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{ | ||
"cells": [], | ||
"metadata": {}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 211, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import numpy.matlib\n", | ||
"import numpy as np" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"2 * 2 matrix" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 212, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"a = np.array([1,2,-1,2])\n", | ||
"a = a.reshape(2,2)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"eigenvalue" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 213, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"g,h = numpy.linalg.eig(a)\n", | ||
"ss = np.zeros(2)\n", | ||
"tt = np.zeros(2)\n", | ||
"ss[0] = g[0].real\n", | ||
"tt[0] = g[0].imag\n", | ||
"ss[1] = g[1].real\n", | ||
"tt[1] = g[1].imag" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 214, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"n = 1000\n", | ||
"s = np.zeros(n)\n", | ||
"t = np.zeros(n)\n", | ||
"p = np.zeros(n)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"SRG:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 215, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"for i in range(n):\n", | ||
" x = np.random.rand(2)\n", | ||
" x = 20*(x.reshape(2,1)-0.5)\n", | ||
" #print(x)\n", | ||
" u = np.matmul(a,x)\n", | ||
" #print(u)\n", | ||
"\n", | ||
" unorm = np.linalg.norm(u)\n", | ||
" xnorm = np.linalg.norm(x)\n", | ||
" r = unorm / xnorm \n", | ||
" #print(unorm,xnorm,r,np.vdot(u,x))\n", | ||
"\n", | ||
" cos_theta = np.vdot(u,x)/(unorm * xnorm)\n", | ||
" theta = np.arccos(cos_theta)\n", | ||
" angle = theta*360/2/np.pi\n", | ||
" #print(theta,angle)\n", | ||
" \n", | ||
" s[i] = r * np.cos(theta)\n", | ||
" t[i] = r * np.sin(theta)\n", | ||
" p[i] = r * np.sin(-theta)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 216, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
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\n", | ||
"text/plain": [ | ||
"<Figure size 432x288 with 1 Axes>" | ||
] | ||
}, | ||
"metadata": { | ||
"needs_background": "light" | ||
}, | ||
"output_type": "display_data" | ||
} | ||
], | ||
"source": [ | ||
"import matplotlib.pyplot as plt\n", | ||
"plt.scatter(s, t)\n", | ||
"plt.scatter(s, p)\n", | ||
"plt.scatter(ss, tt)\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python [conda env:.conda-devito] *", | ||
"language": "python", | ||
"name": "conda-env-.conda-devito-py" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.7.5" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 4 | ||
} |