From 91accc0bbe9adcdccba6e78df52339ffc345f89e Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Sat, 11 Mar 2023 09:33:06 -0600 Subject: [PATCH 1/6] First tutorial notebook commit --- notebooks/tutorial.ipynb | 900 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 894 insertions(+), 6 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index de29eff..cc6b594 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -1,32 +1,920 @@ { "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Process calcium imaging data with DataJoint Elements\n", + "\n", + "This notebook will walk through processing two-photon calcium imaging data collected\n", + "from ScanImage and processed with Suite2p.\n", + "\n", + "The DataJoint Python API and Element Calcium Imaging offer a lot of features to support collaboration, automation, reproducibility, and visualizations.\n", + "\n", + "For more information on these topics, please visit our documentation: \n", + " \n", + "- [DataJoint Core](https://datajoint.com/docs/core/): General principles\n", + "\n", + "- DataJoint [Python](https://datajoint.com/docs/core/datajoint-python/) and\n", + " [MATLAB](https://datajoint.com/docs/core/datajoint-matlab/) APIs: in-depth reviews of\n", + " specifics\n", + "\n", + "- [DataJoint Element Calcium Imaging](https://datajoint.com/docs/elements/element-calcium-imaging/):\n", + " A modular pipeline for calcium imaging analysis" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by importing the packages necessary to run this workflow. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "if os.path.basename(os.getcwd()) == \"notebooks\":\n", + " os.chdir(\"..\")\n", + "\n", + "import datajoint as dj\n", + "import datetime\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Basics:\n", + "\n", + "Any DataJoint workflow can be broken down into basic 3 parts:\n", + "\n", + "- `Insert`\n", + "- `Populate` (or process)\n", + "- `Analyze`\n", + "\n", + "In this demo we will:\n", + "- `Insert` information about an animal subject, recording session, and the metadata and\n", + " parameters related to processing calcium imaging data through Suite2p.\n", + "- `Populate` tables with outputs of image processing including motion correction,\n", + " segmentation, mask classification, fluorescence traces and deconvolved activity traces.\n", + "- `Analyze` the processed data by ***querying*** and plotting activity traces.\n", + "\n", + "Each of these topics will be explained thoroughly in this notebook." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Workflow diagram\n", + "\n", + "This workflow is assembled from 4 DataJoint elements:\n", + "+ [element-lab](https://github.com/datajoint/element-lab)\n", + "+ [element-animal](https://github.com/datajoint/element-animal)\n", + "+ [element-session](https://github.com/datajoint/element-session)\n", + "+ [element-calcium-imaging](https://github.com/datajoint/element-calcium-imaging)\n", + "\n", + "Each element declares its own schema in the database. These schemas can be imported like\n", + "any other Python package. This workflow is composed of schemas from each of the Elements\n", + "above and correspond to a module within `workflow_calcium_imaging.pipeline`.\n", + "\n", + "The schema diagram is a good reference for understanding the order of the tables\n", + "within the workflow, as well as the corresponding table type.\n", + "Let's activate the elements and view the schema diagram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from workflow_calcium_imaging.pipeline import lab, subject, session, scan, imaging, Equipment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " dj.Diagram(subject.Subject)\n", + " + dj.Diagram(session.Session)\n", + " + dj.Diagram(scan)\n", + " + dj.Diagram(imaging)\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the diagram above seems complex at first, it becomes more clear when it's\n", + "approached as a hierarchy of tables that define the order in which DataJoint expects to\n", + "receive data. \n", + "\n", + "+ Tables with a green, or rectangular shape expect to receive data manually using the\n", + "`insert()` function. The tables higher up in the diagram such as `subject.Subject()`\n", + "should be the first to receive data. This ensures data integrity by preventing orphaned\n", + "data within DataJoint schemas. \n", + "+ Tables with a purple oval or red circle can be automatically filled with relevant data\n", + " by calling `populate()`. For example `scan.ScanInfo` and its part-table\n", + " `scan.ScanInfo.Field` are both populated with `scan.ScanInfo.populate()`.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Starting the workflow\n", + "\n", + "### Insert entries into manual tables and populate automated tables\n", + "\n", + "To view details about a table's dependencies and attributes, use functions `.describe()`\n", + "and `.heading`, respectively.\n", + "\n", + "Let's start with the first table in the schema diagram (the `subject` table) and view\n", + "the table attributes we need to insert. There are two ways you can do this: *run each\n", + "of the two cells below*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.describe;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.heading" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cells above show all attribute of the subject table. We will insert data into the\n", + "`subject.Subject` table. These are particularly useful functions if you are new to\n", + "DataJoint Elements and are unsure of the attributes required for each table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.insert1(\n", + " dict(\n", + " subject=\"subject1\",\n", + " sex=\"F\",\n", + " subject_birth_date=\"2020-01-01\",\n", + " subject_description=\"ScanImage acquisition. Suite2p processing.\",\n", + " )\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's repeat the steps above for the `Session` table and see how the output varies between\n", + "`.describe` and `.heading`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session.Session.describe;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session.Session.heading" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cells above show the dependencies and attributes for the `session.Session` table.\n", + "Notice that `describe` shows the dependencies of the table on upstream tables. The\n", + "`Session` table depends on the upstream `Subject` table. Whereas `heading` lists all the\n", + "attributes of the `Session` table, regardless of whether they are declared in an upstream\n", + "table. \n", + "\n", + "\n", + "Here we will demonstrate a very useful way of inserting data by assigning the dictionary\n", + "to a variable `session_key`. This variable can be used to insert entries into tables that\n", + "contain the `Session` table as one of its attributes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session_key = dict(subject=\"subject1\", session_datetime=\"2021-04-30 12:22:15.032\")\n", + "\n", + "session.Session.insert1(session_key)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session.SessionDirectory.insert1(\n", + " dict(**session_key,\n", + " session_dir=\"subject1/session1\")\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll use `describe` and `heading` for the Scan table. Do you notice anything we\n", + "might have missed here? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.Scan.describe;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.Scan.heading" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Scan` table's attributes include the `Session` table **and** the `Equipment` table.\n", + "Let's see what happens when you try to insert data into `Scan` without inserting into\n", + "`Equipment` first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.Scan.insert1(\n", + " dict(\n", + " session_key,\n", + " scanner=\"ScanImage\",\n", + " acq_software=\"ScanImage\",\n", + " scan_notes=\"\",\n", + " )\n", + ")\n", + "\n", + "# This cell will produce an error" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DataJoint issues an error because there is no entry in the `Equipment` table. Let's go\n", + "ahead and insert into the Equipment table after checking its attributes. Then, we will\n", + "try the insert into `Scan` again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Equipment.describe;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Equipment.heading" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Equipment.insert1(dict(scanner=\"ScanImage\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.Scan.insert1(\n", + " dict(\n", + " **session_key,\n", + " scan_id=0,\n", + " scanner=\"ScanImage\",\n", + " acq_software=\"ScanImage\",\n", + " scan_notes=\"\",\n", + " )\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time, we were able to successfully insert data into the scan table. Now we're ready\n", + "to use DataJoint's automation features to populate several downstream tables after\n", + "`Scan`.\n", + "\n", + "### Automatically populate tables\n", + "\n", + "`scan.ScanInfo` is the first table in the pipeline that can be populated automatically.\n", + "If a table contains a part table, this part table is also populated during the\n", + "`populate()` call. Let's view the `scan.ScanInfo` and its part table\n", + "`scan.ScanInfo.Field`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.ScanInfo.describe;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.ScanInfo.heading" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.ScanInfo()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan.scanInfo.Field()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`populate()` takes a few arguments which we will set in the cell below and use it on `ScanInfo`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "populate_settings = {\n", + " \"display_progress\": True,\n", + " \"reserve_jobs\": False,\n", + " \"suppress_errors\": False,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# duration depends on your network bandwidth to s3\n", + "scan.ScanInfo.populate(**populate_settings)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's view that was entered into each of these tables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "scan.scanInfo()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "scan.ScanInfo.Field()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're almost ready to perform image processing with `Suite2p`. An important step before\n", + "processing is managing the parameters which will be used in this step. To do so, we will\n", + "import suite2p and insert the default parameters into a DataJoint table\n", + "`ProcessingParamSet`. This table keeps track of all your image processing\n", + "parameters. You can choose which parameters are used during processing in a later table.\n", + "\n", + "Let's view the attributes and insert data into `imaging.ProcessingParamSet`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "imaging.ProcessingParamSet.describe;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "imaging.ProcessingParamSet.heading" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import suite2p\n", + "\n", + "params_suite2p = suite2p.default_ops()\n", + "params_suite2p['nonrigid']=False\n", + "\n", + "imaging.ProcessingParamSet.insert_new_params(\n", + " processing_method=\"suite2p\",\n", + " paramset_idx=0,\n", + " params=params_suite2p,\n", + " paramset_desc=\"Calcium imaging analysis with Suite2p using default Suite2p parameters\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've inserted suite2p parameters into the `ProcessingParamSet` table,\n", + "we're almost ready to run image processing. DataJoint uses the `ProcessingTask` table to\n", + "determine which parameter set should be used during processing. \n", + "\n", + "The `ProcessingTask` table is important for defining several important aspects of\n", + "downstream processing. Let's view the attributes to get a better understanding. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.ProcessingTask.describe;" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "Coming soon!" + "imaging.ProcessingTask.heading" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `ProcessingParamSet` table adds two attributes that were not in the `Scan` table: \n", + "+ `paramset_idx` \n", + "+ `task_mode` \n", + "\n", + "The `paramset_idx` attribute is intended to help track\n", + "your image processing parameter sets. Suite2p, CaImAn, or EXTRACT\n", + "parameter sets can be inserted into this table. You can also choose the parameter sets on which you want\n", + "to run the image processing analysis. \n", + "\n", + "The `task_mode` attribute can be set to either\n", + "`load` or `trigger`. When set to `load`, running the processing step initiates a search\n", + "for exisiting output files of the image processing algorithm. When set to `trigger`, the\n", + "processing step will run a processing algorithm on the raw data. " ] + }, + { + "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", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice we are setting `task_mode` to `load`. Let's call populate on the `Processing`\n", + "table in the pipeline. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.Processing.populate(**populate_settings)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While processing is complete in the step above, you can optionally curate the output of\n", + "image processing using the `Curation` table." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.Curation.describe;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.Curation.heading" + ] + }, + { + "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=\"2021-04-30 12:22:15.032\",\n", + " curation_output_dir=\"subject1/session1/suite2p\",\n", + " manual_curation=False,\n", + " curation_note=\"\",\n", + " )\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll populate several tables that store the output of image processing, including\n", + "`MotionCorrection`, `Segementation`, `Fluorescence`, and `Activity`.