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import numpy as np
import functools
import sys
import pytest
from numpy.lib.shape_base import (
apply_along_axis, apply_over_axes, array_split, split, hsplit, dsplit,
vsplit, dstack, column_stack, kron, tile, expand_dims, take_along_axis,
put_along_axis
)
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_raises, assert_warns
)
IS_64BIT = sys.maxsize > 2**32
def _add_keepdims(func):
""" hack in keepdims behavior into a function taking an axis """
@functools.wraps(func)
def wrapped(a, axis, **kwargs):
res = func(a, axis=axis, **kwargs)
if axis is None:
axis = 0 # res is now a scalar, so we can insert this anywhere
return np.expand_dims(res, axis=axis)
return wrapped
class TestTakeAlongAxis:
def test_argequivalent(self):
""" Test it translates from arg<func> to <func> """
from numpy.random import rand
a = rand(3, 4, 5)
funcs = [
(np.sort, np.argsort, dict()),
(_add_keepdims(np.min), _add_keepdims(np.argmin), dict()),
(_add_keepdims(np.max), _add_keepdims(np.argmax), dict()),
(np.partition, np.argpartition, dict(kth=2)),
]
for func, argfunc, kwargs in funcs:
for axis in list(range(a.ndim)) + [None]:
a_func = func(a, axis=axis, **kwargs)
ai_func = argfunc(a, axis=axis, **kwargs)
assert_equal(a_func, take_along_axis(a, ai_func, axis=axis))
def test_invalid(self):
""" Test it errors when indices has too few dimensions """
a = np.ones((10, 10))
ai = np.ones((10, 2), dtype=np.intp)
# sanity check
take_along_axis(a, ai, axis=1)
# not enough indices
assert_raises(ValueError, take_along_axis, a, np.array(1), axis=1)
# bool arrays not allowed
assert_raises(IndexError, take_along_axis, a, ai.astype(bool), axis=1)
# float arrays not allowed
assert_raises(IndexError, take_along_axis, a, ai.astype(float), axis=1)
# invalid axis
assert_raises(np.AxisError, take_along_axis, a, ai, axis=10)
def test_empty(self):
""" Test everything is ok with empty results, even with inserted dims """
a = np.ones((3, 4, 5))
ai = np.ones((3, 0, 5), dtype=np.intp)
actual = take_along_axis(a, ai, axis=1)
assert_equal(actual.shape, ai.shape)
def test_broadcast(self):
""" Test that non-indexing dimensions are broadcast in both directions """
a = np.ones((3, 4, 1))
ai = np.ones((1, 2, 5), dtype=np.intp)
actual = take_along_axis(a, ai, axis=1)
assert_equal(actual.shape, (3, 2, 5))
class TestPutAlongAxis:
def test_replace_max(self):
a_base = np.array([[10, 30, 20], [60, 40, 50]])
for axis in list(range(a_base.ndim)) + [None]:
# we mutate this in the loop
a = a_base.copy()
# replace the max with a small value
i_max = _add_keepdims(np.argmax)(a, axis=axis)
put_along_axis(a, i_max, -99, axis=axis)
# find the new minimum, which should max
i_min = _add_keepdims(np.argmin)(a, axis=axis)
assert_equal(i_min, i_max)
def test_broadcast(self):
""" Test that non-indexing dimensions are broadcast in both directions """
a = np.ones((3, 4, 1))
ai = np.arange(10, dtype=np.intp).reshape((1, 2, 5)) % 4
put_along_axis(a, ai, 20, axis=1)
assert_equal(take_along_axis(a, ai, axis=1), 20)
class TestApplyAlongAxis:
def test_simple(self):
a = np.ones((20, 10), 'd')
assert_array_equal(
apply_along_axis(len, 0, a), len(a)*np.ones(a.shape[1]))
def test_simple101(self):
a = np.ones((10, 101), 'd')
assert_array_equal(
apply_along_axis(len, 0, a), len(a)*np.ones(a.shape[1]))
def test_3d(self):
a = np.arange(27).reshape((3, 3, 3))
assert_array_equal(apply_along_axis(np.sum, 0, a),
[[27, 30, 33], [36, 39, 42], [45, 48, 51]])
def test_preserve_subclass(self):
def double(row):
return row * 2
class MyNDArray(np.ndarray):
pass
m = np.array([[0, 1], [2, 3]]).view(MyNDArray)
expected = np.array([[0, 2], [4, 6]]).view(MyNDArray)
result = apply_along_axis(double, 0, m)
assert_(isinstance(result, MyNDArray))
assert_array_equal(result, expected)
