1001 lines
37 KiB
Python
1001 lines
37 KiB
Python
"""Test functions for 1D array set operations.
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"""
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import numpy as np
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from numpy import (
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ediff1d, intersect1d, setxor1d, union1d, setdiff1d, unique, isin
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)
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from numpy.exceptions import AxisError
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from numpy.testing import (assert_array_equal, assert_equal,
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assert_raises, assert_raises_regex)
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import pytest
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class TestSetOps:
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def test_intersect1d(self):
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# unique inputs
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a = np.array([5, 7, 1, 2])
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b = np.array([2, 4, 3, 1, 5])
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ec = np.array([1, 2, 5])
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c = intersect1d(a, b, assume_unique=True)
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assert_array_equal(c, ec)
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# non-unique inputs
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a = np.array([5, 5, 7, 1, 2])
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b = np.array([2, 1, 4, 3, 3, 1, 5])
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ed = np.array([1, 2, 5])
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c = intersect1d(a, b)
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assert_array_equal(c, ed)
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assert_array_equal([], intersect1d([], []))
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def test_intersect1d_array_like(self):
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# See gh-11772
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class Test:
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def __array__(self, dtype=None, copy=None):
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return np.arange(3)
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a = Test()
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res = intersect1d(a, a)
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assert_array_equal(res, a)
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res = intersect1d([1, 2, 3], [1, 2, 3])
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assert_array_equal(res, [1, 2, 3])
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def test_intersect1d_indices(self):
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# unique inputs
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a = np.array([1, 2, 3, 4])
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b = np.array([2, 1, 4, 6])
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c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
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ee = np.array([1, 2, 4])
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assert_array_equal(c, ee)
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assert_array_equal(a[i1], ee)
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assert_array_equal(b[i2], ee)
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# non-unique inputs
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a = np.array([1, 2, 2, 3, 4, 3, 2])
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b = np.array([1, 8, 4, 2, 2, 3, 2, 3])
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c, i1, i2 = intersect1d(a, b, return_indices=True)
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ef = np.array([1, 2, 3, 4])
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assert_array_equal(c, ef)
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assert_array_equal(a[i1], ef)
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assert_array_equal(b[i2], ef)
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# non1d, unique inputs
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a = np.array([[2, 4, 5, 6], [7, 8, 1, 15]])
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b = np.array([[3, 2, 7, 6], [10, 12, 8, 9]])
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c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
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ui1 = np.unravel_index(i1, a.shape)
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ui2 = np.unravel_index(i2, b.shape)
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ea = np.array([2, 6, 7, 8])
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assert_array_equal(ea, a[ui1])
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assert_array_equal(ea, b[ui2])
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# non1d, not assumed to be uniqueinputs
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a = np.array([[2, 4, 5, 6, 6], [4, 7, 8, 7, 2]])
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b = np.array([[3, 2, 7, 7], [10, 12, 8, 7]])
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c, i1, i2 = intersect1d(a, b, return_indices=True)
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ui1 = np.unravel_index(i1, a.shape)
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ui2 = np.unravel_index(i2, b.shape)
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ea = np.array([2, 7, 8])
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assert_array_equal(ea, a[ui1])
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assert_array_equal(ea, b[ui2])
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def test_setxor1d(self):
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a = np.array([5, 7, 1, 2])
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b = np.array([2, 4, 3, 1, 5])
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ec = np.array([3, 4, 7])
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c = setxor1d(a, b)
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assert_array_equal(c, ec)
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a = np.array([1, 2, 3])
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b = np.array([6, 5, 4])
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ec = np.array([1, 2, 3, 4, 5, 6])
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c = setxor1d(a, b)
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assert_array_equal(c, ec)
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a = np.array([1, 8, 2, 3])
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b = np.array([6, 5, 4, 8])
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ec = np.array([1, 2, 3, 4, 5, 6])
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c = setxor1d(a, b)
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assert_array_equal(c, ec)
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assert_array_equal([], setxor1d([], []))
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def test_setxor1d_unique(self):
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a = np.array([1, 8, 2, 3])
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b = np.array([6, 5, 4, 8])
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ec = np.array([1, 2, 3, 4, 5, 6])
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c = setxor1d(a, b, assume_unique=True)
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assert_array_equal(c, ec)
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a = np.array([[1], [8], [2], [3]])
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b = np.array([[6, 5], [4, 8]])
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ec = np.array([1, 2, 3, 4, 5, 6])
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c = setxor1d(a, b, assume_unique=True)
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assert_array_equal(c, ec)
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def test_ediff1d(self):
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zero_elem = np.array([])
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one_elem = np.array([1])
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two_elem = np.array([1, 2])
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assert_array_equal([], ediff1d(zero_elem))
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assert_array_equal([0], ediff1d(zero_elem, to_begin=0))
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assert_array_equal([0], ediff1d(zero_elem, to_end=0))
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assert_array_equal([-1, 0], ediff1d(zero_elem, to_begin=-1, to_end=0))
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assert_array_equal([], ediff1d(one_elem))
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assert_array_equal([1], ediff1d(two_elem))
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assert_array_equal([7, 1, 9], ediff1d(two_elem, to_begin=7, to_end=9))
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assert_array_equal([5, 6, 1, 7, 8],
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ediff1d(two_elem, to_begin=[5, 6], to_end=[7, 8]))
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assert_array_equal([1, 9], ediff1d(two_elem, to_end=9))
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assert_array_equal([1, 7, 8], ediff1d(two_elem, to_end=[7, 8]))
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assert_array_equal([7, 1], ediff1d(two_elem, to_begin=7))
