def test_object_scalar_multiply():
# Tickets #2469 and #4482
# 2018-04-29: moved here from core.tests.test_ufunc
arr = npm.matrix([1, 2], dtype=object)
desired = npm.matrix([[3, 6]], dtype=object)
assert_equal(np.multiply(arr, 3), desired)
assert_equal(np.multiply(3, arr), desired)
def test_inner_scalar_and_matrix_of_objects():
# Ticket #4482
# 2018-04-29: moved here from core.tests.test_multiarray
arr = npm.matrix([1, 2], dtype=object)
desired = npm.matrix([[3, 6]], dtype=object)
assert_equal(np.inner(arr, 3), desired)
assert_equal(np.inner(3, arr), desired)
def test_sort_matrix_none():
# 2018-04-29: moved here from core.tests.test_multiarray
a = npm.matrix([[2, 1, 0]])
actual = np.sort(a, axis=None)
expected = npm.matrix([[0, 1, 2]])
assert_equal(actual, expected)
assert_(type(expected) is npm.matrix)
def test_inner_scalar_and_matrix():
# 2018-04-29: moved here from core.tests.test_multiarray
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
sca = np.array(3, dtype=dt)[()]
arr = npm.matrix([[1, 2], [3, 4]], dtype=dt)
desired = npm.matrix([[3, 6], [9, 12]], dtype=dt)
assert_equal(np.inner(arr, sca), desired)
assert_equal(np.inner(sca, arr), desired)
def test_matrix_properties(self):
# Ticket #125
a = npm.matrix([1.0], dtype=float)
assert_(type(a.real) is npm.matrix)
assert_(type(a.imag) is npm.matrix)
c, d = npm.matrix([0.0]).nonzero()
assert_(type(c) is np.ndarray)
assert_(type(d) is np.ndarray)
def test_max(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x.max(), 6)
assert_equal(x.max(0), matrix([[4, 5, 6]]))
assert_equal(x.max(1), matrix([[3], [6]]))
assert_equal(np.max(x), 6)
assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]]))
assert_equal(np.max(x, axis=1), matrix([[3], [6]]))
def test_min(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x.min(), 1)
assert_equal(x.min(0), matrix([[1, 2, 3]]))
assert_equal(x.min(1), matrix([[1], [4]]))
assert_equal(np.min(x), 1)
assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]]))
assert_equal(np.min(x, axis=1), matrix([[1], [4]]))
def test_diagonal():
b1 = npm.matrix([[1, 2], [3, 4]])
diag_b1 = npm.matrix([[1, 4]])
array_b1 = np.array([1, 4])
assert_equal(b1.diagonal(), diag_b1)
assert_equal(np.diagonal(b1), array_b1)
assert_equal(np.diag(b1), array_b1)
def test_trapz_matrix():
# Test to make sure matrices give the same answer as ndarrays
# 2018-04-29: moved here from core.tests.test_function_base.
x = np.linspace(0, 5)
y = x * x
r = np.trapz(y, x)
mx = npm.matrix(x)
my = npm.matrix(y)
mr = np.trapz(my, mx)
assert_almost_equal(mr, r)
def test_fancy_indexing(self):
a = self.a
x = a[1, [0, 1, 0]]
assert_(isinstance(x, matrix))
assert_equal(x, matrix([[3, 4, 3]]))
x = a[[1, 0]]
assert_(isinstance(x, matrix))
assert_equal(x, matrix([[3, 4], [1, 2]]))
x = a[[[1], [0]], [[1, 0], [0, 1]]]
assert_(isinstance(x, matrix))
assert_equal(x, matrix([[4, 3], [1, 2]]))
def test_matrix_element(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x[0][0], matrix([[1, 2, 3]]))
assert_equal(x[0][0].shape, (1, 3))
assert_equal(x[0].shape, (1, 3))
assert_equal(x[:, 0].shape, (2, 1))
x = matrix(0)
assert_equal(x[0, 0], 0)
assert_equal(x[0], 0)
assert_equal(x[:, 0].shape, x.shape)
def test_average_matrix():
# 2018-04-29: moved here from core.tests.test_function_base.
y = npm.matrix(np.random.rand(5, 5))
assert_array_equal(y.mean(0), np.average(y, 0))
a = npm.matrix([[1, 2], [3, 4]])
w = npm.matrix([[1, 2], [3, 4]])
r = np.average(a, axis=0, weights=w)
assert_equal(type(r), npm.matrix)
assert_equal(r, [[2.5, 10.0 / 3]])
def test_array_almost_equal_matrix():
