Silaev/auto/3.3.5/335kCu.ipynb
2022-10-22 15:03:41 +03:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "869ef02c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.29\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"k = [0.15, 0.45, 0.675, 0.9, 1.215]\n",
"I = [0.2, 0.4, 0.6, 0.8, 1]\n",
"\n",
"a, b = np.polyfit(I, k, deg = 1)\n",
"\n",
"klin = []\n",
"for i in range(5):\n",
" klin.append(a * I[i] + b)\n",
"\n",
"plt.grid()\n",
"plt.scatter(I, k)\n",
"plt.plot(I, klin)\n",
"plt.xlabel('I, А')\n",
"plt.ylabel('k, 10^-6 В/Тл')\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f137f1d",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}