def test_pearsonr(self):
# Tests some computations of Pearson's r
x = ma.arange(10)
with warnings.catch_warnings():
# The tests in this context are edge cases, with perfect
# correlation or anticorrelation, or totally masked data.
# None of these should trigger a RuntimeWarning.
warnings.simplefilter("error", RuntimeWarning)
assert_almost_equal(mstats.pearsonr(x, x)[0], 1.0)
assert_almost_equal(mstats.pearsonr(x, x[::-1])[0], -1.0)
x = ma.array(x, mask=True)
pr = mstats.pearsonr(x, x)
assert_(pr[0] is masked)
assert_(pr[1] is masked)
x1 = ma.array([-1.0, 0.0, 1.0])
y1 = ma.array([0, 0, 3])
r, p = mstats.pearsonr(x1, y1)
assert_almost_equal(r, np.sqrt(3)/2)
assert_almost_equal(p, 1.0/3)
# (x2, y2) have the same unmasked data as (x1, y1).
mask = [False, False, False, True]
x2 = ma.array([-1.0, 0.0, 1.0, 99.0], mask=mask)
y2 = ma.array([0, 0, 3, -1], mask=mask)
r, p = mstats.pearsonr(x2, y2)
assert_almost_equal(r, np.sqrt(3)/2)
assert_almost_equal(p, 1.0/3)
def test_setxor1d(self):
# Test setxor1d
a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
test = setxor1d(a, b)
assert_equal(test, array([3, 4, 7]))
#
a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
b = [1, 2, 3, 4, 5]
test = setxor1d(a, b)
assert_equal(test, array([3, 4, 7, -1], mask=[0, 0, 0, 1]))
#
a = array([1, 2, 3])
b = array([6, 5, 4])
test = setxor1d(a, b)
assert_(isinstance(test, MaskedArray))
assert_equal(test, [1, 2, 3, 4, 5, 6])
#
a = array([1, 8, 2, 3], mask=[0, 1, 0, 0])
b = array([6, 5, 4, 8], mask=[0, 0, 0, 1])
test = setxor1d(a, b)
assert_(isinstance(test, MaskedArray))
assert_equal(test, [1, 2, 3, 4, 5, 6])
#
assert_array_equal([], setxor1d([], []))
def test_pickling_subbaseclass(self):
# Test pickling w/ a subclass of ndarray
a = masked_array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2)
a_pickled = pickle.loads(a.dumps())
assert_equal(a_pickled._mask, a._mask)
assert_equal(a_pickled, a)
assert_(isinstance(a_pickled._data, np.matrix))
def test_masked_binary_operations2(self):
# Tests domained_masked_binary_operation
(x, mx) = self.data
xmx = masked_array(mx.data.__array__(), mask=mx.mask)
assert_(isinstance(divide(mx, mx), MMatrix))
assert_(isinstance(divide(mx, x), MMatrix))
assert_equal(divide(mx, mx), divide(xmx, xmx))
def test_view_simple_dtype(self):
(mrec, a, b, arr) = self.data
ntype = (float, 2)
test = mrec.view(ntype)
assert_(isinstance(test, ma.MaskedArray))
assert_equal(test, np.array(list(zip(a, b)), dtype=float))
assert_(test[3, 1] is ma.masked)
def test_drop_fields(self):
# Test drop_fields
a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
# A basic field
test = drop_fields(a, 'a')
control = np.array([((2, 3.0),), ((5, 6.0),)],
dtype=[('b', [('ba', float), ('bb', int)])])
assert_equal(test, control)
# Another basic field (but nesting two fields)
test = drop_fields(a, 'b')
control = np.array([(1,), (4,)], dtype=[('a', int)])
assert_equal(test, control)
# A nested sub-field
test = drop_fields(a, ['ba', ])
control = np.array([(1, (3.0,)), (4, (6.0,))],
dtype=[('a', int), ('b', [('bb', int)])])
assert_equal(test, control)
# All the nested sub-field from a field: zap that field
test = drop_fields(a, ['ba', 'bb'])
control = np.array([(1,), (4,)], dtype=[('a', int)])
