import numpy as np
from scipy.ndimage import gaussian_filter
[docs]
def hologram_frame(amp, phi,
counts=1000.,
sampling=5.,
visibility=1.,
f_angle=30.,
gaussian_noise=None,
poisson_noise=None,
xp=np):
"""
Generates holograms using phase and amplitude as an input
See :ref:`holography app` for detailed application example
.. versionadded:: 0.3.0
Notes
-----
Theoretical basis for hologram simulations see in:
Lichte, H., and M. Lehmann. Rep. Prog. Phys. 71 (2008): 016102.
doi:10.1088/0034-4885/71/1/016102
:cite:`Lichte2008`
Parameters
----------
amp, phi: np.ndarray, 2d
normalized amplitude and phase images of the same shape
counts: float, default: 1000.
Number of electron counts in vacuum
sampling: float, default: 5.
Hologram fringe sampling (number of pixels per fringe)
visibility: float, default: 1.
Hologram fringe visibility (aka fringe contrast)
f_angle: float, default: 30.
Angle in degrees of hologram fringes with respect to X-axis
gaussian_noise: float or int or None, default: None.
Amount of Gaussian smoothing determined by sigma parameter
applied to the hologram simulating effect of focus spread or
PSF of the detector.
poisson_noise: float or int or None, default: None.
Amount of Poisson applied to the hologram.
Returns
-------
holo: np.ndarray, 2d
hologram image
"""
if not amp.shape == phi.shape:
raise ValueError('Amplitude and phase should be 2d arrays of the same shape.')
sy, sx = phi.shape
x, y = xp.meshgrid(xp.arange(sx), xp.arange(sy))
f_angle = f_angle / 180. * np.pi
amp = xp.asarray(amp)
phi = xp.asarray(phi)
holo = counts / 2 * (1. + amp ** 2 + 2. * amp * visibility
* xp.cos(2. * xp.pi * y / sampling * xp.cos(f_angle)
+ 2. * xp.pi * x / sampling * xp.sin(f_angle)
- phi))
if poisson_noise:
if not isinstance(poisson_noise, (float, int)):
raise ValueError("poisson_noise parameter should be float or int or None.")
noise_scale = poisson_noise * counts
holo = noise_scale * xp.random.poisson(holo / noise_scale)
if gaussian_noise:
if not isinstance(gaussian_noise, (float, int)):
raise ValueError("gaussian_noise parameter should be float or int or None.")
if xp is not np:
from cupyx.scipy.ndimage import gaussian_filter as gaussian_filter_impl
else:
gaussian_filter_impl = gaussian_filter
holo = gaussian_filter_impl(holo, gaussian_noise)
return holo