From 04851badab3f22eb6c9cfd1b1ebd552e8d018e4a Mon Sep 17 00:00:00 2001 From: Ben Lansdell Date: Wed, 7 Feb 2024 23:14:14 -0700 Subject: [PATCH] Add sample notebooks for NWB and SLEAP analyses. Closes #13 --- README.md | 11 +- ethome/__init__.py | 2 +- ethome/data/sleap/sample_sleap.h5 | Bin 0 -> 551501 bytes ethome/io.py | 10 ++ examples/sample_workflow.ipynb | 26 ++-- examples/sample_workflow_nwb.ipynb | 200 +++++++++++++++++++++++++++ examples/sample_workflow_sleap.ipynb | 200 +++++++++++++++++++++++++++ 7 files changed, 430 insertions(+), 19 deletions(-) create mode 100644 ethome/data/sleap/sample_sleap.h5 create mode 100644 examples/sample_workflow_nwb.ipynb create mode 100644 examples/sample_workflow_sleap.ipynb diff --git a/README.md b/README.md index ba08159..bf5c7a5 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,9 @@ That said, you may want a separate environment for running `ethome`. A conda env 3. Run `conda activate ethome` 3. And finally `pip install ethome-ml` -With both install methods, you may want to also install `tensorflow` if you want to use the CNN features for a resident-intruder setup. +### Optional packages + +With both install methods, you may want to also install `tensorflow` (at least version 2.0) if you want to use the CNN features for a resident-intruder setup. ## Quickstart @@ -147,6 +149,13 @@ NWB is a data standard for neurophysiology, providing neuroscientists with a com SLEAP is an open source deep-learning based framework for multi-animal pose tracking. It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading. SLEAP data must be in exported analysis `h5` files, to import into `ethome`. +## Sample notebooks + +Sample notebooks are available ([here](https://github.com/benlansdell/ethome/tree/master/examples)) that you can use as a starting point for your own analyses, using either: +* NWB files +* SLEAP files +* DLC tracking and BORIS annotations + ## Contributing Refer to `CONTRIBUTING.md` for guidelines on how to contribute to the project, and report bugs, etc. diff --git a/ethome/__init__.py b/ethome/__init__.py index 917dd3f..cd9a435 100644 --- a/ethome/__init__.py +++ b/ethome/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.6.0" +__version__ = "0.6.1" # Suppress tensorflow import text... import os diff --git a/ethome/data/sleap/sample_sleap.h5 b/ethome/data/sleap/sample_sleap.h5 new file mode 100644 index 0000000000000000000000000000000000000000..b4f70b3fbbfda003fa4f2884bd9876570d3198b8 GIT binary patch literal 551501 zcmeFXcU+TAxA#jgA|N0lH3|YEASxm~0a3AnN)r)+pi}{=2_z&`5kU|Uk&dFG(gmf4 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Zi@?7K{ENW92>gq{zX<$`!2jO}{2#95c{Kn4 literal 0 HcmV?d00001 diff --git a/ethome/io.py b/ethome/io.py index e7e0fac..bc17cae 100644 --- a/ethome/io.py +++ b/ethome/io.py @@ -583,6 +583,16 @@ def get_sample_nwb_paths(): return os.path.join(cur_dir, "data/sample_nwb_.nwb") +def get_sample_sleap_paths(): + """Get path to a sample SLEAP h5 file with tracking data for testing and dev purposes. + + Returns: + Path to a sample SLEAP file. + """ + cur_dir = os.path.dirname(os.path.abspath(__file__)) + return os.path.join(cur_dir, "data/sleap/sample_sleap.h5") + + def get_sample_data_paths_dlcboris(): """Get path to sample data files provided with package. diff --git a/examples/sample_workflow.ipynb b/examples/sample_workflow.ipynb index 76f9f6c..efb8a40 100644 --- a/examples/sample_workflow.ipynb +++ b/examples/sample_workflow.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -74,17 +74,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All necessary fields provided -- rescaling to 'mm'\n" - ] - } - ], + "outputs": [], "source": [ "#%% Create dataset and add features\n", "dataset = create_dataset(tracking_files, \n", @@ -212,7 +204,7 @@ "metadata": {}, "outputs": [], "source": [ - "#%%################\n", + "###################\n", "## Dim reduction ##\n", "###################\n", "\n", @@ -236,9 +228,9 @@ "source": [ "## Post processing\n", "\n", - "Now we have our model we can make a video of its predictions. Provide the column names whose state we're going to overlay on the video, along with the directory to output the videos.\n", + "Now we have our model we can make a video of its predictions. Provide the column names whose state we're going to print overlaid on the video, along with the directory to output the videos.\n", "\n", - "NOTE: need to have provided 'video' column in the metadata to make movies." + "(NOTE: this won't work for the sample data provided here, as you need to have provided the 'video' column in the metadata to point the dataframe to the corresponding videos. The package doesn't include these sample data video files in it)" ] }, { @@ -267,7 +259,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.8.10" }, "orig_nbformat": 4 }, diff --git a/examples/sample_workflow_nwb.ipynb b/examples/sample_workflow_nwb.ipynb new file mode 100644 index 0000000..27f82bd --- /dev/null +++ b/examples/sample_workflow_nwb.