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160 lines
6.0 KiB
Python
160 lines
6.0 KiB
Python
import numpy as np
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import os
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import sys
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sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
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from fourierOptics import *
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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 compare the Strehl ratio of 50 different instances
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of a generated WFE. Higher spatial frequency perturbations
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should follow the Marechal approximation more closely than
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lower order shape errors only. We verify this using Power
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Spectral Density and Zernike modes (66 coefficients).
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Script made for the SPIE manuscript.
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'''
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def expected_strehl2(rms, l):
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return np.exp( -(2*np.pi*rms/l)**2 )
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def append_to_existing_data(A, filename):
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if os.path.exists(filename):
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A_o = load_variable(filename)
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A2 = np.append(A, A_o)
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else:
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A2 = A
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return A2
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if __name__ == '__main__':
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# Setting some input parameters
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dpix = 160
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n, m = 1001, 1001
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l = 1.49e-3 # wavelength
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zern_rms = 25e-5 # RMS of the zernike component of the WFE
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midspat_rms = 25e-5 # RMS of the PSD component of the WFE
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rmsList = np.linspace(1e-6, 1e-3, 25) # 1 to 1000 nm WFE RMS in mm
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number_of_iterations = 50
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input_field_location = './data/ADC_input_field_1001x1001_1.49_onaxis_obscured.pkl'
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# Initiate ELT-MICADO and wavefront error objects
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mcd = ParaxialMICADO(l=l, n=n, m=m, dpix=dpix, entrance_pupil='open')
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wfe = WavefrontError(n,m)
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print ('Your resolution is {}x{} with a pixel size of {}'.format(n,m,dpix))
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# Propagate unperturbed field
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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=0, hy=0,
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load_up_to_adc=input_field_location)
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# Initiate psf fitter object to calculate the strehl ratios
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psf = PSFFitter(x, y, E)
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# Create some empty lists for later
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strehl_zern_complete, strehl_psd_complete = [], []
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rms_zern_list_complete, rms_psd_list_complete = [], []
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# Repeat the analysis 50 times
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for i in range(number_of_iterations):
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print('Iteration {}/{}'.format(i+1,number_of_iterations))
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# Generate a PSD surface
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psd_surface = wfe.generate_height_map(midspat_rms, 2, 1)
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# Fit the PSD surface using zernike terms
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# Generating a normalized grid for the Zernike evaluation
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yz,xz = np.mgrid[-1:1:wfe.n*1j, -1:1:wfe.m*1j]
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_r = np.sqrt(xz**2 + yz**2)
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_mask = _r > 1
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coeffic = wfe.fit_zernike_coefficients(psd_surface, modes=66)
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StrehlList_zern, StrehlList_psd = [], []
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rms_zern_list, rms_psd_list = [], []
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# Compare results at different RMS values
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for rms in rmsList:
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########################################
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# WFE described by Zernike polynomials #
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########################################
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# Make a new perturbed wavefront error with the required RMS
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coeffic = wfe.scale_zernike_coefficients(coeffic, rms)
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hzern = wfe.zernike_cartesian(coeffic, xz, yz)
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# Add the new wavefront error
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mcd.ELT_ADC.p1.add_wavefront_error(hzern)
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# Performing the propagation using Fresnel approximation to the focal plane.
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Exyz_zern,xzern,yzern = mcd.propagate(l=l,surface=-1, zoom=False, show=False,
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print_progress=False, hx=0, hy=0,
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load_up_to_adc=input_field_location)
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Strehl_zern = np.abs(psf.get_coupling(E, Exyz_zern))**2
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StrehlList_zern.append(Strehl_zern)
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########################################
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########################################
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# WFE described by PSD profile #
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########################################
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factor = rms / midspat_rms
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h_psd = wfe.scale_rms(factor, data=psd_surface)
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# Add the new wavefront error
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mcd.ELT_ADC.p1.add_wavefront_error(h_psd)
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# Performing the propagation using Fresnel approximation to the focal plane.
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Exyz_psd,xpsd,ypsd = mcd.propagate(l=l,surface=-1, zoom=False, show=False,
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print_progress=False, hx=0, hy=0,
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load_up_to_adc=input_field_location)
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Strehl_psd = np.abs(psf.get_coupling(E, Exyz_psd))**2
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StrehlList_psd.append(Strehl_psd)
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########################################
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# Find the RMS value of the wavefront errros
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rms_zern = np.sqrt(np.mean(np.asarray(hzern[~_mask])**2))
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rms_psd = np.sqrt(np.mean(np.asarray(h_psd)**2))
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rms_zern_list.append(rms_zern)
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rms_psd_list.append(rms_psd)
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# Print results
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print('RMS: {:.3f}, S_zern: {:.3f}, S_psd: {:.3f}, \
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RMS_zern: {:.3f}, RMS_psd: {:.3f}'.format(
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rms*1e6, Strehl_zern, Strehl_psd, rms_zern*1e6, rms_psd*1e6))
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# Put the results in the correct list
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strehl_zern_complete.append(StrehlList_zern)
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strehl_psd_complete.append(StrehlList_psd)
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rms_zern_list_complete.append(rms_zern_list)
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rms_psd_list_complete.append(rms_psd_list)
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rms_zern_list_complete = np.asarray(rms_zern_list_complete)
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rms_psd_list_complete = np.asarray(rms_psd_list_complete)
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# Import old results if they are available and append them to the results.
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rmsList_present = rmsList.copy()
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rmsList = append_to_existing_data(rmsList, './data/SR_RMS_values_obscured.pkl')
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strehl_zern_complete = append_to_existing_data(strehl_zern_complete, './data/SR_strehl_zern_obscured.pkl')
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strehl_psd_complete = append_to_existing_data(strehl_psd_complete, './data/SR_strehl_psd_obscured.pkl')
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rms_zern_list_complete = append_to_existing_data(rms_zern_list_complete, './data/SR_rms_zern_complete_obscured.pkl')
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rms_psd_list_complete = append_to_existing_data(rms_psd_list_complete, './data/SR_rms_psd_complete_obscured.pkl')
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mean_strehl_zern = np.mean(strehl_zern_complete, axis=0)
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err_strehl_zern = np.std(strehl_zern_complete, axis=0)
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mean_strehl_psd = np.mean(strehl_psd_complete, axis=0)
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err_strehl_psd = np.std(strehl_psd_complete, axis=0)
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save_variable('./data/SR_RMS_values_obscured.pkl', rmsList)
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save_variable('./data/SR_strehl_zern_obscured.pkl', strehl_zern_complete)
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save_variable('./data/SR_strehl_psd_obscured.pkl', strehl_psd_complete)
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save_variable('./data/SR_rms_zern_complete_obscured.pkl', rms_zern_list_complete)
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save_variable('./data/SR_rms_psd_complete_obscured.pkl', rms_psd_list_complete)
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