Mailman 3 February 2021 - NumPy-Discussion

Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem 18 Feb '23

18 Feb '23

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 do I avoid these messages to appear? I know that I could in principle use numpy.seterr. However, I do NOT want to remove these warnings for other potential divide/multiply/sqrt etc errors. Only when I am using a "where", to in fact avoid such warnings! Note that the warnings only happen once, but since I am going to release that code, I would like to avoid the user to get such messages which are irrelevant here (because I am testing, with the where, when NOT to divide by zero or take a sqrt of a negative number). thanks! Eric

5 4

Documentation Team meeting - Monday June 8th
by Melissa Mendonça 04 Dec '22

04 Dec '22

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 can click this link to get the correct time at your timezone: https://www.timeanddate.com/worldclock/fixedtime.html?msg=NumPy+Documentati… - Melissa

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[Feature Request] Add alias of np.concatenate as np.concat
by Iordanis Fostiropoulos 11 May '22

11 May '22

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. np.expand_dims -> tf.expand_dims. This is done either in debugging (e.g. converting tf to np without eager execution to debug portion of the code), or during prototyping, e.g. develop in numpy and convert in tf. I find myself more than at one occasion to getting syntax errors because of this particular function np.concatenate. It is unnecessarily long. I imagine there are more people that also run into the same problems. Pandas uses concat (torch on the other extreme uses simply cat, which I don't think is as descriptive).

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell 22 Jul '21

22 Jul '21

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 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

10 10

Unreliable crash when converting using numpy.asarray via C buffer interface
by Friedrich Romstedt 29 Mar '21

29 Mar '21

Hi, This is with Python 3.8.2 64-bit and numpy 1.19.2 on Windows 10. I'd like to be able to convert some C++ extension type to a numpy array by using ``numpy.asarray``. The extension type implements the Python buffer interface to support this. The extension type, called "Image" here, holds some chunk of ``double``, C order, contiguous, 2 dimensions. It "owns" the buffer; the buffer is not shared with other objects. The following Python code crashes:: image = <... Image production ...> ar = numpy.asarray(image) However, when I say:: image = <... Image production ...> print("---") ar = numpy.asarray(image) the entire program is executing properly with correct data in the numpy ndarray produced using the buffer interface. The extension type permits reading the pixel values by a method; copying them over by a Python loop works fine. I am ``Py_INCREF``-ing the producer in the C++ buffer view creation function properly. The shapes and strides of the buffer view are ``delete[]``-ed upon releasing the buffer; avoiding this does not prevent the crash. I am catching ``std::exception`` in the view creation function; no such exception occurs. The shapes and strides are allocated by ``new Py_ssize_t[2]``, so they will survive the view creation function. I spent some hours trying to figure out what I am doing wrong. Maybe someone has an idea about this? I double-checked each line of code related to this problem and couldn't find any mistake. Probabaly I am not looking at the right aspect. Best, Friedrich

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NEP: array API standard adoption (NEP 47)
by Ralf Gommers 15 Mar '21

