{ "cells": [ { "cell_type": "markdown", "id": "2780fd26", "metadata": {}, "source": [ "# Formatting your data: feature extraction from motion tracking output" ] }, { "cell_type": "markdown", "id": "874e3ada", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lucasmiranda42/deepof/blob/master/docs/source/tutorial_notebooks/deepof_preprocessing_tutorial.ipynb)" ] }, { "cell_type": "markdown", "id": "b059b912", "metadata": {}, "source": [ "##### What we'll cover:\n", " \n", "* Create and run a project.\n", "* Load a previously generated project.\n", "* Interact with your project: generate coordinates, distances, angles, and areas.\n", "* Exploratory visualizations: heatmaps and processed animations." ] }, { "cell_type": "code", "execution_count": 1, "id": "25ccbea4", "metadata": {}, "outputs": [], "source": [ "# # If using Google colab, uncomment and run this cell and the one below to set up the environment\n", "# # Note: because of how colab handles the installation of local packages, this cell will kill your runtime.\n", "# # This is not an error! Just continue with the cells below.\n", "# import os\n", "# !git clone -q https://github.com/mlfpm/deepof.git\n", "# !pip install -q -e deepof --progress-bar off\n", "# os.chdir(\"deepof\")\n", "# !curl --output tutorial_files.zip https://datashare.mpcdf.mpg.de/s/Hu1XjZkY9zml0mm/download\n", "# !unzip tutorial_files.zip" ] }, { "cell_type": "code", "execution_count": 2, "id": "80e4b2fc", "metadata": {}, "outputs": [], "source": [ "# import os\n", "# os.chdir(\"deepof\")\n", "# import os, warnings\n", "# warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "id": "7632cba8", "metadata": {}, "source": [ "Let's start by importing some packages. We'll use python's os library to handle paths, pickle to load saved objects, pandas to load data frames, and the data entry API within DeepOF, located in deepof.data" ] }, { "cell_type": "code", "execution_count": 3, "id": "4d85f5bf", "metadata": { "scrolled": false }, "outputs": [], "source": [ "import os\n", "import pickle\n", "import deepof.data" ] }, { "cell_type": "markdown", "id": "ffdcefa1", "metadata": {}, "source": [ "We'll also need some plotting gear:" ] }, { "cell_type": "code", "execution_count": 4, "id": "e438d39f", "metadata": {}, "outputs": [], "source": [ "import deepof.visuals\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd" ] }, { "cell_type": "markdown", "id": "9fd9cdbb", "metadata": {}, "source": [ "### Creating and running a project" ] }, { "cell_type": "markdown", "id": "1465ef19", "metadata": {}, "source": [ "With that out of the way, the first thing to do when starting a DeepOF project is to load your videos and DeepLabCut tracking files into a deepof.data.Project object. \n", "\n", "Like depicted in the cell below, the three crucial parameters to input are the project, video and tab paths. \n", "\n", "* project_path specifies where the project folder containing all processing and output files will be created.\n", "* video_path points towards where your DLC or SLEAP labelled videos are stored.\n", "* similarly, table_path should point to the directory containing all tracking files (see section on supported input files below).\n", "* last but not least, you can give your project a name (optional; _deepof_project_ by default)." ] }, { "cell_type": "markdown", "id": "50dcb2d8", "metadata": {}, "source": [ "The dataset used in this tutorial is a subset of the social interaction (SI) dataset used in DeepOF's original paper. It contains six 10-minute-long videos with two animals (a C57Bl6 and a CD1) tracked in a round arena, tracked with DeepLabCut. Three of the C57Bl6 mice have been exposed to chronic social defeat stress (CSDS)." ] }, { "cell_type": "code", "execution_count": 5, "id": "88c90e17", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mInfo! Set arena dimension to 380.0 mm!\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[38;5;208mThe sampling rate of some of your videos differ. The maximum difference is 20191204_Day2_SI_JB08_Test_54: 24.99 fps and 20191204_Day2_SI_JB08_Test_63: 24.994 fps! Proceed with cauthion\u001b[0m\n" ] } ], "source": [ "my_deepof_project_raw = deepof.data.Project(\n", " project_path=os.path.join(\"tutorial_files\"),\n", " video_path=os.path.join(\"tutorial_files/Videos/\"),\n", " table_path=os.path.join(\"tutorial_files/Tables/\"),\n", " project_name=\"deepof_tutorial_project\",\n", " arena=\"circular-autodetect\",\n", " animal_ids=[\"B\", \"W\"],\n", " table_format=\"h5\",\n", " video_format=\".mp4\",\n", " exclude_bodyparts=[\"Tail_1\", \"Tail_2\", \"Tail_tip\"],\n", " video_scale=\"380 mm\",\n", " iterative_imputation=\"partial\", # \"full\",\n", " smooth_alpha=1,\n", " exp_conditions=None,\n", " # number_of_rois=3, # another optional input argument that allows you to define up to 20 different regions of interest during project creation\n", ")" ] }, { "cell_type": "markdown", "id": "dae0d5c8", "metadata": {}, "source": [ "As you may see, there are some extra (optional) parameters in the call above. These specify some details on how the videos and tracks will be processed. Some include:\n", "\n", "* arena: some functions within DeepOF (as you will see in the next tutorial on supervised annotations) require the program to detect the arena. The following section explains how to handle this step in more detail.