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mrocklin/AOLLSM_ITK.ipynb

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AOLLSM_ITK.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup Dask Cluster\n",
"\n",
"ITK doesn't seem to release the GIL, so we use many single-threaded processes.\n",
"\n",
"- See [ITK issue #1134](https://github.com/InsightSoftwareConsortium/ITK/issues/1134)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3 style=\"text-align: left;\">Client</h3>\n",
"<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:45423</li>\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3 style=\"text-align: left;\">Cluster</h3>\n",
"<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
" <li><b>Workers: </b>20</li>\n",
" <li><b>Cores: </b>20</li>\n",
" <li><b>Memory: </b>1.08 TB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:45423' processes=20 cores=20>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client, LocalCluster\n",
"\n",
"cluster = LocalCluster(n_workers=20, threads_per_worker=1)\n",
"client = Client(cluster)\n",
"client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load our data from last time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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" <tr><th> Bytes </th><td> 188.74 GB </td> <td> 316.15 MB </td></tr>\n",
" <tr><th> Shape </th><td> (3, 199, 201, 1024, 768) </td> <td> (1, 1, 201, 1024, 768) </td></tr>\n",
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]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import dask.array as da\n",
"\n",
"imgs = da.from_zarr(\"/public/AOLLSMData_m4_raw.zarr/\", \"data\")\n",
"imgs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load our Point Spread Function (PSF)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"metadata": {},
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}
],
"source": [
"import dask.array.image\n",
"\n",
"psf = dask.array.image.imread(\"/public/AOLLSMData/m4/psfs_z0p1/*.tif\")[:, None, ...]\n",
"psf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convert data to float32 for computation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"imgs = imgs.astype(np.float32)\n",
"psf = psf.astype(np.float32)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Apply Richardson-Lucy Deconvolution"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def richardson_lucy_deconvolution(img, psf, iterations=1):\n",
" \"\"\" Apply deconvolution to a single chunk of data \"\"\"\n",
" import itk\n",
" \n",
" img = img.squeeze() # remove leading two length-one dimensions\n",
" psf = psf.squeeze() # remove leading two length-one dimensions\n",
" \n",
" image = itk.image_view_from_array(img) # Convert to ITK object\n",
" kernel = itk.image_view_from_array(psf) # Convert to ITK object\n",
" \n",
" deconvolved = itk.richardson_lucy_deconvolution_image_filter(image,\n",
" kernel_image=kernel,\n",
" number_of_iterations=iterations)\n",
" \n",
" result = itk.array_from_image(deconvolved) # Convert back to Numpy array\n",
" result = result[None, None, ...] # Add back the leading length-one dimensions\n",
"\n",
" return result\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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"text/plain": [
"dask.array<richardson_lucy_deconvolution, shape=(3, 199, 201, 1024, 768), dtype=float32, chunksize=(1, 1, 201, 1024, 768)>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out = da.map_blocks(richardson_lucy_deconvolution, imgs, psf, dtype=np.float32)\n",
"out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compute and write to disk"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"\n",
"out.to_zarr(\"/raid/mrocklin/tmp.zarr\", \"deconvolved\", overwrite=True)"
]
},
{
"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
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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