Skip to main content

Network Validation using the Spatial Coherence Framework.

Project description

Network Spatial Coherence

How good is your network? This package measures the spatial coherence of a network—how closely it resembles a physical network—. Additionally, it can reconstruct the network's original positions in space using the STRND algorithm. Networks can be simulated (if you don't have any) or imported, and weighted and bipartite networks are supported. For details, see Spatial Coherence and STRND papers.

Features

  • Analyze the spatial coherence of a network
  • Reconstruct images from purely network information - like drawing a country by only knowing which train stations are connected
  • Efficient graph loading and processing (sparse matrices)
  • Handles simulated graphs, custom graphs, unweighted graphs, weighted graphs, bipartite graphs

Install

Python 3.11 is reccomended, although older versions should work. See requirements.txt for dependencies.

pip install network_spatial_coherence

Example Results

OG Image SP Constant Net Dim Gram Mat REC Image
Coherent Network OG Image SP Constant Net Dim Gram Mat REC Image
Incoherent Network OG Image SP Constant Net Dim Gram Mat REC Image

Check out Usage and Examples!

Further information

Contact

[dfb@kth.se]

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

network_spatial_coherence-0.1.199.tar.gz (4.7 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

network_spatial_coherence-0.1.199-py3-none-any.whl (4.8 MB view details)

Uploaded Python 3

File details

Details for the file network_spatial_coherence-0.1.199.tar.gz.

File metadata

File hashes

Hashes for network_spatial_coherence-0.1.199.tar.gz
Algorithm Hash digest
SHA256 139252e0ae7e673387af170979323236e04a4f8499c68d61e07ec8d7cc13e1a1
MD5 03932a89eaaaa578f1d7aa0bc74e3d31
BLAKE2b-256 1c9acbfdcdf6e5758930840a5e8a62d767341929338d4d0882cd1b0fd1be1887

See more details on using hashes here.

File details

Details for the file network_spatial_coherence-0.1.199-py3-none-any.whl.

File metadata

File hashes

Hashes for network_spatial_coherence-0.1.199-py3-none-any.whl
Algorithm Hash digest
SHA256 04c3b180551087bc650b3dd0c3846fb40c1fee82e5a7b0d137b413fe8a4f32ec
MD5 6f4a8c3ba5ba4c03d09a15e09c5ca491
BLAKE2b-256 0ecb13f3d1c05be549cbc969bc92ab6a05e46bbc8865a8310f52aec7bac5cfc1

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page