A package for extracting facial expressions from SLEAP analyses

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  • License: MIT License
  • Author: Analetta (Annie) Ehler
  • Tags SLEAP, analysis, classification, extraction, facial data, facial expression, recognition, tracking analyses
  • Requires: Python >=3.7
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Project description

SLEAPyFaces

A package for extracting facial expressions from SLEAP analyses with sensible assumptions.

Based on these scripts

PyPI - Version PyPI - Python Version Hatch project

Table of Contents

Description

Sleapyfaces is a data analysis package for extracting facial expressions of mice from SLEAP analyses. It is designed to work with the SLEAP software package, which provides a graphical user interface for annotating animal behavioral videos. More information on SLEAP is available at https://sleap.ai. This package also depends on many assumptions about the data format and structure of the SLEAP analyses. It is not intended to be a general tool for extracting facial expressions from SLEAP analyses, but rather a tool for extracting facial expressions from the specific data format and structure used in the lab of Dr. Kay Tye at The Salk Institute for Biological Studies.

Citing:

If you use SLEAPyFaces in your research, this does not fall under standard software according to the Publication Manual for the APA, MLA, AMA, Turabian, IEEE, Vancouver style, Harvard style, or Chicago style guides. Please cite the following:

A. Ehler, J. Delahantey, A. Coley, D. LeDuke, L. Keyes, T.D. Pereira, and K. Tye. SLEAPyFaces: A package for extracting facial expressions from SLEAP analyses with sensible assumptions. SLEAPyFaces python package, v1.0.1, 2023. Retrieved from https://github.com/annie444/sleapyfaces/

BibTeX:

 @misc{Ehler2023sleapyfaces,
    title={SLEAPyFaces: A package for extracting facial expressions from SLEAP analyses with sensible assumptions.},
    author={
        Ehler, Analetta and
        Delahantey, Jeremey and
        Coley, Austin and
        LeDuke, Deryn and
        Keyes, Laurel and
        Pereira, Talmo D and
        Tye, Kay},
    url={https://github.com/annie444/sleapyfaces/},
    journal={SLEAPyFaces python package},
    publisher={GitHub repository},
    volume={v1.0.1},
    year={2023},
    month={Dec},
    day={27}
}

Please also cite the original SLEAP paper:

T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D’Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy. Sleap: A deep learning system for multi-animal pose tracking. Nature Methods, 19(4), 2022

BibTeX:

@ARTICLE{Pereira2022sleap,
   title={SLEAP: A deep learning system for multi-animal pose tracking},
   author={Pereira, Talmo D and
      Tabris, Nathaniel and
      Matsliah, Arie and
      Turner, David M and
      Li, Junyu and
      Ravindranath, Shruthi and
      Papadoyannis, Eleni S and
      Normand, Edna and
      Deutsch, David S and
      Wang, Z. Yan and
      McKenzie-Smith, Grace C and
      Mitelut, Catalin C and
      Castro, Marielisa Diez and
      D'Uva, John and
      Kislin, Mikhail and
      Sanes, Dan H and
      Kocher, Sarah D and
      Samuel S-H and
      Falkner, Annegret L and
      Shaevitz, Joshua W and
      Murthy, Mala},
   journal={Nature Methods},
   volume={19},
   number={4},
   year={2022},
   publisher={Nature Publishing Group}
   }
}

License

sleapyfaces is distributed under the terms of the MIT license. The full license text is available in the LICENSE file.

Getting Started:

Installation

Using pip:

pip install sleapyfaces

Using conda (coming soon):

conda install -c conda-forge sleapyfaces

Basic Usage Step by Step

Importing the package

For basic usage, the package can be imported with the following:

from sleapyfaces.project import Project

Alternatively, the package can be imported with the following:

import sleapyfaces as sf

Creating a project

The project object is the basis for the data analysis. It serves as a container for all of the data produced from a series of experiments, and allows a root object to be created for the data analysis. The project object is initialized with a series of file or file glob patterns, which are used to find the files associated with each experiment. The project object also requires a base path, which is the root directory for the project. The project object can be initialized with the optional get_glob argument set to True, which will automatically find the files associated with each experiment. Alternatively, the get_glob argument can be set to False (default), which will allow the user to manually set the files associated with each experiment as the file arguments. Additionally, there is an iterators argument, which is a dictionary of iterators that can be used to iterate over the project structure. The iterators argument is optional, and if not provided, the project object will assume that the project follows a specific structure.

