Code for analysis of neural data obtained in Packer lab, Oxford.
- Clone repository
- To install the correct Python packages, please build a conda environment from the
pop_off_env.ymlfile. - Change
data_paths.jsonto your local paths - Please set the data path configuration file to 'assume unchanged' in get settings, such its changes are not suggested as commit. Use:
git update-index --assume-unchanged data_paths.json - 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.
- 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?
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)).
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, one needs to install their package. To do so, clone this repo: https://github.com/j-friedrich/OASIS and follow their python installation instructions.
To use demixing PCA, one must install their package. To do so, clone this repo: https://github.com/machenslab/dPCA/tree/master/python/ and follow their python installation instructions.