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assert_array_equal([5, 6, 1], ediff1d(two_elem, to_begin=[5, 6]))
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@pytest.mark.parametrize("ary, prepend, append, expected", [
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# should fail because trying to cast
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# np.nan standard floating point value
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# into an integer array:
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(np.array([1, 2, 3], dtype=np.int64),
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None,
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np.nan,
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'to_end'),
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# should fail because attempting
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# to downcast to int type:
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(np.array([1, 2, 3], dtype=np.int64),
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np.array([5, 7, 2], dtype=np.float32),
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None,
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'to_begin'),
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# should fail because attempting to cast
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# two special floating point values
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# to integers (on both sides of ary),
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# `to_begin` is in the error message as the impl checks this first:
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(np.array([1., 3., 9.], dtype=np.int8),
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np.nan,
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np.nan,
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'to_begin'),
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])
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def test_ediff1d_forbidden_type_casts(self, ary, prepend, append, expected):
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# verify resolution of gh-11490
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# specifically, raise an appropriate
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# Exception when attempting to append or
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# prepend with an incompatible type
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msg = 'dtype of `{}` must be compatible'.format(expected)
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with assert_raises_regex(TypeError, msg):
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ediff1d(ary=ary,
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to_end=append,
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to_begin=prepend)
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@pytest.mark.parametrize(
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"ary,prepend,append,expected",
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[
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(np.array([1, 2, 3], dtype=np.int16),
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2**16, # will be cast to int16 under same kind rule.
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2**16 + 4,
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np.array([0, 1, 1, 4], dtype=np.int16)),
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(np.array([1, 2, 3], dtype=np.float32),
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np.array([5], dtype=np.float64),
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None,
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np.array([5, 1, 1], dtype=np.float32)),
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(np.array([1, 2, 3], dtype=np.int32),
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0,
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0,
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np.array([0, 1, 1, 0], dtype=np.int32)),
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(np.array([1, 2, 3], dtype=np.int64),
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3,
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-9,
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np.array([3, 1, 1, -9], dtype=np.int64)),
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]
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)
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def test_ediff1d_scalar_handling(self,
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ary,
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prepend,
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append,
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expected):
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# maintain backwards-compatibility
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# of scalar prepend / append behavior
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# in ediff1d following fix for gh-11490
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actual = np.ediff1d(ary=ary,
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to_end=append,
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to_begin=prepend)
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assert_equal(actual, expected)
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assert actual.dtype == expected.dtype
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@pytest.mark.parametrize("kind", [None, "sort", "table"])
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def test_isin(self, kind):
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def _isin_slow(a, b):
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b = np.asarray(b).flatten().tolist()
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return a in b
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isin_slow = np.vectorize(_isin_slow, otypes=[bool], excluded={1})
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def assert_isin_equal(a, b):
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x = isin(a, b, kind=kind)
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y = isin_slow(a, b)
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assert_array_equal(x, y)
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# multidimensional arrays in both arguments
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a = np.arange(24).reshape([2, 3, 4])
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b = np.array([[10, 20, 30], [0, 1, 3], [11, 22, 33]])
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assert_isin_equal(a, b)
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# array-likes as both arguments
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c = [(9, 8), (7, 6)]
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d = (9, 7)
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assert_isin_equal(c, d)
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# zero-d array:
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f = np.array(3)
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assert_isin_equal(f, b)
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assert_isin_equal(a, f)
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assert_isin_equal(f, f)
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# scalar:
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assert_isin_equal(5, b)
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assert_isin_equal(a, 6)
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assert_isin_equal(5, 6)
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# empty array-like:
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if kind != "table":
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# An empty list will become float64,
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# which is invalid for kind="table"
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x = []
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assert_isin_equal(x, b)
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assert_isin_equal(a, x)
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assert_isin_equal(x, x)
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# empty array with various types:
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for dtype in [bool, np.int64, np.float64]:
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if kind == "table" and dtype == np.float64:
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continue
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if dtype in {np.int64, np.float64}:
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ar = np.array([10, 20, 30], dtype=dtype)
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elif dtype in {bool}:
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ar = np.array([True, False, False])
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empty_array = np.array([], dtype=dtype)
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assert_isin_equal(empty_array, ar)
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assert_isin_equal(ar, empty_array)
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assert_isin_equal(empty_array, empty_array)
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@pytest.mark.parametrize("kind", [None, "sort", "table"])
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def test_isin(self, kind):
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# we use two different sizes for the b array here to test the
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# two different paths in isin().
