Remove Context.from_global_dim_data...

and fix tests to first make a Distribution object from a global_dim_data, then make a DistArray with the Distribution object.

distarray/context.py

@@ -228,117 +228,6 @@ def empty(self, shape, dtype=float, dist=None, grid_shape=None):
                                   shape=shape, dist=dist,
                                   grid_shape=grid_shape, dtype=dtype)
 
-    def from_global_dim_data(self, global_dim_data, dtype=float):
-        """Make a DistArray from global dim_data structures.
-
-        Parameters
-        ----------
-        global_dim_data : tuple of dict
-            A global dimension dictionary per dimension.  See following `Note`
-            section.
-        dtype : numpy dtype, optional
-            dtype for underlying arrays
-
-        Returns
-        -------
-        result : DistArray
-            An empty DistArray of the specified size, dimensionality, and
-            distribution.
-
-        Note
-        ----
-
-        The `global_dim_data` tuple is a simple, straightforward data structure
-        that allows full control over all aspects of a DistArray's distribution
-        information.  It does not contain any of the array's *data*, only the
-        *metadata* needed to specify how the array is to be distributed.  Each
-        dimension of the array is represented by corresponding dictionary in
-        the tuple, one per dimension.  All dictionaries have a `dist_type` key
-        that specifies whether the array is block, cyclic, or unstructured.
-        The other keys in the dictionary are dependent on the `dist_type` key.
-
-        **Block**
-
-        * ``dist_type`` is ``'b'``.
-
-        * ``bounds`` is a sequence of integers, at least two elements.
-
-          The ``bounds`` sequence always starts with 0 and ends with the global
-          ``size`` of the array.  The other elements indicate the local array
-          global index boundaries, such that successive pairs of elements from
-          ``bounds`` indicates the ``start`` and ``stop`` indices of the
-          corresponding local array.
-
-        * ``comm_padding`` integer, greater than or equal to zero.
-        * ``boundary_padding`` integer, greater than or equal to zero.
-
-        These integer values indicate the communication or boundary padding,
-        respectively, for the local arrays.  Currently only a single value for
-        both ``boundary_padding`` and ``comm_padding`` is allowed for the
-        entire dimension.
-
-        **Cyclic**
-
-        * ``dist_type`` is ``'c'``
-
-        * ``proc_grid_size`` integer, greater than or equal to one.
-
-        The size of the process grid in this dimension.  Equivalent to the
-        number of local arrays in this dimension and determines the number of
-        array sections.
-
-        * ``size`` integer, greater than or equal to zero.
-
-        The global size of the array in this dimension.
-
-        * ``block_size`` integer, optional.  Greater than or equal to one.
-
-        If not present, equivalent to being present with value of one.
-
-        **Unstructured**
-
-        * ``dist_type`` is ``'u'``
-
-        * ``indices`` sequence of one-dimensional numpy integer arrays or
-          buffers.
-
-          The ``len(indices)`` is the number of local unstructured arrays in
-          this dimension.
-
-          To compute the global size of the array in this dimension, compute
-          ``sum(len(ii) for ii in indices)``.
-
-        **Not-distributed**
-
-        The ``'n'`` distribution type is a convenience to specify that an array
-        is not distributed along this dimension.
-
-        * ``dist_type`` is ``'n'``
-
-        * ``size`` integer, greater than or equal to zero.
-
-        The global size of the array in this dimension.
-
-        """
-        # global_dim_data is a sequence of dictionaries, one per dimension.
-        distribution = Distribution(self, global_dim_data)
-        dim_data_per_rank = distribution.get_dim_data_per_rank()
-
-        if len(self.targets) != len(dim_data_per_rank):
-            errmsg = "`dim_data_per_rank` must contain a dim_data for every rank."
-            raise TypeError(errmsg)
-
-        da_key = self._generate_key()
-        subs = ((da_key,) + self._key_and_push(dim_data_per_rank) +
-                (self._comm_key, self._comm_key) + self._key_and_push(dtype))
-
-        cmd = ('%s = distarray.local.LocalArray('
-                    'distarray.local.maps.Distribution(%s[%s.Get_rank()], comm=%s),'
-                    ' dtype=%s)')
-        self._execute(cmd % subs)
-
-        return DistArray.from_localarrays(da_key, self)
-
     def save_dnpy(self, name, da):
         """
         Save a distributed array to files in the ``.dnpy`` format.

distarray/tests/test_client.py

@@ -109,9 +109,10 @@ def test_from_global_dim_data_irregular_block(self):
         bounds = (0, 2, 3, 4, 10)
         glb_dim_data = (
                 {'dist_type': 'b',
-                    'bounds': bounds},
+                 'bounds': bounds},
                 )
-        distarr = self.context.from_global_dim_data(glb_dim_data)
+        distribution = Distribution(self.context, glb_dim_data)
+        distarr = DistArray(distribution, dtype=int)
         for i in range(global_size):
             distarr[i] = i
 
@@ -128,7 +129,8 @@ def test_from_global_dim_data_1d(self):
                     'indices': list_of_indices,
                     },
                 )
-        distarr = self.context.from_global_dim_data(glb_dim_data)
+        distribution = Distribution(self.context, glb_dim_data)
+        distarr = DistArray(distribution, dtype=int)
         for i in range(total_size):
             distarr[i] = i
         localarrays = distarr.get_localarrays()
@@ -152,7 +154,8 @@ def test_from_global_dim_data_bu(self):
                     'indices' : indices
                 },
             )
-        distarr = self.context.from_global_dim_data(glb_dim_data)
+        distribution = Distribution(self.context, glb_dim_data)
+        distarr = DistArray(distribution, dtype=int)
         for i in range(rows):
             for j in range(cols):
                 distarr[i, j] = i*cols + j
@@ -175,7 +178,8 @@ def test_from_global_dim_data_bc(self):
                     'size': cols,
                     'block_size': 2,
                 },)
-        distarr = self.context.from_global_dim_data(global_dim_data)
+        distribution = Distribution(self.context, global_dim_data)
+        distarr = DistArray(distribution, dtype=int)
         for i in range(rows):
             for j in range(cols):
                 distarr[i, j] = i*cols + j
@@ -196,7 +200,8 @@ def test_from_global_dim_data_uu(self):
                 {'dist_type': 'u',
                     'indices' : col_indices},
                 )
-        distarr = self.context.from_global_dim_data(glb_dim_data)
+        distribution = Distribution(self.context, glb_dim_data)
+        distarr = DistArray(distribution, dtype=int)
         for i in range(rows):
             for j in range(cols):
                 distarr[i, j] = i*cols + j
@@ -282,7 +287,8 @@ def test_irregular_block_assignment(self):
                     'bounds': (0, 2, 6, 7, 9),
                 }
             )
-        distarr = self.context.from_global_dim_data(global_dim_data)
+        distribution = Distribution(self.context, global_dim_data)
+        distarr = DistArray(distribution, dtype=int)
         for i in range(global_shape[0]):
             for j in range(global_shape[1]):
                 distarr[i, j] = i + j