Mailman 3 October 2019 - 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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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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argmax() indexes to value
by Daniele Nicolodi Nov. 1, 2019

Nov. 1, 2019

Hello, this is a very basic question, but I cannot find a satisfying answer. Assume a is a 2D array and that I get the index of the maximum value along the second dimension: i = a.argmax(axis=1) Is there a better way to get the value of the maximum array entries along the second axis other than: v = a[np.arange(len(a)), i] ?? Thank you. Cheers, Daniele

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Transonic Vision: unifying Python-Numpy accelerators
by PIERRE AUGIER Oct. 31, 2019

Oct. 31, 2019

Dear Python-Numpy community, Few years ago I started to use a lot Python and Numpy for science. I'd like to thanks all people who contribute to this fantastic community. I used a lot Cython, Pythran and Numba and for the FluidDyn project, we created Transonic, a pure Python package to easily accelerate modern Python-Numpy code with different accelerators. We wrote a long and serious text to explain why we think Transonic could have a positive impact on the scientific Python ecosystem. Here … [View More]

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Numpy community meeting Wed Oct 30
by Matti Picus Oct. 29, 2019

Oct. 29, 2019

Hi all, There will be a NumPy Community meeting Wednesday Oct 30 at 11 am Pacific Time. Everyone is invited to join in and edit the work-in- progress meeting topics and notes: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Matti

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NEP 34 - deprecate automatic dtype=object on ragged arrays
by Matti Picus Oct. 29, 2019

Oct. 29, 2019

After a few iterations by reviewers, I would like to submit NEP 34 to deprecate automatically using dtype=object for ragged arrays. https://github.com/numpy/numpy/pull/14674 and an associated PR for the implementation https://github.com/numpy/numpy/pull/14794 Commments? Matti Abstract -------- When users create arrays with sequences-of-sequences, they sometimes err in matching the lengths of the nested sequences_, commonly called "ragged arrays". Here we will refer to them … [View More]

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NumPy amuse-bouche
by Inessa Pawson Oct. 29, 2019

Oct. 29, 2019

We are looking for code snippets that illustrate NumPy’s unique capabilities. To submit your ideas or for further info please refer to the following GitHub discussion: github.com/numpy/numpy.org/issues/40#issuecomment-534303380 -- Every good wish, Inessa Pawson NumPy Web Team

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New draft of NEP 31 — Context-local and global overrides of the NumPy API
by Hameer Abbasi Oct. 28, 2019