# Matrix slicing keeps things 2-D, while array does not necessarily.
# See gh-8452.
# 2018-04-29: moved here from testing.tests.test_utils.
m1 = npm.matrix([[1., 2.]])
m2 = npm.matrix([[1., np.nan]])
m3 = npm.matrix([[1., -np.inf]])
m4 = npm.matrix([[np.nan, np.inf]])
m5 = npm.matrix([[1., 2.], [np.nan, np.inf]])
for assert_func in assert_array_almost_equal, assert_almost_equal:
for m in m1, m2, m3, m4, m5:
assert_func(m, m)
a = np.array(m)
assert_func(a, m)
assert_func(m, a)
def test_basic(self):
import numpy.linalg as linalg
A = np.array([[1., 2.], [3., 4.]])
mA = matrix(A)
assert_(np.allclose(linalg.inv(A), mA.I))
assert_(np.all(np.array(np.transpose(A) == mA.T)))
assert_(np.all(np.array(np.transpose(A) == mA.H)))
assert_(np.all(A == mA.A))
B = A + 2j * A
mB = matrix(B)
assert_(np.allclose(linalg.inv(B), mB.I))
assert_(np.all(np.array(np.transpose(B) == mB.T)))
assert_(np.all(np.array(np.transpose(B).conj() == mB.H)))
def test_sum(self):
"""Test whether matrix.sum(axis=1) preserves orientation.
Fails in NumPy <= 0.9.6.2127.
"""
M = matrix([[1, 2, 0, 0], [3, 4, 0, 0], [1, 2, 1, 2], [3, 4, 3, 4]])
sum0 = matrix([8, 12, 4, 6])
sum1 = matrix([3, 7, 6, 14]).T
sumall = 30
assert_array_equal(sum0, M.sum(axis=0))
assert_array_equal(sum1, M.sum(axis=1))
assert_equal(sumall, M.sum())
assert_array_equal(sum0, np.sum(M, axis=0))
assert_array_equal(sum1, np.sum(M, axis=1))
assert_equal(sumall, np.sum(M))
def test_nanfunctions_matrices_general():
# Check that it works and that type and
# shape are preserved
# 2018-04-29: moved here from core.tests.test_nanfunctions
mat = npm.matrix(np.eye(3))
for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod, np.nanmean,
np.nanvar, np.nanstd):
res = f(mat, axis=0)
assert_(isinstance(res, npm.matrix))
assert_(res.shape == (1, 3))
res = f(mat, axis=1)
assert_(isinstance(res, npm.matrix))
assert_(res.shape == (3, 1))
res = f(mat)
assert_(np.isscalar(res))
for f in np.nancumsum, np.nancumprod:
res = f(mat, axis=0)
assert_(isinstance(res, npm.matrix))
assert_(res.shape == (3, 3))
res = f(mat, axis=1)
assert_(isinstance(res, npm.matrix))
assert_(res.shape == (3, 3))
res = f(mat)
assert_(isinstance(res, npm.matrix))
assert_(res.shape == (1, 3 * 3))
def test_iter_allocate_output_subtype():
