From d6eb1a1c9af45697f2d7a0d2597b8614754597f8 Mon Sep 17 00:00:00 2001
From: Vishakh Kumar <grokkingstuff@gmail.com>
Date: Thu, 15 May 2025 17:26:50 +0400
Subject: [PATCH] Electrochemical jupyter notebooks

---
 .gitattributes                                |   1 +
 .../.ipynb_checkpoints/EIS-checkpoint.ipynb   |   4 +-
 .../.ipynb_checkpoints/LPR-checkpoint.ipynb   | 319 ++++++++++++++++++
 .../.ipynb_checkpoints/OCP-checkpoint.ipynb   | 248 ++++++++++++++
 .../.ipynb_checkpoints/LPR-checkpoint.cor     |  86 +++++
 .../.ipynb_checkpoints/OCP-checkpoint.cor     |   3 +
 Electrochemical/EIS.ipynb                     |   4 +-
 ...ask2025_05_12_13_00_C001.cor => LPR_2.cor} |   0
 Electrochemical/LPR.ipynb                     | 319 ++++++++++++++++++
 Electrochemical/OCP.ipynb                     | 248 ++++++++++++++
 10 files changed, 1228 insertions(+), 4 deletions(-)
 create mode 100644 Electrochemical/.ipynb_checkpoints/LPR-checkpoint.ipynb
 create mode 100644 Electrochemical/.ipynb_checkpoints/OCP-checkpoint.ipynb
 create mode 100644 Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/LPR-checkpoint.cor
 create mode 100644 Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/OCP-checkpoint.cor
 rename Electrochemical/HIPed_Stellite1_Sample1_Actual/{LPR_Timing task2025_05_12_13_00_C001.cor => LPR_2.cor} (100%)
 create mode 100644 Electrochemical/LPR.ipynb
 create mode 100644 Electrochemical/OCP.ipynb

diff --git a/.gitattributes b/.gitattributes
index 16cba52..1b952cd 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -573,5 +573,6 @@
 /non_academic_paper_references/equipment_manuals/potentiostat/Training[[:space:]]videos/Pitting[[:space:]]corrosion[[:space:]]measurement.mp4 filter=lfs diff=lfs merge=lfs -text
 /non_academic_paper_references/equipment_manuals/potentiostat/Training[[:space:]]videos/Tafel[[:space:]]data[[:space:]]import[[:space:]]to[[:space:]]Origin[[:space:]]for[[:space:]]graphing.mp4 filter=lfs diff=lfs merge=lfs -text
 /non_academic_paper_references/equipment_manuals/potentiostat/Training[[:space:]]videos/Tafel[[:space:]]fitting.mp4 filter=lfs diff=lfs merge=lfs -text
+/Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/OCP-checkpoint.cor filter=lfs diff=lfs merge=lfs -text
 *.jp*g filter=lfs diff=lfs merge=lfs -text
 *.tif filter=lfs diff=lfs merge=lfs -text
diff --git a/Electrochemical/.ipynb_checkpoints/EIS-checkpoint.ipynb b/Electrochemical/.ipynb_checkpoints/EIS-checkpoint.ipynb
index cf8416d..5ccd83c 100644
--- a/Electrochemical/.ipynb_checkpoints/EIS-checkpoint.ipynb
+++ b/Electrochemical/.ipynb_checkpoints/EIS-checkpoint.ipynb
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7dd1298fcac2bbbe10b08c0f6c2a3fca8809bef4d17c11457f811fa3c3b0cc79
-size 429098
+oid sha256:d2486185ffb43aa55840a6b8015a0bc4c6482962e58c1529f10195d6b76ba797
+size 429066
diff --git a/Electrochemical/.ipynb_checkpoints/LPR-checkpoint.ipynb b/Electrochemical/.ipynb_checkpoints/LPR-checkpoint.ipynb
new file mode 100644
index 0000000..2cbd5f4
--- /dev/null
+++ b/Electrochemical/.ipynb_checkpoints/LPR-checkpoint.ipynb
@@ -0,0 +1,319 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "34a7a981-1718-4dcb-af8c-981e0fa84023",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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": 46,
+   "id": "4d796ec7-48d9-4a23-bd98-c607067d330d",
+   "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>-0.325550</td>\n",
+       "      <td>-7.687700e-08</td>\n",
+       "      <td>2.99401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.325052</td>\n",
