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213 lines
7.5 KiB
Python
213 lines
7.5 KiB
Python
import numpy as np
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import sys
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sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/')
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from fourierOptics import *
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from pdb import set_trace
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'''
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Author: Joost van den Born, PhD student at University of Groningen
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Contact: born@astro.rug.nl
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Description: Script to generate the geometric distortions due to WFEs
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off the ADC prisms. The distortions are produced by measuring
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the offset of the PSF centroid with and without WFE as a
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function of field position.
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Script made for the SPIE manuscript.
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'''
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def plot_distortions():
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# Coordinates without WFEs
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xe = load_variable('../data/GD_IFP_obscured_PSF_xcentroid_expected_fs.pkl')
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ye = load_variable('../data/GD_IFP_obscured_PSF_ycentroid_expected_fs.pkl')
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# Coordinates with WFEs
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xf = load_variable('../data/GD_IFP_obscured_PSF_xcentroid_found_with_wfe_airy_fs.pkl')
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yf = load_variable('../data/GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_fs.pkl')
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mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3)
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n = xe.shape[0]
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X = xe.flatten()
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Y = ye.flatten()
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dX = xf - xe
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dY = yf - ye
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dx = dX.flatten()
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dy = dY.flatten()
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# Magnitude of the distortions
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R = np.sqrt(dx**2 + dy**2) * mcd.platescale * 1e6
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# Dummy figure, used for the colorbar colors in the quiver plot
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im = plt.imshow(np.reshape(R, (n,n)))
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plt.close()
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fig = plt.figure()
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ax = fig.add_subplot()
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for i in range(len(xe)):
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ax.plot(xe[:,i], ye[:,i], 'k-', alpha=0.2, linewidth=.5)
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ax.plot(xe[i,:], ye[i,:], 'k-', alpha=0.2, linewidth=.5)
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ax.quiver(X,Y,dx,dy, angles='xy', edgecolor='k', facecolor='None', linewidth=.5, units='dots')
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ax.quiver(X,Y,dx,dy,R, alpha=.5, units='dots')
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ax.set_aspect('equal', adjustable='box')
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ax.set_xlabel('x (mm)')
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ax.set_ylabel('y (mm)')
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fig.colorbar(im, ax=ax, label='PSF centroid shift ($\\mu$as)')
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plt.savefig('../figures/UPDATED_GD_focal_plane_fs.pdf', bbox_inches='tight', transparent=True)
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plt.savefig('../figures/UPDATED_GD_focal_plane_fs.png', bbox_inches='tight', transparent=True)
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plt.tight_layout()
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plt.show()
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if __name__ == '__main__':
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# plt.rc('text', usetex=True)
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plt.rc('font', family='serif')
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plt.rc('axes', labelsize='large')
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# Setup of simulation
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l = 1.49e-3
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mcd = ParaxialMICADO(n=3001, m=3001, dpix=50)
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# mcd.plot_ray_trace()
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# Get x3 and y3 (the coordinates to apply the ADC WFE to):
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# The focal plane area is about 200x200 mm, so we apply the wavefront error to an area
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# of 200x200 mm.
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xfp, yfp = np.meshgrid(np.linspace(-150, 150, mcd.n), np.linspace(-150, 150, mcd.m))
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# _, xfp, yfp = mcd.propagate(l=l,surface=1, zoom=False, show=False,
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# print_progress=False)
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# Generate a wavefront error for the intermediate focal plane.
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print('\nSetting the wavefront errors...')
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wfe_fp = WavefrontError(mcd.n,mcd.m)
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wfe_rms = 15000e-6
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#PSD Wavefront error
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# Loading the phase generated using a Fourier Series
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fs_array = load_variable('wfe_fp_phase_array_{}_{}.pkl'.format(mcd.n, mcd.m))
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# Retrieving the coefficients for the Fourier series
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np.random.seed(2183)
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coeff = np.random.uniform(-1,1,(101,101))
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wfe_fp.coefficients = coeff
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fs_array = fourier_series(xfp, yfp, coeff, Px=30, Py=30, xs=10, ys=10)
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# Setting the initial full window phase array to generate the full window surface height map.