# Make sure that the subtype with priority wins
# 2018-04-29: moved here from core.tests.test_nditer, given the
# matrix specific shape test.
# matrix vs ndarray
a = npm.matrix([[1, 2], [3, 4]])
b = np.arange(4).reshape(2, 2).T
i = np.nditer([a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_(type(i.operands[2]) is npm.matrix)
assert_(type(i.operands[2]) is not np.ndarray)
assert_equal(i.operands[2].shape, (2, 2))
# matrix always wants things to be 2D
b = np.arange(4).reshape(1, 2, 2)
assert_raises(RuntimeError, np.nditer, [a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
# but if subtypes are disabled, the result can still work
i = np.nditer(
[a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate', 'no_subtype']])
assert_(type(i.operands[2]) is np.ndarray)
assert_(type(i.operands[2]) is not npm.matrix)
assert_equal(i.operands[2].shape, (1, 2, 2))
def test_pickling_subbaseclass(self):
# Test pickling w/ a subclass of ndarray
a = masked_array(npm.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2)
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
assert_equal(a_pickled._mask, a._mask)
assert_equal(a_pickled, a)
assert_(isinstance(a_pickled._data, npm.matrix))
def test_compressed(self):
a = masked_array(npm.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0])
b = a.compressed()
assert_equal(b, a)
assert_(isinstance(b, npm.matrix))
a[0, 0] = masked
b = a.compressed()
assert_equal(b, [[2, 3, 4]])
def test_apply_along_axis_matrix():
# this test is particularly malicious because matrix
# refuses to become 1d
# 2018-04-29: moved here from core.tests.test_shape_base.
def double(row):
return row * 2
m = npm.matrix([[0, 1], [2, 3]])
expected = npm.matrix([[0, 2], [4, 6]])
result = np.apply_along_axis(double, 0, m)
assert_(isinstance(result, npm.matrix))
assert_array_equal(result, expected)
result = np.apply_along_axis(double, 1, m)
assert_(isinstance(result, npm.matrix))
assert_array_equal(result, expected)
def test_polynomial_mapdomain():
# test that polynomial preserved matrix subtype.
# 2018-04-29: moved here from polynomial.tests.polyutils.
dom1 = [0, 4]
dom2 = [1, 3]
x = npm.matrix([dom1, dom1])
res = np.polynomial.polyutils.mapdomain(x, dom1, dom2)
assert_(isinstance(res, npm.matrix))
def test_count_mean_with_matrix(self):
m = masked_array(npm.matrix([[1, 2], [3, 4]]), mask=np.zeros((2, 2)))
assert_equal(m.count(axis=0).shape, (1, 2))
assert_equal(m.count(axis=1).shape, (2, 1))
# Make sure broadcasting inside mean and var work
assert_equal(m.mean(axis=0), [[2., 3.]])
assert_equal(m.mean(axis=1), [[1.5], [3.5]])
def test_basic(self):
A = np.array([[1, 2], [3, 4]])
mA = matrix(A)
assert_(np.all(mA.A == A))
B = bmat("A,A;A,A")
C = bmat([[A, A], [A, A]])
D = np.array([[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]])
assert_(np.all(B.A == D))
assert_(np.all(C.A == D))
E = np.array([[5, 6], [7, 8]])
AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
assert_(np.all(bmat([A, E]) == AEresult))
vec = np.arange(5)
mvec = matrix(vec)
assert_(mvec.shape == (1, 5))
def like_function():
# 2018-04-29: moved here from core.tests.test_numeric
a = npm.matrix([[1, 2], [3, 4]])
for like_function in np.zeros_like, np.ones_like, np.empty_like:
b = like_function(a)
assert_(type(b) is npm.matrix)
c = like_function(a, subok=False)
assert_(type(c) is not npm.matrix)
def test_matrix_indexing(self):
# Tests conversions and indexing
x1 = npm.matrix([[1, 2, 3], [4, 3, 2]])
x2 = masked_array(x1, mask=[[1, 0, 0], [0, 1, 0]])
x3 = masked_array(x1, mask=[[0, 1, 0], [1, 0, 0]])
x4 = masked_array(x1)
# test conversion to strings
str(x2) # raises?
repr(x2) # raises?
