{
"cells": [
{
"cell_type": "markdown",
"id": "5851560d",
"metadata": {
"papermill": {
"duration": 0.022012,
"end_time": "2023-11-30T05:22:40.626089",
"exception": false,
"start_time": "2023-11-30T05:22:40.604077",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Identifying experiments and sessions of interest using the data manifest"
]
},
{
"cell_type": "markdown",
"id": "34c30ddc",
"metadata": {
"papermill": {
"duration": 0.020149,
"end_time": "2023-11-30T05:22:40.666684",
"exception": false,
"start_time": "2023-11-30T05:22:40.646535",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"This Jupyter notebook illustrates what data is available as part of the Visual Behavior - 2P dataset , and helps you to understand the experimental design and dimensions of the dataset. The notebook will demonstrate how to identify experiments and sessions that you may be interested in analyzing using the data manifests provided by the `VisualBehaviorOphysProjectCache`, and exploring the metadata columns that describe the experimental conditions including transgenic lines, targeted areas, imaging depths, microscopes that were used, session types, and dataset variants. \n",
"\n",
"We will first install allensdk into your environment by running the appropriate commands below. "
]
},
{
"cell_type": "markdown",
"id": "f5cf03c7",
"metadata": {
"papermill": {
"duration": 0.020091,
"end_time": "2023-11-30T05:22:40.707074",
"exception": false,
"start_time": "2023-11-30T05:22:40.686983",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Install AllenSDK into your local environment"
]
},
{
"cell_type": "markdown",
"id": "7910fadd",
"metadata": {
"papermill": {
"duration": 0.02018,
"end_time": "2023-11-30T05:22:40.747533",
"exception": false,
"start_time": "2023-11-30T05:22:40.727353",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"You can install AllenSDK locally with:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1823b226",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:40.789756Z",
"iopub.status.busy": "2023-11-30T05:22:40.789206Z",
"iopub.status.idle": "2023-11-30T05:22:42.816796Z",
"shell.execute_reply": "2023-11-30T05:22:42.816121Z"
},
"papermill": {
"duration": 2.05088,
"end_time": "2023-11-30T05:22:42.818658",
"exception": false,
"start_time": "2023-11-30T05:22:40.767778",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: allensdk in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (2.16.2)\r\n",
"Requirement already satisfied: psycopg2-binary in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.9.9)\r\n",
"Requirement already satisfied: hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.* in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.11.0)\r\n",
"Requirement already satisfied: h5py in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.10.0)\r\n",
"Requirement already satisfied: matplotlib in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.7.4)\r\n",
"Requirement already satisfied: numpy<1.24 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.23.5)\r\n",
"Requirement already satisfied: pandas==1.5.3 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.5.3)\r\n",
"Requirement already satisfied: jinja2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.1.2)\r\n",
"Requirement already satisfied: scipy<1.11 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.10.1)\r\n",
"Requirement already satisfied: six in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.16.0)\r\n",
"Requirement already satisfied: pynrrd in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.0.0)\r\n",
"Requirement already satisfied: future in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.18.3)\r\n",
"Requirement already satisfied: requests in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.31.0)\r\n",
"Requirement already satisfied: requests-toolbelt in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.0.0)\r\n",
"Requirement already satisfied: simplejson in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.19.2)\r\n",
"Requirement already satisfied: scikit-image in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.21.0)\r\n",
"Requirement already satisfied: scikit-build in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.17.6)\r\n",
"Requirement already satisfied: statsmodels in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.14.0)\r\n",
"Requirement already satisfied: simpleitk in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.3.1)\r\n",
"Requirement already satisfied: argschema in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.0.4)\r\n",
"Requirement already satisfied: glymur in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.12.9.post1)\r\n",
"Requirement already satisfied: xarray<2023.2.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2023.1.0)\r\n",
"Requirement already satisfied: pynwb in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.5.0)\r\n",
"Requirement already satisfied: tables in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.8.0)\r\n",
"Requirement already satisfied: seaborn in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.13.0)\r\n",
"Requirement already satisfied: aiohttp in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.9.1)\r\n",
"Requirement already satisfied: nest-asyncio in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.5.8)\r\n",
"Requirement already satisfied: tqdm in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (4.66.1)\r\n",
"Requirement already satisfied: ndx-events in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.2.0)\r\n",
"Requirement already satisfied: boto3 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.33.4)\r\n",
"Requirement already satisfied: semver in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.0.2)\r\n",
"Requirement already satisfied: cachetools in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (5.3.2)\r\n",
"Requirement already satisfied: sqlalchemy in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.0.23)\r\n",
"Requirement already satisfied: python-dateutil in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.8.2)\r\n",
"Requirement already satisfied: pytz>=2020.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from pandas==1.5.3->allensdk) (2023.3.post1)\r\n",
"Requirement already satisfied: importlib-resources in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (6.1.1)\r\n",
"Requirement already satisfied: jsonschema>=2.6.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (4.20.0)\r\n",
"Requirement already satisfied: ruamel-yaml>=0.16 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.18.5)\r\n",
"Requirement already satisfied: packaging>=21.3 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from xarray<2023.2.0->allensdk) (23.2)\r\n",
"Requirement already satisfied: attrs>=17.3.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (23.1.0)\r\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (6.0.4)\r\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (1.9.3)\r\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (1.4.0)\r\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (1.3.1)\r\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (4.0.3)\r\n",
"Requirement already satisfied: marshmallow<4.0,>=3.0.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from argschema->allensdk) (3.20.1)\r\n",
"Requirement already satisfied: pyyaml in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from argschema->allensdk) (6.0.1)\r\n",
"Requirement already satisfied: botocore<1.34.0,>=1.33.4 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from boto3->allensdk) (1.33.4)\r\n",
"Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from boto3->allensdk) (1.0.1)\r\n",
"Requirement already satisfied: s3transfer<0.9.0,>=0.8.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from boto3->allensdk) (0.8.2)\r\n",
"Requirement already satisfied: lxml in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from glymur->allensdk) (4.9.3)\r\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jinja2->allensdk) (2.1.3)\r\n",
"Requirement already satisfied: contourpy>=1.0.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (1.1.1)\r\n",
"Requirement already satisfied: cycler>=0.10 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (0.12.1)\r\n",
"Requirement already satisfied: fonttools>=4.22.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (4.45.1)\r\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (1.4.5)\r\n",
"Requirement already satisfied: pillow>=6.2.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (10.1.0)\r\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (3.1.1)\r\n",
"Requirement already satisfied: setuptools in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from pynwb->allensdk) (56.0.0)\r\n",
"Requirement already satisfied: nptyping in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from pynrrd->allensdk) (2.5.0)\r\n",
"Requirement already satisfied: typing-extensions in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from pynrrd->allensdk) (4.8.0)\r\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from requests->allensdk) (3.3.2)\r\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from requests->allensdk) (3.6)\r\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from requests->allensdk) (1.26.18)\r\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from requests->allensdk) (2023.11.17)\r\n",
"Requirement already satisfied: distro in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-build->allensdk) (1.8.0)\r\n",
"Requirement already satisfied: tomli in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-build->allensdk) (2.0.1)\r\n",
"Requirement already satisfied: wheel>=0.32.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-build->allensdk) (0.42.0)\r\n",
"Requirement already satisfied: networkx>=2.8 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (3.1)\r\n",
"Requirement already satisfied: imageio>=2.27 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (2.33.0)\r\n",
"Requirement already satisfied: tifffile>=2022.8.12 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (2023.7.10)\r\n",
"Requirement already satisfied: PyWavelets>=1.1.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (1.4.1)\r\n",
"Requirement already satisfied: lazy_loader>=0.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (0.3)\r\n",
"Requirement already satisfied: greenlet!=0.4.17 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from sqlalchemy->allensdk) (3.0.1)\r\n",
"Requirement already satisfied: patsy>=0.5.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from statsmodels->allensdk) (0.5.3)\r\n",
"Requirement already satisfied: cython>=0.29.21 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from tables->allensdk) (3.0.6)\r\n",
"Requirement already satisfied: numexpr>=2.6.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from tables->allensdk) (2.8.6)\r\n",
"Requirement already satisfied: blosc2~=2.0.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from tables->allensdk) (2.0.0)\r\n",
"Requirement already satisfied: py-cpuinfo in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from tables->allensdk) (9.0.0)\r\n",
"Requirement already satisfied: msgpack in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from blosc2~=2.0.0->tables->allensdk) (1.0.7)\r\n",
"Requirement already satisfied: zipp>=3.1.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from importlib-resources->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (3.17.0)\r\n",
"Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (2023.11.1)\r\n",
"Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (1.3.10)\r\n",
"Requirement already satisfied: referencing>=0.28.4 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.31.1)\r\n",
"Requirement already satisfied: rpds-py>=0.7.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.13.2)\r\n",
"Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from ruamel-yaml>=0.16->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.2.8)\r\n",
"\r\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\r\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
]
}
],
"source": [
"!pip install allensdk"
]
},
{
"cell_type": "markdown",
"id": "558d41de",
"metadata": {
"papermill": {
"duration": 0.020836,
"end_time": "2023-11-30T05:22:42.861386",
"exception": false,
"start_time": "2023-11-30T05:22:42.840550",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Install AllenSDK into your notebook environment (good for Google Colab)"
]
},
{
"cell_type": "markdown",
"id": "a5394404",
"metadata": {
"papermill": {
"duration": 0.021113,
"end_time": "2023-11-30T05:22:42.903599",
"exception": false,
"start_time": "2023-11-30T05:22:42.882486",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"You can install AllenSDK into your notebook environment by executing the cell below.\n",
"\n",
"If using Google Colab, click on the RESTART RUNTIME button that appears at the end of the output when this cell is complete,. Note that running this cell will produce a long list of outputs and some error messages. Clicking RESTART RUNTIME at the end will resolve these issues.\n",
"You can minimize the cell after you are done to hide the output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fdc24a64",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:42.947330Z",
"iopub.status.busy": "2023-11-30T05:22:42.946843Z",
"iopub.status.idle": "2023-11-30T05:22:47.791360Z",
"shell.execute_reply": "2023-11-30T05:22:47.790633Z"
},
"papermill": {
"duration": 4.86834,
"end_time": "2023-11-30T05:22:47.793249",
"exception": false,
"start_time": "2023-11-30T05:22:42.924909",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pip in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (23.0.1)\r\n",
"Collecting pip\r\n",
" Downloading pip-23.3.1-py3-none-any.whl.metadata (3.5 kB)\r\n",
"Downloading pip-23.3.1-py3-none-any.whl (2.1 MB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m42.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hInstalling collected packages: pip\r\n",
" Attempting uninstall: pip\r\n",
" Found existing installation: pip 23.0.1\r\n",
" Uninstalling pip-23.0.1:\r\n",
" Successfully uninstalled pip-23.0.1\r\n",
"Successfully installed pip-23.3.1\r\n",
"Requirement already satisfied: allensdk in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (2.16.2)\r\n",
"Requirement already satisfied: psycopg2-binary in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.9.9)\r\n",
"Requirement already satisfied: hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.* in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.11.0)\r\n",
"Requirement already satisfied: h5py in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.10.0)\r\n",
"Requirement already satisfied: matplotlib in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.7.4)\r\n",
"Requirement already satisfied: numpy<1.24 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.23.5)\r\n",
"Requirement already satisfied: pandas==1.5.3 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.5.3)\r\n",
"Requirement already satisfied: jinja2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.1.2)\r\n",
"Requirement already satisfied: scipy<1.11 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.10.1)\r\n",
"Requirement already satisfied: six in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.16.0)\r\n",
"Requirement already satisfied: pynrrd in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.0.0)\r\n",
"Requirement already satisfied: future in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.18.3)\r\n",
"Requirement already satisfied: requests in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.31.0)\r\n",
"Requirement already satisfied: requests-toolbelt in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.0.0)\r\n",
"Requirement already satisfied: simplejson in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.19.2)\r\n",
"Requirement already satisfied: scikit-image in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.21.0)\r\n",
"Requirement already satisfied: scikit-build in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.17.6)\r\n",
"Requirement already satisfied: statsmodels in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.14.0)\r\n",
"Requirement already satisfied: simpleitk in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.3.1)\r\n",
"Requirement already satisfied: argschema in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.0.4)\r\n",
"Requirement already satisfied: glymur in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.12.9.post1)\r\n",
"Requirement already satisfied: xarray<2023.2.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2023.1.0)\r\n",
"Requirement already satisfied: pynwb in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.5.0)\r\n",
"Requirement already satisfied: tables in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.8.0)\r\n",
"Requirement already satisfied: seaborn in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.13.0)\r\n",
"Requirement already satisfied: aiohttp in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.9.1)\r\n",
"Requirement already satisfied: nest-asyncio in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.5.8)\r\n",
"Requirement already satisfied: tqdm in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (4.66.1)\r\n",
"Requirement already satisfied: ndx-events in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (0.2.0)\r\n",
"Requirement already satisfied: boto3 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (1.33.4)\r\n",
"Requirement already satisfied: semver in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (3.0.2)\r\n",
"Requirement already satisfied: cachetools in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (5.3.2)\r\n",
"Requirement already satisfied: sqlalchemy in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.0.23)\r\n",
"Requirement already satisfied: python-dateutil in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from allensdk) (2.8.2)\r\n",
"Requirement already satisfied: pytz>=2020.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from pandas==1.5.3->allensdk) (2023.3.post1)\r\n",
"Requirement already satisfied: importlib-resources in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (6.1.1)\r\n",
"Requirement already satisfied: jsonschema>=2.6.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (4.20.0)\r\n",
"Requirement already satisfied: ruamel-yaml>=0.16 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.18.5)\r\n",
"Requirement already satisfied: packaging>=21.3 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from xarray<2023.2.0->allensdk) (23.2)\r\n",
"Requirement already satisfied: attrs>=17.3.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (23.1.0)\r\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (6.0.4)\r\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (1.9.3)\r\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (1.4.0)\r\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (1.3.1)\r\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from aiohttp->allensdk) (4.0.3)\r\n",
"Requirement already satisfied: marshmallow<4.0,>=3.0.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from argschema->allensdk) (3.20.1)\r\n",
"Requirement already satisfied: pyyaml in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from argschema->allensdk) (6.0.1)\r\n",
"Requirement already satisfied: botocore<1.34.0,>=1.33.4 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from boto3->allensdk) (1.33.4)\r\n",
"Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from boto3->allensdk) (1.0.1)\r\n",
"Requirement already satisfied: s3transfer<0.9.0,>=0.8.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from boto3->allensdk) (0.8.2)\r\n",
"Requirement already satisfied: lxml in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from glymur->allensdk) (4.9.3)\r\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jinja2->allensdk) (2.1.3)\r\n",
"Requirement already satisfied: contourpy>=1.0.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (1.1.1)\r\n",
"Requirement already satisfied: cycler>=0.10 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (0.12.1)\r\n",
"Requirement already satisfied: fonttools>=4.22.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (4.45.1)\r\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (1.4.5)\r\n",
"Requirement already satisfied: pillow>=6.2.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (10.1.0)\r\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from matplotlib->allensdk) (3.1.1)\r\n",
"Requirement already satisfied: setuptools in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from pynwb->allensdk) (56.0.0)\r\n",
"Requirement already satisfied: nptyping in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from pynrrd->allensdk) (2.5.0)\r\n",
"Requirement already satisfied: typing-extensions in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from pynrrd->allensdk) (4.8.0)\r\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from requests->allensdk) (3.3.2)\r\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from requests->allensdk) (3.6)\r\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from requests->allensdk) (1.26.18)\r\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from requests->allensdk) (2023.11.17)\r\n",
"Requirement already satisfied: distro in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-build->allensdk) (1.8.0)\r\n",
"Requirement already satisfied: tomli in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-build->allensdk) (2.0.1)\r\n",
"Requirement already satisfied: wheel>=0.32.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-build->allensdk) (0.42.0)\r\n",
"Requirement already satisfied: networkx>=2.8 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (3.1)\r\n",
"Requirement already satisfied: imageio>=2.27 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (2.33.0)\r\n",
"Requirement already satisfied: tifffile>=2022.8.12 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (2023.7.10)\r\n",
"Requirement already satisfied: PyWavelets>=1.1.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (1.4.1)\r\n",
"Requirement already satisfied: lazy_loader>=0.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from scikit-image->allensdk) (0.3)\r\n",
"Requirement already satisfied: greenlet!=0.4.17 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from sqlalchemy->allensdk) (3.0.1)\r\n",
"Requirement already satisfied: patsy>=0.5.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from statsmodels->allensdk) (0.5.3)\r\n",
"Requirement already satisfied: cython>=0.29.21 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from tables->allensdk) (3.0.6)\r\n",
"Requirement already satisfied: numexpr>=2.6.2 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from tables->allensdk) (2.8.6)\r\n",
"Requirement already satisfied: blosc2~=2.0.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from tables->allensdk) (2.0.0)\r\n",
"Requirement already satisfied: py-cpuinfo in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from tables->allensdk) (9.0.0)\r\n",
"Requirement already satisfied: msgpack in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from blosc2~=2.0.0->tables->allensdk) (1.0.7)\r\n",
"Requirement already satisfied: zipp>=3.1.0 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from importlib-resources->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (3.17.0)\r\n",
"Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (2023.11.1)\r\n",
"Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (1.3.10)\r\n",
"Requirement already satisfied: referencing>=0.28.4 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.31.1)\r\n",
"Requirement already satisfied: rpds-py>=0.7.1 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from jsonschema>=2.6.0->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.13.2)\r\n",
"Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages (from ruamel-yaml>=0.16->hdmf!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*->allensdk) (0.2.8)\r\n"
]
}
],
"source": [
"!pip install --upgrade pip\n",
"!pip install allensdk"
]
},
{
"cell_type": "markdown",
"id": "d26db20b",
"metadata": {
"papermill": {
"duration": 0.022055,
"end_time": "2023-11-30T05:22:47.838616",
"exception": false,
"start_time": "2023-11-30T05:22:47.816561",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Import necessary packages"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "61cecf93",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:47.884264Z",
"iopub.status.busy": "2023-11-30T05:22:47.884042Z",
"iopub.status.idle": "2023-11-30T05:22:52.436910Z",
"shell.execute_reply": "2023-11-30T05:22:52.436315Z"
},
"papermill": {
"duration": 4.577764,
"end_time": "2023-11-30T05:22:52.438614",
"exception": false,
"start_time": "2023-11-30T05:22:47.860850",
"status": "completed"
},
"pycharm": {
"is_executing": true,
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"from allensdk.brain_observatory.behavior.behavior_project_cache import VisualBehaviorOphysProjectCache"
]
},
{
"cell_type": "markdown",
"id": "4bb898d4",
"metadata": {
"papermill": {
"duration": 0.021968,
"end_time": "2023-11-30T05:22:52.483507",
"exception": false,
"start_time": "2023-11-30T05:22:52.461539",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## First, load the project cache - your access point for all tables and data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "003ddb42",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:52.529117Z",
"iopub.status.busy": "2023-11-30T05:22:52.528378Z",
"iopub.status.idle": "2023-11-30T05:22:52.531712Z",
"shell.execute_reply": "2023-11-30T05:22:52.531116Z"
},
"papermill": {
"duration": 0.027464,
"end_time": "2023-11-30T05:22:52.533050",
"exception": false,
"start_time": "2023-11-30T05:22:52.505586",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"# Update this to a valid directory in your filesystem\n",
"output_dir = r\"\\Data\\visual_behavior_ophys_cache_dir\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f0b38233",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:52.630045Z",
"iopub.status.busy": "2023-11-30T05:22:52.629597Z",
"iopub.status.idle": "2023-11-30T05:22:55.646054Z",
"shell.execute_reply": "2023-11-30T05:22:55.645258Z"
},
"papermill": {
"duration": 3.040632,
"end_time": "2023-11-30T05:22:55.647466",
"exception": false,
"start_time": "2023-11-30T05:22:52.606834",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/runner/work/AllenSDK/AllenSDK/allensdk/api/cloud_cache/cloud_cache.py:439: MissingLocalManifestWarning: This cache directory appears to contain data files, but it has no record of what those files are. You might want to consider running\n",
"\n",
"VisualBehaviorOphysProjectCache.construct_local_manifest()\n",
"\n",
"to avoid needlessly downloading duplicates of data files that did not change between data releases. NOTE: running this method will require hashing every data file you have currently downloaded and could be very time consuming.\n",
"\n",
"To avoid this warning in the future, make sure that\n",
"\n",
"/tmp/tmpeiy1ys_0/_downloaded_data.json\n",
"\n",
"is not deleted between instantiations of this cache\n",
" warnings.warn(msg, MissingLocalManifestWarning)\n",
"ophys_session_table.csv: 100%|██████████| 247k/247k [00:00<00:00, 1.63MMB/s] \n",
"behavior_session_table.csv: 100%|██████████| 1.59M/1.59M [00:00<00:00, 7.10MMB/s]\n",
"ophys_experiment_table.csv: 100%|██████████| 657k/657k [00:00<00:00, 4.52MMB/s] \n",
"ophys_cells_table.csv: 100%|██████████| 4.28M/4.28M [00:00<00:00, 11.8MMB/s]\n",
"/home/runner/work/AllenSDK/AllenSDK/allensdk/brain_observatory/behavior/behavior_project_cache/behavior_project_cache.py:135: UpdatedStimulusPresentationTableWarning: \n",
"\tAs of AllenSDK version 2.16.0, the latest Visual Behavior Ophys data has been significantly updated from previous releases. Specifically the user will need to update all processing of the stimulus_presentations tables. These tables now include multiple stimulus types delineated by the columns `stimulus_block` and `stimulus_block_name`.\n",
"\n",
"The data that was available in previous releases are stored in the block name containing 'change_detection' and can be accessed in the pandas table by using: \n",
"\t`stimulus_presentations[stimulus_presentations.stimulus_block_name.str.contains('change_detection')]`\n",
" warnings.warn(\n"
]
}
],
"source": [
"cache = VisualBehaviorOphysProjectCache.from_s3_cache(cache_dir=output_dir)"
]
},
{
"cell_type": "markdown",
"id": "358b9ca2",
"metadata": {
"papermill": {
"duration": 0.023209,
"end_time": "2023-11-30T05:22:55.694700",
"exception": false,
"start_time": "2023-11-30T05:22:55.671491",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The data manifest is comprised of three types of tables: \n",
"\n",
"1. `behavior_session_table` \n",
"2. `ophys_session_table` \n",
"3. `ophys_experiment_table` \n",
"\n",
"The `behavior_session_table` contains metadata for every behavior session in the dataset. Some behavior sessions have 2-photon data associated with them, while others took place during training in the behavior facility. The different training stages that mice are progressed through are described by the `session_type`. \n",
"\n",
"The `ophys_session_table` contains metadata for every 2-photon imaging (aka optical physiology, or ophys) session in the dataset, associated with a unique `ophys_session_id`. An ophys session is one continuous recording session under the microscope, and can contain different numbers of imaging planes (aka experiments) depending on which microscope was used. For Scientifica sessions, there will only be one experiment (aka imaging plane) per session. For Multiscope sessions, there can be up to eight imaging planes per session. Quality Control (QC) is performed on each individual imaging plane within a session, so each can fail QC independent of the others. This means that a Multiscope session may not have exactly eight experiments (imaging planes). \n",
"\n",
"The `ophys_experiment_table` contains metadata for every ophys experiment in the dataset, which corresponds to a single imaging plane recorded in a single session, and associated with a unique `ophys_experiment_id`. A key part of our experimental design is targeting a given population of neurons, contained in one imaging plane, across multiple `session_types` (further described below) to examine the impact of varying sensory and behavioral conditions on single cell responses. The collection of all imaging sessions for a given imaging plane is referred to as an ophys container , associated with a unique `ophys_container_id`. Each ophys container may contain different numbers of sessions, depending on which experiments passed QC, and how many retakes occured (when a given `session_type` fails QC on the first try, an attempt is made to re-acquire the session_type on a different recording day - this is called a retake, also described further below). "
]
},
{
"cell_type": "markdown",
"id": "745f2f80",
"metadata": {
"papermill": {
"duration": 0.022968,
"end_time": "2023-11-30T05:22:55.740774",
"exception": false,
"start_time": "2023-11-30T05:22:55.717806",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### To understand the difference between an `ophys_experiment`, an `ophys_session`, and an `ophys_container`, the following schematic can be helpful"
]
},
{
"cell_type": "markdown",
"id": "5cba01b6",
"metadata": {
"papermill": {
"duration": 0.022791,
"end_time": "2023-11-30T05:22:55.786525",
"exception": false,
"start_time": "2023-11-30T05:22:55.763734",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"
\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "76cd72dd",
"metadata": {
"papermill": {
"duration": 0.023068,
"end_time": "2023-11-30T05:22:55.832927",
"exception": false,
"start_time": "2023-11-30T05:22:55.809859",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Note that this represents a multi-plane imaging dataset. For single-plane imaging, there will only be one plane, corresponding to one row of this diagram."
