The fundamental package for scientific computing with Python.

Overview

NumPy

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NumPy is the fundamental package needed for scientific computing with Python.

It provides:

  • a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code
  • useful linear algebra, Fourier transform, and random number capabilities

Testing:

NumPy requires pytest. Tests can then be run after installation with:

python -c 'import numpy; numpy.test()'

Call for Contributions

The NumPy project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated; issues labeled as "good first issue" may be a good starting point. If you are considering larger contributions to the source code, please contact us through the mailing list first.

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Comments
  • Decide on what the resolution rules for __op__/__rop__/__numpy_ufunc__ actually are

    Decide on what the resolution rules for __op__/__rop__/__numpy_ufunc__ actually are

    There is a complex set of questions around how to handle method resolution in the presence of __numpy_ufunc__. Currently in master is an extremely complicated set of rules that isn't documented and that I don't actually understand (see #5748 for the latest set of changes to this), so it's kinda hard to know whether they are correct, but I suspect not. And this is a blocker for 1.10, b/c whatever we release in 1.10 will be set in stone forever.

    I strongly feel that we cannot include __numpy_ufunc__ in a release without at least having a document somewhere describing what the actual dispatch rules are. I hope that doesn't mean we have to defer __numpy_ufunc__ for another release, but if it does then it does.

    AFAICT this is how a op b dispatch works for ndarrays, BEFORE __numpy_ufunc__ (i.e., this is how 1.9 works):

    • First Python uses the subclass rule to decide whether to invoke a.__op__(b) or b.__rop__(a). So in the case where one of these objects is a proper subclass of the other, that object always gets to do absolutely anything, so that's fine. The interesting cases are the ones where neither is a proper subclass of the other (either because it's like, matrix + masked array, or because it's like ndarray + scipy.sparse). So without loss of generality, let's focus on the case where Python calls a.__op__(b), and a is either an instance of ndarray or else an instance of a subclass of ndarray which has not overridden __op__, i.e. we're getting ndarray.__op__(a, b).
    • ndarray.__op__ has the following logic (see PyArray_GenericBinaryFunction in number.c):
      • If b is not an ndarray at all (even a subclass), and b has a higher __array_priority__ than a, then we return NotImplemented and let control pass to b.__rop__(a).
      • Otherwise, we call np.op(a, b) and let the ufunc machinery take over.
    • np.op(a, b) does the following (see PyUFunc_GenericFunction, PyUFunc_GeneralizedFunction, in ufunc_object.c, and also ufunc_generic_call which converts -2 return values from the previous into NotImplemented so you have to audit their whole call stack):
      • If b is not an ndarray, and calling np.array(b) returns an object array (presumably because coercion failed... though I guess this could also be hit if b.__array__() return an object array or something), AND b has a higher __array_priority__ than a, and b has an __rop__ method, then return NotImplemented.
      • If any of our arrays contain structured dtypes or strings, and there are no special struct ufunc loops registered, but not if any of our arrays contain objects, then return NotImplemented. (This is buried in get_ufunc_arguments, search for return -2.)
      • Otherwise we return the actual ufunc result.

    Now, my suggestion is that the way we would EVENTUALLY like this to look is:

    • First, Python uses the subclass rule to decide whether to invoke a.__op__(b) or b.__rop__(a). As above, let's assume that it invokes ndarray.__op__(a, b).
    • ndarray.__op__(a, b) calls np.op(a, b) (which in turn invokes all the standard ufunc stuff, including __numpy_ufunc__ resolution).
    • There is no step 3.

    I submit that it is obvious that IF we can make this work, then it is obviously the ideal outcome, because it is the simplest possible solution. But is it too simple? To determine this we have to answer two questions: (1) Will it adequately address all the relevant use cases? (2) Can we get there from here?

    So let's compare the current rules to my dream rules.

    First, we observe that everything that currently happens inside the ufunc machinery looks like it's totally wrong. The first check can only be triggered if b is a non-ndarray that has a higher __array_priority__ (among other things), but if we look above, we see that those conditions are sufficient to trigger the check in ndarray.__op__, so checking again at the ufunc level is redundant at best. And the second check is just incoherent nonsense AFAICT. The only reason to return NotImplemented is b/c you want to pass control to another __(r)op__ method, and there's no reason arrays containing structured dtypes in particular should somehow magically have different __(r)op__ methods available than other arrays. So we can just get rid of all the ufunc stuff immediately, great.

    That leaves the __array_priority__ stuff. We have two problems here: we can't just drop this immediately b/c of backcompat issues, and we need to have some way to continue to support all the use cases that this currently supports. The first problem is just a matter of having a deprecation period. For the second, observe that a class which defines a __numpy_ufunc__ method gets complete control over what any ufunc call does, so it has almost as much power as a class that currently sets __array_priority__. The only additional power that __array_priority__ currently gives you is that it lets you distinguish between e.g. a call to ndarray.__add(a, b) versus a call to np.add(a, b). So the only code that really loses out from my proposed change is code which wants a + b and add(a, b) to do different things.

    AFAIK in the entire history of numpy there is only one situation where this power has been used on purpose: the definition of matrix classes where a * b is matmul, but np.multiply(a, b) is elmul. And we've all agreed that such classes should be deprecated and eventually phased out (cite: PEP 465).

    So, I conclude that EVENTUALLY my dream rules should work great. The only problem is that we need some temporary compromises to get us from here to there. Therefore, I propose we use the following dispatch rules in numpy 1.10, with the goal of moving to my "dream rules" in some future version:

    • First, Python uses the subclass rule to decide whether to invoke a.__op__(b) or b.__rop__(a). As above, let's assume that it invokes ndarray.__op__(a, b).
    • ndarray.__op__(a, b) does the following:
      • If b does not define __numpy_ufunc__ and is not an ndarray at all (even a subclass), and b has a higher __array_priority__ than a, then we issue a deprecation warning and return NotImplemented and let control pass to b.__rop__(a). (bolded parts are changes compared to the current behaviour)
      • If __op__ is __mul__ and b->tp_class->tp_name.startswith("scipy.sparse."), then return NotImplemented. (This rule is necessary in addition to the above, because scipy.sparse has already made a release containing __numpy_ufunc__ methods, so the exception above doesn't apply.)
      • Otherwise, we call np.op(a, b) and let the ufunc machinery take over.

    I believe that this is adequate to covers all practical use cases for the current dispatch machinery, and gives us a clean path to better dispatch machinery in the future.

    The main alternative proposal is Pauli's, which involves a very complicated check (I won't try to summarize here, see this comment and following code). The goal of that approach is to continue supporting classes where a + b and add(a, b) do different things. I don't think that keeping substantial additional complexity around indefinitely is worth it in order to support functionality that no-one has ever found a use for except in one very specific case (overriding __mul__), and where we generally agree that that one specific case should be phased out as possible.

    I would very much appreciate feedback from scipy.sparse and astropy in particular on whether the above covers all their concerns.

    (Partial) History: #4815, #5748 CC: @pv, @cowlicks, @mhvk

    component: numpy.core 54 - Needs decision 
    opened by njsmith 328
  • A new PEP for infix matrix multiplication

    A new PEP for infix matrix multiplication

    This is now PEP 465, and a properly rendered version can be found here here.

    A poorly rendered version of the latest draft can be found here.

    Let's use this PR as a central place to discuss this draft PEP for adding an infix matrix multiplication operator to numpy. The PR format is nice in that it allows line by line comments, updating, etc.

    Hopefully this goes without saying, but just in case: We especially welcome comments about how the proposal will work for non-numpy projects (or could work, with fixes); this proposal started in the numpy community but the idea here is to build a consensus about how to make Python better for all projects that care about linear algebra.

    Some possible points of discussion:

    • Does this in fact seem like a good idea?
    • Do people agree with the points where I claim "there is consensus that..."? (You can search on "consensus")
    • Any suggestions on making the argument stronger and clearer?
    opened by njsmith 308
  • Windows wheel package (.whl) on Pypi

    Windows wheel package (.whl) on Pypi

    Please make Windows wheel packages and put them on Pypi.

    Currently it is possible to download Windows wheel packages for numpy here: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy

    It would be great if the wheels were directly available on the Pypi server https://pypi.python.org/pypi/ so that they can be installed with pip.

    component: distribution 
    opened by mcarans 267
  • ENH: Add `__array_ufunc__`

    ENH: Add `__array_ufunc__`

    This reverts commit bac094caf14e420a801cf952080aa443a3865d97 and enables __numpy_ufunc__ for development in the NumPy 1.13.0 development cycle.

    EDIT: Note that the name has been changed to __array_ufunc__ together with various changes to the function signature and implementation.

    01 - Enhancement component: numpy.core 
    opened by charris 234
  • ENH: randomgen

    ENH: randomgen

    ~A start at m~Merging bashtage/randomgen into numpy, as part of NEP 19.

    The original repo was cloned, moved to a subdirectory, and then merged into numpy, as documented in _randomgen/README-git.md. Then I moved the code into numpy/random and the docs into doc/source/random and doc/source/papers.

    ~Still very much a work in progress.~

    01 - Enhancement component: numpy.random 
    opened by mattip 203
  • polyfit and eig regression tests fail after Windows 10 update to 2004

    polyfit and eig regression tests fail after Windows 10 update to 2004

    Tests are failing: FAILED ....\lib\tests\test_regression.py::TestRegression::test_polyfit_build - numpy.linalg.LinAlgError: SVD did not... FAILED ....\linalg\tests\test_regression.py::TestRegression::test_eig_build - numpy.linalg.LinAlgError: Eigenvalues ... FAILED ....\ma\tests\test_extras.py::TestPolynomial::test_polyfit - numpy.linalg.LinAlgError: SVD did not converge i...

    with exceptions:

    err = 'invalid value', flag = 8
        def _raise_linalgerror_lstsq(err, flag):
    >       raise LinAlgError("SVD did not converge in Linear Least Squares")
    E       numpy.linalg.LinAlgError: SVD did not converge in Linear Least Squares
    err        = 'invalid value'
    flag       = 8
    

    and

    err = 'invalid value', flag = 8
        def _raise_linalgerror_eigenvalues_nonconvergence(err, flag):
    >       raise LinAlgError("Eigenvalues did not converge")
    E       numpy.linalg.LinAlgError: Eigenvalues did not converge
    err        = 'invalid value'
    flag       = 8
    

    Steps taken:

    • Create a VM
    • Install latest Windows 10 and update to the latest version 2004 (10.0.19041)
    • Install Python 3.8.3
    • pip install pytest
    • pip install numpy
    • pip install hypothesis
    • run tests in the package

    Same happens issue happens when running on tests in the repository.

    Version 1.19.0 of numpy

    Am I missing any dependencies? Or is it just Windows going bonkers?

    00 - Bug 32 - Installation 
    opened by Caiptain1 182
  • Decide on new PRNG BitGenerator default

    Decide on new PRNG BitGenerator default

    #13163 will be bringing in the long-awaited replacement of numpy's PRNG infrastructure. In the interest of keeping that PR manageable, we will merge it to master before all of the decisions are finalized, like which BitGenerator will be nominated as the default.

    We must make a decision before the first release with the new infrastructure. Once released, we will be stuck with our choice for a while, so we should be sure that we are comfortable with our decision.

    On the other hand, the choice of the default does not have that many consequences. We are not talking about the default BitGenerator underlying the numpy.random.* convenience functions. Per NEP 19, these remain aliases to the legacy RandomState, whose BitGenerator remains MT19937. The only place where the default comes in is when Generator() is instantiated without arguments; i.e. when a user requests a Generator with an arbitrary state, presumably to then call the .seed() method on it. This might probably be pretty rare, as it would be about as easy to just explicitly instantiate it with the seeded BitGenerator that they actually want. A legitimate choice here might actually be to nominate no default and always require the user to specify a BitGenerator.

    Nonetheless, we will have recommendations as to which BitGenerator people should use most of the time, and while we can change recommendations fairly freely, whichever one has pride of place will probably get written about most in books, blogs, tutorials, and such.

    IMO, there are a few main options (with my commentary, please feel free to disagree; I have not attempted to port over all the relevant comments from #13163):

    No default

    Always require Generator(ChosenBitGenerator(maybe_seed)). This is a little unfriendly, but as it's a pretty convenient way to get the generator properly initialized for reproducibility, people may end up doing this anyways, even if we do have a default.

    MT19937

    This would be a good conservative choice. It is certainly no worse than the status quo. As the Mersenne Twister is still widely regarded as "the standard" choice, it might help academic users who need their papers to be reviewed by people who might question "non-standard" choices, regardless of the specific qualities of the PRNG. "No one ever got fired for hiring IBM." The main downsides of MT19937 are mostly that it is slower than some of the available alternatives, due to its very large state, and that it fails some statistical quality tests. In choosing another PRNG, we have an opportunity (but not an obligation, IMO) to be opinionated here and try to move "the standard", if we wish.

    PCG64

    This is likely the one that I'll be using most often, personally. The main downside is that it uses 128-bit integer arithmetic, which is emulated in C if the compiler does not provide such an integer type. The two main platforms for which this is the case are 32-bit CPUs and 64-bit MSVC, which just does not support 128-bit integers even when the CPU does. Personally, I do not suggest letting the performance increasingly-rare 32-bit CPUs dictate our choices. But the MSVC performance is important, though, since our Windows builds do need that compiler and not other Windows compilers. It can probably be addressed with some assembly/compiler intrinsics, but someone would have to write them. The fact that it's only MSVC that we have to do this for makes this somewhat more palatable than other times when we are confronted with assembly.

    Xoshiro256

    Another modern choice for a small, fast PRNG. It does have a few known statistical quirks, but they are unlikely to be a major factor for most uses. Those quirks make me shy away from it, but that's my personal choice for the code I'll be writing.

    15 - Discussion component: numpy.random 
    opened by rkern 166
  • ENH: Nditer as context manager

    ENH: Nditer as context manager

    Resolves issue #9714 by enabling use of nditer as a context manager. Code like this:

    a = np.arange(24, dtype='f8').reshape(2, 3, 4).T
    it = np.nditer(a, [], [['readwrite', 'updateifcopy']],
                casting='same_kind', op_dtypes=[np.dtype('f4')])
    # Check that UPDATEIFCOPY is activated
    it.operands[0][2, 1, 1] = -12.5
    assert  a[2, 1, 1] != -12.5
    it = None                     # magic!!!
    assert a[2, 1, 1] == -12.5
    

    now becomes

    a = np.arange(24, dtype='f8').reshape(2, 3, 4).T
    with np.nditer(a, [], [['readwrite', 'updateifcopy']],
                casting='same_kind', op_dtypes=[np.dtype('f4')]) as it:
        # Check that UPDATEIFCOPY is activated
        it.operands[0][2, 1, 1] = -12.5
        assert  a[2, 1, 1] != -12.5
    assert a[2, 1, 1] == -12.5
    

    No more need to do the it = None assignment to write the data back to the original array. Tests were adjusted. In addition, DeprecationWarning will be raised if a nditer should be using this code pattern and is not, or if the nditer it is used outside the context manager.

    This also allows completing the confusing part of pull request #9639, and now any call to PyArray_SetUpdateIfCopyBase will emit a DeprecationWarning.

    I am not really happy with the way this affects nested_iters, since it returns a tuple there is no convienient way to handle the context manager there, that is why the test tweak is in a seperate commit.

    01 - Enhancement component: numpy.core 24 - PyPy 
    opened by mattip 159
  • numpy.dot crash with numpy.float32 input

    numpy.dot crash with numpy.float32 input

    A user of gensim @fbkarsdorp reported crash (segfault) with NumPy: piskvorky/gensim#131

    The crash seems to have nothing to do with gensim, so I'm transferring the issue here. It happens in dot of matrix*vector in single precision, on his OS X Maverick.

    component: numpy.linalg 
    opened by piskvorky 155
  • ENH: implement NEP-35's `like=` argument

    ENH: implement NEP-35's `like=` argument

    This PR adds the implementation of NEP-35's like= argument, allowing dispatch of array creation functions with __array_function__ based on a reference array.

    There are two ways to dispatch, details below.

    The first is via Python API (as demonstrated by np.ones and np.full), where array_function_dispatch is used, but differently than the existing dispatch for compute functions, where it's dispatched usually on the first argument, this is dispatched on the like= keyword argument, returning (like,). We also check if like is not None as a performance optimization, see https://github.com/numpy/numpy/pull/16935#issuecomment-670675185 .

    The second dispatch occurs via C through splitting array_implement_array_function in two functions. The first function remains very similar to how it was originally implemented (array_implement_array_function) but adds a step to remove like= argument before calling downstream libraries -- downstream libraries shall not add like= to their signatures. The second function (array_implement_c_array_function) will also remove the like= argument, but it will also extract the reference array from it and will gather the public_api Python function by doing an import on np.function_name, where function_name shall be passed by the calling function.

    The usage of the C dispatch is very straightforward and optimized for the case where like=None, adding minimal overhead to such functions. The necessary work will thus be only done when a reference array is passed, such as importing the NumPy Python function -- this can still be improved by using a lookup mechanism to avoid reimporting for each subsequent call but it was decided not to do that in this PR. The caller function will still need to add a like argument to its keyword list and parse that, but it will not be used anywhere other than the dispatcher function, it will also need to call the C dispatcher and check for its return value, if it returns Py_NotImplemented it will continue with NumPy's implementation, otherwise return that value (from downstream library) immediately.

    01 - Enhancement Priority: high component: __array_function__ 
    opened by pentschev 108
  • The PCG implementation provided by Numpy has significant, dangerous self-correlation

    The PCG implementation provided by Numpy has significant, dangerous self-correlation

    The PCG generator used by Numpy has a significant amount self-correlation. That is, for each sequence generated from a seed there is a large number of correlated, nonoverlapping sequences starting from other seeds. By "correlated" I mean that interleaving two such sequences and testing the result you obtain failures that did not appear in each sequence individually.

    The probability that two generators out of large set of terminals get two of those sequences is nonnegligible. Why this happens from a mathematical viewpoint is well known but it is explained here in detail: http://prng.di.unimi.it/pcg.pgp (see "Subsequences within the same generator").

