Merge pull request #504 from MarcCote/doc_lazy_tractogram

MRG: Clarified error messages in LazyTractogram

Clarifies what "generator function" means in relevant error messages. It also makes sure all values contained in a LazyDict object are callable.

Author: matthew-brett

nibabel/streamlines/tests/test_tractogram.py

@@ -778,6 +778,8 @@ def test_lazy_tractogram_creation(self):
         # generators get exhausted and are not reusable unlike generator
         # function.
         assert_raises(TypeError, LazyTractogram, streamlines)
+        assert_raises(TypeError, LazyTractogram,
+                      data_per_point={"none": None})
         assert_raises(TypeError, LazyTractogram,
                       data_per_streamline=data_per_streamline)
         assert_raises(TypeError, LazyTractogram, DATA['streamlines'],

nibabel/streamlines/tractogram.py

@@ -209,8 +209,11 @@ def __getitem__(self, key):
         return self.store[key]()
 
     def __setitem__(self, key, value):
-        if value is not None and not callable(value):  # TODO: why None?
-            raise TypeError("`value` must be a generator function or None.")
+        if not callable(value):
+            msg = ("Values in a `LazyDict` must be generator functions."
+                   " These are functions which, when called, return an"
+                   " instantiated generator.")
+            raise TypeError(msg)
         self.store[key] = value
 
     def __delitem__(self, key):
@@ -682,7 +685,10 @@ def _apply_affine():
 
     def _set_streamlines(self, value):
         if value is not None and not callable(value):
-            raise TypeError("`streamlines` must be a generator function.")
+            msg = ("`streamlines` must be a generator function. That is a"
+                   " function which, when called, returns an instantiated"
+                   " generator.")
+            raise TypeError(msg)
         self._streamlines = value
 
     @property