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@Miura-KR
Last active December 3, 2019 23:19
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Onsager
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Onsager解の計算\n",
"\n",
"西森相転移p169"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 17.5 s\n"
]
}
],
"source": [
"%%time\n",
"import numpy as np\n",
"from scipy.integrate import dblquad\n",
"from math import sin, cos, pi, log1p\n",
"import matplotlib.pyplot as plt\n",
"\n",
"K = np.arange(0.2, 0.7 , 0.0005) # temperature\n",
"k12 = 2 * np.sinh(2 * K) / np.cosh(2 * K)**2 \n",
"f = np.log(2 * np.cosh(2 * K)) # free energy\n",
"\n",
"for k in range(len(K)):\n",
" integrant = lambda y, x: log1p(- k12[k] * cos(x) * cos(y))\n",
" f[k] += 0.5 / pi**2 * dblquad(integrant, 0, pi, lambda x: 0, lambda x: pi)[0]\n",
"\n",
"E = - np.gradient(f, K, edge_order=2)\n",
"C = - np.gradient(E, K, edge_order=2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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"
},
{
"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": [
"plt.plot(K, E, linewidth=\"0\", marker=\".\", markersize=1)\n",
"plt.xlabel('K')\n",
"plt.ylabel('E')\n",
"plt.show()\n",
"\n",
"plt.plot(K, C, linewidth=\"0\", marker=\".\", markersize=1)\n",
"plt.xlabel('K')\n",
"plt.ylabel('C')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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