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"""Test functions for 1D array set operations.
"""
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import (
run_module_suite, TestCase, assert_array_equal, assert_equal, assert_raises
)
from numpy.lib.arraysetops import (
ediff1d, intersect1d, setxor1d, union1d, setdiff1d, unique, in1d, isin
)
class TestSetOps(TestCase):
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_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]))
assert(isinstance(ediff1d(np.matrix(1)), np.matrix))
assert(isinstance(ediff1d(np.matrix(1), to_begin=1), np.matrix))
def test_isin(self):
# 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)
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:
x = []
assert_isin_equal(x, b)
assert_isin_equal(a, x)
assert_isin_equal(x, x)
def test_in1d(self):
# 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)
assert_array_equal(c, ec)
a[0] = 8
ec = np.array([False, False, True, True])
c = in1d(a, b, assume_unique=True)
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)
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)
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)
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)
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)
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)
assert_array_equal(c, ec)
a = np.array([5])
b = np.array([2])
ec = np.array([False])
c = in1d(a, b)
assert_array_equal(c, ec)
assert_array_equal(in1d([], []), [])
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)
def test_in1d_invert(self):
"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)), in1d(a, b, invert=True))
def test_in1d_ravel(self):
# 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), ec)
assert_array_equal(in1d(a, b, assume_unique=False), ec)
assert_array_equal(in1d(a, long_b, assume_unique=True), ec)
assert_array_equal(in1d(a, long_b, assume_unique=False), ec)
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)
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_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(TestCase):
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, 1, 0, 0)
assert_array_equal(v, b, msg)
assert_array_equal(j, i1, msg)
msg = base_msg.format('return_inverse', dt)
v, j = unique(a, 0, 1, 0)
assert_array_equal(v, b, msg)
assert_array_equal(j, i2, msg)
msg = base_msg.format('return_counts', dt)
v, j = unique(a, 0, 0, 1)
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, 1, 1, 0)
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, 1, 0, 1)
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, 0, 1, 1)
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, 1, 1, 1)
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))
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(ValueError, unique, np.arange(10), axis=2)
assert_raises(ValueError, 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)
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 _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.dstack([data] * 3)
result = data3d[..., :1]
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)
if __name__ == "__main__":
run_module_suite()
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