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Added scripts folder with old scripts
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#!/usr/bin/env Python3
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import numpy as np
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from pdb import set_trace
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import matplotlib.pyplot as plt
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from matplotlib.ticker import FormatStrFormatter
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from matplotlib.colors import LogNorm
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import pickle
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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
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'''
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def saveVariable(var, filename):
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# Saving the objects:
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with open(filename, 'wb') as f: # Python 3: open(..., 'wb')
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pickle.dump(var, f)
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def loadVariable(filename):
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# Getting back the objects:
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with open(filename, 'rb') as f: # Python 3: open(..., 'rb')
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obj = pickle.load(f)
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return obj
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def phaseTilt(x,y,tx,ty):
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phaseTilt = x * np.tan(np.deg2rad(tx)) + y * np.tan(np.deg2rad(ty))
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return phaseTilt
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if __name__ == '__main__':
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plt.rc('font', family='serif')
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plt.rc('axes', labelsize='large')
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# Pick a wavelength
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l = 1.35e-3
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# Initiate Fourier Optics
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fou = FourierOptics(l=l)
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# Pick sampling parameters
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dpix = 160
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n, m = 1001, 1001
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oC = [-.75, .75, -.75, .75]
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print ('Your resolution is {}x{} with a pixel size of {}'.format(n,m,dpix))
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# Initiate the profiles class and pick a profile (tophat, obscured tophat or spider)
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prof = Profiles()
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# Aideal = prof.top_hat(38854/2, n, m, dpix=dpix, oversample=1)
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A = prof.ELT_pupil(n,m,dpix)
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# Create an input grid
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x, y = np.meshgrid(np.linspace(-n/2*dpix, n/2*dpix, n), np.linspace(-m/2*dpix, m/2*dpix, m))
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# Add PSD wavefront error to the lens
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wfe = WavefrontError(n,m)
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wfe.generate_height_map(5e-5, 2, 1) # 50 nm RMS for standard
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h_data = wfe.data
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# Setting up empty lists for fitting process
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popt_airy_list, popt_cust_list = [], []
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pcov_airy_list, custom_var_list = [], []
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strehl_list = []
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Exyzlist, PSDlist, surfacelist, EEFlist = [], [], [], []
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# Create a lens with a focal length ~100 mm, with a clear aperture radius of 4 mm and thickness of 1 mm.
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M1 = ParabolicMirror(dx=0, dy=0, name='ELT-paraxial', CA=38854, f=6.84022e5)
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# M1.add_wavefront_error(phaseTilt(x,y,tx=7.5, ty=7.5))
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t_l = M1.phase_delay(x,y,l)
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U_in = A * t_l
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# Propagating the original, unperturbed, system and calculating encircled energy
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Exyz_og,xp,yp = fou.fresnel_propagation(U_in, x, y, dz=M1.f, show=False, outputCoordinates=oC)
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eef_og, r_og = fou.encircled_energy_function(np.abs(Exyz_og)**2, xp, yp)
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plt.imshow(np.abs(Exyz_og), cmap='hot');plt.colorbar();plt.show()
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set_trace()
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zernikeAnalysis = True
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PSDAnalysis = True
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if zernikeAnalysis:
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# For some reason we need to reset the WFE object.
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wfe = WavefrontError(n,m)
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wfe.data = h_data
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psd1d_og = wfe.calculate_1D_PSD(data=h_data)
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# For each Zernike mode, perform analysis.
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# Don't touch the RMS values, it will converge to some value
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# If it goes as expected you'll see some form of convergence,
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# possibly a spiral, of the centroid location as you add more
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# and more modes.
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# Calculate the maximum mode that we want to fit the zernike modes to.
