network-spatial-coherence 0.1.1

Network Validation using the Spatial Coherence Framework.

Network Spatial Coherence

Python library to validate the spatial coherence of a network. It offers tools to analyze network properties, check how "Euclidean" the network is (spatial coherence), and to reconstruct the network. Networks can be both simulated (e.g. a KNN network) or imported.

Features

• Analyze the spatial coherence of a network
• Reconstruction images from purely network information
• Efficient graph loading and processing (using sparse matrices or getting a graph sample)

Install

Python 3.11 is reccomended, although older versions should work.

pip install git+https://github.com/DavidFernandezBonet/Spatial_Constant_Analysis.git

If you require authentication you can use a PAT (a github token) instead. Go to Developer settings > Personal access tokens > Generate new token and then save the token because it will not be displayed again. You should input it in this line of code

pip install git+https://<token>:[e-mail]/DavidFernandezBonet/Spatial_Constant_Analysis.git

Usage

For a detailed tutorial, see the Jupyter Notebook Tutorial in this repository.

1. Access documentation for detailed API usage:

from network_spatial_coherence.docs_util import access_docs
access_docs()

2. Minimum working example

from network_spatial_coherence import nsc_pipeline
from network_spatial_coherence import structure_and_args
structure_and_args.create_project_structure()
graph, args = nsc_pipeline.load_and_initialize_graph()
nsc_pipeline.run_pipeline(graph, args)

Contact

[[e-mail]]