2024-03-24 10:58:49 +03:00
|
|
|
|
{
|
|
|
|
|
|
"cells": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-11-09 11:24:25 +03:00
|
|
|
|
"execution_count": 1,
|
|
|
|
|
|
"id": "f458562b-bc3a-49c6-bc78-4455207673d7",
|
2024-03-24 10:58:49 +03:00
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2024-11-09 11:24:25 +03:00
|
|
|
|
"# %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"
|
2024-03-24 10:58:49 +03:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-11-09 11:24:25 +03:00
|
|
|
|
"execution_count": 2,
|
|
|
|
|
|
"id": "1232debc-1fb6-49a3-a13f-eb0b088a336f",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"data = np.loadtxt(\"data.csv\", delimiter=\",\")\n",
|
|
|
|
|
|
"Utp, R2 = data.T # mV, Ohm\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"f = 622 # Hz\n",
|
|
|
|
|
|
"c = 41e-6 # V/K\n",
|
|
|
|
|
|
"T0 = 25 + 273.15 # K\n",
|
|
|
|
|
|
"T = Utp*1e-3/c + T0\n",
|
|
|
|
|
|
"T_sigma = .01*1e-3/c\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"R1 = 220 # Ohm\n",
|
|
|
|
|
|
"R3 = 560 # Ohm\n",
|
|
|
|
|
|
"l = 40e-3 # m\n",
|
|
|
|
|
|
"S = 4.1e-3 * 4.15e-3 # m^2\n",
|
|
|
|
|
|
"sigma_x = (l / S) * (R1 / R3) * (1 / R2)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 3,
|
|
|
|
|
|
"id": "e8f546af-46c0-4fa4-a8e5-a94c621793a1",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 900x600 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"plt.errorbar(T, sigma_x, xerr=T_sigma, yerr=0, fmt=\".\", markersize=1.5, label=\"Экспериментальные данные\")\n",
|
|
|
|
|
|
"plt.xlabel(\"Температура, K\")\n",
|
|
|
|
|
|
"plt.ylabel(r\"Удельная электропроводность $\\sigma_x$, 1/м$\\cdot$Ом\")\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# plt.yscale(\"log\")\n",
|
|
|
|
|
|
"plt.legend()\n",
|
|
|
|
|
|
"plt.savefig(\"sigmaOnT.png\")\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 5,
|
|
|
|
|
|
"id": "949a988d-840f-4d34-8815-d664e86b6037",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 900x600 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"T_edge = 96 # celcium grad\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"revT = 1 / T\n",
|
|
|
|
|
|
"lnSigma = np.log(sigma_x)\n",
|
|
|
|
|
|
"line = lambda x, k, b: k*x + b\n",
|
|
|
|
|
|
"idx = T > (T_edge + 273.15) # T more than 90 degrees\n",
|
|
|
|
|
|
"popt, pcov = curve_fit(line, revT[idx], lnSigma[idx])\n",
|
|
|
|
|
|
"perr = np.sqrt(np.diag(pcov))\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"k, b = popt\n",
|
|
|
|
|
|
"k_sigma = perr[0]\n",
|
|
|
|
|
|
"b_sigma = perr[1]\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"plt.plot(revT, lnSigma, \".\", markersize=3, label=\"Экспериментальные данные\")\n",
|
|
|
|
|
|
"plt.plot(revT, line(revT, *popt), lw=.75, label=fr\"Аппроксимация прямой для $T > {T_edge}^\\circ$С\")\n",
|
|
|
|
|
|
"plt.fill_between(revT, line(revT, k + k_sigma, b), line(revT, k - k_sigma, b), alpha=.1, color=\"C1\", label=fr\"Среднеквадратичное отклонение\")\n",
|
|
|
|
|
|
"plt.xlabel(\"Обратная температура, 1/K\")\n",
|
|
|
|
|
|
"plt.ylabel(r\"$\\ln \\sigma_x$, усл. ед.\")\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"plt.legend()\n",
|
|
|
|
|
|
"plt.savefig(\"logSigmaOnRevT.png\")\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
|
"id": "534f97cf-1338-4c47-9cce-3fe11bfc59f1",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"13.321019102856487"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"k_sigma"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 8,
|
|
|
|
|
|
"id": "7a703f6a-1a4b-4646-9255-4fa6920a243e",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"4228.052416617957"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"execution_count": 8,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"-k"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 113,
|
|
|
|
|
|
"id": "6fd4474a-ae5c-45df-8aca-48da328c39df",
|
2024-03-24 10:58:49 +03:00
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2024-11-09 11:24:25 +03:00
|
|
|
|
"728.7+-2.3 мэВ\n"
|
2024-03-24 10:58:49 +03:00
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2024-11-09 11:24:25 +03:00
|
|
|
|
"k_B = 1.380649e-23 # Дж/К\n",
|
|
|
|
|
|
"e = 1.602176634e-19 # Кл\n",
|
|
|
|
|
|
"Delta = -2 * k_B * k / e # eV\n",
|
|
|
|
|
|
"Delta_sigma = 2 * k_B * k_sigma / e # eV\n",
|
|
|
|
|
|
"print(f\"{Delta*1e3:.1f}+-{Delta_sigma*1e3:.1f} мэВ\")"
|
2024-03-24 10:58:49 +03:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-11-09 11:24:25 +03:00
|
|
|
|
"execution_count": 114,
|
|
|
|
|
|
"id": "cc9544e5-c8fb-44e0-b3a2-abd0ab9899af",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"69"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"execution_count": 114,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"sum(idx)"
|
|
|
|
|
|
]
|
2024-03-24 10:58:49 +03:00
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"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
|
|
|
|
|
|
}
|
