Environment

This project uses a conda environment.

To create the environment on Linux, run the following commands from within the top level of the repository:

conda env create -f environment.yml
conda activate openscope-differentiation

If you are using macOS or Windows, use environment.cross-platform.yml instead of environment.yml.

Data

The data are available from https://doi.org/10.5281/zenodo.4734580.

Once downloaded, they must be extracted into the top level of the repository:

tar -xzvf data.tar.gz

The data directory is organized as follows:

data
├── dff
│   ├── dff_*.h5    # dF/F traces for each session
├── eye
│   ├── *_area.npy                  # Pupil area for each session
│   └── preprocessing_params.yaml   # Preprocessing parameters for pupillometry data
├── events          # Detected L0 events for each cell in each session
│   └── session_*
│       └── event_cell_*.npz
├── metadata.csv    # Experiment metadata
├── pkl
│   └── pkl_*.pkl   # Stimulus presentation & behavioral data output from the optical physiology rig
├── run
│   ├── preprocessing_params.yaml   # Preprocessing parameters for locomotion data
│   ├── run_speed*.npy              # Locomotion velocity for each session
├── stim
│   ├── metadata.csv        # Stimulus metadata
│   ├── preview             # Stimulus movie files for viewing
│   ├── stimuli             # Stimulus movie files for presentation & analysis (unsigned 8-bit integer arrays)
│   ├── stimulus_df_*.csv   # Stimulus presentation table for each session
└── sync
    └── *_time_synchronization.h5   # Time synchronization data for alignment

Project organization & reproduction

Figures and tables are generated in several notebooks, named accordingly.

Some of these depend on other notebooks that compute intermediate results and write them to disk. Once the notebooks listed below have been run in that order, the figures & statistical analyses can be reproduced by running their respective notebooks.

Notebook	Purpose
main.ipynb	Compute neurophysiological differentiation and decode stimuli
stats.ipynb	Perform the main statistical analyses
stimulus_properties.ipynb	Compute stimulus differentiation & other stimulus properties

These in turn depend on several modules:

Module	Purpose
load.ipynb	Load and preprocess data & stimuli
metadata.py	Load metadata for experiments and stimuli
analysis.ipynb	Analysis and plotting functions shared among notebooks
spectral_differentiation.ipynb	Compute spectral differentiation of timeseries