    To show this problem directly, I wrote this simple C program reusing the Numpy code: http://prng.di.unimi.it/intpcgnumpy.c . The program takes two 128-bit states of two generators (with the same LCG constant or "stream") in the form of high and low bits, interleaves their output and writes it in binary form. Once we send it through PractRand, we should see no statistical failure, as the two streams should be independent. But if try to start from two states with the same 64 lower bits, you get:

    ./intpcgnumpy 0x596d84dfefec2fc7 0x6b79f81ab9f3e37b 0x8d7deae980a64ab0 0x6b79f81ab9f3e37b | stdbuf -oL ~/svn/c/xorshift/practrand/RNG_test stdin -tf 2 -te 1 -tlmaxonly -multithreaded RNG_test using PractRand version 0.94 RNG = RNG_stdin, seed = unknown test set = expanded, folding = extra

    rng=RNG_stdin, seed=unknown
    length= 128 megabytes (2^27 bytes), time= 2.2 seconds
      Test Name                         Raw       Processed     Evaluation
      BCFN(0+0,13-2,T)                  R= +27.6  p =  1.0e-13    FAIL
      BCFN(0+1,13-2,T)                  R= +68.0  p =  2.3e-34    FAIL !!!
      BCFN(0+2,13-3,T)                  R= +90.8  p =  8.8e-43    FAIL !!!
      BCFN(0+3,13-3,T)                  R=+120.6  p =  6.9e-57    FAIL !!!!
      DC6-6x2Bytes-1                    R=  +8.9  p =  4.0e-5   mildly suspicious
      DC6-5x4Bytes-1                    R= +15.7  p =  4.3e-9   very suspicious
      [Low1/8]BCFN(0+0,13-4,T)          R= +11.6  p =  4.9e-5   unusual
      ...and 1074 test result(s) without anomalies
    

    You can even go lower—you just need the same 58 lower bits:

    ./intpcgnumpy 0x596d84dfefec2fc7 0x0579f81ab9f3e37b 0x8d7deae980a64ab0 0x6b79f81ab9f3e37b | stdbuf -oL ~/svn/c/xorshift/practrand/RNG_test stdin -tf 2 -te 1 -tlmaxonly -multithreaded
    
    [...]
    rng=RNG_stdin, seed=unknown
    length= 32 gigabytes (2^35 bytes), time= 453 seconds
      Test Name                         Raw       Processed     Evaluation
      [Low1/16]FPF-14+6/32:cross        R= +11.6  p =  4.0e-10   VERY SUSPICIOUS
      [Low1/32]FPF-14+6/32:cross        R= +16.5  p =  3.2e-14    FAIL
      [Low1/32]FPF-14+6/16:cross        R= +12.8  p =  3.8e-11   VERY SUSPICIOUS
      [Low1/64]FPF-14+6/64:cross        R=  +6.8  p =  4.8e-6   mildly suspicious
      [Low1/64]FPF-14+6/32:cross        R=  +6.0  p =  1.9e-5   unusual
      [Low1/64]FPF-14+6/16:cross        R=  +5.5  p =  5.8e-5   unusual
      [Low4/32]FPF-14+6/64:all          R=  +5.8  p =  5.9e-5   unusual
      [Low4/32]FPF-14+6/32:(0,14-0)     R=  +7.7  p =  1.0e-6   unusual
      [Low4/32]FPF-14+6/32:(1,14-0)     R=  +7.7  p =  9.1e-7   unusual
      [Low4/32]FPF-14+6/32:all          R=  +6.5  p =  1.3e-5   unusual
      [Low4/64]FPF-14+6/64:all          R=  +5.9  p =  5.1e-5   unusual
      [Low4/64]FPF-14+6/64:cross        R=  +8.2  p =  3.0e-7   suspicious
      [Low4/64]FPF-14+6/32:(0,14-0)     R=  +7.6  p =  1.0e-6   unusual
      [Low8/64]FPF-14+6/64:(0,14-0)     R= +17.0  p =  2.2e-15    FAIL
      [Low8/64]FPF-14+6/64:(1,14-0)     R=  +9.1  p =  5.1e-8   mildly suspicious
      [Low8/64]FPF-14+6/64:all          R= +12.7  p =  2.1e-11   VERY SUSPICIOUS
      [Low8/64]FPF-14+6/32:(0,14-0)     R= +12.8  p =  1.7e-11   VERY SUSPICIOUS
      [Low8/64]FPF-14+6/32:all          R= +11.0  p =  9.3e-10   VERY SUSPICIOUS
      ...and 1696 test result(s) without anomalies
    

    Note that to get more the 50% probability that two generators start from two correlated seed (chosen at random) you need just about half a million generators starting at random (birthday paradox). And if you consider the probability that they do not exactly start from the same state, but have significant overlapping correlating sequences, you need much less.

    Any sensible generator from the literature will not behave like that. You can choose adversarially any two starting states of MRG32k3a, SFC64, CMWC, xoshiro256++, etc., and as long as you generate nonoverlapping sequences you will not see the failures above. This is a major drawback that can pop up when a number of devices uses the generator and one assumes (as it should be) that pairwise those sequences should not show correlation. The correlation can induce unwanted behavior that is hard to detect.

    Please at least document somewhere that the generator should not be used on multiple terminals or in a highly parallel environment.

    The same can happen with different "streams", as the sequences generated by an LCG by changing the additive constant are all the same modulo a change of sign and an additive constant. You can see some discussion here: https://github.com/rust-random/rand/issues/907 and a full mathematical discussion of the problem here: https://arxiv.org/abs/2001.05304 .

    component: numpy.random 
    opened by vigna 106
  • BUG: Calling `numpy.histogramdd` with a dictionary of duck arrays does not call the overridden function.

    BUG: Calling `numpy.histogramdd` with a dictionary of duck arrays does not call the overridden function.

    Describe the issue:

    I'm attempting to make an xarray-style ndarray duck type which I'm calling named_arrays. I'm trying to override numpy.histogramdd, but I can't get the numpy dispatch mechanism to call my overridden function.

    For my use case, it makes the most sense to me to have the sample argument of numpy.histogramdd be a dictionary where the keys are strings and the values are instances of my duck ndarray. The keys represent different components and become the names of the axes of the resulting histogram.

    I think it is not working since the dict[str, DuckArray] is just an iterable of strings from the dispatch mechanism's perspective, none of which have an implementation of __array_function__.

    Is there a way to coerce the dispatch mechanism to call the implementation of __array_function__ in the values of samples?

    If not I am willing to open a PR to address this issue if there is a way to resolve this in a way that everyone is happy with.

    Reproduce the code example:

    import dataclasses
    import numpy as np
    
    @dataclasses.dataclass
    class DuckArray:
        data: np.ndarray
    
        def __array_function__(self, func, types, args, kwargs, ):
            if func is np.histogramdd:
                print('Inside DuckArray.__array_function__')
            else:
                return NotImplemented
    
    def test_dict():
        sample = dict(x=DuckArray(np.random.random(11)))
        np.histogramdd(sample)
    
    def test_tuple():
        sample = DuckArray(np.random.random(11)),
        np.histogramdd(sample)
    

    Error message:

    named_arrays\example.py:13 (test_dict)
    def test_dict():
            sample = dict(x=DuckArray(np.random.random(11)))
    >       np.histogramdd(sample)
    
    example.py:16: 
    _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
    <__array_function__ internals>:200: in histogramdd
        ???
    ..\..\..\AppData\Local\Programs\Python\Python310\lib\site-packages\numpy\lib\histograms.py:1001: in histogramdd
        smin, smax = _get_outer_edges(sample[:,i], range[i])
    _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
    
    a = array([{'x': DuckArray(data=array([0.95880504, 0.41589853, 0.94013642, 0.90798121, 0.83272869,
                  0.7402617...6,
                  0.2161453 ]))}                                                                  ],
          dtype=object)
    range = None
    
        def _get_outer_edges(a, range):
            """
            Determine the outer bin edges to use, from either the data or the range
            argument
            """
            if range is not None:
                first_edge, last_edge = range
                if first_edge > last_edge:
                    raise ValueError(
                        'max must be larger than min in range parameter.')
                if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
                    raise ValueError(
                        "supplied range of [{}, {}] is not finite".format(first_edge, last_edge))
            elif a.size == 0:
                # handle empty arrays. Can't determine range, so use 0-1.
                first_edge, last_edge = 0, 1
            else:
                first_edge, last_edge = a.min(), a.max()
    >           if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
    E           TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
    
    ..\..\..\AppData\Local\Programs\Python\Python310\lib\site-packages\numpy\lib\histograms.py:322: TypeError
    

    Runtime information:

    import sys, numpy; print(numpy.version); print(sys.version) 1.24.1 3.10.8 (tags/v3.10.8:aaaf517, Oct 11 2022, 16:50:30) [MSC v.1933 64 bit (AMD64)]

    print(numpy.show_runtime()) WARNING: threadpoolctl not found in system! Install it by pip install threadpoolctl. Once installed, try np.show_runtime again for more detailed build information [{'simd_extensions': {'baseline': ['SSE', 'SSE2', 'SSE3'], 'found': ['SSSE3', 'SSE41', 'POPCNT', 'SSE42', 'AVX', 'F16C', 'FMA3', 'AVX2'], 'not_found': ['AVX512F', 'AVX512CD', 'AVX512_SKX', 'AVX512_CLX', 'AVX512_CNL', 'AVX512_ICL']}}] None

    Context for the issue:

    No response

    00 - Bug 
    opened by byrdie 0
  • MAINT: Move export for scipy arm64 helper into main module

    MAINT: Move export for scipy arm64 helper into main module

    Backport of #22939.

    This is a follow up to gh-22679 which addressed gh-22673.

    The main thing is that we want the functions to be available after importing NumPy, so they need to be part of multiarray. However, npymath is a static library, so the symbols are not really exported there. The former PR did actually work in practice but this seems like it is technically the right place?

    For some reason, I had to add nextafter to be able to do:

    from scipy.spatial.distance import euclidean
    

    with the SciPy 1.9.3 wheels. SciPy test collection works with this for the 1.9.3 wheel, so this should be all the symbols hopefully.

    03 - Maintenance 08 - Backport 
    opened by charris 2
  • Add append and update keywords to savez

    Add append and update keywords to savez

    This PR adds the keywords append and update to numpy.savez and numpy.savez_compressed.

    I like to store results of lengthy calculations into files such as numpy's npz-files and use these in plotting scripts, which run very often to be refined, etc. If I have to redo some of the calculations but not all, then only some of the results in the output files need updating.

    So I implemented that one can simply append new arrays to existing npz-files and also that one can update arrays in existing npz-files. The latter uses a temporary file, just as the zip-utility with -u. Python also only allows 'r', 'w', 'a', and 'x' with zipfile.ZipFile.

    I am a first time contributor. I do not know the dispatch mechanism. So I followed how it is done in the save function with the _save_dispatcher.

    I added tests. However, python runtests.py --coverage gave already 18 times the same error: E AssertionError: Got warnings: [<warnings.WarningMessage object at 0x1a93b92d0>] in numpy/core/tests/test_umath.py::TestSpecialFloats::test_unary_spurious_fpexception before starting the PR. This is still the case.

    opened by mcuntz 5
  • BUG: np.mean(axis=0) return incorrect value for a large size float32 array

    BUG: np.mean(axis=0) return incorrect value for a large size float32 array

    Describe the issue:

    For a large size float32 array with second dimension larger than 1, np.mean(axis=0) return incorrect value as shown follow.

    Python 3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]
    Type 'copyright', 'credits' or 'license' for more information
    IPython 7.22.0 -- An enhanced Interactive Python. Type '?' for help.
    
    In [1]: import numpy as np
    
    In [2]: np.__version__
    Out[2]: '1.24.1'
    
    In [3]: np.ones((20000000, 1), dtype=np.float32).mean(axis=0)
    Out[3]: array([1.], dtype=float32)
    
    In [4]: np.ones((20000000, 2), dtype=np.float32).mean(axis=0)
    Out[4]: array([0.8388608, 0.8388608], dtype=float32)
    
    In [5]: np.ones((20000000, 2), dtype=np.float32).mean()
    Out[5]: 1.0
    

    Reproduce the code example:

    import numpy as np
    # result: [1.]
    print(np.ones((20000000, 1), dtype=np.float32).mean(axis=0))
    # result: [0.8388608, 0.8388608]
    print(np.ones((20000000, 2), dtype=np.float32).mean(axis=0))
    # result: 1.0
    print(np.ones((20000000, 2), dtype=np.float32).mean())
    

    Error message:

    No response

    Runtime information:

    1.24.1 3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]

    Context for the issue:

    No response

    33 - Question 
    opened by cesaryuan 3
  • QUESTION (BUG?): stride of 0 for dimension of size 1 after use of newaxis. Arbitrary strides for dimensions of 1 more generally.

    QUESTION (BUG?): stride of 0 for dimension of size 1 after use of newaxis. Arbitrary strides for dimensions of 1 more generally.

    Describe the issue:

    When using newaxis to add a dimension to an array, the stride of that dimension is set to 0. This is inconsistent with the behaviour of resize(), and results in arr.stride != arr.data.strides.

    It is unclear whether this is actually a bug. The following documentation suggests it is not:

    "Even for contiguous arrays a stride for a given dimension arr.strides[dim] may be arbitrary if arr.shape[dim] == 1 or the array has no elements." https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html

    However, it feels counter-intuitive, and it is not hard to imagine how somebody could naively rely upon a particular value of stride. If this is intentional, it may be worth additional clarification, and probably somewhere more conspicuous.

    Reproduce the code example:

    x1 = np.array([[1,2,3]])
    
    x2 = np.array([1,2,3])
    x2.resize((1,3))
    
    x3 = np.array([1,2,3])[np.newaxis,:]
    
    x1.shape, x2.shape, x3.shape                       # ((1, 3), (1, 3), (1, 3))
    x1.strides, x2.strides, x3.strides                 # ((24, 8), (24, 8), (0, 8))
    x1.data.strides, x2.data.strides, x3.data.strides  # ((24, 8), (24, 8), (24, 8))
    

    Error message:

    No response

    Runtime information:

    1.23.5 3.10.8 (main, Nov 4 2022, 09:21:25) [GCC 12.2.0]

    Context for the issue:

    No response

    00 - Bug 
    opened by gnathand 3
Releases(v1.24.1)
  • v1.24.1(Dec 26, 2022)

    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: BLD: add workaround in setup.py for newer setuptools
    • #22830: BLD: CIRRUS_TAG redux
    • #22831: DOC: fix a couple typos in 1.23 notes
    • #22832: BUG: Fix refcounting errors found using pytest-leaks
    • #22834: BUG, SIMD: Fix invalid value encountered in several ufuncs
    • #22837: TST: ignore more np.distutils.log imports
    • #22839: BUG: Do not use getdata() in np.ma.masked_invalid
    • #22847: BUG: Ensure correct behavior for rows ending in delimiter in...
    • #22848: BUG, SIMD: Fix the bitmask of the boolean comparison
    • #22857: BLD: Help raspian arm + clang 13 about __builtin_mul_overflow
    • #22858: API: Ensure a full mask is returned for masked_invalid
    • #22866: BUG: Polynomials now copy properly (#22669)
    • #22867: BUG, SIMD: Fix memory overlap in ufunc comparison loops
    • #22868: BUG: Fortify string casts against floating point warnings
    • #22875: TST: Ignore nan-warnings in randomized out tests
    • #22883: MAINT: restore npymath implementations needed for freebsd
    • #22884: BUG: Fix integer overflow in in1d for mixed integer dtypes #22877
    • #22887: BUG: Use whole file for encoding checks with charset_normalizer.

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  • v1.24.0(Dec 18, 2022)

    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)

    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)

    Deprecate msort

    The numpy.msort function is deprecated. Use np.sort(a, axis=0) instead.

    (gh-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)

    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)

    • Ragged array creation will now always raise a ValueError unless dtype=object is passed. This includes very deeply nested sequences.

      (gh-22004)

    • Support for Visual Studio 2015 and earlier has been removed.

    • Support for the Windows Interix POSIX interop layer has been removed.

      (gh-22139)

    • Support for Cygwin < 3.3 has been removed.

      (gh-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)

    • 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)

    • 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)

    • 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)

    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)

    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)

    Returned arrays respect uniqueness of dtype kwarg objects

    When the dtype keyword argument is used with :pynp.array(){.interpreted-text role="func"} or :pyasarray(){.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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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 DType <class 'numpy.dtype[str_]'>.
    

    (gh-22055)

    numpy.typing protocols are now runtime checkable

    The protocols used in numpy.typing.ArrayLike and numpy.typing.DTypeLike are now properly marked as runtime checkable, making them easier to use for runtime type checkers.

    (gh-22357)

    Performance improvements and changes

    Faster version of np.isin and np.in1d for integer arrays

    np.in1d (used by np.isin) can now switch to a faster algorithm (up to >10x faster) when it is passed two integer arrays. This is often automatically used, but you can use kind="sort" or kind="table" to force the old or new method, respectively.

    (gh-12065)

    Faster comparison operators

    The comparison functions (numpy.equal, numpy.not_equal, numpy.less, numpy.less_equal, numpy.greater and numpy.greater_equal) are now much faster as they are now vectorized with universal intrinsics. For a CPU with SIMD extension AVX512BW, the performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and boolean data types, respectively (with N=50000).

    (gh-21483)

    Changes

    Better reporting of integer division overflow

    Integer division overflow of scalars and arrays used to provide a RuntimeWarning and the return value was undefined leading to crashes at rare occasions:

    >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
    <stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)
    

    Integer division overflow now returns the input dtype's minimum value and raise the following RuntimeWarning:

    >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
    <stdin>:1: RuntimeWarning: overflow encountered in floor_divide
    array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
           -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
          dtype=int32)
    

    (gh-21506)

    masked_invalid now modifies the mask in-place

    When used with copy=False, numpy.ma.masked_invalid now modifies the input masked array in-place. This makes it behave identically to masked_where and better matches the documentation.

    (gh-22046)

    nditer/NpyIter allows all allocating all operands

    The NumPy iterator available through np.nditer in Python and as NpyIter in C now supports allocating all arrays. The iterator shape defaults to () in this case. The operands dtype must be provided, since a "common dtype" cannot be inferred from the other inputs.

    (gh-22457)

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  • v1.24.0rc2(Dec 4, 2022)

    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,

    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)

    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.

    (gh-22393)

    Deprecate msort

    The numpy.msort function is deprecated. Use np.sort(a, axis=0) instead.

    (gh-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)

    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)

    • Ragged array creation will now always raise a ValueError unless dtype=object is passed. This includes very deeply nested sequences.

      (gh-22004)

    • Support for Visual Studio 2015 and earlier has been removed.

    • Support for the Windows Interix POSIX interop layer has been removed.

      (gh-22139)

    • Support for cygwin < 3.3 has been removed.

      (gh-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)

    • 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)

    • 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)

    • 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)

    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)

    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)

    Returned arrays respect uniqueness of dtype kwarg objects

    When the dtype keyword argument is used with :pynp.array(){.interpreted-text role="func"} or :pyasarray(){.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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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 DType <class 'numpy.dtype[str_]'>.
    

    (gh-22055)

    numpy.typing protocols are now runtime checkable

    The protocols used in numpy.typing.ArrayLike and numpy.typing.DTypeLike are now properly marked as runtime checkable, making them easier to use for runtime type checkers.

    (gh-22357)

    Performance improvements and changes

    Faster version of np.isin and np.in1d for integer arrays

    np.in1d (used by np.isin) can now switch to a faster algorithm (up to >10x faster) when it is passed two integer arrays. This is often automatically used, but you can use kind="sort" or kind="table" to force the old or new method, respectively.

    (gh-12065)

    Faster comparison operators

    The comparison functions (numpy.equal, numpy.not_equal, numpy.less, numpy.less_equal, numpy.greater and numpy.greater_equal) are now much faster as they are now vectorized with universal intrinsics. For a CPU with SIMD extension AVX512BW, the performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and boolean data types, respectively (with N=50000).

