rank_transform() can deal with unknown dissim measure

Author: Jasper van den Bosch

src/rsatoolbox/rdm/transform.py

@@ -2,14 +2,14 @@
 # -*- coding: utf-8 -*-
 """ transforms, which can be applied to RDMs
 """
-
+from __future__ import annotations
 from copy import deepcopy
 import numpy as np
 from scipy.stats import rankdata
 from .rdms import RDMs
 
 
-def rank_transform(rdms, method='average'):
+def rank_transform(rdms: RDMs, method='average'):
     """ applies a rank_transform and generates a new RDMs object
     This assigns a rank to each dissimilarity estimate in the RDM,
     deals with rank ties and saves ranks as new dissimilarity estimates.
@@ -30,9 +30,9 @@ def rank_transform(rdms, method='average'):
     dissimilarities = rdms.get_vectors()
     dissimilarities = np.array([rankdata(dissimilarities[i], method=method)
                                 for i in range(rdms.n_rdm)])
-    measure = rdms.dissimilarity_measure
-    if not measure[-7:] == '(ranks)':
-        measure = measure + ' (ranks)'
+    measure = rdms.dissimilarity_measure or ''
+    if '(ranks)' not in measure:
+        measure = (measure + ' (ranks)').strip()
     rdms_new = RDMs(dissimilarities,
                     dissimilarity_measure=measure,
                     descriptors=deepcopy(rdms.descriptors),
@@ -103,8 +103,9 @@ def transform(rdms, fun):
     """
     dissimilarities = rdms.get_vectors()
     dissimilarities = fun(dissimilarities)
+    meas = 'transformed ' + rdms.dissimilarity_measure
     rdms_new = RDMs(dissimilarities,
-                    dissimilarity_measure='transformed ' + rdms.dissimilarity_measure,
+                    dissimilarity_measure=meas,
                     descriptors=deepcopy(rdms.descriptors),
                     rdm_descriptors=deepcopy(rdms.rdm_descriptors),
                     pattern_descriptors=deepcopy(rdms.pattern_descriptors))

tests/test_rdm.py

@@ -246,6 +246,12 @@ def test_rank_transform(self):
         self.assertEqual(rank_rdm.n_cond, rdms.n_cond)
         self.assertEqual(rank_rdm.dissimilarity_measure, 'Euclidean (ranks)')
 
+    def test_rank_transform_unknown_measure(self):
+        from rsatoolbox.rdm import rank_transform
+        rdms = rsr.RDMs(dissimilarities=np.zeros((8, 10)))
+        rank_rdm = rank_transform(rdms)
+        self.assertEqual(rank_rdm.dissimilarity_measure, '(ranks)')
+
     def test_sqrt_transform(self):
         from rsatoolbox.rdm import sqrt_transform
         dis = np.zeros((8, 10))
@@ -463,17 +469,19 @@ def test_copy(self):
             )
         )
         copy = orig.copy()
-        ## We don't want a reference:
+        # We don't want a reference:
         self.assertIsNot(copy, orig)
         self.assertIsNot(copy.dissimilarities, orig.dissimilarities)
         self.assertIsNot(
             copy.pattern_descriptors.get('order'),
             orig.pattern_descriptors.get('order')
         )
-        ## But check that attributes are equal
+        # But check that attributes are equal
         assert_array_equal(copy.dissimilarities, orig.dissimilarities)
-        self.assertEqual(copy.dissimilarity_measure,
-            orig.dissimilarity_measure)
+        self.assertEqual(
+            copy.dissimilarity_measure,
+            orig.dissimilarity_measure
+        )
         self.assertEqual(copy.descriptors, orig.descriptors)
         self.assertEqual(copy.rdm_descriptors, orig.rdm_descriptors)
         assert_array_equal(