result = apply_along_axis(double, 1, m)
assert_(isinstance(result, MyNDArray))
assert_array_equal(result, expected)
def test_subclass(self):
class MinimalSubclass(np.ndarray):
data = 1
def minimal_function(array):
return array.data
a = np.zeros((6, 3)).view(MinimalSubclass)
assert_array_equal(
apply_along_axis(minimal_function, 0, a), np.array([1, 1, 1])
)
def test_scalar_array(self, cls=np.ndarray):
a = np.ones((6, 3)).view(cls)
res = apply_along_axis(np.sum, 0, a)
assert_(isinstance(res, cls))
assert_array_equal(res, np.array([6, 6, 6]).view(cls))
def test_0d_array(self, cls=np.ndarray):
def sum_to_0d(x):
""" Sum x, returning a 0d array of the same class """
assert_equal(x.ndim, 1)
return np.squeeze(np.sum(x, keepdims=True))
a = np.ones((6, 3)).view(cls)
res = apply_along_axis(sum_to_0d, 0, a)
assert_(isinstance(res, cls))
assert_array_equal(res, np.array([6, 6, 6]).view(cls))
res = apply_along_axis(sum_to_0d, 1, a)
assert_(isinstance(res, cls))
assert_array_equal(res, np.array([3, 3, 3, 3, 3, 3]).view(cls))
def test_axis_insertion(self, cls=np.ndarray):
def f1to2(x):
"""produces an asymmetric non-square matrix from x"""
assert_equal(x.ndim, 1)
return (x[::-1] * x[1:,None]).view(cls)
a2d = np.arange(6*3).reshape((6, 3))
# 2d insertion along first axis
actual = apply_along_axis(f1to2, 0, a2d)
expected = np.stack([
f1to2(a2d[:,i]) for i in range(a2d.shape[1])
], axis=-1).view(cls)
assert_equal(type(actual), type(expected))
assert_equal(actual, expected)
# 2d insertion along last axis
actual = apply_along_axis(f1to2, 1, a2d)
expected = np.stack([
f1to2(a2d[i,:]) for i in range(a2d.shape[0])
], axis=0).view(cls)
assert_equal(type(actual), type(expected))
assert_equal(actual, expected)
# 3d insertion along middle axis
a3d = np.arange(6*5*3).reshape((6, 5, 3))
actual = apply_along_axis(f1to2, 1, a3d)
expected = np.stack([
np.stack([
f1to2(a3d[i,:,j]) for i in range(a3d.shape[0])
], axis=0)
for j in range(a3d.shape[2])
], axis=-1).view(cls)
assert_equal(type(actual), type(expected))
assert_equal(actual, expected)
def test_subclass_preservation(self):
class MinimalSubclass(np.ndarray):
pass
self.test_scalar_array(MinimalSubclass)
self.test_0d_array(MinimalSubclass)
self.test_axis_insertion(MinimalSubclass)
def test_axis_insertion_ma(self):
def f1to2(x):
"""produces an asymmetric non-square matrix from x"""
assert_equal(x.ndim, 1)
res = x[::-1] * x[1:,None]
return np.ma.masked_where(res%5==0, res)
a = np.arange(6*3).reshape((6, 3))
res = apply_along_axis(f1to2, 0, a)
assert_(isinstance(res, np.ma.masked_array))
assert_equal(res.ndim, 3)
assert_array_equal(res[:,:,0].mask, f1to2(a[:,0]).mask)
assert_array_equal(res[:,:,1].mask, f1to2(a[:,1]).mask)
assert_array_equal(res[:,:,2].mask, f1to2(a[:,2]).mask)
def test_tuple_func1d(self):
def sample_1d(x):
return x[1], x[0]
res = np.apply_along_axis(sample_1d, 1, np.array([[1, 2], [3, 4]]))
assert_array_equal(res, np.array([[2, 1], [4, 3]]))
def test_empty(self):
# can't apply_along_axis when there's no chance to call the function
def never_call(x):
assert_(False) # should never be reached
a = np.empty((0, 0))
assert_raises(ValueError, np.apply_along_axis, never_call, 0, a)
assert_raises(ValueError, np.apply_along_axis, never_call, 1, a)
# but it's sometimes ok with some non-zero dimensions
def empty_to_1(x):
assert_(len(x) == 0)
return 1
a = np.empty((10, 0))
actual = np.apply_along_axis(empty_to_1, 1, a)
assert_equal(actual, np.ones(10))
assert_raises(ValueError, np.apply_along_axis, empty_to_1, 0, a)
def test_with_iterable_object(self):
# from issue 5248
d = np.array([
[{1, 11}, {2, 22}, {3, 33}],
[{4, 44}, {5, 55}, {6, 66}]
])
actual = np.apply_along_axis(lambda a: set.union(*a), 0, d)
expected = np.array([{1, 11, 4, 44}, {2, 22, 5, 55}, {3, 33, 6, 66}])
assert_equal(actual, expected)