    (gh-21483)

    Changes

    Better reporting of integer division overflow

    Integer division overflow of scalars and arrays used to provide a RuntimeWarning and the return value was undefined leading to crashes at rare occasions:

    >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
    <stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)
    

    Integer division overflow now returns the input dtype's minimum value and raise the following RuntimeWarning:

    >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
    <stdin>:1: RuntimeWarning: overflow encountered in floor_divide
    array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
           -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
          dtype=int32)
    

    (gh-21506)

    masked_invalid now modifies the mask in-place

    When used with copy=False, numpy.ma.masked_invalid now modifies the input masked array in-place. This makes it behave identically to masked_where and better matches the documentation.

    (gh-22046)

    nditer/NpyIter allows all allocating all operands

    The NumPy iterator available through np.nditer in Python and as NpyIter in C now supports allocating all arrays. The iterator shape defaults to () in this case. The operands dtype must be provided, since a "common dtype" cannot be inferred from the other inputs.

    (gh-22457)

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  • v1.24.0rc1(Nov 24, 2022)

    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,

    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)

    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.

    (gh-22393)

    Deprecate msort

    The numpy.msort function is deprecated. Use np.sort(a, axis=0) instead.

    (gh-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)

    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)

    • Ragged array creation will now always raise a ValueError unless dtype=object is passed. This includes very deeply nested sequences.

      (gh-22004)

    • Support for Visual Studio 2015 and earlier has been removed.

    • Support for the Windows Interix POSIX interop layer has been removed.

      (gh-22139)

    • Support for cygwin < 3.3 has been removed.

      (gh-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)

    • 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)

    • 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)

    • 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)

    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)

    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)

    Returned arrays respect uniqueness of dtype kwarg objects

    When the dtype keyword argument is used with :pynp.array(){.interpreted-text role="func"} or :pyasarray(){.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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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)

    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 DType <class 'numpy.dtype[str_]'>.
    

    (gh-22055)

    numpy.typing protocols are now runtime checkable

    The protocols used in numpy.typing.ArrayLike and numpy.typing.DTypeLike are now properly marked as runtime checkable, making them easier to use for runtime type checkers.

    (gh-22357)

    Performance improvements and changes

    Faster version of np.isin and np.in1d for integer arrays

    np.in1d (used by np.isin) can now switch to a faster algorithm (up to >10x faster) when it is passed two integer arrays. This is often automatically used, but you can use kind="sort" or kind="table" to force the old or new method, respectively.

    (gh-12065)

    Faster comparison operators

    The comparison functions (numpy.equal, numpy.not_equal, numpy.less, numpy.less_equal, numpy.greater and numpy.greater_equal) are now much faster as they are now vectorized with universal intrinsics. For a CPU with SIMD extension AVX512BW, the performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and boolean data types, respectively (with N=50000).

    (gh-21483)

    Changes

    Better reporting of integer division overflow

    Integer division overflow of scalars and arrays used to provide a RuntimeWarning and the return value was undefined leading to crashes at rare occasions:

    >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
    <stdin>:1: RuntimeWarning: divide by zero encountered in floor_divide
    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)
    

    Integer division overflow now returns the input dtype's minimum value and raise the following RuntimeWarning:

    >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
    <stdin>:1: RuntimeWarning: overflow encountered in floor_divide
    array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
           -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
          dtype=int32)
    

    (gh-21506)

    masked_invalid now modifies the mask in-place

    When used with copy=False, numpy.ma.masked_invalid now modifies the input masked array in-place. This makes it behave identically to masked_where and better matches the documentation.

    (gh-22046)

    nditer/NpyIter allows all allocating all operands

    The NumPy iterator available through np.nditer in Python and as NpyIter in C now supports allocating all arrays. The iterator shape defaults to () in this case. The operands dtype must be provided, since a "common dtype" cannot be inferred from the other inputs.

    (gh-22457)

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  • v1.23.5(Nov 20, 2022)

    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.

    • @DWesl
    • Aayush Agrawal +
    • Adam Knapp +
    • Charles Harris
    • Navpreet Singh +
    • Sebastian Berg
    • Tania Allard

    Pull requests merged

    A total of 10 pull requests were merged for this release.

    • #22489: TST, MAINT: Replace most setup with setup_method (also teardown)
    • #22490: MAINT, CI: Switch to cygwin/cygwin-install-action@v2
    • #22494: TST: Make test_partial_iteration_cleanup robust but require leak...
    • #22592: MAINT: Ensure graceful handling of large header sizes
    • #22593: TYP: Spelling alignment for array flag literal
    • #22594: BUG: Fix bounds checking for random.logseries
    • #22595: DEV: Update GH actions and Dockerfile for Gitpod
    • #22596: CI: Only fetch in actions/checkout
    • #22597: BUG: Decrement ref count in gentype_reduce if allocated memory...
    • #22625: BUG: Histogramdd breaks on big arrays in Windows

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    SHA256

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    1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a  numpy-1.23.5.tar.gz
    
    Source code(tar.gz)
    Source code(zip)
    1.23.5-changelog.rst(1.44 KB)
    numpy-1.23.5.tar.gz(10.23 MB)
    README.rst(7.54 KB)
  • v1.23.4(Oct 12, 2022)

    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_iters memory leak, and a fix of complex vector dot for 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.

    • Bas van Beek
    • Charles Harris
    • Matthew Barber
    • Matti Picus
    • Ralf Gommers
    • Ross Barnowski
    • Sebastian Berg
    • Sicheng Zeng +

    Pull requests merged

    A total of 13 pull requests were merged for this release.

    • #22368: BUG: Add __array_api_version__ to numpy.array_api namespace
    • #22370: MAINT: update sde toolkit to 9.0, fix download link
    • #22382: BLD: use macos-11 image on azure, macos-1015 is deprecated
    • #22383: MAINT: random: remove get_info from "extending with Cython"...
    • #22384: BUG: Fix complex vector dot with more than NPY_CBLAS_CHUNK elements
    • #22387: REV: Loosen lookfor's import try/except again
    • #22388: TYP,ENH: Mark numpy.typing protocols as runtime checkable
    • #22389: TYP,MAINT: Change more overloads to play nice with pyright
    • #22390: TST,TYP: Bump mypy to 0.981
    • #22391: DOC: Update delimiter param description.
    • #22392: BUG: Memory leaks in numpy.nested_iters
    • #22413: REL: Prepare for the NumPy 1.23.4 release.
    • #22424: TST: Fix failing aarch64 wheel builds.

    Checksums

    MD5

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    SHA256

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    Source code(tar.gz)
    Source code(zip)
    1.23.4-changelog.rst(1.77 KB)
    numpy-1.23.4.tar.gz(10.23 MB)
    README.rst(8.14 KB)
  • v1.23.3(Sep 9, 2022)

    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.

    • Aaron Meurer
    • Bas van Beek
    • Charles Harris
    • Ganesh Kathiresan
    • Gavin Zhang +
    • Iantra Solari+
    • Jyn Spring 琴春 +
    • Matti Picus
    • Rafael Cardoso Fernandes Sousa
    • Rafael Sousa +
    • Ralf Gommers
    • Rin Cat (鈴猫) +
    • Saransh Chopra +
    • Sayed Adel
    • Sebastian Berg
    • Serge Guelton

    Pull requests merged

    A total of 14 pull requests were merged for this release.

    • #22136: BLD: Add Python 3.11 wheels to aarch64 build
    • #22148: MAINT: Update setup.py for Python 3.11.
    • #22155: CI: Test NumPy build against old versions of GCC(6, 7, 8)
    • #22156: MAINT: support IBM i system
    • #22195: BUG: Fix circleci build
    • #22214: BUG: Expose heapsort algorithms in a shared header
    • #22215: BUG: Support using libunwind for backtrack
    • #22216: MAINT: fix an incorrect pointer type usage in f2py
    • #22220: BUG: change overloads to play nice with pyright.
    • #22221: TST,BUG: Use fork context to fix MacOS savez test
    • #22222: TYP,BUG: Reduce argument validation in C-based __class_getitem__
    • #22223: TST: ensure np.equal.reduce raises a TypeError
    • #22224: BUG: Fix the implementation of numpy.array_api.vecdot
    • #22230: BUG: Better report integer division overflow (backport)

    Checksums

    MD5

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    6efc60a3f6c1b74c849d53fbcc07807b  numpy-1.23.3.tar.gz
    

    SHA256

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    51bf49c0cd1d52be0a240aa66f3458afc4b95d8993d2d04f0d91fa60c10af6cd  numpy-1.23.3.tar.gz
    
    Source code(tar.gz)
    Source code(zip)
    1.23.3-changelog.rst(1.98 KB)
    numpy-1.23.3.tar.gz(10.22 MB)
    README.rst(8.32 KB)
  • v1.23.2(Aug 14, 2022)

    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:

    • Typing changes needed for Python 3.11
    • Wheels for Python 3.11.0rc1

    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.

    • Alexander Grund +
    • Bas van Beek
    • Charles Harris
    • Jon Cusick +
    • Matti Picus
    • Michael Osthege +
    • Pal Barta +
    • Ross Barnowski
    • Sebastian Berg

    Pull requests merged

    A total of 15 pull requests were merged for this release.

    • #22030: ENH: Add __array_ufunc__ typing support to the nin=1 ufuncs
    • #22031: MAINT, TYP: Fix np.angle dtype-overloads
    • #22032: MAINT: Do not let _GenericAlias wrap the underlying classes'...
    • #22033: TYP,MAINT: Allow einsum subscripts to be passed via integer...
    • #22034: MAINT,TYP: Add object-overloads for the np.generic rich comparisons
    • #22035: MAINT,TYP: Allow the squeeze and transpose method to...
    • #22036: BUG: Fix subarray to object cast ownership details
    • #22037: BUG: Use Popen to silently invoke f77 -v
    • #22038: BUG: Avoid errors on NULL during deepcopy
    • #22039: DOC: Add versionchanged for converter callable behavior.
    • #22057: MAINT: Quiet the anaconda uploads.
    • #22078: ENH: reorder includes for testing on top of system installations...
    • #22106: TST: fix test_linear_interpolation_formula_symmetric
    • #22107: BUG: Fix skip condition for test_loss_of_precision[complex256]
    • #22115: BLD: Build python3.11.0rc1 wheels.

    Checksums

    MD5

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    SHA256

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    b78d00e48261fbbd04aa0d7427cf78d18401ee0abd89c7559bbf422e5b1c7d01  numpy-1.23.2.tar.gz
    
    Source code(tar.gz)
    Source code(zip)
    1.23.2-changelog.rst(2.05 KB)
    numpy-1.23.2.tar.gz(10.22 MB)
    README.rst(8.20 KB)
  • v1.23.1(Jul 9, 2022)

    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:

    • Fix searchsorted for float16 NaNs
    • Fix compilation on Apple M1
    • Fix KeyError in crackfortran operator support (Slycot)

    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.

    • Charles Harris
    • Matthias Koeppe +
    • Pranab Das +
    • Rohit Goswami
    • Sebastian Berg
    • Serge Guelton
    • Srimukh Sripada +

    Pull requests merged

    A total of 8 pull requests were merged for this release.

    • #21866: BUG: Fix discovered MachAr (still used within valgrind)
    • #21867: BUG: Handle NaNs correctly for float16 during sorting
    • #21868: BUG: Use keepdims during normalization in np.average and...
    • #21869: DOC: mention changes to max_rows behaviour in np.loadtxt
    • #21870: BUG: Reject non integer array-likes with size 1 in delete
    • #21949: BLD: Make can_link_svml return False for 32bit builds on x86_64
    • #21951: BUG: Reorder extern "C" to only apply to function declarations...
    • #21952: BUG: Fix KeyError in crackfortran operator support

    Checksums

    MD5

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  • v1.23.0(Jun 22, 2022)

    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:

    • Implementation of loadtxt in C, greatly improving its performance.
    • Exposing DLPack at the Python level for easy data exchange.
    • Changes to the promotion and comparisons of structured dtypes.
    • Improvements to f2py.

    See below for the details,

    New functions

    • A masked array specialization of ndenumerate is now available as numpy.ma.ndenumerate. It provides an alternative to numpy.ndenumerate and skips masked values by default.

      (gh-20020)

    • numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

      (gh-21145)

    Deprecations

    • Setting __array_finalize__ to None is deprecated. It must now be a method and may wish to call super().__array_finalize__(obj) after checking for None or if the NumPy version is sufficiently new.

      (gh-20766)

    • Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

      (gh-20920)

    • The hook function PyDataMem_SetEventHook has been deprecated and the 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.distutils has been deprecated, as a result of distutils itself 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.loadtxt will now give a DeprecationWarning when an integer dtype is requested but the value is formatted as a floating point number.

      (gh-21663)

    Expired deprecations

    • The NpzFile.iteritems() and NpzFile.iterkeys() methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.

      (gh-16830)

    • The alen and asscalar functions have been removed.

      (gh-20414)

    • The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before 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 usually ignored. 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] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array 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

    crackfortran parser now understands operator and assignment definitions in a module. They are added in the body list of the module which contains a new key implementedby listing the names of the subroutines 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 public or private statements to specify derived type access properties.

    (gh-15844)

    New parameter ndmin added to genfromtxt

    This parameter behaves the same as ndmin from numpy.loadtxt.

    (gh-20500)

    np.loadtxt now supports quote character and single converter function

    numpy.loadtxt now supports an additional quotechar keyword argument which is not set by default. Using quotechar='"' will read quoted fields as used by the Excel CSV dialect.

    Further, it is now possible to pass a single callable rather than a dictionary for the converters argument.

    (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 construction mechanisms, 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, f2py will generate modname-f2pywrappers.f unconditionally, though these may be empty. For free-form inputs, modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be generated unconditionally, and may be empty. This allows writing generic output rules in cmake or meson and other build systems. Older behavior can be restored by passing --skip-empty-wrappers to f2py. f2py-meson{.interpreted-text role="ref"} details usage.

    (gh-21187)

    keepdims parameter for average

    The parameter keepdims was added to the functions numpy.average and numpy.ma.average. The parameter has the same meaning as it does in reduction functions such as numpy.sum or numpy.mean.

    (gh-21485)

    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)

    Compatibility notes

    1D np.linalg.norm preserves float input types, even for scalar results

    Previously, this would promote to float64 when the ord argument was not one of the explicitly listed values, e.g. ord=3:

    >>> f32 = np.float32([1, 2])
    >>> np.linalg.norm(f32, 2).dtype
    dtype('float32')
    >>> np.linalg.norm(f32, 3)
    dtype('float64')  # numpy 1.22
    dtype('float32')  # numpy 1.23
    

    This change affects only float32 and float16 vectors with ord other than -Inf, 0, 1, 2, and Inf.

    (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:

    >>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
    dtype([('f0', '<i4'), ('f1', '<f8')])
    

    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-text role="ref"} for further details.

    The repr of aligned structures will now never print the long form including offsets and itemsize unless the structure includes padding not 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 is allowed to differ due to padding.
    • "safe" allows mismatching field names and titles
    • The cast safety is limited by the cast safety of each included field.
    • The order of fields is used to decide cast safety of each individual 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_CHECKING has been removed

    NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition.

    (gh-20220)

    np.loadtxt has recieved several changes

    The row counting of numpy.loadtxt was fixed. loadtxt ignores fully empty lines in the file, but counted them towards max_rows. When max_rows is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than max_rows rows even though more data was available to be read. If the old behaviour is required, itertools.islice may be used:

    import itertools
    lines = itertools.islice(open("file"), 0, max_rows)
    result = np.loadtxt(lines, ...)
    

    While generally much faster and improved, numpy.loadtxt may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are:

    • Parsing floating point values such as 1.0 into integers is now deprecated.
    • Parsing hexadecimal floats such as 0x3p3 will fail
    • An _ was previously accepted as a thousands delimiter 100_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 single converter to be used for all columns to make this more convenient. For example, converters=float.fromhex can read hexadecimal float numbers and converters=int will be able to read 100_000.

    Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a ValueError is now always raised when parsing of a single entry fails.

    (gh-20580)

    Improvements

    ndarray.__array_finalize__ is now callable

    This means subclasses can now use super().__array_finalize__(obj) without worrying whether ndarray is their superclass or not. The actual 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.fromiter now accepts objects and subarrays

    The numpy.fromiter function 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-ld backend (compilation for web assembly) and reduces the number of warnings.

    (gh-21154)

    np.kron now maintains subclass information

    np.kron maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs

    >>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
    >>> np.kron(x,x)
    masked_array(
      data=[[1, --, --, --],
            [--, 4, --, --],
            [--, --, 4, --],
            [--, --, --, 16]],
      mask=[[False,  True,  True,  True],
            [ True, False,  True,  True],
            [ True,  True, False,  True],
            [ True,  True,  True, False]],
      fill_value=999999)
    

    :warning: Warning, np.kron output now follows ufunc ordering (multiply) to determine the output class type

    >>> class myarr(np.ndarray):
    >>>    __array_priority__ = -1
    >>> a = np.ones([2, 2])
    >>> ma = myarray(a.shape, a.dtype, a.data)
    >>> type(np.kron(a, ma)) == np.ndarray
    False # Before it was True
    >>> type(np.kron(a, ma)) == myarr
    True
    

    (gh-21262)

    Performance improvements and changes

    Faster np.loadtxt

    numpy.loadtxt is now generally much faster than previously as most of it is now implemented in C.

    (gh-20580)

    Faster reduction operators

    Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce, numpy.logical_and.reduce on contiguous integer-based arrays are now much faster.

    (gh-21001)

    Faster np.where

    numpy.where is now much faster than previously on unpredictable/random input 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.kron

    numpy.kron is about 80% faster as the product is now computed using broadcasting.

    (gh-21354)

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  • v1.23.0rc3(Jun 11, 2022)

    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:

    • Implementation of loadtxt in C, greatly improving its performance.
    • Exposing DLPack at the Python level for easy data exchange.
    • Changes to the promotion and comparisons of structured dtypes.
    • Improvements to f2py.

    See below for the details,

    New functions

    • A masked array specialization of ndenumerate is now available as numpy.ma.ndenumerate. It provides an alternative to numpy.ndenumerate and skips masked values by default.

      (gh-20020)

    • numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

      (gh-21145)

    Deprecations

    • Setting __array_finalize__ to None is deprecated. It must now be a method and may wish to call super().__array_finalize__(obj) after checking for None or if the NumPy version is sufficiently new.

      (gh-20766)

    • Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

      (gh-20920)

    • The hook function PyDataMem_SetEventHook has been deprecated and the 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.distutils has been deprecated, as a result of distutils itself 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.loadtxt will now give a DeprecationWarning when an integer dtype is requested but the value is formatted as a floating point number.

      (gh-21663)

    Expired deprecations

    • The NpzFile.iteritems() and NpzFile.iterkeys() methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.

      (gh-16830)

    • The alen and asscalar functions have been removed.

      (gh-20414)

    • The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before 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 usually ignored. 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] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array 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

    crackfortran parser now understands operator and assignment definitions in a module. They are added in the body list of the module which contains a new key implementedby listing the names of the subroutines 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 public or private statements to specify derived type access properties.

