Python numpy.complex64() Examples

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
                      np.uint32, np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)

            tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
            assert_array_equal(np.cumsum(a, axis=0), tgt)

            tgt = np.array(
                [[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
            assert_array_equal(np.cumsum(a2, axis=0), tgt)

            tgt = np.array(
                [[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
            assert_array_equal(np.cumsum(a2, axis=1), tgt) 

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                assert_raises(ArithmeticError, np.prod, a)
                assert_raises(ArithmeticError, np.prod, a2, 1)
            else:
                assert_equal(a.prod(axis=0), 26400)
                assert_array_equal(a2.prod(axis=0),
                                   np.array([50, 36, 84, 180], ctype))
                assert_array_equal(a2.prod(axis=-1),
                                   np.array([24, 1890, 600], ctype)) 

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
                      np.uint32, np.float32, np.float64, np.complex64,
                      np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)

            tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
            assert_array_equal(np.cumsum(a, axis=0), tgt)

            tgt = np.array(
                [[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
            assert_array_equal(np.cumsum(a2, axis=0), tgt)

            tgt = np.array(
                [[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
            assert_array_equal(np.cumsum(a2, axis=1), tgt) 

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                assert_raises(ArithmeticError, np.cumprod, a)
                assert_raises(ArithmeticError, np.cumprod, a2, 1)
                assert_raises(ArithmeticError, np.cumprod, a)
            else:
                assert_array_equal(np.cumprod(a, axis=-1),
                                   np.array([1, 2, 20, 220,
                                             1320, 6600, 26400], ctype))
                assert_array_equal(np.cumprod(a2, axis=0),
                                   np.array([[1, 2, 3, 4],
                                             [5, 12, 21, 36],
                                             [50, 36, 84, 180]], ctype))
                assert_array_equal(np.cumprod(a2, axis=-1),
                                   np.array([[1, 2, 6, 24],
                                             [5, 30, 210, 1890],
                                             [10, 30, 120, 600]], ctype)) 

def zero_if_close(a, tol=1.e-15):
    """set real and/or imaginary part to 0 if their absolute value is smaller than `tol`.

    Parameters
    ----------
    a : ndarray
        numpy array to be rounded
    tol : float
        the threashold which values to consider as '0'.
    """
    if a.dtype == np.complex128 or a.dtype == np.complex64:
        ar = np.choose(np.abs(a.real) < tol, [a.real, np.zeros(a.shape)])
        ai = np.choose(np.abs(a.imag) < tol, [a.imag, np.zeros(a.shape)])
        return ar + 1j * ai
    else:
        return np.choose(np.abs(a) < tol, [a, np.zeros_like(a)]) 

def _get_output_fourier(output, input):
    if output is None:
        if input.dtype.type in [numpy.complex64, numpy.complex128,
                                numpy.float32]:
            output = numpy.zeros(input.shape, dtype=input.dtype)
        else:
            output = numpy.zeros(input.shape, dtype=numpy.float64)
        return_value = output
    elif type(output) is type:
        if output not in [numpy.complex64, numpy.complex128,
                          numpy.float32, numpy.float64]:
            raise RuntimeError("output type not supported")
        output = numpy.zeros(input.shape, dtype=output)
        return_value = output
    else:
        if output.shape != input.shape:
            raise RuntimeError("output shape not correct")
        return_value = None
    return output, return_value 

def assert_no_overwrite(call, shapes, dtypes=None):
    """
    Test that a call does not overwrite its input arguments
    """

    if dtypes is None:
        dtypes = [np.float32, np.float64, np.complex64, np.complex128]

    for dtype in dtypes:
        for order in ["C", "F"]:
            for faker in [_id, _FakeMatrix, _FakeMatrix2]:
                orig_inputs = [_get_array(s, dtype) for s in shapes]
                inputs = [faker(x.copy(order)) for x in orig_inputs]
                call(*inputs)
                msg = "call modified inputs [%r, %r]" % (dtype, faker)
                for a, b in zip(inputs, orig_inputs):
                    np.testing.assert_equal(a, b, err_msg=msg) 

def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res, res_v = linalg.eigh(a)
        assert_(res_v.dtype.type is np.float64)
        assert_(res.dtype.type is np.float64)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res, res_v = linalg.eigh(a)
        assert_(res_v.dtype.type is np.complex64)
        assert_(res.dtype.type is np.float32)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray)) 

def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res = linalg.eigvalsh(a)
        assert_(res.dtype.type is np.float64)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(res, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res = linalg.eigvalsh(a)
        assert_(res.dtype.type is np.float32)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(res, np.ndarray)) 

def min(self):
    """Returns the minimum representable value in this data type.

