198 lines
28 KiB
Plaintext
198 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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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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"\n",
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"D=np.ones((80,80,40),dtype=\"uint8\")\n",
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"\n",
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"cx = 40\n",
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"cy = 40\n",
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"cz = 0\n",
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"\n",
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"for i in range(0,80):\n",
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" for j in range (0, 80):\n",
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" for k in range (0,40):\n",
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" dist = np.sqrt((i-cx)*(i-cx) + (j-cx)*(j-cx) + (k-cz)*(k-cz))\n",
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" if (dist < 25) :\n",
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" D[i,j,k] = 2\n",
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" if k < 4 : \n",
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" D[i,j,k] = 0\n",
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" \n",
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"D.tofile(\"droplet_40x80x80.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": 3,
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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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"ID = np.fromfile('droplet_40x80x80.raw', dtype = np.uint8)\n",
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"ID.shape = (80,80,40)\n",
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"plt.figure(1)\n",
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"plt.title('segmented image')\n",
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"plt.pcolormesh(np.transpose(ID[:,40,:]),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": 4,
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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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"\n",
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"D=np.ones((256,256,64),dtype=\"uint8\")\n",
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"\n",
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"cx = 128\n",
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"cy = 128\n",
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"cz = 0\n",
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"\n",
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"for i in range(0,256):\n",
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" for j in range (0, 256):\n",
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" for k in range (0,64):\n",
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" dist = np.sqrt((i-cx)*(i-cx) + (j-cx)*(j-cx) + (k-cz)*(k-cz))\n",
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" if (dist < 48) :\n",
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" D[i,j,k] = 2\n",
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" if k < 4 : \n",
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" D[i,j,k] = 0\n",
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" \n",
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"D.tofile(\"droplet_64x256x256.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": 5,
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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",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"ID = np.fromfile('droplet_64x256x256.raw', dtype = np.uint8)\n",
|
|
"ID.shape = (256,256,64)\n",
|
|
"plt.figure(1)\n",
|
|
"plt.title('segmented image')\n",
|
|
"plt.pcolormesh(np.transpose(ID[:,128,:]),cmap='hot')\n",
|
|
"plt.grid(True)\n",
|
|
"plt.axis('equal')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"D=np.ones((200,200,80),dtype=\"uint8\")\n",
|
|
"\n",
|
|
"cx = 100\n",
|
|
"cy = 100\n",
|
|
"cz = 25\n",
|
|
"\n",
|
|
"for i in range(0,200):\n",
|
|
" for j in range (0, 200):\n",
|
|
" for k in range (0,80):\n",
|
|
" dist = np.sqrt((i-cx)*(i-cx) + (j-cx)*(j-cx) + (k-cz)*(k-cz))\n",
|
|
" if (dist < 30) :\n",
|
|
" D[i,j,k] = 2\n",
|
|
" if k < 4 : \n",
|
|
" D[i,j,k] = 0\n",
|
|
" \n",
|
|
"D.tofile(\"droplet_80x200x200.raw\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"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"
|
|
}
|
|
],
|
|
"source": [
|
|
"ID = np.fromfile('droplet_80x200x200.raw', dtype = np.uint8)\n",
|
|
"ID.shape = (200,200,80)\n",
|
|
"plt.figure(1)\n",
|
|
"plt.title('segmented image')\n",
|
|
"plt.pcolormesh(np.transpose(ID[:,100,:]),cmap='hot')\n",
|
|
"plt.grid(True)\n",
|
|
"plt.axis('equal')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": 2
|
|
}
|