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.

6 27

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

1 0

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

1 0

NumPy Development Meeting - Triage Focus
by Sebastian Berg 28 Jan '20

28 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

1 0

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.

2 2

what to clean up, what to leave as is
by Ralf Gommers 24 Jan '20

24 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

4 4

new absolute beginners tutorial merged
by Ralf Gommers 22 Jan '20

22 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

4 6

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

4 4

pip 20 release broke wheels builds.
by Charles R Harris 22 Jan '20

22 Jan '20

Hi All, Just a note that latest pip release broke the numpy wheel builds. The fix is to update multibuild to the latest version. I'm sending this out in case others are affected. Chuck

1 0

Re-enable deprecation warning for "ragged arrays"
by Matti Picus 22 Jan '20

22 Jan '20

A few months ago we merged an implementation of "NEP 34 - Disallow inferring dtype=object from sequences" https://numpy.org/neps/nep-0034.html but it broke downstream packages like scipy, pandas, matplotlib, astropy (those are the ones we know about). Since then, the downstream projects seem to have fixed the problematic code. We have re-merged the implementation. Please check your package against the latest NumPy HEAD and let us know if there are problems. Matti

2 1

NumPy Community Meeting Wednesday, Jan. 22
by Sebastian Berg 21 Jan '20

21 Jan '20

Hi all, There will be a NumPy Community meeting Wednesday January 22 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 Best wishes Sebastian

1 0

ANN: SciPy 1.2.3 (LTS)
by Tyler Reddy 21 Jan '20

21 Jan '20

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.2.3, which is a bug fix release. This is part of the long-term support (LTS) branch that includes Python 2.7. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.2.3 One of a few ways to install this release with pip: pip install scipy==1.2.3 ========================== SciPy 1.2.3 Release Notes ========================== SciPy 1.2.3 is a bug-fix release with no new features compared to 1.2.2. It is part of the long-term support (LTS) release series for Python 2.7. Authors ======= * Geordie McBain * Matt Haberland * David Hagen * Tyler Reddy * Pauli Virtanen * Eric Larson * Yu Feng * ananyashreyjain * Nikolay Mayorov * Evgeni Burovski * Warren Weckesser Issues closed for 1.2.3 -------------------------------- * `#4915 <https://github.com/scipy/scipy/issues/4915>`__: Bug in unique_roots in scipy.signal.signaltools.py for roots with same magnitude * `#5546 <https://github.com/scipy/scipy/issues/5546>`__: ValueError raised if scipy.sparse.linalg.expm recieves array larger than 200x200 * `#7117 <https://github.com/scipy/scipy/issues/7117>`__: Warn users when using float32 input data to curve_fit and friends * `#7906 <https://github.com/scipy/scipy/issues/7906>`__: Wrong result from scipy.interpolate.UnivariateSpline.integral for out-of-bounds * `#9581 <https://github.com/scipy/scipy/issues/9581>`__: Least-squares minimization fails silently when x and y data are different types * `#9901 <https://github.com/scipy/scipy/issues/9901>`__: lsoda fails to detect stiff problem when called from solve_ivp * `#9988 <https://github.com/scipy/scipy/issues/9988>`__: doc build broken with Sphinx 2.0.0 * `#10303 <https://github.com/scipy/scipy/issues/10303>`__: BUG: optimize: `linprog` failing TestLinprogSimplexBland::test_unbounded_below_no_presolve_corrected * `#10376 <https://github.com/scipy/scipy/issues/10376>`__: TST: Travis CI fails (with pytest 5.0 ?) * `#10384 <https://github.com/scipy/scipy/issues/10384>`__: CircleCI doc build failing on new warnings * `#10535 <https://github.com/scipy/scipy/issues/10535>`__: TST: master branch CI failures * `#11121 <https://github.com/scipy/scipy/issues/11121>`__: Calls to `scipy.interpolate.splprep` increase RAM usage. * `#11198 <https://github.com/scipy/scipy/issues/11198>`__: BUG: sparse eigs (arpack) shift-invert drops the smallest eigenvalue for some k * `#11266 <https://github.com/scipy/scipy/issues/11266>`__: Sparse matrix constructor data type detection changes on Numpy 1.18.0 Pull requests for 1.2.3 -------------------------------- * `#9992 <https://github.com/scipy/scipy/pull/9992>`__: MAINT: Undo Sphinx pin * `#10071 <https://github.com/scipy/scipy/pull/10071>`__: DOC: reconstruct SuperLU permutation matrices avoiding SparseEfficiencyWarning * `#10076 <https://github.com/scipy/scipy/pull/10076>`__: BUG: optimize: fix curve_fit for mixed float32/float64 input * `#10138 <https://github.com/scipy/scipy/pull/10138>`__: BUG: special: Invalid arguments to ellip_harm can crash Python. * `#10306 <https://github.com/scipy/scipy/pull/10306>`__: BUG: optimize: Fix for 10303 * `#10309 <https://github.com/scipy/scipy/pull/10309>`__: BUG: Pass jac=None directly to lsoda * `#10377 <https://github.com/scipy/scipy/pull/10377>`__: TST, MAINT: adjustments for pytest 5.0 * `#10379 <https://github.com/scipy/scipy/pull/10379>`__: BUG: sparse: set writeability to be forward-compatible with numpy>=1.17 * `#10426 <https://github.com/scipy/scipy/pull/10426>`__: MAINT: Fix doc build bugs * `#10540 <https://github.com/scipy/scipy/pull/10540>`__: MAINT: Fix Travis and Circle * `#10633 <https://github.com/scipy/scipy/pull/10633>`__: BUG: interpolate: integral(a, b) should be zero when both limits are outside of the interpolation range * `#10833 <https://github.com/scipy/scipy/pull/10833>`__: BUG: Fix subspace_angles for complex values * `#10882 <https://github.com/scipy/scipy/pull/10882>`__: BUG: sparse/arpack: fix incorrect code for complex hermitian M * `#10906 <https://github.com/scipy/scipy/pull/10906>`__: BUG: sparse/linalg: fix expm for np.matrix inputs * `#10961 <https://github.com/scipy/scipy/pull/10961>`__: BUG: Fix signal.unique_roots * `#11126 <https://github.com/scipy/scipy/pull/11126>`__: BUG: interpolate/fitpack: fix memory leak in splprep * `#11199 <https://github.com/scipy/scipy/pull/11199>`__: BUG: sparse.linalg: mistake in unsymm. real shift-invert ARPACK eigenvalue selection * `#11269 <https://github.com/scipy/scipy/pull/11269>`__: Fix: Sparse matrix 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[ANN] pvlib python v0.7.1: predicting power for solar energy
by Dr. Mark Alexander Mikofski PhD 21 Jan '20

