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1 year ago ⚠ Artifact update problem
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The artifact failure details are included below:
File name: poetry.lock
installing v2 tool python v3.10.9
linking tool python v3.10.9
Python 3.10.9
pip 23.0 from /opt/buildpack/tools/python/3.10.9/lib/python3.10/site-packages/pip (python 3.10)
Installed v2 /usr/local/buildpack/tools/v2/python.sh in 21 seconds
skip cleanup, not a docker build: 3b9dcf3c5434
Collecting poetry
Using cached poetry-1.3.2-py3-none-any.whl (218 kB)
Collecting dulwich<0.21.0,>=0.20.46
Using cached dulwich-0.20.50-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (499 kB)
Collecting jsonschema<5.0.0,>=4.10.0
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Collecting pexpect<5.0.0,>=4.7.0
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Requirement already satisfied: virtualenv!=20.4.5,!=20.4.6,<21.0.0,>=20.4.3 in /opt/buildpack/tools/python/3.10.9/lib/python3.10/site-packages (from poetry) (20.17.1)
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Collecting cachecontrol[filecache]<0.13.0,>=0.12.9
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Requirement already satisfied: platformdirs<3.0.0,>=2.5.2 in /opt/buildpack/tools/python/3.10.9/lib/python3.10/site-packages (from poetry) (2.6.2)
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Requirement already satisfied: filelock<4.0.0,>=3.8.0 in /opt/buildpack/tools/python/3.10.9/lib/python3.10/site-packages (from poetry) (3.9.0)
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Requirement already satisfied: distlib<1,>=0.3.6 in /opt/buildpack/tools/python/3.10.9/lib/python3.10/site-packages (from virtualenv!=20.4.5,!=20.4.6,<21.0.0,>=20.4.3->poetry) (0.3.6)
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Installing collected packages: webencodings, trove-classifiers, ptyprocess, msgpack, lockfile, charset-normalizer, zipp, urllib3, tomlkit, tomli, six, shellingham, rapidfuzz, pyrsistent, pycparser, poetry-core, pkginfo, pexpect, packaging, more-itertools, jeepney, idna, crashtest, certifi, attrs, requests, jsonschema, jaraco.classes, importlib-metadata, html5lib, dulwich, cleo, cffi, requests-toolbelt, cryptography, cachecontrol, SecretStorage, keyring, poetry-plugin-export, poetry
Successfully installed SecretStorage-3.3.3 attrs-22.2.0 cachecontrol-0.12.11 certifi-2022.12.7 cffi-1.15.1 charset-normalizer-3.0.1 cleo-2.0.1 crashtest-0.4.1 cryptography-39.0.0 dulwich-0.20.50 html5lib-1.1 idna-3.4 importlib-metadata-6.0.0 jaraco.classes-3.2.3 jeepney-0.8.0 jsonschema-4.17.3 keyring-23.13.1 lockfile-0.12.2 more-itertools-9.0.0 msgpack-1.0.4 packaging-23.0 pexpect-4.8.0 pkginfo-1.9.6 poetry-1.3.2 poetry-core-1.4.0 poetry-plugin-export-1.3.0 ptyprocess-0.7.0 pycparser-2.21 pyrsistent-0.19.3 rapidfuzz-2.13.7 requests-2.28.2 requests-toolbelt-0.10.1 shellingham-1.5.0.post1 six-1.16.0 tomli-2.0.1 tomlkit-0.11.6 trove-classifiers-2023.1.20 urllib3-1.26.14 webencodings-0.5.1 zipp-3.12.0
Updating dependencies
Resolving dependencies...
The current project's Python requirement (>=3.7.1,<3.11) is not compatible with some of the required packages Python requirement:
- numpy requires Python >=3.8, so it will not be satisfied for Python >=3.7.1,<3.8
- numpy requires Python >=3.8, so it will not be satisfied for Python >=3.7.1,<3.8
Because no versions of numpy match >1.24.0,<1.24.1 || >1.24.1,<1.25.0
and numpy (1.24.0) requires Python >=3.8, numpy is forbidden.
