renaming

tfields/core.py

@@ -846,21 +846,12 @@ class Tensors(AbstractNdarray):
         # in current implementation Points3D use always cut method, never MeshMap
         return self.cut(cutOrMeshMap, coordSys=coordSys), None
 
-    def to3DArrays(self, arrayShape):
+    def distances(self, other, **kwargs):
         """
         Args:
-            arrayShape (tuple of lenght 3): how coordinates will be devided.
-                If you initialize Points3D with a grid of dimensions a,b,c and
-                want to retrieve the grid-points use (a,b,c)
-        """
-        if not isinstance(arrayShape, tuple):
-            raise TypeError("ArrayShape is not of type tuple but %s" % type(arrayShape))
-        if not len(arrayShape) == 3:
-            raise TypeError("ArrayShape is not of length 3 but %i" % len(arrayShape))
-        return self.T.reshape((3,) + arrayShape)
-
-    def distances(self, other, metric='euclidean', **kwargs):
-        """
+            other(array)
+            **kwargs:
+                ... is forwarded to sp.spatial.distance.cdist
         Examples:
             >>> p = tfields.Tensors.grid((0, 2, 3j),
             ...                          (0, 2, 3j),
@@ -874,10 +865,21 @@ class Tensors(AbstractNdarray):
             array([ 3.])
 
         """
-        return sp.spatial.distance.cdist(self, other, metric=metric, **kwargs)
+        if issubclass(type(other), Tensors) and self.coordSys != other.coordSys:
+            other = other.copy()
+            other.transform(self.coordSys)
+        return sp.spatial.distance.cdist(self, other, **kwargs)
 
-    def minDistances(self, other=None, metric='euclidean', **kwargs):
+    def minDistances(self, other=None, **kwargs):
         """
+        Args:
+            other(array or None)
+            **kwargs:
+                memory_saving (bool): for very large array comparisons
+                    default False
+                ... rest is forwarded to sp.spatial.distance.cdist
+
+
         Examples:
             >>> p = tfields.Tensors.grid((0, 2, 3),
             ...                          (0, 2, 3),
@@ -888,31 +890,31 @@ class Tensors(AbstractNdarray):
             array([ 1.        ,  1.        ,  1.        ,  1.        ,  1.41421356,
                     1.        ,  1.        ,  1.        ,  1.        ])
 
-            >>> dMin2 = p.minDistances(memorySaving=True)
+            >>> dMin2 = p.minDistances(memory_saving=True)
             >>> bool((dMin2 == dMin).all())
             True
 
         """
-        memorySaving = kwargs.pop('memorySaving', False)
+        memory_saving = kwargs.pop('memory_saving', False)
 
-        if other is not None:
-            raise NotImplementedError("Should be easy but make shure not to remove 0s")
-        else:
+        if other is None:
             other = self
+        else:
+            raise NotImplementedError("Should be easy but make shure not to remove diagonal")
 
         try:
-            if memorySaving:
+            if memory_saving:
                 raise MemoryError()
-            d = self.distances(other, metric=metric, **kwargs)
+            d = self.distances(other, **kwargs)
             return d[d > 0].reshape(d.shape[0], - 1).min(axis=1)
         except MemoryError:
-            minDists = np.empty(self.shape[0])
+            min_dists = np.empty(self.shape[0])
             for i, point in enumerate(other):
-                d = self.distances([point], metric=metric, **kwargs)
-                minDists[i] = d[d > 0].reshape(-1).min()
-            return minDists
+                d = self.distances([point], **kwargs)
+                min_dists[i] = d[d > 0].reshape(-1).min()
+            return min_dists
 
-    def epsilonNeighbourhood(self, epsilon):
+    def epsilon_neighbourhood(self, epsilon):
         """
         Returns:
             indices for those sets of points that lie within epsilon around the other
@@ -923,7 +925,7 @@ class Tensors(AbstractNdarray):
             ...                          (0, 1, 2j),
             ...                          (0, 1, 2j))
             >>> p = tfields.Tensors.merged(p, [[0.5, 0.5, 0.5]])
-            >>> [len(en) for en in p.epsilonNeighbourhood(0.9)]
+            >>> [len(en) for en in p.epsilon_neighbourhood(0.9)]
             [2, 2, 2, 2, 2, 2, 2, 2, 9]
 
         """
@@ -933,8 +935,6 @@ class Tensors(AbstractNdarray):
         return [indices[die] for die in distsInEpsilon]
 
 
-
-
 class TensorFields(Tensors):
     """
     Discrete Tensor Field