コード例 #1
0
def _decision_af(signal, symbols, precision=16):
    """
    Make symbol decisions on  signal array  onto symbols. Arrayfire function.

    Parameters
    ----------
    signal : array_like
        input signal array
    symbols : array_like
        array of symbols to decide onto
    precision : int, optional
        bit precision either 16 for complex128 or 8 for complex 64

    Returns
    -------
    out : array_like
        array of decided symbols

    """
    global NMAX
    if precision == 16:
        prec_dtype = np.complex128
    elif precision == 8:
        prec_dtype = np.complex64
    else:
        raise ValueError(
            "Precision has to be either 16 for double complex or 8 for single complex"
        )
    Nmax = NMAX // len(symbols.flatten()) // 16
    L = signal.flatten().shape[0]
    sig = af.np_to_af_array(signal.flatten().astype(prec_dtype))
    sym = af.transpose(af.np_to_af_array(symbols.flatten().astype(prec_dtype)))
    tmp = af.constant(0, L, dtype=af.Dtype.c64)
    if L < Nmax:
        v, idx = af.imin(af.abs(af.broadcast(lambda x, y: x - y, sig, sym)),
                         dim=1)
        tmp = af.transpose(sym)[idx]
    else:
        steps = L // Nmax
        rem = L % Nmax
        for i in range(steps):
            v, idx = af.imin(af.abs(
                af.broadcast(lambda x, y: x - y, sig[i * Nmax:(i + 1) * Nmax],
                             sym)),
                             dim=1)
            tmp[i * Nmax:(i + 1) * Nmax] = af.transpose(sym)[idx]
        v, idx = af.imin(af.abs(
            af.broadcast(lambda x, y: x - y, sig[steps * Nmax:], sym)),
                         dim=1)
        tmp[steps * Nmax:] = af.transpose(sym)[idx]
    return np.array(tmp)
コード例 #2
0
ファイル: phaserecovery.py プロジェクト: nivir/QAMpy
def _bps_idx_af(E, angles, symbols, N):
    global NMAX
    prec_dtype = E.dtype
    Ntestangles = angles.shape[1]
    Nmax = NMAX // Ntestangles // symbols.shape[0] // 16
    L = E.shape[0]
    EE = E[:, np.newaxis] * np.exp(1.j * angles)
    syms = af.np_to_af_array(symbols.astype(prec_dtype).reshape(1, 1, -1))
    idxnd = np.zeros(L, dtype=np.int32)
    if L <= Nmax + N:
        Eaf = af.np_to_af_array(EE.astype(prec_dtype))
        tmp = af.min(af.abs(af.broadcast(lambda x, y: x - y, Eaf[0:L, :],
                                         syms))**2,
                     dim=2)
        cs = _movavg_af(tmp, 2 * N, axis=0)
        val, idx = af.imin(cs, dim=1)
        idxnd[N:-N] = np.array(idx)
    else:
        K = L // Nmax
        R = L % Nmax
        if R < N:
            R = R + Nmax
            K -= 1
        Eaf = af.np_to_af_array(EE[0:Nmax + N].astype(prec_dtype))
        tmp = af.min(af.abs(af.broadcast(lambda x, y: x - y, Eaf, syms))**2,
                     dim=2)
        tt = np.array(tmp)
        cs = _movavg_af(tmp, 2 * N, axis=0)
        val, idx = af.imin(cs, dim=1)
        idxnd[N:Nmax] = np.array(idx)
        for i in range(1, K):
            Eaf = af.np_to_af_array(EE[i * Nmax - N:(i + 1) * Nmax + N].astype(
                np.complex128))
            tmp = af.min(af.abs(af.broadcast(lambda x, y: x - y, Eaf,
                                             syms))**2,
                         dim=2)
            cs = _movavg_af(tmp, 2 * N, axis=0)
            val, idx = af.imin(cs, dim=1)
            idxnd[i * Nmax:(i + 1) * Nmax] = np.array(idx)
        Eaf = af.np_to_af_array(EE[K * Nmax - N:K * Nmax + R].astype(
            np.complex128))
        tmp = af.min(af.abs(af.broadcast(lambda x, y: x - y, Eaf, syms))**2,
                     dim=2)
        cs = _movavg_af(tmp, 2 * N, axis=0)
        val, idx = af.imin(cs, dim=1)
        idxnd[K * Nmax:-N] = np.array(idx)
    return idxnd
コード例 #3
0
ファイル: _array.py プロジェクト: eeeedgar/model_1
def argmin(a: ndarray,
           axis: tp.Optional[int] = None,
           out: tp.Optional[ndarray] = None) -> ndarray:
    if out:
        raise ValueError('out != None is not supported')