    Raises:
      TypeError: if this is a non-numeric, unordered, or quantized type.

    """
    if (self.is_quantized or self.base_dtype in
        (bool, string, complex64, complex128)):
      raise TypeError("Cannot find minimum value of %s." % self)

    # there is no simple way to get the min value of a dtype, we have to check
    # float and int types separately
    try:
      return np.finfo(self.as_numpy_dtype()).min
    except:  # bare except as possible raises by finfo not documented
      try:
        return np.iinfo(self.as_numpy_dtype()).min
      except:
        raise TypeError("Cannot find minimum value of %s." % self) 

def max(self):
    """Returns the maximum representable value in this data type.

    Raises:
      TypeError: if this is a non-numeric, unordered, or quantized type.

    """
    if (self.is_quantized or self.base_dtype in
        (bool, string, complex64, complex128)):
      raise TypeError("Cannot find maximum value of %s." % self)

    # there is no simple way to get the max value of a dtype, we have to check
    # float and int types separately
    try:
      return np.finfo(self.as_numpy_dtype()).max
    except:  # bare except as possible raises by finfo not documented
      try:
        return np.iinfo(self.as_numpy_dtype()).max
      except:
        raise TypeError("Cannot find maximum value of %s." % self) 

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                self.assertRaises(ArithmeticError, np.prod, a)
                self.assertRaises(ArithmeticError, np.prod, a2, 1)
            else:
                assert_equal(a.prod(axis=0), 26400)
                assert_array_equal(a2.prod(axis=0),
                                   np.array([50, 36, 84, 180], ctype))
                assert_array_equal(a2.prod(axis=-1),
                                   np.array([24, 1890, 600], ctype)) 

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                self.assertRaises(ArithmeticError, np.cumprod, a)
                self.assertRaises(ArithmeticError, np.cumprod, a2, 1)
                self.assertRaises(ArithmeticError, np.cumprod, a)
            else:
                assert_array_equal(np.cumprod(a, axis=-1),
                                   np.array([1, 2, 20, 220,
                                             1320, 6600, 26400], ctype))
                assert_array_equal(np.cumprod(a2, axis=0),
                                   np.array([[1, 2, 3, 4],
                                             [5, 12, 21, 36],
                                             [50, 36, 84, 180]], ctype))
                assert_array_equal(np.cumprod(a2, axis=-1),
                                   np.array([[1, 2, 6, 24],
                                             [5, 30, 210, 1890],
                                             [10, 30, 120, 600]], ctype)) 

def test_scipy_gmres_linop_parameter(self):
    """ This is a test on gmres method with a parameter-dependent linear operator """
    for omega in np.linspace(-10.0, 10.0, 10):
      for eps in np.linspace(-10.0, 10.0, 10):
        
        linop_param = linalg.aslinearoperator(vext2veff_c(omega, eps, n))
        
        Aparam = np.zeros((n,n), np.complex64)
        for i in range(n):
          uv = np.zeros(n, np.complex64); uv[i] = 1.0
          Aparam[:,i] = linop_param.matvec(uv)
        x_ref = np.dot(inv(Aparam), b)
    
        x_itr,info = linalg.lgmres(linop_param, b)
        derr = abs(x_ref-x_itr).sum()/x_ref.size
        self.assertLess(derr, 1e-6) 

def test_signed_zeros(self):
        with np.errstate(all="ignore"):
            for t in [np.complex64, np.complex128]:
                # tupled (numerator, denominator, expected)
                # for testing as expected == numerator/denominator
                data = (
                    (( 0.0,-1.0), ( 0.0, 1.0), (-1.0,-0.0)),
                    (( 0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
                    (( 0.0,-1.0), (-0.0,-1.0), ( 1.0, 0.0)),
                    (( 0.0,-1.0), (-0.0, 1.0), (-1.0, 0.0)),
                    (( 0.0, 1.0), ( 0.0,-1.0), (-1.0, 0.0)),
                    (( 0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
                    ((-0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
                    ((-0.0, 1.0), ( 0.0,-1.0), (-1.0,-0.0))
                )
                for cases in data:
                    n = cases[0]
                    d = cases[1]
                    ex = cases[2]
                    result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))
                    # check real and imag parts separately to avoid comparison
                    # in array context, which does not account for signed zeros
                    assert_equal(result.real, ex[0])
                    assert_equal(result.imag, ex[1]) 

def test_zero_division(self):
        with np.errstate(all="ignore"):
            for t in [np.complex64, np.complex128]:
                a = t(0.0)
                b = t(1.0)
                assert_(np.isinf(b/a))
                b = t(complex(np.inf, np.inf))
                assert_(np.isinf(b/a))
                b = t(complex(np.inf, np.nan))
                assert_(np.isinf(b/a))
                b = t(complex(np.nan, np.inf))
                assert_(np.isinf(b/a))
                b = t(complex(np.nan, np.nan))
                assert_(np.isnan(b/a))
                b = t(0.)
                assert_(np.isnan(b/a)) 