+       "      <td>-6.444170e-08</td>\n",
+       "      <td>5.98802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.324545</td>\n",
+       "      <td>-5.771010e-08</td>\n",
+       "      <td>8.98204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.324054</td>\n",
+       "      <td>-5.352630e-08</td>\n",
+       "      <td>11.97605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.323553</td>\n",
+       "      <td>-5.153480e-08</td>\n",
+       "      <td>14.97006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          E             i         T\n",
+       "0 -0.325550 -7.687700e-08   2.99401\n",
+       "1 -0.325052 -6.444170e-08   5.98802\n",
+       "2 -0.324545 -5.771010e-08   8.98204\n",
+       "3 -0.324054 -5.352630e-08  11.97605\n",
+       "4 -0.323553 -5.153480e-08  14.97006"
+      ]
+     },
+     "execution_count": 46,
+     "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",
+    "    LPR_CS_1_df = lpr_cor_import(\"Cast_Stellite1_Sample1_Actual/LPR.cor\")\n",
+    "    LPR_CS_2_df = lpr_cor_import(\"Cast_Stellite1_Sample2_Actual/LPR.cor\")\n",
+    "    LPR_CS_3_df = lpr_cor_import(\"Cast_Stellite1_Sample3_Actual/LPR.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",
+    "LPR_HS_2_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1267470a-2c4c-4338-ad9e-3a72e2511d20",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a0e44f37-75f5-4ca6-b815-c5ee2e527ea2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_concat = pd.concat((LPR_1_df, LPR_2_df, LPR_3_df))\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": 40,
+   "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>-0.224382</td>\n",
+       "      <td>-1.450166e-08</td>\n",
+       "      <td>2.99401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.223866</td>\n",
+       "      <td>-4.205267e-09</td>\n",
+       "      <td>5.98802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.223365</td>\n",
+       "      <td>2.062133e-09</td>\n",
+       "      <td>8.98204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.222874</td>\n",
+       "      <td>5.875600e-09</td>\n",
+       "      <td>11.97605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.222380</td>\n",
+       "      <td>9.003733e-09</td>\n",
+       "      <td>14.97006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          E             i         T\n",
+       "0 -0.224382 -1.450166e-08   2.99401\n",
+       "1 -0.223866 -4.205267e-09   5.98802\n",
+       "2 -0.223365  2.062133e-09   8.98204\n",
+       "3 -0.222874  5.875600e-09  11.97605\n",
+       "4 -0.222380  9.003733e-09  14.97006"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_means.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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(LPR_CS_1_df[\"i\"].to_numpy()), LPR_CS_1_df[\"E\"].to_numpy()-0.125, markersize=5, label=\"CS 1\")\n",
+    "ax.semilogx(np.abs(LPR_CS_2_df[\"i\"].to_numpy()), LPR_CS_2_df[\"E\"].to_numpy(), markersize=5, label=\"CS 2\")\n",
+    "#ax.semilogx(np.abs(LPR_CS_3_df[\"i\"].to_numpy()), LPR_CS_3_df[\"E\"].to_numpy(), markersize=5, label=\"CS 3\")\n",
+    "ax.semilogx(np.abs(LPR_HS_1_df[\"i\"].to_numpy()), LPR_HS_1_df[\"E\"].to_numpy()+0.03+0.01+0.002, markersize=5, label=\"HIPed 1\")\n",
+    "ax.semilogx(np.abs(LPR_HS_2_df[\"i\"].to_numpy()), LPR_HS_2_df[\"E\"].to_numpy(), markersize=5, label=\"HIPed 2\")\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
+}
