133 lines
17 KiB
Plaintext
133 lines
17 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "51cb8cd8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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",
|
||
"fig, ax = plt.subplots()\n",
|
||
"ax.grid()\n",
|
||
"\n",
|
||
"#I_max_Cu = 1.04 A\n",
|
||
"\n",
|
||
"#A\n",
|
||
"I = [0.03, 0.20, 0.41, 0.62, 0.82, 1.00, 1.10, 1.20, 1.38] #<1.10\n",
|
||
"#mT\n",
|
||
"B = [44.5, 226.6, 410.5, 605.1, 805.5, 998.4, 1092.3, 1129.9, 1181.4]\n",
|
||
"\n",
|
||
"for i in range(len(B)):\n",
|
||
" B[i]/=1000\n",
|
||
"\n",
|
||
"plt.scatter(I, B)\n",
|
||
"plt.plot(I, I)\n",
|
||
"plt.xlabel('I, А')\n",
|
||
"plt.ylabel('B, Тл')\n",
|
||
"\n",
|
||
"#U_0 = 0.15 muV\n",
|
||
"\n",
|
||
"I_Cu = [0] * 5\n",
|
||
"U_Cu = [0] * 5\n",
|
||
"\n",
|
||
"#0.2 A\n",
|
||
"I_Cu[0] = [0.2, 0.4, 0.6, 0.8, 1]\n",
|
||
"U_Cu[0] = [0.03, 0.06, 0.09, 0.12, 0.15]\n",
|
||
"\n",
|
||
"#0.4 A\n",
|
||
"I_Cu[1] = [0.2, 0.4, 0.6, 0.8, 1]\n",
|
||
"U_Cu[1] = [0.09, 0.18, 0.27, 0.36, 0.45]\n",
|
||
"\n",
|
||
"#0.6 A\n",
|
||
"I_Cu[2] = [0.2, 0.4, 0.6, 0.8, 1]\n",
|
||
"U_Cu[2] = [0.15, 0.27, 0.39, 0.54, 0.69]\n",
|
||
"\n",
|
||
"#0.8 A\n",
|
||
"I_Cu[3] = [0.2, 0.4, 0.6, 0.8, 1]\n",
|
||
"U_Cu[3] = [0.18, 0.30, 0.57, 0.78, 0.84]\n",
|
||
"\n",
|
||
"#1.0 A\n",
|
||
"I_Cu[4] = [0.2, 0.4, 0.6, 0.8, 1]\n",
|
||
"U_Cu[4] = [0.24, 0.54, 0.78, 0.99, 1.23]\n",
|
||
"\n",
|
||
"I_Cu_back = [0.2, 0.4, 0.6, 0.8, 1]\n",
|
||
"U_Cu_back = [0.24, 0.51, 0.78, 1.02, 1.17]\n",
|
||
"\n",
|
||
"#U_Cu_34 = 495 muV l = 9 mm L34 = 10 mm a = 0.05 mm\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"#Al\n",
|
||
"\n",
|
||
"#R_24 = 5.567 Ohm\n",
|
||
"#R_34 = 2.788 Ohm\n",
|
||
"\n",
|
||
"#mA\n",
|
||
"I_Al_28 = [100, 80.3, 59.8, 40.2, 20.3]\n",
|
||
"#mV\n",
|
||
"U_Al1_28 = [0.123, 0.097, 0.070, 0.046, 0.021]\n",
|
||
"U_Al2_28 = [0.061, 0.047, 0.034, 0.021, 0.008]\n",
|
||
"\n",
|
||
"#mA\n",
|
||
"I_Al_37 = [100, 80.3, 59.8, 40.2, 20.3]\n",
|
||
"#mV\n",
|
||
"U_Al1_37 = [0.020, 0.008, 0.070, 0.046, 0.021]\n",
|
||
"U_Al2_37 = [0.011, 0.004, 0.034, 0.021, 0.008]\n",
|
||
"\n",
|
||
"#mA\n",
|
||
"I_Al_46 = [100, 80.3, 59.8, 40.2, 20.3]\n",
|
||
"#mV\n",
|
||
"U_Al1_46 = [0.123, 0.097, 0.070, 0.046, 0.021]\n",
|
||
"U_Al2_46 = [0.061, 0.047, 0.034, 0.021, 0.008]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e5eb3d7e",
|
||
"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
|
||
}
|