15 Mar '21

Hi all, Here is a NEP, written together with Stephan Hoyer and Aaron Meurer, for discussion on adoption of the array API standard ( https://data-apis.github.io/array-api/latest/). This will add a new numpy.array_api submodule containing that standardized API. The main purpose of this API is to be able to write code that is portable to other array/tensor libraries like CuPy, PyTorch, JAX, TensorFlow, Dask, and MXNet. We expect this NEP to remain in draft state for quite a while, while we're gaining experience with using it in downstream libraries, discuss adding it to other array libraries, and finishing some of the loose ends (e.g., specifications for linear algebra functions that aren't merged yet, see https://github.com/data-apis/array-api/pulls) in the API standard itself. See https://mail.python.org/pipermail/numpy-discussion/2020-November/081181.html for an initial discussion about this topic. Please keep high-level discussion here and detailed comments on https://github.com/numpy/numpy/pull/18456. Also, you can access a rendered version of the NEP from that PR (see PR description for how), which may be helpful. Cheers, Ralf Abstract -------- We propose to adopt the `Python array API standard`_, developed by the `Consortium for Python Data API Standards`_. Implementing this as a separate new namespace in NumPy will allow authors of libraries which depend on NumPy as well as end users to write code that is portable between NumPy and all other array/tensor libraries that adopt this standard. .. note:: We expect that this NEP will remain in a draft state for quite a while. Given the large scope we don't expect to propose it for acceptance any time soon; instead, we want to solicit feedback on both the high-level design and implementation, and learn what needs describing better in this NEP or changing in either the implementation or the array API standard itself. Motivation and Scope -------------------- Python users have a wealth of choice for libraries and frameworks for numerical computing, data science, machine learning, and deep learning. New frameworks pushing forward the state of the art in these fields are appearing every year. One unintended consequence of all this activity and creativity has been fragmentation in multidimensional array (a.k.a. tensor) libraries - which are the fundamental data structure for these fields. Choices include NumPy, Tensorflow, PyTorch, Dask, JAX, CuPy, MXNet, and others. The APIs of each of these libraries are largely similar, but with enough differences that it’s quite difficult to write code that works with multiple (or all) of these libraries. The array API standard aims to address that issue, by specifying an API for the most common ways arrays are constructed and used. The proposed API is quite similar to NumPy's API, and deviates mainly in places where (a) NumPy made design choices that are inherently not portable to other implementations, and (b) where other libraries consistently deviated from NumPy on purpose because NumPy's design turned out to have issues or unnecessary complexity. For a longer discussion on the purpose of the array API standard we refer to the `Purpose and Scope section of the array API standard < https://data-apis.github.io/array-api/latest/purpose_and_scope.html>`__ and the two blog posts announcing the formation of the Consortium [1]_ and the release of the first draft version of the standard for community review [2]_. The scope of this NEP includes: - Adopting the 2021 version of the array API standard - Adding a separate namespace, tentatively named ``numpy.array_api`` - Changes needed/desired outside of the new namespace, for example new dunder methods on the ``ndarray`` object - Implementation choices, and differences between functions in the new namespace with those in the main ``numpy`` namespace - A new array object conforming to the array API standard - Maintenance effort and testing strategy - Impact on NumPy's total exposed API surface and on other future and under-discussion design choices - Relation to existing and proposed NumPy array protocols (``__array_ufunc__``, ``__array_function__``, ``__array_module__``). - Required improvements to existing NumPy functionality Out of scope for this NEP are: - Changes in the array API standard itself. Those are likely to come up during review of this NEP, but should be upstreamed as needed and this NEP subsequently updated. Usage and Impact ---------------- *This section will be fleshed out later, for now we refer to the use cases given in* `the array API standard Use Cases section < https://data-apis.github.io/array-api/latest/use_cases.html>`__ In addition to those use cases, the new namespace contains functionality that is widely used and supported by many array libraries. As such, it is a good set of functions to teach to newcomers to NumPy and recommend as "best practice". That contrasts with NumPy's main namespace, which contains many functions and objects that have been superceded or we consider mistakes - but that we can't remove because of backwards compatibility reasons. The usage of the ``numpy.array_api`` namespace by downstream libraries is intended to enable them to consume multiple kinds of arrays, *without having to have a hard dependency on all of those array libraries*: .. image:: _static/nep-0047-library-dependencies.png Adoption in downstream libraries ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The prototype implementation of the ``array_api`` namespace will be used with SciPy, scikit-learn and other libraries of interest that depend on NumPy, in order to get more experience with the design and find out if any important parts are missing. The pattern to support multiple array libraries is intended to be something like:: def somefunc(x, y): # Retrieves