\n", "* animal_ids: in case more than one animal is present in the dataset, the IDs assigned to each during tracking need to be specified as a list. For SLEAP projects, this will be detected automatically, but can be overriten.\n", "* exclude_body_parts: while DeepOF originally relies on 14 body part tracking models, body parts along the tail are no longer used. This step could be bypassed using smaller models following an 11-body-part scheme, such as the one presented in the landing page of the documentation. Custom labelling schemes are also supported (see tutorials).\n", "* video_scale: diameter of the arena (in mm) if circular. In the case of polygonal arenas, the length (in mm) of the first edge as specified in the GUI (see next section) should be provided.\n", "* smooth_alpha: smoothing intensity. The higher the value, the more smoothing is applied.\n", "* exp_conditions: dictionary with a video IDs as keys, and data frames with all experimental conditions as values. DeepOF will use this information to enable all sorts of comparisons between conditions, as we will see in the following tutorials. We'll leave it blank for now and update it afterwards.\n", "\n", "For more details, feel free to visit the [full API reference](https://deepof.readthedocs.io/en/latest/#full-api-reference)." ] }, { "cell_type": "markdown", "id": "02ed7ba0", "metadata": {}, "source": [ "Let's then create our first project, by running the `.create()` method in our newly created object:" ] }, { "cell_type": "code", "execution_count": 6, "id": "d2f00bc8", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Setting up project directories...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Preprocessing tables : 100%|██████████| 6/6 [00:02<00:00, 2.08it/s, file=20191204_D..., step=Saving data] \n", "Detecting arenas : 100%|██████████| 6/6 [01:29<00:00, 14.99s/arena]\n", "Rescaling tables : 100%|██████████| 6/6 [00:00<00:00, 556.74table/s]\n", "Computing distances : 100%|██████████| 6/6 [00:00<00:00, 8.14table/s]\n", "Computing angles : 100%|██████████| 6/6 [00:00<00:00, 49.80table/s]\n", "Computing areas : 100%|██████████| 6/6 [00:00<00:00, 6.91table/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Done!\n" ] } ], "source": [ "my_deepof_project = my_deepof_project_raw.create(force=True)" ] }, { "cell_type": "markdown", "id": "12931c30", "metadata": {}, "source": [ "**NOTE**: the `force` parameter allows DeepOF to override an existing project in the same directory. Use with caution!\n", "\n", "**NOTE 2**: the cell above can take a significant amount of time to run in Google colab. Feel free to skip and continue with the loaded results below." ] }, { "cell_type": "markdown", "id": "25a9dc76", "metadata": {}, "source": [ "As you will see, this organizes all required file into a folder in the specified path. Moreover, some processing steps are computed on the go, such as distances, angles and areas. This makes it easier for DeepOF to load all required features later on, without the need to compute them every time." ] }, { "cell_type": "markdown", "id": "870c5c70", "metadata": {}, "source": [ "### Arena detection" ] }, { "cell_type": "markdown", "id": "9a0c3bbc", "metadata": {}, "source": [ "As mentioned above, one of the key aspects of project creation involves setting up the arena detection mechanism, which will be used in downstream tasks, such as climbing detection and overall video scaling.\n", "\n", "You can contrAlong these lines, the package provides tools for detecting elliptical arenas specifically (as seen in this tutorial), and general polygonal shapes (such as squares, rectangles, Y-mazes, etc).\n", "\n", "In principle, DeepOF can detect your arenas automatically by relying in [SAM (Segment Anything Model)](https://segment-anything.com/), a state-of-the-art image segmentation deep learning model. By selecting `arena='circular-autodetect'`, or `arena='polygonal-autodetect'`, users can benefit from this approach. A folder named `Arena_detection` will be created in your project directory, which contains samples of the detected arenas for all videos, so you can visually inspect if DeepOF did a good job.\n", "\n" ] }, { "cell_type": "markdown", "id": "a9d1b43b", "metadata": {}, "source": [ "Moreover, manual annotation can be selected using `arena='circular-manual'`, and `arena='polygonal-manual'`. In this case, you will see a window per video appear, allowing you to manually mark where the arena is with just a few clicks. All results will be stored for further processing.\n", "\n", "Here, clicking anywhere in the video will make an orange marker appear. In the case of polygonal arenas, at least a marker per corner should be used. When dealing with circular (or elliptical) arenas, DeepOF will fit an ellipse to the marked points after a minimum of 5 clicks. The ellipse can always be refined by adding more markers. Once finished with a video, press `q` to save and move to the next one. If you made a mistake and would like to correct it, press `d` to delete the last added marker. Moreover, after you tagged at least one video, you'll see the `p` option appear, which will copy the last marked arena to all remaining videos in the dataset.