mirror of
https://github.com/galera951/experiment-automation.git
synced 2024-11-09 23:55:51 +03:00
202 lines
47 KiB
Plaintext
202 lines
47 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "a21511dc-d48d-4171-bf38-7ed030488d14",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# %load ../processing_tools.py\n",
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"import numpy as np\n",
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"from scipy.optimize import curve_fit\n",
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"import pandas as pd\n",
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"\n",
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"def line(x, k, b):\n",
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" return k * x + b\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"import scienceplots\n",
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"\n",
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"plt.style.use(['science', 'russian-font'])\n",
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"\n",
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"matplotlib.rcParams.update({\n",
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" 'figure.figsize': [6, 4],\n",
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" 'savefig.facecolor': 'white',\n",
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" 'figure.dpi': 150.0,\n",
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" 'font.size': 12.0,\n",
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"})\n"
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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": "ebe7c967-eed8-4701-80a5-3f276e07347b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"h = np.array([10, 20, 40]) # mm\n",
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"ds = np.array([0, 1, 3]) # mm"
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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": 19,
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"id": "549aff15-dfb2-48b0-9802-f33cbfd7c3f5",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"(k, b), _ = curve_fit(line, h**2, ds)"
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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": "c6234bfd-5756-4422-a7c9-557be49f0e67",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"x = np.linspace(0, 1700, 100)"
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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": 28,
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"id": "2160928d-976d-4dd2-9074-ce026fb1f801",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 900x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.scatter(h**2, ds)\n",
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"plt.plot(x, line(x, k, b))\n",
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"\n",
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"plt.xlabel(r'Квадрат диаметра диафрагмы $h^2, \\text{мм}^2$')\n",
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"plt.ylabel(r'Отклонение $\\delta s$, мм')\n",
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"\n",
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"plt.savefig('images/dSonH2.svg')"
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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": 32,
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"id": "2f749ff7-6a94-4a4a-8339-ea843247e055",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.1904761904772945"
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]
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},
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"execution_count": 32,
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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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"line(625, k, b) - line(0, k, b)"
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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": 7,
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"id": "5889dd05-8f4c-42a1-8c27-fe2b8fbcbea7",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"f_chr = 0.5e-3\n",
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"f_D = 5e-2"
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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": 8,
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"id": "e350de8c-412c-4732-a65c-33220c837c19",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"nu = f_D / f_chr"
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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": 9,
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"id": "4e359270-52c7-47e4-ac2d-8685fb69d1d9",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"100.0"
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]
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},
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"execution_count": 9,
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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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"nu"
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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": null,
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"id": "4cf6e3cb-a91f-4dd7-b1ac-efd0b15b32ab",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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