print n subjects
@@ -76,9 +76,9 @@ class HistogramNormalisationLayer(Layer):
if len(mod_to_train) == 0:
print('Normalisation histogram reference models found')
return
- print('training normalisation histogram references for {}'.format(
- mod_to_train.keys()))
array_files = [subject.column(0) for subject in subjects]
+ print("training normalisation histogram references for {}, "
+ "using {} subjects".format(mod_to_train.keys(), len(array_files)))
trained_mapping = hs.create_mapping_from_multimod_arrayfiles(
array_files, mod_to_train, self.cutoff, self.mask_type)
### merging trained_mapping dict and self.mapping dict