\n", "\n", "**NOTE**: If you select `arena='polygonal-autodetect'`, you will still be prompted with the GUI to select the arena just once, so SAM roughly knows how the segmentation it should return looks like. Detection in all remaining videos will be automatic. This is not the case for `arena='circular-autodetect'`, as we already know we're looking for a circle." ] }, { "cell_type": "markdown", "id": "d82da5cc", "metadata": {}, "source": [ "![arena_GUI](./Assets/arena_GUI.png)" ] }, { "cell_type": "markdown", "id": "5398d2a3", "metadata": {}, "source": [ "One last thing is that, in polygonal arenas, the first edge you input will be used to scale the coordinates to the proper distances (pixels to millimeters). Make sure you always mark the same edge first, and that it coincides with the length passed to the \"video_scale\" parameter when creating the project. In saved images of automatically detected polygons, you'll see orange circles marking two arena corners. These should match the first edge you selected when prompting SAM, ensuring data is properly scaled." ] }, { "cell_type": "markdown", "id": "750fc0b5", "metadata": {}, "source": [ "If after all this work you make a mistake while labelling, or see that automatic detection failed in some cases, don't worry! You can always edit manually annotated arenas (or rerun automatic annotation) for specific videos using the `.edit_arenas()` method. Just pass a list with the IDs of the videos you would like to relabel, and the GUI will pop up once again. The same methods described above can be passed to the `arena_type` parameter.\n", "\n", "**NOTE**: If you don't pass any video IDs, all videos will be selected by default." ] }, { "cell_type": "code", "execution_count": 7, "id": "0ea914c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Editing 2 arenas\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Detecting arenas : 100%|██████████| 2/2 [00:02<00:00, 1.16s/arena]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Done!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "my_deepof_project.edit_arenas(\n", " video_keys=['20191204_Day2_SI_JB08_Test_54', '20191204_Day2_SI_JB08_Test_62'],\n", " arena_type=\"circular-autodetect\",\n", ")" ] }, { "cell_type": "markdown", "id": "8dbb3737", "metadata": {}, "source": [ "### Supported input tracking files:" ] }, { "cell_type": "markdown", "id": "3249c4f1", "metadata": {}, "source": [ "DeepOF currently supports input from both [DeepLabCut](url_here) and [SLEAP](url_here). While DeepOF will try to detect automatically which file type you're trying to use, this can be forced with the `table_format` parameter in the `deepof.data.Project()` call depicted in the previous section.\n", "\n", "For DeepLabCut, we support:\n", "\n", "* Single and multi-animal project CSV files (indicated simply with `table_format='csv'`.\n", "* Single and multi-animal project h5 files (indicated simply with `table_format='h5'`.\n", "\n", "For SLEAP, you can use:\n", "\n", "* SLP files (indicated as with `table_format='slp'`.\n", "* Raw .npy files (indicated with `table_format='npy'`.\n", "* h5 files (indicated with `table_format=analysis.h5`.\n", "\n", "Downstream processing should be identical regardless of the files selected, as DeepOF internally brings all these inputs into an equivalent representation. Let's continue!" ] }, { "cell_type": "markdown", "id": "a99d8cf5", "metadata": {}, "source": [ "### Loading a previously generated project" ] }, { "cell_type": "markdown", "id": "0651bdc8", "metadata": {}, "source": [ "Once you ran your project at least once, it can be loaded without much effort from the path you specified in the first place (plus the project name -_deepof_project_, if you didn't specify otherwise-)." ] }, { "cell_type": "code", "execution_count": 8, "id": "899cbab7", "metadata": {}, "outputs": [], "source": [ "# Load a previously saved project\n", "my_deepof_project = deepof.data.load_project(\"./tutorial_files/deepof_tutorial_project/\")" ] }, { "cell_type": "markdown", "id": "63a8e268", "metadata": {}, "source": [ "### Extend a previously generated project" ] }, { "cell_type": "markdown", "id": "b86e9f8d", "metadata": {}, "source": [ "If you'd like to add data (videos and tracks) to a previously processed project, you can use the `extend` method instead of `create`. Just pass the path to your previously processed project, and DeepOF will take care of merging the two. A new directory will be created with all files corresponding to the merged projects.\n", "\n", "**NOTE**: Your old files will not be deleted." ] }, { "cell_type": "code", "execution_count": 9, "id": "b0a85240", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading previous project...\n", "Processing data from 0 experiments...