]
},
{
"cell_type": "markdown",
"id": "ff3bee62",
"metadata": {
"papermill": {
"duration": 0.023038,
"end_time": "2023-11-30T05:22:55.879042",
"exception": false,
"start_time": "2023-11-30T05:22:55.856004",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Lets go through each table and examine what metadata columns are available"
]
},
{
"cell_type": "markdown",
"id": "4938ec29",
"metadata": {
"papermill": {
"duration": 0.022756,
"end_time": "2023-11-30T05:22:55.924959",
"exception": false,
"start_time": "2023-11-30T05:22:55.902203",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Behavior Sessions Table"
]
},
{
"cell_type": "markdown",
"id": "13b629b0",
"metadata": {
"papermill": {
"duration": 0.022783,
"end_time": "2023-11-30T05:22:55.971139",
"exception": false,
"start_time": "2023-11-30T05:22:55.948356",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"In this dataset, mice are trained on a visual change detection task. This task involves a continuous stream of stimuli, and mice learn to lick in response to a change in the stimulus identity to earn a water reward. There are different stages of training in this task, described below. The metadata for each behavior session in the dataset can be found in the `behavior_sessions_table` and can be used to identify behavior sessions you may want to analyze. "
]
},
{
"cell_type": "markdown",
"id": "0750bf10",
"metadata": {
"papermill": {
"duration": 0.022846,
"end_time": "2023-11-30T05:22:56.016897",
"exception": false,
"start_time": "2023-11-30T05:22:55.994051",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Load the `behavior_sessions_table` from the cache"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6de4c43a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:56.064377Z",
"iopub.status.busy": "2023-11-30T05:22:56.063924Z",
"iopub.status.idle": "2023-11-30T05:22:56.084073Z",
"shell.execute_reply": "2023-11-30T05:22:56.083423Z"
},
"papermill": {
"duration": 0.045517,
"end_time": "2023-11-30T05:22:56.085461",
"exception": false,
"start_time": "2023-11-30T05:22:56.039944",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of behavior sessions: 4782\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" ophys_session_id \n",
" ophys_container_id \n",
" mouse_id \n",
" indicator \n",
" full_genotype \n",
" driver_line \n",
" cre_line \n",
" reporter_line \n",
" sex \n",
" age_in_days \n",
" ... \n",
" num_targeted_structures \n",
" catch_trial_count \n",
" false_alarm_trial_count \n",
" miss_trial_count \n",
" correct_reject_trial_count \n",
" file_id \n",
" hit_trial_count \n",
" engaged_trial_count \n",
" go_trial_count \n",
" trial_count \n",
" \n",
" \n",
" behavior_session_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 870987812 \n",
" <NA> \n",
" NaN \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 80 \n",
" ... \n",
" <NA> \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 0 \n",
" 1941 \n",
" 0 \n",
" 70 \n",
" 0 \n",
" 121 \n",
" \n",
" \n",
" 872953842 \n",
" <NA> \n",
" NaN \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 84 \n",
" ... \n",
" <NA> \n",
" 49 \n",
" 4 \n",
" 275 \n",
" 45 \n",
" 1942 \n",
" 18 \n",
" 27 \n",
" 293 \n",
" 564 \n",
" \n",
" \n",
" 873774125 \n",
" <NA> \n",
" NaN \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 85 \n",
" ... \n",
" <NA> \n",
" 41 \n",
" 3 \n",
" 300 \n",
" 38 \n",
" 1943 \n",
" 14 \n",
" 0 \n",
" 314 \n",
" 548 \n",
" \n",
" \n",
" 874790261 \n",
" <NA> \n",
" NaN \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 86 \n",
" ... \n",
" <NA> \n",
" 42 \n",
" 0 \n",
" 312 \n",
" 42 \n",
" 1944 \n",
" 14 \n",
" 0 \n",
" 326 \n",
" 492 \n",
" \n",
" \n",
" 875207834 \n",
" <NA> \n",
" NaN \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 87 \n",
" ... \n",
" <NA> \n",
" 44 \n",
" 3 \n",
" 300 \n",
" 41 \n",
" 1945 \n",
" 22 \n",
" 5 \n",
" 322 \n",
" 504 \n",
" \n",
" \n",
"
\n",
"
5 rows × 34 columns
\n",
"
"
],
"text/plain": [
" ophys_session_id ophys_container_id mouse_id indicator \\\n",
"behavior_session_id \n",
"870987812 NaN 457841 GCaMP6f \n",
"872953842 NaN 457841 GCaMP6f \n",
"873774125 NaN 457841 GCaMP6f \n",
"874790261 NaN 457841 GCaMP6f \n",
"875207834 NaN 457841 GCaMP6f \n",
"\n",
" full_genotype \\\n",
"behavior_session_id \n",
"870987812 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"872953842 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"873774125 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"874790261 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"875207834 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"\n",
" driver_line cre_line reporter_line \\\n",
"behavior_session_id \n",
"870987812 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"872953842 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"873774125 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"874790261 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"875207834 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"\n",
" sex age_in_days ... num_targeted_structures \\\n",
"behavior_session_id ... \n",
"870987812 F 80 ... \n",
"872953842 F 84 ... \n",
"873774125 F 85 ... \n",
"874790261 F 86 ... \n",
"875207834 F 87 ... \n",
"\n",
" catch_trial_count false_alarm_trial_count \\\n",
"behavior_session_id \n",
"870987812 0 0 \n",
"872953842 49 4 \n",
"873774125 41 3 \n",
"874790261 42 0 \n",
"875207834 44 3 \n",
"\n",
" miss_trial_count correct_reject_trial_count file_id \\\n",
"behavior_session_id \n",
"870987812 0 0 1941 \n",
"872953842 275 45 1942 \n",
"873774125 300 38 1943 \n",
"874790261 312 42 1944 \n",
"875207834 300 41 1945 \n",
"\n",
" hit_trial_count engaged_trial_count go_trial_count \\\n",
"behavior_session_id \n",
"870987812 0 70 0 \n",
"872953842 18 27 293 \n",
"873774125 14 0 314 \n",
"874790261 14 0 326 \n",
"875207834 22 5 322 \n",
"\n",
" trial_count \n",
"behavior_session_id \n",
"870987812 121 \n",
"872953842 564 \n",
"873774125 548 \n",
"874790261 492 \n",
"875207834 504 \n",
"\n",
"[5 rows x 34 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"behavior_sessions = cache.get_behavior_session_table()\n",
"\n",
"print(f\"Total number of behavior sessions: {len(behavior_sessions)}\")\n",
"\n",
"behavior_sessions.head()"
]
},
{
"cell_type": "markdown",
"id": "23b2e84c",
"metadata": {
"papermill": {
"duration": 0.023282,
"end_time": "2023-11-30T05:22:56.132559",
"exception": false,
"start_time": "2023-11-30T05:22:56.109277",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### What columns does the behavior_session table have and what values can they take?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4ccde8b1",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:56.180507Z",
"iopub.status.busy": "2023-11-30T05:22:56.180119Z",
"iopub.status.idle": "2023-11-30T05:22:56.184855Z",
"shell.execute_reply": "2023-11-30T05:22:56.184293Z"
},
"papermill": {
"duration": 0.03031,
"end_time": "2023-11-30T05:22:56.186233",
"exception": false,
"start_time": "2023-11-30T05:22:56.155923",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ophys_session_id', 'ophys_container_id', 'mouse_id', 'indicator',\n",
" 'full_genotype', 'driver_line', 'cre_line', 'reporter_line', 'sex',\n",
" 'age_in_days', 'imaging_plane_group_count', 'project_code',\n",
" 'session_type', 'session_number', 'image_set', 'behavior_type',\n",
" 'experience_level', 'prior_exposures_to_session_type',\n",
" 'prior_exposures_to_image_set', 'prior_exposures_to_omissions',\n",
" 'date_of_acquisition', 'equipment_name', 'num_depths_per_area',\n",
" 'ophys_experiment_id', 'num_targeted_structures', 'catch_trial_count',\n",
" 'false_alarm_trial_count', 'miss_trial_count',\n",
" 'correct_reject_trial_count', 'file_id', 'hit_trial_count',\n",
" 'engaged_trial_count', 'go_trial_count', 'trial_count'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"behavior_sessions.columns"
]
},
{
"cell_type": "markdown",
"id": "38a115a0",
"metadata": {
"papermill": {
"duration": 0.023454,
"end_time": "2023-11-30T05:22:56.233154",
"exception": false,
"start_time": "2023-11-30T05:22:56.209700",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### behavior sessions can take place on different experimental systems"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b20a7aae",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:56.280751Z",
"iopub.status.busy": "2023-11-30T05:22:56.280501Z",
"iopub.status.idle": "2023-11-30T05:22:56.284500Z",
"shell.execute_reply": "2023-11-30T05:22:56.283809Z"
},
"papermill": {
"duration": 0.029427,
"end_time": "2023-11-30T05:22:56.285850",
"exception": false,
"start_time": "2023-11-30T05:22:56.256423",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"behavior data could be recorded on these experimental systems:\n",
"\n",
"['BEH.B' 'BEH.B-Box1' 'BEH.B-Box2' 'BEH.B-Box3' 'BEH.B-Box4' 'BEH.B-Box5'\n",
" 'BEH.B-Box6' 'BEH.D' 'BEH.D-Box1' 'BEH.D-Box2' 'BEH.D-Box3' 'BEH.D-Box4'\n",
" 'BEH.D-Box5' 'BEH.D-Box6' 'BEH.F' 'BEH.F-Box1' 'BEH.F-Box2' 'BEH.F-Box3'\n",
" 'BEH.F-Box4' 'BEH.F-Box5' 'BEH.F-Box6' 'BEH.G' 'BEH.G-Box1' 'BEH.G-Box2'\n",
" 'BEH.G-Box3' 'BEH.G-Box4' 'BEH.G-Box5' 'BEH.G-Box6' 'CAM2P.3' 'CAM2P.4'\n",
" 'CAM2P.5' 'MESO.1']\n"
]
}
],
"source": [
"print('behavior data could be recorded on these experimental systems:\\n')\n",
"print(np.sort(behavior_sessions.equipment_name.unique()))"
]
},
{
"cell_type": "markdown",
"id": "f1bc0503",
"metadata": {
"papermill": {
"duration": 0.023517,
"end_time": "2023-11-30T05:22:56.332967",
"exception": false,
"start_time": "2023-11-30T05:22:56.309450",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"`equipment_name` values starting with 'BEH' indicate behavioral training in the behavior facility, while values starting with 'CAM2P' or 'MESO' indicate behavior sessions that took place under a 2-photon microscope - either a Scientifica single plane imaging system ('CAMP2P.4', 'CAM2P.4', or 'CAM2P.5') or a modified Mesoscope system, also called Multiscope, for multi-plane imaging ('MESO.1')."
]
},
{
"cell_type": "markdown",
"id": "ff0ba98e",
"metadata": {
"papermill": {
"duration": 0.023411,
"end_time": "2023-11-30T05:22:56.379828",
"exception": false,
"start_time": "2023-11-30T05:22:56.356417",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Mouse specific metadata"
]
},
{
"cell_type": "markdown",
"id": "a54fc9b0",
"metadata": {
"papermill": {
"duration": 0.023309,
"end_time": "2023-11-30T05:22:56.426717",
"exception": false,
"start_time": "2023-11-30T05:22:56.403408",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The `mouse_id` is a 6-digit unique identifier for each experimental animal in the dataset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5f0573a7",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:56.474787Z",
"iopub.status.busy": "2023-11-30T05:22:56.474577Z",
"iopub.status.idle": "2023-11-30T05:22:56.478491Z",
"shell.execute_reply": "2023-11-30T05:22:56.477773Z"
},
"papermill": {
"duration": 0.029429,
"end_time": "2023-11-30T05:22:56.479861",
"exception": false,
"start_time": "2023-11-30T05:22:56.450432",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"there are 107 mice in the dataset\n"
]
}
],
"source": [
"print('there are ', len(behavior_sessions.mouse_id.unique()), 'mice in the dataset')"
]
},
{
"cell_type": "markdown",
"id": "03c92c4d",
"metadata": {
"papermill": {
"duration": 0.023518,
"end_time": "2023-11-30T05:22:56.527060",
"exception": false,
"start_time": "2023-11-30T05:22:56.503542",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### The transgenic line determines which neurons are labeled in a given mouse, and what they are labeled with"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7818dee1",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:56.575372Z",
"iopub.status.busy": "2023-11-30T05:22:56.574854Z",
"iopub.status.idle": "2023-11-30T05:22:56.578796Z",
"shell.execute_reply": "2023-11-30T05:22:56.578226Z"
},
"papermill": {
"duration": 0.029455,
"end_time": "2023-11-30T05:22:56.580070",
"exception": false,
"start_time": "2023-11-30T05:22:56.550615",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the different transgenic lines included in this dataset are:\n",
"\n",
"['Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/wt'\n",
" 'Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai94(TITL-GCaMP6s)/wt'\n",
" 'Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt'\n",
" 'Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt']\n"
]
}
],
"source": [
"print('the different transgenic lines included in this dataset are:\\n')\n",
"print(np.sort(behavior_sessions.full_genotype.unique()))"
]
},
{
"cell_type": "markdown",
"id": "80b02150",
"metadata": {
"papermill": {
"duration": 0.023431,
"end_time": "2023-11-30T05:22:56.627296",
"exception": false,
"start_time": "2023-11-30T05:22:56.603865",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"`full_genotype` refers to the full name of the transgenic mouse line, including all driver and reporter lines in the cross. `driver_line` and `reporter_line` have their own unique columns in the table. The first element of the `full_genotype` is the `cre_line` (which also has its own column in the table, and is a subset of `driver_line`). The `cre_line` determines which genetically identified neuron type will be labeled by the `reporter_line`. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "59782e41",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:56.675595Z",
"iopub.status.busy": "2023-11-30T05:22:56.675349Z",
"iopub.status.idle": "2023-11-30T05:22:56.679574Z",
"shell.execute_reply": "2023-11-30T05:22:56.678899Z"
},
"papermill": {
"duration": 0.029961,
"end_time": "2023-11-30T05:22:56.680925",
"exception": false,
"start_time": "2023-11-30T05:22:56.650964",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the different cre lines used in this dataset are:\n",
"\n",
"['Slc17a7-IRES2-Cre' 'Sst-IRES-Cre' 'Vip-IRES-Cre']\n"
]
}
],
"source": [
"print('the different cre lines used in this dataset are:\\n')\n",
"print(np.sort(behavior_sessions.cre_line.unique()))"
]
},
{
"cell_type": "markdown",
"id": "25086019",
"metadata": {
"papermill": {
"duration": 0.023473,
"end_time": "2023-11-30T05:22:56.728282",
"exception": false,
"start_time": "2023-11-30T05:22:56.704809",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "db6df690",
"metadata": {
"papermill": {
"duration": 0.023473,
"end_time": "2023-11-30T05:22:56.775361",
"exception": false,
"start_time": "2023-11-30T05:22:56.751888",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"In this dataset, we have 3 `cre_lines`, 'Slc17a7-IRES2-Cre', which labels excitatory neurons across all cortical layers, 'Sst-IRES-Cre' which labels somatostatin expressing inhibitory interneurons, and 'Vip-IRES-Cre', which labels vasoactive intestinal peptide expressing inhibitory interneurons. There are also 3 `reporter_lines`, 'Ai93(TITL-GCaMP6f)' which expresses the genetically encoded calcium indicator GCaMP6f (f is for 'fast', this reporter has fast offset kinetics, but is only moderately sensitive to calcium relative to other sensors) in cre labeled neurons, 'Ai94(TITL-GCaMP6s)' which expresses the indicator GCaMP6s (s is for 'slow', this reporter is very sensitive to calcium but has slow offset kinetics), and 'Ai148(TIT2L-GC6f-ICL-tTA2', which expresses GCaMP6f using a self-enhancing system to achieve higher expression than other reporter lines (which proved necessary to label inhibitory neurons specifically). The specific `indicator` expressed by each `reporter_line` also has its own column in the table."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e15eda2d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:56.823614Z",
"iopub.status.busy": "2023-11-30T05:22:56.823203Z",
"iopub.status.idle": "2023-11-30T05:22:56.827371Z",
"shell.execute_reply": "2023-11-30T05:22:56.826741Z"
},
"papermill": {
"duration": 0.029892,
"end_time": "2023-11-30T05:22:56.828742",
"exception": false,
"start_time": "2023-11-30T05:22:56.798850",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the different reporter lines used in this dataset are:\n",
"\n",
"['Ai148(TIT2L-GC6f-ICL-tTA2)' 'Ai93(TITL-GCaMP6f)' 'Ai94(TITL-GCaMP6s)']\n"
]
}
],
"source": [
"print('the different reporter lines used in this dataset are:\\n')\n",
"print(np.sort(behavior_sessions.reporter_line.unique()))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "60237c34",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:56.877426Z",
"iopub.status.busy": "2023-11-30T05:22:56.877038Z",
"iopub.status.idle": "2023-11-30T05:22:56.881061Z",
"shell.execute_reply": "2023-11-30T05:22:56.880490Z"
},
"papermill": {
"duration": 0.029677,
"end_time": "2023-11-30T05:22:56.882430",
"exception": false,
"start_time": "2023-11-30T05:22:56.852753",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the different indicators used in this dataset are:\n",
"\n",
"['GCaMP6f' 'GCaMP6s']\n"
]
}
],
"source": [
"print('the different indicators used in this dataset are:\\n')\n",
"print(np.sort(behavior_sessions.indicator.unique()))"
]
},
{
"cell_type": "markdown",
"id": "eb33911a",
"metadata": {
"papermill": {
"duration": 0.023506,
"end_time": "2023-11-30T05:22:56.929670",
"exception": false,
"start_time": "2023-11-30T05:22:56.906164",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"* For more information about transgenic lines, see characterization data here: https://observatory.brain-map.org/visualcoding/transgenic\n",
"* for more information on GCaMP6, see this paper: https://www.nature.com/articles/nature12354\n",
"* For more information on reporter lines, see these papers: https://doi.org/10.1016/j.neuron.2015.02.022, https://www.sciencedirect.com/science/article/pii/S0092867418308031"
]
},
{
"cell_type": "markdown",
"id": "031be223",
"metadata": {
"papermill": {
"duration": 0.023433,
"end_time": "2023-11-30T05:22:56.977176",
"exception": false,
"start_time": "2023-11-30T05:22:56.953743",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### how many mice per transgenic line?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8038b9de",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:57.025566Z",
"iopub.status.busy": "2023-11-30T05:22:57.025132Z",
"iopub.status.idle": "2023-11-30T05:22:57.041695Z",
"shell.execute_reply": "2023-11-30T05:22:57.041134Z"
},
"papermill": {
"duration": 0.042062,
"end_time": "2023-11-30T05:22:57.042961",
"exception": false,
"start_time": "2023-11-30T05:22:57.000899",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" mouse_id \n",
" \n",
" \n",
" full_genotype \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/wt \n",
" 47 \n",
" \n",
" \n",
" Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai94(TITL-GCaMP6s)/wt \n",
" 8 \n",
" \n",
" \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" 24 \n",
" \n",
" \n",
" Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" 28 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" mouse_id\n",
"full_genotype \n",
"Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-GC... 47\n",
"Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai94(TITL-GC... 8\n",
"Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt 24\n",
"Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt 28"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"behavior_sessions.groupby(['full_genotype', 'mouse_id']).count().reset_index().groupby('full_genotype').count()[['mouse_id']]"
]
},
{
"cell_type": "markdown",
"id": "ff70ba8f",
"metadata": {
"papermill": {
"duration": 0.023888,
"end_time": "2023-11-30T05:22:57.090841",
"exception": false,
"start_time": "2023-11-30T05:22:57.066953",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Other mouse specific metadata includes `sex` and `age_in_days`"
]
},
{
"cell_type": "markdown",
"id": "e5c3b7e4",
"metadata": {
"papermill": {
"duration": 0.023742,
"end_time": "2023-11-30T05:22:57.138429",
"exception": false,
"start_time": "2023-11-30T05:22:57.114687",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Session Type - a very important piece of information"
]
},
{
"cell_type": "markdown",
"id": "0e79b59f",
"metadata": {
"papermill": {
"duration": 0.023757,
"end_time": "2023-11-30T05:22:57.186166",
"exception": false,
"start_time": "2023-11-30T05:22:57.162409",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The `session_type` for each behavior session indicates the behavioral training stage or 2-photon imaging conditions for that particular session. This determines what stimuli were shown and what task parameters were used. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "7c9cdf28",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:57.235440Z",
"iopub.status.busy": "2023-11-30T05:22:57.234978Z",
"iopub.status.idle": "2023-11-30T05:22:57.240064Z",
"shell.execute_reply": "2023-11-30T05:22:57.239497Z"
},
"papermill": {
"duration": 0.031279,
"end_time": "2023-11-30T05:22:57.241388",
"exception": false,
"start_time": "2023-11-30T05:22:57.210109",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the session_types available in this dataset are:\n",
"\n",
"['OPHYS_0_images_A_habituation' 'OPHYS_0_images_B_habituation'\n",
" 'OPHYS_0_images_G_habituation' 'OPHYS_1_images_A' 'OPHYS_1_images_B'\n",
" 'OPHYS_1_images_G' 'OPHYS_2_images_A_passive' 'OPHYS_2_images_B_passive'\n",
" 'OPHYS_2_images_G_passive' 'OPHYS_3_images_A' 'OPHYS_3_images_B'\n",
" 'OPHYS_3_images_G' 'OPHYS_4_images_A' 'OPHYS_4_images_B'\n",
" 'OPHYS_4_images_H' 'OPHYS_5_images_A_passive' 'OPHYS_5_images_B_passive'\n",
" 'OPHYS_5_images_H_passive' 'OPHYS_6_images_A' 'OPHYS_6_images_B'\n",
" 'OPHYS_6_images_H' 'TRAINING_0_gratings_autorewards_15min'\n",
" 'TRAINING_1_gratings' 'TRAINING_2_gratings_flashed'\n",
" 'TRAINING_3_images_A_10uL_reward' 'TRAINING_3_images_B_10uL_reward'\n",
" 'TRAINING_3_images_G_10uL_reward' 'TRAINING_4_images_A_handoff_lapsed'\n",
" 'TRAINING_4_images_A_handoff_ready' 'TRAINING_4_images_A_training'\n",
" 'TRAINING_4_images_B_training' 'TRAINING_4_images_G_training'\n",
" 'TRAINING_5_images_A_epilogue' 'TRAINING_5_images_A_handoff_lapsed'\n",
" 'TRAINING_5_images_A_handoff_ready' 'TRAINING_5_images_B_epilogue'\n",
" 'TRAINING_5_images_B_handoff_lapsed' 'TRAINING_5_images_B_handoff_ready'\n",
" 'TRAINING_5_images_G_epilogue' 'TRAINING_5_images_G_handoff_lapsed'\n",
" 'TRAINING_5_images_G_handoff_ready']\n"
]
}
],
"source": [
"print('the session_types available in this dataset are:\\n')\n",
"print(np.sort(behavior_sessions.session_type[\n",
" ~behavior_sessions.session_type.isna()].unique()))"
]
},
{
"cell_type": "markdown",
"id": "484ff16b",
"metadata": {
"papermill": {
"duration": 0.023847,
"end_time": "2023-11-30T05:22:57.289093",
"exception": false,
"start_time": "2023-11-30T05:22:57.265246",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Mice are progressed through a series of training stages to shape their behavior prior to 2-photon imaging. Mice are automatically advanced between stages depending on their behavioral performance. For a detailed description of the change detection task and advancement criteria, please see the technical whitepaper: LINK"
]
},
{
"cell_type": "markdown",
"id": "ab075c3d",
"metadata": {
"papermill": {
"duration": 0.023808,
"end_time": "2023-11-30T05:22:57.336869",
"exception": false,
"start_time": "2023-11-30T05:22:57.313061",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "7c9ea148",
"metadata": {
"papermill": {
"duration": 0.023705,
"end_time": "2023-11-30T05:22:57.384230",
"exception": false,
"start_time": "2023-11-30T05:22:57.360525",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Training with the change detection task begins with simple static grating stimuli, changing between 0 and 90 degrees in orientation. On the very first day, mice are automatically given a water reward when the orientation of the stimulus changes (`TRAINING_0_gratings_autorewards_15min`). On subsequent days, mice must lick following the change in order to receive a water reward (`TRAINING_1_gratings`). In the next stage, stimuli are flashed, with a 500ms inter stimulus interal of mean luminance gray screen (`TRAINING_2_gratings_flashed`). "
]
},
{
"cell_type": "markdown",
"id": "e75f59f0",
"metadata": {
"papermill": {
"duration": 0.023835,
"end_time": "2023-11-30T05:22:57.431775",
"exception": false,
"start_time": "2023-11-30T05:22:57.407940",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Once mice perform the task well with gratings, they are transitioned to natural image stimuli. Different groups of mice are trained with different sets of images, image set A or image set B (described further below). In the following description, we use `X` as a placeholder for image set `A` or `B` in the `session_type` name. Training with images begins with a 10ul water reward volume (`TRAINING_3_images_X_10uL_reward`), which is then decreased to 7ul once mice perform the task consistently with images (`TRAINING_4_images_X_training`). When mice have reached criterion to be transferred to the 2-photon imaging portion of the experiment, they are labeled as 'handoff_ready' (`TRAINING_4_images_X_handoff_ready`.) If behavior performance returns to below criterion level, they are labeled as 'handoff_lapsed'(`TRAINING_4_images_X_handoff_lapsed`). "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e6020cbd",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:57.481035Z",
"iopub.status.busy": "2023-11-30T05:22:57.480743Z",
"iopub.status.idle": "2023-11-30T05:22:57.485966Z",
"shell.execute_reply": "2023-11-30T05:22:57.485444Z"
},
"papermill": {
"duration": 0.031301,
"end_time": "2023-11-30T05:22:57.487264",
"exception": false,
"start_time": "2023-11-30T05:22:57.455963",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the different session_types available in this dataset are:\n",
"\n",
"['OPHYS_0_images_A_habituation' 'OPHYS_0_images_B_habituation'\n",
" 'OPHYS_0_images_G_habituation' 'OPHYS_1_images_A' 'OPHYS_1_images_B'\n",
" 'OPHYS_1_images_G' 'OPHYS_2_images_A_passive' 'OPHYS_2_images_B_passive'\n",
" 'OPHYS_2_images_G_passive' 'OPHYS_3_images_A' 'OPHYS_3_images_B'\n",
" 'OPHYS_3_images_G' 'OPHYS_4_images_A' 'OPHYS_4_images_B'\n",
" 'OPHYS_4_images_H' 'OPHYS_5_images_A_passive' 'OPHYS_5_images_B_passive'\n",
" 'OPHYS_5_images_H_passive' 'OPHYS_6_images_A' 'OPHYS_6_images_B'\n",
" 'OPHYS_6_images_H' 'TRAINING_0_gratings_autorewards_15min'\n",
" 'TRAINING_1_gratings' 'TRAINING_2_gratings_flashed'\n",
" 'TRAINING_3_images_A_10uL_reward' 'TRAINING_3_images_B_10uL_reward'\n",
" 'TRAINING_3_images_G_10uL_reward' 'TRAINING_4_images_A_handoff_lapsed'\n",
" 'TRAINING_4_images_A_handoff_ready' 'TRAINING_4_images_A_training'\n",
" 'TRAINING_4_images_B_training' 'TRAINING_4_images_G_training'\n",
" 'TRAINING_5_images_A_epilogue' 'TRAINING_5_images_A_handoff_lapsed'\n",
" 'TRAINING_5_images_A_handoff_ready' 'TRAINING_5_images_B_epilogue'\n",
" 'TRAINING_5_images_B_handoff_lapsed' 'TRAINING_5_images_B_handoff_ready'\n",
" 'TRAINING_5_images_G_epilogue' 'TRAINING_5_images_G_handoff_lapsed'\n",
" 'TRAINING_5_images_G_handoff_ready']\n"
]
}
],
"source": [
"# reminder about possible session types \n",
"print('the different session_types available in this dataset are:\\n')\n",
"print(np.sort(behavior_sessions.session_type[\n",
" ~behavior_sessions.session_type.isna()].unique()))"
]
},
{
"cell_type": "markdown",
"id": "b026a1c6",
"metadata": {
"papermill": {
"duration": 0.02389,
"end_time": "2023-11-30T05:22:57.535180",
"exception": false,
"start_time": "2023-11-30T05:22:57.511290",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
" You will notice that some mice only go up to `TRAINING_4`, while others have the final training stage labeled `TRAINING_5`. This is due to a minor change made partway through data collection, where an `epilogue` stimulus was introduced during the final training stage prior to 2-photon imaging in order to habituate the mice to this stimulus, which is used during 2-photon imaging to aid in session to session registration. The `epilogue` is a 30 minute movie clip repeated 10 times, for a total of 5 minutes, and occurs at the end of the 60 minute behavioral session, followed by 5 minutes of blank gray screen. Training sessions with an epilogue movie include `TRAINING_5_images_X_epilogue`, `TRAINING_5_images_X_handoff_ready` , `TRAINING_5_images_X_handoff_lapsed`. "
]
},
{
"cell_type": "markdown",
"id": "03da3956",
"metadata": {
"papermill": {
"duration": 0.024677,
"end_time": "2023-11-30T05:22:57.583796",
"exception": false,
"start_time": "2023-11-30T05:22:57.559119",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### `session_types` during 2-photon imaging"
]
},
{
"cell_type": "markdown",
"id": "30159c12",
"metadata": {
"papermill": {
"duration": 0.025825,
"end_time": "2023-11-30T05:22:57.635358",
"exception": false,
"start_time": "2023-11-30T05:22:57.609533",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"When mice are transferred to the 2-photon rig for the imaging portion of the experiment, they first undergo 1-3 habituation sessions to get accustomed to the new experimental environment (`OPHYS_0_images_X_habituation`). During these sessions, mice perform the task under the microscope, but no experimental data is recorded. "
]
},
{
"cell_type": "markdown",
"id": "1e5955fc",
"metadata": {
"papermill": {
"duration": 0.02434,
"end_time": "2023-11-30T05:22:57.684735",
"exception": false,
"start_time": "2023-11-30T05:22:57.660395",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "17171d6e",
"metadata": {
"papermill": {
"duration": 0.023907,
"end_time": "2023-11-30T05:22:57.732474",
"exception": false,
"start_time": "2023-11-30T05:22:57.708567",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"During the 2-photon imaging portion of the experiment, mice perform the task with the same set of images they saw during training (either image set `A` or `B`), as well as an additional novel set of images (whichever of `A` or `B` that they did not see during training). This allows evaluation of the impact of different sensory contexts on neural activity - familiarity versus novelty. Sessions with familiar images include those starting with `OPHYS_0`, `OPHYS_1`, `OPHYS_2`, and `OPHYS_3`. Sessions with novel images include those starting with `OPHYS_4`, `OPHYS_5`, and `OPHYS_6`. "
]
},
{
"cell_type": "markdown",
"id": "dd6a406f",
"metadata": {
"papermill": {
"duration": 0.023943,
"end_time": "2023-11-30T05:22:57.781202",
"exception": false,
"start_time": "2023-11-30T05:22:57.757259",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Interleaved between active behavior sessions are passive viewing sessions where mice are given their daily water ahead of the sesssion (and are thus satiated) and view the stimulus with the lick spout retracted so they are unable to earn water rewards. This allows comparison of neural activity in response to stimuli under different behavioral context - active task engagement and passive viewing without reward. Passive sessions include `OPHYS_2_images_A_passive` (passive session with familiar images), and `OPHYS_5_images_A_passive` (passive session with novel images)."
