{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18e08522-4855-4cc9-a7ba-3a33aeb1acfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf352b9-c73c-40b8-8c23-cd78a2278982",
   "metadata": {},
   "source": [
    "# EIS Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "53253dbb-7728-4626-a83f-4d097f9aff24",
   "metadata": {},
   "outputs": [],
   "source": [
    "EIS_10mV_C001 = pd.read_csv(\"EIS_10mV_Timing task2025_04_29_11_50_C001.z60\",\n",
    "                           skiprows=11,\n",
    "                           sep='\\s+',\n",
    "                           names=[\"Freq\", \"Ampl\", \"Bias\", \"Time\", \"Z'\", \"Z''\", \"GD\", \"Err\", \"Range\"],\n",
    "                           header=None\n",
    "                           )\n",
    "\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a4319c8-f20e-4c66-b04e-46bbc01b88ed",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Freq</th>\n",
       "      <th>Ampl</th>\n",
       "      <th>Bias</th>\n",
       "      <th>Time</th>\n",
       "      <th>Z'</th>\n",
       "      <th>Z''</th>\n",
       "      <th>GD</th>\n",
       "      <th>Err</th>\n",
       "      <th>Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>3.49460</td>\n",
       "      <td>15.55570</td>\n",
       "      <td>5.27726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>89051.300000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>5.78299</td>\n",
       "      <td>13.80450</td>\n",
       "      <td>16.94240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79301.400000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>8.05283</td>\n",
       "      <td>13.90250</td>\n",
       "      <td>14.04590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70618.900000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>13.68890</td>\n",
       "      <td>4.37749</td>\n",
       "      <td>1.17738</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62887.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>15.97240</td>\n",
       "      <td>4.40899</td>\n",
       "      <td>1.03117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.014616</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>1041.78000</td>\n",
       "      <td>33173.20000</td>\n",
       "      <td>-7535.82000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0.013016</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>1120.51000</td>\n",
       "      <td>59320.90000</td>\n",
       "      <td>-4182.78000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0.011591</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>1208.77000</td>\n",
       "      <td>47652.70000</td>\n",
       "      <td>-9034.40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.010322</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>1307.67000</td>\n",
       "      <td>38840.50000</td>\n",
       "      <td>-11131.10000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.246816</td>\n",
       "      <td>1409.71000</td>\n",
       "      <td>38631.00000</td>\n",
       "      <td>2761.22000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Freq  Ampl      Bias        Time           Z'          Z''   GD  \\\n",
       "0    100000.000000  10.0 -0.246816     3.49460     15.55570      5.27726  0.0   \n",
       "1     89051.300000  10.0 -0.246816     5.78299     13.80450     16.94240  0.0   \n",
       "2     79301.400000  10.0 -0.246816     8.05283     13.90250     14.04590  0.0   \n",
       "3     70618.900000  10.0 -0.246816    13.68890      4.37749      1.17738  0.0   \n",
       "4     62887.000000  10.0 -0.246816    15.97240      4.40899      1.03117  0.0   \n",
       "..             ...   ...       ...         ...          ...          ...  ...   \n",
       "135       0.014616  10.0 -0.246816  1041.78000  33173.20000  -7535.82000  0.0   \n",
       "136       0.013016  10.0 -0.246816  1120.51000  59320.90000  -4182.78000  0.0   \n",
       "137       0.011591  10.0 -0.246816  1208.77000  47652.70000  -9034.40000  0.0   \n",