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
                      np.uint32, np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)

            tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
            assert_array_equal(np.cumsum(a, axis=0), tgt)

            tgt = np.array(
                [[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
            assert_array_equal(np.cumsum(a2, axis=0), tgt)

            tgt = np.array(
                [[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
            assert_array_equal(np.cumsum(a2, axis=1), tgt) 

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                self.assertRaises(ArithmeticError, np.prod, a)
                self.assertRaises(ArithmeticError, np.prod, a2, 1)
            else:
                assert_equal(a.prod(axis=0), 26400)
                assert_array_equal(a2.prod(axis=0),
                                   np.array([50, 36, 84, 180], ctype))
                assert_array_equal(a2.prod(axis=-1),
                                   np.array([24, 1890, 600], ctype)) 

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                self.assertRaises(ArithmeticError, np.cumprod, a)
                self.assertRaises(ArithmeticError, np.cumprod, a2, 1)
                self.assertRaises(ArithmeticError, np.cumprod, a)
            else:
                assert_array_equal(np.cumprod(a, axis=-1),
                                   np.array([1, 2, 20, 220,
                                             1320, 6600, 26400], ctype))
                assert_array_equal(np.cumprod(a2, axis=0),
                                   np.array([[1, 2, 3, 4],
                                             [5, 12, 21, 36],
                                             [50, 36, 84, 180]], ctype))
                assert_array_equal(np.cumprod(a2, axis=-1),
                                   np.array([[1, 2, 6, 24],
                                             [5, 30, 210, 1890],
                                             [10, 30, 120, 600]], ctype)) 

def test_shuffle(self):
        # Test lists, arrays (of various dtypes), and multidimensional versions
        # of both, c-contiguous or not:
        for conv in [lambda x: np.array([]),
                     lambda x: x,
                     lambda x: np.asarray(x).astype(np.int8),
                     lambda x: np.asarray(x).astype(np.float32),
                     lambda x: np.asarray(x).astype(np.complex64),
                     lambda x: np.asarray(x).astype(object),
                     lambda x: [(i, i) for i in x],
                     lambda x: np.asarray([[i, i] for i in x]),
                     lambda x: np.vstack([x, x]).T,
                     # gh-4270
                     lambda x: np.asarray([(i, i) for i in x],
                                          [("a", object, 1),
                                           ("b", np.int32, 1)])]:
            np.random.seed(self.seed)
            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
            np.random.shuffle(alist)
            actual = alist
            desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
            np.testing.assert_array_equal(actual, desired) 

def test_branch_cuts_complex64(self):
        # check branch cuts and continuity on them
        _check_branch_cut(np.log,   -0.5, 1j, 1, -1, True, np.complex64)
        _check_branch_cut(np.log2,  -0.5, 1j, 1, -1, True, np.complex64)
        _check_branch_cut(np.log10, -0.5, 1j, 1, -1, True, np.complex64)
        _check_branch_cut(np.log1p, -1.5, 1j, 1, -1, True, np.complex64)
        _check_branch_cut(np.sqrt,  -0.5, 1j, 1, -1, True, np.complex64)

        _check_branch_cut(np.arcsin, [ -2, 2],   [1j, 1j], 1, -1, True, np.complex64)
        _check_branch_cut(np.arccos, [ -2, 2],   [1j, 1j], 1, -1, True, np.complex64)
        _check_branch_cut(np.arctan, [0-2j, 2j],  [1,  1], -1, 1, True, np.complex64)

        _check_branch_cut(np.arcsinh, [0-2j,  2j], [1,   1], -1, 1, True, np.complex64)
        _check_branch_cut(np.arccosh, [ -1, 0.5], [1j,  1j], 1, -1, True, np.complex64)
        _check_branch_cut(np.arctanh, [ -2,   2], [1j, 1j], 1, -1, True, np.complex64)

