corrected handling mask

Author: MSenden

code/python/cni_toolbox/IRM.py

@@ -204,7 +204,7 @@ def create_timecourses(self, FUN, xdata):
 
         self.tc_fft = fft(tc, axis = 0)
 
-    def mapping(self, data, threshold = 100, mask = None):
+    def mapping(self, data, threshold = 100, mask = []):
         '''
         identifies the best fitting timecourse for each voxel and
         returns a dictionary with keyes corresponding to the
@@ -230,7 +230,7 @@ def mapping(self, data, threshold = 100, mask = None):
         mean_signal = np.mean(data, axis = 0)
         data = zscore(data, axis = 0)
 
-        if mask.all()==None:
+        if np.size(mask)==0:
                 mask = mean_signal >= threshold
 
         mask = np.reshape(mask,self.n_total)

code/python/cni_toolbox/RRT.py

@@ -169,7 +169,7 @@ def set_design(self, X, convolve = False):
         self.X = zscore(X[0:self.n_samples,], axis = 0)
 
     def optimize_penalty(self, data, candidates, folds = 4,
-                        mask = None):
+                        mask =[]):
         '''
         performs k-fold cross-validation to find an optimal value
         for the penalty parameter.
@@ -189,7 +189,7 @@ def optimize_penalty(self, data, candidates, folds = 4,
 
         '''
 
-        if mask.all()==None:
+        if np.size(mask)==0:
             mask = np.ones(self.n_total).astype(bool)
         else:
             mask = np.reshape(mask,self.n_total)

code/python/cni_toolbox/pRF.py

@@ -287,7 +287,7 @@ def create_timecourses(self, max_radius = 10.0, n_xy = 30,
         sys.stdout.write("creating timecourses [%-20s] %d%%\n" % ('='*20, 100))
         self.tc_fft = fft(tc, axis = 0)
 
-    def mapping(self, data, threshold = 100, mask = None):
+    def mapping(self, data, threshold = 100, mask = []):
         '''
         identifies the best fitting timecourse for each voxel and
         returns a dictionary with the following keys
@@ -320,7 +320,7 @@ def mapping(self, data, threshold = 100, mask = None):
         mean_signal = np.mean(data, axis = 0)
         data = zscore(data, axis = 0)
 
-        if mask.all()==None:
+        if np.size(mask)==0:
                 mask = mean_signal >= threshold
 
         mask = np.reshape(mask,self.n_total)