BF: endian fixes for streamlines code

Getting errors reading and writing on big-endian machines:

http://nipy.bic.berkeley.edu/builders/nibabel-py2.7-ppc/builds/295/steps/shell_5/logs/stdio

Closes #471

Author: matthew-brett

nibabel/streamlines/trk.py

@@ -227,7 +227,8 @@ def decode_value_from_name(encoded_name):
 
 def create_empty_header():
     """ Return an empty compliant TRK header. """
-    header = np.zeros(1, dtype=header_2_dtype)
+    # Enforce little-endian header
+    header = np.zeros(1, dtype=header_2_dtype).newbyteorder('<')
 
     # Default values
     header[Field.MAGIC_NUMBER] = TrkFile.MAGIC_NUMBER
@@ -499,20 +500,20 @@ def save(self, fileobj):
                         for d in t.data_for_points.values())):
                     raise DataError("Missing scalars for some points!")
 
-                points = np.asarray(t.streamline, dtype=f4_dtype)
-                scalars = [np.asarray(t.data_for_points[k], dtype=f4_dtype)
+                points = np.asarray(t.streamline)
+                scalars = [np.asarray(t.data_for_points[k])
                            for k in data_for_points_keys]
-                scalars = np.concatenate([np.ndarray((len(points), 0),
-                                                     dtype=f4_dtype)
+                scalars = np.concatenate([np.ndarray((len(points), 0),)
                                           ] + scalars, axis=1)
-                properties = [np.asarray(t.data_for_streamline[k],
-                                         dtype=f4_dtype)
+                properties = [np.asarray(t.data_for_streamline[k])
                               for k in data_for_streamline_keys]
-                properties = np.concatenate([np.array([], dtype=f4_dtype)
-                                             ] + properties)
+                properties = np.concatenate(
+                    [np.array([])] + properties).astype(f4_dtype)
 
                 data = struct.pack(i4_dtype.str[:-1], len(points))
-                data += np.concatenate([points, scalars], axis=1).tostring()
+                pts_scalars = np.concatenate(
+                    [points, scalars], axis=1).astype(f4_dtype)
+                data += pts_scalars.tostring()
                 data += properties.tostring()
                 f.write(data)