    val, idx = af.imin(a._af_array, dim=axis)
    return _wrap_af_array(idx)
コード例 #4
0
ファイル: matching.py プロジェクト: roaffix/arrayfire-python
def templateMatchingDemo(console):

    root_path = os.path.dirname(os.path.abspath(__file__))
    file_path = root_path
    if console:
        file_path += "/../../assets/examples/images/square.png"
    else:
        file_path += "/../../assets/examples/images/man.jpg"
    img_color = af.load_image(file_path, True)

    # Convert the image from RGB to gray-scale
    img = af.color_space(img_color, af.CSPACE.GRAY, af.CSPACE.RGB)
    iDims = img.dims()
    print("Input image dimensions: ", iDims)

    # Extract a patch from the input image
    patch_size = 100
    tmp_img = img[100:100 + patch_size, 100:100 + patch_size]

    result = af.match_template(img, tmp_img)  # Default disparity metric is
    # Sum of Absolute differences (SAD)
    # Currently supported metrics are
    # AF_SAD, AF_ZSAD, AF_LSAD, AF_SSD,
    # AF_ZSSD, AF_LSSD

    disp_img = img / 255.0
    disp_tmp = tmp_img / 255.0
    disp_res = normalize(result)

    minval, minloc = af.imin(disp_res)
    print("Location(linear index) of minimum disparity value = {}".format(
        minloc))

    if not console:
        marked_res = af.tile(disp_img, 1, 1, 3)
        marked_res = draw_rectangle(marked_res, minloc%iDims[0], minloc/iDims[0],\
                                    patch_size, patch_size)

        print(
            "Note: Based on the disparity metric option provided to matchTemplate function"
        )
        print(
            "either minimum or maximum disparity location is the starting corner"
        )
        print(
            "of our best matching patch to template image in the search image")

        wnd = af.Window(512, 512, "Template Matching Demo")

        while not wnd.close():
            wnd.set_colormap(af.COLORMAP.DEFAULT)
            wnd.grid(2, 2)
            wnd[0, 0].image(disp_img, "Search Image")
            wnd[0, 1].image(disp_tmp, "Template Patch")
            wnd[1, 0].image(marked_res, "Best Match")
            wnd.set_colormap(af.COLORMAP.HEAT)
            wnd[1, 1].image(disp_res, "Disparity Values")
            wnd.show()
コード例 #5
0
ファイル: _array.py プロジェクト: eeeedgar/model_1
    def argmin(self,
               axis: tp.Optional[int] = None,
               out: tp.Optional['ndarray'] = None):
        """
        Return indices of the minimum values along the given axis of a.
        """

        val, idx = af.imin(self._af_array, dim=axis)
        return _wrap_af_array(idx)
コード例 #6
0
ファイル: multiarray.py プロジェクト: Brainiarc7/afnumpy
 def argmin(self, axis=None):
     if axis is None:
         return self.flat.argmin(axis=0)
     if not isinstance(axis, numbers.Number):
         raise TypeError('an integer is required for the axis')
     val, idx = arrayfire.imin(self.d_array, pu.c2f(self.shape, axis))
     shape = list(self.shape)
     shape.pop(axis)
     if(len(shape)):
         return ndarray(shape, dtype=pu.typemap(idx.dtype()), af_array=idx)
     else:
         return ndarray(shape, dtype=pu.typemap(idx.dtype()), af_array=idx)[()]
コード例 #7
0
 def argmin(self, axis=None):
     if axis is None:
         return self.flat.argmin(axis=0)
     if not isinstance(axis, numbers.Number):
         raise TypeError('an integer is required for the axis')
     val, idx = arrayfire.imin(self.d_array, pu.c2f(self.shape, axis))
     shape = list(self.shape)
     shape.pop(axis)
     if (len(shape)):
         return ndarray(shape, dtype=pu.typemap(idx.dtype()), af_array=idx)
     else:
         return ndarray(shape, dtype=pu.typemap(idx.dtype()),
                        af_array=idx)[()]
コード例 #8
0
ファイル: initialize.py プロジェクト: mchandra/Bolt
def initialize_f(r, theta, rdot, thetadot, phidot, params):