\n", + "\n", + "Feel free to create new cells in this notebook to view each table's dependencies and\n", + "attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.MotionCorrection.populate(**populate_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.Segmentation.populate(**populate_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.Fluorescence.populate(**populate_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.Activity.populate(**populate_settings)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that data is in the DataJoint tables, the analysis workflows such as exploratory\n", + "analysis, alignment to behavior, statistical testing or modelling, and other downstream\n", + "analyses can be performed. \n", + "\n", + "\n", + "### Query and fetch\n", + "\n", + "The next section of this tutorial focuses on working with data that is already in the\n", + "database. \n", + "\n", + "We will use `numpy` and `matplotlib` to plot segmentations on an average image. But\n", + "before we can plot we need to interact with the data.\n", + "\n", + "### Querying\n", + "\n", + "DataJoint queries allow you to view data that has been entered into DataJoint tables.\n", + "We've already queried the `ScanInfo` table in this notebook. Let's try that again but\n", + "with the fluorescence table. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imaging.Fluorescence()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's really that simple. Feel free to replace Fluorescence with one of the other tables\n", + "we populated above and view a summary of its contents.\n", + "\n", + "While this is a convenient way to view entries in the table, notice that you cannot see\n", + "the contents of data inserted as a `longblob`. Furthermore, to work on most exploratory\n", + "analyses, data needs to be queried out of the database as a variable, just as you might\n", + "load a `.csv` file. \n", + "\n", + "To query the data out - we'll use `fetch()` and `fetch1()`. There is a subtle difference\n", + "between the two operations. Using `fetch1()` will load the data into a variable as a\n", + "single dictionary where `fetch()` loads the data as a list of dictionaries. \n", + "\n", + "Let's use `fetch1()` to query and load the data into a variable called `trace`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "average_image = (\n", + " imaging.MotionCorrection.Summary & session_key & \"field_idx=0\"\n", + ").fetch1(\"average_image\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask_xpix, mask_ypix = (\n", + " imaging.Segmentation.Mask * imaging.MaskClassification.MaskType\n", + " & session_key\n", + " & \"mask_center_z=0\"\n", + " & \"mask_npix > 130\"\n", + ").fetch(\"mask_xpix\", \"mask_ypix\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mask_image = np.zeros(np.shape(average_image), dtype=bool)\n", + "for xpix, ypix in zip(mask_xpix, mask_ypix):\n", + " mask_image[ypix, xpix] = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(average_image)\n", + "plt.contour(mask_image, colors=\"white\", linewidths=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Drop schemas\n", + "\n", + "+ Schemas are not typically dropped in a production workflow with real data in it. \n", + "+ At the developmental phase, it might be required for the table redesign.\n", + "+ When dropping all schemas is needed, the following is the dependency order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def drop_databases(databases):\n", + " import pymysql.err\n", + " conn = dj.conn()\n", + "\n", + " with dj.config(safemode=False):\n", + " for database in databases:\n", + " schema = dj.Schema(f'{dj.config[\"custom\"][\"database.prefix\"]}{database}')\n", + " while schema.list_tables():\n", + " for table in schema.list_tables():\n", + " try:\n", + " conn.query(f\"DROP TABLE `{schema.database}`.`{table}`\")\n", + " except pymysql.err.OperationalError:\n", + " print(f\"Can't drop `{schema.database}`.`{table}`. Retrying...\")\n", + " schema.drop()\n", + "\n", + "# drop_databases(databases=['imaging_report', 'imaging', 'scan', 'session', 'subject', 'lab', 'reference'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { + "jupytext": { + "encoding": "# -*- coding: utf-8 -*-" + }, "kernelspec": { - "display_name": "python3p10", + "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.10.4 (main, Mar 31 2022, 03:38:35) [Clang 12.0.0 ]" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.16" + }, + "metadata": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } }, - "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "ff52d424e56dd643d8b2ec122f40a2e279e94970100b4e6430cb9025a65ba4cf" + "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1" } } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From cf11ac39611f8eba50abfd03bc4548bb9b6628dc Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Tue, 14 Mar 2023 12:54:39 -0500 Subject: [PATCH 2/6] 60 minute demo notebook --- notebooks/tutorial.ipynb | 98 +++++++++++++++------------------------- 1 file changed, 36 insertions(+), 62 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index cc6b594..0642b81 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -63,7 +63,7 @@ "- `Analyze`\n", "\n", "In this demo we will:\n", - "- `Insert` information about an animal subject, recording session, and the metadata and\n", + "- `Insert` metadata about an animal subject, recording session, and \n", " parameters related to processing calcium imaging data through Suite2p.\n", "- `Populate` tables with outputs of image processing including motion correction,\n", " segmentation, mask classification, fluorescence traces and deconvolved activity traces.\n", @@ -123,8 +123,8 @@ "metadata": {}, "source": [ "While the diagram above seems complex at first, it becomes more clear when it's\n", - "approached as a hierarchy of tables that define the order in which DataJoint expects to\n", - "receive data. \n", + "approached as a hierarchy of tables that define the order in which the workflow expects to\n", + "receive data in each of its tables. \n", "\n", "+ Tables with a green, or rectangular shape expect to receive data manually using the\n", "`insert()` function. The tables higher up in the diagram such as `subject.Subject()`\n", @@ -158,7 +158,7 @@ "metadata": {}, "outputs": [], "source": [ - "subject.Subject.describe;" + "subject.Subject.describe()" ] }, { @@ -175,9 +175,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The cells above show all attribute of the subject table. We will insert data into the\n", - "`subject.Subject` table. These are particularly useful functions if you are new to\n", - "DataJoint Elements and are unsure of the attributes required for each table." + "The cells above show all attributes of the subject table. These are particularly useful functions if you are new to\n", + "DataJoint Elements and are unsure of the attributes required for each table. We will insert data into the\n", + "`subject.Subject` table. " ] }, { @@ -211,7 +211,7 @@ "metadata": {}, "outputs": [], "source": [ - "session.Session.describe;" + "session.Session.describe()" ] }, { @@ -278,7 +278,7 @@ "metadata": {}, "outputs": [], "source": [ - "scan.Scan.describe;" + "scan.Scan.describe()" ] }, { @@ -334,7 +334,7 @@ "metadata": {}, "outputs": [], "source": [ - "Equipment.describe;" + "Equipment.describe()" ] }, { @@ -395,7 +395,7 @@ "metadata": {}, "outputs": [], "source": [ - "scan.ScanInfo.describe;" + "scan.ScanInfo.describe();" ] }, { @@ -467,11 +467,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "scan.scanInfo()" @@ -480,11 +476,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "scan.ScanInfo.Field()" @@ -507,24 +499,16 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ - "imaging.ProcessingParamSet.describe;" + "imaging.ProcessingParamSet.describe();" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, + "metadata": {}, "outputs": [], "source": [ "imaging.ProcessingParamSet.heading" @@ -568,7 +552,7 @@ "metadata": {}, "outputs": [], "source": [ - "imaging.ProcessingTask.describe;" + "imaging.ProcessingTask.describe();" ] }, { @@ -650,7 +634,7 @@ "metadata": {}, "outputs": [], "source": [ - "imaging.Curation.describe;" + "imaging.Curation.describe();" ] }, { @@ -745,14 +729,24 @@ "The next section of this tutorial focuses on working with data that is already in the\n", "database. \n", "\n", - "We will use `numpy` and `matplotlib` to plot segmentations on an average image. But\n", - "before we can plot we need to interact with the data.\n", + "DataJoint queries allow you to view and import data from the database into a python\n", + "variable using the `fetch()` method. \n", "\n", - "### Querying\n", + "There are several important features supported by `fetch()`:\n", + "+ By default, an empty `fetch()` imports a list of dictionaries containing all\n", + " attributes of all entries in the table that is queried.\n", + "+ **`fetch1()`**, on the other hand, imports a dictionary containing all attributes of\n", + " one of the entries in the table. By default, if a table has multiple entries,\n", + " `fetch1()` imports the first entry in the table.\n", + "+ Both `fetch()` and `fetch1()` accept table attributes as an argument to query values\n", + " of that particular attribute.\n", + "+ Recommended best practice is to **restrict** queries by primary key attributes of the\n", + " table to ensure the accuracy of imported data. \n", + " + DataJoint offers a convenient way to fetch the primary key attributes of a table\n", + " using `fetch(\"KEY\")`.\n", "\n", - "DataJoint queries allow you to view data that has been entered into DataJoint tables.\n", - "We've already queried the `ScanInfo` table in this notebook. Let's try that again but\n", - "with the fluorescence table. " + "Let's bring together these concepts of querying together by fetching a fluorescence\n", + "trace with `mask_id` of 10 into a variable `fluor_trace`." ] }, { @@ -761,27 +755,7 @@ "metadata": {}, "outputs": [], "source": [ - "imaging.Fluorescence()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's really that simple. Feel free to replace Fluorescence with one of the other tables\n", - "we populated above and view a summary of its contents.\n", - "\n", - "While this is a convenient way to view entries in the table, notice that you cannot see\n", - "the contents of data inserted as a `longblob`. Furthermore, to work on most exploratory\n", - "analyses, data needs to be queried out of the database as a variable, just as you might\n", - "load a `.csv` file. \n", - "\n", - "To query the data out - we'll use `fetch()` and `fetch1()`. There is a subtle difference\n", - "between the two operations. Using `fetch1()` will load the data into a variable as a\n", - "single dictionary where `fetch()` loads the data as a list of dictionaries. \n", - "\n", - "Let's use `fetch1()` to query and load the data into a variable called `trace`. " + "fluor_trace = (imaging.Fluorescence & \"mask_id = '10'\").fetch1(\"trace\")" ] }, { @@ -902,7 +876,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.16" + "version": "3.9.16" }, "metadata": { "interpreter": { From 324a21be547b9550fb76eb1d05a38ca02153cf05 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Tue, 14 Mar 2023 16:36:56 -0500 Subject: [PATCH 3/6] Refine querying section and perform local testing --- notebooks/tutorial.ipynb | 185 ++++++++++++++++++++++++++++++++------- 1 file changed, 154 insertions(+), 31 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 0642b81..872f5f1 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -140,7 +140,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Starting the workflow\n", + "## Starting the workflow: Insert\n", "\n", "### Insert entries into manual tables and populate automated tables\n", "\n", @@ -309,6 +309,7 @@ "scan.Scan.insert1(\n", " dict(\n", " session_key,\n", + " scan_id=0,\n", " scanner=\"ScanImage\",\n", " acq_software=\"ScanImage\",\n", " scan_notes=\"\",\n", @@ -323,7 +324,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "DataJoint issues an error because there is no entry in the `Equipment` table. Let's go\n", + "DataJoint issues an `IntegrityError` because there is no entry in the `Equipment` table. Let's go\n", "ahead and insert into the Equipment table after checking its attributes. Then, we will\n", "try the insert into `Scan` again." ] @@ -381,6 +382,8 @@ "to use DataJoint's automation features to populate several downstream tables after\n", "`Scan`.\n", "\n", + "## Populate\n", + "\n", "### Automatically populate tables\n", "\n", "`scan.ScanInfo` is the first table in the pipeline that can be populated automatically.\n", @@ -395,7 +398,7 @@ "metadata": {}, "outputs": [], "source": [ - "scan.ScanInfo.describe();" + "scan.ScanInfo.describe()" ] }, { @@ -422,7 +425,7 @@ "metadata": {}, "outputs": [], "source": [ - "scan.scanInfo.Field()" + "scan.ScanInfo.Field()" ] }, { @@ -461,7 +464,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's view that was entered into each of these tables:" + "Let's view the information was entered into each of these tables:" ] }, { @@ -470,7 +473,7 @@ "metadata": {}, "outputs": [], "source": [ - "scan.scanInfo()" + "scan.ScanInfo()" ] }, { @@ -488,10 +491,10 @@ "metadata": {}, "source": [ "We're almost ready to perform image processing with `Suite2p`. An important step before\n", - "processing is managing the parameters which will be used in this step. To do so, we will\n", + "processing is managing the parameters which will be used in that step. To do so, we will\n", "import suite2p and insert the default parameters into a DataJoint table\n", - "`ProcessingParamSet`. This table keeps track of all your image processing\n", - "parameters. You can choose which parameters are used during processing in a later table.