    (gh-15844)

    New parameter ndmin added to genfromtxt

    This parameter behaves the same as ndmin from numpy.loadtxt.

    (gh-20500)

    np.loadtxt now supports quote character and single converter function

    numpy.loadtxt now supports an additional quotechar keyword argument which is not set by default. Using quotechar='"' will read quoted fields as used by the Excel CSV dialect.

    Further, it is now possible to pass a single callable rather than a dictionary for the converters argument.

    (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 construction mechanisms, 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, f2py will generate modname-f2pywrappers.f unconditionally, though these may be empty. For free-form inputs, modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be generated unconditionally, and may be empty. This allows writing generic output rules in cmake or meson and other build systems. Older behavior can be restored by passing --skip-empty-wrappers to f2py. f2py-meson{.interpreted-text role="ref"} details usage.

    (gh-21187)

    keepdims parameter for average

    The parameter keepdims was added to the functions numpy.average and numpy.ma.average. The parameter has the same meaning as it does in reduction functions such as numpy.sum or numpy.mean.

    (gh-21485)

    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)

    Compatibility notes

    1D np.linalg.norm preserves float input types, even for scalar results

    Previously, this would promote to float64 when the ord argument was not one of the explicitly listed values, e.g. ord=3:

    >>> f32 = np.float32([1, 2])
    >>> np.linalg.norm(f32, 2).dtype
    dtype('float32')
    >>> np.linalg.norm(f32, 3)
    dtype('float64')  # numpy 1.22
    dtype('float32')  # numpy 1.23
    

    This change affects only float32 and float16 vectors with ord other than -Inf, 0, 1, 2, and Inf.

    (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:

    >>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
    dtype([('f0', '<i4'), ('f1', '<f8')])
    

    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-text role="ref"} for further details.

    The repr of aligned structures will now never print the long form including offsets and itemsize unless the structure includes padding not 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 is allowed to differ due to padding.
    • "safe" allows mismatching field names and titles
    • The cast safety is limited by the cast safety of each included field.
    • The order of fields is used to decide cast safety of each individual 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_CHECKING has been removed

    NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition.

    (gh-20220)

    np.loadtxt has recieved several changes

    The row counting of numpy.loadtxt was fixed. loadtxt ignores fully empty lines in the file, but counted them towards max_rows. When max_rows is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than max_rows rows even though more data was available to be read. If the old behaviour is required, itertools.islice may be used:

    import itertools
    lines = itertools.islice(open("file"), 0, max_rows)
    result = np.loadtxt(lines, ...)
    

    While generally much faster and improved, numpy.loadtxt may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are:

    • Parsing floating point values such as 1.0 into integers is now deprecated.
    • Parsing hexadecimal floats such as 0x3p3 will fail
    • An _ was previously accepted as a thousands delimiter 100_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 single converter to be used for all columns to make this more convenient. For example, converters=float.fromhex can read hexadecimal float numbers and converters=int will be able to read 100_000.

    Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a ValueError is now always raised when parsing of a single entry fails.

    (gh-20580)

    Improvements

    ndarray.__array_finalize__ is now callable

    This means subclasses can now use super().__array_finalize__(obj) without worrying whether ndarray is their superclass or not. The actual 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.fromiter now accepts objects and subarrays

    The numpy.fromiter function 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-ld backend (compilation for web assembly) and reduces the number of warnings.

    (gh-21154)

    np.kron now maintains subclass information

    np.kron maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs

    >>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
    >>> np.kron(x,x)
    masked_array(
      data=[[1, --, --, --],
            [--, 4, --, --],
            [--, --, 4, --],
            [--, --, --, 16]],
      mask=[[False,  True,  True,  True],
            [ True, False,  True,  True],
            [ True,  True, False,  True],
            [ True,  True,  True, False]],
      fill_value=999999)
    

    :warning: Warning, np.kron output now follows ufunc ordering (multiply) to determine the output class type

    >>> class myarr(np.ndarray):
    >>>    __array_priority__ = -1
    >>> a = np.ones([2, 2])
    >>> ma = myarray(a.shape, a.dtype, a.data)
    >>> type(np.kron(a, ma)) == np.ndarray
    False # Before it was True
    >>> type(np.kron(a, ma)) == myarr
    True
    

    (gh-21262)

    String comparisons now supported in ufuncs

    The comparison ufuncs [np.equal]{.title-ref}, [np.greater]{.title-ref}, etc. now support unicode and byte string inputs (dtypes S and U). Due to this change a FutureWarning is now given when comparing unicode to byte strings. Such comparisons always returned False and continue to do so at this time.

    (gh-21716)

    Performance improvements and changes

    Faster np.loadtxt

    numpy.loadtxt is now generally much faster than previously as most of it is now implemented in C.

    (gh-20580)

    Faster reduction operators

    Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce, numpy.logical_and.reduce on contiguous integer-based arrays are now much faster.

    (gh-21001)

    Faster np.where

    numpy.where is now much faster than previously on unpredictable/random input 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.kron

    numpy.kron is about 80% faster as the product is now computed using broadcasting.

    (gh-21354)

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  • v1.23.0rc2(May 30, 2022)

    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:

    • Implementation of loadtxt in C, greatly improving its performance.
    • Exposing DLPack at the Python level for easy data exchange.
    • Changes to the promotion and comparisons of structured dtypes.
    • Improvements to f2py.

    See below for the details,

    New functions

    • A masked array specialization of ndenumerate is now available as numpy.ma.ndenumerate. It provides an alternative to numpy.ndenumerate and skips masked values by default.

      (gh-20020)

    • numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

      (gh-21145)

    Deprecations

    • Setting __array_finalize__ to None is deprecated. It must now be a method and may wish to call super().__array_finalize__(obj) after checking for None or if the NumPy version is sufficiently new.

      (gh-20766)

    • Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

      (gh-20920)

    • The hook function PyDataMem_SetEventHook has been deprecated and the 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.distutils has been deprecated, as a result of distutils itself 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)

    Expired deprecations

    • The NpzFile.iteritems() and NpzFile.iterkeys() methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.

      (gh-16830)

    • The alen and asscalar functions have been removed.

      (gh-20414)

    • The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before 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 usually ignored. 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] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array 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

    crackfortran parser now understands operator and assignment definitions in a module. They are added in the body list of the module which contains a new key implementedby listing the names of the subroutines 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 public or private statements to specify derived type access properties.

    (gh-15844)

    New parameter ndmin added to genfromtxt

    This parameter behaves the same as ndmin from numpy.loadtxt.

    (gh-20500)

    np.loadtxt now supports quote character and single converter function

    numpy.loadtxt now supports an additional quotechar keyword argument which is not set by default. Using quotechar='"' will read quoted fields as used by the Excel CSV dialect.

    Further, it is now possible to pass a single callable rather than a dictionary for the converters argument.

    (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 construction mechanisms, 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, f2py will generate modname-f2pywrappers.f unconditionally, though these may be empty. For free-form inputs, modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be generated unconditionally, and may be empty. This allows writing generic output rules in cmake or meson and other build systems. Older behavior can be restored by passing --skip-empty-wrappers to f2py. f2py-meson{.interpreted-text role="ref"} details usage.

    (gh-21187)

    keepdims parameter for average

    The parameter keepdims was added to the functions numpy.average and numpy.ma.average. The parameter has the same meaning as it does in reduction functions such as numpy.sum or numpy.mean.

    (gh-21485)

    Compatibility notes

    1D np.linalg.norm preserves float input types, even for scalar results

    Previously, this would promote to float64 when the ord argument was not one of the explicitly listed values, e.g. ord=3:

    >>> f32 = np.float32([1, 2])
    >>> np.linalg.norm(f32, 2).dtype
    dtype('float32')
    >>> np.linalg.norm(f32, 3)
    dtype('float64')  # numpy 1.22
    dtype('float32')  # numpy 1.23
    

    This change affects only float32 and float16 vectors with ord other than -Inf, 0, 1, 2, and Inf.

    (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:

    >>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
    dtype([('f0', '<i4'), ('f1', '<f8')])
    

    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-text role="ref"} for further details.

    The repr of aligned structures will now never print the long form including offsets and itemsize unless the structure includes padding not 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 is allowed to differ due to padding.
    • "safe" allows mismatching field names and titles
    • The cast safety is limited by the cast safety of each included field.
    • The order of fields is used to decide cast safety of each individual 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_CHECKING has been removed

    NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition.

    (gh-20220)

    np.loadtxt has recieved several changes

    The row counting of numpy.loadtxt was fixed. loadtxt ignores fully empty lines in the file, but counted them towards max_rows. When max_rows is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than max_rows rows even though more data was available to be read. If the old behaviour is required, itertools.islice may be used:

    import itertools
    lines = itertools.islice(open("file"), 0, max_rows)
    result = np.loadtxt(lines, ...)
    

    While generally much faster and improved, numpy.loadtxt may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are:

    • Parsing floating point values such as 1.0 into integers will now fail
    • Parsing hexadecimal floats such as 0x3p3 will fail
    • An _ was previously accepted as a thousands delimiter 100_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 single converter to be used for all columns to make this more convenient. For example, converters=float.fromhex can read hexadecimal float numbers and converters=int will be able to read 100_000.

    Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a ValueError is now always raised when parsing of a single entry fails.

    (gh-20580)

    Improvements

    ndarray.__array_finalize__ is now callable

    This means subclasses can now use super().__array_finalize__(obj) without worrying whether ndarray is their superclass or not. The actual 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.fromiter now accepts objects and subarrays

    The numpy.fromiter function 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-ld backend (compilation for web assembly) and reduces the number of warnings.

    (gh-21154)

    np.kron now maintains subclass information

    np.kron maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs

    >>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
    >>> np.kron(x,x)
    masked_array(
      data=[[1, --, --, --],
            [--, 4, --, --],
            [--, --, 4, --],
            [--, --, --, 16]],
      mask=[[False,  True,  True,  True],
            [ True, False,  True,  True],
            [ True,  True, False,  True],
            [ True,  True,  True, False]],
      fill_value=999999)
    

    :warning: Warning, np.kron output now follows ufunc ordering (multiply) to determine the output class type

    >>> class myarr(np.ndarray):
    >>>    __array_priority__ = -1
    >>> a = np.ones([2, 2])
    >>> ma = myarray(a.shape, a.dtype, a.data)
    >>> type(np.kron(a, ma)) == np.ndarray
    False # Before it was True
    >>> type(np.kron(a, ma)) == myarr
    True
    

    (gh-21262)

    Performance improvements and changes

    Faster np.loadtxt

    numpy.loadtxt is now generally much faster than previously as most of it is now implemented in C.

    (gh-20580)

    Faster reduction operators

    Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce, numpy.logical_and.reduce on contiguous integer-based arrays are now much faster.

    (gh-21001)

    Faster np.where

    numpy.where is now much faster than previously on unpredictable/random input 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.kron

    numpy.kron is about 80% faster as the product is now computed using broadcasting.

    (gh-21354)

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  • v1.23.0rc1(May 27, 2022)

    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:

    • Implementation of loadtxt in C, greatly improving its performance.
    • Exposing DLPack at the Python level for easy data exchange.
    • Changes to the promotion and comparisons of structured dtypes.
    • Improvements to f2py.

    See below for the details,

    New functions

    • A masked array specialization of ndenumerate is now available as numpy.ma.ndenumerate. It provides an alternative to numpy.ndenumerate and skips masked values by default.

      (gh-20020)

    • numpy.from_dlpack has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the __dlpack__ and __dlpack_device__ methods and returns a ndarray object which is generally the view of the data of the input object.

      (gh-21145)

    Deprecations

    • Setting __array_finalize__ to None is deprecated. It must now be a method and may wish to call super().__array_finalize__(obj) after checking for None or if the NumPy version is sufficiently new.

      (gh-20766)

    • Using axis=32 (axis=np.MAXDIMS) in many cases had the same meaning as axis=None. This is deprecated and axis=None must be used instead.

      (gh-20920)

    • The hook function PyDataMem_SetEventHook has been deprecated and the 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.distutils has been deprecated, as a result of distutils itself 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)

    Expired deprecations

    • The NpzFile.iteritems() and NpzFile.iterkeys() methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.

      (gh-16830)

    • The alen and asscalar functions have been removed.

      (gh-20414)

    • The UPDATEIFCOPY array flag has been removed together with the enum NPY_ARRAY_UPDATEIFCOPY. The associated (and deprecated) PyArray_XDECREF_ERR was also removed. These were all deprecated in 1.14. They are replaced by WRITEBACKIFCOPY, that requires calling PyArray_ResoveWritebackIfCopy before 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 usually ignored. 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] where ind = [[0, 1], [0, 1]] produced a FutureWarning and was interpreted as a multidimensional index (i.e., arr[tuple(ind)]). Now this example is treated like an array 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

    crackfortran parser now understands operator and assignment definitions in a module. They are added in the body list of the module which contains a new key implementedby listing the names of the subroutines 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 public or private statements to specify derived type access properties.

    (gh-15844)

    New parameter ndmin added to genfromtxt

    This parameter behaves the same as ndmin from numpy.loadtxt.

    (gh-20500)

    np.loadtxt now supports quote character and single converter function

    numpy.loadtxt now supports an additional quotechar keyword argument which is not set by default. Using quotechar='"' will read quoted fields as used by the Excel CSV dialect.

    Further, it is now possible to pass a single callable rather than a dictionary for the converters argument.

    (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 construction mechanisms, 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, f2py will generate modname-f2pywrappers.f unconditionally, though these may be empty. For free-form inputs, modname-f2pywrappers.f, modname-f2pywrappers2.f90 will both be generated unconditionally, and may be empty. This allows writing generic output rules in cmake or meson and other build systems. Older behavior can be restored by passing --skip-empty-wrappers to f2py. f2py-meson{.interpreted-text role="ref"} details usage.

    (gh-21187)

    keepdims parameter for average

    The parameter keepdims was added to the functions numpy.average and numpy.ma.average. The parameter has the same meaning as it does in reduction functions such as numpy.sum or numpy.mean.

    (gh-21485)

    Compatibility notes

    1D np.linalg.norm preserves float input types, even for scalar results

    Previously, this would promote to float64 when the ord argument was not one of the explicitly listed values, e.g. ord=3:

    >>> f32 = np.float32([1, 2])
    >>> np.linalg.norm(f32, 2).dtype
    dtype('float32')
    >>> np.linalg.norm(f32, 3)
    dtype('float64')  # numpy 1.22
    dtype('float32')  # numpy 1.23
    

    This change affects only float32 and float16 vectors with ord other than -Inf, 0, 1, 2, and Inf.

    (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:

    >>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
    dtype([('f0', '<i4'), ('f1', '<f8')])
    

    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-text role="ref"} for further details.

    The repr of aligned structures will now never print the long form including offsets and itemsize unless the structure includes padding not 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 is allowed to differ due to padding.
    • "safe" allows mismatching field names and titles
    • The cast safety is limited by the cast safety of each included field.
    • The order of fields is used to decide cast safety of each individual 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_CHECKING has been removed

    NumPy cannot be compiled with NPY_RELAXED_STRIDES_CHECKING=0 anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition.

    (gh-20220)

    np.loadtxt has recieved several changes

    The row counting of numpy.loadtxt was fixed. loadtxt ignores fully empty lines in the file, but counted them towards max_rows. When max_rows is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than max_rows rows even though more data was available to be read. If the old behaviour is required, itertools.islice may be used:

    import itertools
    lines = itertools.islice(open("file"), 0, max_rows)
    result = np.loadtxt(lines, ...)
    

    While generally much faster and improved, numpy.loadtxt may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are:

    • Parsing floating point values such as 1.0 into integers will now fail
    • Parsing hexadecimal floats such as 0x3p3 will fail
    • An _ was previously accepted as a thousands delimiter 100_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 single converter to be used for all columns to make this more convenient. For example, converters=float.fromhex can read hexadecimal float numbers and converters=int will be able to read 100_000.

    Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a ValueError is now always raised when parsing of a single entry fails.

    (gh-20580)

    Improvements

    ndarray.__array_finalize__ is now callable

    This means subclasses can now use super().__array_finalize__(obj) without worrying whether ndarray is their superclass or not. The actual 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.fromiter now accepts objects and subarrays

    The numpy.fromiter function 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-ld backend (compilation for web assembly) and reduces the number of warnings.

    (gh-21154)

    np.kron now maintains subclass information

    np.kron maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs

    >>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
    >>> np.kron(x,x)
    masked_array(
      data=[[1, --, --, --],
            [--, 4, --, --],
            [--, --, 4, --],
            [--, --, --, 16]],
      mask=[[False,  True,  True,  True],
            [ True, False,  True,  True],
            [ True,  True, False,  True],
            [ True,  True,  True, False]],
      fill_value=999999)
    

    :warning: Warning, np.kron output now follows ufunc ordering (multiply) to determine the output class type

    >>> class myarr(np.ndarray):
    >>>    __array_priority__ = -1
    >>> a = np.ones([2, 2])
    >>> ma = myarray(a.shape, a.dtype, a.data)
    >>> type(np.kron(a, ma)) == np.ndarray
    False # Before it was True
    >>> type(np.kron(a, ma)) == myarr
    True
    

    (gh-21262)

    Performance improvements and changes

    Faster np.loadtxt

    numpy.loadtxt is now generally much faster than previously as most of it is now implemented in C.

    (gh-20580)

    Faster reduction operators

    Reduction operations like numpy.sum, numpy.prod, numpy.add.reduce, numpy.logical_and.reduce on contiguous integer-based arrays are now much faster.

    (gh-21001)

    Faster np.where

    numpy.where is now much faster than previously on unpredictable/random input 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.kron

    numpy.kron is about 80% faster as the product is now computed using broadcasting.

    (gh-21354)

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  • v1.22.4(May 20, 2022)

    NumPy 1.22.4 Release Notes

    NumPy 1.22.4 is a maintenance release that fixes bugs discovered after the 1.22.3 release. In addition, the wheels for this release are built using the recently released Cython 0.29.30, which should fix the reported problems with debugging.

    The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.15 rather than 10.6 that was used in previous NumPy release cycles.

    Contributors

    A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

    • Alexander Shadchin
    • Bas van Beek
    • Charles Harris
    • Hood Chatham
    • Jarrod Millman
    • John-Mark Gurney +
    • Junyan Ou +
    • Mariusz Felisiak +
    • Ross Barnowski
    • Sebastian Berg
    • Serge Guelton
    • Stefan van der Walt

    Pull requests merged

    A total of 22 pull requests were merged for this release.

