Auto stash before merge of "mtnn" and "origin/mtnn"

flake

Author: mayofaulkner

brainbox/io/one.py

@@ -704,7 +704,7 @@ def load_wheel_reaction_times(eid, one=None):
 
 
 def load_trials_df(eid, one=None, maxlen=None, t_before=0., t_after=0., ret_wheel=False,
-                   ret_abswheel=False, wheel_binsize=0.02, addtl_types=[], 
+                   ret_abswheel=False, wheel_binsize=0.02, addtl_types=[],
                    align_event='stimOn_times', keeptrials=None):
     """
     Generate a pandas dataframe of per-trial timing information about a given session.

brainbox/modeling/neural_model.py

@@ -107,7 +107,7 @@ def __init__(self, design_matrix, spk_times, spk_clu,
             spkarrs.append(arr.T)
         y = np.vstack(spkarrs)
         if hasattr(self.design, 'dm'):
-            assert y.shape[0] == self.design.dm.shape[0], f"Oh shit. Indexing error."
+            assert y.shape[0] == self.design.dm.shape[0], "Oh shit. Indexing error."
         self.binnedspikes = y
 
     def combine_weights(self):

brainbox/modeling/utils.py

@@ -38,7 +38,7 @@ def neglog(weights, x, y):
 
 
 class SequentialSelector:
-    def __init__(self, model, n_features_to_select=None, 
+    def __init__(self, model, n_features_to_select=None,
                  direction='forward', scoring=None,
                  train=None, test=None):
         """
@@ -190,4 +190,4 @@ def _get_best_new_feature(self, mask, cells, full_scores=False):
         if full_scores:
             return maxind, trainmax, testmax, trainscores, testscores
         else:
-            return maxind, trainmax, testmax
+            return maxind, trainmax, testmax