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231 lines
8.7 KiB
231 lines
8.7 KiB
import numpy as np
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from numpy.testing import assert_warns
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from numpy.ma.testutils import (assert_, assert_equal, assert_raises,
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assert_array_equal)
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from numpy.ma.core import (masked_array, masked_values, masked, allequal,
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MaskType, getmask, MaskedArray, nomask,
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log, add, hypot, divide)
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from numpy.ma.extras import mr_
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from numpy.compat import pickle
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class MMatrix(MaskedArray, np.matrix,):
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def __new__(cls, data, mask=nomask):
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mat = np.matrix(data)
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_data = MaskedArray.__new__(cls, data=mat, mask=mask)
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return _data
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def __array_finalize__(self, obj):
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np.matrix.__array_finalize__(self, obj)
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MaskedArray.__array_finalize__(self, obj)
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return
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@property
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def _series(self):
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_view = self.view(MaskedArray)
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_view._sharedmask = False
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return _view
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class TestMaskedMatrix:
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def test_matrix_indexing(self):
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# Tests conversions and indexing
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x1 = np.matrix([[1, 2, 3], [4, 3, 2]])
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x2 = masked_array(x1, mask=[[1, 0, 0], [0, 1, 0]])
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x3 = masked_array(x1, mask=[[0, 1, 0], [1, 0, 0]])
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x4 = masked_array(x1)
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# test conversion to strings
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str(x2) # raises?
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repr(x2) # raises?
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# tests of indexing
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assert_(type(x2[1, 0]) is type(x1[1, 0]))
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assert_(x1[1, 0] == x2[1, 0])
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assert_(x2[1, 1] is masked)
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assert_equal(x1[0, 2], x2[0, 2])
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assert_equal(x1[0, 1:], x2[0, 1:])
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assert_equal(x1[:, 2], x2[:, 2])
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assert_equal(x1[:], x2[:])
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assert_equal(x1[1:], x3[1:])
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x1[0, 2] = 9
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x2[0, 2] = 9
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assert_equal(x1, x2)
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x1[0, 1:] = 99
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x2[0, 1:] = 99
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assert_equal(x1, x2)
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x2[0, 1] = masked
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assert_equal(x1, x2)
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x2[0, 1:] = masked
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assert_equal(x1, x2)
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x2[0, :] = x1[0, :]
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x2[0, 1] = masked
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assert_(allequal(getmask(x2), np.array([[0, 1, 0], [0, 1, 0]])))
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x3[1, :] = masked_array([1, 2, 3], [1, 1, 0])
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assert_(allequal(getmask(x3)[1], masked_array([1, 1, 0])))
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assert_(allequal(getmask(x3[1]), masked_array([1, 1, 0])))
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x4[1, :] = masked_array([1, 2, 3], [1, 1, 0])
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assert_(allequal(getmask(x4[1]), masked_array([1, 1, 0])))
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assert_(allequal(x4[1], masked_array([1, 2, 3])))
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x1 = np.matrix(np.arange(5) * 1.0)
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x2 = masked_values(x1, 3.0)
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assert_equal(x1, x2)
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assert_(allequal(masked_array([0, 0, 0, 1, 0], dtype=MaskType),
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x2.mask))
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assert_equal(3.0, x2.fill_value)
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def test_pickling_subbaseclass(self):
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# Test pickling w/ a subclass of ndarray
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a = masked_array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2)
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for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
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a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
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assert_equal(a_pickled._mask, a._mask)
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assert_equal(a_pickled, a)
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assert_(isinstance(a_pickled._data, np.matrix))
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def test_count_mean_with_matrix(self):
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m = masked_array(np.matrix([[1, 2], [3, 4]]), mask=np.zeros((2, 2)))
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assert_equal(m.count(axis=0).shape, (1, 2))
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assert_equal(m.count(axis=1).shape, (2, 1))
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# Make sure broadcasting inside mean and var work
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assert_equal(m.mean(axis=0), [[2., 3.]])
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assert_equal(m.mean(axis=1), [[1.5], [3.5]])
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def test_flat(self):
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# Test that flat can return items even for matrices [#4585, #4615]
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# test simple access
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test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
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assert_equal(test.flat[1], 2)
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assert_equal(test.flat[2], masked)
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assert_(np.all(test.flat[0:2] == test[0, 0:2]))
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# Test flat on masked_matrices
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test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
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test.flat = masked_array([3, 2, 1], mask=[1, 0, 0])
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control = masked_array(np.matrix([[3, 2, 1]]), mask=[1, 0, 0])
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assert_equal(test, control)
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# Test setting
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test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
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testflat = test.flat
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testflat[:] = testflat[[2, 1, 0]]
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assert_equal(test, control)
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testflat[0] = 9
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# test that matrices keep the correct shape (#4615)
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a = masked_array(np.matrix(np.eye(2)), mask=0)
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b = a.flat
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b01 = b[:2]
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assert_equal(b01.data, np.array([[1., 0.]]))
