def test_check_median_stat_length(self):
a = np.arange(100).astype('f')
a[1] = 2.
a[97] = 96.
a = pad(a, (25, 20), 'median', stat_length=(3, 5))
b = np.array(
[ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2.,
0., 2., 2., 3., 4., 5., 6., 7., 8., 9.,
10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
90., 91., 92., 93., 94., 95., 96., 96., 98., 99.,
96., 96., 96., 96., 96., 96., 96., 96., 96., 96.,
96., 96., 96., 96., 96., 96., 96., 96., 96., 96.]
)
assert_array_equal(a, b)
def test_tril_triu_with_inf():
# Issue 4859
arr = np.array([[1, 1, np.inf], [1, 1, 1], [np.inf, 1, 1]])
out_tril = np.array([[1, 0, 0], [1, 1, 0], [np.inf, 1, 1]])
out_triu = out_tril.T
assert_array_equal(np.triu(arr), out_triu)
assert_array_equal(np.tril(arr), out_tril)
def test_object_assign(self):
# Check that the field and object special case using copyto is active.
# The right hand side cannot be converted to an array here.
a = np.arange(5, dtype=object)
b = a.copy()
a[:3] = [1, (1,2), 3]
b[[0, 1, 2]] = [1, (1,2), 3]
assert_array_equal(a, b)
# test same for subspace fancy indexing
b = np.arange(5, dtype=object)[None, :]
b[[0], :3] = [[1, (1,2), 3]]
assert_array_equal(a, b[0])
# Check that swapping of axes works.
# There was a bug that made the later assignment throw a ValueError
# do to an incorrectly transposed temporary right hand side (gh-5714)
b = b.T
b[:3, [0]] = [[1], [(1,2)], [3]]
assert_array_equal(a, b[:, 0])
# Another test for the memory order of the subspace
arr = np.ones((3, 4, 5), dtype=object)
# Equivalent slicing assignment for comparison
cmp_arr = arr.copy()
cmp_arr[:1, ...] = [[[1], [2], [3], [4]]]
arr[[0], ...] = [[[1], [2], [3], [4]]]
assert_array_equal(arr, cmp_arr)
arr = arr.copy('F')
arr[[0], ...] = [[[1], [2], [3], [4]]]
assert_array_equal(arr, cmp_arr)
def test_fix_with_subclass(self):
class MyArray(nx.ndarray):
def __new__(cls, data, metadata=None):
res = nx.array(data, copy=True).view(cls)
res.metadata = metadata
return res
def __array_wrap__(self, obj, context=None):
obj.metadata = self.metadata
return obj
def __array_finalize__(self, obj):
self.metadata = getattr(obj, 'metadata', None)
return self
a = nx.array([1.1, -1.1])
m = MyArray(a, metadata='foo')
f = ufl.fix(m)
assert_array_equal(f, nx.array([1, -1]))
assert_(isinstance(f, MyArray))
assert_equal(f.metadata, 'foo')
# check 0d arrays don't decay to scalars
m0d = m[0,...]
m0d.metadata = 'bar'
f0d = ufl.fix(m0d)
assert_(isinstance(f0d, MyArray))
assert_equal(f0d.metadata, 'bar')
def test_diag_indices():
di = diag_indices(4)
a = np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16]])
a[di] = 100
assert_array_equal(
a, np.array([[100, 2, 3, 4],
[5, 100, 7, 8],
[9, 10, 100, 12],
[13, 14, 15, 100]])
)
# Now, we create indices to manipulate a 3-d array:
d3 = diag_indices(2, 3)
# And use it to set the diagonal of a zeros array to 1:
a = np.zeros((2, 2, 2), int)
a[d3] = 1
assert_array_equal(
a, np.array([[[1, 0],
[0, 0]],
[[0, 0],
[0, 1]]])
)
def test_basic(self):