# issue 8642 - assert_equal doesn't detect this!
for i in np.ndindex(actual.shape):
assert_equal(type(actual[i]), type(expected[i]))
class TestApplyOverAxes:
def test_simple(self):
a = np.arange(24).reshape(2, 3, 4)
aoa_a = apply_over_axes(np.sum, a, [0, 2])
assert_array_equal(aoa_a, np.array([[[60], [92], [124]]]))
class TestExpandDims:
def test_functionality(self):
s = (2, 3, 4, 5)
a = np.empty(s)
for axis in range(-5, 4):
b = expand_dims(a, axis)
assert_(b.shape[axis] == 1)
assert_(np.squeeze(b).shape == s)
def test_axis_tuple(self):
a = np.empty((3, 3, 3))
assert np.expand_dims(a, axis=(0, 1, 2)).shape == (1, 1, 1, 3, 3, 3)
assert np.expand_dims(a, axis=(0, -1, -2)).shape == (1, 3, 3, 3, 1, 1)
assert np.expand_dims(a, axis=(0, 3, 5)).shape == (1, 3, 3, 1, 3, 1)
assert np.expand_dims(a, axis=(0, -3, -5)).shape == (1, 1, 3, 1, 3, 3)
def test_axis_out_of_range(self):
s = (2, 3, 4, 5)
a = np.empty(s)
assert_raises(np.AxisError, expand_dims, a, -6)
assert_raises(np.AxisError, expand_dims, a, 5)
a = np.empty((3, 3, 3))
assert_raises(np.AxisError, expand_dims, a, (0, -6))
assert_raises(np.AxisError, expand_dims, a, (0, 5))
def test_repeated_axis(self):
a = np.empty((3, 3, 3))
assert_raises(ValueError, expand_dims, a, axis=(1, 1))
def test_subclasses(self):
a = np.arange(10).reshape((2, 5))
a = np.ma.array(a, mask=a%3 == 0)
expanded = np.expand_dims(a, axis=1)
assert_(isinstance(expanded, np.ma.MaskedArray))
assert_equal(expanded.shape, (2, 1, 5))
assert_equal(expanded.mask.shape, (2, 1, 5))
class TestArraySplit:
def test_integer_0_split(self):
a = np.arange(10)
assert_raises(ValueError, array_split, a, 0)
def test_integer_split(self):
a = np.arange(10)
res = array_split(a, 1)
desired = [np.arange(10)]
compare_results(res, desired)
res = array_split(a, 2)
desired = [np.arange(5), np.arange(5, 10)]
compare_results(res, desired)
res = array_split(a, 3)
desired = [np.arange(4), np.arange(4, 7), np.arange(7, 10)]
compare_results(res, desired)
res = array_split(a, 4)
desired = [np.arange(3), np.arange(3, 6), np.arange(6, 8),
np.arange(8, 10)]
compare_results(res, desired)
res = array_split(a, 5)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 8), np.arange(8, 10)]
compare_results(res, desired)
res = array_split(a, 6)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 8), np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 7)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 8)
desired = [np.arange(2), np.arange(2, 4), np.arange(4, 5),
np.arange(5, 6), np.arange(6, 7), np.arange(7, 8),
np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 9)
desired = [np.arange(2), np.arange(2, 3), np.arange(3, 4),
np.arange(4, 5), np.arange(5, 6), np.arange(6, 7),
np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 10)
desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10)]
compare_results(res, desired)
res = array_split(a, 11)
desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3),
np.arange(3, 4), np.arange(4, 5), np.arange(5, 6),
np.arange(6, 7), np.arange(7, 8), np.arange(8, 9),
np.arange(9, 10), np.array([])]
compare_results(res, desired)
def test_integer_split_2D_rows(self):
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3, axis=0)
tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]),
np.zeros((0, 10))]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
# Same thing for manual splits:
res = array_split(a, [0, 1], axis=0)
tgt = [np.zeros((0, 10)), np.array([np.arange(10)]),
np.array([np.arange(10)])]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
def test_integer_split_2D_cols(self):
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3, axis=-1)
