Reanalysis for thesis (incl. plate scale change)

scripts/SPIE/Thesis/geometric_distortions.py

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+import numpy as np
+import sys
+# sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+# sys.path.insert(0, '/home/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+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.
+'''
+
+def plot_distortions():
+	# Coordinates without WFEs
+	xe = load_variable('./data/GD_obscured_PSF_xcentroid_expected_zern_psd.pkl')
+	ye = load_variable('./data/GD_obscured_PSF_ycentroid_expected_zern_psd.pkl')
+
+	# Coordinates with WFEs
+	xf = load_variable('./data/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl')
+	yf = load_variable('./data/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/UPDATED_GD_full_field_zern_psd.pdf', bbox_inches='tight', transparent=True)
+	plt.savefig('./figures/UPDATED_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')
+	
+	# 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()
+
+	# 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')
+	
+	# Propagate through the field positions without any WFEs	
+	for i in range(len(hxList)):
+		hx = hxList[i]
+		hy = hyList[i]
+		print('hx: {}	- 	hy: {}'.format(hx,hy) )
+
+		# Initializing MICADO paraxial model
+		mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, entrance_pupil='obscured')
+
+		# Propagate through the MICADO system
+		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.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...')
+		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.c	entroid_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/GD_obscured_PSF_xcentroid_expected_zern_psd.pkl', x_expected)
+	save_variable('./data/GD_obscured_PSF_ycentroid_expected_zern_psd.pkl', 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/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl', x_centroid)
+	save_variable('./data/GD_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl', y_centroid)
+
+	plot_distortions()
+	
+
+

scripts/SPIE/Thesis/geometric_distortions_fp_wfe.py

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+import numpy as np
+import sys
+# sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+# sys.path.insert(0, '/home/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/')
+from fourierOptics import *
+from pdb import set_trace
+
+'''
+	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.
+'''
+
+def plot_distortions():
+	# Coordinates without WFEs
+	xe = load_variable('../data/GD_IFP_obscured_PSF_xcentroid_expected_zern.pkl')
+	ye = load_variable('../data/GD_IFP_obscured_PSF_ycentroid_expected_zern.pkl')
+
+	# Coordinates with WFEs
+	xf = load_variable('../data/GD_IFP_obscured_PSF_xcentroid_found_with_wfe_airy_zern.pkl')
+	yf = load_variable('../data/GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_zern.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/UPDATED_GD_focal_plane_zern.pdf', bbox_inches='tight', transparent=True)
+	plt.savefig('../figures/UPDATED_GD_focal_plane_zern.png', bbox_inches='tight', transparent=True)
+	plt.tight_layout()
+	plt.show()
+
+
+if __name__ == '__main__':
+	# plt.rc('text', usetex=True)
+	# plt.rc('font', family='serif')
+	
+	# Setup of simulation
+	l = 1.49e-3
+	mcd = ParaxialMICADO(n=3001, m=3001, dpix=50)
+	# mcd.plot_ray_trace()
+	wfe = WavefrontError(mcd.n, mcd.m)
+
+	# Get x3 and y3 (the coordinates to apply the ADC WFE to):
+	# The focal plane area is about 200x200 mm, so we apply the wavefront error to an area
+	# of 200x200 mm.
+	xfp, yfp = np.meshgrid(np.linspace(-150, 150, mcd.n), np.linspace(-150, 150, mcd.m))	
+	# _, xfp, yfp = mcd.propagate(l=l,surface=1, zoom=False, show=False, 
+	# 							print_progress=False)
+
+	# Generate a wavefront error for the intermediate focal plane.
+	print('\nSetting the wavefront errors...')
+	wfe_fp 		= WavefrontError(mcd.n,mcd.m)
+	wfe_rms = 50e-6
+	#PSD Wavefront error
+	# surface_map_fp = wfe_fp.generate_height_map(wfe_rms, 2, 1)
+	# plot_real_valued_results(surface_map_fp*1e6, xfp, yfp, logscale=False, fig_size=(8,6.4), grid=False, show=True, 
+							# colormap='jet', labeltext='Surface height (nm)')
+	# save_variable('../data/MCD_IFP_wfe_object_PSD_50nm.pkl', wfe_fp)
+	# wfe_fp = load_variable('../data/MCD_IFP_wfe_object_PSD_50nm.pkl')
+	# surface_map_fp = wfe_fp.data
+
+	# Zernike wavefront error
+	# We re0se the ADC_surface classes because it can be applicable to any surface, if used properly
+	ifp_wfe = ADC_surfaces(xfp, yfp, clearAperture=300)
+	ifp_wfe.generate_ADC_wfe(zernRMS=wfe_rms, psdRMS=0.)
+	mask = np.sqrt(ifp_wfe.xz**2 + ifp_wfe.yz**2) > 1
+	
+	# Plot the wfe
+	# surf_ifp = ifp_wfe.latest_surface.copy()
+	# surf_ifp[mask]=np.nan
+	
+	# surface_map_fp = surf_ifp
+	# wfe_fp.data = surf_ifp
+	wfe_fp = load_variable('../data/MCD_IFP_wfe_object_zern_50nm.pkl')
+	surface_map_fp = wfe_fp.data
+	# save_variable('../data/MCD_IFP_wfe_object_zern_50nm.pkl', wfe_fp)
+	# figp1, axp1 = plot_real_valued_results(surface_map_fp*1e6, xfp, yfp,
+	#  						 				colormap='jet', labeltext='Surface height (nm)', fig_size=None, show=True)
+	plt.show()
+	
+
+	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, hyList = np.mgrid[-.1/11:.1/11:11j,-.1/11:.1/11:11j]-(1/300) # Zoomed in to central area
+	hxList = hxList.flatten()
+	hyList = hyList.flatten()
+
+	# List of performed simulations
+	# Defined by PSD
+	## GD_IFP : -1:1
+	## GD_IFP_zoom  : -1/11 :1/11
+	## GD_IFP_zoom2 : -.5/11:.5/11
+	## GD_IFP_zoom3 : -.1/11:.1/11
+
+	# Defined by zernikes
+	## GD_IFP_obscured_PSF_xcentroid_expected_zern : 50 nm rms 19 zernike modes : -1 : 1
+	## To do; check PSF shape at center field with zern wfe
+	## To do; check interpolated WFE at any zoomed in field point.
+
+	# Propagate through the field positions without any WFEs	
+	for i in range(len(hxList)):
+		hx = hxList[i]
+		hy = hyList[i]
+		print('hx: {}	- 	hy: {}'.format(hx,hy) )
+
+		# Initializing MICADO paraxial model
+		mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, entrance_pupil='obscured')
+
+		# Propagate through the MICADO system
+		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_FP.add_wavefront_error(surface_map_fp, xfp, yfp, wfe=wfe_fp)
+
+
+		# Propagate through the MICADO system
+		print('Propagating with Wavefront Errors applied...')
