def test_diag(self):
assert_equal(
eye(4, k=1),
array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]]))
assert_equal(
eye(4, k=-1),
array([[0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]))
def test_order(self):
mat_c = eye(4, 3, k=-1)
mat_f = eye(4, 3, k=-1, order='F')
assert_equal(mat_c, mat_f)
assert mat_c.flags.c_contiguous
assert not mat_c.flags.f_contiguous
assert not mat_f.flags.c_contiguous
assert mat_f.flags.f_contiguous
def test_out(self):
mat = np.eye(3)
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
resout = np.eye(3)
for axis in (-2, -1, 0, 1):
tgt = rf(mat, axis=axis)
res = nf(mat, axis=axis, out=resout)
assert_almost_equal(res, resout)
assert_almost_equal(res, tgt)
def test_basic(self):
assert_equal(
eye(4),
array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]))
assert_equal(
eye(4, dtype='f'),
array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
'f'))
assert_equal(eye(3) == 1, eye(3, dtype=bool))
def test_eye_bounds(self):
assert_equal(eye(2, 2, 1), [[0, 1], [0, 0]])
assert_equal(eye(2, 2, -1), [[0, 0], [1, 0]])
assert_equal(eye(2, 2, 2), [[0, 0], [0, 0]])
assert_equal(eye(2, 2, -2), [[0, 0], [0, 0]])
assert_equal(eye(3, 2, 2), [[0, 0], [0, 0], [0, 0]])
assert_equal(eye(3, 2, 1), [[0, 1], [0, 0], [0, 0]])
assert_equal(eye(3, 2, -1), [[0, 0], [1, 0], [0, 1]])
assert_equal(eye(3, 2, -2), [[0, 0], [0, 0], [1, 0]])
assert_equal(eye(3, 2, -3), [[0, 0], [0, 0], [0, 0]])
def test_keepdims(self):
mat = np.eye(3)
for axis in [None, 0, 1]:
tgt = np.percentile(mat, 70, axis=axis, out=None,
overwrite_input=False)
res = np.nanpercentile(mat, 70, axis=axis, out=None,
overwrite_input=False)
assert_(res.ndim == tgt.ndim)
d = np.ones((3, 5, 7, 11))
# Randomly set some elements to NaN:
w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
w = w.astype(np.intp)
d[tuple(w)] = np.nan
with suppress_warnings() as sup:
sup.filter(RuntimeWarning)
res = np.nanpercentile(d, 90, axis=None, keepdims=True)
assert_equal(res.shape, (1, 1, 1, 1))
res = np.nanpercentile(d, 90, axis=(0, 1), keepdims=True)
assert_equal(res.shape, (1, 1, 7, 11))
res = np.nanpercentile(d, 90, axis=(0, 3), keepdims=True)
assert_equal(res.shape, (1, 5, 7, 1))
res = np.nanpercentile(d, 90, axis=(1,), keepdims=True)
assert_equal(res.shape, (3, 1, 7, 11))
res = np.nanpercentile(d, 90, axis=(0, 1, 2, 3), keepdims=True)
assert_equal(res.shape, (1, 1, 1, 1))
res = np.nanpercentile(d, 90, axis=(0, 1, 3), keepdims=True)
assert_equal(res.shape, (1, 1, 7, 1))
def test_keepdims(self):
mat = np.eye(3)
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for axis in [None, 0, 1]:
tgt = rf(mat, axis=axis, keepdims=True)
res = nf(mat, axis=axis, keepdims=True)
assert_(res.ndim == tgt.ndim)
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 = np.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, np.matrix))
assert_(res.shape == (1, 3))
res = f(mat, axis=1)
assert_(isinstance(res, np.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, np.matrix))
assert_(res.shape == (3, 3))
res = f(mat, axis=1)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (3, 3))
res = f(mat)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (1, 3*3))
def test_out(self):
mat = np.eye(3)
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
resout = np.zeros(3)
tgt = rf(mat, axis=1)
res = nf(mat, axis=1, out=resout)
assert_almost_equal(res, resout)
assert_almost_equal(res, tgt)
def test_dtype_from_input(self):
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
mat = np.eye(3, dtype=c)
tgt = rf(mat, axis=1).dtype.type
res = nf(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, axis=None).dtype.type
res = nf(mat, axis=None).dtype.type
assert_(res is tgt)
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_append_fields_dtype_list(self):
# Ticket #1676
from numpy1.lib.recfunctions import append_fields
base = np.array([1, 2, 3], dtype=np.int32)
names = ['a', 'b', 'c']
data = np.eye(3).astype(np.int32)
dlist = [np.float64, np.int32, np.int32]
try:
append_fields(base, names, data, dlist)
except Exception:
raise AssertionError()
def test_qr_mode_full_future_warning():
"""Check mode='full' FutureWarning.
