def contribution_percentage(result=None):
if result is None:
result = utils.load_result_pkl(connectivity_file)
contributions = {}
max_k = max(flatten([result[year]['k_components'].keys()
for year in result]))
for G in networks_by_year():
year = G.graph['year']
contributions[year] = {}
devs = set(n for n, d in G.nodes(data=True) if d['bipartite']==1)
total = float(sum(G.degree(devs, weight='weight').values()))
all_devs = float(len(devs))
contributions[year]['total'] = (all_devs, 1, total, 1, total/all_devs)
kcomps = result[year]['k_components']
for k in range(2, max_k + 1):
if k not in kcomps:
contributions[year][k] = (0, 0, 0, 0, 0)
else:
nodes_at_k = set.union(*[nodes[1] for nodes in kcomps[k]])
devs_at_k = nodes_at_k & devs
if not devs_at_k:
print("No developers at level {0} in year {1}".format(k, year))
continue
n_at_k = float(len(devs_at_k))
contrib_at_k = sum(G.degree(devs_at_k, weight='weight').values())
contributions[year][k] = (len(devs_at_k),
(len(devs_at_k) / all_devs) * 100,
contrib_at_k,
(contrib_at_k / total) * 100,
contrib_at_k / n_at_k)
return contributions
def get_developers_by_years(networks=None):
if networks is None:
networks = networks_by_year()
devs_by_year = {}
for G in networks:
year = G.graph['year']
devs = set(n for n, d in G.nodes(data=True) if d['bipartite']==1)
devs_by_year[year] = devs
return devs_by_year
def compute_tenure_by_year(last_year, networks=None):
if networks is None:
networks = networks_by_year()
result = {}
seen = set()
for G in networks:
year = G.graph['year']
if year > last_year:
break
for node in G:
if node not in seen:
result[node] = year
seen.add(node)
return dict((node, 1 + last_year - year ) for node, year in result.items())
def compute_k_components(nets=None, names=None):
datet = datetime.datetime.today()
date = datet.strftime("%Y%m%d%H%M")
if names is None:
names = default_years
if nets is None:
nets = networks_by_year()
result = {}
for name, G in zip(names, nets):
result[name] = {}
print("Analizing {}".format(name))
k_comp, k_num = k_components(G)
result[name]['k_components'] = k_comp
result[name]['k_num'] = k_num
fn = 'years' if name == 2014 else 'branches'
fname = "{0}/k_components_{1}_{2}.pkl".format(results_dir, fn, date)
utils.write_results_pkl(result, fname)
def summay_results(nets=None, years=None):
if years is None:
years = default_years
if nets is None:
nets = networks_by_year()
result = {}
previous_devs = None
for year, G in zip(years, nets):
result[year] = {}
devs = set(n for n, d in G.nodes(data=True) if d['bipartite']==1)
files = set(G) - devs
result[year]['guido_in'] = u'Guido van Rossum' in G
result[year]['density'] = bp.density(G, devs)
cc = sorted(nx.connected_components(G), key=len, reverse=True)
result[year]['cc'] = len(cc[0]) / float(G.order()) if cc else 0
bcc = sorted(nx.biconnected_components(G), key=len, reverse=True)
result[year]['bcc'] = len(bcc[0]) / float(G.order()) if bcc else 0
result[year]['devs'] = len(devs)
result[year]['files'] = len(files)
result[year]['py_files'] = len([f for f in files if f.endswith('.py')])
result[year]['c_files'] = len([f for f in files if f.endswith('.c')
or f.endswith('.h')])
result[year]['doc_files'] = len([f for f in files if f.lower().endswith('.txt')
or f.endswith('.rst')
or f.endswith('.tex')])
result[year]['weight'] = sum(nx.degree(G, devs, weight='weight').values())
result[year]['added'] = sum(nx.degree(G, devs, weight='added').values())
result[year]['deleted'] = sum(nx.degree(G, devs, weight='deleted').values())
result[year]['edits'] = sum(nx.degree(G, devs, weight='edits').values())
result[year]['sq_clustering'] = (sum(nx.square_clustering(G, devs).values())
/ float(len(devs)))
if previous_devs is None:
# First year
result[year]['new_devs'] = len(devs)
result[year]['continue_devs'] = 0
result[year]['lost_devs'] = 0
