gmaneLegacy 0.1.dev27

Utilities for the analysis of the GMANE email list database

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

$ pip install gmaneLegacy

or

$ python setup.py install

For greater control of customization (and debugging), clone the repo and install with pip with -e:

$ git clone https://github.com/ttm/gmaneLegacy.git

$ pip install -e <path_to_repo>

This install method is especially useful with reload function from IPython.lib.deepreload and the standard importlib.

Functionalities are based on physics articles on interaction networks: [1] Stability in human interaction networks: primitive typology of vertex, prominence of measures and activity statistics: http://arxiv.org/abs/1310.7769 [2] A connective differentiation of textual production in interaction networks: http://arxiv.org/abs/1412.7309 [3] Versinus: a visualization method for graphs in evolution: http://arxiv.org/abs/1412.7311

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.

PS. Implemented measures of symmetry in network agents activity by hand (not found in network and numeric packages) according to [1]. PS2. ongoing research in tests/newTextTables.py and tests/makeOverallTextAnalysis.py PS. Also check the gmane Python package https://github.com/ttm/gmane

Usage example

Download messages from one GMANE list:

import gmane as g
dl=g.DownloadGmaneData() # saves into ~/.gmane/
dl.downloadListsIDS() # acquires all GMANE list_ids
dl.downloadListMessages(dl.list_ids[100])
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
lm=g.LoadMessages("gmane.ietf.rfc822",10)

# or access the structures downloaded to your filesystem
dl=g.DownloadGmaneData()
dl.getDownloadedLists()
lms=[]
# and download all messages from 5 lists
for list_id in dl.downloaded_lists[:5]:
    lms.append(g.LoadMessages(list_id))

# to load first three lists with the greated number
# of downloaded messages:
dl.downloadedStats() # might take a while
load_msgs=[]
for list_stat in dl.lists[:3]:
    list_id=list_stat[0]
    load_msgs.append(g.LoadMessages(list_id))

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

# circular (directional) statistics for activity along time
# (hours of the day, days of the week, days of the month, etc):
# mean_vec, mean_angle, size_mean_vec, circular_mean,
# circular_variance, circular dispersion
# and histograms
ts=g.TimeStatistics(ds)
print("made overall circular activity statistics along time")

# make latex tables to observe distributions within bins of interest
hi=100*ts.hours["histogram"]/ts.hours["histogram"].sum()
row_labels=list(range(24))
tstring=g.parcialSums(row_labels,data=[hi],partials=[1,2,3,4,6,12],
            partial_labels=["h","2h","3h","4h","6h","12h"],datarow_labels=["APACHE"])
g.writeTex(tstring,"here.tex")

ps=g.AgentStatistics(ds)
print("made overall activity statistics among participants")

# build the interaction network of the messages:
nw=g.InteractionNetwok(ds)

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

nm=g.NetworkMeasures(nw) # take measures, including symmetry related measures
np=g.NetworkPartitioning(nm) # partition in primitive typology
sa=np.sectorialized_agents # get members of each sector
print("{} agents in periphery, {} are intermediary and {} hubs".format(sa[0],sa[1],sa[2]))
sa=np.sectorialized_agents__ # smoothed histogram for classification
print("{} agents in periphery, {} are intermediary and {} hubs".format(sa[0],sa[1],sa[2]))

# draw
nd=g.NetworkDrawer()
print("drawer started")
nd.makeLayout(nm)
print("gave (x,y) for each author with 5-15-80")
nd2=g.NetworkDrawer()
print("drawer two started")
nd2.makeLayout(nm,np)
print("gave (x,y) for each author with \
sectors by comparison with Erdos-Renyi")
nd.drawNetwork( iN,nm ,"test.png")
nd2.drawNetwork( iN,nm,"test2.png")

# make basic PCA plots of network measures:
npca=g.NetworkPCA(nm)
# Plot PCA with a colored primitive sectors
npca=g.NetworkPCA(nm,np)

# Evolves network with measures, partitions,
# PCA, principal components and Versinus plots saved to disk
lm=lms[0] # loaded messages from list with most messages
ne=g.NetworkEvolution(step_size=10)
ne.evolveRaw(lm.messages,imagerate=4,erdos_sectors=True)
# ne.makeVideo() use this to avoid evolving again just to make video
# see testDrawer.py or g.NetworkEvolution to make movies:
# https://www.youtube.com/watch?v=iS8NwEy291g

# after making network evolution measurements and video,
# you can both make music:
em=g.EvolutionMusic()
print("music is done")
# avconv -i mixY.wav -i evo[..<depends on the evolution done>..].avi final.avi
# delivers you the final.avi animation with a soundtrack relative to network measures
# currently it is the 'four hubs dance' by default:
# https://www.youtube.com/watch?v=YxDiwzAUPeU

# and further analysis of measures and Erdos sectors:
et=g.EvolutionTimelines()
print("Written png files with network measures along evolution timeline")

# Enjoy!

Further documentation is in tests/ folder and object docstrings.