Mailman 3 January 2020 - 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

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

NEP 37: A dispatch protocol for NumPy-like modules
by Stephan Hoyer 12 Apr '20

12 Apr '20

I am pleased to present a new NumPy Enhancement Proposal for discussion: "NEP-37: A dispatch protocol for NumPy-like modules." Feedback would be very welcome! The full text follows. The rendered proposal can also be found online at https://numpy.org/neps/nep-0037-array-module.html Best, Stephan Hoyer =================================================== NEP 37 — A dispatch protocol for NumPy-like modules =================================================== :Author: Stephan Hoyer <shoyer(a)google.com> :Author: Hameer Abbasi :Author: Sebastian Berg :Status: Draft :Type: Standards Track :Created: 2019-12-29 Abstract -------- NEP-18's ``__array_function__`` has been a mixed success. Some projects (e.g., dask, CuPy, xarray, sparse, Pint) have enthusiastically adopted it. Others (e.g., PyTorch, JAX, SciPy) have been more reluctant. Here we propose a new protocol, ``__array_module__``, that we expect could eventually subsume most use-cases for ``__array_function__``. The protocol requires explicit adoption by both users and library authors, which ensures backwards compatibility, and is also significantly simpler than ``__array_function__``, both of which we expect will make it easier to adopt. Why ``__array_function__`` hasn't been enough --------------------------------------------- There are two broad ways in which NEP-18 has fallen short of its goals: 1. **Maintainability concerns**. `__array_function__` has significant implications for libraries that use it: - Projects like `PyTorch <https://github.com/pytorch/pytorch/issues/22402>`_, `JAX <https://github.com/google/jax/issues/1565>`_ and even `scipy.sparse <https://github.com/scipy/scipy/issues/10362>`_ have been reluctant to implement `__array_function__` in part because they are concerned about **breaking existing code**: users expect NumPy functions like ``np.concatenate`` to return NumPy arrays. This is a fundamental limitation of the ``__array_function__`` design, which we chose to allow overriding the existing ``numpy`` namespace. - ``__array_function__`` currently requires an "all or nothing" approach to implementing NumPy's API. There is no good pathway for **incremental adoption**, which is particularly problematic for established projects for which adopting ``__array_function__`` would result in breaking changes. - It is no longer possible to use **aliases to NumPy functions** within modules that support overrides. For example, both CuPy and JAX set ``result_type = np.result_type``. - Implementing **fall-back mechanisms** for unimplemented NumPy functions by using NumPy's implementation is hard to get right (but see the `version from dask <https://github.com/dask/dask/pull/5043>`_), because ``__array_function__`` does not present a consistent interface. Converting all arguments of array type requires recursing into generic arguments of the form ``*args, **kwargs``. 2. **Limitations on what can be overridden.** ``__array_function__`` has some important gaps, most notably array creation and coercion functions: - **Array creation** routines (e.g., ``np.arange`` and those in ``np.random``) need some other mechanism for indicating what type of arrays to create. `NEP 36 <https://github.com/numpy/numpy/pull/14715>`_ proposed adding optional ``like=`` arguments to functions without existing array arguments. However, we still lack any mechanism to override methods on objects, such as those needed by ``np.random.RandomState``. - **Array conversion** can't reuse the existing coercion functions like ``np.asarray``, because ``np.asarray`` sometimes means "convert to an exact ``np.ndarray``" and other times means "convert to something _like_ a NumPy array." This led to the `NEP 30 <https://numpy.org/neps/nep-0030-duck-array-protocol.html>`_ proposal for a separate ``np.duckarray`` function, but this still does not resolve how to cast one duck array into a type matching another duck array. ``get_array_module`` and the ``__array_module__`` protocol ---------------------------------------------------------- We propose a new user-facing mechanism for dispatching to a duck-array implementation, ``numpy.get_array_module``. ``get_array_module`` performs the same type resolution as ``__array_function__`` and returns a module with an API promised to match the standard interface of ``numpy`` that can implement operations on all provided array types. The protocol itself is both simpler and more powerful than ``__array_function__``, because it doesn't need to worry about actually implementing functions. We believe it resolves most of the maintainability and functionality limitations of ``__array_function__``. The new protocol is opt-in, explicit and with local control; see :ref:`appendix-design-choices` for discussion on the importance of these design features. The array module contract ========================= Modules returned by ``get_array_module``/``__array_module__`` should make a best effort to implement NumPy's core functionality on new array types(s). Unimplemented functionality should simply be omitted (e.g., accessing an unimplemented function should raise ``AttributeError``). In the future, we anticipate codifying a protocol for requesting restricted subsets of ``numpy``; see :ref:`requesting-restricted-subsets` for more details. How to use ``get_array_module`` =============================== Code that wants to support generic duck arrays should explicitly call ``get_array_module`` to determine an appropriate array module from which to call functions, rather than using the ``numpy`` namespace directly. For example: .. code:: python # calls the appropriate version of np.something for x and y module = np.get_array_module(x, y) module.something(x, y) Both array creation and array conversion are supported, because dispatching is handled by ``get_array_module`` rather than via the types of