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Meshgrid example
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‎class_07/.ipynb_checkpoints/Try_mesh-checkpoint.ipynb

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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": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"%matplotlib inline\n",
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"import pandas as pd"
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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": 13,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"a = np.zeros((100,))\n",
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"\n",
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"d = pd.DataFrame({'name': a})\n",
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"d.to_csv('out.csv', header=True)"
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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": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(20, 20)\n",
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"(20, 20)\n"
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]
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}
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],
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"source": [
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"z = np.meshgrid(np.arange(-1.0, 1.0, 0.1), np.arange(-1.0, 1.0, 0.1))\n",
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"print z[0].shape\n",
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"print z[1].shape"
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]
52+
}
53+
],
54+
"metadata": {
55+
"kernelspec": {
56+
"display_name": "Python 2",
57+
"language": "python",
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"name": "python2"
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},
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"language_info": {
61+
"codemirror_mode": {
62+
"name": "ipython",
63+
"version": 2
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},
65+
"file_extension": ".py",
66+
"mimetype": "text/x-python",
67+
"name": "python",
68+
"nbconvert_exporter": "python",
69+
"pygments_lexer": "ipython2",
70+
"version": "2.7.12"
71+
}
72+
},
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"nbformat": 4,
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"nbformat_minor": 2
75+
}

