chore(deps): update dependency numpy to v1.23.5 #2
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This PR contains the following updates:
==1.22.2->==1.23.5Release Notes
numpy/numpy
v1.23.5Compare Source
NumPy 1.23.5 Release Notes
NumPy 1.23.5 is a maintenance release that fixes bugs discovered after
the 1.23.4 release and keeps the build infrastructure current. The
Python versions supported for this release are 3.8-3.11.
Contributors
A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 10 pull requests were merged for this release.
random.logseriesChecksums
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v1.23.4Compare Source
NumPy 1.23.4 Release Notes
NumPy 1.23.4 is a maintenance release that fixes bugs discovered after
the 1.23.3 release and keeps the build infrastructure current. The main
improvements are fixes for some annotation corner cases, a fix for a
long time
nested_itersmemory leak, and a fix of complex vector dotfor very large arrays. The Python versions supported for this release
are 3.8-3.11.
Note that the mypy version needs to be 0.981+ if you test using Python
3.10.7, otherwise the typing tests will fail.
Contributors
A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 13 pull requests were merged for this release.
__array_api_version__tonumpy.array_apinamespaceget_infofrom "extending with Cython"...lookfor's import try/except againnumpy.typingprotocols as runtime checkableChecksums
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v1.23.3Compare Source
NumPy 1.23.3 Release Notes
NumPy 1.23.3 is a maintenance release that fixes bugs discovered after
the 1.23.2 release. There is no major theme for this release, the main
improvements are for some downstream builds and some annotation corner
cases. The Python versions supported for this release are 3.8-3.11.
Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the
10.15 version currently used will no longer be supported by our build
infrastructure at that point.
Contributors
A total of 16 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 14 pull requests were merged for this release.
__class_getitem__np.equal.reduceraises aTypeErrorChecksums
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v1.23.2Compare Source
NumPy 1.23.2 Release Notes
NumPy 1.23.2 is a maintenance release that fixes bugs discovered after
the 1.23.1 release. Notable features are:
The Python versions supported for this release are 3.8-3.11.
Contributors
A total of 9 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 15 pull requests were merged for this release.
__array_ufunc__typing support to thenin=1ufuncsnp.angledtype-overloads_GenericAliaswrap the underlying classes'...einsumsubscripts to be passed via integer...np.genericrich comparisonssqueezeandtransposemethod to...Popento silently invoke f77 -vChecksums
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v1.23.1Compare Source
NumPy 1.23.1 Release Notes
The NumPy 1.23.1 is a maintenance release that fixes bugs discovered
after the 1.23.0 release. Notable fixes are:
The Python version supported for this release are 3.8-3.10.
Contributors
A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 8 pull requests were merged for this release.
keepdimsduring normalization innp.averageand...max_rowsbehaviour innp.loadtxtChecksums
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v1.23.0Compare Source
NumPy 1.23.0 Release Notes
The NumPy 1.23.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, clarify
the documentation, and expire old deprecations. The highlights are:
loadtxtin C, greatly improving its performance.See below for the details,
New functions
A masked array specialization of
ndenumerateis now available asnumpy.ma.ndenumerate. It provides an alternative tonumpy.ndenumerateand skips masked values by default.(gh-20020)
numpy.from_dlpackhas been added to allow easy exchange of datausing the DLPack protocol. It accepts Python objects that implement
the
__dlpack__and__dlpack_device__methods and returns andarray object which is generally the view of the data of the input
object.
(gh-21145)
Deprecations
Setting
__array_finalize__toNoneis deprecated. It must now bea method and may wish to call
super().__array_finalize__(obj)after checking for
Noneor if the NumPy version is sufficientlynew.
(gh-20766)
Using
axis=32(axis=np.MAXDIMS) in many cases had the samemeaning as
axis=None. This is deprecated andaxis=Nonemust beused instead.
(gh-20920)
The hook function
PyDataMem_SetEventHookhas been deprecated andthe demonstration of its use in tool/allocation_tracking has been
removed. The ability to track allocations is now built-in to python
via
tracemalloc.(gh-20394)
numpy.distutilshas been deprecated, as a result ofdistutilsitself being deprecated. It will not be present in NumPy for
Python >= 3.12, and will be removed completely 2 years after the
release of Python 3.12 For more details, see
distutils-status-migration{.interpreted-text role="ref"}.(gh-20875)
numpy.loadtxtwill now give aDeprecationWarningwhen an integerdtypeis requested but the value is formatted as a floating point number.(gh-21663)
Expired deprecations
The
NpzFile.iteritems()andNpzFile.iterkeys()methods have beenremoved as part of the continued removal of Python 2 compatibility.
