Python numpy.ComplexWarning() Examples

The following are 30 code examples of numpy.ComplexWarning(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: test_nanfunctions.py    From recruit with Apache License 2.0 7 votes vote down vote up
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) 
Example #2
Source File: test_indexing.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up
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) 
Example #3
Source File: test_regression.py    From Computable with MIT License 6 votes vote down vote up
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) 
Example #4
Source File: test_nanfunctions.py    From lambda-packs with MIT License 6 votes vote down vote up
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) 
Example #5
Source File: test_regression.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up
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) 
Example #6
Source File: test_indexing.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up
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) 
Example #7
Source File: test_indexing.py    From mxnet-lambda with Apache License 2.0 6 votes vote down vote up
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) 
Example #8
Source File: test_indexing.py    From pySINDy with MIT License 6 votes vote down vote up
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) 
Example #9
Source File: test_nanfunctions.py    From pySINDy with MIT License 6 votes vote down vote up
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) 
Example #10
Source File: test_nanfunctions.py    From pySINDy with MIT License 6 votes vote down vote up
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) 
Example #11
Source File: test_base.py    From Computable with MIT License 6 votes vote down vote up
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 ) 
Example #12
Source File: test_pad.py    From cupy with MIT License 6 votes vote down vote up
def test_pad(self, xp, dtype):
        array = xp.array(self.array, dtype=dtype)

        if xp.dtype(dtype).kind in ['i', 'u']:
            # TODO: can remove this skip once cupy/cupy/#2330 is merged
            return array

        # Older version of NumPy(<1.12) can emit ComplexWarning
        def f():
            return xp.pad(array, self.pad_width, mode=self.mode,
                          stat_length=self.stat_length)

        if xp is numpy:
            with warnings.catch_warnings():
                warnings.simplefilter('ignore', numpy.ComplexWarning)
                return f()
        else:
            return f() 
Example #13
Source File: test_pad.py    From cupy with MIT License 6 votes vote down vote up
def test_pad(self, xp, dtype):
        array = xp.array(self.array, dtype=dtype)

        # Older version of NumPy(<1.12) can emit ComplexWarning
        def f():
            if self.mode == 'constant':
                return xp.pad(array, self.pad_width, mode=self.mode,
                              constant_values=self.constant_values)
            elif self.mode in ['minimum', 'maximum']:
                return xp.pad(array, self.pad_width, mode=self.mode,
                              stat_length=self.stat_length)
            elif self.mode in ['reflect', 'symmetric']:
                return xp.pad(array, self.pad_width, mode=self.mode,
                              reflect_type=self.reflect_type)

        if xp is numpy:
            with warnings.catch_warnings():
                warnings.simplefilter('ignore', numpy.ComplexWarning)
                return f()
        else:
            return f() 
Example #14
Source File: test_nanfunctions.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up
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) 
Example #15
Source File: test_nanfunctions.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up
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) 
Example #16
Source File: test_nanfunctions.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up
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) 
Example #17
Source File: test_nanfunctions.py    From pySINDy with MIT License 6 votes vote down vote up
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) 
Example #18
Source File: test_pad.py    From cupy with MIT License 6 votes vote down vote up
def test_pad_default(self, xp, dtype):
        array = xp.array(self.array, dtype=dtype)

        if xp.dtype(dtype).kind in ['i', 'u']:
            # TODO: can remove this skip once cupy/cupy/#2330 is merged
            return array

        # Older version of NumPy(<1.12) can emit ComplexWarning
        def f():
            return xp.pad(array, self.pad_width, mode='mean')

        if xp is numpy:
            with warnings.catch_warnings():
                warnings.simplefilter('ignore', numpy.ComplexWarning)
                return f()
        else:
            return f() 
Example #19
Source File: test_pad.py    From cupy with MIT License 6 votes vote down vote up
def test_pad_default(self, xp, dtype):
        array = xp.array(self.array, dtype=dtype)

        if (xp.dtype(dtype).kind in ['i', 'u'] and
                self.mode == 'linear_ramp'):
            # TODO: can remove this skip once cupy/cupy/#2330 is merged
            return array

        # Older version of NumPy(<1.12) can emit ComplexWarning
        def f():
            return xp.pad(array, self.pad_width, mode=self.mode)

        if xp is numpy:
            with warnings.catch_warnings():
                warnings.simplefilter('ignore', numpy.ComplexWarning)
                return f()
        else:
            return f() 
Example #20
Source File: test_nanfunctions.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
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) 
Example #21
Source File: test_nanfunctions.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
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) 
Example #22
Source File: test_nanfunctions.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
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) 
Example #23
Source File: test_indexing.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
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) 
Example #24
Source File: test_indexing.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up
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) 
Example #25
Source File: test_nanfunctions.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up
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) 
Example #26
Source File: test_signaltools.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
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]) 
Example #27
Source File: test_interpolate.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
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') 
Example #28
Source File: slinalg.py    From attention-lvcsr with MIT License 6 votes vote down vote up
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) 
Example #29
Source File: test_nanfunctions.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up
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) 
Example #30
Source File: test_nanfunctions.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up
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)