diff --git a/Electrochemical/.ipynb_checkpoints/OCP-checkpoint.ipynb b/Electrochemical/.ipynb_checkpoints/OCP-checkpoint.ipynb
new file mode 100644
index 0000000..57b14e9
--- /dev/null
+++ b/Electrochemical/.ipynb_checkpoints/OCP-checkpoint.ipynb
@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "08417046-1a17-422e-96fd-6e4c546798e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.optimize\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aac96ba7-8c92-45bc-8e30-61b2dfd00292",
+   "metadata": {},
+   "source": [
+    "## Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2ef66349-ca7c-4cc5-a426-15b5cd87f64b",
+   "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>-0.326304</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.326281</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.326251</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.326228</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.326211</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143995</th>\n",
+       "      <td>-0.152261</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14399.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143996</th>\n",
+       "      <td>-0.152255</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14399.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143997</th>\n",
+       "      <td>-0.152253</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14399.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143998</th>\n",
+       "      <td>-0.152250</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14399.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143999</th>\n",
+       "      <td>-0.152254</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14400.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>144000 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               E             i        T\n",
+       "0      -0.326304  5.000000e-11      0.1\n",
+       "1      -0.326281  5.000000e-11      0.2\n",
+       "2      -0.326251  5.000000e-11      0.3\n",
+       "3      -0.326228  5.000000e-11      0.4\n",
+       "4      -0.326211  5.000000e-11      0.5\n",
+       "...          ...           ...      ...\n",
+       "143995 -0.152261  5.000000e-11  14399.6\n",
+       "143996 -0.152255  5.000000e-11  14399.7\n",
+       "143997 -0.152253  5.000000e-11  14399.8\n",
+       "143998 -0.152250  5.000000e-11  14399.9\n",
+       "143999 -0.152254  5.000000e-11  14400.0\n",
+       "\n",
+       "[144000 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def ocp_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",
+    "\n",
+    "try:\n",
+    "    OCP_CS_1_df = ocp_cor_import(\"Cast_Stellite1_Sample1_Actual/OCP.cor\")\n",
+    "    OCP_CS_2_df = ocp_cor_import(\"Cast_Stellite1_Sample2_Actual/OCP.cor\")\n",
+    "    OCP_CS_3_df = ocp_cor_import(\"Cast_Stellite1_Sample3_Actual/OCP.cor\")\n",
+    "    OCP_HS_1_df = ocp_cor_import(\"HIPed_Stellite1_Sample1_Actual/OCP.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",
+    "OCP_CS_1_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f065f9b8-3912-493d-8476-5e0d7368b6bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,4), sharex=True, dpi=150)\n",
+    "\n",
+    "ax.plot(OCP_CS_1_df[\"T\"].to_numpy(), OCP_CS_1_df[\"E\"].to_numpy(), label=\"CS 1\")\n",
+    "ax.plot(OCP_CS_2_df[\"T\"].to_numpy(), OCP_CS_2_df[\"E\"].to_numpy(), label=\"CS 2\")\n",
+    "ax.plot(OCP_HS_1_df[\"T\"].to_numpy(), OCP_HS_1_df[\"E\"].to_numpy(), label=\"HS 1\")        \n",
+    "\n",