In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
deprecated. The release date will probably be sometime in the summer
of 2013.
"""
a = np.eye(2)
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')
def test_100(self):
x, w = lag.laggauss(100)
# test orthogonality. Note that the results need to be normalized,
# otherwise the huge values that can arise from fast growing
# functions like Laguerre can be very confusing.
v = lag.lagvander(x, 99)
vv = np.dot(v.T * w, v)
vd = 1 / np.sqrt(vv.diagonal())
vv = vd[:, None] * vv * vd
assert_almost_equal(vv, np.eye(100))
# check that the integral of 1 is correct
tgt = 1.0
assert_almost_equal(w.sum(), tgt)
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
def test_subclass(self):
class MyNDArray(np.ndarray):
pass
# Check that it works and that type and
# shape are preserved
mine = np.eye(3).view(MyNDArray)
for f in self.nanfuncs:
res = f(mine, axis=0)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == (3,))
res = f(mine, axis=1)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == (3,))
res = f(mine)
assert_(res.shape == ())
def test_keepdims(self):
for f, g in zip(self.nanfuncs, self.stdfuncs):
mat = np.eye(3)
for axis in [None, 0, 1]:
tgt = f(mat, axis=axis, out=None)
res = g(mat, axis=axis, out=None)
assert_(res.ndim == tgt.ndim)
for f in self.nanfuncs:
d = np.ones((3, 5, 7, 11))
# Randomly set some elements to NaN:
rs = np.random.RandomState(0)
d[rs.rand(*d.shape) < 0.5] = np.nan
res = f(d, axis=None)
assert_equal(res.shape, (1155,))
for axis in np.arange(4):
res = f(d, axis=axis)
assert_equal(res.shape, (3, 5, 7, 11))
def eye(n, M=None, k=0, dtype=float, order='C'):
"""
Return a matrix with ones on the diagonal and zeros elsewhere.
Parameters
----------
n : int
Number of rows in the output.
M : int, optional
Number of columns in the output, defaults to `n`.
k : int, optional
Index of the diagonal: 0 refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
dtype : dtype, optional
Data-type of the returned matrix.
order : {'C', 'F'}, optional
Whether the output should be stored in row-major (C-style) or
column-major (Fortran-style) order in memory.
.. versionadded:: 1.14.0
Returns
-------
I : matrix
A `n` x `M` matrix where all elements are equal to zero,
except for the `k`-th diagonal, whose values are equal to one.
See Also
--------
numpy.eye : Equivalent array function.
identity : Square identity matrix.
Examples
--------
>>> import numpy1.matlib
>>> np.matlib.eye(3, k=1, dtype=float)
matrix([[ 0., 1., 0.],
[ 0., 0., 1.],
[ 0., 0., 0.]])