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for mult in (1, 10):
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# One check without np.array to make sure lists are handled correct
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a = [5, 7, 1, 2]
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b = [2, 4, 3, 1, 5] * mult
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ec = np.array([True, False, True, True])
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c = isin(a, b, assume_unique=True, kind=kind)
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assert_array_equal(c, ec)
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a[0] = 8
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ec = np.array([False, False, True, True])
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c = isin(a, b, assume_unique=True, kind=kind)
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assert_array_equal(c, ec)
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a[0], a[3] = 4, 8
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ec = np.array([True, False, True, False])
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c = isin(a, b, assume_unique=True, kind=kind)
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assert_array_equal(c, ec)
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a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
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b = [2, 3, 4] * mult
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ec = [False, True, False, True, True, True, True, True, True,
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False, True, False, False, False]
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c = isin(a, b, kind=kind)
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assert_array_equal(c, ec)
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b = b + [5, 5, 4] * mult
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ec = [True, True, True, True, True, True, True, True, True, True,
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True, False, True, True]
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c = isin(a, b, kind=kind)
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assert_array_equal(c, ec)
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a = np.array([5, 7, 1, 2])
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b = np.array([2, 4, 3, 1, 5] * mult)
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ec = np.array([True, False, True, True])
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c = isin(a, b, kind=kind)
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assert_array_equal(c, ec)
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a = np.array([5, 7, 1, 1, 2])
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b = np.array([2, 4, 3, 3, 1, 5] * mult)
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ec = np.array([True, False, True, True, True])
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c = isin(a, b, kind=kind)
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assert_array_equal(c, ec)
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a = np.array([5, 5])
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b = np.array([2, 2] * mult)
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ec = np.array([False, False])
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c = isin(a, b, kind=kind)
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assert_array_equal(c, ec)
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a = np.array([5])
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b = np.array([2])
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ec = np.array([False])
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c = isin(a, b, kind=kind)
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assert_array_equal(c, ec)
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if kind in {None, "sort"}:
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assert_array_equal(isin([], [], kind=kind), [])
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def test_isin_char_array(self):
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a = np.array(['a', 'b', 'c', 'd', 'e', 'c', 'e', 'b'])
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b = np.array(['a', 'c'])
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ec = np.array([True, False, True, False, False, True, False, False])
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c = isin(a, b)
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assert_array_equal(c, ec)
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@pytest.mark.parametrize("kind", [None, "sort", "table"])
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def test_isin_invert(self, kind):
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"Test isin's invert parameter"
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# We use two different sizes for the b array here to test the
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# two different paths in isin().
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for mult in (1, 10):
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a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
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b = [2, 3, 4] * mult
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assert_array_equal(np.invert(isin(a, b, kind=kind)),
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isin(a, b, invert=True, kind=kind))
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# float:
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if kind in {None, "sort"}:
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for mult in (1, 10):
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a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5],
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dtype=np.float32)
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b = [2, 3, 4] * mult
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b = np.array(b, dtype=np.float32)
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assert_array_equal(np.invert(isin(a, b, kind=kind)),
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isin(a, b, invert=True, kind=kind))
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def test_isin_hit_alternate_algorithm(self):
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"""Hit the standard isin code with integers"""
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# Need extreme range to hit standard code
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# This hits it without the use of kind='table'
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a = np.array([5, 4, 5, 3, 4, 4, 1e9], dtype=np.int64)
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b = np.array([2, 3, 4, 1e9], dtype=np.int64)
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expected = np.array([0, 1, 0, 1, 1, 1, 1], dtype=bool)
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assert_array_equal(expected, isin(a, b))
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assert_array_equal(np.invert(expected), isin(a, b, invert=True))
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a = np.array([5, 7, 1, 2], dtype=np.int64)
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b = np.array([2, 4, 3, 1, 5, 1e9], dtype=np.int64)
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ec = np.array([True, False, True, True])
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c = isin(a, b, assume_unique=True)
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assert_array_equal(c, ec)
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@pytest.mark.parametrize("kind", [None, "sort", "table"])
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def test_isin_boolean(self, kind):
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"""Test that isin works for boolean input"""
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a = np.array([True, False])
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b = np.array([False, False, False])
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expected = np.array([False, True])
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assert_array_equal(expected,
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isin(a, b, kind=kind))
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assert_array_equal(np.invert(expected),
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isin(a, b, invert=True, kind=kind))
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@pytest.mark.parametrize("kind", [None, "sort"])
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def test_isin_timedelta(self, kind):
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"""Test that isin works for timedelta input"""
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rstate = np.random.RandomState(0)
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a = rstate.randint(0, 100, size=10)
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b = rstate.randint(0, 100, size=10)
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truth = isin(a, b)
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a_timedelta = a.astype("timedelta64[s]")
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b_timedelta = b.astype("timedelta64[s]")
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assert_array_equal(truth, isin(a_timedelta, b_timedelta, kind=kind))
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def test_isin_table_timedelta_fails(self):
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a = np.array([0, 1, 2], dtype="timedelta64[s]")
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b = a
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# Make sure it raises a value error:
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with pytest.raises(ValueError):
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isin(a, b, kind="table")
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@pytest.mark.parametrize(
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"dtype1,dtype2",
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[
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(np.int8, np.int16),
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(np.int16, np.int8),
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(np.uint8, np.uint16),
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(np.uint16, np.uint8),
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(np.uint8, np.int16),
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(np.int16, np.uint8),
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(np.uint64, np.int64),
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]
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)
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@pytest.mark.parametrize("kind", [None, "sort", "table"])
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def test_isin_mixed_dtype(self, dtype1, dtype2, kind):
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"""Test that isin works as expected for mixed dtype input."""