\n" ] } ], "source": [ "# Extend a previously saved project\n", "my_deepof_project=my_deepof_project_raw.extend(\n", " \"./tutorial_files/deepof_tutorial_project/\",) " ] }, { "cell_type": "markdown", "id": "7079cbfb", "metadata": {}, "source": [ "### Interacting with your project: generating coordinates, distances, angles, and areas." ] }, { "cell_type": "markdown", "id": "73f40230", "metadata": {}, "source": [ "That's it for basic project creation. We now have a DeepOF project up and running! Before ending the tutorial, however, let's explore the object that the commands above produced." ] }, { "cell_type": "markdown", "id": "3a24e71c", "metadata": {}, "source": [ "For starters, if we print it, we see it's a DeepOF analysis of 6 videos. Furthermore, the object belongs to a custom class within DeepOF, called Coordinates. This class allows the package to store all sorts of relevant information required for further processing, as we'll see below:" ] }, { "cell_type": "code", "execution_count": 10, "id": "ee51de4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deepof analysis of 6 videos\n", "\n" ] } ], "source": [ "print(my_deepof_project)\n", "print(type(my_deepof_project))" ] }, { "cell_type": "markdown", "id": "16891425", "metadata": {}, "source": [ "As described before, the `.create()` method runs most of the heavy preprocessing already, which allows us to extract features including coordinates, distances, angles, and areas. Let's see how that works!" ] }, { "cell_type": "markdown", "id": "c887b0e9", "metadata": {}, "source": [ "![preprocessing](./Assets/deepof_preprocessing.png)" ] }, { "cell_type": "markdown", "id": "787fae4a", "metadata": {}, "source": [ "With the `.get_coords()` method, for example, we can obtain the processed (smooth and imputed) tracks for all videos in a dictionary. The returned objects are called table dictionaries (TableDict is the name of the class). They follow a dictionary-like structure, where each value is a data frame. They also provide a plethora of extra methods, some of which we'll cover in these tutorials. Let's retrieve these for one of the animals:" ] }, { "cell_type": "code", "execution_count": 11, "id": "e94d1215", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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14999 rows × 8 columns

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" ], "text/plain": [ " B_head_area B_torso_area B_back_area B_full_area \\\n", "00:00:00 186.379602 286.877920 322.346263 1031.762726 \n", "00:00:00.040002666 186.379602 286.877920 322.346263 1031.762726 \n", "00:00:00.080005333 186.379602 286.877920 322.346263 1031.762726 \n", "00:00:00.120008 220.588695 301.609605 325.358041 1099.050813 \n", "00:00:00.160010667 222.261708 299.870852 335.142348 1120.455237 \n", "... ... ... ... ... \n", "00:09:59.799986665 239.351739 336.178202 369.570132 1236.107480 \n", "00:09:59.839989332 225.022634 337.582968 356.664936 1202.726101 \n", "00:09:59.879991999 254.932258 347.361788 366.539204 1240.352799 \n", "00:09:59.919994666 254.932258 347.361788 366.539204 1240.352799 \n", "00:09:59.959997333 254.932258 347.361788 366.539204 1240.352799 \n", "\n", " W_head_area W_torso_area W_back_area W_full_area \n", "00:00:00 403.882917 432.800448 457.418159 1759.904145 \n", "00:00:00.040002666 403.882917 432.800448 457.418159 1759.904145 \n", "00:00:00.080005333 403.882917 432.800448 457.418159 1759.904145 \n", "00:00:00.120008 436.529889 458.405810 465.829889 1788.957183 \n", "00:00:00.160010667 386.996017 468.328898 464.115153 1794.118011 \n", "... ... ... ... ... \n", "00:09:59.799986665 338.985789 306.291533 398.589194 1296.123235 \n", "00:09:59.839989332 339.620045 307.614991 401.612088 1299.472641 \n", "00:09:59.879991999 339.318555 307.496487 402.713951 1300.378274 \n", "00:09:59.919994666 339.318555 307.496487 402.713951 1300.378274 \n", "00:09:59.959997333 339.318555 307.496487 402.713951 1300.378274 \n", "\n", "[14999 rows x 8 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_deepof_project.get_areas()['20191204_Day2_SI_JB08_Test_54']" ] }, { "cell_type": "markdown", "id": "3fa733d0", "metadata": {}, "source": [ "Last but not least, features can be merged using the `.merge()` method, which can yield combinations of features if needed. For example, the code in the following cell creates an object with both coordinates and areas per time point:" ] }, { "cell_type": "code", "execution_count": 15, "id": "b34be811", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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..................................................................
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14999 rows × 52 columns

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" ], "text/plain": [ " (B_Center, x) (B_Center, y) (B_Left_bhip, x) \\\n", "00:00:00 178.695067 150.903525 178.133481 \n", "00:00:00.040002666 178.695067 150.903525 178.133481 \n", "00:00:00.080005333 178.695067 150.903525 178.133481 \n", "00:00:00.120008 177.009679 149.650527 175.106624 \n", "00:00:00.160010667 177.767518 147.833830 173.779126 \n", "... ... ... ... \n", "00:09:59.799986665 87.802400 245.478268 103.222546 \n", "00:09:59.839989332 87.996004 251.604046 103.329278 \n", "00:09:59.879991999 88.189609 257.729824 101.048434 \n", "00:09:59.919994666 88.189609 257.729824 101.048434 \n", "00:09:59.959997333 88.189609 257.729824 101.048434 \n", "\n", " (B_Left_bhip, y) (B_Left_ear, x) (B_Left_ear, y) \\\n", "00:00:00 164.579831 