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   "source": [
    "# Interacting with PyBOP optimisers\n",
    "\n",
    "This notebook introduces two interfaces to interact with PyBOP's optimiser classes.\n",
    "\n",
    "### Set the Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade pip ipywidgets -q\n",
    "%pip install pybop -q\n",
    "\n",
    "# Import the necessary libraries\n",
    "import numpy as np\n",
    "\n",
    "import pybop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## Setup the model, problem, and cost\n",
    "\n",
    "The code block below sets up the model, problem, and cost objects. For more information on this process, take a look at other notebooks in the examples directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting open-circuit voltage to default function\n"
     ]
    }
   ],
   "source": [
    "# Load the parameters\n",
    "parameter_set = pybop.ParameterSet(\n",
    "    json_path=\"../scripts/parameters/initial_ecm_parameters.json\"\n",
    ")\n",
    "parameter_set.import_parameters()\n",
    "# Define the model\n",
    "model = pybop.empirical.Thevenin(\n",
    "    parameter_set=parameter_set, options={\"number of rc elements\": 1}\n",
    ")\n",
    "\n",
    "# Define the parameters\n",
    "parameters = pybop.Parameter(\n",
    "    \"R0 [Ohm]\",\n",
    "    prior=pybop.Gaussian(0.0002, 0.0001),\n",
    "    bounds=[1e-4, 1e-2],\n",
    ")\n",
    "\n",
    "# Generate synthetic data\n",
    "t_eval = np.arange(0, 900, 2)\n",
    "values = model.predict(t_eval=t_eval)\n",
    "\n",
    "# Form dataset\n",
    "dataset = pybop.Dataset(\n",
    "    {\n",
    "        \"Time [s]\": t_eval,\n",
    "        \"Current function [A]\": values[\"Current [A]\"].data,\n",
    "        \"Voltage [V]\": values[\"Voltage [V]\"].data,\n",
    "    }\n",
    ")\n",
    "\n",
    "# Construct problem and cost\n",
    "problem = pybop.FittingProblem(model, parameters, dataset)\n",
    "cost = pybop.SumSquaredError(problem)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "## Interacting with the Optimisers\n",
    "\n",
    "Now that we have set up the required objects, we can introduce the two interfaces for interacting with PyBOP optimisers. These are:\n",
    " \n",
    "1. The direct optimiser (e.g. `pybop.XNES`)\n",
    "2. The optimisation class (i.e. `pybop.Optimisation`)\n",
    " \n",
    "These two methods provide two equivalent ways of interacting with PyBOP's optimisers. The first method provides a direct way to select the Optimiser, with the second method being a more general method with a default optimiser (`pybop.XNES`) set if you don't provide an optimiser. \n",
    "\n",
    "First, the direct interface is presented. With this interface the user can select from the [list of optimisers](https://github.com/pybop-team/PyBOP?tab=readme-ov-file#supported-methods) supported in PyBOP and construct them directly. Options can be passed as kwargs, or through get() / set() methods in the case of PINTS-based optimisers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "optim_one = pybop.XNES(\n",
    "    cost, max_iterations=50\n",
    ")  # Direct optimiser class with options as kwargs\n",
    "optim_one.set_max_iterations(\n",
    "    50\n",
    ")  # Alternative set() / get() methods for PINTS optimisers\n",
    "x1, final_cost = optim_one.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "Next, the `Optimisation` interface is less direct than the previous one, but provides a single class to work with across PyBOP workflows. The options are passed the same way as the above method, through kwargs or get() / set() methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9",
   "metadata": {},
   "outputs": [],
   "source": [
    "optim_two = pybop.Optimisation(\n",
    "    cost, optimiser=pybop.XNES, max_iterations=50\n",
    ")  # Optimisation class with options as kwargs\n",
    "optim_two.set_max_iterations(\n",
    "    50\n",
    ")  # Alternative set() / get() methods for PINTS optimisers\n",
    "x2, final_cost = optim_two.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "We can show the equivalence of these two methods by comparing the optimiser objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(optim_one, type(optim_two.optimiser))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "For completeness, we can show the optimiser solutions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated parameters x1: [0.00099965]\n",
      "Estimated parameters x2: [0.00099985]\n"
     ]
    }
   ],
   "source": [
    "print(\"Estimated parameters x1:\", x1)\n",
    "print(\"Estimated parameters x2:\", x2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "## Closing Comments\n",
    "\n",
    "As both of these API's provide access to the same optimisers, please use either as you prefer. A couple things to note:\n",
    "\n",
    "- If you are using a SciPy-based optimiser (`pybop.SciPyMinimize`, `pybop.SciPyDifferentialEvolution`), the `set()` / `get()` methods for the optimiser options are not currently supported. These optimisers require options to be passed as kwargs.\n",
    "- The optimiser passed to `pybop.Optimisation` must not be a constructed object."
   ]
  }
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