{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Cycle ageing example\n",
    "\n",
    "This notebook demonstrates how to use the `CycleAgeing` objective function to fit the\n",
    "SEI solvent diffusivity in the \"solvent-diffusion limited\" SEI model using synthetic cycling data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pybamm\n",
    "import ionworkspipeline as iwp\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by generating some synthetic cycling data. We choose the SPMe model and the \"solvent-diffusion limited\" SEI model and simulate 10 cycles of a CCCV charge/discharge cycle. For the data, we record the cycle number, the loss of lithium inventory, and the capacity. We choose the parameter values so that we get a large loss of lithium inventory even after only 10 cycles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pybamm.lithium_ion.SPMe({\"SEI\": \"solvent-diffusion limited\"})\n",
    "parameter_values = iwp.ParameterValues(\"Chen2020\")\n",
    "parameter_values.update(\n",
    "    {\n",
    "        \"SEI solvent diffusivity [m2.s-1]\": 2.5e-18,\n",
    "    }\n",
    ")\n",
    "\n",
    "experiment = pybamm.Experiment(\n",
    "    [\n",
    "        (\n",
    "            \"Discharge at 1C until 2.5 V\",\n",
    "            \"Rest for 1 hour\",\n",
    "            \"Charge at C/2 until 4.2 V\",\n",
    "            \"Hold at 4.2 V until 10 mA\",\n",
    "            \"Rest for 1 hour\",\n",
    "        ),\n",
    "    ]\n",
    "    * 10\n",
    ")\n",
    "\n",
    "sim = iwp.Simulation(\n",
    "    model,\n",
    "    experiment=experiment,\n",
    "    parameter_values=parameter_values,\n",
    ")\n",
    "\n",
    "sol = sim.solve()\n",
    "\n",
    "data = {\"Cycle number\": sol.summary_variables.cycle_number}\n",
    "for var in [\"Loss of lithium inventory [%]\", \"Capacity [A.h]\"]:\n",
    "    data[var] = sol.summary_variables[var]\n",
    "\n",
    "# PyBaMM uses 1-based indexing for cycles but ionworks data format assumes cycles number\n",
    "# starts at zero\n",
    "data[\"Cycle number\"] = data[\"Cycle number\"] - 1\n",
    "\n",
    "data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the synthetic data to see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = data.plot(\n",
    "    x=\"Cycle number\", y=[\"Loss of lithium inventory [%]\", \"Capacity [A.h]\"], kind=\"line\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create the objective function. We pass the data and the options to the `CycleAgeing` objective function. The options include the model, experiment, and the objective variables (i.e. the variables we want to fit to). The `CycleAgeing` objective runs the experiment and calculates the cycling data. By default, it uses the function `ionworkspipeline.data_fits.objectives.get_standard_summary_variables` to extract the summary variables from the solution. Custom summary variables can be used by passing a custom function to the `cycle_variables_function` option.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "objective = iwp.objectives.CycleAgeing(\n",
    "    data,\n",
    "    options={\n",
    "        \"model\": model,\n",
    "        \"experiment\": experiment,\n",
    "        \"objective variables\": [\"Loss of lithium inventory [%]\"],\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then create the data fit. We pass the objective function and the parameters to the `DataFit` class. The parameters include the initial values and bounds for the parameters we want to fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    \"SEI solvent diffusivity [m2.s-1]\": iwp.Parameter(\n",
    "        \"SEI solvent diffusivity [m2.s-1]\",\n",
    "        initial_value=1e-19,\n",
    "        bounds=(1e-21, 1e-17),\n",
    "    )\n",
    "}\n",
    "data_fit = iwp.DataFit(objective, parameters=parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we run the data fit. We pass the known parameters (i.e. those we are not fitting) to the data fit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "known_parameters = {k: v for k, v in parameter_values.items() if k not in parameters}\n",
    "data_fit.run(known_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the fit results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = data_fit.plot_fit_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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