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"""Test functions for 1D array set operations.
"""
import numpy as np
from numpy.testing import (assert_array_equal, assert_equal,
assert_raises, assert_raises_regex)
from numpy.lib.arraysetops import (
ediff1d, intersect1d, setxor1d, union1d, setdiff1d, unique, in1d, isin
)
import pytest
class TestSetOps:
def test_intersect1d(self):
# unique inputs
a = np.array([5, 7, 1, 2])
b = np.array([2, 4, 3, 1, 5])
ec = np.array([1, 2, 5])
c = intersect1d(a, b, assume_unique=True)
assert_array_equal(c, ec)
# non-unique inputs
a = np.array([5, 5, 7, 1, 2])
b = np.array([2, 1, 4, 3, 3, 1, 5])
ed = np.array([1, 2, 5])
c = intersect1d(a, b)
assert_array_equal(c, ed)
assert_array_equal([], intersect1d([], []))
def test_intersect1d_array_like(self):
# See gh-11772
class Test:
def __array__(self):
return np.arange(3)
a = Test()
res = intersect1d(a, a)
assert_array_equal(res, a)
res = intersect1d([1, 2, 3], [1, 2, 3])
assert_array_equal(res, [1, 2, 3])
def test_intersect1d_indices(self):
# unique inputs
a = np.array([1, 2, 3, 4])
b = np.array([2, 1, 4, 6])
c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
ee = np.array([1, 2, 4])
assert_array_equal(c, ee)
assert_array_equal(a[i1], ee)
assert_array_equal(b[i2], ee)
# non-unique inputs
a = np.array([1, 2, 2, 3, 4, 3, 2])
b = np.array([1, 8, 4, 2, 2, 3, 2, 3])
c, i1, i2 = intersect1d(a, b, return_indices=True)
ef = np.array([1, 2, 3, 4])
assert_array_equal(c, ef)
assert_array_equal(a[i1], ef)
assert_array_equal(b[i2], ef)
# non1d, unique inputs
a = np.array([[2, 4, 5, 6], [7, 8, 1, 15]])
b = np.array([[3, 2, 7, 6], [10, 12, 8, 9]])
c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True)
ui1 = np.unravel_index(i1, a.shape)
ui2 = np.unravel_index(i2, b.shape)
ea = np.array([2, 6, 7, 8])
assert_array_equal(ea, a[ui1])
assert_array_equal(ea, b[ui2])
# non1d, not assumed to be uniqueinputs
a = np.array([[2, 4, 5, 6, 6], [4, 7, 8, 7, 2]])
b = np.array([[3, 2, 7, 7], [10, 12, 8, 7]])
c, i1, i2 = intersect1d(a, b, return_indices=True)
ui1 = np.unravel_index(i1, a.shape)
ui2 = np.unravel_index(i2, b.shape)
ea = np.array([2, 7, 8])
assert_array_equal(ea, a[ui1])
assert_array_equal(ea, b[ui2])
def test_setxor1d(self):
a = np.array([5, 7, 1, 2])
b = np.array([2, 4, 3, 1, 5])
ec = np.array([3, 4, 7])
c = setxor1d(a, b)
assert_array_equal(c, ec)
a = np.array([1, 2, 3])
b = np.array([6, 5, 4])
ec = np.array([1, 2, 3, 4, 5, 6])
c = setxor1d(a, b)
assert_array_equal(c, ec)
a = np.array([1, 8, 2, 3])
b = np.array([6, 5, 4, 8])
ec = np.array([1, 2, 3, 4, 5, 6])
c = setxor1d(a, b)
assert_array_equal(c, ec)
assert_array_equal([], setxor1d([], []))
def test_ediff1d(self):
zero_elem = np.array([])
one_elem = np.array([1])
two_elem = np.array([1, 2])
assert_array_equal([], ediff1d(zero_elem))
assert_array_equal([0], ediff1d(zero_elem, to_begin=0))
assert_array_equal([0], ediff1d(zero_elem, to_end=0))
assert_array_equal([-1, 0], ediff1d(zero_elem, to_begin=-1, to_end=0))
assert_array_equal([], ediff1d(one_elem))
assert_array_equal([1], ediff1d(two_elem))
assert_array_equal([7, 1, 9], ediff1d(two_elem, to_begin=7, to_end=9))
assert_array_equal([5, 6, 1, 7, 8],
ediff1d(two_elem, to_begin=[5, 6], to_end=[7, 8]))
assert_array_equal([1, 9], ediff1d(two_elem, to_end=9))
assert_array_equal([1, 7, 8], ediff1d(two_elem, to_end=[7, 8]))
assert_array_equal([7, 1], ediff1d(two_elem, to_begin=7))