Without iterators + with glob:

project = Project(
    DAQFile="*.csv",
    BehFile="*.json",
    SLEAPFile="*.h5",
    VideoFile="*.mp4",
    base="/base/path/to/project",
    get_glob= True
    )

With iterators + without glob:

project = Project(
    DAQFile="DAQOutput.csv",
    BehFile="ExperimentMetadata.json",
    SLEAPFile="SLEAPAnalysis.h5",
    VideoFile="FacialVideo.mp4",
    base="/base/path/to/project",
    iterators={
        "Label 1": "/relative/path/to/week1",
        "Label 2": "/relative/path/to/week2",
        "Label 3": "/relative/path/to/week3",
        "Label 4": "/relative/path/to/week4",
        ...
        }
    }
    )

The iterators paths are relative to the base path. If no iterators are provided, the project object will assume that the project follows the default structure outlined below. The default labels then are "week 1", "week 2", "week 3", "week 4", ....

Building the data

The project object has a buildColumns method, which is used to build the columns of the data. The buildColumns method takes two arguments: columns and values. The columns argument is a list of column titles, and the values argument is a list of values for each column. The buildColumns method will build the columns of the data, and will also build the iterators for each column. The buildColumns method can be called multiple times, and each time it is called, it will add a new column to the data.

project.buildColumns(
    columns=["Mouse"],
    values=["CSE008"]
    )

Building the trials

The project object has a buildTrials method, which is used to build the trials of the data. The buildTrials method takes four arguments: TrackedData, Reduced, start_buffer, and end_buffer. The TrackedData argument is a list of the tracked data columns from the DAQ file that will be used to build the based on the DAQ timestamps. The Reduced argument is a list of boolean values, which indicate whether the tracked data should be reduced to a single value for each trial. This is used in this example for the "LED590_on" column, as the LED turns on and off multiple times during the experiment. To keep the trialData from intitializing a new trial for each timestamp in the "LED590_on" column, reduced is set to true. The start_buffer argument is the number of milliseconds before the trial start time that should be included in the trial. The end_buffer argument is the number of milliseconds after the trial start time that should be included in the trial. The buildTrials method will build the trials of the data, and will also build the iterators for each trial. The buildTrials method can be called multiple times, and each time it is called, it will rebuild the trials of the data. This is useful if you want to build the trials based on different tracked data columns, or if you want to change the start and end buffers.

project.buildTrials(
    TrackedData=["Speaker_on", "LED590_on"],
    Reduced=[False, True],
    start_buffer=10000,
    end_buffer=13000
    )

Normalizing the data

So far there are two normalization methods available:

  • Mean Centering

The meanCenter method will iterate recursively to the smallest data frame in the project, and then mean-center its way up the project tree. More precisely, the meanCenter method first mean centers each trial from each experiment. Then, it will meanCenter each experiment in the project. Finally, it will meanCenter across all the experiments in the project. This method will mean-center the data in place, and will not return anything. However, it does expose a all_data attribute, which is a pd.DataFrame of all the data in the project.

project.meanCenter()
  • Z-Scoring

The zScore method assumes the data has already been mean-centered, and will further z-score the data across all the experiments in the project.

