import numpy as np import sys sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/') from fourierOptics import * ''' Author: Joost van den Born, PhD student at University of Groningen Contact: born@astro.rug.nl Description: Script to generate the geometric distortions due to WFEs off the ADC prisms. The distortions are produced by measuring the offset of the PSF centroid with and without WFE as a function of field position. Script made for the SPIE manuscript. ''' # Rotation of ADC angles - [0.125, 0.25, 0.5, 0.75, 1, 2, 5, 10, 30, 45, 60, 90, 180] def plot_distortions(): # Coordinates without WFEs xe = load_variable('./data/ADC rotation/GD_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye = load_variable('./data/ADC rotation/GD_obscured_PSF_ycentroid_expected_zern_psd.pkl') # Coordinates with WFEs xf = load_variable('./data/ADC rotation/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yf = load_variable('./data/ADC rotation/GD_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3) n = xe.shape[0] X = xe.flatten() Y = ye.flatten() dX = xf - xe dY = yf - ye dx = dX.flatten() dy = dY.flatten() # Magnitude of the distortions R = np.sqrt(dx**2 + dy**2) * mcd.platescale * 1e6 # Dummy figure, used for the colorbar colors in the quiver plot im = plt.imshow(np.reshape(R, (n,n))) plt.close() fig = plt.figure() ax = fig.add_subplot() for i in range(len(xe)): ax.plot(xe[:,i], ye[:,i], 'k-', alpha=0.2, linewidth=.5) ax.plot(xe[i,:], ye[i,:], 'k-', alpha=0.2, linewidth=.5) ax.quiver(X,Y,dx,dy, angles='xy', edgecolor='k', facecolor='None', linewidth=.5, units='dots') ax.quiver(X,Y,dx,dy,R, alpha=.5, units='dots') ax.set_aspect('equal', adjustable='box') ax.set_xlabel('x (mm)') ax.set_ylabel('y (mm)') fig.colorbar(im, ax=ax, label='PSF centroid shift ($\\mu$as)') plt.savefig('./figures/UPDATED2_GD_full_field_zern_psd.pdf', bbox_inches='tight', transparent=True) plt.savefig('./figures/UPDATED2_GD_full_field_zern_psd.png', bbox_inches='tight', transparent=True) plt.tight_layout() plt.show() if __name__ == '__main__': # plt.rc('text', usetex=True) # plt.rc('font', family='serif') # Loading in the WFEs wfe_p1 = load_variable('./data/compressed items/ADC_tWFE_p1_zern_20nm_psd_15nm.pkl') wfe_p2 = load_variable('./data/compressed items/ADC_tWFE_p2_zern_20nm_psd_15nm.pkl') wfe_p3 = load_variable('./data/compressed items/ADC_tWFE_p3_zern_20nm_psd_15nm.pkl') wfe_p4 = load_variable('./data/compressed items/ADC_tWFE_p4_zern_20nm_psd_15nm.pkl') # Rotation of ADC angles - [0.125, 0.25, 0.5, 0.75, 1, 2, 5, 10], 30, 45, 60, 90, 180] thetaList = [0.001, 0.125, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10] # Propagate through the field positions without any WFEs for theta in thetaList: # Setup of simulation l = 1.49e-3 Elist, Eplist = [], [] xlist, ylist = [], [] xplist, yplist = [], [] x_expected_list, y_expected_list = [], [] x_centroid_list, y_centroid_list = [], [] # Set field points hxList, hyList = np.mgrid[-1:1:11j,-1:1:11j]-(1/300) hxList = hxList.flatten() hyList = hyList.flatten() for i in range(len(hxList)): hx = hxList[i] hy = hyList[i] print('Rotation: {:.3f} - hx: {} - hy: {}'.format(theta,hx,hy) ) # Initializing MICADO paraxial model mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, entrance_pupil='obscured') # Propagate through the MICADO system mcd.ELT_ADC.set_orientation(theta) E, x, y = mcd.propagate(l=l,surface=-1, zoom=False, show=False, print_progress=False, hx=hx, hy=hy) # Find approximation for the centroid and amplitude as a first # guess for the Obscured Airy pattern fit. amplitude_guess = np.max(np.abs(E))**2 xbc, ybc, _, _ = mcd.centroid_shift(method='Barycenter') print('Barycenter guess: ', xbc, ybc) # Do the Obscured Airy Pattern Fit for the unperturbed PSF xe, ye, _, _ = mcd.centroid_shift(method='Obscured Airy', initialGuess=(amplitude_guess, xbc, ybc, 0.0, 0.03)) print('Airy result: ', xe, ye) x_expected_list.append(xe) y_expected_list.append(ye) # Load in the WFEs and set them to the corresponding ADC surfaces print('\nSetting the wavefront errors...') # null_wfe = np.zeros((3001,3001)) mcd.ELT_ADC.set_orientation(0) mcd.ELT_ADC.p1.add_wavefront_error(wfe_p1) mcd.ELT_ADC.p2.add_wavefront_error(wfe_p2) mcd.ELT_ADC.p3.add_wavefront_error(wfe_p3) mcd.ELT_ADC.p4.add_wavefront_error(wfe_p4) # Propagate through the MICADO system print('Propagating with Wavefront Errors applied...') mcd.ELT_ADC.set_orientation(theta) Ep, xp, yp = mcd.propagate(l=l,surface=-1, zoom=False, show=False, print_progress=False, hx=hx, hy=hy) # Do the Obscured Airy Pattern Fit for the perturbed PSF xf, yf, _, _ = mcd.centroid_shift(method='Obscured Airy', initialGuess=(amplitude_guess, xe, ye, 0.0, 0.03)) # Resulting centroid shift due to the WFE x_centroid_list.append(xf) y_centroid_list.append(yf) R = np.sqrt( (xf-xe)**2 + (yf-ye)**2 ) * mcd.platescale * 1e6 print('xf: {}, yf: {}, shift (uas): {}'.format(xf,yf, R)) # Clear the memory by emptying the large arrays mcd = None wfe = None E, x, y = None, None, None Ep, xp, yp = None, None, None # Reshaping the arrays shape = (int(np.sqrt(len(x_expected_list))), int(np.sqrt(len(x_expected_list)))) x_expected = np.asarray(x_expected_list).reshape(shape) y_expected = np.asarray(y_expected_list).reshape(shape)#.T # Saving the arrays save_variable('./data/ADC rotation/GD_obscured_PSF_xcentroid_expected_zern_psd_rot{}.pkl'.format(theta), x_expected) save_variable('./data/ADC rotation/GD_obscured_PSF_ycentroid_expected_zern_psd_rot{}.pkl'.format(theta), y_expected) # Reshaping the arrays shape = (int(np.sqrt(len(x_centroid_list))), int(np.sqrt(len(x_centroid_list)))) x_centroid = np.asarray(x_centroid_list).reshape(shape) y_centroid = np.asarray(y_centroid_list).reshape(shape) # Saving the arrays save_variable('./data/ADC rotation/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd_rot{}.pkl'.format(theta), x_centroid) save_variable('./data/ADC rotation/GD_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd_rot{}.pkl'.format(theta), y_centroid) # plot_distortions()