https://github.com/midnighter/rfn-analysis
Data extraction from and analysis of flow networks.
https://github.com/midnighter/rfn-analysis
Last synced: over 1 year ago
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Data extraction from and analysis of flow networks.
- Host: GitHub
- URL: https://github.com/midnighter/rfn-analysis
- Owner: Midnighter
- License: other
- Created: 2013-01-20T13:05:05.000Z (over 13 years ago)
- Default Branch: master
- Last Pushed: 2013-07-16T08:05:32.000Z (almost 13 years ago)
- Last Synced: 2025-01-04T20:15:06.601Z (over 1 year ago)
- Language: R
- Size: 426 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- License: LICENSE.rst
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README
=========================
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.
.. _rfn-generation: https://github.com/Midnighter/rfn-generation
__ rfn-generation_
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.
.. code:: python
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.
.. code:: python
import networkx as nx
net = nx.read_gpickle("standard/node_robust/sim1025_final.pkl")
Requirements
------------
Python:
~~~~~~~
* some home-cooked `utility functions`__
* networkx_
* numpy_
* scipy.linalg_
R:
~~
* ggplot2_
Optional:
~~~~~~~~~
* extraction and storage of network characteristics in HDF5 files pytables_
* reading HDF5 files in R with rhdf5_
.. _meb: https://github.com/Midnighter/Everyday-Utilities
__ meb_
.. _networkx: http://networkx.github.com/
.. _numpy: http://www.numpy.org/
.. _scipy.linalg: http://www.scipy.org/
.. _ggplot2: http://ggplot2.org/
.. _pytables: http://www.pytables.org/
.. _rhdf5: http://www.bioconductor.org/packages/2.12/bioc/html/rhdf5.html
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).