\n", + "`ProcessingParamSet`. This table keeps track of all combinations of your image processing\n", + "parameters. You can choose which parameters are used during processing in a later step.\n", "\n", "Let's view the attributes and insert data into `imaging.ProcessingParamSet`." ] @@ -502,7 +505,7 @@ "metadata": {}, "outputs": [], "source": [ - "imaging.ProcessingParamSet.describe();" + "imaging.ProcessingParamSet.describe()" ] }, { @@ -540,7 +543,7 @@ "source": [ "Now that we've inserted suite2p parameters into the `ProcessingParamSet` table,\n", "we're almost ready to run image processing. DataJoint uses the `ProcessingTask` table to\n", - "determine which parameter set should be used during processing. \n", + "determine which `ProcessingParamSet` should be used during processing. \n", "\n", "The `ProcessingTask` table is important for defining several important aspects of\n", "downstream processing. Let's view the attributes to get a better understanding. " @@ -552,7 +555,7 @@ "metadata": {}, "outputs": [], "source": [ - "imaging.ProcessingTask.describe();" + "imaging.ProcessingTask.describe()" ] }, { @@ -569,19 +572,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `ProcessingParamSet` table adds two attributes that were not in the `Scan` table: \n", + "The `ProcessingParamSet` table adds two important attributes: \n", "+ `paramset_idx` \n", "+ `task_mode` \n", "\n", "The `paramset_idx` attribute is intended to help track\n", - "your image processing parameter sets. Suite2p, CaImAn, or EXTRACT\n", - "parameter sets can be inserted into this table. You can also choose the parameter sets on which you want\n", - "to run the image processing analysis. \n", + "your image processing parameter sets. You can also choose the parameter sets on which\n", + "you want to run the image processing analysis based on this attribute. \n", "\n", - "The `task_mode` attribute can be set to either\n", - "`load` or `trigger`. When set to `load`, running the processing step initiates a search\n", - "for exisiting output files of the image processing algorithm. When set to `trigger`, the\n", - "processing step will run a processing algorithm on the raw data. " + "The `task_mode` attribute can be set to either `load` or `trigger`. When set to `load`,\n", + "running the processing step initiates a search for exisiting output files of the image\n", + "processing algorithm defined in `ProcessingParamSet`. When set to `trigger`, the\n", + "processing step will run image processing on the raw data. " ] }, { @@ -634,7 +636,7 @@ "metadata": {}, "outputs": [], "source": [ - "imaging.Curation.describe();" + "imaging.Curation.describe()" ] }, { @@ -723,6 +725,7 @@ "analysis, alignment to behavior, statistical testing or modelling, and other downstream\n", "analyses can be performed. \n", "\n", + "## Analyze\n", "\n", "### Query and fetch\n", "\n", @@ -745,8 +748,9 @@ " + DataJoint offers a convenient way to fetch the primary key attributes of a table\n", " using `fetch(\"KEY\")`.\n", "\n", - "Let's bring together these concepts of querying together by fetching a fluorescence\n", - "trace with `mask_id` of 10 into a variable `fluor_trace`." + "Let's bring together these concepts of querying together by fetching a\n", + "fluorescence trace with `mask` number 10 into a variable `fluor_trace`, and then\n", + "plotting the activity trace." ] }, { @@ -755,7 +759,32 @@ "metadata": {}, "outputs": [], "source": [ - "fluor_trace = (imaging.Fluorescence & \"mask_id = '10'\").fetch1(\"trace\")" + "fluor_trace = (imaging.Fluorescence.Trace & \"mask = '10'\").fetch1(\"fluorescence\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(fluor_trace)\n", + "plt.title(\"Fluorescence trace for mask 10\");\n", + "plt.xlabel(\"Samples\")\n", + "plt.ylabel(\"a.u.\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DataJoint queries can be far more complex than the example above. After all, plotting\n", + "traces is likely just the start of your analysis workflow. The examples below perform\n", + "several operations using DataJoint queries: \n", + "+ Use multiple restrictions to fetch one image into the variable `average_image`.\n", + "+ Use a join operation and multiple restrictions to fetch mask coordinates from\n", + " `imaging.Segmentation.Mask` and overlay these with the average image." ] }, { @@ -769,6 +798,15 @@ ").fetch1(\"average_image\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(average_image)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -789,8 +827,9 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np" + "mask_image = np.zeros(np.shape(average_image), dtype=bool)\n", + "for xpix, ypix in zip(mask_xpix, mask_ypix):\n", + " mask_image[ypix, xpix] = True" ] }, { @@ -799,9 +838,17 @@ "metadata": {}, "outputs": [], "source": [ - "mask_image = np.zeros(np.shape(average_image), dtype=bool)\n", - "for xpix, ypix in zip(mask_xpix, mask_ypix):\n", - " mask_image[ypix, xpix] = True" + "plt.imshow(average_image)\n", + "plt.contour(mask_image, colors=\"white\", linewidths=0.5);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One more example using complex queries - plot fluorescence and deconvolved activity\n", + "traces: " ] }, { @@ -810,10 +857,86 @@ "metadata": {}, "outputs": [], "source": [ - "plt.imshow(average_image)\n", - "plt.contour(mask_image, colors=\"white\", linewidths=0.5);" + "curation_key = (imaging.Curation & session_key & \"curation_id=0\").fetch1(\"KEY\")\n", + "query_cells = (\n", + " imaging.Segmentation.Mask * imaging.MaskClassification.MaskType\n", + " & curation_key\n", + " & \"mask_center_z=0\"\n", + " & \"mask_npix > 130\"\n", + ").proj()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fluorescence_traces = (imaging.Fluorescence.Trace & query_cells).fetch(\n", + " \"fluorescence\", order_by=\"mask\"\n", + ")\n", + "\n", + "activity_traces = (imaging.Activity.Trace & query_cells).fetch(\n", + " \"activity_trace\", order_by=\"mask\"\n", + ")\n", + "\n", + "sampling_rate = (scan.ScanInfo & curation_key).fetch1(\"fps\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(16, 4))\n", + "ax2 = ax.twinx()\n", + "\n", + "for f, a in zip(fluorescence_traces, activity_traces):\n", + " ax.plot(np.r_[: f.size] * 1 / sampling_rate, f, \"k\", label=\"fluorescence trace\")\n", + " ax2.plot(\n", + " np.r_[: a.size] * 1 / sampling_rate,\n", + " a,\n", + " \"r\",\n", + " alpha=0.5,\n", + " label=\"deconvolved trace\",\n", + " )\n", + "\n", + " break\n", + "\n", + "ax.tick_params(labelsize=14)\n", + "ax2.tick_params(labelsize=14)\n", + "\n", + "ax.legend(loc=\"upper left\", prop={\"size\": 14})\n", + "ax2.legend(loc=\"upper right\", prop={\"size\": 14})\n", + "\n", + "ax.set_xlabel(\"Time (s)\")\n", + "ax.set_ylabel(\"Activity (a.u.)\")\n", + "ax2.set_ylabel(\"Activity (a.u.)\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aligning data to events\n", + "\n", + "Before analyzing your data, it could be necessary to perform alignment to output of\n", + "operant behavior devices or other events such as visual or acoustic stimulus.\n", + "DataJoint's queries simplify this task. \n", + "\n", + "Below, we will populate tables with behavior-related activity information in the `Trial`\n", + "and `Event` schemas " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, From df69884ca7cc61243a6c7b4f94fd571a6571fdb2 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Fri, 17 Mar 2023 17:32:10 -0500 Subject: [PATCH 4/6] Refine tutorial notebook --- notebooks/tutorial.ipynb | 3257 ++++++++++++++++++++++++++++++++++---- 1 file changed, 2949 insertions(+), 308 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 872f5f1..5e6521c 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -8,7 +8,11 @@ "# Process calcium imaging data with DataJoint Elements\n", "\n", "This notebook will walk through processing two-photon calcium imaging data collected\n", - "from ScanImage and processed with Suite2p.\n", + "from ScanImage and processed with Suite2p. While anyone can work through this\n", + "notebook to process calcium imaging data through DataJoint's\n", + "element-calcium-imaging pipeline, for a\n", + "detailed tutorial about the fundamentals of DataJoint including table types,\n", + "make functions, and querying, please see the DataJoint Tutorial.\n", "\n", "The DataJoint Python API and Element Calcium Imaging offer a lot of features to support collaboration, automation, reproducibility, and visualizations.\n", "\n", @@ -34,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -60,14 +64,14 @@ "\n", "- `Insert`\n", "- `Populate` (or process)\n", - "- `Analyze`\n", + "- `Query`\n", "\n", "In this demo we will:\n", "- `Insert` metadata about an animal subject, recording session, and \n", " parameters related to processing calcium imaging data through Suite2p.\n", "- `Populate` tables with outputs of image processing including motion correction,\n", " segmentation, mask classification, fluorescence traces and deconvolved activity traces.\n", - "- `Analyze` the processed data by ***querying*** and plotting activity traces.\n", + "- `Query` the processed data from the database and plot calcium activity traces.\n", "\n", "Each of these topics will be explained thoroughly in this notebook." ] @@ -96,18 +100,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-03-17 22:30:23,259][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n", + "[2023-03-17 22:30:23,274][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-03-17 22:30:23,282][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + ] + } + ], "source": [ - "from workflow_calcium_imaging.pipeline import lab, subject, session, scan, imaging, Equipment" + "from workflow_calcium_imaging.pipeline import lab, subject, session, scan, imaging, Equipment, event, trial, analysis" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": "\n\n%3\n\n\n\n4\n\n4\n\n\n\nimaging.Segmentation.Mask\n\n\nimaging.Segmentation.Mask\n\n\n\n\n\n4->imaging.Segmentation.Mask\n\n\n\n\n3\n\n3\n\n\n\nimaging.MotionCorrection\n\n\nimaging.MotionCorrection\n\n\n\n\n\n3->imaging.MotionCorrection\n\n\n\n\n5\n\n5\n\n\n\nimaging.Fluorescence.Trace\n\n\nimaging.Fluorescence.Trace\n\n\n\n\n\n5->imaging.Fluorescence.Trace\n\n\n\n\nscan.Scan\n\n\nscan.Scan\n\n\n\n\n\nscan.ScanLocation\n\n\nscan.ScanLocation\n\n\n\n\n\nscan.Scan->scan.ScanLocation\n\n\n\n\nscan.ScanInfo\n\n\nscan.ScanInfo\n\n\n\n\n\nscan.Scan->scan.ScanInfo\n\n\n\n\nimaging.ProcessingTask\n\n\nimaging.ProcessingTask\n\n\n\n\n\nscan.Scan->imaging.ProcessingTask\n\n\n\n\nimaging.Segmentation\n\n\nimaging.Segmentation\n\n\n\n\n\nimaging.Segmentation->imaging.Segmentation.Mask\n\n\n\n\nimaging.MaskClassification\n\n\nimaging.MaskClassification\n\n\n\n\n\nimaging.Segmentation->imaging.MaskClassification\n\n\n\n\nimaging.Fluorescence\n\n\nimaging.Fluorescence\n\n\n\n\n\nimaging.Segmentation->imaging.Fluorescence\n\n\n\n\nimaging.MotionCorrection.Summary\n\n\nimaging.MotionCorrection.Summary\n\n\n\n\n\nimaging.MotionCorrection->imaging.MotionCorrection.Summary\n\n\n\n\nimaging.MotionCorrection.RigidMotionCorrection\n\n\nimaging.MotionCorrection.RigidMotionCorrection\n\n\n\n\n\nimaging.MotionCorrection->imaging.MotionCorrection.RigidMotionCorrection\n\n\n\n\nimaging.MotionCorrection.NonRigidMotionCorrection\n\n\nimaging.MotionCorrection.NonRigidMotionCorrection\n\n\n\n\n\nimaging.MotionCorrection->imaging.MotionCorrection.NonRigidMotionCorrection\n\n\n\n\nimaging.ProcessingMethod\n\n\nimaging.ProcessingMethod\n\n\n\n\n\nimaging.ProcessingParamSet\n\n\nimaging.ProcessingParamSet\n\n\n\n\n\nimaging.ProcessingMethod->imaging.ProcessingParamSet\n\n\n\n\nimaging.ProcessingParamSet->imaging.ProcessingTask\n\n\n\n\nimaging.MotionCorrection.Block\n\n\nimaging.MotionCorrection.Block\n\n\n\n\n\nsubject.Subject\n\n\nsubject.Subject\n\n\n\n\n\nsession.Session\n\n\nsession.Session\n\n\n\n\n\nsubject.Subject->session.Session\n\n\n\n\nscan.ScanInfo.Field\n\n\nscan.ScanInfo.Field\n\n\n\n\n\nscan.ScanInfo.Field->imaging.MotionCorrection.Summary\n\n\n\n\nscan.AcquisitionSoftware\n\n\nscan.AcquisitionSoftware\n\n\n\n\n\nscan.AcquisitionSoftware->scan.Scan\n\n\n\n\nimaging.MaskClassification.MaskType\n\n\nimaging.MaskClassification.MaskType\n\n\n\n\n\nimaging.Segmentation.Mask->imaging.MaskClassification.MaskType\n\n\n\n\nimaging.Segmentation.Mask->imaging.Fluorescence.Trace\n\n\n\n\nimaging.MaskClassificationMethod\n\n\nimaging.MaskClassificationMethod\n\n\n\n\n\nimaging.MaskClassificationMethod->imaging.MaskClassification\n\n\n\n\nimaging.CellCompartment\n\n\nimaging.CellCompartment\n\n\n\n\n\nimaging.Processing\n\n\nimaging.Processing\n\n\n\n\n\nimaging.Curation\n\n\nimaging.Curation\n\n\n\n\n\nimaging.Processing->imaging.Curation\n\n\n\n\nimaging.Activity.Trace\n\n\nimaging.Activity.Trace\n\n\n\n\n\nimaging.Fluorescence.Trace->imaging.Activity.Trace\n\n\n\n\nimaging.MaskType\n\n\nimaging.MaskType\n\n\n\n\n\nimaging.MaskType->imaging.MaskClassification.MaskType\n\n\n\n\nimaging.Activity\n\n\nimaging.Activity\n\n\n\n\n\nimaging.Activity->imaging.Activity.Trace\n\n\n\n\nimaging.MotionCorrection.NonRigidMotionCorrection->imaging.MotionCorrection.Block\n\n\n\n\nscan.ScanInfo->scan.ScanInfo.Field\n\n\n\n\nscan.ScanInfo.ScanFile\n\n\nscan.ScanInfo.ScanFile\n\n\n\n\n\nscan.ScanInfo->scan.ScanInfo.ScanFile\n\n\n\n\nimaging.ActivityExtractionMethod\n\n\nimaging.ActivityExtractionMethod\n\n\n\n\n\nimaging.ActivityExtractionMethod->imaging.Activity\n\n\n\n\nimaging.Curation->imaging.Segmentation\n\n\n\n\nimaging.Curation->imaging.MotionCorrection\n\n\n\n\nsession.Session->scan.Scan\n\n\n\n\nimaging.MaskClassification->imaging.MaskClassification.MaskType\n\n\n\n\nimaging.ProcessingTask->imaging.Processing\n\n\n\n\nimaging.Fluorescence->imaging.Fluorescence.Trace\n\n\n\n\nimaging.Fluorescence->imaging.Activity\n\n\n\n\nscan.Channel\n\n\nscan.Channel\n\n\n\n\n\nscan.Channel->4\n\n\n\n\nscan.Channel->3\n\n\n\n\nscan.Channel->5\n\n\n\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " dj.Diagram(subject.Subject)\n", @@ -122,17 +148,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Diagram Breakdown\n", + "\n", "While the diagram above seems complex at first, it becomes more clear when it's\n", - "approached as a hierarchy of tables that define the order in which the workflow expects to\n", - "receive data in each of its tables. \n", + "approached as a hierarchy of tables that **define the order** in which the workflow **expects to\n", + "receive data** in each of its tables. \n", "\n", - "+ Tables with a green, or rectangular shape expect to receive data manually using the\n", - "`insert()` function. The tables higher up in the diagram such as `subject.Subject()`\n", + "- Tables with a green, or rectangular shape expect to receive data manually using the\n", + "`insert()` function. \n", + "- The tables higher up in the diagram such as `subject.Subject()`\n", "should be the first to receive data. This ensures data integrity by preventing orphaned\n", "data within DataJoint schemas. \n", - "+ Tables with a purple oval or red circle can be automatically filled with relevant data\n", + "- Tables with a purple oval or red circle can be automatically filled with relevant data\n", " by calling `populate()`. For example `scan.ScanInfo` and its part-table\n", - " `scan.ScanInfo.Field` are both populated with `scan.ScanInfo.populate()`.