import psf
import utils
import calibration
import noise
### PARAMETERS ###
# PSF bits
N_PIX = 256 # pixels for the Fourier arrays
pix = 30 # pixels to crop the PSF images
WAVE = 1.5 # microns | reference wavelength
SPAX = 4.0 # mas | spaxel scale
RHO_APER = utils.rho_spaxel_scale(spaxel_scale=SPAX, wavelength=WAVE)
RHO_OBSC = 0.30 * RHO_APER # ELT central obscuration
utils.check_spaxel_scale(rho_aper=RHO_APER, wavelength=WAVE)
N_actuators = 16 # Number of actuators in [-1, 1] line
alpha_pc = 20 # Height [percent] at the neighbour actuator (Gaussian Model)
# Machine Learning bits
N_train, N_test = 5000, 500 # Samples for the training of the models
coef_strength = 1.75 / (2 * np.pi) # Strength of the actuator coefficients
rescale = 0.35 # Rescale the coefficients to cover a wide range of RMS
layer_filters = [16, 8] # How many filters per layer
kernel_size = 3
input_shape = (
pix,
pix,
2,
)
epochs = 10 # Training epochs
utils.print_title(message='\nN C P A', font=None, random_font=False)
print("\n -|| NYQUIST ERRORS ||- ")
print("What is the effect of errors in the Nyquist-Shannon sampling criterion?")
print("What happens to the performance when you show the model")
print("PSF images that have a slightly different spaxel scale??\n")
# PSF bits
N_PIX = 256 # pixels for the Fourier arrays
pix = 30 # pixels to crop the PSF images
WAVE = 1.5 # microns | reference wavelength
SPAX = 4.0 # mas | spaxel scale
RHO_APER = utils.rho_spaxel_scale(spaxel_scale=SPAX, wavelength=WAVE)
RHO_OBSC = 0.30 * RHO_APER # ELT central obscuration
print("Nominal Parameters | Spaxel Scale and Wavelength")
utils.check_spaxel_scale(rho_aper=RHO_APER, wavelength=WAVE)
N_actuators = 20 # Number of actuators in [-1, 1] line
alpha_pc = 10 # Height [percent] at the neighbour actuator (Gaussian Model)
N_WAVES = 2 # 2 wavelengths: 1 Nominal, 1 with Nyquist error
# Machine Learning bits
N_train, N_test = 10000, 1000 # Samples for the training of the models
coef_strength = 0.30 # Strength of the actuator coefficients
diversity = 0.55 # Strength of extra diversity commands
rescale = 0.35 # Rescale the coefficients to cover a wide range of RMS
layer_filters = [64, 32, 16, 8] # How many filters per layer
kernel_size = 3
input_shape = (pix, pix, 2,)
SNR = 750 # SNR for the Readout Noise
N_loops, epochs_loop = 5, 5 # How many times to loop over the training