{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b7aa80ce-9d6e-401a-8870-a8a3e990b04a",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "5cc5b64f-cf09-43f8-a7a5-359301e9821c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize\n",
    "# from impedance.models.circuits import CustomCircuit\n",
    "# from impedance.visualization import plot_nyquist # Kept if you want to switch plotting methods\n",
    "\n",
    "import os\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ede9cb-3a55-4e31-9852-b8792e90c2d9",
   "metadata": {},
   "source": [
    "## Data Loading functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f38b45f4-cf5b-49d0-bc65-67489bec0a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Data Loading ---\n",
    "def EIS_z60_import(filename):\n",
    "    \"\"\" Import z60 file as pandas dataframe.\"\"\"\n",
    "    df = pd.read_csv(\n",
    "        filename,\n",
    "        skiprows=11,\n",
    "        sep=r'\\s+',\n",
    "        header=None,\n",
    "        names=[\"Freq\", \"Ampl\", \"Bias\", \"Time\", \"Z'\", \"Z''\", \"GD\", \"Err\", \"Range\"],\n",
    "        index_col=\"Freq\")\n",
    "\n",
    "    df = df.drop(columns=['Ampl', 'Bias', 'Time', 'GD', 'Err', 'Range']) # Drop columns\n",
    "    mask = (df[\"Z'\"] > 0) & (df[\"Z''\"] < 0) # Only keep first quadrant in Nyquist plot\n",
    "\n",
    "    df['Z'] = np.sqrt( (df[\"Z'\"].to_numpy())**2 + \\\n",
    "                       (df[\"Z'\"].to_numpy())**2 )\n",
    "\n",
    "    df['theta'] = np.arctan2(-df[\"Z'\"].to_numpy(), df[\"Z''\"].to_numpy()) * 180 / np.pi\n",
    "    \n",
    "    df[~mask] = None\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7faff843-86d5-43a4-b68a-0e8aaa159065",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z'</th>\n",
       "      <th>Z''</th>\n",
       "      <th>Z</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freq</th>\n",
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       "      <th>100000.000000</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>62887.000000</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-129.860556</td>\n",
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       "    <tr>\n",
       "      <th>0.013016</th>\n",
       "      <td>607638.0</td>\n",
       "      <td>-414233.0</td>\n",
       "      <td>8.593299e+05</td>\n",
       "      <td>-124.282652</td>\n",
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       "    <tr>\n",
       "      <th>0.011591</th>\n",
       "      <td>814114.0</td>\n",
       "      <td>-698537.0</td>\n",
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       "      <td>-130.630696</td>\n",
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       "    <tr>\n",
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       "      <td>848202.0</td>\n",
       "      <td>-689490.0</td>\n",
       "      <td>1.199539e+06</td>\n",
       "      <td>-129.107116</td>\n",
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       "    <tr>\n",
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       "      <td>720312.0</td>\n",
       "      <td>-1031530.0</td>\n",
       "      <td>1.018675e+06</td>\n",
       "      <td>-145.073605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>140 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                     Z'        Z''             Z       theta\n",
       "Freq                                                        \n",
       "100000.000000       NaN        NaN           NaN         NaN\n",
       "89051.300000        NaN        NaN           NaN         NaN\n",
       "79301.400000        NaN        NaN           NaN         NaN\n",
       "70618.900000        NaN        NaN           NaN         NaN\n",
       "62887.000000        NaN        NaN           NaN         NaN\n",
       "...                 ...        ...           ...         ...\n",
       "0.014616       676330.0  -564709.0  9.564751e+05 -129.860556\n",
       "0.013016       607638.0  -414233.0  8.593299e+05 -124.282652\n",
       "0.011591       814114.0  -698537.0  1.151331e+06 -130.630696\n",
