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Turbo87 / utm

Bidirectional UTM-WGS84 converter for python
http://pypi.python.org/pypi/utm
MIT License
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Update dependency numpy to v1.22.0 [SECURITY] #88

Closed renovate[bot] closed 7 months ago

renovate[bot] commented 2 years ago

Mend Renovate

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
numpy (source, changelog) ==1.16.6 -> ==1.22.0 age adoption passing confidence

GitHub Vulnerability Alerts

CVE-2021-41496

Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values.

CVE-2021-41495

Null Pointer Dereference vulnerability exists in numpy.sort in NumPy &lt and 1.19 in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays.

CVE-2021-34141

Incomplete string comparison in the numpy.core component in NumPy1.9.x, which allows attackers to fail the APIs via constructing specific string objects.

CVE-2021-33430

A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service.

NOTE: The vendor does not agree this is a vulnerability; In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.

Release Notes

numpy/numpy (numpy) ### [`v1.22.0`](https://togithub.com/numpy/numpy/releases/tag/v1.22.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.21.6...v1.22.0) # NumPy 1.22.0 Release Notes NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are: - Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. - A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX. - NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. - New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. - A new configurable allocator for use by downstream projects. These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation. The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays. ## Expired deprecations ##### Deprecated numeric style dtype strings have been removed Using the strings `"Bytes0"`, `"Datetime64"`, `"Str0"`, `"Uint32"`, and `"Uint64"` as a dtype will now raise a `TypeError`. ([gh-19539](https://togithub.com/numpy/numpy/pull/19539)) ##### Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio `numpy.loads` was deprecated in v1.15, with the recommendation that users use `pickle.loads` instead. `ndfromtxt` and `mafromtxt` were both deprecated in v1.17 - users should use `numpy.genfromtxt` instead with the appropriate value for the `usemask` parameter. ([gh-19615](https://togithub.com/numpy/numpy/pull/19615)) ## Deprecations ##### Use delimiter rather than delimitor as kwarg in mrecords The misspelled keyword argument `delimitor` of `numpy.ma.mrecords.fromtextfile()` has been changed to `delimiter`, using it will emit a deprecation warning. ([gh-19921](https://togithub.com/numpy/numpy/pull/19921)) ##### Passing boolean `kth` values to (arg-)partition has been deprecated `numpy.partition` and `numpy.argpartition` would previously accept boolean values for the `kth` parameter, which would subsequently be converted into integers. This behavior has now been deprecated. ([gh-20000](https://togithub.com/numpy/numpy/pull/20000)) ##### The `np.MachAr` class has been deprecated The `numpy.MachAr` class and `finfo.machar ` attribute have been deprecated. Users are encouraged to access the property if interest directly from the corresponding `numpy.finfo` attribute. ([gh-20201](https://togithub.com/numpy/numpy/pull/20201)) ## Compatibility notes ##### Distutils forces strict floating point model on clang NumPy now sets the `-ftrapping-math` option on clang to enforce correct floating point error handling for universal functions. Clang defaults to non-IEEE and C99 conform behaviour otherwise. This change (using the equivalent but newer `-ffp-exception-behavior=strict`) was attempted in NumPy 1.21, but was effectively never used. ([gh-19479](https://togithub.com/numpy/numpy/pull/19479)) ##### Removed floor division support for complex types Floor division of complex types will now result in a `TypeError` ```{.python} >>> a = np.arange(10) + 1j* np.arange(10) >>> a // 1 TypeError: ufunc 'floor_divide' not supported for the input types... ``` ([gh-19135](https://togithub.com/numpy/numpy/pull/19135)) ##### `numpy.vectorize` functions now produce the same output class as the base function When a function that respects `numpy.ndarray` subclasses is vectorized using `numpy.vectorize`, the vectorized function will now be subclass-safe also for cases that a signature is given (i.e., when creating a `gufunc`): the output class will be the same as that returned by the first call to the underlying