ndindex Changelog

Version 1.10.0 (2025-05-20)

Major Changes

  • Add support for free threaded builds of CPython (@HaoZeke, @rgommers, @ngoldbaum).

Version 1.9.2 (2024-09-25)

Minor Changes

  • Fixes an issue with pickle and deepcopy serialization introduced in ndindex 1.9.

Version 1.9.1 (2024-09-23)

This version is identical to 1.9, but includes some fixes to the release scripts to ensure that wheels are properly uploaded to PyPI.

Version 1.9 (2024-09-23)

Major Changes

  • ndindex now uses a C extension (using Cython). Currently the constructors for Slice and Tuple have been Cythonized, meaning constructing and using those classes is now much faster. In the future, additional parts of ndindex will be Cythonized as performance needs dictate. This does not have any user-facing changes to functionality.

  • Python 3.8 is no longer supported.

  • The documentation now includes a documentation guide for all NumPy index types, extending the previous guide for slices. The slices guide has also been improved and now has diagrams built using HTML/CSS instead of MathJAX.

Minor Changes

  • Some fixes to incorrect usage of __slots__.

  • Raise an exception earlier for invalid index in ChunkSize.num_subchunks().

  • The CYTHONIZE_NDINDEX environment variable for building has been removed, as Cython support is now required.

  • Fix a compatibility issue with NumPy 2.0.

  • normalize_skip_axes() will now raise AxisError before ValueError for non-unique axes.

  • Keep track of when a Slice has been reduced (with no shape) to avoid recomputing it.

  • Various improvements in the tests.

Version 1.8 (2024-02-15)

Major Changes

  • Breaking broadcast_shapes() no longer returns None in the place of skipped axes. The result is now just the non-skipped axes broadcasted together.

  • The skip_axes flag to iter_indices() and broadcast_shapes() can now be a list of tuples, of skipped axes, which apply to each respective shape independently.

  • Mixing negative and nonnegative skip_axes in iter_indices() and broadcast_shapes() is now supported. The only restriction is that skip axes must refer to unique dimensions for each shape.

  • New index method selected_indices(), which iterates indices corresponding to each element selected by the given index on an array of a given shape.

  • ndindex indices can now be constructed by slicing the ndindex() constructor function, like ndindex[0:10]. This is generally preferred for indices with explicit slices, as this allows using the usual : slice syntax instead of requiring slices to be spelled out with the slice function.

  • Add a negative_int flag to reduce, which makes it normalize integer indices to negative integers when a shape is provided.

  • Slice objects now hash to the same hash value as their corresponding raw slice in Python 3.12, which now allows native slice objects to be hashed.

  • Fix an incorrect result from ChunkSize.as_subchunks() and ChunkSize.num_subchunks() when using multiple array indices or a boolean array index with multiple dimensions.

Minor Changes

  • Drop support for Python 3.7.

  • Fully support the upcoming NumPy 2.0 release. This mostly only relevant for the ndindex test suite, although it does also mean that one ndindex error message has been updated to match updated wording from NumPy.

  • Specify Cython language_level, which leads to shorter code and gets rid of a bunch of warnings. (@keszybz)

  • Rename the default git branch from master to main.

Version 1.7 (2023-04-20)

Major Changes

  • Breaking: the skip_axes argument iter_indices() function now applies the skipped axes before broadcasting, not after. This behavior is more generally useful and matches how functions with stacking work (e.g., np.cross or np.matmul). The best way to get the old behavior is to broadcast the arrays/shapes together first. The skip_axes in iter_indices must be either all negative or all nonnegative to avoid ambiguity. A future version may add support for specifying different skip axes for each shape.

  • iter_indices() no longer requires the skipped axes specified by skip_axes to be broadcast compatible.

  • New method isvalid() to check if an index is valid on a given shape.

  • New function broadcast_shapes() which is the same as np.broadcast_shapes() except it also allows specifying a set of skip_axes which will be ignored when broadcasting.

  • New exceptions BroadcastError and AxisError which are used by iter_indices() and broadcast_shapes().

Minor Changes

  • The documentation theme has been changed to Furo, which has a more clean color scheme based on the ndindex logo, better navigation and layout, mobile support, and dark mode support.

  • Fix some test failures with the latest version of NumPy.

  • Fix some tests that didn’t work properly when run against the sdist.

  • The sdist now includes relevant testing files.

  • Automatically deploy docs from CI again.

  • Add a documentation preview CI job.

  • Test Python 3.11 in CI.

  • Minor improvements to some documentation.

  • Fix a typo in the type confusion docs. (@ruancomelli)

Version 1.6 (2022-01-24)

Major Changes

  • SymPy is no longer a dependency of ndindex.

  • NumPy is now an optional dependency of ndindex. It is only required when constructing array indices BooleanArray or IntegerArray. This does not change the semantics of ndindex. ndindex objects still match NumPy indexing semantics everywhere. Note that NumPy is still a hard requirement for all tests in the ndindex test suite.

