Mailman 3 June 2021 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker July 11, 2023

July 11, 2023

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

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Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem Feb. 18, 2023

Feb. 18, 2023

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How … [View More]

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Documentation Team meeting - Monday June 8th
by Melissa Mendonça Dec. 4, 2022

Dec. 4, 2022

Hi all! A reminder that on Monday, June 8, we have another documentation team meeting at 3PM UTC**. If you wish to join on Zoom, you need to use this link https://zoom.us/j/420005230 Here's the permanent hackmd document with the meeting notes: https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg <https://www.google.com/url?q=https%3A%2F%2Fhackmd.io%2FoB_boakvRqKR-_2jRV-Q…> Hope to see you around (especially if you want to introduce yourself or discuss ideas for Google Season of Docs). ** You … [View More]

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copy="never" discussion and no deprecation cycle?
by Sebastian Berg July 21, 2022

July 21, 2022

Hi all, (sorry for the length, details/discussion below) On the triage call, there seemed a preference to just try to skip the deprecation and introduce `copy="never"`, `copy="if_needed"`, and `copy="always"` (i.e. string options for the `copy` keyword argument). Strictly speaking, this is against the typical policy (one year of warning/errors). But nobody could think of a reasonable chance that anyone actually uses it. (For me just "policy" will be enough of an argument to just take it … [View More]slow.) BUT: If nobody has *any* concerns at all, I think we may just end up introducing the change right away. The PR is: https://github.com/numpy/numpy/pull/19173 ## The Feature There is the idea to add `copy=never` (or similar). This would modify the existing `copy` argument to make it a 3-way decision: * `copy=always` or `copy=True` to force a copy * `copy=if_needed` or `copy=False` to prefer no-copy behavior * `copy=never` to error when no-copy behavior is not possible (this ensures that a view is returned) this would affect the functions: * np.array(object, copy=...) * arr.astype(new_dtype, copy=...) * np.reshape(arr, new_shape, copy=...), and the method arr.reshape() * np.meshgrid and possibly Where `reshape` currently does not have the option and would benefit by allowing for `arr.reshape(-1, copy=never)`, which would guarantee a view. ## The Options We have three options that are currently being discussed: 1. We introduce a new `np.CopyMode` or `np.<something>.Copy` Enum with values `np.CopyMode.NEVER`, `np.CopyMode.IF_NEEDED`, and `np.CopyMode.ALWAYS` * Plus: No compatibility concerns * Downside(?): This would be a first in NumPy, and is untypical API due to that. 2. We introduce `copy="never"`, `copy="if_needed"` and `copy="always"` as strings (all other strings will be a `TypeError`): * Problem: `copy="never"` currently means `copy=True` (the opposite) Which means new code has to take care when it may run on older NumPy versions. And in theory could make old code return the wrong thing. * Plus: Strings are the typical for options in NumPy currently. 3. Same as 2. But we take it very slow: Make strings an error right now and only introduce the new options after two releases as per typical deprecation policy. ## Discussion We discussed it briefly today in the triage call and we were leaning towards strings. I was honestly expecting to converge to option 3 to avoid compatibility issues (mainly surprises with `copy="never"` on older versions). But considering how weird it is to currently pass `copy="never"`, the question was whether we should not change it with a release note. The probability of someone currently passing exactly one of those three (and no other) strings seems exceedingly small. Personally, I don't have a much of an opinion. But if *nobody* voices any concern about just changing the meaning of the string inputs, I think the current default may be to just do it. Cheers, Sebastian [View Less]

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[Feature Request] Add alias of np.concatenate as np.concat
by Iordanis Fostiropoulos May 11, 2022

May 11, 2022

In regard to Feature Request: https://github.com/numpy/numpy/issues/16469 It was suggested to sent to the mailing list. I think I can make a strong point as to why the support for this naming convention would make sense. Such as it would follow other frameworks that often work alongside numpy such as tensorflow. For backward compatibility, it can simply be an alias to np.concatenate I often convert portions of code from tf to np, it is as simple as changing the base module from tf to np. e.g. … [View More]

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request to remove the numpy-aarch64 package from PyPI
by Ralf Gommers March 14, 2022

March 14, 2022

Hi all, FYI, I noticed this package that claimed to be maintained by us: https://pypi.org/project/numpy-aarch64/. That's not ours, so I tried to contact the author (no email provided, but guessed the same username on GitHub) and asked to remove it: https://github.com/tomasriv/DNA_Sequence/issues/1. There are a very large number of packages with "numpy" in the name on PyPI, and there's no way we can audit/police that effectively, but if it's a rebuild that pretends like it's official then I … [View More]

