docs/source/examples/notebooks/change-settings.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Changing settings when solving PyBaMM models\n",
    "\n",
    "[This](./models/SPM.ipynb) example notebook showed how to run an SPM battery model, using the default parameters, discretisation and solvers that were defined for that particular model. Naturally we would like the ability to alter these options on a case by case basis, and this notebook gives an example of how to do this, again using the SPM model. In this notebook we explicitly handle all the stages of setting up, processing and solving the model in order to explain them in detail. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](./simulations_and_experiments/simulation-class.ipynb).\n",
    "\n",
    "\n",
    "### Table of Contents\n",
    "1. Default SPM model\n",
    "1. Changing the parameters\n",
    "1. Changing the discretisation\n",
    "1. Changing the solver"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The default SPM model\n",
    "\n",
    "Below is the code to define and run the default SPM model included in PyBaMM (if this is unfamiliar to you, please see [this](./models/SPM.ipynb) notebook for more details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.\n",
      "You should consider upgrading via the '/home/mrobins/git/PyBaMM/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: 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 os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "os.chdir(pybamm.__path__[0] + \"/..\")\n",
    "\n",
    "# create the model\n",
    "model = pybamm.lithium_ion.SPM()\n",
    "\n",
    "# set the default model geometry\n",
    "geometry = model.default_geometry\n",
    "\n",
    "# set the default model parameters\n",
    "param = model.default_parameter_values\n",
    "\n",
    "# set the parameters for the model and the geometry\n",
    "param.process_model(model)\n",
    "param.process_geometry(geometry)\n",
    "\n",
    "# mesh the domains\n",
    "mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)\n",
    "\n",
    "# discretise the model equations\n",
    "disc = pybamm.Discretisation(mesh, model.default_spatial_methods)\n",
    "disc.process_model(model)\n",
    "\n",
    "# Solve the model at the given time points\n",
    "solver = model.default_solver\n",
    "n = 100\n",
    "t_eval = np.linspace(0, 3600, n)\n",
    "solution = solver.solve(model, t_eval)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify the solution, we can look at a plot of the Voltage over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = solution[\"Time [h]\"].entries\n",
    "voltage = solution[\"Voltage [V]\"].entries\n",
    "plt.plot(time, voltage, lw=2, label=model.name)\n",
    "plt.xlabel(\"Time [h]\", fontsize=15)\n",
    "plt.ylabel(\"Voltage [V]\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the model parameters <a name=\"parameters\"></a>\n",
    "\n",
    "The parameters are defined using the [`pybamm.ParameterValues`](https://docs.pybamm.org/en/latest/source/api/parameters/parameter_values.html) class, which takes either a python dictionary or CSV file with the mapping between parameter names and values.\n",
    "\n",
    "First lets have a look at the default parameters that are included with the SPM model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method ParameterValues.items of {'Thermodynamic factor': 1.0,\n",
       " 'Ambient temperature [K]': 298.15,\n",
       " 'Bulk solvent concentration [mol.m-3]': 2636.0,\n",
       " 'Cation transference number': 0.4,\n",
       " 'Cell cooling surface area [m2]': 0.0569,\n",
       " 'Cell volume [m3]': 7.8e-06,\n",
       " 'Current function [A]': 0.680616,\n",
       " 'EC diffusivity [m2.s-1]': 2e-18,\n",
       " 'EC initial concentration in electrolyte [mol.m-3]': 4541.0,\n",
       " 'Edge heat transfer coefficient [W.m-2.K-1]': 0.3,\n",
       " 'Electrode height [m]': 0.137,\n",
       " 'Electrode width [m]': 0.207,\n",
       " 'Electrolyte conductivity [S.m-1]': <function electrolyte_conductivity_Capiglia1999 at 0x7fb36b261160>,\n",
       " 'Electrolyte diffusivity [m2.s-1]': <function electrolyte_diffusivity_Capiglia1999 at 0x7fb36b2611f0>,\n",
       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
       " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n",
       " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565,\n",
       " 'Initial inner SEI thickness [m]': 2.5e-09,\n",
       " 'Initial outer SEI thickness [m]': 2.5e-09,\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]': 3.105,\n",
       " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n",
       " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n",
