3562 lines
130 KiB
Python
3562 lines
130 KiB
Python
import operator
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import warnings
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import sys
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import decimal
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from fractions import Fraction
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import math
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import pytest
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import hypothesis
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from hypothesis.extra.numpy import arrays
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import hypothesis.strategies as st
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import numpy as np
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from numpy import ma
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from numpy.testing import (
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assert_, assert_equal, assert_array_equal, assert_almost_equal,
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assert_array_almost_equal, assert_raises, assert_allclose, IS_PYPY,
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assert_warns, assert_raises_regex, suppress_warnings, HAS_REFCOUNT,
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)
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import numpy.lib.function_base as nfb
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from numpy.random import rand
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from numpy.lib import (
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add_newdoc_ufunc, angle, average, bartlett, blackman, corrcoef, cov,
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delete, diff, digitize, extract, flipud, gradient, hamming, hanning,
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i0, insert, interp, kaiser, meshgrid, msort, piecewise, place, rot90,
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select, setxor1d, sinc, trapz, trim_zeros, unwrap, unique, vectorize
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)
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def get_mat(n):
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data = np.arange(n)
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data = np.add.outer(data, data)
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return data
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def _make_complex(real, imag):
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"""
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Like real + 1j * imag, but behaves as expected when imag contains non-finite
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values
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"""
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ret = np.zeros(np.broadcast(real, imag).shape, np.complex_)
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ret.real = real
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ret.imag = imag
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return ret
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class TestRot90:
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def test_basic(self):
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assert_raises(ValueError, rot90, np.ones(4))
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assert_raises(ValueError, rot90, np.ones((2,2,2)), axes=(0,1,2))
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assert_raises(ValueError, rot90, np.ones((2,2)), axes=(0,2))
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assert_raises(ValueError, rot90, np.ones((2,2)), axes=(1,1))
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assert_raises(ValueError, rot90, np.ones((2,2,2)), axes=(-2,1))
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a = [[0, 1, 2],
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[3, 4, 5]]
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b1 = [[2, 5],
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[1, 4],
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[0, 3]]
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b2 = [[5, 4, 3],
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[2, 1, 0]]
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b3 = [[3, 0],
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[4, 1],
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[5, 2]]
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b4 = [[0, 1, 2],
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[3, 4, 5]]
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for k in range(-3, 13, 4):
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assert_equal(rot90(a, k=k), b1)
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for k in range(-2, 13, 4):
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assert_equal(rot90(a, k=k), b2)
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for k in range(-1, 13, 4):
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assert_equal(rot90(a, k=k), b3)
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for k in range(0, 13, 4):
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assert_equal(rot90(a, k=k), b4)
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assert_equal(rot90(rot90(a, axes=(0,1)), axes=(1,0)), a)
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assert_equal(rot90(a, k=1, axes=(1,0)), rot90(a, k=-1, axes=(0,1)))
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def test_axes(self):
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a = np.ones((50, 40, 3))
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assert_equal(rot90(a).shape, (40, 50, 3))
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assert_equal(rot90(a, axes=(0,2)), rot90(a, axes=(0,-1)))
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assert_equal(rot90(a, axes=(1,2)), rot90(a, axes=(-2,-1)))
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def test_rotation_axes(self):
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a = np.arange(8).reshape((2,2,2))
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a_rot90_01 = [[[2, 3],
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[6, 7]],
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[[0, 1],
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[4, 5]]]
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a_rot90_12 = [[[1, 3],
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[0, 2]],
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[[5, 7],
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[4, 6]]]
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a_rot90_20 = [[[4, 0],
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[6, 2]],
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[[5, 1],
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[7, 3]]]
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a_rot90_10 = [[[4, 5],
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[0, 1]],
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[[6, 7],
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[2, 3]]]
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assert_equal(rot90(a, axes=(0, 1)), a_rot90_01)
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assert_equal(rot90(a, axes=(1, 0)), a_rot90_10)
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assert_equal(rot90(a, axes=(1, 2)), a_rot90_12)
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for k in range(1,5):
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assert_equal(rot90(a, k=k, axes=(2, 0)),
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rot90(a_rot90_20, k=k-1, axes=(2, 0)))
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class TestFlip:
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def test_axes(self):
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assert_raises(np.AxisError, np.flip, np.ones(4), axis=1)
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assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=2)
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assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=-3)
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assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=(0, 3))
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def test_basic_lr(self):
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a = get_mat(4)
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b = a[:, ::-1]
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assert_equal(np.flip(a, 1), b)
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a = [[0, 1, 2],
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[3, 4, 5]]
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b = [[2, 1, 0],
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[5, 4, 3]]
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assert_equal(np.flip(a, 1), b)
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def test_basic_ud(self):
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a = get_mat(4)
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b = a[::-1, :]
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assert_equal(np.flip(a, 0), b)
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a = [[0, 1, 2],
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[3, 4, 5]]
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b = [[3, 4, 5],
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[0, 1, 2]]
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assert_equal(np.flip(a, 0), b)
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def test_3d_swap_axis0(self):
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a = np.array([[[0, 1],
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[2, 3]],
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[[4, 5],
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[6, 7]]])
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b = np.array([[[4, 5],
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[6, 7]],
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[[0, 1],
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[2, 3]]])
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assert_equal(np.flip(a, 0), b)
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def test_3d_swap_axis1(self):
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a = np.array([[[0, 1],
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[2, 3]],
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[[4, 5],
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[6, 7]]])
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b = np.array([[[2, 3],
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[0, 1]],
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[[6, 7],
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[4, 5]]])
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assert_equal(np.flip(a, 1), b)
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def test_3d_swap_axis2(self):
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a = np.array([[[0, 1],
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[2, 3]],
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[[4, 5],
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[6, 7]]])
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b = np.array([[[1, 0],
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[3, 2]],
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[[5, 4],
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[7, 6]]])
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assert_equal(np.flip(a, 2), b)
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def test_4d(self):
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a = np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5)
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for i in range(a.ndim):
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assert_equal(np.flip(a, i),
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np.flipud(a.swapaxes(0, i)).swapaxes(i, 0))
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def test_default_axis(self):
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a = np.array([[1, 2, 3],
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[4, 5, 6]])
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b = np.array([[6, 5, 4],
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[3, 2, 1]])
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assert_equal(np.flip(a), b)
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def test_multiple_axes(self):
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a = np.array([[[0, 1],
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[2, 3]],
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[[4, 5],
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[6, 7]]])
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assert_equal(np.flip(a, axis=()), a)
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b = np.array([[[5, 4],
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[7, 6]],
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[[1, 0],
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[3, 2]]])
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assert_equal(np.flip(a, axis=(0, 2)), b)
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c = np.array([[[3, 2],
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[1, 0]],
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[[7, 6],
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[5, 4]]])
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assert_equal(np.flip(a, axis=(1, 2)), c)
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class TestAny:
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def test_basic(self):
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y1 = [0, 0, 1, 0]
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y2 = [0, 0, 0, 0]
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y3 = [1, 0, 1, 0]
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assert_(np.any(y1))
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assert_(np.any(y3))
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assert_(not np.any(y2))
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def test_nd(self):
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y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
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assert_(np.any(y1))
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assert_array_equal(np.sometrue(y1, axis=0), [1, 1, 0])
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assert_array_equal(np.sometrue(y1, axis=1), [0, 1, 1])
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class TestAll:
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def test_basic(self):
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y1 = [0, 1, 1, 0]
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y2 = [0, 0, 0, 0]
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y3 = [1, 1, 1, 1]
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assert_(not np.all(y1))
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assert_(np.all(y3))
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assert_(not np.all(y2))
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assert_(np.all(~np.array(y2)))
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def test_nd(self):
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y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
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assert_(not np.all(y1))
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assert_array_equal(np.alltrue(y1, axis=0), [0, 0, 1])
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assert_array_equal(np.alltrue(y1, axis=1), [0, 0, 1])
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class TestCopy:
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def test_basic(self):
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a = np.array([[1, 2], [3, 4]])
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a_copy = np.copy(a)
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assert_array_equal(a, a_copy)
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a_copy[0, 0] = 10
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assert_equal(a[0, 0], 1)
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assert_equal(a_copy[0, 0], 10)
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def test_order(self):
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# It turns out that people rely on np.copy() preserving order by
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# default; changing this broke scikit-learn:
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# github.com/scikit-learn/scikit-learn/commit/7842748cf777412c506a8c0ed28090711d3a3783 # noqa
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a = np.array([[1, 2], [3, 4]])
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assert_(a.flags.c_contiguous)
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assert_(not a.flags.f_contiguous)
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a_fort = np.array([[1, 2], [3, 4]], order="F")
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assert_(not a_fort.flags.c_contiguous)
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assert_(a_fort.flags.f_contiguous)
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a_copy = np.copy(a)
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assert_(a_copy.flags.c_contiguous)
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assert_(not a_copy.flags.f_contiguous)
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a_fort_copy = np.copy(a_fort)
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assert_(not a_fort_copy.flags.c_contiguous)
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assert_(a_fort_copy.flags.f_contiguous)
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def test_subok(self):
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mx = ma.ones(5)
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assert_(not ma.isMaskedArray(np.copy(mx, subok=False)))
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assert_(ma.isMaskedArray(np.copy(mx, subok=True)))
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# Default behavior
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assert_(not ma.isMaskedArray(np.copy(mx)))
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class TestAverage:
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def test_basic(self):
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y1 = np.array([1, 2, 3])
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assert_(average(y1, axis=0) == 2.)
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y2 = np.array([1., 2., 3.])
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assert_(average(y2, axis=0) == 2.)
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y3 = [0., 0., 0.]
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assert_(average(y3, axis=0) == 0.)
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y4 = np.ones((4, 4))
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y4[0, 1] = 0
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y4[1, 0] = 2
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assert_almost_equal(y4.mean(0), average(y4, 0))
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assert_almost_equal(y4.mean(1), average(y4, 1))
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y5 = rand(5, 5)
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assert_almost_equal(y5.mean(0), average(y5, 0))
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assert_almost_equal(y5.mean(1), average(y5, 1))
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def test_weights(self):
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y = np.arange(10)
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w = np.arange(10)
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actual = average(y, weights=w)
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desired = (np.arange(10) ** 2).sum() * 1. / np.arange(10).sum()
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assert_almost_equal(actual, desired)
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y1 = np.array([[1, 2, 3], [4, 5, 6]])
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w0 = [1, 2]
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actual = average(y1, weights=w0, axis=0)
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desired = np.array([3., 4., 5.])
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assert_almost_equal(actual, desired)
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w1 = [0, 0, 1]
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actual = average(y1, weights=w1, axis=1)
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desired = np.array([3., 6.])
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assert_almost_equal(actual, desired)
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# This should raise an error. Can we test for that ?
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# assert_equal(average(y1, weights=w1), 9./2.)
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# 2D Case
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w2 = [[0, 0, 1], [0, 0, 2]]
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desired = np.array([3., 6.])
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assert_array_equal(average(y1, weights=w2, axis=1), desired)
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assert_equal(average(y1, weights=w2), 5.)
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y3 = rand(5).astype(np.float32)
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w3 = rand(5).astype(np.float64)
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assert_(np.average(y3, weights=w3).dtype == np.result_type(y3, w3))
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def test_returned(self):
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y = np.array([[1, 2, 3], [4, 5, 6]])
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# No weights
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avg, scl = average(y, returned=True)
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assert_equal(scl, 6.)
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avg, scl = average(y, 0, returned=True)
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assert_array_equal(scl, np.array([2., 2., 2.]))
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avg, scl = average(y, 1, returned=True)
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assert_array_equal(scl, np.array([3., 3.]))
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# With weights
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w0 = [1, 2]
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avg, scl = average(y, weights=w0, axis=0, returned=True)
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assert_array_equal(scl, np.array([3., 3., 3.]))
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w1 = [1, 2, 3]
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avg, scl = average(y, weights=w1, axis=1, returned=True)
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assert_array_equal(scl, np.array([6., 6.]))
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w2 = [[0, 0, 1], [1, 2, 3]]
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avg, scl = average(y, weights=w2, axis=1, returned=True)
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assert_array_equal(scl, np.array([1., 6.]))
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def test_subclasses(self):
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class subclass(np.ndarray):
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pass
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a = np.array([[1,2],[3,4]]).view(subclass)
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w = np.array([[1,2],[3,4]]).view(subclass)
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assert_equal(type(np.average(a)), subclass)
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assert_equal(type(np.average(a, weights=w)), subclass)
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def test_upcasting(self):
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typs = [('i4', 'i4', 'f8'), ('i4', 'f4', 'f8'), ('f4', 'i4', 'f8'),
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('f4', 'f4', 'f4'), ('f4', 'f8', 'f8')]
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for at, wt, rt in typs:
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a = np.array([[1,2],[3,4]], dtype=at)
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w = np.array([[1,2],[3,4]], dtype=wt)
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assert_equal(np.average(a, weights=w).dtype, np.dtype(rt))
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def test_object_dtype(self):
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a = np.array([decimal.Decimal(x) for x in range(10)])
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w = np.array([decimal.Decimal(1) for _ in range(10)])
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w /= w.sum()
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assert_almost_equal(a.mean(0), average(a, weights=w))
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class TestSelect:
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choices = [np.array([1, 2, 3]),
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np.array([4, 5, 6]),
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np.array([7, 8, 9])]
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conditions = [np.array([False, False, False]),
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np.array([False, True, False]),
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np.array([False, False, True])]
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def _select(self, cond, values, default=0):
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output = []
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for m in range(len(cond)):
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output += [V[m] for V, C in zip(values, cond) if C[m]] or [default]
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return output
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def test_basic(self):
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choices = self.choices
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conditions = self.conditions
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assert_array_equal(select(conditions, choices, default=15),
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self._select(conditions, choices, default=15))
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assert_equal(len(choices), 3)
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assert_equal(len(conditions), 3)
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def test_broadcasting(self):
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conditions = [np.array(True), np.array([False, True, False])]
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choices = [1, np.arange(12).reshape(4, 3)]
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assert_array_equal(select(conditions, choices), np.ones((4, 3)))
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# default can broadcast too:
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assert_equal(select([True], [0], default=[0]).shape, (1,))
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def test_return_dtype(self):
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assert_equal(select(self.conditions, self.choices, 1j).dtype,
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np.complex_)
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# But the conditions need to be stronger then the scalar default
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# if it is scalar.
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choices = [choice.astype(np.int8) for choice in self.choices]
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assert_equal(select(self.conditions, choices).dtype, np.int8)
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d = np.array([1, 2, 3, np.nan, 5, 7])
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m = np.isnan(d)
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assert_equal(select([m], [d]), [0, 0, 0, np.nan, 0, 0])
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def test_deprecated_empty(self):
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assert_raises(ValueError, select, [], [], 3j)
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assert_raises(ValueError, select, [], [])
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def test_non_bool_deprecation(self):
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choices = self.choices
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conditions = self.conditions[:]
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conditions[0] = conditions[0].astype(np.int_)
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assert_raises(TypeError, select, conditions, choices)
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conditions[0] = conditions[0].astype(np.uint8)
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assert_raises(TypeError, select, conditions, choices)
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assert_raises(TypeError, select, conditions, choices)
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def test_many_arguments(self):
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# This used to be limited by NPY_MAXARGS == 32
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conditions = [np.array([False])] * 100
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choices = [np.array([1])] * 100
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select(conditions, choices)
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|
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class TestInsert:
|
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def test_basic(self):
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a = [1, 2, 3]
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assert_equal(insert(a, 0, 1), [1, 1, 2, 3])
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assert_equal(insert(a, 3, 1), [1, 2, 3, 1])
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assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3])
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assert_equal(insert(a, 1, [1, 2, 3]), [1, 1, 2, 3, 2, 3])
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assert_equal(insert(a, [1, -1, 3], 9), [1, 9, 2, 9, 3, 9])
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assert_equal(insert(a, slice(-1, None, -1), 9), [9, 1, 9, 2, 9, 3])
|
|
assert_equal(insert(a, [-1, 1, 3], [7, 8, 9]), [1, 8, 2, 7, 3, 9])
|
|
b = np.array([0, 1], dtype=np.float64)
|
|
assert_equal(insert(b, 0, b[0]), [0., 0., 1.])