21 Jan '20

pvlib has a new minor release, v0.7.1 Release Notes: https://pvlib-python.readthedocs.io/en/v0.7.1/whatsnew.html PyPI: https://pypi.org/project/pvlib/ Read the Docs: https://pvlib-python.readthedocs.io/en/latest/ GitHub: https://github.com/pvlib/pvlib-python New examples gallery: https://pvlib-python.readthedocs.io/en/stable/auto_examples/index.html -- Mark Mikofski, PhD (2005) *Fiat Lux*

1 0

8 Real Distributions
by Michael Lance 20 Jan '20

20 Jan '20

TLDR; I think this could be a useful contribution to NumPy, but I want to get feedback on where it should go (either in NumPy or elsewhere). I have functions using numpy.random which invoke the 8 "Real" data sets as estimated by Ted Micceri in 1989. These can be useful in Monte Carlo simulations. Background info: Parametric inferential statistics generally assume normal distributions (though kurtosis presents less of an issue than skew). However, in "nature", distributions are often not normal. In 1989, Ted Micceri's study ( http://psycnet.apa.org/record/1989-14214-001) on real data sets resulted in the estimation of 8 "Real" distributions. Using these distributions in simulations help to produce more realistic types I and II error rate and power estimates, particularly for smaller samples. A similar module is currently available in Fortran called realpops.