assert_equal(test, control)
test = drop_fields(a, ['a', 'b'])
assert_(test is None)
def test_out(self):
x = masked_array(np.arange(30).reshape(10, 3))
x[:3] = x[-3:] = masked
out = masked_array(np.ones(10))
r = median(x, axis=1, out=out)
assert_equal(r, out)
assert_(type(r) == MaskedArray)
def test_1D_float96(self):
a = ma.array((1,2,3,4), mask=(0,0,0,1))
actual_dt = mstats.hmean(a, dtype=np.float96)
desired_dt = np.asarray(3. / (1./1 + 1./2 + 1./3),
dtype=np.float96)
assert_almost_equal(actual_dt, desired_dt, decimal=14)
assert_(actual_dt.dtype == desired_dt.dtype)
def test_compressed(self):
a = masked_array(np.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0])
b = a.compressed()
assert_equal(b, a)
assert_(isinstance(b, np.matrix))
a[0, 0] = masked
b = a.compressed()
assert_equal(b, [[2, 3, 4]])
def test_pickling_subbaseclass(self):
# Test pickling w/ a subclass of ndarray
a = masked_array(np.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, np.matrix))
def test_view(self):
# Test view w/ flexible dtype
iterator = list(zip(np.arange(10), np.random.rand(10)))
data = np.array(iterator)
a = masked_array(iterator, dtype=[('a', float), ('b', float)])
a.mask[0] = (1, 0)
test = a.view((float, 2), np.matrix)
assert_equal(test, data)
assert_(isinstance(test, np.matrix))
assert_(not isinstance(test, MaskedArray))
def test_pearsonr(self):
"Tests some computations of Pearson's r"
x = ma.arange(10)
assert_almost_equal(mstats.pearsonr(x,x)[0], 1.0)
assert_almost_equal(mstats.pearsonr(x,x[::-1])[0], -1.0)
#
x = ma.array(x, mask=True)
pr = mstats.pearsonr(x,x)
assert_(pr[0] is masked)
assert_(pr[1] is masked)
def test_byview(self):
# Test creation by view
base = self.base
mbase = base.view(mrecarray)
assert_equal(mbase.recordmask, base.recordmask)
assert_equal_records(mbase._mask, base._mask)
assert_(isinstance(mbase._data, recarray))
assert_equal_records(mbase._data, base._data.view(recarray))
for field in ('a', 'b', 'c'):
assert_equal(base[field], mbase[field])
assert_equal_records(mbase.view(mrecarray), mbase)
def test_wmasked_arrays(self):
# Test merge_arrays masked arrays
(_, x, _, _) = self.data
mx = ma.array([1, 2, 3], mask=[1, 0, 0])
test = merge_arrays((x, mx), usemask=True)
control = ma.array([(1, 1), (2, 2), (-1, 3)],
mask=[(0, 1), (0, 0), (1, 0)],
dtype=[('f0', int), ('f1', int)])
assert_equal(test, control)
test = merge_arrays((x, mx), usemask=True, asrecarray=True)
assert_equal(test, control)
assert_(isinstance(test, MaskedRecords))
def test_pearsonr(self):
"Tests some computations of Pearson's r"
x = ma.arange(10)
olderr = np.seterr(all='ignore')
try:
assert_almost_equal(mstats.pearsonr(x,x)[0], 1.0)
assert_almost_equal(mstats.pearsonr(x,x[::-1])[0], -1.0)
x = ma.array(x, mask=True)
pr = mstats.pearsonr(x,x)
finally:
np.seterr(**olderr)
assert_(pr[0] is masked)
assert_(pr[1] is masked)
def assert_mask_equivalent(m1, m2, err_msg=''):
'''
Asserts the equality of two masks.
This is an amended version of ``np.ma.testutils.assert_mask_equal`` to
allow for one test array to have no mask and the other to have a mask array
where each element is ``False``. We want to handle this situation as the
two arrays are functionally equivalent.