+		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/GD_IFP_obscured_PSF_xcentroid_expected_zern.pkl', x_expected)
+	save_variable('../data/GD_IFP_obscured_PSF_ycentroid_expected_zern.pkl', 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/GD_IFP_obscured_PSF_xcentroid_found_with_wfe_airy_zern.pkl', x_centroid)
+	save_variable('../data/GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_zern.pkl', y_centroid)
+
+	plot_distortions()
+	
+
+

scripts/SPIE/Thesis/geometric_distortions_incl_rotations.py

@@ -0,0 +1,174 @@
+import numpy as np
+import sys
+# sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+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()
+	
+
+

scripts/SPIE/Thesis/plotting_detectors.py

@@ -0,0 +1,471 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import matplotlib.ticker as plticker
+from matplotlib.gridspec import GridSpec
+import pandas as pd
+import sys
+sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/')
+from fourierOptics import *
+from pdb import set_trace
+
+'''
+	Date: 			2022-06-20
+	Author: 		Joost van den Born
+	Description: 	Plotting the geometric distortions, that were calculated
+					in an earlier simulation.
+					Here we also include the detector layout in the plot.
+'''
+
+def add_detector_layout(figureObj, lw=0.8, alpha=0.5, show_label=True):
+	'''
+	Adds the detector layout to an existing figure+axes object.
+	The detector outlines are based on the MICADO Hawaii 4RG mosaic
+	and the dimensions are in mm.
+	
+	Parameters
+	----------
+	figureObj : tuple
+	    Tuple containing the figure and axes objects (fig,ax)
+	lw : float, optional
+	    Linewidth of the drawn detectors. Default: 0.8
+	alpha : float, optional
+	    Alpha value to draw the rectangles at
+	show_label : bool, optional
+	    If True, shows the detector ID number in the rectangle
+	
+	Returns
+	-------
+	TYPE
+	    Updated fig, axes objects
+	'''
+	fig, ax = figureObj
+
+	# Detector layout
+	det_hdr = ['id','x_cen', 'y_cen', 'x_size', 'y_size', 'pixel_size', 'angle', 'gain']
+	det_props = np.array([[1, -63.94,  63.94, 4096, 4096, 0.015,   0.0, 1.0],
+						  [2,   0.00,  63.94, 4096, 4096, 0.015, 270.0, 1.0],
+						  [3,  63.94,  63.94, 4096, 4096, 0.015, 180.0, 1.0],
+						  [6, -63.94,   0.00, 4096, 4096, 0.015,   0.0, 1.0],
+						  [5,   0.00,   0.00, 4096, 4096, 0.015, 180.0, 1.0],
+						  [4,  67.94,   0.00, 4096, 4096, 0.015, 180.0, 1.0],
+						  [7, -63.94, -63.94, 4096, 4096, 0.015,   0.0, 1.0],
+						  [8,   0.00, -63.94, 4096, 4096, 0.015,  90.0, 1.0],
+						  [9,  63.94, -63.94, 4096, 4096, 0.015, 180.0, 1.0]])
+	detectors = pd.DataFrame(det_props, columns=det_hdr)
+	detectors["id"] 	= detectors["id"].astype(int)
+	detectors["x_size"] = detectors["x_size"].astype(int)
+	detectors["y_size"] = detectors["y_size"].astype(int)
+
+	for ind, det in detectors.iterrows():
+		x_mm = det['x_cen'] - det['x_size']/2*det['pixel_size']
+		y_mm = det['y_cen'] - det['y_size']/2*det['pixel_size']
+		width = det['x_size']*det['pixel_size']
+		height = det['y_size']*det['pixel_size']
+		x_cen, y_cen = det['x_cen'], det['y_cen']
+		
+		rect = patches.Rectangle((x_mm, y_mm), width, height, 
+				linewidth=lw, edgecolor='k', facecolor='none', alpha=alpha, zorder=1)
+		ax.add_patch(rect)
+		if show_label:
+			ax.text(x_cen, y_cen, det['id'])
+	return fig, ax
+
+def convert_detector_size(x, y, old=True):
+	old_ps 		= 100.13151872258219
+	new_ps 		= ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3).platescale * 1e3
+	conv_fact 	= old_ps / new_ps
+	
+	if old:
+		x *= conv_fact
+		y *= conv_fact
+	elif not old:
+		x /= conv_fact
+		y /= conv_fact
+	
+	return x, y
+	
+
+def plot_distortions(xe, ye, xp, yp, lims=False, add_detectors=True, 
+						save=False, figtitle='geom_distortions',
+						show=True):
+	
+	mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3)
+	n = xe.shape[0]
+	X = xe.flatten()
+	Y = ye.flatten()
+	dX = xp - xe
+	dY = yp - ye
+	dx = dX.flatten()
+	dy = dY.flatten()
+
+	# Magnitude of the distortions
+	R = np.sqrt(dx**2 + dy**2) * mcd.platescale * 1e6
+	print('RMS of the PSF shifts: {:.4f} μas.'.format(np.sqrt(np.mean((R)**2))))
+
+	# Dummy figure, used for the colorbar colors in the quiver plot
+	im = plt.imshow(np.reshape(R, (n,n)))
+	plt.close()
+
+	# Initiate the figure
+	fig = plt.figure(figsize=(6.4,4.8))
+	ax = fig.add_subplot(aspect='equal')
+	
+	# Add the detector layout if so desired.
+	if add_detectors:
+		add_detector_layout((fig,ax), lw=.8, alpha=0.5, show_label=False)
+	
+	# Plot the vector plot
+	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=.8, units='dots', zorder=300)
+	ax.quiver(X,Y,dx,dy,R, alpha=1, units='dots', zorder=300)
+	ax.set_xlabel('x (mm)', fontsize=16)
+	ax.set_ylabel('y (mm)', fontsize=16)
+	ax.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
+										top=True, right=True)
+	cb = fig.colorbar(im, ax=ax)
+	cb.set_label(label='PSF centroid shift (μas)', size=14)
+	cb.ax.tick_params(labelsize=14)
+	if not lims:
+		ax.set_xlim([-125, 115])
+		ax.set_ylim([-115, 115]) 
+	elif lims:
+		ax.set_xlim(lims[0])
+		ax.set_ylim(lims[1]) 
+	plt.tight_layout()
+	if save:
+		plt.savefig('{}.pdf'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+		plt.savefig('{}.png'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+	if show:
+		plt.show()
+	return fig, ax, R
+
+def get_differential_distortion_vectors(xe, ye, xp, yp, xe_ref, ye_ref, xp_ref, yp_ref):
+	# Reference distortions:
+	# Shape of the arrays
+	n = xe_ref.shape[0]
+
+	# Reshaping of the arrays so that they can be used for the quiver plot.
+	X_ref  = xe_ref.flatten()
+	Y_ref  = ye_ref.flatten()
+	dX_ref = xp_ref - xe_ref
+	dY_ref = yp_ref - ye_ref
+
+	dx_ref = dX_ref.flatten()
+	dy_ref = dY_ref.flatten()
+
+	# Distortions to compare
+	# Reshaping of the arrays so that they can be used for the quiver plot.
