249 lines
54 KiB
Plaintext
249 lines
54 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "08417046-1a17-422e-96fd-6e4c546798e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import scipy.optimize\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aac96ba7-8c92-45bc-8e30-61b2dfd00292",
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"metadata": {},
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"source": [
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"## Data Loading"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "2ef66349-ca7c-4cc5-a426-15b5cd87f64b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>E</th>\n",
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" <th>i</th>\n",
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" <th>T</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>-0.326304</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>0.1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>-0.326281</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>0.2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>-0.326251</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>0.3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>-0.326228</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>0.4</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>-0.326211</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>0.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>143995</th>\n",
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" <td>-0.152261</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>14399.6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>143996</th>\n",
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" <td>-0.152255</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>14399.7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>143997</th>\n",
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" <td>-0.152253</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>14399.8</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>143998</th>\n",
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" <td>-0.152250</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>14399.9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>143999</th>\n",
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" <td>-0.152254</td>\n",
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" <td>5.000000e-11</td>\n",
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" <td>14400.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>144000 rows × 3 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" E i T\n",
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"0 -0.326304 5.000000e-11 0.1\n",
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"1 -0.326281 5.000000e-11 0.2\n",
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"2 -0.326251 5.000000e-11 0.3\n",
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"3 -0.326228 5.000000e-11 0.4\n",
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"4 -0.326211 5.000000e-11 0.5\n",
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"... ... ... ...\n",
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"143995 -0.152261 5.000000e-11 14399.6\n",
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"143996 -0.152255 5.000000e-11 14399.7\n",
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"143997 -0.152253 5.000000e-11 14399.8\n",
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"143998 -0.152250 5.000000e-11 14399.9\n",
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"143999 -0.152254 5.000000e-11 14400.0\n",
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"\n",
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"[144000 rows x 3 columns]"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def ocp_cor_import(filename):\n",
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" \"\"\" Import cor file as pandas dataframe.\"\"\"\n",
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" return pd.read_csv(\n",
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" filename,\n",
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" skiprows=26,\n",
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" sep='\\s+',\n",
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" header=None,\n",
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" names=[\"E\", \"i\", \"T\"],\n",
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" ) #index_col=\"Freq\")\n",
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"\n",
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"\n",
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"try:\n",
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" OCP_CS_1_df = ocp_cor_import(\"Cast_Stellite1_Sample1_Actual/OCP.cor\")\n",
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" OCP_CS_2_df = ocp_cor_import(\"Cast_Stellite1_Sample2_Actual/OCP.cor\")\n",
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" OCP_CS_3_df = ocp_cor_import(\"Cast_Stellite1_Sample3_Actual/OCP.cor\")\n",
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" OCP_HS_1_df = ocp_cor_import(\"HIPed_Stellite1_Sample1_Actual/OCP.cor\") \n",
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" \n",
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"except FileNotFoundError as e:\n",
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" print(f\"Error: File was not found.\")\n",
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" print(e.message)\n",
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" print(e.args)\n",
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" exit()\n",
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"except Exception as e:\n",
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" print(f\"Error reading the CSV file: {e}\")\n",
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" exit()\n",
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"\n",
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"OCP_CS_1_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "f065f9b8-3912-493d-8476-5e0d7368b6bc",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[]"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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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
|
||
}
|