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akhanf/zarrnii_example.ipynb

Created November 11, 2025 18:44
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Simple zarrnii example
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zarrnii_example.ipynb
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"source": [
"from zarrnii import ZarrNii, ZarrNiiAtlas\n",
"\n",
"img = ZarrNii.from_ome_zarr('/nfs/trident3/lightsheet/prado/mouse_app_lecanemab_ki3/bids/sub-AS134F3/micr/sub-AS134F3_sample-brain_acq-imaris4x_SPIM.ome.zarr', channel_labels=['Iba1'],level=5,downsample_near_isotropic=True)\n",
"\n"
]
},
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"cell_type": "code",
"execution_count": 2,
"id": "ba5d0f34-03a7-4990-800e-0adf07133940",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" <div style=\"margin-bottom: 10px;\">\n",
" <strong>ZarrNii Object</strong>\n",
" <table style=\"margin-left: 20px; border-collapse: collapse;\">\n",
" <tr><td style=\"padding: 2px;\"><strong>name:</strong></td><td style=\"padding: 2px;\">image</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>shape:</strong></td><td style=\"padding: 2px;\">(1, 192, 312, 212)</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>dims:</strong></td><td style=\"padding: 2px;\">['c', 'z', 'y', 'x']</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>axes_order:</strong></td><td style=\"padding: 2px;\">ZYX</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>xyz_orientation:</strong></td><td style=\"padding: 2px;\">RPI</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>scale:</strong></td><td style=\"padding: 2px;\">{'c': 1, 'z': 0.035216000000000004, 'y': 0.052, 'x': 0.052}</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>dtype:</strong></td><td style=\"padding: 2px;\">uint16</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>chunksize:</strong></td><td style=\"padding: 2px;\">(1, 1, 312, 212)</td></tr>\n",
" </table>\n",
" </div>\n",
" \n",
" <div style=\"margin-top: 10px;\">\n",
" <strong>Dask Array:</strong>\n",
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" <thead>\n",
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" <th> Array </th>\n",
" <th> Chunk </th>\n",
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" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 24.22 MiB </td>\n",
" <td> 129.19 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1, 192, 312, 212) </td>\n",
" <td> (1, 1, 312, 212) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 192 chunks in 4 graph layers </td>\n",
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" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
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"text/plain": [
"ZarrNii(\n",
" name='image', shape=(1, 192, 312, 212), dims=['c', 'z', 'y', 'x'],\n",
" axes_order='ZYX', xyz_orientation='RPI',\n",
" scale={'c': 1, 'z': 0.035216000000000004, 'y': 0.052, 'x': 0.052},\n",
" dtype=uint16, chunksize=(1, 1, 312, 212)\n",
")"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0db711f8-4590-497a-9d85-de41b10b8df9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'iba.nii'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img.to_nifti('iba.nii')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cd0b556a-eaa5-4b95-a4f1-211116a56742",
"metadata": {},
"outputs": [],
"source": [
"atlas = ZarrNiiAtlas.from_files('/nfs/trident3/lightsheet/prado/mouse_app_lecanemab_ki3/derivatives/spimquant_aae813e/sub-AS134F3/micr/sub-AS134F3_sample-brain_acq-imaris4x_seg-all_from-ABAv3_level-5_desc-deform_dseg.nii.gz',\n",
" '/nfs/trident3/lightsheet/prado/mouse_app_lecanemab_ki3/derivatives/spimquant_aae813e/tpl-ABAv3/seg-all_tpl-ABAv3_dseg.tsv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "256d2df7-8d4c-4105-816b-0c0ad2278e84",
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"\n",
" <div style=\"margin-bottom: 10px;\">\n",
" <strong>ZarrNii Object</strong>\n",
" <table style=\"margin-left: 20px; border-collapse: collapse;\">\n",
" <tr><td style=\"padding: 2px;\"><strong>name:</strong></td><td style=\"padding: 2px;\">nifti_image_/nfs/trident3/lightsheet/prado/mouse_app_lecanemab_ki3/derivatives/spimquant_aae813e/sub-AS134F3/micr/sub-AS134F3_sample-brain_acq-imaris4x_seg-all_from-ABAv3_level-5_desc-deform_dseg.nii.gz</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>shape:</strong></td><td style=\"padding: 2px;\">(1, 212, 312, 192)</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>dims:</strong></td><td style=\"padding: 2px;\">['c', 'x', 'y', 'z']</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>axes_order:</strong></td><td style=\"padding: 2px;\">XYZ</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>xyz_orientation:</strong></td><td style=\"padding: 2px;\">RPI</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>scale:</strong></td><td style=\"padding: 2px;\">{'x': 0.052000001072883606, 'y': 0.052000001072883606, 'z': 0.03521599993109703}</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>dtype:</strong></td><td style=\"padding: 2px;\">float64</td></tr>\n",