+	X  = xe.flatten()
+	Y  = ye.flatten()
+	dX = xp - xe
+	dY = yp - ye
+
+	dx = dX.flatten()
+	dy = dY.flatten()
+
+	ddx = dX - dX_ref
+	ddy = dY - dY_ref
+
+	return ddx, ddy
+
+
+def plot_differential_distortions(xe, ye, xp, yp, xe_ref, ye_ref, xp_ref, yp_ref,
+				lims=False, add_detectors=True,	save=False, 
+				figtitle='diff_geom_distortions', show=True):
+	# Reference distortions:
+	# Shape of the arrays
+	n = xe_ref.shape[0]
+
+	# Reshaping of the arrays so that they can be used for the quiver plot.
+	X_ref  = xe_ref.flatten()
+	Y_ref  = ye_ref.flatten()
+	dX_ref = xp_ref - xe_ref
+	dY_ref = yp_ref - ye_ref
+
+	dx_ref = dX_ref.flatten()
+	dy_ref = dY_ref.flatten()
+
+	# Distortions to compare
+	# Reshaping of the arrays so that they can be used for the quiver plot.
+	X  = xe.flatten()
+	Y  = ye.flatten()
+	dX = xp - xe
+	dY = yp - ye
+
+	dx = dX.flatten()
+	dy = dY.flatten()
+
+	ddx = dX - dX_ref
+	ddy = dY - dY_ref
+
+	# Two values are comparatively (extremely large), they are set to 0
+	# to be able to see the other values.
+	mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3)
+	R = (np.sqrt(ddx**2 + ddy**2) * mcd.platescale * 1e6) # Magnitude of the shift in micro arcseconds
+	print('RMS of the PSF shifts compared to the reference position: {:.4f} μas.'.format(np.sqrt(np.mean((R)**2))))
+
+
+	# Dummy figure
+	im = plt.imshow(np.reshape(R, (n,n)))
+	plt.close()
+
+	# Actual plotting of the figure
+	fig = plt.figure(figsize=(6.4, 4.8))
+	ax = fig.add_subplot()
+
+	# Add the detector layout if so desired.
+	if add_detectors:
+		add_detector_layout((fig,ax), lw=.8, alpha=0.5, show_label=False)
+	
+	# Plot the vector plot
+	for i in range(len(xe_ref)):
+	    ax.plot(xe_ref[:,i], ye_ref[:,i], 'k-', alpha=0.2, linewidth=.5)
+	    ax.plot(xe_ref[i,:], ye_ref[i,:], 'k-', alpha=0.2, linewidth=.5)
+	ax.quiver(X,Y,ddx,ddy, angles='xy', edgecolor='k', 
+						facecolor='None', linewidth=.8, units='dots', zorder=300)
+	ax.quiver(X,Y,ddx,ddy,R, alpha=1, units='dots', zorder=300)
+	ax.set_xlabel('x (mm)', fontsize=16)
+	ax.set_ylabel('y (mm)', fontsize=16)
+	ax.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
+										top=True, right=True)
+	cb = fig.colorbar(im, ax=ax)
+	cb.set_label(label='PSF centroid shift (μas)', size=14)
+	cb.ax.tick_params(labelsize=14)
+	if not lims:
+		ax.set_xlim([-125, 115])
+		ax.set_ylim([-115, 115]) 
+	elif lims:
+		ax.set_xlim(lims[0])
+		ax.set_ylim(lims[1])  
+	plt.tight_layout()
+	if save:
+		plt.savefig('{}.pdf'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+		plt.savefig('{}.png'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+	if show:
+		plt.show()
+	return fig, ax, R
+
+def get_data():
+	# Coordinates without WFEs
+	xe = load_variable('../data/GD_obscured_PSF_xcentroid_expected_zern_psd.pkl')
+	ye = load_variable('../data/GD_obscured_PSF_ycentroid_expected_zern_psd.pkl')
+
+	# Coordinates with WFEs
+	xf = load_variable('../data/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl')
+	yf = load_variable('../data/GD_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl')
+	return xe, ye, xf, yf
+
+
+def normalize_relative_angle(theta, degrees=True):
+	if type(theta) in [list, np.ndarray]:
+		values = np.array([normalize_relative_angle(i) for i in theta])
+		return values
+	circle = 360 if degrees else 2*np.pi
+	theta_mod = theta % circle
+	# theta_mod_half_circle = theta % (circle/2)
+	if theta_mod > (circle / 2):
+		rel_angle = theta_mod - (circle)
+	else:
+		rel_angle = theta_mod
+	return rel_angle
+
+if __name__ == '__main__':
+	ps = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3).platescale
+	print(ps)
+
+	plt.rc('font', family='serif')
+	plt.rc('axes', labelsize='large')
+	
+	# convert_detector_size()
+	# Plotting the original distortions
+	xe_og = load_variable('../data/GD full field (Airy small offset)/GD_PSF_xcentroid_expected.pkl')
+	ye_og = load_variable('../data/GD full field (Airy small offset)/GD_PSF_ycentroid_expected.pkl')
+	xp_og = load_variable('../data/GD full field (Airy small offset)/GD_PSF_xcentroid_found_with_wfe_airy.pkl')
+	yp_og = load_variable('../data/GD full field (Airy small offset)/GD_PSF_ycentroid_found_with_wfe_airy.pkl')
+	xe_og, ye_og = convert_detector_size(xe_og, ye_og)
+	xp_og, yp_og = convert_detector_size(xp_og, yp_og)
+	figtitle = '../figures adapted for thesis/GD_full_field_psd'
+	plot_distortions(xe_og, ye_og, xp_og, yp_og, show=False, save=False, figtitle=figtitle,
+					lims=[[-120,130],[-120,120]])
+
+	# Plotting the original distortions
+	xe_og = load_variable('../data/GD_obscured_PSF_xcentroid_expected_zern_psd.pkl')
+	ye_og = load_variable('../data/GD_obscured_PSF_ycentroid_expected_zern_psd.pkl')
+	xp_og = load_variable('../data/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl')
+	yp_og = load_variable('../data/GD_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl')
+	figtitle = '../figures adapted for thesis/GD_full_field_zern_psd'
+	plot_distortions(xe_og, ye_og, xp_og, yp_og, show=False, save=False, figtitle=figtitle)
+
+
+	# Plotting the distortions when the ADC can rotate.