       " 'Negative current collector conductivity [S.m-1]': 59600000.0,\n",
       " 'Negative current collector density [kg.m-3]': 8954.0,\n",
       " 'Negative current collector specific heat capacity [J.kg-1.K-1]': 385.0,\n",
       " 'Negative current collector surface heat transfer coefficient [W.m-2.K-1]': 0.0,\n",
       " 'Negative current collector thermal conductivity [W.m-1.K-1]': 401.0,\n",
       " 'Negative current collector thickness [m]': 2.5e-05,\n",
       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Negative electrode OCP [V]': <function graphite_mcmb2528_ocp_Dualfoil1998 at 0x7fb36b261670>,\n",
       " 'Negative electrode OCP entropic change [V.K-1]': <function graphite_entropic_change_Moura2016 at 0x7fb36b261550>,\n",
       " 'Negative electrode active material volume fraction': 0.6,\n",
       " 'Negative electrode cation signed stoichiometry': -1.0,\n",
       " 'Negative electrode charge transfer coefficient': 0.5,\n",
       " 'Negative electrode conductivity [S.m-1]': 100.0,\n",
       " 'Negative electrode density [kg.m-3]': 1657.0,\n",
       " 'Negative electrode diffusivity [m2.s-1]': <function graphite_mcmb2528_diffusivity_Dualfoil1998 at 0x7fb36b2ad040>,\n",
       " 'Negative electrode double-layer capacity [F.m-2]': 0.2,\n",
       " 'Negative electrode electrons in reaction': 1.0,\n",
       " 'Negative electrode exchange-current density [A.m-2]': <function graphite_electrolyte_exchange_current_density_Dualfoil1998 at 0x7fb36b2615e0>,\n",
       " 'Negative electrode porosity': 0.3,\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]': 0.0001,\n",
       " 'Negative particle radius [m]': 1e-05,\n",
       " 'Negative tab centre y-coordinate [m]': 0.06,\n",
       " 'Negative tab centre z-coordinate [m]': 0.137,\n",
       " 'Negative tab heat transfer coefficient [W.m-2.K-1]': 10.0,\n",
       " 'Negative tab width [m]': 0.04,\n",
       " 'Nominal cell capacity [A.h]': 0.680616,\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]': 35500000.0,\n",
       " 'Positive current collector density [kg.m-3]': 2707.0,\n",
       " 'Positive current collector specific heat capacity [J.kg-1.K-1]': 897.0,\n",
       " 'Positive current collector surface heat transfer coefficient [W.m-2.K-1]': 0.0,\n",
       " 'Positive current collector thermal conductivity [W.m-1.K-1]': 237.0,\n",
       " 'Positive current collector thickness [m]': 2.5e-05,\n",
       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Positive electrode OCP [V]': <function lico2_ocp_Dualfoil1998 at 0x7fb36b2613a0>,\n",
       " 'Positive electrode OCP entropic change [V.K-1]': <function lico2_entropic_change_Moura2016 at 0x7fb36b261280>,\n",
       " 'Positive electrode active material volume fraction': 0.5,\n",
       " 'Positive electrode cation signed stoichiometry': -1.0,\n",
       " 'Positive electrode charge transfer coefficient': 0.5,\n",
       " 'Positive electrode conductivity [S.m-1]': 10.0,\n",
       " 'Positive electrode density [kg.m-3]': 3262.0,\n",
       " 'Positive electrode diffusivity [m2.s-1]': <function lico2_diffusivity_Dualfoil1998 at 0x7fb36b2614c0>,\n",
       " 'Positive electrode double-layer capacity [F.m-2]': 0.2,\n",
       " 'Positive electrode electrons in reaction': 1.0,\n",
       " 'Positive electrode exchange-current density [A.m-2]': <function lico2_electrolyte_exchange_current_density_Dualfoil1998 at 0x7fb36b261310>,\n",
       " 'Positive electrode porosity': 0.3,\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]': 0.0001,\n",
       " 'Positive particle radius [m]': 1e-05,\n",
       " 'Positive tab centre y-coordinate [m]': 0.147,\n",
       " 'Positive tab centre z-coordinate [m]': 0.137,\n",
       " 'Positive tab heat transfer coefficient [W.m-2.K-1]': 10.0,\n",
       " 'Positive tab width [m]': 0.04,\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': 1.0,\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]': 2.5e-05,\n",
       " 'Total heat transfer coefficient [W.m-2.K-1]': 10.0,\n",
       " 'Typical current [A]': 0.680616,\n",
       " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
       " 'Upper voltage cut-off [V]': 4.1,\n",
       " 'citations': ['Marquis2019']}>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.default_parameter_values.items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PARAMETER                                                                                   VALUE\n",
      "-------------------------------------------------------------------------------------------------\n",
      "Ratio of lithium moles to SEI moles                                                           2.0\n",
      "Inner SEI reaction proportion                                                                 0.5\n",