|
|
assert_equal(insert(b, [], []), b)
|
|
# Bools will be treated differently in the future:
|
|
# assert_equal(insert(a, np.array([True]*4), 9), [9, 1, 9, 2, 9, 3, 9])
|
|
with warnings.catch_warnings(record=True) as w:
|
|
warnings.filterwarnings('always', '', FutureWarning)
|
|
assert_equal(
|
|
insert(a, np.array([True] * 4), 9), [1, 9, 9, 9, 9, 2, 3])
|
|
assert_(w[0].category is FutureWarning)
|
|
|
|
def test_multidim(self):
|
|
a = [[1, 1, 1]]
|
|
r = [[2, 2, 2],
|
|
[1, 1, 1]]
|
|
assert_equal(insert(a, 0, [1]), [1, 1, 1, 1])
|
|
assert_equal(insert(a, 0, [2, 2, 2], axis=0), r)
|
|
assert_equal(insert(a, 0, 2, axis=0), r)
|
|
assert_equal(insert(a, 2, 2, axis=1), [[1, 1, 2, 1]])
|
|
|
|
a = np.array([[1, 1], [2, 2], [3, 3]])
|
|
b = np.arange(1, 4).repeat(3).reshape(3, 3)
|
|
c = np.concatenate(
|
|
(a[:, 0:1], np.arange(1, 4).repeat(3).reshape(3, 3).T,
|
|
a[:, 1:2]), axis=1)
|
|
assert_equal(insert(a, [1], [[1], [2], [3]], axis=1), b)
|
|
assert_equal(insert(a, [1], [1, 2, 3], axis=1), c)
|
|
# scalars behave differently, in this case exactly opposite:
|
|
assert_equal(insert(a, 1, [1, 2, 3], axis=1), b)
|
|
assert_equal(insert(a, 1, [[1], [2], [3]], axis=1), c)
|
|
|
|
a = np.arange(4).reshape(2, 2)
|
|
assert_equal(insert(a[:, :1], 1, a[:, 1], axis=1), a)
|
|
assert_equal(insert(a[:1,:], 1, a[1,:], axis=0), a)
|
|
|
|
# negative axis value
|
|
a = np.arange(24).reshape((2, 3, 4))
|
|
assert_equal(insert(a, 1, a[:,:, 3], axis=-1),
|
|
insert(a, 1, a[:,:, 3], axis=2))
|
|
assert_equal(insert(a, 1, a[:, 2,:], axis=-2),
|
|
insert(a, 1, a[:, 2,:], axis=1))
|
|
|
|
# invalid axis value
|
|
assert_raises(np.AxisError, insert, a, 1, a[:, 2, :], axis=3)
|
|
assert_raises(np.AxisError, insert, a, 1, a[:, 2, :], axis=-4)
|
|
|
|
# negative axis value
|
|
a = np.arange(24).reshape((2, 3, 4))
|
|
assert_equal(insert(a, 1, a[:, :, 3], axis=-1),
|
|
insert(a, 1, a[:, :, 3], axis=2))
|
|
assert_equal(insert(a, 1, a[:, 2, :], axis=-2),
|
|
insert(a, 1, a[:, 2, :], axis=1))
|
|
|
|
def test_0d(self):
|
|
a = np.array(1)
|
|
with pytest.raises(np.AxisError):
|
|
insert(a, [], 2, axis=0)
|
|
with pytest.raises(TypeError):
|
|
insert(a, [], 2, axis="nonsense")
|
|
|
|
def test_subclass(self):
|
|
class SubClass(np.ndarray):
|
|
pass
|
|
a = np.arange(10).view(SubClass)
|
|
assert_(isinstance(np.insert(a, 0, [0]), SubClass))
|
|
assert_(isinstance(np.insert(a, [], []), SubClass))
|
|
assert_(isinstance(np.insert(a, [0, 1], [1, 2]), SubClass))
|
|
assert_(isinstance(np.insert(a, slice(1, 2), [1, 2]), SubClass))
|
|
assert_(isinstance(np.insert(a, slice(1, -2, -1), []), SubClass))
|
|
# This is an error in the future:
|
|
a = np.array(1).view(SubClass)
|
|
assert_(isinstance(np.insert(a, 0, [0]), SubClass))
|
|
|
|
def test_index_array_copied(self):
|
|
x = np.array([1, 1, 1])
|
|
np.insert([0, 1, 2], x, [3, 4, 5])
|
|
assert_equal(x, np.array([1, 1, 1]))
|
|
|
|
def test_structured_array(self):
|
|
a = np.array([(1, 'a'), (2, 'b'), (3, 'c')],
|
|
dtype=[('foo', 'i'), ('bar', 'a1')])
|
|
val = (4, 'd')
|
|
b = np.insert(a, 0, val)
|
|
assert_array_equal(b[0], np.array(val, dtype=b.dtype))
|
|
val = [(4, 'd')] * 2
|
|
b = np.insert(a, [0, 2], val)
|
|
assert_array_equal(b[[0, 3]], np.array(val, dtype=b.dtype))
|
|
|
|
def test_index_floats(self):
|
|
with pytest.raises(IndexError):
|
|
np.insert([0, 1, 2], np.array([1.0, 2.0]), [10, 20])
|
|
with pytest.raises(IndexError):
|
|
np.insert([0, 1, 2], np.array([], dtype=float), [])
|
|
|
|
|
|
class TestAmax:
|
|
|
|
def test_basic(self):
|
|
a = [3, 4, 5, 10, -3, -5, 6.0]
|
|
assert_equal(np.amax(a), 10.0)
|
|
b = [[3, 6.0, 9.0],
|
|
[4, 10.0, 5.0],
|
|
[8, 3.0, 2.0]]
|
|
assert_equal(np.amax(b, axis=0), [8.0, 10.0, 9.0])
|
|
assert_equal(np.amax(b, axis=1), [9.0, 10.0, 8.0])
|
|
|
|
|
|
class TestAmin:
|
|
|
|
def test_basic(self):
|
|
a = [3, 4, 5, 10, -3, -5, 6.0]
|
|
assert_equal(np.amin(a), -5.0)
|
|
b = [[3, 6.0, 9.0],
|
|
[4, 10.0, 5.0],
|
|
[8, 3.0, 2.0]]
|
|
assert_equal(np.amin(b, axis=0), [3.0, 3.0, 2.0])
|
|
assert_equal(np.amin(b, axis=1), [3.0, 4.0, 2.0])
|
|
|
|
|
|
class TestPtp:
|
|
|
|
def test_basic(self):
|
|
a = np.array([3, 4, 5, 10, -3, -5, 6.0])
|
|
assert_equal(a.ptp(axis=0), 15.0)
|
|
b = np.array([[3, 6.0, 9.0],
|
|
[4, 10.0, 5.0],
|
|
[8, 3.0, 2.0]])
|
|
assert_equal(b.ptp(axis=0), [5.0, 7.0, 7.0])
|
|
assert_equal(b.ptp(axis=-1), [6.0, 6.0, 6.0])
|
|
|
|
assert_equal(b.ptp(axis=0, keepdims=True), [[5.0, 7.0, 7.0]])
|
|
assert_equal(b.ptp(axis=(0,1), keepdims=True), [[8.0]])
|
|
|
|
|
|
class TestCumsum:
|
|
|
|
def test_basic(self):
|
|
ba = [1, 2, 10, 11, 6, 5, 4]
|
|
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
|
|
for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
|
|
np.uint32, np.float32, np.float64, np.complex64,
|
|
np.complex128]:
|
|
a = np.array(ba, ctype)
|
|
a2 = np.array(ba2, ctype)
|
|
|
|
tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
|
|
assert_array_equal(np.cumsum(a, axis=0), tgt)
|
|
|
|
tgt = np.array(
|
|
[[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
|
|
assert_array_equal(np.cumsum(a2, axis=0), tgt)
|
|
|
|
tgt = np.array(
|
|
[[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
|
|
assert_array_equal(np.cumsum(a2, axis=1), tgt)
|
|
|
|
|
|
class TestProd:
|
|
|
|
def test_basic(self):
|
|
ba = [1, 2, 10, 11, 6, 5, 4]
|
|
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
|
|
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
|
|
np.float32, np.float64, np.complex64, np.complex128]:
|
|
a = np.array(ba, ctype)
|
|
a2 = np.array(ba2, ctype)
|
|
if ctype in ['1', 'b']:
|
|
assert_raises(ArithmeticError, np.prod, a)
|
|
assert_raises(ArithmeticError, np.prod, a2, 1)
|
|
else:
|
|
assert_equal(a.prod(axis=0), 26400)
|
|
assert_array_equal(a2.prod(axis=0),
|
|
np.array([50, 36, 84, 180], ctype))
|
|
assert_array_equal(a2.prod(axis=-1),
|
|
np.array([24, 1890, 600], ctype))
|
|
|
|
|
|
class TestCumprod:
|
|
|
|
def test_basic(self):
|
|
ba = [1, 2, 10, 11, 6, 5, 4]
|
|
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
|
|
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
|
|
np.float32, np.float64, np.complex64, np.complex128]:
|
|
a = np.array(ba, ctype)
|
|
a2 = np.array(ba2, ctype)
|
|
if ctype in ['1', 'b']:
|
|
assert_raises(ArithmeticError, np.cumprod, a)
|
|
assert_raises(ArithmeticError, np.cumprod, a2, 1)
|
|
assert_raises(ArithmeticError, np.cumprod, a)
|
|
else:
|
|
assert_array_equal(np.cumprod(a, axis=-1),
|
|
np.array([1, 2, 20, 220,
|
|
1320, 6600, 26400], ctype))
|
|
assert_array_equal(np.cumprod(a2, axis=0),
|
|
np.array([[1, 2, 3, 4],
|
|
[5, 12, 21, 36],
|
|
[50, 36, 84, 180]], ctype))
|
|
assert_array_equal(np.cumprod(a2, axis=-1),
|
|
np.array([[1, 2, 6, 24],
|
|
[5, 30, 210, 1890],
|
|
[10, 30, 120, 600]], ctype))
|
|
|
|
|
|
class TestDiff:
|
|
|
|
def test_basic(self):
|
|
x = [1, 4, 6, 7, 12]
|
|
out = np.array([3, 2, 1, 5])
|
|
out2 = np.array([-1, -1, 4])
|
|
out3 = np.array([0, 5])
|
|
assert_array_equal(diff(x), out)
|
|
assert_array_equal(diff(x, n=2), out2)
|
|
assert_array_equal(diff(x, n=3), out3)
|
|
|
|
x = [1.1, 2.2, 3.0, -0.2, -0.1]
|
|
out = np.array([1.1, 0.8, -3.2, 0.1])
|
|
assert_almost_equal(diff(x), out)
|
|
|
|
x = [True, True, False, False]
|
|
out = np.array([False, True, False])
|
|
out2 = np.array([True, True])
|
|
assert_array_equal(diff(x), out)
|
|
assert_array_equal(diff(x, n=2), out2)
|
|
|
|
def test_axis(self):
|
|
x = np.zeros((10, 20, 30))
|
|
x[:, 1::2, :] = 1
|
|
exp = np.ones((10, 19, 30))
|
|
exp[:, 1::2, :] = -1
|
|
assert_array_equal(diff(x), np.zeros((10, 20, 29)))
|
|
assert_array_equal(diff(x, axis=-1), np.zeros((10, 20, 29)))
|
|
assert_array_equal(diff(x, axis=0), np.zeros((9, 20, 30)))
|
|
assert_array_equal(diff(x, axis=1), exp)
|
|
assert_array_equal(diff(x, axis=-2), exp)
|
|
assert_raises(np.AxisError, diff, x, axis=3)
|
|
assert_raises(np.AxisError, diff, x, axis=-4)
|
|
|
|
x = np.array(1.11111111111, np.float64)
|
|
assert_raises(ValueError, diff, x)
|
|
|
|
def test_nd(self):
|
|
x = 20 * rand(10, 20, 30)
|
|
out1 = x[:, :, 1:] - x[:, :, :-1]
|
|
out2 = out1[:, :, 1:] - out1[:, :, :-1]
|
|
out3 = x[1:, :, :] - x[:-1, :, :]
|
|
out4 = out3[1:, :, :] - out3[:-1, :, :]
|
|
assert_array_equal(diff(x), out1)
|
|
assert_array_equal(diff(x, n=2), out2)
|
|
assert_array_equal(diff(x, axis=0), out3)
|
|
assert_array_equal(diff(x, n=2, axis=0), out4)
|
|
|
|
def test_n(self):
|
|
x = list(range(3))
|
|
assert_raises(ValueError, diff, x, n=-1)
|
|
output = [diff(x, n=n) for n in range(1, 5)]
|
|
expected = [[1, 1], [0], [], []]
|
|
assert_(diff(x, n=0) is x)
|
|
for n, (expected, out) in enumerate(zip(expected, output), start=1):
|
|
assert_(type(out) is np.ndarray)
|
|
assert_array_equal(out, expected)
|
|
assert_equal(out.dtype, np.int_)
|
|
assert_equal(len(out), max(0, len(x) - n))
|
|
|
|
def test_times(self):
|
|
x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
|
|
expected = [
|
|
np.array([1, 1], dtype='timedelta64[D]'),
|
|
np.array([0], dtype='timedelta64[D]'),
|
|
]
|
|
expected.extend([np.array([], dtype='timedelta64[D]')] * 3)
|
|
for n, exp in enumerate(expected, start=1):
|
|
out = diff(x, n=n)
|
|
assert_array_equal(out, exp)
|
|
assert_equal(out.dtype, exp.dtype)
|
|
|
|
def test_subclass(self):
|
|
x = ma.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]],
|
|
mask=[[False, False], [True, False],
|
|
[False, True], [True, True], [False, False]])
|
|
out = diff(x)
|
|
assert_array_equal(out.data, [[1], [1], [1], [1], [1]])
|
|
assert_array_equal(out.mask, [[False], [True],
|
|
[True], [True], [False]])
|
|
assert_(type(out) is type(x))
|
|
|
|
out3 = diff(x, n=3)
|
|
assert_array_equal(out3.data, [[], [], [], [], []])
|
|
assert_array_equal(out3.mask, [[], [], [], [], []])
|
|
assert_(type(out3) is type(x))
|
|
|
|
def test_prepend(self):
|
|
x = np.arange(5) + 1
|
|
assert_array_equal(diff(x, prepend=0), np.ones(5))
|
|
assert_array_equal(diff(x, prepend=[0]), np.ones(5))
|
|
assert_array_equal(np.cumsum(np.diff(x, prepend=0)), x)
|
|
assert_array_equal(diff(x, prepend=[-1, 0]), np.ones(6))
|
|
|
|
x = np.arange(4).reshape(2, 2)
|
|
result = np.diff(x, axis=1, prepend=0)
|
|
expected = [[0, 1], [2, 1]]
|
|
assert_array_equal(result, expected)
|
|
result = np.diff(x, axis=1, prepend=[[0], [0]])
|
|
assert_array_equal(result, expected)
|
|
|
|
result = np.diff(x, axis=0, prepend=0)
|
|
expected = [[0, 1], [2, 2]]
|
|
assert_array_equal(result, expected)
|
|
result = np.diff(x, axis=0, prepend=[[0, 0]])
|
|
assert_array_equal(result, expected)
|
|
|
|
assert_raises(ValueError, np.diff, x, prepend=np.zeros((3,3)))
|
|
|
|
assert_raises(np.AxisError, diff, x, prepend=0, axis=3)
|
|
|
|
def test_append(self):
|
|
x = np.arange(5)
|
|
result = diff(x, append=0)
|
|
expected = [1, 1, 1, 1, -4]
|
|
assert_array_equal(result, expected)
|
|
result = diff(x, append=[0])
|
|
assert_array_equal(result, expected)
|
|
result = diff(x, append=[0, 2])
|
|
expected = expected + [2]
|
|
assert_array_equal(result, expected)
|
|
|
|
x = np.arange(4).reshape(2, 2)
|
|
result = np.diff(x, axis=1, append=0)
|
|
expected = [[1, -1], [1, -3]]
|
|
assert_array_equal(result, expected)
|
|
result = np.diff(x, axis=1, append=[[0], [0]])
|
|
assert_array_equal(result, expected)
|
|
|
|
result = np.diff(x, axis=0, append=0)
|
|
expected = [[2, 2], [-2, -3]]
|
|
assert_array_equal(result, expected)
|
|
result = np.diff(x, axis=0, append=[[0, 0]])
|
|
assert_array_equal(result, expected)
|
|
|
|
assert_raises(ValueError, np.diff, x, append=np.zeros((3,3)))
|
|
|
|
assert_raises(np.AxisError, diff, x, append=0, axis=3)
|
|
|
|
|
|
class TestDelete:
|
|
|
|
def setup(self):
|
|
self.a = np.arange(5)
|
|
self.nd_a = np.arange(5).repeat(2).reshape(1, 5, 2)
|
|
|
|
def _check_inverse_of_slicing(self, indices):
|
|
a_del = delete(self.a, indices)
|
|
nd_a_del = delete(self.nd_a, indices, axis=1)
|
|
msg = 'Delete failed for obj: %r' % indices
|
|
assert_array_equal(setxor1d(a_del, self.a[indices, ]), self.a,
|
|
err_msg=msg)
|
|
xor = setxor1d(nd_a_del[0,:, 0], self.nd_a[0, indices, 0])
|
|
assert_array_equal(xor, self.nd_a[0,:, 0], err_msg=msg)
|
|
|
|
def test_slices(self):
|
|
lims = [-6, -2, 0, 1, 2, 4, 5]
|
|
steps = [-3, -1, 1, 3]
|
|
for start in lims:
|
|
for stop in lims:
|
|
for step in steps:
|
|
s = slice(start, stop, step)
|
|
self._check_inverse_of_slicing(s)
|
|
|
|
def test_fancy(self):
|
|
self._check_inverse_of_slicing(np.array([[0, 1], [2, 1]]))
|
|
with pytest.raises(IndexError):
|
|
delete(self.a, [100])
|
|
with pytest.raises(IndexError):
|
|
delete(self.a, [-100])
|
|
|
|
self._check_inverse_of_slicing([0, -1, 2, 2])
|
|
|
|
self._check_inverse_of_slicing([True, False, False, True, False])
|
|
|
|
# not legal, indexing with these would change the dimension
|
|
with pytest.raises(ValueError):
|
|
delete(self.a, True)
|
|
with pytest.raises(ValueError):
|
|
delete(self.a, False)
|
|
|
|
# not enough items
|
|
with pytest.raises(ValueError):
|
|
delete(self.a, [False]*4)
|
|
|
|
def test_single(self):
|
|
self._check_inverse_of_slicing(0)
|
|
self._check_inverse_of_slicing(-4)
|
|
|
|
def test_0d(self):
|
|
a = np.array(1)
|
|
with pytest.raises(np.AxisError):
|
|
delete(a, [], axis=0)
|
|
with pytest.raises(TypeError):
|
|
delete(a, [], axis="nonsense")
|
|
|
|
def test_subclass(self):
|
|
class SubClass(np.ndarray):
|
|
pass
|
|
a = self.a.view(SubClass)
|
|
assert_(isinstance(delete(a, 0), SubClass))
|
|
assert_(isinstance(delete(a, []), SubClass))
|
|
assert_(isinstance(delete(a, [0, 1]), SubClass))
|
|
assert_(isinstance(delete(a, slice(1, 2)), SubClass))
|
|
assert_(isinstance(delete(a, slice(1, -2)), SubClass))
|
|
|
|
def test_array_order_preserve(self):
|
|
# See gh-7113
|
|
k = np.arange(10).reshape(2, 5, order='F')
|
|
m = delete(k, slice(60, None), axis=1)
|
|
|
|
# 'k' is Fortran ordered, and 'm' should have the
|
|
# same ordering as 'k' and NOT become C ordered
|
|
assert_equal(m.flags.c_contiguous, k.flags.c_contiguous)
|
|
assert_equal(m.flags.f_contiguous, k.flags.f_contiguous)
|
|
|
|
def test_index_floats(self):
|
|
with pytest.raises(IndexError):
|
|
np.delete([0, 1, 2], np.array([1.0, 2.0]))
|
|
with pytest.raises(IndexError):
|
|
np.delete([0, 1, 2], np.array([], dtype=float))
|
|
|
|
|
|
class TestGradient:
|
|
|
|
def test_basic(self):
|
|
v = [[1, 1], [3, 4]]
|
|
x = np.array(v)
|
|
dx = [np.array([[2., 3.], [2., 3.]]),
|
|
np.array([[0., 0.], [1., 1.]])]
|
|
assert_array_equal(gradient(x), dx)
|
|
assert_array_equal(gradient(v), dx)
|
|
|
|
def test_args(self):
|
|
dx = np.cumsum(np.ones(5))
|
|
dx_uneven = [1., 2., 5., 9., 11.]
|
|
f_2d = np.arange(25).reshape(5, 5)
|
|
|
|
# distances must be scalars or have size equal to gradient[axis]
|
|
gradient(np.arange(5), 3.)
|
|
gradient(np.arange(5), np.array(3.))
|
|
gradient(np.arange(5), dx)
|
|
# dy is set equal to dx because scalar
|
|
gradient(f_2d, 1.5)
|
|
gradient(f_2d, np.array(1.5))
|
|
|
|
gradient(f_2d, dx_uneven, dx_uneven)
|
|
# mix between even and uneven spaces and
|
|
# mix between scalar and vector
|
|
gradient(f_2d, dx, 2)
|
|
|
|
# 2D but axis specified
|
|
gradient(f_2d, dx, axis=1)
|
|
|
|
# 2d coordinate arguments are not yet allowed
|
|
assert_raises_regex(ValueError, '.*scalars or 1d',
|
|
gradient, f_2d, np.stack([dx]*2, axis=-1), 1)
|
|
|
|
def test_badargs(self):
|
|
f_2d = np.arange(25).reshape(5, 5)
|
|
x = np.cumsum(np.ones(5))
|
|
|
|
# wrong sizes
|
|
assert_raises(ValueError, gradient, f_2d, x, np.ones(2))
|
|
assert_raises(ValueError, gradient, f_2d, 1, np.ones(2))
|
|
assert_raises(ValueError, gradient, f_2d, np.ones(2), np.ones(2))
|
|
# wrong number of arguments
|
|
assert_raises(TypeError, gradient, f_2d, x)
|
|
assert_raises(TypeError, gradient, f_2d, x, axis=(0,1))
|
|
assert_raises(TypeError, gradient, f_2d, x, x, x)
|
|
assert_raises(TypeError, gradient, f_2d, 1, 1, 1)
|
|
assert_raises(TypeError, gradient, f_2d, x, x, axis=1)
|
|
assert_raises(TypeError, gradient, f_2d, 1, 1, axis=1)
|
|
|
|
def test_datetime64(self):
|
|
# Make sure gradient() can handle special types like datetime64
|
|
x = np.array(
|
|
['1910-08-16', '1910-08-11', '1910-08-10', '1910-08-12',
|
|
'1910-10-12', '1910-12-12', '1912-12-12'],
|
|
dtype='datetime64[D]')
|
|
dx = np.array(
|
|
[-5, -3, 0, 31, 61, 396, 731],
|
|
dtype='timedelta64[D]')
|
|
assert_array_equal(gradient(x), dx)
|
|
assert_(dx.dtype == np.dtype('timedelta64[D]'))
|
|
|
|
def test_masked(self):
|
|
# Make sure that gradient supports subclasses like masked arrays
|
|
x = np.ma.array([[1, 1], [3, 4]],
|
|
mask=[[False, False], [False, False]])
|
|
out = gradient(x)[0]
|
|
assert_equal(type(out), type(x))
|
|
# And make sure that the output and input don't have aliased mask
|
|
# arrays
|
|
assert_(x._mask is not out._mask)
|
|
# Also check that edge_order=2 doesn't alter the original mask
|
|
x2 = np.ma.arange(5)
|
|
x2[2] = np.ma.masked
|
|
np.gradient(x2, edge_order=2)
|
|
assert_array_equal(x2.mask, [False, False, True, False, False])
|
|
|
|
def test_second_order_accurate(self):
|
|
# Testing that the relative numerical error is less that 3% for
|
|
# this example problem. This corresponds to second order
|
|
# accurate finite differences for all interior and boundary
|
|
# points.
|
|
x = np.linspace(0, 1, 10)
|
|
dx = x[1] - x[0]
|
|
y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
|
|
analytical = 6 * x ** 2 + 8 * x + 2
|
|
num_error = np.abs((np.gradient(y, dx, edge_order=2) / analytical) - 1)
|
|
assert_(np.all(num_error < 0.03) == True)
|
|
|
|
# test with unevenly spaced
|
|
np.random.seed(0)
|
|
x = np.sort(np.random.random(10))
|
|
y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
|
|
analytical = 6 * x ** 2 + 8 * x + 2
|
|
num_error = np.abs((np.gradient(y, x, edge_order=2) / analytical) - 1)
|
|
assert_(np.all(num_error < 0.03) == True)
|
|
|
|
def test_spacing(self):
|
|
f = np.array([0, 2., 3., 4., 5., 5.])