       "138       0.010322  10.0 -0.246816  1307.67000  38840.50000 -11131.10000  0.0   \n",
       "139       0.010000  10.0 -0.246816  1409.71000  38631.00000   2761.22000  0.0   \n",
       "\n",
       "     Err  Range  \n",
       "0      0      0  \n",
       "1      0      0  \n",
       "2      0      0  \n",
       "3      0      0  \n",
       "4      0      0  \n",
       "..   ...    ...  \n",
       "135    0      0  \n",
       "136    0      0  \n",
       "137    0      0  \n",
       "138    0      0  \n",
       "139    0      0  \n",
       "\n",
       "[140 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EIS_10mV_C001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783cdc19-c873-4c91-83ea-174f42de0070",
   "metadata": {},
   "source": [
    "# OCP Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "890e7898-e314-4401-9f94-228dd3c7af83",
   "metadata": {},
   "outputs": [],
   "source": [
    "OCP_C001 = pd.read_csv(\"OCP_Timing task2025_04_29_11_50_C001.cor\",\n",
    "                       skiprows=25,\n",
    "                       sep='\\s+',\n",
    "                       names=[\"E\", \"i\", \"T\"],\n",
    "                       header=None\n",
    "                       )                       \n",
    "\n",
    "#E(V)\t\ti(A/cm²)\t\tT(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0df7fffd-275f-4b50-8548-b21756cb35ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E</th>\n",
       "      <th>i</th>\n",
       "      <th>T</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.247772</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>0.1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.247790</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.247798</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.000000e-10</td>\n",
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       "    <tr>\n",
       "      <th>17996</th>\n",
       "      <td>-0.247102</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>1799.6</td>\n",
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       "    <tr>\n",
       "      <th>17997</th>\n",
       "      <td>-0.247068</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>1799.7</td>\n",
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       "    <tr>\n",
       "      <th>17998</th>\n",
       "      <td>-0.247039</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>1799.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17999</th>\n",
       "      <td>-0.247008</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>1799.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18000</th>\n",
       "      <td>-0.247001</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              E             i       T\n",
       "0     -0.247764  1.000000e-10     0.0\n",
       "1     -0.247772  1.000000e-10     0.1\n",
       "2     -0.247790  1.000000e-10     0.2\n",
       "3     -0.247798  1.000000e-10     0.3\n",
       "4     -0.247809  1.000000e-10     0.4\n",
       "...         ...           ...     ...\n",
       "17996 -0.247102  1.000000e-10  1799.6\n",
       "17997 -0.247068  1.000000e-10  1799.7\n",
       "17998 -0.247039  1.000000e-10  1799.8\n",
       "17999 -0.247008  1.000000e-10  1799.9\n",
       "18000 -0.247001  1.000000e-10  1800.0\n",
       "\n",
       "[18001 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OCP_C001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8a80b4e-15ed-47f1-97ca-f2854581bcb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x78512cfdab30>]"
      ]
     },
     "execution_count": 23,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(OCP_C001[\"T\"], OCP_C001[\"E\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c9dcfe28-58ad-4dc0-b844-bed6f0a000e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x78511d739e40>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "files = [f for f in os.listdir('.') if os.path.isfile(f) and 'cor' in f]\n",
    "files = sorted(files)\n",
    "files\n",
    "\n",
    "OCP = {}\n",
    "for i, file in enumerate(files):\n",
