100 lines
11 KiB
Plaintext
100 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pylab as plt\n",
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"import pandas as pd\n",
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"\n",
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"Nx=3\n",
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"Ny=128\n",
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"Nz=128\n",
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"dx = 1.0/(Nz-1)\n",
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"ID = np.ones(Nx*Ny*Nz,dtype='uint8')\n",
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"ID.shape = (Nz,Ny,Nx)"
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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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"outputs": [],
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"source": [
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"for idx in range(len(D)):\n",
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" #print(idx)\n",
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" cx=D['cx'][idx] / dx\n",
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" cy=D['cy'][idx] /dx\n",
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" r=D['r'][idx] /dx\n",
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" for i in range(0,Nz):\n",
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" for j in range(0,Ny):\n",
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" if ( (cx-i)*(cx-i) + (cy-j)*(cy-j) < r*r ):\n",
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" ID[i,j,0] = 0\n",
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" ID[i,j,1] = 0\n",
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" ID[i,j,2] = 0\n",
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"\n",
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" \n",
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"ID.tofile(\"discs_3x128x128.raw\")"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.figure(1)\n",
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"plt.title('segmented image')\n",
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"plt.pcolormesh(ID[:,:,1],cmap='hot')\n",
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"plt.grid(True)\n",
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"plt.axis('equal')\n",
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"plt.show()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"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
|
|
}
|