|
|
f = np.tile(f, (6,1)) + f.reshape(-1, 1)
|
|
x_uneven = np.array([0., 0.5, 1., 3., 5., 7.])
|
|
x_even = np.arange(6.)
|
|
|
|
fdx_even_ord1 = np.tile([2., 1.5, 1., 1., 0.5, 0.], (6,1))
|
|
fdx_even_ord2 = np.tile([2.5, 1.5, 1., 1., 0.5, -0.5], (6,1))
|
|
fdx_uneven_ord1 = np.tile([4., 3., 1.7, 0.5, 0.25, 0.], (6,1))
|
|
fdx_uneven_ord2 = np.tile([5., 3., 1.7, 0.5, 0.25, -0.25], (6,1))
|
|
|
|
# evenly spaced
|
|
for edge_order, exp_res in [(1, fdx_even_ord1), (2, fdx_even_ord2)]:
|
|
res1 = gradient(f, 1., axis=(0,1), edge_order=edge_order)
|
|
res2 = gradient(f, x_even, x_even,
|
|
axis=(0,1), edge_order=edge_order)
|
|
res3 = gradient(f, x_even, x_even,
|
|
axis=None, edge_order=edge_order)
|
|
assert_array_equal(res1, res2)
|
|
assert_array_equal(res2, res3)
|
|
assert_almost_equal(res1[0], exp_res.T)
|
|
assert_almost_equal(res1[1], exp_res)
|
|
|
|
res1 = gradient(f, 1., axis=0, edge_order=edge_order)
|
|
res2 = gradient(f, x_even, axis=0, edge_order=edge_order)
|
|
assert_(res1.shape == res2.shape)
|
|
assert_almost_equal(res2, exp_res.T)
|
|
|
|
res1 = gradient(f, 1., axis=1, edge_order=edge_order)
|
|
res2 = gradient(f, x_even, axis=1, edge_order=edge_order)
|
|
assert_(res1.shape == res2.shape)
|
|
assert_array_equal(res2, exp_res)
|
|
|
|
# unevenly spaced
|
|
for edge_order, exp_res in [(1, fdx_uneven_ord1), (2, fdx_uneven_ord2)]:
|
|
res1 = gradient(f, x_uneven, x_uneven,
|
|
axis=(0,1), edge_order=edge_order)
|
|
res2 = gradient(f, x_uneven, x_uneven,
|
|
axis=None, edge_order=edge_order)
|
|
assert_array_equal(res1, res2)
|
|
assert_almost_equal(res1[0], exp_res.T)
|
|
assert_almost_equal(res1[1], exp_res)
|
|
|
|
res1 = gradient(f, x_uneven, axis=0, edge_order=edge_order)
|
|
assert_almost_equal(res1, exp_res.T)
|
|
|
|
res1 = gradient(f, x_uneven, axis=1, edge_order=edge_order)
|
|
assert_almost_equal(res1, exp_res)
|
|
|
|
# mixed
|
|
res1 = gradient(f, x_even, x_uneven, axis=(0,1), edge_order=1)
|
|
res2 = gradient(f, x_uneven, x_even, axis=(1,0), edge_order=1)
|
|
assert_array_equal(res1[0], res2[1])
|
|
assert_array_equal(res1[1], res2[0])
|
|
assert_almost_equal(res1[0], fdx_even_ord1.T)
|
|
assert_almost_equal(res1[1], fdx_uneven_ord1)
|
|
|
|
res1 = gradient(f, x_even, x_uneven, axis=(0,1), edge_order=2)
|
|
res2 = gradient(f, x_uneven, x_even, axis=(1,0), edge_order=2)
|
|
assert_array_equal(res1[0], res2[1])
|
|
assert_array_equal(res1[1], res2[0])
|
|
assert_almost_equal(res1[0], fdx_even_ord2.T)
|
|
assert_almost_equal(res1[1], fdx_uneven_ord2)
|
|
|
|
def test_specific_axes(self):
|
|
# Testing that gradient can work on a given axis only
|
|
v = [[1, 1], [3, 4]]
|
|
x = np.array(v)
|
|
dx = [np.array([[2., 3.], [2., 3.]]),
|
|
np.array([[0., 0.], [1., 1.]])]
|
|
assert_array_equal(gradient(x, axis=0), dx[0])
|
|
assert_array_equal(gradient(x, axis=1), dx[1])
|
|
assert_array_equal(gradient(x, axis=-1), dx[1])
|
|
assert_array_equal(gradient(x, axis=(1, 0)), [dx[1], dx[0]])
|
|
|
|
# test axis=None which means all axes
|
|
assert_almost_equal(gradient(x, axis=None), [dx[0], dx[1]])
|
|
# and is the same as no axis keyword given
|
|
assert_almost_equal(gradient(x, axis=None), gradient(x))
|
|
|
|
# test vararg order
|
|
assert_array_equal(gradient(x, 2, 3, axis=(1, 0)),
|
|
[dx[1]/2.0, dx[0]/3.0])
|
|
# test maximal number of varargs
|
|
assert_raises(TypeError, gradient, x, 1, 2, axis=1)
|
|
|
|
assert_raises(np.AxisError, gradient, x, axis=3)
|
|
assert_raises(np.AxisError, gradient, x, axis=-3)
|
|
# assert_raises(TypeError, gradient, x, axis=[1,])
|
|
|
|
def test_timedelta64(self):
|
|
# Make sure gradient() can handle special types like timedelta64
|
|
x = np.array(
|
|
[-5, -3, 10, 12, 61, 321, 300],
|
|
dtype='timedelta64[D]')
|
|
dx = np.array(
|
|
[2, 7, 7, 25, 154, 119, -21],
|
|
dtype='timedelta64[D]')
|
|
assert_array_equal(gradient(x), dx)
|
|
assert_(dx.dtype == np.dtype('timedelta64[D]'))
|
|
|
|
def test_inexact_dtypes(self):
|
|
for dt in [np.float16, np.float32, np.float64]:
|
|
# dtypes should not be promoted in a different way to what diff does
|
|
x = np.array([1, 2, 3], dtype=dt)
|
|
assert_equal(gradient(x).dtype, np.diff(x).dtype)
|
|
|
|
def test_values(self):
|
|
# needs at least 2 points for edge_order ==1
|
|
gradient(np.arange(2), edge_order=1)
|
|
# needs at least 3 points for edge_order ==1
|
|
gradient(np.arange(3), edge_order=2)
|
|
|
|
assert_raises(ValueError, gradient, np.arange(0), edge_order=1)
|
|
assert_raises(ValueError, gradient, np.arange(0), edge_order=2)
|
|
assert_raises(ValueError, gradient, np.arange(1), edge_order=1)
|
|
assert_raises(ValueError, gradient, np.arange(1), edge_order=2)
|
|
assert_raises(ValueError, gradient, np.arange(2), edge_order=2)
|
|
|
|
@pytest.mark.parametrize('f_dtype', [np.uint8, np.uint16,
|
|
np.uint32, np.uint64])
|
|
def test_f_decreasing_unsigned_int(self, f_dtype):
|
|
f = np.array([5, 4, 3, 2, 1], dtype=f_dtype)
|
|
g = gradient(f)
|
|
assert_array_equal(g, [-1]*len(f))
|
|
|
|
@pytest.mark.parametrize('f_dtype', [np.int8, np.int16,
|
|
np.int32, np.int64])
|
|
def test_f_signed_int_big_jump(self, f_dtype):
|
|
maxint = np.iinfo(f_dtype).max
|
|
x = np.array([1, 3])
|
|
f = np.array([-1, maxint], dtype=f_dtype)
|
|
dfdx = gradient(f, x)
|
|
assert_array_equal(dfdx, [(maxint + 1) // 2]*2)
|
|
|
|
@pytest.mark.parametrize('x_dtype', [np.uint8, np.uint16,
|
|
np.uint32, np.uint64])
|
|
def test_x_decreasing_unsigned(self, x_dtype):
|
|
x = np.array([3, 2, 1], dtype=x_dtype)
|
|
f = np.array([0, 2, 4])
|
|
dfdx = gradient(f, x)
|
|
assert_array_equal(dfdx, [-2]*len(x))
|
|
|
|
@pytest.mark.parametrize('x_dtype', [np.int8, np.int16,
|
|
np.int32, np.int64])
|
|
def test_x_signed_int_big_jump(self, x_dtype):
|
|
minint = np.iinfo(x_dtype).min
|
|
maxint = np.iinfo(x_dtype).max
|
|
x = np.array([-1, maxint], dtype=x_dtype)
|
|
f = np.array([minint // 2, 0])
|
|
dfdx = gradient(f, x)
|
|
assert_array_equal(dfdx, [0.5, 0.5])
|
|
|
|
|
|
class TestAngle:
|
|
|
|
def test_basic(self):
|
|
x = [1 + 3j, np.sqrt(2) / 2.0 + 1j * np.sqrt(2) / 2,
|
|
1, 1j, -1, -1j, 1 - 3j, -1 + 3j]
|
|
y = angle(x)
|
|
yo = [
|
|
np.arctan(3.0 / 1.0),
|
|
np.arctan(1.0), 0, np.pi / 2, np.pi, -np.pi / 2.0,
|
|
-np.arctan(3.0 / 1.0), np.pi - np.arctan(3.0 / 1.0)]
|
|
z = angle(x, deg=True)
|
|
zo = np.array(yo) * 180 / np.pi
|
|
assert_array_almost_equal(y, yo, 11)
|
|
assert_array_almost_equal(z, zo, 11)
|
|
|
|
def test_subclass(self):
|
|
x = np.ma.array([1 + 3j, 1, np.sqrt(2)/2 * (1 + 1j)])
|
|
x[1] = np.ma.masked
|
|
expected = np.ma.array([np.arctan(3.0 / 1.0), 0, np.arctan(1.0)])
|
|
expected[1] = np.ma.masked
|
|
actual = angle(x)
|
|
assert_equal(type(actual), type(expected))
|
|
assert_equal(actual.mask, expected.mask)
|
|
assert_equal(actual, expected)
|
|
|
|
|
|
class TestTrimZeros:
|
|
|
|
a = np.array([0, 0, 1, 0, 2, 3, 4, 0])
|
|
b = a.astype(float)
|
|
c = a.astype(complex)
|
|
d = a.astype(object)
|
|
|
|
def values(self):
|
|
attr_names = ('a', 'b', 'c', 'd')
|
|
return (getattr(self, name) for name in attr_names)
|
|
|
|
def test_basic(self):
|
|
slc = np.s_[2:-1]
|
|
for arr in self.values():
|
|
res = trim_zeros(arr)
|
|
assert_array_equal(res, arr[slc])
|
|
|
|
def test_leading_skip(self):
|
|
slc = np.s_[:-1]
|
|
for arr in self.values():
|
|
res = trim_zeros(arr, trim='b')
|
|
assert_array_equal(res, arr[slc])
|
|
|
|
def test_trailing_skip(self):
|
|
slc = np.s_[2:]
|
|
for arr in self.values():
|
|
res = trim_zeros(arr, trim='F')
|
|
assert_array_equal(res, arr[slc])
|
|
|
|
def test_all_zero(self):
|
|
for _arr in self.values():
|
|
arr = np.zeros_like(_arr, dtype=_arr.dtype)
|
|
|
|
res1 = trim_zeros(arr, trim='B')
|
|
assert len(res1) == 0
|
|
|
|
res2 = trim_zeros(arr, trim='f')
|
|
assert len(res2) == 0
|
|
|
|
def test_size_zero(self):
|
|
arr = np.zeros(0)
|
|
res = trim_zeros(arr)
|
|
assert_array_equal(arr, res)
|
|
|
|
@pytest.mark.parametrize(
|
|
'arr',
|
|
[np.array([0, 2**62, 0]),
|
|
np.array([0, 2**63, 0]),
|
|
np.array([0, 2**64, 0])]
|
|
)
|
|
def test_overflow(self, arr):
|
|
slc = np.s_[1:2]
|
|
res = trim_zeros(arr)
|
|
assert_array_equal(res, arr[slc])
|
|
|
|
def test_no_trim(self):
|
|
arr = np.array([None, 1, None])
|
|
res = trim_zeros(arr)
|
|
assert_array_equal(arr, res)
|
|
|
|
|
|
def test_list_to_list(self):
|
|
res = trim_zeros(self.a.tolist())
|
|
assert isinstance(res, list)
|
|
|
|
class TestExtins:
|
|
|
|
def test_basic(self):
|
|
a = np.array([1, 3, 2, 1, 2, 3, 3])
|
|
b = extract(a > 1, a)
|
|
assert_array_equal(b, [3, 2, 2, 3, 3])
|
|
|
|
def test_place(self):
|
|
# Make sure that non-np.ndarray objects
|
|
# raise an error instead of doing nothing
|
|
assert_raises(TypeError, place, [1, 2, 3], [True, False], [0, 1])
|
|
|
|
a = np.array([1, 4, 3, 2, 5, 8, 7])
|
|
place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6])
|
|
assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7])
|
|
|
|
place(a, np.zeros(7), [])
|
|
assert_array_equal(a, np.arange(1, 8))
|
|
|
|
place(a, [1, 0, 1, 0, 1, 0, 1], [8, 9])
|
|
assert_array_equal(a, [8, 2, 9, 4, 8, 6, 9])
|
|
assert_raises_regex(ValueError, "Cannot insert from an empty array",
|
|
lambda: place(a, [0, 0, 0, 0, 0, 1, 0], []))
|
|
|
|
# See Issue #6974
|
|
a = np.array(['12', '34'])
|
|
place(a, [0, 1], '9')
|
|
assert_array_equal(a, ['12', '9'])
|
|
|
|
def test_both(self):
|
|
a = rand(10)
|
|
mask = a > 0.5
|
|
ac = a.copy()
|
|
c = extract(mask, a)
|
|
place(a, mask, 0)
|
|
place(a, mask, c)
|
|
assert_array_equal(a, ac)
|
|
|
|
|
|
# _foo1 and _foo2 are used in some tests in TestVectorize.
|
|
|
|
def _foo1(x, y=1.0):
|
|
return y*math.floor(x)
|
|
|
|
|
|
def _foo2(x, y=1.0, z=0.0):
|
|
return y*math.floor(x) + z
|
|
|
|
|
|
class TestVectorize:
|
|
|
|
def test_simple(self):
|
|
def addsubtract(a, b):
|
|
if a > b:
|
|
return a - b
|
|
else:
|
|
return a + b
|
|
|
|
f = vectorize(addsubtract)
|
|
r = f([0, 3, 6, 9], [1, 3, 5, 7])
|
|
assert_array_equal(r, [1, 6, 1, 2])
|
|
|
|
def test_scalar(self):
|
|
def addsubtract(a, b):
|
|
if a > b:
|
|
return a - b
|
|
else:
|
|
return a + b
|
|
|
|
f = vectorize(addsubtract)
|
|
r = f([0, 3, 6, 9], 5)
|
|
assert_array_equal(r, [5, 8, 1, 4])
|
|
|
|
def test_large(self):
|
|
x = np.linspace(-3, 2, 10000)
|
|
f = vectorize(lambda x: x)
|
|
y = f(x)
|
|
assert_array_equal(y, x)
|
|
|
|
def test_ufunc(self):
|
|
f = vectorize(math.cos)
|
|
args = np.array([0, 0.5 * np.pi, np.pi, 1.5 * np.pi, 2 * np.pi])
|
|
r1 = f(args)
|
|
r2 = np.cos(args)
|
|
assert_array_almost_equal(r1, r2)
|
|
|
|
def test_keywords(self):
|
|
|
|
def foo(a, b=1):
|
|
return a + b
|
|
|
|
f = vectorize(foo)
|
|
args = np.array([1, 2, 3])
|
|
r1 = f(args)
|
|
r2 = np.array([2, 3, 4])
|
|
assert_array_equal(r1, r2)
|
|
r1 = f(args, 2)
|
|
r2 = np.array([3, 4, 5])
|
|
assert_array_equal(r1, r2)
|
|
|
|
def test_keywords_with_otypes_order1(self):
|
|
# gh-1620: The second call of f would crash with
|
|
# `ValueError: invalid number of arguments`.
|
|
f = vectorize(_foo1, otypes=[float])
|
|
# We're testing the caching of ufuncs by vectorize, so the order
|
|
# of these function calls is an important part of the test.
|
|
r1 = f(np.arange(3.0), 1.0)
|
|
r2 = f(np.arange(3.0))
|
|
assert_array_equal(r1, r2)
|
|
|
|
def test_keywords_with_otypes_order2(self):
|
|
# gh-1620: The second call of f would crash with
|
|
# `ValueError: non-broadcastable output operand with shape ()
|
|
# doesn't match the broadcast shape (3,)`.
|
|
f = vectorize(_foo1, otypes=[float])
|
|
# We're testing the caching of ufuncs by vectorize, so the order
|
|
# of these function calls is an important part of the test.
|
|
r1 = f(np.arange(3.0))
|
|
r2 = f(np.arange(3.0), 1.0)
|
|
assert_array_equal(r1, r2)
|
|
|
|
def test_keywords_with_otypes_order3(self):
|
|
# gh-1620: The third call of f would crash with
|
|
# `ValueError: invalid number of arguments`.
|
|
f = vectorize(_foo1, otypes=[float])
|
|
# We're testing the caching of ufuncs by vectorize, so the order
|
|
# of these function calls is an important part of the test.
|
|
r1 = f(np.arange(3.0))
|
|
r2 = f(np.arange(3.0), y=1.0)
|
|
r3 = f(np.arange(3.0))
|
|
assert_array_equal(r1, r2)
|
|
assert_array_equal(r1, r3)
|
|
|
|
def test_keywords_with_otypes_several_kwd_args1(self):
|
|
# gh-1620 Make sure different uses of keyword arguments
|
|
# don't break the vectorized function.
|
|
f = vectorize(_foo2, otypes=[float])
|
|
# We're testing the caching of ufuncs by vectorize, so the order
|
|
# of these function calls is an important part of the test.
|
|
r1 = f(10.4, z=100)
|
|
r2 = f(10.4, y=-1)
|
|
r3 = f(10.4)
|
|
assert_equal(r1, _foo2(10.4, z=100))
|
|
assert_equal(r2, _foo2(10.4, y=-1))
|
|
assert_equal(r3, _foo2(10.4))
|
|
|
|
def test_keywords_with_otypes_several_kwd_args2(self):
|
|
# gh-1620 Make sure different uses of keyword arguments
|
|
# don't break the vectorized function.
|
|
f = vectorize(_foo2, otypes=[float])
