def plot_rc(g, controls, ax, **kwargs):
rc = richclub.rich_club_coefficient(g, **kwargs)
rich_club_coefficient = vectorize(richclub.rich_club_coefficient)
rc_controls = []
for c in controls:
rc_controls.append(richclub.rich_club_coefficient(c, **kwargs))
rc_controls = array(rc_controls)
control_mean = mean(rc_controls, axis=0)
if 'rank' in kwargs:
x = kwargs['rank']
else:
if 'mode' in kwargs:
mode = kwargs['mode']
else:
mode = 'percentile'
if mode == 'percentile':
x = arange(10.0, 100.0, 10.0)
else:
x = unique(g.degree())
from scipy.stats import scoreatpercentile
upper95 = array(
[scoreatpercentile(rc_controls[:, i], 95) for i in range(len(x))])
lower95 = array(
[scoreatpercentile(rc_controls[:, i], 5) for i in range(len(x))])
ax.plot(x, rc, color='r')
ax.plot(x, control_mean, color='b')
ax.fill_between(x,
lower95,
upper95,
where=(lower95 > 0) & (upper95 > 0),
alpha=.1,
color='b')
ax.set_yscale('log')
#ax.set_xscale('log')
rc_norm = rc / control_mean
ax2 = ax.twinx()
ax2.plot(x, rc_norm, linewidth=4, color='k')
ax2.plot(ax.get_xlim(), (1, 1), linestyle='--', color='k')
ax2.set_ylim(bottom=0)
if ax2.get_ylim()[1] < 2:
ax2.set_ylim(top=2)
ax2.set_ylabel(r"$\phi_{norm}$, normalized rich club coefficient")
ax2.fill_between(x,
1,
rc_norm,
where=(rc > upper95) | (rc < lower95),
alpha=.05,
color='g')
return ax, rc, lower95, upper95
def test_rich_club_coefficient(self):
print """Testing rich_club_coefficient"""
self.assertTrue(True)
ns = [
60,
]
ms = [60, 10, 600]
directeds = [True, False]
highests = [True, False]
scoress = [None, 'r']
test_cases = [(n, m, directed, highest, score) for n in ns for m in ms
for directed in directeds for highest in highests
for score in scoress]
rc_properties = [
'intensity_P_wm', 'intensity_L_wm', 'intensity_P_local',
'intensity_L_local', 'intensity_L_global', 'intensity_P_global'
]
for n, m, directed, highest, score in test_cases:
g = Graph.Erdos_Renyi(n=n, m=m, directed=directed)
# print "%i nodes, %i links, directed: %i, highest: %i, "\
# "score: %s"\
for p in rc_properties:
print p
rc = richclub.rich_club_coefficient(g, club_property=p)
self.assertTrue(True)
def plot_rc(g, controls, ax, **kwargs):
rc = richclub.rich_club_coefficient(g, **kwargs)
rich_club_coefficient = vectorize(richclub.rich_club_coefficient)
rc_controls = []
for c in controls:
rc_controls.append(richclub.rich_club_coefficient(c, **kwargs))
rc_controls = array(rc_controls)
control_mean = mean(rc_controls, axis=0)
if 'rank' in kwargs:
x = kwargs['rank']
else:
if 'mode' in kwargs:
mode = kwargs['mode']
else:
mode = 'percentile'
if mode=='percentile':
x = arange(10.0,100.0, 10.0)
else:
x = unique(g.degree())
from scipy.stats import scoreatpercentile
upper95 = array([scoreatpercentile(rc_controls[:,i], 95) for i in range(len(x))])
lower95 = array([scoreatpercentile(rc_controls[:,i], 5) for i in range(len(x))])
ax.plot(x, rc, color='r')
ax.plot(x, control_mean, color='b')
ax.fill_between(x, lower95, upper95, where=(lower95>0)&(upper95>0), alpha=.1, color='b')
ax.set_yscale('log')
#ax.set_xscale('log')
rc_norm = rc/control_mean
ax2 = ax.twinx()
ax2.plot(x, rc_norm, linewidth=4, color='k')
ax2.plot(ax.get_xlim(), (1,1), linestyle='--', color='k')
ax2.set_ylim(bottom=0)
if ax2.get_ylim()[1]<2:
ax2.set_ylim(top=2)
ax2.set_ylabel(r"$\phi_{norm}$, normalized rich club coefficient")
ax2.fill_between(x, 1,rc_norm, where=(rc>upper95) | (rc<lower95), alpha=.05, color='g')
return ax, rc, lower95, upper95
def test_rich_club_coefficient(self):
print """Testing rich_club_coefficient"""
self.assertTrue(True)
ns = [60]
ms = [60, 10, 600]
directeds = [True, False]
highests = [True, False]
scoress = [None, "r"]
test_cases = [
(n, m, directed, highest, score)
for n in ns
for m in ms
for directed in directeds
for highest in highests
for score in scoress
]
rc_properties = [
"intensity_P_wm",
"intensity_L_wm",
"intensity_P_local",
"intensity_L_local",
"intensity_L_global",
"intensity_P_global",
]
for n, m, directed, highest, score in test_cases:
g = Graph.Erdos_Renyi(n=n, m=m, directed=directed)
# print "%i nodes, %i links, directed: %i, highest: %i, "\
# "score: %s"\
for p in rc_properties:
print p
rc = richclub.rich_club_coefficient(g, club_property=p)
self.assertTrue(True)
def calculate_rc_codelength_in(g):
return richclub.rich_club_coefficient(
g, richness='in_strength',
club_property='codelength')
def calculate_rc_q_infomap_in(g):
return richclub.rich_club_coefficient(
g, richness='in_strength',
club_property='q_infomap')
def calculate_rc_n_infomap_out(g):
return richclub.rich_club_coefficient(
g, richness='out_strength',
club_property='n_infomap')
def calculate_rc_clustering_out(g):
return richclub.rich_club_coefficient(
g, richness='out_strength',
club_property='clustering')
def calculate_rc_iNpwmg_in(g):
return richclub.rich_club_coefficient(
g, richness='in_strength',
club_property='intensity_topNpweightmax_global')
def calculate_rc_iNpg_out(g):
return richclub.rich_club_coefficient(
g, richness='out_strength',
club_property='intensity_topNp_global')
def calculate_rc_itg_in(g):
return richclub.rich_club_coefficient(
g, richness='in_strength',
club_property='intensity_total_global')
def calculate_rc_iNpwml_out(g):
return richclub.rich_club_coefficient(
g, richness='out_strength',
club_property='intensity_topNpweightmax_local')
def calculate_rc_iNpl_in(g):
return richclub.rich_club_coefficient(
g, richness='in_strength',
club_property='intensity_topNp_local')
def calculate_rc_itl_out(g):
return richclub.rich_club_coefficient(
g, richness='out_strength',
club_property='intensity_total_local')