VectorData Refactor Expandable

src/hdmf/backends/hdf5/h5tools.py

@@ -383,7 +383,10 @@ def copy_file(self, **kwargs):
              'default': True},
             {'name': 'herd', 'type': 'hdmf.common.resources.HERD',
              'doc': 'A HERD object to populate with references.',
-             'default': None})
+             'default': None},
+            {'name': 'expandable', 'type': bool, 'default': True,
+             'doc': ('Bool to set whether datasets are expandable by setting the max shape for all dimensions',
+                     'of a dataset to None and enabling auto-chunking by default.')})
     def write(self, **kwargs):
         """Write the container to an HDF5 file."""
         if self.__mode == 'r':
@@ -826,10 +829,16 @@ def close_linked_files(self):
              'doc': 'exhaust DataChunkIterators one at a time. If False, exhaust them concurrently',
              'default': True},
             {'name': 'export_source', 'type': str,
-             'doc': 'The source of the builders when exporting', 'default': None})
+             'doc': 'The source of the builders when exporting', 'default': None},
+            {'name': 'expandable', 'type': bool, 'default': True,
+             'doc': ('Bool to set whether datasets are expandable by setting the max shape for all dimensions',
+                     'of a dataset to None and enabling auto-chunking by default.')})
     def write_builder(self, **kwargs):
         f_builder = popargs('builder', kwargs)
-        link_data, exhaust_dci, export_source = getargs('link_data', 'exhaust_dci', 'export_source', kwargs)
+        link_data, exhaust_dci, export_source = getargs('link_data',
+                                                        'exhaust_dci',
+                                                        'export_source',
+                                                        kwargs)
         self.logger.debug("Writing GroupBuilder '%s' to path '%s' with kwargs=%s"
                           % (f_builder.name, self.source, kwargs))
         for name, gbldr in f_builder.groups.items():
@@ -1000,6 +1009,9 @@ def _filler():
              'default': True},
             {'name': 'export_source', 'type': str,
              'doc': 'The source of the builders when exporting', 'default': None},
+            {'name': 'expandable', 'type': bool, 'default': True,
+             'doc': ('Bool to set whether datasets are expandable by setting the max shape for all dimensions',
+                     'of a dataset to None and enabling auto-chunking by default.')},
             returns='the Group that was created', rtype=Group)
     def write_group(self, **kwargs):
         parent, builder = popargs('parent', 'builder', kwargs)
@@ -1100,21 +1112,25 @@ def write_link(self, **kwargs):
              'default': True},
             {'name': 'export_source', 'type': str,
              'doc': 'The source of the builders when exporting', 'default': None},
+            {'name': 'expandable', 'type': bool, 'default': True,
+             'doc': ('Bool to set whether datasets are expandable by setting the max shape for all dimensions',
+                     'of a dataset to None and enabling auto-chunking by default.')},
             returns='the Dataset that was created', rtype=Dataset)
     def write_dataset(self, **kwargs):  # noqa: C901
         """ Write a dataset to HDF5
 