]
},
{
"cell_type": "markdown",
"id": "b7a28754",
"metadata": {
"papermill": {
"duration": 0.024326,
"end_time": "2023-11-30T05:22:57.831289",
"exception": false,
"start_time": "2023-11-30T05:22:57.806963",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The final session during the 2-photon imaging phase is `OPHYS_7_receptive_field_mapping`, however 2-photon data is not available for these sessions in this data release (but will be made available in a subsequent release)."
]
},
{
"cell_type": "markdown",
"id": "901b0840",
"metadata": {
"papermill": {
"duration": 0.023881,
"end_time": "2023-11-30T05:22:57.879171",
"exception": false,
"start_time": "2023-11-30T05:22:57.855290",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Dataset variants - different mice were subject to different experimental conditions"
]
},
{
"cell_type": "markdown",
"id": "6274c13d",
"metadata": {
"papermill": {
"duration": 0.024418,
"end_time": "2023-11-30T05:22:57.927905",
"exception": false,
"start_time": "2023-11-30T05:22:57.903487",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"As hinted to above, some mice were trained with image set A, and others with image set B. Including these two groups of mice, with swapped stimulus conditions, was included in the dataset as a control for the effects of novelty, to ensure that any observed changes were truly due to lack of familiarity with the novel image set, rather than a result of specific features of the image set that was used. In addition, some mice were imaged on the Scientifica single plane imaging systems, and other mice were imaged on Multiscope for multi-plane imaging. These distinct groups of mice are referred to as dataset variants and can be identified using the `project_code` column"
]
},
{
"cell_type": "markdown",
"id": "1da1c171",
"metadata": {
"papermill": {
"duration": 0.024424,
"end_time": "2023-11-30T05:22:57.976763",
"exception": false,
"start_time": "2023-11-30T05:22:57.952339",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "6065bf63",
"metadata": {
"papermill": {
"duration": 0.023988,
"end_time": "2023-11-30T05:22:58.024925",
"exception": false,
"start_time": "2023-11-30T05:22:58.000937",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Project_code is only defined for ophys sessions, for technical reasons, so let's fill in the gaps so that all mice have a project_code"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "6606e286",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:58.074600Z",
"iopub.status.busy": "2023-11-30T05:22:58.074120Z",
"iopub.status.idle": "2023-11-30T05:22:58.087150Z",
"shell.execute_reply": "2023-11-30T05:22:58.086481Z"
},
"papermill": {
"duration": 0.039358,
"end_time": "2023-11-30T05:22:58.088459",
"exception": false,
"start_time": "2023-11-30T05:22:58.049101",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" mouse_id \n",
" project_code \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 457841 \n",
" VisualBehaviorMultiscope \n",
" \n",
" \n",
" 83 \n",
" 476970 \n",
" VisualBehaviorTask1B \n",
" \n",
" \n",
" 159 \n",
" 412036 \n",
" VisualBehavior \n",
" \n",
" \n",
" 206 \n",
" 429952 \n",
" VisualBehavior \n",
" \n",
" \n",
" 239 \n",
" 449441 \n",
" VisualBehavior \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 4494 \n",
" 467302 \n",
" VisualBehavior \n",
" \n",
" \n",
" 4551 \n",
" 489065 \n",
" VisualBehaviorMultiscope4areasx2d \n",
" \n",
" \n",
" 4612 \n",
" 403491 \n",
" VisualBehavior \n",
" \n",
" \n",
" 4683 \n",
" 523927 \n",
" VisualBehavior \n",
" \n",
" \n",
" 4753 \n",
" 489066 \n",
" VisualBehaviorTask1B \n",
" \n",
" \n",
"
\n",
"
107 rows × 2 columns
\n",
"
"
],
"text/plain": [
" mouse_id project_code\n",
"0 457841 VisualBehaviorMultiscope\n",
"83 476970 VisualBehaviorTask1B\n",
"159 412036 VisualBehavior\n",
"206 429952 VisualBehavior\n",
"239 449441 VisualBehavior\n",
"... ... ...\n",
"4494 467302 VisualBehavior\n",
"4551 489065 VisualBehaviorMultiscope4areasx2d\n",
"4612 403491 VisualBehavior\n",
"4683 523927 VisualBehavior\n",
"4753 489066 VisualBehaviorTask1B\n",
"\n",
"[107 rows x 2 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get a table of the project code for each mouse\n",
"project_code_lookup = behavior_sessions[behavior_sessions.project_code.isnull()==False].reset_index().drop_duplicates('mouse_id')[['mouse_id','project_code']]\n",
"project_code_lookup"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "59cfd3f4",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:58.141910Z",
"iopub.status.busy": "2023-11-30T05:22:58.141696Z",
"iopub.status.idle": "2023-11-30T05:22:58.152451Z",
"shell.execute_reply": "2023-11-30T05:22:58.151800Z"
},
"papermill": {
"duration": 0.037113,
"end_time": "2023-11-30T05:22:58.153909",
"exception": false,
"start_time": "2023-11-30T05:22:58.116796",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"behavior_sessions = behavior_sessions.merge(project_code_lookup, on='mouse_id',\n",
" how='left', suffixes=('_session', '_mouse'))\n",
"behavior_sessions = behavior_sessions.drop(columns='project_code_session')\n",
"behavior_sessions = behavior_sessions.rename(columns={'project_code_mouse': 'project_code'})"
]
},
{
"cell_type": "markdown",
"id": "18e78600",
"metadata": {
"papermill": {
"duration": 0.024047,
"end_time": "2023-11-30T05:22:58.202415",
"exception": false,
"start_time": "2023-11-30T05:22:58.178368",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### What `project_codes` are available? What `session_types` belong to each? "
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "2893504f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:58.252064Z",
"iopub.status.busy": "2023-11-30T05:22:58.251532Z",
"iopub.status.idle": "2023-11-30T05:22:58.256197Z",
"shell.execute_reply": "2023-11-30T05:22:58.255661Z"
},
"papermill": {
"duration": 0.030789,
"end_time": "2023-11-30T05:22:58.257465",
"exception": false,
"start_time": "2023-11-30T05:22:58.226676",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array(['VisualBehaviorMultiscope', 'VisualBehaviorTask1B',\n",
" 'VisualBehavior', 'VisualBehaviorMultiscope4areasx2d'],\n",
" dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"behavior_sessions.project_code.unique()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "93050515",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:58.307166Z",
"iopub.status.busy": "2023-11-30T05:22:58.306967Z",
"iopub.status.idle": "2023-11-30T05:22:58.318601Z",
"shell.execute_reply": "2023-11-30T05:22:58.318001Z"
},
"papermill": {
"duration": 0.037865,
"end_time": "2023-11-30T05:22:58.319899",
"exception": false,
"start_time": "2023-11-30T05:22:58.282034",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" project_code: VisualBehaviorMultiscope\n",
"\n",
" has these session types:\n",
" ['OPHYS_0_images_A_habituation' 'OPHYS_1_images_A'\n",
" 'OPHYS_2_images_A_passive' 'OPHYS_3_images_A' 'OPHYS_3_images_B'\n",
" 'OPHYS_4_images_B' 'OPHYS_5_images_A_passive' 'OPHYS_5_images_B_passive'\n",
" 'OPHYS_6_images_A' 'OPHYS_6_images_B'\n",
" 'TRAINING_0_gratings_autorewards_15min' 'TRAINING_1_gratings'\n",
" 'TRAINING_2_gratings_flashed' 'TRAINING_3_images_A_10uL_reward'\n",
" 'TRAINING_4_images_A_training' 'TRAINING_5_images_A_epilogue'\n",
" 'TRAINING_5_images_A_handoff_lapsed' 'TRAINING_5_images_A_handoff_ready']\n",
"\n",
"\n",
"\n",
" project_code: VisualBehaviorTask1B\n",
"\n",
" has these session types:\n",
" ['OPHYS_0_images_A_habituation' 'OPHYS_0_images_B_habituation'\n",
" 'OPHYS_1_images_B' 'OPHYS_2_images_B_passive' 'OPHYS_3_images_B'\n",
" 'OPHYS_4_images_A' 'OPHYS_4_images_B' 'OPHYS_5_images_A_passive'\n",
" 'OPHYS_6_images_A' 'TRAINING_0_gratings_autorewards_15min'\n",
" 'TRAINING_1_gratings' 'TRAINING_2_gratings_flashed'\n",
" 'TRAINING_3_images_B_10uL_reward' 'TRAINING_4_images_B_training'\n",
" 'TRAINING_5_images_B_epilogue' 'TRAINING_5_images_B_handoff_lapsed'\n",
" 'TRAINING_5_images_B_handoff_ready']\n",
"\n",
"\n",
"\n",
" project_code: VisualBehavior\n",
"\n",
" has these session types:\n",
" ['OPHYS_0_images_A_habituation' 'OPHYS_0_images_B_habituation'\n",
" 'OPHYS_1_images_A' 'OPHYS_1_images_B' 'OPHYS_2_images_A_passive'\n",
" 'OPHYS_2_images_B_passive' 'OPHYS_3_images_A' 'OPHYS_3_images_B'\n",
" 'OPHYS_4_images_A' 'OPHYS_4_images_B' 'OPHYS_5_images_A_passive'\n",
" 'OPHYS_5_images_B_passive' 'OPHYS_6_images_A' 'OPHYS_6_images_B'\n",
" 'TRAINING_0_gratings_autorewards_15min' 'TRAINING_1_gratings'\n",
" 'TRAINING_2_gratings_flashed' 'TRAINING_3_images_A_10uL_reward'\n",
" 'TRAINING_3_images_B_10uL_reward' 'TRAINING_4_images_A_handoff_lapsed'\n",
" 'TRAINING_4_images_A_handoff_ready' 'TRAINING_4_images_A_training'\n",
" 'TRAINING_4_images_B_training' 'TRAINING_5_images_A_epilogue'\n",
" 'TRAINING_5_images_A_handoff_lapsed' 'TRAINING_5_images_A_handoff_ready'\n",
" 'TRAINING_5_images_B_epilogue' 'TRAINING_5_images_B_handoff_ready']\n",
"\n",
"\n",
"\n",
" project_code: VisualBehaviorMultiscope4areasx2d\n",
"\n",
" has these session types:\n",
" ['OPHYS_0_images_G_habituation' 'OPHYS_1_images_G'\n",
" 'OPHYS_2_images_G_passive' 'OPHYS_3_images_G' 'OPHYS_4_images_H'\n",
" 'OPHYS_5_images_H_passive' 'OPHYS_6_images_H'\n",
" 'TRAINING_0_gratings_autorewards_15min' 'TRAINING_1_gratings'\n",
" 'TRAINING_2_gratings_flashed' 'TRAINING_3_images_G_10uL_reward'\n",
" 'TRAINING_4_images_G_training' 'TRAINING_5_images_G_epilogue'\n",
" 'TRAINING_5_images_G_handoff_lapsed' 'TRAINING_5_images_G_handoff_ready']\n",
"\n",
"\n"
]
}
],
"source": [
"for project_code in behavior_sessions.project_code.unique(): \n",
" project_sessions = behavior_sessions[behavior_sessions.project_code==project_code]\n",
" print('\\n project_code:', project_code)\n",
" print('\\n has these session types:\\n', np.sort(\n",
" project_sessions.session_type[~project_sessions.session_type.isna()].unique()))\n",
" print('\\n')"
]
},
{
"cell_type": "markdown",
"id": "983a12b2",
"metadata": {
"papermill": {
"duration": 0.024304,
"end_time": "2023-11-30T05:22:58.368753",
"exception": false,
"start_time": "2023-11-30T05:22:58.344449",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Notice that for `project_codes` `VisualBehavior` and `VisualBehaviorMultiscope`, mice are trained on image set A, while for `VisualBehaviorTask1B`, mice are trained on image set B"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e04c6d3e",
"metadata": {
"papermill": {
"duration": 0.024284,
"end_time": "2023-11-30T05:22:58.417511",
"exception": false,
"start_time": "2023-11-30T05:22:58.393227",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "cf0fc554",
"metadata": {
"papermill": {
"duration": 0.024098,
"end_time": "2023-11-30T05:22:58.466240",
"exception": false,
"start_time": "2023-11-30T05:22:58.442142",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Ophys Sessions Table"
]
},
{
"cell_type": "markdown",
"id": "05b97a3e",
"metadata": {
"papermill": {
"duration": 0.024288,
"end_time": "2023-11-30T05:22:58.515114",
"exception": false,
"start_time": "2023-11-30T05:22:58.490826",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The `ophys_session_table` includes all of the metadata columns available in the `behavior_session_table`, as well as additional information specific to 2-photon imaging, namely the list of `ophys_experiment_ids` and `ophys_container_ids` associated with each `ophys_session_id`. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "8831dcb6",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:58.595375Z",
"iopub.status.busy": "2023-11-30T05:22:58.594873Z",
"iopub.status.idle": "2023-11-30T05:22:58.615648Z",
"shell.execute_reply": "2023-11-30T05:22:58.615088Z"
},
"papermill": {
"duration": 0.047637,
"end_time": "2023-11-30T05:22:58.617021",
"exception": false,
"start_time": "2023-11-30T05:22:58.569384",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of ophys sessions: 703\n",
"\n",
"Index(['behavior_session_id', 'ophys_container_id', 'mouse_id', 'indicator',\n",
" 'full_genotype', 'driver_line', 'cre_line', 'reporter_line', 'sex',\n",
" 'age_in_days', 'imaging_plane_group_count', 'project_code',\n",
" 'session_type', 'session_number', 'image_set', 'behavior_type',\n",
" 'experience_level', 'prior_exposures_to_session_type',\n",
" 'prior_exposures_to_image_set', 'prior_exposures_to_omissions',\n",
" 'date_of_acquisition', 'equipment_name', 'num_depths_per_area',\n",
" 'ophys_experiment_id', 'num_targeted_structures'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" behavior_session_id \n",
" ophys_container_id \n",
" mouse_id \n",
" indicator \n",
" full_genotype \n",
" driver_line \n",
" cre_line \n",
" reporter_line \n",
" sex \n",
" age_in_days \n",
" ... \n",
" behavior_type \n",
" experience_level \n",
" prior_exposures_to_session_type \n",
" prior_exposures_to_image_set \n",
" prior_exposures_to_omissions \n",
" date_of_acquisition \n",
" equipment_name \n",
" num_depths_per_area \n",
" ophys_experiment_id \n",
" num_targeted_structures \n",
" \n",
" \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 951410079 \n",
" 951520319 \n",
" [1018028339, 1018028342, 1018028345, 101802835... \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 206 \n",
" ... \n",
" active_behavior \n",
" Familiar \n",
" 0 \n",
" 65 \n",
" 0 \n",
" 2019-09-20 09:59:38.837000+00:00 \n",
" MESO.1 \n",
" 4 \n",
" [951980471, 951980473, 951980475, 951980479, 9... \n",
" 2 \n",
" \n",
" \n",
" 952430817 \n",
" 952554548 \n",
" [1018028339, 1018028345, 1018028354, 1018028357] \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 209 \n",
" ... \n",
" passive_viewing \n",
" Familiar \n",
" 0 \n",
" 66 \n",
" 1 \n",
" 2019-09-23 08:45:38.490000+00:00 \n",
" MESO.1 \n",
" 4 \n",
" [953659743, 953659745, 953659749, 953659752] \n",
" 2 \n",
" \n",
" \n",
" 954954402 \n",
" 953982960 \n",
" [1018028339, 1018028342, 1018028345, 101802835... \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 210 \n",
" ... \n",
" active_behavior \n",
" Familiar \n",
" 0 \n",
" 67 \n",
" 2 \n",
" 2019-09-24 09:01:31.582000+00:00 \n",
" MESO.1 \n",
" 4 \n",
" [958527464, 958527471, 958527474, 958527479, 9... \n",
" 2 \n",
" \n",
" \n",
" 955775716 \n",
" 956010809 \n",
" [1018028339, 1018028342, 1018028345] \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 212 \n",
" ... \n",
" active_behavior \n",
" Familiar \n",
" 1 \n",
" 68 \n",
" 3 \n",
" 2019-09-26 09:22:21.772000+00:00 \n",
" MESO.1 \n",
" 4 \n",
" [956941841, 956941844, 956941846] \n",
" 2 \n",
" \n",
" \n",
" 957020350 \n",
" 957032492 \n",
" [1018028339, 1018028342, 1018028345, 101802835... \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" 213 \n",
" ... \n",
" active_behavior \n",
" Novel 1 \n",
" 0 \n",
" 0 \n",
" 4 \n",
" 2019-09-27 08:58:37.005000+00:00 \n",
" MESO.1 \n",
" 4 \n",
" [957759562, 957759564, 957759566, 957759570, 9... \n",
" 2 \n",
" \n",
" \n",
"
\n",
"
5 rows × 25 columns
\n",
"
"
],
"text/plain": [
" behavior_session_id \\\n",
"ophys_session_id \n",
"951410079 951520319 \n",
"952430817 952554548 \n",
"954954402 953982960 \n",
"955775716 956010809 \n",
"957020350 957032492 \n",
"\n",
" ophys_container_id mouse_id \\\n",
"ophys_session_id \n",
"951410079 [1018028339, 1018028342, 1018028345, 101802835... 457841 \n",
"952430817 [1018028339, 1018028345, 1018028354, 1018028357] 457841 \n",
"954954402 [1018028339, 1018028342, 1018028345, 101802835... 457841 \n",
"955775716 [1018028339, 1018028342, 1018028345] 457841 \n",
"957020350 [1018028339, 1018028342, 1018028345, 101802835... 457841 \n",
"\n",
" indicator full_genotype \\\n",
"ophys_session_id \n",
"951410079 GCaMP6f Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"952430817 GCaMP6f Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"954954402 GCaMP6f Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"955775716 GCaMP6f Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"957020350 GCaMP6f Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"\n",
" driver_line cre_line reporter_line \\\n",
"ophys_session_id \n",
"951410079 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"952430817 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"954954402 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"955775716 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"957020350 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"\n",
" sex age_in_days ... behavior_type experience_level \\\n",
"ophys_session_id ... \n",
"951410079 F 206 ... active_behavior Familiar \n",
"952430817 F 209 ... passive_viewing Familiar \n",
"954954402 F 210 ... active_behavior Familiar \n",
"955775716 F 212 ... active_behavior Familiar \n",
"957020350 F 213 ... active_behavior Novel 1 \n",
"\n",
" prior_exposures_to_session_type \\\n",
"ophys_session_id \n",
"951410079 0 \n",
"952430817 0 \n",
"954954402 0 \n",
"955775716 1 \n",
"957020350 0 \n",
"\n",
" prior_exposures_to_image_set prior_exposures_to_omissions \\\n",
"ophys_session_id \n",
"951410079 65 0 \n",
"952430817 66 1 \n",
"954954402 67 2 \n",
"955775716 68 3 \n",
"957020350 0 4 \n",
"\n",
" date_of_acquisition equipment_name \\\n",
"ophys_session_id \n",
"951410079 2019-09-20 09:59:38.837000+00:00 MESO.1 \n",
"952430817 2019-09-23 08:45:38.490000+00:00 MESO.1 \n",
"954954402 2019-09-24 09:01:31.582000+00:00 MESO.1 \n",
"955775716 2019-09-26 09:22:21.772000+00:00 MESO.1 \n",
"957020350 2019-09-27 08:58:37.005000+00:00 MESO.1 \n",
"\n",
" num_depths_per_area \\\n",
"ophys_session_id \n",
"951410079 4 \n",
"952430817 4 \n",
"954954402 4 \n",
"955775716 4 \n",
"957020350 4 \n",
"\n",
" ophys_experiment_id \\\n",
"ophys_session_id \n",
"951410079 [951980471, 951980473, 951980475, 951980479, 9... \n",
"952430817 [953659743, 953659745, 953659749, 953659752] \n",
"954954402 [958527464, 958527471, 958527474, 958527479, 9... \n",
"955775716 [956941841, 956941844, 956941846] \n",
"957020350 [957759562, 957759564, 957759566, 957759570, 9... \n",
"\n",
" num_targeted_structures \n",
"ophys_session_id \n",
"951410079 2 \n",
"952430817 2 \n",
"954954402 2 \n",
"955775716 2 \n",
"957020350 2 \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ophys_sessions = cache.get_ophys_session_table()\n",
"\n",
"print(f\"Total number of ophys sessions: {len(ophys_sessions)}\\n\")\n",
"\n",
"print(ophys_sessions.columns)\n",
"\n",
"ophys_sessions.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ca9bb487",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:58.667714Z",
"iopub.status.busy": "2023-11-30T05:22:58.667304Z",
"iopub.status.idle": "2023-11-30T05:22:58.681603Z",
"shell.execute_reply": "2023-11-30T05:22:58.681092Z"
},
"papermill": {
"duration": 0.040924,
"end_time": "2023-11-30T05:22:58.682960",
"exception": false,
"start_time": "2023-11-30T05:22:58.642036",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" ophys_experiment_id \n",
" ophys_container_id \n",
" equipment_name \n",
" \n",
" \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 951410079 \n",
" [951980471, 951980473, 951980475, 951980479, 9... \n",
" [1018028339, 1018028342, 1018028345, 101802835... \n",
" MESO.1 \n",
" \n",
" \n",
" 952430817 \n",
" [953659743, 953659745, 953659749, 953659752] \n",
" [1018028339, 1018028345, 1018028354, 1018028357] \n",
" MESO.1 \n",
" \n",
" \n",
" 954954402 \n",
" [958527464, 958527471, 958527474, 958527479, 9... \n",
" [1018028339, 1018028342, 1018028345, 101802835... \n",
" MESO.1 \n",
" \n",
" \n",
" 955775716 \n",
" [956941841, 956941844, 956941846] \n",
" [1018028339, 1018028342, 1018028345] \n",
" MESO.1 \n",
" \n",
" \n",
" 957020350 \n",
" [957759562, 957759564, 957759566, 957759570, 9... \n",
" [1018028339, 1018028342, 1018028345, 101802835... \n",
" MESO.1 \n",
" \n",
" \n",
" 958105827 \n",
" [958741219, 958741222, 958741230, 958741232, 9... \n",
" [1018028342, 1018028345, 1018028351, 101802835... \n",
" MESO.1 \n",
" \n",
" \n",
" 958772311 \n",
" [959388788, 959388790, 959388792, 959388796, 9... \n",
" [1018028339, 1018028342, 1018028345, 101802835... \n",
" MESO.1 \n",
" \n",
" \n",
" 959458018 \n",
" [960410023, 960410026, 960410028, 960410038, 9... \n",
" [1018028339, 1018028342, 1018028345, 101802835... \n",
" MESO.1 \n",
" \n",
" \n",
" 993727065 \n",
" [993862620] \n",
" [991913453] \n",
" CAM2P.4 \n",
" \n",
" \n",
" 993984066 \n",
" [994053903] \n",
" [991913453] \n",
" CAM2P.4 \n",
" \n",
" \n",
" 994199725 \n",
" [994278281] \n",
" [991913453] \n",
" CAM2P.4 \n",
" \n",
" \n",
" 994731371 \n",
" [994790561] \n",
" [991913453] \n",
" CAM2P.4 \n",
" \n",
" \n",
" 994883056 \n",
" [994955425] \n",
" [991913453] \n",
" CAM2P.4 \n",
" \n",
" \n",
" 995115654 \n",
" [995280513] \n",
" [991913453] \n",
" CAM2P.4 \n",
" \n",
" \n",
" 995359867 \n",
" [995439942] \n",
" [991913453] \n",
" CAM2P.4 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" ophys_experiment_id \\\n",
"ophys_session_id \n",
"951410079 [951980471, 951980473, 951980475, 951980479, 9... \n",
"952430817 [953659743, 953659745, 953659749, 953659752] \n",
"954954402 [958527464, 958527471, 958527474, 958527479, 9... \n",
"955775716 [956941841, 956941844, 956941846] \n",
"957020350 [957759562, 957759564, 957759566, 957759570, 9... \n",
"958105827 [958741219, 958741222, 958741230, 958741232, 9... \n",
"958772311 [959388788, 959388790, 959388792, 959388796, 9... \n",
"959458018 [960410023, 960410026, 960410028, 960410038, 9... \n",
"993727065 [993862620] \n",
"993984066 [994053903] \n",
"994199725 [994278281] \n",
"994731371 [994790561] \n",
"994883056 [994955425] \n",
"995115654 [995280513] \n",
"995359867 [995439942] \n",
"\n",
" ophys_container_id \\\n",
"ophys_session_id \n",
"951410079 [1018028339, 1018028342, 1018028345, 101802835... \n",
"952430817 [1018028339, 1018028345, 1018028354, 1018028357] \n",
"954954402 [1018028339, 1018028342, 1018028345, 101802835... \n",
"955775716 [1018028339, 1018028342, 1018028345] \n",
"957020350 [1018028339, 1018028342, 1018028345, 101802835... \n",
"958105827 [1018028342, 1018028345, 1018028351, 101802835... \n",
"958772311 [1018028339, 1018028342, 1018028345, 101802835... \n",
"959458018 [1018028339, 1018028342, 1018028345, 101802835... \n",
"993727065 [991913453] \n",
"993984066 [991913453] \n",
"994199725 [991913453] \n",
"994731371 [991913453] \n",
"994883056 [991913453] \n",
"995115654 [991913453] \n",
"995359867 [991913453] \n",
"\n",
" equipment_name \n",
"ophys_session_id \n",
"951410079 MESO.1 \n",
"952430817 MESO.1 \n",
"954954402 MESO.1 \n",
"955775716 MESO.1 \n",
"957020350 MESO.1 \n",
"958105827 MESO.1 \n",
"958772311 MESO.1 \n",
"959458018 MESO.1 \n",
"993727065 CAM2P.4 \n",
"993984066 CAM2P.4 \n",
"994199725 CAM2P.4 \n",
"994731371 CAM2P.4 \n",
"994883056 CAM2P.4 \n",
"995115654 CAM2P.4 \n",
"995359867 CAM2P.4 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# what do the ophys_experiment_id and ophys_container_id columns look like? \n",
"# are there always the same number of experiments and containers in different sessions? \n",
"# does the number of experiments and containers depend on the microscope used? \n",
"ophys_sessions[['ophys_experiment_id', 'ophys_container_id', 'equipment_name']][:15]"
]
},
{
"cell_type": "markdown",
"id": "5cc382f2",
"metadata": {
"papermill": {
"duration": 0.024793,
"end_time": "2023-11-30T05:22:58.732976",
"exception": false,
"start_time": "2023-11-30T05:22:58.708183",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Session order "
