Getting Started

Getting Started#

Installation#

The project uses conda to manage dependencies. If you don’t already have conda, you can download and install it from the official website. We suggest reading Getting Started from the conda website for an introduction on how to use Conda.

Make a copy of the repo (e.g. with git clone), then cd into the root folder of the repo.

Recreate the conda environment with the dependencies listed in environment.yml in the repo’s root:

conda env create -f environment.yml

Activate the environment:

conda activate snazzy-env

Organization#

The code is split in two packages. Parsing raw data into csv files with the relevant ROI metrics is done using snazzy_processing. The data analysis and GUI access is done using snazzy_analysis.

Each one of the packages has the following structure:

  • snazzy_[pkg]: core code.

  • tests: contains tests for the code.

  • data: contains the data for the analysis. This folder is kept out of github, and should be populated in your local copy.

  • results: contains the results of the analyses. It is also kept out of github and will be populated by performing the analyses.

  • notebooks: illustrates how individual steps of the pipeline work. Also used for more specific visualizations.

Running the code#

Refer to the Getting Started session of each package for how to run the code.

Two sample datasets with a reduced number of samples (to reduce dataset size) were uploaded to zenodo. Please find the datasets here: https://doi.org/10.5281/zenodo.17295552.

To process raw data, start with Getting Started. To analyze the output of the processing step, go to Getting Started.

The analyses can be executed using the provided jupyter notebooks, or using the GUI.

Community Guidelines#

Thank you for being interested in snazzy! Check more information about how to get involved in the Contributing section of the Github repository’s Readme.

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