         The function uses other dataset-dependent write functions, e.g,
         ``__scalar_fill__``, ``__list_fill__``, and ``__setup_chunked_dset__`` to write the data.
         """
-        parent, builder = popargs('parent', 'builder', kwargs)
+        parent, builder, expandable = popargs('parent', 'builder', 'expandable', kwargs)
         link_data, exhaust_dci, export_source = getargs('link_data', 'exhaust_dci', 'export_source', kwargs)
         self.logger.debug("Writing DatasetBuilder '%s' to parent group '%s'" % (builder.name, parent.name))
         if self.get_written(builder):
             self.logger.debug("    DatasetBuilder '%s' is already written" % builder.name)
             return None
         name = builder.name
         data = builder.data
+        matched_spec_shape = builder.spec_shapes
         dataio = None
         options = dict()  # dict with additional
         if isinstance(data, H5DataIO):
@@ -1228,7 +1244,7 @@ def _filler():
                 return
             # If the compound data type contains only regular data (i.e., no references) then we can write it as usual
             else:
-                dset = self.__list_fill__(parent, name, data, options)
+                dset = self.__list_fill__(parent, name, data, matched_spec_shape, expandable, options)
         # Write a dataset containing references, i.e., a region or object reference.
         # NOTE: we can ignore options['io_settings'] for scalar data
         elif self.__is_ref(options['dtype']):
@@ -1323,7 +1339,7 @@ def _filler():
                 self.__dci_queue.append(dataset=dset, data=data)
             # Write a regular in memory array (e.g., numpy array, list etc.)
             elif hasattr(data, '__len__'):
-                dset = self.__list_fill__(parent, name, data, options)
+                dset = self.__list_fill__(parent, name, data, matched_spec_shape, expandable, options)
             # Write a regular scalar dataset
             else:
                 dset = self.__scalar_fill__(parent, name, data, options)
@@ -1451,7 +1467,7 @@ def __chunked_iter_fill__(cls, parent, name, data, options=None):
         return dset
 
     @classmethod
-    def __list_fill__(cls, parent, name, data, options=None):
+    def __list_fill__(cls, parent, name, data, matched_spec_shape, expandable, options=None):
         # define the io settings and data type if necessary
         io_settings = {}
         dtype = None
@@ -1473,7 +1489,13 @@ def __list_fill__(cls, parent, name, data, options=None):
             data_shape = (len(data),)
         else:
             data_shape = get_data_shape(data)
-
+        if expandable:
+            # Don't override existing settings
+            if 'maxshape' not in io_settings:
+                if matched_spec_shape is not None:
+                    io_settings['maxshape'] = tuple([None]*len(matched_spec_shape))
+                else:
+                    io_settings['maxshape'] = tuple([None]*len(data_shape))
         # Create the dataset
         try:
             dset = parent.create_dataset(name, shape=data_shape, dtype=dtype, **io_settings)

src/hdmf/build/builders.py

@@ -330,6 +330,9 @@ class DatasetBuilder(BaseBuilder):
              'doc': 'The datatype of this dataset.', 'default': None},
             {'name': 'attributes', 'type': dict,
              'doc': 'A dictionary of attributes to create in this dataset.', 'default': dict()},
+            {'name': 'spec_shapes', 'type': tuple,
+             'doc': ('The shape(s) defined in the spec.'),
+             'default': None},
             {'name': 'dimension_labels', 'type': tuple,
              'doc': ('A list of labels for each dimension of this dataset from the spec. Currently this is '
                      'supplied only on build.'),
@@ -341,22 +344,27 @@ class DatasetBuilder(BaseBuilder):
             {'name': 'source', 'type': str, 'doc': 'The source of the data in this builder.', 'default': None})
     def __init__(self, **kwargs):
         """ Create a Builder object for a dataset """
-        name, data, dtype, attributes, dimension_labels, maxshape, chunks, parent, source = getargs(
-            'name', 'data', 'dtype', 'attributes', 'dimension_labels', 'maxshape', 'chunks', 'parent', 'source',
-            kwargs
-        )
+        name, data, dtype, attributes, spec_shapes, dimension_labels, maxshape, chunks, parent, source = getargs(
+            'name', 'data', 'dtype', 'attributes', 'spec_shapes', 'dimension_labels', 'maxshape', 'chunks',
+            'parent', 'source', kwargs)
         super().__init__(name, attributes, parent, source)
         self['data'] = data
         self['attributes'] = _copy.copy(attributes)
         self.__dimension_labels = dimension_labels
         self.__chunks = chunks
+        self.__spec_shapes = spec_shapes
         self.__maxshape = maxshape
         if isinstance(data, BaseBuilder):
             if dtype is None:
                 dtype = self.OBJECT_REF_TYPE
         self.__dtype = dtype
         self.__name = name
 
