def find_actuator_defocus(self):
i_ref = np.argwhere(self.wave_range == self.wave_ref)[0][0]
PSF_nom = psf.PointSpreadFunction(self.raw_matrices[i_ref],
N_pix=self.N_PIX,
crop_pix=self.pix,
diversity_coef=np.zeros(self.N_act))
# Create a Zernike model so we can mimic the defocus
zernike_matrix, pupil_mask_zernike, flat_zernike = psf.zernike_matrix(
N_levels=5,
rho_aper=self.rho_aper,
rho_obsc=self.rho_obsc,
N_PIX=self.N_PIX,
radial_oversize=1.0)
zernike_matrices = [zernike_matrix, pupil_mask_zernike, flat_zernike]
plt.figure()
plt.imshow(pupil_mask_zernike)
PSF_zernike = psf.PointSpreadFunction(matrices=zernike_matrices,
N_pix=self.N_PIX,
crop_pix=self.pix,
diversity_coef=np.zeros(
zernike_matrix.shape[-1]))
# Use Least Squares to find the actuator commands that mimic a Zernike defocus
zernike_fit = calibration.Zernike_fit(PSF_zernike,
PSF_nom,
wavelength=self.wave_ref,
rho_aper=self.rho_aper)
defocus_zernike = np.zeros((1, zernike_matrix.shape[-1]))
defocus_zernike[0, 1] = 1.0
defocus_actuators = zernike_fit.fit_zernike_wave_to_actuators(
defocus_zernike, plot=True, cmap='bwr')[:, 0]
# Update the Diversity Map on the actuator model so that it matches Defocus
return defocus_actuators
rho_obsc=RHO_OBSC,
N_PIX=N_PIX)
actuator_matrices = [actuator_matrix, pupil_mask, flat_actuator]
diversity_actuators = np.zeros(N_act)
PSF_actuators = psf.PointSpreadFunction(matrices=actuator_matrices,
N_pix=N_PIX,
crop_pix=pix,
diversity_coef=diversity_actuators)
plt.show()
# Use Least Squares to find the actuator commands that mimic the Zernike defocus
zernike_fit = calibration.Zernike_fit(PSF_zernike,
PSF_actuators,
wavelength=WAVE,
rho_aper=RHO_APER)
defocus_zernike = np.zeros((1, zernike_matrix.shape[-1]))
defocus_zernike[0, 1] = 1.0
defocus_actuators = zernike_fit.fit_zernike_wave_to_actuators(
defocus_zernike, plot=True, cmap='Reds')[:, 0]
# Loop over the strength of defocus to see what is the optimum value
rms_before, rms_after = [], []
diversities, rms_div = [], []
mean_peak_foc = []
# Noise RMS
N_cases = 30
noiseSNR = np.array([500, 250, 100])
N_noise = noiseSNR.shape[0]
plt.show()
###
# Temporarily use a single wavelength PSF to calculate the Zernike defocus coefficients
_PSF = psf.PointSpreadFunction(actuator_matrices[0], N_pix=N_PIX, crop_pix=pix, diversity_coef=np.zeros(N_act))
# Create a Zernike model so we can mimic the defocus
zernike_matrix, pupil_mask_zernike, flat_zernike = psf.zernike_matrix(N_levels=5, rho_aper=RHO_APER,
rho_obsc=RHO_OBSC,
N_PIX=N_PIX, radial_oversize=1.0)
zernike_matrices = [zernike_matrix, pupil_mask_zernike, flat_zernike]
PSF_zernike = psf.PointSpreadFunction(matrices=zernike_matrices, N_pix=N_PIX,
crop_pix=pix, diversity_coef=np.zeros(zernike_matrix.shape[-1]))
# Use Least Squares to find the actuator commands that mimic a Zernike defocus
zernike_fit = calibration.Zernike_fit(PSF_zernike, _PSF, wavelength=WAVE, rho_aper=RHO_APER)
defocus_zernike = np.zeros((1, zernike_matrix.shape[-1]))
defocus_zernike[0, 1] = 1.0
defocus_actuators = zernike_fit.fit_zernike_wave_to_actuators(defocus_zernike, plot=True, cmap='bwr')[:, 0]
diversity_defocus = diversity / (2 * np.pi) * defocus_actuators
###
PSFs = psf.PointSpreadFunctionMultiwave(matrices=actuator_matrices, N_waves=N_WAVES, wave0=WAVE0, waveN=WAVEN,
wave_ref=WAVE, N_pix=N_PIX, crop_pix=pix, diversity_coef=diversity_defocus)
# Show the PSF as a function of wavelength
c_act = 0.3 * np.random.uniform(-1, 1, size=N_act)
cmap = 'hot'
fig, axes = plt.subplots(1, N_WAVES)
for k in range(N_WAVES):