576 lines
19 KiB
Python
576 lines
19 KiB
Python
from __future__ import division, absolute_import, print_function
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import numpy as np
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import warnings
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from numpy.lib.shape_base import (
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apply_along_axis, apply_over_axes, array_split, split, hsplit, dsplit,
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vsplit, dstack, column_stack, kron, tile, expand_dims,
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)
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from numpy.testing import (
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run_module_suite, assert_, assert_equal, assert_array_equal, assert_raises,
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assert_warns
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)
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class TestApplyAlongAxis(object):
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def test_simple(self):
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a = np.ones((20, 10), 'd')
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assert_array_equal(
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apply_along_axis(len, 0, a), len(a)*np.ones(a.shape[1]))
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def test_simple101(self):
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a = np.ones((10, 101), 'd')
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assert_array_equal(
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apply_along_axis(len, 0, a), len(a)*np.ones(a.shape[1]))
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def test_3d(self):
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a = np.arange(27).reshape((3, 3, 3))
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assert_array_equal(apply_along_axis(np.sum, 0, a),
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[[27, 30, 33], [36, 39, 42], [45, 48, 51]])
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def test_preserve_subclass(self):
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# this test is particularly malicious because matrix
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# refuses to become 1d
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def double(row):
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return row * 2
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m = np.matrix([[0, 1], [2, 3]])
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expected = np.matrix([[0, 2], [4, 6]])
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result = apply_along_axis(double, 0, m)
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assert_(isinstance(result, np.matrix))
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assert_array_equal(result, expected)
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result = apply_along_axis(double, 1, m)
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assert_(isinstance(result, np.matrix))
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assert_array_equal(result, expected)
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def test_subclass(self):
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class MinimalSubclass(np.ndarray):
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data = 1
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def minimal_function(array):
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return array.data
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a = np.zeros((6, 3)).view(MinimalSubclass)
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assert_array_equal(
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apply_along_axis(minimal_function, 0, a), np.array([1, 1, 1])
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)
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def test_scalar_array(self, cls=np.ndarray):
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a = np.ones((6, 3)).view(cls)
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res = apply_along_axis(np.sum, 0, a)
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assert_(isinstance(res, cls))
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assert_array_equal(res, np.array([6, 6, 6]).view(cls))
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def test_0d_array(self, cls=np.ndarray):
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def sum_to_0d(x):
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""" Sum x, returning a 0d array of the same class """
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assert_equal(x.ndim, 1)
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return np.squeeze(np.sum(x, keepdims=True))
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a = np.ones((6, 3)).view(cls)
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res = apply_along_axis(sum_to_0d, 0, a)
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assert_(isinstance(res, cls))
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assert_array_equal(res, np.array([6, 6, 6]).view(cls))
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res = apply_along_axis(sum_to_0d, 1, a)
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assert_(isinstance(res, cls))
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assert_array_equal(res, np.array([3, 3, 3, 3, 3, 3]).view(cls))
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def test_axis_insertion(self, cls=np.ndarray):
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def f1to2(x):
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"""produces an assymmetric non-square matrix from x"""
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assert_equal(x.ndim, 1)
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return (x[::-1] * x[1:,None]).view(cls)
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a2d = np.arange(6*3).reshape((6, 3))
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# 2d insertion along first axis
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actual = apply_along_axis(f1to2, 0, a2d)
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expected = np.stack([
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f1to2(a2d[:,i]) for i in range(a2d.shape[1])
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], axis=-1).view(cls)
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assert_equal(type(actual), type(expected))
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assert_equal(actual, expected)
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# 2d insertion along last axis
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actual = apply_along_axis(f1to2, 1, a2d)
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expected = np.stack([
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f1to2(a2d[i,:]) for i in range(a2d.shape[0])
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], axis=0).view(cls)
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assert_equal(type(actual), type(expected))
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assert_equal(actual, expected)
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# 3d insertion along middle axis
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a3d = np.arange(6*5*3).reshape((6, 5, 3))
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actual = apply_along_axis(f1to2, 1, a3d)
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expected = np.stack([
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np.stack([
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f1to2(a3d[i,:,j]) for i in range(a3d.shape[0])
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], axis=0)
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for j in range(a3d.shape[2])
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], axis=-1).view(cls)
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assert_equal(type(actual), type(expected))