"""
return asmatrix(np.eye(n, M=M, k=k, dtype=dtype, order=order))
def test_subclass(self):
class MyNDArray(np.ndarray):
pass
# Check that it works and that type and
# shape are preserved
mine = np.eye(3).view(MyNDArray)
for f in self.nanfuncs:
res = f(mine, axis=0)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == (3,))
res = f(mine, axis=1)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == (3,))
res = f(mine)
assert_(res.shape == ())
# check that rows of nan are dealt with for subclasses (#4628)
mine[1] = np.nan
for f in self.nanfuncs:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mine, axis=0)
assert_(isinstance(res, MyNDArray))
assert_(not np.any(np.isnan(res)))
assert_(len(w) == 0)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mine, axis=1)
assert_(isinstance(res, MyNDArray))
assert_(np.isnan(res[1]) and not np.isnan(res[0])
and not np.isnan(res[2]))
assert_(len(w) == 1, 'no warning raised')
assert_(issubclass(w[0].category, RuntimeWarning))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mine)
assert_(res.shape == ())
assert_(res != np.nan)
assert_(len(w) == 0)
def test_subclass(self):
class MyNDArray(np.ndarray):
pass
# Check that it works and that type and
# shape are preserved
array = np.eye(3)
mine = array.view(MyNDArray)
for f in self.nanfuncs:
expected_shape = f(array, axis=0).shape
res = f(mine, axis=0)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == expected_shape)
expected_shape = f(array, axis=1).shape
res = f(mine, axis=1)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == expected_shape)
expected_shape = f(array).shape
res = f(mine)
assert_(isinstance(res, MyNDArray))
assert_(res.shape == expected_shape)
def test_nanfunctions_matrices():
# Check that it works and that type and
# shape are preserved
# 2018-04-29: moved here from core.tests.test_nanfunctions
mat = np.matrix(np.eye(3))
for f in [np.nanmin, np.nanmax]:
res = f(mat, axis=0)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (1, 3))
res = f(mat, axis=1)
assert_(isinstance(res, np.matrix))
assert_(res.shape == (3, 1))
res = f(mat)
assert_(np.isscalar(res))
# check that rows of nan are dealt with for subclasses (#4628)
mat[1] = np.nan
for f in [np.nanmin, np.nanmax]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mat, axis=0)
assert_(isinstance(res, np.matrix))
assert_(not np.any(np.isnan(res)))
assert_(len(w) == 0)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mat, axis=1)
assert_(isinstance(res, np.matrix))
assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0])
and not np.isnan(res[2, 0]))
assert_(len(w) == 1, 'no warning raised')
assert_(issubclass(w[0].category, RuntimeWarning))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(mat)
assert_(np.isscalar(res))
assert_(res != np.nan)
assert_(len(w) == 0)
def test_simple(self):
x = array(
[0.41702200, 0.72032449, 1.1437481e-4, 0.302332573, 0.146755891])
y = array([0.09233859, 0.18626021, 0.34556073, 0.39676747, 0.53881673])
xedges = np.linspace(0, 1, 10)
yedges = np.linspace(0, 1, 10)
H = histogram2d(x, y, (xedges, yedges))[0]
answer = array([[0, 0, 0, 1, 0, 0, 0, 0,
0], [0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0,
0], [1, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0,
0], [0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0,
0], [0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]])
assert_array_equal(H.T, answer)
H = histogram2d(x, y, xedges)[0]
assert_array_equal(H.T, answer)
H, xedges, yedges = histogram2d(list(range(10)), list(range(10)))
assert_array_equal(H, eye(10, 10))
assert_array_equal(xedges, np.linspace(0, 9, 11))
assert_array_equal(yedges, np.linspace(0, 9, 11))
def test_flat(self):
# Test that flat can return items even for matrices [#4585, #4615]
# test simple access
test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
assert_equal(test.flat[1], 2)
assert_equal(test.flat[2], masked)
assert_(np.all(test.flat[0:2] == test[0, 0:2]))
# Test flat on masked_matrices
test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
test.flat = masked_array([3, 2, 1], mask=[1, 0, 0])
control = masked_array(np.matrix([[3, 2, 1]]), mask=[1, 0, 0])
assert_equal(test, control)
# Test setting
test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
testflat = test.flat
testflat[:] = testflat[[2, 1, 0]]
assert_equal(test, control)
testflat[0] = 9
# test that matrices keep the correct shape (#4615)
a = masked_array(np.matrix(np.eye(2)), mask=0)
b = a.flat
b01 = b[:2]
assert_equal(b01.data, np.array([[1., 0.]]))
assert_equal(b01.mask, np.array([[False, False]]))
def test_no_lists(self):
assert_equal(np.block(1), np.array(1))
assert_equal(np.block(np.eye(3)), np.eye(3))
def test_large_svd_32bit(self):
# See gh-4442, 64bit would require very large/slow matrices.
x = np.eye(1000, 66)
np.linalg.svd(x)
def test_bool(self):
assert_equal(eye(2, 2, dtype=bool), [[True, False], [False, True]])
def test_strings(self):
assert_equal(eye(2, 2, dtype='S3'), [[b'1', b''], [b'', b'1']])
def test_diag2d(self):
assert_equal(eye(3, 4, k=2),
array([[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]]))
assert_equal(eye(4, 3, k=-2),
array([[0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 0]]))
def test_2d(self):
assert_equal(eye(4, 3),
array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 0]]))
assert_equal(eye(3, 4),
array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]))
def mul():
np.mat(np.eye(2)) * np.ones(2)