    • #21191: TYP, BUG: Fix np.lib.stride_tricks re-exported under the...
    • #21192: TST: Bump mypy from 0.931 to 0.940
    • #21243: MAINT: Explicitly re-export the types in numpy._typing
    • #21245: MAINT: Specify sphinx, numpydoc versions for CI doc builds
    • #21275: BUG: Fix typos
    • #21277: ENH, BLD: Fix math feature detection for wasm
    • #21350: MAINT: Fix failing simd and cygwin tests.
    • #21438: MAINT: Fix failing Python 3.8 32-bit Windows test.
    • #21444: BUG: add linux guard per #21386
    • #21445: BUG: Allow legacy dtypes to cast to datetime again
    • #21446: BUG: Make mmap handling safer in frombuffer
    • #21447: BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11.
    • #21448: ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS
    • #21472: BUG: Ensure compile errors are raised correclty
    • #21473: BUG: Fix segmentation fault
    • #21474: MAINT: Update doc requirements
    • #21475: MAINT: Mark npy_memchr with no_sanitize("alignment") on clang
    • #21512: DOC: Proposal - make the doc landing page cards more similar...
    • #21525: MAINT: Update Cython version to 0.29.30.
    • #21536: BUG: Fix GCC error during build configuration
    • #21541: REL: Prepare for the NumPy 1.22.4 release.
    • #21547: MAINT: Skip tests that fail on PyPy.

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  • v1.21.6(Apr 12, 2022)

    NumPy 1.21.6 Release Notes

    NumPy 1.21.6 is a very small release that achieves two things:

    • Backs out the mistaken backport of C++ code into 1.21.5.
    • Provides a 32 bit Windows wheel for Python 3.10.

    The provision of the 32 bit wheel is intended to make life easier for oldest-supported-numpy.

    Checksums

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  • v1.22.3(Mar 7, 2022)

    NumPy 1.22.3 Release Notes

    NumPy 1.22.3 is a maintenance release that fixes bugs discovered after the 1.22.2 release. The most noticeable fixes may be those for DLPack. One that may cause some problems is disallowing strings as inputs to logical ufuncs. It is still undecided how strings should be treated in those functions and it was thought best to simply disallow them until a decision was reached. That should not cause problems with older code.

    The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.14 rather than 10.9 that was used in previous NumPy release cycles. 10.14 is the oldest release supported by Apple.

    Contributors

    A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

    • @GalaxySnail +
    • Alexandre de Siqueira
    • Bas van Beek
    • Charles Harris
    • Melissa Weber Mendonça
    • Ross Barnowski
    • Sebastian Berg
    • Tirth Patel
    • Matthieu Darbois

    Pull requests merged

    A total of 10 pull requests were merged for this release.

    • #21048: MAINT: Use "3.10" instead of "3.10-dev" on travis.
    • #21106: TYP,MAINT: Explicitly allow sequences of array-likes in np.concatenate
    • #21137: BLD,DOC: skip broken ipython 8.1.0
    • #21138: BUG, ENH: np._from_dlpack: export correct device information
    • #21139: BUG: Fix numba DUFuncs added loops getting picked up
    • #21140: BUG: Fix unpickling an empty ndarray with a non-zero dimension...
    • #21141: BUG: use ThreadPoolExecutor instead of ThreadPool
    • #21142: API: Disallow strings in logical ufuncs
    • #21143: MAINT, DOC: Fix SciPy intersphinx link
    • #21148: BUG,ENH: np._from_dlpack: export arrays with any strided size-1...

    Checksums

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  • v1.22.2(Feb 4, 2022)

    NumPy 1.22.2 Release Notes

    The NumPy 1.22.2 is maintenance release that fixes bugs discovered after the 1.22.1 release. Notable fixes are:

    • Several build related fixes for downstream projects and other platforms.
    • Various Annotation fixes/additions.
    • Numpy wheels for Windows will use the 1.41 tool chain, fixing downstream link problems for projects using NumPy provided libraries on Windows.
    • Deal with CVE-2021-41495 complaint.

    The Python versions supported for this release are 3.8-3.10.

    Contributors

    A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

    • Andrew J. Hesford +
    • Bas van Beek
    • Brénainn Woodsend +
    • Charles Harris
    • Hood Chatham
    • Janus Heide +
    • Leo Singer
    • Matti Picus
    • Mukulika Pahari
    • Niyas Sait
    • Pearu Peterson
    • Ralf Gommers
    • Sebastian Berg
    • Serge Guelton

    Pull requests merged

    A total of 21 pull requests were merged for this release.

    • #20842: BLD: Add NPY_DISABLE_SVML env var to opt out of SVML
    • #20843: BUG: Fix build of third party extensions with Py_LIMITED_API
    • #20844: TYP: Fix pyright being unable to infer the real and imag...
    • #20845: BUG: Fix comparator function signatures
    • #20906: BUG: Avoid importing numpy.distutils on import numpy.testing
    • #20907: MAINT: remove outdated mingw32 fseek support
    • #20908: TYP: Relax the return type of np.vectorize
    • #20909: BUG: fix f2py's define for threading when building with Mingw
    • #20910: BUG: distutils: fix building mixed C/Fortran extensions
    • #20912: DOC,TST: Fix Pandas code example as per new release
    • #20935: TYP, MAINT: Add annotations for flatiter.__setitem__
    • #20936: MAINT, TYP: Added missing where typehints in fromnumeric.pyi
    • #20937: BUG: Fix build_ext interaction with non numpy extensions
    • #20938: BUG: Fix missing intrinsics for windows/arm64 target
    • #20945: REL: Prepare for the NumPy 1.22.2 release.
    • #20982: MAINT: f2py: don't generate code that triggers -Wsometimes-uninitialized.
    • #20983: BUG: Fix incorrect return type in reduce without initial value
    • #20984: ENH: review return values for PyArray_DescrNew
    • #20985: MAINT: be more tolerant of setuptools >= 60
    • #20986: BUG: Fix misplaced return.
    • #20992: MAINT: Further small return value validation fixes

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  • v1.22.1(Jan 14, 2022)

    NumPy 1.22.1 Release Notes

    The NumPy 1.22.1 is maintenance release that fixes bugs discovered after the 1.22.0 release. Notable fixes are:

    • Fix f2PY docstring problems (SciPy)
    • Fix reduction type problems (AstroPy)
    • Fix various typing bugs.

    The Python versions supported for this release are 3.8-3.10.

    Contributors

    A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

    • Arryan Singh
    • Bas van Beek
    • Charles Harris
    • Denis Laxalde
    • Isuru Fernando
    • Kevin Sheppard
    • Matthew Barber
    • Matti Picus
    • Melissa Weber Mendonça
    • Mukulika Pahari
    • Omid Rajaei +
    • Pearu Peterson
    • Ralf Gommers
    • Sebastian Berg

    Pull requests merged

    A total of 20 pull requests were merged for this release.

    • #20702: MAINT, DOC: Post 1.22.0 release fixes.
    • #20703: DOC, BUG: Use pngs instead of svgs.
    • #20704: DOC: Fixed the link on user-guide landing page
    • #20714: BUG: Restore vc141 support
    • #20724: BUG: Fix array dimensions solver for multidimensional arguments...
    • #20725: TYP: change type annotation for __array_namespace__ to ModuleType
    • #20726: TYP, MAINT: Allow ndindex to accept integer tuples
    • #20757: BUG: Relax dtype identity check in reductions
    • #20763: TYP: Allow time manipulation functions to accept date and timedelta...
    • #20768: TYP: Relax the type of ndarray.__array_finalize__
    • #20795: MAINT: Raise RuntimeError if setuptools version is too recent.
    • #20796: BUG, DOC: Fixes SciPy docs build warnings
    • #20797: DOC: fix OpenBLAS version in release note
    • #20798: PERF: Optimize array check for bounded 0,1 values
    • #20805: BUG: Fix that reduce-likes honor out always (and live in the...
    • #20806: BUG: array_api.argsort(descending=True) respects relative...
    • #20807: BUG: Allow integer inputs for pow-related functions in array_api
    • #20814: DOC: Refer to NumPy, not pandas, in main page
    • #20815: DOC: Update Copyright to 2022 [License]
    • #20819: BUG: Return correctly shaped inverse indices in array_api set...

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  • v1.22.0(Dec 31, 2021)

    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)

    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)

    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)

    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)

    The np.MachAr class has been deprecated

    The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have been deprecated. Users are encouraged to access the property if interest directly from the corresponding numpy.finfo attribute.

    (gh-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)

    Removed floor division support for complex types

    Floor division of complex types will now result in a TypeError

    >>> a = np.arange(10) + 1j* np.arange(10)
    >>> a // 1
    TypeError: ufunc 'floor_divide' not supported for the input types...
    

    (gh-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)

    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)

    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': ['<i8'], 'offsets': [2], 'itemsize': 10}), whereas spaces where previously omitted after colons and between fields.

    The old behavior can be restored via np.set_printoptions(legacy="1.21").

    (gh-19687)

    Corrected advance in PCG64DSXM and PCG64

    Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug only affects results when the step was larger than $2^{64}$ on platforms that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

    (gh-20049)

    Change in generation of random 32 bit floating point variates

    There was bug in the generation of 32 bit floating point values from the uniform distribution that would result in the least significant bit of the random variate always being 0. This has been fixed.

    This change affects the variates produced by the random.Generator methods random, standard_normal, standard_exponential, and standard_gamma, but only when the dtype is specified as numpy.float32.

    (gh-20314)

    C API changes

    Masked inner-loops cannot be customized anymore

    The masked inner-loop selector is now never used. A warning will be given in the unlikely event that it was customized.

    We do not expect that any code uses this. If you do use it, you must unset the selector on newer NumPy version. Please also contact the NumPy developers, we do anticipate providing a new, more specific, mechanism.

    The customization was part of a never-implemented feature to allow for faster masked operations.

    (gh-19259)

    New Features

    NEP 49 configurable allocators

    As detailed in NEP 49, the function used for allocation of the data segment of a ndarray can be changed. The policy can be set globally or in a context. For more information see the NEP and the data_memory{.interpreted-text role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY override to warn on dangerous use of transfering ownership by setting NPY_ARRAY_OWNDATA.

    (gh-17582)

    Implementation of the NEP 47 (adopting the array API standard)

    An initial implementation of NEP47, adoption of the array API standard, has been added as numpy.array_api. The implementation is experimental and will issue a UserWarning on import, as the array API standard is still in draft state. numpy.array_api is a conforming implementation of the array API standard, which is also minimal, meaning that only those functions and behaviors that are required by the standard are implemented (see the NEP for more info). Libraries wishing to make use of the array API standard are encouraged to use numpy.array_api to check that they are only using functionality that is guaranteed to be present in standard conforming implementations.

    (gh-18585)

    Generate C/C++ API reference documentation from comments blocks is now possible

    This feature depends on Doxygen in the generation process and on Breathe to integrate it with Sphinx.

    (gh-18884)

    Assign the platform-specific c_intp precision via a mypy plugin

    The mypy plugin, introduced in numpy/numpy#17843, has again been expanded: the plugin now is now responsible for setting the platform-specific precision of numpy.ctypeslib.c_intp, the latter being used as data type for various numpy.ndarray.ctypes attributes.

    Without the plugin, aforementioned type will default to ctypes.c_int64.

    To enable the plugin, one must add it to their mypy configuration file:

    [mypy]
    plugins = numpy.typing.mypy_plugin
    

    (gh-19062)

    Add NEP 47-compatible dlpack support

    Add a ndarray.__dlpack__() method which returns a dlpack C structure wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function, where obj supports __dlpack__(), and returns an ndarray.

    (gh-19083)

    keepdims optional argument added to numpy.argmin, numpy.argmax

    keepdims argument is added to numpy.argmin, numpy.argmax. If set to True, the axes which are reduced are left in the result as dimensions with size one. The resulting array has the same number of dimensions and will broadcast with the input array.

    (gh-19211)

    bit_count to compute the number of 1-bits in an integer

    Computes the number of 1-bits in the absolute value of the input. This works on all the numpy integer types. Analogous to the builtin int.bit_count or popcount in C++.

    >>> np.uint32(1023).bit_count()
    10
    >>> np.int32(-127).bit_count()
    7
    

    (gh-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)

    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)

    Added support for LoongArch

    LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit.

    (gh-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)

    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.

    >>> np.float32(-2.0).is_integer()
    True
    >>> np.float64(3.2).is_integer()
    False
    >>> np.int32(-2).is_integer()
    True
    

    (gh-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)

    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.

    >>> 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)

    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__<os.PathLike.__fspath__>{.interpreted-text role="meth"} protocol.

    (gh-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)

    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)

    numpy.fromregex now accepts os.PathLike implementations

    numpy.fromregex now accepts objects implementing the __fspath__<os.PathLike> protocol, e.g. pathlib.Path.

    (gh-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)

    Missing parameters have been added to the nan<x> functions

    A number of the nan<x> functions previously lacked parameters that were present in their <x>-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<x> 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)

    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)

    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)

    OpenBLAS v0.3.18

    Update the OpenBLAS used in testing and in wheels to v0.3.18

    (gh-20058)

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  • v1.21.5(Dec 20, 2021)

    NumPy 1.21.5 Release Notes

    NumPy 1.21.5 is a maintenance release 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: MAINT: Do not forward __(deep)copy__ calls of _GenericAlias...
    • #20462: BUG: Fix float16 einsum fastpaths using wrong tempvar
    • #20463: BUG, DIST: Print os error message when the executable not exist
    • #20464: BLD: Verify the ability to compile C++ sources before initiating...
    • #20465: BUG: Force npymath to respect npy_longdouble
    • #20466: BUG: Fix failure to create aligned, empty structured dtype
    • #20467: ENH: provide a convenience function to replace npy_load_module
    • #20495: MAINT: update wheel to version that supports python3.10
    • #20497: BUG: Clear errors correctly in F2PY conversions
    • #20613: DEV: add a warningfilter to fix pytest workflow.
    • #20618: MAINT: Help boost::python libraries at least not crash

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  • v1.22.0rc3(Dec 18, 2021)

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 152 contributers spread over 602 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)

    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)

    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)

    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)

    The np.MachAr class has been deprecated

    The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have been deprecated. Users are encouraged to access the property if interest directly from the corresponding numpy.finfo attribute.

    (gh-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)

    Removed floor division support for complex types

    Floor division of complex types will now result in a TypeError

    >>> a = np.arange(10) + 1j* np.arange(10)
    >>> a // 1
    TypeError: ufunc 'floor_divide' not supported for the input types...
    

    (gh-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)

    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)

    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': ['<i8'], 'offsets': [2], 'itemsize': 10}), whereas spaces where previously omitted after colons and between fields.

    The old behavior can be restored via np.set_printoptions(legacy="1.21").

    (gh-19687)

    Corrected advance in PCG64DSXM and PCG64

    Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug only affects results when the step was larger than $2^{64}$ on platforms that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

    (gh-20049)

    Change in generation of random 32 bit floating point variates

    There was bug in the generation of 32 bit floating point values from the uniform distribution that would result in the least significant bit of the random variate always being 0. This has been fixed.

    This change affects the variates produced by the random.Generator methods random, standard_normal, standard_exponential, and standard_gamma, but only when the dtype is specified as numpy.float32.

    (gh-20314)

    C API changes

    Masked inner-loops cannot be customized anymore

    The masked inner-loop selector is now never used. A warning will be given in the unlikely event that it was customized.

    We do not expect that any code uses this. If you do use it, you must unset the selector on newer NumPy version. Please also contact the NumPy developers, we do anticipate providing a new, more specific, mechanism.

    The customization was part of a never-implemented feature to allow for faster masked operations.

    (gh-19259)

    New Features

    NEP 49 configurable allocators

    As detailed in NEP 49, the function used for allocation of the data segment of a ndarray can be changed. The policy can be set globally or in a context. For more information see the NEP and the data_memory{.interpreted-text role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY override to warn on dangerous use of transfering ownership by setting NPY_ARRAY_OWNDATA.

    (gh-17582)

    Implementation of the NEP 47 (adopting the array API standard)

    An initial implementation of NEP47, adoption of the array API standard, has been added as numpy.array_api. The implementation is experimental and will issue a UserWarning on import, as the array API standard is still in draft state. numpy.array_api is a conforming implementation of the array API standard, which is also minimal, meaning that only those functions and behaviors that are required by the standard are implemented (see the NEP for more info). Libraries wishing to make use of the array API standard are encouraged to use numpy.array_api to check that they are only using functionality that is guaranteed to be present in standard conforming implementations.

    (gh-18585)

    Generate C/C++ API reference documentation from comments blocks is now possible

    This feature depends on Doxygen in the generation process and on Breathe to integrate it with Sphinx.

    (gh-18884)

    Assign the platform-specific c_intp precision via a mypy plugin

    The mypy plugin, introduced in numpy/numpy#17843, has again been expanded: the plugin now is now responsible for setting the platform-specific precision of numpy.ctypeslib.c_intp, the latter being used as data type for various numpy.ndarray.ctypes attributes.

    Without the plugin, aforementioned type will default to ctypes.c_int64.

    To enable the plugin, one must add it to their mypy configuration file:

    [mypy]
    plugins = numpy.typing.mypy_plugin
    

    (gh-19062)

    Add NEP 47-compatible dlpack support

    Add a ndarray.__dlpack__() method which returns a dlpack C structure wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function, where obj supports __dlpack__(), and returns an ndarray.

    (gh-19083)

    keepdims optional argument added to numpy.argmin, numpy.argmax

    keepdims argument is added to numpy.argmin, numpy.argmax. If set to True, the axes which are reduced are left in the result as dimensions with size one. The resulting array has the same number of dimensions and will broadcast with the input array.

    (gh-19211)

    bit_count to compute the number of 1-bits in an integer

    Computes the number of 1-bits in the absolute value of the input. This works on all the numpy integer types. Analogous to the builtin int.bit_count or popcount in C++.

    >>> np.uint32(1023).bit_count()
    10
    >>> np.int32(-127).bit_count()
    7
    

    (gh-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)

    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)

    Added support for LoongArch

    LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit.

    (gh-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)

    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.

    >>> np.float32(-2.0).is_integer()
    True
    >>> np.float64(3.2).is_integer()
    False
    >>> np.int32(-2).is_integer()
    True
    

    (gh-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)

    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.

    >>> 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)

    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__<os.PathLike.__fspath__>{.interpreted-text role="meth"} protocol.

    (gh-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)

    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)

    numpy.fromregex now accepts os.PathLike implementations

    numpy.fromregex now accepts objects implementing the __fspath__<os.PathLike> protocol, e.g. pathlib.Path.

    (gh-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)

    Missing parameters have been added to the nan<x> functions

    A number of the nan<x> functions previously lacked parameters that were present in their <x>-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<x> 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)

    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)

    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)

    OpenBLAS v0.3.17

    Update the OpenBLAS used in testing and in wheels to v0.3.17

    (gh-19462)

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    Source code(zip)
    1.22.0-changelog.rst(66.95 KB)
    numpy-1.22.0rc3.tar.gz(9.88 MB)
    numpy-1.22.0rc3.zip(10.77 MB)
    README.rst(23.34 KB)
  • v1.22.0rc2(Dec 10, 2021)

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 151 contributers spread over 589 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)

    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)

    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)

    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)

    The np.MachAr class has been deprecated

    The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have been deprecated. Users are encouraged to access the property if interest directly from the corresponding numpy.finfo attribute.