+    "ax.legend()\n",
+    "ax.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c066c0e-227b-4908-99c1-c26f1a7d0a21",
+   "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
+}
diff --git a/Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/LPR-checkpoint.cor b/Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/LPR-checkpoint.cor
new file mode 100644
index 0000000..73153a1
--- /dev/null
+++ b/Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/LPR-checkpoint.cor
@@ -0,0 +1,86 @@
+CORRW ASCII
+CorrTest for Windows: SN CS310M2108624  V6.5.1230.3  V3.0.210914.1
+ID_PotDynamic
+Data:2025-05-13   Time:17:10:02
+ExpParmas:ExpType=ID_PotDynamic&-&InitE=-0.015&-&MidEOne=-0.1&-&MidETwo=-0.1&-&FinalE=0.015&-&ScanRate=0.167&-&InitEVsOcp=vsOCP&-&MidEOneVsOcp=vsOCP&-&MidETwoVsOcp=vsOCP&-&FinalEVsOcp=vsOCP&-&MidEOneEnable=False&-&MidETwoEnable=False&-&ScanStopEnable=False&-&ScanReverse=True&-&MaxCurrent=2&-&MinCurrent=-2&-&ScanRateUnit=0&-&IsReve=False&-&FileName=C:\Users\vp2039\Desktop\thesis\Electrochemical\Cast_Stellite1_Sample1_Actual\LPR.cor&-&FilePath=C:\Users\vp2039\Desktop\thesis\Electrochemical\Cast_Stellite1_Sample1_Actual\LPR.cor&-&Comment=&-&IsFrq=False&-&SampleInter=0.5&-&SampleFrq=2&-&Number=0&-&EnableAxisType=True&-&AxisType=EvsLogi&-&OcpValue=-0.157233908772469&-&IsStopEvent=False&-&Frq=0.334&-&ExpTime=179.6407&-&DevParam=VoltRange=2&|&EnableWaitSecond=True&|&IsWaitSecond=True&|&WaitSecondEx=5&|&IsCurrAutoRange=True&|&IsVoltAutoRange=True&|&OnlyInc=True&|&AutoMinCurrRange=5&|&OnlyIncE=True&|&AutoMinVoltRange=4&|&IsIR=False&|&IrModeEx=0&|&IrValue=100&|&IsVoltRule=True&|&IsCurrRule=True&|&XiaoboqiEnable=True&|&Xianboqi=True&|&ManualCurRange=3&|&FilterCap=3&|&IsContainsUnit=False&|&LowFilter=True&|&OneKHzFilter=True&|&DigitalSmoothModeEx=1&|&DigitLPFCount=5&|&LineFre=0&|&IsIgnoreFre=True&|&OcpState=False&|&DeltaMaxOcp=10&|&WEMode=0&|&GroundingMode=0&|&IsLowSpeedFilter=False&|&IsEnableCY=False&|&OutputMode=0&|&OutputTime=100&-&CellParam=Area=2&|&AreaMode=0&|&Density=7.8&|&Dangliang=28&|&Type=0&|&REValue=0&|&Temp=25&|&Stern=26&|&Enable=True&|&IsGetTemp=False&|&EnableSpeed=False&|&RotatingSpeed=3200&-&ExpType=ID_PotDynamic&-&Issampling=True&-&EnablePause=True&-&EnablePolar=True&-&Cycles=1&-&DefaultE=0&-&DefaultI=0,ExpInfo:Init E(V):-0.015 vs OCP.,Final E(V):0.015 vs OCP.,Scan Rate(mV/s):0.167,Freq(Hz):0.334
+Open Circuit Potential (V):-0.15723
+
+Temperature(℃):   25
+  Begin Information:            Cell Information
+      Surface Area:           2
+      Density:                7.8
+      Weight:                 28
+      Polarity:               0
+      PolarityI:              0
+      Corrosion Unit Type:    1
+      Reference Type:         0
+      Reference Potential:    0
+      Reference User-Defined: 0
+      Stern-Geary:            26
+  End Information:              Cell Information
+  Begin Experiment:
+      Axes Type:              4
+  End Experiment:
+E(V)		i(A/cm²)		T(s)
+End Comments
+-1.74089E-01	-8.56811E-08	0.00000
+-1.74013E-01	-7.01619E-08	2.99401
+-1.73515E-01	-5.16582E-08	5.98802
+-1.73024E-01	-4.03670E-08	8.98204
+-1.72538E-01	-3.39670E-08	11.97605
+-1.72039E-01	-2.78544E-08	14.97006
+-1.71530E-01	-2.22852E-08	17.96407
+-1.71015E-01	-1.80910E-08	20.95808
+-1.70515E-01	-1.56981E-08	23.95210
+-1.70036E-01	-1.28611E-08	26.94611
+-1.69548E-01	-9.32889E-09	29.94012
+-1.69057E-01	-6.55977E-09	32.93413
+-1.68544E-01	-5.03984E-09	35.92814
+-1.68027E-01	-1.54873E-09	38.92216
+-1.67537E-01	2.20151E-09	41.91617
+-1.67035E-01	3.84952E-09	44.91018
