Python numpy.ComplexWarning方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_dtype_from_dtype(self):
        mat = np.eye(3)
        codes = 'efdgFDG'
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            for c in codes:
                with suppress_warnings() as sup:
                    if nf in {np.nanstd, np.nanvar} and c in 'FDG':
                        # Giving the warning is a small bug, see gh-8000
                        sup.filter(np.ComplexWarning)
                    tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
                    res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
                    assert_(res is tgt)
                    # scalar case
                    tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
                    res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
                    assert_(res is tgt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_dtype_from_char(self):
        mat = np.eye(3)
        codes = 'efdgFDG'
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            for c in codes:
                with suppress_warnings() as sup:
                    if nf in {np.nanstd, np.nanvar} and c in 'FDG':
                        # Giving the warning is a small bug, see gh-8000
                        sup.filter(np.ComplexWarning)
                    tgt = rf(mat, dtype=c, axis=1).dtype.type
                    res = nf(mat, dtype=c, axis=1).dtype.type
                    assert_(res is tgt)
                    # scalar case
                    tgt = rf(mat, dtype=c, axis=None).dtype.type
                    res = nf(mat, dtype=c, axis=None).dtype.type
                    assert_(res is tgt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_ddof_too_big(self):
        nanfuncs = [np.nanvar, np.nanstd]
        stdfuncs = [np.var, np.std]
        dsize = [len(d) for d in _rdat]
        for nf, rf in zip(nanfuncs, stdfuncs):
            for ddof in range(5):
                with suppress_warnings() as sup:
                    sup.record(RuntimeWarning)
                    sup.filter(np.ComplexWarning)
                    tgt = [ddof >= d for d in dsize]
                    res = nf(_ndat, axis=1, ddof=ddof)
                    assert_equal(np.isnan(res), tgt)
                    if any(tgt):
                        assert_(len(sup.log) == 1)
                    else:
                        assert_(len(sup.log) == 0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_boolean_index_cast_assign(self):
        # Setup the boolean index and float arrays.
        shape = (8, 63)
        bool_index = np.zeros(shape).astype(bool)
        bool_index[0, 1] = True
        zero_array = np.zeros(shape)

        # Assigning float is fine.
        zero_array[bool_index] = np.array([1])
        assert_equal(zero_array[0, 1], 1)

        # Fancy indexing works, although we get a cast warning.
        assert_warns(np.ComplexWarning,
                     zero_array.__setitem__, ([0], [1]), np.array([2 + 1j]))
        assert_equal(zero_array[0, 1], 2)  # No complex part

        # Cast complex to float, throwing away the imaginary portion.
        assert_warns(np.ComplexWarning,
                     zero_array.__setitem__, bool_index, np.array([1j]))
        assert_equal(zero_array[0, 1], 0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_ticket_1539(self):
        dtypes = [x for x in np.typeDict.values()
                  if (issubclass(x, np.number)
                      and not issubclass(x, np.timedelta64))]
        a = np.array([], dtypes[0])
        failures = []
        # ignore complex warnings
        with warnings.catch_warnings():
            warnings.simplefilter('ignore', np.ComplexWarning)
            for x in dtypes:
                b = a.astype(x)
                for y in dtypes:
                    c = a.astype(y)
                    try:
                        np.dot(b, c)
                    except TypeError:
                        failures.append((x, y))
        if failures:
            raise AssertionError("Failures: %r" % failures) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def perform(self, node, inputs, outputs):
        # Kalbfleisch and Lawless, J. Am. Stat. Assoc. 80 (1985) Equation 3.4
        # Kind of... You need to do some algebra from there to arrive at
        # this expression.
        (A, gA) = inputs
        (out,) = outputs
        w, V = scipy.linalg.eig(A, right=True)
        U = scipy.linalg.inv(V).T

