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...numpy.sf.net. The Numerical Python mailing list: numpy-discussion. Resources The SourceForge project page for Numerical Python The new Topic Guide for Python and Scientific Computing
...numpy, for example, it is technically possible to switch between the conventions, because numpy provides two different types with different __mul__ methods. For numpy.ndarray objects, * performs elementwise multiplication, and matrix multiplication must use a function call (numpy.dot). For numpy.matrix objects, * performs matrix multiplication, and elementwise multiplication requires function syntax. Writing code using numpy.ndarray works fine. Writing code using numpy.matrix also works fine....
...Numpy https://github.com/numpy/numpy/pull/12011 [12]Benchmark pickling Numpy arrays with different pickle protocols https://github.com/numpy/numpy/issues/11161#issuecomment-424035962 Copyright This document has been placed into the public domain. Source: https://github.com/python/peps/blob/master/pep-0574.rst
...NumPy folks brought up a somewhat separate issue: for them, the most common use case is chained comparisons (e.g. A < B < C). To understand this observation, we first need to look at how comparisons work with NumPy arrays: >>> import numpy as np >>> increasing = np.arange(5) >>> increasing array([0, 1, 2, 3, 4]) >>> decreasing = np.arange(4, -1, -1) >>> decreasing array([4, 3, 2, 1, 0]) >>> increasing < decreasing array([ True, Tru...
...Numpy is aimed at those doing heavy numerical work, and may be intimidating to those who don't have a background in computational mathematics and computer science. For example, numpy.mean takes four arguments: mean(a, axis=None, dtype=None, out=None) although fortunately for the beginner or casual numpy user, three are optional and numpy.mean does the right thing in simple cases: >>> numpy.mean([1, 2, 3, 4]) 2.5 For many people, installing numpy may be difficult or impossible. F...
...NumPy Arrays #----------------------------------------------------------------- # # This example creates a subclass of numpy array to which # 'and', 'or' and 'not' can be applied, producing an array # of booleans. # #----------------------------------------------------------------- from numpy import array, ndarray class BArray(ndarray): def __str__(self): return "barray(%s)" % ndarray.__str__(self) def __and2__(self, other): return (self & other) def _...
...numpy-stubs]Stubs for numpy (https://github.com/numpy/numpy-stubs) [pkg_checker]Sample package checker (https://github.com/ethanhs/check_typedpkg) Copyright This document has been placed in the public domain. Source: https://github.com/python/peps/blob/master/pep-0561.rst
...numpy isclose() http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.isclose.html The numpy package provides the vectorized functions isclose() and allclose(), for similar use cases as this proposal: isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False) Returns a boolean array where two arrays are element-wise equal within a tolerance. The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added tog...
...NumPy). Specifically, NumPy allows the creation of multidimensional arrays, which support most of the numeric operators. Thus: x = array((1,2,3,4)) y = array((2,2,4,4)) are two NumPy arrays. While they can be added elementwise,: z = x + y # z == array((3,4,7,8)) they cannot be compared in the current framework - the released version of NumPy compares the pointers, (thus yielding junk information) which was the only solution before the recent addition of the ability (in 1.5) to raise...
...numpy [numpy], Argument Clinic [argumentclinic], pytypedecl [pytypedecl], numba [numba], obiwan [obiwan]. Existing Approaches in Other Languages ActionScript ActionScript [actionscript] is a class-based, single inheritance, object-oriented superset of ECMAScript. It supports inferfaces and strong runtime-checked static typing. Compilation supports a “strict dialect” where type mismatches are reported at compile-time. Example code with types: package { import flash.events.Event; publi...
...NumPy 1.22 is made of 307,300 lines of C code, and adapting NumPy to the this PEP only modified 11 lines (use Py_SET_TYPE and Py_SET_SIZE) and adding 4 lines (to define Py_SET_TYPE and Py_SET_SIZE for Python 3.8 and older). The beginnings of the NumPy port to HPy already required modifying more lines than that. Right now, it's hard to bet which approach is the best: fixing the current C API, or focusing on HPy. It would be risky to only focus on HPy. Rejected Idea: Leave the macros as they ar...
...NumPy, PIL, etc.). Quality of the software. Quality of the output. One possible goal for the API project is to develop a package- independent API for plots, which would produce reasonably similar results regardless of the plotting package used as a backend (PLPlot, Gist/Yorick, Gnuplot, etc.), in the same spirit as the interface defined by the DB-SIG. Package-specific extensions could naturally be provided as well. The goals for the new framework nee...
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...numpy numpy is expected to only download an sdist for numpy, since we do not need to check for dependencies, and both the name and version are available by introspecting the downloaded file name. pip, however, cannot assume the downloaded archive follows the convention, and must build and check the metadata. For a PEP 518 project, this means running the prepare_metadata_for_build_wheel hook specified in PEP 517, which incurs significant overhead. Rationale By creating a special file name schem...
...NumPy, data-base interfaces, etc.) There is no way to share discontiguous memory (except through the sequence of segments notion). There are two widely used libraries that use the concept of discontiguous memory: PIL and NumPy. Their view of discontiguous arrays is different, though. The proposed buffer interface allows sharing of either memory model. Exporters will typically use only one approach and consumers may choose to support discontiguous arrays of each type however they choose. NumP...
...numpy arrays with labelled dimensions, supports indexing by additional dimensions (so called "non-dimension coordinates") that don't correspond directly to the dimensions of the underlying numpy array, and those have no position to match up to. In other words, anonymous indexes are a plausible use case that this solution would remove, although it could be argued that using *args would solve that issue. Adding an adapter function Similar to the above, in the sense that a pre-function would be...
...NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world's leading scientists and engineers. If you need to ...
...NumPy, JAX, Xarray), but who is not so familiar with the details of Python's type system, it is reassuring to see that a broad range of use-cases related to type checking of named axes & shapes have been considered, and could build upon the infrastructure in this PEP. Type checking for shapes is something the NumPy community is very interested in -- there are more thumbs up on the relevant issue on NumPy's GitHub than any others (https://github.com/numpy/numpy/issues/7370) and we recently ad...
...NumPy's numpy.errstate (which exposes the error-handling settings provided by the IEEE 754 floating point standard), and the handling of logging context or HTTP request context in many server application frameworks. However, there is currently no ergonomic way to manage such local changes to global state when writing a generator or coroutine. For example, this code: def f(): with warnings.catch_warnings(): for x in g(): yield x may or may not successfully catch warnings...
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