pgncc = networkx.number_connected_components(pgu)
print 'number_connected_components: %s' % (pgncc)
ped_deg = pyp_network.get_node_degrees(pg)
ped_hist = pyp_network.get_node_degree_histograms(ped_deg)
print 'ped_hist:\t'
for k, v in ped_hist.iteritems():
print '\t\t', k, '\t', v
print 'nodes:\t\t', pg.order()
print 'edges:\t\t', pg.size()
density = pyp_network.graph_density(pg)
print 'density:\t', density
census = pyp_network.dyad_census(pg)
print 'max dyads:\t', ((pg.order() * (pg.order() - 1)) / 2)
print 'census:\t\t', census
geodesic = pyp_network.mean_geodesic(pg)
print 'geodesic:\t', geodesic
centrality = pyp_network.mean_degree_centrality(pg)
print 'centrality:\t', centrality
centrality = pyp_network.mean_degree_centrality(pg, normalize=1)
print 'normed central:\t', centrality
close = pyp_network.get_closeness_centrality(pg)
mean_close = pyp_network.mean_value(close)
print 'mean closeness:\t', mean_close
pgncc = networkx.number_connected_components(pgu)
print 'number_connected_components: %s' % ( pgncc )
ped_deg = pyp_network.get_node_degrees(pg)
ped_hist = pyp_network.get_node_degree_histograms(ped_deg)
print 'ped_hist:\t'
for k,v in ped_hist.iteritems():
print '\t\t', k, '\t', v
print 'nodes:\t\t', pg.order()
print 'edges:\t\t', pg.size()
density = pyp_network.graph_density(pg)
print 'density:\t', density
census = pyp_network.dyad_census(pg)
print 'max dyads:\t', ( ( pg.order()*(pg.order()-1) ) / 2 )
print 'census:\t\t', census
geodesic = pyp_network.mean_geodesic(pg)
print 'geodesic:\t', geodesic
centrality = pyp_network.mean_degree_centrality(pg)
print 'centrality:\t', centrality
centrality = pyp_network.mean_degree_centrality(pg,normalize=1)
print 'normed central:\t', centrality
close = pyp_network.get_closeness_centrality(pg)
mean_close = pyp_network.mean_value(close)
print 'mean closeness:\t', mean_close
#coi_by_year
#print ib
#for _e in example.pedigree:
#print _e.name, _e.fa
print example.kw['paper_size']
#print example.backmap
## Use with new_renumbering.ped.
#pyp_reports.pdf3GenPed([56,72], example)
## Use with horse.ped.
#pyp_reports.pdf3GenPed(["Pie's Joseph","Green's Dingo"], example)
#pyp_reports.pdf3GenPed("Green's Dingo", example,reportfile='greens_dingo_pedigree.pdf')
#pyp_reports.pdf3GenPed(example.namemap.keys(), example)
pyp_graphics.draw_pedigree(example, gfilename='greens_dingo_pedigree', \
gtitle="Green's Dingo pedigree", gname=1, gformat='ps', garrow=1)
#matings = {}
#for s in example.metadata.unique_sire_list:
# for d in example.metadata.unique_dam_list:
# matings[example.pedigree[s-1].name] = example.pedigree[d-1].name
#pyp_metrics.mating_coi_group(matings,example,names=1)
pg = pyp_network.ped_to_graph(example)
#print pg, pg.degree(), pg.nodes()
census = pyp_network.dyad_census(pg, debug=1)
print 'max dyads:\t', ((pg.order() * (pg.order() - 1)) / 2)
print 'census:\t\t', census
#coi_by_year
#print ib
#for _e in example.pedigree:
#print _e.name, _e.fa
print example.kw['paper_size']
#print example.backmap
## Use with new_renumbering.ped.
#pyp_reports.pdf3GenPed([56,72], example)
## Use with horse.ped.
#pyp_reports.pdf3GenPed(["Pie's Joseph","Green's Dingo"], example)
#pyp_reports.pdf3GenPed("Green's Dingo", example,reportfile='greens_dingo_pedigree.pdf')
#pyp_reports.pdf3GenPed(example.namemap.keys(), example)
pyp_graphics.draw_pedigree(example, gfilename='greens_dingo_pedigree', \
gtitle="Green's Dingo pedigree", gname=1, gformat='ps', garrow=1)
#matings = {}
#for s in example.metadata.unique_sire_list:
# for d in example.metadata.unique_dam_list:
# matings[example.pedigree[s-1].name] = example.pedigree[d-1].name
#pyp_metrics.mating_coi_group(matings,example,names=1)
pg = pyp_network.ped_to_graph(example)
#print pg, pg.degree(), pg.nodes()
census = pyp_network.dyad_census(pg,debug=1)
print 'max dyads:\t', ( ( pg.order()*(pg.order()-1) ) / 2 )
print 'census:\t\t', census