def test_subclasspreservation(self): # Checks that masked_array(...,subok=True) preserves the class. x = np.arange(5) m = [0, 0, 1, 0, 0] xinfo = [(i, j) for (i, j) in zip(x, m)] xsub = MSubArray(x, mask=m, info={'xsub': xinfo}) # mxsub = masked_array(xsub, subok=False) assert_(not isinstance(mxsub, MSubArray)) assert_(isinstance(mxsub, MaskedArray)) assert_equal(mxsub._mask, m) # mxsub = asarray(xsub) assert_(not isinstance(mxsub, MSubArray)) assert_(isinstance(mxsub, MaskedArray)) assert_equal(mxsub._mask, m) # mxsub = masked_array(xsub, subok=True) assert_(isinstance(mxsub, MSubArray)) assert_equal(mxsub.info, xsub.info) assert_equal(mxsub._mask, xsub._mask) # mxsub = asanyarray(xsub) assert_(isinstance(mxsub, MSubArray)) assert_equal(mxsub.info, xsub.info) assert_equal(mxsub._mask, m)
def test_attributepropagation(self): x = array(arange(5), mask=[0] + [1] * 4) my = masked_array(subarray(x)) ym = msubarray(x) # z = (my + 1) assert_(isinstance(z, MaskedArray)) assert_(not isinstance(z, MSubArray)) assert_(isinstance(z._data, SubArray)) assert_equal(z._data.info, {}) # z = (ym + 1) assert_(isinstance(z, MaskedArray)) assert_(isinstance(z, MSubArray)) assert_(isinstance(z._data, SubArray)) assert_(z._data.info['added'] > 0) # Test that inplace methods from data get used (gh-4617) ym += 1 assert_(isinstance(ym, MaskedArray)) assert_(isinstance(ym, MSubArray)) assert_(isinstance(ym._data, SubArray)) assert_(ym._data.info['iadded'] > 0) # ym._set_mask([1, 0, 0, 0, 1]) assert_equal(ym._mask, [1, 0, 0, 0, 1]) ym._series._set_mask([0, 0, 0, 0, 1]) assert_equal(ym._mask, [0, 0, 0, 0, 1]) # xsub = subarray(x, info={'name': 'x'}) mxsub = masked_array(xsub) assert_(hasattr(mxsub, 'info')) assert_equal(mxsub.info, xsub.info)
def test_subclass_repr(self): """test that repr uses the name of the subclass and 'array' for np.ndarray""" x = np.arange(5) mx = masked_array(x, mask=[True, False, True, False, False]) assert_startswith(repr(mx), 'masked_array') xsub = SubArray(x) mxsub = masked_array(xsub, mask=[True, False, True, False, False]) assert_startswith( repr(mxsub), 'masked_{0}(data=[--, 1, --, 3, 4]'.format(SubArray.__name__))
def test_subclass_str(self): """test str with subclass that has overridden str, setitem""" # first without override x = np.arange(5) xsub = SubArray(x) mxsub = masked_array(xsub, mask=[True, False, True, False, False]) assert_equal(str(mxsub), '[-- 1 -- 3 4]') xcsub = ComplicatedSubArray(x) assert_raises(ValueError, xcsub.__setitem__, 0, np.ma.core.masked_print_option) mxcsub = masked_array(xcsub, mask=[True, False, True, False, False]) assert_equal(str(mxcsub), 'myprefix [-- 1 -- 3 4] mypostfix')
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), msubarray)) assert_(isinstance(divide(mx, x), msubarray)) assert_equal(divide(mx, mx), divide(xmx, xmx))
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_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_data_subclassing(self): # Tests whether the subclass is kept. x = np.arange(5) m = [0, 0, 1, 0, 0] xsub = SubArray(x) xmsub = masked_array(xsub, mask=m) assert_(isinstance(xmsub, MaskedArray)) assert_equal(xmsub._data, xsub) assert_(isinstance(xmsub._data, SubArray))
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_subclass_nomask_items(self): x = np.arange(5) xcsub = ComplicatedSubArray(x) mxcsub_nomask = masked_array(xcsub) assert_(isinstance(mxcsub_nomask[1, ...].data, ComplicatedSubArray)) assert_(isinstance(mxcsub_nomask[0, ...].data, ComplicatedSubArray)) assert_(isinstance(mxcsub_nomask[1], ComplicatedSubArray)) assert_(isinstance(mxcsub_nomask[0], ComplicatedSubArray))
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_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_subclass_items(self): """test that getter and setter go via baseclass""" x = np.arange(5) xcsub = ComplicatedSubArray(x) mxcsub = masked_array(xcsub, mask=[True, False, True, False, False]) # getter should return a ComplicatedSubArray, even for single item # first check we wrote ComplicatedSubArray correctly assert_(isinstance(xcsub[1], ComplicatedSubArray)) assert_(isinstance(xcsub[1, ...], ComplicatedSubArray)) assert_(isinstance(xcsub[1:4], ComplicatedSubArray)) # now that it propagates inside the MaskedArray assert_(isinstance(mxcsub[1], ComplicatedSubArray)) assert_(isinstance(mxcsub[1, ...].data, ComplicatedSubArray)) assert_(mxcsub[0] is masked) assert_(isinstance(mxcsub[0, ...].data, ComplicatedSubArray)) assert_(isinstance(mxcsub[1:4].data, ComplicatedSubArray)) # also for flattened version (which goes via MaskedIterator) assert_(isinstance(mxcsub.flat[1].data, ComplicatedSubArray)) assert_(mxcsub.flat[0] is masked) assert_(isinstance(mxcsub.flat[1:4].base, ComplicatedSubArray)) # setter should only work with ComplicatedSubArray input # first check we wrote ComplicatedSubArray correctly assert_raises(ValueError, xcsub.__setitem__, 1, x[4]) # now that it propagates inside the MaskedArray assert_raises(ValueError, mxcsub.__setitem__, 1, x[4]) assert_raises(ValueError, mxcsub.__setitem__, slice(1, 4), x[1:4]) mxcsub[1] = xcsub[4] mxcsub[1:4] = xcsub[1:4] # also for flattened version (which goes via MaskedIterator) assert_raises(ValueError, mxcsub.flat.__setitem__, 1, x[4]) assert_raises(ValueError, mxcsub.flat.__setitem__, slice(1, 4), x[1:4]) mxcsub.flat[1] = xcsub[4] mxcsub.flat[1:4] = xcsub[1:4]
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_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_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)