  • Added a new function iter_indices() which is a generalization of the np.ndindex() function (which is otherwise unrelated) to allow multiple broadcast compatible shapes, and to allow skipping axes.

  • Added a new method ChunkSize.containing_block, which computes the smallest continuous block of chunks containing a given index.

  • ndindex can now be installed with optional Cythonization support. This is still experimental and is only enabled when installing ndindex from source when Cython is installed (see the installation instructions). This improves the general performance of ndindex.

Minor Changes

  • Fix an issue with the Tuple constructor with broadcast incompatible arrays with the latest version of NumPy.

  • Small performance improvement to Tuple.reduce.

  • Add better support for boolean scalar indices in various ChunkSize methods.

  • Better NotImplementedError messages from ChunkSize methods.

  • Switch from Travis CI to GitHub Actions.

  • Update CI to test Python 3.10.

  • Remove Codecov from CI.

  • The docs now have a cleaner sidebar which always stays fixed on screen.

Version 1.5.2 (2021-04-06)

Major Changes

  • ndindex now has a logo: ndindex logo

    Thanks to Irina Fumarel for the logo design.

  • Improve ChunkSize.as_subchunks() to never use the slow fallback method. This in particular improves the performance for array indices.

  • Add a new function ChunkSize.num_subchunks(). This is a more efficient way of computing len(list(chunk_size.as_subindex(idx, shape))).

Minor Changes

Version 1.5.1 (2021-02-03)

Major Changes

  • The ChunkSize.as_subchunks method now only iterates the chunk indices. Previously it iterated (c, index.as_subindex(c)). But the subindex can always be computed manually (there was nothing more efficient about the way it was computed previously), and this is much slower if the subindex is not actually needed. This is a backwards incompatible change, but since the ChunkSize object was only introduced in the previous release, it should hopefully not have a major impact.

  • Made improvements to performance throughout the library. The improvements in some instances are drastic.

  • Added a benchmarking suite using airspeed velocity. Graphs of the benchmarks can be viewed at https://quansight-labs.github.io/ndindex/benchmarks/.

Version 1.5 (2020-12-23)

Major Changes

  • ndindex has been moved to the Quansight-Labs organization on Github. ndindex is now a Quansight Labs project.

  • Python 3.6 support has been dropped. ndindex has been tested with Python 3.7-3.9.

  • Slice.reduce() now gives a fully canonical result, meaning that two slices s1 and s2 are equal on all array shapes if and only if s1.reduce() == s2.reduce(), and are equal on an array of shape shape if and only if s1.reduce(shape) == s2.reduce(shape) (note that s1 == s2 is only True if s1 and s2 are exactly equal). See the documentation for more information on what properties are true after canonicalization.

  • Make np.ndarray == ndindex(array) give True or False (@telamonian).

  • Add ChunkSize, a new object to represent chunking over an array and manipulate indices over chunks.

Minor Changes

  • Various performance improvements.

  • Make hash(idx) == hash(idx.raw) whenever idx.raw is hashable.

  • Fix the background color for some monospace text in the docs.

  • Fix math formatting in the slices documentation.

Version 1.4 (2020-09-14)

Major Changes

  • New object Newaxis to represent np.newaxis (i.e., None).

  • New object BooleanArray to represent boolean array indices (i.e., masks).

  • New object IntegerArray to represent integer array indices (i.e., fancy indexing).

With these three new objects, ndindex can now represent all valid NumPy index types. However, note that two corner cases with tuples of arrays are not implemented:

  • separating arrays by slices, ellipsis, or newaxis (e.g., a[[0], :, [0]])

  • mixing boolean scalars (True or False) with other non-boolean scalar arrays (e.g., a[True, [0]])

The first case in particular may not ever be implemented, as it is considered to be a mistake that it is allowed in the first place in the NumPy, so if you need it, please let me know.

Additionally, some corner cases of array semantics are either deprecated or fixed as bugs in newer versions of NumPy, with some only being fixed in the unreleased NumPy 1.20. ndindex follows the NumPy 1.20 behavior, and whenever something is deprecated, ndindex follows the post-deprecation semantics. For example, in some cases in NumPy, using a list as an index results in it being interpreted as a tuple and a deprecation warning raised. ndindex always treats lists as arrays. As another example, there are some cases involving integer array indices where NumPy does not check bounds but raises a deprecation warning, and in these cases ndindex does check bounds (when relevant, e.g., in idx.reduce(shape)). ndindex should work just fine with older versions of NumPy, but at least 1.20 (the development version) is required to run the ndindex test suite due to the way ndindex tests itself against NumPy.

  • New method broadcast_arrays(). This will convert all boolean arrays into the equivalent integer arrays and broadcast all arrays in a Tuple together so that they have the same shape. idx.broadcast_arrays() is equivalent to idx in all cases where idx does not give IndexError. Note that broadcastability itself is checked in the Tuple constructor, so if you only wish to check if an index is valid, it is not necessary to call broadcast_arrays().

