MAINT: Slight simplification

Author: cpelley

stratify/_conservative.pyx

@@ -74,7 +74,6 @@ cdef apply_weights(np.ndarray[np.float64_t, ndim=3] src_point,
     cdef Py_ssize_t ind
     cdef np.ndarray[np.float64_t, ndim=3] results, weighted_contrib
     cdef np.ndarray[np.float64_t, ndim=2] weights
-    cdef np.ndarray[np.int64_t, ndim=2] nan_src_contrib
     results = np.zeros(
         [src_data.shape[0], tgt_point.shape[0], src_data.shape[2]],
         dtype='float64')
@@ -90,8 +89,8 @@ cdef apply_weights(np.ndarray[np.float64_t, ndim=3] src_point,
             np.nansum(weighted_contrib, axis=1))
         # Return nan values for those target cells, where there is any
         # contribution of nan data from the source data.
-        nan_src_contrib = ((weights > 0) * np.isnan(weighted_contrib)).sum(axis=1)
-        results[..., ind][nan_src_contrib>0] = np.nan
+        results[..., ind][
+            ((weights > 0) * np.isnan(weighted_contrib)).any(axis=1)] = np.nan
 
         # Return np.nan for those target cells where no source contributes.
         results[:, weights.sum(axis=0) == 0, ind] = np.nan