmHSpElYtWoVNm/ejFatWlW5vnv37nB2dq7SLiUlBWlpaXbXbsCAAUhKSkJiYqLpq0ePHhg7dqzpn9nqij59+lz3FvHjx4+jRYsWAIBWrVohODi4SquCggLs2bPH7lqVlJTAwaHqj2ZHR0cYjUYAbFUTkTYxMTHIy8tDfHy86TabN2+G0WhEr169LD6zTFcXIidOnMCmTZsQEBBQ5XqLtqrXw2EVsnTpUs3V1VVbtGiRduTIEe2f//yn5ufnp2VmZsoeTaqnnnpK8/X11bZu3aplZGSYvkpKSky3efLJJ7XmzZtrmzdv1vbv36/FxMRoMTExEqe2Hte+m0bT2OqqvXv3ak5OTto777yjnThxQvv+++81Dw8P7bvvvjPd5r333tP8/Py0NWvWaIcOHdLuu+8+rVWrVlppaanEyS1v/PjxWtOmTbW1a9dqqamp2sqVK7XGjRtrzz//vOk29tyqsLBQO3DggHbgwAENgPbxxx9rBw4cML0DRKTNkCFDtK5du2p79uzRduzYoYWHh2tjxoyR9S01mJpalZeXa/fee6/WrFkzLTExscrPe71eb3oMS7Wy28WIpmnaf/7zH6158+aai4uL1rNnT2337t2yR5IOQLVfCxcuNN2mtLRU+9e//qU1atRI8/Dw0O6//34tIyND3tBW5K+LEbb6088//6xFRkZqrq6uWvv27bXPP/+8yvVGo1F79dVXtaCgIM3V1VUbMGCAlpKSImlaeQoKCrTJkydrzZs319zc3LTWrVtrL7/8cpVfEPbcasuWLdX+jBo/frymaWJtLl26pI0ZM0bz8vLSfHx8tAkTJmiFhYUSvpuGVVOr1NTUG/6837Jli+kxLNVKp2nXnNaPiIiIyMLs8pgRIiIish5cjBAREZFUXIwQERGRVFyMEBERkVRcjBAREZFUXIwQERGRVFyMEBERkVRcjBAREZFUXIwQERGRVFyMEBERkVRcjBAREZFUXIwQERGRVP8P6CcaK9fs7LwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index in range(len(solutions)):\n",
    "    tt=solutions[index][\"Time [h]\"].entries\n",
    "    xx=solutions[index][\"X-averaged inner SEI thickness [m]\"].entries\n",
    "    plt.plot(tt,xx, label=SEIModels[index])\n",
    "plt.legend()\n",
    "a = np.sign(solutions[0][\"Power [W]\"].entries)\n",
    "pp=solutions[0][\"Power [W]\"].entries\n",
    "tt=solutions[0][\"Time [h]\"].entries\n",
    "SS=[\"C\", \"R\", \"D\", \"R\"]\n",
    "for ii in np.arange(20,tt[-1],20):\n",
    "     plt.axvline(x=ii, color='gray', linewidth=0.5, linestyle=\"dashed\" ) # red line at x=5\n",
    "     jj=(np.remainder((ii//20-1) , 4)).astype(int)\n",
    "     plt.annotate(SS[jj], xy=(ii-10, 1e-9), xytext=(ii-10, 3e-9))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af98ec20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index in range(len(solutions)):\n",
    "    tt=solutions[index][\"Time [h]\"].entries\n",
    "    Qt = solutions[index][\"Throughput capacity [A.h]\"].entries\n",