assert_array_equal([5, 6, 1], ediff1d(two_elem, to_begin=[5, 6]))
@pytest.mark.parametrize("ary, prepend, append, expected", [
# should fail because trying to cast
# np.nan standard floating point value
# into an integer array:
(np.array([1, 2, 3], dtype=np.int64),
None,
np.nan,
'to_end'),
# should fail because attempting
# to downcast to int type:
(np.array([1, 2, 3], dtype=np.int64),
np.array([5, 7, 2], dtype=np.float32),
None,
'to_begin'),
# should fail because attempting to cast
# two special floating point values
# to integers (on both sides of ary),
# `to_begin` is in the error message as the impl checks this first:
(np.array([1., 3., 9.], dtype=np.int8),
np.nan,
np.nan,
'to_begin'),
])
def test_ediff1d_forbidden_type_casts(self, ary, prepend, append, expected):
# verify resolution of gh-11490
# specifically, raise an appropriate
# Exception when attempting to append or
# prepend with an incompatible type
msg = 'dtype of `{}` must be compatible'.format(expected)
with assert_raises_regex(TypeError, msg):
ediff1d(ary=ary,
to_end=append,
to_begin=prepend)
@pytest.mark.parametrize(
"ary,prepend,append,expected",
[
(np.array([1, 2, 3], dtype=np.int16),
2**16, # will be cast to int16 under same kind rule.
2**16 + 4,
np.array([0, 1, 1, 4], dtype=np.int16)),
(np.array([1, 2, 3], dtype=np.float32),
np.array([5], dtype=np.float64),
None,
np.array([5, 1, 1], dtype=np.float32)),
(np.array([1, 2, 3], dtype=np.int32),
0,
0,
np.array([0, 1, 1, 0], dtype=np.int32)),
(np.array([1, 2, 3], dtype=np.int64),
3,
-9,
np.array([3, 1, 1, -9], dtype=np.int64)),
]
)
def test_ediff1d_scalar_handling(self,
ary,
prepend,
append,
expected):
# maintain backwards-compatibility
# of scalar prepend / append behavior
# in ediff1d following fix for gh-11490
actual = np.ediff1d(ary=ary,
to_end=append,
to_begin=prepend)
assert_equal(actual, expected)
assert actual.dtype == expected.dtype
@pytest.mark.parametrize("kind", [None, "sort", "table"])
def test_isin(self, kind):
# the tests for in1d cover most of isin's behavior
# if in1d is removed, would need to change those tests to test
# isin instead.
def _isin_slow(a, b):
b = np.asarray(b).flatten().tolist()
return a in b
isin_slow = np.vectorize(_isin_slow, otypes=[bool], excluded={1})
def assert_isin_equal(a, b):
x = isin(a, b, kind=kind)
y = isin_slow(a, b)
assert_array_equal(x, y)
# multidimensional arrays in both arguments
a = np.arange(24).reshape([2, 3, 4])
b = np.array([[10, 20, 30], [0, 1, 3], [11, 22, 33]])
assert_isin_equal(a, b)
# array-likes as both arguments
c = [(9, 8), (7, 6)]
d = (9, 7)
assert_isin_equal(c, d)
# zero-d array:
f = np.array(3)
assert_isin_equal(f, b)
assert_isin_equal(a, f)
assert_isin_equal(f, f)
# scalar:
assert_isin_equal(5, b)
assert_isin_equal(a, 6)
assert_isin_equal(5, 6)
# empty array-like:
if kind != "table":
# An empty list will become float64,
# which is invalid for kind="table"
x = []
assert_isin_equal(x, b)
assert_isin_equal(a, x)
assert_isin_equal(x, x)
# empty array with various types:
for dtype in [bool, np.int64, np.float64]:
if kind == "table" and dtype == np.float64:
continue
if dtype in {np.int64, np.float64}:
ar = np.array([10, 20, 30], dtype=dtype)
elif dtype in {bool}:
ar = np.array([True, False, False])
empty_array = np.array([], dtype=dtype)
assert_isin_equal(empty_array, ar)
assert_isin_equal(ar, empty_array)
assert_isin_equal(empty_array, empty_array)
@pytest.mark.parametrize("kind", [None, "sort", "table"])
def test_in1d(self, kind):