]
},
{
"cell_type": "markdown",
"id": "18de9c0a",
"metadata": {
"papermill": {
"duration": 0.025305,
"end_time": "2023-11-30T05:22:58.783401",
"exception": false,
"start_time": "2023-11-30T05:22:58.758096",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### The `ophys_session_table` only includes sessions that pass ophys QC "
]
},
{
"cell_type": "markdown",
"id": "9d820569",
"metadata": {
"papermill": {
"duration": 0.024839,
"end_time": "2023-11-30T05:22:58.834015",
"exception": false,
"start_time": "2023-11-30T05:22:58.809176",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### (but the `behavior_session_table` includes all the sessions)"
]
},
{
"cell_type": "markdown",
"id": "a7c8920b",
"metadata": {
"papermill": {
"duration": 0.024977,
"end_time": "2023-11-30T05:22:58.884129",
"exception": false,
"start_time": "2023-11-30T05:22:58.859152",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The `ophys_session_table` only includes sessions with 2-photon imaging data that passed our QC criteria. Importantly, sessions that took place during 2-photon imaging, but did NOT pass QC, can be found in the `behavior_session_table`, as it includes the full training history for every mouse. In the `behavior_session_table`, only sessions with passing ophys data will have an `ophys_session_id`. We can use this to identify ophys sessions that didnt pass QC, but still have behavior data. "
]
},
{
"cell_type": "markdown",
"id": "7684ef65",
"metadata": {
"papermill": {
"duration": 0.024962,
"end_time": "2023-11-30T05:22:58.934195",
"exception": false,
"start_time": "2023-11-30T05:22:58.909233",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### Let's look at all the behavior sessions that took place on a 2-photon rig for one mouse, in order of acquisition date"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "9a3d4298",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:58.985519Z",
"iopub.status.busy": "2023-11-30T05:22:58.985103Z",
"iopub.status.idle": "2023-11-30T05:22:58.995231Z",
"shell.execute_reply": "2023-11-30T05:22:58.994580Z"
},
"papermill": {
"duration": 0.037552,
"end_time": "2023-11-30T05:22:58.996614",
"exception": false,
"start_time": "2023-11-30T05:22:58.959062",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"scrolled": false,
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" session_type \n",
" date_of_acquisition \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [session_type, date_of_acquisition, ophys_session_id]\n",
"Index: []"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pick a mouse\n",
"mouse_id = 445002 \n",
"# get behavior sessions that took place on the microscope\n",
"mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&\n",
" (behavior_sessions.equipment_name=='CAM2P.3')]\n",
"# only look at the relevant columns\n",
"mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['session_type', 'date_of_acquisition', 'ophys_session_id']]"
]
},
{
"cell_type": "markdown",
"id": "e0db92d4",
"metadata": {
"papermill": {
"duration": 0.025018,
"end_time": "2023-11-30T05:22:59.046886",
"exception": false,
"start_time": "2023-11-30T05:22:59.021868",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Notice that only a subset of all OPHYS sessions have an `ophys_session_id` - these are the sessions that passed QC. Sessions with NaN as the `ophys_session_id` either do not have 2P data recorded (as in habituation sessions), or failed QC and were retaken on a subsequent day, such as `OPHYS_5_images_B_passive` in this case"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "895d8679",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:59.098229Z",
"iopub.status.busy": "2023-11-30T05:22:59.097621Z",
"iopub.status.idle": "2023-11-30T05:22:59.101450Z",
"shell.execute_reply": "2023-11-30T05:22:59.100782Z"
},
"papermill": {
"duration": 0.031062,
"end_time": "2023-11-30T05:22:59.102841",
"exception": false,
"start_time": "2023-11-30T05:22:59.071779",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"there are 0 ophys sessions in the behavior_session_table for this mouse\n",
"this includes ophys sessions that failed QC for ophys, but still have behavior data\n"
]
}
],
"source": [
"print('there are', len(mouse_ophys_sessions), 'ophys sessions in the behavior_session_table for this mouse')\n",
"print('this includes ophys sessions that failed QC for ophys, but still have behavior data')"
]
},
{
"cell_type": "markdown",
"id": "4347ef89",
"metadata": {
"papermill": {
"duration": 0.025413,
"end_time": "2023-11-30T05:22:59.153863",
"exception": false,
"start_time": "2023-11-30T05:22:59.128450",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### What is available in the `ophys_session_table` for this mouse? "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "9d0e7372",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:59.205430Z",
"iopub.status.busy": "2023-11-30T05:22:59.205169Z",
"iopub.status.idle": "2023-11-30T05:22:59.210511Z",
"shell.execute_reply": "2023-11-30T05:22:59.209783Z"
},
"papermill": {
"duration": 0.032709,
"end_time": "2023-11-30T05:22:59.211864",
"exception": false,
"start_time": "2023-11-30T05:22:59.179155",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"scrolled": true,
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"there are 0 sessions in the ophys_session_table for this mouse\n",
"these are the sessions with valid ophys data\n"
]
}
],
"source": [
"print('there are', len(ophys_sessions[ophys_sessions.mouse_id==mouse_id]), 'sessions in the ophys_session_table for this mouse')\n",
"print('these are the sessions with valid ophys data')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "9a478a25",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:59.263348Z",
"iopub.status.busy": "2023-11-30T05:22:59.262840Z",
"iopub.status.idle": "2023-11-30T05:22:59.270428Z",
"shell.execute_reply": "2023-11-30T05:22:59.269865Z"
},
"papermill": {
"duration": 0.034586,
"end_time": "2023-11-30T05:22:59.271713",
"exception": false,
"start_time": "2023-11-30T05:22:59.237127",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" \n",
" \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type]\n",
"Index: []"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ophys_sessions[ophys_sessions.mouse_id==mouse_id][['date_of_acquisition', 'session_type']]"
]
},
{
"cell_type": "markdown",
"id": "f82f9443",
"metadata": {
"papermill": {
"duration": 0.025242,
"end_time": "2023-11-30T05:22:59.322532",
"exception": false,
"start_time": "2023-11-30T05:22:59.297290",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Due to QC failures and retakes, session types in the `ophys_session_table` do not always occur in sequential order"
]
},
{
"cell_type": "markdown",
"id": "bbe5b15f",
"metadata": {
"papermill": {
"duration": 0.025222,
"end_time": "2023-11-30T05:22:59.373061",
"exception": false,
"start_time": "2023-11-30T05:22:59.347839",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The schematic above depicts ophys sessions OPHYS1-6 in a specific order, however this order is rarely perfectly maintained due to QC failures. The example above shows OPHYS_1-4 in the correct order, but then OPHYS_5 comes after OPHYS_6 because the first attempt at OPHYS_5 failed (as we can see from the behavior_sessions for this mouse), and had to be retaken after OPHYS_6. "
]
},
{
"cell_type": "markdown",
"id": "1fb21893",
"metadata": {
"papermill": {
"duration": 0.02537,
"end_time": "2023-11-30T05:22:59.423922",
"exception": false,
"start_time": "2023-11-30T05:22:59.398552",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### Let's look at the session order for a different mouse, imaged on the Multiscope"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "281336e5",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:59.476426Z",
"iopub.status.busy": "2023-11-30T05:22:59.475905Z",
"iopub.status.idle": "2023-11-30T05:22:59.485528Z",
"shell.execute_reply": "2023-11-30T05:22:59.484877Z"
},
"papermill": {
"duration": 0.037083,
"end_time": "2023-11-30T05:22:59.486881",
"exception": false,
"start_time": "2023-11-30T05:22:59.449798",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type, ophys_session_id]\n",
"Index: []"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pick a mouse\n",
"mouse_id = 453911\n",
"# get behavior sessions that took place on the microscope\n",
"mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&\n",
" (behavior_sessions.equipment_name=='MESO.1')]\n",
"# only look at the relevant columns\n",
"mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id']]"
]
},
{
"cell_type": "markdown",
"id": "934f44ca",
"metadata": {
"papermill": {
"duration": 0.025195,
"end_time": "2023-11-30T05:22:59.537586",
"exception": false,
"start_time": "2023-11-30T05:22:59.512391",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Looks like lots of retakes for this one (where `ophys_session_id` = NaN). Also note that there are multiple retakes for some `session_types`. This can happen for mice imaged on Multiscope, because retakes can be triggered by QC failure of any one of the 8 imaging planes in the session. "
]
},
{
"cell_type": "markdown",
"id": "2da74236",
"metadata": {
"papermill": {
"duration": 0.025129,
"end_time": "2023-11-30T05:22:59.588070",
"exception": false,
"start_time": "2023-11-30T05:22:59.562941",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### Let's look at how failures and retakes affects the session order in the `ophys_sessions` table for this mouse"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "4619658c",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:59.639904Z",
"iopub.status.busy": "2023-11-30T05:22:59.639532Z",
"iopub.status.idle": "2023-11-30T05:22:59.646994Z",
"shell.execute_reply": "2023-11-30T05:22:59.646337Z"
},
"papermill": {
"duration": 0.034884,
"end_time": "2023-11-30T05:22:59.648283",
"exception": false,
"start_time": "2023-11-30T05:22:59.613399",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" \n",
" \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type]\n",
"Index: []"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ophys_sessions[ophys_sessions.mouse_id==mouse_id][['date_of_acquisition', 'session_type']]"
]
},
{
"cell_type": "markdown",
"id": "b4a5f2a9",
"metadata": {
"papermill": {
"duration": 0.025363,
"end_time": "2023-11-30T05:22:59.699467",
"exception": false,
"start_time": "2023-11-30T05:22:59.674104",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"It looks like the first set of sessions are taken in sequential order, but after that there are a few retakes of some of the `session_types`. This suggests that some of the imaging planes for this Multiscope mouse passed QC on the first time around, but retakes were needed to get passing ophys data for other imaging planes. "
]
},
{
"cell_type": "markdown",
"id": "ba86289b",
"metadata": {
"papermill": {
"duration": 0.025292,
"end_time": "2023-11-30T05:22:59.750261",
"exception": false,
"start_time": "2023-11-30T05:22:59.724969",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### But they're not always out of order, sometimes things go perfectly! "
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "66199121",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:22:59.803128Z",
"iopub.status.busy": "2023-11-30T05:22:59.802692Z",
"iopub.status.idle": "2023-11-30T05:22:59.812810Z",
"shell.execute_reply": "2023-11-30T05:22:59.812227Z"
},
"papermill": {
"duration": 0.0383,
"end_time": "2023-11-30T05:22:59.814114",
"exception": false,
"start_time": "2023-11-30T05:22:59.775814",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type, ophys_session_id]\n",
"Index: []"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pick a mouse\n",
"mouse_id = 438912\n",
"# get behavior sessions that took place on the microscope\n",
"mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&\n",
" (behavior_sessions.equipment_name=='MESO.1')]\n",
"# only look at the relevant columns\n",
"mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id']]"
]
},
{
"cell_type": "markdown",
"id": "98aeda1d",
"metadata": {
"papermill": {
"duration": 0.02553,
"end_time": "2023-11-30T05:22:59.865311",
"exception": false,
"start_time": "2023-11-30T05:22:59.839781",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Well, nearly perfectly, OPHYS_5 came after OPHYS_6"
]
},
{
"cell_type": "markdown",
"id": "b1e021a9",
"metadata": {
"papermill": {
"duration": 0.025415,
"end_time": "2023-11-30T05:22:59.916283",
"exception": false,
"start_time": "2023-11-30T05:22:59.890868",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Prior Exposures"
]
},
{
"cell_type": "markdown",
"id": "2f9fee8c",
"metadata": {
"papermill": {
"duration": 0.025442,
"end_time": "2023-11-30T05:22:59.967224",
"exception": false,
"start_time": "2023-11-30T05:22:59.941782",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Because the session types can be out of order due to retakes, and because of some of our other experimental design decisions, it is helpful to know some information about the history of the mouse relative to a given session. To serve this purpose, we have included metadata describing the `prior_exposures_to_image_set`, `prior_exposures_to_session_type`, and `prior_exposures_to_omissions` as columns in all the manifest data tables. "
]
},
{
"cell_type": "markdown",
"id": "f6693273",
"metadata": {
"papermill": {
"duration": 0.025914,
"end_time": "2023-11-30T05:23:00.018929",
"exception": false,
"start_time": "2023-11-30T05:22:59.993015",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### `prior_exposures_to_image_set`"
]
},
{
"cell_type": "markdown",
"id": "3a7a18d4",
"metadata": {
"papermill": {
"duration": 0.025457,
"end_time": "2023-11-30T05:23:00.070289",
"exception": false,
"start_time": "2023-11-30T05:23:00.044832",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"A key aspect of our experimental design is the inclusion of novel stimuli during the imaging phase of the experiment. However, after the very first session with these novel images, they actually start to become more and more familiar. So, it is important to know whether a given session is truly the first exposure to that image set. In addition, it is useful to know whether subsequent sessions are the second, third, fourth, etc. exposure to that image set, for analysis of changes in activity with experience following novelty. The `prior_exposures_to_image_set` column describes the number of sessions that a given mouse has observed the stimulus set that was shown in that session, prior to the start of that session. For the very first exposure to a novel image set, the value of `prior_exposures_to_image_set` will be 0. "
]
},
{
"cell_type": "markdown",
"id": "39a69f2e",
"metadata": {
"papermill": {
"duration": 0.025339,
"end_time": "2023-11-30T05:23:00.121432",
"exception": false,
"start_time": "2023-11-30T05:23:00.096093",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### Let's look at the `prior_exposures_to_image_set` column for one of the mice we looked at above, first in the `behavior_session_table`, which contains all sessions the mouse experienced, then in the `ophys_session_table`, which only includes sessions that passed ophys QC"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "1e4cda23",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:00.174508Z",
"iopub.status.busy": "2023-11-30T05:23:00.173893Z",
"iopub.status.idle": "2023-11-30T05:23:00.184338Z",
"shell.execute_reply": "2023-11-30T05:23:00.183799Z"
},
"papermill": {
"duration": 0.038681,
"end_time": "2023-11-30T05:23:00.185663",
"exception": false,
"start_time": "2023-11-30T05:23:00.146982",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" session_type \n",
" date_of_acquisition \n",
" ophys_session_id \n",
" prior_exposures_to_image_set \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [session_type, date_of_acquisition, ophys_session_id, prior_exposures_to_image_set]\n",
"Index: []"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mouse_id = 445002 \n",
"# get behavior sessions that took place on the microscope\n",
"mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&\n",
" (behavior_sessions.equipment_name=='CAM2P.3')]\n",
"# only look at the relevant columns\n",
"mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['session_type', 'date_of_acquisition', 'ophys_session_id', 'prior_exposures_to_image_set']]"
]
},
{
"cell_type": "markdown",
"id": "53d7c797",
"metadata": {
"papermill": {
"duration": 0.025509,
"end_time": "2023-11-30T05:23:00.237189",
"exception": false,
"start_time": "2023-11-30T05:23:00.211680",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Note that `prior_exposures_to_image_set` is a high number for `OPHYS_0-3`, because that is the image set the mouse was trained on, and that it re-sets to zero for the first exposure to the novel image set in `OPHYS_4`"
]
},
{
"cell_type": "markdown",
"id": "e0fbaddf",
"metadata": {
"papermill": {
"duration": 0.025474,
"end_time": "2023-11-30T05:23:00.288098",
"exception": false,
"start_time": "2023-11-30T05:23:00.262624",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### Let's double check the full training history for this mouse"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "c60129c2",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:00.340760Z",
"iopub.status.busy": "2023-11-30T05:23:00.340287Z",
"iopub.status.idle": "2023-11-30T05:23:00.349492Z",
"shell.execute_reply": "2023-11-30T05:23:00.348811Z"
},
"papermill": {
"duration": 0.036962,
"end_time": "2023-11-30T05:23:00.350895",
"exception": false,
"start_time": "2023-11-30T05:23:00.313933",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" session_type \n",
" date_of_acquisition \n",
" ophys_session_id \n",
" prior_exposures_to_image_set \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [session_type, date_of_acquisition, ophys_session_id, prior_exposures_to_image_set]\n",
"Index: []"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mouse_id = 445002 \n",
"# get behavior sessions that took place on the microscope\n",
"mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)]\n",
"# only look at the relevant columns\n",
"mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['session_type', 'date_of_acquisition', 'ophys_session_id', 'prior_exposures_to_image_set']]"
]
},
{
"cell_type": "markdown",
"id": "5edd5efc",
"metadata": {
"papermill": {
"duration": 0.025514,
"end_time": "2023-11-30T05:23:00.402352",
"exception": false,
"start_time": "2023-11-30T05:23:00.376838",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Knowing the prior exposures number is especially important for the `ophys_session_table`, because the sessions that failed ophys QC are not visible there, so it is difficult to know whether a given session was the first of its type or a retake. "
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "9027e240",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:00.455523Z",
"iopub.status.busy": "2023-11-30T05:23:00.455113Z",
"iopub.status.idle": "2023-11-30T05:23:00.462750Z",
"shell.execute_reply": "2023-11-30T05:23:00.462093Z"
},
"papermill": {
"duration": 0.036003,
"end_time": "2023-11-30T05:23:00.464084",
"exception": false,
"start_time": "2023-11-30T05:23:00.428081",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" prior_exposures_to_image_set \n",
" \n",
" \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type, prior_exposures_to_image_set]\n",
"Index: []"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ophys_sessions[ophys_sessions.mouse_id==mouse_id][['date_of_acquisition', 'session_type', 'prior_exposures_to_image_set']]"
]
},
{
"cell_type": "markdown",
"id": "0ec46b22",
"metadata": {
"papermill": {
"duration": 0.02559,
"end_time": "2023-11-30T05:23:00.515663",
"exception": false,
"start_time": "2023-11-30T05:23:00.490073",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### `prior_exposures_to_session_type`"
]
},
{
"cell_type": "markdown",
"id": "6be4c69a",
"metadata": {
"papermill": {
"duration": 0.025792,
"end_time": "2023-11-30T05:23:00.567216",
"exception": false,
"start_time": "2023-11-30T05:23:00.541424",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"In some cases, you may want to know how many times a given `session_type` was seen by the mouse. For example, to know whether a passive viewing session was the very first time the mouse experienced a passive session with no lick spout, as there may be a difference in expectation of reward between the first passive session and a later one where the mouse has become accustomed to sometimes having the lick spout removed. "
]
},
{
"cell_type": "markdown",
"id": "eae9b601",
"metadata": {
"papermill": {
"duration": 0.025695,
"end_time": "2023-11-30T05:23:00.618492",
"exception": false,
"start_time": "2023-11-30T05:23:00.592797",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### compare `prior_exposures_to_session_type` in the `behavior_session_table` with the `ophys_session_table` for a given mouse"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "a0ab006e",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:00.671911Z",
"iopub.status.busy": "2023-11-30T05:23:00.671470Z",
"iopub.status.idle": "2023-11-30T05:23:00.682566Z",
"shell.execute_reply": "2023-11-30T05:23:00.681840Z"
},
"papermill": {
"duration": 0.039804,
"end_time": "2023-11-30T05:23:00.684290",
"exception": false,
"start_time": "2023-11-30T05:23:00.644486",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" ophys_session_id \n",
" prior_exposures_to_session_type \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type, ophys_session_id, prior_exposures_to_session_type]\n",
"Index: []"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pick a mouse\n",
"mouse_id = 456915\n",
"# get behavior sessions that took place on the microscope\n",
"mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&\n",
" (behavior_sessions.equipment_name=='MESO.1')]\n",
"# only look at the relevant columns\n",
"mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id', 'prior_exposures_to_session_type']]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "8e318120",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:00.740072Z",
"iopub.status.busy": "2023-11-30T05:23:00.739605Z",
"iopub.status.idle": "2023-11-30T05:23:00.747792Z",
"shell.execute_reply": "2023-11-30T05:23:00.747182Z"
},
"papermill": {
"duration": 0.036848,
"end_time": "2023-11-30T05:23:00.749151",
"exception": false,
"start_time": "2023-11-30T05:23:00.712303",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" prior_exposures_to_session_type \n",
" \n",
" \n",
" ophys_session_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type, prior_exposures_to_session_type]\n",
"Index: []"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ophys_sessions[ophys_sessions.mouse_id==mouse_id][['date_of_acquisition', 'session_type', 'prior_exposures_to_session_type']]"
]
},
{
"cell_type": "markdown",
"id": "39290a73",
"metadata": {
"papermill": {
"duration": 0.025912,
"end_time": "2023-11-30T05:23:00.801244",
"exception": false,
