# Generate simple result figure (using pathomx libs) from pathomx.figures import histogram View = histogram(input_data, styles=styles)
for n, a in enumerate(args[:labels_l]):
if a:
ol.append(str(a))
elif n in subs_ix:
ol.append(str( args[subs_ix[n]]))
la.append('-'.join(ol))
df['UniqueLabel'] = la
df.set_index(['UniqueLabel'] + other_indices, inplace=True)
df = df.filter(regex='^([MLH]/[MLH] \d\w)$', axis=1)
# Add the reverse ratios
for a,b in [('H','L'), ('H','M'), ('M','L')]:
ds = df.filter(regex='%s/%s' % (a,b) )
ds.columns = pd.Index([l.replace('%s/%s' % (a,b),'%s/%s' % (b,a)) for l in ds.columns.values])
df = pd.concat([df, 1.0/ ds], axis=1)
df = df.T
classes = [c[:3] for c in df.index.values]
df.index = pd.MultiIndex.from_tuples(zip(df.index.values,classes), names=['Label', 'Class'])
output_data = df
df = None
ds = None
from pathomx.figures import histogram
Histogram = histogram(output_data)