    (gh-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)

    Removed floor division support for complex types

    Floor division of complex types will now result in a TypeError

    >>> a = np.arange(10) + 1j* np.arange(10)
    >>> a // 1
    TypeError: ufunc 'floor_divide' not supported for the input types...
    

    (gh-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)

    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)

    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': ['<i8'], 'offsets': [2], 'itemsize': 10}), whereas spaces where previously omitted after colons and between fields.

    The old behavior can be restored via np.set_printoptions(legacy="1.21").

    (gh-19687)

    Corrected advance in PCG64DSXM and PCG64

    Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug only affects results when the step was larger than $2^{64}$ on platforms that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

    (gh-20049)

    Change in generation of random 32 bit floating point variates

    There was bug in the generation of 32 bit floating point values from the uniform distribution that would result in the least significant bit of the random variate always being 0. This has been fixed.

    This change affects the variates produced by the random.Generator methods random, standard_normal, standard_exponential, and standard_gamma, but only when the dtype is specified as numpy.float32.

    (gh-20314)

    C API changes

    Masked inner-loops cannot be customized anymore

    The masked inner-loop selector is now never used. A warning will be given in the unlikely event that it was customized.

    We do not expect that any code uses this. If you do use it, you must unset the selector on newer NumPy version. Please also contact the NumPy developers, we do anticipate providing a new, more specific, mechanism.

    The customization was part of a never-implemented feature to allow for faster masked operations.

    (gh-19259)

    New Features

    NEP 49 configurable allocators

    As detailed in NEP 49, the function used for allocation of the data segment of a ndarray can be changed. The policy can be set globally or in a context. For more information see the NEP and the data_memory{.interpreted-text role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY override to warn on dangerous use of transfering ownership by setting NPY_ARRAY_OWNDATA.

    (gh-17582)

    Implementation of the NEP 47 (adopting the array API standard)

    An initial implementation of NEP47, adoption of the array API standard, has been added as numpy.array_api. The implementation is experimental and will issue a UserWarning on import, as the array API standard is still in draft state. numpy.array_api is a conforming implementation of the array API standard, which is also minimal, meaning that only those functions and behaviors that are required by the standard are implemented (see the NEP for more info). Libraries wishing to make use of the array API standard are encouraged to use numpy.array_api to check that they are only using functionality that is guaranteed to be present in standard conforming implementations.

    (gh-18585)

    Generate C/C++ API reference documentation from comments blocks is now possible

    This feature depends on Doxygen in the generation process and on Breathe to integrate it with Sphinx.

    (gh-18884)

    Assign the platform-specific c_intp precision via a mypy plugin

    The mypy plugin, introduced in numpy/numpy#17843, has again been expanded: the plugin now is now responsible for setting the platform-specific precision of numpy.ctypeslib.c_intp, the latter being used as data type for various numpy.ndarray.ctypes attributes.

    Without the plugin, aforementioned type will default to ctypes.c_int64.

    To enable the plugin, one must add it to their mypy configuration file:

    [mypy]
    plugins = numpy.typing.mypy_plugin
    

    (gh-19062)

    Add NEP 47-compatible dlpack support

    Add a ndarray.__dlpack__() method which returns a dlpack C structure wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function, where obj supports __dlpack__(), and returns an ndarray.

    (gh-19083)

    keepdims optional argument added to numpy.argmin, numpy.argmax

    keepdims argument is added to numpy.argmin, numpy.argmax. If set to True, the axes which are reduced are left in the result as dimensions with size one. The resulting array has the same number of dimensions and will broadcast with the input array.

    (gh-19211)

    bit_count to compute the number of 1-bits in an integer

    Computes the number of 1-bits in the absolute value of the input. This works on all the numpy integer types. Analogous to the builtin int.bit_count or popcount in C++.

    >>> np.uint32(1023).bit_count()
    10
    >>> np.int32(-127).bit_count()
    7
    

    (gh-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)

    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)

    Added support for LoongArch

    LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit.

    (gh-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)

    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.

    >>> np.float32(-2.0).is_integer()
    True
    >>> np.float64(3.2).is_integer()
    False
    >>> np.int32(-2).is_integer()
    True
    

    (gh-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)

    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.

    >>> 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)

    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__<os.PathLike.__fspath__>{.interpreted-text role="meth"} protocol.

    (gh-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)

    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)

    numpy.fromregex now accepts os.PathLike implementations

    numpy.fromregex now accepts objects implementing the __fspath__<os.PathLike> protocol, e.g. pathlib.Path.

    (gh-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)

    Missing parameters have been added to the nan<x> functions

    A number of the nan<x> functions previously lacked parameters that were present in their <x>-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<x> 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)

    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)

    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)

    OpenBLAS v0.3.17

    Update the OpenBLAS used in testing and in wheels to v0.3.17

    (gh-19462)

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  • v1.22.0rc1(Nov 23, 2021)

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 150 contributers spread over 575 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 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)

    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)

    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)

    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)

    The np.MachAr class has been deprecated

    The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have been deprecated. Users are encouraged to access the property if interest directly from the corresponding numpy.finfo attribute.

    (gh-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)

    Removed floor division support for complex types

    Floor division of complex types will now result in a TypeError

    >>> a = np.arange(10) + 1j* np.arange(10)
    >>> a // 1
    TypeError: ufunc 'floor_divide' not supported for the input types...
    

    (gh-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)

    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)

    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': ['<i8'], 'offsets': [2], 'itemsize': 10}), whereas spaces where previously omitted after colons and between fields.

    The old behavior can be restored via np.set_printoptions(legacy="1.21").

    (gh-19687)

    Corrected advance in PCG64DSXM and PCG64

    Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug only affects results when the step was larger than $2^{64}$ on platforms that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

    (gh-20049)

    Change in generation of random 32 bit floating point variates

    There was bug in the generation of 32 bit floating point values from the uniform distribution that would result in the least significant bit of the random variate always being 0. This has been fixed.

    This change affects the variates produced by the random.Generator methods random, standard_normal, standard_exponential, and standard_gamma, but only when the dtype is specified as numpy.float32.

    (gh-20314)

    C API changes

    Masked inner-loops cannot be customized anymore

    The masked inner-loop selector is now never used. A warning will be given in the unlikely event that it was customized.

    We do not expect that any code uses this. If you do use it, you must unset the selector on newer NumPy version. Please also contact the NumPy developers, we do anticipate providing a new, more specific, mechanism.

    The customization was part of a never-implemented feature to allow for faster masked operations.

    (gh-19259)

    New Features

    NEP 49 configurable allocators

    As detailed in NEP 49, the function used for allocation of the data segment of a ndarray can be changed. The policy can be set globally or in a context. For more information see the NEP and the data_memory{.interpreted-text role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY override to warn on dangerous use of transfering ownership by setting NPY_ARRAY_OWNDATA.

    (gh-17582)

    Implementation of the NEP 47 (adopting the array API standard)

    An initial implementation of NEP 47 (adoption the array API standard) has been added as numpy.array_api. The implementation is experimental and will issue a UserWarning on import, as the array API standard is still in draft state. numpy.array_api is a conforming implementation of the array API standard, which is also minimal, meaning that only those functions and behaviors that are required by the standard are implemented (see the NEP for more info). Libraries wishing to make use of the array API standard are encouraged to use numpy.array_api to check that they are only using functionality that is guaranteed to be present in standard conforming implementations.

    (gh-18585)

    Generate C/C++ API reference documentation from comments blocks is now possible

    This feature depends on Doxygen in the generation process and on Breathe to integrate it with Sphinx.

    (gh-18884)

    Assign the platform-specific c_intp precision via a mypy plugin

    The mypy plugin, introduced in numpy/numpy#17843, has again been expanded: the plugin now is now responsible for setting the platform-specific precision of numpy.ctypeslib.c_intp, the latter being used as data type for various numpy.ndarray.ctypes attributes.

    Without the plugin, aforementioned type will default to ctypes.c_int64.

    To enable the plugin, one must add it to their mypy configuration file:

    [mypy]
    plugins = numpy.typing.mypy_plugin
    

    (gh-19062)

    Add NEP 47-compatible dlpack support

    Add a ndarray.__dlpack__() method which returns a dlpack C structure wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function, where obj supports __dlpack__(), and returns an ndarray.

    (gh-19083)

    keepdims optional argument added to numpy.argmin, numpy.argmax

    keepdims argument is added to numpy.argmin, numpy.argmax. If set to True, the axes which are reduced are left in the result as dimensions with size one. The resulting array has the same number of dimensions and will broadcast with the input array.

    (gh-19211)

    bit_count to compute the number of 1-bits in an integer

    Computes the number of 1-bits in the absolute value of the input. This works on all the numpy integer types. Analogous to the builtin int.bit_count or popcount in C++.

    >>> np.uint32(1023).bit_count()
    10
    >>> np.int32(-127).bit_count()
    7
    

    (gh-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)

    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)

    Added support for LoongArch

    LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit.

    (gh-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)

    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.

    >>> np.float32(-2.0).is_integer()
    True
    >>> np.float64(3.2).is_integer()
    False
    >>> np.int32(-2).is_integer()
    True
    

    (gh-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)

    ndarray, dtype and number are now runtime-subscriptable

    Mimicking 585{.interpreted-text role="pep"}, 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.

    >>> 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)

    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__<os.PathLike.__fspath__>{.interpreted-text role="meth"} protocol.

    (gh-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)

    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)

    numpy.fromregex now accepts os.PathLike implementations

    numpy.fromregex now accepts objects implementing the __fspath__<os.PathLike> protocol, e.g. pathlib.Path.

    (gh-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)

    Missing parameters have been added to the nan<x> functions

    A number of the nan<x> functions previously lacked parameters that were present in their <x>-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<x> 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)

    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)

    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)

    OpenBLAS v0.3.17

    Update the OpenBLAS used in testing and in wheels to v0.3.17

    (gh-19462)

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  • v1.21.4(Nov 5, 2021)

    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: BUG: Fix shadowed reference of dtype in type stub
    • #20293: BUG: Fix headers for universal2 builds
    • #20294: BUG: VOID_nonzero could sometimes mutate alignment flag
    • #20295: BUG: Do not use nonzero fastpath on unaligned arrays
    • #20296: BUG: Distutils patch to allow for 2 as a minor version (!)
    • #20297: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar
    • #20298: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC...
    • #20300: REL: Prepare for the NumPy 1.21.4 release.
    • #20302: TST: Fix a Arrayterator typing test failure

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  • v1.21.3(Oct 20, 2021)

    NumPy 1.21.3 Release Notes

    The NumPy 1.21.3 is a maintenance release 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: ENH: Add dtype-support to 3 `generic/ndarray methods
    • #19955: BUG: Resolve Divide by Zero on Apple silicon + test failures...
    • #19958: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases...
    • #19994: BUG: np.tan(np.inf) test failure
    • #20080: BUG: Correct incorrect advance in PCG with emulated int128
    • #20081: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime...
    • #20082: DOC: Ensure that we add documentation also as to the dict for...
    • #20106: BUG: core: result_type(0, np.timedelta64(4)) would seg. fault.

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  • v1.21.2(Aug 15, 2021)

    NumPy 1.21.2 Release Notes

    The NumPy 1.21.2 is maintenance release that fixes bugs discovered after 1.21.1. 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, check your version if you are using gcc-11.

    Contributors

    A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

    • Bas van Beek
    • Carl Johnsen +
    • Charles Harris
    • Gwyn Ciesla +
    • Matthieu Dartiailh
    • Matti Picus
    • Niyas Sait +
    • Ralf Gommers
    • Sayed Adel
    • Sebastian Berg

    Pull requests merged

    A total of 18 pull requests were merged for this release.

    • #19497: MAINT: set Python version for 1.21.x to <3.11
    • #19533: BUG: Fix an issue wherein importing numpy.typing could raise
    • #19646: MAINT: Update Cython version for Python 3.10.
    • #19648: TST: Bump the python 3.10 test version from beta4 to rc1
    • #19651: TST: avoid distutils.sysconfig in runtests.py
    • #19652: MAINT: add missing dunder method to nditer type hints
    • #19656: BLD, SIMD: Fix testing extra checks when -Werror isn't applicable...
    • #19657: BUG: Remove logical object ufuncs with bool output
    • #19658: MAINT: Include .coveragerc in source distributions to support...
    • #19659: BUG: Fix bad write in masked iterator output copy paths
    • #19660: ENH: Add support for windows on arm targets
    • #19661: BUG: add base to templated arguments for platlib
    • #19662: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated
    • #19666: MAINT: Add Python 3.10 to supported versions.
    • #19668: TST,BUG: Sanitize path-separators when running runtest.py
    • #19671: BLD: load extra flags when checking for libflame
    • #19676: BLD: update circleCI docker image
    • #19677: REL: Prepare for 1.21.2 release.

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    Source code(tar.gz)
    Source code(zip)
    1.21.2-changelog.rst(2.35 KB)
    numpy-1.21.2.tar.gz(8.99 MB)
    numpy-1.21.2.zip(9.78 MB)
    README.rst(9.68 KB)
  • v1.21.1(Jul 18, 2021)

    NumPy 1.21.1 Release Notes

    The NumPy 1.21.1 is maintenance release that fixes bugs discovered after the 1.21.0 release and updates OpenBLAS to v0.3.17 to deal with problems on arm64.

    The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with development Python 3.10. Python 3.10 will be officially supported after it is released.

    :warning: There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1.

    • Optimization level -O3 results in many incorrect warnings when running the tests.
    • On some hardware NumPY will hang in an infinite loop.

    Contributors

    A total of 11 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

    • Bas van Beek
    • Charles Harris
    • Ganesh Kathiresan
    • Gregory R. Lee
    • Hugo Defois +
    • Kevin Sheppard
    • Matti Picus
    • Ralf Gommers
    • Sayed Adel
    • Sebastian Berg
    • Thomas J. Fan

    Pull requests merged

    A total of 26 pull requests were merged for this release.

    • #19311: REV,BUG: Replace NotImplemented with typing.Any
    • #19324: MAINT: Fixed the return-dtype of ndarray.real and imag
    • #19330: MAINT: Replace "dtype[Any]" with dtype in the definiton of...
    • #19342: DOC: Fix some docstrings that crash pdf generation.
    • #19343: MAINT: bump scipy-mathjax
    • #19347: BUG: Fix arr.flat.index for large arrays and big-endian machines
    • #19348: ENH: add numpy.f2py.get_include function
    • #19349: BUG: Fix reference count leak in ufunc dtype handling
    • #19350: MAINT: Annotate missing attributes of np.number subclasses
    • #19351: BUG: Fix cast safety and comparisons for zero sized voids
    • #19352: BUG: Correct Cython declaration in random
    • #19353: BUG: protect against accessing base attribute of a NULL subarray
    • #19365: BUG, SIMD: Fix detecting AVX512 features on Darwin
    • #19366: MAINT: remove print()'s in distutils template handling
    • #19390: ENH: SIMD architectures to show_config
    • #19391: BUG: Do not raise deprecation warning for all nans in unique...
    • #19392: BUG: Fix NULL special case in object-to-any cast code
    • #19430: MAINT: Use arm64-graviton2 for testing on travis
    • #19495: BUILD: update OpenBLAS to v0.3.17
    • #19496: MAINT: Avoid unicode characters in division SIMD code comments
    • #19499: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11
    • #19500: BUG: fix a numpy.npiter leak in npyiter_multi_index_set
    • #19501: TST: Fix a GenericAlias test failure for python 3.9.0
    • #19502: MAINT: Start testing with Python 3.10.0b3.
    • #19503: MAINT: Add missing dtype overloads for object- and ctypes-based...
    • #19510: REL: Prepare for NumPy 1.21.1 release.

    Checksums

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    Source code(tar.gz)
    Source code(zip)
    1.21.1-changelog.rst(3.25 KB)
    numpy-1.21.1.tar.gz(8.99 MB)
    numpy-1.21.1.zip(9.78 MB)
    README.rst(10.04 KB)
  • v1.21.0(Jun 22, 2021)

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • universal2 wheels for Python 3.8 and Python 3.9 on Mac,
    • improved documentation,
    • improved annotations,
    • new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    :warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

    • Optimization level -O3 results in many wrong warnings when running the tests.
    • On some hardware NumPy will hang in an infinite loop.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64 for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

      (gh-17900)

    • The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

      (gh-18697)

    • The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

      (gh-18697)

    • The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

      (gh-18963)

    • The unused PolyError and PolyDomainError exceptions are removed.

      (gh-18963)

    Deprecations

    The .dtype attribute must return a dtype

    A DeprecationWarning is now given if the .dtype attribute of an object passed into np.dtype or as a dtype=obj argument is not a dtype. NumPy will stop attempting to recursively coerce the result of .dtype.

    (gh-13578)

    Inexact matches for numpy.convolve and numpy.correlate are deprecated

    numpy.convolve and numpy.correlate now emit a warning when there are case insensitive and/or inexact matches found for mode argument in the functions. Pass full "same", "valid", "full" strings instead of "s", "v", "f" for the mode argument.

    (gh-17492)

    np.typeDict has been formally deprecated

    np.typeDict is a deprecated alias for np.sctypeDict and has been so for over 14 years (6689502). A deprecation warning will now be issued whenever getting np.typeDict.

    (gh-17586)

    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 usually ignored. A warning is now given when the exception is anything but AttributeError. To silence the warning, the type raising the exception has to be adapted to raise an AttributeError.

    (gh-19001)

    Four ndarray.ctypes methods have been deprecated

    Four methods of the ndarray.ctypes object have been deprecated, as they are (undocumentated) implementation artifacts of their respective properties.

    The methods in question are:

    • _ctypes.get_data (use _ctypes.data instead)
    • _ctypes.get_shape (use _ctypes.shape instead)
    • _ctypes.get_strides (use _ctypes.strides instead)
    • _ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

    (gh-19031)

    Expired deprecations

    • The shape argument numpy.unravel_index] cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

      (gh-17900)

    • The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

      (gh-18697)

    • The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

      (gh-18697)

    Remove deprecated PolyBase and unused PolyError and PolyDomainError

    The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

    Furthermore, the unused PolyError and PolyDomainError exceptions are removed from the numpy.polynomial.

    (gh-18963)

    Compatibility notes

    Error type changes in universal functions

    The universal functions may now raise different errors on invalid input in some cases. The main changes should be that a RuntimeError was replaced with a more fitting TypeError. When multiple errors were present in the same call, NumPy may now raise a different one.

    (gh-15271)

    __array_ufunc__ argument validation

    NumPy will now partially validate arguments before calling __array_ufunc__. Previously, it was possible to pass on invalid arguments (such as a non-existing keyword argument) when dispatch was known to occur.

    (gh-15271)

    __array_ufunc__ and additional positional arguments

    Previously, all positionally passed arguments were checked for __array_ufunc__ support. In the case of reduce, accumulate, and reduceat all arguments may be passed by position. This means that when they were passed by position, they could previously have been asked to handle the ufunc call via __array_ufunc__. Since this depended on the way the arguments were passed (by position or by keyword), NumPy will now only dispatch on the input and output array. For example, NumPy will never dispatch on the where array in a reduction such as np.add.reduce.

