Reuse multidim_daskstack in merge + fast um loading.

lib/iris/_lazy_data.py

@@ -87,3 +87,31 @@ def array_masked_to_nans(array, mask=None):
         array = array.astype(np.dtype('f8'))
     array[mask] = np.nan
     return array
+
+
+def multidim_lazy_stack(stack):
+    """
+    Recursively build a multidimensional stacked dask array.
+
+    This is needed because dask.array.stack only accepts a 1-dimensional list.
+
+    Args:
+
+    * stack:
++        An ndarray of dask arrays.
+
+    Returns:
+        The input array converted to a lazy dask array.
+
+    """
+    if stack.ndim == 0:
+        # A 0-d array cannot be stacked.
+        result = stack.item()
+    elif stack.ndim == 1:
+        # Another base case : simple 1-d goes direct in dask.
+        result = da.stack(list(stack))
+    else:
+        # Recurse because dask.stack does not do multi-dimensional.
+        result = da.stack([multidim_lazy_stack(subarray)
+                           for subarray in stack])
+    return result

lib/iris/_merge.py

@@ -33,7 +33,7 @@
 import numpy as np
 import numpy.ma as ma
 
-from iris._lazy_data import array_masked_to_nans, as_lazy_data, is_lazy_data
+from iris._lazy_data import as_lazy_data, is_lazy_data, multidim_lazy_stack
 import iris.cube
 import iris.coords
 import iris.exceptions
@@ -1069,27 +1069,6 @@ def derive_space(groups, relation_matrix, positions, function_matrix=None):
     return space
 
 
-def _multidim_daskstack(stack):
-    """
-    Recursively build a multidensional stacked dask array.
-
-    The argument is an ndarray of dask arrays.
-    This is needed because dask.array.stack only accepts a 1-dimensional list.
-
-    """
-    if stack.ndim == 0:
-        # A 0-d array cannot be merged.
-        result = stack.item()
-    elif stack.ndim == 1:
-        # 'Another' base case : simple 1-d goes direct in dask.
-        result = da.stack(list(stack))
-    else:
-        # Recurse because dask.stack does not do multi-dimensional.
-        result = da.stack([_multidim_daskstack(subarray)
-                           for subarray in stack])
-    return result
-
-
 class ProtoCube(object):
     """
     Framework for merging source-cubes into one or more higher
@@ -1234,7 +1213,7 @@ def merge(self, unique=True):
                     data = as_lazy_data(data, chunks=data.shape)
                 stack[nd_index] = data
 
-            merged_data = _multidim_daskstack(stack)
+            merged_data = multidim_lazy_stack(stack)
             if all_have_data:
                 # All inputs were concrete, so turn the result back into a
                 # normal array.

lib/iris/fileformats/um/_fast_load_structured_fields.py

@@ -32,12 +32,10 @@
 from netCDF4 import netcdftime
 import numpy as np
 
+from iris._lazy_data import as_lazy_data, multidim_lazy_stack
 from iris.fileformats.um._optimal_array_structuring import \
     optimal_array_structure
 
-from iris.fileformats.pp import PPField3
-from iris._lazy_data import as_lazy_data
-
 
 class FieldCollation(object):
     """
@@ -89,15 +87,11 @@ def data(self):
         if not self._structure_calculated:
             self._calculate_structure()
         if self._data_cache is None:
-            data_arrays = [as_lazy_data(f._data, chunks=f._data.shape)
-                           for f in self.fields]
-            vector_dims_list = list(self.vector_dims_shape)
-            vector_dims_list.reverse()
-            self._data_cache = data_arrays
-            for size in vector_dims_list:
-                self._data_cache = [da.stack(self._data_cache[i:i+size]) for i
-                                    in range(0, len(self._data_cache), size)]
-            self._data_cache, = self._data_cache
+            stack = np.empty(np.prod(self.vector_dims_shape), 'object')
+            for index, f in enumerate(self.fields):
+                stack[index] = as_lazy_data(f._data, chunks=f._data.shape)
+            stack = stack.reshape(self.vector_dims_shape)
+            self._data_cache = multidim_lazy_stack(stack)
         return self._data_cache
 
     @property

lib/iris/tests/unit/lazy_data/test_array_masked_to_nans.py

@@ -14,7 +14,7 @@
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with Iris.  If not, see <http://www.gnu.org/licenses/>.
-"""Test :meth:`iris._lazy data.array_masked_to_nans` method."""
+"""Test function :func:`iris._lazy data.array_masked_to_nans`."""
 
 from __future__ import (absolute_import, division, print_function)
 from six.moves import (filter, input, map, range, zip)  # noqa

lib/iris/tests/unit/lazy_data/test_is_lazy_data.py

@@ -14,7 +14,7 @@
 #
 # You should have received a copy of the GNU Lesser General Public License
 # along with Iris.  If not, see <http://www.gnu.org/licenses/>.
-"""Test :meth:`iris._lazy data.is_lazy_data` method."""
+"""Test function :func:`iris._lazy data.is_lazy_data`."""
 
 from __future__ import (absolute_import, division, print_function)
 from six.moves import (filter, input, map, range, zip)  # noqa

lib/iris/tests/unit/lazy_data/test_multidim_lazy_stack.py

@@ -0,0 +1,57 @@
+# (C) British Crown Copyright 2017, Met Office
+#
+# This file is part of Iris.
+#
+# Iris is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the
+# Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Iris is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with Iris.  If not, see <http://www.gnu.org/licenses/>.
+"""Test function :func:`iris._lazy data.multidim_lazy_stack`."""
+
+from __future__ import (absolute_import, division, print_function)
+from six.moves import (filter, input, map, range, zip)  # noqa
+
+# Import iris.tests first so that some things can be initialised before
+# importing anything else.
+import iris.tests as tests
+
+import numpy as np
+import dask.array as da
+
+from iris._lazy_data import as_lazy_data, multidim_lazy_stack
+
+
+class Test_multidim_lazy_stack(tests.IrisTest):
+    def _check(self, stack_shape):
+        vals = np.arange(np.prod(stack_shape)).reshape(stack_shape)
+        stack = np.empty(stack_shape, 'object')
+        stack_element_shape = (4, 5)
+        for index, val in np.ndenumerate(vals):
+            stack[index] = as_lazy_data(val*np.ones(stack_element_shape))
+        result = multidim_lazy_stack(stack)
+        self.assertEqual(result.shape, stack_shape + stack_element_shape)
+        self.assertIsInstance(result, da.core.Array)
+
+    def test_0d_lazy_stack(self):
+        shape = ()
+        result = self._check(shape)
+
+    def test_1d_lazy_stack(self):
+        shape = (2,)
+        result = self._check(shape)
+
+    def test_2d_lazy_stack(self):
+        shape = (3, 2)
+        result = self._check(shape)
+
+
+if __name__ == '__main__':
+    tests.main()