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell July 22, 2021

July 22, 2021

David Froger <david.froger.info <at> gmail.com> writes: > Hy,My question is about reading Fortran binary file (oh no this question > again...) I've posted this before, but I finally got it cleaned up for the Cookbook. For this purpose I use a subclass of file that has methods for reading unformatted Fortran data. See http://www.scipy.org/Cookbook/FortranIO/FortranFile. I'd gladly see this in numpy or scipy somewhere, but I'm not sure where it belongs. > program … [View More]makeArray > implicit none > integer,parameter:: nx=10,ny=20 > real(4),dimension(nx,ny):: ux,uy,p > integer :: i,j > open(11,file='uxuyp.bin',form='unformatted') > do i = 1,nx > do j = 1,ny > ux(i,j) = real(i*j) > uy(i,j) = real(i)/real(j) > p (i,j) = real(i) + real(j) > enddo > enddo > write(11) ux,uy > write(11) p > close(11) > end program makeArray When I run the above program compiled with gfortran on my Intel Mac, I can read it back with:: >>> import numpy as np >>> from fortranfile import FortranFile >>> f=FortranFile('uxuyp.bin', endian='<') >>> uxuy = f.readReals(prec='f') # 'f' for default reals >>> len(uxuy) 400 >>> ux = np.array(uxuy[:200]).reshape((20,10)).T >>> uy = np.array(uxuy[200:]).reshape((20,10)).T >>> p = f.readReals('f').reshape((20,10)).T >>> ux array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40.], [ 3., 6., 9., 12., 15., 18., 21., 24., 27., 30., 33., 36., 39., 42., 45., 48., 51., 54., 57., 60.], [ 4., 8., 12., 16., 20., 24., 28., 32., 36., 40., 44., 48., 52., 56., 60., 64., 68., 72., 76., 80.], [ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.], [ 6., 12., 18., 24., 30., 36., 42., 48., 54., 60., 66., 72., 78., 84., 90., 96., 102., 108., 114., 120.], [ 7.,Proxy-Connection: keep-alive Cache-Control: max-age=0 14., 21., 28., 35., 42., 49., 56., 63., 70., 77., 84., 91., 98., 105., 112., 119., 126., 133., 140.], [ 8., 16., 24., 32., 40., 48., 56., 64., 72., 80., 88., 96., 104., 112., 120., 128., 136., 144., 152., 160.], [ 9., 18., 27., 36., 45., 54., 63., 72., 81., 90., 99., 108., 117., 126., 135., 144., 153., 162., 171., 180.], [ 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130., 140., 150., 160., 170., 180., 190., 200.]]) >>> uy array([[ 1. , 0.5 , 0.33333334, 0.25 , 0.2 , 0.16666667, 0.14285715, 0.125 , 0.11111111, 0.1 , 0.09090909, 0.08333334, 0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353, 0.05555556, 0.05263158, 0.05 ], [ 2. , 1. , 0.66666669, 0.5 , 0.40000001, 0.33333334, 0.2857143 , 0.25 , 0.22222222, 0.2 , 0.18181819, 0.16666667, 0.15384616, 0.14285715, 0.13333334, 0.125 , 0.11764706, 0.11111111, 0.10526316, 0.1 ], [ 3. , 1.5 , 1. , 0.75 , 0.60000002, 0.5 , 0.42857143, 0.375 , 0.33333334, 0.30000001, 0.27272728, 0.25 , 0.23076923, 0.21428572, 0.2 , 0.1875 , 0.17647059, 0.16666667, 0.15789473, 0.15000001], [ 4. , 2. , 1.33333337, 1. , 0.80000001, 0.66666669, 0.5714286 , 0.5 , 0.44444445, 0.40000001, 0.36363637, 0.33333334, 0.30769232, 0.2857143 , 0.26666668, 0.25 , 0.23529412, 0.22222222, 0.21052632, 0.2 ], [ 5. , 2.5 , 1.66666663, 1.25 , 1. , 0.83333331, 0.71428573, 0.625 , 0.55555558, 0.5 , 0.45454547, 0.41666666, 0.38461539, 0.35714287, 0.33333334, 0.3125 , 0.29411766, 0.27777779, 0.2631579 , 0.25 ], [ 6. , 3. , 2. , 1.5 , 1.20000005, 1. , 0.85714287, 0.75 , 0.66666669, 0.60000002, 0.54545456, 0.5 , 0.46153846, 0.42857143, 0.40000001, 0.375 , 0.35294119, 0.33333334, 0.31578946, 0.30000001], [ 7. , 3.5 , 2.33333325, 1.75 , 1.39999998, 1.16666663, 1. , 0.875 , 0.77777779, 0.69999999, 0.63636363, 0.58333331, 0.53846157, 0.5 , 0.46666667, 0.4375 , 0.41176471, 0.3888889 , 0.36842105, 0.34999999], [ 8. , 4. , 2.66666675, 2. , 1.60000002, 1.33333337, 1.14285719, 1. , 0.8888889 , 0.80000001, 0.72727275, 0.66666669, 0.61538464, 0.5714286 , 0.53333336, 0.5 , 0.47058824, 0.44444445, 0.42105263, 0.40000001], [ 9. , 4.5 , 3. , 2.25 , 1.79999995, 1.5 , 1.28571427, 1.125 , 1. , 0.89999998, 0.81818181, 0.75 , 0.69230771, 0.64285713, 0.60000002, 0.5625 , 0.52941179, 0.5 , 0.47368422, 0.44999999], [ 10. , 5. , 3.33333325, 2.5 , 2. , 1.66666663, 1.42857146, 1.25 , 1.11111116, 1. , 0.90909094, 0.83333331, 0.76923078, 0.71428573, 0.66666669, 0.625 , 0.58823532, 0.55555558, 0.52631581, 0.5 ]]) >>> p array([[ 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.], [ 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.], [ 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.], [ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.], [ 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.], [ 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.], [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.], [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.], [ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.], [ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]]) Note that you have to provide the shape information for ux and uy because fortran writes them together as a stream of 400 numbers. -Neil [View Less]