+-1.66539E-01	4.99575E-09	47.90419
+-1.66050E-01	6.52187E-09	50.89820
+-1.65546E-01	9.26893E-09	53.89222
+-1.65037E-01	1.22240E-08	56.88623
+-1.64541E-01	1.35572E-08	59.88024
+-1.64058E-01	1.59415E-08	62.87425
+-1.63568E-01	1.89249E-08	65.86827
+-1.63053E-01	2.15111E-08	68.86228
+-1.62545E-01	2.35669E-08	71.85629
+-1.62055E-01	2.60926E-08	74.85030
+-1.61567E-01	2.69345E-08	77.84431
+-1.61069E-01	2.88566E-08	80.83833
+-1.60562E-01	3.14372E-08	83.83234
+-1.60066E-01	3.38394E-08	86.82635
+-1.59547E-01	3.54857E-08	89.82036
+-1.59023E-01	3.68801E-08	92.81437
+-1.58509E-01	3.80910E-08	95.80839
+-1.57997E-01	4.00863E-08	98.80240
+-1.57501E-01	4.29400E-08	101.79641
+-1.56994E-01	4.54830E-08	104.79042
+-1.56486E-01	4.79224E-08	107.78443
+-1.55999E-01	4.99293E-08	110.77845
+-1.55505E-01	5.10459E-08	113.77246
+-1.54999E-01	5.31591E-08	116.76647
+-1.54498E-01	5.60521E-08	119.76048
+-1.53986E-01	5.84589E-08	122.75449
+-1.53483E-01	6.00073E-08	125.74851
+-1.52993E-01	6.09213E-08	128.74252
+-1.52490E-01	6.24695E-08	131.73653
+-1.51994E-01	6.39804E-08	134.73054
+-1.51494E-01	6.50312E-08	137.72455
+-1.50982E-01	6.53815E-08	140.71857
+-1.50495E-01	6.68910E-08	143.71258
+-1.50002E-01	6.98315E-08	146.70659
+-1.49504E-01	7.28013E-08	149.70060
+-1.49004E-01	7.55819E-08	152.69461
+-1.48486E-01	7.76528E-08	155.68863
+-1.47987E-01	7.94871E-08	158.68264
+-1.47491E-01	8.01483E-08	161.67665
+-1.46988E-01	8.23584E-08	164.67066
+-1.46491E-01	8.39407E-08	167.66467
+-1.45990E-01	8.60101E-08	170.65869
+-1.45493E-01	8.76449E-08	173.65270
+-1.45008E-01	8.72686E-08	176.64671
+-1.44493E-01	9.15961E-08	179.64072
\ No newline at end of file
diff --git a/Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/OCP-checkpoint.cor b/Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/OCP-checkpoint.cor
new file mode 100644
index 0000000..b8da737
--- /dev/null
+++ b/Electrochemical/Cast_Stellite1_Sample1_Actual/.ipynb_checkpoints/OCP-checkpoint.cor
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73090329861cc95a0f4f0fa74b01c70254f05558ba1d2b4aa1184281563f2fc6
+size 5219119
diff --git a/Electrochemical/EIS.ipynb b/Electrochemical/EIS.ipynb
index cf8416d..5ccd83c 100644
--- a/Electrochemical/EIS.ipynb
+++ b/Electrochemical/EIS.ipynb
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7dd1298fcac2bbbe10b08c0f6c2a3fca8809bef4d17c11457f811fa3c3b0cc79
-size 429098
+oid sha256:d2486185ffb43aa55840a6b8015a0bc4c6482962e58c1529f10195d6b76ba797
+size 429066
diff --git a/Electrochemical/HIPed_Stellite1_Sample1_Actual/LPR_Timing task2025_05_12_13_00_C001.cor b/Electrochemical/HIPed_Stellite1_Sample1_Actual/LPR_2.cor
similarity index 100%
rename from Electrochemical/HIPed_Stellite1_Sample1_Actual/LPR_Timing task2025_05_12_13_00_C001.cor
rename to Electrochemical/HIPed_Stellite1_Sample1_Actual/LPR_2.cor
diff --git a/Electrochemical/LPR.ipynb b/Electrochemical/LPR.ipynb
new file mode 100644
index 0000000..2cbd5f4
--- /dev/null
+++ b/Electrochemical/LPR.ipynb
@@ -0,0 +1,319 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "34a7a981-1718-4dcb-af8c-981e0fa84023",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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": 46,
+   "id": "4d796ec7-48d9-4a23-bd98-c607067d330d",
+   "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>-0.325550</td>\n",
+       "      <td>-7.687700e-08</td>\n",
+       "      <td>2.99401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.325052</td>\n",
+       "      <td>-6.444170e-08</td>\n",
