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Data extraction from and analysis of flow networks.
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rfn_analysis
LICENSE.rst
README.rst
analysis.R
artificial_output_patterns.ipynb
complexity_prediction.ipynb
complexity_vs_compression.ipynb
draw_sample_networks.ipynb
extract_data.py
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setup.py

README.rst

Analysis of Flow Networks

Flow networks robust against damages are simple model networks described in a series of publications by Kaluza et al.[1, 2, 3].

This repository provides a series of Python scripts to extract relevant data from flow networks generated by code from a related repository and some R scripts for their analysis and plotting.

Installation

Please follow the relevant documentation for the installation of external packages.

This package can be installed like any other Python package but if you only use the extract_data.py script, it doesn't have to be.

sudo python setup.py install

Usage

If you don't want to make a system-wide installation, you can simply add the location of the package to the path variable.

import sys
sys.path.append("/home/you/location/rfn-analysis")
import rfn_analysis as ra

With the class definitions imported, you can unpickle the networks.

import networkx as nx
net = nx.read_gpickle("standard/node_robust/sim1025_final.pkl")

Requirements

Python:

R:

Optional:

  • extraction and storage of network characteristics in HDF5 files pytables
  • reading HDF5 files in R with rhdf5

References

[1]Kaluza, P., Ipsen, M., Vingron, M. & Mikhailov, A. S. Design and statistical properties of robust functional networks: A model study of biological signal transduction. Physical Review E 75, 15101 (2007).
[2]Kaluza, P. & Mikhailov, A. S. Evolutionary design of functional networks robust against noise. Europhysics Letters 79, 48001 (2007).
[3]Kaluza, P., Vingron, M. & Mikhailov, A. S. Self-correcting networks: function, robustness, and motif distributions in biological signal processing. Chaos 18, 026113 (2008).
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