function arguments. For example, to use random number generation functions or methods, we can simply pull out the appropriate submodule: .. code:: python def duckarray_add_random(array): module = np.get_array_module(array) noise = module.random.randn(*array.shape) return array + noise We can also write the duck-array ``stack`` function from `NEP 30 <https://numpy.org/neps/nep-0030-duck-array-protocol.html>`_, without the need for a new ``np.duckarray`` function: .. code:: python def duckarray_stack(arrays): module = np.get_array_module(*arrays) arrays = [module.asarray(arr) for arr in arrays] shapes = {arr.shape for arr in arrays} if len(shapes) != 1: raise ValueError('all input arrays must have the same shape') expanded_arrays = [arr[module.newaxis, ...] for arr in arrays] return module.concatenate(expanded_arrays, axis=0) By default, ``get_array_module`` will return the ``numpy`` module if no arguments are arrays. This fall-back can be explicitly controlled by providing the ``module`` keyword-only argument. It is also possible to indicate that an exception should be raised instead of returning a default array module by setting ``module=None``. How to implement ``__array_module__`` ===================================== Libraries implementing a duck array type that want to support ``get_array_module`` need to implement the corresponding protocol, ``__array_module__``. This new protocol is based on Python's dispatch protocol for arithmetic, and is essentially a simpler version of ``__array_function__``. Only one argument is passed into ``__array_module__``, a Python collection of unique array types passed into ``get_array_module``, i.e., all arguments with an ``__array_module__`` attribute. The special method should either return an namespace with an API matching ``numpy``, or ``NotImplemented``, indicating that it does not know how to handle the operation: .. code:: python class MyArray: def __array_module__(self, types): if not all(issubclass(t, MyArray) for t in types): return NotImplemented return my_array_module Returning custom objects from ``__array_module__`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``my_array_module`` will typically, but need not always, be a Python module. Returning a custom objects (e.g., with functions implemented via ``__getattr__``) may be useful for some advanced use cases. For example, custom objects could allow for partial implementations of duck array modules that fall-back to NumPy (although this is not recommended in general because such fall-back behavior can be error prone): .. code:: python class MyArray: def __array_module__(self, types): if all(issubclass(t, MyArray) for t in types): return ArrayModule() else: return NotImplemented class ArrayModule: def __getattr__(self, name): import base_module return getattr(base_module, name, getattr(numpy, name)) Subclassing from ``numpy.ndarray`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ All of the same guidance about well-defined type casting hierarchies from NEP-18 still applies. ``numpy.ndarray`` itself contains a matching implementation of ``__array_module__``, which is convenient for subclasses: .. code:: python class ndarray: def __array_module__(self, types): if all(issubclass(t, ndarray) for t in types): return numpy else: return NotImplemented NumPy's internal machinery ========================== The type resolution rules of ``get_array_module`` follow the same model as Python and NumPy's existing dispatch protocols: subclasses are called before super-classes, and otherwise left to right. ``__array_module__`` is guaranteed to be called only a single time on each unique type. The actual implementation of `get_array_module` will be in C, but should be equivalent to this Python code: .. code:: python def get_array_module(*arrays, default=numpy): implementing_arrays, types = _implementing_arrays_and_types(arrays) if not implementing_arrays and default is not None: return default for array in implementing_arrays: module = array.__array_module__(types) if module is not NotImplemented: return module raise TypeError("no common array module found") def _implementing_arrays_and_types(relevant_arrays): types = [] implementing_arrays = [] for array in relevant_arrays: t = type(array) if t not in types and hasattr(t, '__array_module__'): types.append(t) # Subclasses before superclasses, otherwise left to right index = len(implementing_arrays) for i, old_array in enumerate(implementing_arrays): if issubclass(t, type(old_array)): index = i break implementing_arrays.insert(index, array) return implementing_arrays, types Relationship with ``__array_ufunc__`` and ``__array_function__`` ---------------------------------------------------------------- These older protocols have distinct use-cases and should remain =============================================================== ``__array_module__`` is intended to resolve limitations of ``__array_function__``, so it is natural to consider whether it could entirely replace ``__array_function__``. This would offer dual benefits: (1) simplifying the user-story about how to override NumPy and (2) removing the slowdown associated with checking for dispatch when calling every NumPy function. However, ``__array_module__`` and ``__array_function__`` are pretty different from a user perspective: it requires explicit calls to ``get_array_function``, rather than simply reusing original ``numpy`` functions. This is probably fine for *libraries* that rely on duck-arrays, but may be frustratingly verbose for interactive use. Some of the dispatching use-cases for ``__array_ufunc__`` are also solved by ``__array_module__``, but not all of them. For example, it is still useful to be able to define non-NumPy ufuncs (e.g., from Numba or SciPy) in a generic way on non-NumPy arrays (e.g., with dask.array). Given their existing adoption and distinct use cases, we don't think it makes sense to remove or deprecate ``__array_function__`` and ``__array_ufunc__`` at this time. Mixin classes to implement ``__array_function__`` and ``__array_ufunc__`` ========================================================================= Despite the user-facing differences, ``__array_module__`` and a module implementing NumPy's API still contain sufficient functionality needed to implement dispatching with the existing duck array protocols. For example, the following mixin classes would provide sensible defaults for these special methods in terms of ``get_array_module`` and ``__array_module__``: .. code:: python class ArrayUfuncFromModuleMixin: def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): arrays = inputs + kwargs.get('out', ()) try: array_module = np.get_array_module(*arrays) except TypeError: return NotImplemented try: # Note this may have false positive matches, if ufunc.