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Edit File: test_masked_matrix.py
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))