Changelog
### 2.1.0
```
NumPy 2.1.0 provides support for the upcoming Python 3.13 release and
drops support for Python 3.9. In addition to the usual bug fixes and
updated Python support, it helps get us back into our usual release
cycle after the extended development of 2.0. The highlights for this
release are:
- Support for the array-api 2023.12 standard.
- Support for Python 3.13.
- Preliminary support for free threaded Python 3.13.
Python versions 3.10-3.13 are supported in this release.
New functions
New function `numpy.unstack`
A new function `np.unstack(array, axis=...)` was added, which splits an
array into a tuple of arrays along an axis. It serves as the inverse of
[numpy.stack]{.title-ref}.
([gh-26579](https://github.com/numpy/numpy/pull/26579))
Deprecations
- The `fix_imports` keyword argument in `numpy.save` is deprecated.
Since NumPy 1.17, `numpy.save` uses a pickle protocol that no longer
supports Python 2, and ignored `fix_imports` keyword. This keyword
is kept only for backward compatibility. It is now deprecated.
([gh-26452](https://github.com/numpy/numpy/pull/26452))
- Passing non-integer inputs as the first argument of
[bincount]{.title-ref} is now deprecated, because such inputs are
silently cast to integers with no warning about loss of precision.
([gh-27076](https://github.com/numpy/numpy/pull/27076))
Expired deprecations
- Scalars and 0D arrays are disallowed for `numpy.nonzero` and
`numpy.ndarray.nonzero`.
([gh-26268](https://github.com/numpy/numpy/pull/26268))
- `set_string_function` internal function was removed and
`PyArray_SetStringFunction` was stubbed out.
([gh-26611](https://github.com/numpy/numpy/pull/26611))
C API changes
API symbols now hidden but customizable
NumPy now defaults to hide the API symbols it adds to allow all NumPy
API usage. This means that by default you cannot dynamically fetch the
NumPy API from another library (this was never possible on windows).
If you are experiencing linking errors related to `PyArray_API` or
`PyArray_RUNTIME_VERSION`, you can define the `NPY_API_SYMBOL_ATTRIBUTE`
to opt-out of this change.
If you are experiencing problems due to an upstream header including
NumPy, the solution is to make sure you
`include "numpy/ndarrayobject.h"` before their header and import NumPy
yourself based on `including-the-c-api`.
([gh-26103](https://github.com/numpy/numpy/pull/26103))
Many shims removed from npy_3kcompat.h
Many of the old shims and helper functions were removed from
`npy_3kcompat.h`. If you find yourself in need of these, vendor the
previous version of the file into your codebase.
([gh-26842](https://github.com/numpy/numpy/pull/26842))
New `PyUFuncObject` field `process_core_dims_func`
The field `process_core_dims_func` was added to the structure
`PyUFuncObject`. For generalized ufuncs, this field can be set to a
function of type `PyUFunc_ProcessCoreDimsFunc` that will be called when
the ufunc is called. It allows the ufunc author to check that core
dimensions satisfy additional constraints, and to set output core
dimension sizes if they have not been provided.
([gh-26908](https://github.com/numpy/numpy/pull/26908))
New Features
- `numpy.reshape` and `numpy.ndarray.reshape` now support `shape` and
`copy` arguments.
([gh-26292](https://github.com/numpy/numpy/pull/26292))
- NumPy now supports DLPack v1, support for older versions will be
deprecated in the future.
([gh-26501](https://github.com/numpy/numpy/pull/26501))
- `numpy.asanyarray` now supports `copy` and `device` arguments,
matching `numpy.asarray`.
([gh-26580](https://github.com/numpy/numpy/pull/26580))
- `numpy.printoptions`, `numpy.get_printoptions`, and
`numpy.set_printoptions` now support a new option, `override_repr`,
for defining custom `repr(array)` behavior.
([gh-26611](https://github.com/numpy/numpy/pull/26611))
- `numpy.cumulative_sum` and `numpy.cumulative_prod` were added as
Array API compatible alternatives for `numpy.cumsum` and
`numpy.cumprod`. The new functions can include a fixed initial
(zeros for `sum` and ones for `prod`) in the result.