So, because numpy (1.24.1) requires Python >=3.8
and skqulacs depends on numpy (~1.24.0), version solving failed.
• Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties
For numpy, a possible solution would be to set the `python` property to ">=3.8,<3.11"
For numpy, a possible solution would be to set the `python` property to ">=3.8,<3.11"
https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
https://python-poetry.org/docs/dependency-specification/#using-environment-markers
This PR contains the following updates:
~1.21.0->~1.24.0Release Notes
numpy/numpy
### [`v1.24.1`](https://togithub.com/numpy/numpy/releases/tag/v1.24.1) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.24.0...v1.24.1) ### NumPy 1.24.1 Release Notes NumPy 1.24.1 is a maintenance release that fixes bugs and regressions discovered after the 1.24.0 release. The Python versions supported by this release are 3.8-3.11. #### Contributors A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Andrew Nelson - Ben Greiner + - Charles Harris - Clément Robert - Matteo Raso - Matti Picus - Melissa Weber Mendonça - Miles Cranmer - Ralf Gommers - Rohit Goswami - Sayed Adel - Sebastian Berg #### Pull requests merged A total of 18 pull requests were merged for this release. - [#22820](https://togithub.com/numpy/numpy/pull/22820): BLD: add workaround in setup.py for newer setuptools - [#22830](https://togithub.com/numpy/numpy/pull/22830): BLD: CIRRUS_TAG redux - [#22831](https://togithub.com/numpy/numpy/pull/22831): DOC: fix a couple typos in 1.23 notes - [#22832](https://togithub.com/numpy/numpy/pull/22832): BUG: Fix refcounting errors found using pytest-leaks - [#22834](https://togithub.com/numpy/numpy/pull/22834): BUG, SIMD: Fix invalid value encountered in several ufuncs - [#22837](https://togithub.com/numpy/numpy/pull/22837): TST: ignore more np.distutils.log imports - [#22839](https://togithub.com/numpy/numpy/pull/22839): BUG: Do not use getdata() in np.ma.masked_invalid - [#22847](https://togithub.com/numpy/numpy/pull/22847): BUG: Ensure correct behavior for rows ending in delimiter in... - [#22848](https://togithub.com/numpy/numpy/pull/22848): BUG, SIMD: Fix the bitmask of the boolean comparison - [#22857](https://togithub.com/numpy/numpy/pull/22857): BLD: Help raspian arm + clang 13 about \__builtin_mul_overflow - [#22858](https://togithub.com/numpy/numpy/pull/22858): API: Ensure a full mask is returned for masked_invalid - [#22866](https://togithub.com/numpy/numpy/pull/22866): BUG: Polynomials now copy properly ([#22669](https://togithub.com/numpy/numpy/issues/22669)) - [#22867](https://togithub.com/numpy/numpy/pull/22867): BUG, SIMD: Fix memory overlap in ufunc comparison loops - [#22868](https://togithub.com/numpy/numpy/pull/22868): BUG: Fortify string casts against floating point warnings - [#22875](https://togithub.com/numpy/numpy/pull/22875): TST: Ignore nan-warnings in randomized out tests - [#22883](https://togithub.com/numpy/numpy/pull/22883): MAINT: restore npymath implementations needed for freebsd - [#22884](https://togithub.com/numpy/numpy/pull/22884): BUG: Fix integer overflow in in1d for mixed integer dtypes [#22877](https://togithub.com/numpy/numpy/issues/22877) - [#22887](https://togithub.com/numpy/numpy/pull/22887): BUG: Use whole file for encoding checks with `charset_normalizer`. #### Checksums ##### MD5 9e543db90493d6a00939bd54c2012085 numpy-1.24.1-cp310-cp310-macosx_10_9_x86_64.whl 4ebd7af622bf617b4876087e500d7586 numpy-1.24.1-cp310-cp310-macosx_11_0_arm64.whl 0c0a3012b438bb455a6c2fadfb1be76a numpy-1.24.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 0bddb527345449df624d3cb9aa0e1b75 numpy-1.24.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b246beb773689d97307f7b4c2970f061 numpy-1.24.1-cp310-cp310-win32.whl 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ad2925567f43643f51255220424c23d204024ed428afc5aad0f86f3ffc080086 numpy-1.24.