Pickle memory maps in SliceableDataChunkIterator #536
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CodyCBakerPhD
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Jul 30, 2023
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| " use DataInterface.add_to_nwbfile()." | ||
| " use DataInterface.add_to_nwbfile().", | ||
| stacklevel=2, |
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Found this useful in a separate conversion project; kept getting the warning but was unsure where it was coming from (had several data interfaces in the conversion)
CodyCBakerPhD
commented
Jul 30, 2023
src/neuroconv/tools/hdmf.py
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| from hdmf.data_utils import GenericDataChunkIterator as HDMFGenericDataChunkIterator | ||
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| # from hdmf.data_utils import GenericDataChunkIterator as HDMFGenericDataChunkIterator | ||
| from hdmf.data_utils import GenericDataChunkIterator | ||
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| class GenericDataChunkIterator(HDMFGenericDataChunkIterator): | ||
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| # class GenericDataChunkIterator(HDMFGenericDataChunkIterator): | ||
| class _GenericDataChunkIterator(GenericDataChunkIterator): |
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This was a temporary reversion, I think I can restore it - just need to test it doesn't break the serialization
CodyCBakerPhD
commented
Jul 30, 2023
| @@ -90,3 +95,42 @@ def _get_maxshape(self) -> tuple: | |||
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| def _get_data(self, selection: Tuple[slice]) -> np.ndarray: | |||
| return self.data[selection] | |||
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| def is_pickleable(self) -> bool: | |||
| if isinstance(self.data, (np.memmap, np.array)): | |||
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Will need to remove np.array here; easy support is only offered for memory-map like lazy access to source data
CodyCBakerPhD
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Jul 30, 2023
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| if np.issubdtype(extended_data.dtype, np.object_): | ||
| extended_data = extended_data.astype("str", copy=False) | ||
| # if np.issubdtype(extended_data.dtype, np.object_): | ||
| # extended_data = extended_data.astype("str", copy=False) |
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Codecov Report
@@ Coverage Diff @@
## main #536 +/- ##
==========================================
- Coverage 91.59% 91.06% -0.54%
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Files 96 96
Lines 4806 4833 +27
==========================================
- Hits 4402 4401 -1
- Misses 404 432 +28
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sister PR to hdmf-dev/hdmf#924 and hdmf-dev/hdmf-zarr#111
This acts as a first test implementation by allowing efficient pickling of a memory-map based iterator. Will add and test more conditions over time
Also, to be clear, a
np.memmap(and Zarr arrays for that matter) are pickleable by default; however, they do it by pickling the data they contain, not a packaged set of instructions for re-instantiating that object after reloading (in our case, on a separate process)So we want to override such methods to more efficiently allow re-initialization of the lazy mappings to the source data
Opening in draft for now