|
|
# We're testing the caching of ufuncs by vectorize, so the order
|
|
# of these function calls is an important part of the test.
|
|
r1 = f(z=100, x=10.4, y=-1)
|
|
r2 = f(1, 2, 3)
|
|
assert_equal(r1, _foo2(z=100, x=10.4, y=-1))
|
|
assert_equal(r2, _foo2(1, 2, 3))
|
|
|
|
def test_keywords_no_func_code(self):
|
|
# This needs to test a function that has keywords but
|
|
# no func_code attribute, since otherwise vectorize will
|
|
# inspect the func_code.
|
|
import random
|
|
try:
|
|
vectorize(random.randrange) # Should succeed
|
|
except Exception:
|
|
raise AssertionError()
|
|
|
|
def test_keywords2_ticket_2100(self):
|
|
# Test kwarg support: enhancement ticket 2100
|
|
|
|
def foo(a, b=1):
|
|
return a + b
|
|
|
|
f = vectorize(foo)
|
|
args = np.array([1, 2, 3])
|
|
r1 = f(a=args)
|
|
r2 = np.array([2, 3, 4])
|
|
assert_array_equal(r1, r2)
|
|
r1 = f(b=1, a=args)
|
|
assert_array_equal(r1, r2)
|
|
r1 = f(args, b=2)
|
|
r2 = np.array([3, 4, 5])
|
|
assert_array_equal(r1, r2)
|
|
|
|
def test_keywords3_ticket_2100(self):
|
|
# Test excluded with mixed positional and kwargs: ticket 2100
|
|
def mypolyval(x, p):
|
|
_p = list(p)
|
|
res = _p.pop(0)
|
|
while _p:
|
|
res = res * x + _p.pop(0)
|
|
return res
|
|
|
|
vpolyval = np.vectorize(mypolyval, excluded=['p', 1])
|
|
ans = [3, 6]
|
|
assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3]))
|
|
assert_array_equal(ans, vpolyval([0, 1], p=[1, 2, 3]))
|
|
assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3]))
|
|
|
|
def test_keywords4_ticket_2100(self):
|
|
# Test vectorizing function with no positional args.
|
|
@vectorize
|
|
def f(**kw):
|
|
res = 1.0
|
|
for _k in kw:
|
|
res *= kw[_k]
|
|
return res
|
|
|
|
assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8])
|
|
|
|
def test_keywords5_ticket_2100(self):
|
|
# Test vectorizing function with no kwargs args.
|
|
@vectorize
|
|
def f(*v):
|
|
return np.prod(v)
|
|
|
|
assert_array_equal(f([1, 2], [3, 4]), [3, 8])
|
|
|
|
def test_coverage1_ticket_2100(self):
|
|
def foo():
|
|
return 1
|
|
|
|
f = vectorize(foo)
|
|
assert_array_equal(f(), 1)
|
|
|
|
def test_assigning_docstring(self):
|
|
def foo(x):
|
|
"""Original documentation"""
|
|
return x
|
|
|
|
f = vectorize(foo)
|
|
assert_equal(f.__doc__, foo.__doc__)
|
|
|
|
doc = "Provided documentation"
|
|
f = vectorize(foo, doc=doc)
|
|
assert_equal(f.__doc__, doc)
|
|
|
|
def test_UnboundMethod_ticket_1156(self):
|
|
# Regression test for issue 1156
|
|
class Foo:
|
|
b = 2
|
|
|
|
def bar(self, a):
|
|
return a ** self.b
|
|
|
|
assert_array_equal(vectorize(Foo().bar)(np.arange(9)),
|
|
np.arange(9) ** 2)
|
|
assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)),
|
|
np.arange(9) ** 2)
|
|
|
|
def test_execution_order_ticket_1487(self):
|
|
# Regression test for dependence on execution order: issue 1487
|
|
f1 = vectorize(lambda x: x)
|
|
res1a = f1(np.arange(3))
|
|
res1b = f1(np.arange(0.1, 3))
|
|
f2 = vectorize(lambda x: x)
|
|
res2b = f2(np.arange(0.1, 3))
|
|
res2a = f2(np.arange(3))
|
|
assert_equal(res1a, res2a)
|
|
assert_equal(res1b, res2b)
|
|
|
|
def test_string_ticket_1892(self):
|
|
# Test vectorization over strings: issue 1892.
|
|
f = np.vectorize(lambda x: x)
|
|
s = '0123456789' * 10
|
|
assert_equal(s, f(s))
|
|
|
|
def test_cache(self):
|
|
# Ensure that vectorized func called exactly once per argument.
|
|
_calls = [0]
|
|
|
|
@vectorize
|
|
def f(x):
|
|
_calls[0] += 1
|
|
return x ** 2
|
|
|
|
f.cache = True
|
|
x = np.arange(5)
|
|
assert_array_equal(f(x), x * x)
|
|
assert_equal(_calls[0], len(x))
|
|
|
|
def test_otypes(self):
|
|
f = np.vectorize(lambda x: x)
|
|
f.otypes = 'i'
|
|
x = np.arange(5)
|
|
assert_array_equal(f(x), x)
|
|
|
|
def test_parse_gufunc_signature(self):
|
|
assert_equal(nfb._parse_gufunc_signature('(x)->()'), ([('x',)], [()]))
|
|
assert_equal(nfb._parse_gufunc_signature('(x,y)->()'),
|
|
([('x', 'y')], [()]))
|
|
assert_equal(nfb._parse_gufunc_signature('(x),(y)->()'),
|
|
([('x',), ('y',)], [()]))
|
|
assert_equal(nfb._parse_gufunc_signature('(x)->(y)'),
|
|
([('x',)], [('y',)]))
|
|
assert_equal(nfb._parse_gufunc_signature('(x)->(y),()'),
|
|
([('x',)], [('y',), ()]))
|
|
assert_equal(nfb._parse_gufunc_signature('(),(a,b,c),(d)->(d,e)'),
|
|
([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')]))
|
|
with assert_raises(ValueError):
|
|
nfb._parse_gufunc_signature('(x)(y)->()')
|
|
with assert_raises(ValueError):
|
|
nfb._parse_gufunc_signature('(x),(y)->')
|
|
with assert_raises(ValueError):
|
|
nfb._parse_gufunc_signature('((x))->(x)')
|
|
|
|
def test_signature_simple(self):
|
|
def addsubtract(a, b):
|
|
if a > b:
|
|
return a - b
|
|
else:
|
|
return a + b
|
|
|
|
f = vectorize(addsubtract, signature='(),()->()')
|
|
r = f([0, 3, 6, 9], [1, 3, 5, 7])
|
|
assert_array_equal(r, [1, 6, 1, 2])
|
|
|
|
def test_signature_mean_last(self):
|
|
def mean(a):
|
|
return a.mean()
|
|
|
|
f = vectorize(mean, signature='(n)->()')
|
|
r = f([[1, 3], [2, 4]])
|
|
assert_array_equal(r, [2, 3])
|
|
|
|
def test_signature_center(self):
|
|
def center(a):
|
|
return a - a.mean()
|
|
|
|
f = vectorize(center, signature='(n)->(n)')
|
|
r = f([[1, 3], [2, 4]])
|
|
assert_array_equal(r, [[-1, 1], [-1, 1]])
|
|
|
|
def test_signature_two_outputs(self):
|
|
f = vectorize(lambda x: (x, x), signature='()->(),()')
|
|
r = f([1, 2, 3])
|
|
assert_(isinstance(r, tuple) and len(r) == 2)
|
|
assert_array_equal(r[0], [1, 2, 3])
|
|
assert_array_equal(r[1], [1, 2, 3])
|
|
|
|
def test_signature_outer(self):
|
|
f = vectorize(np.outer, signature='(a),(b)->(a,b)')
|
|
r = f([1, 2], [1, 2, 3])
|
|
assert_array_equal(r, [[1, 2, 3], [2, 4, 6]])
|
|
|
|
r = f([[[1, 2]]], [1, 2, 3])
|
|
assert_array_equal(r, [[[[1, 2, 3], [2, 4, 6]]]])
|
|
|
|
r = f([[1, 0], [2, 0]], [1, 2, 3])
|
|
assert_array_equal(r, [[[1, 2, 3], [0, 0, 0]],
|
|
[[2, 4, 6], [0, 0, 0]]])
|
|
|
|
r = f([1, 2], [[1, 2, 3], [0, 0, 0]])
|
|
assert_array_equal(r, [[[1, 2, 3], [2, 4, 6]],
|
|
[[0, 0, 0], [0, 0, 0]]])
|
|
|
|
def test_signature_computed_size(self):
|
|
f = vectorize(lambda x: x[:-1], signature='(n)->(m)')
|
|
r = f([1, 2, 3])
|
|
assert_array_equal(r, [1, 2])
|
|
|
|
r = f([[1, 2, 3], [2, 3, 4]])
|
|
assert_array_equal(r, [[1, 2], [2, 3]])
|
|
|
|
def test_signature_excluded(self):
|
|
|
|
def foo(a, b=1):
|
|
return a + b
|
|
|
|
f = vectorize(foo, signature='()->()', excluded={'b'})
|
|
assert_array_equal(f([1, 2, 3]), [2, 3, 4])
|
|
assert_array_equal(f([1, 2, 3], b=0), [1, 2, 3])
|
|
|
|
def test_signature_otypes(self):
|
|
f = vectorize(lambda x: x, signature='(n)->(n)', otypes=['float64'])
|
|
r = f([1, 2, 3])
|
|
assert_equal(r.dtype, np.dtype('float64'))
|
|
assert_array_equal(r, [1, 2, 3])
|
|
|
|
def test_signature_invalid_inputs(self):
|
|
f = vectorize(operator.add, signature='(n),(n)->(n)')
|
|
with assert_raises_regex(TypeError, 'wrong number of positional'):
|
|
f([1, 2])
|
|
with assert_raises_regex(
|
|
ValueError, 'does not have enough dimensions'):
|
|
f(1, 2)
|
|
with assert_raises_regex(
|
|
ValueError, 'inconsistent size for core dimension'):
|
|
f([1, 2], [1, 2, 3])
|
|
|
|
f = vectorize(operator.add, signature='()->()')
|
|
with assert_raises_regex(TypeError, 'wrong number of positional'):
|
|
f(1, 2)
|
|
|
|
def test_signature_invalid_outputs(self):
|
|
|
|
f = vectorize(lambda x: x[:-1], signature='(n)->(n)')
|
|
with assert_raises_regex(
|
|
ValueError, 'inconsistent size for core dimension'):
|
|
f([1, 2, 3])
|
|
|
|
f = vectorize(lambda x: x, signature='()->(),()')
|
|
with assert_raises_regex(ValueError, 'wrong number of outputs'):
|
|
f(1)
|
|
|
|
f = vectorize(lambda x: (x, x), signature='()->()')
|
|
with assert_raises_regex(ValueError, 'wrong number of outputs'):
|
|
f([1, 2])
|
|
|
|
def test_size_zero_output(self):
|
|
# see issue 5868
|
|
f = np.vectorize(lambda x: x)
|
|
x = np.zeros([0, 5], dtype=int)
|
|
with assert_raises_regex(ValueError, 'otypes'):
|
|
f(x)
|
|
|
|
f.otypes = 'i'
|
|
assert_array_equal(f(x), x)
|
|
|
|
f = np.vectorize(lambda x: x, signature='()->()')
|
|
with assert_raises_regex(ValueError, 'otypes'):
|
|
f(x)
|
|
|
|
f = np.vectorize(lambda x: x, signature='()->()', otypes='i')
|
|
assert_array_equal(f(x), x)
|
|
|
|
f = np.vectorize(lambda x: x, signature='(n)->(n)', otypes='i')
|
|
assert_array_equal(f(x), x)
|
|
|
|
f = np.vectorize(lambda x: x, signature='(n)->(n)')
|
|
assert_array_equal(f(x.T), x.T)
|
|
|
|
f = np.vectorize(lambda x: [x], signature='()->(n)', otypes='i')
|
|
with assert_raises_regex(ValueError, 'new output dimensions'):
|
|
f(x)
|
|
|
|
|
|
class TestLeaks:
|
|
class A:
|
|
iters = 20
|
|
|
|
def bound(self, *args):
|
|
return 0
|
|
|
|
@staticmethod
|
|
def unbound(*args):
|
|
return 0
|
|
|
|
@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
|
|
@pytest.mark.parametrize('name, incr', [
|
|
('bound', A.iters),
|
|
('unbound', 0),
|
|
])
|
|
def test_frompyfunc_leaks(self, name, incr):
|
|
# exposed in gh-11867 as np.vectorized, but the problem stems from
|
|
# frompyfunc.
|
|
# class.attribute = np.frompyfunc(<method>) creates a
|
|
# reference cycle if <method> is a bound class method. It requires a
|
|
# gc collection cycle to break the cycle (on CPython 3)
|
|
import gc
|
|
A_func = getattr(self.A, name)
|
|
gc.disable()
|
|
try:
|
|
refcount = sys.getrefcount(A_func)
|
|
for i in range(self.A.iters):
|
|
a = self.A()
|
|
a.f = np.frompyfunc(getattr(a, name), 1, 1)
|
|
out = a.f(np.arange(10))
|
|
a = None
|
|
# A.func is part of a reference cycle if incr is non-zero
|
|
assert_equal(sys.getrefcount(A_func), refcount + incr)
|
|
for i in range(5):
|
|
gc.collect()
|
|
assert_equal(sys.getrefcount(A_func), refcount)
|
|
finally:
|
|
gc.enable()
|
|
|
|
class TestDigitize:
|
|
|
|
def test_forward(self):
|
|
x = np.arange(-6, 5)
|
|
bins = np.arange(-5, 5)
|
|
assert_array_equal(digitize(x, bins), np.arange(11))
|
|
|
|
def test_reverse(self):
|
|
x = np.arange(5, -6, -1)
|
|
bins = np.arange(5, -5, -1)
|
|
assert_array_equal(digitize(x, bins), np.arange(11))
|
|
|
|
def test_random(self):
|
|
x = rand(10)
|
|
bin = np.linspace(x.min(), x.max(), 10)
|
|
assert_(np.all(digitize(x, bin) != 0))
|
|
|
|
def test_right_basic(self):
|
|
x = [1, 5, 4, 10, 8, 11, 0]
|
|
bins = [1, 5, 10]
|
|
default_answer = [1, 2, 1, 3, 2, 3, 0]
|
|
assert_array_equal(digitize(x, bins), default_answer)
|
|
right_answer = [0, 1, 1, 2, 2, 3, 0]
|
|
assert_array_equal(digitize(x, bins, True), right_answer)
|
|
|
|
def test_right_open(self):
|
|
x = np.arange(-6, 5)
|
|
bins = np.arange(-6, 4)
|
|
assert_array_equal(digitize(x, bins, True), np.arange(11))
|
|
|
|
def test_right_open_reverse(self):
|
|
x = np.arange(5, -6, -1)
|
|
bins = np.arange(4, -6, -1)
|
|
assert_array_equal(digitize(x, bins, True), np.arange(11))
|
|
|
|
def test_right_open_random(self):
|
|
x = rand(10)
|
|
bins = np.linspace(x.min(), x.max(), 10)
|
|
assert_(np.all(digitize(x, bins, True) != 10))
|
|
|
|
def test_monotonic(self):
|
|
x = [-1, 0, 1, 2]
|
|
bins = [0, 0, 1]
|
|
assert_array_equal(digitize(x, bins, False), [0, 2, 3, 3])
|
|
assert_array_equal(digitize(x, bins, True), [0, 0, 2, 3])
|
|
bins = [1, 1, 0]
|
|
assert_array_equal(digitize(x, bins, False), [3, 2, 0, 0])
|
|
assert_array_equal(digitize(x, bins, True), [3, 3, 2, 0])
|
|
bins = [1, 1, 1, 1]
|
|
assert_array_equal(digitize(x, bins, False), [0, 0, 4, 4])
|
|
assert_array_equal(digitize(x, bins, True), [0, 0, 0, 4])
|
|
bins = [0, 0, 1, 0]
|
|
assert_raises(ValueError, digitize, x, bins)
|
|
bins = [1, 1, 0, 1]
|
|
assert_raises(ValueError, digitize, x, bins)
|
|
|
|
def test_casting_error(self):
|
|
x = [1, 2, 3 + 1.j]
|
|
bins = [1, 2, 3]
|
|
assert_raises(TypeError, digitize, x, bins)
|
|
x, bins = bins, x
|
|
assert_raises(TypeError, digitize, x, bins)
|
|
|
|
def test_return_type(self):
|
|
# Functions returning indices should always return base ndarrays
|
|
class A(np.ndarray):
|
|
pass
|
|
a = np.arange(5).view(A)
|
|
b = np.arange(1, 3).view(A)
|
|
assert_(not isinstance(digitize(b, a, False), A))
|
|
assert_(not isinstance(digitize(b, a, True), A))
|
|
|
|
def test_large_integers_increasing(self):
|
|
# gh-11022
|
|
x = 2**54 # loses precision in a float
|
|
assert_equal(np.digitize(x, [x - 1, x + 1]), 1)
|
|
|
|
@pytest.mark.xfail(
|
|
reason="gh-11022: np.core.multiarray._monoticity loses precision")
|
|
def test_large_integers_decreasing(self):
|
|
# gh-11022
|
|
x = 2**54 # loses precision in a float
|
|
assert_equal(np.digitize(x, [x + 1, x - 1]), 1)
|
|
|
|
|
|
class TestUnwrap:
|
|
|
|
def test_simple(self):
|
|
# check that unwrap removes jumps greater that 2*pi
|
|
assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1])
|
|
# check that unwrap maintains continuity
|
|
assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi))
|
|
|
|
|
|
class TestFilterwindows:
|
|
|
|
def test_hanning(self):
|
|
# check symmetry
|
|
w = hanning(10)
|
|
assert_equal(w, flipud(w))
|
|
# check known value
|
|
assert_almost_equal(np.sum(w, axis=0), 4.500, 4)
|
|
|
|
def test_hamming(self):
|
|
# check symmetry
|
|
w = hamming(10)
|
|
assert_equal(w, flipud(w))
|
|
# check known value
|
|
assert_almost_equal(np.sum(w, axis=0), 4.9400, 4)
|
|
|
|
def test_bartlett(self):
|
|
# check symmetry
|
|
w = bartlett(10)
|
|
assert_equal(w, flipud(w))
|
|
# check known value
|
|
assert_almost_equal(np.sum(w, axis=0), 4.4444, 4)
|
|
|
|
def test_blackman(self):
|
|
# check symmetry
|
|
w = blackman(10)
|
|
assert_equal(w, flipud(w))
|
|
# check known value
|
|
assert_almost_equal(np.sum(w, axis=0), 3.7800, 4)
|
|
|
|
|
|
class TestTrapz:
|
|
|
|
def test_simple(self):
|
|
x = np.arange(-10, 10, .1)
|
|
r = trapz(np.exp(-.5 * x ** 2) / np.sqrt(2 * np.pi), dx=0.1)
|
|
# check integral of normal equals 1
|
|
assert_almost_equal(r, 1, 7)
|
|
|
|
def test_ndim(self):
|
|
x = np.linspace(0, 1, 3)
|
|
y = np.linspace(0, 2, 8)
|
|
z = np.linspace(0, 3, 13)
|
|
|
|
wx = np.ones_like(x) * (x[1] - x[0])
|
|
wx[0] /= 2
|
|
wx[-1] /= 2
|
|
wy = np.ones_like(y) * (y[1] - y[0])
|
|
wy[0] /= 2
|
|
wy[-1] /= 2
|
|
wz = np.ones_like(z) * (z[1] - z[0])
|
|
wz[0] /= 2
|
|
wz[-1] /= 2
|
|
|
|
q = x[:, None, None] + y[None,:, None] + z[None, None,:]
|
|
|
|
qx = (q * wx[:, None, None]).sum(axis=0)
|
|
qy = (q * wy[None, :, None]).sum(axis=1)
|
|
qz = (q * wz[None, None, :]).sum(axis=2)
|
|
|
|
# n-d `x`
|
|
r = trapz(q, x=x[:, None, None], axis=0)
|
|
assert_almost_equal(r, qx)
|
|
r = trapz(q, x=y[None,:, None], axis=1)
|
|
assert_almost_equal(r, qy)
|
|
r = trapz(q, x=z[None, None,:], axis=2)
|
|
assert_almost_equal(r, qz)
|
|
|
|
# 1-d `x`
|
|
r = trapz(q, x=x, axis=0)
|
|
assert_almost_equal(r, qx)
|
|
r = trapz(q, x=y, axis=1)
|
|
assert_almost_equal(r, qy)
|
|
r = trapz(q, x=z, axis=2)
|
|
assert_almost_equal(r, qz)
|
|
|
|
def test_masked(self):
|
|
# Testing that masked arrays behave as if the function is 0 where
|
|
# masked
|
|
x = np.arange(5)
|
|
y = x * x
|
|
mask = x == 2
|
|
ym = np.ma.array(y, mask=mask)
|
|
r = 13.0 # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16))
|
|
assert_almost_equal(trapz(ym, x), r)
|
|
|
|
xm = np.ma.array(x, mask=mask)
|
|
assert_almost_equal(trapz(ym, xm), r)
|
|
|
|
xm = np.ma.array(x, mask=mask)
|
|
assert_almost_equal(trapz(y, xm), r)
|
|
|
|
|
|
class TestSinc:
|
|
|
|
def test_simple(self):
|
|
assert_(sinc(0) == 1)
|
|
w = sinc(np.linspace(-1, 1, 100))
|
|
# check symmetry
|
|
assert_array_almost_equal(w, flipud(w), 7)
|
|
|
|
def test_array_like(self):
|
|
x = [0, 0.5]
|
|
y1 = sinc(np.array(x))
|
|
y2 = sinc(list(x))
|
|
y3 = sinc(tuple(x))
|
|
assert_array_equal(y1, y2)
|
|
assert_array_equal(y1, y3)
|
|
|
|
|
|
class TestUnique:
|
|
|
|
def test_simple(self):
|
|
x = np.array([4, 3, 2, 1, 1, 2, 3, 4, 0])
|
|
assert_(np.all(unique(x) == [0, 1, 2, 3, 4]))
|
|
assert_(unique(np.array([1, 1, 1, 1, 1])) == np.array([1]))
|
|
x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham']
|
|
assert_(np.all(unique(x) == ['bar', 'foo', 'ham', 'widget']))
|
|
x = np.array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j])
|
|
assert_(np.all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10]))
|
|
|
|
|
|
class TestCheckFinite:
|
|
|
|
def test_simple(self):
|
|
a = [1, 2, 3]
|
|
b = [1, 2, np.inf]
|
|
c = [1, 2, np.nan]
|
|
np.lib.asarray_chkfinite(a)
|
|
assert_raises(ValueError, np.lib.asarray_chkfinite, b)
|
|
assert_raises(ValueError, np.lib.asarray_chkfinite, c)
|
|
|
|
def test_dtype_order(self):
|
|
# Regression test for missing dtype and order arguments
|
|
a = [1, 2, 3]
|
|
a = np.lib.asarray_chkfinite(a, order='F', dtype=np.float64)
|
|
assert_(a.dtype == np.float64)
|
|
|
|
|
|
class TestCorrCoef:
|
|
A = np.array(
|
|
[[0.15391142, 0.18045767, 0.14197213],
|
|
[0.70461506, 0.96474128, 0.27906989],
|
|
[0.9297531, 0.32296769, 0.19267156]])
|
|
B = np.array(
|
|
[[0.10377691, 0.5417086, 0.49807457],
|
|
[0.82872117, 0.77801674, 0.39226705],
|
|
[0.9314666, 0.66800209, 0.03538394]])
|
|
res1 = np.array(
|
|
[[1., 0.9379533, -0.04931983],
|
|
[0.9379533, 1., 0.30007991],
|
|
[-0.04931983, 0.30007991, 1.]])