__name__ # matches the name of a ufunc defined by NumPy. Unfortunately # there is no way to determine in which module a ufunc was # defined. new_ufunc = getattr(array_module, ufunc.__name__) except AttributeError: return NotImplemented try: callable = getattr(new_ufunc, method) except AttributeError: return NotImplemented return callable(*inputs, **kwargs) class ArrayFunctionFromModuleMixin: def __array_function__(self, func, types, args, kwargs): array_module = self.__array_module__(types) if array_module is NotImplemented: return NotImplemented # Traverse submodules to find the appropriate function modules = func.__module__.split('.') assert modules[0] == 'numpy' for submodule in modules[1:]: module = getattr(module, submodule, None) new_func = getattr(module, func.__name__, None) if new_func is None: return NotImplemented return new_func(*args, **kwargs) To make it easier to write duck arrays, we could also add these mixin classes into ``numpy.lib.mixins`` (but the examples above may suffice). Alternatives considered ----------------------- Naming ====== We like the name ``__array_module__`` because it mirrors the existing ``__array_function__`` and ``__array_ufunc__`` protocols. Another reasonable choice could be ``__array_namespace__``. It is less clear what the NumPy function that calls this protocol should be called (``get_array_module`` in this proposal). Some possible alternatives: ``array_module``, ``common_array_module``, ``resolve_array_module``, ``get_namespace``, ``get_numpy``, ``get_numpylike_module``, ``get_duck_array_module``. .. _requesting-restricted-subsets: Requesting restricted subsets of NumPy's API ============================================ Over time, NumPy has accumulated a very large API surface, with over 600 attributes in the top level ``numpy`` module alone. It is unlikely that any duck array library could or would want to implement all of these functions and classes, because the frequently used subset of NumPy is much smaller. We think it would be useful exercise to define "minimal" subset(s) of NumPy's API, omitting rarely used or non-recommended functionality. For example, minimal NumPy might include ``stack``, but not the other stacking functions ``column_stack``, ``dstack``, ``hstack`` and ``vstack``. This could clearly indicate to duck array authors and users want functionality is core and what functionality they can skip. Support for requesting a restricted subset of NumPy's API would be a natural feature to include in ``get_array_function`` and ``__array_module__``, e.g., .. code:: python # array_module is only guaranteed to contain "minimal" NumPy array_module = np.get_array_module(*arrays, request='minimal') To facilitate testing with NumPy and use with any valid duck array library, NumPy itself would return restricted versions of the ``numpy`` module when ``get_array_module`` is called only on NumPy arrays. Omitted functions would simply not exist. Unfortunately, we have not yet figured out what these restricted subsets should be, so it doesn't make sense to do this yet. When/if we do, we could either add new keyword arguments to ``get_array_module`` or add new top level functions, e.g., ``get_minimal_array_module``. We would also need to add either a new protocol patterned off of ``__array_module__`` (e.g., ``__array_module_minimal__``), or could add an optional second argument to ``__array_module__`` (catching errors with ``try``/``except``). A new namespace for implicit dispatch ===================================== Instead of supporting overrides in the main `numpy` namespace with ``__array_function__``, we could create a new opt-in namespace, e.g., ``numpy.api``, with versions of NumPy functions that support dispatching. These overrides would need new opt-in protocols, e.g., ``__array_function_api__`` patterned off of ``__array_function__``. This would resolve the biggest limitations of ``__array_function__`` by being opt-in and would also allow for unambiguously overriding functions like ``asarray``, because ``np.api.asarray`` would always mean "convert an array-like object." But it wouldn't solve all the dispatching needs met by ``__array_module__``, and would leave us with supporting a considerably more complex protocol both for array users and implementors. We could potentially implement such a new namespace *via* the ``__array_module__`` protocol. Certainly some users would find this convenient, because it is slightly less boilerplate. But this would leave users with a confusing choice: when should they use `get_array_module` vs. `np.api.something`. Also, we would have to add and maintain a whole new module, which is considerably more expensive than merely adding a function. Dispatching on both types and arrays instead of only types ========================================================== Instead of supporting dispatch only via unique array types, we could also support dispatch via array objects, e.g., by passing an ``arrays`` argument as part of the ``__array_module__`` protocol. This could potentially be useful for dispatch for arrays with metadata, such provided by Dask and Pint, but would impose costs in terms of type safety and complexity. For example, a library that supports arrays on both CPUs and GPUs might decide on which device to create a new arrays