3 4

Adding an nd generalization of np.ma.mask_rowscols
by Eric Wieser 17 Jan '20

17 Jan '20

Today, numpy has a np.ma.mask_rowcols function, which stretches masks along the full length of an axis. For example, given the matrix:: >>> a2d = np.zeros((3, 3), dtype=int) >>> a2d[1, 1] = 1 >>> a2d = np.ma.masked_equal(a2d, 1) >>> print(a2d) [[0 0 0] [0 -- 0] [0 0 0]] The API allows:: >>> print(np.ma.mask_rowcols(a2d, axis=0)) [[0 0 0] [-- -- --] [0 0 0]] >>> print(np.ma.mask_rowcols(a2d, axis=1)) [[0 -- 0] [0 -- 0] [0 -- 0]] >>> print(np.ma.mask_rowcols(a2d, axis=None)) [[0 -- 0] [-- -- --] [0 -- 0]] However, this function only works for 2D arrays. It would be useful to generalize this to work on ND arrays as well. Unfortunately, the current function is messy to generalize, because axis=0 means “spread the mask along axis 1”, and vice versa. Additionally, the name is not particularly good for an ND function. My proposal in PR 14998 <https://github.com/numpy/numpy/pull/14998> is to introduce a new function, mask_extend_axis, which fixes this shortcoming. Given an 3D array:: >>> a3d = np.zeros((2, 2, 2), dtype=int) >>> a3d[0, 0, 0] = 1 >>> a3d = np.ma.masked_equal(a3d, 1) >>> print(a3d) [[[-- 0] [0 0]] [[0 0] [0 0]]] This, in my opinion, has clearer axis semantics: >>> print(np.ma.mask_extend_axis(a2d, axis=0)) [[[-- 0] [0 0]] [[-- 0] [0 0]]] >>> print(np.ma.mask_extend_axis(a2d, axis=1)) [[[-- 0] [-- 0]] [[0 0] [0 0]]] >>> print(np.ma.mask_extend_axis(a2d, axis=2)) [[[-- --] [0 0]] [[0 0] [0 0]]] Stretching over multiple axes remains possible: >>> print(np.ma.mask_extend_axis(a2d, axis=(1, 2))) [[[-- --] [-- 0]] [[0 0] [0 0]]] # extending sequentially is not the same as extending in parallel >>> print(np.ma.mask_extend_axis(np.ma.mask_extend_axis(a2d, axis=1), axis=2)) [[[-- --] [-- --]] [[0 0] [0 0]]] Questions for the mailing list then: - Can you think of a better name than mask_extend_axis? - Does my proposed meaning of axis make more sense to you than the one used by mask_rowcols?

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Accuracy improvement in np.mean
by mpro 13 Jan '20

13 Jan '20

Hi, 18 days ago I submitted pull request that improves accuracy of mean function by calculating mean sequentially along certain axis. Happy to get some feedback. Magdalena -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

2 1

Numpy support for Microblaze
by pnp 12 Jan '20

12 Jan '20

Are there some steps to add support to a new cpu such as microblaze? Regards. -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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Fwd: [NumFOCUS Projects] Job: Open Source Developer Advocate
by Ralf Gommers 09 Jan '20

09 Jan '20

---------- Forwarded message --------- From: Nicole Foster <nicole(a)numfocus.org> Date: Wed, Jan 8, 2020 at 10:25 PM Subject: [NumFOCUS Projects] Job: Open Source Developer Advocate To: Fiscally Sponsored Project Representatives <projects(a)numfocus.org>, Affiliated Projects <affiliated(a)numfocus.org> Hi Everyone, We recently posted an ad for an Open Source Developer Advocate <https://www.indeedjobs.com/numfocus/jobs/344b7c30fe13ddb6d8ee>position with NumFOCUS. We would be thrilled to have a member of our community work with us here at NumFOCUS so we ask that you please share this job posting with anyone you know that might be a good fit. Please feel free to reach out if you have any questions. Thank you for your time and consideration as we work to fill this role. Best, -- Nicole Foster Executive Operations Administrator, NumFOCUS nicole(a)numfocus.org 512-831-2870 <http://www.numfocus.org/> <https://twitter.com/NumFOCUS> <https://www.facebook.com/NumFOCUS/> -- You received this message because you are subscribed to the Google Groups "Fiscally Sponsored Project Representatives" group. To unsubscribe from this group and stop receiving emails from it, send an email to projects+unsubscribe(a)numfocus.org. To view this discussion on the web visit https://groups.google.com/a/numfocus.org/d/msgid/projects/CAJLwxPF5ezbsAEWk… <https://groups.google.com/a/numfocus.org/d/msgid/projects/CAJLwxPF5ezbsAEWk…> .

