odak.learn.wave.transfer_function_fresnel¶

A definition to calculate convolution based Fresnel approximation for beam propagation.

Parameters:

Name	Type	Description	Default
`field`	`torch.complex`	Complex field (MxN).	required
`k`	`odak.wave.wavenumber`	Wave number of a wave, see odak.wave.wavenumber for more.	required
`distance`	`float`	Propagation distance.	required
`dx`	`float`	Size of one single pixel in the field grid (in meters).	required
`wavelength`	`float`	Wavelength of the electric field.	required

Returns:

Type	Description
`torch.complex`	Final complex field (MxN).

Source code in odak/learn/wave/classical.py

def transfer_function_fresnel(field, k, distance, dx, wavelength, zero_padding = False):
    """
    A definition to calculate convolution based Fresnel approximation for beam propagation.

    Parameters
    ----------
    field            : torch.complex
                       Complex field (MxN).
    k                : odak.wave.wavenumber
                       Wave number of a wave, see odak.wave.wavenumber for more.
    distance         : float
                       Propagation distance.
    dx               : float
                       Size of one single pixel in the field grid (in meters).
    wavelength       : float
                       Wavelength of the electric field.

    Returns
    -------
    result           : torch.complex
                       Final complex field (MxN).

    """
    distance = torch.tensor([distance]).to(field.device)
    nv, nu = field.shape[-1], field.shape[-2]
    fx = torch.linspace(-1./2./dx, 1./2./dx, nu,
                        dtype=torch.float32).to(field.device)
    fy = torch.linspace(-1./2./dx, 1./2./dx, nv,
                        dtype=torch.float32).to(field.device)
    FY, FX = torch.meshgrid(fx, fy, indexing='ij')
    H = torch.exp(1j*k*distance*(1-(FX*wavelength)**2-(FY*wavelength)**2)**0.5)
    H = H.to(field.device)
    U1 = torch.fft.fftshift(torch.fft.fft2(torch.fft.fftshift(field)))
    if zero_padding == False:
        U2 = H*U1
    elif zero_padding == True:
        U2 = zero_pad(H*U1)
    result = torch.fft.ifftshift(torch.fft.ifft2(torch.fft.ifftshift(U2)))
    return result

Notes¶

Unless you know what you are doing, we do not suggest you to use this function directly. Rather stick to odak.learn.wave.propagate_beam for your beam propagation code.

odak.learn.wave.transfer_function_fresnel¶

Notes¶

See also¶