The zScore method will iterate recursively to the smallest data frame in the project, and then z-score its way up the project tree. This method will z-score the data in place, and will not return anything.

project.zScore()

For convenience, you can also normalize the data in one step:

project.analyze()

Complete Example

This implementation of the data objects is roughly 10x faster than the scripted version of the data analysis. The full example is shown below:

from sleapyfaces.project import Project

project = Project(
    DAQFile="*.csv",
    BehFile="*.json",
    SLEAPFile="*.h5",
    VideoFile="*.mp4",
    base="/base/path/to/project",
    get_glob= True
    )

project.buildColumns(
    columns=["Mouse"],
    values=["CSE008"]
    )

project.buildTrials(
    TrackedData=["Speaker_on", "LED590_on"],
    Reduced=[False, True],
    start_buffer=10000,
    end_buffer=13000
    )

project.analyze()

project.visualize()

data = project.pcas["pca3d"]

Project Structure

Data Files Formats

DAQ file

This file is the output of the DAQ collection, which is used to record the TTL pulses from all of the stimuli used in the experiment. The DAQ file is usually a CSV file, which contains the timestamps for each TTL pulse, and a column for each TTL pulse being tracked. These timestamps are all relative to the experiment start time and are in milliseconds. The TTL pulses are named according to the following convention:

  • Stimuli_on - The TTL pulse for when the stimuli starts
  • Stimuli_on - The TTL pulse for when the stimuli ends

Behavioral metadata file

This file is the output of the experimental parameters output from bruker_control as a JSON file. This file contains the experimental parameters for the experiment, including the trial types used, the number of trials, and the inter-trial interval. SLEAPyFaces assumed that the file follows the following structure:

{
    "beh_metadata": {
        ...
        "any_keys": "any_values",
        ...
        "trialArray": [1, 1, 1, 0, 1, 0, 1, 1, ...],
        "ITIArray": [16848, 23012, 25678, 19107, ...],
        ...
        "any_keys": "any_values",
        ...
    }
}

Note that this is the default output from bruker_control and does not need to be modified.

SLEAP file

The SLEAP file is the output of the sleap-convert command from the SLEAP software package, which is used to track the facial expressions of the mice. The SLEAP file is a HDF5 file, which contains the tracking data for each frame of the video using a common skeleton across all experiments. The SLEAP file also contains the metadata for the experiment, which includes the tracking accuracy, the number of frames in the video, and the number of mice in the video. The SLEAP analysis file can be obtained from the following command:

sleap-convert --format analysis -o /path/to/output.h5 /path/to/input.slp

or

sleap-convert --format h5 -o /path/to/output.h5 /path/to/input.slp

Further documentation on this command is available on this SLEAP documentation page.

Video file

The video file is the raw video file that was used to generate the facial trackings. The video file is used to extract the frame rate of the video, which is used to accurately calculate the timestamps and append them to the SLEAP data. The video file can be any video format that is supported by ffmpeg, which includes .mp4, .avi, .mov, .mkv, and many more.

Project Directory Structure

/base/path/to/project
    │
    └───20231208
    │   │   DAQOutput.csv
    │   │   ExperimentMetadata.json
    |   |   SLEAPAnalysis.h5
    |   |   FacialVideo.mp4
    │
    └───20231215
    |   │   DAQOutput.csv
    │   │   ExperimentMetadata.json
    |   |   SLEAPAnalysis.h5
    |   |   FacialVideo.mp4
    |
    └─── ...

Note that the files do not have to be named as shown above, but the file extensions must be correct for the DAQ file as *.csv, the metadata file as *.json, and the SLEAP analysis file as *.h5. The video file can be any video format that is supported by ffmpeg.

By default, the sub-directories can be named anything, as long as the date proceeds any other naming. The dates must be in the format YYYYMMDD, so the experiments can be sorted by date.

e.g. 20231208_Exp1 or 20231208_Exp2 are valid file names, but Exp1_20231208 is not

However, this functionality can be disabled by specifying the sub-directory paths and their respective labels in the iterators argument of the Project object. For example, if the project directory structure is as follows:

