minor corrections

Author: Bertrand THIRION

ibc_public/utils_contrasts.py

@@ -628,7 +628,10 @@ def faces_aomic(design_matrix_columns):
                       'male-female',
                       'female-male',
                       'mediterranean-european',
-                      'european-mediterranean']
+                      'european-mediterranean',
+                      'positive-neutral',
+                      'negative-neutral',
+                      'positive-negative',]
     if design_matrix_columns is None:
         return dict([(name, []) for name in contrast_names])
     con = _elementary_contrasts(design_matrix_columns)
@@ -656,6 +659,9 @@ def faces_aomic(design_matrix_columns):
                  'male-female':male-female,
                  'european-mediterranean': european - mediterranean,
                  'mediterranean-european': mediterranean - european,
+                 'positive-neutral': joy + pride - 2 * neutral,
+                 'negative-neutral': contempt + anger - 2 * neutral,
+                 'positive-negative': joy + pride - (contempt + anger),
     }
     assert((sorted(contrasts.keys()) == sorted(contrast_names)))
     _append_derivative_contrast(design_matrix_columns, contrasts)

ibc_public/utils_paradigm.py

@@ -435,9 +435,9 @@ def post_process(df, paradigm_id):
     if paradigm_id == 'OptimismBias':
         df.drop(df[df.trial_type == 'start'].index, inplace=True)
     if paradigm_id == 'HarririAomic':
-        df.drop(df[df.trial_type.isin(['ttl', 'iti'])].index, 0, inplace=True)
+        df.drop(df[df.trial_type.isin(['ttl', 'iti'])].index, inplace=True)
     if paradigm_id == 'StroopAomic':
-        df.drop(df[df.trial_type.isin(['ttl', 'iti'])].index, 0, inplace=True)
+        df.drop(df[df.trial_type.isin(['ttl', 'iti'])].index, inplace=True)
         # todo: distinguish incorrect vs female
     if paradigm_id == 'Emotion':
         to_drop  = ['block_divider']

ibc_public/utils_pipeline.py

@@ -186,7 +186,7 @@ def run_glm(dmtx, contrasts, fmri_data, mask_img, subject_dic,
         for map_type in ['z_score', 'stat', 'effect_size', 'effect_variance']:
             stat_map = fmri_glm.compute_contrast(
                 contrast_val, output_type=map_type)
-            if map_type == 'effect_size' and contrast_val.ndim == 1:
+            if len(stat_map.shape) == 4 and contrast_val.ndim == 1:
                 stat_map = index_img(stat_map, 0)
             map_dir = os.path.join(
                 subject_session_output_dir, '%s_maps' % map_type)