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assert_equal(actual, expected)
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def test_subclass_preservation(self):
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class MinimalSubclass(np.ndarray):
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pass
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self.test_scalar_array(MinimalSubclass)
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self.test_0d_array(MinimalSubclass)
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self.test_axis_insertion(MinimalSubclass)
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def test_axis_insertion_ma(self):
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def f1to2(x):
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"""produces an assymmetric non-square matrix from x"""
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assert_equal(x.ndim, 1)
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res = x[::-1] * x[1:,None]
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return np.ma.masked_where(res%5==0, res)
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a = np.arange(6*3).reshape((6, 3))
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res = apply_along_axis(f1to2, 0, a)
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assert_(isinstance(res, np.ma.masked_array))
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assert_equal(res.ndim, 3)
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assert_array_equal(res[:,:,0].mask, f1to2(a[:,0]).mask)
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assert_array_equal(res[:,:,1].mask, f1to2(a[:,1]).mask)
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assert_array_equal(res[:,:,2].mask, f1to2(a[:,2]).mask)
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def test_tuple_func1d(self):
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def sample_1d(x):
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return x[1], x[0]
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res = np.apply_along_axis(sample_1d, 1, np.array([[1, 2], [3, 4]]))
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assert_array_equal(res, np.array([[2, 1], [4, 3]]))
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def test_empty(self):
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# can't apply_along_axis when there's no chance to call the function
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def never_call(x):
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assert_(False) # should never be reached
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a = np.empty((0, 0))
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assert_raises(ValueError, np.apply_along_axis, never_call, 0, a)
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assert_raises(ValueError, np.apply_along_axis, never_call, 1, a)
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# but it's sometimes ok with some non-zero dimensions
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def empty_to_1(x):
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assert_(len(x) == 0)
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return 1
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a = np.empty((10, 0))
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actual = np.apply_along_axis(empty_to_1, 1, a)
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assert_equal(actual, np.ones(10))
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assert_raises(ValueError, np.apply_along_axis, empty_to_1, 0, a)
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def test_with_iterable_object(self):
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# from issue 5248
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d = np.array([
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[set([1, 11]), set([2, 22]), set([3, 33])],
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[set([4, 44]), set([5, 55]), set([6, 66])]
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])
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actual = np.apply_along_axis(lambda a: set.union(*a), 0, d)
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expected = np.array([{1, 11, 4, 44}, {2, 22, 5, 55}, {3, 33, 6, 66}])
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assert_equal(actual, expected)
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# issue 8642 - assert_equal doesn't detect this!
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for i in np.ndindex(actual.shape):
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assert_equal(type(actual[i]), type(expected[i]))
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class TestApplyOverAxes(object):
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def test_simple(self):
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a = np.arange(24).reshape(2, 3, 4)
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aoa_a = apply_over_axes(np.sum, a, [0, 2])
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assert_array_equal(aoa_a, np.array([[[60], [92], [124]]]))
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class TestExpandDims(object):
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def test_functionality(self):
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s = (2, 3, 4, 5)
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a = np.empty(s)
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for axis in range(-5, 4):
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b = expand_dims(a, axis)
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assert_(b.shape[axis] == 1)
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assert_(np.squeeze(b).shape == s)
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def test_deprecations(self):
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# 2017-05-17, 1.13.0
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s = (2, 3, 4, 5)
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a = np.empty(s)
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with warnings.catch_warnings():
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warnings.simplefilter("always")
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assert_warns(DeprecationWarning, expand_dims, a, -6)
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assert_warns(DeprecationWarning, expand_dims, a, 5)
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class TestArraySplit(object):
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def test_integer_0_split(self):
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a = np.arange(10)
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assert_raises(ValueError, array_split, a, 0)
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def test_integer_split(self):
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a = np.arange(10)
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res = array_split(a, 1)
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desired = [np.arange(10)]
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compare_results(res, desired)
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res = array_split(a, 2)
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desired = [np.arange(5), np.arange(5, 10)]
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compare_results(res, desired)
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res = array_split(a, 3)
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desired = [np.arange(4), np.arange(4, 7), np.arange(7, 10)]
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compare_results(res, desired)
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res = array_split(a, 4)
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desired = [np.arange(3), np.arange(3, 6), np.arange(6, 8),
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np.arange(8, 10)]
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compare_results(res, desired)
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res = array_split(a, 5)