    # The initialization is performed to setup particles
    # between r = 0.9 and 1.1 at theta = 0
    rho = af.select(r < 2, 1 * r**0, 0)
    rho = af.select(r > 1, rho, 0)
    rho = af.select(theta == 0, rho, 0)

    # Getting f at the right shape:
    f = rdot * rho

    f[:] = 0

    # Changing to velocities_expanded form:
    f = af.moddims(f, N_p1, N_p2, N_p3, r.shape[2] * r.shape[3])
    rdot = af.moddims(rdot, N_p1, N_p2, N_p3)
    thetadot = af.moddims(thetadot, N_p1, N_p2, N_p3)

    # Assigning the velocities such that thetadot ∝ 1 / r
    for i in range(r.shape[2]):
        for j in range(r.shape[3]):

            rho_new = rho * 0
            rho_new[:, :, i, j] = rho[:, :, i, j]
            rho_new = af.moddims(rho_new, 1, 1, 1, r.shape[2] * r.shape[3])

            # We set rdot = rdot[64] for all particles and
            # thetadot is inversely proportional to r
            # At initialization:
            thetadot1d = af.flat(thetadot[0, :, 0])
            ind = af.imin(af.abs(thetadot1d - 1 / af.sum(r[:, :, i, j])))[1]

            print('rdot     =', af.sum(rdot[64, ind, 0]))
            print('thetadot =', af.sum(thetadot[64, ind, 0]))
            f[64, ind, 0] += rho_new

    f = af.moddims(f, N_p1 * N_p2 * N_p3, 1, r.shape[2], r.shape[3])

    af.eval(f)
    return (f)
コード例 #9
0
ファイル: intro.py プロジェクト: Brainiarc7/arrayfire-python
    A[1,:] = B[2,:]
    af.display(A)

    print("\n---- Bitwise operations\n")
    af.display(A & B)
    af.display(A | B)
    af.display(A ^ B)

    print("\n---- Transpose\n")
    af.display(A)
    af.display(af.transpose(A))

    print("\n---- Flip Vertically / Horizontally\n")
    af.display(A)
    af.display(af.flip(A, 0))
    af.display(af.flip(A, 1))

    print("\n---- Sum, Min, Max along row / columns\n")
    af.display(A)
    af.display(af.sum(A, 0))
    af.display(af.min(A, 0))
    af.display(af.max(A, 0))
    af.display(af.sum(A, 1))
    af.display(af.min(A, 1))
    af.display(af.max(A, 1))

    print("\n---- Get minimum with index\n")
    (min_val, min_idx) = af.imin(A, 0)
    af.display(min_val)
    af.display(min_idx)
コード例 #10
0
    A[1, :] = B[2, :]
    af.display(A)

    print("\n---- Bitwise operations\n")
    af.display(A & B)
    af.display(A | B)
    af.display(A ^ B)

    print("\n---- Transpose\n")
    af.display(A)
    af.display(af.transpose(A))

    print("\n---- Flip Vertically / Horizontally\n")
    af.display(A)
    af.display(af.flip(A, 0))
    af.display(af.flip(A, 1))

    print("\n---- Sum, Min, Max along row / columns\n")
    af.display(A)
    af.display(af.sum(A, 0))
    af.display(af.min(A, 0))
    af.display(af.max(A, 0))
    af.display(af.sum(A, 1))
    af.display(af.min(A, 1))
    af.display(af.max(A, 1))

    print("\n---- Get minimum with index\n")
    (min_val, min_idx) = af.imin(A, 0)
    af.display(min_val)
    af.display(min_idx)