standard namespace. Raises if x and y have different # namespaces. See Appendix for possible get_namespace implementation xp = get_namespace(x, y) out = xp.mean(x, axis=0) + 2*xp.std(y, axis=0) return out The ``get_namespace`` call is effectively the library author opting in to using the standard API namespace, and thereby explicitly supporting all conforming array libraries. The ``asarray`` / ``asanyarray`` pattern ```````````````````````````````````````` Many existing libraries use the same ``asarray`` (or ``asanyarray``) pattern as NumPy itself does; accepting any object that can be coerced into a ``np.ndarray``. We consider this design pattern problematic - keeping in mind the Zen of Python, *"explicit is better than implicit"*, as well as the pattern being historically problematic in the SciPy ecosystem for ``ndarray`` subclasses and with over-eager object creation. All other array/tensor libraries are more strict, and that works out fine in practice. We would advise authors of new libraries to avoid the ``asarray`` pattern. Instead they should either accept just NumPy arrays or, if they want to support multiple kinds of arrays, check if the incoming array object supports the array API standard by checking for ``__array_namespace__`` as shown in the example above. Existing libraries can do such a check as well, and only call ``asarray`` if the check fails. This is very similar to the ``__duckarray__`` idea in :ref:`NEP30`. .. _adoption-application-code: Adoption in application code ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The new namespace can be seen by end users as a cleaned up and slimmed down version of NumPy's main namespace. Encouraging end users to use this namespace like:: import numpy.array_api as xp x = xp.linspace(0, 2*xp.pi, num=100) y = xp.cos(x) seems perfectly reasonable, and potentially beneficial - users get offered only one function for each purpose (the one we consider best-practice), and they then write code that is more easily portable to other libraries. Backward compatibility ---------------------- No deprecations or removals of existing NumPy APIs or other backwards incompatible changes are proposed. High-level design ----------------- The array API standard consists of approximately 120 objects, all of which have a direct NumPy equivalent. This figure shows what is included at a high level: .. image:: _static/nep-0047-scope-of-array-API.png The most important changes compared to what NumPy currently offers are: - A new array object which: - conforms to the casting rules and indexing behaviour specified by the standard, - does not have methods other than dunder methods, - does not support the full range of NumPy indexing behaviour. Advanced indexing with integers is not supported. Only boolean indexing with a single (possibly multi-dimensional) boolean array is supported. An indexing expression that selects a single element returns a 0-D array rather than a scalar. - Functions in the ``array_api`` namespace: - do not accept ``array_like`` inputs, only NumPy arrays and Python scalars - do not support ``__array_ufunc__`` and ``__array_function__``, - use positional-only and keyword-only parameters in their signatures, - have inline type annotations, - may have minor changes to signatures and semantics of individual functions compared to their equivalents already present in NumPy, - only support dtype literals, not format strings or other ways of specifying dtypes - DLPack_ support will be added to NumPy, - New syntax for "device support" will be added, through a ``.device`` attribute on the new array object, and ``device=`` keywords in array creation functions in the ``array_api`` namespace, - Casting rules that differ from those NumPy currently has. Output dtypes can be derived from input dtypes (i.e. no value-based casting), and 0-D arrays are treated like >=1-D arrays. - Not all dtypes NumPy has are part of the standard. Only boolean, signed and unsigned integers, and floating-point dtypes up to ``float64`` are supported. Complex dtypes are expected to be added in the next version of the standard. Extended precision, string, void, object and datetime dtypes, as well as structured dtypes, are not included. Improvements to existing NumPy functionality that are needed include: - Add support for stacks of matrices to some functions in ``numpy.linalg`` that are currently missing such support. - Add the ``keepdims`` keyword to ``np.argmin`` and ``np.argmax``. - Add a "never copy" mode to ``np.asarray``. Functions in the ``array_api`` namespace ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Let's start with an example of a function implementation that shows the most important differences with the equivalent function in the main namespace:: def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False ) -> array: """ Array API compatible wrapper for :py:func:`np.max <numpy.max>`. """ return np.max._implementation(x, axis=axis, keepdims=keepdims) This function does not accept ``array_like`` inputs, only ``ndarray``. There are multiple reasons for this. Other array libraries all work like this. Letting the user do coercion of lists, generators, or other foreign objects separately results in a cleaner design with less unexpected behaviour. It's higher-performance - less overhead from ``asarray`` calls. Static typing is easier. Subclasses will work as expected. And the slight increase in verbosity because users have to explicitly coerce to ``ndarray`` on rare occasions seems like a small price to pay. This function does not support ``__array_ufunc__`` nor ``__array_function__``. These protocols serve a similar purpose as the array API standard module itself, but through a different mechanisms. Because only ``ndarray`` instances are accepted, dispatching via one of these protocols isn't useful anymore. This function uses positional-only parameters in its signature. This makes code more portable - writing ``max(x=x, ...)