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is_dtype2_signed = np.issubdtype(dtype2, np.signedinteger)
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ar1 = np.array([0, 0, 1, 1], dtype=dtype1)
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if is_dtype2_signed:
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ar2 = np.array([-128, 0, 127], dtype=dtype2)
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else:
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ar2 = np.array([127, 0, 255], dtype=dtype2)
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expected = np.array([True, True, False, False])
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expect_failure = kind == "table" and (
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dtype1 == np.int16 and dtype2 == np.int8)
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if expect_failure:
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with pytest.raises(RuntimeError, match="exceed the maximum"):
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isin(ar1, ar2, kind=kind)
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else:
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assert_array_equal(isin(ar1, ar2, kind=kind), expected)
|
|
|
|
@pytest.mark.parametrize("data", [
|
|
np.array([2**63, 2**63+1], dtype=np.uint64),
|
|
np.array([-2**62, -2**62-1], dtype=np.int64),
|
|
])
|
|
@pytest.mark.parametrize("kind", [None, "sort", "table"])
|
|
def test_isin_mixed_huge_vals(self, kind, data):
|
|
"""Test values outside intp range (negative ones if 32bit system)"""
|
|
query = data[1]
|
|
res = np.isin(data, query, kind=kind)
|
|
assert_array_equal(res, [False, True])
|
|
# Also check that nothing weird happens for values can't possibly
|
|
# in range.
|
|
data = data.astype(np.int32) # clearly different values
|
|
res = np.isin(data, query, kind=kind)
|
|
assert_array_equal(res, [False, False])
|
|
|
|
@pytest.mark.parametrize("kind", [None, "sort", "table"])
|
|
def test_isin_mixed_boolean(self, kind):
|
|
"""Test that isin works as expected for bool/int input."""
|
|
for dtype in np.typecodes["AllInteger"]:
|
|
a = np.array([True, False, False], dtype=bool)
|
|
b = np.array([0, 0, 0, 0], dtype=dtype)
|
|
expected = np.array([False, True, True], dtype=bool)
|
|
assert_array_equal(isin(a, b, kind=kind), expected)
|
|
|
|
a, b = b, a
|
|
expected = np.array([True, True, True, True], dtype=bool)
|
|
assert_array_equal(isin(a, b, kind=kind), expected)
|
|
|
|
def test_isin_first_array_is_object(self):
|
|
ar1 = [None]
|
|
ar2 = np.array([1]*10)
|
|
expected = np.array([False])
|
|
result = np.isin(ar1, ar2)
|
|
assert_array_equal(result, expected)
|
|
|
|
def test_isin_second_array_is_object(self):
|
|
ar1 = 1
|
|
ar2 = np.array([None]*10)
|
|
expected = np.array([False])
|
|
result = np.isin(ar1, ar2)
|
|
assert_array_equal(result, expected)
|
|
|
|
def test_isin_both_arrays_are_object(self):
|
|
ar1 = [None]
|
|
ar2 = np.array([None]*10)
|
|
expected = np.array([True])
|
|
result = np.isin(ar1, ar2)
|
|
assert_array_equal(result, expected)
|
|
|
|
def test_isin_both_arrays_have_structured_dtype(self):
|
|
# Test arrays of a structured data type containing an integer field
|
|
# and a field of dtype `object` allowing for arbitrary Python objects
|
|
dt = np.dtype([('field1', int), ('field2', object)])
|
|
ar1 = np.array([(1, None)], dtype=dt)
|
|
ar2 = np.array([(1, None)]*10, dtype=dt)
|
|
expected = np.array([True])
|
|
result = np.isin(ar1, ar2)
|
|
assert_array_equal(result, expected)
|
|
|
|
def test_isin_with_arrays_containing_tuples(self):
|
|
ar1 = np.array([(1,), 2], dtype=object)
|
|
ar2 = np.array([(1,), 2], dtype=object)
|
|
expected = np.array([True, True])
|
|
result = np.isin(ar1, ar2)
|
|
assert_array_equal(result, expected)
|
|
result = np.isin(ar1, ar2, invert=True)
|
|
assert_array_equal(result, np.invert(expected))
|
|
|
|
# An integer is added at the end of the array to make sure
|
|
# that the array builder will create the array with tuples
|
|
# and after it's created the integer is removed.
|
|
# There's a bug in the array constructor that doesn't handle
|
|
# tuples properly and adding the integer fixes that.
|
|
ar1 = np.array([(1,), (2, 1), 1], dtype=object)
|
|
ar1 = ar1[:-1]
|
|
ar2 = np.array([(1,), (2, 1), 1], dtype=object)
|
|
ar2 = ar2[:-1]
|
|
expected = np.array([True, True])
|
|
result = np.isin(ar1, ar2)
|
|
assert_array_equal(result, expected)
|
|
result = np.isin(ar1, ar2, invert=True)
|
|
assert_array_equal(result, np.invert(expected))
|
|
|
|
ar1 = np.array([(1,), (2, 3), 1], dtype=object)
|
|
ar1 = ar1[:-1]
|
|
ar2 = np.array([(1,), 2], dtype=object)
|
|
expected = np.array([True, False])
|
|
result = np.isin(ar1, ar2)
|
|
assert_array_equal(result, expected)
|
|
result = np.isin(ar1, ar2, invert=True)
|
|
assert_array_equal(result, np.invert(expected))
|
|
|
|
def test_isin_errors(self):
|
|
"""Test that isin raises expected errors."""