/base/path/to/project
    │
    └───Day1
    |   |
    |   └───Exp1
    │   │   |   DAQOutput.csv
    │   │   |   ExperimentMetadata.json
    |   |   |   SLEAPAnalysis.h5
    |   |   |   FacialVideo.mp4
    |   |
    |   └───Exp2
    │   │   |   DAQOutput.csv
    │   │   |   ExperimentMetadata.json
    |   |   |   SLEAPAnalysis.h5
    |   |   |   FacialVideo.mp4
    │
    └───Day2
    |   |
    |   └───Exp1
    │   │   |   DAQOutput.csv
    │   │   |   ExperimentMetadata.json
    |   |   |   SLEAPAnalysis.h5
    |   |   |   FacialVideo.mp4
    |   |
    |   └───Exp2
    │   │   |   DAQOutput.csv
    │   │   |   ExperimentMetadata.json
    |   |   |   SLEAPAnalysis.h5
    |   |   |   FacialVideo.mp4
    |
    └─── ...

Then the Project object can be initialized as follows:

proj = Project(
    DAQFile="DAQOutput.csv",
    BehFile="ExperimentMetadata.json",
    SLEAPFile="SLEAPAnalysis.h5",
    VideoFile="FacialVideo.mp4",
    base="/base/path/to/project",
    iterators={
        "Day1-Exp1": "/Day1/Exp1",
        "Day1-Exp2": "/Day1/Exp2",
        "Day2-Exp1": "/Day2/Exp1",
        "Day2-Exp2": "/Day2/Exp2",
        ...
        }
    }
    )

API Documentation

sleapyfaces.project.Project

proj = sleapyfaces.project.Project(
    DAQFile: str,
    BehFile: str,
    SLEAPFile: str,
    VideoFile: str,
    base: str,
    iterator: dict[str, str] | None, # Optional
    get_glob: bool = False, # Optional
    )
  • Project is the main class for the sleapyfaces package. It is used to initialize the project and iterate over the project structure.
    • Args:
      • DAQFile (string): The naming convention for the DAQ files (e.g. "*_events.csv" or "DAQOutput.csv")
      • ExprMetaFile (string): The naming convention for the experimental structure files (e.g. "*_config.json" or "BehMetadata.json")
      • SLEAPFile (string): The naming convention for the SLEAP files (e.g. "*_sleap.h5" or "SLEAP.h5")
      • VideoFile (string): The naming convention for the video files (e.g. "*.mp4" or "video.avi")
      • base (string): Base path of the project (e.g. "/specialk_cs/2p/raw/CSE009")
      • iterator (dictionary[string, string], optional): Iterator for the project files, with keys as the label and values as the folder name (e.g. {"week 1": "20211105", "week 2": "20211112"})
      • get_glob (boolean, default = False): Whether to use glob to find the files (e.g. True or False). Default is False.
        • NOTE: if glob is True, make sure to at least include the file extension with an asterisk in the naming convention
        • e.g. DAQFile="*.csv" or SLEAPFile="*.h5"
    • Attributes:
      • exprs (list[Experiment]): List of the experiment objects
      • files (list[FileConstructor]): FileConstructor object

sleapyfaces.project.Project.buildColumns

proj.buildColumns(
    columns: list[str],
    values: list[str],
    )
  • Builds the custom columns for the project and builds the data for each experiment
    • Args:
      • columns (list[string]): the column titles
      • values (list[any]): the data for each column
    • Initializes attributes:
      • custom_columns (list[CustomColumn]): list of custom columns
      • all_data (pd.DataFrame): the data for all experiments concatenated together

sleapyfaces.project.Project.buildTrials

proj.buildTrials(
    TrackedData: list[str],
    Reduced: list[bool],
    start_buffer: int = 10000, # Optional
    end_buffer: int = 13000, # Optional
    )
  • Parses the data from each experiment into its individual trials
    • Args:
      • TrackedData (list[string]): The title of the columns from the DAQ data to be tracked
      • Reduced (list[boolean]): The corresponding boolean for whether the DAQ data is to be reduced (True) or not (False)
      • start_buffer (integer, optional): The time in milliseconds before the trial start to capture. Defaults to 10000.
      • end_buffer (integer, optional): The time in milliseconds after the trial start to capture. Defaults to 13000.
    • Initializes attributes:
      • exprs[i].trials (pd.DataFrame): the data frame containing the concatenated trial data for each experiment
      • exprs[i].trialData (list[pd.DataFrame]): the list of data frames containing the trial data for each trial for each experiment