160.236194 129.369839 \n", "00:00:00.040002666 164.579831 160.236194 129.369839 \n", "00:00:00.080005333 164.579831 160.236194 129.369839 \n", "00:00:00.120008 163.666017 163.807835 124.262824 \n", "00:00:00.160010667 162.437004 167.520924 121.969512 \n", "... ... ... ... \n", "00:09:59.799986665 240.694477 86.352888 275.400892 \n", "00:09:59.839989332 244.701389 84.727063 283.767821 \n", "00:09:59.879991999 252.178232 83.101238 292.134750 \n", "00:09:59.919994666 252.178232 83.101238 292.134750 \n", "00:09:59.959997333 252.178232 83.101238 292.134750 \n", "\n", " (B_Left_fhip, x) (B_Left_fhip, y) (B_Nose, x) \\\n", "00:00:00 162.932410 149.543005 166.288734 \n", "00:00:00.040002666 162.932410 149.543005 166.288734 \n", "00:00:00.080005333 162.932410 149.543005 166.288734 \n", "00:00:00.120008 161.761899 147.278549 174.192032 \n", "00:00:00.160010667 163.445137 144.056996 179.274246 \n", "... ... ... ... \n", "00:09:59.799986665 96.059204 258.098620 74.008872 \n", "00:09:59.839989332 97.459237 262.037312 75.703366 \n", "00:09:59.879991999 96.435058 270.932928 70.169244 \n", "00:09:59.919994666 96.435058 270.932928 70.169244 \n", "00:09:59.959997333 96.435058 270.932928 70.169244 \n", "\n", " (B_Nose, y) ... (W_Tail_base, x) (W_Tail_base, y) \\\n", "00:00:00 115.459213 ... 284.276208 81.953327 \n", "00:00:00.040002666 115.459213 ... 284.276208 81.953327 \n", "00:00:00.080005333 115.459213 ... 284.276208 81.953327 \n", "00:00:00.120008 112.040097 ... 293.070473 82.577959 \n", "00:00:00.160010667 108.299092 ... 299.036433 86.246318 \n", "... ... ... ... ... \n", "00:09:59.799986665 292.358497 ... 217.058911 419.505907 \n", "00:09:59.839989332 297.466054 ... 217.025629 419.515154 \n", "00:09:59.879991999 305.492857 ... 217.112048 419.540870 \n", "00:09:59.919994666 305.492857 ... 217.112048 419.540870 \n", "00:09:59.959997333 305.492857 ... 217.112048 419.540870 \n", "\n", " B_head_area B_torso_area B_back_area B_full_area \\\n", "00:00:00 186.379602 286.877920 322.346263 1031.762726 \n", "00:00:00.040002666 186.379602 286.877920 322.346263 1031.762726 \n", "00:00:00.080005333 186.379602 286.877920 322.346263 1031.762726 \n", "00:00:00.120008 220.588695 301.609605 325.358041 1099.050813 \n", "00:00:00.160010667 222.261708 299.870852 335.142348 1120.455237 \n", "... ... ... ... ... \n", "00:09:59.799986665 239.351739 336.178202 369.570132 1236.107480 \n", "00:09:59.839989332 225.022634 337.582968 356.664936 1202.726101 \n", "00:09:59.879991999 254.932258 347.361788 366.539204 1240.352799 \n", "00:09:59.919994666 254.932258 347.361788 366.539204 1240.352799 \n", "00:09:59.959997333 254.932258 347.361788 366.539204 1240.352799 \n", "\n", " W_head_area W_torso_area W_back_area W_full_area \n", "00:00:00 403.882917 432.800448 457.418159 1759.904145 \n", "00:00:00.040002666 403.882917 432.800448 457.418159 1759.904145 \n", "00:00:00.080005333 403.882917 432.800448 457.418159 1759.904145 \n", "00:00:00.120008 436.529889 458.405810 465.829889 1788.957183 \n", "00:00:00.160010667 386.996017 468.328898 464.115153 1794.118011 \n", "... ... ... ... ... \n", "00:09:59.799986665 338.985789 306.291533 398.589194 1296.123235 \n", "00:09:59.839989332 339.620045 307.614991 401.612088 1299.472641 \n", "00:09:59.879991999 339.318555 307.496487 402.713951 1300.378274 \n", "00:09:59.919994666 339.318555 307.496487 402.713951 1300.378274 \n", "00:09:59.959997333 339.318555 307.496487 402.713951 1300.378274 \n", "\n", "[14999 rows x 52 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_deepof_project.get_coords().merge(my_deepof_project.get_areas())['20191204_Day2_SI_JB08_Test_54']" ] }, { "cell_type": "markdown", "id": "0afd4280", "metadata": {}, "source": [ "### Loading experimental conditions" ] }, { "cell_type": "markdown", "id": "b9e4c272", "metadata": {}, "source": [ "So far, DeepOF does not know to which condition each animal belongs. This can be either set up when creating the project (as described above) or specified afterward using the `.load_exp_conditions()` method. We just need to pass the path to a CSV file containing all conditions per animal as extra columns. The only hard requirement is that the first column should have the experiment IDs.\n", "\n", "Here is an example:" ] }, { "cell_type": "code", "execution_count": 16, "id": "2c9b2648", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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520191204_Day2_SI_JB08_Test_64Nonstressed