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desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
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np.arange(6, 8), np.arange(8, 10)]
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compare_results(res, desired)
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res = array_split(a, 6)
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desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
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np.arange(6, 8), np.arange(8, 9), np.arange(9, 10)]
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compare_results(res, desired)
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res = array_split(a, 7)
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desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
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np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
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np.arange(9, 10)]
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compare_results(res, desired)
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res = array_split(a, 8)
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desired = [np.arange(2), np.arange(2, 4), np.arange(4, 5),
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np.arange(5, 6), np.arange(6, 7), np.arange(7, 8),
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np.arange(8, 9), np.arange(9, 10)]
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compare_results(res, desired)
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res = array_split(a, 9)
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desired = [np.arange(2), np.arange(2, 3), np.arange(3, 4),
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np.arange(4, 5), np.arange(5, 6), np.arange(6, 7),
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np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)]
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compare_results(res, desired)
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res = array_split(a, 10)
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desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
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np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
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np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
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np.arange(9, 10)]
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compare_results(res, desired)
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res = array_split(a, 11)
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desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
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np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
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np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
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np.arange(9, 10), np.array([])]
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compare_results(res, desired)
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def test_integer_split_2D_rows(self):
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a = np.array([np.arange(10), np.arange(10)])
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res = array_split(a, 3, axis=0)
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tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]),
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np.zeros((0, 10))]
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compare_results(res, tgt)
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assert_(a.dtype.type is res[-1].dtype.type)
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# Same thing for manual splits:
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res = array_split(a, [0, 1, 2], axis=0)
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tgt = [np.zeros((0, 10)), np.array([np.arange(10)]),
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np.array([np.arange(10)])]
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compare_results(res, tgt)
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assert_(a.dtype.type is res[-1].dtype.type)
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def test_integer_split_2D_cols(self):
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a = np.array([np.arange(10), np.arange(10)])
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res = array_split(a, 3, axis=-1)
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desired = [np.array([np.arange(4), np.arange(4)]),
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np.array([np.arange(4, 7), np.arange(4, 7)]),
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np.array([np.arange(7, 10), np.arange(7, 10)])]
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compare_results(res, desired)
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def test_integer_split_2D_default(self):
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""" This will fail if we change default axis
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"""
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a = np.array([np.arange(10), np.arange(10)])
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res = array_split(a, 3)
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tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]),
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np.zeros((0, 10))]
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compare_results(res, tgt)
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assert_(a.dtype.type is res[-1].dtype.type)
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# perhaps should check higher dimensions
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def test_index_split_simple(self):
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a = np.arange(10)
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indices = [1, 5, 7]
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res = array_split(a, indices, axis=-1)
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desired = [np.arange(0, 1), np.arange(1, 5), np.arange(5, 7),
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np.arange(7, 10)]
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compare_results(res, desired)
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def test_index_split_low_bound(self):
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a = np.arange(10)
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indices = [0, 5, 7]
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res = array_split(a, indices, axis=-1)
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desired = [np.array([]), np.arange(0, 5), np.arange(5, 7),
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np.arange(7, 10)]
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compare_results(res, desired)
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def test_index_split_high_bound(self):
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a = np.arange(10)
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indices = [0, 5, 7, 10, 12]
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res = array_split(a, indices, axis=-1)
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desired = [np.array([]), np.arange(0, 5), np.arange(5, 7),
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np.arange(7, 10), np.array([]), np.array([])]
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compare_results(res, desired)
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class TestSplit(object):
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# The split function is essentially the same as array_split,
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# except that it test if splitting will result in an
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# equal split. Only test for this case.