‎class_07/Try_mesh.ipynb

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{
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"cells": [
3+
{
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"cell_type": "code",
5+
"execution_count": 7,
6+
"metadata": {
7+
"collapsed": true
8+
},
9+
"outputs": [],
10+
"source": [
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"import numpy as np\n",
12+
"from matplotlib import pyplot as plt\n",
13+
"%matplotlib inline\n",
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"import pandas as pd"
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]
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},
17+
{
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"cell_type": "code",
19+
"execution_count": 13,
20+
"metadata": {
21+
"collapsed": false
22+
},
23+
"outputs": [],
24+
"source": [
25+
"a = np.zeros((100,))\n",
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"\n",
27+
"d = pd.DataFrame({'name': a})\n",
28+
"d.to_csv('out.csv', header=True)"
29+
]
30+
},
31+
{
32+
"cell_type": "code",
33+
"execution_count": 39,
34+
"metadata": {
35+
"collapsed": false
36+
},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(200, 200)\n",
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"(200, 200)\n",
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"(40000, 2)\n"
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]
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},
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{
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"data": {
49+
"text/plain": [
50+
"<matplotlib.collections.PathCollection at 0x7f10bbd66810>"
51+
]
52+
},
53+
"execution_count": 39,
54+
"metadata": {},
55+
"output_type": "execute_result"
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},
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{
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"data": {
59+
"image/png": 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aZb+dSS5L8kCSx4/wsQHgWHRiprDY2Xmna4qLMcbDSR7unMDSfd9fVXuSXJjk/yRJVT0n\nyauS/PYh5nTjHHMCgGNA2xGL/eb8nIuzqurcJGcnOa6qzl1cTl4ac19VvX5pt/+Y5D1V9Y+r6mVJ\nrk/y9SR/ONc8AYBec57QeVWStyxd33/+zz9K8j8W//33kpyyf8AY48NVdVKSTyR5bpL/meSSMcYT\nM84TAGhUY6x2riQAwNpsms+5AAC2BnEBALQ6KuPCl6JtLVV1alXdUFWPVtUjVXXt8om/K+zzJ1W1\nb+nyVFX9znrNmR9WVVdU1f1V9d2qur2qLjjE+J+vqnsX4++pqkvWa64c2lrWs6ouX3oO7n8+Prae\n82VlVfUzVXVLVX1jsTaXHsY+r66q3VX1eFV9eY1f45HkKI2L+FK0rebGJC/J9Dbk1yX5h5lO6l3N\nSHJNps9F2Zbk7yT51RnnyAqq6k1JPpLkfUnOS3JPpufWaSuM35FpzT+Z5BVJbk5yc1W9dH1mzGrW\nup4Lj2Z6Hu6/nD33PDlsJye5O8kVWf0DKZMkVXVOkj/K9FUc5yb5WJJrq+qitTzoUX1C56KmPjrG\n+FuHMfbBJP9hjPHRxfXnZPqwrsvHGL4UbYNU1U8k+WKS7WOMuxbbLk7ymSTPG2PsWWG/P05y1xjj\nnes2WQ6qqm5PcscY48rF9Uryl0l+Y4zx4YOM/89JThpjXLq0bVem9Xz7Ok2bFRzBeh72v8NsrKra\nl+QNY4xbVhnzoUzv0nz50rabkpwyxvi5w32so/XIxZr4UrRNbUeSR/aHxcKtmQr7VYfY97Kqeqiq\nvlBVH6iqH51tlhxUVZ2Q6ZOgl59bI9MarvTc2rG4fdnOVcazTo5wPZPkx6rqgar6i6pyFOro9lNp\neH5upi8um9ORfika89uW5FvLG8YYTy3OpVltbW5I8rUkDyZ5eZIPJ/nxJG+caZ4c3GlJjsvBn1sv\nXmGfbSuM91zceEeynl9K8tZMn6x8SpJ/m+S2qvr7Y4xvzDVRZrPS8/M5VfUjY4zvHc6dbJq42KRf\nisYROtz1XO0ussrajDGuXbr654uPjr+1qp4/xrh/TZNlDmt9bnkubm4rrs8Y4/Ykt/9g4PQS171J\n3pbpvA2OfrX4edjP0U0TF9mcX4rGkTvc9dyTaR1+oKqOS3Jqnl7Pq7kj0xq/KIm4WD/fTvJUpufW\nstOz8vrtWeN41s+RrOcPGWM8WVV3ZXoucvRZ6fn5nbV8WvamiYvN+KVoHLnDXc/FXznPrarzls67\nuDBTKNyxhoc8L1NVf3Otc+XIjTG+X1W7M63ZLckPTgC8MMlvrLDbroPcftFiOxvoCNfzh1TVs5L8\nZJL/Otc8mdWuJAe+Nfxns8bn51F5QqcvRds6xhj3ZTpZ6JNVdUFV/XSS30xy0/53ilTVmYvPRHjl\n4voLquo9VXV+VZ29eN/2dUk+P8b4s436fzmGXZ3kbVX1lsW7fz6e5KQkn0qSqrq+qj6wNP5jSS6p\nqndW1Yur6t9lOonwt9Z32qxgTetZVe+tqouq6vlVdV6m86HOTnLt0++a9VZVJy9+P75isekFi+tn\nLW7/YFVdt7TLx5O8sKo+tHh+vj3TuWxXr+VxN82RizXypWhbyy9k+sVya5J9SX4/yZVLt5+Q6WTN\nkxbXn0jymsWYkzO9Te73krx/nebLkjHGpxefgXBVpsOpdye5eIzx0GLI85I8uTR+V1W9OdN6vT/J\nV5K8fozxxfWdOQez1vXM9BLmNZlOBHwkye4kOxZ/OLDxXpnkjzMd2R2ZPsMkmf4ge2umdTtr/+Ax\nxgNV9bpMMfGvMv0R/ktjjAPfQbKqo/pzLgCAzeeofFkEANi8xAUA0EpcAACtxAUA0EpcAACtxAUA\n0EpcAACtxAUA0EpcAACtxAUA0EpcAACt/j9VSmx490nxBAAAAABJRU5ErkJggg==\n",
60+
"text/plain": [
61+
"<matplotlib.figure.Figure at 0x7f10bbdd6250>"
62+
]
63+
},
64+
"metadata": {},
65+
"output_type": "display_data"
66+
}
67+
],
68+
"source": [
69+
"z = np.meshgrid(np.arange(-1.0, 1.0, 0.01), np.arange(-1.0, 1.0, 0.01))\n",
70+
"print z[0].shape\n",
71+
"print z[1].shape\n",
72+
"# print z[0].ravel()\n",
73+
"k = np.c_[z[0].ravel(), z[1].ravel()]\n",
74+
"print k.shape\n",
75+
"\n",
76+
"plt.figure(0)\n",
77+
"plt.xlim(-1.0, 1.0)\n",
78+
"plt.ylim(-1.0, 1.0)\n",
79+
"\n",
80+
"plt.scatter(k[:k.shape[0]/2, 0], k[:k.shape[0]/2, 1], linewidths=0.1, color='red')\n",
81+
"plt.scatter(k[k.shape[0]/2:, 0], k[k.shape[0]/2:, 1], linewidths=0.1, color='green')"
82+
]
83+
}
84+
],
85+
"metadata": {
86+
"kernelspec": {
87+
"display_name": "Python 2",
88+
"language": "python",
89+
"name": "python2"
90+
},
91+
"language_info": {
92+
"codemirror_mode": {
93+
"name": "ipython",
94+
"version": 2
95+
},
96+
"file_extension": ".py",
97+
"mimetype": "text/x-python",
98+
"name": "python",
99+
"nbconvert_exporter": "python",
100+
"pygments_lexer": "ipython2",
101+
"version": "2.7.12"
102+
}
103+
},
104+
"nbformat": 4,
105+
"nbformat_minor": 2
106+
}

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