from functions like ``ones`` based on input arguments: .. code:: python class Array: def __array_module__(self, types, arrays): useful_arrays = tuple(a in arrays if isinstance(a, Array)) if not useful_arrays: return NotImplemented prefer_gpu = any(a.prefer_gpu for a in useful_arrays) return ArrayModule(prefer_gpu) class ArrayModule: def __init__(self, prefer_gpu): self.prefer_gpu = prefer_gpu def __getattr__(self, name): import base_module base_func = getattr(base_module, name) return functools.partial(base_func, prefer_gpu=self.prefer_gpu) This might be useful, but it's not clear if we really need it. Pint seems to get along OK without any explicit array creation routines (favoring multiplication by units, e.g., ``np.ones(5) * ureg.m``), and for the most part Dask is also OK with existing ``__array_function__`` style overides (e.g., favoring ``np.ones_like`` over ``np.ones``). Choosing whether to place an array on the CPU or GPU could be solved by `making array creation lazy <https://github.com/google/jax/pull/1668>`_. .. _appendix-design-choices: Appendix: design choices for API overrides ------------------------------------------ There is a large range of possible design choices for overriding NumPy's API. Here we discuss three major axes of the design decision that guided our design for ``__array_module__``. Opt-in vs. opt-out for users ============================ The ``__array_ufunc__`` and ``__array_function__`` protocols provide a mechanism for overriding NumPy functions *within NumPy's existing namespace*. This means that users need to explicitly opt-out if they do not want any overridden behavior, e.g., by casting arrays with ``np.asarray()``. In theory, this approach lowers the barrier for adopting these protocols in user code and libraries, because code that uses the standard NumPy namespace is automatically compatible. But in practice, this hasn't worked out. For example, most well-maintained libraries that use NumPy follow the best practice of casting all inputs with ``np.asarray()``, which they would have to explicitly relax to use ``__array_function__``. Our experience has been that making a library compatible with a new duck array type typically requires at least a small amount of work to accommodate differences in the data model and operations that can be implemented efficiently. These opt-out approaches also considerably complicate backwards compatibility for libraries that adopt these protocols, because by opting in as a library they also opt-in their users, whether they expect it or not. For winning over libraries that have been unable to adopt ``__array_function__``, an opt-in approach seems like a must. Explicit vs. implicit choice of implementation ============================================== Both ``__array_ufunc__`` and ``__array_function__`` have implicit control over dispatching: the dispatched functions are determined via the appropriate protocols in every function call. This generalizes well to handling many different types of objects, as evidenced by its use for implementing arithmetic operators in Python, but it has two downsides: 1. *Speed*: it imposes additional overhead in every function call, because each function call needs to inspect each of its arguments for overrides. This is why arithmetic on builtin Python numbers is slow. 2. *Readability*: it is not longer immediately evident to readers of code what happens when a function is called, because the function's implementation could be overridden by any of its arguments. In contrast, importing a new library (e.g., ``import dask.array as da``) with an API matching NumPy is entirely explicit. There is no overhead from dispatch or ambiguity about which implementation is being used. Explicit and implicit choice of implementations are not mutually exclusive options. Indeed, most implementations of NumPy API overrides via ``__array_function__`` that we are familiar with (namely, dask, CuPy and sparse, but not Pint) also include an explicit way to use their version of NumPy's API by importing a module directly (``dask.array``, ``cupy`` or ``sparse``, respectively). Local vs. non-local vs. global control ====================================== The final design axis is how users control the choice of API: - **Local control**, as exemplified by multiple dispatch and Python protocols for arithmetic, determines which implementation to use either by checking types or calling methods on the direct arguments of a function. - **Non-local control** such as `np.errstate <https://docs.scipy.org/doc/numpy/reference/generated/numpy.errstate.html >`_ overrides behavior with global-state via function decorators or context-managers. Control is determined hierarchically, via the inner-most context. - **Global control** provides a mechanism for users to set default behavior, either via function calls or configuration files. For example, matplotlib allows setting a global choice of plotting backend. Local control is generally considered a best practice for API design, because control flow is entirely explicit, which makes it the easiest to understand. Non-local and global control are occasionally used, but generally either due to ignorance or a lack of better alternatives. In the case of duck typing for NumPy's public API, we think non-local or global control would be mistakes, mostly because they **don't compose well**. If one library sets/needs one set of overrides and then internally calls a routine that expects another set of overrides, the resulting behavior may be very surprising. Higher order functions are especially problematic, because the context in which functions are evaluated may not be the context in which they are defined. One class of override use cases where we think non-local and global control are appropriate is for choosing a backend system that is guaranteed to have an entirely consistent interface, such as a faster alternative implementation of ``numpy.fft`` on NumPy arrays. However, these are out of scope for the current proposal, which is focused on duck arrays.