`` is no longer valid, hence if other libraries call the first parameter ``input`` rather than ``x``, that is fine. The rationale for keyword-only parameters (not shown in the above example) is two-fold: clarity of end user code, and it being easier to extend the signature in the future with keywords in the desired order. This function has inline type annotations. Inline annotations are far easier to maintain than separate stub files. And because the types are simple, this will not result in a large amount of clutter with type aliases or unions like in the current stub files NumPy has. DLPack support for zero-copy data interchange ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The ability to convert one kind of array into another kind is valuable, and indeed necessary when downstream libraries want to support multiple kinds of arrays. This requires a well-specified data exchange protocol. NumPy already supports two of these, namely the buffer protocol (i.e., PEP 3118), and the ``__array_interface__`` (Python side) / ``__array_struct__`` (C side) protocol. Both work similarly, letting the "producer" describe how the data is laid out in memory so the "consumer" can construct its own kind of array with a view on that data. DLPack works in a very similar way. The main reasons to prefer DLPack over the options already present in NumPy are: 1. DLPack is the only protocol with device support (e.g., GPUs using CUDA or ROCm drivers, or OpenCL devices). NumPy is CPU-only, but other array libraries are not. Having one protocol per device isn't tenable, hence device support is a must. 2. Widespread support. DLPack has the widest adoption of all protocols, only NumPy is missing support. And the experiences of other libraries with it are positive. This contrasts with the protocols NumPy does support, which are used very little - when other libraries want to interoperate with NumPy, they typically use the (more limited, and NumPy-specific) ``__array__`` protocol. Adding support for DLPack to NumPy entails: - Adding a ``ndarray.__dlpack__`` method - Adding a ``from_dlpack`` function, which takes as input an object supporting ``__dlpack__``, and returns an ``ndarray``. DLPack is currently a ~200 LoC header, and is meant to be included directly, so no external dependency is needed. Implementation should be straightforward. Syntax for device support ~~~~~~~~~~~~~~~~~~~~~~~~~ NumPy itself is CPU-only, so it clearly doesn't have a need for device support. However, other libraries (e.g. TensorFlow, PyTorch, JAX, MXNet) support multiple types of devices: CPU, GPU, TPU, and more exotic hardware. To write portable code on systems with multiple devices, it's often necessary to create new arrays on the same device as some other array, or check that two arrays live on the same device. Hence syntax for that is needed. The array object will have a ``.device`` attribute which enables comparing devices of different arrays (they only should compare equal if both arrays are from the same library and it's the same hardware device). Furthermore, ``device=`` keywords in array creation functions are needed. For example:: def empty(shape: Union[int, Tuple[int, ...]], /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: """ Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`. """ return np.empty(shape, dtype=dtype, device=device) The implementation for NumPy may be as simple as setting the device attribute to the string ``'cpu'`` and raising an exception if array creation functions encounter any other value. Dtypes and casting rules ~~~~~~~~~~~~~~~~~~~~~~~~ The supported dtypes in this namespace are boolean, 8/16/32/64-bit signed and unsigned integer, and 32/64-bit floating-point dtypes. These will be added to the namespace as dtype literals with the expected names (e.g., ``bool``, ``uint16``, ``float64``). The most obvious omissions are the complex dtypes. The rationale for the lack of complex support in the first version of the array API standard is that several libraries (PyTorch, MXNet) are still in the process of adding support for complex dtypes. The next version of the standard is expected to include ``complex64`` and ``complex128`` (see `this issue < https://github.com/data-apis/array-api/issues/102>`__ for more details). Specifying dtypes to functions, e.g. via the ``dtype=`` keyword, is expected to only use the dtype literals. Format strings, Python builtin dtypes, or string representations of the dtype literals are not accepted - this will improve readability and portability of code at little cost. Casting rules are only defined between different dtypes of the same kind. The rationale for this is that mixed-kind (e.g., integer to floating-point) casting behavior differs between libraries. NumPy's mixed-kind casting behavior doesn't need to be changed or restricted, it only needs to be documented that if users use mixed-kind casting, their code may not be portable. .. image:: _static/nep-0047-casting-rules-lattice.png *Type promotion diagram. Promotion between any two types is given by their join on this lattice. Only the types of participating arrays matter, not their values. Dashed lines indicate that behaviour for Python scalars is undefined on overflow. Boolean, integer and floating-point dtypes are not connected, indicating mixed-kind promotion is undefined.