+       "      <td>5.98802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.324545</td>\n",
+       "      <td>-5.771010e-08</td>\n",
+       "      <td>8.98204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.324054</td>\n",
+       "      <td>-5.352630e-08</td>\n",
+       "      <td>11.97605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.323553</td>\n",
+       "      <td>-5.153480e-08</td>\n",
+       "      <td>14.97006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          E             i         T\n",
+       "0 -0.325550 -7.687700e-08   2.99401\n",
+       "1 -0.325052 -6.444170e-08   5.98802\n",
+       "2 -0.324545 -5.771010e-08   8.98204\n",
+       "3 -0.324054 -5.352630e-08  11.97605\n",
+       "4 -0.323553 -5.153480e-08  14.97006"
+      ]
+     },
+     "execution_count": 46,
+     "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",
+    "    LPR_CS_1_df = lpr_cor_import(\"Cast_Stellite1_Sample1_Actual/LPR.cor\")\n",
+    "    LPR_CS_2_df = lpr_cor_import(\"Cast_Stellite1_Sample2_Actual/LPR.cor\")\n",
+    "    LPR_CS_3_df = lpr_cor_import(\"Cast_Stellite1_Sample3_Actual/LPR.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",
+    "LPR_HS_2_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1267470a-2c4c-4338-ad9e-3a72e2511d20",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a0e44f37-75f5-4ca6-b815-c5ee2e527ea2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_concat = pd.concat((LPR_1_df, LPR_2_df, LPR_3_df))\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": 40,
+   "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>-0.224382</td>\n",
+       "      <td>-1.450166e-08</td>\n",
+       "      <td>2.99401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.223866</td>\n",
+       "      <td>-4.205267e-09</td>\n",
+       "      <td>5.98802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.223365</td>\n",
+       "      <td>2.062133e-09</td>\n",
+       "      <td>8.98204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.222874</td>\n",
+       "      <td>5.875600e-09</td>\n",
+       "      <td>11.97605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.222380</td>\n",
+       "      <td>9.003733e-09</td>\n",
+       "      <td>14.97006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          E             i         T\n",
+       "0 -0.224382 -1.450166e-08   2.99401\n",
+       "1 -0.223866 -4.205267e-09   5.98802\n",
+       "2 -0.223365  2.062133e-09   8.98204\n",
+       "3 -0.222874  5.875600e-09  11.97605\n",
+       "4 -0.222380  9.003733e-09  14.97006"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_means.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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(LPR_CS_1_df[\"i\"].to_numpy()), LPR_CS_1_df[\"E\"].to_numpy()-0.125, markersize=5, label=\"CS 1\")\n",
+    "ax.semilogx(np.abs(LPR_CS_2_df[\"i\"].to_numpy()), LPR_CS_2_df[\"E\"].to_numpy(), markersize=5, label=\"CS 2\")\n",
+    "#ax.semilogx(np.abs(LPR_CS_3_df[\"i\"].to_numpy()), LPR_CS_3_df[\"E\"].to_numpy(), markersize=5, label=\"CS 3\")\n",
+    "ax.semilogx(np.abs(LPR_HS_1_df[\"i\"].to_numpy()), LPR_HS_1_df[\"E\"].to_numpy()+0.03+0.01+0.002, markersize=5, label=\"HIPed 1\")\n",
+    "ax.semilogx(np.abs(LPR_HS_2_df[\"i\"].to_numpy()), LPR_HS_2_df[\"E\"].to_numpy(), markersize=5, label=\"HIPed 2\")\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
+}
diff --git a/Electrochemical/OCP.ipynb b/Electrochemical/OCP.ipynb
new file mode 100644
index 0000000..57b14e9
--- /dev/null
