Lugovtsov/5.2.1-frank-herz-experiment/Untitled.ipynb
2023-09-15 14:01:17 +03:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"id": "4f99854c-1d5d-405a-99c4-90bc90e0a863",
"metadata": {},
"outputs": [],
"source": [
"# %load /home/glebi/git/experiment-automation/processing_tools.py\n",
"import numpy as np\n",
"from scipy.optimize import curve_fit\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import scienceplots\n",
"\n",
"plt.style.use(['science', 'russian-font'])\n",
"\n",
"matplotlib.rcParams.update({\n",
" 'figure.figsize': [6, 4],\n",
" 'savefig.facecolor': 'white',\n",
" 'figure.dpi': 150.0,\n",
" 'font.size': 12.0,\n",
"})\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "20cb3c10-6356-4c6d-bb4b-13f19fa677ed",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 900x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv(\"data.csv\")\n",
"c = df.columns\n",
"U = df[c[0]][:2000]\n",
"I = df[c[1]][:2000]\n",
"plt.plot(U, I, lw=0.2)\n",
"\n",
"plt.savefig(\"5v_plot.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b01fad92-ddc1-47d4-8b78-e533e4b8fb70",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}