# 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)