|
|
res2 = np.array(
|
|
[[1., 0.9379533, -0.04931983, 0.30151751, 0.66318558, 0.51532523],
|
|
[0.9379533, 1., 0.30007991, -0.04781421, 0.88157256, 0.78052386],
|
|
[-0.04931983, 0.30007991, 1., -0.96717111, 0.71483595, 0.83053601],
|
|
[0.30151751, -0.04781421, -0.96717111, 1., -0.51366032, -0.66173113],
|
|
[0.66318558, 0.88157256, 0.71483595, -0.51366032, 1., 0.98317823],
|
|
[0.51532523, 0.78052386, 0.83053601, -0.66173113, 0.98317823, 1.]])
|
|
|
|
def test_non_array(self):
|
|
assert_almost_equal(np.corrcoef([0, 1, 0], [1, 0, 1]),
|
|
[[1., -1.], [-1., 1.]])
|
|
|
|
def test_simple(self):
|
|
tgt1 = corrcoef(self.A)
|
|
assert_almost_equal(tgt1, self.res1)
|
|
assert_(np.all(np.abs(tgt1) <= 1.0))
|
|
|
|
tgt2 = corrcoef(self.A, self.B)
|
|
assert_almost_equal(tgt2, self.res2)
|
|
assert_(np.all(np.abs(tgt2) <= 1.0))
|
|
|
|
def test_ddof(self):
|
|
# ddof raises DeprecationWarning
|
|
with suppress_warnings() as sup:
|
|
warnings.simplefilter("always")
|
|
assert_warns(DeprecationWarning, corrcoef, self.A, ddof=-1)
|
|
sup.filter(DeprecationWarning)
|
|
# ddof has no or negligible effect on the function
|
|
assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1)
|
|
assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2)
|
|
assert_almost_equal(corrcoef(self.A, ddof=3), self.res1)
|
|
assert_almost_equal(corrcoef(self.A, self.B, ddof=3), self.res2)
|
|
|
|
def test_bias(self):
|
|
# bias raises DeprecationWarning
|
|
with suppress_warnings() as sup:
|
|
warnings.simplefilter("always")
|
|
assert_warns(DeprecationWarning, corrcoef, self.A, self.B, 1, 0)
|
|
assert_warns(DeprecationWarning, corrcoef, self.A, bias=0)
|
|
sup.filter(DeprecationWarning)
|
|
# bias has no or negligible effect on the function
|
|
assert_almost_equal(corrcoef(self.A, bias=1), self.res1)
|
|
|
|
def test_complex(self):
|
|
x = np.array([[1, 2, 3], [1j, 2j, 3j]])
|
|
res = corrcoef(x)
|
|
tgt = np.array([[1., -1.j], [1.j, 1.]])
|
|
assert_allclose(res, tgt)
|
|
assert_(np.all(np.abs(res) <= 1.0))
|
|
|
|
def test_xy(self):
|
|
x = np.array([[1, 2, 3]])
|
|
y = np.array([[1j, 2j, 3j]])
|
|
assert_allclose(np.corrcoef(x, y), np.array([[1., -1.j], [1.j, 1.]]))
|
|
|
|
def test_empty(self):
|
|
with warnings.catch_warnings(record=True):
|
|
warnings.simplefilter('always', RuntimeWarning)
|
|
assert_array_equal(corrcoef(np.array([])), np.nan)
|
|
assert_array_equal(corrcoef(np.array([]).reshape(0, 2)),
|
|
np.array([]).reshape(0, 0))
|
|
assert_array_equal(corrcoef(np.array([]).reshape(2, 0)),
|
|
np.array([[np.nan, np.nan], [np.nan, np.nan]]))
|
|
|
|
def test_extreme(self):
|
|
x = [[1e-100, 1e100], [1e100, 1e-100]]
|
|
with np.errstate(all='raise'):
|
|
c = corrcoef(x)
|
|
assert_array_almost_equal(c, np.array([[1., -1.], [-1., 1.]]))
|
|
assert_(np.all(np.abs(c) <= 1.0))
|
|
|
|
@pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble])
|
|
def test_corrcoef_dtype(self, test_type):
|
|
cast_A = self.A.astype(test_type)
|
|
res = corrcoef(cast_A, dtype=test_type)
|
|
assert test_type == res.dtype
|
|
|
|
|
|
class TestCov:
|
|
x1 = np.array([[0, 2], [1, 1], [2, 0]]).T
|
|
res1 = np.array([[1., -1.], [-1., 1.]])
|
|
x2 = np.array([0.0, 1.0, 2.0], ndmin=2)
|
|
frequencies = np.array([1, 4, 1])
|
|
x2_repeats = np.array([[0.0], [1.0], [1.0], [1.0], [1.0], [2.0]]).T
|
|
res2 = np.array([[0.4, -0.4], [-0.4, 0.4]])
|
|
unit_frequencies = np.ones(3, dtype=np.int_)
|
|
weights = np.array([1.0, 4.0, 1.0])
|
|
res3 = np.array([[2. / 3., -2. / 3.], [-2. / 3., 2. / 3.]])
|
|
unit_weights = np.ones(3)
|
|
x3 = np.array([0.3942, 0.5969, 0.7730, 0.9918, 0.7964])
|
|
|
|
def test_basic(self):
|
|
assert_allclose(cov(self.x1), self.res1)
|
|
|
|
def test_complex(self):
|
|
x = np.array([[1, 2, 3], [1j, 2j, 3j]])
|
|
res = np.array([[1., -1.j], [1.j, 1.]])
|
|
assert_allclose(cov(x), res)
|
|
assert_allclose(cov(x, aweights=np.ones(3)), res)
|
|
|
|
def test_xy(self):
|
|
x = np.array([[1, 2, 3]])
|
|
y = np.array([[1j, 2j, 3j]])
|
|
assert_allclose(cov(x, y), np.array([[1., -1.j], [1.j, 1.]]))
|
|
|
|
def test_empty(self):
|
|
with warnings.catch_warnings(record=True):
|
|
warnings.simplefilter('always', RuntimeWarning)
|
|
assert_array_equal(cov(np.array([])), np.nan)
|
|
assert_array_equal(cov(np.array([]).reshape(0, 2)),
|
|
np.array([]).reshape(0, 0))
|
|
assert_array_equal(cov(np.array([]).reshape(2, 0)),
|
|
np.array([[np.nan, np.nan], [np.nan, np.nan]]))
|
|
|
|
def test_wrong_ddof(self):
|
|
with warnings.catch_warnings(record=True):
|
|
warnings.simplefilter('always', RuntimeWarning)
|
|
assert_array_equal(cov(self.x1, ddof=5),
|
|
np.array([[np.inf, -np.inf],
|
|
[-np.inf, np.inf]]))
|
|
|
|
def test_1D_rowvar(self):
|
|
assert_allclose(cov(self.x3), cov(self.x3, rowvar=False))
|
|
y = np.array([0.0780, 0.3107, 0.2111, 0.0334, 0.8501])
|
|
assert_allclose(cov(self.x3, y), cov(self.x3, y, rowvar=False))
|
|
|
|
def test_1D_variance(self):
|
|
assert_allclose(cov(self.x3, ddof=1), np.var(self.x3, ddof=1))
|
|
|
|
def test_fweights(self):
|
|
assert_allclose(cov(self.x2, fweights=self.frequencies),
|
|
cov(self.x2_repeats))
|
|
assert_allclose(cov(self.x1, fweights=self.frequencies),
|
|
self.res2)
|
|
assert_allclose(cov(self.x1, fweights=self.unit_frequencies),
|
|
self.res1)
|
|
nonint = self.frequencies + 0.5
|
|
assert_raises(TypeError, cov, self.x1, fweights=nonint)
|
|
f = np.ones((2, 3), dtype=np.int_)
|
|
assert_raises(RuntimeError, cov, self.x1, fweights=f)
|
|
f = np.ones(2, dtype=np.int_)
|
|
assert_raises(RuntimeError, cov, self.x1, fweights=f)
|
|
f = -1 * np.ones(3, dtype=np.int_)
|
|
assert_raises(ValueError, cov, self.x1, fweights=f)
|
|
|
|
def test_aweights(self):
|
|
assert_allclose(cov(self.x1, aweights=self.weights), self.res3)
|
|
assert_allclose(cov(self.x1, aweights=3.0 * self.weights),
|
|
cov(self.x1, aweights=self.weights))
|
|
assert_allclose(cov(self.x1, aweights=self.unit_weights), self.res1)
|
|
w = np.ones((2, 3))
|
|
assert_raises(RuntimeError, cov, self.x1, aweights=w)
|
|
w = np.ones(2)
|
|
assert_raises(RuntimeError, cov, self.x1, aweights=w)
|
|
w = -1.0 * np.ones(3)
|
|
assert_raises(ValueError, cov, self.x1, aweights=w)
|
|
|
|
def test_unit_fweights_and_aweights(self):
|
|
assert_allclose(cov(self.x2, fweights=self.frequencies,
|
|
aweights=self.unit_weights),
|
|
cov(self.x2_repeats))
|
|
assert_allclose(cov(self.x1, fweights=self.frequencies,
|
|
aweights=self.unit_weights),
|
|
self.res2)
|
|
assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
|
|
aweights=self.unit_weights),
|
|
self.res1)
|
|
assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
|
|
aweights=self.weights),
|
|
self.res3)
|
|
assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
|
|
aweights=3.0 * self.weights),
|
|
cov(self.x1, aweights=self.weights))
|
|
assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
|
|
aweights=self.unit_weights),
|
|
self.res1)
|
|
|
|
@pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble])
|
|
def test_cov_dtype(self, test_type):
|
|
cast_x1 = self.x1.astype(test_type)
|
|
res = cov(cast_x1, dtype=test_type)
|
|
assert test_type == res.dtype
|
|
|
|
|
|
class Test_I0:
|
|
|
|
def test_simple(self):
|
|
assert_almost_equal(
|
|
i0(0.5),
|
|
np.array(1.0634833707413234))
|
|
|
|
# need at least one test above 8, as the implementation is piecewise
|
|
A = np.array([0.49842636, 0.6969809, 0.22011976, 0.0155549, 10.0])
|
|
expected = np.array([1.06307822, 1.12518299, 1.01214991, 1.00006049, 2815.71662847])
|
|
assert_almost_equal(i0(A), expected)
|
|
assert_almost_equal(i0(-A), expected)
|
|
|
|
B = np.array([[0.827002, 0.99959078],
|
|
[0.89694769, 0.39298162],
|
|
[0.37954418, 0.05206293],
|
|
[0.36465447, 0.72446427],
|
|
[0.48164949, 0.50324519]])
|
|
assert_almost_equal(
|
|
i0(B),
|
|
np.array([[1.17843223, 1.26583466],
|
|
[1.21147086, 1.03898290],
|
|
[1.03633899, 1.00067775],
|
|
[1.03352052, 1.13557954],
|
|
[1.05884290, 1.06432317]]))
|
|
# Regression test for gh-11205
|
|
i0_0 = np.i0([0.])
|
|
assert_equal(i0_0.shape, (1,))
|
|
assert_array_equal(np.i0([0.]), np.array([1.]))
|
|
|
|
def test_non_array(self):
|
|
a = np.arange(4)
|
|
|
|
class array_like:
|
|
__array_interface__ = a.__array_interface__
|
|
|
|
def __array_wrap__(self, arr):
|
|
return self
|
|
|
|
# E.g. pandas series survive ufunc calls through array-wrap:
|
|
assert isinstance(np.abs(array_like()), array_like)
|
|
exp = np.i0(a)
|
|
res = np.i0(array_like())
|
|
|
|
assert_array_equal(exp, res)
|
|
|
|
def test_complex(self):
|
|
a = np.array([0, 1 + 2j])
|
|
with pytest.raises(TypeError, match="i0 not supported for complex values"):
|
|
res = i0(a)
|
|
|
|
class TestKaiser:
|
|
|
|
def test_simple(self):
|
|
assert_(np.isfinite(kaiser(1, 1.0)))
|
|
assert_almost_equal(kaiser(0, 1.0),
|
|
np.array([]))
|
|
assert_almost_equal(kaiser(2, 1.0),
|
|
np.array([0.78984831, 0.78984831]))
|
|
assert_almost_equal(kaiser(5, 1.0),
|
|
np.array([0.78984831, 0.94503323, 1.,
|
|
0.94503323, 0.78984831]))
|
|
assert_almost_equal(kaiser(5, 1.56789),
|
|
np.array([0.58285404, 0.88409679, 1.,
|
|
0.88409679, 0.58285404]))
|
|
|
|
def test_int_beta(self):
|
|
kaiser(3, 4)
|
|
|
|
|
|
class TestMsort:
|
|
|
|
def test_simple(self):
|
|
A = np.array([[0.44567325, 0.79115165, 0.54900530],
|
|
[0.36844147, 0.37325583, 0.96098397],
|
|
[0.64864341, 0.52929049, 0.39172155]])
|
|
assert_almost_equal(
|
|
msort(A),
|
|
np.array([[0.36844147, 0.37325583, 0.39172155],
|
|
[0.44567325, 0.52929049, 0.54900530],
|
|
[0.64864341, 0.79115165, 0.96098397]]))
|
|
|
|
|
|
class TestMeshgrid:
|
|
|
|
def test_simple(self):
|
|
[X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7])
|
|
assert_array_equal(X, np.array([[1, 2, 3],
|
|
[1, 2, 3],
|
|
[1, 2, 3],
|
|
[1, 2, 3]]))
|
|
assert_array_equal(Y, np.array([[4, 4, 4],
|
|
[5, 5, 5],
|
|
[6, 6, 6],
|
|
[7, 7, 7]]))
|
|
|
|
def test_single_input(self):
|
|
[X] = meshgrid([1, 2, 3, 4])
|
|
assert_array_equal(X, np.array([1, 2, 3, 4]))
|
|
|
|
def test_no_input(self):
|
|
args = []
|
|
assert_array_equal([], meshgrid(*args))
|
|
assert_array_equal([], meshgrid(*args, copy=False))
|
|
|
|
def test_indexing(self):
|
|
x = [1, 2, 3]
|
|
y = [4, 5, 6, 7]
|
|
[X, Y] = meshgrid(x, y, indexing='ij')
|
|
assert_array_equal(X, np.array([[1, 1, 1, 1],
|
|
[2, 2, 2, 2],
|
|
[3, 3, 3, 3]]))
|
|
assert_array_equal(Y, np.array([[4, 5, 6, 7],
|
|
[4, 5, 6, 7],
|
|
[4, 5, 6, 7]]))
|
|
|
|
# Test expected shapes:
|
|
z = [8, 9]
|
|
assert_(meshgrid(x, y)[0].shape == (4, 3))
|
|
assert_(meshgrid(x, y, indexing='ij')[0].shape == (3, 4))
|
|
assert_(meshgrid(x, y, z)[0].shape == (4, 3, 2))
|
|
assert_(meshgrid(x, y, z, indexing='ij')[0].shape == (3, 4, 2))
|
|
|
|
assert_raises(ValueError, meshgrid, x, y, indexing='notvalid')
|
|
|
|
def test_sparse(self):
|
|
[X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7], sparse=True)
|
|
assert_array_equal(X, np.array([[1, 2, 3]]))
|
|
assert_array_equal(Y, np.array([[4], [5], [6], [7]]))
|
|
|
|
def test_invalid_arguments(self):
|
|
# Test that meshgrid complains about invalid arguments
|
|
# Regression test for issue #4755:
|
|
# https://github.com/numpy/numpy/issues/4755
|
|
assert_raises(TypeError, meshgrid,
|
|
[1, 2, 3], [4, 5, 6, 7], indices='ij')
|
|
|
|
def test_return_type(self):
