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364 lines
12 KiB
Python
364 lines
12 KiB
Python
"""Tests of interaction of matrix with other parts of numpy.
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Note that tests with MaskedArray and linalg are done in separate files.
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"""
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from __future__ import division, absolute_import, print_function
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import pytest
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import textwrap
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import warnings
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import numpy as np
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from numpy.testing import (assert_, assert_equal, assert_raises,
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assert_raises_regex, assert_array_equal,
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assert_almost_equal, assert_array_almost_equal)
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def test_fancy_indexing():
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# The matrix class messes with the shape. While this is always
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# weird (getitem is not used, it does not have setitem nor knows
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# about fancy indexing), this tests gh-3110
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# 2018-04-29: moved here from core.tests.test_index.
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m = np.matrix([[1, 2], [3, 4]])
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assert_(isinstance(m[[0, 1, 0], :], np.matrix))
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# gh-3110. Note the transpose currently because matrices do *not*
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# support dimension fixing for fancy indexing correctly.
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x = np.asmatrix(np.arange(50).reshape(5, 10))
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assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
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def test_polynomial_mapdomain():
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# test that polynomial preserved matrix subtype.
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# 2018-04-29: moved here from polynomial.tests.polyutils.
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dom1 = [0, 4]
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dom2 = [1, 3]
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x = np.matrix([dom1, dom1])
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res = np.polynomial.polyutils.mapdomain(x, dom1, dom2)
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assert_(isinstance(res, np.matrix))
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def test_sort_matrix_none():
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# 2018-04-29: moved here from core.tests.test_multiarray
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a = np.matrix([[2, 1, 0]])
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actual = np.sort(a, axis=None)
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expected = np.matrix([[0, 1, 2]])
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assert_equal(actual, expected)
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assert_(type(expected) is np.matrix)
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def test_partition_matrix_none():
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# gh-4301
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# 2018-04-29: moved here from core.tests.test_multiarray
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a = np.matrix([[2, 1, 0]])
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actual = np.partition(a, 1, axis=None)
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expected = np.matrix([[0, 1, 2]])
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assert_equal(actual, expected)
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assert_(type(expected) is np.matrix)
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def test_dot_scalar_and_matrix_of_objects():
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# Ticket #2469
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# 2018-04-29: moved here from core.tests.test_multiarray
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arr = np.matrix([1, 2], dtype=object)
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desired = np.matrix([[3, 6]], dtype=object)
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assert_equal(np.dot(arr, 3), desired)
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assert_equal(np.dot(3, arr), desired)
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def test_inner_scalar_and_matrix():
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# 2018-04-29: moved here from core.tests.test_multiarray
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for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
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sca = np.array(3, dtype=dt)[()]
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arr = np.matrix([[1, 2], [3, 4]], dtype=dt)
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desired = np.matrix([[3, 6], [9, 12]], dtype=dt)
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assert_equal(np.inner(arr, sca), desired)
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assert_equal(np.inner(sca, arr), desired)
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def test_inner_scalar_and_matrix_of_objects():
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# Ticket #4482
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# 2018-04-29: moved here from core.tests.test_multiarray
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arr = np.matrix([1, 2], dtype=object)
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desired = np.matrix([[3, 6]], dtype=object)
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assert_equal(np.inner(arr, 3), desired)
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assert_equal(np.inner(3, arr), desired)
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def test_iter_allocate_output_subtype():
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# Make sure that the subtype with priority wins
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# 2018-04-29: moved here from core.tests.test_nditer, given the
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# matrix specific shape test.