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" ], "text/plain": [ " experiment_id CSDS\n", "0 20191204_Day2_SI_JB08_Test_54 Nonstressed\n", "1 20191204_Day2_SI_JB08_Test_56 Stressed\n", "2 20191204_Day2_SI_JB08_Test_61 Stressed\n", "3 20191204_Day2_SI_JB08_Test_62 Stressed\n", "4 20191204_Day2_SI_JB08_Test_63 Nonstressed\n", "5 20191204_Day2_SI_JB08_Test_64 Nonstressed" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(\"./tutorial_files/tutorial_exp_conditions.csv\", index_col=0)" ] }, { "cell_type": "markdown", "id": "bcbf676f", "metadata": {}, "source": [ "Great! Now that we understand how the CSV should be formatted, let's then load it onto our project:" ] }, { "cell_type": "code", "execution_count": 17, "id": "5cfa481c", "metadata": {}, "outputs": [], "source": [ "my_deepof_project.load_exp_conditions(\"./tutorial_files/tutorial_exp_conditions.csv\")" ] }, { "cell_type": "markdown", "id": "dced0767", "metadata": {}, "source": [ "And we're done!. Let's explore what's in there with the .get_exp_conditions property:" ] }, { "cell_type": "code", "execution_count": 18, "id": "a0da64d3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'20191204_Day2_SI_JB08_Test_54': CSDS\n", "0 Nonstressed, '20191204_Day2_SI_JB08_Test_56': CSDS\n", "1 Stressed, '20191204_Day2_SI_JB08_Test_61': CSDS\n", "2 Stressed, '20191204_Day2_SI_JB08_Test_62': CSDS\n", "3 Stressed, '20191204_Day2_SI_JB08_Test_63': CSDS\n", "4 Nonstressed, '20191204_Day2_SI_JB08_Test_64': CSDS\n", "5 Nonstressed}\n" ] } ], "source": [ "print(my_deepof_project.get_exp_conditions)" ] }, { "cell_type": "markdown", "id": "a2b8650a", "metadata": {}, "source": [ "We can see that the property retrieves a dictionary with all animal experiments as keys, and data frames with conditions as values. Although in this case we only have the CSDS condition, which can take two values (\"Nonstressed\" and \"Stressed\", with three animals each), adding more just requires us to add extra columns to the CSV file." ] }, { "cell_type": "markdown", "id": "5333813e", "metadata": {}, "source": [ "### Filtering DeepOF objects" ] }, { "cell_type": "markdown", "id": "24364e60", "metadata": {}, "source": [ "Now that experimental conditions were added, we'll explore some filtering tools that DeepOF provides for table dictionary objects." ] }, { "cell_type": "markdown", "id": "374fc091", "metadata": {}, "source": [ "For starters, imagine you want to subset your data to only contain stressed animals. You can do that with the `.filter_condition()` method, which takes a dictionary as input with the experimental condition to filter on as key, and the value you'd like to keep as value:" ] }, { "cell_type": "code", "execution_count": 19, "id": "84884409", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The original dataset has 6 videos\n", "The filtered dataset has only 3 videos\n" ] } ], "source": [ "# Let's use coords as an example\n", "coords = my_deepof_project.get_coords()\n", "print(\"The original dataset has {} videos\".format(len(coords)))\n", "\n", "# Let's keep only those experiments where the subject is stressed:\n", "coords = coords.filter_condition({\"CSDS\": \"Stressed\"})\n", "print(\"The filtered dataset has only {} videos\".format(len(coords)))" ] }, { "cell_type": "markdown", "id": "2a810cc3", "metadata": {}, "source": [ "We can also filter specific experiments with the `.filter_videos()` method, which takes a list of experiment IDs as input:" ] }, { "cell_type": "code", "execution_count": 20, "id": "82c69dc8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The new filtered dataset has only 1 video\n" ] } ], "source": [ "single_video_coords = coords.filter_videos(['20191204_Day2_SI_JB08_Test_56'])\n", "print(\"The new filtered dataset has only {} video\".format(len(single_video_coords)))" ] }, { "cell_type": "markdown", "id": "21a2c256", "metadata": {}, "source": [ "Last but not least, we can also keep all videos, but filter certain animals whose coordinates we'd like to keep for further analysis. As seen above, the dataset used in this tutorial contains two animals per video: a C57Bl6 (labelled \"B\") and a CD1 (labelled \"W\"). Let's see how we can keep only the C57B16 with the `.filter_id()` method:" ] }, { "cell_type": "markdown", "id": "39d4d7b8", "metadata": {}, "source": [ "Let's first point out that, before filtering, a given experiment in the coords object has 44 features (22 from each animal)." ] }, { "cell_type": "code", "execution_count": 21, "id": "eb2539f5", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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00:09:59.919989331334.90139496.142181350.01032599.189031326.892150117.030927338.705496107.086259324.202601130.295730...310.410430116.226902320.16795994.313141325.642692102.384981346.16632091.468633359.23022288.757696
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14998 rows × 22 columns

\n", "
" ], "text/plain": [ "bodyparts B_Center B_Left_bhip \\\n", "coords x y x y \n", "00:00:00 431.582129 199.886157 443.317284 191.049059 \n", "00:00:00.040005334 431.582129 199.886157 443.317284 191.049059 \n", "00:00:00.080010668 431.582129 199.886157 443.317284 191.049059 \n", "00:00:00.120016002 431.571022 200.002112 443.335878 190.993253 \n", "00:00:00.160021336 431.597640 199.745468 443.281956 190.945019 \n", "... ... ... ... ... \n", "00:09:59.799973329 334.474567 96.498638 348.224364 97.645028 \n", "00:09:59.839978663 335.718348 97.174041 350.550550 100.151544 \n", "00:09:59.879983997 334.901394 96.142181 350.010325 99.189031 \n", "00:09:59.919989331 334.901394 96.142181 350.010325 99.189031 \n", "00:09:59.959994665 334.901394 96.142181 350.010325 99.189031 \n", "\n", "bodyparts B_Left_ear B_Left_fhip \\\n", "coords x y x y \n", "00:00:00 422.038894 239.280726 444.320813 211.530190 \n", "00:00:00.040005334 422.038894 239.280726 444.320813 211.530190 \n", "00:00:00.080010668 422.038894 239.280726 444.320813 211.530190 \n", "00:00:00.120016002 422.030086 239.327994 444.329816 211.562289 \n", "00:00:00.160021336 421.874045 239.267099 444.379187 211.410798 \n", "... ... ... ... ... \n", "00:09:59.799973329 326.814349 119.492518 337.246908 106.565620 \n", "00:09:59.839978663 327.655304 119.156520 339.572653 108.236325 \n", "00:09:59.879983997 326.892150 117.030927 338.705496 107.086259 \n", "00:09:59.919989331 326.892150 117.030927 338.705496 107.086259 \n", "00:09:59.959994665 326.892150 117.030927 338.705496 107.086259 \n", "\n", "bodyparts B_Nose ... B_Right_ear \\\n", "coords x y ... x y \n", "00:00:00 398.565403 241.218546 ... 