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assert_equal(b01.mask, np.array([[False, False]]))
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def test_allany_onmatrices(self):
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x = np.array([[0.13, 0.26, 0.90],
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[0.28, 0.33, 0.63],
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[0.31, 0.87, 0.70]])
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X = np.matrix(x)
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m = np.array([[True, False, False],
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[False, False, False],
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[True, True, False]], dtype=np.bool_)
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mX = masked_array(X, mask=m)
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mXbig = (mX > 0.5)
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mXsmall = (mX < 0.5)
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assert_(not mXbig.all())
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assert_(mXbig.any())
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assert_equal(mXbig.all(0), np.matrix([False, False, True]))
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assert_equal(mXbig.all(1), np.matrix([False, False, True]).T)
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assert_equal(mXbig.any(0), np.matrix([False, False, True]))
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assert_equal(mXbig.any(1), np.matrix([True, True, True]).T)
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assert_(not mXsmall.all())
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assert_(mXsmall.any())
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assert_equal(mXsmall.all(0), np.matrix([True, True, False]))
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assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T)
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assert_equal(mXsmall.any(0), np.matrix([True, True, False]))
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assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T)
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def test_compressed(self):
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a = masked_array(np.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0])
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b = a.compressed()
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assert_equal(b, a)
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assert_(isinstance(b, np.matrix))
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a[0, 0] = masked
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b = a.compressed()
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assert_equal(b, [[2, 3, 4]])
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def test_ravel(self):
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a = masked_array(np.matrix([1, 2, 3, 4, 5]), mask=[[0, 1, 0, 0, 0]])
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aravel = a.ravel()
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assert_equal(aravel.shape, (1, 5))
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assert_equal(aravel._mask.shape, a.shape)
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def test_view(self):
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# Test view w/ flexible dtype
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iterator = list(zip(np.arange(10), np.random.rand(10)))
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data = np.array(iterator)
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a = masked_array(iterator, dtype=[('a', float), ('b', float)])
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a.mask[0] = (1, 0)
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test = a.view((float, 2), np.matrix)
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assert_equal(test, data)
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assert_(isinstance(test, np.matrix))
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assert_(not isinstance(test, MaskedArray))
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class TestSubclassing:
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# Test suite for masked subclasses of ndarray.
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def setup(self):
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x = np.arange(5, dtype='float')
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mx = MMatrix(x, mask=[0, 1, 0, 0, 0])
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self.data = (x, mx)
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def test_maskedarray_subclassing(self):
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# Tests subclassing MaskedArray
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(x, mx) = self.data
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assert_(isinstance(mx._data, np.matrix))
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def test_masked_unary_operations(self):
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# Tests masked_unary_operation
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(x, mx) = self.data
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with np.errstate(divide='ignore'):
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assert_(isinstance(log(mx), MMatrix))
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assert_equal(log(x), np.log(x))
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def test_masked_binary_operations(self):
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# Tests masked_binary_operation
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(x, mx) = self.data
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# Result should be a MMatrix
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assert_(isinstance(add(mx, mx), MMatrix))
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assert_(isinstance(add(mx, x), MMatrix))
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# Result should work
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assert_equal(add(mx, x), mx+x)
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assert_(isinstance(add(mx, mx)._data, np.matrix))
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with assert_warns(DeprecationWarning):
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assert_(isinstance(add.outer(mx, mx), MMatrix))
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assert_(isinstance(hypot(mx, mx), MMatrix))
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assert_(isinstance(hypot(mx, x), MMatrix))
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def test_masked_binary_operations2(self):
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# Tests domained_masked_binary_operation
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(x, mx) = self.data
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xmx = masked_array(mx.data.__array__(), mask=mx.mask)
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assert_(isinstance(divide(mx, mx), MMatrix))
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assert_(isinstance(divide(mx, x), MMatrix))
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assert_equal(divide(mx, mx), divide(xmx, xmx))
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class TestConcatenator:
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# Tests for mr_, the equivalent of r_ for masked arrays.
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def test_matrix_builder(self):
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assert_raises(np.ma.MAError, lambda: mr_['1, 2; 3, 4'])
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def test_matrix(self):
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# Test consistency with unmasked version. If we ever deprecate
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# matrix, this test should either still pass, or both actual and
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# expected should fail to be build.
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actual = mr_['r', 1, 2, 3]
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expected = np.ma.array(np.r_['r', 1, 2, 3])
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assert_array_equal(actual, expected)
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# outer type is masked array, inner type is matrix
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assert_equal(type(actual), type(expected))
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assert_equal(type(actual.data), type(expected.data))
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