    "    Q_SEI = solutions[index][\"Loss of capacity to SEI [A.h]\"].entries\n",
    "    plt.plot(Qt, Q_SEI,label=SEIModels[index])\n",
    "plt.xlabel(\"Throughput capacity [A.h]\")\n",
    "plt.ylabel(\"Capacity loss [A.h]\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be0ec14b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt=solutions[0][\"Time [h]\"].entries\n",
    "Qt = solutions[0][\"Negative electrode exchange current density [A.m-2]\"].entries\n",
    "cnn = solutions[0]['Negative particle concentration'].entries\n",
    "cce = solutions[0][\"Electrolyte concentration concatenation [mol.m-3]\"].entries\n",
    "for ii in np.arange(0,10,2):\n",
    "    plt.plot(tt,np.sqrt(cnn[ii,3,:]*(1-cnn[ii,3,:])), label=ii)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75cc5a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.shape(cnn)\n",
    "F=param['Faraday constant [C.mol-1]']\n",
    "mn=6.48*1e-7\n",
    "an=3.84*1e5\n",
    "ce_int=4541.0\n",
    "cn_Max=33133.0\n",
    "i0=4.5/100\n",
    "L=8.52e-05+7.56e-05+1.2e-05\n",
    "t0=F*cn_Max*L/i0\n",
    "Crn=F/(mn*np.sqrt(ce_int)*t0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92fa2a8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7212,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i0 = solutions[0]['Current [A]'].entries\n",
    "np.shape(i0)\n",
    "# for ii in np.arange(0,10,2):\n",
    "#     plt.plot(tt,cnn[ii,3,:], label=ii)\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ecfcca86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb7d76acf40>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGdCAYAAADaPpOnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgy0lEQVR4nO3df3BU9f3v8Vd+wIZfSYhAYiAYoH4NFAQkJQ3q1zpkDMpFqdYqjYKUwm0lFY21gK2g5dqgRUpFLlTqj9tbKNSpUGUqd2L4NdhIIAErP+VrQShxE5CSDQkkIXvuH3xZ3ZLEBdmcfcPzMbMz5uw5u+98WJInJ2dNlOM4jgAAAIyIdnsAAACAC0G8AAAAU4gXAABgCvECAABMIV4AAIApxAsAADCFeAEAAKYQLwAAwJRYtwe41Px+vyoqKtSlSxdFRUW5PQ4AAAiB4ziqqalRamqqoqNbP7dy2cVLRUWF0tLS3B4