diff options
Diffstat (limited to 'numpy/ma/tests')
| -rw-r--r-- | numpy/ma/tests/test_core.py | 129 | ||||
| -rw-r--r-- | numpy/ma/tests/test_extras.py | 3 | ||||
| -rw-r--r-- | numpy/ma/tests/test_subclassing.py | 45 |
3 files changed, 22 insertions, 155 deletions
diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py index 9caf38b56..63703f6cd 100644 --- a/numpy/ma/tests/test_core.py +++ b/numpy/ma/tests/test_core.py @@ -335,49 +335,6 @@ class TestMaskedArray(object): assert_equal(s1, s2) assert_(x1[1:1].shape == (0,)) - def test_matrix_indexing(self): - # Tests conversions and indexing - x1 = np.matrix([[1, 2, 3], [4, 3, 2]]) - x2 = array(x1, mask=[[1, 0, 0], [0, 1, 0]]) - x3 = array(x1, mask=[[0, 1, 0], [1, 0, 0]]) - x4 = 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], array([1, 1, 0]))) - assert_(allequal(getmask(x3[1]), array([1, 1, 0]))) - x4[1, :] = masked_array([1, 2, 3], [1, 1, 0]) - assert_(allequal(getmask(x4[1]), array([1, 1, 0]))) - assert_(allequal(x4[1], array([1, 2, 3]))) - x1 = np.matrix(np.arange(5) * 1.0) - x2 = masked_values(x1, 3.0) - assert_equal(x1, x2) - assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask)) - assert_equal(3.0, x2.fill_value) - @suppress_copy_mask_on_assignment def test_copy(self): # Tests of some subtle points of copying and sizing. @@ -611,11 +568,13 @@ class TestMaskedArray(object): def test_pickling_subbaseclass(self): # Test pickling w/ a subclass of ndarray - a = array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2) + x = np.array([(1.0, 2), (3.0, 4)], + dtype=[('x', float), ('y', int)]).view(np.recarray) + a = masked_array(x, mask=[(True, False), (False, True)]) 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)) + assert_(isinstance(a_pickled._data, np.recarray)) def test_pickling_maskedconstant(self): # Test pickling MaskedConstant @@ -1448,16 +1407,6 @@ class TestMaskedArrayArithmetic(object): assert_(result is output) assert_(output[0] is masked) - def test_count_mean_with_matrix(self): - m = np.ma.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_eq_on_structured(self): # Test the equality of structured arrays ndtype = [('A', int), ('B', int)] @@ -1740,23 +1689,6 @@ class TestMaskedArrayAttributes(object): def test_flat(self): # Test that flat can return all types of items [#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 - assert_equal(test[0, 0], 9) # test 2-D record array # ... on structured array w/ masked records x = array([[(1, 1.1, 'one'), (2, 2.2, 'two'), (3, 3.3, 'thr')], @@ -1784,12 +1716,6 @@ class TestMaskedArrayAttributes(object): if i >= x.shape[-1]: i = 0 j += 1 - # 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, array([[1., 0.]])) - assert_equal(b01.mask, array([[False, False]])) def test_assign_dtype(self): # check that the mask's dtype is updated when dtype is changed @@ -2893,32 +2819,6 @@ class TestMaskedArrayMethods(object): assert_equal(mxsmall.any(0), [True, True, False]) assert_equal(mxsmall.any(1), [True, True, 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_allany_oddities(self): # Some fun with all and any store = empty((), dtype=bool) @@ -3017,14 +2917,6 @@ class TestMaskedArrayMethods(object): b = a.compressed() assert_equal(b, [2, 3, 4]) - a = 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_empty(self): # Tests empty/like datatype = [('a', int), ('b', float), ('c', '|S8')] @@ -3139,10 +3031,6 @@ class TestMaskedArrayMethods(object): a = array([0, 0], mask=[1, 1]) aravel = a.ravel() assert_equal(aravel._mask.shape, a.shape) - a = 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) # Checks that small_mask is preserved a = array([1, 2, 3, 4], mask=[0, 0, 0, 0], shrink=False) assert_equal(a.ravel()._mask, [0, 0, 0, 0]) @@ -4607,10 +4495,6 @@ class TestMaskedFields(object): assert_equal(test, data) assert_equal(test.mask, controlmask.reshape(-1, 2)) - test = a.view((float, 2), np.matrix) - assert_equal(test, data) - assert_(isinstance(test, np.matrix)) - def test_getitem(self): ndtype = [('a', float), ('b', float)] a = array(list(zip(np.random.rand(10), np.arange(10))), dtype=ndtype) @@ -4794,11 +4678,12 @@ class TestMaskedView(object): def test_view_to_dtype_and_type(self): (data, a, controlmask) = self.data - test = a.view((float, 2), np.matrix) + test = a.view((float, 2), np.recarray) assert_equal(test, data) - assert_(isinstance(test, np.matrix)) + assert_(isinstance(test, np.recarray)) assert_(not isinstance(test, MaskedArray)) + class TestOptionalArgs(object): def test_ndarrayfuncs(self): # test axis arg behaves the same as ndarray (including multiple axes) diff --git a/numpy/ma/tests/test_extras.py b/numpy/ma/tests/test_extras.py index a7a32b628..2d5e30b2c 100644 --- a/numpy/ma/tests/test_extras.py +++ b/numpy/ma/tests/test_extras.py @@ -311,6 +311,9 @@ class TestConcatenator(object): 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) diff --git a/numpy/ma/tests/test_subclassing.py b/numpy/ma/tests/test_subclassing.py index b61a46278..f8ab52bb9 100644 --- a/numpy/ma/tests/test_subclassing.py +++ b/numpy/ma/tests/test_subclassing.py @@ -75,27 +75,6 @@ class MSubArray(SubArray, MaskedArray): msubarray = MSubArray -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 - - def _get_series(self): - _view = self.view(MaskedArray) - _view._sharedmask = False - return _view - _series = property(fget=_get_series) - -mmatrix = MMatrix - - # Also a subclass that overrides __str__, __repr__ and __setitem__, disallowing # setting to non-class values (and thus np.ma.core.masked_print_option) # and overrides __array_wrap__, updating the info dict, to check that this @@ -180,7 +159,7 @@ class TestSubclassing(object): def setup(self): x = np.arange(5, dtype='float') - mx = mmatrix(x, mask=[0, 1, 0, 0, 0]) + mx = msubarray(x, mask=[0, 1, 0, 0, 0]) self.data = (x, mx) def test_data_subclassing(self): @@ -196,34 +175,34 @@ class TestSubclassing(object): def test_maskedarray_subclassing(self): # Tests subclassing MaskedArray (x, mx) = self.data - assert_(isinstance(mx._data, np.matrix)) + assert_(isinstance(mx._data, subarray)) 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_(isinstance(log(mx), msubarray)) 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 be a msubarray + assert_(isinstance(add(mx, mx), msubarray)) + assert_(isinstance(add(mx, x), msubarray)) # 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)) + assert_(isinstance(add(mx, mx)._data, subarray)) + assert_(isinstance(add.outer(mx, mx), msubarray)) + assert_(isinstance(hypot(mx, mx), msubarray)) + assert_(isinstance(hypot(mx, x), msubarray)) 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_(isinstance(divide(mx, mx), msubarray)) + assert_(isinstance(divide(mx, x), msubarray)) assert_equal(divide(mx, mx), divide(xmx, xmx)) def test_attributepropagation(self): |