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Add smallest_normal and smallest_subnormal attributes to finfo
by Stephannie Jiménez Gacha July 19, 2021

July 19, 2021

Good afternoon, Given the discussions happened in the Data API consortium when looking into the attributes of `finfo` used in the wild, we found that `tiny` is used regularly but in a good amount of cases not for its intended purpose but rather as "just give me a small number". Following this we are proposing the addition of `smallest_normal` and `smallest_subnormal` attributes. Personally, I think that the `tiny` name is a little bit odd and misleading, so it will be great to leave that as an … [View More]

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Proposal to accept NEP 49: Data allocation strategies
by Matti Picus July 18, 2021

July 18, 2021

Here is the current rendering of the NEP:https://numpy.org/neps/nep-0049.html The mailing list discussion, started on April 20 did not bring up any objections to the proposal, nor were there objections in the discussion around the text of the NEP. There were questions around details of the implementation, thank you reviewers for carefully looking at them and suggesting improvements. If there are no substantive objections within 7 days from this email, then the NEP will be accepted; … [View More]

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is_triangular, is_diagonal, is_symmetric et al. in NumPy or SciPy linalg
by Ilhan Polat July 2, 2021

July 2, 2021

Dear all, I'm writing some helper Cythpm functions for scipy.linalg which is kinda performant and usable. And there is still quite some wiggle room for more. In many linalg routines there is a lot of performance benefit if the structure can be discovered in a cheap and reliable way at the outset. For example if symmetric then eig can delegate to eigh or if triangular then triangular solvers can be used in linalg.solve and lstsq so forth Here is the Cythonized version for Jupyter notebook to … [View More]paste to discover the lower/upper bandwidth of square array A that competes well with A != 0 just to use some low level function (note the latter returns an array hence more cost is involved) There is a higher level supervisor function that checks C-contiguousness otherwise specializes to different versions of it Initial cell %load_ext Cython %load_ext line_profiler import cython import line_profiler Then another cell %%cython # cython: language_level=3 # cython: linetrace=True # cython: binding = True # distutils: define_macros=CYTHON_TRACE=1 # distutils: define_macros=CYTHON_TRACE_NOGIL=1 cimport cython cimport numpy as cnp import numpy as np import line_profiler ctypedef fused np_numeric_t: cnp.int8_t cnp.int16_t cnp.int32_t cnp.int64_t cnp.uint8_t cnp.uint16_t cnp.uint32_t cnp.uint64_t cnp.float32_t cnp.float64_t cnp.complex64_t cnp.complex128_t cnp.int_t cnp.long_t cnp.longlong_t cnp.uint_t cnp.ulong_t cnp.ulonglong_t cnp.intp_t cnp.uintp_t cnp.float_t cnp.double_t cnp.longdouble_t @cython.linetrace(True) @cython.initializedcheck(False) @cython.boundscheck(False) @cython.wraparound(False) cpdef inline (int, int) band_check_internal(np_numeric_t[:, ::1]A): cdef Py_ssize_t n = A.shape[0], lower_band = 0, upper_band = 0, r, c cdef np_numeric_t zero = 0 for r in xrange(n): # Only bother if outside the existing band: for c in xrange(r-lower_band): if A[r, c] != zero: lower_band = r - c break for c in xrange(n - 1, r + upper_band, -1): if A[r, c] != zero: upper_band = c - r break return lower_band, upper_band Final cell for use-case --------------- # Make arbitrary lower-banded array n = 50 # array size k = 3 # k'th subdiagonal R = np.zeros([n, n], dtype=np.float32) R[[x for x in range(n)], [x for x in range(n)]] = 1 R[[x for x in range(n-1)], [x for x in range(1,n)]] = 1 R[[x for x in range(1,n)], [x for x in range(n-1)]] = 1 R[[x for x in range(k,n)], [x for x in range(n-k)]] = 2 Some very haphazardly put together metrics %timeit band_check_internal(R) 2.59 µs ± 84.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) %timeit np.linalg.solve(R, zzz) 824 µs ± 6.24 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) %timeit R != 0. 1.65 µs ± 43.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) So the worst case cost is negligible in general (note that the given code is slower as it uses the fused type however if I go with tempita standalone version is faster) Two questions: 1) This is missing np.half/float16 functionality since any arithmetic with float16 is might not be reliable including nonzero check. IS it safe to view it as np.uint16 and use that specialization? I'm not sure about the sign bit hence the question. I can leave this out since almost all linalg suite rejects this datatype due to well-known lack of supprt. 2) Should this be in NumPy or SciPy linalg? It is quite relevant to be on SciPy but then again this stuff is purely about array structures. But if the opinion is for NumPy then I would need a volunteer because NumPy codebase flies way above my head. All feedback welcome Best ilhan [View Less]