+	# First for a very small rotation, this will serve as the reference distortions
+	# that are calibrated for
+	xe = load_variable('../data/ADC rotation/GD_obscured_PSF_xcentroid_expected_zern_psd_rot0.001.pkl')
+	ye = load_variable('../data/ADC rotation/GD_obscured_PSF_ycentroid_expected_zern_psd_rot0.001.pkl')
+	xp = load_variable('../data/ADC rotation/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd_rot0.001.pkl')
+	yp = load_variable('../data/ADC rotation/GD_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd_rot0.001.pkl')
+	figtitle = '../figures adapted for thesis/ADC rotation/GD_full_field_zern_psd_0'
+	plot_distortions(xe, ye, xp, yp, show=False, save=False, figtitle=figtitle, lims=None)
+	
+	# And comparing it to the original (i.e. no rotation)
+	figtitle = '../figures adapted for thesis/ADC rotation/DGD_full_field_zern_psd_0'
+	plot_differential_distortions(xe, ye, xp, yp, xe_og, ye_og, xp_og, yp_og, 
+				show=False, save=False, figtitle=figtitle, lims=None)
+
+	# Next we plot all the simulated rotation values
+	# rotangles = [0.001, 0.125, 0.25, 0.5, 0.75, 1, 2, 5, 10, 30, 45, 90, 180]
+	rotangles = [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]
+	rms_of_distortions, max_of_distortions = [], []
+	vector_magnitude = np.zeros((121,len(rotangles)))
+	for i,rx in enumerate(rotangles):
+		# Full field
+		xe2 = load_variable('../data/ADC rotation/GD_obscured_PSF_xcentroid_expected_zern_psd_rot{}.pkl'.format(rx))
+		ye2 = load_variable('../data/ADC rotation/GD_obscured_PSF_ycentroid_expected_zern_psd_rot{}.pkl'.format(rx))
+		xp2 = load_variable('../data/ADC rotation/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd_rot{}.pkl'.format(rx))
+		yp2 = load_variable('../data/ADC rotation/GD_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd_rot{}.pkl'.format(rx))
+		
+		figtitle = '../figures adapted for thesis/ADC rotation/DGD_full_field_zern_psd_{}'.format(rx)	
+		_,_, R = plot_differential_distortions(xe2, ye2, xp2, yp2, xe, ye, xp, yp, show=False, save=False,
+			figtitle=figtitle, lims=[[-120,120],[-120,120]])
+
+		rms_of_distortions.append(np.sqrt(np.mean((R)**2)))
+		max_of_distortions.append(np.max(R))
+		Rflat = R.flatten()
+		for j in range(len(Rflat)):
+			vector_magnitude[j,i] = Rflat[j]
+
+
+	gs = GridSpec(1, 2, width_ratios=[3,2])
+	fig = plt.figure(figsize=(6.4*2,4.8))
+	ax = fig.add_subplot(gs[0,0])
+	ax2 = fig.add_subplot(gs[0,1], projection='polar')
+	
+	relangle = normalize_relative_angle(2*np.asarray(rotangles))
+	for i in range(len(Rflat)):
+		ax.plot(relangle, vector_magnitude[i], 'b-', linewidth=0.5, alpha=0.1)
+	ax.plot(relangle, rms_of_distortions, 'b-', label='RMS', zorder=30)
+	ax.plot(relangle, rms_of_distortions, 'bx', zorder=30)
+	ax.plot(relangle, max_of_distortions, 'r-', label='Maximum', zorder=30)
+	ax.plot(relangle, max_of_distortions, 'rx', zorder=30)
+
+	ax.axhline(50, color='k', linestyle='-', linewidth=1, label='50 μas', zorder=4)
+	ax.axhline(10, color='k', linestyle='-.', linewidth=1, label='10 μas', zorder=4)
+	ax.grid(True, alpha=0.3)
+	ax.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
+										top=True, right=True)
+	ax.set_xlabel('Relative prism rotation (deg)', fontsize=16)
+	ax.set_ylabel('Differential distortion (μas)', fontsize=16)
+	ax.set_xlim([0,20])
+	ax.set_ylim([-3,100])
+	# plt.tight_layout()
+	# save = False
+	# if save:
+	# 	figtitle = '../figures adapted for thesis/rotation_relation_relangles'
+	# 	plt.savefig('{}.pdf'.format(figtitle), 
+	# 		bbox_inches='tight', 
+	# 		transparent=False, 
+	# 		facecolor=(1, 1, 0, 0.0))
+	# 	plt.savefig('{}.png'.format(figtitle), 
+	# 		bbox_inches='tight', 
+	# 		transparent=False, 
+	# 		facecolor=(1, 1, 0, 0.0))
+
+	# Trying to plot the change in the distortion vector as a function of ADC rotation
+	
+	ax2.grid(True, alpha=0.3)
+	vxs, vys = [], []
+	for i,rx in enumerate(rotangles):
+		xe2 = load_variable('../data/ADC rotation/GD_obscured_PSF_xcentroid_expected_zern_psd_rot{}.pkl'.format(rx))
+		ye2 = load_variable('../data/ADC rotation/GD_obscured_PSF_ycentroid_expected_zern_psd_rot{}.pkl'.format(rx))
+		xp2 = load_variable('../data/ADC rotation/GD_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd_rot{}.pkl'.format(rx))
+		yp2 = load_variable('../data/ADC rotation/GD_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd_rot{}.pkl'.format(rx))
+
+		# Find the distortion vector
+		ddx, ddy = get_differential_distortion_vectors(xe2, ye2, xp2, yp2, xe, ye, xp, yp)
+		cx = xe2.flatten()
+		cy = ye2.flatten()
+		# print(cx[ci], cy[ci])
+		vx = ddx.flatten() * ps * 1e6
+		vy = ddy.flatten() * ps * 1e6
+		
+		vxs.append([vx])
+		vys.append([vy])
+
+	phi, mag = [],[]
+	for i in range(121):
+		# set_trace()
+		# Array slicing: rotation, 0, grid index
+		vx_evo = np.asarray(vxs)[:,0,i]
+		vy_evo = np.asarray(vys)[:,0,i]
+
+		phi_evo = np.arctan2(vy_evo, vx_evo)
+		mag_evo = np.sqrt(vx_evo**2 + vy_evo**2)
+
+		phi.append(phi_evo[-1])
+		mag.append(mag_evo[-1])
+
+		# Plot the vectors on the central detectors separately
+		if i in [48, 49, 50, 59, 60, 61, 70, 71, 72]:
+			if i == 48:
+				ax.plot(relangle, vector_magnitude[i], 'g-', linewidth=1, alpha=1, zorder=20, label='Central detector')
+				ax2.plot(phi_evo, mag_evo, 'g-', linewidth=2, zorder=300)
+			else:
+				ax.plot(relangle, vector_magnitude[i], 'g-', linewidth=1, alpha=1, zorder=20)
+				ax2.plot(phi_evo, mag_evo, 'g-', linewidth=2, zorder=300)
+		else:
+			ax2.plot(phi_evo, mag_evo, 'b-', linewidth=1, zorder=1)
+	
+	# Making a legend on the first sub figure in the right order
+	handles, labels = ax.get_legend_handles_labels()
+	order = [0,1,4,2,3]
+
+	ax.legend([handles[idx] for idx in order],[labels[idx] for idx in order], fontsize=14)
+
+	# Slicing the polar plot to the relevant part.