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def test_equal_split(self):
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a = np.arange(10)
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res = split(a, 2)
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desired = [np.arange(5), np.arange(5, 10)]
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compare_results(res, desired)
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def test_unequal_split(self):
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a = np.arange(10)
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assert_raises(ValueError, split, a, 3)
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class TestColumnStack(object):
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def test_non_iterable(self):
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assert_raises(TypeError, column_stack, 1)
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class TestDstack(object):
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def test_non_iterable(self):
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assert_raises(TypeError, dstack, 1)
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def test_0D_array(self):
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a = np.array(1)
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b = np.array(2)
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res = dstack([a, b])
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desired = np.array([[[1, 2]]])
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assert_array_equal(res, desired)
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def test_1D_array(self):
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a = np.array([1])
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b = np.array([2])
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res = dstack([a, b])
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desired = np.array([[[1, 2]]])
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assert_array_equal(res, desired)
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def test_2D_array(self):
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a = np.array([[1], [2]])
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b = np.array([[1], [2]])
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res = dstack([a, b])
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desired = np.array([[[1, 1]], [[2, 2, ]]])
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assert_array_equal(res, desired)
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def test_2D_array2(self):
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a = np.array([1, 2])
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b = np.array([1, 2])
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res = dstack([a, b])
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desired = np.array([[[1, 1], [2, 2]]])
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assert_array_equal(res, desired)
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# array_split has more comprehensive test of splitting.
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# only do simple test on hsplit, vsplit, and dsplit
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class TestHsplit(object):
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"""Only testing for integer splits.
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"""
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def test_non_iterable(self):
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assert_raises(ValueError, hsplit, 1, 1)
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def test_0D_array(self):
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a = np.array(1)
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try:
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hsplit(a, 2)
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assert_(0)
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except ValueError:
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pass
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def test_1D_array(self):
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a = np.array([1, 2, 3, 4])
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res = hsplit(a, 2)
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desired = [np.array([1, 2]), np.array([3, 4])]
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compare_results(res, desired)
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def test_2D_array(self):
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a = np.array([[1, 2, 3, 4],
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[1, 2, 3, 4]])
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res = hsplit(a, 2)
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desired = [np.array([[1, 2], [1, 2]]), np.array([[3, 4], [3, 4]])]
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compare_results(res, desired)
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class TestVsplit(object):
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"""Only testing for integer splits.
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"""
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def test_non_iterable(self):
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assert_raises(ValueError, vsplit, 1, 1)