# Test that indexing in various ways produces SubClass instances,
# and that the base is set up correctly: the original subclass
# instance for views, and a new ndarray for advanced/boolean indexing
# where a copy was made (latter a regression test for gh-11983).
class SubClass(np.ndarray):
pass
a = np.arange(5)
s = a.view(SubClass)
s_slice = s[:3]
assert_(type(s_slice) is SubClass)
assert_(s_slice.base is s)
assert_array_equal(s_slice, a[:3])
s_fancy = s[[0, 1, 2]]
assert_(type(s_fancy) is SubClass)
assert_(s_fancy.base is not s)
assert_(type(s_fancy.base) is np.ndarray)
assert_array_equal(s_fancy, a[[0, 1, 2]])
assert_array_equal(s_fancy.base, a[[0, 1, 2]])
s_bool = s[s > 0]
assert_(type(s_bool) is SubClass)
assert_(s_bool.base is not s)
assert_(type(s_bool.base) is np.ndarray)
assert_array_equal(s_bool, a[a > 0])
assert_array_equal(s_bool.base, a[a > 0])
def test_char(self):
strings = np.array(['ab', 'cd', 'ef'], dtype='c').T
inp, out = self.module.char_test.change_strings(strings, strings.shape[1])
assert_array_equal(inp, strings)
expected = strings.copy()
expected[1, :] = 'AAA'
assert_array_equal(out, expected)
def test_tril_triu_ndim3():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.array([
[[1, 1], [1, 1]],
[[1, 1], [1, 0]],
[[1, 1], [0, 0]],
],
dtype=dtype)
a_tril_desired = np.array([
[[1, 0], [1, 1]],
[[1, 0], [1, 0]],
[[1, 0], [0, 0]],
],
dtype=dtype)
a_triu_desired = np.array([
[[1, 1], [0, 1]],
[[1, 1], [0, 0]],
[[1, 1], [0, 0]],
],
dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
assert_array_equal(a_triu_observed, a_triu_desired)
assert_array_equal(a_tril_observed, a_tril_desired)
assert_equal(a_triu_observed.dtype, a.dtype)
assert_equal(a_tril_observed.dtype, a.dtype)
def test_weights(self):
v = np.random.rand(100)
w = np.ones(100) * 5
a, b = histogram(v)
na, nb = histogram(v, density=True)
wa, wb = histogram(v, weights=w)
nwa, nwb = histogram(v, weights=w, density=True)
assert_array_almost_equal(a * 5, wa)
assert_array_almost_equal(na, nwa)
# Check weights are properly applied.
v = np.linspace(0, 10, 10)
w = np.concatenate((np.zeros(5), np.ones(5)))
wa, wb = histogram(v, bins=np.arange(11), weights=w)
assert_array_almost_equal(wa, w)
# Check with integer weights
wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1])
assert_array_equal(wa, [4, 5, 0, 1])
wa, wb = histogram([1, 2, 2, 4],
bins=4,
weights=[4, 3, 2, 1],
density=True)
assert_array_almost_equal(wa, np.array([4, 5, 0, 1]) / 10. / 3. * 4)
# Check weights with non-uniform bin widths
a, b = histogram(np.arange(9), [0, 1, 3, 6, 10],
weights=[2, 1, 1, 1, 1, 1, 1, 1, 1],
density=True)
assert_almost_equal(a, [.2, .1, .1, .075])
def test_cov_parameters(self):
# Ticket #91
x = np.random.random((3, 3))
y = x.copy()
np.cov(x, rowvar=1)
np.cov(y, rowvar=0)
assert_array_equal(x, y)
def test_broadcast_to_succeeds():
data = [
[np.array(0), (0, ), np.array(0)],
[np.array(0), (1, ), np.zeros(1)],
[np.array(0), (3, ), np.zeros(3)],
[np.ones(1), (1, ), np.ones(1)],
[np.ones(1), (2, ), np.ones(2)],
[np.ones(1), (1, 2, 3), np.ones((1, 2, 3))],
[np.arange(3), (3, ), np.arange(3)],
[np.arange(3), (1, 3),
np.arange(3).reshape(1, -1)],
[np.arange(3), (2, 3),
np.array([[0, 1, 2], [0, 1, 2]])],
# test if shape is not a tuple
[np.ones(0), 0, np.ones(0)],
[np.ones(1), 1, np.ones(1)],
[np.ones(1), 2, np.ones(2)],
# these cases with size 0 are strange, but they reproduce the behavior
# of broadcasting with ufuncs (see test_same_as_ufunc above)
[np.ones(1), (0, ), np.ones(0)],
[np.ones((1, 2)), (0, 2), np.ones((0, 2))],
[np.ones((2, 1)), (2, 0), np.ones((2, 0))],
]
for input_array, shape, expected in data:
actual = broadcast_to(input_array, shape)
assert_array_equal(expected, actual)
def test_check_large_pad(self):
a = np.arange(12)
a = np.reshape(a, (3, 4))
a = pad(a, (10, 12), 'wrap')
b = np.array(
[[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11],
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
3, 0, 1, 2, 3, 0, 1, 2, 3],
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
7, 4, 5, 6, 7, 4, 5, 6, 7],
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11],
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
3, 0, 1, 2, 3, 0, 1, 2, 3],
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
7, 4, 5, 6, 7, 4, 5, 6, 7],
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11],
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
3, 0, 1, 2, 3, 0, 1, 2, 3],
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
7, 4, 5, 6, 7, 4, 5, 6, 7],
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11],
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
3, 0, 1, 2, 3, 0, 1, 2, 3],
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
7, 4, 5, 6, 7, 4, 5, 6, 7],
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11],