       "0.010322       848202.0  -689490.0  1.199539e+06 -129.107116\n",
       "0.010000       720312.0 -1031530.0  1.018675e+06 -145.073605\n",
       "\n",
       "[140 rows x 4 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EIS_z60_import(\"EIS_10mV_Timing task2025_07_11_17_19_C001.z60\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd32fc6-fd73-48e0-b705-cbf037e941c0",
   "metadata": {},
   "source": [
    "## Loading all files from timed repeated measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3bbd1375-9e48-483b-b3a0-cbc097396470",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "\n",
    "EIS_10 = [file \n",
    "          for file in os.listdir('.') \n",
    "          if file.startswith('EIS_10mV') and file.endswith('.z60')]\n",
    "EIS_10_df_list = [] # 1. Create an empty list to hold the DataFrames\n",
    "for filename in EIS_10:\n",
    "    base_name = filename.removesuffix('.z60')\n",
    "    repeat_number = \"\".join(char for char in base_name.rsplit('_', 1)[-1] if char.isdigit())\n",
    "    repeat_number = int(repeat_number) \n",
    "    time = (repeat_number - 1)*2\n",
    "    #print(f\"{base_name}  {time}\")\n",
    "    df = EIS_z60_import(filename)\n",
    "    df[\"Time\"] = time\n",
    "    df.set_index('Time', append=True, inplace=True)    \n",
    "    df_swapped = df.swaplevel()\n",
    "    df_sorted = df_swapped.sort_index()\n",
    "    EIS_10_df_list.append(df_sorted) # 2. Add the processed DataFrame to the list\n",
    "\n",
    "EIS_20 = [file \n",
    "          for file in os.listdir('.') \n",
    "          if file.startswith('EIS_20mV') and file.endswith('.z60')]\n",
    "EIS_20_df_list = [] # 1. Create an empty list to hold the DataFrames\n",
    "for filename in EIS_20:\n",
    "    base_name = filename.removesuffix('.z60')\n",
    "    repeat_number = \"\".join(char for char in base_name.rsplit('_', 1)[-1] if char.isdigit())\n",
    "    repeat_number = int(repeat_number) \n",
    "    time = (repeat_number - 1)*2\n",
    "    #print(f\"{base_name}  {time}\")\n",
    "    df = EIS_z60_import(filename)\n",
    "    df[\"Time\"] = time\n",
    "    df.set_index('Time', append=True, inplace=True)    \n",
    "    df_swapped = df.swaplevel()\n",
    "    df_sorted = df_swapped.sort_index()\n",
    "    EIS_20_df_list.append(df_sorted) # 2. Add the processed DataFrame to the list\n",
    "\n",
    "if EIS_10_df_list:\n",
    "    EIS_10_df = pd.concat(EIS_10_df_list)\n",
    "else:\n",
    "    EIS_10_df = pd.DataFrame() # Ensure EIS_10_df is an empty DataFrame if no files were found\n",
    "    raise ValueError(\"No EIS 10mV files found\")\n",
    "\n",
    "if EIS_20_df_list:\n",
    "    EIS_20_df = pd.concat(EIS_20_df_list)\n",
    "else:\n",
    "    EIS_20_df = pd.DataFrame() # Ensure EIS_10_df is an empty DataFrame if no files were found\n",
    "    raise ValueError(\"No EIS 20mV files found\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "61ab95b2-4dd7-4a05-a990-d54ab001b285",
   "metadata": {},
   "outputs": [
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       "      <th>79301.400000</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>100000.000000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>1960 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                          Z'         Z''             Z       theta\n",
       "Time Freq                                                         \n",
       "0    0.010000       720312.0 -1031530.00  1.018675e+06 -145.073605\n",
       "     0.010322       848202.0  -689490.00  1.199539e+06 -129.107116\n",
       "     0.011591       814114.0  -698537.00  1.151331e+06 -130.630696\n",
       "     0.013016       607638.0  -414233.00  8.593299e+05 -124.282652\n",
       "     0.014616       676330.0  -564709.00  9.564751e+05 -129.860556\n",
       "...                      ...         ...           ...         ...\n",
       "26   62887.000000   170147.0    -5666.63  2.406242e+05  -91.907492\n",
       "     70618.900000   125131.0   -26409.20  1.769620e+05 -101.917519\n",