+    @property
+    def spec_shapes(self):
+        """The shapes defined in the spec."""
+        return self.__spec_shapes
+
     @property
     def data(self):
         """The data stored in the dataset represented by this builder."""

src/hdmf/build/objectmapper.py

@@ -723,7 +723,7 @@ def build(self, **kwargs):
                     msg = "'container' must be of type Data with DatasetSpec"
                     raise ValueError(msg)
                 spec_dtype, spec_shape, spec_dims, spec = self.__check_dset_spec(self.spec, spec_ext)
-                dimension_labels = self.__get_dimension_labels_from_spec(container.data, spec_shape, spec_dims)
+                dimension_labels, matched_shape = self.__get_spec_info(container.data, spec_shape, spec_dims)
                 if isinstance(spec_dtype, RefSpec):
                     self.logger.debug("Building %s '%s' as a dataset of references (source: %s)"
                                       % (container.__class__.__name__, container.name, repr(source)))
@@ -734,6 +734,7 @@ def build(self, **kwargs):
                         parent=parent,
                         source=source,
                         dtype=spec_dtype.reftype,
+                        spec_shapes=matched_shape,
                         dimension_labels=dimension_labels,
                     )
                     manager.queue_ref(self.__set_dataset_to_refs(builder, spec_dtype, spec_shape, container, manager))
@@ -748,6 +749,7 @@ def build(self, **kwargs):
                         parent=parent,
                         source=source,
                         dtype=spec_dtype,
+                        spec_shapes=matched_shape,
                         dimension_labels=dimension_labels,
                     )
                     manager.queue_ref(self.__set_compound_dataset_to_refs(builder, spec, spec_dtype, container,
@@ -766,6 +768,7 @@ def build(self, **kwargs):
                             parent=parent,
                             source=source,
                             dtype="object",
+                            spec_shapes=matched_shape,
                             dimension_labels=dimension_labels,
                         )
                         manager.queue_ref(self.__set_untyped_dataset_to_refs(builder, container, manager))
@@ -789,6 +792,7 @@ def build(self, **kwargs):
                             parent=parent,
                             source=source,
                             dtype=dtype,
+                            spec_shapes=matched_shape,
                             dimension_labels=dimension_labels,
                         )
 
@@ -820,9 +824,10 @@ def __check_dset_spec(self, orig, ext):
             spec = ext
         return dtype, shape, dims, spec
 
-    def __get_dimension_labels_from_spec(self, data, spec_shape, spec_dims) -> tuple:
+    def __get_spec_info(self, data, spec_shape, spec_dims):
+        """This will return the dimension labels and shape by matching the data shape to a permissible spec shape."""
         if spec_shape is None or spec_dims is None:
-            return None
+            return None, None
         data_shape = get_data_shape(data)
         # if shape is a list of allowed shapes, find the index of the shape that matches the data
         if isinstance(spec_shape[0], list):
@@ -842,22 +847,22 @@ def __get_dimension_labels_from_spec(self, data, spec_shape, spec_dims) -> tuple
             # use the most specific match -- the one with the fewest Nones
             if match_shape_inds:
                 if len(match_shape_inds) == 1:
-                    return tuple(spec_dims[match_shape_inds[0]])
+                    return tuple(spec_dims[match_shape_inds[0]]), tuple(spec_shape[match_shape_inds[0]])
                 else:
                     count_nones = [len([x for x in spec_shape[k] if x is None]) for k in match_shape_inds]
                     index_min_count = count_nones.index(min(count_nones))
                     best_match_ind = match_shape_inds[index_min_count]
-                    return tuple(spec_dims[best_match_ind])
+                    return tuple(spec_dims[best_match_ind]), tuple(spec_shape[best_match_ind])
             else:
                 # no matches found
                 msg = "Shape of data does not match any allowed shapes in spec '%s'" % self.spec.path
                 warnings.warn(msg, IncorrectDatasetShapeBuildWarning)
-                return None
+                return None, None
         else:
             if len(data_shape) != len(spec_shape):
                 msg = "Shape of data does not match shape in spec '%s'" % self.spec.path
                 warnings.warn(msg, IncorrectDatasetShapeBuildWarning)
-                return None
+                return None, None
             # check each dimension. None means any length is allowed
             match = True
             for j, d in enumerate(data_shape):
@@ -867,9 +872,9 @@ def __get_dimension_labels_from_spec(self, data, spec_shape, spec_dims) -> tuple
             if not match:
                 msg = "Shape of data does not match shape in spec '%s'" % self.spec.path
                 warnings.warn(msg, IncorrectDatasetShapeBuildWarning)
-                return None
+                return None, None
             # shape is a single list of allowed dimension lengths
-            return tuple(spec_dims)
+            return tuple(spec_dims), tuple(spec_shape)
 
