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Merge pull request #106 from kabilar/main
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Update demo notebooks
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@kushalbakshi
kushalbakshi authored Mar 9, 2023
2 parents 4d38edd + 3d0b582 commit 88aeb23
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4 changes: 3 additions & 1 deletion .devcontainer/Dockerfile
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Expand Up @@ -24,4 +24,6 @@ ENV DJ_HOST fakeservices.datajoint.io
ENV DJ_USER root
ENV DJ_PASS simple

ENV DJ_PUBLIC_S3_MOUNT_PATH /workspaces/workflow-calcium-imaging/example_data
ENV DJ_PUBLIC_S3_MOUNT_PATH /workspaces/workflow-calcium-imaging/example_data
ENV IMAGING_ROOT_DATA_DIR /workspaces/workflow-calcium-imaging/example_data
ENV DATABASE_PREFIX neuro_
184 changes: 184 additions & 0 deletions notebooks/demo_prepare.ipynb
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Demo Preparation Notebook\n",
"\n",
"**Please Note**: This notebook and demo are NOT intended to be used as learning materials. To gain\n",
"a thorough understanding of the DataJoint workflow for calcium imaging, please\n",
"see the [`tutorial`](./tutorial.ipynb) notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datajoint as dj\n",
"import datetime\n",
"import numpy as np\n",
"from workflow_calcium_imaging.pipeline import subject, session, scan, imaging, Equipment\n",
"from element_calcium_imaging import imaging_report\n",
"import suite2p"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"subject.Subject.insert1(\n",
" dict(\n",
" subject='subject1',\n",
" subject_birth_date='2023-01-01',\n",
" sex='U',\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Equipment.insert1(dict(scanner=\"Mesoscope\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"session_key = dict(subject='subject1', \n",
" session_datetime=datetime.datetime.now())\n",
"\n",
"session.Session.insert1(session_key)\n",
"\n",
"session.SessionDirectory.insert1(\n",
" dict(\n",
" session_key, \n",
" session_dir='subject1/session1'\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scan.Scan.insert1(\n",
" dict(\n",
" session_key,\n",
" scan_id=0,\n",
" scanner=\"Mesoscope\",\n",
" acq_software='ScanImage',\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"populate_settings = {\"display_progress\": True}\n",
"\n",
"scan.ScanInfo.populate(**populate_settings)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"suite2p_params = suite2p.default_ops()\n",
"suite2p_params['nonrigid']=False\n",
"\n",
"imaging.ProcessingParamSet.insert_new_params(\n",
" processing_method=\"suite2p\",\n",
" paramset_idx=0,\n",
" params=suite2p_params,\n",
" paramset_desc='Default parameter set for suite2p'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"imaging.ProcessingTask.insert1(\n",
" dict(\n",
" session_key,\n",
" scan_id=0,\n",
" paramset_idx=0,\n",
" task_mode='load', # load or trigger\n",
" processing_output_dir='subject1/session1/suite2p',\n",
" )\n",
")\n",
"\n",
"imaging.Processing.populate(**populate_settings)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"imaging.Curation.insert1(\n",
" dict(\n",
" session_key,\n",
" scan_id=0,\n",
" paramset_idx=0,\n",
" curation_id=0,\n",
" curation_time=datetime.datetime.now(),\n",
" curation_output_dir='subject1/session1/suite2p',\n",
" manual_curation=False,\n",
" )\n",
")\n",
"\n",
"imaging.MotionCorrection.populate(**populate_settings)\n",
"imaging.Segmentation.populate(**populate_settings)\n",
"imaging.Fluorescence.populate(**populate_settings)\n",
"imaging.Activity.populate(**populate_settings)\n",
"imaging_report.ScanLevelReport.populate(**populate_settings)\n",
"imaging_report.TraceReport.populate(**populate_settings)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.16"
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"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
154 changes: 154 additions & 0 deletions notebooks/demo_run.ipynb
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# DataJoint Workflow for Calcium Imaging\n",
"\n",
"+ This notebook demonstrates using the open-source DataJoint Element to build a workflow for\n",
"calcium imaging.\n",
"+ For a detailed tutorial, please see the [tutorial notebook](./tutorial.ipynb)."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src =../images/rawscans.gif title=\"value\" width=\"200\" height=\"200\"/>\n",
"<img src =../images/motioncorrectedscans.gif width=\"200\" height=\"200\"/>\n",
"<img src =../images/cellsegmentation.png width=\"200\" height=\"200\"/>\n",
"<img src =../images/calciumtraces.png width=\"200\" height=\"200\"/> \n",
"\n",
"Left to right: Raw scans, Motion corrected scans, Cell segmentations, Calcium events"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"import datajoint as dj\n",
"from workflow_calcium_imaging.pipeline import subject, session, scan, imaging\n",
"from element_calcium_imaging.plotting.widget import main"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### View workflow"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + dj.Diagram(scan) + dj.Diagram(imaging)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Insert an entry in a manual table by calling the `insert()` method\n",
"\n",
"```python\n",
"subject.Subject.insert1(\n",
" dict(subject='subject1',\n",
" subject_birth_date='2023-01-01',\n",
" sex='U'\n",
" )\n",
")\n",
"```"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Automatically process data with the `populate()` method\n",
"\n",
"+ Once data is inserted into manual tables, the `populate()` function automatically runs the ingestion and processing routines. \n",
"\n",
"+ For example, to run Suite2p processing in the `Processing` table:\n",
"\n",
" ```python\n",
" imaging.Processing.populate()\n",
" ```"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize processed data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"main(imaging)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"For an in-depth tutorial please see the [tutorial notebook](./tutorial.ipynb)."
]
}
],
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