abhi-glitchhg/gsoc23.md

## gsoc23.md

      
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              gsoc23.md
            
          
    Project Description:

The Virtual Brain (TVB) is a scientific simulation platform that provides all
means to generate, manipulate and visualize connectivity and network dynamics
of the brain networks. Researchers in Computational Neuroscience use brain
network models to understand the dynamic behavior of the healthy and diseased
brain, as measured by various neuroimaging techniques such as fMRI, EEG, and
MEG. TVB is currently the only neuroinformatics project providing a platform for
researchers to work together on modeling studies using brain network models
with realistic connectivity.
The modeling of the simulations requires datasets generally hosted on
data storage platforms like Zenodo, OpenSourceBrain, EBRAINS, etc. One has
to download the dataset manually, unzip it and then use it inside the web GUI.
Instead of manually downloading and unzipping the data, the project's goal
is to have a dedicated framework to manage the downloading and unzipping the
dataset from remote sources. Right now, we are focusing on the Zenodo
platform
My work on this feature can be found in this branch: https://github.com/the-virtual-brain/tvb-root/tree/TVB-1999-deo or in
this pr against the master : https://github.com/the-virtual-brain/pulls/691 
Usage:

In the proposed idea, there is a Zenodo class through which one will interact with the Zenodo API.
The response from the Zenodo API is stored in the Record class.
One can use the above Zenodo and Record classes like,
#instantiate the Zenodo class
zen = Zenodo() 

# Suppose you are interested in working with a specific repository.
# for this example lets consider https://zenodo.org/record/4263723 as the repository you are interested in.

#then we can get the `record` object back using the get_record method of the zenodo class. In our case, "4263723" is the record id.

record = zen.get_record("4263723")

print(record)
# this will print the formatted json response from the zenodo api

print(record.describe())
# this will print the project description 

# to donwload the files associated with this record at the current working directory, 
record.download(path = "./")
There is a special class created to work with the tvb_data called TVBZenodoDataset.
# you can specify which version of the data you are interested in, and where you want to download and extract the data.
dataset = TVBZenodoDataset(version= "2.0.3", extract_dir=<enter your path here>)


# once you have instantiated the TVBZenodoDataset class, you can fetch files using `fetch_data` method.
# this method returns the absolute path of the file. 

connectivity_66_file_path = dataset.fetch_data("connectivity_66.zip")

# you can check if the file exists in the file system or not by
import pathlib

assert pathlib.Path(connectivity_66_file_path).is_file()
TO DO

Implement data caching in CI/CD pipeline to improve the testing speed.
Extend the datasets for EBRAINS and other similar platform.
Acknowledgement

I'd like to thank Google Summer of Code (GSOC) for providing an amazing platform, and  my organization INCF for giving me the chance to work on this project.
I also want to express my gratitude to my mentors, Lia Domide and Romina Baila, for their incredible support and guidance throughout this project.
It's been an awesome learning experience!