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   "source": [
    "# Aged parameter set example\n",
    "\n",
    "For scenario modelling it can be useful to take a parameter set for a fresh cell and apply a degradation state to it. In this example we will use the `apply_degradation_state` method to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ionworkspipeline as iwp\n",
    "import pybamm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by loading in a fresh parameter set. For this example we will use the `Chen2020` parameter set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fresh_parameter_values = iwp.ParameterValues(\"Chen2020\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define a degradation state. The degradation state to apply to the parameter values is a dictionary with the following keys:\n",
    "\n",
    "* \"LAM_ne [%]\": Loss of active material in negative electrode (%)\n",
    "* \"LAM_pe [%]\": Loss of active material in positive electrode (%)\n",
    "* \"LLI [%]\": Loss of lithium inventory (%)\n",
    "* \"R0_addn [Ohm]\": Additional ohmic resistance (Ohm). This is achieved by adding to the contact resistance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "degradation_state = {\n",
    "    \"LLI [%]\": 8,\n",
    "    \"LAM_ne [%]\": 3,\n",
    "    \"LAM_pe [%]\": 5,\n",
    "    \"R0_addn [Ohm]\": 0.03,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then apply the degradation state to the parameter values, passing `inplace=False` so that the original parameter values are not modified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aged_parameter_values = fresh_parameter_values.apply_degradation_state(\n",
    "    degradation_state, inplace=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a model and solve it for both the fresh and aged parameter values, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pybamm.lithium_ion.SPMe(options={\"contact resistance\": \"true\"})\n",
    "experiment = pybamm.Experiment(\n",
    "    [\n",
    "        \"Discharge at 1C until 2.8V\",\n",
    "        \"Rest for 1 hour\",\n",
    "        \"Charge at 1C until 4.2V\",\n",
    "        \"Hold at 4.2V until C/20\",\n",
    "        \"Rest for 1 hour\",\n",
    "    ]\n",
    ")\n",
    "sols = []\n",
    "for parameter_values in [fresh_parameter_values, aged_parameter_values]:\n",
    "    sim = pybamm.Simulation(\n",
    "        model,\n",
    "        parameter_values=parameter_values,\n",
    "        experiment=experiment,\n",
    "    )\n",
    "    sol = sim.solve()\n",
    "    sols.append(sol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and compare the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pybamm.dynamic_plot(\n",
    "    sols,\n",
    "    [\n",
    "        \"Current [A]\",\n",
    "        \"Voltage [V]\",\n",
    "        \"X-averaged negative electrode reaction overpotential [V]\",\n",
    "        \"X-averaged positive electrode reaction overpotential [V]\",\n",
    "        \"X-averaged electrolyte overpotential [V]\",\n",
    "        \"Electrolyte concentration [mol.m-3]\",\n",
    "    ],\n",
    "    labels=[\"Fresh\", \"Aged\"],\n",
    ")"
   ]
  }
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