|
|
|
|
# Error 1: `kind` is not one of 'sort' 'table' or None.
|
|
ar1 = np.array([1, 2, 3, 4, 5])
|
|
ar2 = np.array([2, 4, 6, 8, 10])
|
|
assert_raises(ValueError, isin, ar1, ar2, kind='quicksort')
|
|
|
|
# Error 2: `kind="table"` does not work for non-integral arrays.
|
|
obj_ar1 = np.array([1, 'a', 3, 'b', 5], dtype=object)
|
|
obj_ar2 = np.array([1, 'a', 3, 'b', 5], dtype=object)
|
|
assert_raises(ValueError, isin, obj_ar1, obj_ar2, kind='table')
|
|
|
|
for dtype in [np.int32, np.int64]:
|
|
ar1 = np.array([-1, 2, 3, 4, 5], dtype=dtype)
|
|
# The range of this array will overflow:
|
|
overflow_ar2 = np.array([-1, np.iinfo(dtype).max], dtype=dtype)
|
|
|
|
# Error 3: `kind="table"` will trigger a runtime error
|
|
# if there is an integer overflow expected when computing the
|
|
# range of ar2
|
|
assert_raises(
|
|
RuntimeError,
|
|
isin, ar1, overflow_ar2, kind='table'
|
|
)
|
|
|
|
# Non-error: `kind=None` will *not* trigger a runtime error
|
|
# if there is an integer overflow, it will switch to
|
|
# the `sort` algorithm.
|
|
result = np.isin(ar1, overflow_ar2, kind=None)
|
|
assert_array_equal(result, [True] + [False] * 4)
|
|
result = np.isin(ar1, overflow_ar2, kind='sort')
|
|
assert_array_equal(result, [True] + [False] * 4)
|
|
|
|
def test_union1d(self):
|
|
a = np.array([5, 4, 7, 1, 2])
|
|
b = np.array([2, 4, 3, 3, 2, 1, 5])
|
|
|
|
ec = np.array([1, 2, 3, 4, 5, 7])
|
|
c = union1d(a, b)
|
|
assert_array_equal(c, ec)
|
|
|
|
# Tests gh-10340, arguments to union1d should be
|
|
# flattened if they are not already 1D
|
|
x = np.array([[0, 1, 2], [3, 4, 5]])
|
|
y = np.array([0, 1, 2, 3, 4])
|
|
ez = np.array([0, 1, 2, 3, 4, 5])
|
|
z = union1d(x, y)
|
|
assert_array_equal(z, ez)
|
|
|
|
assert_array_equal([], union1d([], []))
|
|
|
|
def test_setdiff1d(self):
|
|
a = np.array([6, 5, 4, 7, 1, 2, 7, 4])
|
|
b = np.array([2, 4, 3, 3, 2, 1, 5])
|
|
|
|
ec = np.array([6, 7])
|
|
c = setdiff1d(a, b)
|
|
assert_array_equal(c, ec)
|
|
|
|
a = np.arange(21)
|
|
b = np.arange(19)
|
|
ec = np.array([19, 20])
|
|
c = setdiff1d(a, b)
|
|
assert_array_equal(c, ec)
|
|
|
|
assert_array_equal([], setdiff1d([], []))
|
|
a = np.array((), np.uint32)
|
|
assert_equal(setdiff1d(a, []).dtype, np.uint32)
|
|
|
|
def test_setdiff1d_unique(self):
|
|
a = np.array([3, 2, 1])
|
|
b = np.array([7, 5, 2])
|
|
expected = np.array([3, 1])
|
|
actual = setdiff1d(a, b, assume_unique=True)
|
|
assert_equal(actual, expected)
|
|
|
|
def test_setdiff1d_char_array(self):
|
|
a = np.array(['a', 'b', 'c'])
|
|
b = np.array(['a', 'b', 's'])
|
|
assert_array_equal(setdiff1d(a, b), np.array(['c']))
|
|
|
|
def test_manyways(self):
|
|
a = np.array([5, 7, 1, 2, 8])
|
|
b = np.array([9, 8, 2, 4, 3, 1, 5])
|
|
|
|
c1 = setxor1d(a, b)
|
|
aux1 = intersect1d(a, b)
|
|
aux2 = union1d(a, b)
|
|
c2 = setdiff1d(aux2, aux1)
|
|
assert_array_equal(c1, c2)
|
|
|
|
|
|
class TestUnique:
|
|
|
|
def test_unique_1d(self):
|
|
|
|
def check_all(a, b, i1, i2, c, dt):
|
|
base_msg = 'check {0} failed for type {1}'
|
|
|
|
msg = base_msg.format('values', dt)
|
|
v = unique(a)
|
|
assert_array_equal(v, b, msg)
|
|
|
|
msg = base_msg.format('return_index', dt)
|
|
v, j = unique(a, True, False, False)
|
|
assert_array_equal(v, b, msg)
|
|
assert_array_equal(j, i1, msg)
|
|
|
|
msg = base_msg.format('return_inverse', dt)
|
|
v, j = unique(a, False, True, False)
|
|
assert_array_equal(v, b, msg)
|
|
assert_array_equal(j, i2, msg)
|
|
|
|
msg = base_msg.format('return_counts', dt)
|
|
v, j = unique(a, False, False, True)
|
|
assert_array_equal(v, b, msg)
|
|
assert_array_equal(j, c, msg)
|
|
|
|
msg = base_msg.format('return_index and return_inverse', dt)
|
|
v, j1, j2 = unique(a, True, True, False)
|
|
assert_array_equal(v, b, msg)
|
|
assert_array_equal(j1, i1, msg)
|
|
assert_array_equal(j2, i2, msg)
|
|
|
|
msg = base_msg.format('return_index and return_counts', dt)
|
|
v, j1, j2 = unique(a, True, False, True)
|
|
assert_array_equal(v, b, msg)
|
|
assert_array_equal(j1, i1, msg)
|
|
assert_array_equal(j2, c, msg)
|
|
|
|
msg = base_msg.format('return_inverse and return_counts', dt)
|
|
v, j1, j2 = unique(a, False, True, True)
|
|
assert_array_equal(v, b, msg)
|
|
assert_array_equal(j1, i2, msg)
|
|
assert_array_equal(j2, c, msg)
|
|
|
|
msg = base_msg.format(('return_index, return_inverse '
|
|
'and return_counts'), dt)
|
|
v, j1, j2, j3 = unique(a, True, True, True)
|
|
assert_array_equal(v, b, msg)
|
|
assert_array_equal(j1, i1, msg)
|
|
assert_array_equal(j2, i2, msg)
|
|
assert_array_equal(j3, c, msg)
|
|
|
|
a = [5, 7, 1, 2, 1, 5, 7]*10
|
|
b = [1, 2, 5, 7]
|
|
i1 = [2, 3, 0, 1]
|
|
i2 = [2, 3, 0, 1, 0, 2, 3]*10
|
|
c = np.multiply([2, 1, 2, 2], 10)
|
|
|
|
# test for numeric arrays
|
|
types = []
|
|
types.extend(np.typecodes['AllInteger'])
|
|
types.extend(np.typecodes['AllFloat'])