'''
if m1 is np.ma.nomask or not np.any(m1):
assert_(m2 is np.ma.nomask or not np.any(m2), msg=err_msg)
if m2 is np.ma.nomask or not np.any(m2):
assert_(m1 is np.ma.nomask or not np.any(m1), msg=err_msg)
assert_array_equal(m1, m2, err_msg=err_msg)
def test_allany_onmatrices(self):
x = np.array([[0.13, 0.26, 0.90],
[0.28, 0.33, 0.63],
[0.31, 0.87, 0.70]])
X = np.matrix(x)
m = np.array([[True, False, False],
[False, False, False],
[True, True, False]], dtype=np.bool_)
mX = masked_array(X, mask=m)
mXbig = (mX > 0.5)
mXsmall = (mX < 0.5)
assert_(not mXbig.all())
assert_(mXbig.any())
assert_equal(mXbig.all(0), np.matrix([False, False, True]))
assert_equal(mXbig.all(1), np.matrix([False, False, True]).T)
assert_equal(mXbig.any(0), np.matrix([False, False, True]))
assert_equal(mXbig.any(1), np.matrix([True, True, True]).T)
assert_(not mXsmall.all())
assert_(mXsmall.any())
assert_equal(mXsmall.all(0), np.matrix([True, True, False]))
assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T)
assert_equal(mXsmall.any(0), np.matrix([True, True, False]))
assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T)
def test_dot_returns_maskedarray(self):
# See gh-6611
a = np.eye(3)
b = array(a)
assert_(type(dot(a, a)) is MaskedArray)
assert_(type(dot(a, b)) is MaskedArray)
assert_(type(dot(b, a)) is MaskedArray)
assert_(type(dot(b, b)) is MaskedArray)
def test_1D(self):
a = (1,2,3,4)
actual = mstats.gmean(a)
desired = np.power(1*2*3*4,1./4.)
assert_almost_equal(actual, desired, decimal=14)
desired1 = mstats.gmean(a,axis=-1)
assert_almost_equal(actual, desired1, decimal=14)
assert_(not isinstance(desired1, ma.MaskedArray))
a = ma.array((1,2,3,4),mask=(0,0,0,1))
actual = mstats.gmean(a)
desired = np.power(1*2*3,1./3.)
assert_almost_equal(actual, desired,decimal=14)
desired1 = mstats.gmean(a,axis=-1)
assert_almost_equal(actual, desired1, decimal=14)
def test_fromtextfile(self):
# Tests reading from a text file.
fcontent = (
"""#
'One (S)','Two (I)','Three (F)','Four (M)','Five (-)','Six (C)'
'strings',1,1.0,'mixed column',,1
'with embedded "double quotes"',2,2.0,1.0,,1
'strings',3,3.0E5,3,,1
'strings',4,-1e-10,,,1
""")
with temppath() as path:
with open(path, 'w') as f:
f.write(fcontent)
mrectxt = fromtextfile(path, delimitor=',', varnames='ABCDEFG')
assert_(isinstance(mrectxt, MaskedRecords))
assert_equal(mrectxt.F, [1, 1, 1, 1])
assert_equal(mrectxt.E._mask, [1, 1, 1, 1])
assert_equal(mrectxt.C, [1, 2, 3.e+5, -1e-10])
def test_chisquare_masked_arrays():
# The other tests were taken from the tests for stats.chisquare, so
# they don't test the function with masked arrays. Here masked arrays
# are tested.
obs = np.array([[8, 8, 16, 32, -1], [-1, -1, 3, 4, 5]]).T
mask = np.array([[0, 0, 0, 0, 1], [1, 1, 0, 0, 0]]).T
mobs = ma.masked_array(obs, mask)
expected_chisq = np.array([24.0, 0.5])
chisq, p = mstats.chisquare(mobs)
assert_array_equal(chisq, expected_chisq)
assert_array_almost_equal(p, stats.chisqprob(expected_chisq, mobs.count(axis=0) - 1))
chisq, p = mstats.chisquare(mobs.T, axis=1)
assert_array_equal(chisq, expected_chisq)
assert_array_almost_equal(p, stats.chisqprob(expected_chisq, mobs.T.count(axis=1) - 1))