\n" + " `scan.ScanInfo.Field` are both populated with `scan.ScanInfo.populate()`.\n", + "- Tables connected by a solid line depend on attributes (entries) in the table\n", + " above it\n", + "\n", + "#### Table Types\n", + "\n", + "There are 5 table types in DataJoint. Each of these appear in the diagram above.\n", + "\n", + "- **Manual table**: green box, manually inserted table, expect new entries daily, e.g. Subject, ProbeInsertion. \n", + "- **Lookup table**: gray box, pre inserted table, commonly used for general facts or parameters. e.g. Strain, ClusteringMethod, ClusteringParamSet. \n", + "- **Imported table**: blue oval, auto-processing table, the processing depends on the importing of external files. e.g. process of Clustering requires output files from kilosort2. \n", + "- **Computed table**: red circle, auto-processing table, the processing does not depend on files external to the database, commonly used for \n", + "- **Part table**: plain text, as an appendix to the master table, all the part\n", + " entries of a given master entry represent a intact set of the master entry.\n", + " e.g. Unit of a CuratedClustering.\n" ] }, { @@ -142,7 +185,7 @@ "source": [ "## Starting the workflow: Insert\n", "\n", - "### Insert entries into manual tables and populate automated tables\n", + "### Insert entries into manual tables\n", "\n", "To view details about a table's dependencies and attributes, use functions `.describe()`\n", "and `.heading`, respectively.\n", @@ -154,18 +197,46 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'subject : varchar(8) \\n---\\nsubject_nickname=\"\" : varchar(64) \\nsex : enum(\\'M\\',\\'F\\',\\'U\\') \\nsubject_birth_date : date \\nsubject_description=\"\" : varchar(1024) \\n'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subject.Subject.describe()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "---\n", + "subject_nickname=\"\" : varchar(64) # \n", + "sex : enum('M','F','U') # \n", + "subject_birth_date : date # \n", + "subject_description=\"\" : varchar(1024) # " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subject.Subject.heading" ] @@ -182,9 +253,103 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject1F2020-01-01ScanImage acquisition. Suite2p processing.
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\n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+----------+ +------------+ +-----+ +------------+ +------------+\n", + "subject1 F 2020-01-01 ScanImage acqu\n", + " (Total: 1)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subject.Subject.insert1(\n", " dict(\n", @@ -193,7 +358,8 @@ " subject_birth_date=\"2020-01-01\",\n", " subject_description=\"ScanImage acquisition. Suite2p processing.\",\n", " )\n", - ")" + ")\n", + "subject.Subject()" ] }, { @@ -207,18 +373,42 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'-> subject.Subject\\nsession_datetime : datetime \\n'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "session.Session.describe()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "session.Session.heading" ] @@ -230,10 +420,10 @@ "source": [ "The cells above show the dependencies and attributes for the `session.Session` table.\n", "Notice that `describe` shows the dependencies of the table on upstream tables. The\n", - "`Session` table depends on the upstream `Subject` table. Whereas `heading` lists all the\n", - "attributes of the `Session` table, regardless of whether they are declared in an upstream\n", - "table. \n", + "`Session` table depends on the upstream `Subject` table. \n", "\n", + "Whereas `heading` lists all the attributes of the `Session` table, regardless of\n", + "whether they are declared in an upstream table. \n", "\n", "Here we will demonstrate a very useful way of inserting data by assigning the dictionary\n", "to a variable `session_key`. This variable can be used to insert entries into tables that\n", @@ -242,52 +432,250 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject1 2021-04-30 12:\n", + " (Total: 1)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "session_key = dict(subject=\"subject1\", session_datetime=\"2021-04-30 12:22:15.032\")\n", "\n", - "session.Session.insert1(session_key)" + "session.Session.insert1(session_key)\n", + "session.Session()" ] }, { - "cell_type": "code", - "execution_count": null, + "attachments": {}, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "session.SessionDirectory.insert1(\n", - " dict(**session_key,\n", - " session_dir=\"subject1/session1\")\n", - ")" + "The `SessionDirectory` table locates the relevant data files in a directory path\n", + "relative to the root directory defined in your `dj.config[\"custom\"]`. More\n", + "information about `dj.config` is provided at the end of this tutorial and is\n", + "particularly useful for local deployments of this workflow." ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 10, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'-> session.Session\\n---\\nsession_dir : varchar(256) # Path to the data directory for a session\\n'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Next, we'll use `describe` and `heading` for the Scan table. Do you notice anything we\n", - "might have missed here? " + "session.SessionDirectory.describe()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "---\n", + "session_dir : varchar(256) # Path to the data directory for a session" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "scan.Scan.describe()" + "session.SessionDirectory.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet session_dir \n", + "+----------+ +------------+ +------------+\n", + "subject1 2021-04-30 12: subject1/sessi\n", + " (Total: 1)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "scan.Scan.heading" + "session.SessionDirectory.insert1(\n", + " dict(**session_key,\n", + " session_dir=\"subject1/session1\")\n", + ")\n", + "session.SessionDirectory()" ] }, { @@ -295,38 +683,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `Scan` table's attributes include the `Session` table **and** the `Equipment` table.\n", - "Let's see what happens when you try to insert data into `Scan` without inserting into\n", - "`Equipment` first." + "Next, we'll use `describe` and `heading` for the Scan table. Do you notice anything we\n", + "might have missed here? " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'-> session.Session\\nscan_id : int \\n---\\n-> [nullable] Equipment\\n-> scan.AcquisitionSoftware\\nscan_notes=\"\" : varchar(4095) \\n'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "scan.Scan.insert1(\n", - " dict(\n", - " session_key,\n", - " scan_id=0,\n", - " scanner=\"ScanImage\",\n", - " acq_software=\"ScanImage\",\n", - " scan_notes=\"\",\n", - " )\n", - ")\n", - "\n", - "# This cell will produce an error" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DataJoint issues an `IntegrityError` because there is no entry in the `Equipment` table. Let's go\n", - "ahead and insert into the Equipment table after checking its attributes. Then, we will\n", - "try the insert into `Scan` again." + "scan.Scan.describe()" ] }, { @@ -335,42 +713,142 @@ "metadata": {}, "outputs": [], "source": [ - "Equipment.describe()" + "scan.Scan.heading" ] }, { - "cell_type": "code", - "execution_count": null, + "attachments": {}, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "Equipment.heading" + "The `Scan` table's attributes include the `Session` table **and** the `Equipment` table.\n", + "Let's insert into the Equipment table after checking its attributes. Then, we will\n", + "insert into `Scan`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "Equipment.insert1(dict(scanner=\"ScanImage\"))" + "Equipment.insert1(dict(scanner=\"ScannerA\"))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *scan_id scanner acq_software scan_notes \n", + "+----------+ +------------+ +---------+ +----------+ +------------+ +------------+\n", + "subject1 2021-04-30 12: 0 ScannerA ScanImage \n", + " (Total: 1)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "scan.Scan.insert1(\n", " dict(\n", " **session_key,\n", " scan_id=0,\n", - " scanner=\"ScanImage\",\n", + " scanner=\"ScannerA\",\n", " acq_software=\"ScanImage\",\n", " scan_notes=\"\",\n", " )\n", - ")" + ")\n", + "scan.Scan()" ] }, { @@ -378,85 +856,371 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This time, we were able to successfully insert data into the scan table. Now we're ready\n", - "to use DataJoint's automation features to populate several downstream tables after\n", - "`Scan`.\n", - "\n", "## Populate\n", "\n", "### Automatically populate tables\n", "\n", "`scan.ScanInfo` is the first table in the pipeline that can be populated automatically.\n", "If a table contains a part table, this part table is also populated during the\n", - "`populate()` call. Let's view the `scan.ScanInfo` and its part table\n", - "`scan.ScanInfo.Field`. " + "`populate()` call. `populate()` takes several arguments including the a session\n", + "key. This key restricts `populate()` to performing the operation on the session\n", + "of interest rather than all possible sessions which could be a time-intensive\n", + "process for databases with lots of entries.\n", + "\n", + "Let's view the `scan.ScanInfo` and its part table\n", + "`scan.ScanInfo.Field` and populate both through a single `populate()` call." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# General data about the resoscans/mesoscans from header\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "scan_id : int # \n", + "---\n", + "nfields : tinyint # number of fields\n", + "nchannels : tinyint # number of channels\n", + "ndepths : int # Number of scanning depths (planes)\n", + "nframes : int # number of recorded frames\n", + "nrois : tinyint # number of ROIs (see scanimage's multi ROI imaging)\n", + "x=null : float # (um) ScanImage's 0 point in the motor coordinate system\n", + "y=null : float # (um) ScanImage's 0 point in the motor coordinate system\n", + "z=null : float # (um) ScanImage's 0 point in the motor coordinate system\n", + "fps : float # (Hz) frames per second - Volumetric Scan Rate\n", + "bidirectional : tinyint # true = bidirectional scanning\n", + "usecs_per_line=null : float # microseconds per scan line\n", + "fill_fraction=null : float # raster scan temporal fill fraction (see scanimage)\n", + "scan_datetime=null : datetime # datetime of the scan\n", + "scan_duration=null : float # (seconds) duration of the scan\n", + "bidirectional_z=null : tinyint # true = bidirectional z-scan" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "scan.ScanInfo.describe()" + "scan.ScanInfo.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scan.ScanInfo.heading" + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# field-specific scan information\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "scan_id : int # \n", + "field_idx : int # \n", + "---\n", + "px_height : smallint # height in pixels\n", + "px_width : smallint # width in pixels\n", + "um_height=null : float # height in microns\n", + "um_width=null : float # width in microns\n", + "field_x=null : float # (um) center of field in the motor coordinate system\n", + "field_y=null : float # (um) center of field in the motor coordinate system\n", + "field_z=null : float # (um) relative depth of field\n", + "delay_image=null : longblob # (ms) delay between the start of the scan and pixels in this field\n", + "roi=null : int # the scanning roi (as recorded in the acquisition software) containing this field - only relevant to mesoscale scans" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scan.ScanInfo.Field.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " General data about the resoscans/mesoscans from header\n", + "