      "Inner SEI partial molar volume [m3.mol-1]                                               9.585e-05\n",
      "Outer SEI partial molar volume [m3.mol-1]                                               9.585e-05\n",
      "SEI reaction exchange current density [A.m-2]                                             1.5e-07\n",
      "SEI resistivity [Ohm.m]                                                                  200000.0\n",
      "Outer SEI solvent diffusivity [m2.s-1]                                       2.5000000000000002e-22\n",
      "Bulk solvent concentration [mol.m-3]                                                       2636.0\n",
      "Inner SEI open-circuit potential [V]                                                          0.1\n",
      "Outer SEI open-circuit potential [V]                                                          0.8\n",
      "Inner SEI electron conductivity [S.m-1]                                                  8.95e-14\n",
      "Inner SEI lithium interstitial diffusivity [m2.s-1]                                         1e-20\n",
      "Lithium interstitial reference concentration [mol.m-3]                                       15.0\n",
      "Initial inner SEI thickness [m]                                                           2.5e-09\n",
      "Initial outer SEI thickness [m]                                                           2.5e-09\n",
      "EC initial concentration in electrolyte [mol.m-3]                                          4541.0\n",
      "EC diffusivity [m2.s-1]                                                                     2e-18\n",
      "SEI kinetic rate constant [m.s-1]                                                           1e-12\n",
      "SEI open-circuit potential [V]                                                                0.4\n",
      "SEI growth activation energy [J.mol-1]                                                        0.0\n",
      "Negative electrode reaction-driven LAM factor [m3.mol-1]                                      0.0\n",
      "Positive electrode reaction-driven LAM factor [m3.mol-1]                                      0.0\n",
      "Negative current collector thickness [m]                                                  2.5e-05\n",
      "Negative electrode thickness [m]                                                           0.0001\n",
      "Separator thickness [m]                                                                   2.5e-05\n",
      "Positive electrode thickness [m]                                                           0.0001\n",
      "Positive current collector thickness [m]                                                  2.5e-05\n",
      "Electrode height [m]                                                                        0.137\n",
      "Electrode width [m]                                                                         0.207\n",
      "Negative tab width [m]                                                                       0.04\n",
      "Negative tab centre y-coordinate [m]                                                         0.06\n",
      "Negative tab centre z-coordinate [m]                                                        0.137\n",
      "Positive tab width [m]                                                                       0.04\n",
      "Positive tab centre y-coordinate [m]                                                        0.147\n",
      "Positive tab centre z-coordinate [m]                                                        0.137\n",
      "Cell cooling surface area [m2]                                                             0.0569\n",
      "Cell volume [m3]                                                                          7.8e-06\n",
      "Negative current collector conductivity [S.m-1]                                        59600000.0\n",
      "Positive current collector conductivity [S.m-1]                                        35500000.0\n",
      "Negative current collector density [kg.m-3]                                                8954.0\n",
      "Positive current collector density [kg.m-3]                                                2707.0\n",
      "Negative current collector specific heat capacity [J.kg-1.K-1]                              385.0\n",
      "Positive current collector specific heat capacity [J.kg-1.K-1]                              897.0\n",
      "Negative current collector thermal conductivity [W.m-1.K-1]                                 401.0\n",
      "Positive current collector thermal conductivity [W.m-1.K-1]                                 237.0\n",
      "Nominal cell capacity [A.h]                                                              0.680616\n",