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Interface for wrapping random distribution functions (__array_function__, __ufunc__, ?)
by Yoann Mocquin June 30, 2021

June 30, 2021

Hello there, here is a feature request for the possibility to wrap numpy random distributions to be wrapped by a mechanism like __array_function__ or __array_ufunc__. You can find the GH issue at : https://github.com/numpy/numpy/issues/19382 <https://github.com/numpy/numpy/issues/19382>. This post follows [this one](https://github.com/numpy/numpy/issues/18902 <https://github.com/numpy/numpy/issues/18902>). I figured I should open another issue for `np.random.normal`, but this … [View More]applies for all distributions I guess. ## Feature Basicaly, I would like that `numpy.random.*` distributions could trigger an interface when passed instances from custom classes, "à la" `__array_ufunc__` or `__array_function__`. Here is an example concept : ```python arr = np.arange(10) # Reference result print(np.mean(arr)) print(np.random.normal(arr)) custom_obj = MyArrayLike(arr) print(np.mean(custom_obj)) # OK : np.mean will trigger __array_function__ interface print(np.random.normal(custom_obj)) # KO : np.random.normal will "only" try to cast the object to float ``` And here is a MWE : ```python import numpy as np np.random.seed(1234) HANDLED_FUNCTIONS = {} class NumericalLabeled(): def __init__(self, value, label=""): self.value = value self.label = label def __repr__(self): return "NumericalLabelled<"+str(self.value) + "," + self.label+">" def __array_function__(self, func, types, args, kwargs): if func not in HANDLED_FUNCTIONS: return NotImplemented return HANDLED_FUNCTIONS[func](*args, **kwargs) def make_numericallabelled(x, label=""): """ Helper function to cast anything into a NumericalLabelled object. """ if isinstance(x, NumericalLabeled): return x else: return NumericalLabeled(x, label=label) # Numpy functions # Override functions - used with __array_function__ def implements(np_function): def decorator(func): HANDLED_FUNCTIONS[np_function] = func return func return decorator @implements(np.random.normal) def np_random_normal(loc=0.0, scale=1.0, **kwargs): # cast both loc and scale into Numericallabelled loc = make_numericallabelled(loc) scale = make_numericallabelled(scale) # check their label is "compatible" if not loc.label == scale.label: raise ValueError return NumericalLabeled(np.random.rand(loc=loc.value, scale=scale.value, **kwargs), loc.label+scale.label) @implements(np.mean) def np_mean(a, *args, **kwargs): return NumericalLabeled(np.mean(a.value, *args, **kwargs), a.label) def main(): # reference result for standard array arr = np.arange(10) print(np.mean(arr)) print(np.random.normal(arr)) # array-like object num_labeled = NumericalLabeled(arr, "toto") print(np.mean(num_labeled)) try: print(np.random.normal(num_labeled)) except Exception as e: print(e) main() ``` which results in ``` 4.5 [ 0.47143516 -0.19097569 3.43270697 2.6873481 3.27941127 5.88716294 6.85958841 6.3634765 8.01569637 6.75731505] NumericalLabelled<4.5,toto> float() argument must be a string or a number, not 'NumericalLabeled' ``` Since the distribution functions accept array as input, I would expect them to be wrappable like many other array functions. ### Versions ``` Python : 3.8.5 (default, Sep 4 2020, 02:22:02) [Clang 10.0.0 ] Numpy : 1.21.0 ``` Cheers [View Less]