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cfa1161c6ac8f92dea03d625c2d0c05e084668f4a06568b77a25a89111621566 numpy-1.24.1-pp38-pypy38_pp73-win_amd64.whl 2386da9a471cc00a1f47845e27d916d5ec5346ae9696e01a8a34760858fe9dd2 numpy-1.24.1.tar.gz ### [`v1.24.0`](https://togithub.com/numpy/numpy/releases/tag/v1.24.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.23.5...v1.24.0) ### NumPy 1.24 Release Notes The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There are also a large number of new and expired deprecations due to changes in promotion and cleanups. This might be called a deprecation release. Highlights are - Many new deprecations, check them out. - Many expired deprecations, - New F2PY features and fixes. - New "dtype" and "casting" keywords for stacking functions. See below for the details, This release supports Python versions 3.8-3.11. #### Deprecations ##### Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose The `numpy.fastCopyAndTranspose` function has been deprecated. Use the corresponding copy and transpose methods directly: arr.T.copy() The underlying C function `PyArray_CopyAndTranspose` has also been deprecated from the NumPy C-API. ([gh-22313](https://togithub.com/numpy/numpy/pull/22313)) ##### Conversion of out-of-bound Python integers Attempting a conversion from a Python integer to a NumPy value will now always check whether the result can be represented by NumPy. This means the following examples will fail in the future and give a `DeprecationWarning` now: np.uint8(-1) np.array([3000], dtype=np.int8) Many of these did succeed before. Such code was mainly useful for unsigned integers with negative values such as `np.uint8(-1)` giving `np.iinfo(np.uint8).max`. Note that conversion between NumPy integers is unaffected, so that `np.array(-1).astype(np.uint8)` continues to work and use C integer overflow logic. For negative values, it will also work to view the array: `np.array(-1, dtype=np.int8).view(np.uint8)`. In some cases, using `np.iinfo(np.uint8).max` or `val % 2**8` may also work well. In rare cases input data may mix both negative values and very large unsigned values (i.e. `-1` and `2**63`). There it is unfortunately necessary to use `%` on the Python value or use signed or unsigned conversion depending on whether negative values are expected. ([gh-22385](https://togithub.com/numpy/numpy/pull/22385)) ##### Deprecate `msort` The `numpy.msort` function is deprecated. Use `np.sort(a, axis=0)` instead. ([gh-22456](https://togithub.com/numpy/numpy/pull/22456)) ##### `np.str0` and similar are now deprecated The scalar type aliases ending in a 0 bit size: `np.object0`, `np.str0`, `np.bytes0`, `np.void0`, `np.int0`, `np.uint0` as well as `np.bool8` are now deprecated and will eventually be removed. ([gh-22607](https://togithub.com/numpy/numpy/pull/22607)) #### Expired deprecations - The `normed` keyword argument has been removed from \[np.histogram]{.title-ref}, \[np.histogram2d]{.title-ref}, and \[np.histogramdd]{.title-ref}. Use `density` instead. If `normed` was passed by position, `density` is now used. ([gh-21645](https://togithub.com/numpy/numpy/pull/21645)) - Ragged array creation will now always raise a `ValueError` unless `dtype=object` is passed. This includes very deeply nested sequences. ([gh-22004](https://togithub.com/numpy/numpy/pull/22004)) - Support for Visual Studio 2015 and earlier has been removed. - Support for the Windows Interix POSIX interop layer has been removed. ([gh-22139](https://togithub.com/numpy/numpy/pull/22139)) - Support for Cygwin < 3.3 has been removed. ([gh-22159](https://togithub.com/numpy/numpy/pull/22159)) - The mini() method of `np.ma.MaskedArray` has been removed. Use either `np.ma.MaskedArray.min()` or `np.ma.minimum.reduce()`. - The single-argument form of `np.ma.minimum` and `np.ma.maximum` has been removed. Use `np.ma.minimum.reduce()` or `np.ma.maximum.reduce()` instead. ([gh-22228](https://togithub.com/numpy/numpy/pull/22228)) - Passing dtype instances other than the canonical (mainly native byte-order) ones to `dtype=` or `signature=` in ufuncs