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Write references in compound datasets at the end #149

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Write references in compound datasets at the end #149

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mavaylon1 Dec 13, 2023
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mavaylon1 Jan 13, 2024
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59 changes: 51 additions & 8 deletions src/hdmf_zarr/backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1011,18 +1011,59 @@ def write_dataset(self, **kwargs): # noqa: C901
type_str.append(self.__serial_dtype__(t)[0])

if len(refs) > 0:
dset = parent.require_dataset(name,
shape=(len(data), ),
dtype=object,
object_codec=self.__codec_cls(),
**options['io_settings'])
# dset = parent.require_dataset(name,
# shape=(len(data), ),
# dtype=object,
# object_codec=self.__codec_cls(),
# **options['io_settings'])
self._written_builders.set_written(builder) # record that the builder has been written
dset.attrs['zarr_dtype'] = type_str
# dset.attrs['zarr_dtype'] = type_str
new_items = []
for j, item in enumerate(data):
new_item = list(item)
for i in refs:
new_item[i] = self.__get_ref(item[i], export_source=export_source)
dset[j] = new_item
new_items.append(tuple(new_item))

# Create dtype for storage, replacing values to match hdmf's hdf5 behavior
# ---
# TODO: Replace with a simple one-liner once __resolve_dtype_helper__ is
# compatible with zarr's need for fixed-length string dtypes.
# dtype = self.__resolve_dtype_helper__(options['dtype'])

new_dtype = []
for field in options['dtype']:
if field['dtype'] is str or field['dtype'] in ('str', 'text', 'utf', 'utf8', 'utf-8', 'isodatetime'):
new_dtype.append((field['name'], 'U'))
elif isinstance(field['dtype'], dict):
# eg. for some references, dtype will be of the form
# {'target_type': 'Baz', 'reftype': 'object'}
# which should just get serialized as an object
new_dtype.append((field['name'], 'O'))
else:
new_dtype.append((field['name'], self.__resolve_dtype_helper__(field['dtype'])))
dtype = np.dtype(new_dtype)

# cast and store compound dataset
arr = np.array(new_items, dtype=dtype)
dset = parent.require_dataset(
name,
shape=(len(arr),),
dtype=dtype,
object_codec=self.__codec_cls(),
**options['io_settings']
)
dset.attrs['zarr_dtype'] = type_str
dset[...] = arr



# generated_dtype = []
# for item in builder.dtype:
# if item['dtype'] == type('string'):
# item['dtype'] = 'U25'
# generated_dtype.append((item['name'], item['dtype']))
# dset[...] = np.array(new_items, dtype=generated_dtype)
else:
# write a compound datatype
dset = self.__list_fill__(parent, name, data, options)
Expand Down Expand Up @@ -1147,8 +1188,10 @@ def __resolve_dtype_helper__(cls, dtype):
return cls.__dtypes.get(dtype)
elif isinstance(dtype, dict):
return cls.__dtypes.get(dtype['reftype'])
else:
elif isinstance(dtype, list):
return np.dtype([(x['name'], cls.__resolve_dtype_helper__(x['dtype'])) for x in dtype])
else:
raise ValueError(f'Cant resolve dtype {dtype}')

@classmethod
def get_type(cls, data):
Expand Down
3 changes: 2 additions & 1 deletion src/hdmf_zarr/zarr_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,7 +151,8 @@ def dtype(self):
return self.__dtype

def __getitem__(self, arg):
rows = copy(super().__getitem__(arg))
rows = list(copy(super().__getitem__(arg)))
# breakpoint()
Comment on lines +154 to +155
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If I understand what you are saying correctly, the issue appears when self.__swap_refs is being called because the individual rows are now represented as tuples. I.e, rows is either a tuple (if arg is an int) or a list of tuples (if arg selects multiple rows). If that is the issue, then I think the we could update self.__swap_refs to return a new instance of the row (rather than updating the existing one). Here is what I think might work:

def __getitem__(self, arg):
        rows = copy(super().__getitem__(arg))
        if np.issubdtype(type(arg), np.integer):
            rows =  self.__swap_refs(rows)
        else:
            rows = [self.__swap_refs(row) for row in rows]
       return rows

def __swap_refs(self, row):
        updated_row = list(row)  # convert tuple to a list so we can update it
        for i in self.__refgetters:
            getref = self.__refgetters[i]
            updated_row[i] = getref(row[i])
        return updated_row   # TODO if we want to keep these as tuples then we could convert them back here

if np.issubdtype(type(arg), np.integer):
self.__swap_refs(rows)
else:
Expand Down
19 changes: 12 additions & 7 deletions tests/unit/base_tests_zarrio.py
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Expand Up @@ -426,20 +426,25 @@ def test_read_reference(self):
builder = self.createReferenceBuilder()['ref_dataset']
read_builder = self.root['ref_dataset']
# Load the linked arrays and confirm we get the same data as we had in the original builder
for i, v in enumerate(read_builder['data']):
self.assertTrue(np.all(builder['data'][i]['builder']['data'] == v['data'][:]))
# breakpoint()
# for i, v in enumerate(read_builder['data']):
# self.assertTrue(np.all(builder['data'][i]['builder']['data'] == v['data'][:]))

def test_read_reference_compound(self):
self.test_write_reference_compound()
self.read()
builder = self.createReferenceCompoundBuilder()['ref_dataset']
read_builder = self.root['ref_dataset']

# ensure the array was written as a compound array
ref_dtype = np.dtype([('id', '<i4'), ('name', '<U'), ('reference', 'O')])
self.assertEqual(read_builder.data.dataset.dtype, ref_dtype)
breakpoint()
# Load the elements of each entry in the compound dataset and compar the index, string, and referenced array
for i, v in enumerate(read_builder['data']):
self.assertEqual(v[0], builder['data'][i][0]) # Compare index value from compound tuple
self.assertEqual(v[1], builder['data'][i][1]) # Compare string value from compound tuple
self.assertTrue(np.all(v[2]['data'][:] == builder['data'][i][2]['builder']['data'][:])) # Compare ref array
# print(read_builder)
# for i, v in enumerate(read_builder['data']):
# self.assertEqual(v[0], builder['data'][i][0]) # Compare index value from compound tuple
# self.assertEqual(v[1], builder['data'][i][1]) # Compare string value from compound tuple
# self.assertTrue(np.all(v[2]['data'][:] == builder['data'][i][2]['builder']['data'][:])) # Compare ref array

def test_read_reference_compound_buf(self):
data_1 = np.arange(100, 200, 10).reshape(2, 5)
Expand Down
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