sleapyfaces.project.Project.meanCenter

proj.meanCenter()
  • Recursively mean centers the data for each trial for each experiment
    • Initializes attributes:
      • all_data (pd.DataFrame): the mean centered data for all trials and experiments concatenated together

sleapyfaces.project.Project.zScore

proj.zScore()
  • Z scores the mean centered data for each experiment
    • Updates attributes:
      • all_data (pd.DataFrame): the z-scored data for all experiments concatenated together

sleapyfaces.project.Project.analyze

proj.analyze()
  • Runs the mean centering and z scoring functions

sleapyfaces.project.Project.visualize

proj.visualize()
  • Reduces all_data to 2 and 3 dimensions using PCA
    • Initializes attributes:
      • pcas (dictionary[string, pd.DataFrame]): a dictionary containing the 2 and 3 dimensional PCA data for each experiment
      • the dictionary keys are 'pca2d', 'pca3d'

sleapyfaces.experiment.Experiment

expr = sleapyfaces.experiment.Experiment(
    name: str,
    files: sleapyfaces.structs.FileConstructor,
    )
  • Class constructor for the Experiment object.
    • Args:
      • name (string): The name of the experiment.
      • files (FileConstructor): The FileConstructor object containing the paths to the experiment files.
    • Attributes:
      • name (string): The name of the experiment.
      • files (FileConstructor): The FileConstructor object containing the paths to the experiment files.
      • sleap (SLEAPanalysis): The SLEAPanalysis object containing the SLEAP data.
      • beh (BehMetadata): The BehMetadata object containing the behavior metadata.
      • video (VideoMetadata): The VideoMetadata object containing the video metadata.
      • daq (DAQData): The DAQData object containing the DAQ data.
      • numeric_columns (list[string]): A list of the titles of the numeric columns in the SLEAP data.

sleapyfaces.experiment.Experiment.buildData

expr.buildData(
    CustomColumns: list[sleapyfaces.structs.CustomColumn]
    )
  • Builds the data for the experiment.
    • Args:
      • CustomColumns (list[CustomColumn]): A list of the CustomColumn objects to be added to the experiment.
    • Initializes attributes:
      • sleap.tracks (pd.DataFrame): The SLEAP data.
      • custom_columns (pd.DataFrame): The non-numeric columns.

sleapyfaces.experiment.Experiment.append

expr.append(
    item: pd.Series | pd.DataFrame
    )
  • Appends a column to the SLEAP data.
    • Args:
      • item (pd.Series or pd.DataFrame): A pandas series or dataframe to be appended to the SLEAP data.
    • Updates attributes:
      • sleap.tracks (pd.DataFrame): The SLEAP data.
      • custom_columns (pd.DataFrame): The non-numeric columns.

sleapyfaces.experiment.Experiment.buildTrials

expr.buildTrials(
    TrackedData: list[str],
    Reduced: list[bool],
    start_buffer: int = 10000, # Optional
    end_buffer: int = 13000, # Optional
    )
  • Converts the data into trial by trial format.
    • Args:
      • TrackedData (list[string]): the list of columns from the DAQ data that signify the START of each trial.
      • Reduced (list[boolean]): a boolean list with the same length as the TrackedData list that signifies the columns from the tracked data with quick TTL pulses that occur during the trial.
        • (e.g. the LED TTL pulse may signify the beginning of a trial, but during the trial the LED turns on and off, so the LED TTL column should be marked as True)
      • start_buffer (integer, optional): The time in miliseconds you want to capture before the trial starts. Defaults to 10000 (i.e. 10 seconds).
      • end_buffer (integer, optional): The time in miliseconds you want to capture after the trial starts. Defaults to 13000 (i.e. 13 seconds).
    • Initializes attributes:
      • trials (pd.DataFrame): the dataframe with the data in trial by trial format, with a metaindex of trial number and frame number
      • trialData (list[pd.DataFrame]): a list of the dataframes with the individual trial data.