+	phi = np.asarray(phi)
+	ax2.set_thetamin(np.degrees(np.min(phi%(2*np.pi)))-10)
+	ax2.set_thetamax(np.degrees(np.max(phi%(2*np.pi)))+10)
+
+	label_position=ax2.get_rlabel_position()
+	ax2.text(np.radians(label_position-10),ax2.get_rmax()/2.,'Differential\ndistortion (μas)',
+	        rotation=np.degrees(np.min(phi%(2*np.pi)))-10,ha='center',va='center', fontsize=14)
+
+	loc = plticker.MultipleLocator(base=np.radians(10)) # this locator puts ticks at regular intervals
+	ax2.xaxis.set_major_locator(loc)
+	ticks = [40, '', 60,'', 80, '', 100, '', 120, '', 140, '', 160, '', 180, '', 200, '', 220]
+	ax2.xaxis.set_ticklabels(ticks)
+	ax2.tick_params(axis='both', direction='in', labelsize=12, zorder=-1,
+										top=True, right=False, left=True)
+	plt.tight_layout()
+	save = True
+	if save:
+		figtitle = '../figures adapted for thesis/rotation_relation_with_polar_plot'
+		plt.savefig('{}.pdf'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+		plt.savefig('{}.png'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+	plt.show()
+
+
+

scripts/SPIE/Thesis/plotting_geometric_distortions_IFP.py

@@ -0,0 +1,280 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import matplotlib.ticker as plticker
+from matplotlib.gridspec import GridSpec
+import pandas as pd
+import sys
+sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/')
+from fourierOptics import *
+from pdb import set_trace
+from matplotlib.colors import Normalize, LogNorm
+
+'''
+	Date: 			2022-06-20
+	Author: 		Joost van den Born
+	Description: 	Plotting the geometric distortions, that were calculated
+					in an earlier simulation.
+					Here we also include the detector layout in the plot.
+'''
+
+def add_detector_layout(figureObj, lw=0.8, alpha=0.5, show_label=True):
+	'''
+	Adds the detector layout to an existing figure+axes object.
+	The detector outlines are based on the MICADO Hawaii 4RG mosaic
+	and the dimensions are in mm.
+	
+	Parameters
+	----------
+	figureObj : tuple
+	    Tuple containing the figure and axes objects (fig,ax)
+	lw : float, optional
+	    Linewidth of the drawn detectors. Default: 0.8
+	alpha : float, optional
+	    Alpha value to draw the rectangles at
+	show_label : bool, optional
+	    If True, shows the detector ID number in the rectangle
+	
+	Returns
+	-------
+	TYPE
+	    Updated fig, axes objects
+	'''
+	fig, ax = figureObj
+
+	# Detector layout
+	det_hdr = ['id','x_cen', 'y_cen', 'x_size', 'y_size', 'pixel_size', 'angle', 'gain']
+	det_props = np.array([[1, -63.94,  63.94, 4096, 4096, 0.015,   0.0, 1.0],
+						  [2,   0.00,  63.94, 4096, 4096, 0.015, 270.0, 1.0],
+						  [3,  63.94,  63.94, 4096, 4096, 0.015, 180.0, 1.0],
+						  [6, -63.94,   0.00, 4096, 4096, 0.015,   0.0, 1.0],
+						  [5,   0.00,   0.00, 4096, 4096, 0.015, 180.0, 1.0],
+						  [4,  67.94,   0.00, 4096, 4096, 0.015, 180.0, 1.0],
+						  [7, -63.94, -63.94, 4096, 4096, 0.015,   0.0, 1.0],
+						  [8,   0.00, -63.94, 4096, 4096, 0.015,  90.0, 1.0],
+						  [9,  63.94, -63.94, 4096, 4096, 0.015, 180.0, 1.0]])
+	detectors = pd.DataFrame(det_props, columns=det_hdr)
+	detectors["id"] 	= detectors["id"].astype(int)
+	detectors["x_size"] = detectors["x_size"].astype(int)
+	detectors["y_size"] = detectors["y_size"].astype(int)
+
+	for ind, det in detectors.iterrows():
+		x_mm = det['x_cen'] - det['x_size']/2*det['pixel_size']
+		y_mm = det['y_cen'] - det['y_size']/2*det['pixel_size']
+		width = det['x_size']*det['pixel_size']
+		height = det['y_size']*det['pixel_size']
+		x_cen, y_cen = det['x_cen'], det['y_cen']
+		
+		rect = patches.Rectangle((x_mm, y_mm), width, height, 
+				linewidth=lw, edgecolor='k', facecolor='none', alpha=alpha, zorder=1)
+		ax.add_patch(rect)
+		if show_label:
+			ax.text(x_cen, y_cen, det['id'])
+	return fig, ax
+
+def convert_detector_size(x, y, old=True):
+	old_ps 		= 100.13151872258219
+	new_ps 		= ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3).platescale * 1e3
+	conv_fact 	= old_ps / new_ps
+	
+	if old:
+		x *= conv_fact
+		y *= conv_fact
+	elif not old:
+		x /= conv_fact
+		y /= conv_fact
+	
+	return x, y
+	
+
+def plot_distortions(xe, ye, xp, yp, lims=False, add_detectors=True, 
+						save=False, figtitle='geom_distortions',
+						show=True, cmap='magma'):
+	vmin, vmax = 0.1, 100
+	mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3)
+	n = xe.shape[0]
+	X = xe.flatten()
+	Y = ye.flatten()
+	dX = xp - xe
+	dY = yp - ye
+	dx = dX.flatten()
+	dy = dY.flatten()
+
+	# Magnitude of the distortions
+	R = np.sqrt(dx**2 + dy**2) * mcd.platescale * 1e6
+	print('RMS of the PSF shifts: {:.4f} μas.'.format(np.sqrt(np.mean((R)**2))))
+
+	# Dummy figure, used for the colorbar colors in the quiver plot
+	# im = plt.imshow(np.reshape(R, (n,n)))
+	im = plt.imshow(np.reshape(R[:121], (11,11)))
+	im = plt.scatter(X, Y, s=R, c=np.log10(R), cmap=cmap, vmin=np.log10(vmin), vmax=np.log10(vmax))
+	# im = plt.scatter(X, Y, s=R, c=R, cmap='jet')
+	plt.close()
+
+	# Initiate the figure
+	# fig = plt.figure(figsize=(9.6,4.8))
+	# ax = fig.add_subplot(121, aspect='equal')
+	# ax2 = fig.add_subplot(122, aspect='equal')
+	fig, axes = plt.subplots(ncols=2, 
+						   nrows=1, 
+						   gridspec_kw={'width_ratios': [1,1]},
+						   figsize=(9.6,4.8))
+	ax, ax2 = axes
+	ax.set_aspect('equal')
+	ax2.set_aspect('equal')
+	# Add the detector layout if so desired.