       "     79301.400000        NaN         NaN           NaN         NaN\n",
       "     89051.300000        NaN         NaN           NaN         NaN\n",
       "     100000.000000       NaN         NaN           NaN         NaN\n",
       "\n",
       "[1960 rows x 4 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EIS_10_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "77b7bb99-499c-4811-8cc4-07fe2710dad8",
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"5\" valign=\"top\">26</th>\n",
       "      <th>62887.000000</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>79301.400000</th>\n",
       "      <td>319716.0</td>\n",
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       "      <td>4.521467e+05</td>\n",
       "      <td>-101.271863</td>\n",
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       "    <tr>\n",
       "      <th>89051.300000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000.000000</th>\n",
       "      <td>96997.8</td>\n",
       "      <td>-139224.0</td>\n",
       "      <td>1.371756e+05</td>\n",
       "      <td>-145.134952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1960 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Z'       Z''             Z       theta\n",
       "Time Freq                                                       \n",
       "0    0.010000       798861.0 -436871.0  1.129760e+06 -118.672809\n",
       "     0.010322       870890.0 -353106.0  1.231624e+06 -112.070300\n",
       "     0.011591       700737.0 -637478.0  9.909918e+05 -132.293576\n",
       "     0.013016       635843.0 -702820.0  8.992178e+05 -137.864277\n",
       "     0.014616       679839.0 -543164.0  9.614375e+05 -128.623447\n",
       "...                      ...       ...           ...         ...\n",
       "26   62887.000000        NaN       NaN           NaN         NaN\n",
       "     70618.900000        NaN       NaN           NaN         NaN\n",
       "     79301.400000   319716.0  -63722.3  4.521467e+05 -101.271863\n",
       "     89051.300000        NaN       NaN           NaN         NaN\n",
       "     100000.000000   96997.8 -139224.0  1.371756e+05 -145.134952\n",
       "\n",
       "[1960 rows x 4 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EIS_20_df\n",
    "# In order to get all the values for the zeroth hour, use the following:\n",
    "# EIS_20_df.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f431d5-bfc1-4600-a83a-bc6d7ccae13a",
   "metadata": {},
   "source": [
    "## Fit data to Randles equivalent circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2559f6f1-cf17-4650-be09-7c7b6801fc47",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import functools\n",
    "import numpy as np\n",
    "import scipy.optimize\n",
    "import pandas as pd # Added for the example\n",
    "\n",
    "def get_resistance_values(\n",
    "    frequencies,\n",
    "    z_real,\n",
    "    z_imag,\n",
    "    initial_guesses=None,\n",
    "    bounds=None,\n",
    "    sigma_real=None, # Changed to optional\n",
    "    sigma_imag=None, # Changed to optional\n",
    "    max_evaluations=5000000\n",
    "):\n",
    "    \"\"\"\n",
    "    Fits EIS data to a Randles equivalent circuit and returns resistance values.\n",
    "\n",
    "    This function can perform either a weighted (if sigma is provided) or\n",
    "    unweighted (if sigma is None) least squares fit.\n",
    "\n",
    "    Args:\n",
    "        frequencies (array-like): Array of frequency values.\n",
    "        z_real (array-like): Real part of the impedance (Z').\n",
    "        z_imag (array-like): Imaginary part of the impedance (Z'').\n",
    "        initial_guesses (list or tuple): Initial guesses for [Rs, Rp, Q, alpha].\n",
    "        bounds (tuple): A 2-tuple of lists, e.g., ([low_bounds], [high_bounds]).\n",
    "        sigma_real (array-like, optional): Error for the real impedance. Defaults to None.\n",
    "        sigma_imag (array-like, optional): Error for the imaginary impedance. Defaults to None.\n",