sleapyfaces.io.DAQData

daq = sleapyfaces.io.DAQData(
    path: str | PathLike[string]
    )
  • Cache for DAQ data.
    • Args:
      • path (string or PathLike[string]): Path to the directory containing the DAQ data.
    • Attrs:
      • cache (pd.DataFrame): Pandas DataFrame containing the DAQ data.
      • columns (list[string]): List of column names in the cache.

sleapyfaces.io.DAQData.append

daq.append(
    name: str,
    value: list[any]
    )
  • Takes in a list with a name and appends it to the cache as a column
    • Args:
      • name (string): The column name.
      • value (list[any]): The column data.
    • Updates attributes:
      • cache (pd.DataFrame): Pandas DataFrame containing the DAQ data.
      • columns (list[string]): List of column names in the cache.

sleapyfaces.io.DAQData.saveData

daq.saveData(
    filename: str | PathLike[str] | FileIO,
    )
  • saves the cached data to a csv file
    • Args:
      • filename (Text | PathLike[Text] | BufferedWriter): the path and name of the file to save the data to

sleapyfaces.io.SLEAPanalysis

sleap = sleapyfaces.io.SLEAPanalysis(
    path: str | PathLike[string]
    )
  • a class for reading and storing SLEAP analysis files
    • Args:
      • path (string or PathLike[string]): path to the directory containing the SLEAP analysis file
    • Attributes:
      • data (dictionary): dictionary of all the data from the SLEAP analysis file
      • track_names (list): list of the track names from the SLEAP analysis file
      • tracks (pd.DataFrame): a pandas DataFrame containing the tracks from the SLEAP analysis file (with missing frames filled in using a linear interpolation method)

sleapyfaces.io.SLEAPanalysis.getDatasets

sleap.getDatasets()
  • gets the datasets from the SLEAP analysis file
    • Initializes Attributes:
      • data (dictionary): dictionary of all the data from the SLEAP analysis file

sleapyfaces.io.SLEAPanalysis.getTracks

sleap.getTracks()
  • gets the tracks from the SLEAP analysis file
    • Initializes Attributes:
      • tracks (pd.DataFrame): a pandas DataFrame containing the tracks from the SLEAP analysis file (with missing frames filled in using a linear interpolation method)

sleapyfaces.io.SLEAPanalysis.getTrackNames

sleap.getTrackNames()
  • gets the track names from the SLEAP analysis file
    • Initializes Attributes:
      • track_names (List): list of the track names from the SLEAP analysis file

sleapyfaces.io.SLEAPanalysis.append

sleap.append(
    item: pd.Series | pd.DataFrame
)
  • Appends a column to the tracks DataFrame
    • Args:
      • item (pd.Series or pd.DataFrame): The column to append to the tracks DataFrame
    • Updates Attributes:
      • tracks (pd.DataFrame): a pandas DataFrame containing the tracks from the SLEAP analysis file

sleapyfaces.io.SLEAPanalysis.saveData

sleap.saveData(
    filename: str | PathLike[str],
    path: str="SLEAP" # Optional
)
  • saves the modified SLEAP analysis data to an HDF5 file
    • Args:
      • filename (string or PathLike[string]): the path and name of the file to save the data to
      • path (string, optional): the internal HDF5 path to save the data to. Defaults to "SLEAP".