* The most important difference between the casting rules in NumPy and in the array API standard is how scalars and 0-dimensional arrays are handled. In the standard, array scalars do not exist and 0-dimensional arrays follow the same casting rules as higher-dimensional arrays. See the `Type Promotion Rules section of the array API standard < https://data-apis.github.io/array-api/latest/API_specification/type_promoti… >`__ for more details. .. note:: It is not clear what the best way is to support the different casting rules for 0-dimensional arrays and no value-based casting. One option may be to implement this second set of casting rules, keep them private, mark the array API functions with a private attribute that says they adhere to these different rules, and let the casting machinery check whether for that attribute. This needs discussion. Indexing ~~~~~~~~ An indexing expression that would return a scalar with ``ndarray``, e.g. ``arr_2d[0, 0]``, will return a 0-D array with the new array object. There are several reasons for that: array scalars are largely considered a design mistake which no other array library copied; it works better for non-CPU libraries (typically arrays can live on the device, scalars live on the host); and it's simply a consistent design. To get a Python scalar out of a 0-D array, one can simply use the builtin for the type, e.g. ``float(arr_0d)``. The other `indexing modes in the standard < https://data-apis.github.io/array-api/latest/API_specification/indexing.html >`__ do work largely the same as they do for ``numpy.ndarray``. One noteworthy difference is that clipping in slice indexing (e.g., ``a[:n]`` where ``n`` is larger than the size of the first axis) is unspecified behaviour, because that kind of check can be expensive on accelerators. The lack of advanced indexing, and boolean indexing being limited to a single n-D boolean array, is due to those indexing modes not being suitable for all types of arrays or JIT compilation. Their absence does not seem to be problematic; if a user or library author wants to use them, they can do so through zero-copy conversion to ``numpy.ndarray``. This will signal correctly to whomever reads the code that it is then NumPy-specific rather than portable to all conforming array types. The array object ~~~~~~~~~~~~~~~~ The array object in the standard does not have methods other than dunder methods. The rationale for that is that not all array libraries have methods on their array object (e.g., TensorFlow does not). It also provides only a single way of doing something, rather than have functions and methods that are effectively duplicate. Mixing operations that may produce views (e.g., indexing, ``nonzero``) in combination with mutation (e.g., item or slice assignment) is `explicitly documented in the standard to not be supported < https://data-apis.github.io/array-api/latest/design_topics/copies_views_and… >`__. This cannot easily be prohibited in the array object itself; instead this will be guidance to the user via documentation. The standard current does not prescribe a name for the array object itself. We propose to simply name it ``ndarray``. This is the most obvious name, and because of the separate namespace should not clash with ``numpy.ndarray``. Implementation -------------- .. note:: This section needs a lot more detail, which will gradually be added when the implementation progresses. A prototype of the ``array_api`` namespace can be found in https://github.com/data-apis/numpy/tree/array-api/numpy/_array_api. The docstring in its ``__init__.py`` has notes on completeness of the implementation. The code for the wrapper functions also contains ``# Note:`` comments everywhere there is a difference with the NumPy API. Two important parts that are not implemented yet are the new array object and DLPack support. Functions may need changes to ensure the changed casting rules are respected. The array object ~~~~~~~~~~~~~~~~ Regarding the array object implementation, we plan to start with a regular Python class that wraps a ``numpy.ndarray`` instance. Attributes and methods can forward to that wrapped instance, applying input validation and implementing changed behaviour as needed. The casting rules are probably the most challenging part. The in-progress dtype system refactor (NEPs 40-43) should make implementing the correct casting behaviour easier - it is already moving away from value-based casting for example. The dtype objects ~~~~~~~~~~~~~~~~~ We must be able to compare dtypes for equality, and expressions like these must be possible:: np.array_api.some_func(..., dtype=x.dtype) The above implies it would be nice to have ``np.array_api.float32 == np.array_api.ndarray(...).dtype``. Dtypes should not be assumed to have a class hierarchy by users, however we are free to implement it with a class hierarchy if that's convenient. We considered the following options to implement dtype objects: 1. Alias dtypes to those in the main namespace. E.g., ``np.array_api.float32 = np.float32``. 2. Make the dtypes instances of ``np.dtype``. E.g., ``np.array_api.float32 = np.dtype(np.float32)``. 3. Create new singleton classes with only the required methods/attributes (currently just ``__eq__``). It seems like (2) would be easiest from the perspective of interacting with functions outside the main namespace. And (3) would adhere best to the standard. TBD: the standard does not yet have a good way to inspect properties of a dtype, to ask questions like "is this an integer dtype?". Perhaps this is easy enough to do for users, like so:: def _get_dtype(dt_or_arr): return dt_or_arr.dtype if hasattr(dt_or_arr, 'dtype') else dt_or_arr def is_floating(dtype_or_array): dtype = _get_dtype(dtype_or_array) return dtype in (float32, float64) def is_integer(dtype_or_array): dtype = _get_dtype(dtype_or_array) return dtype in (uint8, uint16, uint32, uint64, int8, int16, int32, int64) However it could make sense to add to the standard. Note that NumPy itself currently does not have a great for asking such questions, see `gh-17325 <https://github.com/numpy/numpy/issues/17325>`__.