# When axis=None, the two values should have type np.float64.
chisq, p = mstats.chisquare([1,2,3], axis=None)
assert_(isinstance(chisq, np.float64))
assert_(isinstance(p, np.float64))
assert_equal(chisq, 1.0)
assert_almost_equal(p, stats.chisqprob(1.0, 2))
def test_view_flexible_type(self):
(mrec, a, b, arr) = self.data
alttype = [('A', float), ('B', float)]
test = mrec.view(alttype)
assert_(isinstance(test, MaskedRecords))
assert_equal_records(test, arr.view(alttype))
assert_(test['B'][3] is masked)
assert_equal(test.dtype, np.dtype(alttype))
assert_(test._fill_value is None)
def test_view_flexible_type(self):
(mrec, a, b, arr) = self.data
alttype = [('A', np.float), ('B', np.float)]
test = mrec.view(alttype)
assert_(isinstance(test, MaskedRecords))
assert_equal_records(test, arr.view(alttype))
assert_(test['B'][3] is masked)
assert_equal(test.dtype, np.dtype(alttype))
assert_(test._fill_value is None)
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_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_masked_1d(self):
x = array(np.arange(5), mask=True)
assert_equal(np.ma.median(x), np.ma.masked)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is np.ma.core.MaskedConstant)
x = array(np.arange(5), mask=False)
assert_equal(np.ma.median(x), 2.)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
x = array(np.arange(5), mask=[0,1,0,0,0])
assert_equal(np.ma.median(x), 2.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
x = array(np.arange(5), mask=[0,1,1,1,1])
assert_equal(np.ma.median(x), 0.)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
def test_masked_1d(self):
x = array(np.arange(5), mask=True)
assert_equal(np.ma.median(x), np.ma.masked)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is np.ma.core.MaskedConstant)
x = array(np.arange(5), mask=False)
assert_equal(np.ma.median(x), 2.)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
x = array(np.arange(5), mask=[0,1,0,0,0])
assert_equal(np.ma.median(x), 2.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
x = array(np.arange(5), mask=[0,1,1,1,1])
assert_equal(np.ma.median(x), 0.)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
def test_non_masked(self):
x = np.arange(9)
assert_equal(np.ma.median(x), 4.)
assert_(type(np.ma.median(x)) is not MaskedArray)
x = range(9)
assert_equal(np.ma.median(x), 4.)
assert_(type(np.ma.median(x)) is not MaskedArray)
x = 5
assert_equal(np.ma.median(x), 5.)
assert_(type(np.ma.median(x)) is not MaskedArray)
def test_non_masked(self):
x = np.arange(9)
assert_equal(np.ma.median(x), 4.)
assert_(type(np.ma.median(x)) is not MaskedArray)
x = range(9)
assert_equal(np.ma.median(x), 4.)
assert_(type(np.ma.median(x)) is not MaskedArray)
x = 5
assert_equal(np.ma.median(x), 5.)
assert_(type(np.ma.median(x)) is not MaskedArray)
def test_2d_waxis(self):
# Tests median w/ 2D arrays and different axis.
x = masked_array(np.arange(30).reshape(10, 3))
x[:3] = x[-3:] = masked
assert_equal(median(x), 14.5)
assert_(type(np.ma.median(x)) is not MaskedArray)
assert_equal(median(x, axis=0), [13.5, 14.5, 15.5])
assert_(type(np.ma.median(x, axis=0)) is MaskedArray)
assert_equal(median(x, axis=1), [0, 0, 0, 10, 13, 16, 19, 0, 0, 0])
assert_(type(np.ma.median(x, axis=1)) is MaskedArray)
assert_equal(median(x, axis=1).mask, [1, 1, 1, 0, 0, 0, 0, 1, 1, 1])
def test_2d_waxis(self):
# Tests median w/ 2D arrays and different axis.
x = masked_array(np.arange(30).reshape(10, 3))
x[:3] = x[-3:] = masked
assert_equal(median(x), 14.5)
assert_(type(np.ma.median(x)) is not MaskedArray)
assert_equal(median(x, axis=0), [13.5, 14.5, 15.5])
assert_(type(np.ma.median(x, axis=0)) is MaskedArray)
assert_equal(median(x, axis=1), [0, 0, 0, 10, 13, 16, 19, 0, 0, 0])
assert_(type(np.ma.median(x, axis=1)) is MaskedArray)
assert_equal(median(x, axis=1).mask, [1, 1, 1, 0, 0, 0, 0, 1, 1, 1])
def test_get(self):
# Tests fields retrieval
base = self.base.copy()
mbase = base.view(mrecarray)
# As fields..........
for field in ("a", "b", "c"):
assert_equal(getattr(mbase, field), mbase[field])
assert_equal(base[field], mbase[field])