# tests of indexing
assert_(type(x2[1, 0]) is type(x1[1, 0]))
assert_(x1[1, 0] == x2[1, 0])
assert_(x2[1, 1] is masked)
assert_equal(x1[0, 2], x2[0, 2])
assert_equal(x1[0, 1:], x2[0, 1:])
assert_equal(x1[:, 2], x2[:, 2])
assert_equal(x1[:], x2[:])
assert_equal(x1[1:], x3[1:])
x1[0, 2] = 9
x2[0, 2] = 9
assert_equal(x1, x2)
x1[0, 1:] = 99
x2[0, 1:] = 99
assert_equal(x1, x2)
x2[0, 1] = masked
assert_equal(x1, x2)
x2[0, 1:] = masked
assert_equal(x1, x2)
x2[0, :] = x1[0, :]
x2[0, 1] = masked
assert_(allequal(getmask(x2), np.array([[0, 1, 0], [0, 1, 0]])))
x3[1, :] = masked_array([1, 2, 3], [1, 1, 0])
assert_(allequal(getmask(x3)[1], masked_array([1, 1, 0])))
assert_(allequal(getmask(x3[1]), masked_array([1, 1, 0])))
x4[1, :] = masked_array([1, 2, 3], [1, 1, 0])
assert_(allequal(getmask(x4[1]), masked_array([1, 1, 0])))
assert_(allequal(x4[1], masked_array([1, 2, 3])))
x1 = npm.matrix(np.arange(5) * 1.0)
x2 = masked_values(x1, 3.0)
assert_equal(x1, x2)
assert_(
allequal(masked_array([0, 0, 0, 1, 0], dtype=MaskType), x2.mask))
assert_equal(3.0, x2.fill_value)
def test_numpy_ravel_order(self):
x = np.array([[1, 2, 3], [4, 5, 6]])
assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
class TestShape:
a = np.array([[1], [2]])
m = matrix([[1], [2]])
def test_shape(self):
assert_equal(self.a.shape, (2, 1))
assert_equal(self.m.shape, (2, 1))
def test_numpy_ravel(self):
assert_equal(np.ravel(self.a).shape, (2, ))
assert_equal(np.ravel(self.m).shape, (2, ))
def test_member_ravel(self):
assert_equal(self.a.ravel().shape, (2, ))
assert_equal(self.m.ravel().shape, (1, 2))
def test_member_flatten(self):
assert_equal(self.a.flatten().shape, (2, ))
assert_equal(self.m.flatten().shape, (1, 2))
def test_numpy_ravel_order(self):
x = np.array([[1, 2, 3], [4, 5, 6]])
assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
def test_matrix_ravel_order(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]])
assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]])
assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]])
assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]])
def test_array_memory_sharing(self):
assert_(np.may_share_memory(self.a, self.a.ravel()))
assert_(not np.may_share_memory(self.a, self.a.flatten()))
def test_matrix_memory_sharing(self):
assert_(np.may_share_memory(self.m, self.m.ravel()))
assert_(not np.may_share_memory(self.m, self.m.flatten()))
def test_expand_dims_matrix(self):
# matrices are always 2d - so expand_dims only makes sense when the
# type is changed away from matrix.
a = np.arange(10).reshape((2, 5)).view(npm.matrix)
expanded = np.expand_dims(a, axis=1)
assert_equal(expanded.ndim, 3)
assert_(not isinstance(expanded, npm.matrix))
def test_notimplemented(self):
'''Check that 'not implemented' operations produce a failure.'''
A = matrix([[1., 2.], [3., 4.]])
# __rpow__
with assert_raises(TypeError):
1.0**A
# __mul__ with something not a list, ndarray, tuple, or scalar
with assert_raises(TypeError):
A * object()
def test_pow(self):
"""Test raising a matrix to an integer power works as expected."""
m = matrix("1. 2.; 3. 4.")
m2 = m.copy()
m2 **= 2
mi = m.copy()
mi **= -1
m4 = m2.copy()
m4 **= 2
assert_array_almost_equal(m2, m**2)
assert_array_almost_equal(m4, np.dot(m2, m2))
assert_array_almost_equal(np.dot(mi, m), np.eye(2))
def test_array_equal_error_message_matrix():
# 2018-04-29: moved here from testing.tests.test_utils.
with pytest.raises(AssertionError) as exc_info:
assert_equal(np.array([1, 2]), npm.matrix([1, 2]))
msg = str(exc_info.value)
msg_reference = textwrap.dedent("""\
Arrays are not equal
(shapes (2,), (1, 2) mismatch)
x: array([1, 2])
y: matrix([[1, 2]])""")
assert_equal(msg, msg_reference)