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Guidelines for floating point comparison
by Bernard Knaepen 28 Feb '21

28 Feb '21

Hi all, We are developing a code that heavily relies on NumPy. Some of our regression tests rely on floating point number comparisons and we are a bit lost in determining how to choose atol and rtol (we are trying to do all operations in double precision). We would like to set atol and rtol as low as possible but still have the tests pass if we run on different architectures or introduce some ‘cosmetic’ changes like using different similar NumPy routines. For example, let’s say we want some powers of the matrix A and compute them as: A = np.array(some_array) A2 = np.dot(A, A) A3 = np.dot(A2, A) A4 = np.dot(A3, A) If we alternatively computed A4 like: A4 = np.linalg.matrix_power(A, 4), we get different values in our final outputs because obviously the operations are not equivalent up to machine accuracy. Is there any reference that one could share providing guidelines on how to choose reasonable values for atol and rtol in this kind of situation? For example, does the NumPy package use a fixed set of values for its own development? the default ones? Thanks in advance for any help, Cheers, Bernard.

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C-coded dot 1000x faster than numpy?
by Neal Becker 24 Feb '21

24 Feb '21

I have code that performs dot product of a 2D matrix of size (on the order of) [1000,16] with a vector of size [1000]. The matrix is float64 and the vector is complex128. I was using numpy.dot but it turned out to be a bottleneck. So I coded dot2x1 in c++ (using xtensor-python just for the interface). No fancy simd was used, unless g++ did it on it's own. On a simple benchmark using timeit I find my hand-coded routine is on the order of 1000x faster than numpy? Here is the test code: My custom c++ code is dot2x1. I'm not copying it here because it has some dependencies. Any idea what is going on? import numpy as np from dot2x1 import dot2x1 a = np.ones ((1000,16)) b = np.array([ 0.80311816+0.80311816j, 0.80311816-0.80311816j, -0.80311816+0.80311816j, -0.80311816-0.80311816j, 1.09707981+0.29396165j, 1.09707981-0.29396165j, -1.09707981+0.29396165j, -1.09707981-0.29396165j, 0.29396165+1.09707981j, 0.29396165-1.09707981j, -0.29396165+1.09707981j, -0.29396165-1.09707981j, 0.25495815+0.25495815j, 0.25495815-0.25495815j, -0.25495815+0.25495815j, -0.25495815-0.25495815j]) def F1(): d = dot2x1 (a, b) def F2(): d = np.dot (a, b) from timeit import timeit print (timeit ('F1()', globals=globals(), number=1000)) print (timeit ('F2()', globals=globals(), number=1000)) In [13]: 0.013910860987380147 << 1st timeit 28.608758996007964 << 2nd timeit -- Those who don't understand recursion are doomed to repeat it

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg 24 Feb '21

24 Feb '21

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, Feb 24th at 11 am Pacific Time (19:00 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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Re: [Numpy-discussion] ENH: Proposal to add KML_BLAS support
by ChunLin Fang 23 Feb '21

23 Feb '21

Thanks for asking, this is a simple explanation for your questions: 1. The download link of KML_BLAS: The Chinese page is https://www.huaweicloud.com/kunpeng/software/KML_BLAS.html The English page is https://kunpeng.huawei.com/en/#/developer/devkit/library, you can find a "Math Library" Navigation entry in the bottom of this page. "KML_BLAS" lies in there. 2. The license/redistribution policy of KML_BLAS: The license is very similar to intel MKL, The license file is in the process of making. 3.How to support KML_BLAS: The support process is similar to BLIS, just need to add to numpy.distutils, KML_BLAS will not open source in the near future. 4.What kind of ARM chips are supported: any ARMV8 chip is supported.

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NumPy-Discussion February 2021