    "    OCP[f\"C{i:>03}\"] = pd.read_csv(file,\n",
    "                       skiprows=25,\n",
    "                       sep='\\s+',\n",
    "                       names=[\"E\", \"i\", \"T\"],\n",
    "                       header=None\n",
    "                       )             \n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "for cycle, data in OCP.items():\n",
    "    ax.plot(data[\"T\"], data[\"E\"], label=cycle) \n",
    "\n",
    "ax.legend(loc=\"lower left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb0100e-3305-4628-8431-235011e8fe2a",
   "metadata": {},
   "source": [
    "# Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f866e7eb-8bad-47e9-9f4b-514ff9045c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "from impedance.models.circuits import CustomCircuit\n",
    "\n",
    "circuit = 'R0-p(R1,C1)-p(R2-Wo1,C2)'\n",
    "initial_guess = [.01, .01, 100, .01, .05, 100, 1]\n",
    "\n",
    "circuit = CustomCircuit(circuit, initial_guess=initial_guess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76b2221a-9d83-46b1-9b68-a51a38136970",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "32cb48a2-6b48-4b67-a20d-ab21d674de37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "EIS_10mV_C001 = pd.read_csv(\"EIS_10mV_Timing task2025_04_29_11_50_C001.z60\",\n",
    "                           skiprows=11,\n",
    "                           sep='\\s+',\n",
    "                           names=[\"Freq\", \"Ampl\", \"Bias\", \"Time\", \"Z'\", \"Z''\", \"GD\", \"Err\", \"Range\"],\n",
    "                           header=None\n",
    "                           )\n",
    "\n",
    "frequencies = EIS_10mV_C001[\"Freq\"].to_numpy()\n",
    "Z = EIS_10mV_C001[\"Z'\"].to_numpy() + EIS_10mV_C001[\"Z''\"].to_numpy() * 1j\n",
    "\n",
    "# keep only the impedance data in the first quandrant\n",
    "frequencies, Z = preprocessing.ignoreBelowX(frequencies, Z)\n",
    "\n",
    "from impedance.models.circuits import CustomCircuit\n",
    "\n",
    "circuit = 'R0-p(R1,C1)'\n",
    "initial_guess = [.01, .01, 100]\n",
    "circuit = CustomCircuit(circuit, initial_guess=initial_guess)\n",
    "circuit.fit(frequencies, Z)\n",
    "\n",
    "Z_fit = circuit.predict(frequencies)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from impedance.visualization import plot_nyquist\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(np.real(Z),     -np.imag(Z), \n",
    "        's', label=\"Data\")\n",
    "ax.plot(np.real(Z_fit), -np.imag(Z_fit), \n",
    "        '-', label=\"Fit\")\n",
    "\n",
    "# Make the axes square\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "# Set the labels to -imaginary vs real\n",
    "ax.set_xlabel(r\"$Z^{\\prime} [\\Omega]$\")\n",
    "ax.set_ylabel(r\"$Z^{\\prime\\prime} [\\Omega]$\")\n",
    "\n",
    "\n",
    "# Make the tick labels larger\n",
    "ax.tick_params(axis='both', which='major')\n",
    "\n",
    "\n",
    "# Change the number of labels on each axis to five\n",
    "ax.locator_params(axis='x', nbins=5, tight=True)\n",
    "ax.locator_params(axis='y', nbins=5, tight=True)\n",
    "\n",
    "\n",
    "# Add a light grid\n",
    "ax.grid(visible=True, which='major', axis='both', alpha=.5)\n",
    "\n",
    "\n",
    "# Change axis units to 10**log10(scale) and resize the offset text\n",
    "#ax.ticklabel_format(style='sci', axis='both',\n",
    "#                        scilimits=(limits, limits))\n",
    "#y_offset = ax.yaxis.get_offset_text()\n",
    "#y_offset.set_size(18)\n",
    "#t = ax.xaxis.get_offset_text()\n",
    "#t.set_size(18)\n",
    "\n",
    "\n",
    "\n",
    "#plot_nyquist(Z, fmt='o', scale=10, ax=ax)\n",
    "#plot_nyquist(Z_fit, fmt='-', scale=10, ax=ax)\n",
    "\n",
    "#plt.legend(['Data', 'Fit'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50ab399d-2bf5-4618-b1d7-2e368cf6cbb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "64399943-7f01-4aa1-b3bb-3a5aa5daa176",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "BaseCircuit.__init__() got an unexpected keyword argument 'circuit_str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[83], line 61\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;66;03m# --- Circuit Fitting ---\u001b[39;00m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;66;03m# Ensure there's data to fit\u001b[39;00m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(frequencies_to_fit) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(Z_to_fit) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 61\u001b[0m     circuit_model \u001b[38;5;241m=\u001b[39m \u001b[43mCustomCircuit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcircuit_str\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCIRCUIT_STRING\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_guess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mINITIAL_GUESS\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     63\u001b[0m         circuit_model\u001b[38;5;241m.\u001b[39mfit(frequencies_to_fit, Z_to_fit)\n",
      "File \u001b[0;32m~/.conda/envs/EIS/lib/python3.10/site-packages/impedance/models/circuits/circuits.py:433\u001b[0m, in \u001b[0;36mCustomCircuit.__init__\u001b[0;34m(self, circuit, **kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, circuit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    410\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\" Constructor for a customizable equivalent circuit model\u001b[39;00m\n\u001b[1;32m    411\u001b[0m \n\u001b[1;32m    412\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    430\u001b[0m \n\u001b[1;32m    431\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 433\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    434\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcircuit \u001b[38;5;241m=\u001b[39m circuit\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    436\u001b[0m     circuit_len \u001b[38;5;241m=\u001b[39m calculateCircuitLength(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcircuit)\n",
      "\u001b[0;31mTypeError\u001b[0m: BaseCircuit.__init__() got an unexpected keyword argument 'circuit_str'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from impedance.models.circuits import CustomCircuit\n",
    "# from impedance.visualization import plot_nyquist # Kept if you want to switch plotting methods\n",
    "\n",
    "# --- Configuration ---\n",
    "FILENAME = \"EIS_10mV_Timing task2025_04_29_11_50_C001.z60\"\n",
    "CIRCUIT_STRING = 'R0-p(R1,C1)'\n",
    "INITIAL_GUESS = [0.01, 0.01, 100] # R0, R1, C1\n",
    "\n",
    "# --- Data Loading ---\n",
    "try:\n",
    "    data_df = pd.read_csv(\n",
    "        FILENAME,\n",
    "        skiprows=11,\n",
    "        sep='\\s+',\n",
    "        names=[\"Freq\", \"Ampl\", \"Bias\", \"Time\", \"Z'\", \"Z''\", \"GD\", \"Err\", \"Range\"],\n",
    "        header=None\n",
    "    )\n",
    "except FileNotFoundError:\n",
    "    print(f\"Error: The file '{FILENAME}' was not found.\")\n",
    "    exit()\n",
    "except Exception as e:\n",
    "    print(f\"Error reading the CSV file: {e}\")\n",
    "    exit()\n",
    "\n",
    "# Extract frequencies and impedance components\n",
    "frequencies = data_df[\"Freq\"].to_numpy()\n",
    "# Z' is the real part, Z'' is the imaginary part\n",
    "Z_real = data_df[\"Z'\"].to_numpy()\n",
    "Z_imag = data_df[\"Z''\"].to_numpy()\n",
    "Z = Z_real + 1j * Z_imag\n",
    "\n",
    "# --- Preprocessing ---\n",
    "# Keep only the impedance data in the \"first quadrant\" of a Nyquist plot\n",
    "# (Re(Z) > 0 and -Im(Z) > 0  => Im(Z) < 0)\n",
    "mask = (Z.real > 0) & (Z.imag < 0)\n",
    "frequencies_filtered = frequencies[mask]\n",
    "Z_filtered = Z[mask]\n",
    "\n",
    "if len(Z_filtered) == 0:\n",
    "    print(\"Warning: After filtering for the first quadrant, no data points remain.\")\n",
    "    # Decide how to proceed: exit, or try to fit with unfiltered data, etc.\n",
    "    # For now, we'll try to fit with unfiltered if filtered is empty.\n",
    "    if len(Z) > 0:\n",
    "        print(\"Attempting to fit with unfiltered data.\")\n",
