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MASS is Music and Audio in Sample Sequences

Project description

Python utilities for the analysis of the GMANE email list database

This project delivers helper classes for the analysis of the GMANE
email database.

Functionalities are based on physics articles on interaction networks:
- Stability in human interaction networks: primitive typology of vertex, prominence of measures and activity statistics:
- A connective differentiation of textual production in interaction networks:
- Versinus: a visualization method for graphs in evolution:

With core concepts of 1) analysis of topological structure; 2) analysis of textual production; 3) visualization of evolving structures. Activity distribution along time and among participants are also approached through specific routines and indirectly through 1), 2) and 3).

Ideally, this package should ease:
- Downloading GMANE email list data.
- Building elementary data structures with downloaded data.
- Analysis of data through complex networks and NLP criteria.
- Visualization through diverse layout methods.

Usage example
Download messages from one GMANE list:

.. code:: python

import gmane as g
dl=g.DownloadGmaneData() # saves into ~/.gmane/
dl.downloadListsIDS() # acquires all GMANE list_ids
dl.cleanDownloadedLists() # remove empty messages for coherence
dl.downloadedStats() # creates ~/.gmane/stats.txt

# to load message contents to Python objects:
# load 10 messages from list with list_id gmane.ietf.rfc822

# or access the structures downloaded to your filesystem
# and download all messages from 5 lists
for list_id in dl.downloaded_lists[:5]:

# to load first three lists with the greated number
# of downloaded messages:
dl.downloadedStats() # might take a while
for list_stat in dl.lists[:3]:

# to make basic datastructures of a list with
# greatest number of messages:
print("first: ", mm[ids[0]][2], "last:", mm[ids[-1]][2])

# build the interaction network of the messages:

print("number of nodes: {}, number of edges: {}".format(
nw.g.number_of_nodes(), nw.g.number_of_edges()))


print("{} agents in periphery,\
{} are intermediary and {} hubs".format(sa[0],sa[1],sa[2]))
sa=np.sectorialized_agents__ # smoothed histogram
print("{} agents in periphery,\
{} are intermediary and {} hubs".format(sa[0],sa[1],sa[2]))

# Enjoy!

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