This concludes the deprecation from 1.15.
(gh-16830)
The
alenandasscalarfunctions have been removed.(gh-20414)
The
UPDATEIFCOPYarray flag has been removed together with theenum
NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated)PyArray_XDECREF_ERRwas also removed. These were all deprecated in1.14. They are replaced by
WRITEBACKIFCOPY, that requires callingPyArray_ResoveWritebackIfCopybefore the array is deallocated.(gh-20589)
Exceptions will be raised during array-like creation. When an object
raised an exception during access of the special attributes
__array__or__array_interface__, this exception was usuallyignored. This behaviour was deprecated in 1.21, and the exception
will now be raised.
(gh-20835)
Multidimensional indexing with non-tuple values is not allowed.
Previously, code such as
arr[ind]whereind = [[0, 1], [0, 1]]produced a
FutureWarningand was interpreted as a multidimensionalindex (i.e.,
arr[tuple(ind)]). Now this example is treated like anarray index over a single dimension (
arr[array(ind)]).Multidimensional indexing with anything but a tuple was deprecated
in NumPy 1.15.
(gh-21029)
Changing to a dtype of different size in F-contiguous arrays is no
longer permitted. Deprecated since Numpy 1.11.0. See below for an
extended explanation of the effects of this change.
(gh-20722)
New Features
crackfortran has support for operator and assignment overloading
crackfortranparser now understands operator and assignmentdefinitions in a module. They are added in the
bodylist of the modulewhich contains a new key
implementedbylisting the names of thesubroutines or functions implementing the operator or assignment.
(gh-15006)
f2py supports reading access type attributes from derived type statements
As a result, one does not need to use
publicorprivatestatementsto specify derived type access properties.
(gh-15844)
New parameter
ndminadded togenfromtxtThis parameter behaves the same as
ndminfromnumpy.loadtxt.(gh-20500)
np.loadtxtnow supports quote character and single converter functionnumpy.loadtxtnow supports an additionalquotecharkeyword argumentwhich is not set by default. Using
quotechar='"'will read quotedfields as used by the Excel CSV dialect.
Further, it is now possible to pass a single callable rather than a
dictionary for the
convertersargument.(gh-20580)
Changing to dtype of a different size now requires contiguity of only the last axis
Previously, viewing an array with a dtype of a different item size
required that the entire array be C-contiguous. This limitation would
unnecessarily force the user to make contiguous copies of non-contiguous
arrays before being able to change the dtype.
This change affects not only
ndarray.view, but other constructionmechanisms, including the discouraged direct assignment to
ndarray.dtype.This change expires the deprecation regarding the viewing of
F-contiguous arrays, described elsewhere in the release notes.
(gh-20722)
Deterministic output files for F2PY
For F77 inputs,
f2pywill generatemodname-f2pywrappers.funconditionally, though these may be empty. For free-form inputs,
modname-f2pywrappers.f,modname-f2pywrappers2.f90will both begenerated unconditionally, and may be empty. This allows writing generic
output rules in
cmakeormesonand other build systems. Olderbehavior can be restored by passing
--skip-empty-wrapperstof2py.f2py-meson{.interpreted-text role="ref"} details usage.(gh-21187)
keepdimsparameter foraverageThe parameter
keepdimswas added to the functionsnumpy.averageandnumpy.ma.average. The parameter has the same meaning as it does inreduction functions such as
numpy.sumornumpy.mean.(gh-21485)
New parameter
equal_nanadded tonp.uniquenp.uniquewas changed in 1.21 to treat allNaNvalues as equal andreturn a single
NaN. Settingequal_nan=Falsewill restore pre-1.21behavior to treat
NaNsas unique. Defaults toTrue.(gh-21623)
Compatibility notes
1D
np.linalg.normpreserves float input types, even for scalar resultsPreviously, this would promote to
float64when theordargument wasnot one of the explicitly listed values, e.g.
ord=3:This change affects only
float32andfloat16vectors withordother than
-Inf,0,1,2, andInf.(gh-17709)
Changes to structured (void) dtype promotion and comparisons
In general, NumPy now defines correct, but slightly limited, promotion
for structured dtypes by promoting the subtypes of each field instead of
raising an exception:
For promotion matching field names, order, and titles are enforced,
however padding is ignored. Promotion involving structured dtypes now
always ensures native byte-order for all fields (which may change the
result of
np.concatenate) and ensures that the result will be"packed", i.e. all fields are ordered contiguously and padding is
removed. See
structured_dtype_comparison_and_promotion{.interpreted-textrole="ref"} for further details.