        exp_w = numpy.exp(w)
        X = numpy.subtract.outer(exp_w, exp_w) / numpy.subtract.outer(w, w)
        numpy.fill_diagonal(X, exp_w)
        Y = U.dot(V.T.dot(gA).dot(U) * X).dot(V.T)

        with warnings.catch_warnings():
            warnings.simplefilter("ignore", numpy.ComplexWarning)
            out[0] = Y.astype(A.dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_ticket_1539(self):
        dtypes = [x for x in np.typeDict.values()
                  if (issubclass(x, np.number)
                      and not issubclass(x, np.timedelta64))]
        a = np.array([], dtypes[0])
        failures = []
        # ignore complex warnings
        with warnings.catch_warnings():
            warnings.simplefilter('ignore', np.ComplexWarning)
            for x in dtypes:
                b = a.astype(x)
                for y in dtypes:
                    c = a.astype(y)
                    try:
                        np.dot(b, c)
                    except TypeError as e:
                        failures.append((x, y))
        if failures:
            raise AssertionError("Failures: %r" % failures) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_from_sparse(self):
        D = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]])
        S = csr_matrix(D)
        assert_array_equal(self.spmatrix(S).toarray(), D)
        S = self.spmatrix(D)
        assert_array_equal(self.spmatrix(S).toarray(), D)

        with warnings.catch_warnings():
            warnings.simplefilter("ignore", category=np.ComplexWarning)
            D = array([[1.0 + 3j, 0, 0],
                       [0, 2.0 + 5, 0],
                       [0, 0, 0]])
            S = csr_matrix(D)
            assert_array_equal(self.spmatrix(S).toarray(), D)
            assert_array_equal(self.spmatrix(S, dtype='int16').toarray(), D.astype('int16'))
            S = self.spmatrix(D)
            assert_array_equal(self.spmatrix(S).toarray(), D)
            assert_array_equal(self.spmatrix(S, dtype='int16').toarray(), D.astype('int16'))

    # def test_array(self):
    #    """test array(A) where A is in sparse format"""
    #    assert_equal( array(self.datsp), self.dat ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_fillvalue_deprecations(self):
        # Deprecated 2017-07, scipy version 1.0.0
        with suppress_warnings() as sup:
            sup.filter(np.ComplexWarning, "Casting complex values to real")
            r = sup.record(DeprecationWarning, "could not cast `fillvalue`")
            convolve2d([[1]], [[1, 2]], fillvalue=1j)
            assert_(len(r) == 1)
            warnings.filterwarnings(
                "error", message="could not cast `fillvalue`",
                category=DeprecationWarning)
            assert_raises(DeprecationWarning, convolve2d, [[1]], [[1, 2]],
                          fillvalue=1j)

        with suppress_warnings():
            warnings.filterwarnings(
                "always", message="`fillvalue` must be scalar or an array ",
                category=DeprecationWarning)
            assert_warns(DeprecationWarning, convolve2d, [[1]], [[1, 2]],
                         fillvalue=[1, 2])
            warnings.filterwarnings(
                "error", message="`fillvalue` must be scalar or an array ",
                category=DeprecationWarning)
            assert_raises(DeprecationWarning, convolve2d, [[1]], [[1, 2]],
                          fillvalue=[1, 2]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ComplexWarning [as 別名]
def test_complex(self):
        x, y, values = self._sample_2d_data()
        points = (x, y)
        values = values - 2j*values

        sample = np.array([[1, 2.3, 5.3, 0.5, 3.3, 1.2, 3],
                           [1, 3.3, 1.2, 4.0, 5.0, 1.0, 3]]).T

        for method in ['linear', 'nearest']:
            v1 = interpn(points, values, sample, method=method)
            v2r = interpn(points, values.real, sample, method=method)
            v2i = interpn(points, values.imag, sample, method=method)
            v2 = v2r + 1j*v2i
            assert_allclose(v1, v2)

        # Complex-valued data not supported by spline2fd
        _assert_warns(np.ComplexWarning, interpn, points, values,
                      sample, method='splinef2d')