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Python 3.9
by Charles R Harris 31 Jan '20

31 Jan '20

Hi All, Out of curiosity, I ran the numpy tests on Python3.9a3 to see if we hit any of the expired deprecations. The only error looked like a Python bug in fractions.Fraction. For others who may want to try this for a bit of fun: - Download and altinstall Python3.9a3 - Clone and install cython in python3.9 from the cython master branch - Install pytest in python3.9 This worked for me on Fedora 31. Chuck

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SciPy 2020 Call for Submissions is open!
by Melissa Mendonça 29 Jan '20

29 Jan '20

Apologies for crossposting. ===== SciPy 2020, the 19th annual Scientific Computing with Python conference, will be held July 6-12, 2020 in Austin, Texas. The annual SciPy Conference brings together over 900 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The call for SciPy 2020 talks, posters, and tutorials is now open through February 11, 2020. Talks and Posters (July 8-10, 2020) In addition to the general track, this year will have the following specialized tracks, mini symposia, and sessions: Tracks High Performance Python Machine Learning and Data Science Mini Symposia Astronomy and Astrophysics Biology and Bioinformatics Earth, Ocean, Geo and Atmospheric Science Materials Science Special Sessions Maintainers Track SciPy Tools Plenary Session For additional details and instructions, please see the conference website. <https://www.scipy2020.scipy.org/> Tutorials (July 6-7, 2020) Tutorials should be focused on covering a well-defined topic in a hands-on manner. We are looking for awesome techniques or packages, helping new or advanced Python programmers develop better or faster scientific applications. We encourage submissions to be designed to allow at least 50% of the time for hands-on exercises even if this means the subject matter needs to be limited. Tutorials will be 4 hours in duration. In your tutorial application, you can indicate what prerequisite skills and knowledge will be needed for your tutorial, and the approximate expected level of knowledge of your students (i.e., beginner, intermediate, advanced). Instructors of accepted tutorials will receive a stipend. For examples of content and format, you can refer to tutorials from past SciPy tutorial sessions (SciPy 2018 <https://na-admin.eventscloud.com/emarketing/go.php?i=735095&e=amlsbGNAZW50a…>, SciPy2019 <https://na-admin.eventscloud.com/emarketing/go.php?i=735095&e=amlsbGNAZW50a…> ) and some accepted submissions <https://na-admin.eventscloud.com/emarketing/go.php?i=735095&e=amlsbGNAZW50a…>. For additional details and instructions see the conference website <https://na-admin.eventscloud.com/emarketing/go.php?i=735095&e=amlsbGNAZW50a…> . Submission page: https://easychair.org/conferences/?conf=scipy2020 Submission Deadline: February 11, 2020 - Melissa Weber Mendonça Diversity Committee Co-Chair