will now raise a `TypeError`. We recommend passing the strings `"int8"` or scalar types `np.int8` since the byte-order, datetime/timedelta unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.) ([gh-22540](https://togithub.com/numpy/numpy/pull/22540)) - The `dtype=` argument to comparison ufuncs is now applied correctly. That means that only `bool` and `object` are valid values and `dtype=object` is enforced. ([gh-22541](https://togithub.com/numpy/numpy/pull/22541)) - The deprecation for the aliases `np.object`, `np.bool`, `np.float`, `np.complex`, `np.str`, and `np.int` is expired (introduces NumPy 1.20). Some of these will now give a FutureWarning in addition to raising an error since they will be mapped to the NumPy scalars in the future. ([gh-22607](https://togithub.com/numpy/numpy/pull/22607)) #### Compatibility notes ##### `array.fill(scalar)` may behave slightly different `numpy.ndarray.fill` may in some cases behave slightly different now due to the fact that the logic is aligned with item assignment: arr = np.array([1]) # with any dtype/value arr.fill(scalar) ### is now identical to: arr[0] = scalar Previously casting may have produced slightly different answers when using values that could not be represented in the target `dtype` or when the target had `object` dtype. ([gh-20924](https://togithub.com/numpy/numpy/pull/20924)) ##### Subarray to object cast now copies Casting a dtype that includes a subarray to an object will now ensure a copy of the subarray. Previously an unsafe view was returned: arr = np.ones(3, dtype=[("f", "i", 3)]) subarray_fields = arr.astype(object)[0] subarray = subarray_fields[0] # "f" field np.may_share_memory(subarray, arr) Is now always false. While previously it was true for the specific cast. ([gh-21925](https://togithub.com/numpy/numpy/pull/21925)) ##### Returned arrays respect uniqueness of dtype kwarg objects When the `dtype` keyword argument is used with :py`np.array()`{.interpreted-text role="func"} or :py`asarray()`{.interpreted-text role="func"}, the dtype of the returned array now always exactly matches the dtype provided by the caller. In some cases this change means that a *view* rather than the input array is returned. The following is an example for this on 64bit Linux where `long` and `longlong` are the same precision but different `dtypes`: >>> arr = np.array([1, 2, 3], dtype="long") >>> new_dtype = np.dtype("longlong") >>> new = np.asarray(arr, dtype=new_dtype) >>> new.dtype is new_dtype True >>> new is arr False Before the change, the `dtype` did not match because `new is arr` was `True`. ([gh-21995](https://togithub.com/numpy/numpy/pull/21995)) ##### DLPack export raises `BufferError` When an array buffer cannot be exported via DLPack a `BufferError` is now always raised where previously `TypeError` or `RuntimeError` was raised. This allows falling back to the buffer protocol or `__array_interface__` when DLPack was tried first. ([gh-22542](https://togithub.com/numpy/numpy/pull/22542)) ##### NumPy builds are no longer tested on GCC-6 Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available on Ubuntu 20.04, so builds using that compiler are no longer tested. We still test builds using GCC-7 and GCC-8. ([gh-22598](https://togithub.com/numpy/numpy/pull/22598)) #### New Features ##### New attribute `symbol` added to polynomial classes The polynomial classes in the `numpy.polynomial` package have a new `symbol` attribute which is used to represent the indeterminate of the polynomial. This can be used to change the value of the variable when printing: >>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y") >>> print(P_y) 1.0 + 0.0·y¹ - 1.0·y² Note that the polynomial classes only support 1D polynomials, so operations that involve polynomials with different symbols are disallowed when the result would be multivariate: >>> P = np.polynomial.Polynomial([1, -1]) # default symbol is "x" >>> P_z = np.polynomial.Polynomial([1, 1], symbol="z") >>> P * P_z Traceback (most recent