        # check against bogus branch cuts: assert continuity between quadrants
        _check_branch_cut(np.arcsin, [0-2j, 2j], [ 1,  1], 1, 1, False, np.complex64)
        _check_branch_cut(np.arccos, [0-2j, 2j], [ 1,  1], 1, 1, False, np.complex64)
        _check_branch_cut(np.arctan, [ -2,  2], [1j, 1j], 1, 1, False, np.complex64)

        _check_branch_cut(np.arcsinh, [ -2,  2, 0], [1j, 1j, 1], 1, 1, False, np.complex64)
        _check_branch_cut(np.arccosh, [0-2j, 2j, 2], [1,  1,  1j], 1, 1, False, np.complex64)
        _check_branch_cut(np.arctanh, [0-2j, 2j, 0], [1,  1,  1j], 1, 1, False, np.complex64) 

def stft_from_frames(frames, window='hann', dtype=np.complex64):
    """
    Variation of the librosa.core.stft function,
    that computes the short-time-fourier-transfrom from frames instead from the signal.

    See http://librosa.github.io/librosa/_modules/librosa/core/spectrum.html#stft
    """

    win_length = frames.shape[0]
    n_fft = win_length

    fft_window = filters.get_window(window, win_length, fftbins=True)

    # Reshape so that the window can be broadcast
    fft_window = fft_window.reshape((-1, 1))

    # Pre-allocate the STFT matrix
    stft_matrix = np.empty((int(1 + n_fft // 2), frames.shape[1]),
                           dtype=dtype,
                           order='F')

    # how many columns can we fit within MAX_MEM_BLOCK?
    n_columns = int(util.MAX_MEM_BLOCK / (stft_matrix.shape[0] *
                                          stft_matrix.itemsize))

    for bl_s in range(0, stft_matrix.shape[1], n_columns):
        bl_t = min(bl_s + n_columns, stft_matrix.shape[1])

        # RFFT and Conjugate here to match phase from DPWE code
        stft_matrix[:, bl_s:bl_t] = fft.fft(fft_window *
                                            frames[:, bl_s:bl_t],
                                            axis=0)[:stft_matrix.shape[0]].conj()

    return stft_matrix 

def _set_CLAPACK_callsignatures(lapack_lib):
    """define the call signature of the CLAPACK functions which we need.

    See http://www.netlib.org/lapack/explore-html/d8/d70/group__lapack.html
    for the (fortran) signature.
    In the C version, all arguments must be given as pointers of the corresponding C types.
    """
    # Shorthand data type for the arrays.
    # s/d/c/z = fortran single/double/complex_single/complex_double
    s_arr = np.ctypeslib.ndpointer(dtype=np.float32, ndim=1)
    d_arr = np.ctypeslib.ndpointer(dtype=np.float64, ndim=1)
    c_arr = np.ctypeslib.ndpointer(dtype=np.complex64, ndim=1)
    z_arr = np.ctypeslib.ndpointer(dtype=np.complex128, ndim=1)
    s_2arr = np.ctypeslib.ndpointer(dtype=np.float32, ndim=2)
    d_2arr = np.ctypeslib.ndpointer(dtype=np.float64, ndim=2)
    c_2arr = np.ctypeslib.ndpointer(dtype=np.complex64, ndim=2)
    z_2arr = np.ctypeslib.ndpointer(dtype=np.complex128, ndim=2)

    lapack_lib.sgesvd_.argtypes = \
        [POINTER(c_char), POINTER(c_char), POINTER(c_int), POINTER(c_int),
            s_2arr, POINTER(c_int), s_arr, s_2arr, POINTER(c_int), s_2arr,
            POINTER(c_int), s_arr, POINTER(c_int), POINTER(c_int)]
    lapack_lib.dgesvd_.argtypes = \
        [POINTER(c_char), POINTER(c_char), POINTER(c_int), POINTER(c_int),
            d_2arr, POINTER(c_int), d_arr, d_2arr, POINTER(c_int), d_2arr,
            POINTER(c_int), d_arr, POINTER(c_int), POINTER(c_int)]
    lapack_lib.cgesvd_.argtypes = \
        [POINTER(c_char), POINTER(c_char), POINTER(c_int), POINTER(c_int),
            c_2arr, POINTER(c_int), s_arr, c_2arr, POINTER(c_int),
            c_2arr, POINTER(c_int), c_arr, POINTER(c_int), s_arr,
            POINTER(c_int)]
    lapack_lib.zgesvd_.argtypes = \
        [POINTER(c_char), POINTER(c_char), POINTER(c_int), POINTER(c_int),
            z_2arr, POINTER(c_int), d_arr, z_2arr, POINTER(c_int),
            z_2arr, POINTER(c_int), z_arr, POINTER(c_int), d_arr,
            POINTER(c_int)] 

