Python numpy.complex64方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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.eigvals(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.eigvals(a)
        assert_(res.dtype.type is np.complex64)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(res, np.ndarray)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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)]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
def __stream_to_complex(self, stream):
        bytes_np = numpy.ctypeslib.as_array(stream)
        iq = bytes_np.astype(numpy.float32).view(numpy.complex64)
        iq /= 255 / 2
        iq -= 1 + 1j

        return iq 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import complex64 [as 別名]
def __on_freq(self, freq):
        _l, freqs = psd(numpy.zeros(2, dtype=numpy.complex64),
                        BINS, SAMPLE_RATE)
        freqs /= 1e6
        freqs += freq
        self._freqs = freqs.tolist()

        if self._receive is not None:
            self._receive.set_frequency(freq)