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet *scan_id *field_idx px_height px_width um_height um_width field_x field_y field_z delay_imag roi \n", + "+----------+ +------------+ +---------+ +-----------+ +-----------+ +----------+ +-----------+ +----------+ +---------+ +---------+ +-----------+ +--------+ +------+\n", + "subject1 2021-04-30 12: 0 0 512 512 nan nan 13441.9 15745.0 -205821.0 =BLOB= None \n", + " (Total: 1)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "scan.ScanInfo.Field()" ] @@ -501,27 +1537,45 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.ProcessingParamSet.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Processing Parameter Set\n", + "paramset_idx : smallint # Unique parameter set ID.\n", + "---\n", + "processing_method : char(8) # \n", + "paramset_desc : varchar(1280) # Parameter-set description\n", + "param_set_hash : uuid # A universally unique identifier for the parameter set\n", + "params : longblob # Parameter Set, a dictionary of all applicable parameters to the analysis suite." + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "imaging.ProcessingParamSet.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: cellpose did not import\n", + "No module named 'cellpose'\n", + "cannot use anatomical mode, but otherwise suite2p will run normally\n" + ] + } + ], "source": [ "import suite2p\n", "\n", @@ -542,27 +1596,56 @@ "metadata": {}, "source": [ "Now that we've inserted suite2p parameters into the `ProcessingParamSet` table,\n", - "we're almost ready to run image processing. DataJoint uses the `ProcessingTask` table to\n", - "determine which `ProcessingParamSet` should be used during processing. \n", + "we're almost ready to run image processing. DataJoint uses a `ProcessingTask` table to\n", + "manage which `Scan` and `ProcessingParamSet` should be used during processing. \n", "\n", - "The `ProcessingTask` table is important for defining several important aspects of\n", + "This table is important for defining several important aspects of\n", "downstream processing. Let's view the attributes to get a better understanding. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'# Manual table for defining a processing task ready to be run\\n-> scan.Scan\\n-> imaging.ProcessingParamSet\\n---\\nprocessing_output_dir : varchar(255) # Output directory of the processed scan relative to root data directory\\ntask_mode=\"load\" : enum(\\'load\\',\\'trigger\\') # \\'load\\': load computed analysis results, \\'trigger\\': trigger computation\\n'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "imaging.ProcessingTask.describe()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Manual table for defining a processing task ready to be run\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "scan_id : int # \n", + "paramset_idx : smallint # Unique parameter set ID.\n", + "---\n", + "processing_output_dir : varchar(255) # Output directory of the processed scan relative to root data directory\n", + "task_mode=\"load\" : enum('load','trigger') # 'load': load computed analysis results, 'trigger': trigger computation" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "imaging.ProcessingTask.heading" ] @@ -572,13 +1655,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `ProcessingParamSet` table adds two important attributes: \n", + "The `ProcessingParamSet` table contains two important attributes: \n", "+ `paramset_idx` \n", "+ `task_mode` \n", "\n", - "The `paramset_idx` attribute is intended to help track\n", - "your image processing parameter sets. You can also choose the parameter sets on which\n", - "you want to run the image processing analysis based on this attribute. \n", + "The `paramset_idx` attribute is tracks\n", + "your image processing parameter sets. You can choose the parameter set on which\n", + "you want to run image processing analysis based on this attribute. For example,\n", + "`paramset_idx=0` may contain default parameters for suite2p processing whereas\n", + "`paramset_idx=1` contains your custom parameters for suite2p processing. This\n", + "attribute tells the `Processing` table which set of parameters you are\n", + "processing in a given `populate()`.\n", "\n", "The `task_mode` attribute can be set to either `load` or `trigger`. When set to `load`,\n", "running the processing step initiates a search for exisiting output files of the image\n", @@ -588,7 +1675,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -614,11 +1701,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing: 100%|██████████| 1/1 [00:00<00:00, 28.35it/s]\n" + ] + } + ], "source": [ - "imaging.Processing.populate(**populate_settings)" + "imaging.Processing.populate(session_key, display_progress=True)" ] }, { @@ -632,25 +1727,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "imaging.Curation.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Curation(s) results\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "scan_id : int # \n", + "paramset_idx : smallint # Unique parameter set ID.\n", + "curation_id : int # \n", + "---\n", + "curation_time : datetime # Time of generation of this set of curated results\n", + "curation_output_dir : varchar(255) # Output directory of the curated results, relative to root data directory\n", + "manual_curation : tinyint # Has manual curation been performed on this result?\n", + "curation_note=\"\" : varchar(2000) # " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "imaging.Curation.heading" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -682,38 +1789,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MotionCorrection: 100%|██████████| 1/1 [00:00<00:00, 1.43it/s]\n" + ] + } + ], "source": [ - "imaging.MotionCorrection.populate(**populate_settings)" + "imaging.MotionCorrection.populate(session_key, display_progress=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Segmentation: 100%|██████████| 1/1 [00:00<00:00, 1.42it/s]\n" + ] + } + ], "source": [ - "imaging.Segmentation.populate(**populate_settings)" + "imaging.Segmentation.populate(session_key, display_progress=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fluorescence: 100%|██████████| 1/1 [00:02<00:00, 2.97s/it]\n" + ] + } + ], "source": [ - "imaging.Fluorescence.populate(**populate_settings)" + "imaging.Fluorescence.populate(session_key, display_progress=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Activity: 100%|██████████| 1/1 [00:00<00:00, 1.30it/s]\n" + ] + } + ], "source": [ - "imaging.Activity.populate(**populate_settings)" + "imaging.Activity.populate(session_key, display_progress=True)" ] }, { @@ -721,41 +1860,47 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that data is in the DataJoint tables, the analysis workflows such as exploratory\n", - "analysis, alignment to behavior, statistical testing or modelling, and other downstream\n", - "analyses can be performed. \n", + "Now that we've populated the tables in this workflow, there are one of\n", + "several options for next steps. If you have a standard workflow for\n", + "aligning calcium activity to behavior data or other stimuli, you can easily\n", + "invoke `element-event` or define your custom DataJoint tables to extend the\n", + "pipeline.\n", "\n", - "## Analyze\n", + "In this tutorial, we will fetch the data from the database and create a few plots.\n", "\n", - "### Query and fetch\n", + "## Query\n", "\n", - "The next section of this tutorial focuses on working with data that is already in the\n", + "This section focuses on working with data that is already in the\n", "database. \n", "\n", "DataJoint queries allow you to view and import data from the database into a python\n", "variable using the `fetch()` method. \n", "\n", "There are several important features supported by `fetch()`:\n", - "+ By default, an empty `fetch()` imports a list of dictionaries containing all\n", + "- By default, an empty `fetch()` imports a list of dictionaries containing all\n", " attributes of all entries in the table that is queried.\n", - "+ **`fetch1()`**, on the other hand, imports a dictionary containing all attributes of\n", + "- **`fetch1()`**, on the other hand, imports a dictionary containing all attributes of\n", " one of the entries in the table. By default, if a table has multiple entries,\n", " `fetch1()` imports the first entry in the table.\n", - "+ Both `fetch()` and `fetch1()` accept table attributes as an argument to query values\n", - " of that particular attribute.\n", - "+ Recommended best practice is to **restrict** queries by primary key attributes of the\n", - " table to ensure the accuracy of imported data. \n", - " + DataJoint offers a convenient way to fetch the primary key attributes of a table\n", - " using `fetch(\"KEY\")`.\n", + "- Both `fetch()` and `fetch1()` accept table attributes as an argument to query\n", + " that particular attribute. For example `fetch1('fps')` will fetch the first\n", + " value of the `fps` attribute if it exists in the table.\n", + "- Recommended best practice is to **restrict** queries by primary key attributes of the\n", + " table to ensure the accuracy of imported data.\n", + " - The most common restriction for entries in DataJoint tables is performed\n", + " using the `&` operator. For example to fetch all session start times belonging to\n", + " `subject1`, a possible query could be `subject1_sessions =\n", + " (session.Session & \"subject = 'subject1'\").fetch(\"session_datetime\")`. \n", + "- `fetch()` can also be used to obtain the primary keys of a table. To fetch the primary\n", + " keys of a table use `.fetch(\"KEY\")` syntax.\n", "\n", - "Let's bring together these concepts of querying together by fetching a\n", - "fluorescence trace with `mask` number 10 into a variable `fluor_trace`, and then\n", - "plotting the activity trace." + "Let's walk through these concepts of querying by moving from simple to more\n", + "complex queries." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -763,10 +1908,33 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "attachments": {}, + "cell_type": "markdown", "metadata": {}, - "outputs": [], + "source": [ + "In the query above, we fetch a single `fluorescence` attribute from the `Trace` part\n", + "table belonging to the `Fluorescence` table. We also restrict the query to mask\n", + "number 10. \n", + "\n", + "Let's go ahead and plot this trace." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(fluor_trace)\n", "plt.title(\"Fluorescence trace for mask 10\");\n", @@ -779,17 +1947,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "DataJoint queries can be far more complex than the example above. After all, plotting\n", - "traces is likely just the start of your analysis workflow. The examples below perform\n", - "several operations using DataJoint queries: \n", - "+ Use multiple restrictions to fetch one image into the variable `average_image`.\n", - "+ Use a join operation and multiple restrictions to fetch mask coordinates from\n", + "DataJoint queries can be a highly flexible tool to manipulate and visualize your data.\n", + "After all, plotting traces is likely just the start of your analysis workflow. This can\n", + "also make the queries seem more complex at first. However, we'll walk through them\n", + "slowly to simplify their content in this notebook. \n", + "\n", + "The examples below perform several operations using DataJoint queries: \n", + "- Use **multiple restrictions** to fetch one image into the variable `average_image`.\n", + "- Use a **join** operation and **multiple restrictions** to fetch mask coordinates from\n", " `imaging.Segmentation.Mask` and overlay these with the average image." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -799,17 +1970,1320 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "attachments": {}, + "cell_type": "markdown", "metadata": {}, - "outputs": [], + "source": [ + "The query above restricts the `average_image` attribute of the `Summary` part\n", + "table of the `MotionCorrection` table by the `session_key` variable containing primary\n", + "key attributes of the current session and the `field_idx` attribute of the\n", + "`Summary` table. " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.imshow(average_image)" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next query introduces the **join** feature of DataJoint queries. DataJoint\n", + "tables can be joined to combine their primary key attributes and display the\n", + "attributes of all the tables. It also performs 3 restrictions with the `&`\n", + "operator and simultaneously fetches the contents of 2 different attributes to\n", + "two different variables.\n", + "\n", + "Let's see the full query and then build it up one step at a time: \n", + "```python\n", + "mask_xpix, mask_ypix = (\n", + " imaging.Segmentation.Mask * imaging.MaskClassification.MaskType\n", + " & session_key\n", + " & \"mask_center_z=0\"\n", + " & \"mask_npix > 130\"\n", + ").fetch(\"mask_xpix\", \"mask_ypix\")\n", + "```\n", + "\n", + "This query joins `imaging.Segmentation.Mask` with `imaging.MaskClassification.MaskType`.\n", + "Below we will: \n", + "\n", + "1. See the contents of each table individually.\n", + "2. See the contents of the two tables joined together without restrictions.\n", + "3. See the contents of the two tables with the restrictions.\n", + "4. Execute the query to fetch the data." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " A mask produced by segmentation.\n", + "
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subject12021-04-30 12:22:15000003051761490=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:15000101771563770=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:15000201871601650=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:1500030208482850=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:15000401531483050=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:15000502472562770=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:15000601461042610=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:15000704132761890=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:1500080223922770=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:150009015034090=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:15000100140336690=BLOB==BLOB==BLOB==BLOB=
subject12021-04-30 12:22:150001101841363490=BLOB==BLOB==BLOB==BLOB=
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Total: 1276