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demo workflow\n", + "\n", + "Demonstrate simple workflow for case where only pose data is available (no behavior labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ethome import create_dataset, interpolate_lowconf_points\n", + "from ethome.io import get_sample_nwb_paths\n", + "from ethome.unsupervised import compute_umap_embedding\n", + "from ethome.plot import plot_embedding, interactive_tracks" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gather some sample tracking files to play with" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nwb_file = get_sample_nwb_paths()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nwb files already contain metedata about the tracking, so we don't have to provide this ourselves:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#%% Create dataset\n", + "dataset = create_dataset(nwb_file)\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.pose.body_parts" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checkout the tracks with a widget" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "filename = dataset.metadata.videos[0]\n", + "interactive_tracks(dataset,\n", + " filename)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Smooth over low-confidence points" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "interpolate_lowconf_points(dataset)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create features on this dataset. Can use pre-built featuresets, or make your own. \n", + "As we don't have a resident-intruder setup, here we use generic featuresets. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.features.add('distances')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unsupervised learning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#%%################\n", + "## Dim reduction ##\n", + "###################\n", + "\n", + "embedding = compute_umap_embedding(dataset, dataset.features.active, N_rows = 10000)\n", + "dataset[['embedding_0', 'embedding_1']] = embedding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plot_embedding(dataset) " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Post processing\n", + "\n", + "Now we have our model we can make a video of its predictions. Provide the column names whose state we're going to overlay on the video, along with the directory to output the videos.\n", + "\n", + "NOTE: need to have provided 'video' column in the metadata to make movies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.io.save_movie(['label', 'prediction'], '.')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ethome", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/sample_workflow_sleap.ipynb b/examples/sample_workflow_sleap.ipynb new file mode 100644 index 0000000..9490ce1 --- /dev/null +++ b/examples/sample_workflow_sleap.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demo workflow - SLEAP\n", + "\n", + "Demonstrate simple workflow for case where only pose data is available (no behavior labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ethome import create_dataset, interpolate_lowconf_points\n", + "from ethome.io import get_sample_sleap_paths\n", + "from ethome.unsupervised import compute_umap_embedding\n", + "from ethome.plot import plot_embedding, interactive_tracks" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gather some sample tracking files to play with" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sleap_file = get_sample_sleap_paths()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nwb files already contain metedata about the tracking, so we don't have to provide this ourselves:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#%% Create dataset\n", + "dataset = create_dataset(sleap_file, fps = 30)\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.pose.body_parts" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checkout the tracks with a widget" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "filename = dataset.metadata.videos[0]\n", + "interactive_tracks(dataset,\n", + " filename)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Smooth over low-confidence points" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "interpolate_lowconf_points(dataset)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create features on this dataset. Can use pre-built featuresets, or make your own. \n", + "As we don't have a resident-intruder setup, here we use generic featuresets. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.features.add('intrabodypartspeeds')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unsupervised learning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#%%################\n", + "## Dim reduction ##\n", + "###################\n", + "\n", + "embedding = compute_umap_embedding(dataset, dataset.features.active, N_rows = 10000)\n", + "dataset[['embedding_0', 'embedding_1']] = embedding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plot_embedding(dataset) " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Post processing\n", + "\n", + "Now we have our model we can make a video of its predictions. Provide the column names whose state we're going to overlay on the video, along with the directory to output the videos.\n", + "\n", + "NOTE: need to have provided 'video' column in the metadata to make movies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.io.save_movie(['label', 'prediction'], '.')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ethome", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}