{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Circuit Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we demonstrate how to generate a pool of equivalent circuit models from electrochemical impedance spectroscopy (EIS) measurements that best fit the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AutoEIS relies on `EquivalentCircuits.jl` package to perform the EIS analysis. The package is not written in Python, so we need to install it first. AutoEIS ships with `julia_helpers` module that helps to install and manage Julia dependencies with minimal user interaction. For convenience, installing Julia and the required packages is done automatically when you import `autoeis` for the first time. If you have Julia installed already (discoverable in system PATH), it'll get detected and used, otherwise, it'll be installed automatically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "\n",
    "If this is the first time you're importing AutoEIS, executing the next cell will take a while, outputting a lot of logs. Re-run the cell to get rid of the logs.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:30:18.218516Z",
     "iopub.status.busy": "2024-09-16T20:30:18.217880Z",
     "iopub.status.idle": "2024-09-16T20:30:21.717443Z",
     "shell.execute_reply": "2024-09-16T20:30:21.716819Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import autoeis as ae\n",
    "\n",
    "ae.visualization.set_plot_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load EIS data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the environment is set up, we can load the EIS data. You can use [`pyimpspec`](https://vyrjana.github.io/pyimpspec/guide_data.html) to load EIS data from a variety of popular formats. Eventually, AutoEIS requires two arrays: `Z` and `freq`. `Z` is a complex impedance array, and `freq` is a frequency array. Both arrays must be 1D and have the same length. The impedance array must be in Ohms, and the frequency array must be in Hz.\n",
    "\n",
    "For convenience, we provide a function `load_test_dataset()` in `autoeis.io` to load a test dataset. The function returns a tuple of `freq` and `Z`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:30:21.721039Z",
     "iopub.status.busy": "2024-09-16T20:30:21.720478Z",
     "iopub.status.idle": "2024-09-16T20:30:21.759234Z",
     "shell.execute_reply": "2024-09-16T20:30:21.758916Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2;36m[20:30:21]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m \u001b[1;36m10\u001b[0m% of data filtered out.                                   \n"
     ]
    }
   ],
   "source": [
    "freq, Z = ae.io.load_test_dataset(preprocess=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "\n",
    "If your EIS data is stored as text, you can easily load them using `numpy.loadtxt`. See NumPy's documentation for more details.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the test dataset before we proceed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:30:21.761592Z",
     "iopub.status.busy": "2024-09-16T20:30:21.761452Z",
     "iopub.status.idle": "2024-09-16T20:30:22.153714Z",
     "shell.execute_reply": "2024-09-16T20:30:22.153269Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x325 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 336,
       "width": 868
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ae.visualization.plot_impedance_combo(freq, Z);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate equivalent circuits\n",
    "\n",
    "Now that we have loaded the EIS data, we can generate a pool of candidate equivalent circuits using the `generate_equivalent_circuits` function. The function takes the impedance data and frequency as input and returns a list of equivalent circuits. It also takes many optional arguments to control the circuit generation process. The most important ones are:\n",
    "\n",
    "- `iters`: Number of circuits to generate.\n",
    "- `complexity`: Maximum number of elements in a circuit.\n",
    "- `parallel`: Whether to run the circuit generation in parallel.\n",
    "- `tol`: Tolerance for accepting a circuit as a good fit.\n",
    "\n",
    "The function uses a gene expression programming (GEP) algorithm to generate the circuits. The GEP algorithm is a genetic algorithm that evolves circuits by combining and mutating genes. The algorithm starts with a population of random circuits and evolves them over many generations to find the best circuits that fit the data. The following parameters control the GEP algorithm:\n",
    "\n",
    "- `generations`: Number of generations to run the genetic algorithm.\n",
    "- `population_size`: Number of circuits in the population for\n",
    "\n",
