Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

Installation

conda create -n pyfferaph python=3.6
conda activate pyfferaph

On linux:

pip install --upgrade pyfferaph

On Mac/Windows, you need first to install the dependencies.

conda install -c conda-forge MDAnalysis
pip install --upgrade pyfferaph
# filter_pyff
# ============
#
# filter_pyff script works on adjacency matrix files, such as as those outputted by Pyinteraph (add reference)
# The script is designed to:

#1) return and save to file a macro_iin.dat file starting from separete interaction matrices (salt bridges, hydrogen bonds, hydrophobic interactions)
# Each interaction in each interaction matrix is retained only if above a certaint treshold value (-p option) if provided (default value 0.0).
# The filtered matrices arte then combined to generate a macro_iin.dat file,
# an edge between two nodes is drawn if that interaction is above treshold in at least one filterd interaction matrix

threshold=5.0
filter_pyff -d sb_graph.dat -d hb_graph.dat -d hc_graph.dat -p $threshold -o out_macro_iin.dat

#2) Generate an intercation network G based on either separate interaction matrices or a macro IIN file. A topology file is required (option -g)
# compute all shortest paths between a set of source and target residues defined in a json formatted input file (option -z).
# A json-formatted file can be obtained with:

filter_pyff -j template.json

# A score is assigned for each path of a given source-target pair.
# Identify the best path (or equally best pahts) among all paths connecting source and target residues
# Calculate the communication robustness index for each source-target pathway (a pathway is define as the set of all the shortest paths connecting source and taget)
# Print to file all the above mentioned informations. The script saves one file for each source residue. Eache file contains all patht between that source and all the target resiudes.

#starting from separete interaction matrices:

filter_pyff -d sb_graph.dat -d hb_graph.dat -d hc_graph.dat -p $treshold -o out_macro_iin.dat -g topol.gro -z z_file.json

#starting from a macro_iin.dat file

filter_pyff -i macro_iin.dat -g topol.gro -z z_file.json

#3) Compute the selective betweeness for a given redidue (option -s) considering all shortest path between a given source and target file (option -t)

filter_pyff -d sb_graph.dat -d hb_graph.dat -d hc_graph.dat -p $treshold -o out_macro_iin.dat -g topol.gro -s RES1 -t RES2 RES3

#or

filter_pyff -i macro_iin.dat -g topol.gro -s RES1 -t RES2 RES3

Release files for pyfferaph 0.0.1a4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pyfferaph 0.0.1a4
File Size Uploaded
pyfferaph-0.0.1a4.tar.gz 9.7 kB Details

Release files / pyfferaph-0.0.1a4.tar.gz

Download URL pyfferaph-0.0.1a4.tar.gz
Size 9.7 kB
Tags Source
SHA-256 checksum
How to use checksums
2566fde9e73c95a63aa3eb93cda749918043d7d2ea5557e40ee6005a11c80645
BLAKE2b-256 checksum
How to use checksums
d34a6d262b28c9e36a061c67fb1caba9efcf020b240ddf1a287b0cd9bfbe287f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.54.0 CPython/3.8.5
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page