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *scan_id *paramset_idx *curation_id *mask segmentation_c mask_npix mask_center_x mask_center_y mask_center_z mask_xpix mask_ypix mask_zpix mask_weigh\n", + "+----------+ +------------+ +---------+ +------------+ +------------+ +------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n", + "subject1 2021-04-30 12: 0 0 0 0 0 305 176 149 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 1 0 177 156 377 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 2 0 187 160 165 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 3 0 208 48 285 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 4 0 153 148 305 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 5 0 247 256 277 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 6 0 146 104 261 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 7 0 413 276 189 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 8 0 223 92 277 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 9 0 150 340 9 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 10 0 140 336 69 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + "subject1 2021-04-30 12: 0 0 0 11 0 184 136 349 0 =BLOB= =BLOB= =BLOB= =BLOB= \n", + " ...\n", + " (Total: 1276)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "imaging.Segmentation.Mask()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12021-04-30 12:22:15000suite2p_default_classifier0soma0.81364
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subject12021-04-30 12:22:15000suite2p_default_classifier2soma0.747744
subject12021-04-30 12:22:15000suite2p_default_classifier3soma0.856031
subject12021-04-30 12:22:15000suite2p_default_classifier4soma0.985041
subject12021-04-30 12:22:15000suite2p_default_classifier5soma0.825305
subject12021-04-30 12:22:15000suite2p_default_classifier6soma0.99609
subject12021-04-30 12:22:15000suite2p_default_classifier7soma0.947971
subject12021-04-30 12:22:15000suite2p_default_classifier8soma0.963464
subject12021-04-30 12:22:15000suite2p_default_classifier9soma0.913962
subject12021-04-30 12:22:15000suite2p_default_classifier10soma0.951404
subject12021-04-30 12:22:15000suite2p_default_classifier11soma0.922488
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subject12021-04-30 12:22:150000suite2p_default_classifier03051761490=BLOB==BLOB==BLOB==BLOB=soma0.81364
subject12021-04-30 12:22:150001suite2p_default_classifier01771563770=BLOB==BLOB==BLOB==BLOB=soma0.850127
subject12021-04-30 12:22:150002suite2p_default_classifier01871601650=BLOB==BLOB==BLOB==BLOB=soma0.747744
subject12021-04-30 12:22:150003suite2p_default_classifier0208482850=BLOB==BLOB==BLOB==BLOB=soma0.856031
subject12021-04-30 12:22:150004suite2p_default_classifier01531483050=BLOB==BLOB==BLOB==BLOB=soma0.985041
subject12021-04-30 12:22:150005suite2p_default_classifier02472562770=BLOB==BLOB==BLOB==BLOB=soma0.825305
subject12021-04-30 12:22:150006suite2p_default_classifier01461042610=BLOB==BLOB==BLOB==BLOB=soma0.99609
subject12021-04-30 12:22:150007suite2p_default_classifier04132761890=BLOB==BLOB==BLOB==BLOB=soma0.947971
subject12021-04-30 12:22:150008suite2p_default_classifier0223922770=BLOB==BLOB==BLOB==BLOB=soma0.963464
subject12021-04-30 12:22:150009suite2p_default_classifier015034090=BLOB==BLOB==BLOB==BLOB=soma0.913962
subject12021-04-30 12:22:1500010suite2p_default_classifier0140336690=BLOB==BLOB==BLOB==BLOB=soma0.951404
subject12021-04-30 12:22:1500011suite2p_default_classifier01841363490=BLOB==BLOB==BLOB==BLOB=soma0.922488
\n", + "