"start_time": "2023-11-30T05:23:00.775332",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Without the `prior_exposures_to_session_type` column in the `ophys_session_table`, it would be difficult to know that `OPHYS_2_images_A_passive` was actually the second time (1 prior exposure) that the mouse had experienced a passive vieweing session"
]
},
{
"cell_type": "markdown",
"id": "9c0f83d1",
"metadata": {
"papermill": {
"duration": 0.02599,
"end_time": "2023-11-30T05:23:00.853186",
"exception": false,
"start_time": "2023-11-30T05:23:00.827196",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### `prior_exposures_to_omissions`"
]
},
{
"cell_type": "markdown",
"id": "7d825055",
"metadata": {
"papermill": {
"duration": 0.026075,
"end_time": "2023-11-30T05:23:00.905221",
"exception": false,
"start_time": "2023-11-30T05:23:00.879146",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Another unique aspect of the experimental design of this dataset is the inclusion of stimulus omissions in the 2-photon portion of the experiment. During behavioral training, mice experience a highly regular cadence of himage presentations, with 250ms per stimulus presentation, with a 500ms gray screen in between. During imaging sessions, stimulus presentations (other than the change and pre-change images) are omitted with a 5% probability, resulting in some inter stimlus intervals appearing as an extended gray screen period. This allows exploration of potential effects of temporal expectation on neural activity. "
]
},
{
"cell_type": "markdown",
"id": "fd3599f6",
"metadata": {
"papermill": {
"duration": 0.026617,
"end_time": "2023-11-30T05:23:00.958010",
"exception": false,
"start_time": "2023-11-30T05:23:00.931393",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "62088262",
"metadata": {
"papermill": {
"duration": 0.026959,
"end_time": "2023-11-30T05:23:01.011612",
"exception": false,
"start_time": "2023-11-30T05:23:00.984653",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### Let's look at `prior_exposures_to_omissions` in a few mice"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "9d31c411",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:01.066398Z",
"iopub.status.busy": "2023-11-30T05:23:01.065752Z",
"iopub.status.idle": "2023-11-30T05:23:01.072346Z",
"shell.execute_reply": "2023-11-30T05:23:01.071696Z"
},
"papermill": {
"duration": 0.035552,
"end_time": "2023-11-30T05:23:01.073674",
"exception": false,
"start_time": "2023-11-30T05:23:01.038122",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array(['412036', '412366', '423606', '425493', '425496', '429952',\n",
" '431252', '436662', '442709', '447663', '448900', '453913',\n",
" '456564', '462468', '467953', '468866', '476970', '479458',\n",
" '480753', '489066', '492395', '523927', '524274', '533161',\n",
" '533162', '538219', '539518', '544261', '549976'], dtype=object)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sort(behavior_sessions[behavior_sessions.equipment_name=='CAM2P.4'].mouse_id.unique())"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "79764ccc",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:01.128662Z",
"iopub.status.busy": "2023-11-30T05:23:01.127920Z",
"iopub.status.idle": "2023-11-30T05:23:01.138527Z",
"shell.execute_reply": "2023-11-30T05:23:01.137852Z"
},
"papermill": {
"duration": 0.039201,
"end_time": "2023-11-30T05:23:01.139897",
"exception": false,
"start_time": "2023-11-30T05:23:01.100696",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" ophys_session_id \n",
" equipment_name \n",
" prior_exposures_to_omissions \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type, ophys_session_id, equipment_name, prior_exposures_to_omissions]\n",
"Index: []"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pick a mouse\n",
"mouse_id = 436662\n",
"# get behavior sessions that took place on the microscope\n",
"mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)&\n",
" (behavior_sessions.equipment_name=='CAM2P.4')]\n",
"# only look at the relevant columns\n",
"mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id', 'equipment_name', 'prior_exposures_to_omissions']]"
]
},
{
"cell_type": "markdown",
"id": "e99daa09",
"metadata": {
"papermill": {
"duration": 0.026491,
"end_time": "2023-11-30T05:23:01.193857",
"exception": false,
"start_time": "2023-11-30T05:23:01.167366",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"In this case (and in most cases), omissions do not occur until the first true imaging session on the 2-photon rig, `OPHYS_1`, i.e. they are not included in habituation sessions. However, in a small number of mice from the beginning of our data collection process, omissions did occur in habituation sessions (but never during training). This is something to be careful of if you are looking at something like the change in omission related activity with experience. "
]
},
{
"cell_type": "markdown",
"id": "77c1cab6",
"metadata": {
"papermill": {
"duration": 0.026381,
"end_time": "2023-11-30T05:23:01.246664",
"exception": false,
"start_time": "2023-11-30T05:23:01.220283",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Here is a mouse that saw omissions during habituation sessions. Also note that the first two habituation sessions took place on different microscopes (this is extremely rare, every effort is made to image a given mouse on the same 2-photon rig during its entire lifetime). "
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "6e8ca8f0",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:01.301371Z",
"iopub.status.busy": "2023-11-30T05:23:01.300723Z",
"iopub.status.idle": "2023-11-30T05:23:01.310662Z",
"shell.execute_reply": "2023-11-30T05:23:01.310083Z"
},
"papermill": {
"duration": 0.038798,
"end_time": "2023-11-30T05:23:01.312031",
"exception": false,
"start_time": "2023-11-30T05:23:01.273233",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" date_of_acquisition \n",
" session_type \n",
" ophys_session_id \n",
" equipment_name \n",
" prior_exposures_to_omissions \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [date_of_acquisition, session_type, ophys_session_id, equipment_name, prior_exposures_to_omissions]\n",
"Index: []"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pick a mouse\n",
"mouse_id = 423606\n",
"# get behavior sessions - include training as well\n",
"mouse_ophys_sessions = behavior_sessions[(behavior_sessions.mouse_id==mouse_id)]\n",
"# only look at the relevant columns\n",
"mouse_ophys_sessions.sort_values(by='date_of_acquisition')[['date_of_acquisition', 'session_type', 'ophys_session_id', 'equipment_name', 'prior_exposures_to_omissions']]"
]
},
{
"cell_type": "markdown",
"id": "408ba8d1",
"metadata": {
"papermill": {
"duration": 0.026376,
"end_time": "2023-11-30T05:23:01.365247",
"exception": false,
"start_time": "2023-11-30T05:23:01.338871",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Here is how to identify all mice that saw omissions during habituation sessions"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "bf60216c",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:01.419201Z",
"iopub.status.busy": "2023-11-30T05:23:01.418835Z",
"iopub.status.idle": "2023-11-30T05:23:01.425785Z",
"shell.execute_reply": "2023-11-30T05:23:01.425141Z"
},
"papermill": {
"duration": 0.03547,
"end_time": "2023-11-30T05:23:01.427218",
"exception": false,
"start_time": "2023-11-30T05:23:01.391748",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14 mice had omissions during habituation\n"
]
}
],
"source": [
"# get all behavior sessions that were habituation sessions (image set A or B) \n",
"# where the prior exposures to omissions was not zero\n",
"habituation_with_omission = behavior_sessions[((behavior_sessions.session_type=='OPHYS_0_images_A_habituation')|\n",
" (behavior_sessions.session_type=='OPHYS_0_images_B_habituation'))&\n",
" (behavior_sessions.prior_exposures_to_omissions>0)]\n",
"\n",
"mice_with_omission_during_habituation = habituation_with_omission.mouse_id.unique()\n",
"\n",
"print(len(mice_with_omission_during_habituation), ' mice had omissions during habituation')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3ba42ef",
"metadata": {
"papermill": {
"duration": 0.026555,
"end_time": "2023-11-30T05:23:01.480623",
"exception": false,
"start_time": "2023-11-30T05:23:01.454068",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "9dd5f5e5",
"metadata": {
"papermill": {
"duration": 0.027206,
"end_time": "2023-11-30T05:23:01.534911",
"exception": false,
"start_time": "2023-11-30T05:23:01.507705",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Ophys Experiment Table"
]
},
{
"cell_type": "markdown",
"id": "9ec2a91c",
"metadata": {
"papermill": {
"duration": 0.026924,
"end_time": "2023-11-30T05:23:01.589458",
"exception": false,
"start_time": "2023-11-30T05:23:01.562534",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The `ophys_experiment_table` contains all ophys data that passes QC, organized according to individual imaging planes in individual sessions, each associated with an `ophys_experiment_id`. The `ophys_experiment_table` contains all the columns in `ophys_session_table`, plus a few additional ones specific to individual imaging planes, namely `imaging_depth` and `targeted_structure`."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "4d0f19fd",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:01.644261Z",
"iopub.status.busy": "2023-11-30T05:23:01.643763Z",
"iopub.status.idle": "2023-11-30T05:23:01.661918Z",
"shell.execute_reply": "2023-11-30T05:23:01.661335Z"
},
"papermill": {
"duration": 0.047278,
"end_time": "2023-11-30T05:23:01.663292",
"exception": false,
"start_time": "2023-11-30T05:23:01.616014",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of ophys experiments: 1936\n",
"\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" behavior_session_id \n",
" ophys_session_id \n",
" ophys_container_id \n",
" mouse_id \n",
" indicator \n",
" full_genotype \n",
" driver_line \n",
" cre_line \n",
" reporter_line \n",
" sex \n",
" ... \n",
" passive \n",
" experience_level \n",
" prior_exposures_to_session_type \n",
" prior_exposures_to_image_set \n",
" prior_exposures_to_omissions \n",
" date_of_acquisition \n",
" equipment_name \n",
" published_at \n",
" isi_experiment_id \n",
" file_id \n",
" \n",
" \n",
" ophys_experiment_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 951980471 \n",
" 951520319 \n",
" 951410079 \n",
" 1018028342 \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 65 \n",
" 0 \n",
" 2019-09-20 09:59:38.837000+00:00 \n",
" MESO.1 \n",
" 2021-03-25 \n",
" 848974280 \n",
" 0 \n",
" \n",
" \n",
" 951980473 \n",
" 951520319 \n",
" 951410079 \n",
" 1018028345 \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 65 \n",
" 0 \n",
" 2019-09-20 09:59:38.837000+00:00 \n",
" MESO.1 \n",
" 2021-03-25 \n",
" 848974280 \n",
" 1 \n",
" \n",
" \n",
" 951980475 \n",
" 951520319 \n",
" 951410079 \n",
" 1018028339 \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 65 \n",
" 0 \n",
" 2019-09-20 09:59:38.837000+00:00 \n",
" MESO.1 \n",
" 2021-03-25 \n",
" 848974280 \n",
" 2 \n",
" \n",
" \n",
" 951980479 \n",
" 951520319 \n",
" 951410079 \n",
" 1018028354 \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 65 \n",
" 0 \n",
" 2019-09-20 09:59:38.837000+00:00 \n",
" MESO.1 \n",
" 2021-03-25 \n",
" 848974280 \n",
" 3 \n",
" \n",
" \n",
" 951980481 \n",
" 951520319 \n",
" 951410079 \n",
" 1018028357 \n",
" 457841 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 65 \n",
" 0 \n",
" 2019-09-20 09:59:38.837000+00:00 \n",
" MESO.1 \n",
" 2021-03-25 \n",
" 848974280 \n",
" 4 \n",
" \n",
" \n",
"
\n",
"
5 rows × 30 columns
\n",
"
"
],
"text/plain": [
" behavior_session_id ophys_session_id \\\n",
"ophys_experiment_id \n",
"951980471 951520319 951410079 \n",
"951980473 951520319 951410079 \n",
"951980475 951520319 951410079 \n",
"951980479 951520319 951410079 \n",
"951980481 951520319 951410079 \n",
"\n",
" ophys_container_id mouse_id indicator \\\n",
"ophys_experiment_id \n",
"951980471 1018028342 457841 GCaMP6f \n",
"951980473 1018028345 457841 GCaMP6f \n",
"951980475 1018028339 457841 GCaMP6f \n",
"951980479 1018028354 457841 GCaMP6f \n",
"951980481 1018028357 457841 GCaMP6f \n",
"\n",
" full_genotype \\\n",
"ophys_experiment_id \n",
"951980471 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"951980473 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"951980475 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"951980479 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"951980481 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"\n",
" driver_line cre_line reporter_line \\\n",
"ophys_experiment_id \n",
"951980471 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"951980473 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"951980475 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"951980479 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"951980481 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"\n",
" sex ... passive experience_level \\\n",
"ophys_experiment_id ... \n",
"951980471 F ... False Familiar \n",
"951980473 F ... False Familiar \n",
"951980475 F ... False Familiar \n",
"951980479 F ... False Familiar \n",
"951980481 F ... False Familiar \n",
"\n",
" prior_exposures_to_session_type \\\n",
"ophys_experiment_id \n",
"951980471 0 \n",
"951980473 0 \n",
"951980475 0 \n",
"951980479 0 \n",
"951980481 0 \n",
"\n",
" prior_exposures_to_image_set \\\n",
"ophys_experiment_id \n",
"951980471 65 \n",
"951980473 65 \n",
"951980475 65 \n",
"951980479 65 \n",
"951980481 65 \n",
"\n",
" prior_exposures_to_omissions \\\n",
"ophys_experiment_id \n",
"951980471 0 \n",
"951980473 0 \n",
"951980475 0 \n",
"951980479 0 \n",
"951980481 0 \n",
"\n",
" date_of_acquisition equipment_name \\\n",
"ophys_experiment_id \n",
"951980471 2019-09-20 09:59:38.837000+00:00 MESO.1 \n",
"951980473 2019-09-20 09:59:38.837000+00:00 MESO.1 \n",
"951980475 2019-09-20 09:59:38.837000+00:00 MESO.1 \n",
"951980479 2019-09-20 09:59:38.837000+00:00 MESO.1 \n",
"951980481 2019-09-20 09:59:38.837000+00:00 MESO.1 \n",
"\n",
" published_at isi_experiment_id file_id \n",
"ophys_experiment_id \n",
"951980471 2021-03-25 848974280 0 \n",
"951980473 2021-03-25 848974280 1 \n",
"951980475 2021-03-25 848974280 2 \n",
"951980479 2021-03-25 848974280 3 \n",
"951980481 2021-03-25 848974280 4 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ophys_experiments = cache.get_ophys_experiment_table()\n",
"\n",
"print(f\"Total number of ophys experiments: {len(ophys_experiments)}\\n\")\n",
"\n",
"ophys_experiments.head()"
]
},
{
"cell_type": "markdown",
"id": "90e742ca",
"metadata": {
"papermill": {
"duration": 0.026511,
"end_time": "2023-11-30T05:23:01.717043",
"exception": false,
"start_time": "2023-11-30T05:23:01.690532",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### Compare the columns of `ophys_sessions_table` with `ophys_experiments_table`"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "2935b6a8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:01.771687Z",
"iopub.status.busy": "2023-11-30T05:23:01.771182Z",
"iopub.status.idle": "2023-11-30T05:23:01.775995Z",
"shell.execute_reply": "2023-11-30T05:23:01.775331Z"
},
"papermill": {
"duration": 0.033669,
"end_time": "2023-11-30T05:23:01.777280",
"exception": false,
"start_time": "2023-11-30T05:23:01.743611",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['behavior_session_id', 'ophys_container_id', 'mouse_id', 'indicator',\n",
" 'full_genotype', 'driver_line', 'cre_line', 'reporter_line', 'sex',\n",
" 'age_in_days', 'imaging_plane_group_count', 'project_code',\n",
" 'session_type', 'session_number', 'image_set', 'behavior_type',\n",
" 'experience_level', 'prior_exposures_to_session_type',\n",
" 'prior_exposures_to_image_set', 'prior_exposures_to_omissions',\n",
" 'date_of_acquisition', 'equipment_name', 'num_depths_per_area',\n",
" 'ophys_experiment_id', 'num_targeted_structures'],\n",
" dtype='object')"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ophys_sessions.columns"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "a5fb01f7",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:01.832857Z",
"iopub.status.busy": "2023-11-30T05:23:01.832359Z",
"iopub.status.idle": "2023-11-30T05:23:01.836796Z",
"shell.execute_reply": "2023-11-30T05:23:01.836163Z"
},
"papermill": {
"duration": 0.03382,
"end_time": "2023-11-30T05:23:01.838259",
"exception": false,
"start_time": "2023-11-30T05:23:01.804439",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['behavior_session_id', 'ophys_session_id', 'ophys_container_id',\n",
" 'mouse_id', 'indicator', 'full_genotype', 'driver_line', 'cre_line',\n",
" 'reporter_line', 'sex', 'age_in_days', 'imaging_depth',\n",
" 'targeted_structure', 'targeted_imaging_depth', 'imaging_plane_group',\n",
" 'project_code', 'session_type', 'session_number', 'image_set',\n",
" 'behavior_type', 'passive', 'experience_level',\n",
" 'prior_exposures_to_session_type', 'prior_exposures_to_image_set',\n",
" 'prior_exposures_to_omissions', 'date_of_acquisition', 'equipment_name',\n",
" 'published_at', 'isi_experiment_id', 'file_id'],\n",
" dtype='object')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ophys_experiments.columns"
]
},
{
"cell_type": "markdown",
"id": "731e2ab8",
"metadata": {
"papermill": {
"duration": 0.026682,
"end_time": "2023-11-30T05:23:01.892376",
"exception": false,
"start_time": "2023-11-30T05:23:01.865694",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### What `imaging_depths` and `targeted_structures` are available? Are they different depending on `project_code`?"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "31028431",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:01.947363Z",
"iopub.status.busy": "2023-11-30T05:23:01.946759Z",
"iopub.status.idle": "2023-11-30T05:23:01.957185Z",
"shell.execute_reply": "2023-11-30T05:23:01.956533Z"
},
"papermill": {
"duration": 0.03941,
"end_time": "2023-11-30T05:23:01.958496",
"exception": false,
"start_time": "2023-11-30T05:23:01.919086",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"imaging_depths available for VisualBehaviorMultiscope include: [150 225 75 300 169 263 73 359 271 85 267 361 167 275 79 363 177 269\n",
" 365 175 283 367 165 373 81 371 369 171 259 77 279 83 377 274 152 71\n",
" 146 227 158 144 276 160 87 142 217 69 51 140 219 215 156 211 162 148\n",
" 223 166 231 174 282 93 213 221 229 306 320 298 232 218 179 285 270 272\n",
" 145 251 273 154 308 302 278 312 383 161 381 265 261 230 290 228 61 284\n",
" 233 304 292 65 289 235 310 375 280 281 187 91 197 295 183 355 277 181\n",
" 185 291 95 173 67 321 149 287 322 220 226 222 315 147 294 286 132 316\n",
" 296 350 348 379 340 345]\n",
"\n",
"targeted_structures available for VisualBehaviorMultiscope include: ['VISp' 'VISl']\n",
"\n",
"\n",
"\n",
"imaging_depths available for VisualBehaviorTask1B include: [275 175 375]\n",
"\n",
"targeted_structures available for VisualBehaviorTask1B include: ['VISp']\n",
"\n",
"\n",
"\n",
"imaging_depths available for VisualBehavior include: [175 375 275 290]\n",
"\n",
"targeted_structures available for VisualBehavior include: ['VISp']\n",
"\n",
"\n",
"\n",
"imaging_depths available for VisualBehaviorMultiscope4areasx2d include: [187 163 263 179 265 167 267 183 171 255 181 169 271 175 273 277 275 269\n",
" 193 185 189 283 177 165 173 159 279 281 150 260 240 160 205 155 210 145\n",
" 125 270 300 182 174 257 285 170 245 295 195 250 156 251 297 249 190 305\n",
" 176 244 200 310 107 153 227 123 103 157 111 161 139 225 109 110 137 221\n",
" 101 135 219 121 151 105 261 213 217 143 211 133 241 117 147 223 287 308\n",
" 178 180 290 286 280 268 141 253 289 201 254 235 230 128 224 132 222 233\n",
" 229 130 124 236 134 191]\n",
"\n",
"targeted_structures available for VisualBehaviorMultiscope4areasx2d include: ['VISp' 'VISl' 'VISal' 'VISam']\n",
"\n",
"\n"
]
}
],
"source": [
"# loop through project codes and print the available imaging_depths and targeted_structures\n",
"for project_code in ophys_experiments.project_code.unique():\n",
" \n",
" project_experiments = ophys_experiments[ophys_experiments.project_code==project_code]\n",
" print('\\nimaging_depths available for', project_code, 'include: ', project_experiments.imaging_depth.unique())\n",
" print('\\ntargeted_structures available for', project_code, 'include: ', project_experiments.targeted_structure.unique())\n",
" print('\\n')"
]
},
{
"cell_type": "markdown",
"id": "b875e804",
"metadata": {
"papermill": {
"duration": 0.026844,
"end_time": "2023-11-30T05:23:02.012473",
"exception": false,
"start_time": "2023-11-30T05:23:01.985629",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### `ophys_experiment_table` is useful for identifying `ophys_containers` to analyze"
]
},
{
"cell_type": "markdown",
"id": "f9211fc5",
"metadata": {
"papermill": {
"duration": 0.027102,
"end_time": "2023-11-30T05:23:02.066618",
"exception": false,
"start_time": "2023-11-30T05:23:02.039516",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Compare the `ophys_container_id` column of the `ophys_experiment_table` with the `ophys_session_table`. In `ophys_session_table`, each `ophys_session_id` is associated with one or more imaging planes (`ophys_experiment_ids`), while in the `ophys_experiment_table`, you can evaluate each of those imaging planes indepdently. This is particularly helpful for identifying `ophys_containers` that you want to analyze - the set of all imaging sessions for a given imaging plane. "
]
},
{
"cell_type": "markdown",
"id": "cc59f9dc",
"metadata": {
"papermill": {
"duration": 0.027232,
"end_time": "2023-11-30T05:23:02.120817",
"exception": false,
"start_time": "2023-11-30T05:23:02.093585",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"The `ophys_experient_table` has all the same columns as `ophys_session_table`, just reorgnized by `ophys_experiment_id`"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "54f91b4f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:02.177861Z",
"iopub.status.busy": "2023-11-30T05:23:02.177294Z",
"iopub.status.idle": "2023-11-30T05:23:02.181328Z",
"shell.execute_reply": "2023-11-30T05:23:02.180665Z"
},
"papermill": {
"duration": 0.034282,
"end_time": "2023-11-30T05:23:02.182742",
"exception": false,
"start_time": "2023-11-30T05:23:02.148460",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['behavior_session_id', 'ophys_session_id', 'ophys_container_id',\n",
" 'mouse_id', 'indicator', 'full_genotype', 'driver_line', 'cre_line',\n",
" 'reporter_line', 'sex', 'age_in_days', 'imaging_depth',\n",
" 'targeted_structure', 'targeted_imaging_depth', 'imaging_plane_group',\n",
" 'project_code', 'session_type', 'session_number', 'image_set',\n",
" 'behavior_type', 'passive', 'experience_level',\n",
" 'prior_exposures_to_session_type', 'prior_exposures_to_image_set',\n",
" 'prior_exposures_to_omissions', 'date_of_acquisition', 'equipment_name',\n",
" 'published_at', 'isi_experiment_id', 'file_id'],\n",
" dtype='object')\n"
]
}
],
"source": [
"print(ophys_experiments.columns)"
]
},
{
"cell_type": "markdown",
"id": "d6c74213",
"metadata": {
"papermill": {
"duration": 0.027452,
"end_time": "2023-11-30T05:23:02.238654",
"exception": false,
"start_time": "2023-11-30T05:23:02.211202",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"This means that each `ophys_experiment_id` has a single `ophys_container_id`."