1 0

Is the build-system section of pyproject.toml a maintained list of the build dependencies?
by Warren Weckesser 08 Jan '20

08 Jan '20

I'm doing some work on the travis-ci scripts, and I'd like to remove some redundant calls of 'pip install'. The scripts should get the build dependencies from a configuration file instead of having hard-coded pip commands. Is pyproject.toml the appropriate file to use for this? (Note: we also have test_requirements.txt, but as the name says, those are the dependencies for running the tests, not for building numpy.) Warren

3 4

Deprecate numpy.dual?
by Warren Weckesser 07 Jan '20

07 Jan '20

In response to some work on improving the documentation of `numpy.linalg` and how it compares to `scipy.linalg`, Kevin Sheppard suggested that the documentation of the module `numpy.dual` should also be improved. When I mentioned this suggestion in the community meeting on December 11, it was suggested that we should probably deprecate `numpy.dual`. I think some current NumPy developers (myself included at the time the topic came up) are unfamiliar with the history and purpose of this module, so I spent some time reading code and github issues and wrote up some notes. These notes are available at https://github.com/WarrenWeckesser/numpy-notes/blob/master/numpy-dual.md If you are not familiar with `numpy.dual`, you might find those notes useful. Now that I know a bit more about `numpy.dual`, I'm not sure it should be deprecated. It provides a hook for other libraries to selectively replace the use of the exposed functions in internal NumPy code, so if a library has a better version of, say, `linalg.eigh`, it can configure `numpy.dual` to use its version. Then, for example, NumPy multivariate normal distribution code could benefit from the use of that library's version of `eigh`. The NumPy documentation of `numpy.dual` refers specifically to SciPy, but it could be used by any library. Does anyone know if any other libraries use `register_func` to put their functions into the `numpy.dual` namespace? SciPy currently registers some functions, but there is an open issue in which it is proposed that SciPy no longer register its functions with `numpy.dual`: https://github.com/scipy/scipy/issues/10441 This email is to start the discussion of the future of `numpy.dual`. Some of the options: 1. Quietly continue the status quo. 2. Deprecate `numpy.dual`. 3. Spend time improving the documentation of this feature, and perhaps even expand the functions that are supported. What do you think? For those who were involved in the creation of `numpy.dual`: is it working out like you expected? If not, is it worthwhile maintaining it? Warren

4 4

NumPy Community Meeting Wednesday, Jan. 8
by Sebastian Berg 07 Jan '20

07 Jan '20

Hi all, After the holiday break, there will be a NumPy Community meeting Wednesday January 8 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 Best wishes Sebastian

1 0

NumPy 1.18.1 released
by Charles R Harris 07 Jan '20

07 Jan '20

Hi All, On behalf of the NumPy team I am pleased to announce that NumPy 1.18.1 has been released. This release contains fixes for bugs reported against NumPy 1.18.0. Two bugs in particular that caused widespread problems downstream were: - The cython random extension test was not using a temporary directory for building, resulting in a permission violation. Fixed. - Numpy distutils was appending -std=c99 to all C compiler runs, leading to changed behavior and compile problems downstream. That flag is now only applied when building numpy C code. The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cython >= 0.29.14 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.18.1>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.18.1>. *Contributors* A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris - Matti Picus - Maxwell Aladago - Pauli Virtanen - Ralf Gommers - Tyler Reddy - Warren Weckesser *Pull requests merged* A total of 13 pull requests were merged for this release. - `#15158 <https://github.com/numpy/numpy/pull/15158>`__: MAINT: Update pavement.py for towncrier. - `#15159 <https://github.com/numpy/numpy/pull/15159>`__: DOC: add moved modules to 1.18 release note - `#15161 <https://github.com/numpy/numpy/pull/15161>`__: MAINT, DOC: Minor backports and updates for 1.18.x - `#15176 <https://github.com/numpy/numpy/pull/15176>`__: TST: Add assert_array_equal test for big integer arrays - `#15184 <https://github.com/numpy/numpy/pull/15184>`__: BUG: use tmp dir and check version for cython test. - `#15220 <https://github.com/numpy/numpy/pull/15220>`__: BUG: distutils: fix msvc+gfortran openblas handling corner case - `#15221 <https://github.com/numpy/numpy/pull/15221>`__: BUG: remove -std=c99 for c++ compilation - `#15222 <https://github.com/numpy/numpy/pull/15222>`__: MAINT: unskip test on win32 - `#15223 <https://github.com/numpy/numpy/pull/15223>`__: TST: add BLAS ILP64 run in Travis & Azure - `#15245 <https://github.com/numpy/numpy/pull/15245>`__: MAINT: only add --std=c99 where needed - `#15246 <https://github.com/numpy/numpy/pull/15246>`__: BUG: lib: Fix handling of integer arrays by gradient. - `#15247 <https://github.com/numpy/numpy/pull/15247>`__: MAINT: Do not use private Python function in testing - `#15250 <https://github.com/numpy/numpy/pull/15250>`__: REL: Prepare for the NumPy 1.18.1 release. Cheers, Charles Harris