([gh-26724](https://github.com/numpy/numpy/pull/26724))
- `numpy.clip` now supports `max` and `min` keyword arguments which
are meant to replace `a_min` and `a_max`. Also, for `np.clip(a)` or
`np.clip(a, None, None)` a copy of the input array will be returned
instead of raising an error.
([gh-26724](https://github.com/numpy/numpy/pull/26724))
- `numpy.astype` now supports `device` argument.
([gh-26724](https://github.com/numpy/numpy/pull/26724))
`f2py` can generate freethreading-compatible C extensions
Pass `--freethreading-compatible` to the f2py CLI tool to produce a C
extension marked as compatible with the free threading CPython
interpreter. Doing so prevents the interpreter from re-enabling the GIL
at runtime when it imports the C extension. Note that `f2py` does not
analyze fortran code for thread safety, so you must verify that the
wrapped fortran code is thread safe before marking the extension as
compatible.
([gh-26981](https://github.com/numpy/numpy/pull/26981))
Improvements
`histogram` auto-binning now returns bin sizes \>=1 for integer input data
For integer input data, bin sizes smaller than 1 result in spurious
empty bins. This is now avoided when the number of bins is computed
using one of the algorithms provided by `histogram_bin_edges`.
([gh-12150](https://github.com/numpy/numpy/pull/12150))
`ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]`
Static typing for `ndarray` is a long-term effort that continues with
this change. It is a generic type with type parameters for the shape and
the data type. Previously, the shape type parameter could be any value.
This change restricts it to a tuple of ints, as one would expect from
using `ndarray.shape`. Further, the shape-type parameter has been
changed from invariant to covariant. This change also applies to the
subtypes of `ndarray`, e.g. `numpy.ma.MaskedArray`. See the [typing
docs](https://typing.readthedocs.io/en/latest/reference/generics.html#variance-of-generic-types)
for more information.
([gh-26081](https://github.com/numpy/numpy/pull/26081))
`np.quantile` with method `closest_observation` chooses nearest even order statistic
This changes the definition of nearest for border cases from the nearest
odd order statistic to nearest even order statistic. The numpy
implementation now matches other reference implementations.
([gh-26656](https://github.com/numpy/numpy/pull/26656))
`lapack_lite` is now thread safe
NumPy provides a minimal low-performance version of LAPACK named
`lapack_lite` that can be used if no BLAS/LAPACK system is detected at
build time.
Until now, `lapack_lite` was not thread safe. Single-threaded use cases
did not hit any issues, but running linear algebra operations in
multiple threads could lead to errors, incorrect results, or segfaults
due to data races.
We have added a global lock, serializing access to `lapack_lite` in
multiple threads.
([gh-26750](https://github.com/numpy/numpy/pull/26750))
The `numpy.printoptions` context manager is now thread and async-safe
In prior versions of NumPy, the printoptions were defined using a
combination of Python and C global variables. We have refactored so the
state is stored in a python `ContextVar`, making the context manager
thread and async-safe.
([gh-26846](https://github.com/numpy/numpy/pull/26846))
Performance improvements and changes
- `numpy.save` now uses pickle protocol version 4 for saving arrays
with object dtype, which allows for pickle objects larger than 4GB
and improves saving speed by about 5% for large arrays.
([gh-26388](https://github.com/numpy/numpy/pull/26388))
- OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on
benchmarking, there are 5 clusters of performance around these
kernels: `PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX`.
([gh-27147](https://github.com/numpy/numpy/pull/27147))
- OpenBLAS on windows is linked without quadmath, simplifying
licensing
([gh-27147](https://github.com/numpy/numpy/pull/27147))
- Due to a regression in OpenBLAS on windows, the performance
improvements when using multiple threads for OpenBLAS 0.3.26 were
reverted.
([gh-27147](https://github.com/numpy/numpy/pull/27147))
`ma.cov` and `ma.corrcoef` are now significantly faster
The private function has been refactored along with `ma.cov` and
`ma.corrcoef`. They are now significantly faster, particularly on large,
masked arrays.
([gh-26285](https://github.com/numpy/numpy/pull/26285))
Changes
- As `numpy.vecdot` is now a ufunc it has a less precise signature.
This is due to the limitations of ufunc\'s typing stub.