]
},
{
"cell_type": "markdown",
"id": "b8fe48de",
"metadata": {
"papermill": {
"duration": 0.02684,
"end_time": "2023-11-30T05:23:02.292726",
"exception": false,
"start_time": "2023-11-30T05:23:02.265886",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### Let's pick an `ophys_container_id` and see what `ophys_experiments` it contains? "
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "5c519ede",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:02.348181Z",
"iopub.status.busy": "2023-11-30T05:23:02.347618Z",
"iopub.status.idle": "2023-11-30T05:23:02.351364Z",
"shell.execute_reply": "2023-11-30T05:23:02.350701Z"
},
"papermill": {
"duration": 0.0329,
"end_time": "2023-11-30T05:23:02.352638",
"exception": false,
"start_time": "2023-11-30T05:23:02.319738",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"ophys_container_id = ophys_experiments.ophys_container_id.unique()[50]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "1c04f4fd",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:02.407810Z",
"iopub.status.busy": "2023-11-30T05:23:02.407452Z",
"iopub.status.idle": "2023-11-30T05:23:02.425387Z",
"shell.execute_reply": "2023-11-30T05:23:02.424730Z"
},
"papermill": {
"duration": 0.047147,
"end_time": "2023-11-30T05:23:02.426829",
"exception": false,
"start_time": "2023-11-30T05:23:02.379682",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" behavior_session_id \n",
" ophys_session_id \n",
" ophys_container_id \n",
" mouse_id \n",
" indicator \n",
" full_genotype \n",
" driver_line \n",
" cre_line \n",
" reporter_line \n",
" sex \n",
" ... \n",
" passive \n",
" experience_level \n",
" prior_exposures_to_session_type \n",
" prior_exposures_to_image_set \n",
" prior_exposures_to_omissions \n",
" date_of_acquisition \n",
" equipment_name \n",
" published_at \n",
" isi_experiment_id \n",
" file_id \n",
" \n",
" \n",
" ophys_experiment_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 975608418 \n",
" 975239345 \n",
" 975050422 \n",
" 1018027765 \n",
" 484408 \n",
" GCaMP6f \n",
" Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
" [Slc17a7-IRES2-Cre, Camk2a-tTA] \n",
" Slc17a7-IRES2-Cre \n",
" Ai93(TITL-GCaMP6f) \n",
" M \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 9 \n",
" 1 \n",
" 2019-11-01 09:25:23.468000+00:00 \n",
" MESO.1 \n",
" 2021-08-12 \n",
" 942701302 \n",
" 293 \n",
" \n",
" \n",
" 976300323 \n",
" 976039179 \n",
" 975929719 \n",
" 1018027765 \n",
" 484408 \n",
" GCaMP6f \n",
" Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
" [Slc17a7-IRES2-Cre, Camk2a-tTA] \n",
" Slc17a7-IRES2-Cre \n",
" Ai93(TITL-GCaMP6f) \n",
" M \n",
" ... \n",
" True \n",
" Familiar \n",
" 0 \n",
" 10 \n",
" 2 \n",
" 2019-11-04 09:21:00.916000+00:00 \n",
" MESO.1 \n",
" 2021-08-12 \n",
" 942701302 \n",
" 301 \n",
" \n",
" \n",
" 977220493 \n",
" 976964099 \n",
" 976865980 \n",
" 1018027765 \n",
" 484408 \n",
" GCaMP6f \n",
" Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
" [Slc17a7-IRES2-Cre, Camk2a-tTA] \n",
" Slc17a7-IRES2-Cre \n",
" Ai93(TITL-GCaMP6f) \n",
" M \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 11 \n",
" 3 \n",
" 2019-11-05 13:35:33.124000+00:00 \n",
" MESO.1 \n",
" 2021-08-12 \n",
" 942701302 \n",
" 309 \n",
" \n",
" \n",
" 977975802 \n",
" 977451727 \n",
" 977288921 \n",
" 1018027765 \n",
" 484408 \n",
" GCaMP6f \n",
" Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
" [Slc17a7-IRES2-Cre, Camk2a-tTA] \n",
" Slc17a7-IRES2-Cre \n",
" Ai93(TITL-GCaMP6f) \n",
" M \n",
" ... \n",
" False \n",
" Novel 1 \n",
" 0 \n",
" 0 \n",
" 4 \n",
" 2019-11-06 09:02:04.020000+00:00 \n",
" MESO.1 \n",
" 2021-08-12 \n",
" 942701302 \n",
" 317 \n",
" \n",
" \n",
" 978866908 \n",
" 978554430 \n",
" 978323049 \n",
" 1018027765 \n",
" 484408 \n",
" GCaMP6f \n",
" Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
" [Slc17a7-IRES2-Cre, Camk2a-tTA] \n",
" Slc17a7-IRES2-Cre \n",
" Ai93(TITL-GCaMP6f) \n",
" M \n",
" ... \n",
" False \n",
" Novel >1 \n",
" 0 \n",
" 2 \n",
" 6 \n",
" 2019-11-08 08:52:05.141000+00:00 \n",
" MESO.1 \n",
" 2021-08-12 \n",
" 942701302 \n",
" 332 \n",
" \n",
" \n",
"
\n",
"
5 rows × 30 columns
\n",
"
"
],
"text/plain": [
" behavior_session_id ophys_session_id \\\n",
"ophys_experiment_id \n",
"975608418 975239345 975050422 \n",
"976300323 976039179 975929719 \n",
"977220493 976964099 976865980 \n",
"977975802 977451727 977288921 \n",
"978866908 978554430 978323049 \n",
"\n",
" ophys_container_id mouse_id indicator \\\n",
"ophys_experiment_id \n",
"975608418 1018027765 484408 GCaMP6f \n",
"976300323 1018027765 484408 GCaMP6f \n",
"977220493 1018027765 484408 GCaMP6f \n",
"977975802 1018027765 484408 GCaMP6f \n",
"978866908 1018027765 484408 GCaMP6f \n",
"\n",
" full_genotype \\\n",
"ophys_experiment_id \n",
"975608418 Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
"976300323 Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
"977220493 Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
"977975802 Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
"978866908 Slc17a7-IRES2-Cre/wt;Camk2a-tTA/wt;Ai93(TITL-G... \n",
"\n",
" driver_line cre_line \\\n",
"ophys_experiment_id \n",
"975608418 [Slc17a7-IRES2-Cre, Camk2a-tTA] Slc17a7-IRES2-Cre \n",
"976300323 [Slc17a7-IRES2-Cre, Camk2a-tTA] Slc17a7-IRES2-Cre \n",
"977220493 [Slc17a7-IRES2-Cre, Camk2a-tTA] Slc17a7-IRES2-Cre \n",
"977975802 [Slc17a7-IRES2-Cre, Camk2a-tTA] Slc17a7-IRES2-Cre \n",
"978866908 [Slc17a7-IRES2-Cre, Camk2a-tTA] Slc17a7-IRES2-Cre \n",
"\n",
" reporter_line sex ... passive experience_level \\\n",
"ophys_experiment_id ... \n",
"975608418 Ai93(TITL-GCaMP6f) M ... False Familiar \n",
"976300323 Ai93(TITL-GCaMP6f) M ... True Familiar \n",
"977220493 Ai93(TITL-GCaMP6f) M ... False Familiar \n",
"977975802 Ai93(TITL-GCaMP6f) M ... False Novel 1 \n",
"978866908 Ai93(TITL-GCaMP6f) M ... False Novel >1 \n",
"\n",
" prior_exposures_to_session_type \\\n",
"ophys_experiment_id \n",
"975608418 0 \n",
"976300323 0 \n",
"977220493 0 \n",
"977975802 0 \n",
"978866908 0 \n",
"\n",
" prior_exposures_to_image_set \\\n",
"ophys_experiment_id \n",
"975608418 9 \n",
"976300323 10 \n",
"977220493 11 \n",
"977975802 0 \n",
"978866908 2 \n",
"\n",
" prior_exposures_to_omissions \\\n",
"ophys_experiment_id \n",
"975608418 1 \n",
"976300323 2 \n",
"977220493 3 \n",
"977975802 4 \n",
"978866908 6 \n",
"\n",
" date_of_acquisition equipment_name \\\n",
"ophys_experiment_id \n",
"975608418 2019-11-01 09:25:23.468000+00:00 MESO.1 \n",
"976300323 2019-11-04 09:21:00.916000+00:00 MESO.1 \n",
"977220493 2019-11-05 13:35:33.124000+00:00 MESO.1 \n",
"977975802 2019-11-06 09:02:04.020000+00:00 MESO.1 \n",
"978866908 2019-11-08 08:52:05.141000+00:00 MESO.1 \n",
"\n",
" published_at isi_experiment_id file_id \n",
"ophys_experiment_id \n",
"975608418 2021-08-12 942701302 293 \n",
"976300323 2021-08-12 942701302 301 \n",
"977220493 2021-08-12 942701302 309 \n",
"977975802 2021-08-12 942701302 317 \n",
"978866908 2021-08-12 942701302 332 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"container_experiments = ophys_experiments[ophys_experiments.ophys_container_id==ophys_container_id]\n",
"container_experiments"
]
},
{
"cell_type": "markdown",
"id": "08c3f8d5",
"metadata": {
"papermill": {
"duration": 0.027531,
"end_time": "2023-11-30T05:23:02.482339",
"exception": false,
"start_time": "2023-11-30T05:23:02.454808",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"Thats 7 different recording sessions for this single imaging plane. Remember that one `ophys_container_id` is linked to one imaging plane, recorded in multiple sessions"
]
},
{
"cell_type": "markdown",
"id": "a9dc1c8f",
"metadata": {
"papermill": {
"duration": 0.026858,
"end_time": "2023-11-30T05:23:02.536538",
"exception": false,
"start_time": "2023-11-30T05:23:02.509680",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"#### What are the session types for this container? "
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "eea0fa3d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:02.592131Z",
"iopub.status.busy": "2023-11-30T05:23:02.591629Z",
"iopub.status.idle": "2023-11-30T05:23:02.596554Z",
"shell.execute_reply": "2023-11-30T05:23:02.596037Z"
},
"papermill": {
"duration": 0.034269,
"end_time": "2023-11-30T05:23:02.597842",
"exception": false,
"start_time": "2023-11-30T05:23:02.563573",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array(['OPHYS_1_images_G', 'OPHYS_2_images_G_passive', 'OPHYS_3_images_G',\n",
" 'OPHYS_4_images_H', 'OPHYS_6_images_H'], dtype=object)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"container_experiments.session_type.unique()"
]
},
{
"cell_type": "markdown",
"id": "f765cf8d",
"metadata": {
"papermill": {
"duration": 0.027029,
"end_time": "2023-11-30T05:23:02.653029",
"exception": false,
"start_time": "2023-11-30T05:23:02.626000",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Reminder about structure & terminology of the dataset"
]
},
{
"cell_type": "markdown",
"id": "2354be59",
"metadata": {
"papermill": {
"duration": 0.028303,
"end_time": "2023-11-30T05:23:02.708596",
"exception": false,
"start_time": "2023-11-30T05:23:02.680293",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "768b2dd4",
"metadata": {
"papermill": {
"duration": 0.02757,
"end_time": "2023-11-30T05:23:02.764813",
"exception": false,
"start_time": "2023-11-30T05:23:02.737243",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Reminder about cre lines"
]
},
{
"cell_type": "markdown",
"id": "3985b896",
"metadata": {
"papermill": {
"duration": 0.027404,
"end_time": "2023-11-30T05:23:02.819924",
"exception": false,
"start_time": "2023-11-30T05:23:02.792520",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "54d1a1d3",
"metadata": {
"papermill": {
"duration": 0.027554,
"end_time": "2023-11-30T05:23:02.875074",
"exception": false,
"start_time": "2023-11-30T05:23:02.847520",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Reminder about dataset variants aka project_codes"
]
},
{
"cell_type": "markdown",
"id": "d982faa5",
"metadata": {
"papermill": {
"duration": 0.027157,
"end_time": "2023-11-30T05:23:02.930073",
"exception": false,
"start_time": "2023-11-30T05:23:02.902916",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "757f68db",
"metadata": {
"papermill": {
"duration": 0.027393,
"end_time": "2023-11-30T05:23:02.984956",
"exception": false,
"start_time": "2023-11-30T05:23:02.957563",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Reminder about session types"
]
},
{
"cell_type": "markdown",
"id": "2853c8c8",
"metadata": {
"papermill": {
"duration": 0.027092,
"end_time": "2023-11-30T05:23:03.039465",
"exception": false,
"start_time": "2023-11-30T05:23:03.012373",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"id": "ec343aef",
"metadata": {
"papermill": {
"duration": 0.027118,
"end_time": "2023-11-30T05:23:03.094113",
"exception": false,
"start_time": "2023-11-30T05:23:03.066995",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52140d97",
"metadata": {
"papermill": {
"duration": 0.027026,
"end_time": "2023-11-30T05:23:03.148509",
"exception": false,
"start_time": "2023-11-30T05:23:03.121483",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "12c7f170",
"metadata": {
"papermill": {
"duration": 0.027474,
"end_time": "2023-11-30T05:23:03.203683",
"exception": false,
"start_time": "2023-11-30T05:23:03.176209",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Identifying experiments and sessions of interest"
]
},
{
"cell_type": "markdown",
"id": "1cedb900",
"metadata": {
"papermill": {
"duration": 0.027697,
"end_time": "2023-11-30T05:23:03.259115",
"exception": false,
"start_time": "2023-11-30T05:23:03.231418",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Get all experiments for one container from an Sst-IRES-Cre mouse in the VisualBehaviorTask1B project code "
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "67ec7003",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:03.316860Z",
"iopub.status.busy": "2023-11-30T05:23:03.316388Z",
"iopub.status.idle": "2023-11-30T05:23:03.322615Z",
"shell.execute_reply": "2023-11-30T05:23:03.321987Z"
},
"papermill": {
"duration": 0.036418,
"end_time": "2023-11-30T05:23:03.323928",
"exception": false,
"start_time": "2023-11-30T05:23:03.287510",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"941373529\n"
]
}
],
"source": [
"# get all Sst experiments in the relevant project code\n",
"sst_experiments = ophys_experiments[(ophys_experiments.cre_line=='Sst-IRES-Cre')&\n",
" (ophys_experiments.project_code=='VisualBehaviorTask1B')]\n",
"\n",
"# pick some container from this set\n",
"ophys_container_id = sst_experiments.ophys_container_id.unique()[1]\n",
"print(ophys_container_id)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "04008a46",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:03.380526Z",
"iopub.status.busy": "2023-11-30T05:23:03.380078Z",
"iopub.status.idle": "2023-11-30T05:23:03.398590Z",
"shell.execute_reply": "2023-11-30T05:23:03.397966Z"
},
"papermill": {
"duration": 0.048219,
"end_time": "2023-11-30T05:23:03.399925",
"exception": false,
"start_time": "2023-11-30T05:23:03.351706",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" behavior_session_id \n",
" ophys_session_id \n",
" ophys_container_id \n",
" mouse_id \n",
" indicator \n",
" full_genotype \n",
" driver_line \n",
" cre_line \n",
" reporter_line \n",
" sex \n",
" ... \n",
" passive \n",
" experience_level \n",
" prior_exposures_to_session_type \n",
" prior_exposures_to_image_set \n",
" prior_exposures_to_omissions \n",
" date_of_acquisition \n",
" equipment_name \n",
" published_at \n",
" isi_experiment_id \n",
" file_id \n",
" \n",
" \n",
" ophys_experiment_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 955276580 \n",
" 955110047 \n",
" 954981981 \n",
" 941373529 \n",
" 470784 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 20 \n",
" 0 \n",
" 2019-09-25 13:31:46+00:00 \n",
" CAM2P.5 \n",
" 2021-03-25 \n",
" 899181668 \n",
" 577 \n",
" \n",
" \n",
" 956903375 \n",
" 956213722 \n",
" 955991376 \n",
" 941373529 \n",
" 470784 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" True \n",
" Familiar \n",
" 0 \n",
" 21 \n",
" 1 \n",
" 2019-09-26 11:13:06.028000+00:00 \n",
" CAM2P.5 \n",
" 2021-03-25 \n",
" 899181668 \n",
" 578 \n",
" \n",
" \n",
" 957652800 \n",
" 957331258 \n",
" 957189583 \n",
" 941373529 \n",
" 470784 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Familiar \n",
" 0 \n",
" 22 \n",
" 2 \n",
" 2019-09-27 11:57:27.017000+00:00 \n",
" CAM2P.5 \n",
" 2021-03-25 \n",
" 899181668 \n",
" 579 \n",
" \n",
" \n",
" 959337347 \n",
" 958994511 \n",
" 958931715 \n",
" 941373529 \n",
" 470784 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Novel >1 \n",
" 2 \n",
" 2 \n",
" 5 \n",
" 2019-10-01 11:20:24.445000+00:00 \n",
" CAM2P.5 \n",
" 2021-03-25 \n",
" 899181668 \n",
" 580 \n",
" \n",
" \n",
" 960351917 \n",
" 959893902 \n",
" 959751299 \n",
" 941373529 \n",
" 470784 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" True \n",
" Novel >1 \n",
" 0 \n",
" 3 \n",
" 6 \n",
" 2019-10-02 11:46:04.405000+00:00 \n",
" CAM2P.5 \n",
" 2021-03-25 \n",
" 899181668 \n",
" 581 \n",
" \n",
" \n",
" 960960480 \n",
" 960697332 \n",
" 960593969 \n",
" 941373529 \n",
" 470784 \n",
" GCaMP6f \n",
" Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
" [Sst-IRES-Cre] \n",
" Sst-IRES-Cre \n",
" Ai148(TIT2L-GC6f-ICL-tTA2) \n",
" F \n",
" ... \n",
" False \n",
" Novel >1 \n",
" 0 \n",
" 4 \n",
" 7 \n",
" 2019-10-03 11:25:31.172000+00:00 \n",
" CAM2P.5 \n",
" 2021-03-25 \n",
" 899181668 \n",
" 582 \n",
" \n",
" \n",
"
\n",
"
6 rows × 30 columns
\n",
"
"
],
"text/plain": [
" behavior_session_id ophys_session_id \\\n",
"ophys_experiment_id \n",
"955276580 955110047 954981981 \n",
"956903375 956213722 955991376 \n",
"957652800 957331258 957189583 \n",
"959337347 958994511 958931715 \n",
"960351917 959893902 959751299 \n",
"960960480 960697332 960593969 \n",
"\n",
" ophys_container_id mouse_id indicator \\\n",
"ophys_experiment_id \n",
"955276580 941373529 470784 GCaMP6f \n",
"956903375 941373529 470784 GCaMP6f \n",
"957652800 941373529 470784 GCaMP6f \n",
"959337347 941373529 470784 GCaMP6f \n",
"960351917 941373529 470784 GCaMP6f \n",
"960960480 941373529 470784 GCaMP6f \n",
"\n",
" full_genotype \\\n",
"ophys_experiment_id \n",
"955276580 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"956903375 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"957652800 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"959337347 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"960351917 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"960960480 Sst-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt \n",
"\n",
" driver_line cre_line reporter_line \\\n",
"ophys_experiment_id \n",
"955276580 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"956903375 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"957652800 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"959337347 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"960351917 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"960960480 [Sst-IRES-Cre] Sst-IRES-Cre Ai148(TIT2L-GC6f-ICL-tTA2) \n",
"\n",
" sex ... passive experience_level \\\n",
"ophys_experiment_id ... \n",
"955276580 F ... False Familiar \n",
"956903375 F ... True Familiar \n",
"957652800 F ... False Familiar \n",
"959337347 F ... False Novel >1 \n",
"960351917 F ... True Novel >1 \n",
"960960480 F ... False Novel >1 \n",
"\n",
" prior_exposures_to_session_type \\\n",
"ophys_experiment_id \n",
"955276580 0 \n",
"956903375 0 \n",
"957652800 0 \n",
"959337347 2 \n",
"960351917 0 \n",
"960960480 0 \n",
"\n",
" prior_exposures_to_image_set \\\n",