      "Typical current [A]                                                                      0.680616\n",
      "Current function [A]                                                                     0.680616\n",
      "Negative electrode conductivity [S.m-1]                                                     100.0\n",
      "Maximum concentration in negative electrode [mol.m-3]                            24983.2619938437\n",
      "Negative electrode diffusivity [m2.s-1]                                      <function graphite_mcmb2528_diffusivity_Dualfoil1998 at 0x7fb36b2ad040>\n",
      "Negative electrode OCP [V]                                                   <function graphite_mcmb2528_ocp_Dualfoil1998 at 0x7fb36b261670>\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 cation signed stoichiometry                                               -1.0\n",
      "Negative electrode electrons in reaction                                                      1.0\n",
      "Negative electrode charge transfer coefficient                                                0.5\n",
      "Negative electrode double-layer capacity [F.m-2]                                              0.2\n",
      "Negative electrode exchange-current density [A.m-2]                          <function graphite_electrolyte_exchange_current_density_Dualfoil1998 at 0x7fb36b2615e0>\n",
      "Negative electrode density [kg.m-3]                                                        1657.0\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 OCP entropic change [V.K-1]                               <function graphite_entropic_change_Moura2016 at 0x7fb36b261550>\n",
      "Positive electrode conductivity [S.m-1]                                                      10.0\n",
      "Maximum concentration in positive electrode [mol.m-3]                            51217.9257309275\n",
      "Positive electrode diffusivity [m2.s-1]                                      <function lico2_diffusivity_Dualfoil1998 at 0x7fb36b2614c0>\n",
      "Positive electrode OCP [V]                                                   <function lico2_ocp_Dualfoil1998 at 0x7fb36b2613a0>\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 cation signed stoichiometry                                               -1.0\n",
      "Positive electrode electrons in reaction                                                      1.0\n",
      "Positive electrode charge transfer coefficient                                                0.5\n",
      "Positive electrode double-layer capacity [F.m-2]                                              0.2\n",
      "Positive electrode exchange-current density [A.m-2]                          <function lico2_electrolyte_exchange_current_density_Dualfoil1998 at 0x7fb36b261310>\n",
      "Positive electrode density [kg.m-3]                                                        3262.0\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 OCP entropic change [V.K-1]                               <function lico2_entropic_change_Moura2016 at 0x7fb36b261280>\n",
      "Separator porosity                                                                            1.0\n",
      "Separator Bruggeman coefficient (electrolyte)                                                 1.5\n",
      "Separator density [kg.m-3]                                                                  397.0\n",
      "Separator specific heat capacity [J.kg-1.K-1]                                               700.0\n",
      "Separator thermal conductivity [W.m-1.K-1]                                                   0.16\n",
      "Typical electrolyte concentration [mol.m-3]                                                1000.0\n",
      "Initial concentration in electrolyte [mol.m-3]                                             1000.0\n",
      "Cation transference number                                                                    0.4\n",
      "Thermodynamic factor                                                                                 1.0\n",
      "Electrolyte diffusivity [m2.s-1]                                             <function electrolyte_diffusivity_Capiglia1999 at 0x7fb36b2611f0>\n",
      "Electrolyte conductivity [S.m-1]                                             <function electrolyte_conductivity_Capiglia1999 at 0x7fb36b261160>\n",
      "Reference temperature [K]                                                                  298.15\n",
      "Ambient temperature [K]                                                                    298.15\n",
      "Negative current collector surface heat transfer coefficient [W.m-2.K-1]                      0.0\n",