+	if add_detectors:
+		add_detector_layout((fig,ax), lw=.8, alpha=1, show_label=False)
+	
+	# Plot the vector plot
+	xe = np.asarray(xe[:121]).reshape((11,11))
+	ye = np.asarray(ye[:121]).reshape((11,11))
+	# 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,R, alpha=1, units='dots', zorder=300)
+	
+	for k in range(4):
+		print()
+		ind_end = (k+1) * 121 
+		ind_start = k * 121
+		dpix = np.diff(Y)[ind_start:ind_end][1]
+		print(k, ind_start, ind_end, dpix)
+
+		xplot = np.asarray(X[ind_start:ind_end]).reshape((11,11)) 
+		yplot = np.asarray(Y[ind_start:ind_end]).reshape((11,11))
+		yplot, xplot = np.mgrid[yplot.min()+dpix/2:yplot.max()-dpix/2:12j,
+								xplot.min()+dpix/2:xplot.max()-dpix/2:12j]
+
+
+		Rtemp  = np.asarray(R[ind_start:ind_end]).reshape((11,11))
+		ax.pcolor(-1*yplot.T, -1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
+		# ax2.pcolor(-1*xplot.T, -1*yplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=1, vmax=100))
+		shiftx = X[ind_start:ind_end][61]#np.abs(dpix)/2
+		shifty = Y[ind_start:ind_end][60]
+		print(Y[ind_start:ind_end])
+		print('shiftx:', shiftx)
+		print('shifty:', shifty)
+
+		# ax2.pcolor(k*shifty-1*yplot.T, k*shiftx-1*xplot.T, Rtemp.T*10, cmap=cmap, norm=LogNorm(vmin=1, vmax=100))
+		if k == 1: ax2.pcolor(shifty-1*yplot.T, shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
+		elif k == 2: ax2.pcolor(2*shifty-1*yplot.T, 2*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
+		elif k == 3: ax2.pcolor(2*shifty-1*yplot.T, 2*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
+		if k == 0:
+			ax.quiver(X[ind_start:ind_end],Y[ind_start:ind_end],
+					 dx[ind_start:ind_end],dy[ind_start:ind_end], 
+					 angles='xy', edgecolor='k', facecolor='k', 
+					 linewidth=.8, zorder=300, alpha=1, scale=1.4e-5, 
+					 units='dots')
+		elif k in [2,3]:
+			ax2.quiver(X[ind_start:ind_end],Y[ind_start:ind_end],
+					 dx[ind_start:ind_end],dy[ind_start:ind_end], 
+					 angles='xy', edgecolor='k', facecolor='k', 
+					 linewidth=.8, zorder=300, alpha=1, scale=0.2e-5, 
+					 units='dots')
+		# ax.pcolor(xplot * (1 - dpix/105), yplot * (1 - dpix/105), Rtemp, cmap='jet', norm=LogNorm(vmin=1, vmax=100))
+				   
+	ax.set_xlabel('x (mm)', fontsize=16)
+	ax.set_ylabel('y (mm)', fontsize=16)
+	ax.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
+										top=True, right=True)
+	ax2.set_xlabel('x (mm)', fontsize=16)
+	ax2.set_ylabel('y (mm)', fontsize=16)
+	ax2.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
+										top=True, right=True)
+	
+	
+	cb = fig.colorbar(im, ax=axes, fraction=0.025, pad=0.04)
+	cb.set_label(label='PSF centroid shift (μas)', size=14)
+	cb.ax.tick_params(labelsize=14)
+	
+	lbls = 10**np.asarray(cb.ax.get_yticks())
+	print(lbls)
+	newlbls = ['{:.0f}'.format(x) for x in 10**np.asarray(cb.ax.get_yticks())]
+	newlbls[1] = '{:.1f}'.format(lbls[1])
+	newlbls[0] = '{:.1f}'.format(lbls[0])
+	cb.ax.set_yticklabels(newlbls)
+	print(cb.ax.get_yticks())
+	if not lims:
+		ax.set_xlim([-125, 115])
+		ax.set_ylim([-115, 115]) 
+	elif lims:
+		ax.set_xlim(lims[0])
+		ax.set_ylim(lims[1])
+	ax2.set_xlim([-2.01,2.75])
+	ax2.set_ylim([-2.01,2.75])
+	ax.indicate_inset_zoom(ax2, edgecolor="black") 
+	fig.subplots_adjust(top=1.0,
+						bottom=0.0,
+						left=0.1,
+						right=0.8,
+						hspace=0.135,
+						wspace=0.3)
+	# plt.tight_layout()
+	if save:
+		plt.savefig('{}.pdf'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+		plt.savefig('{}.png'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+	if show:
+		plt.show()
+	return fig, ax, R
+
+
+if __name__ == '__main__':
+	ps = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3).platescale
+	print(ps)
+
+	plt.rc('font', family='serif')
+	plt.rc('axes', labelsize='large')
+	
+	# convert_detector_size()
+	# Plotting the original distortions
+	data_dir = '../data/GD Focal Plane WFEs/'
+	xe_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_xcentroid_expected_zern_psd.pkl')
+	ye_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_ycentroid_expected_zern_psd.pkl')
+	xp_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl')
+	yp_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl')
+
+	# Zoom 1: 
+	xe_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_xcentroid_expected_zern_psd.pkl')
+	ye_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_ycentroid_expected_zern_psd.pkl')
+	xp_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl')
+	yp_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl')
+
+	# Zoom 2
+	xe_og2 = load_variable(data_dir + 'GD_IFP_zoom2_obscured_PSF_xcentroid_expected_zern_psd.pkl')
+	ye_og2 = load_variable(data_dir + 'GD_IFP_zoom2_obscured_PSF_ycentroid_expected_zern_psd.pkl')
+	xp_og2 = load_variable(data_dir + 'GD_IFP_zoom2_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl')
+	yp_og2 = load_variable(data_dir + 'GD_IFP_zoom2_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl')
+
+	# Zoom 3
+	xe_og3 = load_variable(data_dir + 'GD_IFP_zoom3_obscured_PSF_xcentroid_expected_zern_psd.pkl')
+	ye_og3 = load_variable(data_dir + 'GD_IFP_zoom3_obscured_PSF_ycentroid_expected_zern_psd.pkl')
+	xp_og3 = load_variable(data_dir + 'GD_IFP_zoom3_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl')
+	yp_og3 = load_variable(data_dir + 'GD_IFP_zoom3_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl')
+
+	xe = np.append(xe_og, [xe_og1, xe_og2, xe_og3])
+	ye = np.append(ye_og, [ye_og1, ye_og2, ye_og3])
+	xp = np.append(xp_og, [xp_og1, xp_og2, xp_og3])
+	yp = np.append(yp_og, [yp_og1, yp_og2, yp_og3])
+
+	# xe = np.append(xe_og2, xe_og3)
+	# ye = np.append(ye_og2, ye_og3)
+	# xp = np.append(xp_og2, xp_og3)
+	# yp = np.append(yp_og2, yp_og3)
+
+
+	figtitle = '../figures adapted for thesis/Focal Plane/GD_IFP_zern'
+	plot_distortions(xe, ye, xp, yp, show=True, save=True, figtitle=figtitle,
+					lims=[[-120,120],[-120,120]])
+
+	
+
+
+

scripts/SPIE/Thesis/plotting_geometric_distortions_IFP_zern.py

@@ -0,0 +1,332 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import matplotlib.ticker as plticker
+from matplotlib.gridspec import GridSpec
+import pandas as pd
+import sys
+sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/')
+from fourierOptics import *
+from pdb import set_trace
+from matplotlib.colors import Normalize, LogNorm
+
+'''
+	Date: 			2022-06-20
+	Author: 		Joost van den Born
+	Description: 	Plotting the geometric distortions, that were calculated
+					in an earlier simulation.