else:
result[year]['new_devs'] = len(devs - previous_devs)
result[year]['continue_devs'] = len(devs & previous_devs)
result[year]['lost_devs'] = len(previous_devs - devs)
previous_devs = devs
return result
def build_mobility_network(connectivity):
H = nx.DiGraph()
networks = networks_by_year()
skip = next(networks)
years = range(1992, 2015)
for year, G in zip(years, networks):
devs = {n for n, d in G.nodes(data=True) if d['bipartite']==1}
kcomps = connectivity[year]['k_components']
max_k = max(kcomps)
devs = set(n for n, d in G.nodes(data=True) if d['bipartite']==1)
these_devs = set(u for u in set.union(*[v[1] for v in kcomps[max_k]]) if u in devs)
H.add_node(year, devs=these_devs, number_devs=len(these_devs), total_devs=len(devs))
for year in years:
seen = set()
for future_year in range(year+1, 2015):
common = H.node[year]['devs'] & H.node[future_year]['devs']
if common:
to_add = common - seen
if to_add:
H.add_edge(year, future_year, devs=to_add, weight=len(to_add))
seen.update(to_add)
seen = set()
for year in years:
devs = H.node[year]['devs']
if year == max(years):
future_devs = set()
else:
future_devs = set.union(*[H.node[n]['devs'] for n in range(year+1, 2015)])
out_devs = devs - future_devs
if out_devs:
if year != max(years):
H.add_node("%s-out" % year, devs=out_devs, number_devs=len(out_devs))
H.add_edge(year, "%s-out" % year, devs=out_devs, weight=len(out_devs))
new_devs = devs - seen
if new_devs:
H.add_node("%s-in" % year, devs=new_devs, number_devs=len(new_devs))
H.add_edge("%s-in" % year, year, devs=new_devs, weight=len(new_devs))
seen.update(devs)
return H
def compute_centrality(nets=None, names=None):
datet = datetime.datetime.today()
date = datet.strftime("%Y%m%d%H%M")
if names is None:
names = default_years
if nets is None:
nets = networks_by_year()
result = {}
for name, G in zip(names, nets):
result[name] = {}
print("computing centrality for {}".format(name))
devs = set(n for n, d in G.nodes(data=True) if d['bipartite']==1)
result[name]['deg'] = bp.degree_centrality(G, devs)
try:
result[name]['bet'] = bp.betweenness_centrality(G, devs)
except ZeroDivisionError:
result[name]['bet'] = dict()
result[name]['clos'] = bp.closeness_centrality(G, devs)
result[name]['ev'] = nx.eigenvector_centrality_numpy(G)
fn = 'years' if name == 2014 else 'branches'
fname = "{0}/bipartite_centrality_{1}_{2}.pkl".format(results_dir, fn, date)
utils.write_results_pkl(result, fname)
def compute_layouts(nets=None, names=None):
datet = datetime.datetime.today()
date = datet.strftime("%Y%m%d%H%M")
if names is None:
names = default_years
if nets is None:
nets = networks_by_year()
result = {}
for name, G in zip(names, nets):
# This is likely a pydot bug, nodes cannot have a 'name' attribute
for node, data in G.node.items():
if 'name' in data:
del data['name']
result[name] = {}
print("Analizing {}".format(name))
result[name]['pos_kk'] = nx.graphviz_layout(G, prog='neato')
try:
result[name]['pos_fdp'] = nx.graphviz_layout(G, prog='fdp')
except:
result[name]['pos_fdp'] = result[name]['pos_kk']
fn = 'years' if name == 2014 else 'branches'
fname = "{0}/layouts_{1}_{2}.pkl".format(results_dir, fn, date)
utils.write_results_pkl(result, fname)
def main():
parser = OptionParser()
parser.add_option('-c','--conn_years',
action='store_true',
dest='connectivity_years',
help='Connectivity analysis by year',
default=False)
parser.add_option('-d','--conn_branches',
action='store_true',
dest='connectivity_branches',
help='Connectivity analysis by branch',
default=False)
parser.add_option('-e','--cent_years',
action='store_true',
dest='centrality_years',
help='Centrality analysis by year',
default=False)
parser.add_option('-f','--cent_branches',
action='store_true',
dest='centrality_branches',
help='Centrality analysis by branch',
default=False)
