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IBL tutorials #40

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Sep 6, 2023
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IBL tutorials #40

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ec4f918
IBL tutorials
oliche Sep 5, 2023
82523d8
create an IBL folder in 000409 and add Readme
oliche Sep 5, 2023
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250 changes: 250 additions & 0 deletions 000409/IBL/01_list_datasets.ipynb
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"cells": [
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"id": "f981241e-e235-4bdb-898d-81fba5ca95d6",
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},
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{
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>path</th>\n",
" <th>subject</th>\n",
" <th>filesize</th>\n",
" <th>name</th>\n",
" </tr>\n",
" <tr>\n",
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" path \\\n",
"eid \n",
"ae8787b1-4229-4d56-b0c2-566b61a25b77 sub-NR-0027/sub-NR-0027_ses-ae8787b1-4229-4d56... \n",
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"\n",
" subject filesize \\\n",
"eid \n",
"ae8787b1-4229-4d56-b0c2-566b61a25b77 NR-0027 1209097503 \n",
"ae8787b1-4229-4d56-b0c2-566b61a25b77 NR-0027 7173043026 \n",
"ae8787b1-4229-4d56-b0c2-566b61a25b77 NR-0027 9430320168 \n",
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"metadata": {},
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],
"source": [
"from pathlib import Path\n",
"import re\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from dandi.dandiapi import DandiAPIClient\n",
"\n",
"DANDISET_ID = '000409'\n",
"\n",
"client = DandiAPIClient()\n",
"dandiset = client.get_dandiset(DANDISET_ID, 'draft')\n",
"meta_data = dandiset.get_metadata()\n",
"\n",
"assets = list(dandiset.get_assets())\n",
"\n",
"# I'm going to build a dataframe containing the asset paths and filesizes so I can navigate and filter the data files\n",
"df_assets = pd.DataFrame(index=np.arange(len(assets)), columns=['path', 'eid', 'subject', 'filesize', 'name'])\n",
"\n",
"for i, asset in enumerate(assets):\n",
" parts = asset.path.split('/')\n",
" # gets what is after 'sub-' in the first element of parts\n",
" subject = re.search('sub-(.*)', parts[0]).group(1)\n",
" eid = re.search(f'sub-{subject}_ses-(.*)', parts[1]).group(1)[:36]\n",
" df_assets.loc[i, 'subject'] = subject\n",
" df_assets.loc[i, 'eid'] = eid\n",
" df_assets.loc[i, 'path'] = asset.path\n",
" df_assets.loc[i, 'filesize'] = asset.size\n",
" df_assets.loc[i, 'name'] = Path(asset.path).name\n",
"df_assets = df_assets.set_index('eid')\n",
"df_assets.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e3f1acac-9b90-4388-921a-a82611d81ef5",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['sub-NYU-46/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e_behavior+ecephys+image/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e_OriginalVideoBodyCamera.mp4'\n",
" 'sub-NYU-46/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e_behavior+ecephys+image/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e_OriginalVideoRightCamera.mp4'\n",
" 'sub-NYU-46/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e_behavior+ecephys+image/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e_OriginalVideoLeftCamera.mp4'\n",
" 'sub-NYU-46/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e_behavior+ecephys+image.nwb'\n",
" 'sub-NYU-46/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e-processed-only_behavior.nwb']\n"
]
}
],
"source": [
"# pick the experiment ID from https://viz.internationalbrainlab.org/\n",
"eid = \"64e3fb86-928c-4079-865c-b364205b502e\"\n",
"print(df_assets.loc[eid, 'path'].values)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1d3a9700-da45-4481-972c-01ec11db0e28",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%capture output\n",
"!pip install one-api\n",
"from one.api import ONE\n",
"ONE.setup(base_url='https://openalyx.internationalbrainlab.org', silent=True)\n",
"one = ONE(password='international')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "7748fcf8-8c4f-472f-b6cf-ede765fbef33",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No. of detected sessions: 7\n",
"sub-KS094/sub-KS094_ses-3f859b5c-e73a-4044-b49e-34bb81e96715-processed-only_behavior.nwb\n",
"sub-KS043/sub-KS043_ses-e5c75b62-6871-4135-b3d0-f6464c2d90c0-processed-only_behavior.nwb\n",
"sub-NYU-46/sub-NYU-46_ses-64e3fb86-928c-4079-865c-b364205b502e-processed-only_behavior.nwb\n",
"sub-ibl-witten-29/sub-ibl-witten-29_ses-821f1883-27f3-411d-afd3-fb8241bbc39a-processed-only_behavior.nwb\n",
"sub-CSH-ZAD-025/sub-CSH-ZAD-025_ses-ff48aa1d-ef30-4903-ac34-8c41b738c1b9-processed-only_behavior.nwb\n",
"sub-SWC-043/sub-SWC-043_ses-6fb1e12c-883b-46d1-a745-473cde3232c8-processed-only_behavior.nwb\n",
"sub-KS046/sub-KS046_ses-0ac8d013-b91e-4732-bc7b-a1164ff3e445-processed-only_behavior.nwb\n"
]
}
],
"source": [
"brain_acronym = 'SSs5'\n",
"# query sessions endpoint\n",
"_, insertions = one.search_insertions(atlas_acronym=brain_acronym, details=True)\n",
"sessions = list(set([ins['session'] for ins in insertions]))\n",
"\n",
"# print the name of assets that contain the desired brain regions\n",
"print(f'No. of detected sessions: {len(sessions)}')\n",
"for _, asset in df_assets.loc[sessions].iterrows():\n",
" if 'only_behavior' in asset.path:\n",
" print(asset.path)\n"
]
}
],
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