This is a package for extracting facial expressions from SLEAP analyses.

Things that it does:

1. iterate (repeatedly) over each mouse and each week (each mouse and each experiment)
   • get project files (experimental) structure
   • initialize an iterator over 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 subkey “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 theres 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)

Table of Contents

• SLEAPyFaces
  • To Do:
  • Installation
  • License

pip install sleapyfaces

sleapyfaces is distributed under the terms of the MIT license.