desired = [np.array([np.arange(4), np.arange(4)]),
np.array([np.arange(4, 7), np.arange(4, 7)]),
np.array([np.arange(7, 10), np.arange(7, 10)])]
compare_results(res, desired)
def test_integer_split_2D_default(self):
""" This will fail if we change default axis
"""
a = np.array([np.arange(10), np.arange(10)])
res = array_split(a, 3)
tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]),
np.zeros((0, 10))]
compare_results(res, tgt)
assert_(a.dtype.type is res[-1].dtype.type)
# perhaps should check higher dimensions
@pytest.mark.skipif(not IS_64BIT, reason="Needs 64bit platform")
def test_integer_split_2D_rows_greater_max_int32(self):
a = np.broadcast_to([0], (1 << 32, 2))
res = array_split(a, 4)
chunk = np.broadcast_to([0], (1 << 30, 2))
tgt = [chunk] * 4
for i in range(len(tgt)):
assert_equal(res[i].shape, tgt[i].shape)
def test_index_split_simple(self):
a = np.arange(10)
indices = [1, 5, 7]
res = array_split(a, indices, axis=-1)
desired = [np.arange(0, 1), np.arange(1, 5), np.arange(5, 7),
np.arange(7, 10)]
compare_results(res, desired)
def test_index_split_low_bound(self):
a = np.arange(10)
indices = [0, 5, 7]
res = array_split(a, indices, axis=-1)
desired = [np.array([]), np.arange(0, 5), np.arange(5, 7),
np.arange(7, 10)]
compare_results(res, desired)
def test_index_split_high_bound(self):
a = np.arange(10)
indices = [0, 5, 7, 10, 12]
res = array_split(a, indices, axis=-1)
desired = [np.array([]), np.arange(0, 5), np.arange(5, 7),
np.arange(7, 10), np.array([]), np.array([])]
compare_results(res, desired)
class TestSplit:
# The split function is essentially the same as array_split,
# except that it test if splitting will result in an
# equal split. Only test for this case.
def test_equal_split(self):
a = np.arange(10)
res = split(a, 2)
desired = [np.arange(5), np.arange(5, 10)]
compare_results(res, desired)
def test_unequal_split(self):
a = np.arange(10)
assert_raises(ValueError, split, a, 3)
class TestColumnStack:
def test_non_iterable(self):
assert_raises(TypeError, column_stack, 1)
def test_1D_arrays(self):
# example from docstring
a = np.array((1, 2, 3))
b = np.array((2, 3, 4))
expected = np.array([[1, 2],
[2, 3],
[3, 4]])
actual = np.column_stack((a, b))
assert_equal(actual, expected)
def test_2D_arrays(self):
# same as hstack 2D docstring example
a = np.array([[1], [2], [3]])
b = np.array([[2], [3], [4]])
expected = np.array([[1, 2],
[2, 3],
[3, 4]])
actual = np.column_stack((a, b))
assert_equal(actual, expected)
def test_generator(self):
with pytest.raises(TypeError, match="arrays to stack must be"):
column_stack((np.arange(3) for _ in range(2)))
class TestDstack:
def test_non_iterable(self):
assert_raises(TypeError, dstack, 1)
def test_0D_array(self):
a = np.array(1)
b = np.array(2)
res = dstack([a, b])
desired = np.array([[[1, 2]]])
assert_array_equal(res, desired)
def test_1D_array(self):
a = np.array([1])
b = np.array([2])
res = dstack([a, b])
desired = np.array([[[1, 2]]])
assert_array_equal(res, desired)
def test_2D_array(self):
a = np.array([[1], [2]])
b = np.array([[1], [2]])
res = dstack([a, b])
desired = np.array([[[1, 1]], [[2, 2, ]]])
assert_array_equal(res, desired)
def test_2D_array2(self):
a = np.array([1, 2])
b = np.array([1, 2])
res = dstack([a, b])
desired = np.array([[[1, 1], [2, 2]]])
assert_array_equal(res, desired)
def test_generator(self):
with pytest.raises(TypeError, match="arrays to stack must be"):
dstack((np.arange(3) for _ in range(2)))
# array_split has more comprehensive test of splitting.
# only do simple test on hsplit, vsplit, and dsplit
class TestHsplit:
"""Only testing for integer splits.