|
|
# Test for appropriate dtype in returned arrays.
|
|
# Regression test for issue #5297
|
|
# https://github.com/numpy/numpy/issues/5297
|
|
x = np.arange(0, 10, dtype=np.float32)
|
|
y = np.arange(10, 20, dtype=np.float64)
|
|
|
|
X, Y = np.meshgrid(x,y)
|
|
|
|
assert_(X.dtype == x.dtype)
|
|
assert_(Y.dtype == y.dtype)
|
|
|
|
# copy
|
|
X, Y = np.meshgrid(x,y, copy=True)
|
|
|
|
assert_(X.dtype == x.dtype)
|
|
assert_(Y.dtype == y.dtype)
|
|
|
|
# sparse
|
|
X, Y = np.meshgrid(x,y, sparse=True)
|
|
|
|
assert_(X.dtype == x.dtype)
|
|
assert_(Y.dtype == y.dtype)
|
|
|
|
def test_writeback(self):
|
|
# Issue 8561
|
|
X = np.array([1.1, 2.2])
|
|
Y = np.array([3.3, 4.4])
|
|
x, y = np.meshgrid(X, Y, sparse=False, copy=True)
|
|
|
|
x[0, :] = 0
|
|
assert_equal(x[0, :], 0)
|
|
assert_equal(x[1, :], X)
|
|
|
|
|
|
class TestPiecewise:
|
|
|
|
def test_simple(self):
|
|
# Condition is single bool list
|
|
x = piecewise([0, 0], [True, False], [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
# List of conditions: single bool list
|
|
x = piecewise([0, 0], [[True, False]], [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
# Conditions is single bool array
|
|
x = piecewise([0, 0], np.array([True, False]), [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
# Condition is single int array
|
|
x = piecewise([0, 0], np.array([1, 0]), [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
# List of conditions: int array
|
|
x = piecewise([0, 0], [np.array([1, 0])], [1])
|
|
assert_array_equal(x, [1, 0])
|
|
|
|
x = piecewise([0, 0], [[False, True]], [lambda x:-1])
|
|
assert_array_equal(x, [0, -1])
|
|
|
|
assert_raises_regex(ValueError, '1 or 2 functions are expected',
|
|
piecewise, [0, 0], [[False, True]], [])
|
|
assert_raises_regex(ValueError, '1 or 2 functions are expected',
|
|
piecewise, [0, 0], [[False, True]], [1, 2, 3])
|
|
|
|
def test_two_conditions(self):
|
|
x = piecewise([1, 2], [[True, False], [False, True]], [3, 4])
|
|
assert_array_equal(x, [3, 4])
|
|
|
|
def test_scalar_domains_three_conditions(self):
|
|
x = piecewise(3, [True, False, False], [4, 2, 0])
|
|
assert_equal(x, 4)
|
|
|
|
def test_default(self):
|
|
# No value specified for x[1], should be 0
|
|
x = piecewise([1, 2], [True, False], [2])
|
|
assert_array_equal(x, [2, 0])
|
|
|
|
# Should set x[1] to 3
|
|
x = piecewise([1, 2], [True, False], [2, 3])
|
|
assert_array_equal(x, [2, 3])
|
|
|
|
def test_0d(self):
|
|
x = np.array(3)
|
|
y = piecewise(x, x > 3, [4, 0])
|
|
assert_(y.ndim == 0)
|
|
assert_(y == 0)
|
|
|
|
x = 5
|
|
y = piecewise(x, [True, False], [1, 0])
|
|
assert_(y.ndim == 0)
|
|
assert_(y == 1)
|
|
|
|
# With 3 ranges (It was failing, before)
|
|
y = piecewise(x, [False, False, True], [1, 2, 3])
|
|
assert_array_equal(y, 3)
|
|
|
|
def test_0d_comparison(self):
|
|
x = 3
|
|
y = piecewise(x, [x <= 3, x > 3], [4, 0]) # Should succeed.
|
|
assert_equal(y, 4)
|
|
|
|
# With 3 ranges (It was failing, before)
|
|
x = 4
|
|
y = piecewise(x, [x <= 3, (x > 3) * (x <= 5), x > 5], [1, 2, 3])
|
|
assert_array_equal(y, 2)
|
|
|
|
assert_raises_regex(ValueError, '2 or 3 functions are expected',
|
|
piecewise, x, [x <= 3, x > 3], [1])
|
|
assert_raises_regex(ValueError, '2 or 3 functions are expected',
|
|
piecewise, x, [x <= 3, x > 3], [1, 1, 1, 1])
|
|
|
|
def test_0d_0d_condition(self):
|
|
x = np.array(3)
|
|
c = np.array(x > 3)
|
|
y = piecewise(x, [c], [1, 2])
|
|
assert_equal(y, 2)
|
|
|
|
def test_multidimensional_extrafunc(self):
|
|
x = np.array([[-2.5, -1.5, -0.5],
|
|
[0.5, 1.5, 2.5]])
|
|
y = piecewise(x, [x < 0, x >= 2], [-1, 1, 3])
|
|
assert_array_equal(y, np.array([[-1., -1., -1.],
|
|
[3., 3., 1.]]))
|
|
|
|
|
|
class TestBincount:
|
|
|
|
def test_simple(self):
|
|
y = np.bincount(np.arange(4))
|
|
assert_array_equal(y, np.ones(4))
|
|
|
|
def test_simple2(self):
|
|
y = np.bincount(np.array([1, 5, 2, 4, 1]))
|
|
assert_array_equal(y, np.array([0, 2, 1, 0, 1, 1]))
|
|
|
|
def test_simple_weight(self):
|
|
x = np.arange(4)
|
|
w = np.array([0.2, 0.3, 0.5, 0.1])
|
|
y = np.bincount(x, w)
|
|
assert_array_equal(y, w)
|
|
|
|
def test_simple_weight2(self):
|
|
x = np.array([1, 2, 4, 5, 2])
|
|
w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
|
|
y = np.bincount(x, w)
|
|
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))
|
|
|
|
def test_with_minlength(self):
|
|
x = np.array([0, 1, 0, 1, 1])
|
|
y = np.bincount(x, minlength=3)
|
|
assert_array_equal(y, np.array([2, 3, 0]))
|
|
x = []
|
|
y = np.bincount(x, minlength=0)
|
|
assert_array_equal(y, np.array([]))
|
|
|
|
def test_with_minlength_smaller_than_maxvalue(self):
|
|
x = np.array([0, 1, 1, 2, 2, 3, 3])
|
|
y = np.bincount(x, minlength=2)
|
|
assert_array_equal(y, np.array([1, 2, 2, 2]))
|
|
y = np.bincount(x, minlength=0)
|
|
assert_array_equal(y, np.array([1, 2, 2, 2]))
|
|
|
|
def test_with_minlength_and_weights(self):
|
|
x = np.array([1, 2, 4, 5, 2])
|
|
w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
|
|
y = np.bincount(x, w, 8)
|
|
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0]))
|
|
|
|
def test_empty(self):
|
|
x = np.array([], dtype=int)
|
|
y = np.bincount(x)
|
|
assert_array_equal(x, y)
|
|
|
|
def test_empty_with_minlength(self):
|
|
x = np.array([], dtype=int)
|
|
y = np.bincount(x, minlength=5)
|
|
assert_array_equal(y, np.zeros(5, dtype=int))
|
|
|
|
def test_with_incorrect_minlength(self):
|
|
x = np.array([], dtype=int)
|
|
assert_raises_regex(TypeError,
|
|
"'str' object cannot be interpreted",
|
|
lambda: np.bincount(x, minlength="foobar"))
|
|
assert_raises_regex(ValueError,
|
|
"must not be negative",
|
|
lambda: np.bincount(x, minlength=-1))
|
|
|
|
x = np.arange(5)
|
|
assert_raises_regex(TypeError,
|
|
"'str' object cannot be interpreted",
|
|
lambda: np.bincount(x, minlength="foobar"))
|
|
assert_raises_regex(ValueError,
|
|
"must not be negative",
|
|
lambda: np.bincount(x, minlength=-1))
|
|
|
|
@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
|
|
def test_dtype_reference_leaks(self):
|
|
# gh-6805
|
|
intp_refcount = sys.getrefcount(np.dtype(np.intp))
|
|
double_refcount = sys.getrefcount(np.dtype(np.double))
|
|
|
|
for j in range(10):
|
|
np.bincount([1, 2, 3])
|
|
assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount)
|
|
assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount)
|
|
|
|
for j in range(10):
|
|
np.bincount([1, 2, 3], [4, 5, 6])
|
|
assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount)
|
|
assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount)
|
|
|
|
@pytest.mark.parametrize("vals", [[[2, 2]], 2])
|
|
def test_error_not_1d(self, vals):
|
|
# Test that values has to be 1-D (both as array and nested list)
|
|
vals_arr = np.asarray(vals)
|
|
with assert_raises(ValueError):
|
|
np.bincount(vals_arr)
|
|
with assert_raises(ValueError):
|
|
np.bincount(vals)
|
|
|
|
|
|
class TestInterp:
|
|
|
|
def test_exceptions(self):
|
|
assert_raises(ValueError, interp, 0, [], [])
|
|
assert_raises(ValueError, interp, 0, [0], [1, 2])
|
|
assert_raises(ValueError, interp, 0, [0, 1], [1, 2], period=0)
|
|
assert_raises(ValueError, interp, 0, [], [], period=360)
|
|
assert_raises(ValueError, interp, 0, [0], [1, 2], period=360)
|
|
|
|
def test_basic(self):
|
|
x = np.linspace(0, 1, 5)
|
|
y = np.linspace(0, 1, 5)
|
|
x0 = np.linspace(0, 1, 50)
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
|
|
def test_right_left_behavior(self):
|
|
# Needs range of sizes to test different code paths.
|
|
# size ==1 is special cased, 1 < size < 5 is linear search, and
|
|
# size >= 5 goes through local search and possibly binary search.
|
|
for size in range(1, 10):
|
|
xp = np.arange(size, dtype=np.double)
|
|
yp = np.ones(size, dtype=np.double)
|
|
incpts = np.array([-1, 0, size - 1, size], dtype=np.double)
|
|
decpts = incpts[::-1]
|
|
|
|
incres = interp(incpts, xp, yp)
|
|
decres = interp(decpts, xp, yp)
|
|
inctgt = np.array([1, 1, 1, 1], dtype=float)
|
|
dectgt = inctgt[::-1]
|
|
assert_equal(incres, inctgt)
|
|
assert_equal(decres, dectgt)
|
|
|
|
incres = interp(incpts, xp, yp, left=0)
|
|
decres = interp(decpts, xp, yp, left=0)
|
|
inctgt = np.array([0, 1, 1, 1], dtype=float)
|
|
dectgt = inctgt[::-1]
|
|
assert_equal(incres, inctgt)
|
|
assert_equal(decres, dectgt)
|
|
|
|
incres = interp(incpts, xp, yp, right=2)
|
|
decres = interp(decpts, xp, yp, right=2)
|
|
inctgt = np.array([1, 1, 1, 2], dtype=float)
|
|
dectgt = inctgt[::-1]
|
|
assert_equal(incres, inctgt)
|
|
assert_equal(decres, dectgt)
|
|
|
|
incres = interp(incpts, xp, yp, left=0, right=2)
|
|
decres = interp(decpts, xp, yp, left=0, right=2)
|
|
inctgt = np.array([0, 1, 1, 2], dtype=float)
|
|
dectgt = inctgt[::-1]
|
|
assert_equal(incres, inctgt)
|
|
assert_equal(decres, dectgt)
|
|
|
|
def test_scalar_interpolation_point(self):
|
|
x = np.linspace(0, 1, 5)
|
|
y = np.linspace(0, 1, 5)
|
|
x0 = 0
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
x0 = .3
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
x0 = np.float32(.3)
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
x0 = np.float64(.3)
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
x0 = np.nan
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
|
|
def test_non_finite_behavior_exact_x(self):
|
|
x = [1, 2, 2.5, 3, 4]
|
|
xp = [1, 2, 3, 4]
|
|
fp = [1, 2, np.inf, 4]
|
|
assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.inf, np.inf, 4])
|
|
fp = [1, 2, np.nan, 4]
|
|
assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.nan, np.nan, 4])
|
|
|
|
@pytest.fixture(params=[
|
|
lambda x: np.float_(x),
|
|
lambda x: _make_complex(x, 0),
|
|
lambda x: _make_complex(0, x),
|
|
lambda x: _make_complex(x, np.multiply(x, -2))
|
|
], ids=[
|
|
'real',
|
|
'complex-real',
|
|
'complex-imag',
|
|
'complex-both'
|
|
])
|
|
def sc(self, request):
|
|
""" scale function used by the below tests """
|
|
return request.param
|
|
|
|
def test_non_finite_any_nan(self, sc):
|
|
""" test that nans are propagated """
|
|
assert_equal(np.interp(0.5, [np.nan, 1], sc([ 0, 10])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, np.nan], sc([ 0, 10])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, 1], sc([np.nan, 10])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, 1], sc([ 0, np.nan])), sc(np.nan))
|
|
|
|
def test_non_finite_inf(self, sc):
|
|
""" Test that interp between opposite infs gives nan """
|
|
assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([ 0, 10])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, 1], sc([-np.inf, +np.inf])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, 1], sc([+np.inf, -np.inf])), sc(np.nan))
|
|
|
|
# unless the y values are equal
|
|
assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([ 10, 10])), sc(10))
|
|
|
|
def test_non_finite_half_inf_xf(self, sc):
|
|
""" Test that interp where both axes have a bound at inf gives nan """
|
|
assert_equal(np.interp(0.5, [-np.inf, 1], sc([-np.inf, 10])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [-np.inf, 1], sc([+np.inf, 10])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [-np.inf, 1], sc([ 0, -np.inf])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [-np.inf, 1], sc([ 0, +np.inf])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, +np.inf], sc([-np.inf, 10])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, +np.inf], sc([+np.inf, 10])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, +np.inf], sc([ 0, -np.inf])), sc(np.nan))
|
|
assert_equal(np.interp(0.5, [ 0, +np.inf], sc([ 0, +np.inf])), sc(np.nan))
|
|
|
|
def test_non_finite_half_inf_x(self, sc):
|
|
""" Test interp where the x axis has a bound at inf """
|
|
assert_equal(np.interp(0.5, [-np.inf, -np.inf], sc([0, 10])), sc(10))
|
|
assert_equal(np.interp(0.5, [-np.inf, 1 ], sc([0, 10])), sc(10))
|
|
assert_equal(np.interp(0.5, [ 0, +np.inf], sc([0, 10])), sc(0))
|
|
assert_equal(np.interp(0.5, [+np.inf, +np.inf], sc([0, 10])), sc(0))
|
|
|
|
def test_non_finite_half_inf_f(self, sc):
|
|
""" Test interp where the f axis has a bound at inf """
|
|
assert_equal(np.interp(0.5, [0, 1], sc([ 0, -np.inf])), sc(-np.inf))
|
|
assert_equal(np.interp(0.5, [0, 1], sc([ 0, +np.inf])), sc(+np.inf))
|
|
assert_equal(np.interp(0.5, [0, 1], sc([-np.inf, 10])), sc(-np.inf))
|
|
assert_equal(np.interp(0.5, [0, 1], sc([+np.inf, 10])), sc(+np.inf))
|
|
assert_equal(np.interp(0.5, [0, 1], sc([-np.inf, -np.inf])), sc(-np.inf))
|
|
assert_equal(np.interp(0.5, [0, 1], sc([+np.inf, +np.inf])), sc(+np.inf))
|
|
|
|
def test_complex_interp(self):
|
|
# test complex interpolation
|
|
x = np.linspace(0, 1, 5)
|
|
y = np.linspace(0, 1, 5) + (1 + np.linspace(0, 1, 5))*1.0j
|
|
x0 = 0.3
|
|
y0 = x0 + (1+x0)*1.0j
|
|
assert_almost_equal(np.interp(x0, x, y), y0)
|
|
# test complex left and right
|
|
x0 = -1
|
|
left = 2 + 3.0j
|
|
assert_almost_equal(np.interp(x0, x, y, left=left), left)
|
|
x0 = 2.0
|
|
right = 2 + 3.0j
|
|
assert_almost_equal(np.interp(x0, x, y, right=right), right)
|
|
# test complex non finite
|
|
x = [1, 2, 2.5, 3, 4]
|
|
xp = [1, 2, 3, 4]
|
|
fp = [1, 2+1j, np.inf, 4]
|
|
y = [1, 2+1j, np.inf+0.5j, np.inf, 4]
|
|
assert_almost_equal(np.interp(x, xp, fp), y)
|
|
# test complex periodic
|
|
x = [-180, -170, -185, 185, -10, -5, 0, 365]
|
|
xp = [190, -190, 350, -350]
|
|
fp = [5+1.0j, 10+2j, 3+3j, 4+4j]
|
|
y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j,
|
|
3.5+3.5j, 3.75+3.75j]
|
|
assert_almost_equal(np.interp(x, xp, fp, period=360), y)
|
|
|
|
def test_zero_dimensional_interpolation_point(self):
|
|
x = np.linspace(0, 1, 5)
|
|
y = np.linspace(0, 1, 5)
|
|
x0 = np.array(.3)
|
|
assert_almost_equal(np.interp(x0, x, y), x0)
|
|
|
|
xp = np.array([0, 2, 4])
|
|
fp = np.array([1, -1, 1])
|
|
|
|
actual = np.interp(np.array(1), xp, fp)
|
|
assert_equal(actual, 0)
|
|
assert_(isinstance(actual, np.float64))
|
|
|
|
actual = np.interp(np.array(4.5), xp, fp, period=4)
|
|
assert_equal(actual, 0.5)
|
|
assert_(isinstance(actual, np.float64))
|
|
|
|
def test_if_len_x_is_small(self):
|
|
xp = np.arange(0, 10, 0.0001)
|
|
fp = np.sin(xp)
|
|
assert_almost_equal(np.interp(np.pi, xp, fp), 0.0)
|
|
|
|
def test_period(self):
|
|
x = [-180, -170, -185, 185, -10, -5, 0, 365]
|
|
xp = [190, -190, 350, -350]
|
|
fp = [5, 10, 3, 4]
|
|
y = [7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75]
|
|
assert_almost_equal(np.interp(x, xp, fp, period=360), y)
|
|
x = np.array(x, order='F').reshape(2, -1)
|
|
y = np.array(y, order='C').reshape(2, -1)
|
|
assert_almost_equal(np.interp(x, xp, fp, period=360), y)
|
|
|
|
|
|
def compare_results(res, desired):
|
|
for i in range(len(desired)):
|
|
assert_array_equal(res[i], desired[i])
|
|
|
|
|
|
class TestPercentile:
|
|
|
|
def test_basic(self):
|
|
x = np.arange(8) * 0.5
|
|
assert_equal(np.percentile(x, 0), 0.)
