mirror of
https://github.com/galera951/experiment-automation.git
synced 2024-11-15 02:15:58 +03:00
237 lines
90 KiB
Plaintext
237 lines
90 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": 6,
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"id": "d83b90d9-2c29-41df-a57d-4b882e24c3cf",
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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 /home/glebi/git/experiment-automation/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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"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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" 'savefig.dpi': 250.0\n",
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"})\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "33e783a5-ce1b-46b5-98d6-3c12dc541ab1",
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"metadata": {},
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"source": [
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"Тводы = 24 +- 0.5 град"
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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": "8d94c051-6f00-449a-8bbc-7c75d0ec3eeb",
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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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"T0 = 24\n",
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"k = 0.041 # мв/град.цельсия"
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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": "2707c9c0-f2f0-4293-9a88-7d7e53e04fdd",
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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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"df = pd.read_csv(\"data.csv\", sep=\"\\t\")\n",
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"cols = df.columns\n",
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"U = df[cols[0]]\n",
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"f = df[cols[1]]\n",
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"f0 = 857 # кГц"
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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": 39,
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"id": "c119caab-9f68-4646-816d-9f73e62c7d45",
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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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"T = U/k + T0 + 273.15\n",
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"kappaRev = f**2 / (f0**2 - f**2)\n",
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"\n",
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"plt.plot(T, kappaRev, \".--\", lw=.75, markersize=8)\n",
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"plt.xlabel(fr\"Температура, $K$\")\n",
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"plt.ylabel(r\"Магн. прониц., $\\frac 1 \\kappa \\sim \\dfrac {f^2} {f_0^2 - f^2}$\")\n",
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"\n",
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"plt.savefig(\"plot1.svg\")\n",
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"plt.show()"
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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": 40,
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"id": "3636222e-9c1f-46bb-a6f9-97605e6001fd",
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"metadata": {},
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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": [
|
||
|
"<Figure size 900x600 with 1 Axes>"
|
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]
|
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},
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"metadata": {},
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||
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"output_type": "display_data"
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||
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}
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||
|
],
|
||
|
"source": [
|
||
|
"line = lambda x, k, b: k*x + b\n",
|
||
|
"si = 38\n",
|
||
|
"T_tail = T[si:]\n",
|
||
|
"kappaRev_tail = kappaRev[si:]\n",
|
||
|
"popt, pcov = curve_fit(line, T_tail, kappaRev_tail)\n",
|
||
|
"\n",
|
||
|
"plt.plot(T, kappaRev, \".--\", lw=.75, markersize=4)\n",
|
||
|
"plt.plot(T_tail, line(T_tail, *popt), lw=1.5, c=\"C3\")\n",
|
||
|
"plt.plot()\n",
|
||
|
"plt.xlabel(fr\"Температура, $K$\")\n",
|
||
|
"plt.ylabel(r\"Магн. прониц., $\\frac 1 \\kappa \\sim \\dfrac {f^2} {f_0^2 - f^2}$\")\n",
|
||
|
"\n",
|
||
|
"plt.savefig(\"plot2.svg\")\n",
|
||
|
"plt.show()"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 41,
|
||
|
"id": "f5e0e9df-c3c2-4980-a0ba-e0eb2cb86ff4",
|
||
|
"metadata": {
|
||
|
"tags": []
|
||
|
},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"(array([ 10.83492707, -3177.29142511]),\n",
|
||
|
" array([[ 1.47017342e-01, -4.44001012e+01],\n",
|
||
|
" [-4.44001012e+01, 1.34151101e+04]]))"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 41,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"popt, pcov"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 42,
|
||
|
"id": "4da5f1c6-0eae-4946-99f9-19cbb000b51d",
|
||
|
"metadata": {
|
||
|
"tags": []
|
||
|
},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"293.2452987878977"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 42,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"Theta = -popt[1]/popt[0]\n",
|
||
|
"Theta"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 43,
|
||
|
"id": "8facb2b3-e789-4f70-a276-44fc1e90ab74",
|
||
|
"metadata": {
|
||
|
"tags": []
|
||
|
},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"115.82361625604054"
|
||
|
]
|
||
|
},
|
||
|
"execution_count": 43,
|
||
|
"metadata": {},
|
||
|
"output_type": "execute_result"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"pcov[1][1]**.5"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"id": "17b27e0c-9e6b-43c5-8583-d797319b73cd",
|
||
|
"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
|
||
|
}
|