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NumPy Development Meeting - Triage Focus
by Sebastian Berg 29 Jan '20

29 Jan '20

Hi all, Our bi-weekly triage-focused NumPy development meeting is tomorrow (Wednesday, January 29) at 11 am Pacific Time. Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg Best regards, Sebastian

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Deprecate unused but exposed C-API functions
by Sebastian Berg 25 Jan '20

25 Jan '20

Hi all, I would like to deprecate three C-API functions (more may come later) in https://github.com/numpy/numpy/pull/15427 The functions are: * PyArray_GetArrayParamsFromObject * PyUfunc_GenericCall * PyUFunc_SetUsesArraysAsData I could not find downstream usage for any of these and they seem useful only on first glance to me. The first one is too complex (regarding scalars), simply converting to an array first should be just as well. The second one is almost identical to `PyObject_Call`. And the third one is not even documented and while the idea is sound, I am not sure that there are any cases where it is useful [0] My idea is to add the deprecation warning now, and if nobody notices it remove them as soon as 1.19 is released. - Sebastian [0] It seems useful for user datatypes/loop, but using it for those does not seem possible (or at least straight forward), since they are usually stored in a way not accessible through this function.

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what to clean up, what to leave as is
by Ralf Gommers 25 Jan '20

25 Jan '20

Hi all, It's great to see that people are jumping at the chance to clean up Python 2 support. I would however caution about overdoing it on other cleanups. As a reminder, we normally do not want pure style PRs (e.g. PEP8 cleanups), because they make the code history (git blame, commits on particular files, etc.) harder to work with, have review overhead, and may introduce new bugs for little gain. Imho that same rationale applies to things like converting strings to f-strings. There's of course some gray area, for example removing "from ... import *" can guard against accidentally exposing new API, so can be considered a valuable cleanup. As a separate/additional point: numpy.distutils and numpy.f2py are largely untested, PRs are hard to test locally because of platform-specific code, and changes often introduce regressions. So even for some cleanups that are okay for other files, please do not do them on those modules. Cheers, Ralf

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new absolute beginners tutorial merged
by Ralf Gommers 23 Jan '20

23 Jan '20

Hi all, I think this is worth announcing: we have a new tutorial in the user guide titled "NumPy: the absolute basics for beginners". This was Anne Bonner's Season of Docs project, and it is a big step in the right direction for making are docs more accessible to new users. Up at https://numpy.org/devdocs/user/absolute_beginners.html Cheers, Ralf

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Interest to port Numpy to Haskell, with Accelerate/Repa to do the crunching?
by YueCompl 22 Jan '20

22 Jan '20

Hello folks, Myself a long time Numpy user, and recently done some Haskell stuff, Please have a look at https://github.com/e-wrks/edh/tree/master/Tour#defining-more-magic-methods <https://github.com/e-wrks/edh/tree/master/Tour#defining-more-magic-methods> and https://github.com/e-wrks/edh/tree/master/Tour#indexing <https://github.com/e-wrks/edh/tree/master/Tour#indexing> , any interest to port Numpy to Haskell, with http://hackage.haskell.org/package/accelerate <http://hackage.haskell.org/package/accelerate> and/or http://hackage.haskell.org/package/repa <http://hackage.haskell.org/package/repa> doing the number crunching? Best regards, Compl

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NumPy-Discussion January 2020