      "Positive current collector surface heat transfer coefficient [W.m-2.K-1]                      0.0\n",
      "Negative tab heat transfer coefficient [W.m-2.K-1]                                           10.0\n",
      "Positive tab heat transfer coefficient [W.m-2.K-1]                                           10.0\n",
      "Edge heat transfer coefficient [W.m-2.K-1]                                                    0.3\n",
      "Total heat transfer coefficient [W.m-2.K-1]                                                  10.0\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\n",
      "citations                                                                         ['Marquis2019']\n"
     ]
    }
   ],
   "source": [
    "format_str = \"{:<75}  {:>20}\"\n",
    "print(format_str.format(\"PARAMETER\", \"VALUE\"))\n",
    "print(\"-\" * 97)\n",
    "for key, value in model.default_parameter_values.items():\n",
    "    try:\n",
    "        print(format_str.format(key, value))\n",
    "    except TypeError:\n",
    "        print(format_str.format(key, value.__str__()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the parameters in this list have numerical values. Some have string values, that point to particular python functions within PyBaMM. These denote parameters that vary over time and/or space, in a manner defined by the given python function. For the moment we will ignore these, and focus on altering one of the numerical parameters.\n",
    "\n",
    "The class [`pybamm.ParameterValues`](https://docs.pybamm.org/en/latest/source/api/parameters/parameter_values.html) acts like the normal python `dict` data structure, so you can read or write individual parameters accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current function [A] was 0.680616\n",
      "Current function [A] now is 1.4\n"
     ]
    }
   ],
   "source": [
    "variable = \"Current function [A]\"\n",
    "old_value = param[variable]\n",
    "param[variable] = 1.4\n",
    "new_value = param[variable]\n",
    "print(variable, \"was\", old_value)\n",
    "print(variable, \"now is\", param[variable])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to compare solutions with different parameter values, parameters must be changed to `InputParameter` objects, whose value can then be specified when solving. For example, we can compare the Voltage calculated using both the old and new current values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up\n",
    "model = pybamm.lithium_ion.SPM()\n",
    "solver = model.default_solver\n",
    "param[\"Current function [A]\"] = \"[input]\"\n",
    "param.process_model(model)\n",
    "disc.process_model(model)\n",
    "\n",
    "# Solution with current = 0.68\n",
    "old_solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 0.68})\n",
    "old_time = old_solution[\"Time [h]\"].entries\n",
    "old_voltage = old_solution[\"Voltage [V]\"].entries\n",
    "\n",
    "# Solution with current = 1.4\n",
    "new_solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 1.4})\n",
    "new_time = new_solution[\"Time [h]\"].entries\n",
    "new_voltage = new_solution[\"Voltage [V]\"].entries\n",
    "\n",
    "plt.plot(old_time, old_voltage, lw=2, label=\"Current = 0.68\")\n",
    "plt.plot(new_time, new_voltage, lw=2, label=\"Current = 1.4\")\n",
    "plt.xlabel(\"Time [h]\", fontsize=15)\n",
    "plt.ylabel(\"Voltage [V]\", fontsize=15)\n",
    "plt.legend(fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that there is a small number of parameters for which this is not possible, since the discretisation depends on them (for example, *Negative electrode thickness*). In this case you would need to remesh the geometry as the relative length of the electrodes would have altered. This would in turn require you to re-discretise the model equations."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the discretisation <a name=\"discretisation\"></a>\n",
    "\n",
    "The chosen spatial discretisation method to use for each domain is passed into the [`pybamm.Discretisation`](https://docs.pybamm.org/en/latest/source/api/spatial_methods/discretisation.html) class as one of its arguments. The default spatial methods for the SPM class are given as:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DOMAIN                                                                             DISCRETISED BY\n",
      "----------------------------------------------------------------------------------\n",
      "macroscale                                                                           FiniteVolume\n",
      "negative particle                                                                    FiniteVolume\n",
      "positive particle                                                                    FiniteVolume\n",
