Commit f0160f8a authored by dmar's avatar dmar
Browse files

Adding the folder for the 4th exercise

parent 855d31e1
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from ase.io import read\n",
"from ase.visualize import view\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"sigma = 3.405\n",
"epsilon = 119.8*8.616733e-5 # eV"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def g_step(box, n_bin, d):\n",
" box += 1\n",
" box *= np.sqrt(2)/sigma\n",
" del_bin = box/n_bin #width of a bin\n",
" \n",
" g = np.zeros([n_bin,n_bin])\n",
" g[0] = np.linspace(0,box,n_bin)\n",
" g[0]+= del_bin*0.5\n",
" \n",
" for i in range(len(d)):\n",
" for j in range(i+1,len(d)):\n",
" g_index = d[i][j]/(del_bin)\n",
" g[1,g_index.astype(int)] += 2\n",
" # we count both for i and j.\n",
" \n",
" \n",
" #nomalized by number of particles\n",
" g[1] /= len(d[0])\n",
" #and by bin volume\n",
" \n",
" for k in range(len(g[1])):\n",
" g[1][k] /= ((k+1)**3 -k**3)*del_bin**3\n",
" \n",
" #side of the optimized cell: 5,269\n",
" vol = (5.269/sigma)**3\n",
" rho = 4./vol\n",
" g[1] /= np.pi*(4/3)*rho\n",
" return g;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 38 atom"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"trajectory = read('production_38-pos-1.xyz', index='::2')\n",
"N = len(trajectory[0]) #number of atoms\n",
"n_step = len(trajectory) #number of step\n",
"\n",
"#create a distance array\n",
"dist = np.empty([0,N,N])\n",
"for frame in trajectory:\n",
" dist = np.append(dist, [frame.get_all_distances()],axis=0)\n",
"\n",
"#Lennard Jones units\n",
"\n",
"dist /= sigma"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"#understand what the distance array is:\n",
"\n",
"#dist0 = trajectory[0].get_all_distances()\n",
"# print(dist0.shape)\n",
"# print(dist0)\n",
"# print(dist0[0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "IndexError",
"evalue": "index 200 is out of bounds for axis 1 with size 200",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-4be1c11f7d6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#average during the thermalized part of the simulation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mg_38\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mg_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_bin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mg_38\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mn_step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-3-79309bc65b61>\u001b[0m in \u001b[0;36mg_step\u001b[0;34m(box, n_bin, d)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mg_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdel_bin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mg_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;31m# we count both for i and j.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: index 200 is out of bounds for axis 1 with size 200"
]
}
],
"source": [
"n_bin = 200 #number of bin in the graph\n",
"box = 25 #side of the bon in the simulation \n",
"g_38 = np.zeros([n_bin,n_bin])\n",
"\n",
"#average during the thermalized part of the simulation\n",
"for i in range(n_step):\n",
" g_38 += g_step(box,n_bin,dist[i])\n",
"g_38 /= n_step\n",
"\n",
"plt.plot(g_38[0],g_38[1])\n",
"plt.xlim(0,box/(2*sigma))\n",
"plt.xlabel('distance [ units of $\\sigma$]')\n",
"plt.ylabel('radial distribution function')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 150 atoms"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"trajectory = read('production_150-pos-1.xyz', index='::2')\n",
"N = len(trajectory[0]) #number of atoms\n",
"n_step = len(trajectory) #number of step\n",
"\n",
"dist = np.empty([0,N,N])\n",
"for frame in trajectory:\n",
" dist = np.append(dist, [frame.get_all_distances()],axis=0)\n",
"dist /= sigma\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'radial distribution function')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n_bin = 400 \n",
"box = 30 \n",
"g_150 = np.zeros([n_bin,n_bin])\n",
"\n",
"\n",
"for i in range(n_step):\n",
" g_150 += g_step(box,n_bin,dist[i])\n",
"g_150 /= n_step\n",
"\n",
"plt.plot(g_150[0],g_150[1])\n",
"plt.xlim(0,box/(2*sigma))\n",
"plt.xlabel('distance [ units of $\\sigma$]')\n",