# as elements .......
mbase_first = mbase[0]
assert_(isinstance(mbase_first, mrecarray))
assert_equal(mbase_first.dtype, mbase.dtype)
assert_equal(mbase_first.tolist(), (1, 1.1, b"one"))
# Used to be mask, now it's recordmask
assert_equal(mbase_first.recordmask, nomask)
assert_equal(mbase_first._mask.item(), (False, False, False))
assert_equal(mbase_first["a"], mbase["a"][0])
mbase_last = mbase[-1]
assert_(isinstance(mbase_last, mrecarray))
assert_equal(mbase_last.dtype, mbase.dtype)
assert_equal(mbase_last.tolist(), (None, None, None))
# Used to be mask, now it's recordmask
assert_equal(mbase_last.recordmask, True)
assert_equal(mbase_last._mask.item(), (True, True, True))
assert_equal(mbase_last["a"], mbase["a"][-1])
assert_((mbase_last["a"] is masked))
# as slice ..........
mbase_sl = mbase[:2]
assert_(isinstance(mbase_sl, mrecarray))
assert_equal(mbase_sl.dtype, mbase.dtype)
# Used to be mask, now it's recordmask
assert_equal(mbase_sl.recordmask, [0, 1])
assert_equal_records(
mbase_sl.mask,
np.array(
[(False, False, False), (True, True, True)], dtype=mbase._mask.dtype
),
)
assert_equal_records(mbase_sl, base[:2].view(mrecarray))
for field in ("a", "b", "c"):
assert_equal(getattr(mbase_sl, field), base[:2][field])
def test_docstring_examples(self):
"test the examples given in the docstring of ma.median"
x = array(np.arange(8), mask=[0]*4 + [1]*4)
assert_equal(np.ma.median(x), 1.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
x = array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4)
assert_equal(np.ma.median(x), 2.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
ma_x = np.ma.median(x, axis=-1, overwrite_input=True)
assert_equal(ma_x, [2., 5.])
assert_equal(ma_x.shape, (2,), "shape mismatch")
assert_(type(ma_x) is MaskedArray)
def test_docstring_examples(self):
"test the examples given in the docstring of ma.median"
x = array(np.arange(8), mask=[0]*4 + [1]*4)
assert_equal(np.ma.median(x), 1.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
x = array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4)
assert_equal(np.ma.median(x), 2.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
ma_x = np.ma.median(x, axis=-1, overwrite_input=True)
assert_equal(ma_x, [2., 5.])
assert_equal(ma_x.shape, (2,), "shape mismatch")
assert_(type(ma_x) is MaskedArray)
def test_get(self):
# Tests fields retrieval
base = self.base.copy()
mbase = base.view(mrecarray)
# As fields..........
for field in ('a', 'b', 'c'):
assert_equal(getattr(mbase, field), mbase[field])
assert_equal(base[field], mbase[field])
# as elements .......
mbase_first = mbase[0]
assert_(isinstance(mbase_first, mrecarray))
assert_equal(mbase_first.dtype, mbase.dtype)
assert_equal(mbase_first.tolist(), (1, 1.1, asbytes('one')))
# Used to be mask, now it's recordmask
assert_equal(mbase_first.recordmask, nomask)
assert_equal(mbase_first._mask.item(), (False, False, False))
assert_equal(mbase_first['a'], mbase['a'][0])
mbase_last = mbase[-1]
assert_(isinstance(mbase_last, mrecarray))
assert_equal(mbase_last.dtype, mbase.dtype)
assert_equal(mbase_last.tolist(), (None, None, None))
# Used to be mask, now it's recordmask
assert_equal(mbase_last.recordmask, True)
assert_equal(mbase_last._mask.item(), (True, True, True))
assert_equal(mbase_last['a'], mbase['a'][-1])
assert_((mbase_last['a'] is masked))
# as slice ..........
mbase_sl = mbase[:2]
assert_(isinstance(mbase_sl, mrecarray))
assert_equal(mbase_sl.dtype, mbase.dtype)
# Used to be mask, now it's recordmask
assert_equal(mbase_sl.recordmask, [0, 1])
assert_equal_records(mbase_sl.mask,
np.array([(False, False, False),
(True, True, True)],
dtype=mbase._mask.dtype))
assert_equal_records(mbase_sl, base[:2].view(mrecarray))
for field in ('a', 'b', 'c'):
assert_equal(getattr(mbase_sl, field), base[:2][field])
def test_hardmask(self):
# Test hardmask
base = self.base.copy()
mbase = base.view(mrecarray)
mbase.harden_mask()
assert_(mbase._hardmask)
mbase.mask = nomask
assert_equal_records(mbase._mask, base._mask)
mbase.soften_mask()
assert_(not mbase._hardmask)