    (gh-15271)

    Validate input values in Generator.uniform

    Checked that high - low >= 0 in np.random.Generator.uniform. Raises ValueError if low > high. Previously out-of-order inputs were accepted and silently swapped, so that if low > high, the value generated was high + (low - high) * random().

    (gh-17921)

    /usr/include removed from default include paths

    The default include paths when building a package with numpy.distutils no longer include /usr/include. This path is normally added by the compiler, and hardcoding it can be problematic. In case this causes a problem, please open an issue. A workaround is documented in PR 18658.

    (gh-18658)

    Changes to comparisons with dtype=...

    When the dtype= (or signature) arguments to comparison ufuncs (equal, less, etc.) is used, this will denote the desired output dtype in the future. This means that:

    np.equal(2, 3, dtype=object)

    will give a FutureWarning that it will return an object array in the future, which currently happens for:

    np.equal(None, None, dtype=object)

    due to the fact that np.array(None) is already an object array. (This also happens for some other dtypes.)

    Since comparisons normally only return boolean arrays, providing any other dtype will always raise an error in the future and give a DeprecationWarning now.

    (gh-18718)

    Changes to dtype and signature arguments in ufuncs

    The universal function arguments dtype and signature which are also valid for reduction such as np.add.reduce (which is the implementation for np.sum) will now issue a warning when the dtype provided is not a "basic" dtype.

    NumPy almost always ignored metadata, byteorder or time units on these inputs. NumPy will now always ignore it and raise an error if byteorder or time unit changed. The following are the most important examples of changes which will give the error. In some cases previously the information stored was not ignored, in all of these an error is now raised:

    # Previously ignored the byte-order (affect if non-native)
    np.add(3, 5, dtype=">i32")
    
    # The biggest impact is for timedelta or datetimes:
    arr = np.arange(10, dtype="m8[s]")
    
    # The examples always ignored the time unit "ns":
    np.add(arr, arr, dtype="m8[ns]")
    np.maximum.reduce(arr, dtype="m8[ns]")
    
    # The following previously did use "ns" (as opposed to `arr.dtype`)
    np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
    np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)
    

    The same applies for functions like np.sum which use these internally. This change is necessary to achieve consistent handling within NumPy.

    If you run into these, in most cases pass for example dtype=np.timedelta64 which clearly denotes a general timedelta64 without any unit or byte-order defined. If you need to specify the output dtype precisely, you may do so by either casting the inputs or providing an output array using out=.

    NumPy may choose to allow providing an exact output dtype here in the future, which would be preceded by a FutureWarning.

    (gh-18718)

    Ufunc signature=... and dtype= generalization and casting

    The behaviour for np.ufunc(1.0, 1.0, signature=...) or np.ufunc(1.0, 1.0, dtype=...) can now yield different loops in 1.21 compared to 1.20 because of changes in promotion. When signature was previously used, the casting check on inputs was relaxed, which could lead to downcasting inputs unsafely especially if combined with casting="unsafe".

    Casting is now guaranteed to be safe. If a signature is only partially provided, for example using signature=("float64", None, None), this could lead to no loop being found (an error). In that case, it is necessary to provide the complete signature to enforce casting the inputs. If dtype="float64" is used or only outputs are set (e.g. signature=(None, None, "float64") the is unchanged. We expect that very few users are affected by this change.

    Further, the meaning of dtype="float64" has been slightly modified and now strictly enforces only the correct output (and not input) DTypes. This means it is now always equivalent to:

    signature=(None, None, "float64")
    

    (If the ufunc has two inputs and one output). Since this could lead to no loop being found in some cases, NumPy will normally also search for the loop:

    signature=("float64", "float64", "float64")
    

    if the first search failed. In the future, this behaviour may be customized to achieve the expected results for more complex ufuncs. (For some universal functions such as np.ldexp inputs can have different DTypes.)

    (gh-18880)

    Distutils forces strict floating point model on clang

    NumPy distutils will now always add the -ffp-exception-behavior=strict compiler flag when compiling with clang. Clang defaults to a non-strict version, which allows the compiler to generate code that does not set floating point warnings/errors correctly.

    (gh-19049)

    C API changes

    Use of ufunc->type_resolver and "type tuple"

    NumPy now normalizes the "type tuple" argument to the type resolver functions before calling it. Note that in the use of this type resolver is legacy behaviour and NumPy will not do so when possible. Calling ufunc->type_resolver or PyUFunc_DefaultTypeResolver is strongly discouraged and will now enforce a normalized type tuple if done. Note that this does not affect providing a type resolver, which is expected to keep working in most circumstances. If you have an unexpected use-case for calling the type resolver, please inform the NumPy developers so that a solution can be found.

    (gh-18718)

    New Features

    Added a mypy plugin for handling platform-specific numpy.number precisions

    A mypy plugin is now available for automatically assigning the (platform-dependent) precisions of certain numpy.number subclasses, including the likes of numpy.int_, numpy.intp and numpy.longlong. See the documentation on scalar types <arrays.scalars.built-in> for a comprehensive overview of the affected classes.

    Note that while usage of the plugin is completely optional, without it the precision of above-mentioned classes will be inferred as typing.Any.

    To enable the plugin, one must add it to their mypy [configuration file] (https://mypy.readthedocs.io/en/stable/config_file.html):

    [mypy]
    plugins = numpy.typing.mypy_plugin
    

    (gh-17843)

    Let the mypy plugin manage extended-precision numpy.number subclasses

    The mypy plugin, introduced in numpy/numpy#17843, has been expanded: the plugin now removes annotations for platform-specific extended-precision types that are not available to the platform in question. For example, it will remove numpy.float128 when not available.

    Without the plugin all extended-precision types will, as far as mypy is concerned, be available on all platforms.

    To enable the plugin, one must add it to their mypy configuration file:

    [mypy]
    plugins = numpy.typing.mypy_plugin
                                                                            cn
    

    (gh-18322)

    New min_digits argument for printing float values

    A new min_digits argument has been added to the dragon4 float printing functions numpy.format_float_positional and numpy.format_float_scientific. This kwd guarantees that at least the given number of digits will be printed when printing in unique=True mode, even if the extra digits are unnecessary to uniquely specify the value. It is the counterpart to the precision argument which sets the maximum number of digits to be printed. When unique=False in fixed precision mode, it has no effect and the precision argument fixes the number of digits.

    (gh-18629)

    f2py now recognizes Fortran abstract interface blocks

    numpy.f2py can now parse abstract interface blocks.

    (gh-18695)

    BLAS and LAPACK configuration via environment variables

    Autodetection of installed BLAS and LAPACK libraries can be bypassed by using the NPY_BLAS_LIBS and NPY_LAPACK_LIBS environment variables. Instead, the link flags in these environment variables will be used directly, and the language is assumed to be F77. This is especially useful in automated builds where the BLAS and LAPACK that are installed are known exactly. A use case is replacing the actual implementation at runtime via stub library links.

    If NPY_CBLAS_LIBS is set (optional in addition to NPY_BLAS_LIBS), this will be used as well, by defining HAVE_CBLAS and appending the environment variable content to the link flags.

    (gh-18737)

    A runtime-subcriptable alias has been added for ndarray

    numpy.typing.NDArray has been added, a runtime-subscriptable alias for np.ndarray[Any, np.dtype[~Scalar]]. The new type alias can be used for annotating arrays with a given dtype and unspecified shape.

    NumPy does not support the annotating of array shapes as of 1.21, this is expected to change in the future though (see 646{.interpreted-text role="pep"}).

    Examples

    >>> import numpy as np
    >>> import numpy.typing as npt
    
    >>> print(npt.NDArray)
    numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]]
    
    >>> print(npt.NDArray[np.float64])
    numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
    
    >>> NDArrayInt = npt.NDArray[np.int_]
    >>> a: NDArrayInt = np.arange(10)
    
    >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
    ...     return np.array(a)
    

    (gh-18935)

    Improvements

    Arbitrary period option for numpy.unwrap

    The size of the interval over which phases are unwrapped is no longer restricted to 2 * pi. This is especially useful for unwrapping degrees, but can also be used for other intervals.

    >>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180
    >>> phase_deg
    array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
           -180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
           -180.])
    
    >>> unwrap(phase_deg, period=360)
    array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
            180.,  220.,  260.,  300.,  340.,  380.,  420.,  460.,  500.,
            540.])
    

    (gh-16987)

    np.unique now returns single NaN

    When np.unique operated on an array with multiple NaN entries, its return included a NaN for each entry that was NaN in the original array. This is now improved such that the returned array contains just one NaN as the last element.

    Also for complex arrays all NaN values are considered equivalent (no matter whether the NaN is in the real or imaginary part). As the representant for the returned array the smallest one in the lexicographical order is chosen - see np.sort for how the lexicographical order is defined for complex arrays.

    (gh-18070)

    Generator.rayleigh and Generator.geometric performance improved

    The performance of Rayleigh and geometric random variate generation in Generator has improved. These are both transformation of exponential random variables and the slow log-based inverse cdf transformation has been replaced with the Ziggurat-based exponential variate generator.

    This change breaks the stream of variates generated when variates from either of these distributions are produced.

    (gh-18666)

    Placeholder annotations have been improved

    All placeholder annotations, that were previously annotated as typing.Any, have been improved. Where appropiate they have been replaced with explicit function definitions, classes or other miscellaneous objects.

    (gh-18934)

    Performance improvements

    Improved performance in integer division of NumPy arrays

    Integer division of NumPy arrays now uses libdivide when the divisor is a constant. With the usage of libdivide and other minor optimizations, there is a large speedup. The // operator and np.floor_divide makes use of the new changes.

    (gh-17727)

    Improve performance of np.save and np.load for small arrays

    np.save is now a lot faster for small arrays.

    np.load is also faster for small arrays, but only when serializing with a version >= (3, 0).

    Both are done by removing checks that are only relevant for Python 2, while still maintaining compatibility with arrays which might have been created by Python 2.

    (gh-18657)

    Changes

    numpy.piecewise output class now matches the input class

    When numpy.ndarray subclasses are used on input to numpy.piecewise, they are passed on to the functions. The output will now be of the same subclass as well.

    (gh-18110)

    Enable Accelerate Framework

    With the release of macOS 11.3, several different issues that numpy was encountering when using Accelerate Framework's implementation of BLAS and LAPACK should be resolved. This change enables the Accelerate Framework as an option on macOS. If additional issues are found, please file a bug report against Accelerate using the developer feedback assistant tool (https://developer.apple.com/bug-reporting/). We intend to address issues promptly and plan to continue supporting and updating our BLAS and LAPACK libraries.

    (gh-18874)

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    Source code(tar.gz)
    Source code(zip)
    1.21.0-changelog.rst(64.32 KB)
    numpy-1.21.0.tar.gz(8.98 MB)
    numpy-1.21.0.zip(9.77 MB)
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  • v1.21.0rc2(Jun 8, 2021)

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • universal2 wheels for Python 3.8 and Python 3.9 on Mac,
    • improved documentation,
    • improved annotations,
    • new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64 for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

      (gh-17900)

    • The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

      (gh-18697)

    • The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

      (gh-18697)

    • The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

      (gh-18963)

    • The unused PolyError and PolyDomainError exceptions are removed.

      (gh-18963)

    Deprecations

    Inexact matches for numpy.convolve and numpy.correlate are deprecated

    numpy.convolve and numpy.correlate now emit a warning when there are case insensitive and/or inexact matches found for mode argument in the functions. Pass full "same", "valid", "full" strings instead of "s", "v", "f" for the mode argument.

    (gh-17492)

    np.typeDict has been formally deprecated

    np.typeDict is a deprecated alias for np.sctypeDict and has been so for over 14 years (6689502). A deprecation warning will now be issued whenever getting np.typeDict.

    (gh-17586)

    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 usually ignored. A warning is now given when the exception is anything but AttributeError. To silence the warning, the type raising the exception has to be adapted to raise an AttributeError.

    (gh-19001)

    Four ndarray.ctypes methods have been deprecated

    Four methods of the ndarray.ctypes object have been deprecated, as they are (undocumentated) implementation artifacts of their respective properties.

    The methods in question are:

    • _ctypes.get_data (use _ctypes.data instead)
    • _ctypes.get_shape (use _ctypes.shape instead)
    • _ctypes.get_strides (use _ctypes.strides instead)
    • _ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

    (gh-19031)

    Expired deprecations

    • The shape argument numpy.unravel_index] cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

      (gh-17900)

    • The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

      (gh-18697)

    • The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

      (gh-18697)

    Remove deprecated PolyBase and unused PolyError and PolyDomainError

    The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

    Furthermore, the unused PolyError and PolyDomainError exceptions are removed from the numpy.polynomial.

    (gh-18963)

    Compatibility notes

    Error type changes in universal functions

    The universal functions may now raise different errors on invalid input in some cases. The main changes should be that a RuntimeError was replaced with a more fitting TypeError. When multiple errors were present in the same call, NumPy may now raise a different one.

    (gh-15271)

    __array_ufunc__ argument validation

    NumPy will now partially validate arguments before calling __array_ufunc__. Previously, it was possible to pass on invalid arguments (such as a non-existing keyword argument) when dispatch was known to occur.

    (gh-15271)

    __array_ufunc__ and additional positional arguments

    Previously, all positionally passed arguments were checked for __array_ufunc__ support. In the case of reduce, accumulate, and reduceat all arguments may be passed by position. This means that when they were passed by position, they could previously have been asked to handle the ufunc call via __array_ufunc__. Since this depended on the way the arguments were passed (by position or by keyword), NumPy will now only dispatch on the input and output array. For example, NumPy will never dispatch on the where array in a reduction such as np.add.reduce.

    (gh-15271)

    Validate input values in Generator.uniform

    Checked that high - low >= 0 in np.random.Generator.uniform. Raises ValueError if low > high. Previously out-of-order inputs were accepted and silently swapped, so that if low > high, the value generated was high + (low - high) * random().

    (gh-17921)

    /usr/include removed from default include paths

    The default include paths when building a package with numpy.distutils no longer include /usr/include. This path is normally added by the compiler, and hardcoding it can be problematic. In case this causes a problem, please open an issue. A workaround is documented in PR 18658.

    (gh-18658)

    Changes to comparisons with dtype=...

    When the dtype= (or signature) arguments to comparison ufuncs (equal, less, etc.) is used, this will denote the desired output dtype in the future. This means that:

    np.equal(2, 3, dtype=object)

    will give a FutureWarning that it will return an object array in the future, which currently happens for:

    np.equal(None, None, dtype=object)

    due to the fact that np.array(None) is already an object array. (This also happens for some other dtypes.)

    Since comparisons normally only return boolean arrays, providing any other dtype will always raise an error in the future and give a DeprecationWarning now.

    (gh-18718)

    Changes to dtype and signature arguments in ufuncs

    The universal function arguments dtype and signature which are also valid for reduction such as np.add.reduce (which is the implementation for np.sum) will now issue a warning when the dtype provided is not a "basic" dtype.

    NumPy almost always ignored metadata, byteorder or time units on these inputs. NumPy will now always ignore it and raise an error if byteorder or time unit changed. The following are the most important examples of changes which will give the error. In some cases previously the information stored was not ignored, in all of these an error is now raised:

    # Previously ignored the byte-order (affect if non-native)
    np.add(3, 5, dtype=">i32")
    
    # The biggest impact is for timedelta or datetimes:
    arr = np.arange(10, dtype="m8[s]")
    
    # The examples always ignored the time unit "ns":
    np.add(arr, arr, dtype="m8[ns]")
    np.maximum.reduce(arr, dtype="m8[ns]")
    
    # The following previously did use "ns" (as opposed to `arr.dtype`)
    np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
    np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)
    

    The same applies for functions like np.sum which use these internally. This change is necessary to achieve consistent handling within NumPy.

    If you run into these, in most cases pass for example dtype=np.timedelta64 which clearly denotes a general timedelta64 without any unit or byte-order defined. If you need to specify the output dtype precisely, you may do so by either casting the inputs or providing an output array using out=.

    NumPy may choose to allow providing an exact output dtype here in the future, which would be preceded by a FutureWarning.

    (gh-18718)

    Ufunc signature=... and dtype= generalization and casting

    The behaviour for np.ufunc(1.0, 1.0, signature=...) or np.ufunc(1.0, 1.0, dtype=...) can now yield different loops in 1.21 compared to 1.20 because of changes in promotion. When signature was previously used, the casting check on inputs was relaxed, which could lead to downcasting inputs unsafely especially if combined with casting="unsafe".

    Casting is now guaranteed to be safe. If a signature is only partially provided, for example using signature=("float64", None, None), this could lead to no loop being found (an error). In that case, it is necessary to provide the complete signature to enforce casting the inputs. If dtype="float64" is used or only outputs are set (e.g. signature=(None, None, "float64") the is unchanged. We expect that very few users are affected by this change.

    Further, the meaning of dtype="float64" has been slightly modified and now strictly enforces only the correct output (and not input) DTypes. This means it is now always equivalent to:

    signature=(None, None, "float64")
    

    (If the ufunc has two inputs and one output). Since this could lead to no loop being found in some cases, NumPy will normally also search for the loop:

    signature=("float64", "float64", "float64")
    

    if the first search failed. In the future, this behaviour may be customized to achieve the expected results for more complex ufuncs. (For some universal functions such as np.ldexp inputs can have different DTypes.)

    (gh-18880)

    Distutils forces strict floating point model on clang

    NumPy distutils will now always add the -ffp-exception-behavior=strict compiler flag when compiling with clang. Clang defaults to a non-strict version, which allows the compiler to generate code that does not set floating point warnings/errors correctly.

    (gh-19049)

    C API changes

    Use of ufunc->type_resolver and "type tuple"

    NumPy now normalizes the "type tuple" argument to the type resolver functions before calling it. Note that in the use of this type resolver is legacy behaviour and NumPy will not do so when possible. Calling ufunc->type_resolver or PyUFunc_DefaultTypeResolver is strongly discouraged and will now enforce a normalized type tuple if done. Note that this does not affect providing a type resolver, which is expected to keep working in most circumstances. If you have an unexpected use-case for calling the type resolver, please inform the NumPy developers so that a solution can be found.

    (gh-18718)

    New Features

    Added a mypy plugin for handling platform-specific numpy.number precisions

    A mypy plugin is now available for automatically assigning the (platform-dependent) precisions of certain numpy.number subclasses, including the likes of numpy.int_, numpy.intp and numpy.longlong. See the documentation on scalar types <arrays.scalars.built-in> for a comprehensive overview of the affected classes.

    Note that while usage of the plugin is completely optional, without it the precision of above-mentioned classes will be inferred as typing.Any.

    To enable the plugin, one must add it to their mypy [configuration file] (https://mypy.readthedocs.io/en/stable/config_file.html):

    [mypy]
    plugins = numpy.typing.mypy_plugin
    

    (gh-17843)

    Let the mypy plugin manage extended-precision numpy.number subclasses

    The mypy plugin, introduced in numpy/numpy#17843, has been expanded: the plugin now removes annotations for platform-specific extended-precision types that are not available to the platform in question. For example, it will remove numpy.float128 when not available.

