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kushalbakshikushalbakshi
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Merge pull request #106 from kabilar/main
Update demo notebooks
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‎.devcontainer/Dockerfile‎

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@@ -24,4 +24,6 @@ ENV DJ_HOST fakeservices.datajoint.io
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ENV DJ_USER root
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ENV DJ_PASS simple
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ENV DJ_PUBLIC_S3_MOUNT_PATH /workspaces/workflow-calcium-imaging/example_data
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ENV DJ_PUBLIC_S3_MOUNT_PATH /workspaces/workflow-calcium-imaging/example_data
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ENV IMAGING_ROOT_DATA_DIR /workspaces/workflow-calcium-imaging/example_data
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ENV DATABASE_PREFIX neuro_

‎notebooks/demo_prepare.ipynb‎

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Demo Preparation Notebook\n",
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"\n",
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"**Please Note**: This notebook and demo are NOT intended to be used as learning materials. To gain\n",
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"a thorough understanding of the DataJoint workflow for calcium imaging, please\n",
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"see the [`tutorial`](./tutorial.ipynb) notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import datajoint as dj\n",
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"import datetime\n",
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"import numpy as np\n",
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"from workflow_calcium_imaging.pipeline import subject, session, scan, imaging, Equipment\n",
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"from element_calcium_imaging import imaging_report\n",
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"import suite2p"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"subject.Subject.insert1(\n",
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" dict(\n",
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" subject='subject1',\n",
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" subject_birth_date='2023-01-01',\n",
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" sex='U',\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"Equipment.insert1(dict(scanner=\"Mesoscope\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"session_key = dict(subject='subject1', \n",
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" session_datetime=datetime.datetime.now())\n",
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"\n",
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"session.Session.insert1(session_key)\n",
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"\n",
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"session.SessionDirectory.insert1(\n",
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" dict(\n",
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" session_key, \n",
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" session_dir='subject1/session1'\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"scan.Scan.insert1(\n",
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" dict(\n",
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" session_key,\n",
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" scan_id=0,\n",
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" scanner=\"Mesoscope\",\n",
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" acq_software='ScanImage',\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"populate_settings = {\"display_progress\": True}\n",
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"\n",
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"scan.ScanInfo.populate(**populate_settings)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"suite2p_params = suite2p.default_ops()\n",
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"suite2p_params['nonrigid']=False\n",
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"\n",
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"imaging.ProcessingParamSet.insert_new_params(\n",
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" processing_method=\"suite2p\",\n",
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" paramset_idx=0,\n",
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" params=suite2p_params,\n",
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" paramset_desc='Default parameter set for suite2p'\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"imaging.ProcessingTask.insert1(\n",
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" dict(\n",
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" session_key,\n",
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" scan_id=0,\n",
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" paramset_idx=0,\n",
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" task_mode='load', # load or trigger\n",
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" processing_output_dir='subject1/session1/suite2p',\n",
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" )\n",
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")\n",
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"\n",
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"imaging.Processing.populate(**populate_settings)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"imaging.Curation.insert1(\n",
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" dict(\n",
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" session_key,\n",
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" scan_id=0,\n",
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" paramset_idx=0,\n",
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" curation_id=0,\n",
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" curation_time=datetime.datetime.now(),\n",
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" curation_output_dir='subject1/session1/suite2p',\n",
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" manual_curation=False,\n",
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" )\n",
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")\n",
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"\n",
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"imaging.MotionCorrection.populate(**populate_settings)\n",
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"imaging.Segmentation.populate(**populate_settings)\n",
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"imaging.Fluorescence.populate(**populate_settings)\n",
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"imaging.Activity.populate(**populate_settings)\n",
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"imaging_report.ScanLevelReport.populate(**populate_settings)\n",
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"imaging_report.TraceReport.populate(**populate_settings)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.16"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎notebooks/demo_run.ipynb‎

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# DataJoint Workflow for Calcium Imaging\n",
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"\n",
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"+ This notebook demonstrates using the open-source DataJoint Element to build a workflow for\n",
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"calcium imaging.\n",
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"+ For a detailed tutorial, please see the [tutorial notebook](./tutorial.ipynb)."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src =../images/rawscans.gif title=\"value\" width=\"200\" height=\"200\"/>\n",
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"<img src =../images/motioncorrectedscans.gif width=\"200\" height=\"200\"/>\n",
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"<img src =../images/cellsegmentation.png width=\"200\" height=\"200\"/>\n",
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"<img src =../images/calciumtraces.png width=\"200\" height=\"200\"/> \n",
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"\n",
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"Left to right: Raw scans, Motion corrected scans, Cell segmentations, Calcium events"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Import dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"hide-input"
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]
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},
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"outputs": [],
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"source": [
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"import datajoint as dj\n",
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"from workflow_calcium_imaging.pipeline import subject, session, scan, imaging\n",
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"from element_calcium_imaging.plotting.widget import main"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### View workflow"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dj.Diagram(subject.Subject) + dj.Diagram(session.Session) + dj.Diagram(scan) + dj.Diagram(imaging)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Insert an entry in a manual table by calling the `insert()` method\n",
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"\n",
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"```python\n",
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"subject.Subject.insert1(\n",
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" dict(subject='subject1',\n",
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" subject_birth_date='2023-01-01',\n",
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" sex='U'\n",
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" )\n",
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")\n",
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"```"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Automatically process data with the `populate()` method\n",
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"\n",
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"+ Once data is inserted into manual tables, the `populate()` function automatically runs the ingestion and processing routines. \n",
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"\n",
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"+ For example, to run Suite2p processing in the `Processing` table:\n",
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"\n",
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" ```python\n",
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" imaging.Processing.populate()\n",
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" ```"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Visualize processed data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"main(imaging)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For an in-depth tutorial please see the [tutorial notebook](./tutorial.ipynb)."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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