"plt.ylabel('radial distribution function')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 450 atoms"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"trajectory = read('production_450-pos-1.xyz', index='::2')\n",
"N = len(trajectory[0]) #number of atoms\n",
"n_step = len(trajectory) #number of step\n",
"\n",
"dist = np.empty([0,N,N])\n",
"for frame in trajectory:\n",
" dist = np.append(dist, [frame.get_all_distances()],axis=0)\n",
"dist /= sigma\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'radial distribution function')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"n_bin = 400 \n",
"box = 40 \n",
"g_450 = np.zeros([n_bin,n_bin])\n",
"\n",
"\n",
"for i in range(n_step):\n",
" g_450 += g_step(box,n_bin,dist[i])\n",
"g_450 /= n_step\n",
"\n",
"plt.plot(g_450[0],g_450[1])\n",
"plt.xlim(0,box/(2*sigma))\n",
"plt.xlabel('distance [ units of $\\sigma$]')\n",
"plt.ylabel('radial distribution function')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 817 atoms"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"trajectory = read('production_817-pos-1.xyz', index='::2')\n",
"N = len(trajectory[0]) #number of atoms\n",
"n_step = len(trajectory) #number of step\n",
"\n",
"dist = np.empty([0,N,N])\n",
"for frame in trajectory:\n",
" dist = np.append(dist, [frame.get_all_distances()],axis=0)\n",
"dist /= sigma\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'radial distribution function')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"n_bin = 400 \n",
"box = 45 \n",
"g_817 = np.zeros([n_bin,n_bin])\n",
"\n",
"\n",
"for i in range(n_step):\n",
" g_817 += g_step(box,n_bin,dist[i])\n",
"g_817 /= n_step\n",
"\n",
"plt.plot(g_817[0],g_817[1])\n",
"plt.xlim(0,box/(2*sigma))\n",
"plt.xlabel('distance [ units of $\\sigma$]')\n",
"plt.ylabel('radial distribution function')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### temperature"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'T')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"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": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Thermalization plots (NVT)\n",
"\n",
"in the .energ file you can find\n",
"\n",
"- column 0: step\n",
"- column 1: time \n",
"- column 2: kinetic energy\n",
"- column 3: temperature\n",
"- column 4: potential energy\n",
"\n",
"Try to plot these values and describe what you see and what is conserved"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data38 = np.loadtxt('thermalization_38-1.ener' )\n",
"data150 = np.loadtxt('thermalization_150-1.ener')\n",
"data450 = np.loadtxt('thermalization_450-1.ener')\n",
"data817 = np.loadtxt('thermalization_817-1.ener')\n",
"\n",
"data38 = np.transpose(data38)\n",
"data150 = np.transpose(data150)\n",
"data450 = np.transpose(data450)\n",
"data817 = np.transpose(data817)\n",
"\n",
"plt.plot(data38[1],data38[3], label='38')\n",
"plt.plot(data150[1],data150[3], label='150')\n",
"plt.plot(data450[1],data450[3], label='450')\n",
"plt.plot(data817[1],data817[3], label='817')\n",
"\n",
"plt.legend( loc='upper right')\n",
"\n",
"plt.xlabel(' time [fs]')\n",
"plt.ylabel('T [K]')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Production plots (NVE)\n",
"\n",
"in the .energ file you can find\n",
"\n",
"- column 0: step\n",
"- column 1: time \n",
"- column 2: kinetic energy\n",
"- column 3: temperature\n",
"- column 4: potential energy\n",
"\n",
"Try to plot these values and describe what you see and what is conserved"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data38 = np.loadtxt('production_38-1.ener' )\n",
"data150 = np.loadtxt('production_150-1.ener')\n",
"data450 = np.loadtxt('production_450-1.ener')\n",
"data817 = np.loadtxt('production_817-1.ener')\n",
"\n",
"data38 = np.transpose(data38)\n",
"data150 = np.transpose(data150)\n",
"data450 = np.transpose(data450)\n",
"data817 = np.transpose(data817)\n",
"\n",
"plt.plot(data38[1],data38[3], label='38')\n",
"plt.plot(data150[1],data150[3], label='150')\n",
"plt.plot(data450[1],data450[3], label='450')\n",
"plt.plot(data817[1],data817[3], label='817')\n",
"\n",
"plt.legend( loc='upper right')\n",
"\n",
"plt.xlabel(' time [fs]')\n",
"plt.ylabel('T [K]')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},