Oct. 28, 2019

Hello everyone, I’ve improved upon the content of NEP 31 to make it simpler, and also according to the new NEP template, only part of the NEP is being sent out to the mailing list. For the full nep, please see PR 14793<https://github.com/numpy/numpy/pull/14793>. ============================================================ NEP 31 — Context-local and global overrides of the NumPy API ============================================================ :Author: Hameer Abbasi <habbasi(a)… [View More]quansight.com> :Author: Ralf Gommers <rgommers(a)quansight.com> :Author: Peter Bell <pbell(a)quansight.com> :Status: Draft :Type: Standards Track :Created: 2019-08-22 Abstract -------- This NEP proposes to make all of NumPy's public API overridable via an extensible backend mechanism. Acceptance of this NEP means NumPy would provide global and context-local overrides in a separate namespace, as well as a dispatch mechanism similar to NEP-18 [2]_. First experiences with ``__array_function__`` show that it is necessary to be able to override NumPy functions that *do not take an array-like argument*, and hence aren't overridable via ``__array_function__``. The most pressing need is array creation and coercion functions, such as ``numpy.zeros`` or ``numpy.asarray``; see e.g. NEP-30 [9]_. This NEP proposes to allow, in an opt-in fashion, overriding any part of the NumPy API. It is intended as a comprehensive resolution to NEP-22 [3]_, and obviates the need to add an ever-growing list of new protocols for each new type of function or object that needs to become overridable. Motivation and Scope -------------------- The primary end-goal of this NEP is to make the following possible: .. code:: python # On the library side import numpy.overridable as unp def library_function(array): array = unp.asarray(array) # Code using unumpy as usual return array # On the user side: import numpy.overridable as unp import uarray as ua import dask.array as da ua.register_backend(da) # Can be done within Dask itself library_function(dask_array) # works and returns dask_array with unp.set_backend(da): library_function([1, 2, 3, 4]) # actually returns a Dask array. Here, ``backend`` can be any compatible object defined either by NumPy or an external library, such as Dask or CuPy. Ideally, it should be the module ``dask.array`` or ``cupy`` itself. These kinds of overrides are useful for both the end-user as well as library authors. End-users may have written or wish to write code that they then later speed up or move to a different implementation, say PyData/Sparse. They can do this simply by setting a backend. Library authors may also wish to write code that is portable across array implementations, for example ``sklearn`` may wish to write code for a machine learning algorithm that is portable across array implementations while also using array creation functions. This NEP takes a holistic approach: It assumes that there are parts of the API that need to be overridable, and that these will grow over time. It provides a general framework and a mechanism to avoid a design of a new protocol each time this is required. This was the goal of ``uarray``: to allow for overrides in an API without needing the design of a new protocol. This NEP proposes the following: That ``unumpy`` [8]_ becomes the recommended override mechanism for the parts of the NumPy API not yet covered by ``__array_function__`` or ``__array_ufunc__``, and that ``uarray`` is vendored into a new namespace within NumPy to give users and downstream dependencies access to these overrides. This vendoring mechanism is similar to what SciPy decided to do for making ``scipy.fft`` overridable (see [10]_). The motivation behind ``uarray`` is manyfold: First, there have been several attempts to allow dispatch of parts of the NumPy API, including (most prominently), the ``__array_ufunc__`` protocol in NEP-13 [4]_, and the ``__array_function__`` protocol in NEP-18 [2]_, but this has shown the need for further protocols to be developed, including a protocol for coercion (see [5]_, [9]_). The reasons these overrides are needed have been extensively discussed in the references, and this NEP will not attempt to go into the details of why these are needed; but in short: It is necessary for library authors to be able to coerce arbitrary objects into arrays of their own types, such as CuPy needing to coerce to a CuPy array, for example, instead of a NumPy array. In simpler words, one needs things like ``np.asarray(...)`` or an alternative to "just work" and return duck-arrays. Usage and Impact ---------------- This NEP allows for global and context-local overrides, as well as automatic overrides a-la ``__array_function__``. Here are some use-cases this NEP would enable, besides the first one stated in the motivation section: The first is allowing alternate dtypes to return their respective arrays. .. code:: python # Returns an XND array x = unp.ones((5, 5), dtype=xnd_dtype) # Or torch dtype The second is allowing overrides for parts of the API. This is to allow alternate and/or optimised implementations for ``np.linalg``, BLAS, and ``np.random``. .. code:: python import numpy as np import pyfftw # Or mkl_fft # Makes pyfftw the default for FFT np.set_global_backend(pyfftw) # Uses pyfftw without monkeypatching np.fft.fft(numpy_array) with np.set_backend(pyfftw) # Or mkl_fft, or numpy # Uses the backend you specified np.fft.fft(numpy_array) This will allow an official way for overrides to work with NumPy without monkeypatching or distributing a modified version of NumPy. Here are a few other use-cases, implied but not already stated: .. code:: python data = da.from_zarr('myfile.zarr') # result should still be dask, all things being equal result = library_function(data) result.to_zarr('output.zarr') This second one would work if ``magic_library`` was built on top of ``unumpy``. .. code:: python from dask import array as da from magic_library import pytorch_predict data = da.from_zarr('myfile.zarr') # normally here one would use e.g. data.map_overlap result = pytorch_predict(data) result.to_zarr('output.zarr') Backward compatibility ---------------------- There are no backward incompatible changes proposed in this NEP. [View Less]

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proposed NEP template change
by Ralf Gommers Oct. 24, 2019

Oct. 24, 2019

Hi all, In https://github.com/numpy/numpy/pull/14734 we're working on a NEP template update, with the goal of better separating content that's relevant for users and authors of packages that depend on NumPy from implementation details. Please have a look and comment on the PR if you're interested in this topic. Cheers, Ralf

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