parser.add_option('-l','--layouts_years',
action='store_true',
dest='layouts_years',
help='Compute layouts by year',
default=False)
parser.add_option('-m','--layouts_branches',
action='store_true',
dest='layouts_branches',
help='Compute layouts by branch',
default=False)
parser.add_option('-s','--survival',
action='store_true',
dest='survival',
help='Build and save survival Data Frame',
default=False)
parser.add_option('-z','--contrib',
action='store_true',
dest='contrib',
help='Build and save contribution and PEP authoring DF',
default=False)
options, args = parser.parse_args()
if options.connectivity_years:
compute_k_components(nets=networks_by_year(), names=default_years)
if options.connectivity_branches:
compute_k_components(nets=networks_by_branches(), names=default_branches)
if options.centrality_years:
compute_centrality(nets=networks_by_year(), names=default_years)
if options.centrality_branches:
compute_centrality(nets=networks_by_branches(), names=default_branches)
if options.layouts_years:
compute_layouts(nets=networks_by_year(), names=default_years)
if options.layouts_branches:
compute_layouts(nets=networks_by_branches(), names=default_branches)
if options.survival:
build_survival_data_frame(fname=survival_file)
if options.contrib:
write_developer_contrib_df(fname='data/developer_contributions_df.csv')
def write_developer_contrib_df(fname='data/developer_contributions_df.csv'):
ids = utils.UniqueIdGenerator()
peps = [pep for pep in get_peps() if pep.created is not None]
connectivity = utils.load_result_pkl(connectivity_file)
centrality = utils.load_result_pkl(centrality_file)
networks_gen = networks_by_year()
skip = next(networks_gen)
networks = list(networks_gen)
years = range(1992, 2015)
devs_by_year = get_developers_by_years(networks=networks)
with open(fname, 'wb') as f:
out = csv.writer(f)
out.writerow([
'id', 'year', 'dev', 'has_written_peps', 'has_written_acc_peps',
'is_delegate', 'peps_this_year', 'total_peps',
'accepted_peps_year', 'total_accepted_peps',
'degree', 'contributions_sc', 'contributions_edits',
'contributions_added', 'contributions_deleted',
'collaborators', 'knum', 'aknum', 'top', 'top2',
'tenure', 'betweenness', 'closeness', 'degree_cent',
'file_mean_degree', 'clus_sq', 'clus_dot', 'clus_red',
])
for year, G in zip(years, networks):
print("Analyzing {}".format(G.name))
bdfl_delegates = get_delegates_by_year(year, peps=peps)
peps_this_year = peps_by_developer_that_year(year, peps=peps)
peps_until_year = peps_by_developer_until_year(year, peps=peps)
acc_peps_this_year = accepted_peps_by_developer_that_year(year, peps=peps)
acc_peps_until_year = accepted_peps_by_developer_until_year(year, peps=peps)
top = get_developers_top_connectivity_by_year(G, year,
connectivity=connectivity)
top2 = get_developers_top_connectivity_by_year_new(G, year,
connectivity=connectivity)
devs = devs_by_year[year]
tenure = compute_tenure_by_year(year, networks=networks)
k_num = connectivity[year]['k_num']
bet = normalize(centrality[year]['bet'])
clos = normalize(centrality[year]['clos'])
deg = normalize(centrality[year]['deg'])
clus_sq = nx.square_clustering(G)
clus_dot = bp.clustering(G)
clus_red = bp.node_redundancy(G)
for dev in devs:
out.writerow([
ids[dev],
year,
dev.encode('utf8'),
1 if dev in peps_until_year else 0, # developer has written at least a pep
1 if dev in acc_peps_until_year else 0, # developer has written at least an acc. pep
1 if dev in bdfl_delegates else 0, # developer has been BDFL delegate
peps_this_year[dev] if dev in peps_this_year else 0, # peps written this year
peps_until_year[dev] if dev in peps_until_year else 0, # peps written until this year
acc_peps_this_year[dev] if dev in acc_peps_this_year else 0, # peps acc. this year
acc_peps_until_year[dev] if dev in acc_peps_until_year else 0, # total peps acc.