|
|
types.append('datetime64[D]')
|
|
types.append('timedelta64[D]')
|
|
for dt in types:
|
|
aa = np.array(a, dt)
|
|
bb = np.array(b, dt)
|
|
check_all(aa, bb, i1, i2, c, dt)
|
|
|
|
# test for object arrays
|
|
dt = 'O'
|
|
aa = np.empty(len(a), dt)
|
|
aa[:] = a
|
|
bb = np.empty(len(b), dt)
|
|
bb[:] = b
|
|
check_all(aa, bb, i1, i2, c, dt)
|
|
|
|
# test for structured arrays
|
|
dt = [('', 'i'), ('', 'i')]
|
|
aa = np.array(list(zip(a, a)), dt)
|
|
bb = np.array(list(zip(b, b)), dt)
|
|
check_all(aa, bb, i1, i2, c, dt)
|
|
|
|
# test for ticket #2799
|
|
aa = [1. + 0.j, 1 - 1.j, 1]
|
|
assert_array_equal(np.unique(aa), [1. - 1.j, 1. + 0.j])
|
|
|
|
# test for ticket #4785
|
|
a = [(1, 2), (1, 2), (2, 3)]
|
|
unq = [1, 2, 3]
|
|
inv = [[0, 1], [0, 1], [1, 2]]
|
|
a1 = unique(a)
|
|
assert_array_equal(a1, unq)
|
|
a2, a2_inv = unique(a, return_inverse=True)
|
|
assert_array_equal(a2, unq)
|
|
assert_array_equal(a2_inv, inv)
|
|
|
|
# test for chararrays with return_inverse (gh-5099)
|
|
a = np.char.chararray(5)
|
|
a[...] = ''
|
|
a2, a2_inv = np.unique(a, return_inverse=True)
|
|
assert_array_equal(a2_inv, np.zeros(5))
|
|
|
|
# test for ticket #9137
|
|
a = []
|
|
a1_idx = np.unique(a, return_index=True)[1]
|
|
a2_inv = np.unique(a, return_inverse=True)[1]
|
|
a3_idx, a3_inv = np.unique(a, return_index=True,
|
|
return_inverse=True)[1:]
|
|
assert_equal(a1_idx.dtype, np.intp)
|
|
assert_equal(a2_inv.dtype, np.intp)
|
|
assert_equal(a3_idx.dtype, np.intp)
|
|
assert_equal(a3_inv.dtype, np.intp)
|
|
|
|
# test for ticket 2111 - float
|
|
a = [2.0, np.nan, 1.0, np.nan]
|
|
ua = [1.0, 2.0, np.nan]
|
|
ua_idx = [2, 0, 1]
|
|
ua_inv = [1, 2, 0, 2]
|
|
ua_cnt = [1, 1, 2]
|
|
assert_equal(np.unique(a), ua)
|
|
assert_equal(np.unique(a, return_index=True), (ua, ua_idx))
|
|
assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))
|
|
assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))
|
|
|
|
# test for ticket 2111 - complex
|
|
a = [2.0-1j, np.nan, 1.0+1j, complex(0.0, np.nan), complex(1.0, np.nan)]
|
|
ua = [1.0+1j, 2.0-1j, complex(0.0, np.nan)]
|
|
ua_idx = [2, 0, 3]
|
|
ua_inv = [1, 2, 0, 2, 2]
|
|
ua_cnt = [1, 1, 3]
|
|
assert_equal(np.unique(a), ua)
|
|
assert_equal(np.unique(a, return_index=True), (ua, ua_idx))
|
|
assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))
|
|
assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))
|
|
|
|
# test for ticket 2111 - datetime64
|
|
nat = np.datetime64('nat')
|
|
a = [np.datetime64('2020-12-26'), nat, np.datetime64('2020-12-24'), nat]
|
|
ua = [np.datetime64('2020-12-24'), np.datetime64('2020-12-26'), nat]
|
|
ua_idx = [2, 0, 1]
|
|
ua_inv = [1, 2, 0, 2]
|
|
ua_cnt = [1, 1, 2]
|
|
assert_equal(np.unique(a), ua)
|
|
assert_equal(np.unique(a, return_index=True), (ua, ua_idx))
|
|
assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))
|
|
assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))
|
|
|
|
# test for ticket 2111 - timedelta
|
|
nat = np.timedelta64('nat')
|
|
a = [np.timedelta64(1, 'D'), nat, np.timedelta64(1, 'h'), nat]
|
|
ua = [np.timedelta64(1, 'h'), np.timedelta64(1, 'D'), nat]
|
|
ua_idx = [2, 0, 1]
|
|
ua_inv = [1, 2, 0, 2]
|
|
ua_cnt = [1, 1, 2]
|
|
assert_equal(np.unique(a), ua)
|
|
assert_equal(np.unique(a, return_index=True), (ua, ua_idx))
|
|
assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))
|
|
assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))
|
|
|
|
# test for gh-19300
|
|
all_nans = [np.nan] * 4
|
|
ua = [np.nan]
|
|
ua_idx = [0]
|
|
ua_inv = [0, 0, 0, 0]
|
|
ua_cnt = [4]
|
|
assert_equal(np.unique(all_nans), ua)
|
|
assert_equal(np.unique(all_nans, return_index=True), (ua, ua_idx))
|
|
assert_equal(np.unique(all_nans, return_inverse=True), (ua, ua_inv))
|
|
assert_equal(np.unique(all_nans, return_counts=True), (ua, ua_cnt))
|
|
|
|
def test_unique_axis_errors(self):
|
|
assert_raises(TypeError, self._run_axis_tests, object)
|
|
assert_raises(TypeError, self._run_axis_tests,
|
|
[('a', int), ('b', object)])
|
|
|
|
assert_raises(AxisError, unique, np.arange(10), axis=2)
|
|
assert_raises(AxisError, unique, np.arange(10), axis=-2)
|
|
|
|
def test_unique_axis_list(self):
|
|
msg = "Unique failed on list of lists"
|
|
inp = [[0, 1, 0], [0, 1, 0]]
|
|
inp_arr = np.asarray(inp)
|
|
assert_array_equal(unique(inp, axis=0), unique(inp_arr, axis=0), msg)
|
|
assert_array_equal(unique(inp, axis=1), unique(inp_arr, axis=1), msg)
|
|
|
|
def test_unique_axis(self):
|
|
types = []
|
|
types.extend(np.typecodes['AllInteger'])
|
|
types.extend(np.typecodes['AllFloat'])
|
|
types.append('datetime64[D]')
|
|
types.append('timedelta64[D]')
|
|
types.append([('a', int), ('b', int)])
|
|
types.append([('a', int), ('b', float)])
|
|
|
|
for dtype in types:
|
|
self._run_axis_tests(dtype)
|
|
|
|
msg = 'Non-bitwise-equal booleans test failed'
|
|