DAABchMOHD6tXr16t7nPZxUuXLl0knf3k4+PjXZ4GAACEwufzKS0tLfB9vDWXXbyc+1FRfHw88QIAgDGhXPLBBbsAAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYMpl94sZERk2fXRU6/dVuT3GedrHRGvc8N5K79bJ7VFwEQ4eq9Uftx5Swxm/26OcJ6d/sm78Wje3x8BFqDndqFc3H9SJUw1uj3KegakJumdYL7fHiDjEC8LisZU79Flt5H0hkKSjNfWaf98Qt8fARXhx3X69WX7E7TGatXanVyUzR7o9Bi7CWx9U6NfvfuT2GC36z//oru5dPG6PEVGIF4RFXUOTJOmuIanq1bWDy9OctfOITxs/OqrahjNuj4KLdOq/X1cJHdrpgW/2dnmas/5V16jlWw6ptp7XlVXnXleSNPXWfi5OEuy3G/+hM35HpxubvnznKwzxgrD6yW3XKS2po9tjSJL+8P4n2vjRUbfHwCXwk9zr9OA3r3F7DEnSP46e1PIth9weA5fAt4f21BO5GW6PEfDaewd1poFwaQ4X7AIAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATGmTeFm0aJHS09MVFxenrKwslZaWhnTcihUrFBUVpbFjx4Z3QAAAYEbY42XlypUqKCjQ7NmzVV5ersGDBys3N1dVVVWtHnfw4EH95Cc/0c033xzuEQEAgCFhj5f58+dr8uTJmjhxogYMGKAlS5aoY8eOevXVV1s8pqmpSXl5eXrmmWfUt2/fcI8IAAAMCWu8NDQ0qKysTDk5OZ8/YXS0cnJyVFJS0uJxv/jFL9SjRw9NmjTpS5+jvr5ePp8v6AYAAC5fYY2XY8eOqampScnJyUHbk5OT5fV6mz1m8+bNeuWVV7R06dKQnqOwsFAJCQmBW1pa2leeGwAARK6IerdRTU2NHnzwQS1dulTdunUL6ZiZM2equro6cDt8+HCYpwQAAG6KDeeDd+vWTTExMaqsrAzaXllZqZSUlPP2//jjj3Xw4EGNGTMmsM3v958dNDZW+/btU79+/YKO8Xg88ng8YZgeAABEorCeeWnfvr2GDRum4uLiwDa/36/i4mJlZ2eft39GRoY+/PBD7dixI3C78847deutt2rHjh38SAgAAIT3zIskFRQUaMKECcrMzNTw4cO1YMEC1dbWauLEiZKk8ePHq2fPniosLFRcXJwGDhwYdHxiYqIknbcdAABcmcIeL/fdd5+OHj2qWbNmyev1asiQIVq7dm3gIt5Dhw4pOjqiLr0BAAARLOzxIkn5+fnKz89v9r4NGza0euzrr79+6QcCAABmccoDAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAlDaJl0WLFik9PV1xcXHKyspSaWlpi/suXbpUN998s7p27aquXbsqJyen1f0BAMCVJezxsnLlShUUFGj27NkqLy/X4MGDlZubq6qqqmb337Bhg8aNG6f169erpKREaWlpuu2223TkyJFwjwoAAAwIe7zMnz9fkydP1sSJEzVgwAAtWbJEHTt21Kuvvtrs/suWLdPDDz+sIUOGKCMjQ7/73e/k9/tVXFwc7lEBAIABYY2XhoYGlZWVKScn5/MnjI5WTk6OSkpKQnqMuro6NTY2Kikpqdn76+vr5fP5gm4AAODyFdZ4OXbsmJqampScnBy0PTk5WV6vN6THmD59ulJTU4MC6IsKCwuVkJAQuKWlpX3luQEAQOSK6HcbzZ07VytWrNCqVasUFxfX7D4zZ85UdXV14Hb48OE2nhIAALSl2HA+eLdu3RQTE6PKysqg7ZWVlUpJSWn12Hnz5mnu3Ll69913df3117e4n8fjkcfjuSTzAgCAyBfWMy/t27fXsGHDgi62PXfxbXZ2dovHPf/885ozZ47Wrl2rzMzMcI4IAACMCeuZF0kqKCjQhAkTlJmZqeHDh2vBggWqra3VxIkTJUnjx49Xz549