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# matrix vs ndarray
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a = np.matrix([[1, 2], [3, 4]])
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b = np.arange(4).reshape(2, 2).T
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i = np.nditer([a, b, None], [],
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[['readonly'], ['readonly'], ['writeonly', 'allocate']])
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assert_(type(i.operands[2]) is np.matrix)
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assert_(type(i.operands[2]) is not np.ndarray)
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assert_equal(i.operands[2].shape, (2, 2))
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# matrix always wants things to be 2D
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b = np.arange(4).reshape(1, 2, 2)
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assert_raises(RuntimeError, np.nditer, [a, b, None], [],
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[['readonly'], ['readonly'], ['writeonly', 'allocate']])
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# but if subtypes are disabled, the result can still work
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i = np.nditer([a, b, None], [],
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[['readonly'], ['readonly'],
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['writeonly', 'allocate', 'no_subtype']])
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assert_(type(i.operands[2]) is np.ndarray)
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assert_(type(i.operands[2]) is not np.matrix)
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assert_equal(i.operands[2].shape, (1, 2, 2))
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def like_function():
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# 2018-04-29: moved here from core.tests.test_numeric
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a = np.matrix([[1, 2], [3, 4]])
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for like_function in np.zeros_like, np.ones_like, np.empty_like:
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b = like_function(a)
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assert_(type(b) is np.matrix)
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c = like_function(a, subok=False)
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assert_(type(c) is not np.matrix)
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def test_array_astype():
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# 2018-04-29: copied here from core.tests.test_api
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# subok=True passes through a matrix
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a = np.matrix([[0, 1, 2], [3, 4, 5]], dtype='f4')
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b = a.astype('f4', subok=True, copy=False)
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assert_(a is b)
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# subok=True is default, and creates a subtype on a cast
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b = a.astype('i4', copy=False)
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assert_equal(a, b)
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assert_equal(type(b), np.matrix)
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# subok=False never returns a matrix
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b = a.astype('f4', subok=False, copy=False)
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assert_equal(a, b)
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assert_(not (a is b))
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assert_(type(b) is not np.matrix)
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def test_stack():
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# 2018-04-29: copied here from core.tests.test_shape_base
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# check np.matrix cannot be stacked
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m = np.matrix([[1, 2], [3, 4]])
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assert_raises_regex(ValueError, 'shape too large to be a matrix',
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np.stack, [m, m])
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def test_object_scalar_multiply():
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# Tickets #2469 and #4482
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# 2018-04-29: moved here from core.tests.test_ufunc
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arr = np.matrix([1, 2], dtype=object)
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desired = np.matrix([[3, 6]], dtype=object)
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assert_equal(np.multiply(arr, 3), desired)
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assert_equal(np.multiply(3, arr), desired)
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def test_nanfunctions_matrices():
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# Check that it works and that type and
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# shape are preserved
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# 2018-04-29: moved here from core.tests.test_nanfunctions
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mat = np.matrix(np.eye(3))
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for f in [np.nanmin, np.nanmax]:
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res = f(mat, axis=0)
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assert_(isinstance(res, np.matrix))
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assert_(res.shape == (1, 3))
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res = f(mat, axis=1)
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assert_(isinstance(res, np.matrix))
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assert_(res.shape == (3, 1))
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res = f(mat)
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assert_(np.isscalar(res))
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# check that rows of nan are dealt with for subclasses (#4628)
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mat[1] = np.nan
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for f in [np.nanmin, np.nanmax]:
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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res = f(mat, axis=0)
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assert_(isinstance(res, np.matrix))
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assert_(not np.any(np.isnan(res)))
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assert_(len(w) == 0)
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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res = f(mat, axis=1)
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assert_(isinstance(res, np.matrix))
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assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0])
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and not np.isnan(res[2, 0]))
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assert_(len(w) == 1, 'no warning raised')
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assert_(issubclass(w[0].category, RuntimeWarning))
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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res = f(mat)
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assert_(np.isscalar(res))
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assert_(res != np.nan)
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assert_(len(w) == 0)
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def test_nanfunctions_matrices_general():
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# Check that it works and that type and
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# shape are preserved
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# 2018-04-29: moved here from core.tests.test_nanfunctions
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mat = np.matrix(np.eye(3))
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for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod,
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np.nanmean, np.nanvar, np.nanstd):
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res = f(mat, axis=0)
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assert_(isinstance(res, np.matrix))
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assert_(res.shape == (1, 3))
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res = f(mat, axis=1)
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assert_(isinstance(res, np.matrix))
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assert_(res.shape == (3, 1))
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res = f(mat)
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assert_(np.isscalar(res))
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for f in np.nancumsum, np.nancumprod:
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res = f(mat, axis=0)
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assert_(isinstance(res, np.matrix))
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assert_(res.shape == (3, 3))
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res = f(mat, axis=1)
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assert_(isinstance(res, np.matrix))
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assert_(res.shape == (3, 3))
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res = f(mat)
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assert_(isinstance(res, np.matrix))
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assert_(res.shape == (1, 3*3))
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def test_average_matrix():
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# 2018-04-29: moved here from core.tests.test_function_base.