  • expand() now broadcasts all array inputs (same as broadcast_arrays()), and combines multiple scalar booleans.

  • Tuple.reduce() now combines multiple scalar booleans.

  • as_subindex() now supports many cases involving BooleanArray and IntegerArray. There are still many instances where as_subindex() raises NotImplementedError however. If you need support for these, please open an issue to let me know.

  • Add a new document to the documentation on type confusion. The document stresses that ndindex types should not be confused with the built-in/NumPy types that they wrap, and outlines some pitfalls and best practices to avoid them when using ndindex.

Minor Changes

  • There is now only one docstring for expand(), on the NDindex base class.

  • Calling Tuple with a tuple argument now raises ValueError. Previously it raised NotImplementedError, because NumPy sometimes treats tuples as arrays. It was decided to not allow treating a tuple as an array to avoid the type confusion between Tuple((1, 2)) and Tuple(1, 2) (only the latter form is correct).

  • Document the .args attribute.

  • New internal function operator_index(), which acts like operator.index() except it disallows boolean types. A consequence of this is that calling the Integer or Slice constructors with boolean arguments will now result in a TypeError. Note that scalar booleans (False and True) are valid indices, but they are not the same as the integer indices 0 and 1.

Version 1.3.1 (2020-07-22)

Major Changes

  • as_subindex now supports more input types. In particular, Integer is now supported better.

  • as_subindex will now raise ValueError in some cases when the two indices do not intersect with each other. This is because representing the correct answer is either impossible or requires an index type that is not yet implemented in ndindex.

Minor Changes

  • as_subindex correctly gives NotImplementedError for Tuples with ellipses.

  • ndindex(list/array/bool/None) now correctly raise NotImplementedError instead of TypeError.

  • ndindex() now raises IndexError instead of TypeError on invalid index types, with error messages that match NumPy. This also applies to various API functions that call ndindex() on their arguments.

  • Update the “too many indices for array” error messages to match NumPy 1.19.

  • Better checking of arguments for functions that take a shape. The allowed shapes and exceptions should now match NumPy more closely (although unknown (negative) dimensions do not make sense and are not allowed). Shapes with NumPy integer types now work properly.

Version 1.3 (2020-06-29)

Major Changes

  • New method expand, which always returns a Tuple which is as explicit as possible.

  • New method newshape, which returns the shape of an array of shape shape after being indexed by idx.

  • New method as_subindex produces an index k such that a[j][k] gives all the elements of a[j] that are also in a[i] (see the documentation for more information). This is useful for re-indexing an index onto chunks of an array. Note that this raises NotImplementedError for some index types that are not yet implemented.

  • New method isempty returns True if an index always indexes to an empty array (an array with a 0 in its shape). isempty can also be called with a shape like idx.isempty(shape).

  • SymPy is now a hard dependency of ndindex.

  • Added extensive documentation on slice semantics to the documentation.

Minor Changes

  • Made Slice.reduce() give a canonical empty slice in some more cases.

  • Move the documentation from recommonmark to Myst.

  • Add a documentation style guide.

  • Add “See Also” sections to various docstrings.

  • Replace the “Fork me on GitHub” ribbon in the documentation with a pure CSS version.

  • Fix a font name typo in the docs CSS.

  • Use a custom doctest runner instead of pytest-doctest. Among other things, this now ensures all library doctests include all imports required for the doctest to run in a fresh interpreter.

  • Various improvements to test coverage.

  • Various minor improvements to documentation.

Version 1.2 (2020-05-01)

Major Changes

  • Added ellipsis to represent ellipsis indices (...). See ellipsis.

Minor Changes

  • Make str(Tuple) more readable.

  • Fix a bug in == when comparing against non-ndindex types.

  • Make == give True when comparing against equivalent non-ndindex types.

  • Make inspect.signature give the correct thing for ndindex types.

  • Fix Tuple.reduce() with no arguments.

  • ndindex now has 100% test coverage.

Version 1.1 (2020-04-23)

Major Changes

  • ndindex objects no longer automatically canonicalize on instantiation. To canonicalize an index, call idx.reduce() with no arguments. This will put it in a canonical form that is equivalent for all array shapes (assuming no IndexErrors). idx.reduce(shape) can be used to further canonicalize an index given that it will index an array of shape shape.

Minor Changes

  • Added internal classes to the Sphinx documentation.

  • Fixed incorrect Slice.stop and Slice.step.

  • Added the LICENSE file to the source distribution (@synapticarbors).

Version 1.0 (2020-04-08)

Major Changes

  • Initial ndindex release

  • Includes support for Slice, Integer, and Tuple

  • Implements basic canonicalization, .args, hashability, .raw, len(), and reduce.