Manipulate arrays of complex data structures as easily as Numpy
https://github.com/scikit-hep/awkward-1.0
Awkward Array is a library for nested, variable-sized data, including
arbitrary-length lists, records, mixed types, and missing data, using
NumPy-like idioms.
Arrays are dynamically typed, but operations on them are compiled and fast.
Their behavior coincides with NumPy when array dimensions are regular and
generalizes when they're not.
- Sources inherited from project devel:languages:python:numeric
- Devel package for openSUSE:Factory
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Source Files
| Filename | Size | Changed |
|---|---|---|
| _multibuild | 0000000085 85 Bytes | |
| awkward-2.7.1.tar.gz | 0006149828 5.86 MB | |
| python-awkward.changes | 0000047721 46.6 KB | |
| python-awkward.rpmlintrc | 0000000045 45 Bytes | |
| python-awkward.spec | 0000003876 3.79 KB |
Revision 79 (latest revision is 80)
Dirk Mueller (dirkmueller)
committed
(revision 79)
- update to 2.7.1:
* fix: test from_raggedtensor on CUDA
* fix: use None when ndims can't be inferred from a layout-like
obj
* fix: ak.argcombinations should allow negative axis
* fix: restrict `named_axis` inferring to
`ak.Arrays/ak.Records/ak.HighLevelContexts` by default
- update to 2.7.0:
* chore: remove Python 3.8, ensure Python 3.13 is included
- update to 2.6.10:
* feat: named axis for `ak.Array`
* feat: to/from TensorFlow Tensor
* feat: updated LayoutBuilder.h (added functors) for C++11
compatibility
* fix: make sure 'at' is a cupy zero dim array
* fix: add cuda backend support for `to_raggedtensor` and
`from_raggedtensor` functions
* fix: test `from_raggedtensor` on GPU
* fix: correct handling of keepdims and mask_identity for
weighted mean
- update to 2.6.9:
* feat: Add to_cudf
* feat: to/from PyTorch Tensor
* perf: avoid inflating UnmaskedArrays in broadcasting when you
can
* fix: TypeError fix for `can_cast`
* fix: ListArray slicing on GPU
* fix: ak.typetracer.length_one_if_typetracer with option and
union types
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