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def test_0D_array(self):
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a = np.array(1)
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assert_raises(ValueError, vsplit, a, 2)
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def test_1D_array(self):
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a = np.array([1, 2, 3, 4])
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try:
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vsplit(a, 2)
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assert_(0)
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except ValueError:
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pass
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def test_2D_array(self):
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a = np.array([[1, 2, 3, 4],
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[1, 2, 3, 4]])
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res = vsplit(a, 2)
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desired = [np.array([[1, 2, 3, 4]]), np.array([[1, 2, 3, 4]])]
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compare_results(res, desired)
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class TestDsplit(object):
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# Only testing for integer splits.
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def test_non_iterable(self):
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assert_raises(ValueError, dsplit, 1, 1)
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def test_0D_array(self):
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a = np.array(1)
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assert_raises(ValueError, dsplit, a, 2)
|
|
|
|
def test_1D_array(self):
|
|
a = np.array([1, 2, 3, 4])
|
|
assert_raises(ValueError, dsplit, a, 2)
|
|
|
|
def test_2D_array(self):
|
|
a = np.array([[1, 2, 3, 4],
|
|
[1, 2, 3, 4]])
|
|
try:
|
|
dsplit(a, 2)
|
|
assert_(0)
|
|
except ValueError:
|
|
pass
|
|
|
|
def test_3D_array(self):
|
|
a = np.array([[[1, 2, 3, 4],
|
|
[1, 2, 3, 4]],
|
|
[[1, 2, 3, 4],
|
|
[1, 2, 3, 4]]])
|
|
res = dsplit(a, 2)
|
|
desired = [np.array([[[1, 2], [1, 2]], [[1, 2], [1, 2]]]),
|
|
np.array([[[3, 4], [3, 4]], [[3, 4], [3, 4]]])]
|
|
compare_results(res, desired)
|
|
|
|
|
|
class TestSqueeze(object):
|
|
def test_basic(self):
|
|
from numpy.random import rand
|
|
|
|
a = rand(20, 10, 10, 1, 1)
|
|
b = rand(20, 1, 10, 1, 20)
|
|
c = rand(1, 1, 20, 10)
|
|
assert_array_equal(np.squeeze(a), np.reshape(a, (20, 10, 10)))
|
|
assert_array_equal(np.squeeze(b), np.reshape(b, (20, 10, 20)))
|
|
assert_array_equal(np.squeeze(c), np.reshape(c, (20, 10)))
|
|
|
|
# Squeezing to 0-dim should still give an ndarray
|
|
a = [[[1.5]]]
|
|
res = np.squeeze(a)
|
|
assert_equal(res, 1.5)
|
|
assert_equal(res.ndim, 0)
|
|
assert_equal(type(res), np.ndarray)
|
|
|
|
|
|
class TestKron(object):
|
|
def test_return_type(self):
|
|
a = np.ones([2, 2])
|
|
m = np.asmatrix(a)
|
|
assert_equal(type(kron(a, a)), np.ndarray)
|
|
assert_equal(type(kron(m, m)), np.matrix)
|
|
assert_equal(type(kron(a, m)), np.matrix)
|
|
assert_equal(type(kron(m, a)), np.matrix)
|
|
|
|
class myarray(np.ndarray):
|
|
__array_priority__ = 0.0
|
|
|
|
ma = myarray(a.shape, a.dtype, a.data)
|
|
assert_equal(type(kron(a, a)), np.ndarray)
|
|
assert_equal(type(kron(ma, ma)), myarray)
|
|
assert_equal(type(kron(a, ma)), np.ndarray)
|
|
assert_equal(type(kron(ma, a)), myarray)
|
|
|
|
|
|
class TestTile(object):
|
|
def test_basic(self):
|
|
a = np.array([0, 1, 2])
|
|
b = [[1, 2], [3, 4]]
|
|
assert_equal(tile(a, 2), [0, 1, 2, 0, 1, 2])
|
|
assert_equal(tile(a, (2, 2)), [[0, 1, 2, 0, 1, 2], [0, 1, 2, 0, 1, 2]])
|
|
assert_equal(tile(a, (1, 2)), [[0, 1, 2, 0, 1, 2]])
|
|
assert_equal(tile(b, 2), [[1, 2, 1, 2], [3, 4, 3, 4]])
|
|
assert_equal(tile(b, (2, 1)), [[1, 2], [3, 4], [1, 2], [3, 4]])
|
|
assert_equal(tile(b, (2, 2)), [[1, 2, 1, 2], [3, 4, 3, 4],
|
|
[1, 2, 1, 2], [3, 4, 3, 4]])
|
|
|
|
def test_tile_one_repetition_on_array_gh4679(self):
|
|
a = np.arange(5)
|
|
b = tile(a, 1)
|
|
b += 2
|
|
assert_equal(a, np.arange(5))
|
|
|
|
def test_empty(self):
|
|
a = np.array([[[]]])
|
|
b = np.array([[], []])
|
|
c = tile(b, 2).shape
|
|
d = tile(a, (3, 2, 5)).shape
|
|
assert_equal(c, (2, 0))
|
|
assert_equal(d, (3, 2, 0))
|
|
|
|
def test_kroncompare(self):
|
|
from numpy.random import randint
|
|
|
|
reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)]
|
|
shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
|
|
for s in shape:
|
|
b = randint(0, 10, size=s)
|
|
for r in reps:
|
|
a = np.ones(r, b.dtype)
|
|
large = tile(b, r)
|
|
klarge = kron(a, b)
|
|
assert_equal(large, klarge)
|
|
|
|
|
|
class TestMayShareMemory(object):
|
|
def test_basic(self):
|
|
d = np.ones((50, 60))
|
|
d2 = np.ones((30, 60, 6))
|
|
assert_(np.may_share_memory(d, d))
|
|
assert_(np.may_share_memory(d, d[::-1]))
|
|
assert_(np.may_share_memory(d, d[::2]))
|
|
assert_(np.may_share_memory(d, d[1:, ::-1]))
|
|
|
|
assert_(not np.may_share_memory(d[::-1], d2))
|
|
assert_(not np.may_share_memory(d[::2], d2))
|
|
assert_(not np.may_share_memory(d[1:, ::-1], d2))
|
|
assert_(np.may_share_memory(d2[1:, ::-1], d2))
|
|
|
|
|
|
# Utility
|
|
def compare_results(res, desired):
|
|
for i in range(len(desired)):
|
|
assert_array_equal(res[i], desired[i])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_module_suite()
|