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y = np.matrix(np.random.rand(5, 5))
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assert_array_equal(y.mean(0), np.average(y, 0))
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a = np.matrix([[1, 2], [3, 4]])
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w = np.matrix([[1, 2], [3, 4]])
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r = np.average(a, axis=0, weights=w)
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assert_equal(type(r), np.matrix)
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assert_equal(r, [[2.5, 10.0/3]])
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def test_trapz_matrix():
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# Test to make sure matrices give the same answer as ndarrays
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# 2018-04-29: moved here from core.tests.test_function_base.
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x = np.linspace(0, 5)
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y = x * x
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r = np.trapz(y, x)
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mx = np.matrix(x)
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my = np.matrix(y)
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mr = np.trapz(my, mx)
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assert_almost_equal(mr, r)
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def test_ediff1d_matrix():
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# 2018-04-29: moved here from core.tests.test_arraysetops.
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assert(isinstance(np.ediff1d(np.matrix(1)), np.matrix))
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assert(isinstance(np.ediff1d(np.matrix(1), to_begin=1), np.matrix))
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def test_apply_along_axis_matrix():
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# this test is particularly malicious because matrix
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# refuses to become 1d
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# 2018-04-29: moved here from core.tests.test_shape_base.
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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 = np.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 = np.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_kron_matrix():
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# 2018-04-29: moved here from core.tests.test_shape_base.
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a = np.ones([2, 2])
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m = np.asmatrix(a)
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assert_equal(type(np.kron(a, a)), np.ndarray)
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assert_equal(type(np.kron(m, m)), np.matrix)
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assert_equal(type(np.kron(a, m)), np.matrix)
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assert_equal(type(np.kron(m, a)), np.matrix)
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class TestConcatenatorMatrix(object):
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# 2018-04-29: moved here from core.tests.test_index_tricks.
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def test_matrix(self):
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a = [1, 2]
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b = [3, 4]
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ab_r = np.r_['r', a, b]
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ab_c = np.r_['c', a, b]
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assert_equal(type(ab_r), np.matrix)
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assert_equal(type(ab_c), np.matrix)
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assert_equal(np.array(ab_r), [[1, 2, 3, 4]])
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assert_equal(np.array(ab_c), [[1], [2], [3], [4]])
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assert_raises(ValueError, lambda: np.r_['rc', a, b])
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def test_matrix_scalar(self):
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r = np.r_['r', [1, 2], 3]
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assert_equal(type(r), np.matrix)
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assert_equal(np.array(r), [[1, 2, 3]])
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def test_matrix_builder(self):
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a = np.array([1])
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b = np.array([2])
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c = np.array([3])
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d = np.array([4])
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actual = np.r_['a, b; c, d']
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expected = np.bmat([[a, b], [c, d]])
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assert_equal(actual, expected)
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assert_equal(type(actual), type(expected))
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def test_array_equal_error_message_matrix():
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# 2018-04-29: moved here from testing.tests.test_utils.
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try:
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assert_equal(np.array([1, 2]), np.matrix([1, 2]))
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except AssertionError as e:
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msg = str(e)
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msg2 = msg.replace("shapes (2L,), (1L, 2L)", "shapes (2,), (1, 2)")
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msg_reference = textwrap.dedent("""\
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Arrays are not equal
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(shapes (2,), (1, 2) mismatch)
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x: array([1, 2])
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y: matrix([[1, 2]])""")
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try:
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assert_equal(msg, msg_reference)
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except AssertionError:
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assert_equal(msg2, msg_reference)
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else:
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raise AssertionError("Did not raise")
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def test_array_almost_equal_matrix():
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# Matrix slicing keeps things 2-D, while array does not necessarily.
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# See gh-8452.
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# 2018-04-29: moved here from testing.tests.test_utils.
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m1 = np.matrix([[1., 2.]])
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m2 = np.matrix([[1., np.nan]])
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m3 = np.matrix([[1., -np.inf]])
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m4 = np.matrix([[np.nan, np.inf]])
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m5 = np.matrix([[1., 2.], [np.nan, np.inf]])
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for assert_func in assert_array_almost_equal, assert_almost_equal:
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for m in m1, m2, m3, m4, m5:
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assert_func(m, m)
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a = np.array(m)
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assert_func(a, m)
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assert_func(m, a)
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