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Fix conversion of Zarr string dataset to HDF5 #1171

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Fix conversion of Zarr string dataset to HDF5 #1171

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db51abd
Fix conversion of Zarr string dataset to HDF5
oruebel Aug 15, 2024
b39d20f
Updated changelog
oruebel Aug 15, 2024
6f759a6
Fix spelling
oruebel Aug 15, 2024
2b90d21
Merge branch 'dev' into fix/zarr_str_export
rly Aug 19, 2024
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7 changes: 5 additions & 2 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,9 @@
- Adjusted stacklevel of warnings to point to user code when possible. @rly [#1166](https://github.com/hdmf-dev/hdmf/pull/1166)
- Improved "already exists" error message when adding a container to a `MultiContainerInterface`. @rly [#1165](https://github.com/hdmf-dev/hdmf/pull/1165)

### Bug fixes
- Fixed bug when converting string datasets from Zarr to HDF5. @oruebel [#1171](https://github.com/hdmf-dev/hdmf/pull/1171)

## HDMF 3.14.3 (July 29, 2024)

### Enhancements
Expand All @@ -22,10 +25,10 @@ is available on build (during the write process), but not on read of a dataset f
- Warn when unexpected keys are present in specs. @rly [#1134](https://github.com/hdmf-dev/hdmf/pull/1134)
- Support appending to zarr arrays. @mavaylon1 [#1136](https://github.com/hdmf-dev/hdmf/pull/1136)
- Support specifying "value" key in DatasetSpec. @rly [#1143](https://github.com/hdmf-dev/hdmf/pull/1143)
- Add support for numpy 2. @rly [#1139](https://github.com/hdmf-dev/hdmf/pull/1139)
- Added support for numpy 2. @rly [#1139](https://github.com/hdmf-dev/hdmf/pull/1139)

### Bug fixes
- Fix iterator increment causing an extra +1 added after the end of completion. @CodyCBakerPhD [#1128](https://github.com/hdmf-dev/hdmf/pull/1128)
- Fixed iterator increment causing an extra +1 added after the end of completion. @CodyCBakerPhD [#1128](https://github.com/hdmf-dev/hdmf/pull/1128)

## HDMF 3.14.1 (June 6, 2024)

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6 changes: 6 additions & 0 deletions src/hdmf/backends/hdf5/h5tools.py
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Expand Up @@ -923,6 +923,12 @@
# TODO: These values exist, but I haven't solved them yet
# binary
# number

# Use text dtype for Zarr datasets of strings. Zarr stores variable length strings
# as objects, so we need to detect this special case here
if hasattr(data, 'attrs') and 'zarr_dtype' in data.attrs and data.attrs['zarr_dtype'] == 'str':
return cls.__dtypes['text']

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If we add:

try:
    from zarr import Array as ZarrArray
    import numcodecs
    ZARR_INSTALLED = True
except ImportError:
    ZARR_INSTALLED = False

def _is_zarr_array(value):
    return ZARR_INSTALLED and isinstance(value, ZarrArray)

Can we change these lines to:

        if _is_zarr_array(data) and data.filters:
            if numcodecs.VLenUTF8() in data.filters:
                return cls.__dtypes['text']
            elif numcodecs.VLenBytes() in data.filters:
                return cls.__dtypes['ascii']

It would be nice if HDF5IO can accept any Zarr array just like it accepts any Numpy array, instead of accepting only Zarr arrays that have been written with hdmf-zarr. The Zarr array filters should show that the array dtype is a variable-length string and its encoding.

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That said, I don't seem to be able to create a test case where these lines (regardless of yours or mine) matters. Could you add one?

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Note from discussion: this probably can be removed and was there to be overly cautious. Ryan TODO: try a conversion and confirm.


dtype = cls.__resolve_dtype_helper__(dtype)
if dtype is None:
dtype = cls.get_type(data)
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12 changes: 10 additions & 2 deletions src/hdmf/build/objectmapper.py
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Expand Up @@ -209,10 +209,18 @@
if (isinstance(value, np.ndarray) or
(hasattr(value, 'astype') and hasattr(value, 'dtype'))):
if spec_dtype_type is _unicode:
ret = value.astype('U')
if hasattr(value, 'attrs') and 'zarr_dtype' in value.attrs:
# Zarr stores strings as objects, so we cannot convert to unicode dtype
ret = value

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else:
ret = value.astype('U')
ret_dtype = "utf8"
elif spec_dtype_type is _ascii:
ret = value.astype('S')
if hasattr(value, 'attrs') and 'zarr_dtype' in value.attrs:
# Zarr stores strings as objects, so we cannot convert to unicode dtype
ret = value

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else:
ret = value.astype('S')
ret_dtype = "ascii"
else:
dtype_func, warning_msg = cls.__resolve_numeric_dtype(value.dtype, spec_dtype_type)
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