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@NicerNewerCar
Created August 8, 2024 17:15
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import nrrd\n",
"from tqdm.notebook import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"ct, header = nrrd.read(\"./Data/tal.nrrd\")\n",
"scale = [\n",
" header[\"space directions\"][0,0],\n",
" header[\"space directions\"][1,1],\n",
" header[\"space directions\"][2,2]\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21437654228746b083de6708761ff9c0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/118 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_dim, y_dim, z_dim = ct.shape\n",
"density_sum = 0\n",
"avg_location = np.array([0,0,0], dtype=np.uint64)\n",
"for i in tqdm(range(x_dim)):\n",
" for j in range(y_dim):\n",
" for k in range(z_dim):\n",
" density_sum += ct[i,j,k]\n",
" avg_location = avg_location + (np.array([i,j,k]) * ct[i,j,k])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20.232262548793365 21.750312875643534 24.423974407400138\n"
]
}
],
"source": [
"COM_IDX = avg_location/density_sum\n",
"print(COM_IDX[0] *scale[0], COM_IDX[1] * scale[1], COM_IDX[2] * scale[2])"
]
}
],
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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