+					Here we also include the detector layout in the plot.
+'''
+
+def add_detector_layout(figureObj, lw=0.8, alpha=0.5, show_label=True):
+	'''
+	Adds the detector layout to an existing figure+axes object.
+	The detector outlines are based on the MICADO Hawaii 4RG mosaic
+	and the dimensions are in mm.
+	
+	Parameters
+	----------
+	figureObj : tuple
+	    Tuple containing the figure and axes objects (fig,ax)
+	lw : float, optional
+	    Linewidth of the drawn detectors. Default: 0.8
+	alpha : float, optional
+	    Alpha value to draw the rectangles at
+	show_label : bool, optional
+	    If True, shows the detector ID number in the rectangle
+	
+	Returns
+	-------
+	TYPE
+	    Updated fig, axes objects
+	'''
+	fig, ax = figureObj
+
+	# Detector layout
+	det_hdr = ['id','x_cen', 'y_cen', 'x_size', 'y_size', 'pixel_size', 'angle', 'gain']
+	det_props = np.array([[1, -63.94,  63.94, 4096, 4096, 0.015,   0.0, 1.0],
+						  [2,   0.00,  63.94, 4096, 4096, 0.015, 270.0, 1.0],
+						  [3,  63.94,  63.94, 4096, 4096, 0.015, 180.0, 1.0],
+						  [6, -63.94,   0.00, 4096, 4096, 0.015,   0.0, 1.0],
+						  [5,   0.00,   0.00, 4096, 4096, 0.015, 180.0, 1.0],
+						  [4,  67.94,   0.00, 4096, 4096, 0.015, 180.0, 1.0],
+						  [7, -63.94, -63.94, 4096, 4096, 0.015,   0.0, 1.0],
+						  [8,   0.00, -63.94, 4096, 4096, 0.015,  90.0, 1.0],
+						  [9,  63.94, -63.94, 4096, 4096, 0.015, 180.0, 1.0]])
+	detectors = pd.DataFrame(det_props, columns=det_hdr)
+	detectors["id"] 	= detectors["id"].astype(int)
+	detectors["x_size"] = detectors["x_size"].astype(int)
+	detectors["y_size"] = detectors["y_size"].astype(int)
+
+	for ind, det in detectors.iterrows():
+		x_mm = det['x_cen'] - det['x_size']/2*det['pixel_size']
+		y_mm = det['y_cen'] - det['y_size']/2*det['pixel_size']
+		width = det['x_size']*det['pixel_size']
+		height = det['y_size']*det['pixel_size']
+		x_cen, y_cen = det['x_cen'], det['y_cen']
+		
+		rect = patches.Rectangle((x_mm, y_mm), width, height, 
+				linewidth=lw, edgecolor='k', facecolor='none', alpha=alpha, zorder=1)
+		ax.add_patch(rect)
+		if show_label:
+			ax.text(x_cen, y_cen, det['id'])
+	return fig, ax
+
+def convert_detector_size(x, y, old=True):
+	old_ps 		= 100.13151872258219
+	new_ps 		= ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3).platescale * 1e3
+	conv_fact 	= old_ps / new_ps
+	
+	if old:
+		x *= conv_fact
+		y *= conv_fact
+	elif not old:
+		x /= conv_fact
+		y /= conv_fact
+	
+	return x, y
+	
+
+def plot_distortions(xe, ye, xp, yp, lims=False, add_detectors=True, 
+						save=False, figtitle='geom_distortions',
+						show=True):
+	
+	mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3)
+	n = xe.shape[0]
+	X = xe.flatten()
+	Y = ye.flatten()
+	dX = xp - xe
+	dY = yp - ye
+	dx = dX.flatten()
+	dy = dY.flatten()
+
+	# Magnitude of the distortions
+	R = np.sqrt(dx**2 + dy**2) * mcd.platescale * 1e6
+	print('RMS of the PSF shifts: {:.4f} μas.'.format(np.sqrt(np.mean((R)**2))))
+
+	# Dummy figure, used for the colorbar colors in the quiver plot
+	im = plt.imshow(np.reshape(R, (n,n)))
+	plt.close()
+
+	# Initiate the figure
+	fig = plt.figure(figsize=(6.4,4.8))
+	ax = fig.add_subplot(aspect='equal')
+	
+	# Add the detector layout if so desired.
+	if add_detectors:
+		add_detector_layout((fig,ax), lw=.8, alpha=0.5, show_label=False)
+	
+	# Plot the vector plot
+	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=.8, units='dots', zorder=300)
+	ax.quiver(X,Y,dx,dy,R, alpha=1, units='dots', zorder=300)
+	ax.set_xlabel('x (mm)', fontsize=16)
+	ax.set_ylabel('y (mm)', fontsize=16)
+	ax.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
+										top=True, right=True)
+	cb = fig.colorbar(im, ax=ax)
+	cb.set_label(label='PSF centroid shift (μas)', size=14)
+	cb.ax.tick_params(labelsize=14)
+	if not lims:
+		ax.set_xlim([-125, 115])
+		ax.set_ylim([-115, 115]) 
+	elif lims:
+		ax.set_xlim(lims[0])
+		ax.set_ylim(lims[1]) 
+	plt.tight_layout()
+	if save:
+		plt.savefig('{}.pdf'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+		plt.savefig('{}.png'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+	if show:
+		plt.show()
+	return fig, ax, R
+
+
+def plot_distortions2(xe, ye, xp, yp, lims=False, add_detectors=True, 
+						save=False, figtitle='geom_distortions',
+						show=True, cmap='magma'):
+	vmin, vmax = 0.1, 100
+	mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3)
+	n = xe.shape[0]
+	X = xe.flatten()
+	Y = ye.flatten()
+	dX = xp - xe
+	dY = yp - ye
+	dx = dX.flatten()
+	dy = dY.flatten()
+
+	# Magnitude of the distortions
+	R = np.sqrt(dx**2 + dy**2) * mcd.platescale * 1e6
+	print('RMS of the PSF shifts: {:.4f} μas.'.format(np.sqrt(np.mean((R)**2))))
+
+	# Dummy figure, used for the colorbar colors in the quiver plot
+	# im = plt.imshow(np.reshape(R, (n,n)))
+	im = plt.imshow(np.reshape(R[:121], (11,11)))
+	im = plt.scatter(X, Y, s=R, c=np.log10(R), cmap=cmap, vmin=np.log10(vmin), vmax=np.log10(vmax))
+	# im = plt.scatter(X, Y, s=R, c=R, cmap='jet')
+	plt.close()
+
+	# Initiate the figure
+	# fig = plt.figure(figsize=(9.6,4.8))
+	# ax = fig.add_subplot(121, aspect='equal')
+	# ax2 = fig.add_subplot(122, aspect='equal')
+	fig, axes = plt.subplots(ncols=2, 
+						   nrows=1, 
+						   gridspec_kw={'width_ratios': [1,1]},
+						   figsize=(9.6,4.8))
+	ax, ax2 = axes
+	ax.set_aspect('equal')
+	ax2.set_aspect('equal')
+	# Add the detector layout if so desired.