|
|
assert_equal(np.percentile(x, 100), 3.5)
|
|
assert_equal(np.percentile(x, 50), 1.75)
|
|
x[1] = np.nan
|
|
assert_equal(np.percentile(x, 0), np.nan)
|
|
assert_equal(np.percentile(x, 0, interpolation='nearest'), np.nan)
|
|
|
|
def test_fraction(self):
|
|
x = [Fraction(i, 2) for i in range(8)]
|
|
|
|
p = np.percentile(x, Fraction(0))
|
|
assert_equal(p, Fraction(0))
|
|
assert_equal(type(p), Fraction)
|
|
|
|
p = np.percentile(x, Fraction(100))
|
|
assert_equal(p, Fraction(7, 2))
|
|
assert_equal(type(p), Fraction)
|
|
|
|
p = np.percentile(x, Fraction(50))
|
|
assert_equal(p, Fraction(7, 4))
|
|
assert_equal(type(p), Fraction)
|
|
|
|
def test_api(self):
|
|
d = np.ones(5)
|
|
np.percentile(d, 5, None, None, False)
|
|
np.percentile(d, 5, None, None, False, 'linear')
|
|
o = np.ones((1,))
|
|
np.percentile(d, 5, None, o, False, 'linear')
|
|
|
|
def test_2D(self):
|
|
x = np.array([[1, 1, 1],
|
|
[1, 1, 1],
|
|
[4, 4, 3],
|
|
[1, 1, 1],
|
|
[1, 1, 1]])
|
|
assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1])
|
|
|
|
def test_linear(self):
|
|
|
|
# Test defaults
|
|
assert_equal(np.percentile(range(10), 50), 4.5)
|
|
|
|
# explicitly specify interpolation_method 'linear' (the default)
|
|
assert_equal(np.percentile(range(10), 50,
|
|
interpolation='linear'), 4.5)
|
|
|
|
def test_lower_higher(self):
|
|
|
|
# interpolation_method 'lower'/'higher'
|
|
assert_equal(np.percentile(range(10), 50,
|
|
interpolation='lower'), 4)
|
|
assert_equal(np.percentile(range(10), 50,
|
|
interpolation='higher'), 5)
|
|
|
|
def test_midpoint(self):
|
|
assert_equal(np.percentile(range(10), 51,
|
|
interpolation='midpoint'), 4.5)
|
|
assert_equal(np.percentile(range(11), 51,
|
|
interpolation='midpoint'), 5.5)
|
|
assert_equal(np.percentile(range(11), 50,
|
|
interpolation='midpoint'), 5)
|
|
|
|
def test_nearest(self):
|
|
assert_equal(np.percentile(range(10), 51,
|
|
interpolation='nearest'), 5)
|
|
assert_equal(np.percentile(range(10), 49,
|
|
interpolation='nearest'), 4)
|
|
|
|
def test_sequence(self):
|
|
x = np.arange(8) * 0.5
|
|
assert_equal(np.percentile(x, [0, 100, 50]), [0, 3.5, 1.75])
|
|
|
|
def test_axis(self):
|
|
x = np.arange(12).reshape(3, 4)
|
|
|
|
assert_equal(np.percentile(x, (25, 50, 100)), [2.75, 5.5, 11.0])
|
|
|
|
r0 = [[2, 3, 4, 5], [4, 5, 6, 7], [8, 9, 10, 11]]
|
|
assert_equal(np.percentile(x, (25, 50, 100), axis=0), r0)
|
|
|
|
r1 = [[0.75, 1.5, 3], [4.75, 5.5, 7], [8.75, 9.5, 11]]
|
|
assert_equal(np.percentile(x, (25, 50, 100), axis=1), np.array(r1).T)
|
|
|
|
# ensure qth axis is always first as with np.array(old_percentile(..))
|
|
x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
|
|
assert_equal(np.percentile(x, (25, 50)).shape, (2,))
|
|
assert_equal(np.percentile(x, (25, 50, 75)).shape, (3,))
|
|
assert_equal(np.percentile(x, (25, 50), axis=0).shape, (2, 4, 5, 6))
|
|
assert_equal(np.percentile(x, (25, 50), axis=1).shape, (2, 3, 5, 6))
|
|
assert_equal(np.percentile(x, (25, 50), axis=2).shape, (2, 3, 4, 6))
|
|
assert_equal(np.percentile(x, (25, 50), axis=3).shape, (2, 3, 4, 5))
|
|
assert_equal(
|
|
np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6))
|
|
assert_equal(np.percentile(x, (25, 50),
|
|
interpolation="higher").shape, (2,))
|
|
assert_equal(np.percentile(x, (25, 50, 75),
|
|
interpolation="higher").shape, (3,))
|
|
assert_equal(np.percentile(x, (25, 50), axis=0,
|
|
interpolation="higher").shape, (2, 4, 5, 6))
|
|
assert_equal(np.percentile(x, (25, 50), axis=1,
|
|
interpolation="higher").shape, (2, 3, 5, 6))
|
|
assert_equal(np.percentile(x, (25, 50), axis=2,
|
|
interpolation="higher").shape, (2, 3, 4, 6))
|
|
assert_equal(np.percentile(x, (25, 50), axis=3,
|
|
interpolation="higher").shape, (2, 3, 4, 5))
|
|
assert_equal(np.percentile(x, (25, 50, 75), axis=1,
|
|
interpolation="higher").shape, (3, 3, 5, 6))
|
|
|
|
def test_scalar_q(self):
|
|
# test for no empty dimensions for compatibility with old percentile
|
|
x = np.arange(12).reshape(3, 4)
|
|
assert_equal(np.percentile(x, 50), 5.5)
|
|
assert_(np.isscalar(np.percentile(x, 50)))
|
|
r0 = np.array([4., 5., 6., 7.])
|
|
assert_equal(np.percentile(x, 50, axis=0), r0)
|
|
assert_equal(np.percentile(x, 50, axis=0).shape, r0.shape)
|
|
r1 = np.array([1.5, 5.5, 9.5])
|
|
assert_almost_equal(np.percentile(x, 50, axis=1), r1)
|
|
assert_equal(np.percentile(x, 50, axis=1).shape, r1.shape)
|
|
|
|
out = np.empty(1)
|
|
assert_equal(np.percentile(x, 50, out=out), 5.5)
|
|
assert_equal(out, 5.5)
|
|
out = np.empty(4)
|
|
assert_equal(np.percentile(x, 50, axis=0, out=out), r0)
|
|
assert_equal(out, r0)
|
|
out = np.empty(3)
|
|
assert_equal(np.percentile(x, 50, axis=1, out=out), r1)
|
|
assert_equal(out, r1)
|
|
|
|
# test for no empty dimensions for compatibility with old percentile
|
|
x = np.arange(12).reshape(3, 4)
|
|
assert_equal(np.percentile(x, 50, interpolation='lower'), 5.)
|
|
assert_(np.isscalar(np.percentile(x, 50)))
|
|
r0 = np.array([4., 5., 6., 7.])
|
|
c0 = np.percentile(x, 50, interpolation='lower', axis=0)
|
|
assert_equal(c0, r0)
|
|
assert_equal(c0.shape, r0.shape)
|
|
r1 = np.array([1., 5., 9.])
|
|
c1 = np.percentile(x, 50, interpolation='lower', axis=1)
|
|
assert_almost_equal(c1, r1)
|
|
assert_equal(c1.shape, r1.shape)
|
|
|
|
out = np.empty((), dtype=x.dtype)
|
|
c = np.percentile(x, 50, interpolation='lower', out=out)
|
|
assert_equal(c, 5)
|
|
assert_equal(out, 5)
|
|
out = np.empty(4, dtype=x.dtype)
|
|
c = np.percentile(x, 50, interpolation='lower', axis=0, out=out)
|
|
assert_equal(c, r0)
|
|
assert_equal(out, r0)
|
|
out = np.empty(3, dtype=x.dtype)
|
|
c = np.percentile(x, 50, interpolation='lower', axis=1, out=out)
|
|
assert_equal(c, r1)
|
|
assert_equal(out, r1)
|
|
|
|
def test_exception(self):
|
|
assert_raises(ValueError, np.percentile, [1, 2], 56,
|
|
interpolation='foobar')
|
|
assert_raises(ValueError, np.percentile, [1], 101)
|
|
assert_raises(ValueError, np.percentile, [1], -1)
|
|
assert_raises(ValueError, np.percentile, [1], list(range(50)) + [101])
|
|
assert_raises(ValueError, np.percentile, [1], list(range(50)) + [-0.1])
|
|
|
|
def test_percentile_list(self):
|
|
assert_equal(np.percentile([1, 2, 3], 0), 1)
|
|
|
|
def test_percentile_out(self):
|
|
x = np.array([1, 2, 3])
|
|
y = np.zeros((3,))
|
|
p = (1, 2, 3)
|
|
np.percentile(x, p, out=y)
|
|
assert_equal(y, np.percentile(x, p))
|
|
|
|
x = np.array([[1, 2, 3],
|
|
[4, 5, 6]])
|
|
|
|
y = np.zeros((3, 3))
|
|
np.percentile(x, p, axis=0, out=y)
|
|
assert_equal(y, np.percentile(x, p, axis=0))
|
|
|
|
y = np.zeros((3, 2))
|
|
np.percentile(x, p, axis=1, out=y)
|
|
assert_equal(y, np.percentile(x, p, axis=1))
|
|
|
|
x = np.arange(12).reshape(3, 4)
|
|
# q.dim > 1, float
|
|
r0 = np.array([[2., 3., 4., 5.], [4., 5., 6., 7.]])
|
|
out = np.empty((2, 4))
|
|
assert_equal(np.percentile(x, (25, 50), axis=0, out=out), r0)
|
|
assert_equal(out, r0)
|
|
r1 = np.array([[0.75, 4.75, 8.75], [1.5, 5.5, 9.5]])
|
|
out = np.empty((2, 3))
|
|
assert_equal(np.percentile(x, (25, 50), axis=1, out=out), r1)
|
|
assert_equal(out, r1)
|
|
|
|
# q.dim > 1, int
|
|
r0 = np.array([[0, 1, 2, 3], [4, 5, 6, 7]])
|
|
out = np.empty((2, 4), dtype=x.dtype)
|
|
c = np.percentile(x, (25, 50), interpolation='lower', axis=0, out=out)
|
|
assert_equal(c, r0)
|
|
assert_equal(out, r0)
|
|
r1 = np.array([[0, 4, 8], [1, 5, 9]])
|
|
out = np.empty((2, 3), dtype=x.dtype)
|
|
c = np.percentile(x, (25, 50), interpolation='lower', axis=1, out=out)
|
|
assert_equal(c, r1)
|
|
assert_equal(out, r1)
|
|
|
|
def test_percentile_empty_dim(self):
|
|
# empty dims are preserved
|
|
d = np.arange(11 * 2).reshape(11, 1, 2, 1)
|
|
assert_array_equal(np.percentile(d, 50, axis=0).shape, (1, 2, 1))
|
|
assert_array_equal(np.percentile(d, 50, axis=1).shape, (11, 2, 1))
|
|
assert_array_equal(np.percentile(d, 50, axis=2).shape, (11, 1, 1))
|
|
assert_array_equal(np.percentile(d, 50, axis=3).shape, (11, 1, 2))
|
|
assert_array_equal(np.percentile(d, 50, axis=-1).shape, (11, 1, 2))
|
|
assert_array_equal(np.percentile(d, 50, axis=-2).shape, (11, 1, 1))
|
|
assert_array_equal(np.percentile(d, 50, axis=-3).shape, (11, 2, 1))
|
|
assert_array_equal(np.percentile(d, 50, axis=-4).shape, (1, 2, 1))
|
|
|
|
assert_array_equal(np.percentile(d, 50, axis=2,
|
|
interpolation='midpoint').shape,
|
|
(11, 1, 1))
|
|
assert_array_equal(np.percentile(d, 50, axis=-2,
|
|
interpolation='midpoint').shape,
|
|
(11, 1, 1))
|
|
|
|
assert_array_equal(np.array(np.percentile(d, [10, 50], axis=0)).shape,
|
|
(2, 1, 2, 1))
|
|
assert_array_equal(np.array(np.percentile(d, [10, 50], axis=1)).shape,
|
|
(2, 11, 2, 1))
|
|
assert_array_equal(np.array(np.percentile(d, [10, 50], axis=2)).shape,
|
|
(2, 11, 1, 1))
|
|
assert_array_equal(np.array(np.percentile(d, [10, 50], axis=3)).shape,
|
|
(2, 11, 1, 2))
|
|
|
|
def test_percentile_no_overwrite(self):
|
|
a = np.array([2, 3, 4, 1])
|
|
np.percentile(a, [50], overwrite_input=False)
|
|
assert_equal(a, np.array([2, 3, 4, 1]))
|
|
|
|
a = np.array([2, 3, 4, 1])
|
|
np.percentile(a, [50])
|
|
assert_equal(a, np.array([2, 3, 4, 1]))
|
|
|
|
def test_no_p_overwrite(self):
|
|
p = np.linspace(0., 100., num=5)
|
|
np.percentile(np.arange(100.), p, interpolation="midpoint")
|
|
assert_array_equal(p, np.linspace(0., 100., num=5))
|
|
p = np.linspace(0., 100., num=5).tolist()
|
|
np.percentile(np.arange(100.), p, interpolation="midpoint")
|
|
assert_array_equal(p, np.linspace(0., 100., num=5).tolist())
|
|
|
|
def test_percentile_overwrite(self):
|
|
a = np.array([2, 3, 4, 1])
|
|
b = np.percentile(a, [50], overwrite_input=True)
|
|
assert_equal(b, np.array([2.5]))
|
|
|
|
b = np.percentile([2, 3, 4, 1], [50], overwrite_input=True)
|
|
assert_equal(b, np.array([2.5]))
|
|
|
|
def test_extended_axis(self):
|
|
o = np.random.normal(size=(71, 23))
|
|
x = np.dstack([o] * 10)
|
|
assert_equal(np.percentile(x, 30, axis=(0, 1)), np.percentile(o, 30))
|
|
x = np.moveaxis(x, -1, 0)
|
|
assert_equal(np.percentile(x, 30, axis=(-2, -1)), np.percentile(o, 30))
|
|
x = x.swapaxes(0, 1).copy()
|
|
assert_equal(np.percentile(x, 30, axis=(0, -1)), np.percentile(o, 30))
|
|
x = x.swapaxes(0, 1).copy()
|
|
|
|
assert_equal(np.percentile(x, [25, 60], axis=(0, 1, 2)),
|
|
np.percentile(x, [25, 60], axis=None))
|
|
assert_equal(np.percentile(x, [25, 60], axis=(0,)),
|
|
np.percentile(x, [25, 60], axis=0))
|
|
|
|
d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11))
|
|
np.random.shuffle(d.ravel())
|
|
assert_equal(np.percentile(d, 25, axis=(0, 1, 2))[0],
|
|
np.percentile(d[:,:,:, 0].flatten(), 25))
|
|
assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1],
|
|
np.percentile(d[:,:, 1,:].flatten(), [10, 90]))
|
|
assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2],
|
|
np.percentile(d[:,:, 2,:].flatten(), 25))
|
|
assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2],
|
|
np.percentile(d[2,:,:,:].flatten(), 25))
|
|
assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1],
|
|
np.percentile(d[2, 1,:,:].flatten(), 25))
|
|
assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1],
|
|
np.percentile(d[2,:,:, 1].flatten(), 25))
|
|
assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2],
|
|
np.percentile(d[2,:, 2,:].flatten(), 25))
|
|
|
|
def test_extended_axis_invalid(self):
|
|
d = np.ones((3, 5, 7, 11))
|
|
assert_raises(np.AxisError, np.percentile, d, axis=-5, q=25)
|
|
assert_raises(np.AxisError, np.percentile, d, axis=(0, -5), q=25)
|
|
assert_raises(np.AxisError, np.percentile, d, axis=4, q=25)
|
|
assert_raises(np.AxisError, np.percentile, d, axis=(0, 4), q=25)
|
|
# each of these refers to the same axis twice
|
|
assert_raises(ValueError, np.percentile, d, axis=(1, 1), q=25)
|
|
assert_raises(ValueError, np.percentile, d, axis=(-1, -1), q=25)
|
|
assert_raises(ValueError, np.percentile, d, axis=(3, -1), q=25)
|
|
|
|
def test_keepdims(self):
|
|
d = np.ones((3, 5, 7, 11))
|
|
assert_equal(np.percentile(d, 7, axis=None, keepdims=True).shape,
|
|
(1, 1, 1, 1))
|
|
assert_equal(np.percentile(d, 7, axis=(0, 1), keepdims=True).shape,
|
|
(1, 1, 7, 11))
|
|
assert_equal(np.percentile(d, 7, axis=(0, 3), keepdims=True).shape,
|
|
(1, 5, 7, 1))
|
|
assert_equal(np.percentile(d, 7, axis=(1,), keepdims=True).shape,
|
|
(3, 1, 7, 11))
|
|
assert_equal(np.percentile(d, 7, (0, 1, 2, 3), keepdims=True).shape,
|
|
(1, 1, 1, 1))
|
|
assert_equal(np.percentile(d, 7, axis=(0, 1, 3), keepdims=True).shape,
|
|
(1, 1, 7, 1))
|
|
|
|
assert_equal(np.percentile(d, [1, 7], axis=(0, 1, 3),
|
|
keepdims=True).shape, (2, 1, 1, 7, 1))
|
|
assert_equal(np.percentile(d, [1, 7], axis=(0, 3),
|
|
keepdims=True).shape, (2, 1, 5, 7, 1))
|
|
|
|
def test_out(self):
|
|
o = np.zeros((4,))
|
|
d = np.ones((3, 4))
|
|
assert_equal(np.percentile(d, 0, 0, out=o), o)
|
|
assert_equal(np.percentile(d, 0, 0, interpolation='nearest', out=o), o)
|
|
o = np.zeros((3,))
|
|
assert_equal(np.percentile(d, 1, 1, out=o), o)
|
|
assert_equal(np.percentile(d, 1, 1, interpolation='nearest', out=o), o)
|
|
|
|
o = np.zeros(())
|
|
assert_equal(np.percentile(d, 2, out=o), o)
|
|
assert_equal(np.percentile(d, 2, interpolation='nearest', out=o), o)
|
|
|
|
def test_out_nan(self):
|
|
with warnings.catch_warnings(record=True):
|
|
warnings.filterwarnings('always', '', RuntimeWarning)
|
|
o = np.zeros((4,))
|
|
d = np.ones((3, 4))
|
|
d[2, 1] = np.nan
|
|
assert_equal(np.percentile(d, 0, 0, out=o), o)
|
|
assert_equal(
|
|
np.percentile(d, 0, 0, interpolation='nearest', out=o), o)
|
|
o = np.zeros((3,))
|
|
assert_equal(np.percentile(d, 1, 1, out=o), o)
|
|
assert_equal(
|
|
np.percentile(d, 1, 1, interpolation='nearest', out=o), o)
|
|
o = np.zeros(())
|
|
assert_equal(np.percentile(d, 1, out=o), o)
|
|
assert_equal(
|
|
np.percentile(d, 1, interpolation='nearest', out=o), o)
|
|
|
|
def test_nan_behavior(self):
|
|
a = np.arange(24, dtype=float)
|
|
a[2] = np.nan
|
|
assert_equal(np.percentile(a, 0.3), np.nan)
|
|
assert_equal(np.percentile(a, 0.3, axis=0), np.nan)
|
|
assert_equal(np.percentile(a, [0.3, 0.6], axis=0),
|
|
np.array([np.nan] * 2))
|
|
|
|
a = np.arange(24, dtype=float).reshape(2, 3, 4)
|
|
a[1, 2, 3] = np.nan
|
|
a[1, 1, 2] = np.nan
|
|
|
|
# no axis
|
|
assert_equal(np.percentile(a, 0.3), np.nan)
|
|
assert_equal(np.percentile(a, 0.3).ndim, 0)
|
|
|
|
# axis0 zerod
|
|
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 0)
|
|
b[2, 3] = np.nan
|
|
b[1, 2] = np.nan
|
|
assert_equal(np.percentile(a, 0.3, 0), b)
|
|
|
|
# axis0 not zerod
|
|
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
|
|
[0.3, 0.6], 0)
|
|
b[:, 2, 3] = np.nan
|
|
b[:, 1, 2] = np.nan
|
|
assert_equal(np.percentile(a, [0.3, 0.6], 0), b)
|
|
|
|
# axis1 zerod
|
|
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 1)
|
|
b[1, 3] = np.nan
|
|
b[1, 2] = np.nan
|
|
assert_equal(np.percentile(a, 0.3, 1), b)
|
|
# axis1 not zerod
|
|
b = np.percentile(
|
|
np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1)
|
|
b[:, 1, 3] = np.nan
|
|
b[:, 1, 2] = np.nan
|
|
assert_equal(np.percentile(a, [0.3, 0.6], 1), b)
|
|
|
|
# axis02 zerod
|
|
b = np.percentile(
|
|
np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2))
|
|
b[1] = np.nan
|
|
b[2] = np.nan
|
|
assert_equal(np.percentile(a, 0.3, (0, 2)), b)
|
|
# axis02 not zerod
|
|
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
|
|
[0.3, 0.6], (0, 2))
|
|
b[:, 1] = np.nan
|
|
b[:, 2] = np.nan
|
|
assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b)
|
|
# axis02 not zerod with nearest interpolation
|
|
b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
|
|
[0.3, 0.6], (0, 2), interpolation='nearest')
|
|
b[:, 1] = np.nan
|
|
b[:, 2] = np.nan
|
|
assert_equal(np.percentile(
|
|
a, [0.3, 0.6], (0, 2), interpolation='nearest'), b)
|
|
|
|
|
|
class TestQuantile:
|
|
# most of this is already tested by TestPercentile
|
|
|
|
def test_basic(self):
|
|
x = np.arange(8) * 0.5
|
|
assert_equal(np.quantile(x, 0), 0.)