sleapyfaces.io.BehMetadata

beh = sleapyfaces.io.BehMetadata(
    path: str | PathLike[string],
    MetaDataKey: str="beh_metadata", # Optional
    TrialArrayKey: str="trialArray", # Optional
    ITIArrayKey: str="ITIArray", # Optional
    )
  • Cache for JSON data.
    • Args:
      • path (string of PathLike[string]): Path to the file containing the JSON data.
      • MetaDataKey (string, optional): Key for the metadata in the JSON data. Defaults to "beh_metadata" based on bruker_control.
      • TrialArrayKey (string, optional): Key for the trial array in the JSON data. Defaults to "trialArray" based on bruker_control.
      • ITIArrayKey (string, optional): Key for the inter-trial interval array in the JSON data. Defaults to "ITIArray" based on bruker_control.
    • Attributes:
      • cache (pd.DataFrame): Pandas DataFrame containing the JSON data.
      • columns (list): List of column names in the cache.

sleapyfaces.io.BehMetadata.saveData

beh.saveData(
    filename: str | PathLike[str] | FileIO,
    )
  • Saves the DAQ data to a CSV file.
    • Args:
      • filename (string or PathLike[string] or FileIO): The name and path of the file to save the data to.

sleapyfaces.io.VideoMetadata

video = sleapyfaces.io.VideoMetadata(
    path: str | PathLike[string],
    )
  • A class for caching the video metadata.
    • Args:
      • path (string of PathLike[string]): Path to the directory containing the video metadata.
    • Attributes:
      • cache (dictionary): Dictionary containing the video metadata from ffmpeg.
      • fps (float): The precise frames per second of the video.

sleapyfaces.io.VideoMetadata.saveData

video.saveData(
    filename: str | PathLike[str] | FileIO,
    )
  • Saves the video metadata to a json file.
    • Args:
      • filename (string or PathLike[string] or FileIO): the name and path of the file to save the data to.

sleapyfaces.normalize.mean_center

sleapyfaces.normalize.mean_center(
    data: pd.DataFrame,
    track_names: list[str]
    ) -> pd.DataFrame
  • Mean centers the data for each track.
    • Args:
      • data (pd.DataFrame): The data to mean center.
      • track_names (list[str]): The names of the tracks to mean center.
    • Returns:
      • pd.DataFrame: The mean centered data.

sleapyfaces.normalize.z_score

sleapyfaces.normalize.z_score(
    data: pd.DataFrame,
    track_names: list[str]
    ) -> pd.DataFrame
  • Z-score the data.
    • Args:
      • data (pd.DataFrame): The data to z-score.
      • track_names (list[str]): The names of the tracks to z-score.
    • Returns:
      • pd.DataFrame: The z-scored data.

sleapyfaces.normalize.pca

sleapyfaces.normalize.pca(
    data: pd.DataFrame,
    track_names: list[str]
    ) -> dict[str, pd.DataFrame]
  • Runs 2D and 3D PCA dimensionality reduction on the data.
    • Args:
      • data (pd.DataFrame): The data to be reduced.
      • track_names (list[str]): The names of the tracks to be reduced.
    • Returns:
      • dictionary[string, pd.DataFrame]: The reduced data with keys "pca2d" and "pca3d". -i.e. {"pca2d": pd.DataFrame, "pca3d": pd.DataFrame}

sleapyfaces.structs.File

file = sleapyfaces.structs.File(
    basepath: str | PathLike[str],
    filename: str,
    get_glob: bool=False, # Optional
    )
  • A structured file object that contains the base path and filename of a file.
    • Args:
      • basepath (string): the path to the file.
      • filename (string): the name of the file, or a glob pattern if get_glob is True.
      • get_glob (bool, optional): whether or not to get the glob pattern. Defaults to False.
    • Attributes:
      • file (string): the complete filepath
      • iPath(i) (string): the path to the ith file in the glob pattern.

sleapyfaces.structs.FileConstructor

files = sleapyfaces.structs.FileConstructor(
    daq: File,
    sleap: File,
    beh: File,
    video: File
    )
  • Takes in the base paths and filenames of the experimental data and returns them as a structured object.
    • Args/Attributes:
      • daq (File): The location of the DAQ data file.
      • sleap (File): The location of the SLEAP analysis file.
      • beh (File): The location of the behavioral metadata file.
      • video (File): The location of the video file.