" <tr><td style=\"padding: 2px;\"><strong>chunksize:</strong></td><td style=\"padding: 2px;\">(1, 212, 312, 192)</td></tr>\n",
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" \n",
" <div style=\"margin-top: 10px;\">\n",
" <strong>Dask Array:</strong>\n",
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" <tr>\n",
" <td>\n",
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" <thead>\n",
" <tr>\n",
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" <th> Chunk </th>\n",
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" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 96.89 MiB </td>\n",
" <td> 96.89 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1, 212, 312, 192) </td>\n",
" <td> (1, 212, 312, 192) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"text/plain": [
"ZarrNiiAtlas(ngff_image=NgffImage(data=dask.array<getitem, shape=(1, 212, 312, 192), dtype=float64, chunksize=(1, 212, 312, 192), chunktype=numpy.ndarray>, dims=['c', 'x', 'y', 'z'], scale={'x': 0.052000001072883606, 'y': 0.052000001072883606, 'z': 0.03521599993109703}, translation={'x': -0.0, 'y': -0.0, 'z': 0.0}, name='nifti_image_/nfs/trident3/lightsheet/prado/mouse_app_lecanemab_ki3/derivatives/spimquant_aae813e/sub-AS134F3/micr/sub-AS134F3_sample-brain_acq-imaris4x_seg-all_from-ABAv3_level-5_desc-deform_dseg.nii.gz', axes_units=None, axes_orientations=None, computed_callbacks=[]), axes_order='XYZ', xyz_orientation='RPI', _omero=None, labels_df= index name abbreviation color\n",
"0 0 left root root-L 000000\n",
"1 1 left Basic cell groups and regions grey-L BFDAE3\n",
"2 2 left Cerebrum CH-L B0F0FF\n",
"3 3 left Cerebral cortex CTX-L B0FFB8\n",
"4 4 left Cortical plate CTXpl-L 70FF70\n",
"... ... ... ... ...\n",
"2649 11322 right intraparafloccular fissure ipf-R A9A9AA\n",
"2650 11323 right paramedian sulcus pms-R A9A9A9\n",
"2651 11324 right parafloccular sulcus pfs-R AAAAA8\n",
"2652 11325 right Interpeduncular fossa IPF-R A9A8A9\n",
"2653 11326 right retina retina-R 7F2E7E\n",
"\n",
"[2654 rows x 4 columns], label_column='index', name_column='name', abbrev_column='abbreviation')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"atlas\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2e8c7e85-b140-4802-9eea-3e8db42fcb1e",
"metadata": {},
"outputs": [],
"source": [
"cropped = img.crop_with_bounding_box(*atlas.get_region_bounding_box(regex='Hipp'),ras_coords=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f3e8eb06-c648-41af-9420-97798c6009ed",
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"text/plain": [
"ZarrNii(\n",
" name='image', shape=(1, 144, 116, 158), dims=['c', 'z', 'y', 'x'],\n",
" axes_order='ZYX', xyz_orientation='RPI',\n",
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")"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cropped"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "36d0f991-7695-4cef-98e4-4a60d6a0001f",
"metadata": {},
"outputs": [],
"source": [
"fullresimg = ZarrNii.from_ome_zarr('/nfs/trident3/lightsheet/prado/mouse_app_lecanemab_ki3/bids/sub-AS134F3/micr/sub-AS134F3_sample-brain_acq-imaris4x_SPIM.ome.zarr', channel_labels=['Iba1'],level=0)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8b4cd37c-6cf9-4de2-b0e9-9ad889e4370d",
"metadata": {},
"outputs": [],
"source": [
"croppedfullres = fullresimg.crop_with_bounding_box(*atlas.get_region_bounding_box(regex='Left Hipp'),ras_coords=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0cdc6e4d-a6fb-4b38-be5a-f961f7ac8ddf",
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"</svg>\n",
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" </tr>\n",
"</table>\n",
" </div>\n",
" "
],
"text/plain": [
"ZarrNii(\n",
" name='image', shape=(1, 2096, 2496, 2368), dims=['c', 'z', 'y', 'x'],\n",
" axes_order='ZYX', xyz_orientation='RPI',\n",
" scale={'c': 1, 'z': 0.0022010000000000003, 'y': 0.001625, 'x': 0.001625},\n",
" dtype=uint16, chunksize=(1, 1, 2496, 2368)\n",
")"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"croppedfullres"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3cde61c1-844b-470e-9907-f663c7fb235e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[########################################] | 100% Completed | 202.25 ms\n",
"[########################################] | 100% Completed | 202.07 ms\n",
"[########################################] | 100% Completed | 204.44 ms\n",
"[########################################] | 100% Completed | 202.75 ms\n",
"[########################################] | 100% Completed | 22.72 s\n"
]
}
],
"source": [
"from dask.diagnostics import ProgressBar\n",
"\n",
"with ProgressBar():\n",
" ZarrNii.from_ome_zarr('/nfs/trident3/lightsheet/prado/mouse_app_lecanemab_ki3/bids/sub-AS134F3/micr/sub-AS134F3_sample-brain_acq-imaris4x_SPIM.ome.zarr', channel_labels=['Abeta'],level=2, downsample_near_isotropic=True).crop_with_bounding_box(*atlas.get_region_bounding_box(regex='Field CA1'),ras_coords=True).to_nifti('abeta_lhipp.nii')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4847a7ce-1fee-4606-a3df-9e3ca9adf22b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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