    "        max_evaluations (int, optional): Max function evaluations. Defaults to 5,000,000.\n",
    "\n",
    "    Returns:\n",
    "        dict: A dictionary of optimized parameters and errors. Returns None if fitting fails.\n",
    "    \"\"\"\n",
    "\n",
    "    if initial_guesses is None:\n",
    "        # Initial guesses for the parameters (R_s, R_ct, Q, alpha)\n",
    "        initial_guesses = [10.0, 7e6,  1e-6, 0.87]\n",
    "\n",
    "    if bounds is None:\n",
    "        # Helps to keep parameters within physically realistic ranges. \n",
    "        bounds=([0,    0,    1e-9, 0], \n",
    "                [1000, 1e15, 1e-2, 1])\n",
    "        \n",
    "    # --- Helper functions for circuit elements and combinations ---\n",
    "    def R(f, R_val): return np.full_like(f, R_val, dtype=np.complex128)\n",
    "    def CPE(f, Q, alpha): return 1.0 / (Q * (1j * 2 * np.pi * f) ** alpha)\n",
    "    def series(*args): return functools.reduce(np.add, args)\n",
    "    def parallel(*args): return 1.0 / functools.reduce(np.add, [1.0/z for z in args])\n",
    "\n",
    "    # --- Equivalent circuit model ---\n",
    "    def randles_circuit(f, Rs, Rp, Q, alpha):\n",
    "        return series(R(f, Rs), parallel(R(f, Rp), CPE(f, Q, alpha)))\n",
    "\n",
    "    def model_func_for_fitter(f, Rs, Rp, Q, alpha):\n",
    "        Z_fit = randles_circuit(f, Rs, Rp, Q, alpha)\n",
    "        return np.concatenate([Z_fit.real, Z_fit.imag])\n",
    "\n",
    "    # --- Prepare data for fitting ---\n",
    "    valid_indices = ~np.isnan(z_real) & ~np.isnan(z_imag)\n",
    "    freq_data = np.array(frequencies)[valid_indices]\n",
    "    y_data = np.concatenate([\n",
    "        np.array(z_real)[valid_indices],\n",
    "        np.array(z_imag)[valid_indices]\n",
    "    ])\n",
    "\n",
    "    try:    \n",
    "        if sigma_real is not None and sigma_imag is not None:\n",
    "            print(\"Sigma values provided. Performing weighted least squares fit.\")\n",
    "            sigma = np.concatenate([\n",
    "                np.array(sigma_real)[valid_indices],\n",
    "                np.array(sigma_imag)[valid_indices]\n",
    "            ])\n",
    "            absolute_sigma = True # Treat sigma as true standard deviations\n",
    "\n",
    "            popt, pcov = scipy.optimize.curve_fit(\n",
    "                f=model_func_for_fitter,\n",
    "                xdata=freq_data,\n",
    "                ydata=y_data,\n",
    "                p0=initial_guesses,\n",
    "                bounds=bounds,\n",
    "                maxfev=max_evaluations,\n",
    "            )\n",
    "        else:\n",
    "            popt, pcov = scipy.optimize.curve_fit(\n",
    "                f=model_func_for_fitter,\n",
    "                xdata=freq_data,\n",
    "                ydata=y_data,\n",
    "                p0=initial_guesses,\n",
    "                bounds=bounds,\n",
    "                maxfev=max_evaluations,\n",
    "            )\n",
    "        #print(\"Curve fitting finished successfully! ✅\")\n",
    "\n",
    "        # --- Extract and return results ---\n",
    "        perr = np.sqrt(np.diag(pcov))\n",
    "        results = {\n",
    "            'Rs': popt[0], 'Rs_err': perr[0],\n",
    "            'Rp': popt[1], 'Rp_err': perr[1],\n",
    "            'Q': popt[2], 'Q_err': perr[2],\n",
    "            'alpha': popt[3], 'alpha_err': perr[3],\n",
    "        }\n",
    "        return results\n",
    "    \n",
    "    except (RuntimeError, ValueError) as e:\n",
    "        print(f\"Curve fitting failed: {e} ❌\")\n",
    "        print(\"Try adjusting initial guesses, bounds, or the model itself.\")\n",
    "        return None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "91f3af6e-93f2-487e-9b84-9c054fbeabaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "EIS_10_results = {}\n",
    "for time in list(set([time for (time, freq) in EIS_10_df.index])):\n",
    "   EIS_10_results[time] = get_resistance_values(\n",
    "        frequencies=EIS_10_df.loc[time].index,\n",
    "        z_real=EIS_10_df.loc[time][\"Z'\"],\n",