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
3, 0, 1, 2, 3, 0, 1, 2, 3],
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
7, 4, 5, 6, 7, 4, 5, 6, 7],
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11],
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
3, 0, 1, 2, 3, 0, 1, 2, 3],
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
7, 4, 5, 6, 7, 4, 5, 6, 7],
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11],
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
3, 0, 1, 2, 3, 0, 1, 2, 3],
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
7, 4, 5, 6, 7, 4, 5, 6, 7],
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11],
[2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
3, 0, 1, 2, 3, 0, 1, 2, 3],
[6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
7, 4, 5, 6, 7, 4, 5, 6, 7],
[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
11, 8, 9, 10, 11, 8, 9, 10, 11]]
)
assert_array_equal(a, b)
def test_one_off():
x = np.array([[1, 2, 3]])
y = np.array([[1], [2], [3]])
bx, by = broadcast_arrays(x, y)
bx0 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
by0 = bx0.T
assert_array_equal(bx0, bx)
assert_array_equal(by0, by)
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_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_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 test_shuffle_mixed_dimension(self):
# Test for trac ticket #2074
for t in [[1, 2, 3, None], [(1, 1), (2, 2), (3, 3), None],
[1, (2, 2), (3, 3), None], [(1, 1), 2, 3, None]]:
np.random.seed(12345)
shuffled = list(t)
random.shuffle(shuffled)
assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]])
def test_wide_matrix(self):
a = np.zeros((3, 10), int)
fill_diagonal(a, 5)
assert_array_equal(
a, np.array([[5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 5, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 5, 0, 0, 0, 0, 0, 0, 0]])
)
def test_reference_types():
input_array = np.array('a', dtype=object)
expected = np.array(['a'] * 3, dtype=object)
actual = broadcast_to(input_array, (3, ))
assert_array_equal(expected, actual)
actual, _ = broadcast_arrays(input_array, np.ones(3))
assert_array_equal(expected, actual)
def test_3D_array(self):
a = array([[1, 2], [1, 2]])
b = array([[2, 3], [2, 3]])
a = array([a, a])
b = array([b, b])
res = [atleast_2d(a), atleast_2d(b)]
desired = [a, b]
assert_array_equal(res, desired)
def test_keywords(self):
x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
# We must be specific about the endianness here:
y = x.view(dtype='<i2', type=np.matrix)
assert_array_equal(y, [[513]])
assert_(isinstance(y, np.matrix))
assert_equal(y.dtype, np.dtype('<i2'))
def test_mask_indices():
# simple test without offset
iu = mask_indices(3, np.triu)
a = np.arange(9).reshape(3, 3)
assert_array_equal(a[iu], array([0, 1, 2, 4, 5, 8]))
# Now with an offset
iu1 = mask_indices(3, np.triu, 1)
assert_array_equal(a[iu1], array([1, 2, 5]))
def test_unpackbits():
# Copied from the docstring.
a = np.array([[2], [7], [23]], dtype=np.uint8)
b = np.unpackbits(a, axis=1)
assert_equal(b.dtype, np.uint8)
assert_array_equal(b, np.array([[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 0, 1, 1, 1]]))
def test_reversed_strides_result_allocation(self):
# Test a bug when calculating the output strides for a result array
# when the subspace size was 1 (and test other cases as well)
a = np.arange(10)[:, None]
i = np.arange(10)[::-1]
assert_array_equal(a[i], a[i.copy('C')])
a = np.arange(20).reshape(-1, 2)
def test_basic(self):
a = np.zeros((3, 3), int)
fill_diagonal(a, 5)
assert_array_equal(
a, np.array([[5, 0, 0],
[0, 5, 0],
[0, 0, 5]])
)
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_basic(self):
a = np.random.rand(10)
b = real_if_close(a + 1e-15j)
assert_all(isrealobj(b))
assert_array_equal(a, b)
b = real_if_close(a + 1e-7j)
assert_all(iscomplexobj(b))
b = real_if_close(a + 1e-7j, tol=1e-6)
assert_all(isrealobj(b))
def test_indexing_array_negative_strides(self):
# From gh-8264,
# core dumps if negative strides are used in iteration
arro = np.zeros((4, 4))
arr = arro[::-1, ::-1]
slices = (slice(None), [0, 1, 2, 3])
arr[slices] = 10
assert_array_equal(arr, 10.)
def test_call_within_randomstate(self):
# Check that custom RandomState does not call into global state
m = np.random.RandomState()
res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
for i in range(3):
np.random.seed(i)
m.seed(4321)
# If m.state is not honored, the result will change
assert_array_equal(m.choice(10, size=10, p=np.ones(10) / 10.), res)
def test_lapack_endian(self):
# For bug #1482
a = array([[5.7998084, -2.1825367], [-2.1825367, 9.85910595]],
dtype='>f8')
b = array(a, dtype='<f8')
ap = linalg.cholesky(a)
bp = linalg.cholesky(b)
assert_array_equal(ap, bp)