data = np.arange(10, dtype=np.uint8).reshape(-1, 2).view(bool)
|
|
result = np.array([[False, True], [True, True]], dtype=bool)
|
|
assert_array_equal(unique(data, axis=0), result, msg)
|
|
|
|
msg = 'Negative zero equality test failed'
|
|
data = np.array([[-0.0, 0.0], [0.0, -0.0], [-0.0, 0.0], [0.0, -0.0]])
|
|
result = np.array([[-0.0, 0.0]])
|
|
assert_array_equal(unique(data, axis=0), result, msg)
|
|
|
|
@pytest.mark.parametrize("axis", [0, -1])
|
|
def test_unique_1d_with_axis(self, axis):
|
|
x = np.array([4, 3, 2, 3, 2, 1, 2, 2])
|
|
uniq = unique(x, axis=axis)
|
|
assert_array_equal(uniq, [1, 2, 3, 4])
|
|
|
|
@pytest.mark.parametrize("axis", [None, 0, -1])
|
|
def test_unique_inverse_with_axis(self, axis):
|
|
x = np.array([[4, 4, 3], [2, 2, 1], [2, 2, 1], [4, 4, 3]])
|
|
uniq, inv = unique(x, return_inverse=True, axis=axis)
|
|
assert_equal(inv.ndim, x.ndim if axis is None else 1)
|
|
assert_array_equal(x, np.take(uniq, inv, axis=axis))
|
|
|
|
def test_unique_axis_zeros(self):
|
|
# issue 15559
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single_zero = np.empty(shape=(2, 0), dtype=np.int8)
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uniq, idx, inv, cnt = unique(single_zero, axis=0, return_index=True,
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return_inverse=True, return_counts=True)
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# there's 1 element of shape (0,) along axis 0
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assert_equal(uniq.dtype, single_zero.dtype)
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assert_array_equal(uniq, np.empty(shape=(1, 0)))
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assert_array_equal(idx, np.array([0]))
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assert_array_equal(inv, np.array([0, 0]))
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assert_array_equal(cnt, np.array([2]))
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# there's 0 elements of shape (2,) along axis 1
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uniq, idx, inv, cnt = unique(single_zero, axis=1, return_index=True,
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return_inverse=True, return_counts=True)
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assert_equal(uniq.dtype, single_zero.dtype)
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assert_array_equal(uniq, np.empty(shape=(2, 0)))
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assert_array_equal(idx, np.array([]))
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assert_array_equal(inv, np.array([]))
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assert_array_equal(cnt, np.array([]))
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# test a "complicated" shape
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shape = (0, 2, 0, 3, 0, 4, 0)
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multiple_zeros = np.empty(shape=shape)
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for axis in range(len(shape)):
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expected_shape = list(shape)
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if shape[axis] == 0:
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expected_shape[axis] = 0
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else:
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expected_shape[axis] = 1
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assert_array_equal(unique(multiple_zeros, axis=axis),
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np.empty(shape=expected_shape))
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def test_unique_masked(self):
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# issue 8664
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x = np.array([64, 0, 1, 2, 3, 63, 63, 0, 0, 0, 1, 2, 0, 63, 0],
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dtype='uint8')
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y = np.ma.masked_equal(x, 0)
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v = np.unique(y)
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v2, i, c = np.unique(y, return_index=True, return_counts=True)
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msg = 'Unique returned different results when asked for index'
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assert_array_equal(v.data, v2.data, msg)
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assert_array_equal(v.mask, v2.mask, msg)
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def test_unique_sort_order_with_axis(self):
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# These tests fail if sorting along axis is done by treating subarrays
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# as unsigned byte strings. See gh-10495.