    "The default values for these parameters are usually good enough for most cases. However, you can adjust them to get better results (e.g., increase both arguments if you're not satisfied with the generated circuits, or decrease them if you want to speed up the process)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "\n",
    "Circuit generation is a lengthy process. It may take one minute per iteration on a modern CPU. We recommend generating at least 50 circuits to get a good pool of candidate circuits, which may take about an hour.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:30:22.156111Z",
     "iopub.status.busy": "2024-09-16T20:30:22.156012Z",
     "iopub.status.idle": "2024-09-16T20:47:04.735543Z",
     "shell.execute_reply": "2024-09-16T20:47:04.732483Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da1db759dbde462097efd4fa91af4b73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating Candidate ECMs:   0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>circuitstring</th>\n",
       "      <th>Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[L1,R2]-L3-P4,P5]</td>\n",
       "      <td>{'L1': 4.999196815066165, 'R2': 142.1925571982...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[P1-[L2,R3]-[P4,R5],R6]</td>\n",
       "      <td>{'P1w': 1.0692875796913132e-05, 'P1n': 0.99933...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[P1,R2],P3-R4]</td>\n",
       "      <td>{'P1w': 4.0628711141873436e-07, 'P1n': 0.16679...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[P1,R2]-P3-[L4,R5],R6]</td>\n",
       "      <td>{'P1w': 2.26469783112177e-06, 'P1n': 0.9413257...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[P1,R2],P3]-R4</td>\n",
       "      <td>{'P1w': 4.062341607346847e-07, 'P1n': 0.166768...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[[P1,R2-R3],P4-R5]</td>\n",
       "      <td>{'P1w': 4.067227880682277e-07, 'P1n': 0.166463...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[P1-[R2,[L3,R4]],[P5,R6]]</td>\n",
       "      <td>{'P1w': 1.711705365594271e-06, 'P1n': 0.963974...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[P1,[P2,R3]-[R4,L5]]</td>\n",
       "      <td>{'P1w': 4.055870416931093e-07, 'P1n': 0.166440...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[[P1,R2],P3]-[R4,L5]</td>\n",
       "      <td>{'P1w': 4.0574609425532184e-07, 'P1n': 0.16657...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[[L1-P2-L3-R4,P5-R6],R7]</td>\n",
       "      <td>{'L1': 4.999733247944559, 'P2w': 2.12123178869...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[P1,[R2,P3]]-[R4,L5]-P6-R7</td>\n",
       "      <td>{'P1w': 1.7871141432179293e-06, 'P1n': 0.99976...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[P1-R2-P3,P4-R5]</td>\n",
       "      <td>{'P1w': 2.2940626507716466e-06, 'P1n': 0.40149...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 circuitstring  \\\n",
       "0           [[L1,R2]-L3-P4,P5]   \n",
       "1      [P1-[L2,R3]-[P4,R5],R6]   \n",
       "2              [[P1,R2],P3-R4]   \n",
       "3      [[P1,R2]-P3-[L4,R5],R6]   \n",
       "4              [[P1,R2],P3]-R4   \n",
       "5           [[P1,R2-R3],P4-R5]   \n",
       "6    [P1-[R2,[L3,R4]],[P5,R6]]   \n",
       "7         [P1,[P2,R3]-[R4,L5]]   \n",
       "8         [[P1,R2],P3]-[R4,L5]   \n",
       "9     [[L1-P2-L3-R4,P5-R6],R7]   \n",
       "10  [P1,[R2,P3]]-[R4,L5]-P6-R7   \n",
       "11            [P1-R2-P3,P4-R5]   \n",
       "\n",
       "                                           Parameters  \n",
       "0   {'L1': 4.999196815066165, 'R2': 142.1925571982...  \n",
       "1   {'P1w': 1.0692875796913132e-05, 'P1n': 0.99933...  \n",
       "2   {'P1w': 4.0628711141873436e-07, 'P1n': 0.16679...  \n",
       "3   {'P1w': 2.26469783112177e-06, 'P1n': 0.9413257...  \n",
       "4   {'P1w': 4.062341607346847e-07, 'P1n': 0.166768...  \n",
       "5   {'P1w': 4.067227880682277e-07, 'P1n': 0.166463...  \n",
       "6   {'P1w': 1.711705365594271e-06, 'P1n': 0.963974...  \n",
       "7   {'P1w': 4.055870416931093e-07, 'P1n': 0.166440...  \n",
       "8   {'P1w': 4.0574609425532184e-07, 'P1n': 0.16657...  \n",
       "9   {'L1': 4.999733247944559, 'P2w': 2.12123178869...  \n",
       "10  {'P1w': 1.7871141432179293e-06, 'P1n': 0.99976...  \n",
       "11  {'P1w': 2.2940626507716466e-06, 'P1n': 0.40149...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kwargs = {\n",
    "    \"iters\": 12,\n",
    "    \"complexity\": 12,\n",
    "    \"population_size\": 100,\n",
    "    \"generations\": 30,\n",
    "    \"tol\": 1e-3,\n",
    "    \"parallel\": True,\n",
    "}\n",
    "circuits_unfiltered = ae.core.generate_equivalent_circuits(freq, Z, **kwargs)\n",