"ophys_experiment_id \n",
"955276580 20 \n",
"956903375 21 \n",
"957652800 22 \n",
"959337347 2 \n",
"960351917 3 \n",
"960960480 4 \n",
"\n",
" prior_exposures_to_omissions \\\n",
"ophys_experiment_id \n",
"955276580 0 \n",
"956903375 1 \n",
"957652800 2 \n",
"959337347 5 \n",
"960351917 6 \n",
"960960480 7 \n",
"\n",
" date_of_acquisition equipment_name \\\n",
"ophys_experiment_id \n",
"955276580 2019-09-25 13:31:46+00:00 CAM2P.5 \n",
"956903375 2019-09-26 11:13:06.028000+00:00 CAM2P.5 \n",
"957652800 2019-09-27 11:57:27.017000+00:00 CAM2P.5 \n",
"959337347 2019-10-01 11:20:24.445000+00:00 CAM2P.5 \n",
"960351917 2019-10-02 11:46:04.405000+00:00 CAM2P.5 \n",
"960960480 2019-10-03 11:25:31.172000+00:00 CAM2P.5 \n",
"\n",
" published_at isi_experiment_id file_id \n",
"ophys_experiment_id \n",
"955276580 2021-03-25 899181668 577 \n",
"956903375 2021-03-25 899181668 578 \n",
"957652800 2021-03-25 899181668 579 \n",
"959337347 2021-03-25 899181668 580 \n",
"960351917 2021-03-25 899181668 581 \n",
"960960480 2021-03-25 899181668 582 \n",
"\n",
"[6 rows x 30 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# what experiments are there for this container? \n",
"sst_container_experiments = sst_experiments[sst_experiments.ophys_container_id==ophys_container_id]\n",
"sst_container_experiments"
]
},
{
"cell_type": "markdown",
"id": "d298a228",
"metadata": {
"papermill": {
"duration": 0.02754,
"end_time": "2023-11-30T05:23:03.455748",
"exception": false,
"start_time": "2023-11-30T05:23:03.428208",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Load the BehaviorOphysExperiment dataset for each ophys_experiment_id in the container and plot the max intensity projection - are they well aligned across sessions? can you identify the same neurons? "
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "53012df3",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:03.512295Z",
"iopub.status.busy": "2023-11-30T05:23:03.511823Z",
"iopub.status.idle": "2023-11-30T05:23:03.759000Z",
"shell.execute_reply": "2023-11-30T05:23:03.758328Z"
},
"papermill": {
"duration": 0.277234,
"end_time": "2023-11-30T05:23:03.760660",
"exception": false,
"start_time": "2023-11-30T05:23:03.483426",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "783a85a4",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:23:03.818414Z",
"iopub.status.busy": "2023-11-30T05:23:03.817700Z",
"iopub.status.idle": "2023-11-30T05:25:02.490525Z",
"shell.execute_reply": "2023-11-30T05:25:02.489808Z"
},
"papermill": {
"duration": 118.702737,
"end_time": "2023-11-30T05:25:02.492109",
"exception": false,
"start_time": "2023-11-30T05:23:03.789372",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"behavior_ophys_experiment_955276580.nwb: 100%|██████████| 313M/313M [00:13<00:00, 22.8MMB/s]\n",
"behavior_ophys_experiment_956903375.nwb: 100%|██████████| 322M/322M [00:13<00:00, 23.4MMB/s]\n",
"behavior_ophys_experiment_957652800.nwb: 100%|██████████| 318M/318M [00:13<00:00, 22.8MMB/s]\n",
"behavior_ophys_experiment_959337347.nwb: 100%|██████████| 342M/342M [00:14<00:00, 22.8MMB/s]\n",
"behavior_ophys_experiment_960351917.nwb: 100%|██████████| 327M/327M [00:14<00:00, 22.6MMB/s]\n",
"behavior_ophys_experiment_960960480.nwb: 100%|██████████| 332M/332M [00:18<00:00, 18.0MMB/s]\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ophys_experiment_ids are the index of the ophys_experiment_table\n",
"ophys_experiment_ids = sst_container_experiments.index.values\n",
"\n",
"# create figure axis\n",
"fig, ax = plt.subplots(1, len(ophys_experiment_ids), figsize=(20,5))\n",
"# enumerate over experiments in this container\n",
"for i, ophys_experiment_id in enumerate(ophys_experiment_ids): \n",
" # get the dataset object\n",
" dataset = cache.get_behavior_ophys_experiment(ophys_experiment_id=ophys_experiment_id)\n",
" # get the max intensity projection and plot on the appropriate axis\n",
" ax[i].imshow(dataset.max_projection.data, cmap='gray')\n",
" ax[i].set_title(ophys_experiment_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e307cb12",
"metadata": {
"papermill": {
"duration": 0.056747,
"end_time": "2023-11-30T05:25:02.607397",
"exception": false,
"start_time": "2023-11-30T05:25:02.550650",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "916a6eb1",
"metadata": {
"papermill": {
"duration": 0.056206,
"end_time": "2023-11-30T05:25:02.720165",
"exception": false,
"start_time": "2023-11-30T05:25:02.663959",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Get all imaging planes recorded during one session with novel images in a Vip mouse imaged on Multiscope"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "8583aaf9",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:25:02.834393Z",
"iopub.status.busy": "2023-11-30T05:25:02.833848Z",
"iopub.status.idle": "2023-11-30T05:25:02.839664Z",
"shell.execute_reply": "2023-11-30T05:25:02.839045Z"
},
"papermill": {
"duration": 0.064565,
"end_time": "2023-11-30T05:25:02.841042",
"exception": false,
"start_time": "2023-11-30T05:25:02.776477",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"# get all Vip sessions in the Multiscope project code\n",
"vip_sessions = ophys_sessions[(ophys_sessions.cre_line=='Vip-IRES-Cre')&\n",
" (ophys_sessions.project_code=='VisualBehaviorMultiscope')&\n",
" (ophys_sessions.prior_exposures_to_image_set==0)]\n",
"\n",
"# ophys_session_id is the index of the ophys_session_table\n",
"ophys_session_id = vip_sessions.index.values[0]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "78e63532",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:25:02.955790Z",
"iopub.status.busy": "2023-11-30T05:25:02.955186Z",
"iopub.status.idle": "2023-11-30T05:25:02.961504Z",
"shell.execute_reply": "2023-11-30T05:25:02.960970Z"
},
"papermill": {
"duration": 0.065012,
"end_time": "2023-11-30T05:25:02.962853",
"exception": false,
"start_time": "2023-11-30T05:25:02.897841",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"behavior_session_id 1050620316\n",
"ophys_container_id [1046576772, 1046576775, 1046576784, 104657678...\n",
"mouse_id 523922\n",
"indicator GCaMP6f\n",
"full_genotype Vip-IRES-Cre/wt;Ai148(TIT2L-GC6f-ICL-tTA2)/wt\n",
"driver_line [Vip-IRES-Cre]\n",
"cre_line Vip-IRES-Cre\n",
"reporter_line Ai148(TIT2L-GC6f-ICL-tTA2)\n",
"sex M\n",
"age_in_days 183\n",
"imaging_plane_group_count 4\n",
"project_code VisualBehaviorMultiscope\n",
"session_type OPHYS_4_images_B\n",
"session_number 4\n",
"image_set images_B\n",
"behavior_type active_behavior\n",
"experience_level Novel 1\n",
"prior_exposures_to_session_type 0\n",
"prior_exposures_to_image_set 0\n",
"prior_exposures_to_omissions 7\n",
"date_of_acquisition 2020-09-15 09:31:58.082000+00:00\n",
"equipment_name MESO.1\n",
"num_depths_per_area 4\n",
"ophys_experiment_id [1050762966, 1050762969, 1050762972, 105076297...\n",
"num_targeted_structures 2\n",
"Name: 1050597678, dtype: object"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# look at info for this ophys session\n",
"vip_sessions.loc[ophys_session_id]"
]
},
{
"cell_type": "markdown",
"id": "ef194001",
"metadata": {
"papermill": {
"duration": 0.05659,
"end_time": "2023-11-30T05:25:03.076200",
"exception": false,
"start_time": "2023-11-30T05:25:03.019610",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"### Plot the average dF/F trace for each of the experiments in this session for a 5 minute time period "
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "d035286a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:25:03.190793Z",
"iopub.status.busy": "2023-11-30T05:25:03.190486Z",
"iopub.status.idle": "2023-11-30T05:25:03.195054Z",
"shell.execute_reply": "2023-11-30T05:25:03.194334Z"
},
"papermill": {
"duration": 0.063709,
"end_time": "2023-11-30T05:25:03.196395",
"exception": false,
"start_time": "2023-11-30T05:25:03.132686",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1050762966, 1050762969, 1050762972, 1050762974, 1050762975, 1050762977]\n"
]
}
],
"source": [
"# get all the ophys_experiment_ids (corresponding to imaging planes) for this session\n",
"ophys_experiment_ids = vip_sessions.loc[ophys_session_id].ophys_experiment_id\n",
"print(ophys_experiment_ids)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "0bf45dda",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:25:03.339365Z",
"iopub.status.busy": "2023-11-30T05:25:03.338808Z",
"iopub.status.idle": "2023-11-30T05:26:34.968106Z",
"shell.execute_reply": "2023-11-30T05:26:34.967413Z"
},
"papermill": {
"duration": 91.71657,
"end_time": "2023-11-30T05:26:34.969568",
"exception": false,
"start_time": "2023-11-30T05:25:03.252998",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"behavior_ophys_experiment_1050762966.nwb: 100%|██████████| 248M/248M [00:10<00:00, 22.6MMB/s]\n",
"behavior_ophys_experiment_1050762969.nwb: 100%|██████████| 258M/258M [00:11<00:00, 23.3MMB/s]\n",
"behavior_ophys_experiment_1050762972.nwb: 100%|██████████| 251M/251M [00:10<00:00, 25.1MMB/s]\n",
"behavior_ophys_experiment_1050762974.nwb: 100%|██████████| 251M/251M [00:10<00:00, 23.6MMB/s]\n",
"behavior_ophys_experiment_1050762975.nwb: 100%|██████████| 259M/259M [00:10<00:00, 25.0MMB/s]\n",
"behavior_ophys_experiment_1050762977.nwb: 100%|██████████| 250M/250M [00:10<00:00, 23.9MMB/s]\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create figure axis\n",
"fig, ax = plt.subplots(1,1, figsize=(15,4))\n",
"# enumerate over experiments in this session\n",
"for i, ophys_experiment_id in enumerate(ophys_experiment_ids): \n",
" # get the dataset object\n",
" dataset = cache.get_behavior_ophys_experiment(ophys_experiment_id=ophys_experiment_id)\n",
" # get ophys timestamps\n",
" ophys_timestamps = dataset.ophys_timestamps\n",
" # get the population average dF/F trace\n",
" dff_traces = dataset.dff_traces\n",
" # dff_traces is a dataframe with a column 'dff'\n",
" # get the values of this column and turn into a matrix of n_cells x timepoints\n",
" dff_traces = np.vstack(dff_traces.dff.values)\n",
" # take the mean over the cell axis\n",
" average_dFF = np.mean(dff_traces, axis=0)\n",
" # get the imaging_depth and targeted_structure for this experiment\n",
" imaging_depth = dataset.metadata['imaging_depth']\n",
" targeted_structure = dataset.metadata['targeted_structure']\n",
" # plot it, including the imaging_depth and targeted_structure in the legend label\n",
" ax.plot(ophys_timestamps, average_dFF, label=targeted_structure+'_'+str(imaging_depth))\n",
" ax.set_title(dataset.metadata['cre_line']+', ophys_session_id: '+str(ophys_session_id))\n",
"ax.set_ylabel('dF/F')\n",
"ax.set_xlabel('time (seconds)')\n",
"ax.set_xlim(5*60, 10*60)\n",
"ax.legend()"
]
},
{
"cell_type": "markdown",
"id": "2f9f57a1",
"metadata": {
"papermill": {
"duration": 0.07764,
"end_time": "2023-11-30T05:26:35.127164",
"exception": false,
"start_time": "2023-11-30T05:26:35.049524",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"source": [
"## Ophys Cells Table"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "0b9608e5",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:26:35.282441Z",
"iopub.status.busy": "2023-11-30T05:26:35.281885Z",
"iopub.status.idle": "2023-11-30T05:26:35.289024Z",
"shell.execute_reply": "2023-11-30T05:26:35.288381Z"
},
"papermill": {
"duration": 0.08622,
"end_time": "2023-11-30T05:26:35.290423",
"exception": false,
"start_time": "2023-11-30T05:26:35.204203",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" ophys_experiment_id \n",
" cell_specimen_id \n",
" \n",
" \n",
" cell_roi_id \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" 1080884343 \n",
" 775614751 \n",
" 1086496928 \n",
" \n",
" \n",
" 1080884173 \n",
" 775614751 \n",
" 1086496914 \n",
" \n",
" \n",
" 1080883843 \n",
" 775614751 \n",
" 1086496838 \n",
" \n",
" \n",
" 1080886674 \n",
" 775614751 \n",
" 1086491756 \n",
" \n",
" \n",
" 1080885658 \n",
" 775614751 \n",
" 1086491699 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" ophys_experiment_id cell_specimen_id\n",
"cell_roi_id \n",
"1080884343 775614751 1086496928\n",
"1080884173 775614751 1086496914\n",
"1080883843 775614751 1086496838\n",
"1080886674 775614751 1086491756\n",
"1080885658 775614751 1086491699"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cells_table = cache.get_ophys_cells_table()\n",
"\n",
"cells_table.head()"
]
},
{
"cell_type": "markdown",
"id": "6b4a6555",
"metadata": {
"papermill": {
"duration": 0.077376,
"end_time": "2023-11-30T05:26:35.446001",
"exception": false,
"start_time": "2023-11-30T05:26:35.368625",
"status": "completed"
},
"tags": []
},
"source": [
"### How many cells per experiment?"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "2da8849b",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:26:35.601584Z",
"iopub.status.busy": "2023-11-30T05:26:35.601033Z",
"iopub.status.idle": "2023-11-30T05:26:35.787977Z",
"shell.execute_reply": "2023-11-30T05:26:35.787401Z"
},
"papermill": {
"duration": 0.265959,
"end_time": "2023-11-30T05:26:35.789378",
"exception": false,
"start_time": "2023-11-30T05:26:35.523419",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4LElEQVR4nO3de3RNd/7/8deJXAjOCSo5TONWlLhfilNKZ5KRatqpb1ODpagaphr3S9ELalpRnemFUb5jtMwMY6Y6dBoEjVsRQQjqVpRGy0m0JHGpiGT//ujP+c5pmOYk5ySx83ystddqPp/P2fu9P2OS19r7s/exGIZhCAAAwKT8yroAAAAAXyLsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU/Mv6wLKg4KCAp07d07Vq1eXxWIp63IAAEARGIahy5cvq27duvLzu/P1G8KOpHPnzik8PLysywAAAMVw9uxZ3XvvvXfsJ+xIql69uqQfJstqtZZxNQAAoChycnIUHh7u+jt+J4QdyXXrymq1EnYAALjL/NQSFBYoAwAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU/Mv6wLMrsGUNT855szsmFKoBACAiokrOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNTKPOx88803evrpp1WrVi1VqVJFrVq10t69e139hmFo2rRpqlOnjqpUqaKoqCidOHHCbR8XL17UgAEDZLVaFRISoqFDh+rKlSulfSoAAKAcKtOwc+nSJXXt2lUBAQFat26djhw5oj/84Q+qUaOGa8ycOXM0d+5cLVy4UCkpKapataqio6N1/fp115gBAwbo8OHD2rhxoxISErRt2zYNHz68LE4JAACUMxbDMIyyOviUKVO0Y8cOffbZZ7ftNwxDdevW1YQJEzRx4kRJUnZ2tsLCwrRkyRL169dPR48eVUREhPbs2aOOHTtKkhITE/Xoo4/q66+/Vt26dX+yjpycHNlsNmVnZ8tqtXrvBCU1mLLmJ8ecmR3j1WMCAFARFPXvd5le2fn3v/+tjh07qk+fPgoNDVW7du20aNEiV//p06fldDoVFRXlarPZbOrcubOSk5MlScnJyQoJCXEFHUmKioqSn5+fUlJSbnvc3Nxc5eTkuG0AAMCcyjTsfPnll1qwYIGaNGmi9evXa8SIERo9erSWLl0qSXI6nZKksLAwt8+FhYW5+pxOp0JDQ936/f39VbNmTdeYH4uPj5fNZnNt4eHh3j41AABQTpRp2CkoKFD79u01a9YstWvXTsOHD9ewYcO0cOFCnx536tSpys7Odm1nz5716fEAAEDZKdOwU6dOHUVERLi1NW/eXOnp6ZIku90uScrIyHAbk5GR4eqz2+3KzMx0679586YuXrzoGvNjQUFBslqtbhsAADCnMg07Xbt21fHjx93avvjiC9WvX1+S1LBhQ9ntdiUlJbn6c3JylJKSIofDIUlyOBzKyspSamqqa8ymTZtUUFCgzp07l8JZAACA8sy/LA8+btw4Pfjgg5o1a5Z+/etfa/fu3frTn/6kP/3pT5Iki8WisWPH6rXXXlOTJk3UsGFDvfLKK6pbt6569+4t6YcrQY888ojr9ldeXp5Gjhypfv36FelJLAAAYG5lGnYeeOABrVq1SlOnTtXMmTPVsGFDvfPOOxowYIBrzAsvvKCrV69q+PDhysrKUrdu3ZSYmKjKlSu7xixbtkwjR45UZGSk/Pz8FBsbq7lz55bFKQEAgHKmTN+zU17wnh0AAO4+d8V7dgAAAHyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEytTMPOjBkzZLFY3LZmzZq5+q9fv664uDjVqlVL1apVU2xsrDIyMtz2kZ6erpiYGAUHBys0NFSTJk3SzZs3S/tUAABAOeVf1gW0aNFCn376qetnf///K2ncuHFas2aNPvzwQ9lsNo0cOVJPPvmkduzYIUnKz89XTEyM7Ha7du7cqfPnz2vQoEEKCAjQrFmzSv1cAABA+VPmYcff3192u71Qe3Z2thYvXqzly5frF7/4hSTpgw8+UPPmzbVr1y516dJFGzZs0JEjR/Tpp58qLCxMbdu21e9+9ztNnjxZM2bMUGBgYGmfDgAAKGdKfBsrPz9faWlpunTpUrE+f+LECdWtW1eNGjXSgAEDlJ6eLklKTU1VXl6eoqKiXGObNWumevXqKTk5WZKUnJysVq1aKSwszDUmOjpaOTk5Onz48B2PmZubq5ycHLcNAACYk8dhZ+zYsVq8eLGkH4JOjx491L59e4WHh2vLli0e7atz585asmSJEhMTtWDBAp0+fVoPPfSQLl++LKfTqcDAQIWEhLh9JiwsTE6nU5LkdDrdgs6t/lt9dxIfHy+bzebawsPDPaobAADcPTwOOytXrlSbNm0kSZ988olOnz6tY8eOady4cXrppZc82levXr3Up08ftW7dWtHR0Vq7dq2ysrL0z3/+09OyPDJ16lRlZ2e7trNnz/r0eAAAoOx4HHa+/fZb1xqbtWvXqk+fPmratKmeffZZHTp0qETFhISEqGnTpjp58qTsdrtu3LihrKwstzEZGRmu49vt9kJPZ936+XbrgG4JCgqS1Wp12wAAgDl5HHbCwsJ05MgR5efnKzExUb/85S8lSdeuXVOlSpVKVMyVK1d06tQp1alTRx06dFBAQICSkpJc/cePH1d6erocDockyeFw6NChQ8rMzHSN2bhxo6xWqyIiIkpUCwAAMAePn8YaMmSIfv3rX6tOnTqyWCyuBcQpKSlu78gpiokTJ+rxxx9X/fr1de7cOU2fPl2VKlVS//79ZbPZNHToUI0fP141a9aU1WrVqFGj5HA41KVLF0lSz549FRERoYEDB2rOnDlyOp16+eWXFRcXp6CgIE9PDQAAmJDHYWfGjBlq2bKlzp49qz59+rhCRaVKlTRlyhSP9vX111+rf//++u6771S7dm1169ZNu3btUu3atSVJb7/9tvz8/BQbG6vc3FxFR0frvffec32+UqVKSkhI0IgRI+RwOFS1alUNHjxYM2fO9PS0AACASVkMwzA8+cBf/vIX9e3bt9CVkxs3bmjFihUaNGiQVwssDTk5ObLZbMrOzvb6+p0GU9b85Jgzs2O8ekwAACqCov799njNzpAhQ5SdnV2o/fLlyxoyZIinuwMAAPApj8OOYRiyWCyF2r/++mvZbDavFAUAAOAtRV6z065dO9eXdUZGRrp9h1V+fr5Onz6tRx55xCdFAgAAFFeRw07v3r0lSWlpaYqOjla1atVcfYGBgWrQoIFiY2O9XiAAAEBJFDnsTJ8+XZLUoEED9e3bV5UrV/ZZUQAAAN7i8aPngwcPlvTD01eZmZkqKChw669Xr553KgMAAPACj8POiRMn9Oyzz2rnzp1u7bcWLufn53utOAAAgJLyOOw888wz8vf3V0JCgustygAAAOWVx2EnLS1NqampHn81BAAAQFnw+D07ERER+vbbb31RCwAAgNd5HHbeeOMNvfDCC9qyZYu+++475eTkuG0AAADlice3sW59y3lkZKRbOwuUAQBAeeRx2Nm8ebMv6gAAAPAJj8NOjx49fFEHAACAT3i8ZkeSPvvsMz399NN68MEH9c0330iS/vrXv2r79u1eLQ4AAKCkPA47H330kaKjo1WlShXt27dPubm5kqTs7GzNmjXL6wUCAACUhMdh57XXXtPChQu1aNEiBQQEuNq7du2qffv2ebU4AACAkvI47Bw/flzdu3cv1G6z2ZSVleWNmgAAALzG47Bjt9t18uTJQu3bt29Xo0aNvFIUAACAt3gcdoYNG6YxY8YoJSVFFotF586d07JlyzRx4kSNGDHCFzUCAAAUm8ePnk+ZMkUFBQWKjIzUtWvX1L17dwUFBWnixIkaNWqUL2oEAAAoNo/DjsVi0UsvvaRJkybp5MmTunLliiIiIlStWjVf1AcAAFAiHoedWwIDAxUREeHNWgAAALzO47Bz/fp1zZs3T5s3b1ZmZqYKCgrc+nn8HAAAlCceh52hQ4dqw4YNeuqpp9SpUydZLBZf1AUAAOAVHoedhIQErV27Vl27dvVFPQAAAF7l8aPnP/vZz1S9enVf1AIAAOB1HoedP/zhD5o8ebK++uorX9QDAADgVR7fxurYsaOuX7+uRo0aKTg42O37sSTp4sWLXisOAACgpDwOO/3799c333yjWbNmKSwsjAXKAACgXPM47OzcuVPJyclq06aNL+oBAADwKo/X7DRr1kzff/+9L2oBAADwOo/DzuzZszVhwgRt2bJF3333nXJyctw2AACA8sTj21iPPPKIJCkyMtKt3TAMWSwW5efne6cyAAAAL/A47GzevNkXdQAAAPiEx2GnR48evqgDAADAJ4oUdg4ePKiWLVvKz89PBw8e/K9jW7du7ZXCAAAAvKFIYadt27ZyOp0KDQ1V27ZtZbFYZBhGoXGs2QEAAOVNkcLO6dOnVbt2bdd/AwAA3C2KFHbq168vScrLy9Orr76qV155RQ0bNvRpYQAAAN7g0Xt2AgIC9NFHH/mqFgAAAK/z+KWCvXv31urVq31QCgAAgPd5HHaaNGmimTNn6qmnnlJ8fLzmzp3rthXX7NmzZbFYNHbsWFfb9evXFRcXp1q1aqlatWqKjY1VRkaG2+fS09MVExOj4OBghYaGatKkSbp582ax6wAAAObi8Xt2Fi9erJCQEKWmpio1NdWtz2