mbase.mask = nomask
# So, the mask of a field is no longer set to nomask...
assert_equal_records(mbase._mask, ma.make_mask_none(base.shape, base.dtype))
assert_(ma.make_mask(mbase["b"]._mask) is nomask)
assert_equal(mbase["a"]._mask, mbase["b"]._mask)
def test_hardmask(self):
# Test hardmask
base = self.base.copy()
mbase = base.view(mrecarray)
mbase.harden_mask()
assert_(mbase._hardmask)
mbase.mask = nomask
assert_equal_records(mbase._mask, base._mask)
mbase.soften_mask()
assert_(not mbase._hardmask)
mbase.mask = nomask
# So, the mask of a field is no longer set to nomask...
assert_equal_records(mbase._mask,
ma.make_mask_none(base.shape, base.dtype))
assert_(ma.make_mask(mbase['b']._mask) is nomask)
assert_equal(mbase['a']._mask, mbase['b']._mask)
def test_allany_onmatrices(self):
x = np.array([[0.13, 0.26, 0.90], [0.28, 0.33, 0.63],
[0.31, 0.87, 0.70]])
X = npm.matrix(x)
m = np.array(
[[True, False, False], [False, False, False], [True, True, False]],
dtype=np.bool_)
mX = masked_array(X, mask=m)
mXbig = (mX > 0.5)
mXsmall = (mX < 0.5)
assert_(not mXbig.all())
assert_(mXbig.any())
assert_equal(mXbig.all(0), npm.matrix([False, False, True]))
assert_equal(mXbig.all(1), npm.matrix([False, False, True]).T)
assert_equal(mXbig.any(0), npm.matrix([False, False, True]))
assert_equal(mXbig.any(1), npm.matrix([True, True, True]).T)
assert_(not mXsmall.all())
assert_(mXsmall.any())
assert_equal(mXsmall.all(0), npm.matrix([True, True, False]))
assert_equal(mXsmall.all(1), npm.matrix([False, False, False]).T)
assert_equal(mXsmall.any(0), npm.matrix([True, True, False]))
assert_equal(mXsmall.any(1), npm.matrix([True, True, False]).T)
def test_empty(self):
res1 = mstats.ttest_1samp([], 1)
assert_(np.all(np.isnan(res1)))
def test_1D_float96(self):
a = ma.array((1, 2, 3, 4), mask=(0, 0, 0, 1))
actual_dt = mstats.gmean(a, dtype=np.float96)
desired_dt = np.power(1 * 2 * 3, 1. / 3.).astype(np.float96)
assert_almost_equal(actual_dt, desired_dt, decimal=14)
assert_(actual_dt.dtype == desired_dt.dtype)
def test_empty(self):
res1 = mstats.ttest_ind([], [])
assert_(np.all(np.isnan(res1)))
def test_empty(self):
res1 = mstats.ttest_1samp([], 1)
assert_(np.all(np.isnan(res1)))
def test_matrix_indexing(self):
# Tests conversions and indexing
x1 = np.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 = np.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_masked_binary_operations(self):
# Tests masked_binary_operation
(x, mx) = self.data
# Result should be a MMatrix
assert_(isinstance(add(mx, mx), MMatrix))
assert_(isinstance(add(mx, x), MMatrix))
# Result should work
assert_equal(add(mx, x), mx+x)
assert_(isinstance(add(mx, mx)._data, np.matrix))
assert_(isinstance(add.outer(mx, mx), MMatrix))
assert_(isinstance(hypot(mx, mx), MMatrix))
assert_(isinstance(hypot(mx, x), MMatrix))
def test_masked_unary_operations(self):
# Tests masked_unary_operation
(x, mx) = self.data
with np.errstate(divide='ignore'):
assert_(isinstance(log(mx), MMatrix))
assert_equal(log(x), np.log(x))
def test_maskedarray_subclassing(self):
# Tests subclassing MaskedArray
(x, mx) = self.data
assert_(isinstance(mx._data, np.matrix))
def test_dot_out(self):
a = array(np.eye(3))
out = array(np.zeros((3, 3)))
res = dot(a, a, out=out)
assert_(res is out)
assert_equal(a, res)
def test_view_by_itself(self):
(mrec, a, b, arr) = self.data
test = mrec.view()
assert_(isinstance(test, MaskedRecords))
assert_equal_records(test, mrec)
assert_equal_records(test._mask, mrec._mask)