call last) ... ValueError: Polynomial symbols differ The symbol can be any valid Python identifier. The default is `symbol=x`, consistent with existing behavior. ([gh-16154](https://togithub.com/numpy/numpy/pull/16154)) ##### F2PY support for Fortran `character` strings F2PY now supports wrapping Fortran functions with: - character (e.g. `character x`) - character array (e.g. `character, dimension(n) :: x`) - character string (e.g. `character(len=10) x`) - and character string array (e.g. `character(len=10), dimension(n, m) :: x`) arguments, including passing Python unicode strings as Fortran character string arguments. ([gh-19388](https://togithub.com/numpy/numpy/pull/19388)) ##### New function `np.show_runtime` A new function `numpy.show_runtime` has been added to display the runtime information of the machine in addition to `numpy.show_config` which displays the build-related information. ([gh-21468](https://togithub.com/numpy/numpy/pull/21468)) ##### `strict` option for `testing.assert_array_equal` The `strict` option is now available for `testing.assert_array_equal`. Setting `strict=True` will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type. ([gh-21595](https://togithub.com/numpy/numpy/pull/21595)) ##### New parameter `equal_nan` added to `np.unique` `np.unique` was changed in 1.21 to treat all `NaN` values as equal and return a single `NaN`. Setting `equal_nan=False` will restore pre-1.21 behavior to treat `NaNs` as unique. Defaults to `True`. ([gh-21623](https://togithub.com/numpy/numpy/pull/21623)) ##### `casting` and `dtype` keyword arguments for `numpy.stack` The `casting` and `dtype` keyword arguments are now available for `numpy.stack`. To use them, write `np.stack(..., dtype=None, casting='same_kind')`. ##### `casting` and `dtype` keyword arguments for `numpy.vstack` The `casting` and `dtype` keyword arguments are now available for `numpy.vstack`. To use them, write `np.vstack(..., dtype=None, casting='same_kind')`. ##### `casting` and `dtype` keyword arguments for `numpy.hstack` The `casting` and `dtype` keyword arguments are now available for `numpy.hstack`. To use them, write `np.hstack(..., dtype=None, casting='same_kind')`. ([gh-21627](https://togithub.com/numpy/numpy/pull/21627)) ##### The bit generator underlying the singleton RandomState can be changed The singleton `RandomState` instance exposed in the `numpy.random` module is initialized at startup with the `MT19937` bit generator. The new function `set_bit_generator` allows the default bit generator to be replaced with a user-provided bit generator. This function has been introduced to provide a method allowing seamless integration of a high-quality, modern bit generator in new code with existing code that makes use of the singleton-provided random variate generating functions. The companion function `get_bit_generator` returns the current bit generator being used by the singleton `RandomState`. This is provided to simplify restoring the original source of randomness if required. The preferred method to generate reproducible random numbers is to use a modern bit generator in an instance of `Generator`. The function `default_rng` simplifies instantiation: >>> rg = np.random.default_rng(3728973198) >>> rg.random() The same bit generator can then be shared with the singleton instance so that calling functions in the `random` module will use the same bit generator: >>> orig_bit_gen = np.random.get_bit_generator() >>> np.random.set_bit_generator(rg.bit_generator) >>> np.random.normal() The swap is permanent (until reversed) and so any call to functions in the `random` module will use the new bit generator. The original can be restored if required for code to run correctly: >>> np.random.set_bit_generator(orig_bit_gen) ([gh-21976](https://togithub.com/numpy/numpy/pull/21976)) ##### `np.void` now has a `dtype` argument NumPy now allows constructing structured void scalars directly by passing the `dtype` argument to `np.void`. ([gh-22316](https://togithub.com/numpy/numpy/pull/22316)) #### Improvements ##### F2PY Improvements - The generated extension modules don't use the deprecated NumPy-C API anymore - Improved `f2py` generated exception messages - Numerous bug and `flake8` warning fixes - various CPP macros that one can use within C-expressions of signature files are prefixed with `f2py_`. For example, one should use `f2py_len(x)` instead of `len(x)` - A new construct `character(f2py_len=...)