    "circuits_unfiltered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Convergence\n",
    "\n",
    "The Circuit generation algorithm is sensitive to the `tol` parameter, meaning that the order of magnitude of the `tol` needs to be proportional to the order of magnitude of the impedance data. There's no one-size-fits-all value for `tol`, and we're trying to make the algorithm `tol`-agnostic in future releases. For now, we've hacked a heuristic that internally scales the `tol` based on the impedance data. Nevertheless, you may still need to adjust the `tol` if you end up with no circuits (increase `tol`) or too many circuits (decrease `tol`). The default value is `1e-3`. When increasing or decreasing `tol`, try doubling or halving the value to see if it helps.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter candidate equivalent circuits\n",
    "\n",
    "Note that all these circuits generated by the GEP process probably fit the data well, but they may not be physically meaningful. Therefore, we need to filter them to find the ones that are most plausible. AutoEIS uses \"statistical plausibility\" as a proxy for gauging \"physical plausibility\". To this end, AutoEIS provides a function to filter the candidate circuits based on some heuristics (read our [paper](https://doi.org/10.1149/1945-7111/aceab2) for the exact steps and the supporting rationale)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:47:04.757449Z",
     "iopub.status.busy": "2024-09-16T20:47:04.757092Z",
     "iopub.status.idle": "2024-09-16T20:47:04.813371Z",
     "shell.execute_reply": "2024-09-16T20:47:04.812215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>circuitstring</th>\n",
       "      <th>Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[P1,R2],P3]-R4</td>\n",
       "      <td>{'P1w': 4.062341607346847e-07, 'P1n': 0.166768...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[P1,[R2,P3]]-[R4,L5]-P6-R7</td>\n",
       "      <td>{'P1w': 1.7871141432179293e-06, 'P1n': 0.99976...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                circuitstring  \\\n",
       "0             [[P1,R2],P3]-R4   \n",
       "1  [P1,[R2,P3]]-[R4,L5]-P6-R7   \n",
       "\n",
       "                                          Parameters  \n",
       "0  {'P1w': 4.062341607346847e-07, 'P1n': 0.166768...  \n",
       "1  {'P1w': 1.7871141432179293e-06, 'P1n': 0.99976...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuits = ae.core.filter_implausible_circuits(circuits_unfiltered)\n",
    "circuits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how well the generated circuits fit the data. You can either use the parameters' values at the end of the GEP process (stored in the `circuits` dataframe), or use `fit_circuit_parameters` to further refine the parameters (recommended)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "\n",
    "Normally, the found circuits are good enough, but since we didn't run the algorithm for long enough (to not timeout our CI on GitHub), we will use a custom circuit for evaluation. If you're running this notebook on your own data, try using `iters >= 200` together with a more stringet `tol` to get a good pool of circuits. We're currently working on changing the evolutionary algorithm backend to speed up the process, so you no longer need to wait for hours to get a good pool of circuits.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:47:04.818217Z",
     "iopub.status.busy": "2024-09-16T20:47:04.817985Z",
     "iopub.status.idle": "2024-09-16T20:47:05.999520Z",
     "shell.execute_reply": "2024-09-16T20:47:05.999170Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 550x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 385,
       "width": 503
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "use_custom_circuit = True\n",
    "\n",
    "if not use_custom_circuit:\n",
    "    circuit = circuits.iloc[3][\"circuitstring\"]\n",
    "    p = circuits.iloc[3][\"Parameters\"]\n",
    "    # Refine the circuit parameters\n",
    "    p = ae.utils.fit_circuit_parameters(circuit, freq, Z, p0=p, max_iters=25)\n",
    "else:\n",
    "    circuit = \"R4-[P1, R3-P2]\"\n",
    "    p = ae.utils.fit_circuit_parameters(circuit, freq, Z, max_iters=25)\n",
    "\n",
    "# Simulate Z using the circuit and the fitted parameters\n",
    "circuit_fn = ae.utils.generate_circuit_fn(circuit)\n",
    "Z_sim = circuit_fn(freq, list(p.values()))\n",
    "\n",
    "# Plot against ground truth\n",
    "fig, ax = plt.subplots(figsize=(5.5, 4))\n",
    "ae.visualization.plot_nyquist(Z_sim, fmt=\"-\", ax=ax, label=\"simulated\")\n",
    "ae.visualization.plot_nyquist(Z, fmt=\".\", ax=ax, label=\"data\");"
   ]
  }
 ],
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