    "        frequencies_to_fit = frequencies\n",
    "        Z_to_fit = Z\n",
    "    else:\n",
    "        print(\"Error: No data to fit.\")\n",
    "        exit()\n",
    "else:\n",
    "    frequencies_to_fit = frequencies_filtered\n",
    "    Z_to_fit = Z_filtered\n",
    "\n",
    "\n",
    "# --- Circuit Fitting ---\n",
    "# Ensure there's data to fit\n",
    "if len(frequencies_to_fit) > 0 and len(Z_to_fit) > 0:\n",
    "    circuit_model = CustomCircuit(circuit_str=CIRCUIT_STRING, initial_guess=INITIAL_GUESS)\n",
    "    try:\n",
    "        circuit_model.fit(frequencies_to_fit, Z_to_fit)\n",
    "        Z_fit = circuit_model.predict(frequencies_to_fit)\n",
    "        fit_successful = True\n",
    "    except Exception as e:\n",
    "        print(f\"Error during circuit fitting: {e}\")\n",
    "        Z_fit = np.array([]) # Empty array if fit fails\n",
    "        fit_successful = False\n",
    "else:\n",
    "    print(\"Skipping circuit fitting as there is no data after preprocessing.\")\n",
    "    Z_fit = np.array([]) # Empty array if no data to fit\n",
    "    fit_successful = False\n",
    "\n",
    "# --- Plotting ---\n",
    "fig, ax = plt.subplots(figsize=(8, 8)) # You can adjust figsize\n",
    "\n",
    "# Plot experimental data (use Z_to_fit which is potentially filtered)\n",
    "ax.plot(Z_to_fit.real, -Z_to_fit.imag,\n",
    "        marker='s', linestyle='none', markersize=6, label=\"Experimental Data\")\n",
    "\n",
    "# Plot fitted data if successful\n",
    "if fit_successful and len(Z_fit) > 0:\n",
    "    ax.plot(Z_fit.real, -Z_fit.imag,\n",
    "            linestyle='-', color='red', linewidth=2, label=\"Fit\")\n",
    "\n",
    "# Make the axes square\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "# Set the labels\n",
    "ax.set_xlabel(r\"$Z^{\\prime} [\\Omega]$\")\n",
    "ax.set_ylabel(r\"$-Z^{\\prime\\prime} [\\Omega]$\") # Corrected label for negative imaginary axis\n",
    "ax.set_title(\"Nyquist Plot\")\n",
    "\n",
    "# Make the tick labels larger (adjust '12' as needed)\n",
    "ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "# Change the number of ticks (locator_params finds \"nice\" locations)\n",
    "ax.locator_params(axis='x', nbins=6, tight=True)\n",
    "ax.locator_params(axis='y', nbins=6, tight=True)\n",
    "\n",
    "# Add a light grid\n",
    "ax.grid(visible=True, which='major', axis='both', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Add legend\n",
    "ax.legend()\n",
    "\n",
    "# Optional: Uncomment and adjust if scientific notation for axes is desired\n",
    "# limits = 0 # Example: display in 10^0 (normal) or 10^3 (kilo) etc.\n",
    "# ax.ticklabel_format(style='sci', axis='both', scilimits=(limits, limits))\n",
    "# y_offset = ax.yaxis.get_offset_text()\n",
    "# if y_offset.get_text(): # Check if offset text exists\n",
    "#     y_offset.set_size(12) # Adjust size\n",
    "# x_offset = ax.xaxis.get_offset_text()\n",
    "# if x_offset.get_text(): # Check if offset text exists\n",
    "#     x_offset.set_size(12) # Adjust size\n",
    "\n",
    "# Alternative plotting using impedance.visualization (if you prefer)\n",
    "# if fit_successful and len(Z_fit) > 0 :\n",
    "#     fig_nyquist, ax_nyquist = plt.subplots(figsize=(8,8))\n",
    "#     plot_nyquist(Z_to_fit, fmt='s', ax=ax_nyquist, label='Data')\n",
    "#     plot_nyquist(Z_fit, fmt='-', ax=ax_nyquist, label='Fit')\n",
    "#     ax_nyquist.legend()\n",
    "#     ax_nyquist.set_xlabel(r\"$Z^{\\prime} [\\Omega]$\")\n",
    "#     ax_nyquist.set_ylabel(r\"$-Z^{\\prime\\prime} [\\Omega]$\")\n",
    "#     ax_nyquist.set_title(\"Nyquist Plot (impedance.visualization)\")\n",
    "\n",
    "plt.tight_layout() # Adjusts plot to prevent labels from overlapping\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "c5463e26-a2ac-4099-bae6-deff569a13c1",
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   "outputs": [],
   "source": []
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