{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34a7a981-1718-4dcb-af8c-981e0fa84023",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "390c33fa-ab42-4d69-ac06-604beb2c69db",
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a055f3f-6b2e-4fa8-8395-acebacade488",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d796ec7-48d9-4a23-bd98-c607067d330d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>E</th>\n",
       "      <th>i</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.174013</td>\n",
       "      <td>-7.016190e-08</td>\n",
       "      <td>2.99401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.173515</td>\n",
       "      <td>-5.165820e-08</td>\n",
       "      <td>5.98802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.173024</td>\n",
       "      <td>-4.036700e-08</td>\n",
       "      <td>8.98204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.172538</td>\n",
       "      <td>-3.396700e-08</td>\n",
       "      <td>11.97605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172039</td>\n",
       "      <td>-2.785440e-08</td>\n",
       "      <td>14.97006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          E             i         T\n",
       "0 -0.174013 -7.016190e-08   2.99401\n",
       "1 -0.173515 -5.165820e-08   5.98802\n",
       "2 -0.173024 -4.036700e-08   8.98204\n",
       "3 -0.172538 -3.396700e-08  11.97605\n",
       "4 -0.172039 -2.785440e-08  14.97006"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Data Loading ---\n",
    "\n",
    "def lpr_cor_import(filename):\n",
    "    \"\"\" Import cor file as pandas dataframe.\"\"\"\n",
    "    return pd.read_csv(\n",
    "        filename,\n",
    "        skiprows=26,\n",
    "        sep='\\s+',\n",
    "        header=None,\n",
    "        names=[\"E\", \"i\", \"T\"],\n",
    "    ) #index_col=\"Freq\")\n",
    "    \n",
    "try:\n",
    "    CS_LPR_1 = lpr_cor_import(\"Cast_Stellite1_LPR/LPR_1.cor\")\n",
    "    #LPR_HS_1_df = lpr_cor_import(\"HIPed_Stellite1_Sample1_Actual/LPR.cor\")    \n",
    "    #LPR_HS_2_df = lpr_cor_import(\"HIPed_Stellite1_Sample1_Actual/LPR_2.cor\")    \n",
    "    \n",
    "except FileNotFoundError as e:\n",
    "    print(f\"Error: File was not found.\")\n",
    "    print(e.message)\n",
    "    print(e.args)\n",
    "    exit()\n",
    "except Exception as e:\n",
    "    print(f\"Error reading the CSV file: {e}\")\n",
    "    exit()\n",
    "\n",
    "CS_LPR_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1267470a-2c4c-4338-ad9e-3a72e2511d20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0e44f37-75f5-4ca6-b815-c5ee2e527ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_concat = pd.concat((CS_LPR_1, CS_LPR_2, CS_LPR_3))\n",
    "df_means = df_concat.groupby(df_concat.index).mean()\n",
    "df_err = df_concat.groupby(df_concat.index).std()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "07827e93-a83b-4020-856b-f8391fdf8c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>E</th>\n",
       "      <th>i</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.213767</td>\n",
       "      <td>-4.673386e-08</td>\n",
       "      <td>2.99401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.213257</td>\n",
       "      <td>-3.026957e-08</td>\n",
       "      <td>5.98802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.212761</td>\n",
       "      <td>-2.028820e-08</td>\n",
       "      <td>8.98204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.212273</td>\n",
       "      <td>-1.442430e-08</td>\n",
       "      <td>11.97605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.211776</td>\n",
       "      <td>-9.352567e-09</td>\n",
       "      <td>14.97006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          E             i         T\n",
       "0 -0.213767 -4.673386e-08   2.99401\n",
       "1 -0.213257 -3.026957e-08   5.98802\n",
       "2 -0.212761 -2.028820e-08   8.98204\n",
       "3 -0.212273 -1.442430e-08  11.97605\n",
       "4 -0.211776 -9.352567e-09  14.97006"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d717162-2cbf-4d0b-acf5-d7b2528539c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4,4), sharex=True, dpi=150)\n",
    "\n",
    "ax.semilogx(np.abs(CS_LPR_1[\"i\"].to_numpy()), CS_LPR_2[\"E\"].to_numpy(), markersize=5, label=\"CS 1\")\n",
    "\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fef0e0c4-1f42-46a0-b731-1ed4b82e673c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5159c47-93d9-4993-a073-f7b80cd4a81e",
   "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
}