VVhYKEl67rnnNGvWLC1fvlzp6emBa2M6d+6szp07h3tcAAAQ4cIeL/fdd5+OHj2qWbNmyev1asiQIVq7dm3gIt5Dhw4pOvrzE0CLFy9WQ0ODvvOd7wQ9zuzZs/X000+He1wAABDhwh4vkpSfn6/8/Pxm79uwYUPQxwcPHgz/QAAAwKyIfrcRAADAvyNeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYEqbxMuiRYuUnp6uuLg4ZWVlqbS0tNX933jjDWVkZCguLk6DBg3SX//617YYEwAAGBD2eFm5cqUKCgo0e/ZslZeXa/DgwcrNzVVVVVWz+//tb3/TuHHjNGnSJG3fvl1jx47V2LFjtXPnznCPCgAADAh7vMyfP1+TJ0/WxIkTNWDAAC1ZskQdO3bUq6++2uz+v/nNbzRq1Cg98cQT6t+/v+bMmaMbbrhBL730UrhHBQAABsSG88EbGhpUVlammTNnBrZFR0crJydHJSUlzR5TUlKigoKCoG25ublavXp1s/vX19ervr4+8LHP5/vqgxtytKZe/+dvB1XbcMbtUYI0NPndHqFF/29XpZ55e5fbYwTpmdhB37+xj6Kjo9weRZLk9zt67W8H9c9/1bk9SpDdn0bu32/f6TMR97rq4onVhBHpuqqzx+1RAtbu9GrLgc/cHiPIriOR+7qSpGfe3q20pA5ujxGkX/fOeuCb17j2/GGNl2PHjqmpqUnJyclB25OTk7V3795mj/F6vc3u7/V6m92/sLBQzzzzzKUZ2KD/+/4nemn9f7k9RrOioqROnrC+xC5Il7jPZ3ntvYPuDdKCob27atg1Xd0eQ5K0458nNGfNbrfHaFGXCHpddfZE9uvK0y5GU2/9mttjSJIazvj1yIrtajgTmf+46RxBryvp7Dx1DU16d0+l26Oc5z//o/vlGy9tYebMmUFnanw+n9LS0lycqG2d+sIZl6m39nNxkvMNTE1QUqf2bo8RkPv1FD31PwboeG39l+/chlaUHtZntQ2qi6CzZ6camgL/HWmvq6ROHo0amOL2GAE94uO0cNxQ7fVG1r/eN+8/pg/+Wa3a+sh5XfkdJxAuD41IVydPjMsTfc4TG6PvZkbW946XvneDNn7U/PWhbku/qpOrzx/WeOnWrZtiYmJUWRlcjZWVlUpJaf6LT0pKygXt7/F45PFEzilRt/zPW/rqidwMt8eIaHHtYjTppj5uj3Ge4j1V+qy2we0xmpWR0oXXVQjGDE7VmMGpbo8R5FTDbn3wz2q3x2jRE7nXRdSZ2Ug0vE+ShvdJcnuMiBTWC3bbt2+vYcOGqbi4OLDN7/eruLhY2dnZzR6TnZ0dtL8kFRUVtbg/AAC4soQ9ewsKCjRhwgRlZmZq+PDhWrBggWprazVx4kRJ0vjx49WzZ08VFhZKkqZNm6ZbbrlFL7zwgkaPHq0VK1Zo27Ztevnll8M9KgAAMCDs8XLffffp6NGjmjVrlrxer4YMGaK1a9cGLso9dOiQoqM/PwE0YsQILV++XD//+c/15JNP6tprr9Xq1as1cODAcI8KAAAMaJMfOObn5ys/P7/Z+zZs2HDetnvvvVf33ntvmKcCAAAW8buNAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKWGLl+PHjysvL0/x8fFKTEzUpEmTdPLkyVb3//GPf6zrrrtOHTp0UO/evfXII4+ouro6XCMCAACDwhYveXl52rVrl4qKirRmzRpt2rRJU6ZMaXH/iooKVVRUaN68edq5c6def/11rV27VpMmTQrXiAAAwKDYcDzonj17tHbtWm3dulWZmZmSpIULF+qOO+7QvHnzlJqaet4xAwcO1J///OfAx/369dOzzz6rBx54QGfOnFFsbFhGBQAAxoTlzEtJSYkSExMD4SJJOTk5io6O1pYtW0J+nOrqasXHx7caLvX19fL5fEE3AABw+QpLvHi9XvXo0SNoW2xsrJKSkuT1ekN6jGPHjmnOnDmt/qhJkgoLC5WQkBC4paWlXfTcAAAg8l1QvMyYMUNRUVGt3vbu3fuVh/L5fBo9erQGDBigp59+utV9Z86cqerq6sDt8OHDX/n5AQBA5LqgC0kef/xxPfTQQ63u07dvX6WkpKiqqipo+5kzZ3T8+HGlpKS0enxNTY1GjRqlLl26aNWqVWrXrl2r+3s8Hnk8npDmBwAA9l1QvHTv3l3du3f/0v2ys7N14sQJlZWVadiwYZKkdevWye/3Kysrq8XjfD6fcnNz5fF49NZbbykuLu5CxgMAAFeAsFzz0r9/f40aNUqTJ09WaWmp3nvvPeXn5+v+++8PvNPoyJEjysjIUGlpqaSz4XLbbbeptrZWr7zyinw+n7xer7xer5qamsIxJgAAMChs7z9etmyZ8vPzNXLkSEVHR+uee+7Riy++GLi/sbFR+/btU11dnSSpvLw88E6kr33ta0GPdeDAAaWnp4drVAAAYEjY4iUpKUnLly9v8f709HQ5jhP4+Fvf+lbQxwAAAM3hdxsBAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYQrwAAABTiBcAAGAK8QIAAEwhXgAAgCnECwAAMIV4AQAAphAvAADAFOIFAACYErZ4OX78uPLy8hQfH6/ExERNmjRJJ0+eDOlYx3F0++23KyoqSqtXrw7XiAAAwKCwxUteXp527dqloqIirVmzRps2bdKUKVNCOnbBggWKiooK12gAAMCw2HA86J49e7R27Vpt3bpVmZmZkqSFCxfqjjvu0Lx585SamtrisTt27NALL7ygbdu26eqrrw7HeAAAwLCwnHkpKSlRYmJiIFwkKScnR9HR0dqyZUuLx9XV1el73/ueFi1apJSUlJCeq76+Xj6fL+gGAAAuX2GJF6/Xqx49egRti42NVVJSkrxeb4vHPfbYYxoxYoTuuuuukJ+rsLBQCQkJgVtaWtpFzw0AACLfBcXLjBkzFBUV1ept7969FzXIW2+9pXXr1mnBggUXdNzMmTNVXV0duB0+fPiinh8AANhwQde8PP7443rooYda3adv375KSUlRVVVV0PYzZ87o+PHjLf44aN26dfr444+VmJgYtP2ee+7RzTffrA0bNjR7nMfjkcfjCfVTAAAAxl1QvHTv3l3du3f/0v2ys7N14sQJlZWVadiwYZLOxonf71dWVlazx8yYMUM/+MEPgrYNGjRIv/71rzVmzJgLGRMAAFzGwvJuo/79+2vUqFGaPHmylixZosbGRuXn5+v+++8PvNPoyJEjGjlypH7/+99r+PDhSklJafasTO/evdWnT59wjAkAAAwK2//nZdmyZcrIyNDIkSN1xx136KabbtLLL78cuL+xsVH79u1TXV1duEYAAACXobCceZGkpKQkLV++vMX709PT5ThOq4/xZfcDAIArD7/bCAAAmEK8AAAAU4gXAABgCvECAABMIV4AAIApxAsAADCFeAEAAKYQLwAAwBTiBQAAmEK8AAAAU4gXAABgCvECAABMIV4AAIApxAsAADCFeAEAAKYQLwAAwBTiBQAAmEK8AAAAU4gXAABgCvECAABMIV4AAIApxAsAADCFeAEAAKYQLwAAwBTiBQAAmBLr9gBW/FfVSS3b8onbY5yn9MBxt0fAJfKLt3frpmu7uT2GJKnixCm3R8Al8r83fKxTjU1ujyFJavI7bo+AywTxEqKKE6f02nsH3R6jRV08/FFa1SXu7J/d/qqT2l910uVpgnXmdWVW57jP/+wi7WuXJzZasTFRbo8Bw/jKFKK0pI6aems/t8doVidPrL43vLfbY+Ai/a+xg7Tm7xXyO5H1r9LoqCiNvv5qt8fARXpoRLo8sdGqazjj9ijnyUxPkic2xu0xYFiU40TYV8yvyOfzKSEhQdXV1YqPj3d7HAAAEIIL+f7NBbsAAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATCFeAACAKcQLAAAwhXgBAACmEC8AAMAU4gUAAJhCvAAAAFOIFwAAYArxAgAATIl1e4BL7dwvyfb5fC5PAgAAQnXu+/a57+OtuezipaamRpKUlpbm8iQAAOBC1dTUKCEhodV9opxQEscQv9+viooKdenSRVFRUZf0sX0+n9LS0nT48GHFx8df0se+3LBWoWOtQsdahY61ujCsV+jCtVaO46impkapqamKjm79qpbL7sxLdHS0evXqFdbniI+P58UdItYqdKxV6Fir0LFWF4b1Cl041urLzricwwW7AADAFOIFAACYQrxcAI/Ho9mzZ8vj8bg9SsRjrULHWoWOtQoda3VhWK/QRcJaXXYX7AIAgMsbZ14AAIApxAsAADCFeAEAAKYQLwAAwBTiJUSLFi1Senq64uLilJWVpdLSUrdHcl1hYaG+8Y1vqEuXLurRo4fGjh2rffv2Be1z+vRpTZ06VVdddZU6d+6se+65R5WVlS5NHDnmzp2rqKgoPfroo4FtrNXnjhw5ogceeEBXXXWVOnTooEGDBmnbtm2B+x3H0axZs3T11VerQ4cOysnJ0f79+12c2D1NTU166qmn1KdPH3Xo0EH9+vXTnDlzgn4/zJW6Xps2bdKYMWOUmpqqqKgorV69Ouj+UNbl+PHjysvLU3x8vBITEzVp0iSdPHmyDT+LttHaWjU2Nmr69OkaNGiQOnXqpNTUVI0fP14VFRVBj9GWa0W8hGDlypUqKCjQ7NmzVV5ersGDBys3N1dVVVVuj+aqjRs3aurUqXr//fdVVFSkxsZG3XbbbaqtrQ3s89hjj+ntt9/WG2+8oY0bN6qiokJ33323i1O7b+vWrfrtb3+r66+/Pmg7a3XWv/71L914441q166d3nnnHe3evVsvvPCCunbtGtjn+eef14svvqglS5Zoy5Yt6tSpk3Jzc3X69GkXJ3fHc889p8WLF+ull17Snj179Nxzz+n555/XwoULA/tcqetVW1urwYMHa9GiRc3eH8q65OXladeuXSoqKtKaNWu0adMmTZkypa0+hTbT2lrV1dWpvLxcTz31lMrLy/Xmm29q3759uvPOO4P2a9O1cvClhg8f7kydOjXwcVNTk5OamuoUFha6OFXkqaqqciQ5GzdudBzHcU6cOOG0a9fOeeONNwL77Nmzx5HklJSUuDWmq2pqapxrr73WKSoqcm655RZn2rRpjuOwVl80ffp056abbmrxfr/f76SkpDi/+tWvAttOnDjheDwe549//GNbjBhRRo8e7Xz/+98P2nb33Xc7eXl5juOwXudIclatWhX4OJR12b17tyPJ2bp1a2Cfd955x4mKinKOHDnSZrO3tX9fq+aUlpY6kpxPPvnEcZy2XyvOvHyJhoYGlZWVKScnJ7AtOjpaOTk5KikpcXGyyFNdXS1JSkpKkiSVlZWpsbExaO0yMjLUu3fvK3btpk6dqtGjRweticRafdFbb72lzMxM3XvvverRo4eGDh2qpUuXBu4/cOCAvF5v0FolJCQoKyvrilsrSRoxYoSKi4v10UcfSZI++OADbd68Wbfffrsk1qsloaxLSUmJEhMTlZmZGdgnJydH0dHR2rJlS5vPHEmqq6sVFRWlxMRESW2/VpfdL2a81I4dO6ampiYlJycHbU9OTtbevXtdmiry+P1+Pfroo7rxxhs1cOBASZLX61X79u0DL+5zkpOT5fV6XZjSXStWrFB5ebm2bt163n2s1ef+8Y9/aPHixSooKNCTTz6prVu36pFHHlH79u01YcKEwHo093fySlsrSZoxY4Z8Pp8yMjIUExOjpqYmPfvss8rLy5Mk1qsFoayL1+tVjx49gu6PjY1VUlLSFb12p0+f1vTp0zVu3LjAL2Zs67UiXnBJTJ06VTt37tTmzZvdHiUiHT58WNOmTVNRUZHi4uLcHiei+f1+ZWZm6pe//KUkaejQodq5c6eWLFmiCRMmuDxd5PnTn/6kZcuWafny5fr617+uHTt26NFHH1VqairrhUuusbFR3/3ud+U4jhYvXuzaHPzY6Et069ZNMTEx573ro7KyUikpKS5NFVny8/O1Zs0arV+/Xr169QpsT0lJUUNDg06cOBG0/5W4dmVlZaqqqtINN9yg2NhYxcbGauPGjXrxxRcVGxur5ORk1uq/XX311RowYEDQtv79++vQoUOSFFgP/k6e9cQTT2jGjBm6//77NWjQID344IN67LHHVFhYKIn1akko65KSknLeGzPOnDmj48ePX5Frdy5cPvnkExUVFQXOukhtv1bEy5do3769hg0bpuLi4sA2v9+v4uJiZWdnuziZ+xzHUX5+vlatWqV169apT58+QfcPGzZM7dq1C1q7ffv26dChQ1fc2o0cOVIffvihduzYEbhlZmYqLy8v8N+s1Vk33njjeW+5/+ijj3TNNddIkvr06aOUlJSgtfL5fNqyZcsVt1bS2XeCREcHfymPiYmR3++XxHq1JJR1yc7O1okTJ1RWVhbYZ926dfL7/crKymrzmd10Llz279+vd999V1dddVXQ/W2+Vpf8EuDL0IoVKxyPx+O8/vrrzu7du50pU6Y4iYmJjtfrdXs0V/3oRz9yEhISnA0bNjiffvpp4FZXVxfY54c//KHTu3dvZ926dc62bduc7OxsJzs728WpI8cX323kOKzVOaWlpU5sbKzz7LPPOvv373eWLVvmdOzY0fnDH/4Q2Gfu3LlOYmKi85e//MX5+9//7tx1111Onz59nFOnTrk4uTsmTJjg9OzZ01mzZo1z4MAB580333S6devm/PSnPw3sc6WuV01NjbN9+3Zn+/btjiRn/vz5zvbt2wPvkAllXUaNGuUMHTrU2bJli7N582bn2muvdcaNG+fWpxQ2ra1VQ0ODc+eddzq9evVyduzYEfT1vr6+PvAYbblWxEuIFi5c6PTu3dtp3769M3z4cOf99993eyTXSWr29tprrwX2OXXqlPPwww87Xbt2dTp27Oh8+9vfdj799FP3ho4g/x4vrNXn3n77bWfgwIGOx+NxMjIynJdffjnofr/f7zz11FNOcnKy4/F4nJEjRzr79u1zaVp3+Xw+Z9q0aU7v3r2duLg4p2/fvs7PfvazoG8qV+p6rV+/vtmvURMmTHAcJ7R1+eyzz5xx48Y5nTt3duLj452JEyc6NTU1Lnw24dXaWh04cKDFr/fr168PPEZbrlWU43zhf8MIAAAQ4bjmBQAAmEK8AAAAU4gXAABgCvECAABMIV4AAIApxAsAADCFeAEAAKYQLwAAwBTiBQAAmEK8AAAAU4gXAABgCvECAABM+f9bCLmKFw0KhgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tt,i0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b30b00f",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa7c55c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt=solutions[0][\"Time [h]\"].entries\n",
    "cnn = solutions[0]['Negative electrode exchange current density [A.m-2]'].entries\n",
    "# np.shape(cnn)\n",
    "# plt.plot(tt,np.sqrt(cnn*(1-cnn)), label=ii)\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "# np.shape(cnn)\n",
    "for ii in np.arange(0,5,2):\n",
    "    plt.plot(tt,np.arcsinh(1/cnn[ii,:]), label=ii)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c405b1be",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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