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NumPy Development Meeting Wednesday - Triage Focus (now earlier!)
by Sebastian Berg June 29, 2021

June 29, 2021

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, June 30th at 9 am Pacific Time (18:00 UTC). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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ANN: SfePy 2021.2
by Robert Cimrman June 29, 2021

June 29, 2021

I am pleased to announce the release of SfePy 2021.2. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by finite element methods. It is distributed under the new BSD license. Home page: https://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------… [View More]

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Moving triage meeting two hours earlier
by Sebastian Berg June 28, 2021

June 28, 2021

Hi all, I would like to move the triage meeting to two hours earlier. That would make it 9am Pacific time and 4pm UTC (6pm central European summer time). That is to allow eastern time zones to join more easily. If nobody of the core team has any objections, I will adjust the time for this Wednesday and announce with the new time tomorrow. Cheers, Sebastian

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NumPy 1.21.0 release
by Charles R Harris June 24, 2021

June 24, 2021

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.21.0. The 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. This NumPy release is the result of 581 merged pull requests contributed by 175 … [View More]people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.21.0/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.21.0>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 175 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @8bitmp3 + - @DWesl + - @Endolith - @Illviljan + - @Lbogula + - @Lisa + - @Patrick + - @Scian + - @h-vetinari + - @h6197627 + - @jbCodeHub + - @legoffant + - @sfolje0 + - @tautaus + - @yetanothercheer + - Abhay Raghuvanshi + - Adrian Price-Whelan + - Aerik Pawson + - Agbonze Osazuwa + - Aitik Gupta + - Al-Baraa El-Hag - Alex Henrie - Alexander Hunt + - Alizé Papp + - Allan Haldane - Amarnath1904 + - Amrit Krishnan + - Andras Deak - AngelGris + - Anne Archibald - Anthony Vo + - Antony Lee - Atharva-Vidwans + - Ayush Verma + - Bas van Beek - Bharat Raghunathan - Bhargav V + - Brian Soto - Carl Michal + - Charles Harris - Charles Stern + - Chiara Marmo + - Chris Barnes + - Chris Vavaliaris - Christina Hedges + - Christoph Gohlke - Christopher Dahlin + - Christos Efstathiou + - Chunlin Fang - Constanza Fierro + - Daniel Evans + - Daniel Montes + - Dario Mory + - David Carlier + - David Stansby - Deepyaman Datta + - Derek Homeier - Dong Keun Oh + - Dylan Cutler + - Eric Larson - Eric Wieser - Eva Jau + - Evgeni Burovski - FX Coudert + - Faris A Chugthai + - Filip Ter + - Filip Trojan + - François Le Lay + - Ganesh Kathiresan - Giannis Zapantis + - Giulio Procopio + - Greg Lucas + - Hollow Man + - Holly Corbett + - I-Shen Leong + - Inessa Pawson - Isabela Presedo-Floyd - Ismael Jimenez + - Isuru Fernando - Jakob Jakobson - James Gerity + - Jamie Macey + - Jasmin Classen + - Jody Klymak + - Joseph Fox-Rabinovitz - Jérome Eertmans + - Jérôme Kieffer + - Kamil Choudhury + - Kasia Leszek + - Keller Meier + - Kenichi Maehashi - Kevin Sheppard - Kulin Seth + - Kumud Lakara + - Laura Kopf + - Laura Martens + - Leo Singer + - Leonardus Chen + - Lima Tango + - Lumir Balhar + - Maia Kaplan + - Mainak Debnath + - Marco Aurélio da Costa + - Marta Lemanczyk + - Marten van Kerkwijk - Mary Conley + - Marysia Winkels + - Mateusz Sokół + - Matt Haberland - Matt Hall + - Matt Ord + - Matthew Badin + - Matthias Bussonnier - Matthias Geier - Matti Picus - Matías Ríos + - Maxim Belkin + - Melissa Weber Mendonça - Meltem Eren Copur + - Michael Dubravski + - Michael Lamparski - Michal W. Tarnowski + - Michał Górny + - Mike Boyle + - Mike Toews - Misal Raj + - Mitchell Faas + - Mukulikaa Parhari + - Neil Girdhar + - Nicholas McKibben + - Nico Schlömer - Nicolas Hug + - Nilo Kruchelski + - Nirjas Jakilim + - Ohad Ravid + - Olivier Grisel - Pamphile ROY + - Panos Mavrogiorgos + - Patrick T. Komiske III + - Pearu Peterson - Peter Hawkins + - Raghuveer Devulapalli - Ralf Gommers - Raúl Montón Pinillos + - Rin Arakaki + - Robert Kern - Rohit Sanjay - Roman Yurchak - Ronan Lamy - Ross Barnowski - Ryan C Cooper - Ryan Polley + - Ryan Soklaski - Sabrina Simao + - Sayed Adel - Sebastian Berg - Shen Zhou + - Stefan van der Walt - Sylwester Arabas + - Takanori Hirano - Tania Allard + - Thomas J. Fan + - Thomas Orgis + - Tim Hoffmann - Tomoki, Karatsu + - Tong Zou + - Touqir Sajed + - Tyler Reddy - Wansoo Kim - Warren Weckesser - Weh Andreas + - Yang Hau - Yashasvi Misra + - Zolboo Erdenebaatar + - Zolisa Bleki Cheers, Charles Harris [View Less]