     def __is_reftype(self, data):
         if (isinstance(data, AbstractDataChunkIterator) or

tests/unit/test_io_hdf5.py

@@ -225,5 +225,5 @@ def test_dataset_shape(self):
         io.write_builder(self.builder)
         builder = io.read_builder()
         dset = builder['test_bucket']['foo_holder']['foo1']['my_data'].data
-        self.assertEqual(get_data_shape(dset), (10,))
+        self.assertEqual(get_data_shape(dset), (None,))
         io.close()

tests/unit/test_io_hdf5_h5tools.py

@@ -28,6 +28,7 @@
 from hdmf.testing import TestCase, remove_test_file
 from hdmf.common.resources import HERD
 from hdmf.term_set import TermSet, TermSetWrapper
+from hdmf.utils import get_data_shape
 
 
 from tests.unit.helpers.utils import (Foo, FooBucket, FooFile, get_foo_buildmanager,
@@ -739,12 +740,12 @@ def test_copy_h5py_dataset_h5dataio_input(self):
                              self.f['test_copy'][:].tolist())
 
     def test_list_fill_empty(self):
-        dset = self.io.__list_fill__(self.f, 'empty_dataset', [], options={'dtype': int, 'io_settings': {}})
+        dset = self.io.__list_fill__(self.f, 'empty_dataset', [], None, True, options={'dtype': int, 'io_settings': {}})
         self.assertTupleEqual(dset.shape, (0,))
 
     def test_list_fill_empty_no_dtype(self):
         with self.assertRaisesRegex(Exception, r"cannot add \S+ to [/\S]+ - could not determine type"):
-            self.io.__list_fill__(self.f, 'empty_dataset', [])
+            self.io.__list_fill__(self.f, 'empty_dataset', [], None, True)
 
     def test_read_str(self):
         a = ['a', 'bb', 'ccc', 'dddd', 'e']
@@ -3725,3 +3726,25 @@ def test_set_data_io(self):
         self.data.set_data_io(H5DataIO, dict(chunks=True))
         assert isinstance(self.data.data, H5DataIO)
         assert self.data.data.io_settings["chunks"]
+
+
+class TestExpand(TestCase):
+    def setUp(self):
+        self.manager = get_foo_buildmanager()
+        self.path = get_temp_filepath()
+
+    def test_expand_false(self):
+        # Setup all the data we need
+        foo1 = Foo('foo1', [1, 2, 3, 4, 5], "I am foo1", 17, 3.14)
+        foobucket = FooBucket('bucket1', [foo1])
+        foofile = FooFile(buckets=[foobucket])
+
+        with HDF5IO(self.path, manager=self.manager, mode='w') as io:
+            io.write(foofile, expandable=False)
+
+        io = HDF5IO(self.path, manager=self.manager, mode='r')
+        read_foofile = io.read()
+        self.assertListEqual(foofile.buckets['bucket1'].foos['foo1'].my_data,
+                                 read_foofile.buckets['bucket1'].foos['foo1'].my_data[:].tolist())
+        self.assertEqual(get_data_shape(read_foofile.buckets['bucket1'].foos['foo1'].my_data),
+                        (5,))