    "        z_imag=EIS_10_df.loc[time][\"Z''\"],\n",
    "    )\n",
    "\n",
    "EIS_20_results = {}\n",
    "for time in list(set([time for (time, freq) in EIS_20_df.index])):\n",
    "   EIS_20_results[time] = get_resistance_values(\n",
    "        frequencies=EIS_20_df.loc[time].index,\n",
    "        z_real=EIS_20_df.loc[time][\"Z'\"],\n",
    "        z_imag=EIS_20_df.loc[time][\"Z''\"],\n",
    "    )\n",
    "    \n",
    "\n",
    "EIS_10_results_df = pd.DataFrame.from_dict(EIS_10_results, orient='index')\n",
    "EIS_20_results_df = pd.DataFrame.from_dict(EIS_20_results, orient='index')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c544ad4f-0231-44fa-865e-fbb7be597ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x296ea6b13a0>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "EIS_10_results_df[\"Rp\"].plot(ax=ax, label=\"10 mV\")\n",
    "EIS_20_results_df[\"Rp\"].plot(ax=ax, label=\"20 mV\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "88d6cbff-d2cf-4838-bbd4-0fe75eeab386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 1. Set a professional style\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "\n",
    "# 2. Create a 2x2 grid of subplots\n",
    "# `sharex=True` links the x-axes for easier comparison.\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 4), dpi=120, sharex=True)\n",
    "\n",
    "# Flatten the 2x2 array of axes to easily loop through them\n",
    "axes = axes.flatten()\n",
    "\n",
    "# 3. Define parameters and labels for each subplot\n",
    "params = ['Rs', 'Rp', 'Q', 'alpha']\n",
    "titles = [\n",
    "    'Solution Resistance ($R_s$)',\n",
    "    'Polarization Resistance ($R_p$)',\n",
    "    'Constant Phase Element (Q)',\n",
    "    'Alpha Coefficient (α)'\n",
    "]\n",
    "ylabels = [\n",
    "    '$R_s$ / $\\\\Omega$',\n",
    "    '$R_p$ / $\\\\Omega$',\n",
    "    'Q / $s^{\\\\alpha} \\\\Omega^{-1}$',\n",
    "    'Alpha (α)'\n",
    "]\n",
    "\n",
    "# 4. Loop through each parameter and create a subplot\n",
    "for i, ax in enumerate(axes):\n",
    "    param = params[i]\n",
    "\n",
    "    # Plot data from both DataFrames on the current axis\n",
    "    ax.plot(EIS_10_results_df.index, EIS_10_results_df[param],\n",
    "            label=\"10 mV\", marker='o', linestyle='-', linewidth=2, color='#007ACC')\n",
    "\n",
    "    ax.plot(EIS_20_results_df.index, EIS_20_results_df[param],\n",
    "            label=\"20 mV\", marker='s', linestyle='--', linewidth=2, color='#D62728')\n",
    "\n",
    "    # Customize titles and labels for each subplot\n",
    "    ax.set_title(titles[i], fontsize=14, fontweight='bold')\n",
    "    ax.set_ylabel(ylabels[i], fontsize=12)\n",
    "\n",
    "    # Customize grid and spines\n",
    "    ax.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.tick_params(axis='both', which='major', labelsize=10)\n",
    "\n",
    "    # Apply scientific notation where needed (e.g., for Rp and Q)\n",
    "    if param in ['Rp', 'Q']:\n",
    "        ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0), useMathText=True)\n",
    "        ax.yaxis.get_offset_text().set_fontsize(10)\n",
    "\n",
    "# 5. Global customizations\n",
    "# Add a shared x-axis label to the bottom plots\n",
    "axes[2].set_xlabel('Time (hr)', fontsize=12)\n",
    "axes[3].set_xlabel('Time (hr)', fontsize=12)\n",
    "\n",
    "# Create a single, shared legend for the entire figure\n",
    "handles, labels = axes[0].get_legend_handles_labels()\n",
    "fig.legend(handles, labels, loc='upper right', fontsize=11, bbox_to_anchor=(0.98, 0.98))\n",
    "\n",
    "# Add a main title for the whole figure\n",
    "fig.suptitle('EIS Parameter Evolution vs. Cycle Number', fontsize=18, fontweight='bold')\n",
    "\n",
    "# 6. Adjust layout to prevent titles/labels from overlapping\n",
    "fig.tight_layout(rect=[0, 0, 1, 0.96]) # Adjust rect to make space for suptitle\n",
    "\n",
    "# 7. Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74ccb4af-035b-4292-8c93-a3e72246a378",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}