([gh-26313](https://github.com/numpy/numpy/pull/26313))
- `numpy.floor`, `numpy.ceil`, and `numpy.trunc` now won\'t perform
casting to a floating dtype for integer and boolean dtype input
arrays.
([gh-26766](https://github.com/numpy/numpy/pull/26766))
`ma.corrcoef` may return a slightly different result
A pairwise observation approach is currently used in `ma.corrcoef` to
calculate the standard deviations for each pair of variables. This has
been changed as it is being used to normalise the covariance, estimated
using `ma.cov`, which does not consider the observations for each
variable in a pairwise manner, rendering it unnecessary. The
normalisation has been replaced by the more appropriate standard
deviation for each variable, which significantly reduces the wall time,
but will return slightly different estimates of the correlation
coefficients in cases where the observations between a pair of variables
are not aligned. However, it will return the same estimates in all other
cases, including returning the same correlation matrix as `corrcoef`
when using a masked array with no masked values.
([gh-26285](https://github.com/numpy/numpy/pull/26285))
Cast-safety fixes in `copyto` and `full`
`copyto` now uses NEP 50 correctly and applies this to its cast safety.
Python integer to NumPy integer casts and Python float to NumPy float
casts are now considered \"safe\" even if assignment may fail or
precision may be lost. This means the following examples change
slightly:
-
`np.copyto(int8_arr, 1000)` previously performed an unsafe/same-kind cast
: of the Python integer. It will now always raise, to achieve an
unsafe cast you must pass an array or NumPy scalar.
- `np.copyto(uint8_arr, 1000, casting="safe")` will raise an
OverflowError rather than a TypeError due to same-kind casting.
- `np.copyto(float32_arr, 1e300, casting="safe")` will overflow to
`inf` (float32 cannot hold `1e300`) rather raising a TypeError.
Further, only the dtype is used when assigning NumPy scalars (or 0-d
arrays), meaning that the following behaves differently:
- `np.copyto(float32_arr, np.float64(3.0), casting="safe")` raises.
- `np.coptyo(int8_arr, np.int64(100), casting="safe")` raises.
Previously, NumPy checked whether the 100 fits the `int8_arr`.
This aligns `copyto`, `full`, and `full_like` with the correct NumPy 2
behavior.
([gh-27091](https://github.com/numpy/numpy/pull/27091))
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```
Links
- PyPI: https://pypi.org/project/numpy
- Changelog: https://data.safetycli.com/changelogs/numpy/
- Homepage: https://numpy.org
pyup-bot
commented
1 month ago
This PR updates numpy from 2.0.1 to 2.1.0.
Changelog
### 2.1.0 ``` NumPy 2.1.0 provides support for the upcoming Python 3.13 release and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get us back into our usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for the array-api 2023.12 standard. - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. Python versions 3.10-3.13 are supported in this release. New functions New function `numpy.unstack` A new function `np.unstack(array, axis=...)` was added, which splits an array into a tuple of arrays along an axis. It serves as the inverse of [numpy.stack]{.title-ref}. ([gh-26579](https://github.com/numpy/numpy/pull/26579)) Deprecations - The `fix_imports` keyword argument in `numpy.save` is deprecated. Since NumPy 1.17, `numpy.save` uses a pickle protocol that no longer supports Python 2, and ignored `fix_imports` keyword. This keyword is kept only for backward compatibility. It is now deprecated. ([gh-26452](https://github.com/numpy/numpy/pull/26452)) - Passing non-integer inputs as the first argument of [bincount]{.title-ref} is now deprecated, because such inputs are silently cast to integers with no warning about loss of precision. ([gh-27076](https://github.com/numpy/numpy/pull/27076)) Expired deprecations - Scalars and 0D arrays are disallowed for `numpy.nonzero` and `numpy.ndarray.nonzero`. ([gh-26268](https://github.com/numpy/numpy/pull/26268)) - `set_string_function` internal function was removed and `PyArray_SetStringFunction` was stubbed out. ([gh-26611](https://github.com/numpy/numpy/pull/26611)) C API changes API symbols now hidden but customizable NumPy now defaults to hide the API symbols it adds to allow all NumPy API usage. This means that by default you cannot dynamically fetch the NumPy API from another library (this was never possible on windows). If you are experiencing linking