The
reprof aligned structures will now never print the long formincluding
offsetsanditemsizeunless the structure includes paddingnot guaranteed by
align=True.In alignment with the above changes to the promotion logic, the casting
safety has been updated:
"equiv"enforces matching names and titles. The itemsize isallowed to differ due to padding.
"safe"allows mismatching field names and titlesfield.
field. Previously, the field names were used and only unsafe casts
were possible when names mismatched.
The main important change here is that name mismatches are now
considered "safe" casts.
(gh-19226)
NPY_RELAXED_STRIDES_CHECKINGhas been removedNumPy cannot be compiled with
NPY_RELAXED_STRIDES_CHECKING=0anymore.Relaxed strides have been the default for many years and the option was
initially introduced to allow a smoother transition.
(gh-20220)
np.loadtxthas recieved several changesThe row counting of
numpy.loadtxtwas fixed.loadtxtignores fullyempty lines in the file, but counted them towards
max_rows. Whenmax_rowsis used and the file contains empty lines, these will now notbe counted. Previously, it was possible that the result contained fewer
than
max_rowsrows even though more data was available to be read. Ifthe old behaviour is required,
itertools.islicemay be used:While generally much faster and improved,
numpy.loadtxtmay now failto converter certain strings to numbers that were previously
successfully read. The most important cases for this are:
1.0into integers is nowdeprecated.
0x3p3will fail_was previously accepted as a thousands delimiter100_000.This will now result in an error.
If you experience these limitations, they can all be worked around by
passing appropriate
converters=. NumPy now supports passing a singleconverter to be used for all columns to make this more convenient. For
example,
converters=float.fromhexcan read hexadecimal float numbersand
converters=intwill be able to read100_000.Further, the error messages have been generally improved. However, this
means that error types may differ. In particularly, a
ValueErrorisnow always raised when parsing of a single entry fails.
(gh-20580)
Improvements
ndarray.__array_finalize__is now callableThis means subclasses can now use
super().__array_finalize__(obj)without worrying whether
ndarrayis their superclass or not. Theactual call remains a no-op.
(gh-20766)
Add support for VSX4/Power10
With VSX4/Power10 enablement, the new instructions available in Power
ISA 3.1 can be used to accelerate some NumPy operations, e.g.,
floor_divide, modulo, etc.
(gh-20821)
np.fromiternow accepts objects and subarraysThe
numpy.fromiterfunction now supports object and subarray dtypes.Please see he function documentation for examples.
(gh-20993)
Math C library feature detection now uses correct signatures
Compiling is preceded by a detection phase to determine whether the
underlying libc supports certain math operations. Previously this code
did not respect the proper signatures. Fixing this enables compilation
for the
wasm-ldbackend (compilation for web assembly) and reduces thenumber of warnings.
(gh-21154)
np.kronnow maintains subclass informationnp.kronmaintains subclass information now such as masked arrays whilecomputing the Kronecker product of the inputs
np.kronoutput now followsufuncordering (multiply) to determinethe output class type
(gh-21262)
Performance improvements and changes
Faster
np.loadtxtnumpy.loadtxtis now generally much faster than previously as most ofit is now implemented in C.
(gh-20580)
Faster reduction operators
Reduction operations like
numpy.sum,numpy.prod,numpy.add.reduce,numpy.logical_and.reduceon contiguous integer-based arrays are nowmuch faster.
(gh-21001)
Faster
np.wherenumpy.whereis now much faster than previously on unpredictable/randominput data.
(gh-21130)
Faster operations on NumPy scalars
Many operations on NumPy scalars are now significantly faster, although
rare operations (e.g. with 0-D arrays rather than scalars) may be slower
in some cases. However, even with these improvements users who want the
best performance for their scalars, may want to convert a known NumPy
scalar into a Python one using
scalar.item().(gh-21188)
Faster
np.kronnumpy.kronis about 80% faster as the product is now computed usingbroadcasting.
(gh-21354)
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