1 0

Announcing Numexpr 2.7.1
by Robert McLeod 05 Jan '20

05 Jan '20

Hi everyone, This is a version bump to add support for Python 3.8 and NumPy 1.18. We are also removing support for Python 3.4. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.7.0 to 2.7.1 ---------------------------- - Python 3.8 support has been added. - Python 3.4 support is discontinued. - The tests are now compatible with NumPy 1.18. - `site.cfg.example` was updated to use the `libraries` tag instead of `mkl_libs`, which is recommended for newer version of NumPy. What's Numexpr? --------------- Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It has multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. Where I can find Numexpr? ------------------------- The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Robert McLeod robbmcleod(a)gmail.com robert.mcleod(a)hitachi-hhtc.ca

1 0

Providing functions to convert axes of object arrays to and from lists
by Przemysław Simajchel 04 Jan '20

04 Jan '20

Hi, A feature to implement functions converting last axes of object arrays to and from lists has been requested in the following issue: https://github.com/numpy/numpy/issues/14478 I've implemented this feature, and the code can be viewed in the following pull request: https://github.com/numpy/numpy/pull/15091 The original issue asked for functions operating on just the last axis of this array, but I decided to allow to choose a specific axis for conversion from array of objects to array of lists, because this allows for the functions to be somewhat inverses of eachother. The linked pull request provides an example showing where the inverse property wouldn't hold with only last axis conversions. I would like to hear your opinions about this, and if it's agreed upon,also discuss the choice of the default value of the axis parameter. At the moment it's the first axis, but I assume that this being the last axis would also make sense. Also regarding a bit more cosmetic issues, the names of the functions and their place in the project (currently lib/shape_base.py) should probably be discussed, as their current names are long and clunky, yet I haven't come up with anything prettier, that would also be this expressive. Please note that this is my first contribution to the project and so I'd also appreciate a little help regarding the failing tests on the pull request, locally everything passed so I'm not sure what's wrong. Cheers, Przemysław Simajchel

1 0

Cleaning up Python 2.7 code.
by Charles R Harris 03 Jan '20

03 Jan '20

Hi All, Just want to propose cleaning up the Python 2.7 compatibility code for NumPy 1.19. Any objections? Chuck

4 5

NumPy 1.17.5 Released
by Charles R Harris 01 Jan '20

01 Jan '20

Hi All, On behalf of the NumPy team I am pleased to announce that NumPy 1.17.5 has been released. This release fixes bugs reported against the 1.17.4 release. The supported Python versions are 3.5-3.7. This is the last planned release that supports Python 3.5. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.17.5>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.17.5>. Downstream developers building this release should use Cython >= 0.29.14 and, if using OpenBLAS, OpenBLAS >= v0.3.7. It is recommended that developers interested in the new random bit generators upgrade to the NumPy 1.18.x series, as it has updated documentation and many small improvements. *Highlights* - The ``np.testing.utils`` functions have been updated from 1.19.0-dev0. This improves the function documentation and error messages as well extending the ``assert_array_compare`` function to additional types. *Contributors* A total of 6 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris - Eric Wieser - Ilhan Polat - Matti Picus - Michael Hudson-Doyle - Ralf Gommers *Pull requests merged* A total of 8 pull requests were merged for this release. - `#14593 <https://github.com/numpy/numpy/pull/14593>`__: MAINT: backport Cython API cleanup to 1.17.x, remove docs - `#14937 <https://github.com/numpy/numpy/pull/14937>`__: BUG: fix integer size confusion in handling array's ndmin argument - `#14939 <https://github.com/numpy/numpy/pull/14939>`__: BUILD: remove SSE2 flag from numpy.random builds - `#14993 <https://github.com/numpy/numpy/pull/14993>`__: MAINT: Added Python3.8 branch to dll lib discovery - `#15038 <https://github.com/numpy/numpy/pull/15038>`__: BUG: Fix refcounting in ufunc object loops - `#15067 <https://github.com/numpy/numpy/pull/15067>`__: BUG: Exceptions tracebacks are dropped - `#15175 <https://github.com/numpy/numpy/pull/15175>`__: ENH: Backport improvements to testing functions. - `#15213 <https://github.com/numpy/numpy/pull/15213>`__: REL: Prepare for the NumPy 1.17.5 release. Cheers, Charles Harris

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NumPy-Discussion January 2020