errors related to `PyArray_API` or `PyArray_RUNTIME_VERSION`, you can define the `NPY_API_SYMBOL_ATTRIBUTE` to opt-out of this change. If you are experiencing problems due to an upstream header including NumPy, the solution is to make sure you `include "numpy/ndarrayobject.h"` before their header and import NumPy yourself based on `including-the-c-api`. ([gh-26103](https://github.com/numpy/numpy/pull/26103)) Many shims removed from npy_3kcompat.h Many of the old shims and helper functions were removed from `npy_3kcompat.h`. If you find yourself in need of these, vendor the previous version of the file into your codebase. ([gh-26842](https://github.com/numpy/numpy/pull/26842)) New `PyUFuncObject` field `process_core_dims_func` The field `process_core_dims_func` was added to the structure `PyUFuncObject`. For generalized ufuncs, this field can be set to a function of type `PyUFunc_ProcessCoreDimsFunc` that will be called when the ufunc is called. It allows the ufunc author to check that core dimensions satisfy additional constraints, and to set output core dimension sizes if they have not been provided. ([gh-26908](https://github.com/numpy/numpy/pull/26908)) New Features - `numpy.reshape` and `numpy.ndarray.reshape` now support `shape` and `copy` arguments. ([gh-26292](https://github.com/numpy/numpy/pull/26292)) - NumPy now supports DLPack v1, support for older versions will be deprecated in the future. ([gh-26501](https://github.com/numpy/numpy/pull/26501)) - `numpy.asanyarray` now supports `copy` and `device` arguments, matching `numpy.asarray`. ([gh-26580](https://github.com/numpy/numpy/pull/26580)) - `numpy.printoptions`, `numpy.get_printoptions`, and `numpy.set_printoptions` now support a new option, `override_repr`, for defining custom `repr(array)` behavior. ([gh-26611](https://github.com/numpy/numpy/pull/26611)) - `numpy.cumulative_sum` and `numpy.cumulative_prod` were added as Array API compatible alternatives for `numpy.cumsum` and `numpy.cumprod`. The new functions can include a fixed initial (zeros for `sum` and ones for `prod`) in the result. ([gh-26724](https://github.com/numpy/numpy/pull/26724)) - `numpy.clip` now supports `max` and `min` keyword arguments which are meant to replace `a_min` and `a_max`. Also, for `np.clip(a)` or `np.clip(a, None, None)` a copy of the input array will be returned instead of raising an error. ([gh-26724](https://github.com/numpy/numpy/pull/26724)) - `numpy.astype` now supports `device` argument. ([gh-26724](https://github.com/numpy/numpy/pull/26724)) `f2py` can generate freethreading-compatible C extensions Pass `--freethreading-compatible` to the f2py CLI tool to produce a C extension marked as compatible with the free threading CPython interpreter. Doing so prevents the interpreter from re-enabling the GIL at runtime when it imports the C extension. Note that `f2py` does not analyze fortran code for thread safety, so you must verify that the wrapped fortran code is thread safe before marking the extension as compatible. ([gh-26981](https://github.com/numpy/numpy/pull/26981)) Improvements `histogram` auto-binning now returns bin sizes \>=1 for integer input data For integer input data, bin sizes smaller than 1 result in spurious empty bins. This is now avoided when the number of bins is computed using one of the algorithms provided by `histogram_bin_edges`. ([gh-12150](https://github.com/numpy/numpy/pull/12150)) `ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]` Static typing for `ndarray` is a long-term effort that continues with this change. It is a generic type with type parameters for the shape and the data type. Previously, the shape type parameter could be any value. This change restricts it to a tuple of ints, as one would expect from using `ndarray.shape`. Further, the shape-type parameter has been changed from invariant to covariant. This change also applies to the subtypes of `ndarray`, e.g. `numpy.ma.MaskedArray`. See the [typing docs](https://typing.readthedocs.io/en/latest/reference/generics.html#variance-of-generic-types) for more information. ([gh-26081](https://github.com/numpy/numpy/pull/26081)) `np.quantile` with method `closest_observation` chooses nearest even order statistic This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations. ([gh-26656](https://github.com/numpy/numpy/pull/26656)) `lapack_lite` is now thread safe NumPy provides a minimal low-performance version of LAPACK named `lapack_lite` that can be used if no BLAS/LAPACK system