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Total: 481

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *scan_id *paramset_idx *curation_id *mask *mask_classifi segmentation_c mask_npix mask_center_x mask_center_y mask_center_z mask_xpix mask_ypix mask_zpix mask_weigh mask_type confidence \n", + "+----------+ +------------+ +---------+ +------------+ +------------+ +------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+ +-----------+ +------------+\n", + "subject1 2021-04-30 12: 0 0 0 0 suite2p_defaul 0 305 176 149 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.81364 \n", + "subject1 2021-04-30 12: 0 0 0 1 suite2p_defaul 0 177 156 377 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.850127 \n", + "subject1 2021-04-30 12: 0 0 0 2 suite2p_defaul 0 187 160 165 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.747744 \n", + "subject1 2021-04-30 12: 0 0 0 3 suite2p_defaul 0 208 48 285 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.856031 \n", + "subject1 2021-04-30 12: 0 0 0 4 suite2p_defaul 0 153 148 305 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.985041 \n", + "subject1 2021-04-30 12: 0 0 0 5 suite2p_defaul 0 247 256 277 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.825305 \n", + "subject1 2021-04-30 12: 0 0 0 6 suite2p_defaul 0 146 104 261 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.99609 \n", + "subject1 2021-04-30 12: 0 0 0 7 suite2p_defaul 0 413 276 189 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.947971 \n", + "subject1 2021-04-30 12: 0 0 0 8 suite2p_defaul 0 223 92 277 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.963464 \n", + "subject1 2021-04-30 12: 0 0 0 9 suite2p_defaul 0 150 340 9 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.913962 \n", + "subject1 2021-04-30 12: 0 0 0 10 suite2p_defaul 0 140 336 69 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.951404 \n", + "subject1 2021-04-30 12: 0 0 0 11 suite2p_defaul 0 184 136 349 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.922488 \n", + " ...\n", + " (Total: 481)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "imaging.Segmentation.Mask * imaging.MaskClassification.MaskType" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject

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session_datetime

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scan_id

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paramset_idx

\n", + " Unique parameter set ID.\n", + "
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curation_id

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mask

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mask_classification_method

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segmentation_channel

\n", + " 0-based indexing\n", + "
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mask_npix

\n", + " number of pixels in ROIs\n", + "
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mask_center_x

\n", + " center x coordinate in pixel\n", + "
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mask_center_y

\n", + " center y coordinate in pixel\n", + "
\n", + "

mask_center_z

\n", + " center z coordinate in pixel\n", + "
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mask_xpix

\n", + " x coordinates in pixels\n", + "
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mask_ypix

\n", + " y coordinates in pixels\n", + "
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mask_zpix

\n", + " z coordinates in pixels\n", + "
\n", + "

mask_weights

\n", + " weights of the mask at the indices above\n", + "
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mask_type

\n", + " \n", + "
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confidence

\n", + " \n", + "
subject12021-04-30 12:22:150000suite2p_default_classifier03051761490=BLOB==BLOB==BLOB==BLOB=soma0.81364
subject12021-04-30 12:22:150001suite2p_default_classifier01771563770=BLOB==BLOB==BLOB==BLOB=soma0.850127
subject12021-04-30 12:22:150002suite2p_default_classifier01871601650=BLOB==BLOB==BLOB==BLOB=soma0.747744
subject12021-04-30 12:22:150003suite2p_default_classifier0208482850=BLOB==BLOB==BLOB==BLOB=soma0.856031
subject12021-04-30 12:22:150004suite2p_default_classifier01531483050=BLOB==BLOB==BLOB==BLOB=soma0.985041
subject12021-04-30 12:22:150005suite2p_default_classifier02472562770=BLOB==BLOB==BLOB==BLOB=soma0.825305
subject12021-04-30 12:22:150006suite2p_default_classifier01461042610=BLOB==BLOB==BLOB==BLOB=soma0.99609
subject12021-04-30 12:22:150007suite2p_default_classifier04132761890=BLOB==BLOB==BLOB==BLOB=soma0.947971
subject12021-04-30 12:22:150008suite2p_default_classifier0223922770=BLOB==BLOB==BLOB==BLOB=soma0.963464
subject12021-04-30 12:22:150009suite2p_default_classifier015034090=BLOB==BLOB==BLOB==BLOB=soma0.913962
subject12021-04-30 12:22:1500010suite2p_default_classifier0140336690=BLOB==BLOB==BLOB==BLOB=soma0.951404
subject12021-04-30 12:22:1500011suite2p_default_classifier01841363490=BLOB==BLOB==BLOB==BLOB=soma0.922488
\n", + "

...