# we use two different sizes for the b array here to test the
# two different paths in in1d().
for mult in (1, 10):
# One check without np.array to make sure lists are handled correct
a = [5, 7, 1, 2]
b = [2, 4, 3, 1, 5] * mult
ec = np.array([True, False, True, True])
c = in1d(a, b, assume_unique=True, kind=kind)
assert_array_equal(c, ec)
a[0] = 8
ec = np.array([False, False, True, True])
c = in1d(a, b, assume_unique=True, kind=kind)
assert_array_equal(c, ec)
a[0], a[3] = 4, 8
ec = np.array([True, False, True, False])
c = in1d(a, b, assume_unique=True, kind=kind)
assert_array_equal(c, ec)
a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
b = [2, 3, 4] * mult
ec = [False, True, False, True, True, True, True, True, True,
False, True, False, False, False]
c = in1d(a, b, kind=kind)
assert_array_equal(c, ec)
b = b + [5, 5, 4] * mult
ec = [True, True, True, True, True, True, True, True, True, True,
True, False, True, True]
c = in1d(a, b, kind=kind)
assert_array_equal(c, ec)
a = np.array([5, 7, 1, 2])
b = np.array([2, 4, 3, 1, 5] * mult)
ec = np.array([True, False, True, True])
c = in1d(a, b, kind=kind)
assert_array_equal(c, ec)
a = np.array([5, 7, 1, 1, 2])
b = np.array([2, 4, 3, 3, 1, 5] * mult)
ec = np.array([True, False, True, True, True])
c = in1d(a, b, kind=kind)
assert_array_equal(c, ec)
a = np.array([5, 5])
b = np.array([2, 2] * mult)
ec = np.array([False, False])
c = in1d(a, b, kind=kind)
assert_array_equal(c, ec)
a = np.array([5])
b = np.array([2])
ec = np.array([False])
c = in1d(a, b, kind=kind)
assert_array_equal(c, ec)
if kind in {None, "sort"}:
assert_array_equal(in1d([], [], kind=kind), [])
def test_in1d_char_array(self):
a = np.array(['a', 'b', 'c', 'd', 'e', 'c', 'e', 'b'])
b = np.array(['a', 'c'])
ec = np.array([True, False, True, False, False, True, False, False])
c = in1d(a, b)
assert_array_equal(c, ec)
@pytest.mark.parametrize("kind", [None, "sort", "table"])
def test_in1d_invert(self, kind):
"Test in1d's invert parameter"
# We use two different sizes for the b array here to test the
# two different paths in in1d().
for mult in (1, 10):
a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5])
b = [2, 3, 4] * mult
assert_array_equal(np.invert(in1d(a, b, kind=kind)),
in1d(a, b, invert=True, kind=kind))
# float:
if kind in {None, "sort"}:
for mult in (1, 10):
a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5],
dtype=np.float32)
b = [2, 3, 4] * mult
b = np.array(b, dtype=np.float32)
assert_array_equal(np.invert(in1d(a, b, kind=kind)),
in1d(a, b, invert=True, kind=kind))
@pytest.mark.parametrize("kind", [None, "sort", "table"])
def test_in1d_ravel(self, kind):
