Merge pull request #360 from cosanlab/http

Minor bug fixes

Author: ljchang

nltools/data/adjacency.py

@@ -908,6 +908,9 @@ def isc(self, n_bootstraps=5000, metric='median', ci_percentile=95, exclude_self
         if metric not in ['mean', 'median']:
             raise ValueError("metric must be ['mean', 'median']")
 
+        if not self.is_single_matrix:
+            raise NotImplementedError('Currently we can only compute ISC values on single Adjacency matrices')
+        
         if metric =='mean':
             isc = np.tanh(self.r_to_z().mean())
         elif metric =='median':

nltools/data/brain_data.py

@@ -112,7 +112,7 @@ def __init__(self, data=None, Y=None, X=None, mask=None, output_file=None,
 
         if data is not None:
             if isinstance(data, six.string_types):
-                if 'http://' in data:
+                if 'http://' in data or 'https://' in data:
                     from nltools.datasets import download_nifti
                     tmp_dir = os.path.join(tempfile.gettempdir(),
                                            str(os.times()[-1]))

nltools/mask.py

@@ -213,7 +213,11 @@ def roi_to_brain(data, mask_x):
         raise ValueError('Data must have the same number of rows as mask has ROIs.')
 
     if isinstance(data, pd.Series):
-        return Brain_Data([mask_x[x]*data[x] for x in data.keys()]).sum()
+        out = mask_x[0].copy()
+        out.data = np.zeros(out.data.shape)
+        for roi in range(len(mask_x)):
+            out.data[np.where(mask_x.data[roi,:])] = data[roi]
+        return out
     else:
         out = mask_x.copy()
         out.data = np.ones((data.shape[1], out.data.shape[1]))