` is introduced to support returning assumed length character strings (e.g. `character(len=*)`) from wrapper functions A hook to support rewriting `f2py` internal data structures after reading all its input files is introduced. This is required, for instance, for BC of SciPy support where character arguments are treated as character strings arguments in `C` expressions. ([gh-19388](https://togithub.com/numpy/numpy/pull/19388)) ##### IBM zSystems Vector Extension Facility (SIMD) Added support for SIMD extensions of zSystem (z13, z14, z15), through the universal intrinsics interface. This support leads to performance improvements for all SIMD kernels implemented using the universal intrinsics, including the following operations: rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal, not_equal, greater, greater_equal, less, less_equal, maximum, minimum, fmax, fmin, argmax, argmin, add, subtract, multiply, divide. ([gh-20913](https://togithub.com/numpy/numpy/pull/20913)) ##### NumPy now gives floating point errors in casts In most cases, NumPy previously did not give floating point warnings or errors when these happened during casts. For examples, casts like: np.array([2e300]).astype(np.float32) # overflow for float32 np.array([np.inf]).astype(np.int64) Should now generally give floating point warnings. These warnings should warn that floating point overflow occurred. For errors when converting floating point values to integers users should expect invalid value warnings. Users can modify the behavior of these warnings using `np.errstate`. Note that for float to int casts, the exact warnings that are given may be platform dependent. For example: arr = np.full(100, value=1000, dtype=np.float64) arr.astype(np.int8) May give a result equivalent to (the intermediate cast means no warning is given): arr.astype(np.int64).astype(np.int8) May return an undefined result, with a warning set: RuntimeWarning: invalid value encountered in cast The precise behavior is subject to the C99 standard and its implementation in both software and hardware. ([gh-21437](https://togithub.com/numpy/numpy/pull/21437)) ##### F2PY supports the value attribute The Fortran standard requires that variables declared with the `value` attribute must be passed by value instead of reference. F2PY now supports this use pattern correctly. So `integer, intent(in), value :: x` in Fortran codes will have correct wrappers generated. ([gh-21807](https://togithub.com/numpy/numpy/pull/21807)) ##### Added pickle support for third-party BitGenerators The pickle format for bit generators was extended to allow each bit generator to supply its own constructor when during pickling. Previous versions of NumPy only supported unpickling `Generator` instances created with one of the core set of bit generators supplied with NumPy. Attempting to unpickle a `Generator` that used a third-party bit generators would fail since the constructor used during the unpickling was only aware of the bit generators included in NumPy. ([gh-22014](https://togithub.com/numpy/numpy/pull/22014)) ##### arange() now explicitly fails with dtype=str Previously, the `np.arange(n, dtype=str)` function worked for `n=1` and `n=2`, but would raise a non-specific exception message for other values of `n`. Now, it raises a \[TypeError]{.title-ref} informing that `arange` does not support string dtypes: >>> np.arange(2, dtype=str) Traceback (most recent call last) ... TypeError: arange() not supported for inputs with DTypeConfiguration
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