"""
def test_non_iterable(self):
assert_raises(ValueError, hsplit, 1, 1)
def test_0D_array(self):
a = np.array(1)
try:
hsplit(a, 2)
assert_(0)
except ValueError:
pass
def test_1D_array(self):
a = np.array([1, 2, 3, 4])
res = hsplit(a, 2)
desired = [np.array([1, 2]), np.array([3, 4])]
compare_results(res, desired)
def test_2D_array(self):
a = np.array([[1, 2, 3, 4],
[1, 2, 3, 4]])
res = hsplit(a, 2)
desired = [np.array([[1, 2], [1, 2]]), np.array([[3, 4], [3, 4]])]
compare_results(res, desired)
class TestVsplit:
"""Only testing for integer splits.
"""
def test_non_iterable(self):
assert_raises(ValueError, vsplit, 1, 1)
def test_0D_array(self):
a = np.array(1)
assert_raises(ValueError, vsplit, a, 2)
def test_1D_array(self):
a = np.array([1, 2, 3, 4])
try:
vsplit(a, 2)
assert_(0)
except ValueError:
pass
def test_2D_array(self):
a = np.array([[1, 2, 3, 4],
[1, 2, 3, 4]])
res = vsplit(a, 2)
desired = [np.array([[1, 2, 3, 4]]), np.array([[1, 2, 3, 4]])]
compare_results(res, desired)
class TestDsplit:
# Only testing for integer splits.
def test_non_iterable(self):
assert_raises(ValueError, dsplit, 1, 1)
def test_0D_array(self):
a = np.array(1)
assert_raises(ValueError, dsplit, a, 2)
def test_1D_array(self):
a = np.array([1, 2, 3, 4])
assert_raises(ValueError, dsplit, a, 2)
def test_2D_array(self):
a = np.array([[1, 2, 3, 4],
[1, 2, 3, 4]])
try:
dsplit(a, 2)
assert_(0)
except ValueError:
pass
def test_3D_array(self):
a = np.array([[[1, 2, 3, 4],
[1, 2, 3, 4]],
[[1, 2, 3, 4],
[1, 2, 3, 4]]])
res = dsplit(a, 2)
desired = [np.array([[[1, 2], [1, 2]], [[1, 2], [1, 2]]]),
np.array([[[3, 4], [3, 4]], [[3, 4], [3, 4]]])]
compare_results(res, desired)
class TestSqueeze:
def test_basic(self):
from numpy.random import rand
a = rand(20, 10, 10, 1, 1)
b = rand(20, 1, 10, 1, 20)
c = rand(1, 1, 20, 10)
assert_array_equal(np.squeeze(a), np.reshape(a, (20, 10, 10)))
assert_array_equal(np.squeeze(b), np.reshape(b, (20, 10, 20)))
assert_array_equal(np.squeeze(c), np.reshape(c, (20, 10)))
# Squeezing to 0-dim should still give an ndarray
a = [[[1.5]]]
res = np.squeeze(a)
assert_equal(res, 1.5)
assert_equal(res.ndim, 0)
assert_equal(type(res), np.ndarray)
class TestKron:
def test_basic(self):
# Using 0-dimensional ndarray
a = np.array(1)
b = np.array([[1, 2], [3, 4]])
k = np.array([[1, 2], [3, 4]])
assert_array_equal(np.kron(a, b), k)
a = np.array([[1, 2], [3, 4]])
b = np.array(1)
assert_array_equal(np.kron(a, b), k)
# Using 1-dimensional ndarray
a = np.array([3])
b = np.array([[1, 2], [3, 4]])
k = np.array([[3, 6], [9, 12]])
assert_array_equal(np.kron(a, b), k)
a = np.array([[1, 2], [3, 4]])
b = np.array([3])
assert_array_equal(np.kron(a, b), k)
# Using 3-dimensional ndarray
a = np.array([[[1]], [[2]]])
b = np.array([[1, 2], [3, 4]])
k = np.array([[[1, 2], [3, 4]], [[2, 4], [6, 8]]])
assert_array_equal(np.kron(a, b), k)
a = np.array([[1, 2], [3, 4]])
b = np.array([[[1]], [[2]]])
k = np.array([[[1, 2], [3, 4]], [[2, 4], [6, 8]]])
assert_array_equal(np.kron(a, b), k)
def test_return_type(self):
class myarray(np.ndarray):