function. ([gh-19356](https://togithub.com/numpy/numpy/pull/19356)) ##### Python 3.7 is no longer supported Python support has been dropped. This is rather strict, there are changes that require Python >= 3.8. ([gh-19665](https://togithub.com/numpy/numpy/pull/19665)) ##### str/repr of complex dtypes now include space after punctuation The repr of `np.dtype({"names": ["a"], "formats": [int], "offsets": [2]})` is now `dtype({'names': ['a'], 'formats': ['>> np.uint32(1023).bit_count() 10 >>> np.int32(-127).bit_count() 7 ``` ([gh-19355](https://togithub.com/numpy/numpy/pull/19355)) ##### The `ndim` and `axis` attributes have been added to `numpy.AxisError` The `ndim` and `axis` parameters are now also stored as attributes within each `numpy.AxisError` instance. ([gh-19459](https://togithub.com/numpy/numpy/pull/19459)) ##### Preliminary support for `windows/arm64` target `numpy` added support for windows/arm64 target. Please note `OpenBLAS` support is not yet available for windows/arm64 target. ([gh-19513](https://togithub.com/numpy/numpy/pull/19513)) ##### Added support for LoongArch LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit. ([gh-19527](https://togithub.com/numpy/numpy/pull/19527)) ##### A `.clang-format` file has been added Clang-format is a C/C++ code formatter, together with the added `.clang-format` file, it produces code close enough to the NumPy C_STYLE_GUIDE for general use. Clang-format version 12+ is required due to the use of several new features, it is available in Fedora 34 and Ubuntu Focal among other distributions. ([gh-19754](https://togithub.com/numpy/numpy/pull/19754)) ##### `is_integer` is now available to `numpy.floating` and `numpy.integer` Based on its counterpart in Python `float` and `int`, the numpy floating point and integer types now support `float.is_integer`. Returns `True` if the number is finite with integral value, and `False` otherwise. ```{.python} >>> np.float32(-2.0).is_integer() True >>> np.float64(3.2).is_integer() False >>> np.int32(-2).is_integer() True ``` ([gh-19803](https://togithub.com/numpy/numpy/pull/19803)) ##### Symbolic parser for Fortran dimension specifications A new symbolic parser has been added to f2py in order to correctly parse dimension specifications. The parser is the basis for future improvements and provides compatibility with Draft Fortran 202x. ([gh-19805](https://togithub.com/numpy/numpy/pull/19805)) ##### `ndarray`, `dtype` and `number` are now runtime-subscriptable Mimicking PEP-585, the `numpy.ndarray`, `numpy.dtype` and `numpy.number` classes are now subscriptable for python 3.9 and later. Consequently, expressions that were previously only allowed in .pyi stub files or with the help of `from __future__ import annotations` are now also legal during runtime. ```{.python} >>> import numpy as np >>> from typing import Any >>> np.ndarray[Any, np.dtype[np.float64]] numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] ``` ([gh-19879](https://togithub.com/numpy/numpy/pull/19879)) ## Improvements ##### `ctypeslib.load_library` can now take any path-like object All parameters in the can now take any `python:path-like object`{.interpreted-text role="term"}. This includes the likes of strings, bytes and objects implementing the `__fspath__`{.interpreted-text role="meth"} protocol. ([gh-17530](https://togithub.com/numpy/numpy/pull/17530)) ##### Add `smallest_normal` and `smallest_subnormal` attributes to `finfo` The attributes `smallest_normal` and `smallest_subnormal` are available as an extension of `finfo` class for any floating-point data type. To use these new attributes, write `np.finfo(np.float64).smallest_normal` or `np.finfo(np.float64).smallest_subnormal`. ([gh-18536](https://togithub.com/numpy/numpy/pull/18536)) ##### `numpy.linalg.qr` accepts stacked matrices as inputs `numpy.linalg.qr` is able to produce results for stacked matrices as inputs. Moreover, the implementation of QR decomposition has been shifted to C from Python. ([gh-19151](https://togithub.com/numpy/numpy/pull/19151)) ##### `numpy.fromregex` now accepts `os.PathLike` implementations `numpy.fromregex` now accepts objects implementing the `__fspath__` protocol, *e.g.* `pathlib.Path`. ([gh-19680](https://togithub.com/numpy/numpy/pull/19680)) ##### Add new methods for `quantile` and `percentile` `quantile` and `percentile` now have have a `method=` keyword argument supporting 13 different methods. This replaces the `interpolation=` keyword argument. The methods are now aligned with nine methods which can be found in scientific literature and the R language. The remaining methods are the previous discontinuous variations of the default "linear" one. Please see the documentation of `numpy.percentile` for more information. ([gh-19857](https://togithub.com/numpy/numpy/pull/19857)) ##### Missing parameters have been added to the `nan` functions A number of the `nan` functions previously lacked parameters that were present in their ``-based counterpart, *e.g.