KxaPTo0R4XsWfPHv3v//5vocfWx40bpzVr1ujDDz+UzWbTyJEj9eSTT2rHjh2SpPz8fMXExMhut2vnzp06f/68Bg0apICAAM2aNcvjOgAAgPl4HHa8/TTWlStXNGDAAC1atEivvfaaqz07O1uLFy/W8uXL9Ytf/EKS9MEHH6h58+batWuXunTpog0bNujIkSP69NNPFRYWprZt2+p3v/udJk+erBkzZigwMNCrtQIAgLuPx7exbrlx44aOHz9e4ltGcXFxiomJUVRUlFt7amqq8vLy3NqbNWumevXqKTk5WZKUnJysVq1aKSwszDUmOjpaOTk5Onz48B2PmZubyxeYAgBQQXgcdq5du6ahQ4cqODhYLVq0UHp6uiRp1KhRmj17tkf7WrFihfbt26f4+PhCfU6nU4GBgQoJCXFrDwsLk9PpdI35z6Bzq/9W353Ex8fLZrO5tvDwcI/qBgAAdw+Pw87UqVN14MABbdmyRZUrV3a1R0VF6R//+EeR93P27FmNGTNGy5Ytc9tPaZg6daqys7Nd29mzZ0v1+AAAoPR4vGZn9erV+sc//qEuXbrIYrG42lu0aKFTp04VeT+pqanKzMxU+/btXW35+fnatm2b/vjHP2r9+vW6ceOGsrKy3K7uZGRkyG63S5Lsdrt2797ttt9bT2vdGnM7QUFBCgoKKnKtvtZgypqfHHNmdkwpVAIAgPl4fGXnwoULCg0NLdR+9epVt/DzUyIjI3Xo0CGlpaW5to4dO2rAgAGu/w4ICFBSUpLrM8ePH1d6erocDockyeFw6NChQ8rMzHSN2bhxo6xWqyIiIjw9NQAAYEIeX9np2LGj1qxZo1GjRkmSK+D8+c9/doWQoqhevbpatmzp1la1alXVqlXL1T506FCNHz9eNWvWlNVq1ahRo+RwONSlSxdJUs+ePRUREaGBAwdqzpw5cjqdevnllxUXF1eurtwAAICy43HYmTVrlnr16qUjR47o5s2bevfdd3XkyBHt3LlTW7du9Wpxb7/9tvz8/BQbG6vc3FxFR0frvffec/VXqlRJCQkJGjFihBwOh6pWrarBgwdr5syZXq0DAADcvSyGYRiefujUqVOaPXu2Dhw4oCtXrqh9+/aaPHmyWrVq5YsafS4nJ0c2m03Z2dmyWq1e3XdR1uMUBWt2AABwV9S/3x5f2ZGk++67T4sWLSp2cQAAAKWlWGEnPz9fq1at0tGjRyVJEREReuKJJ+TvX6zdAQAA+IzH6eTw4cP61a9+JafTqfvvv1+S9MYbb6h27dr65JNPCi06BgAAKEseP3r+m9/8Ri1atNDXX3+tffv2ad++fTp79qxat26t4cOH+6JGAACAYvP4yk5aWpr27t2rGjVquNpq1Kih119/XQ888IBXiwMAACgpj6/sNG3a1PWW4v+UmZmpxo0be6UoAAAAb/E47MTHx2v06NFauXKlvv76a3399ddauXKlxo4dqzfeeINvEgcAAOWKx7exHnvsMUnSr3/9a9fbk2+9qufxxx93/WyxWJSfn++tOgEAAIrF47CzefNmX9QBAADgEx6HnR49eviiDgAAAJ/weM3OjBkzVFBQUKg9Oztb/fv390pRAAAA3uJx2Fm8eLG6deumL7/80tW2ZcsWtWrVSqdOnfJqcQAAACXlcdg5ePCg7r33XrVt21aLFi3SpEmT1LNnTw0cOFA7d+70RY0AAADF5vGanRo1auif//ynXnzxRf32t7+Vv7+/1q1bp8jISF/UBwAAUCIeX9mRpHnz5undd99V//791ahRI40ePVoHDhzwdm0AAAAl5nHYeeSRR/Tqq69q6dKlWrZsmfbv36/u3burS5cumjNnji9qBAAAKDaPw05+fr4OHjyop556SpJUpUoVLViwQCtXrtTbb7/t9QIBAABKwuM1Oxs3brxte0xMjA4dOlTiggAAALypWGt2PvvsMz399NNyOBz65ptvJEl//etfdezYMa8WBwAAUFIeh52PPvpI0dHRqlKlivbv36/c3FxJP7xUcNasWV4vEAAAoCQ8DjuvvfaaFi5cqEWLFikgIMDV3rVrV+3bt8+rxQEAAJSUx2Hn+PHj6t69e6F2m82mrKwsb9QEAADgNR6HHbvdrpMnTxZq3759uxo1auSVogAAALzF47AzbNgwjRkzRikpKbJYLDp37pyWLVumiRMnasSIEb6oEQAAoNg8fvR8ypQpKigoUGRkpK5du6bu3bsrKChIEydO1KhRo3xRIwAAQLF5HHYsFoteeuklTZo0SSdPntSVK1cUERGhatWq+aI+AACAEvE47NwSGBioiIgIb9YCAADgdcV6qSAAAMDdgrADAABMjbADAABMrUhhp3379rp06ZIkaebMmbp27ZpPiwIAAPCWIoWdo0eP6urVq5KkV199VVeuXPFpUQAAAN5SpKex2rZtqyFDhqhbt24yDEO///3v7/io+bRp07xaIAAAQEkUKewsWbJE06dPV0JCgiwWi9atWyd//8IftVgshB0AAFCuFCns3H///VqxYoUkyc/PT0lJSQoNDfVpYQAAAN7g8UsFCwoKfFEHAACATxTrDcqnTp3SO++8o6NHj0qSIiIiNGbMGN13331eLQ4AAKCkPH7Pzvr16xUREaHdu3erdevWat26tVJSUtSiRQtt3LjRFzUCAAAUW7G+9XzcuHGaPXt2ofbJkyfrl7/8pdeKAwAAKCmPr+wcPXpUQ4cOLdT+7LPP6siRI14pCgAAwFs8Dju1a9dWWlpaofa0tDSe0AIAAOWOx7exhg0bpuHDh+vLL7/Ugw8+KEnasWOH3njjDY0fP97rBQIAAJSEx1d2XnnlFU2bNk3z5s1Tjx491KNHD/3xj3/UjBkz9PLLL3u0rwULFqh169ayWq2yWq1yOBxat26dq//69euKi4tTrVq1VK1aNcXGxiojI8NtH+np6YqJiVFwcLBCQ0M1adIk3bx509PTAgAAJuXxlR2LxaJx48Zp3Lhxunz5siSpevXqxTr4vffeq9mzZ6tJkyYyDENLly7VE088of3796tFixYaN26c1qxZow8//FA2m00jR47Uk08+qR07dkiS8vPzFRMTI7vdrp07d+r8+fMaNGiQAgICNGvWrGLVBAAAzMViGIZR1kX8p5o1a+rNN9/UU089pdq1a2v58uV66qmnJEnHjh1T8+bNlZycrC5dumjdunV67LHHdO7cOYWFhUmSFi5cqMmTJ+vChQsKDAws0jFzcnJks9mUnZ0tq9Xq1fNpMGWNV/ZzZnaMV/YDAIBZFPXvt8e3sXwlPz9fK1as0NWrV+VwOJSamqq8vDxFRUW5xjRr1kz16tVTcnKyJCk5OVmtWrVyBR1Jio6OVk5Ojg4fPnzHY+Xm5ionJ8dtAwAA5lTmYefQoUOqVq2agoKC9Nxzz2nVqlWKiIiQ0+lUYGCgQkJC3MaHhYXJ6XRKkpxOp1vQudV/q+9O4uPjZbPZXFt4eLh3TwoAAJQbZR527r//fqWlpSklJUUjRozQ4MGDff6+nqlTpyo7O9u1nT171qfHAwAAZcejsJOXl6fIyEidOHHCawUEBgaqcePG6tChg+Lj49WmTRu9++67stvtunHjhrKystzGZ2RkyG63S5Lsdnuhp7Nu/XxrzO0EBQW5ngC7tQEAAHPyKOwEBATo4MGDvqpF0g/fqp6bm6sOHTooICBASUlJrr7jx48rPT1dDodDkuRwOHTo0CFlZma6xmzcuFFWq1URERE+rRMAANwdPH70/Omnn9bixYsLfTdWcUydOlW9evVSvXr1dPnyZS1fvlxbtmzR+vXrZbPZNHToUI0fP141a9aU1WrVqFGj5HA41KVLF0lSz549FRERoYEDB2rOnDlyOp16+eWXFRcXp6CgoBLXBwAA7n4eh52bN2/q/fff16effqoOHTqoatWqbv1vvfVWkfeVmZmpQYMG6fz587LZbGrdurXWr1/v+jLRt99+W35+foqNjVVubq6io6P13nvvuT5fqVIlJSQkaMSIEXI4HKpataoGDx6smTNnenpaAADApDx+z87Pf/7zO+/MYtGmTZtKXFRp4z07AADcfYr699vjKzubN28uUWEAAAClqdiPnp88eVLr16/X999/L0kqZy9iBgAAkFSMsPPdd98pMjJSTZs21aOPPqrz589LkoYOHaoJEyZ4vUAAAICS8DjsjBs3TgEBAUpPT1dwcLCrvW/fvkpMTPRqcQAAACXl8ZqdDRs2aP369br33nvd2ps0aaKvvvrKa4UBAAB4g8dXdq5evep2ReeWixcv8m4bAABQ7ngcdh566CH95S9/cf1ssVhUUFCgOXPm/NfH0gEAAMqCx7ex5syZo8jISO3du1c3btzQCy+8oMOHD+vixYvasWOHL2oEAAAoNo+v7LRs2VJffPGFunXrpieeeEJXr17Vk08+qf379+u+++7zRY0AAADF5vGVHUmy2Wx66aWXvF0LAACA1xUr7Fy6dEmLFy/W0aNHJUkREREaMmSIatas6dXiAAAASsrj21jbtm1TgwYNNHfuXF26dEmXLl3S3Llz1bBhQ23bts0XNQIAABSbx1d24uLi1LdvXy1YsECVKlWSJOXn5+v5559XXFycDh065PUiAQAAisvjKzsnT57UhAkTXEFHkipVqqTx48fr5MmTXi0OAACgpDwOO+3bt3et1flPR48eVZs2bbxSFAAAgLcU6TbWwYMHXf89evRojRkzRidPnlSXLl0kSbt27dL8+fM1e/Zs31QJAABQTBbDMIyfGuTn5yeLxaKfGmqxWJSfn++14kpLTk6ObDabsrOzZbVavbrvBlPWeGU/Z2bHeGU/AACYRVH/fhfpys7p06e9VhgAAEBpKlLYqV+/vq/rAAAA8IlivVTw3Llz2r59uzIzM1VQUODWN3r0aK8UBgAA4A0eh50lS5bot7/9rQIDA1WrVi1ZLBZXn8ViIewAAIByxeOw88orr2jatGmaOnWq/Pw8fnIdAACgVHmcVq5du6Z+/foRdAAAwF3B48QydOhQffjhh76oBQAAwOs8vo0VHx+vxx57TImJiWrVqpUCAgLc+t966y2vFQcAAFBSxQo769ev1/333y9JhRYoAwAAlCceh50//OEPev/99/XMM8/4oBwAAADv8njNTlBQkLp27eqLWgAAALzO47AzZswYzZs3zxe1AAAAeJ3Ht7F2796tTZs2KSEhQS1atCi0QPlf//qX14oDAAAoKY/DTkhIiJ588klf1AIAAOB1HoedDz74wBd1AAAA+ASvQQYAAKbm8ZWdhg0b/tf36Xz55ZclKggAAMCbPA47Y8eOdfs5Ly9P+/fvV2JioiZNmuStugAAALzC47AzZsyY27bPnz9fe/fuLXFBAAAA3uS1NTu9evXSRx995K3dAQAAeIXXws7KlStVs2ZNb+0OAADAKzy+jdWuXTu3BcqGYcjpdOrChQt67733vFocAABASXkcdnr37u32s5+fn2rXrq2HH35YzZo181ZdAAAAXuFx2Jk+fbov6gAAAPAJXioIAABMrchhx8/PT5UqVfqvm7+/ZxeK4uPj9cADD6h69eoKDQ1V7969dfz4cbcx169fV1xcnGrVqqVq1aopNjZWGRkZbmPS09MVExOj4OBghYaGatKkSbp586ZHtQAAAHMqcjpZtWrVHfuSk5M1d+5cFRQUeHTwrVu3Ki4uTg888IBu3rypF198UT179tSRI0dUtWpVSdK4ceO0Zs0affjhh7LZbBo5cqSefPJJ7dixQ5KUn5+vmJgY2e127dy5U+fPn9egQYMUEBCgWbNmeVQPAAAwH4thGEZxP3z8+HFNmTJFn3zyiQYMGKCZM2eqfv36xS7mwoULCg0N1datW9W9e3dlZ2erdu3aWr58uZ566ilJ0rFjx9S8eXMlJyerS5cuWrdunR577DGdO3dOYWFhkqSFCxdq8uTJunDhggIDA3/yuDk5ObLZbMrOzpbVai12/bfTYMoar+znzOwYr+wHAACzKOrf72Kt2Tl37pyGDRumVq1a6ebNm0pLS9PSpUtLFHQkKTs7W5Jc7+tJTU1VXl6eoqKiXGOaNWumevXqKTk5WdIPV5VatWrlCjqSFB0drZycHB0+fPi2x8nNzVVOTo7bBgAAzMmjsJOdna3JkyercePGOnz4sJKSkvTJJ5+oZcuWJS6koKBAY8eOVdeuXV37czqdCgwMVEhIiNvYsLAwOZ1O15j/DDq3+m/13U58fLxsNptrCw8PL3H9AACgfCpy2JkzZ44aNWqkhIQE/f3vf9fOnTv10EMPea2QuLg4ff7551qxYoXX9nknU6dOVXZ2tms7e/asz48JAADKRpEXKE+ZMkVVqlRR48aNtXTpUi1duvS24/71r395XMTIkSOVkJCgbdu26d5773W12+123bhxQ1lZWW5XdzIyMmS3211jdu/e7ba/W09r3RrzY0FBQQoKCvK4TgAAcPcpctgZNGiQ29dEeINhGBo1apRWrVqlLVu2qGHDhm79HTp0UEBAgJKSkhQbGyvph0XR6enpcjgckiSHw6HXX39dmZmZCg0NlSRt3LhRVqtVERERXq0XAADcfYocdpYsWeL1g8fFxWn58uX6+OOPVb16ddcaG5vNpipVqshms2no0KEaP368atasKavVqlGjRsnhcKhLly6SpJ49eyoiIkIDBw7UnDlz5HQ69fLLLysuLo6rNwAAwPOvi/CmBQsWSJIefvhht/YPPvhAzzzzjCTp7bfflp+fn2JjY5Wbm6vo6Gi3LxytVKmSEhISNGLECDkcDlWtWlWDBw/WzJkzS+s0AABAOVai9+yYBe/ZAQDg7uPT9+wAAADcLQg7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Mo07Gzbtk2PP/646tatK4vFotWrV7v1G4ahadOmqU6dOqpSpYqioqJ04sQJtzEXL17UgAEDZLVaFRISoqFDh+rKlSuleBYAAKA8K9Owc/XqVbVp00bz58+/bf+cOXM0d+5cLVy4UCkpKapataqio6N1/fp115gBAwbo8OHD2rhxoxISErRt2zYNHz68tE4BAACUc/5lefBevXqpV69et+0zDEPvvPOOXn75ZT3xxBOSpL/85S8KCwvT6tWr1a9fPx09elSJiYnas2ePOnbsKEmaN2+eHn30Uf3+979X3bp1S+1cAABA+VRu1+ycPn1aTqdTUVFRrjabzabOnTsrOTlZkpScnKyQkBBX0JGkqKgo+fn5KSUl5Y77zs3NVU5OjtsGAADMqdyGHafTKUkKCwtzaw8LC3P1OZ1OhYaGuvX7+/urZs2arjG3Ex8fL5vN5trCw8O9XD0AACgvym3Y8aWpU6cqOzvbtZ09e7asSwIAAD5SbsOO3W6XJGVkZLi1Z2RkuPrsdrsyMzPd+m/evKmLFy+6xtxOUFCQrFar2wYAAMyp3Iadhg0bym63KykpydWWk5OjlJQUORwOSZLD4VBWVpZSU1NdYzZt2qSCggJ17ty51GsGAADlT5k+jXXlyhWdPHnS9fPp06eVlpammjVrql69eho7dqxee+01NWnSRA0bNtQrr7yiunXrqnfv3pKk5s2b65FHHtGwYcO0cOFC5eXlaeTIkerXrx9PYgEAAEllHHb27t2rn//8566fx48fL0kaPHiwlixZohdeeEFXr17V8OHDlZWVpW7duikxMVGVK1d2fWbZsmUaOXKkIiMj5efnp9jYWM2dO7fUzwUAAJRPFsMwjLIuoqzl5OTIZrMpOzvb6+t3GkxZ45X9nJkd45X9AABgFkX9+12mV3ZQdEUJTaUZiMpbPQAA3Em5XaAMAADgDYQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgarxnp4Lh/TgAgIqGsGMi3npbMwAAZkLYQZniShMAwNdYswMAAEyNsAMAAEyNsAMAAEyNNTvwGRZMAwDKA67sAAAAU+PKDso9ntgCAJQEYQeFcPsJAGAmhB1UGFwhAoCKiTU7AADA1Ag7AADA1Ag7AADA1FizA1NgUTUA4E64sgMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNR88BD3nrMXe+mgIASgdXdgAAgKlxZQf4D7ycEADMhys7AADA1Ag7AADA1Ag7AADA1Ag7AADA1FigDJSRoiyG5vF0ACg5wg5QQRCuAFRU3MYCAACmxpUdoBwr6nt/uCIDAHdG2AFMgJchAsCdEXYAlAnWEAEoLaYJO/Pnz9ebb74pp9OpNm3aaN68eerUqVNZlwXcVQggQGH8/+LuZ4qw849//EPjx4/XwoUL1blzZ73zzjuKjo7W8ePHFRoaWtblAabCL37cibdup/LvB95mirDz1ltvadiwYRoyZIgkaeHChVqzZo3ef/99TZkypYyrAyoe1hCVjtIMF+Xtf1NCNzxhMQzDKOsiSuLGjRsKDg7WypUr1bt3b1f74MGDlZWVpY8//rjQZ3Jzc5Wbm+v6OTs7W/Xq1dPZs2dltVq9Wl/L6eu9uj8AAH7K569Gl9qxivJ3zlf15OTkKDw8XFlZWbLZbHccd9df2fn222+Vn5+vsLAwt/awsDAdO3bstp+Jj4/Xq6++Wqg9PDzcJzUCAFCabO+UdQXufF3P5cuXzR12imPq1KkaP3686+eCggJdvHhRtWrVksVi8dpxbiVOX1wxutswF+6YD3fMhzvmwx3z8X+YC3eGYejy5cuqW7fufx1314ede+65R5UqVVJGRoZbe0ZGhux2+20/ExQUpKCgILe2kJAQX5Uoq9XKP8r/j7lwx3y4Yz7cMR/umI//w1z8n/92ReeWu/7rIgIDA9WhQwclJSW52goKCpSUlCSHw1GGlQEAgPLgrr+yI0njx4/X4MGD1bFjR3Xq1EnvvPOOrl696no6CwAAVFymCDt9+/bVhQsXNG3aNDmdTrVt21aJiYmFFi2XtqCgIE2fPr3QLbOKiLlwx3y4Yz7cMR/umI//w1wUz13/6DkAAMB/c9ev2QEAAPhvCDsAAMDUCDsAAMDUCDsAAMDUCDs+Mn/+fDVo0ECVK1dW586dtXv37rIuySe2bdumxx9/XHXr1pXFYtHq1avd+g3D0LRp01SnTh1VqVJFUVFROnHihNuYixcvasCAAbJarQoJCdHQoUN15cqVUjwL74iPj9cDDzyg6tWrKzQ0VL1799bx48fdxly/fl1xcXGqVauWqlWrptjY2EIvxExPT1dMTIyCg4MVGhqqSZMm6ebNm6V5Kl6xYMECtW7d2vXyM4fDoXXr1rn6K9Jc/Njs2bNlsVg0duxYV1tFmo8ZM2bIYrG4bc2aNXP1V6S5uOWbb77R008/rVq1aqlKlSpq1aqV9u7d6+qvSL9LfcKA161YscIIDAw03n//fePw4cPGsGHDjJCQECMjI6OsS/O6tWvXGi+99JLxr3/9y5BkrFq1yq1/9uzZhs1mM1avXm0cOHDA+NWvfmU0bNjQ+P77711jHnnkEaNNmzbGrl27jM8++8xo3Lix0b9//1I+k5KLjo42PvjgA+Pzzz830tLSjEcffdSoV6+eceXKFdeY5557zggPDzeSkpKMvXv3Gl26dDEefPBBV//NmzeNli1bGlFRUcb+/fuNtWvXGvfcc48xderUsjilEvn3v/9trFmzxvjiiy+M48ePGy+++KIREBBgfP7554ZhVKy5+E+7d+82GjRoYLRu3doYM2aMq70izcf06dONFi1aGOfPn3dtFy5ccPVXpLkwDMO4ePGiUb9+feOZZ54xUlJSjC+//NJYv369cfLkSdeYivS71BcIOz7QqVMnIy4uzvVzfn6+UbduXSM+Pr4Mq/K9H4edgoICw263G2+++aarLSsrywgKCjL+/ve/G4ZhGEeOHDEkGXv27HGNWbdunWGxWIxvvvmm1Gr3hczMTEOSsXXrVsMwfjj3gIAA48MPP3SNOXr0qCHJSE5ONgzjh/Do5+dnOJ1O15gFCxYYVqvVyM3NLd0T8IEaNWoYf/7znyvsXFy+fNlo0qSJsXHjRqNHjx6usFPR5mP69OlGmzZtbttX0ebCMAxj8uTJRrdu3e7YX9F/l3oDt7G87MaNG0pNTVVUVJSrzc/PT1FRUUpOTi7Dykrf6dOn5XQ63ebCZrOpc+fOrrlITk5WSEiIOnbs6BoTFRUlPz8/paSklHrN3pSdnS1JqlmzpiQpNTVVeXl5bvPRrFkz1atXz20+WrVq5fZCzOjoaOXk5Ojw4cOlWL135efna8WKFbp69aocDkeFnYu4uDjFxMS4nbdUMf9tnDhxQnXr1lWjRo00YMAApaenS6qYc/Hvf/9bHTt2VJ8+fRQaGqp27dpp0aJFrv6K/rvUGwg7Xvbtt98qPz+/0Nubw8LC5HQ6y6iqsnHrfP/bXDidToWGhrr1+/v7q2bNmnf1fBUUFGjs2LHq2rWrWrZsKemHcw0MDCz0pbM/no/bzdetvrvNoUOHVK1aNQUFBem5557TqlWrFBERUSHnYsWKFdq3b5/i4+ML9VW0+ejcubOWLFmixMRELViwQKdPn9ZDDz2ky5cvV7i5kKQvv/xSCxYsUJMmTbR+/XqNGDFCo0eP1tKlSyVV7N+l3mKKr4sAypu4uDh9/vnn2r59e1mXUqbuv/9+paWlKTs7WytXrtTgwYO1devWsi6r1J09e1ZjxozRxo0bVbly5bIup8z16tXL9d+tW7dW586dVb9+ff3zn/9UlSpVyrCyslFQUKCOHTtq1qxZkqR27drp888/18KFCzV48OAyrs4cuLLjZffcc48qVapU6MmBjIwM2e32MqqqbNw63/82F3a7XZmZmW79N2/e1MWLF+/a+Ro5cqQSEhK0efNm3Xvvva52u92uGzduKCsry238j+fjdvN1q+9uExgYqMaNG6tDhw6Kj49XmzZt9O6771a4uUhNTVVmZqbat28vf39/+fv7a+vWrZo7d678/f0VFhZWoebjx0JCQtS0aVOdPHmywv3bkKQ6deooIiLCra158+auW3sV9XepNxF2vCwwMFAdOnRQUlKSq62goEBJSUlyOBxlWFnpa9iwoex2u9tc5OTkKCUlxTUXDodDWVlZSk1NdY3ZtGmTCgoK1Llz51KvuSQMw9DIkSO1atUqbdq0SQ0bNnTr79ChgwICAtzm4/jx40pPT3ebj0OHDrn90tq4caOsVmuhX4Z3o4KCAuXm5la4uYiMjNShQ4eUlpbm2jp27KgBAwa4/rsizcePXblyRadOnVKdOnUq3L8NSeratWuh11R88cUXql+/vqSK97vUJ8p6hbQZrVixwggKCjKWLFliHDlyxBg+fLgREhLi9uSAWVy+fNnYv3+/sX//fkOS8dZbbxn79+83vvrqK8MwfnhcMiQkxPj444+NgwcPGk888cRtH5ds166dkZKSYmzfvt1o0qTJXfm45IgRIwybzWZs2bLF7ZHaa9euucY899xzRr169YxNmzYZe/fuNRwOh+FwOFz9tx6p7dmzp5GWlmYkJiYatWvXvisfqZ0yZYqxdetW4/Tp08bBgweNKVOmGBaLxdiwYYNhGBVrLm7nP5/GMoyKNR8TJkwwtmzZYpw+fdrYsWOHERUVZdxzzz1GZmamYRgVay4M44fXEfj7+xuvv/66ceLECWPZsmVGcHCw8be//c01piL9LvUFwo6PzJs3z6hXr54RGBhodOrUydi1a1dZl+QTmzdvNiQV2gYPHmwYxg+PTL7yyitGWFiYERQUZERGRhrHjx9328d3331n9O/f36hWrZphtVqNIUOGGJcvXy6DsymZ282DJOODDz5wjfn++++N559/3qhRo4YRHBxs/M///I9x/vx5t/2cOXPG6NWrl1GlShXjnnvuMSZMmGDk5eWV8tmU3LPPPmvUr1/fCAwMNGrXrm1ERka6go5hVKy5uJ0fh52KNB99+/Y16tSpYwQGBho/+9nPjL59+7q9U6YizcUtn3zyidGyZUsjKCjIaNasmfGnP/3Jrb8i/S71BYthGEbZXFMCAADwPdbsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsACj3ZsyYobZt27p+fuaZZ9S7d+8yqwfA3YWwA8CnnE6nRo0apUaNGikoKEjh4eF6/PHH3b7U0KwefvhhjR07tqzLACo8/7IuAIB5nTlzRl27dlVISIjefPNNtWrVSnl5eVq/fr3i4uJ07Nixsi4RQAXAlR0APvP888/LYrFo9+7dio2NVdOmTdWiRQuNHz9eu3btco3LysrSb37zG9WuXVtWq1W/+MUvdODAgRIde8eOHXr44YcVHBysGjVqKDo6WpcuXZIk5ebmavTo0QoNDVXlypXVrVs37dmzx/XZJUuWKCQkxG1/q1evlsVicf1869baX//6VzVo0EA2m039+vXT5cuXJf1wq23r1q169913ZbFYZLFYdObMmRKdE4DiIewA8ImLFy8qMTFRcXFxqlq1aqH+/wwTffr0UWZmptatW6fU1FS1b99ekZGRunjxYrGOnZaWpsjISEVERCg5OVnbt2/X448/rvz8fEnSCy+8oI8++khLly7Vvn371LhxY0VHR3t8vFOnTmn16tVKSEhQQkKCtm7dqtmzZ0uS3n33XTkcDg0bNkznz5/X+fPnFR4eXqzzAVAy3MYC4BMnT56UYRhq1qzZfx23fft27d69W5mZmQoKCpIk/f73v9fq1au1cuVKDR8+3ONjz5kzRx07dtR7773namvRooUk6erVq1qwYIGWLFmiXr16SZIWLVqkjRs3avHixZo0aVKRj1NQUKAlS5aoevXqkqSBAwcqKSlJr7/+umw2mwIDAxUcHCy73e7xOQDwHsIOAJ8wDKNI4w4cOKArV66oVq1abu3ff/+9Tp06Vaxjp6WlqU+fPrftO3XqlPLy8tS1a1dXW0BAgDp16qSjR496dJwGDRq4go4k1alTR5mZmcWqGYDvEHYA+ESTJk1ksVh+chHylStXVKdOHW3ZsqVQ34/XzRRVlSpVivW5W/z8/AqFtby8vELjAgIC3H62WCwqKCgo0bEBeB9rdgD4RM2aNRUdHa358+fr6tWrhfqzsrIkSe3bt5fT6ZS/v78aN27stt1zzz3FOnbr1q3v+Gj7fffdp8DAQO3YscPVlpeXpz179igiIkKSVLt2bV2+fNmt7rS0NI/rCAwMdK0TAlB2CDsAfGb+/PnKz89Xp06d9NFHH+nEiRM6evSo5s6dK4fDIUmKioqSw+FQ7969tWHDBp05c0Y7d+7USy+9pL179xbruFOnTtWePXv0/PPP6+DBgzp27JgWLFigb7/9VlWrVtWIESM0adIkJSYm6siRIxo2bJiuXbumoUOHSpI6d+6s4OBgvfjiizp16pSWL1+uJUuWeFxHgwYNlJKSojNnzujbb7/lqg9QRgg7AHymUaNG2rdvn37+859rwoQJatmypX75y18qKSlJCxYskPTDrZ+1a9eqe/fuGjJkiJo2bap+/frpq6++UlhYWLGO27RpU23YsEEHDhxQp06d5HA49PHHH8vf/4c797Nnz1ZsbKwGDhyo9u3b6+TJk1q/fr1q1Kgh6YerUn/729+0du1atWrVSn//+981Y8YMj+uYOHGiKlWqpIiICNWuXVvp6enFOh8AJWMxirqKEAAA4C7ElR0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBq/w+xSomQOETUqwAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"count 1936.000000\n",
"mean 68.732438\n",
"std 95.376045\n",
"min 1.000000\n",
"25% 11.000000\n",
"50% 22.000000\n",
"75% 99.000000\n",
"max 666.000000\n",
"dtype: float64"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cell_per_exp = cells_table.groupby('ophys_experiment_id').size()\n",
"fig = plt.hist(cell_per_exp, bins=50)\n",
"plt.xlabel('Cell count')\n",
"plt.ylabel('Number of experiments')\n",
"plt.show()\n",
"cell_per_exp.describe()"
]
},
{
"cell_type": "markdown",
"id": "acbf3e5a",
"metadata": {
"papermill": {
"duration": 0.077028,
"end_time": "2023-11-30T05:26:35.945512",
"exception": false,
"start_time": "2023-11-30T05:26:35.868484",
"status": "completed"
},
"tags": []
},
"source": [
"Merge the cell counts into the ophys experiments table"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "d584bdac",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:26:36.196089Z",
"iopub.status.busy": "2023-11-30T05:26:36.195491Z",
"iopub.status.idle": "2023-11-30T05:26:36.200079Z",
"shell.execute_reply": "2023-11-30T05:26:36.199409Z"
},
"papermill": {
"duration": 0.15559,
"end_time": "2023-11-30T05:26:36.201472",
"exception": false,
"start_time": "2023-11-30T05:26:36.045882",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"ophys_experiments['n_cells'] = ophys_experiments.index.map(cell_per_exp)"
]
},
{
"cell_type": "markdown",
"id": "4b5272f7",
"metadata": {
"papermill": {
"duration": 0.103864,
"end_time": "2023-11-30T05:26:36.417107",
"exception": false,
"start_time": "2023-11-30T05:26:36.313243",
"status": "completed"
},
"tags": []
},
"source": [
"Now we can look at the cell count by depth, for example"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "85244312",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:26:36.574075Z",
"iopub.status.busy": "2023-11-30T05:26:36.573434Z",
"iopub.status.idle": "2023-11-30T05:26:36.823973Z",
"shell.execute_reply": "2023-11-30T05:26:36.823215Z"
},
"papermill": {
"duration": 0.33008,
"end_time": "2023-11-30T05:26:36.825308",
"exception": false,
"start_time": "2023-11-30T05:26:36.495228",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(30, 10))\n",
"ax.scatter(ophys_experiments['imaging_depth'], ophys_experiments['n_cells'], alpha=.3)\n",
"ax.set_xlabel('Imaging depth (microns)')\n",
"ax.set_ylabel('Cell count')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e84eb564",
"metadata": {
"papermill": {
"duration": 0.080114,
"end_time": "2023-11-30T05:26:36.987578",
"exception": false,
"start_time": "2023-11-30T05:26:36.907464",
"status": "completed"
},
"tags": []
},
"source": [
"Or by cre-line"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "864c2ee4",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-30T05:26:37.148462Z",
"iopub.status.busy": "2023-11-30T05:26:37.148190Z",
"iopub.status.idle": "2023-11-30T05:26:37.311821Z",
"shell.execute_reply": "2023-11-30T05:26:37.311053Z"
},
"papermill": {
"duration": 0.24573,
"end_time": "2023-11-30T05:26:37.313371",
"exception": false,
"start_time": "2023-11-30T05:26:37.067641",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.boxplot(data=ophys_experiments, x='n_cells', y='cre_line')"
]
}
],
"metadata": {
"interpreter": {
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
},
"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": "3.8.18"
},
"papermill": {
"default_parameters": {},
"duration": 238.139136,
"end_time": "2023-11-30T05:26:38.013498",
"environment_variables": {},
"exception": null,
"input_path": "doc_template/examples_root/examples/nb/visual_behavior_ophys_dataset_manifest.ipynb",
"output_path": "/tmp/tmpeiy1ys_0/scratch_nb.ipynb",
"parameters": {
"output_dir": "/tmp/tmpeiy1ys_0",
"resources_dir": "/home/runner/work/AllenSDK/AllenSDK/allensdk/internal/notebooks/resources"
},
"start_time": "2023-11-30T05:22:39.874362",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}