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nmax, mmax = 80, 80
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zernikeLimit = int((nmax*(nmax+2)+mmax)/2)
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# Fit the zernike coefficients for all modes up to zernikeLimit
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print('Fitting Zernike up to j={:.0f}'.format(zernikeLimit))
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zernikeCoefficients = wfe.fit_zernike_coefficients(h_data, modes=zernikeLimit)
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# Result lists:
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x0zern, y0zern = [], []
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x0zern_var, y0zern_var = [], []
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zernCouplingList = []
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eef_zern_list = []
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zernImageResponse = []
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psd1d_list_zern = []
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for nmax_i in range(nmax):
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print (' Zernike analysis: {0}/{1}'.format(nmax_i, nmax), end="\r")
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zernikeModeMax = int((nmax_i*(nmax_i+2)+nmax_i)/2+1)
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# The zernike coefficients for this particular mode
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zC = zernikeCoefficients[:zernikeModeMax]
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xz, yz = np.mgrid[-1:1:n*1j, -1:1:m*1j]
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wfeFilteredZern = wfe.zernike_cartesian(zC, xz, yz)
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# Adding the wavefront error
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M1.add_wavefront_error(wfeFilteredZern)
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wfe_t_l = M1.phase_delay(x,y,l)
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U_in = A * wfe_t_l
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# Performing the propagation using Fresnel approximation to the focal plane.
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Exyz_zern,xz,yz = fou.fresnel_propagation(U_in, x, y, dz=M1.f, show=False, outputCoordinates=oC)
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eef_zern, _ = fou.encircled_energy_function(np.abs(Exyz_zern)**2, xz, yz, originalPSF=np.abs(Exyz_og)**2)
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# Fitting the centroid location using the coupling integral
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# Set up fitting object
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psfFit_zern = PSFFitter(xz.real,yz.real,np.abs(Exyz_zern)**2)
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custFitted, popt_cust, cust_var, cust_coupling = psfFit_zern.fit_custom_field(Exyz_og, Exyz_zern, return_coupling=True)
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# Returning the found PSD curve
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psd1d_list_zern.append(wfe.calculate_1D_PSD(data=wfeFilteredZern))
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x0zern.append(popt_cust[0])
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y0zern.append(popt_cust[1])
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x0zern_var.append(cust_var[0])
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y0zern_var.append(cust_var[1])
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zernCouplingList.append(cust_coupling)
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eef_zern_list.append(eef_zern.tolist())
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zernImageResponse.append(Exyz_zern)
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saveVariable(x0zern, './data/x0zern.pck')
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saveVariable(y0zern, './data/y0zern.pck')
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saveVariable(x0zern_var, './data/x0zern_var.pck')
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saveVariable(y0zern_var, './data/y0zern_var.pck')
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saveVariable(zernCouplingList, './data/zernCouplingList.pck')
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saveVariable(eef_zern_list, './data/eef_zern_list.pck')
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saveVariable(zernImageResponse, './data/zernImageResponse.pck')
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saveVariable(psd1d_list_zern, './data/psd1d_list_zern.pck')
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saveVariable(zernikeCoefficients, './data/zernikeCoefficients.pcks')
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if PSDAnalysis:
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# For each spatial frequency, perform the analysis
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# Don't touch the RMS value. It will converge to some value
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# For some reason we need to reset the WFE object.
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wfe = None
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wfe = WavefrontError(n,m)
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wfe.data = h_data
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psd1d_og = wfe.calculate_1D_PSD(data=h_data)
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# Result lists:
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x0psd, y0psd = [], []
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x0psd_var, y0psd_var =[], []
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psdCouplingList = []
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eef_psd_list = []
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psdImageResponse = []
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frequencyRange = np.asarray([1,2,3,4,5,6,7,8,9] + np.linspace(10,int(np.sqrt(2)*n/2+1),70).tolist())
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psd1d_list_psd = []
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for lowpassFrequency in frequencyRange:
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i1 = frequencyRange.tolist().index(lowpassFrequency)
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i2 = len(frequencyRange)
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print (' Power Spectral Density analysis: {0}/{1}'.format(i1, i2), end="\r")
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# Filter the spatial frequencies
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wfeFilteredPSD = wfe.set_lowpass_filter(lowpassFrequency)
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# Adding the wavefront error
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M1.add_wavefront_error(wfeFilteredPSD)
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wfe_t_l = M1.phase_delay(x,y,l)
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U_in = A * wfe_t_l
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# Performing the propagation using Fresnel approximation to the focal plane.
