diff options
| -rw-r--r-- | doc/source/reference/routines.polynomials.classes.rst | 15 | ||||
| -rw-r--r-- | numpy/core/tests/test_arrayprint.py | 10 | ||||
| -rw-r--r-- | numpy/lib/shape_base.py | 4 | ||||
| -rw-r--r-- | numpy/lib/tests/test_shape_base.py | 14 | ||||
| -rw-r--r-- | numpy/polynomial/_polybase.py | 5 | ||||
| -rwxr-xr-x | setup.py | 27 |
6 files changed, 49 insertions, 26 deletions
diff --git a/doc/source/reference/routines.polynomials.classes.rst b/doc/source/reference/routines.polynomials.classes.rst index f44ddd46c..da0394305 100644 --- a/doc/source/reference/routines.polynomials.classes.rst +++ b/doc/source/reference/routines.polynomials.classes.rst @@ -35,11 +35,11 @@ degree :math:`n`, but could just as easily be the basis functions of any of the other classes. The convention for all the classes is that the coefficient :math:`c[i]` goes with the basis function of degree i. -All of the classes have the same methods, and especially they implement the -Python numeric operators +, -, \*, //, %, divmod, \*\*, ==, -and !=. The last two can be a bit problematic due to floating point -roundoff errors. We now give a quick demonstration of the various -operations using NumPy version 1.7.0. +All of the classes are immutable and have the same methods, and +especially they implement the Python numeric operators +, -, \*, //, %, +divmod, \*\*, ==, and !=. The last two can be a bit problematic due to +floating point roundoff errors. We now give a quick demonstration of the +various operations using NumPy version 1.7.0. Basics ------ @@ -187,6 +187,11 @@ Which gives the polynomial `p` in Chebyshev form. This works because the original polynomial. However, all the multiplications and divisions will be done using Chebyshev series, hence the type of the result. +It is intended that all polynomial instances are immutable, therefore +augmented operations (``+=``, ``-=``, etc.) and any other functionality that +would violate the immutablity of a polynomial instance are intentionally +unimplemented. + Calculus -------- diff --git a/numpy/core/tests/test_arrayprint.py b/numpy/core/tests/test_arrayprint.py index 6214e325c..6522c6e8a 100644 --- a/numpy/core/tests/test_arrayprint.py +++ b/numpy/core/tests/test_arrayprint.py @@ -8,6 +8,7 @@ import pytest import numpy as np from numpy.testing import ( assert_, assert_equal, assert_raises, assert_warns, HAS_REFCOUNT, + assert_raises_regex, ) import textwrap @@ -210,6 +211,15 @@ class TestArray2String(object): assert_(np.array2string(a, max_line_width=4, legacy='1.13') == '[0 1\n 2]') assert_(np.array2string(a, max_line_width=4) == '[0\n 1\n 2]') + def test_unexpected_kwarg(self): + # ensure than an appropriate TypeError + # is raised when array2string receives + # an unexpected kwarg + + with assert_raises_regex(TypeError, 'nonsense'): + np.array2string(np.array([1, 2, 3]), + nonsense=None) + def test_format_function(self): """Test custom format function for each element in array.""" def _format_function(x): diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py index d31d8a939..66f534734 100644 --- a/numpy/lib/shape_base.py +++ b/numpy/lib/shape_base.py @@ -688,7 +688,7 @@ def array_split(ary, indices_or_sections, axis=0): except AttributeError: Ntotal = len(ary) try: - # handle scalar case. + # handle array case. Nsections = len(indices_or_sections) + 1 div_points = [0] + list(indices_or_sections) + [Ntotal] except TypeError: @@ -700,7 +700,7 @@ def array_split(ary, indices_or_sections, axis=0): section_sizes = ([0] + extras * [Neach_section+1] + (Nsections-extras) * [Neach_section]) - div_points = _nx.array(section_sizes).cumsum() + div_points = _nx.array(section_sizes, dtype=_nx.intp).cumsum() sub_arys = [] sary = _nx.swapaxes(ary, axis, 0) diff --git a/numpy/lib/tests/test_shape_base.py b/numpy/lib/tests/test_shape_base.py index 6d24dd624..6e4cd225d 100644 --- a/numpy/lib/tests/test_shape_base.py +++ b/numpy/lib/tests/test_shape_base.py @@ -3,6 +3,8 @@ from __future__ import division, absolute_import, print_function import numpy as np import warnings import functools +import sys +import pytest from numpy.lib.shape_base import ( apply_along_axis, apply_over_axes, array_split, split, hsplit, dsplit, @@ -14,6 +16,9 @@ from numpy.testing import ( ) +IS_64BIT = sys.maxsize > 2**32 + + def _add_keepdims(func): """ hack in keepdims behavior into a function taking an axis """ @functools.wraps(func) @@ -403,6 +408,15 @@ class TestArraySplit(object): assert_(a.dtype.type is res[-1].dtype.type) # perhaps should check higher dimensions + @pytest.mark.skipif(not IS_64BIT, reason="Needs 64bit platform") + def test_integer_split_2D_rows_greater_max_int32(self): + a = np.broadcast_to([0], (1 << 32, 2)) + res = array_split(a, 4) + chunk = np.broadcast_to([0], (1 << 30, 2)) + tgt = [chunk] * 4 + for i in range(len(tgt)): + assert_equal(res[i].shape, tgt[i].shape) + def test_index_split_simple(self): a = np.arange(10) indices = [1, 5, 7] diff --git a/numpy/polynomial/_polybase.py b/numpy/polynomial/_polybase.py index ccbf30bda..dc72e7661 100644 --- a/numpy/polynomial/_polybase.py +++ b/numpy/polynomial/_polybase.py @@ -17,7 +17,7 @@ from . import polyutils as pu __all__ = ['ABCPolyBase'] class ABCPolyBase(object): - """An abstract base class for series classes. + """An abstract base class for immutable series classes. ABCPolyBase provides the standard Python numerical methods '+', '-', '*', '//', '%', 'divmod', '**', and '()' along with the @@ -512,9 +512,6 @@ class ABCPolyBase(object): rem = self.__class__(rem, self.domain, self.window) return quo, rem - # Enhance me - # some augmented arithmetic operations could be added here - def __eq__(self, other): res = (isinstance(other, self.__class__) and np.all(self.domain == other.domain) and @@ -1,23 +1,20 @@ #!/usr/bin/env python -"""NumPy: array processing for numbers, strings, records, and objects. +""" NumPy is the fundamental package for array computing with Python. -NumPy is a general-purpose array-processing package designed to -efficiently manipulate large multi-dimensional arrays of arbitrary -records without sacrificing too much speed for small multi-dimensional -arrays. NumPy is built on the Numeric code base and adds features -introduced by numarray as well as an extended C-API and the ability to -create arrays of arbitrary type which also makes NumPy suitable for -interfacing with general-purpose data-base applications. +It provides: -There are also basic facilities for discrete fourier transform, -basic linear algebra and random number generation. +- a powerful N-dimensional array object +- sophisticated (broadcasting) functions +- tools for integrating C/C++ and Fortran code +- useful linear algebra, Fourier transform, and random number capabilities +- and much more -All numpy wheels distributed from pypi are BSD licensed. +Besides its obvious scientific uses, NumPy can also be used as an efficient +multi-dimensional container of generic data. Arbitrary data-types can be +defined. This allows NumPy to seamlessly and speedily integrate with a wide +variety of databases. -Windows wheels are linked against the ATLAS BLAS / LAPACK library, restricted -to SSE2 instructions, so may not give optimal linear algebra performance for -your machine. See https://docs.scipy.org/doc/numpy/user/install.html for -alternatives. +All NumPy wheels distributed on PyPI are BSD licensed. """ from __future__ import division, print_function |