* the `where` parameter was present in `numpy.mean` but absent from `numpy.nanmean`. The following parameters have now been added to the `nan` functions: - nanmin: `initial` & `where` - nanmax: `initial` & `where` - nanargmin: `keepdims` & `out` - nanargmax: `keepdims` & `out` - nansum: `initial` & `where` - nanprod: `initial` & `where` - nanmean: `where` - nanvar: `where` - nanstd: `where` ([gh-20027](https://togithub.com/numpy/numpy/pull/20027)) ##### Annotating the main Numpy namespace Starting from the 1.20 release, PEP 484 type annotations have been included for parts of the NumPy library; annotating the remaining functions being a work in progress. With the release of 1.22 this process has been completed for the main NumPy namespace, which is now fully annotated. Besides the main namespace, a limited number of sub-packages contain annotations as well. This includes, among others, `numpy.testing`, `numpy.linalg` and `numpy.random` (available since 1.21). ([gh-20217](https://togithub.com/numpy/numpy/pull/20217)) ##### Vectorize umath module using AVX-512 By leveraging Intel Short Vector Math Library (SVML), 18 umath functions (`exp2`, `log2`, `log10`, `expm1`, `log1p`, `cbrt`, `sin`, `cos`, `tan`, `arcsin`, `arccos`, `arctan`, `sinh`, `cosh`, `tanh`, `arcsinh`, `arccosh`, `arctanh`) are vectorized using AVX-512 instruction set for both single and double precision implementations. This change is currently enabled only for Linux users and on processors with AVX-512 instruction set. It provides an average speed up of 32x and 14x for single and double precision functions respectively. ([gh-19478](https://togithub.com/numpy/numpy/pull/19478)) ##### OpenBLAS v0.3.18 Update the OpenBLAS used in testing and in wheels to v0.3.18 ([gh-20058](https://togithub.com/numpy/numpy/pull/20058)) #### Checksums ##### MD5 66757b963ad5835038b9a2a9df852c84 numpy-1.22.0-cp310-cp310-macosx_10_9_universal2.whl 86b7f3a94c09dbd6869614c4d7f9ba5e numpy-1.22.0-cp310-cp310-macosx_10_9_x86_64.whl 5184db17d8e5e6ecdc53e2f0a6964c35 numpy-1.22.0-cp310-cp310-macosx_11_0_arm64.whl 6643e9a076cce736cfbe15face4db9db numpy-1.22.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 6efef45bf63594703c094b2ad729e648 numpy-1.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 7a1a21bb0958a3eb920deeef9e745935 numpy-1.22.0-cp310-cp310-win_amd64.whl 45241fb5f31ea46e2b6f1321a63c8e1c numpy-1.22.0-cp38-cp38-macosx_10_9_universal2.whl 472f24a5d35116634fcc57e9bda899bc numpy-1.22.0-cp38-cp38-macosx_10_9_x86_64.whl 6c15cf7847b20101ae281ade6121b79e numpy-1.22.0-cp38-cp38-macosx_11_0_arm64.whl 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that fixes a few bugs discovered after the 1.21.4 release and does some maintenance to extend the 1.21.x lifetime. The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11, you will need to use gcc-11.2+ to avoid problems. #### Contributors A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Matti Picus - Rohit Goswami - Ross Barnowski - Sayed Adel - Sebastian Berg #### Pull requests merged A total of 11 pull requests were merged for this release. - [#​20357](https://togithub.com/numpy/numpy/pull/20357): MAINT: Do not forward `__(deep)copy__` calls of `_GenericAlias`... - [#​20462](https://togithub.com/numpy/numpy/pull/20462): BUG: Fix float16 einsum fastpaths using wrong tempvar - [#​20463](https://togithub.com/numpy/numpy/pull/20463): BUG, DIST: Print os error message when the executable not exist - [#​20464](https://togithub.com/numpy/numpy/pull/20464): BLD: Verify the ability to compile C++ sources before initiating... - [#​20465](https://togithub.com/numpy/numpy/pull/20465): BUG: Force `npymath` to respect `npy_longdouble` - [#​20466](https://togithub.com/numpy/numpy/pull/20466): BUG: Fix failure to create aligned, empty structured dtype - [#​20467](https://togithub.com/numpy/numpy/pull/20467): ENH: provide a convenience function to replace `npy_load_module` - [#​20495](https://togithub.com/numpy/numpy/pull/20495): MAINT: update wheel to version that supports python3.10 - [#​20497](https://togithub.com/numpy/numpy/pull/20497): BUG: Clear errors correctly in F2PY conversions - [#​20613](https://togithub.com/numpy/numpy/pull/20613): DEV: add a warningfilter to fix pytest workflow. - [#​20618](https://togithub.com/numpy/numpy/pull/20618): MAINT: Help boost::python libraries at least not crash #### Checksums ##### MD5 e00a3c2e1461dd2920ab4af6b753d3da numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl 50e0526fa29110fb6033fa8285fba4e1 numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl bdbb19e7656d66250aa67bd1c7924764 numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl c5c982a07797c8963b8fec44aae6db09 numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 8b27b622f58caeeb7f14472651d655e3 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1a7ee0ffb35dc7489aebe5185a483f4c43b0d2cf784c3c9940f975a7dde56506 numpy-1.21.5.tar.gz 6a5928bc6241264dce5ed509e66f33676fc97f464e7a919edc672fb5532221ee numpy-1.21.5.zip ### [`v1.21.4`](https://togithub.com/numpy/numpy/releases/tag/v1.21.4) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.21.3...v1.21.4) # NumPy 1.21.4 Release Notes The NumPy 1.21.4 is a maintenance release that fixes a few bugs discovered after 1.21.3. The most important fix here is a fix for the NumPy header files to make them work for both x86\_64 and M1 hardware when included in the Mac universal2 wheels. Previously, the header files only worked for M1 and this caused problems for folks building x86\_64 extensions. This problem was not seen before Python 3.10 because there were thin wheels for x86\_64 that had precedence. This release also provides thin x86\_64 Mac wheels for Python 3.10. The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11, you will need to use gcc-11.2+ to avoid problems. ## Contributors A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Isuru Fernando - Matthew Brett - Sayed Adel - Sebastian Berg - 傅立业（Chris Fu） + ## Pull requests merged A total of 9 pull requests were merged for this release. - [#​20278](https://togithub.com/numpy/numpy/pull/20278): BUG: Fix shadowed reference of `dtype` in type stub - [#​20293](https://togithub.com/numpy/numpy/pull/20293): BUG: Fix headers for universal2 builds - [#​20294](https://togithub.com/numpy/numpy/pull/20294): BUG: `VOID_nonzero` could sometimes mutate alignment flag - [#​20295](https://togithub.com/numpy/numpy/pull/20295): BUG: Do not use nonzero fastpath on unaligned arrays - [#​20296](https://togithub.com/numpy/numpy/pull/20296): BUG: Distutils patch to allow for 2 as a minor version (!) - [#​20297](https://togithub.com/numpy/numpy/pull/20297): BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar - [#​20298](https://togithub.com/numpy/numpy/pull/20298): BUG, 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the fixes a few bugs discovered after 1.21.2. It also provides 64 bit Python 3.10.0 wheels. Note a few oddities about Python 3.10: - There are no 32 bit wheels for Windows, Mac, or Linux. - The Mac Intel builds are only available in universal2 wheels. The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11 you will need to use gcc-11.2+ to avoid problems. ## Contributors A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Aaron Meurer - Bas van Beek - Charles Harris - Developer-Ecosystem-Engineering + - Kevin Sheppard - Sebastian Berg - Warren Weckesser ## Pull requests merged A total of 8 pull requests were merged for this release. - [#​19745](https://togithub.com/numpy/numpy/pull/19745): ENH: Add dtype-support to 3 `` `generic ``/`ndarray` methods - [#​19955](https://togithub.com/numpy/numpy/pull/19955): BUG: Resolve Divide by Zero on Apple silicon + test failures... - [#​19958](https://togithub.com/numpy/numpy/pull/19958): MAINT: Mark type-check-only ufunc subclasses as ufunc aliases... - [#​19994](https://togithub.com/numpy/numpy/pull/19994): BUG: np.tan(np.inf) test failure - [#​20080](https://togithub.com/numpy/numpy/pull/20080): BUG: Correct incorrect advance in PCG with emulated int128 - [#​20081](https://togithub.com/numpy/numpy/pull/20081): BUG: Fix NaT handling in the PyArray_CompareFunc for datetime... - 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It also provides 64 bit manylinux Python 3.10.0rc1 wheels for downstream testing. Note that Python 3.10 is not yet final. There is also preliminary support for Windows on ARM64 builds, but there is no OpenBLAS for that platform and no wheels are available. The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with Python 3.10.0rc1 and Python 3.10 will be officially supported after it is released. The previous problems with gcc-11.1 have been fixed by gcc-11.2, che

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