Zarrdataset

Latest version: v0.2.0

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0.2.0

Description
New capabilities were added to `PatchSampler` and `ImageBase` to handle padded samples and incomplete patches.
This allows to use `ZarrDataset` in an inference workflow and not only for training.

Additionally, patch sampling is now based on multiples of the `patch_size` instead of the chunk size from input files. This allow to sample patches from "incomplete" chunks (those smaller than the file's chunk size).

Changes
- Added `allow_incomplete_patches` parameter to `PatchSampler` class. This allows to retrieve patches from chunks that are smaller than the input file's chunk size. Previously, these chunks and all patches that could potentially be extracted were discarded.
- Added `pad` parameter to `PatchSampler` to add padding into all spatial axes when generating the selection slices.
- Added `stride` parameter to `PatchSampler` to introduce spacing between sampled patches.
- Updated `ImageBase` to handle indices that request pixels from outside the actual image by adding padding to cached chunks.

0.1.4

Documentation ready at https://thejacksonlaboratory.github.io/zarrdataset/index.html and the package for installing from PyPI

0.1.2

This release is for python 3.9 and should be able to publish ZarrDataset to PyPI

0.1.1

First release of ZarrDataset for testing with TestPyPI

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