+++ b/Electrochemical/OCP.ipynb
@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "08417046-1a17-422e-96fd-6e4c546798e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.optimize\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aac96ba7-8c92-45bc-8e30-61b2dfd00292",
+   "metadata": {},
+   "source": [
+    "## Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2ef66349-ca7c-4cc5-a426-15b5cd87f64b",
+   "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>-0.326304</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.326281</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.326251</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.326228</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.326211</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143995</th>\n",
+       "      <td>-0.152261</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14399.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143996</th>\n",
+       "      <td>-0.152255</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14399.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143997</th>\n",
+       "      <td>-0.152253</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14399.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143998</th>\n",
+       "      <td>-0.152250</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14399.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143999</th>\n",
+       "      <td>-0.152254</td>\n",
+       "      <td>5.000000e-11</td>\n",
+       "      <td>14400.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>144000 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               E             i        T\n",
+       "0      -0.326304  5.000000e-11      0.1\n",
+       "1      -0.326281  5.000000e-11      0.2\n",
+       "2      -0.326251  5.000000e-11      0.3\n",
+       "3      -0.326228  5.000000e-11      0.4\n",
+       "4      -0.326211  5.000000e-11      0.5\n",
+       "...          ...           ...      ...\n",
+       "143995 -0.152261  5.000000e-11  14399.6\n",
+       "143996 -0.152255  5.000000e-11  14399.7\n",
+       "143997 -0.152253  5.000000e-11  14399.8\n",
+       "143998 -0.152250  5.000000e-11  14399.9\n",
+       "143999 -0.152254  5.000000e-11  14400.0\n",
+       "\n",
+       "[144000 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def ocp_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",
+    "\n",
+    "try:\n",
+    "    OCP_CS_1_df = ocp_cor_import(\"Cast_Stellite1_Sample1_Actual/OCP.cor\")\n",
+    "    OCP_CS_2_df = ocp_cor_import(\"Cast_Stellite1_Sample2_Actual/OCP.cor\")\n",
+    "    OCP_CS_3_df = ocp_cor_import(\"Cast_Stellite1_Sample3_Actual/OCP.cor\")\n",
+    "    OCP_HS_1_df = ocp_cor_import(\"HIPed_Stellite1_Sample1_Actual/OCP.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",
+    "OCP_CS_1_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f065f9b8-3912-493d-8476-5e0d7368b6bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,4), sharex=True, dpi=150)\n",
+    "\n",
+    "ax.plot(OCP_CS_1_df[\"T\"].to_numpy(), OCP_CS_1_df[\"E\"].to_numpy(), label=\"CS 1\")\n",
+    "ax.plot(OCP_CS_2_df[\"T\"].to_numpy(), OCP_CS_2_df[\"E\"].to_numpy(), label=\"CS 2\")\n",
+    "ax.plot(OCP_HS_1_df[\"T\"].to_numpy(), OCP_HS_1_df[\"E\"].to_numpy(), label=\"HS 1\")        \n",
+    "\n",
+    "ax.legend()\n",
+    "ax.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c066c0e-227b-4908-99c1-c26f1a7d0a21",
+   "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
+}