# Test that in1d ravels its input arrays. This is not documented
# behavior however. The test is to ensure consistentency.
a = np.arange(6).reshape(2, 3)
b = np.arange(3, 9).reshape(3, 2)
long_b = np.arange(3, 63).reshape(30, 2)
ec = np.array([False, False, False, True, True, True])
assert_array_equal(in1d(a, b, assume_unique=True, kind=kind),
ec)
assert_array_equal(in1d(a, b, assume_unique=False,
kind=kind),
ec)
assert_array_equal(in1d(a, long_b, assume_unique=True,
kind=kind),
ec)
assert_array_equal(in1d(a, long_b, assume_unique=False,
kind=kind),
ec)
def test_in1d_hit_alternate_algorithm(self):
"""Hit the standard isin code with integers"""
# Need extreme range to hit standard code
# This hits it without the use of kind='table'
a = np.array([5, 4, 5, 3, 4, 4, 1e9], dtype=np.int64)
b = np.array([2, 3, 4, 1e9], dtype=np.int64)
expected = np.array([0, 1, 0, 1, 1, 1, 1], dtype=bool)
assert_array_equal(expected, in1d(a, b))
assert_array_equal(np.invert(expected), in1d(a, b, invert=True))
a = np.array([5, 7, 1, 2], dtype=np.int64)
b = np.array([2, 4, 3, 1, 5, 1e9], dtype=np.int64)
ec = np.array([True, False, True, True])
c = in1d(a, b, assume_unique=True)
assert_array_equal(c, ec)
@pytest.mark.parametrize("kind", [None, "sort", "table"])
def test_in1d_boolean(self, kind):
"""Test that in1d works for boolean input"""
a = np.array([True, False])
b = np.array([False, False, False])
expected = np.array([False, True])
assert_array_equal(expected,
in1d(a, b, kind=kind))
assert_array_equal(np.invert(expected),
in1d(a, b, invert=True, kind=kind))
@pytest.mark.parametrize("kind", [None, "sort"])
def test_in1d_timedelta(self, kind):
"""Test that in1d works for timedelta input"""
rstate = np.random.RandomState(0)
a = rstate.randint(0, 100, size=10)
b = rstate.randint(0, 100, size=10)
truth = in1d(a, b)
a_timedelta = a.astype("timedelta64[s]")
b_timedelta = b.astype("timedelta64[s]")
assert_array_equal(truth, in1d(a_timedelta, b_timedelta, kind=kind))
def test_in1d_table_timedelta_fails(self):
a = np.array([0, 1, 2], dtype="timedelta64[s]")
b = a
# Make sure it raises a value error:
with pytest.raises(ValueError):
in1d(a, b, kind="table")
@pytest.mark.parametrize(
"dtype1,dtype2",
[
(np.int8, np.int16),
(np.int16, np.int8),
(np.uint8, np.uint16),
(np.uint16, np.uint8),
(np.uint8, np.int16),
(np.int16, np.uint8),
]
)
@pytest.mark.parametrize("kind", [None, "sort", "table"])
def test_in1d_mixed_dtype(self, dtype1, dtype2, kind):
"""Test that in1d works as expected for mixed dtype input."""
is_dtype2_signed = np.issubdtype(dtype2, np.signedinteger)
ar1 = np.array([0, 0, 1, 1], dtype=dtype1)
if is_dtype2_signed:
ar2 = np.array([-128, 0, 127], dtype=dtype2)
else:
ar2 = np.array([127, 0, 255], dtype=dtype2)
expected = np.array([True, True, False, False])
expect_failure = kind == "table" and any((
dtype1 == np.int8 and dtype2 == np.int16,
dtype1 == np.int16 and dtype2 == np.int8
))
if expect_failure:
with pytest.raises(RuntimeError, match="exceed the maximum"):
in1d(ar1, ar2, kind=kind)
else:
assert_array_equal(in1d(ar1, ar2, kind=kind), expected)
@pytest.mark.parametrize("kind", [None, "sort", "table"])
def test_in1d_mixed_boolean(self, kind):
"""Test that in1d 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(in1d(a, b, kind=kind), expected)
a, b = b, a
expected = np.array([True, True, True, True], dtype=bool)
assert_array_equal(in1d(a, b, kind=kind), expected)
def test_in1d_first_array_is_object(self):
ar1 = [None]
ar2 = np.array([1]*10)
expected = np.array([False])
result = np.in1d(ar1, ar2)
assert_array_equal(result, expected)
def test_in1d_second_array_is_object(self):
ar1 = 1
ar2 = np.array([None]*10)
expected = np.array([False])
result = np.in1d(ar1, ar2)
assert_array_equal(result, expected)
def test_in1d_both_arrays_are_object(self):
ar1 = [None]
ar2 = np.array([None]*10)
expected = np.array([True])
result = np.in1d(ar1, ar2)
assert_array_equal(result, expected)
def test_in1d_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.in1d(ar1, ar2)
assert_array_equal(result, expected)
def test_in1d_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.in1d(ar1, ar2)
assert_array_equal(result, expected)
result = np.in1d(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.in1d(ar1, ar2)
assert_array_equal(result, expected)
result = np.in1d(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.in1d(ar1, ar2)
assert_array_equal(result, expected)
result = np.in1d(ar1, ar2, invert=True)