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2020 NumPy survey results
by Inessa Pawson June 23, 2021

June 23, 2021

Hi, everyone! In 2020, the NumPy survey team in partnership with students and faculty from a Master’s course in Survey Methodology jointly hosted by the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. We are finally ready to share with you the findings of this survey. You can … [View More]

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NumPy Community Meeting Wednesday
by Sebastian Berg June 23, 2021

June 23, 2021

Hi all, There will be a NumPy Community meeting Wednesday June 23rd at 20:00 UTC. Everyone is invited and encouraged to join in and edit the work-in-progress meeting topics and notes at: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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ANN: SciPy 1.7.0
by Tyler Reddy June 21, 2021

June 21, 2021

Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.7.0. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.7.0 One of a few ways to install this release with pip: pip install scipy==1.7.0 ===================== SciPy 1.7.0 Release Notes ===================== SciPy 1.7.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, … [View More]

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Acceptance of NEP 36 - fair play
by Matti Picus June 20, 2021

June 20, 2021

Stefan has submitted a PR [0] to accept "NEP 36 -fair play" as accepted. It was discussed recently on the mailing list [1] and in the original PR [2] . If there is no opposition in a week (27 June) the PR will be merged. [0]https://github.com/numpy/numpy/pull/19284 [1] https://mail.python.org/pipermail/numpy-discussion/2021-June/081873.html [2] https://github.com/numpy/numpy/pull/14779

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NumPy Development Meeting Wednesday - Triage Focus (Soon today!)
by Sebastian Berg June 16, 2021

June 16, 2021

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, June 16nd at 11 am Pacific Time (18:00 UTC). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian PS: I am considering moving the meeting to slightly earlier if everyone agrees.

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Tensor Typing presentation and discussion on Wednesday! (As part of the NumPy Community Meeting)
by Sebastian Berg June 15, 2021

June 15, 2021

Hi all, There will be a NumPy Community meeting Wednesday June 9th at 20:00 UTC. This meeting will be dedicated to a presentation by Matthew and Pradeep about typing arrays. The title is: "Better types for numerical computing - arrays generic in dtype and shape" Abstract: An overview of why more detailed types for arrays would be awesome, the work being done to enable those types in PEP 646, and what the future of this effort could look like. If you are interested in typing and/or missed … [View More]

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ANN: SciPy 1.7.0rc2 -- please test
by Tyler Reddy June 14, 2021

June 14, 2021

Hi all, On behalf of the SciPy development team I'm pleased to announce the release candidate SciPy 1.7.0rc2. Please help us test this pre-release, which notably no longer includes Cython- generated C source files in the sdist. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.7.0rc2 <https://github.com/scipy/scipy/releases/tag/v1.7.0rc1> One of a few ways to install the release candidate with pip: pip … [View More]

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PR for keywordlabeler
by Charles R Harris June 13, 2021

June 13, 2021

Hi All, There is a pending PR <https://github.com/numpy/numpy/pull/19198> that uses keywordlabeler <https://github.com/marketplace/keywordlabeler> to automatically label PRs and issues. Installing the app requires giving it write permissions to PRs and issues. This isn't different than with the current labeler, but I note that we don't have a procedure for deciding such issues that may have security implications. So I am posting here before proceeding with app installation. Thoughts? Chuck

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Checking if all array elements are whole numbers using the C-API
by zoj613 June 13, 2021