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Exyz_psd,xp,yp = fou.fresnel_propagation(U_in, x, y, dz=M1.f, show=False, outputCoordinates=oC)
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eef_psd, _ = fou.encircled_energy_function(np.abs(Exyz_psd)**2, xp, yp, originalPSF=np.abs(Exyz_og)**2)
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# Fitting the centroid location using the coupling integral
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# Set up fitting object
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psfFit_psd = PSFFitter(xp.real,yp.real,np.abs(Exyz_psd)**2)
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custFitted, popt_cust, cust_var, cust_coupling = psfFit_psd.fit_custom_field(Exyz_og, Exyz_psd, return_coupling=True)
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# Returning the found PSD curve
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psd1d_list_psd.append(wfe.calculate_1D_PSD(data=wfeFilteredPSD))
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x0psd.append(popt_cust[0])
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y0psd.append(popt_cust[1])
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x0psd_var.append(cust_var[0])
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y0psd_var.append(cust_var[1])
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psdCouplingList.append(cust_coupling)
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eef_psd_list.append(eef_psd)
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psdImageResponse.append(Exyz_psd)
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saveVariable(x0psd, './data/x0psd.pck')
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saveVariable(y0psd, './data/y0psd.pck')
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saveVariable(x0psd_var, './data/x0psd_var.pck')
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saveVariable(y0psd_var, './data/y0psd_var.pck')
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saveVariable(psdCouplingList, './data/psdCouplingList.pck')
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saveVariable(eef_psd_list, './data/eef_psd_list.pck')
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saveVariable(psdImageResponse, './data/psdImageResponse.pck')
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saveVariable(psd1d_list_psd, './data/psd1d_list_psd.pck')
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# Plot some of the results:
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if PSDAnalysis:
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# Power Spectral Density distribution for PSD analysis
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.loglog(psd1d_og, 'r-') # The original PSD curve
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# The PSD curve for each lowpass filtered PSD WFE
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for i in range(len(frequencyRange)):
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ax.loglog(psd1d_list_psd[i], 'b-', label='data')
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ax.grid(True, alpha=0.3)
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ax.set_xlabel('Spatial frequency')
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ax.set_ylabel('PSD')
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plt.show(block=False)
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if zernikeAnalysis:
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# Power Spectral Density distribution for Zernike Analysis
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fig = plt.figure()
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ax = fig.add_subplot(111)
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# The original PSD curve
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ax.loglog(psd1d_og, 'r-')
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# The PSD curve for each of the zernike fits
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for i in range(nmax):
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ax.loglog(psd1d_list_zern[i], 'b-', label='data')
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ax.grid(True, alpha=0.3)
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ax.set_xlabel('Spatial frequency')
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ax.set_ylabel('PSD')
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plt.show()
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# Plot of the centroid shift
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fig = plt.figure()
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ax = fig.add_subplot(111)
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if zernikeAnalysis:
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ax.plot(x0zern, y0zern, 'b-', label='data')
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ax.plot(x0zern, y0zern, 'bx', label='data')
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ax.plot(x0zern[0], y0zern[0], 'rx')
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if PSDAnalysis:
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ax.plot(x0psd, y0psd, 'r-', label='data')
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ax.plot(x0psd, y0psd, 'rx', label='data')
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ax.plot(x0psd[0], y0psd[0], 'bx')
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ax.grid(True, alpha=0.3)
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ax.set_xlabel('x [mm]')
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ax.set_ylabel('y [mm]')
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plt.show()
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