413.290642 223.459014 \n", "00:00:00.040005334 398.565403 241.218546 ... 413.290642 223.459014 \n", "00:00:00.080010668 398.565403 241.218546 ... 413.290642 223.459014 \n", "00:00:00.120016002 398.595961 241.294169 ... 413.300820 223.461412 \n", "00:00:00.160021336 398.593955 241.218815 ... 413.300526 223.434426 \n", "... ... ... ... ... ... \n", "00:09:59.799973329 323.700053 135.141625 ... 312.159399 118.444302 \n", "00:09:59.839978663 323.334586 134.646345 ... 311.967124 117.222504 \n", "00:09:59.879983997 324.202601 130.295730 ... 310.410430 116.226902 \n", "00:09:59.919989331 324.202601 130.295730 ... 310.410430 116.226902 \n", "00:09:59.959994665 324.202601 130.295730 ... 310.410430 116.226902 \n", "\n", "bodyparts B_Right_fhip B_Spine_1 \\\n", "coords x y x y \n", "00:00:00 420.766537 207.134779 429.961921 216.150904 \n", "00:00:00.040005334 420.766537 207.134779 429.961921 216.150904 \n", "00:00:00.080010668 420.766537 207.134779 429.961921 216.150904 \n", "00:00:00.120016002 420.767516 207.192591 429.975867 216.158488 \n", "00:00:00.160021336 420.761057 207.029504 429.993971 216.054461 \n", "... ... ... ... ... \n", "00:09:59.799973329 322.335877 95.900822 325.605505 103.833966 \n", "00:09:59.839978663 322.339963 95.787418 326.444674 103.855875 \n", "00:09:59.879983997 320.167959 94.313141 325.642692 102.384981 \n", "00:09:59.919989331 320.167959 94.313141 325.642692 102.384981 \n", "00:09:59.959994665 320.167959 94.313141 325.642692 102.384981 \n", "\n", "bodyparts B_Spine_2 B_Tail_base \n", "coords x y x y \n", "00:00:00 430.374313 184.700537 425.421944 169.926050 \n", "00:00:00.040005334 430.374313 184.700537 425.421944 169.926050 \n", "00:00:00.080010668 430.374313 184.700537 425.421944 169.926050 \n", "00:00:00.120016002 430.366680 184.675558 425.426543 169.916887 \n", "00:00:00.160021336 430.355523 184.650934 425.416170 169.926563 \n", "... ... ... ... ... \n", "00:09:59.799973329 345.151219 91.364484 359.482856 88.716496 \n", "00:09:59.839978663 346.788969 92.072933 359.465680 89.018248 \n", "00:09:59.879983997 346.166320 91.468633 359.230222 88.757696 \n", "00:09:59.919989331 346.166320 91.468633 359.230222 88.757696 \n", "00:09:59.959994665 346.166320 91.468633 359.230222 88.757696 \n", "\n", "[14998 rows x 22 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coords.filter_id(\"B\")['20191204_Day2_SI_JB08_Test_56']" ] }, { "cell_type": "markdown", "id": "b7d15f4a", "metadata": {}, "source": [ "Now that we have a basic understanding of how to create and interact with a project, coordinates, and table dictionaries, let's show some plots!" ] }, { "cell_type": "markdown", "id": "95b88fbd", "metadata": {}, "source": [ "### Basic visual exploration" ] }, { "cell_type": "markdown", "id": "5540f140", "metadata": {}, "source": [ "Let's first see some basic heatmaps per condition. All plotting functions within DeepOF are hosted in the deepof.visuals module. Among many other things, we can plot average heatmaps per experimental condition! Let's see if we can visualize ant interesting patterns on the available data:" ] }, { "cell_type": "code", "execution_count": 23, "id": "69d82405", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_context(\"notebook\")\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n", "\n", "deepof.visuals.plot_heatmaps(\n", " my_deepof_project, \n", " [\"B_Nose\"],\n", " center=\"arena\", \n", " exp_condition=\"CSDS\",\n", " condition_value=\"Nonstressed\",\n", " ax=ax1,\n", " show=False,\n", " display_arena=True,\n", " experiment_id=\"average\",\n", ")\n", "\n", "deepof.visuals.plot_heatmaps(\n", " my_deepof_project,\n", " [\"B_Nose\"],\n", " center=\"arena\", \n", " exp_condition=\"CSDS\",\n", " condition_value=\"Stressed\",\n", " ax=ax2,\n", " show=False,\n", " display_arena=True,\n", " experiment_id=\"average\",\n", ")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "fc5551a3", "metadata": {}, "source": [ "It seems stressed animals spend more time closer to the walls of the arena, and less time in the center! For details on how deepof.visuals.plot_heatmap() works, feel free to check the full API reference or the function docstring." ] }, { "cell_type": "markdown", "id": "82473db4", "metadata": {}, "source": [ "We can also have a more detailed look at our data. As in most deepof plot functions, we can limit the time range that we want to include in our plot. For this the optional input arguments bin_index and bin_size can be used. They allow to either specify a bin size in seconds that is used to bin all data and then select one of the resulting bins via bin_index, or to directly