def stacked_L(r, c, g, color='b', linestyle='solid', label=None, figax=None):
if not isinstance(c, np.ndarray):
c = np.array(c)
if not isinstance(g, np.ndarray):
g = np.array(g)
if figax==None:
fig = plt.figure(figsize=default_figsize)
axh = fig.add_axes(stacked_axhpos)
axc = fig.add_axes(stacked_axcpos)
else:
fig, axh, axc = figax
truth = c > 0
if label:
axh.plot( r[truth], g[truth]/c[truth], color=color, linestyle=linestyle, label=label)
else:
axh.plot( r[truth], g[truth]/c[truth], color=color, linestyle=linestyle)
G = idq.kde_to_ckde( g )
C = idq.kde_to_ckde( c )
Truth = C > 0
if label:
axc.plot( r[Truth], G[Truth]/C[Truth], color=color, linestyle=linestyle, label=label)
else:
axc.plot( r[Truth], G[Truth]/C[Truth], color=color, linestyle=linestyle)
axh.grid(True, which='both')
axc.grid(True, which='both')
axh.set_xlim(xmin=0, xmax=1)
axc.set_xlim(xmin=0, xmax=1)
axh.set_yscale('log')
axc.set_yscale('log')
if np.any(truth):
axh.set_ylim(ymin=0, ymax=max(axh.get_ylim()[1], max(g[truth]/c[truth])*1.1))
if np.any(Truth):
axc.set_ylim(ymin=0, ymax=max(axc.get_ylim()[1], max(G[Truth]/C[Truth])*1.1))
plt.setp(axc.get_xticklabels(), visible=False)
axc.set_ylabel('cdf(g)/cdf(c)')
axh.set_xlabel('rank')
axh.set_ylabel('pdf(g)/pdf(c)')
return fig, axh, axc
def stacked_kde(r, s, color='b', linestyle='solid', label=None, figax=None):
if figax==None:
fig = plt.figure(figsize=default_figsize)
axh = fig.add_axes(stacked_axhpos)
axc = fig.add_axes(stacked_axcpos)
else:
fig, axh, axc = figax
axh.plot( r, s, color=color, linestyle=linestyle, label=label)
c = idq.kde_to_ckde( s )
axc.plot( r, c, color=color, linestyle=linestyle, label=label)
axh.grid(True, which="both")
axc.grid(True, which="both")
axh.set_xlim(xmin=0, xmax=1)
axc.set_xlim(xmin=0, xmax=1)
axh.set_ylim(ymin=0, ymax=max(axh.get_ylim()[1], max(s)*1.1) )
axc.set_ylim(ymin=0, ymax=1.0)
plt.setp(axc.get_xticklabels(), visible=False)
axc.set_ylabel('cumulative fraction of events')
axh.set_xlabel('rank')
axh.set_ylabel('p(rank)')
return fig, axh, axc
kde_range = idq.best_range(gps, kde_ranges.keys())
kde_cln_name, kde_gch_name = kde_ranges[kde_range]
kde_cln_file = event.gzopen(kde_cln_name, 'r')
kde, kde_cln = numpy.load(kde_cln_file)
kde_cln_file.close()
kde_gch_file = event.gzopen(kde_gch_name, 'r')
_, kde_gch = numpy.load(kde_gch_file)
kde_gch_file.close()
### store kdes
kdeD[classifier]['kde'] = kde
kdeD[classifier]['kde_cln'] = kde_cln
kdeD[classifier]['ckde_cln'] = idq.kde_to_ckde(kde_cln)
kdeD[classifier]['kde_gch'] = kde_gch
kdeD[classifier]['ckde_gch'] = idq.kde_to_ckde(kde_gch)
kdeD[classifier]['kde_cln_name'] = kde_cln_name
kdeD[classifier]['kde_gch_name'] = kde_gch_name
kdeD[classifier]['kde_range'] = kde_range
n_samples = len(
outputs[classifier]['rank']
) ### assumes all outputs have the same length, which they should
for combiner in combiners:
flavor = combinersD[combiner][
kde_range = idq.best_range( gps, kde_ranges.keys() )
kde_cln_name, kde_gch_name = kde_ranges[kde_range]
kde_cln_file = event.gzopen(kde_cln_name, 'r')
kde, kde_cln = numpy.load(kde_cln_file)
kde_cln_file.close()
kde_gch_file = event.gzopen(kde_gch_name, 'r')
_ , kde_gch = numpy.load(kde_gch_file)
kde_gch_file.close()
### store kdes
kdeD[classifier]['kde'] = kde
kdeD[classifier]['kde_cln'] = kde_cln
kdeD[classifier]['ckde_cln'] = idq.kde_to_ckde( kde_cln )
kdeD[classifier]['kde_gch'] = kde_gch
kdeD[classifier]['ckde_gch'] = idq.kde_to_ckde( kde_gch )
kdeD[classifier]['kde_cln_name'] = kde_cln_name
kdeD[classifier]['kde_gch_name'] = kde_gch_name
kdeD[classifier]['kde_range'] = kde_range
n_samples = len(outputs[classifier]['rank']) ### assumes all outputs have the same length, which they should
for combiner in combiners:
flavor = combinersD[combiner]['flavor'] ### should always be None, but we pull the reference nonetheless
these_classifiers = combinersD[combiner]['classifiers'] ### the classifiers this combiner combines