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fmt = "sort order incorrect for integer type '%s'"
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for dt in 'bhilq':
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a = np.array([[-1], [0]], dt)
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b = np.unique(a, axis=0)
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assert_array_equal(a, b, fmt % dt)
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def _run_axis_tests(self, dtype):
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data = np.array([[0, 1, 0, 0],
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[1, 0, 0, 0],
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[0, 1, 0, 0],
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[1, 0, 0, 0]]).astype(dtype)
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msg = 'Unique with 1d array and axis=0 failed'
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result = np.array([0, 1])
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assert_array_equal(unique(data), result.astype(dtype), msg)
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msg = 'Unique with 2d array and axis=0 failed'
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result = np.array([[0, 1, 0, 0], [1, 0, 0, 0]])
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assert_array_equal(unique(data, axis=0), result.astype(dtype), msg)
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msg = 'Unique with 2d array and axis=1 failed'
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result = np.array([[0, 0, 1], [0, 1, 0], [0, 0, 1], [0, 1, 0]])
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assert_array_equal(unique(data, axis=1), result.astype(dtype), msg)
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msg = 'Unique with 3d array and axis=2 failed'
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data3d = np.array([[[1, 1],
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[1, 0]],
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[[0, 1],
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[0, 0]]]).astype(dtype)
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result = np.take(data3d, [1, 0], axis=2)
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assert_array_equal(unique(data3d, axis=2), result, msg)
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uniq, idx, inv, cnt = unique(data, axis=0, return_index=True,
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return_inverse=True, return_counts=True)
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msg = "Unique's return_index=True failed with axis=0"
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assert_array_equal(data[idx], uniq, msg)
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msg = "Unique's return_inverse=True failed with axis=0"
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assert_array_equal(np.take(uniq, inv, axis=0), data)
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msg = "Unique's return_counts=True failed with axis=0"
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assert_array_equal(cnt, np.array([2, 2]), msg)
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uniq, idx, inv, cnt = unique(data, axis=1, return_index=True,
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return_inverse=True, return_counts=True)
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msg = "Unique's return_index=True failed with axis=1"
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assert_array_equal(data[:, idx], uniq)
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msg = "Unique's return_inverse=True failed with axis=1"
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assert_array_equal(np.take(uniq, inv, axis=1), data)
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msg = "Unique's return_counts=True failed with axis=1"
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assert_array_equal(cnt, np.array([2, 1, 1]), msg)
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def test_unique_nanequals(self):
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# issue 20326
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a = np.array([1, 1, np.nan, np.nan, np.nan])
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unq = np.unique(a)
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not_unq = np.unique(a, equal_nan=False)
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assert_array_equal(unq, np.array([1, np.nan]))
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assert_array_equal(not_unq, np.array([1, np.nan, np.nan, np.nan]))
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def test_unique_array_api_functions(self):
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arr = np.array([np.nan, 1, 4, 1, 3, 4, np.nan, 5, 1])
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for res_unique_array_api, res_unique in [
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(
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np.unique_values(arr),
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np.unique(arr, equal_nan=False)
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),
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(
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np.unique_counts(arr),
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np.unique(arr, return_counts=True, equal_nan=False)
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),
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(
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np.unique_inverse(arr),
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np.unique(arr, return_inverse=True, equal_nan=False)
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),
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(
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np.unique_all(arr),
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np.unique(
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arr,
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return_index=True,
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return_inverse=True,
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return_counts=True,
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equal_nan=False
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)
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)
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]:
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assert len(res_unique_array_api) == len(res_unique)
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for actual, expected in zip(res_unique_array_api, res_unique):
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assert_array_equal(actual, expected)
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def test_unique_inverse_shape(self):
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# Regression test for https://github.com/numpy/numpy/issues/25552
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arr = np.array([[1, 2, 3], [2, 3, 1]])
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expected_values, expected_inverse = np.unique(arr, return_inverse=True)
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expected_inverse = expected_inverse.reshape(arr.shape)
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for func in np.unique_inverse, np.unique_all:
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result = func(arr)
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assert_array_equal(expected_values, result.values)
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assert_array_equal(expected_inverse, result.inverse_indices)
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assert_array_equal(arr, result.values[result.inverse_indices])
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