sleapyfaces.structs.CustomColumn

column = sleapyfaces.structs.CustomColumn(
    ColumnTitle: str,
    ColumnData: str | int | float | bool
    )
  • Takes in the base paths and filenames of the experimental data and returns them as a structured object.
    • Args:
      • ColumnTitle (string): The title of the column.
      • ColumnData (string or integer or float or boolean): The data to be added to the column.

sleapyfaces.structs.CustomColumn.buildColumn

column.buildColumn(
    length: int,
    )
  • Initializes a column of a given length.
    • Args:
      • length (integer): The length of the column to be built.
    • Initializes Attributes:
      • Column (pd.DataFrame): The initialized column at a given length.

Changelog:

Version 1.0.0 (2022-12-27)

  1. iterate (repeatedly) over each mouse and each week (each mouse and each experiment)
    • get project files (experimental) structure
    • initialize an iterator over the project structure
  2. get daq data from CSV file
    • read CSV files
    • save each column from CSV file
      • Note: CSV columns are of differing lengths
  3. get “beh_metadata” from json metadata
    • read JSON file
    • grab the values for key “beh_metadata”
      • get the values of sub key “trialArray”
      • get the values of sub-key “ITIArray”
  4. get video metadata from *.mp4 file (with ffmpeg.probe)
    • read in the *.mp4 metadata
    • select the correct video stream
    • get the average frames per second
  5. get SLEAP data from *.h5 file
    • open h5 file
    • get transposed values of key “tracks” (tracking_locations)
    • fill missing locations (linear regress. fit)
    • get transposed values of key “edge_inds”
    • get values of key “edge_names”
    • get transposed values of “instance_scores”
    • get transposed values of “point_scores”
    • get values of “track_occupancy”
    • get transposed values of “tracking_scores”
    • get decoded values of “node_names” (make sure there's no encoding issues)
  6. deconstruct SLEAP points into x and y points (across all frames)
    • iterate over each node
    • breakup the 4D array “tracks” into 1D array for x and y values respectively
      • Note: [frame, node, x/y, color] for greyscale the color dimension is 1D (i.e. essentially the 4D array is 3D because the color dimension is constant)
    • iterate over each frame
    • assign mouse, week, frame #, and timestamp (using average frames per second)
  7. Split data into individual trials by trial type using the Speaker and LED data from the CSV daq data
    • initialize trial iterators for the consistently documented points from the daq CSV
    • iterate over each trial in “trialArray”
    • get the index of 10sec before and 13sec after trial start
    • for each feature, grab the start and end indices
    • store data from each trial in a pd.dataframe
    • concatenate all pd.dataframes together for each video
    • concatenate the pd.dataframes from each video together for each mouse (base expr split)
  8. Prepare the data
    • (opt.) mean center across all points for a single trial
    • mean center across all trials for a single experiment
    • mean center across all experiments for a single mouse
    • mean center across all mice
    • (opt.) z-score mean-centered data
  9. Analyze the data
    • Perform 2D and 3D PCAs on all data (raw, centered, by trial, by week, by mouse, etc…)
    • apply gaussian kernel to PCA outputs
  10. Save the data
    • write everything to HDF5 file(s)

Version 1.0.1

  • add exhaustive documentation
  • add inline documentation
  • strengthen type hints

Version 1.1.0 (in progress)

  • add support for multiple mice
  • add clustering/prediction algorithm(s)
  • add velocity, acceleration, and jerk calculations
  • add save option for all data
  • add plotting functions
  • clustering features
    • distance to a point
      • vector to a point (theta, magnitude) or (angle, distance)
    • velocity/acceleration
    • distance to centroid
    • distance between given points

Project details

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  • License: MIT License
  • Author: Analetta (Annie) Ehler
  • Tags SLEAP, analysis, classification, extraction, facial data, facial expression, recognition, tracking analyses
  • Requires: Python >=3.7
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