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
pix,
pix,
2,
)
epochs = 10 # Training epochs
SNR = 500
if __name__ == """__main__""":
plt.rc('font', family='serif')
plt.rc('text', usetex=False)
# (1) We begin by creating a Zernike PSF model with Defocus as diversity
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=1.1)
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]))
# Calculate the Actuator Centres
centers = psf.actuator_centres(N_actuators,
rho_aper=RHO_APER,
rho_obsc=RHO_OBSC,
radial=True)
N_act = len(centers[0])
import importlib
importlib.reload(calibration)
if __name__ == """__main__""":
plt.rc('font', family='serif')
plt.rc('text', usetex=False)
### (0) Show the impact of ellipticity variations
# Define a nominal PSF Zernike model with no anamorphic errors. Perfectly circular pupil
zernike_matrix, pupil_mask_zernike, flat_zernike = psf.zernike_matrix(N_levels=N_levels, rho_aper=RHO_APER,
rho_obsc=RHO_OBSC, N_PIX=N_PIX,
radial_oversize=1.0, anamorphic_ratio=1.0)
N_zern = zernike_matrix.shape[-1]
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]))
defocus_zernike = np.zeros(zernike_matrix.shape[-1])
defocus_zernike[1] = diversity / (2 * np.pi)
PSF_zernike.define_diversity(defocus_zernike)
# Define a model with some anamorphic error
ratio = 1.10 # a / b for the ellipse. > 1.0 elongated PSF along Y
zernike_matrix_anam, pupil_mask_zernike_anam, flat_zernike_anam = psf.zernike_matrix(N_levels=N_levels, rho_aper=RHO_APER,
rho_obsc=RHO_OBSC, N_PIX=N_PIX,
readout_copies = 2 # How many copies with Readout Noise to use
N_iter = 3 # How many iterations to run the calibration (testing)
import importlib
importlib.reload(calibration)
if __name__ == """__main__""":
plt.rc('font', family='serif')
plt.rc('text', usetex=False)
### (0) Define a nominal PSF Zernike model with no anamorphic mag. Perfectly circular pupil
zernike_matrix, pupil_mask_zernike, flat_zernike = psf.zernike_matrix(
N_levels=N_levels,
rho_aper=RHO_APER,
rho_obsc=RHO_OBSC,
N_PIX=N_PIX,
radial_oversize=1.0,
anamorphic_ratio=1.0)
N_zern = zernike_matrix.shape[-1]
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]))
defocus_zernike = np.zeros(zernike_matrix.shape[-1])
defocus_zernike[1] = diversity / (2 * np.pi)
PSF_zernike.define_diversity(defocus_zernike)
if __name__ == """__main__""":
plt.rc('font', family='serif')
plt.rc('text', usetex=False)
### (0) Define a model with a sampling of 2.0 mas Spaxels
### PSF sampled with 2.0 mas spaxels
SPAX2 = 2.0 # mas | spaxel scale
RHO_APER2 = utils.rho_spaxel_scale(spaxel_scale=SPAX2, wavelength=WAVE)
RHO_OBSC2 = 0.30 * RHO_APER2 # ELT central obscuration
zernike_matrix2, pupil_mask_zernike2, flat_zernike2 = psf.zernike_matrix(N_levels=N_levels, rho_aper=RHO_APER2,
rho_obsc=RHO_OBSC2, N_PIX=N_PIX,
radial_oversize=1.0, anamorphic_ratio=1.0)
N_zern = zernike_matrix2.shape[-1]
zernike_matrices2 = [zernike_matrix2, pupil_mask_zernike2, flat_zernike2]
PSF_zernike = psf.PointSpreadFunction(matrices=zernike_matrices2, N_pix=N_PIX,
crop_pix=pix, diversity_coef=np.zeros(N_zern))
defocus_zernike = np.zeros(N_zern)
defocus_zernike[1] = diversity / (2 * np.pi)
PSF_zernike.define_diversity(defocus_zernike)
train_PSF, train_coef, test_PSF, test_coef = calibration.generate_dataset(PSF_zernike, N_train, N_test,
coef_strength=coef_strength, rescale=rescale)
# plt.figure()