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Total: 129

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *scan_id *paramset_idx *curation_id *mask *mask_classifi segmentation_c mask_npix mask_center_x mask_center_y mask_center_z mask_xpix mask_ypix mask_zpix mask_weigh mask_type confidence \n", + "+----------+ +------------+ +---------+ +------------+ +------------+ +------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+ +-----------+ +------------+\n", + "subject1 2021-04-30 12: 0 0 0 0 suite2p_defaul 0 305 176 149 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.81364 \n", + "subject1 2021-04-30 12: 0 0 0 1 suite2p_defaul 0 177 156 377 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.850127 \n", + "subject1 2021-04-30 12: 0 0 0 2 suite2p_defaul 0 187 160 165 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.747744 \n", + "subject1 2021-04-30 12: 0 0 0 3 suite2p_defaul 0 208 48 285 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.856031 \n", + "subject1 2021-04-30 12: 0 0 0 4 suite2p_defaul 0 153 148 305 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.985041 \n", + "subject1 2021-04-30 12: 0 0 0 5 suite2p_defaul 0 247 256 277 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.825305 \n", + "subject1 2021-04-30 12: 0 0 0 6 suite2p_defaul 0 146 104 261 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.99609 \n", + "subject1 2021-04-30 12: 0 0 0 7 suite2p_defaul 0 413 276 189 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.947971 \n", + "subject1 2021-04-30 12: 0 0 0 8 suite2p_defaul 0 223 92 277 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.963464 \n", + "subject1 2021-04-30 12: 0 0 0 9 suite2p_defaul 0 150 340 9 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.913962 \n", + "subject1 2021-04-30 12: 0 0 0 10 suite2p_defaul 0 140 336 69 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.951404 \n", + "subject1 2021-04-30 12: 0 0 0 11 suite2p_defaul 0 184 136 349 0 =BLOB= =BLOB= =BLOB= =BLOB= soma 0.922488 \n", + " ...\n", + " (Total: 129)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " imaging.Segmentation.Mask * imaging.MaskClassification.MaskType\n", + " & session_key\n", + " & \"mask_center_z=0\"\n", + " & \"mask_npix > 130\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -821,9 +3295,18 @@ ").fetch(\"mask_xpix\", \"mask_ypix\")" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using this query, we've fetched the coordinates of segmented masks. We can overlay these\n", + "masks onto our average image." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -834,9 +3317,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.imshow(average_image)\n", "plt.contour(mask_image, colors=\"white\", linewidths=0.5);" @@ -847,15 +3341,206 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "One more example using complex queries - plot fluorescence and deconvolved activity\n", - "traces: " + "One more example using queries - plot fluorescence and deconvolved activity\n", + "traces:\n", + "\n", + "Here we fetch the primary key attributes of the entry with `curation_id=0` for the\n", + "current session in the `imaging.Curation` table. \n", + "\n", + "Then, we fetch all cells that fit the\n", + "restriction criteria from `imaging.Segmentation.Mask` and\n", + "`imaging.MaskClassification.MaskType` as a `projection`. \n", + "\n", + "We then use this projection as\n", + "a restriction to fetch and plot fluorescence and deconvolved activity traces from the\n", + "`imaging.Fluorescence.Trace` and `imaging.Activity.Trace` tables, respectively." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject

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session_datetime

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scan_id

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paramset_idx

\n", + " Unique parameter set ID.\n", + "
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curation_id

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mask

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mask_classification_method

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subject12021-04-30 12:22:150000suite2p_default_classifier
subject12021-04-30 12:22:150001suite2p_default_classifier
subject12021-04-30 12:22:150002suite2p_default_classifier
subject12021-04-30 12:22:150003suite2p_default_classifier
subject12021-04-30 12:22:150004suite2p_default_classifier
subject12021-04-30 12:22:150005suite2p_default_classifier
subject12021-04-30 12:22:150006suite2p_default_classifier
subject12021-04-30 12:22:150007suite2p_default_classifier
subject12021-04-30 12:22:150008suite2p_default_classifier
subject12021-04-30 12:22:150009suite2p_default_classifier
subject12021-04-30 12:22:1500010suite2p_default_classifier
subject12021-04-30 12:22:1500011suite2p_default_classifier
\n", + "

...

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Total: 129

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *scan_id *paramset_idx *curation_id *mask *mask_classifi\n", + "+----------+ +------------+ +---------+ +------------+ +------------+ +------+ +------------+\n", + "subject1 2021-04-30 12: 0 0 0 0 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 1 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 2 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 3 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 4 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 5 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 6 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 7 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 8 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 9 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 10 suite2p_defaul\n", + "subject1 2021-04-30 12: 0 0 0 11 suite2p_defaul\n", + " ...\n", + " (Total: 129)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "curation_key = (imaging.Curation & session_key & \"curation_id=0\").fetch1(\"KEY\")\n", "query_cells = (\n", @@ -863,12 +3548,14 @@ " & curation_key\n", " & \"mask_center_z=0\"\n", " & \"mask_npix > 130\"\n", - ").proj()" + ").proj()\n", + "\n", + "query_cells" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -885,9 +3572,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(16, 4))\n", "ax2 = ax.twinx()\n", @@ -915,63 +3613,6 @@ "ax2.set_ylabel(\"Activity (a.u.)\");" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Aligning data to events\n", - "\n", - "Before analyzing your data, it could be necessary to perform alignment to output of\n", - "operant behavior devices or other events such as visual or acoustic stimulus.\n", - "DataJoint's queries simplify this task. \n", - "\n", - "Below, we will populate tables with behavior-related activity information in the `Trial`\n", - "and `Event` schemas " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Drop schemas\n", - "\n", - "+ Schemas are not typically dropped in a production workflow with real data in it. \n", - "+ At the developmental phase, it might be required for the table redesign.\n", - "+ When dropping all schemas is needed, the following is the dependency order." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def drop_databases(databases):\n", - " import pymysql.err\n", - " conn = dj.conn()\n", - "\n", - " with dj.config(safemode=False):\n", - " for database in databases:\n", - " schema = dj.Schema(f'{dj.config[\"custom\"][\"database.prefix\"]}{database}')\n", - " while schema.list_tables():\n", - " for table in schema.list_tables():\n", - " try:\n", - " conn.query(f\"DROP TABLE `{schema.database}`.`{table}`\")\n", - " except pymysql.err.OperationalError:\n", - " print(f\"Can't drop `{schema.database}`.`{table}`. Retrying...\")\n", - " schema.drop()\n", - "\n", - "# drop_databases(databases=['imaging_report', 'imaging', 'scan', 'session', 'subject', 'lab', 'reference'])" - ] - }, { "cell_type": "code", "execution_count": null, From e8f72289a31dea5dd4f8a73fde425059f2fd2bc1 Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Fri, 17 Mar 2023 17:44:46 -0500 Subject: [PATCH 5/6] Add local config info, update version + CHANGELOG --- CHANGELOG.md | 5 +++++ notebooks/tutorial.ipynb | 30 ++++++++++++++++++++++++++--- workflow_calcium_imaging/version.py | 2 +- 3 files changed, 33 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 95f7a52..88210a6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,10 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. +## [0.4.0] - 2023-03-17 + ++ Add - 60 minute tutorial notebook using CodeSpaces and DevContainers + ## [0.3.1] - 2023-03-09 + Update - Simplify dependencies to reduce Docker image size for GitHub Codespaces @@ -54,6 +58,7 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and + Add - Containerization for pytests + Comment - Phase previously designated 0.1.0 -> 0.0.0 +[0.4.0]: https://github.com/datajoint/workflow-calcium-imaging/releases/tag/0.4.0 [0.3.1]: https://github.com/datajoint/workflow-calcium-imaging/releases/tag/0.3.1 [0.3.0]: https://github.com/datajoint/workflow-calcium-imaging/releases/tag/0.3.0 [0.2.0]: https://github.com/datajoint/workflow-calcium-imaging/releases/tag/0.2.0 diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 5e6521c..e3af011 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -3614,10 +3614,34 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run this tutorial notebook on your own data, please use the following steps:\n", + "- Download the mysql-docker image for DataJoint and run the container according to the\n", + " instructions provide in the repository.\n", + "- Create a fork of this repository to your GitHub account.\n", + "- Clone the repository and open the files using your IDE.\n", + "- Add a code cell immediately after the first code cell in the notebook - we will setup\n", + " the local connection using this cell. In this cell, type in the following code. \n", + "\n", + "```python\n", + "import datajoint as dj\n", + "dj.config[\"database.host\"] = \"localhost\"\n", + "dj.config[\"database.user\"] = \"\"\n", + "dj.config[\"database.password\"] = \"\"\n", + "dj.config[\"custom\"] = {\"imaging_root_data_dir\": \"path/to/your/data/dir\",\n", + "\"database_prefix\": \"\"}\n", + "dj.conn()\n", + "```\n", + "\n", + "- Run this code cell and proceed with the rest of the notebook." + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [] } ], diff --git a/workflow_calcium_imaging/version.py b/workflow_calcium_imaging/version.py index 8a1319a..ff84a92 100644 --- a/workflow_calcium_imaging/version.py +++ b/workflow_calcium_imaging/version.py @@ -3,4 +3,4 @@ Update the Docker image tag in `docker-compose-test.yaml` and `docker-compose-dev.yaml` to match """ -__version__ = "0.3.1" +__version__ = "0.4.0" From 253ea50de4d20ca666013ab9aed068d9d2f28aee Mon Sep 17 00:00:00 2001 From: kushalbakshi Date: Fri, 17 Mar 2023 17:45:08 -0500 Subject: [PATCH 6/6] Add unsaved notebook changes --- notebooks/tutorial.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index e3af011..248a279 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -3633,6 +3633,7 @@ "dj.config[\"database.password\"] = \"\"\n", "dj.config[\"custom\"] = {\"imaging_root_data_dir\": \"path/to/your/data/dir\",\n", "\"database_prefix\": \"\"}\n", + "dj.config.save_local()\n", "dj.conn()\n", "```\n", "\n",