is detected at build time. Until now, `lapack_lite` was not thread safe. Single-threaded use cases did not hit any issues, but running linear algebra operations in multiple threads could lead to errors, incorrect results, or segfaults due to data races. We have added a global lock, serializing access to `lapack_lite` in multiple threads. ([gh-26750](https://github.com/numpy/numpy/pull/26750)) The `numpy.printoptions` context manager is now thread and async-safe In prior versions of NumPy, the printoptions were defined using a combination of Python and C global variables. We have refactored so the state is stored in a python `ContextVar`, making the context manager thread and async-safe. ([gh-26846](https://github.com/numpy/numpy/pull/26846)) Performance improvements and changes - `numpy.save` now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays. ([gh-26388](https://github.com/numpy/numpy/pull/26388)) - OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on benchmarking, there are 5 clusters of performance around these kernels: `PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX`. ([gh-27147](https://github.com/numpy/numpy/pull/27147)) - OpenBLAS on windows is linked without quadmath, simplifying licensing ([gh-27147](https://github.com/numpy/numpy/pull/27147)) - Due to a regression in OpenBLAS on windows, the performance improvements when using multiple threads for OpenBLAS 0.3.26 were reverted. ([gh-27147](https://github.com/numpy/numpy/pull/27147)) `ma.cov` and `ma.corrcoef` are now significantly faster The private function has been refactored along with `ma.cov` and `ma.corrcoef`. They are now significantly faster, particularly on large, masked arrays. ([gh-26285](https://github.com/numpy/numpy/pull/26285)) Changes - As `numpy.vecdot` is now a ufunc it has a less precise signature. This is due to the limitations of ufunc\'s typing stub. ([gh-26313](https://github.com/numpy/numpy/pull/26313)) - `numpy.floor`, `numpy.ceil`, and `numpy.trunc` now won\'t perform casting to a floating dtype for integer and boolean dtype input arrays. ([gh-26766](https://github.com/numpy/numpy/pull/26766)) `ma.corrcoef` may return a slightly different result A pairwise observation approach is currently used in `ma.corrcoef` to calculate the standard deviations for each pair of variables. This has been changed as it is being used to normalise the covariance, estimated using `ma.cov`, which does not consider the observations for each variable in a pairwise manner, rendering it unnecessary. The normalisation has been replaced by the more appropriate standard deviation for each variable, which significantly reduces the wall time, but will return slightly different estimates of the correlation coefficients in cases where the observations between a pair of variables are not aligned. However, it will return the same estimates in all other cases, including returning the same correlation matrix as `corrcoef` when using a masked array with no masked values. ([gh-26285](https://github.com/numpy/numpy/pull/26285)) Cast-safety fixes in `copyto` and `full` `copyto` now uses NEP 50 correctly and applies this to its cast safety. Python integer to NumPy integer casts and Python float to NumPy float casts are now considered \"safe\" even if assignment may fail or precision may be lost. This means the following examples change slightly: - `np.copyto(int8_arr, 1000)` previously performed an unsafe/same-kind cast : of the Python integer. It will now always raise, to achieve an unsafe cast you must pass an array or NumPy scalar. - `np.copyto(uint8_arr, 1000, casting="safe")` will raise an OverflowError rather than a TypeError due to same-kind casting. - `np.copyto(float32_arr, 1e300, casting="safe")` will overflow to `inf` (float32 cannot hold `1e300`) rather raising a TypeError. Further, only the dtype is used when assigning NumPy scalars (or 0-d arrays), meaning that the following behaves differently: - `np.copyto(float32_arr, np.float64(3.0), casting="safe")` raises. - `np.coptyo(int8_arr, np.int64(100), casting="safe")` raises. Previously, NumPy checked whether the 100 fits the `int8_arr`. This aligns `copyto`, `full`, and `full_like` with the correct NumPy 2 behavior. 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dc7ce867d277aa74555c67b93ef2a6f78bd7bd73e6c2bbafeb96f8bccd05b9d9 numpy-2.1.0rc1.tar.gz ```Links
- PyPI: https://pypi.org/project/numpy - Changelog: https://data.safetycli.com/changelogs/numpy/ - Homepage: https://numpy.org