specify the start time and duration of the segment you want to plot. Below you see the syntax for plotting the third minute 0:2:0-0:3:0 (or the third 60 seconds bin i.e. bin no. 2 as bin numbering starts at 0) for our data:" ] }, { "cell_type": "code", "execution_count": 24, "id": "190f0cb2", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_context(\"notebook\")\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n", "\n", "deepof.visuals.plot_heatmaps(\n", " my_deepof_project, \n", " [\"B_Nose\"],\n", " center=\"arena\", \n", " exp_condition=\"CSDS\",\n", " condition_value=\"Nonstressed\",\n", " ax=ax1,\n", " show=False,\n", " display_arena=True,\n", " experiment_id=\"average\",\n", " bin_index=2, #plots only the second 60 seconds of teh data\n", " bin_size=60\n", ")\n", "\n", "deepof.visuals.plot_heatmaps(\n", " my_deepof_project,\n", " [\"B_Nose\"],\n", " center=\"arena\", \n", " exp_condition=\"CSDS\",\n", " condition_value=\"Nonstressed\", \n", " ax=ax2,\n", " show=False,\n", " display_arena=True,\n", " experiment_id=\"average\",\n", " bin_index=\"00:02:00\", #does the same as the above\n", " bin_size=\"00:01:00\",\n", ")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ecb86918", "metadata": {}, "source": [ "If you want very specific samples to get plotted, you also can use the precomputed_bins options and enter a boolean array. Here we do the same plot as above but by directly stating which samples should be included. The remaining samples not specified in the list get filled up with \"False\"." ] }, { "cell_type": "code", "execution_count": 25, "id": "188912db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6, 6))\n", "\n", "\n", "deepof.visuals.plot_heatmaps(\n", " my_deepof_project,\n", " [\"B_Nose\"],\n", " center=\"arena\", \n", " exp_condition=\"CSDS\",\n", " condition_value=\"Nonstressed\", \n", " show=False,\n", " display_arena=True,\n", " experiment_id=\"average\",\n", " precomputed_bins=[False]*2998+[True]*1498,\n", ")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e973f13a", "metadata": {}, "source": [ "Furthermore, you can use regions of interest (ROIs) for spatial exploration, if you initialized them during project definition. Covering this here would be a bit much, so please have a look at the [ROI tutorial](https://deepof.readthedocs.io/en/latest/tutorial_notebooks/deepof_roi_tutorial.html) for more details. For now, this is only a plot how ROIs in your data could look like:" ] }, { "cell_type": "code", "execution_count": 26, "id": "c38f9ae4", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load a previously saved project\n", "my_deepof_project_with_rois = deepof.data.load_project(\"./tutorial_files/sample_project/\")\n", "# And load the experiment conditions\n", "#my_deepof_project_with_rois.load_exp_conditions(\"./tutorial_files/tutorial_exp_conditions.csv\")\n", "\n", "# we now only plot \n", "\n", "sns.set_context(\"notebook\")\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n", "\n", "# Nose based plot\n", "deepof.visuals.plot_heatmaps(\n", " my_deepof_project_with_rois, \n", " [\"B_Center\"],\n", " center=\"arena\", \n", " exp_condition=\"CSDS\",\n", " condition_value=\"Nonstressed\",\n", " ax=ax1,\n", " show=False,\n", " display_arena=True,\n", " experiment_id=\"average\",\n", " bin_index=2, \n", " bin_size=60,\n", " roi_number=1,\n", " animals_in_roi=\"B\",\n", ")\n", "\n", "# Center based plot\n", "deepof.visuals.plot_heatmaps(\n", " my_deepof_project_with_rois,\n", " [\"B_Center\"],\n", " center=\"arena\", \n", " exp_condition=\"CSDS\",\n", " condition_value=\"Nonstressed\", \n", " ax=ax2,\n", " show=False,\n", " display_arena=True,\n", " experiment_id=\"average\",\n", " bin_index=2, \n", " bin_size=60,\n", " roi_number=2,\n", " animals_in_roi=\"B\",\n", ")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4c301645", "metadata": {}, "source": [ "Finally, let's create an animated video showing our newly preprocessed data. DeepOF can produce reconstructions of the tracks and show them as videos. All animals and the arena are displayed by default. This is particularly useful when interpreting clusters and visualizing embeddings in the unsupervised pipeline, as we'll see in a later turorial." ] }, { "cell_type": "code", "execution_count": 28, "id": "b262b7b0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython import display\n", "\n", "video = deepof.visuals.animate_skeleton(\n", " my_deepof_project,\n", " experiment_id=\"20191204_Day2_SI_JB08_Test_54\",\n", " bin_index=0,\n", " bin_size=20,\n", " sampling_rate=15,\n", " dpi=60,\n", ")\n", "\n", "html = display.HTML(video)\n", "display.display(html)\n", "plt.close()" ] }, { "cell_type": "markdown", "id": "8ded862f", "metadata": {}, "source": [ "### What's next" ] }, { "cell_type": "markdown", "id": "d4f08308", "metadata": {}, "source": [ "That's it for this tutorial. [Next](https://deepof.readthedocs.io/en/latest/tutorial_notebooks/deepof_supervised_tutorial.html), we'll see how to run a supervised annotation pipeline with pretrained models!" ] } ], "metadata": { "kernelspec": { "display_name": "dof", "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": "3.10.11" } }, "nbformat": 4, "nbformat_minor": 5 }