len(G[dev]), #G.degree(dev, weight=None),
G.degree(dev, weight='weight'), # lines of code added plus deleted
G.degree(dev, weight='edits'), # number files edit
G.degree(dev, weight='added'), # lines of code added
G.degree(dev, weight='deleted'), # lines of code removed
second_order_nbrs(G, dev), # second order neighbors
k_num[dev][0], # k-component number
k_num[dev][1], # Average k-component number
1 if dev in top else 0, # top connectivity level
1 if dev in top2 else 0, # top 2 connectivity level
tenure[dev],
bet[dev],
clos[dev],
deg[dev],
sum(len(G[n]) for n in G[dev]) / float(len(G[dev])),
clus_sq[dev],
clus_dot[dev],
clus_red[dev],
])
def build_survival_data_frame(fname=survival_file):
nan = float('nan')
ids = utils.UniqueIdGenerator()
connectivity = utils.load_result_pkl(connectivity_file)
centrality = utils.load_result_pkl(centrality_file)
peps = [pep for pep in get_peps() if pep.created is not None]
networks = list(networks_by_year())
devs = get_developers_by_years(networks=networks)
skip = networks.pop(0) # skip 1991
G_start = networks.pop(0) # start with 1992
devs_start = set(n for n, d in G_start.nodes(data=True) if d['bipartite']==1)
years = range(1993, 2015)
with open(fname, 'wb') as f:
out = csv.writer(f)
out.writerow([
'id', 'dev', 'period', 'rstart', 'rstop', 'status',
'has_written_peps', 'has_written_acc_peps',
'peps_this_year', 'total_peps',
'accepted_peps_year', 'total_accepted_peps',
'biconnected', 'top', 'tenure', 'colaborators',
'knum', 'aknum', 'clus_sq', 'clus_dot', 'clus_red',
'degree', 'contributions', 'dcentrality',
'betweenness', 'closeness',
])
previous_devs = devs_start
previous_year = 1992
previous_G = G_start
for i, (year, G) in enumerate(zip(years, networks)):
print("processing year {}".format(previous_year))
clus_sq = nx.square_clustering(previous_G)
these_devs = devs[year]
remaining_devs = get_all_remaining_devs(devs, years[i:])
top_devs = get_developers_top_connectivity(
connectivity[previous_year]['k_components'],
previous_devs)
tenure = compute_tenure_by_year(previous_year)
bet = normalize(centrality[previous_year]['bet'])
clos = normalize(centrality[previous_year]['bet'])
deg = normalize(centrality[previous_year]['deg'])
clus_sq = nx.square_clustering(previous_G)
clus_dot = bp.clustering(previous_G)
clus_red = bp.node_redundancy(previous_G)
peps_this_year = peps_by_developer_that_year(previous_year, peps=peps)
peps_until_year = peps_by_developer_until_year(previous_year, peps=peps)
acc_peps_this_year = accepted_peps_by_developer_that_year(previous_year, peps=peps)
acc_peps_until_year = accepted_peps_by_developer_until_year(previous_year, peps=peps)
for dev in previous_devs:
out.writerow([
ids[dev], # developer numerical ID
dev.encode('utf8'), # developer name
i + 1, # period
i, # start
i + 1, # stop
0 if dev in remaining_devs else 1, # status (censored)
1 if dev in peps_until_year else 0, # developer has written at least a pep
1 if dev in acc_peps_until_year else 0, # developer has written at least an acc. pep
peps_this_year[dev] if dev in peps_this_year else 0, # peps written this year
peps_until_year[dev] if dev in peps_until_year else 0, # peps written until this year
acc_peps_this_year[dev] if dev in acc_peps_this_year else 0, # peps acc. this year
acc_peps_until_year[dev] if dev in acc_peps_until_year else 0, # total peps acc.
0 if connectivity[previous_year]['k_num'][dev][0] < 2 else 1,#biconnected
0 if dev not in top_devs else 1, # member of the top connectivity level
tenure[dev], # tenure in years
second_order_nbrs(previous_G, dev), # collaborators
connectivity[previous_year]['k_num'].get(dev, (nan,nan))[0], # knum
connectivity[previous_year]['k_num'].get(dev, (nan,nan))[1], # aknum
clus_sq.get(dev, nan),
clus_dot.get(dev, nan),
clus_red.get(dev, nan),
previous_G.degree(dev), # degree
previous_G.degree(dev, weight='weight'), # contributions
deg.get(dev, nan),
bet.get(dev, nan),
clos.get(dev, nan),
])
previous_devs = these_devs
previous_year = year
previous_G = G