June 13, 2021

Hi All, Is there a C-API analogue for `np.asarray(a, dtype=int) != np.asarray(a)` assuming that `a` is an array or python sequence object of whole numbers but is assigned the float type (e.g a=array([1., 2., 3.]) or [1., 2., 3.])? I tried `np.PyArray_FROM_OT(a, np.NPY_LONG) != np.PyArray_FROM_O(a)` but I get the error message: `TypeError: Cannot cast array data from dtype('float64') to dtype('int64') according to the rule 'safe'`. Is there a way I can achieve what I want using the numpy … [View More]

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Proposals for NumPy 1.22.0
by Charles R Harris June 10, 2021

June 10, 2021

Hi All, Here are some proposals for the NumPy 1.22.0 release currently in development. 1. Drop support for Python 3.7 (3.10 will be available). 2. Drop manylinux1 wheels. 3. Drop manylinux2010 wheels? 4. Use ILP64 BLAS for 64 bit wheels. Thoughts? Chuck

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Accepting NEP 35 `like=array_like` for use with `__array_function__`
by Sebastian Berg June 9, 2021

June 9, 2021

Hi all, I have opened a PR (https://github.com/numpy/numpy/pull/19188) to *finalize* NEP 35: https://numpy.org/neps/nep-0035-array-creation-dispatch-with-array-function… Which added `like=` keyword argument to `np.array`, `np.asarray`, `np.arange`, etc. as final. The previous acceptance mail was here: https://mail.python.org/pipermail/numpy-discussion/2021-May/081761.html But, since it may have flown under the radar. Please don't hesitate to voice any concerns! We are planning to … [View More]

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NumPy 1.21.0rc2 released
by Charles R Harris June 8, 2021

June 8, 2021

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.21.0rc2. The 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. This NumPy release is the result of 571 merged pull requests contributed by 173 … [View More]people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.21.0rc2/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.21.0rc2>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 171 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @8bitmp3 + - @DWesl + - @Endolith - @Illviljan + - @Lbogula + - @Lisa + - @Patrick + - @Scian + - @h-vetinari + - @h6197627 + - @jbCodeHub + - @legoffant + - @sfolje0 + - @tautaus + - @yetanothercheer + - Abhay Raghuvanshi + - Adrian Price-Whelan + - Aerik Pawson + - Agbonze Osazuwa + - Aitik Gupta + - Al-Baraa El-Hag - Alex Henrie - Alexander Hunt + - Alizé Papp + - Allan Haldane - Amarnath1904 + - Amrit Krishnan + - Andras Deak - AngelGris + - Anne Archibald - Anthony Vo + - Antony Lee - Atharva-Vidwans + - Ayush Verma + - Bas van Beek - Bharat Raghunathan - Bhargav V + - Brian Soto - Carl Michal + - Charles Harris - Charles Stern + - Chiara Marmo + - Chris Barnes + - Chris Vavaliaris - Christina Hedges + - Christoph Gohlke - Christopher Dahlin + - Christos Efstathiou + - Chunlin Fang - Constanza Fierro + - Daniel Evans + - Daniel Montes + - Dario Mory + - David Carlier + - David Stansby - Deepyaman Datta + - Derek Homeier - Dong Keun Oh + - Dylan Cutler + - Eric Larson - Eric Wieser - Eva Jau + - Evgeni Burovski - FX Coudert + - Faris A Chugthai + - Filip Ter + - Filip Trojan + - François Le Lay + - Ganesh Kathiresan - Giannis Zapantis + - Giulio Procopio + - Greg Lucas + - Hollow Man + - Holly Corbett + - I-Shen Leong + - Inessa Pawson - Isabela Presedo-Floyd - Ismael Jimenez + - Isuru Fernando - Jakob Jakobson - James Gerity + - Jamie Macey + - Jasmin Classen + - Jody Klymak + - Joseph Fox-Rabinovitz - Jérome Eertmans + - Jérôme Kieffer + - Kamil Choudhury + - Kasia Leszek + - Keller Meier + - Kevin Sheppard - Kulin Seth + - Kumud Lakara + - Laura Kopf + - Laura Martens + - Leo Singer + - Leonardus Chen + - Lima Tango + - Lumir Balhar + - Maia Kaplan + - Mainak Debnath + - Marco Aurélio da Costa + - Marta Lemanczyk + - Marten van Kerkwijk - Mary Conley + - Marysia Winkels + - Mateusz Sokół + - Matt Haberland - Matt Hall + - Matt Ord + - Matthew Badin + - Matthias Bussonnier - Matthias Geier - Matti Picus - Matías Ríos + - Maxim Belkin + - Melissa Weber Mendonça - Meltem Eren Copur + - Michael Dubravski + - Michael Lamparski - Michal W. Tarnowski + - Michał Górny + - Mike Boyle + - Mike Toews - Misal Raj + - Mitchell Faas + - Mukulikaa Parhari + - Neil Girdhar + - Nicholas McKibben + - Nico Schlömer - Nicolas Hug + - Nilo Kruchelski + - Nirjas Jakilim + - Ohad Ravid + - Olivier Grisel - Pamphile ROY + - Panos Mavrogiorgos + - Patrick T. Komiske III + - Pearu Peterson - Raghuveer Devulapalli - Ralf Gommers - Raúl Montón Pinillos + - Rin Arakaki + - Robert Kern - Rohit Sanjay - Roman Yurchak - Ronan Lamy - Ross Barnowski - Ryan C Cooper - Ryan Polley + - Ryan Soklaski - Sabrina Simao + - Sayed Adel - Sebastian Berg - Shen Zhou + - Stefan van der Walt - Sylwester Arabas + - Takanori Hirano - Tania Allard + - Thomas J. Fan + - Thomas Orgis + - Tim Hoffmann - Tomoki, Karatsu + - Tong Zou + - Touqir Sajed + - Tyler Reddy - Wansoo Kim - Warren Weckesser - Weh Andreas + - Yang Hau - Yashasvi Misra + - Zolboo Erdenebaatar + - Zolisa Bleki Cheers, Charles Harris Reply allReplyForward [View Less]