      "negative primary particle                                                            FiniteVolume\n",
      "positive primary particle                                                            FiniteVolume\n",
      "negative secondary particle                                                          FiniteVolume\n",
      "positive secondary particle                                                          FiniteVolume\n",
      "negative particle size                                                               FiniteVolume\n",
      "positive particle size                                                               FiniteVolume\n",
      "current collector                                                            ZeroDimensionalSpatialMethod\n"
     ]
    }
   ],
   "source": [
    "print(format_str.format(\"DOMAIN\", \"DISCRETISED BY\"))\n",
    "print(\"-\" * 82)\n",
    "for key, value in model.default_spatial_methods.items():\n",
    "    print(format_str.format(key, value.__class__.__name__))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change the discretisation for a particular domain, you can update the spatial methods `dict` with the new discretiation class  that you wish to use, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_methods = model.default_spatial_methods\n",
    "spatial_methods[\"negative particle\"] = pybamm.SpectralVolume()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also update the submeshes (meshes used for each domain) in a similar way. We'll generate a spectral mesh to use with our spectral volume method in the negative particle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "submesh_types = model.default_submesh_types\n",
    "submesh_types[\"negative particle\"] = pybamm.MeshGenerator(\n",
    "    pybamm.SpectralVolume1DSubMesh\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have set the new discretisation method, we can proceed to re-discretise and then solve the model. Note that in this case we need to regenerate the model using `pybamm.lithium_ion.SPM`, as the original model information was lost when we discretised it [above](#the-default-spm-model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# re-generate the model and set parameters (with fixed current function this time)\n",
    "model = pybamm.lithium_ion.SPM()\n",
    "solver = model.default_solver\n",
    "param[\"Current function [A]\"] = 0.68\n",
    "param.process_model(model)\n",
    "\n",
    "# re-discretise the model with the new mesh...\n",
    "mesh = pybamm.Mesh(geometry, submesh_types, model.default_var_pts)\n",
    "disc = pybamm.Discretisation(mesh, spatial_methods)\n",
    "disc.process_model(model)\n",
    "\n",
    "# ... and finally solve it\n",
    "solution = solver.solve(model, t_eval)\n",
    "pybamm.QuickPlot(solution).plot(0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the solver <a name=\"solver\"></a>\n",
    "\n",
    "Which method you use to integrate the discretised model in time can also be changed. PyBaMM has a number of different solvers available, all of which are described in the [documentation](https://docs.pybamm.org/en/latest/source/api/solvers/index.html).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default solver for SPM model: CasadiSolver\n"
     ]
    }
   ],
   "source": [
    "print(\"Default solver for SPM model:\", type(model.default_solver).__name__)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change this, simply create a new solver using one of the available classes and use it to solve your discretised model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_solver = pybamm.ScipySolver()\n",
    "new_solution = new_solver.solve(model, t_eval)\n",
    "pybamm.QuickPlot(new_solution).plot(0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "The relevant papers for this notebook are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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] 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",
      "[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] 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",
      "[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] Z. J. Wang. Spectral (finite) volume method for conservation laws on unstructured grids. Journal of Computational Physics, 178(1):210–251, 2002. doi:10.1006/jcph.2002.7041.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pybamm.print_citations()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pybamm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
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  "vscode": {
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 },
 "nbformat": 4,
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}