Photostimulation Of Population Obviously Fuels Firing

Code for analysis of all-optical neural data obtained in Packer lab, Oxford. Figures were generated in whole by code, using notebooks in popping-off/notebooks/Paper Figures/. This is the code of the following preprint:

https://www.biorxiv.org/content/10.1101/2021.12.28.474343v1

Instructions for installation repo:

Clone or fork this repository
Clone the dependence Vape: https://github.com/Packer-Lab/Vape
To install the correct Python packages, please build a conda environment from the pope.yml file, using the following terminal command conda env create -f pope.yml.
Additionally; run pip install google-api-python-client and pip install google-auth-oauthlib
Add a profile to popping-off/data_paths.json, with links to your local paths. (base_path should be the path that the .pkl data set is in. Vape_path should be the directory the Vape repository (also from Packer-Lab Github) is in. The other two entries are not needed for data analysis (only for pre-processing).
Install pop-off by going to your local repo path /popping-off/popoff/ and running python setup.py develop
Get started by opening the data-loading routine and short tutorial on data structure, available in popping-off/notebooks/Example notebook to load data.ipynb
Figures from the bioRxiv preprint were generated by code, available in popping-off/notebooks/Paper figures/

Additional detail (not necessary for getting started)

Data:

To build sessions.pkl files run 'python Session.py' from command line and enter flu_flavour through cli. A new .pkl file will be built for each flu_flavour
Each .pkl contains a dictionary of SessionLite objects.

SessionLite Attributes

behaviour_trials (float64): 3d array of imaging data as defined by flu_flavour [n_cells x n_trials x n_frames].
outcome (str): what was the behavioural response to the trial?
decision (bool): did the animal lick or not?
photostim (int): 0 = no stim; 1 = test trial; 2 = easy trial.
trial_subsets (int): how many cells were stimulated on each trial?
s1_bool (bool): is the cell in s1?
s2_bool (bool): is the cell in s2?

info folders:

This repo attempts to follow the directory structure as recommmended by: https://drivendata.github.io/cookiecutter-data-science/ . Most of the code is wrapped in objects (functions and classes), which are called in notebooks to plot results. In summary, there are four main folders:

figures (saved figures (preferably pdf or svg))
notebooks (Jupyter notebooks that run the functions)
popoff (contains all relevant modules for the notebooks)
scripts (code that is not relevant for notebooks, but is used in other stages of the project (e.g. data pre-processing)).

Additional info:

One requires the repo VAPE for data pre-processing. Furthermore, some routines in scripts/Session.py were taken from VAPE. VAPE can be cloned here: https://github.com/neuromantic99/Vape

To use OASIS for spike deconvolution in some places, one needs to install their package. To do so, clone this repo: https://github.com/j-friedrich/OASIS and follow their python installation instructions. (Not necessary for the majority of analyses)

vdplasthijs / popping-off_backup