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wfe_fp.phase = fs_array
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surface_map_fp = wfe_fp.generate_height_map(wfe_rms, 2, 1)
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# plot_real_valued_results(surface_map_fp*1e6, xfp, yfp, logscale=False, fig_size=(8,6.4), grid=False, show=True,
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# colormap='jet', labeltext='Surface height (nm)')
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# save_variable('../data/MCD_IFP_wfe_object_PSD_50nm.pkl', wfe_fp)
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# wfe_fp = load_variable('../data/MCD_IFP_wfe_object_PSD_50nm.pkl')
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# surface_map_fp = wfe_fp.data
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# plt.show()
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Elist, Eplist = [], []
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xlist, ylist = [], []
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xplist, yplist = [], []
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x_expected_list, y_expected_list = [], []
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x_centroid_list, y_centroid_list = [], []
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# Set field points
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hxList, hyList = np.mgrid[-1:1:11j,-1:1:11j]-(1/300)
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# hxList, hyList = np.mgrid[-.1/11:.1/11:11j,-.1/11:.1/11:11j]-(1/300) # Zoomed in to central area
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hxList = hxList.flatten()
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hyList = hyList.flatten()
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# hxList, hyList = [0.0], [0.0]
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# List of performed simulations
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# Defined by PSD
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## GD_IFP : -1:1
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## GD_IFP_zoom : -1/11 :1/11
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## GD_IFP_zoom2 : -.5/11:.5/11
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## GD_IFP_zoom3 : -.1/11:.1/11
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# Defined by zernikes
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## GD_IFP_obscured_PSF_xcentroid_expected_zern : 50 nm rms 19 zernike modes : -1 : 1
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## To do; check PSF shape at center field with zern wfe
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## To do; check interpolated WFE at any zoomed in field point.
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# Propagate through the field positions without any WFEs
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for i in range(len(hxList)):
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hx = hxList[i]
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hy = hyList[i]
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print('Data point: i={:.0f} of i_max={:.0f}'.format(i, len(hxList)))
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print('hx: {} - hy: {}'.format(hx,hy) )
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# Initializing MICADO paraxial model
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mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, entrance_pupil='obscured')
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# Propagate through the MICADO system
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E, x, y = mcd.propagate(l=l,surface=-1, zoom=False, show=False,
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print_progress=False, hx=hx, hy=hy)
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# Find approximation for the centroid and amplitude as a first
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# guess for the Obscured Airy pattern fit.
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amplitude_guess = np.max(np.abs(E))**2
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xbc, ybc, _, _ = mcd.centroid_shift(method='Barycenter')
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print('Barycenter guess: ', xbc, ybc)
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# Do the Obscured Airy Pattern Fit for the unperturbed PSF
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xe, ye, _, _ = mcd.centroid_shift(method='Obscured Airy', initialGuess=(amplitude_guess, xbc, ybc, 0.0, 0.03))
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print('Airy result: ', xe, ye)
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x_expected_list.append(xe)
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y_expected_list.append(ye)
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# Load in the WFEs and set them to the corresponding ADC surfaces
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print('\nSetting the wavefront errors...')
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# null_wfe = np.zeros((3001,3001))
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mcd.ELT_FP.add_wavefront_error(surface_map_fp, xfp, yfp, wfe=wfe_fp)
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# Propagate through the MICADO system
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print('Propagating with Wavefront Errors applied...')
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Ep, xp, yp = mcd.propagate(l=l,surface=-1, zoom=False, show=True,
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print_progress=False, hx=hx, hy=hy)
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# Do the Obscured Airy Pattern Fit for the perturbed PSF
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xf, yf, _, _ = mcd.centroid_shift(method='Obscured Airy', initialGuess=(amplitude_guess, xe, ye, 0.0, 0.03))
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# Resulting centroid shift due to the WFE
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x_centroid_list.append(xf)
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y_centroid_list.append(yf)
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R = np.sqrt( (xf-xe)**2 + (yf-ye)**2 ) * mcd.platescale * 1e6
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print('xf: {}, yf: {}, shift (uas): {}'.format(xf,yf, R))
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data_tuple = (i, hx, hy, xe, ye, xf, yf)
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data_name = 'gd_point_{:03d}_hx{}_hy{}.pkl'.format(i, hx, hy)
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save_variable('../data/GD_IFP_intermediate_results/{}'.format(data_name), data_tuple)
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# Clear the memory by emptying the large arrays
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mcd = None
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wfe = None
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E, x, y = None, None, None
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Ep, xp, yp = None, None, None
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# Reshaping the arrays
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shape = (int(np.sqrt(len(x_expected_list))), int(np.sqrt(len(x_expected_list))))
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x_expected = np.asarray(x_expected_list).reshape(shape)
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y_expected = np.asarray(y_expected_list).reshape(shape)#.T
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# Saving the arrays
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save_variable('../data/GD_IFP_obscured_PSF_xcentroid_expected_fs.pkl', x_expected)
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save_variable('../data/GD_IFP_obscured_PSF_ycentroid_expected_fs.pkl', y_expected)
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# Reshaping the arrays
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shape = (int(np.sqrt(len(x_centroid_list))), int(np.sqrt(len(x_centroid_list))))
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x_centroid = np.asarray(x_centroid_list).reshape(shape)
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y_centroid = np.asarray(y_centroid_list).reshape(shape)
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# Saving the arrays
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save_variable('../data/GD_IFP_obscured_PSF_xcentroid_found_with_wfe_airy_fs.pkl', x_centroid)
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save_variable('../data/GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_fs.pkl', y_centroid)
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plot_distortions()
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