assert_array_equal(result, np.invert(expected))
def test_in1d_errors(self):
"""Test that in1d 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, in1d, 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, in1d, 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,
in1d, 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.in1d(ar1, overflow_ar2, kind=None)
assert_array_equal(result, [True] + [False] * 4)
result = np.in1d(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.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(np.AxisError, unique, np.arange(10), axis=2)
assert_raises(np.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])
def test_unique_axis_zeros(self):
# issue 15559
single_zero = np.empty(shape=(2, 0), dtype=np.int8)
uniq, idx, inv, cnt = unique(single_zero, axis=0, return_index=True,
return_inverse=True, return_counts=True)
# there's 1 element of shape (0,) along axis 0
assert_equal(uniq.dtype, single_zero.dtype)
assert_array_equal(uniq, np.empty(shape=(1, 0)))
assert_array_equal(idx, np.array([0]))
assert_array_equal(inv, np.array([0, 0]))
assert_array_equal(cnt, np.array([2]))
# there's 0 elements of shape (2,) along axis 1
uniq, idx, inv, cnt = unique(single_zero, axis=1, return_index=True,
return_inverse=True, return_counts=True)
assert_equal(uniq.dtype, single_zero.dtype)
assert_array_equal(uniq, np.empty(shape=(2, 0)))
assert_array_equal(idx, np.array([]))
assert_array_equal(inv, np.array([]))
assert_array_equal(cnt, np.array([]))
# test a "complicated" shape
shape = (0, 2, 0, 3, 0, 4, 0)
multiple_zeros = np.empty(shape=shape)
for axis in range(len(shape)):
expected_shape = list(shape)
if shape[axis] == 0:
expected_shape[axis] = 0
else:
expected_shape[axis] = 1
assert_array_equal(unique(multiple_zeros, axis=axis),
np.empty(shape=expected_shape))
def test_unique_masked(self):
# issue 8664
x = np.array([64, 0, 1, 2, 3, 63, 63, 0, 0, 0, 1, 2, 0, 63, 0],
dtype='uint8')
y = np.ma.masked_equal(x, 0)
v = np.unique(y)
v2, i, c = np.unique(y, return_index=True, return_counts=True)
msg = 'Unique returned different results when asked for index'
assert_array_equal(v.data, v2.data, msg)
assert_array_equal(v.mask, v2.mask, msg)
def test_unique_sort_order_with_axis(self):
# These tests fail if sorting along axis is done by treating subarrays
# as unsigned byte strings. See gh-10495.
fmt = "sort order incorrect for integer type '%s'"
for dt in 'bhilq':
a = np.array([[-1], [0]], dt)
b = np.unique(a, axis=0)
assert_array_equal(a, b, fmt % dt)
def _run_axis_tests(self, dtype):
data = np.array([[0, 1, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[1, 0, 0, 0]]).astype(dtype)
msg = 'Unique with 1d array and axis=0 failed'
result = np.array([0, 1])
assert_array_equal(unique(data), result.astype(dtype), msg)
msg = 'Unique with 2d array and axis=0 failed'
result = np.array([[0, 1, 0, 0], [1, 0, 0, 0]])
assert_array_equal(unique(data, axis=0), result.astype(dtype), msg)
msg = 'Unique with 2d array and axis=1 failed'
result = np.array([[0, 0, 1], [0, 1, 0], [0, 0, 1], [0, 1, 0]])
assert_array_equal(unique(data, axis=1), result.astype(dtype), msg)
msg = 'Unique with 3d array and axis=2 failed'
data3d = np.array([[[1, 1],
[1, 0]],
[[0, 1],
[0, 0]]]).astype(dtype)
result = np.take(data3d, [1, 0], axis=2)
assert_array_equal(unique(data3d, axis=2), result, msg)
uniq, idx, inv, cnt = unique(data, axis=0, return_index=True,
return_inverse=True, return_counts=True)
msg = "Unique's return_index=True failed with axis=0"
assert_array_equal(data[idx], uniq, msg)
msg = "Unique's return_inverse=True failed with axis=0"
assert_array_equal(uniq[inv], data)
msg = "Unique's return_counts=True failed with axis=0"
assert_array_equal(cnt, np.array([2, 2]), msg)
uniq, idx, inv, cnt = unique(data, axis=1, return_index=True,
return_inverse=True, return_counts=True)
msg = "Unique's return_index=True failed with axis=1"
assert_array_equal(data[:, idx], uniq)
msg = "Unique's return_inverse=True failed with axis=1"
assert_array_equal(uniq[:, inv], data)
msg = "Unique's return_counts=True failed with axis=1"
assert_array_equal(cnt, np.array([2, 1, 1]), msg)
def test_unique_nanequals(self):
# issue 20326
a = np.array([1, 1, np.nan, np.nan, np.nan])
unq = np.unique(a)
not_unq = np.unique(a, equal_nan=False)
assert_array_equal(unq, np.array([1, np.nan]))
assert_array_equal(not_unq, np.array([1, np.nan, np.nan, np.nan]))
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