Return outcomes as first dimension in activation matrix

Harmonizes return value dimension order with activation dict of dicts and weight
matrices.
So it's always weights[outcome][cue] and activations[outcome][event]

pyndl/activation.py

@@ -40,7 +40,7 @@ def activation(event_list, weights, number_of_threads=1, remove_duplicates=None,
     Returns
     -------
     activations : xarray.DataArray
-        with dimensions 'events' and 'outcomes'. Contains coords for the outcomes.
+        with dimensions 'outcomes' and 'events'. Contains coords for the outcomes.
         returned if weights is instance of xarray.DataArray
 
     or
@@ -84,7 +84,7 @@ def enforce_no_duplicates(cues):
                             coords={
                                 'outcomes': outcomes
                             },
-                            dims=('events', 'outcomes'))
+                            dims=('outcomes', 'events'))
     elif isinstance(weights, dict):
         assert number_of_threads == 1, "Estimating activations with multiprocessing is not implemented for dicts."
         activations = defaultdict(lambda: np.zeros(len(event_cues_list)))
@@ -118,7 +118,7 @@ def _run_mp_activation_matrix(event_index, cue_indices):
     Calculate activation for all outcomes while a event.
 
     """
-    activations[event_index, :] = weights[:, cue_indices].sum(axis=1)
+    activations[:, event_index] = weights[:, cue_indices].sum(axis=1)
 
 
 def _activation_matrix(indices_list, weights, number_of_threads):
@@ -139,16 +139,16 @@ def _activation_matrix(indices_list, weights, number_of_threads):
     Returns
     -------
     activation_matrix : numpy.array
-        estimated activations as matrix with shape (events, outcomes)
+        estimated activations as matrix with shape (outcomes, events)
 
     """
     assert number_of_threads >= 1, "Can't run with less than 1 thread"
 
-    activations_dim = (len(indices_list), weights.shape[0])
+    activations_dim = (weights.shape[0], len(indices_list))
     if number_of_threads == 1:
         activations = np.empty(activations_dim, dtype=np.float64)
         for row, event_cues in enumerate(indices_list):
-            activations[row, :] = weights[:, event_cues].sum(axis=1)
+            activations[:, row] = weights[:, event_cues].sum(axis=1)
         return activations
     else:
         shared_activations = mp.RawArray(ctypes.c_double, int(np.prod(activations_dim)))

tests/test_activation.py

@@ -46,7 +46,7 @@ def test_activation_matrix():
               (['c1', 'c3'], []),
               (['c2'], []),
               (['c1', 'c1'], [])]
-    reference_activations = np.array([[1, 1], [0, 1], [1, 0], [0, 1]])
+    reference_activations = np.array([[1, 1], [0, 1], [1, 0], [0, 1]]).T
 
     with pytest.raises(ValueError):
         activations = activation(events, weights, number_of_threads=1)
@@ -70,7 +70,7 @@ def test_ignore_missing_cues():
               (['c1', 'c3'], []),
               (['c2', 'c4'], []),
               (['c1', 'c1'], [])]
-    reference_activations = np.array([[1, 1], [0, 1], [1, 0], [0, 1]])
+    reference_activations = np.array([[1, 1], [0, 1], [1, 0], [0, 1]]).T
 
     with pytest.raises(KeyError):
         activations = activation(events, weights, number_of_threads=1,