    Without the plugin all extended-precision types will, as far as mypy is concerned, be available on all platforms.

    To enable the plugin, one must add it to their mypy configuration file:

    [mypy]
    plugins = numpy.typing.mypy_plugin
                                                                            cn
    

    (gh-18322)

    New min_digits argument for printing float values

    A new min_digits argument has been added to the dragon4 float printing functions numpy.format_float_positional and numpy.format_float_scientific. This kwd guarantees that at least the given number of digits will be printed when printing in unique=True mode, even if the extra digits are unnecessary to uniquely specify the value. It is the counterpart to the precision argument which sets the maximum number of digits to be printed. When unique=False in fixed precision mode, it has no effect and the precision argument fixes the number of digits.

    (gh-18629)

    f2py now recognizes Fortran abstract interface blocks

    numpy.f2py can now parse abstract interface blocks.

    (gh-18695)

    BLAS and LAPACK configuration via environment variables

    Autodetection of installed BLAS and LAPACK libraries can be bypassed by using the NPY_BLAS_LIBS and NPY_LAPACK_LIBS environment variables. Instead, the link flags in these environment variables will be used directly, and the language is assumed to be F77. This is especially useful in automated builds where the BLAS and LAPACK that are installed are known exactly. A use case is replacing the actual implementation at runtime via stub library links.

    If NPY_CBLAS_LIBS is set (optional in addition to NPY_BLAS_LIBS), this will be used as well, by defining HAVE_CBLAS and appending the environment variable content to the link flags.

    (gh-18737)

    A runtime-subcriptable alias has been added for ndarray

    numpy.typing.NDArray has been added, a runtime-subscriptable alias for np.ndarray[Any, np.dtype[~Scalar]]. The new type alias can be used for annotating arrays with a given dtype and unspecified shape. ^1^

    ^1^ NumPy does not support the annotating of array shapes as of 1.21, this is expected to change in the future though (see 646{.interpreted-text role="pep"}).

    Examples

    >>> import numpy as np
    >>> import numpy.typing as npt
    
    >>> print(npt.NDArray)
    numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]]
    
    >>> print(npt.NDArray[np.float64])
    numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
    
    >>> NDArrayInt = npt.NDArray[np.int_]
    >>> a: NDArrayInt = np.arange(10)
    
    >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
    ...     return np.array(a)
    

    (gh-18935)

    Improvements

    Arbitrary period option for numpy.unwrap

    The size of the interval over which phases are unwrapped is no longer restricted to 2 * pi. This is especially useful for unwrapping degrees, but can also be used for other intervals.

    >>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180
    >>> phase_deg
    array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
           -180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
           -180.])
    
    >>> unwrap(phase_deg, period=360)
    array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
            180.,  220.,  260.,  300.,  340.,  380.,  420.,  460.,  500.,
            540.])
    

    (gh-16987)

    np.unique now returns single NaN

    When np.unique operated on an array with multiple NaN entries, its return included a NaN for each entry that was NaN in the original array. This is now improved such that the returned array contains just one NaN as the last element.

    Also for complex arrays all NaN values are considered equivalent (no matter whether the NaN is in the real or imaginary part). As the representant for the returned array the smallest one in the lexicographical order is chosen - see np.sort for how the lexicographical order is defined for complex arrays.

    (gh-18070)

    Generator.rayleigh and Generator.geometric performance improved

    The performance of Rayleigh and geometric random variate generation in Generator has improved. These are both transformation of exponential random variables and the slow log-based inverse cdf transformation has been replaced with the Ziggurat-based exponential variate generator.

    This change breaks the stream of variates generated when variates from either of these distributions are produced.

    (gh-18666)

    Placeholder annotations have been improved

    All placeholder annotations, that were previously annotated as typing.Any, have been improved. Where appropiate they have been replaced with explicit function definitions, classes or other miscellaneous objects.

    (gh-18934)

    Performance improvements

    Improved performance in integer division of NumPy arrays

    Integer division of NumPy arrays now uses libdivide when the divisor is a constant. With the usage of libdivide and other minor optimizations, there is a large speedup. The // operator and np.floor_divide makes use of the new changes.

    (gh-17727)

    Improve performance of np.save and np.load for small arrays

    np.save is now a lot faster for small arrays.

    np.load is also faster for small arrays, but only when serializing with a version >= (3, 0).

    Both are done by removing checks that are only relevant for Python 2, while still maintaining compatibility with arrays which might have been created by Python 2.

    (gh-18657)

    Changes

    numpy.piecewise output class now matches the input class

    When numpy.ndarray subclasses are used on input to numpy.piecewise, they are passed on to the functions. The output will now be of the same subclass as well.

    (gh-18110)

    Enable Accelerate Framework

    With the release of macOS 11.3, several different issues that numpy was encountering when using Accelerate Framework's implementation of BLAS and LAPACK should be resolved. This change enables the Accelerate Framework as an option on macOS. If additional issues are found, please file a bug report against Accelerate using the developer feedback assistant tool (https://developer.apple.com/bug-reporting/). We intend to address issues promptly and plan to continue supporting and updating our BLAS and LAPACK libraries.

    (gh-18874)

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    Source code(tar.gz)
    Source code(zip)
    1.21.0-changelog.rst(63.18 KB)
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    numpy-1.21.0rc2.zip(8.57 MB)
    README.rst(28.56 KB)
  • v1.21.0rc1(May 24, 2021)

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • improved documentation,
    • improved annotations,
    • the new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64{.interpreted-text role="ref"} for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument of numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

      (gh-17900)

    • The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

      (gh-18697)

    • The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

      (gh-18697)

    • The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

      (gh-18963)

    • The unused PolyError and PolyDomainError exceptions are removed.

      (gh-18963)

    Deprecations

    Inexact matches for numpy.convolve and numpy.correlate are deprecated

    numpy.convolve and numpy.correlate now emit a warning when there are case insensitive and/or inexact matches found for mode argument in the functions. Pass full "same", "valid", "full" strings instead of "s", "v", "f" for the mode argument.

    (gh-17492)

    np.typeDict has been formally deprecated

    np.typeDict is a deprecated alias for np.sctypeDict and has been so for over 14 years (6689502). A deprecation warning will now be issued whenever getting np.typeDict.

    (gh-17586)

    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 usually ignored. A warning is now given when the exception is anything but AttributeError. To silence the warning, the type raising the exception has to be adapted to raise an AttributeError.

    (gh-19001)

    Four ndarray.ctypes methods have been deprecated

    Four methods of the ndarray.ctypes object have been deprecated, as they are (undocumentated) implementation artifacts of their respective properties.

    The methods in question are:

    • _ctypes.get_data (use _ctypes.data instead)
    • _ctypes.get_shape (use _ctypes.shape instead)
    • _ctypes.get_strides (use _ctypes.strides instead)
    • _ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

    (gh-19031)

    Future Changes

    Promotion of strings with numbers and bools will be deprecated

    Any promotion of numbers and strings is deprecated and will give a FutureWarning the main affected functionalities are:

    • numpy.promote_types and numpy.result_type which will raise an error in this case in the future.
    • numpy.concatenate will raise an error when concatenating a string and numeric array. You can use dtype="S" to explicitly request a string result.
    • numpy.array and related functions will start returning object arrays because these functions use object as a fallback when no common dtype can be found. However, it may happen that future releases of NumPy will generally error in these cases.

    This will mainly affect code such as:

    np.asarray(['string', 0])
    

    and:

    np.concatenate((['string'], [0]))
    

    in both cases adding dtype="U" or dtype="S" will give the previous (string) result, while dtype=object will ensure an array with object dtype is returned.

    Comparisons, universal functions, and casting are not affected by this.

    (gh-18116)

    Compatibility notes

    Error type changes in universal functions

    The universal functions may now raise different errors on invalid input in some cases. The main changes should be that a RuntimeError was replaced with a more fitting TypeError. When multiple errors were present in the same call, NumPy may now raise a different one.

    (gh-15271)

    __array_ufunc__ argument validation

    NumPy will now partially validate arguments before calling __array_ufunc__. Previously, it was possible to pass on invalid arguments (such as a non-existing keyword argument) when dispatch was known to occur.

    (gh-15271)

    __array_ufunc__ and additional positional arguments

    Previously, all positionally passed arguments were checked for __array_ufunc__ support. In the case of reduce, accumulate, and reduceat all arguments may be passed by position. This means that when they were passed by position, they could previously have been asked to handle the ufunc call via __array_ufunc__. Since this depended on the way the arguments were passed (by position or by keyword), NumPy will now only dispatch on the input and output array. For example, NumPy will never dispatch on the where array in a reduction such as np.add.reduce.

    (gh-15271)

    Validate input values in Generator.uniform

    Checked that high - low >= 0 in np.random.Generator.uniform. Raises ValueError if low > high. Previously out-of-order inputs were accepted and silently swapped, so that if low > high, the value generated was high + (low - high) * random().

    (gh-17921)

    /usr/include removed from default include paths

    The default include paths when building a package with numpy.distutils no longer include /usr/include. This path is normally added by the compiler, and hardcoding it can be problematic. In case this causes a problem, please open an issue. A workaround is documented in PR 18658.

    (gh-18658)

    Changes to comparisons with dtype=...

    When the dtype= (or signature) arguments to comparison ufuncs (equal, less, etc.) is used, this will denote the desired output dtype in the future. This means that:

    np.equal(2, 3, dtype=object)

    will give a FutureWarning that it will return an object array in the future, which currently happens for:

    np.equal(None, None, dtype=object)

    due to the fact that np.array(None) is already an object array. (This also happens for some other dtypes.)

    Since comparisons normally only return boolean arrays, providing any other dtype will always raise an error in the future and give a DeprecationWarning now.

    (gh-18718)

    Changes to dtype and signature arguments in ufuncs

    The universal function arguments dtype and signature which are also valid for reduction such as np.add.reduce (which is the implementation for np.sum) will now issue a warning when the dtype provided is not a "basic" dtype.

    NumPy almost always ignored metadata, byteorder or time units on these inputs. NumPy will now always ignore it and raise an error if byteorder or time unit changed. The following are the most important examples of changes which will give the error. In some cases previously the information stored was not ignored, in all of these an error is now raised:

    # Previously ignored the byte-order (affect if non-native)
    np.add(3, 5, dtype=">i32")
    
    # The biggest impact is for timedelta or datetimes:
    arr = np.arange(10, dtype="m8[s]")
    # The examples always ignored the time unit "ns":
    np.add(arr, arr, dtype="m8[ns]")
    np.maximum.reduce(arr, dtype="m8[ns]")
    
    # The following previously did use "ns" (as opposed to `arr.dtype`)
    np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
    np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)
    

    The same applies for functions like np.sum which use these internally. This change is necessary to achieve consistent handling within NumPy.

    If you run into these, in most cases pass for example dtype=np.timedelta64 which clearly denotes a general timedelta64 without any unit or byte-order defined. If you need to specify the output dtype precisely, you may do so by either casting the inputs or providing an output array using out=.

    NumPy may choose to allow providing an exact output dtype here in the future, which would be preceded by a FutureWarning.

    (gh-18718)

    Ufunc signature=... and dtype= generalization and casting

    The behaviour for np.ufunc(1.0, 1.0, signature=...) or np.ufunc(1.0, 1.0, dtype=...) can now yield different loops in 1.21 compared to 1.20 because of changes in promotion. When signature was previously used, the casting check on inputs was relaxed, which could lead to downcasting inputs unsafely especially if combined with casting="unsafe".

    Casting is now guaranteed to be safe. If a signature is only partially provided, for example using signature=("float64", None, None), this could lead to no loop being found (an error). In that case, it is necessary to provide the complete signature to enforce casting the inputs. If dtype="float64" is used or only outputs are set (e.g. signature=(None, None, "float64") the is unchanged. We expect that very few users are affected by this change.

    Further, the meaning of dtype="float64" has been slightly modified and now strictly enforces only the correct output (and not input) DTypes. This means it is now always equivalent to:

    signature=(None, None, "float64")
    

    (If the ufunc has two inputs and one output). Since this could lead to no loop being found in some cases, NumPy will normally also search for the loop:

    signature=("float64", "float64", "float64")
    

    if the first search failed. In the future, this behaviour may be customized to achieve the expected results for more complex ufuncs. (For some universal functions such as np.ldexp inputs can have different DTypes.)

    (gh-18880)

    Distutils forces strict floating point model on clang

    NumPy distutils will now always add the -ffp-exception-behavior=strict compiler flag when compiling with clang. Clang defaults to a non-strict version, which allows the compiler to generate code that does not set floating point warnings/errors correctly.

    (gh-19049)

    C API changes

    Use of ufunc->type_resolver and "type tuple"

    NumPy now normalizes the "type tuple" argument to the type resolver functions before calling it. Note that in the use of this type resolver is legacy behaviour and NumPy will not do so when possible. Calling ufunc->type_resolver or PyUFunc_DefaultTypeResolver is strongly discouraged and will now enforce a normalized type tuple if done. Note that this does not affect providing a type resolver, which is expected to keep working in most circumstances. If you have an unexpected use-case for calling the type resolver, please inform the NumPy developers so that a solution can be found.

    (gh-18718)

    New Features

    Added a mypy plugin for handling platform-specific numpy.number precisions

    A mypy plugin is now available for automatically assigning the (platform-dependent) precisions of certain numpy.number subclasses, including the likes of numpy.int_, numpy.intp and numpy.longlong. See the documentation on scalar types <arrays.scalars.built-in>{.interpreted-text role="ref"} for a comprehensive overview of the affected classes.

    Note that while usage of the plugin is completely optional, without it the precision of above-mentioned classes will be inferred as typing.Any.

    To enable the plugin, one must add it to their mypy configuration file:

    [mypy]
    plugins = numpy.typing.mypy_plugin
    

    (gh-17843)

    Let the mypy plugin manage extended-precision numpy.number subclasses

    The mypy plugin, introduced in numpy/numpy#17843, has been expanded: the plugin now removes annotations for platform-specific extended-precision types that are not available to the platform in question. For example, it will remove numpy.float128 when not available.

    Without the plugin all extended-precision types will, as far as mypy is concerned, be available on all platforms.

    To enable the plugin, one must add it to their mypy configuration file:

    [mypy]
    plugins = numpy.typing.mypy_plugin
    

    (gh-18322)

    New min_digits argument for printing float values

    A new min_digits argument has been added to the dragon4 float printing functions numpy.format_float_positional and numpy.format_float_scientific . This kwd guarantees that at least the given number of digits will be printed when printing in unique=True mode, even if the extra digits are unnecessary to uniquely specify the value. It is the counterpart to the precision argument which sets the maximum number of digits to be printed. When unique=False in fixed precision mode, it has no effect and the precision argument fixes the number of digits.

    (gh-18629)

    f2py now recognizes Fortran abstract interface blocks

    numpy.f2py can now parse abstract interface blocks.

    (gh-18695)

    BLAS and LAPACK configuration via environment variables

    Autodetection of installed BLAS and LAPACK libraries can be bypassed by using the NPY_BLAS_LIBS and NPY_LAPACK_LIBS environment variables. Instead, the link flags in these environment variables will be used directly, and the language is assumed to be F77. This is especially useful in automated builds where the BLAS and LAPACK that are installed are known exactly. A use case is replacing the actual implementation at runtime via stub library links.

    If NPY_CBLAS_LIBS is set (optional in addition to NPY_BLAS_LIBS), this will be used as well, by defining HAVE_CBLAS and appending the environment variable content to the link flags.

    (gh-18737)

    A runtime-subcriptable alias has been added for ndarray

    numpy.typing.NDArray has been added, a runtime-subscriptable alias for np.ndarray[Any, np.dtype[~Scalar]]. The new type alias can be used for annotating arrays with a given dtype and unspecified shape. ^1^

    ^1^ NumPy does not support the annotating of array shapes as of 1.21, this is expected to change in the future though (see 646{.interpreted-text role="pep"}).

    Examples

    >>> import numpy as np
    >>> import numpy.typing as npt
    
    >>> print(npt.NDArray)
    numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]]
    
    >>> print(npt.NDArray[np.float64])
    numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
    
    >>> NDArrayInt = npt.NDArray[np.int_]
    >>> a: NDArrayInt = np.arange(10)
    
    >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
    ...     return np.array(a)
    

    (gh-18935)

    Improvements

    Arbitrary period option for numpy.unwrap

    The size of the interval over which phases are unwrapped is no longer restricted to 2 * pi. This is especially useful for unwrapping degrees, but can also be used for other intervals.

    >>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180
    >>> phase_deg
    array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
           -180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
           -180.])
    
    >>> unwrap(phase_deg, period=360)
    array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
            180.,  220.,  260.,  300.,  340.,  380.,  420.,  460.,  500.,
            540.])
    

    (gh-16987)

    np.unique now returns single NaN

    When np.unique operated on an array with multiple NaN entries, its return included a NaN for each entry that was NaN in the original array. This is now improved such that the returned array contains just one NaN as the last element.

    Also for complex arrays all NaN values are considered equivalent (no matter whether the NaN is in the real or imaginary part). As the representant for the returned array the smallest one in the lexicographical order is chosen - see np.sort for how the lexicographical order is defined for complex arrays.

    (gh-18070)

    Generator.rayleigh and Generator.geometric performance improved

    The performance of Rayleigh and geometric random variate generation in Generator has improved. These are both transformation of exponential random variables and the slow log-based inverse cdf transformation has been replaced with the Ziggurat-based exponential variate generator.

    This change breaks the stream of variates generated when variates from either of these distributions are produced.

    (gh-18666)

    Placeholder annotations have been improved

    All placeholder annotations, that were previously annotated as typing.Any, have been improved. Where appropiate they have been replaced with explicit function definitions, classes or other miscellaneous objects.

    (gh-18934)

    Performance improvements

    Improved performance in integer division of NumPy arrays

    Integer division of NumPy arrays now uses libdivide when the divisor is a constant. With the usage of libdivide and other minor optimizations, there is a large speedup. The // operator and np.floor_divide makes use of the new changes.

    (gh-17727)

    Improve performance of np.save and np.load for small arrays

    np.save is now a lot faster for small arrays.

    np.load is also faster for small arrays, but only when serializing with a version >= (3, 0).

    Both are done by removing checks that are only relevant for Python 2, while still maintaining compatibility with arrays which might have been created by Python 2.

    (gh-18657)

    Changes

    numpy.piecewise output class now matches the input class

    When numpy.ndarray subclasses are used on input to numpy.piecewise, they are passed on to the functions. The output will now be of the same subclass as well.

    (gh-18110)

    Enable Accelerate Framework

    With the release of macOS 11.3, several different issues that numpy was encountering when using Accelerate Framework's implementation of BLAS and LAPACK should be resolved. This change enables the Accelerate Framework as an option on macOS. If additional issues are found, please file a bug report against Accelerate using the developer feedback assistant tool (https://developer.apple.com/bug-reporting/). We intend to address issues promptly and plan to continue supporting and updating our BLAS and LAPACK libraries.

    (gh-18874)

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