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import numpy as np
from numpy.testing import assert_warns
from numpy.ma.testutils import (assert_, assert_equal, assert_raises,
assert_array_equal)
from numpy.ma.core import (masked_array, masked_values, masked, allequal,
MaskType, getmask, MaskedArray, nomask,
log, add, hypot, divide)
from numpy.ma.extras import mr_
from numpy.compat import pickle
class MMatrix(MaskedArray, np.matrix,):
def __new__(cls, data, mask=nomask):
mat = np.matrix(data)
_data = MaskedArray.__new__(cls, data=mat, mask=mask)
return _data
def __array_finalize__(self, obj):
np.matrix.__array_finalize__(self, obj)
MaskedArray.__array_finalize__(self, obj)
return
@property
def _series(self):
_view = self.view(MaskedArray)
_view._sharedmask = False
return _view
class TestMaskedMatrix:
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_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_count_mean_with_matrix(self):
m = masked_array(np.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_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_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_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_ravel(self):
a = masked_array(np.matrix([1, 2, 3, 4, 5]), mask=[[0, 1, 0, 0, 0]])
aravel = a.ravel()
assert_equal(aravel.shape, (1, 5))
assert_equal(aravel._mask.shape, a.shape)
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))
class TestSubclassing:
# Test suite for masked subclasses of ndarray.
def setup_method(self):
x = np.arange(5, dtype='float')
mx = MMatrix(x, mask=[0, 1, 0, 0, 0])
self.data = (x, mx)
def test_maskedarray_subclassing(self):
# Tests subclassing MaskedArray
(x, mx) = self.data
assert_(isinstance(mx._data, np.matrix))
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_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))
with assert_warns(DeprecationWarning):
assert_(isinstance(add.outer(mx, mx), MMatrix))
assert_(isinstance(hypot(mx, mx), MMatrix))
assert_(isinstance(hypot(mx, x), MMatrix))
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))
class TestConcatenator:
# Tests for mr_, the equivalent of r_ for masked arrays.
def test_matrix_builder(self):
assert_raises(np.ma.MAError, lambda: mr_['1, 2; 3, 4'])
def test_matrix(self):
# Test consistency with unmasked version. If we ever deprecate
# matrix, this test should either still pass, or both actual and
# expected should fail to be build.
actual = mr_['r', 1, 2, 3]
expected = np.ma.array(np.r_['r', 1, 2, 3])
assert_array_equal(actual, expected)
# outer type is masked array, inner type is matrix
assert_equal(type(actual), type(expected))
assert_equal(type(actual.data), type(expected.data))
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