|
|
assert_equal(np.quantile(x, 1), 3.5)
|
|
assert_equal(np.quantile(x, 0.5), 1.75)
|
|
|
|
def test_correct_quantile_value(self):
|
|
a = np.array([True])
|
|
tf_quant = np.quantile(True, False)
|
|
assert_equal(tf_quant, a[0])
|
|
assert_equal(type(tf_quant), a.dtype)
|
|
a = np.array([False, True, True])
|
|
quant_res = np.quantile(a, a)
|
|
assert_array_equal(quant_res, a)
|
|
assert_equal(a.dtype, quant_res.dtype)
|
|
|
|
def test_fraction(self):
|
|
# fractional input, integral quantile
|
|
x = [Fraction(i, 2) for i in range(8)]
|
|
|
|
q = np.quantile(x, 0)
|
|
assert_equal(q, 0)
|
|
assert_equal(type(q), Fraction)
|
|
|
|
q = np.quantile(x, 1)
|
|
assert_equal(q, Fraction(7, 2))
|
|
assert_equal(type(q), Fraction)
|
|
|
|
q = np.quantile(x, Fraction(1, 2))
|
|
assert_equal(q, Fraction(7, 4))
|
|
assert_equal(type(q), Fraction)
|
|
|
|
# repeat with integral input but fractional quantile
|
|
x = np.arange(8)
|
|
assert_equal(np.quantile(x, Fraction(1, 2)), Fraction(7, 2))
|
|
|
|
def test_no_p_overwrite(self):
|
|
# this is worth retesting, because quantile does not make a copy
|
|
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
|
|
p = p0.copy()
|
|
np.quantile(np.arange(100.), p, interpolation="midpoint")
|
|
assert_array_equal(p, p0)
|
|
|
|
p0 = p0.tolist()
|
|
p = p.tolist()
|
|
np.quantile(np.arange(100.), p, interpolation="midpoint")
|
|
assert_array_equal(p, p0)
|
|
|
|
def test_quantile_monotonic(self):
|
|
# GH 14685
|
|
# test that the return value of quantile is monotonic if p0 is ordered
|
|
p0 = np.arange(0, 1, 0.01)
|
|
quantile = np.quantile(np.array([0, 1, 1, 2, 2, 3, 3, 4, 5, 5, 1, 1, 9, 9, 9,
|
|
8, 8, 7]) * 0.1, p0)
|
|
assert_equal(np.sort(quantile), quantile)
|
|
|
|
@hypothesis.given(
|
|
arr=arrays(dtype=np.float64,
|
|
shape=st.integers(min_value=3, max_value=1000),
|
|
elements=st.floats(allow_infinity=False, allow_nan=False,
|
|
min_value=-1e300, max_value=1e300)))
|
|
def test_quantile_monotonic_hypo(self, arr):
|
|
p0 = np.arange(0, 1, 0.01)
|
|
quantile = np.quantile(arr, p0)
|
|
assert_equal(np.sort(quantile), quantile)
|
|
|
|
|
|
class TestLerp:
|
|
@hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=0, max_value=1),
|
|
t1=st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=0, max_value=1),
|
|
a = st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=-1e300, max_value=1e300),
|
|
b = st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=-1e300, max_value=1e300))
|
|
def test_lerp_monotonic(self, t0, t1, a, b):
|
|
l0 = np.lib.function_base._lerp(a, b, t0)
|
|
l1 = np.lib.function_base._lerp(a, b, t1)
|
|
if t0 == t1 or a == b:
|
|
assert l0 == l1 # uninteresting
|
|
elif (t0 < t1) == (a < b):
|
|
assert l0 <= l1
|
|
else:
|
|
assert l0 >= l1
|
|
|
|
@hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=0, max_value=1),
|
|
a=st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=-1e300, max_value=1e300),
|
|
b=st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=-1e300, max_value=1e300))
|
|
def test_lerp_bounded(self, t, a, b):
|
|
if a <= b:
|
|
assert a <= np.lib.function_base._lerp(a, b, t) <= b
|
|
else:
|
|
assert b <= np.lib.function_base._lerp(a, b, t) <= a
|
|
|
|
@hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=0, max_value=1),
|
|
a=st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=-1e300, max_value=1e300),
|
|
b=st.floats(allow_nan=False, allow_infinity=False,
|
|
min_value=-1e300, max_value=1e300))
|
|
def test_lerp_symmetric(self, t, a, b):
|
|
# double subtraction is needed to remove the extra precision of t < 0.5
|
|
left = np.lib.function_base._lerp(a, b, 1 - (1 - t))
|
|
right = np.lib.function_base._lerp(b, a, 1 - t)
|
|
assert left == right
|
|
|
|
def test_lerp_0d_inputs(self):
|
|
a = np.array(2)
|
|
b = np.array(5)
|
|
t = np.array(0.2)
|
|
assert np.lib.function_base._lerp(a, b, t) == 2.6
|
|
|
|
|
|
class TestMedian:
|
|
|
|
def test_basic(self):
|
|
a0 = np.array(1)
|
|
a1 = np.arange(2)
|
|
a2 = np.arange(6).reshape(2, 3)
|
|
assert_equal(np.median(a0), 1)
|
|
assert_allclose(np.median(a1), 0.5)
|
|
assert_allclose(np.median(a2), 2.5)
|
|
assert_allclose(np.median(a2, axis=0), [1.5, 2.5, 3.5])
|
|
assert_equal(np.median(a2, axis=1), [1, 4])
|
|
assert_allclose(np.median(a2, axis=None), 2.5)
|
|
|
|
a = np.array([0.0444502, 0.0463301, 0.141249, 0.0606775])
|
|
assert_almost_equal((a[1] + a[3]) / 2., np.median(a))
|
|
a = np.array([0.0463301, 0.0444502, 0.141249])
|
|
assert_equal(a[0], np.median(a))
|
|
a = np.array([0.0444502, 0.141249, 0.0463301])
|
|
assert_equal(a[-1], np.median(a))
|
|
# check array scalar result
|
|
assert_equal(np.median(a).ndim, 0)
|
|
a[1] = np.nan
|
|
assert_equal(np.median(a).ndim, 0)
|
|
|
|
def test_axis_keyword(self):
|
|
a3 = np.array([[2, 3],
|
|
[0, 1],
|
|
[6, 7],
|
|
[4, 5]])
|
|
for a in [a3, np.random.randint(0, 100, size=(2, 3, 4))]:
|
|
orig = a.copy()
|
|
np.median(a, axis=None)
|
|
for ax in range(a.ndim):
|
|
np.median(a, axis=ax)
|
|
assert_array_equal(a, orig)
|
|
|
|
assert_allclose(np.median(a3, axis=0), [3, 4])
|
|
assert_allclose(np.median(a3.T, axis=1), [3, 4])
|
|
assert_allclose(np.median(a3), 3.5)
|
|
assert_allclose(np.median(a3, axis=None), 3.5)
|
|
assert_allclose(np.median(a3.T), 3.5)
|
|
|
|
def test_overwrite_keyword(self):
|
|
a3 = np.array([[2, 3],
|
|
[0, 1],
|
|
[6, 7],
|
|
[4, 5]])
|
|
a0 = np.array(1)
|
|
a1 = np.arange(2)
|
|
a2 = np.arange(6).reshape(2, 3)
|
|
assert_allclose(np.median(a0.copy(), overwrite_input=True), 1)
|
|
assert_allclose(np.median(a1.copy(), overwrite_input=True), 0.5)
|
|
assert_allclose(np.median(a2.copy(), overwrite_input=True), 2.5)
|
|
assert_allclose(np.median(a2.copy(), overwrite_input=True, axis=0),
|
|
[1.5, 2.5, 3.5])
|
|
assert_allclose(
|
|
np.median(a2.copy(), overwrite_input=True, axis=1), [1, 4])
|
|
assert_allclose(
|
|
np.median(a2.copy(), overwrite_input=True, axis=None), 2.5)
|
|
assert_allclose(
|
|
np.median(a3.copy(), overwrite_input=True, axis=0), [3, 4])
|
|
assert_allclose(np.median(a3.T.copy(), overwrite_input=True, axis=1),
|
|
[3, 4])
|
|
|
|
a4 = np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5))
|
|
np.random.shuffle(a4.ravel())
|
|
assert_allclose(np.median(a4, axis=None),
|
|
np.median(a4.copy(), axis=None, overwrite_input=True))
|
|
assert_allclose(np.median(a4, axis=0),
|
|
np.median(a4.copy(), axis=0, overwrite_input=True))
|
|
assert_allclose(np.median(a4, axis=1),
|
|
np.median(a4.copy(), axis=1, overwrite_input=True))
|
|
assert_allclose(np.median(a4, axis=2),
|
|
np.median(a4.copy(), axis=2, overwrite_input=True))
|
|
|
|
def test_array_like(self):
|
|
x = [1, 2, 3]
|
|
assert_almost_equal(np.median(x), 2)
|
|
x2 = [x]
|
|
assert_almost_equal(np.median(x2), 2)
|
|
assert_allclose(np.median(x2, axis=0), x)
|
|
|
|
def test_subclass(self):
|
|
# gh-3846
|
|
class MySubClass(np.ndarray):
|
|
|
|
def __new__(cls, input_array, info=None):
|
|
obj = np.asarray(input_array).view(cls)
|
|
obj.info = info
|
|
return obj
|
|
|
|
def mean(self, axis=None, dtype=None, out=None):
|
|
return -7
|
|
|
|
a = MySubClass([1, 2, 3])
|
|
assert_equal(np.median(a), -7)
|
|
|
|
def test_out(self):
|
|
o = np.zeros((4,))
|
|
d = np.ones((3, 4))
|
|
assert_equal(np.median(d, 0, out=o), o)
|
|
o = np.zeros((3,))
|
|
assert_equal(np.median(d, 1, out=o), o)
|
|
o = np.zeros(())
|
|
assert_equal(np.median(d, out=o), o)
|
|
|
|
def test_out_nan(self):
|
|
with warnings.catch_warnings(record=True):
|
|
warnings.filterwarnings('always', '', RuntimeWarning)
|
|
o = np.zeros((4,))
|
|
d = np.ones((3, 4))
|
|
d[2, 1] = np.nan
|
|
assert_equal(np.median(d, 0, out=o), o)
|
|
o = np.zeros((3,))
|
|
assert_equal(np.median(d, 1, out=o), o)
|
|
o = np.zeros(())
|
|
assert_equal(np.median(d, out=o), o)
|
|
|
|
def test_nan_behavior(self):
|
|
a = np.arange(24, dtype=float)
|
|
a[2] = np.nan
|
|
assert_equal(np.median(a), np.nan)
|
|
assert_equal(np.median(a, axis=0), np.nan)
|
|
|
|
a = np.arange(24, dtype=float).reshape(2, 3, 4)
|
|
a[1, 2, 3] = np.nan
|
|
a[1, 1, 2] = np.nan
|
|
|
|
# no axis
|
|
assert_equal(np.median(a), np.nan)
|
|
assert_equal(np.median(a).ndim, 0)
|
|
|
|
# axis0
|
|
b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 0)
|
|
b[2, 3] = np.nan
|
|
b[1, 2] = np.nan
|
|
assert_equal(np.median(a, 0), b)
|
|
|
|
# axis1
|
|
b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 1)
|
|
b[1, 3] = np.nan
|
|
b[1, 2] = np.nan
|
|
assert_equal(np.median(a, 1), b)
|
|
|
|
# axis02
|
|
b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), (0, 2))
|
|
b[1] = np.nan
|
|
b[2] = np.nan
|
|
assert_equal(np.median(a, (0, 2)), b)
|
|
|
|
def test_empty(self):
|
|
# mean(empty array) emits two warnings: empty slice and divide by 0
|
|
a = np.array([], dtype=float)
|
|
with warnings.catch_warnings(record=True) as w:
|
|
warnings.filterwarnings('always', '', RuntimeWarning)
|
|
assert_equal(np.median(a), np.nan)
|
|
assert_(w[0].category is RuntimeWarning)
|
|
assert_equal(len(w), 2)
|
|
|
|
# multiple dimensions
|
|
a = np.array([], dtype=float, ndmin=3)
|
|
# no axis
|
|
with warnings.catch_warnings(record=True) as w:
|
|
warnings.filterwarnings('always', '', RuntimeWarning)
|
|
assert_equal(np.median(a), np.nan)
|
|
assert_(w[0].category is RuntimeWarning)
|
|
|
|
# axis 0 and 1
|
|
b = np.array([], dtype=float, ndmin=2)
|
|
assert_equal(np.median(a, axis=0), b)
|
|
assert_equal(np.median(a, axis=1), b)
|
|
|
|
# axis 2
|
|
b = np.array(np.nan, dtype=float, ndmin=2)
|
|
with warnings.catch_warnings(record=True) as w:
|
|
warnings.filterwarnings('always', '', RuntimeWarning)
|
|
assert_equal(np.median(a, axis=2), b)
|
|
assert_(w[0].category is RuntimeWarning)
|
|
|
|
def test_object(self):
|
|
o = np.arange(7.)
|
|
assert_(type(np.median(o.astype(object))), float)
|
|
o[2] = np.nan
|
|
assert_(type(np.median(o.astype(object))), float)
|
|
|
|
def test_extended_axis(self):
|
|
o = np.random.normal(size=(71, 23))
|
|
x = np.dstack([o] * 10)
|
|
assert_equal(np.median(x, axis=(0, 1)), np.median(o))
|
|
x = np.moveaxis(x, -1, 0)
|
|
assert_equal(np.median(x, axis=(-2, -1)), np.median(o))
|
|
x = x.swapaxes(0, 1).copy()
|
|
assert_equal(np.median(x, axis=(0, -1)), np.median(o))
|
|
|
|
assert_equal(np.median(x, axis=(0, 1, 2)), np.median(x, axis=None))
|
|
assert_equal(np.median(x, axis=(0, )), np.median(x, axis=0))
|
|
assert_equal(np.median(x, axis=(-1, )), np.median(x, axis=-1))
|
|
|
|
d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11))
|
|
np.random.shuffle(d.ravel())
|
|
assert_equal(np.median(d, axis=(0, 1, 2))[0],
|
|
np.median(d[:,:,:, 0].flatten()))
|
|
assert_equal(np.median(d, axis=(0, 1, 3))[1],
|
|
np.median(d[:,:, 1,:].flatten()))
|
|
assert_equal(np.median(d, axis=(3, 1, -4))[2],
|
|
np.median(d[:,:, 2,:].flatten()))
|
|
assert_equal(np.median(d, axis=(3, 1, 2))[2],
|
|
np.median(d[2,:,:,:].flatten()))
|
|
assert_equal(np.median(d, axis=(3, 2))[2, 1],
|
|
np.median(d[2, 1,:,:].flatten()))
|
|
assert_equal(np.median(d, axis=(1, -2))[2, 1],
|
|
np.median(d[2,:,:, 1].flatten()))
|
|
assert_equal(np.median(d, axis=(1, 3))[2, 2],
|
|
np.median(d[2,:, 2,:].flatten()))
|
|
|
|
def test_extended_axis_invalid(self):
|
|
d = np.ones((3, 5, 7, 11))
|
|
assert_raises(np.AxisError, np.median, d, axis=-5)
|
|
assert_raises(np.AxisError, np.median, d, axis=(0, -5))
|
|
assert_raises(np.AxisError, np.median, d, axis=4)
|
|
assert_raises(np.AxisError, np.median, d, axis=(0, 4))
|
|
assert_raises(ValueError, np.median, d, axis=(1, 1))
|
|
|
|
def test_keepdims(self):
|
|
d = np.ones((3, 5, 7, 11))
|
|
assert_equal(np.median(d, axis=None, keepdims=True).shape,
|
|
(1, 1, 1, 1))
|
|
assert_equal(np.median(d, axis=(0, 1), keepdims=True).shape,
|
|
(1, 1, 7, 11))
|
|
assert_equal(np.median(d, axis=(0, 3), keepdims=True).shape,
|
|
(1, 5, 7, 1))
|
|
assert_equal(np.median(d, axis=(1,), keepdims=True).shape,
|
|
(3, 1, 7, 11))
|
|
assert_equal(np.median(d, axis=(0, 1, 2, 3), keepdims=True).shape,
|
|
(1, 1, 1, 1))
|
|
assert_equal(np.median(d, axis=(0, 1, 3), keepdims=True).shape,
|
|
(1, 1, 7, 1))
|
|
|
|
|
|
class TestAdd_newdoc_ufunc:
|
|
|
|
def test_ufunc_arg(self):
|
|
assert_raises(TypeError, add_newdoc_ufunc, 2, "blah")
|
|
assert_raises(ValueError, add_newdoc_ufunc, np.add, "blah")
|
|
|
|
def test_string_arg(self):
|
|
assert_raises(TypeError, add_newdoc_ufunc, np.add, 3)
|
|
|
|
|
|
class TestAdd_newdoc:
|
|
|
|
@pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
|
|
@pytest.mark.xfail(IS_PYPY, reason="PyPy does not modify tp_doc")
|
|
def test_add_doc(self):
|
|
# test that np.add_newdoc did attach a docstring successfully:
|
|
tgt = "Current flat index into the array."
|
|
assert_equal(np.core.flatiter.index.__doc__[:len(tgt)], tgt)
|
|
assert_(len(np.core.ufunc.identity.__doc__) > 300)
|
|
assert_(len(np.lib.index_tricks.mgrid.__doc__) > 300)
|
|
|
|
@pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
|
|
def test_errors_are_ignored(self):
|
|
prev_doc = np.core.flatiter.index.__doc__
|
|
# nothing changed, but error ignored, this should probably
|
|
# give a warning (or even error) in the future.
|
|
np.add_newdoc("numpy.core", "flatiter", ("index", "bad docstring"))
|
|
assert prev_doc == np.core.flatiter.index.__doc__
|
|
|
|
|
|
class TestAddDocstring():
|
|
# Test should possibly be moved, but it also fits to be close to
|
|
# the newdoc tests...
|
|
@pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
|
|
@pytest.mark.skipif(IS_PYPY, reason="PyPy does not modify tp_doc")
|
|
def test_add_same_docstring(self):
|
|
# test for attributes (which are C-level defined)
|
|
np.add_docstring(np.ndarray.flat, np.ndarray.flat.__doc__)
|
|
# And typical functions:
|
|
def func():
|
|
"""docstring"""
|
|
return
|
|
|
|
np.add_docstring(func, func.__doc__)
|
|
|
|
@pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
|
|
def test_different_docstring_fails(self):
|
|
# test for attributes (which are C-level defined)
|
|
with assert_raises(RuntimeError):
|
|
np.add_docstring(np.ndarray.flat, "different docstring")
|
|
# And typical functions:
|
|
def func():
|
|
"""docstring"""
|
|
return
|
|
|
|
with assert_raises(RuntimeError):
|
|
np.add_docstring(func, "different docstring")
|
|
|
|
|
|
class TestSortComplex:
|
|
|
|
@pytest.mark.parametrize("type_in, type_out", [
|
|
('l', 'D'),
|
|
('h', 'F'),
|
|
('H', 'F'),
|
|
('b', 'F'),
|
|
('B', 'F'),
|
|
('g', 'G'),
|
|
])
|
|
def test_sort_real(self, type_in, type_out):
|
|
# sort_complex() type casting for real input types
|
|
a = np.array([5, 3, 6, 2, 1], dtype=type_in)
|
|
actual = np.sort_complex(a)
|
|
expected = np.sort(a).astype(type_out)
|
|
assert_equal(actual, expected)
|
|
assert_equal(actual.dtype, expected.dtype)
|
|
|
|
def test_sort_complex(self):
|
|
# sort_complex() handling of complex input
|
|
a = np.array([2 + 3j, 1 - 2j, 1 - 3j, 2 + 1j], dtype='D')
|
|
expected = np.array([1 - 3j, 1 - 2j, 2 + 1j, 2 + 3j], dtype='D')
|
|
actual = np.sort_complex(a)
|
|
assert_equal(actual, expected)
|
|
assert_equal(actual.dtype, expected.dtype)
|