__array_priority__ = 1.0
a = np.ones([2, 2])
ma = myarray(a.shape, a.dtype, a.data)
assert_equal(type(kron(a, a)), np.ndarray)
assert_equal(type(kron(ma, ma)), myarray)
assert_equal(type(kron(a, ma)), myarray)
assert_equal(type(kron(ma, a)), myarray)
@pytest.mark.parametrize(
"array_class", [np.asarray, np.mat]
)
def test_kron_smoke(self, array_class):
a = array_class(np.ones([3, 3]))
b = array_class(np.ones([3, 3]))
k = array_class(np.ones([9, 9]))
assert_array_equal(np.kron(a, b), k)
def test_kron_ma(self):
x = np.ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
k = np.ma.array(np.diag([1, 4, 4, 16]),
mask=~np.array(np.identity(4), dtype=bool))
assert_array_equal(k, np.kron(x, x))
@pytest.mark.parametrize(
"shape_a,shape_b", [
((1, 1), (1, 1)),
((1, 2, 3), (4, 5, 6)),
((2, 2), (2, 2, 2)),
((1, 0), (1, 1)),
((2, 0, 2), (2, 2)),
((2, 0, 0, 2), (2, 0, 2)),
])
def test_kron_shape(self, shape_a, shape_b):
a = np.ones(shape_a)
b = np.ones(shape_b)
normalised_shape_a = (1,) * max(0, len(shape_b)-len(shape_a)) + shape_a
normalised_shape_b = (1,) * max(0, len(shape_a)-len(shape_b)) + shape_b
expected_shape = np.multiply(normalised_shape_a, normalised_shape_b)
k = np.kron(a, b)
assert np.array_equal(
k.shape, expected_shape), "Unexpected shape from kron"
class TestTile:
def test_basic(self):
a = np.array([0, 1, 2])
b = [[1, 2], [3, 4]]
assert_equal(tile(a, 2), [0, 1, 2, 0, 1, 2])
assert_equal(tile(a, (2, 2)), [[0, 1, 2, 0, 1, 2], [0, 1, 2, 0, 1, 2]])
assert_equal(tile(a, (1, 2)), [[0, 1, 2, 0, 1, 2]])
assert_equal(tile(b, 2), [[1, 2, 1, 2], [3, 4, 3, 4]])
assert_equal(tile(b, (2, 1)), [[1, 2], [3, 4], [1, 2], [3, 4]])
assert_equal(tile(b, (2, 2)), [[1, 2, 1, 2], [3, 4, 3, 4],
[1, 2, 1, 2], [3, 4, 3, 4]])
def test_tile_one_repetition_on_array_gh4679(self):
a = np.arange(5)
b = tile(a, 1)
b += 2
assert_equal(a, np.arange(5))
def test_empty(self):
a = np.array([[[]]])
b = np.array([[], []])
c = tile(b, 2).shape
d = tile(a, (3, 2, 5)).shape
assert_equal(c, (2, 0))
assert_equal(d, (3, 2, 0))
def test_kroncompare(self):
from numpy.random import randint
reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)]
shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)]
for s in shape:
b = randint(0, 10, size=s)
for r in reps:
a = np.ones(r, b.dtype)
large = tile(b, r)
klarge = kron(a, b)
assert_equal(large, klarge)
class TestMayShareMemory:
def test_basic(self):
d = np.ones((50, 60))
d2 = np.ones((30, 60, 6))
assert_(np.may_share_memory(d, d))
assert_(np.may_share_memory(d, d[::-1]))
assert_(np.may_share_memory(d, d[::2]))
assert_(np.may_share_memory(d, d[1:, ::-1]))
assert_(not np.may_share_memory(d[::-1], d2))
assert_(not np.may_share_memory(d[::2], d2))
assert_(not np.may_share_memory(d[1:, ::-1], d2))
assert_(np.may_share_memory(d2[1:, ::-1], d2))
# Utility
def compare_results(res, desired):
"""Compare lists of arrays."""
if len(res) != len(desired):
raise ValueError("Iterables have different lengths")
# See also PEP 618 for Python 3.10
for x, y in zip(res, desired):
assert_array_equal(x, y)
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