def _raw_fftn_dispatch(x, shape, axes, overwrite_x, direction):
    tmp = _asfarray(x)

    try:
        work_function = _DTYPE_TO_FFTN[tmp.dtype]
    except KeyError:
        raise ValueError("type %s is not supported" % tmp.dtype)

    if not (istype(tmp, numpy.complex64) or istype(tmp, numpy.complex128)):
        overwrite_x = 1

    overwrite_x = overwrite_x or _datacopied(tmp, x)
    return _raw_fftnd(tmp,shape,axes,direction,overwrite_x,work_function) 

def test_unhandledDtype(self, model, index):
        dataFrame = pandas.DataFrame([92.289+151.96j], columns=['A'])
        dataFrame['A'] = dataFrame['A'].astype(numpy.complex64)
        model.setDataFrame(dataFrame)
        assert not model.dataFrame().empty
        assert model.dataFrame() is dataFrame

        assert index.isValid()
        model.enableEditing(True)
        with pytest.raises(TypeError) as excinfo:
            model.setData(index, numpy.complex64(92+151j))
        assert "unhandled data type" in unicode(excinfo.value) 

def test_unhandledDtype(self, model, index):
        dataFrame = pandas.DataFrame([92.289+151.96j], columns=['A'])
        dataFrame['A'] = dataFrame['A'].astype(numpy.complex64)
        model.setDataFrame(dataFrame)
        assert not model.dataFrame().empty
        assert model.dataFrame() is dataFrame

        assert index.isValid()
        assert model.data(index) == None
        # with pytest.raises(TypeError) as excinfo:
        #     model.data(index)
        # assert "unhandled data type" in unicode(excinfo.value) 

def load(self, path, code, shape, header):
        "Load SARData object for a given month code"
        self.shape = shape
        self.size = shape[0]*shape[1]
        extension = ".emi" if header else ""
        self.hhhh = read_sar_file(path + '/{}/{}hhhh{}'.format(code, code, extension), np.float32, header)
        self.hhhv = read_sar_file(path + '/{}/{}hhhv{}'.format(code, code, extension), np.complex64, header)
        self.hvhv = read_sar_file(path + '/{}/{}hvhv{}'.format(code, code, extension), np.float32, header)
        self.hhvv = read_sar_file(path + '/{}/{}hhvv{}'.format(code, code, extension), np.complex64, header)
        self.hvvv = read_sar_file(path + '/{}/{}hvvv{}'.format(code, code, extension), np.complex64, header)
        self.vvvv = read_sar_file(path + '/{}/{}vvvv{}'.format(code, code, extension), np.float32, header)
        return self 

def test_constructor_complex_dtypes(self):
        # GH10952
        a = np.random.rand(10).astype(np.complex64)
        b = np.random.rand(10).astype(np.complex128)

        df = DataFrame({'a': a, 'b': b})
        assert a.dtype == df.a.dtype
        assert b.dtype == df.b.dtype 

def test_branches(self):
        with np.errstate(all="ignore"):
            for t in [np.complex64, np.complex128]:
                # tupled (numerator, denominator, expected)
                # for testing as expected == numerator/denominator
                data = list()

                # trigger branch: real(fabs(denom)) > imag(fabs(denom))
                # followed by else condition as neither are == 0
                data.append((( 2.0, 1.0), ( 2.0, 1.0), (1.0, 0.0)))

                # trigger branch: real(fabs(denom)) > imag(fabs(denom))
                # followed by if condition as both are == 0
                # is performed in test_zero_division(), so this is skipped

                # trigger else if branch: real(fabs(denom)) < imag(fabs(denom))
                data.append((( 1.0, 2.0), ( 1.0, 2.0), (1.0, 0.0)))

                for cases in data:
                    n = cases[0]
                    d = cases[1]
                    ex = cases[2]
                    result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))
                    # check real and imag parts separately to avoid comparison
                    # in array context, which does not account for signed zeros
                    assert_equal(result.real, ex[0])
                    assert_equal(result.imag, ex[1]) 

def test_modular_power(self):
        # modular power is not implemented, so ensure it errors
        a = 5
        b = 4
        c = 10
        expected = pow(a, b, c)  # noqa: F841
        for t in (np.int32, np.float32, np.complex64):
            # note that 3-operand power only dispatches on the first argument
            assert_raises(TypeError, operator.pow, t(a), b, c)
            assert_raises(TypeError, operator.pow, np.array(t(a)), b, c)