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numpy histogram data
by Keith Sloan June 8, 2021

June 8, 2021

Okay I have some data which I have created a scatter plot <http://numpy-discussion.10968.n7.nabble.com/file/t2643/A2D75C32-6DAF-4DA7-B…> I have also created a histogram of red Galaxy counts for redshift with RedEllipticalMasses.keep_columns(['uminusr','Z_1']) RedEllipticalMasses.sort(['Z_1','uminusr']) counts, bins = np.histogram(RedEllipticalMasses['Z_1'],bins=80) fig, ax = plt.subplots(1, 1, figsize=(12,8)) # make the figure with the size 10 x 8 inches fig.suptitle("2 - Histogram … [View More]

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Re: [Numpy-discussion] Best fit linear piecewise function?
by Mark Bakker June 7, 2021

June 7, 2021

My students are using this and seem to like it: https://jekel.me/piecewise_linear_fit_py/about.html > Date: Tue, 1 Jun 2021 17:22:52 -0700 > From: Chris Barker <chris.barker(a)noaa.gov> > To: Discussion of Numerical Python <numpy-discussion(a)python.org> > Subject: [Numpy-discussion] Best fit linear piecewise function? > Message-ID: > <CALGmxELHB3MUT= > i58WZ0dL_292GvntmFRte88ik98h2NqT1YJw(a)mail.gmail.com> > Content-Type: text/plain; charset=… [View More]

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RFC: supporting complex inputs in np.logaddexp
by Filippo Vicentini June 7, 2021

June 7, 2021

Hello all, I would like to ask if the maintainers would be in favour of adding support to np.logaddexp in order to support complex numbers. scipy.special.logsumexp already does, and it would be relatively straightforward to support them in numpy too. Complex numbers are very common when working in quantum physics, where we have complex-valued probability amplitudes. Best, Filippo Vicentini

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ANN: SciPy 1.7.0rc1 -- please test
by Tyler Reddy June 6, 2021

June 6, 2021

Hi all, On behalf of the SciPy development team I'm pleased to announce the release candidate SciPy 1.7.0rc1. Please help us test this pre-release. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.7.0rc1 One of a few ways to install the release candidate with pip: pip install scipy==1.7.0rc1 ===================== SciPy 1.7.0 Release Notes ===================== Note: Scipy 1.7.0 is not released yet! SciPy 1.7.… [View More]

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Broadcasting array with Ursina intersect
by klark--kent＠yandex.ru June 6, 2021

June 6, 2021

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Best fit linear piecewise function?
by Chris Barker June 2, 2021

June 2, 2021

Do any of you know of code for finding an optimum linear piecewise fit to a set of points? Somethin like what is described in this article: https://www.hindawi.com/journals/mpe/2015/876862/ At a glance, that looked just hard enough to code up that I'm hoping someone has already done it :-) -CHB -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (… [View More]

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NumPy Development Meeting Wednesday - Triage Focus (Soon today!)
by Sebastian Berg June 2, 2021

June 2, 2021

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, June 2nd at 11 am Pacific Time (18:00 UTC). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian PS: Sorry for the late reminder!

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