+	if add_detectors:
+		add_detector_layout((fig,ax), lw=.8, alpha=1, show_label=False)
+	
+	# Plot the vector plot
+	xe = np.asarray(xe[:121]).reshape((11,11))
+	ye = np.asarray(ye[:121]).reshape((11,11))
+	# 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,R, alpha=1, units='dots', zorder=300)
+	
+	for k in range(1):
+		print()
+		ind_end = (k+1) * 121 
+		ind_start = k * 121
+		dpix = np.diff(Y)[ind_start:ind_end][1]
+		print(k, ind_start, ind_end, dpix)
+
+		xplot = np.asarray(X[ind_start:ind_end]).reshape((11,11)) 
+		yplot = np.asarray(Y[ind_start:ind_end]).reshape((11,11))
+		yplot, xplot = np.mgrid[yplot.min()+dpix/2:yplot.max()-dpix/2:12j,
+								xplot.min()+dpix/2:xplot.max()-dpix/2:12j]
+
+
+		Rtemp  = np.asarray(R[ind_start:ind_end]).reshape((11,11))
+		ax.pcolor(-1*yplot.T, -1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
+		# ax2.pcolor(-1*xplot.T, -1*yplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=1, vmax=100))
+		shiftx = X[ind_start:ind_end][61]#np.abs(dpix)/2
+		shifty = Y[ind_start:ind_end][60]
+		print(Y[ind_start:ind_end])
+		print('shiftx:', shiftx)
+		print('shifty:', shifty)
+
+		# ax2.pcolor(k*shifty-1*yplot.T, k*shiftx-1*xplot.T, Rtemp.T*10, cmap=cmap, norm=LogNorm(vmin=1, vmax=100))
+		if k == 1: ax2.pcolor(shifty-1*yplot.T, shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
+		elif k == 2: ax2.pcolor(2*shifty-1*yplot.T, 2*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
+		elif k == 3: ax2.pcolor(2*shifty-1*yplot.T, 2*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
+		if k == 0:
+			ax.quiver(X[ind_start:ind_end],Y[ind_start:ind_end],
+					 dx[ind_start:ind_end],dy[ind_start:ind_end], 
+					 angles='xy', edgecolor='k', facecolor='k', 
+					 linewidth=.8, zorder=300, alpha=1, scale=1.4e-5, 
+					 units='dots')
+		elif k in [2,3]:
+			ax2.quiver(X[ind_start:ind_end],Y[ind_start:ind_end],
+					 dx[ind_start:ind_end],dy[ind_start:ind_end], 
+					 angles='xy', edgecolor='k', facecolor='k', 
+					 linewidth=.8, zorder=300, alpha=1, scale=0.2e-5, 
+					 units='dots')
+		# ax.pcolor(xplot * (1 - dpix/105), yplot * (1 - dpix/105), Rtemp, cmap='jet', norm=LogNorm(vmin=1, vmax=100))
+				   
+	ax.set_xlabel('x (mm)', fontsize=16)
+	ax.set_ylabel('y (mm)', fontsize=16)
+	ax.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
+										top=True, right=True)
+	ax2.set_xlabel('x (mm)', fontsize=16)
+	ax2.set_ylabel('y (mm)', fontsize=16)
+	ax2.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
+										top=True, right=True)
+	
+	
+	cb = fig.colorbar(im, ax=axes, fraction=0.025, pad=0.04)
+	cb.set_label(label='PSF centroid shift (μas)', size=14)
+	cb.ax.tick_params(labelsize=14)
+	
+	lbls = 10**np.asarray(cb.ax.get_yticks())
+	print(lbls)
+	newlbls = ['{:.0f}'.format(x) for x in 10**np.asarray(cb.ax.get_yticks())]
+	newlbls[1] = '{:.1f}'.format(lbls[1])
+	newlbls[0] = '{:.1f}'.format(lbls[0])
+	cb.ax.set_yticklabels(newlbls)
+	print(cb.ax.get_yticks())
+	if not lims:
+		ax.set_xlim([-125, 115])
+		ax.set_ylim([-115, 115]) 
+	elif lims:
+		ax.set_xlim(lims[0])
+		ax.set_ylim(lims[1])
+	ax2.set_xlim([-2.01,2.75])
+	ax2.set_ylim([-2.01,2.75])
+	ax.indicate_inset_zoom(ax2, edgecolor="black") 
+	fig.subplots_adjust(top=1.0,
+						bottom=0.0,
+						left=0.1,
+						right=0.8,
+						hspace=0.135,
+						wspace=0.3)
+	# plt.tight_layout()
+	if save:
+		plt.savefig('{}.pdf'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+		plt.savefig('{}.png'.format(figtitle), 
+			bbox_inches='tight', 
+			transparent=False, 
+			facecolor=(1, 1, 0, 0.0))
+	if show:
+		plt.show()
+	return fig, ax, R
+
+
+if __name__ == '__main__':
+	ps = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3).platescale
+	print(ps)
+
+	plt.rc('font', family='serif')
+	plt.rc('axes', labelsize='large')
+	
+	# convert_detector_size()
+	# Plotting the original distortions
+	data_dir = '../data/GD Focal Plane WFEs/'
+	xe_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_xcentroid_expected_zern.pkl')
+	ye_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_ycentroid_expected_zern.pkl')
+	xp_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_xcentroid_found_with_wfe_airy_zern.pkl')
+	yp_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_zern.pkl')
+
+	# # Zoom 1: 
+	# xe_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_xcentroid_expected_zern.pkl')
+	# ye_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_ycentroid_expected_zern.pkl')
+	# xp_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_xcentroid_found_with_wfe_airy_zern.pkl')
+	# yp_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_ycentroid_found_with_wfe_airy_zern.pkl')
+
+	xe = xe_og#np.append(xe_og, xe_og1)
+	ye = ye_og#np.append(ye_og, ye_og1)
+	xp = xp_og#np.append(xp_og, xp_og1)
+	yp = yp_og#np.append(yp_og, yp_og1)
+
+	# xe = np.append(xe_og2, xe_og3)
+	# ye = np.append(ye_og2, ye_og3)
+	# xp = np.append(xp_og2, xp_og3)
+	# yp = np.append(yp_og2, yp_og3)
+
+
+	figtitle = '../figures adapted for thesis/Focal Plane/GD_IFP_zern'
+	plot_distortions(xe, ye, xp, yp, show=True, save=False, figtitle=figtitle,
+					lims=[[-120,120],[-120,120]])
+
+	
+
+
+

scripts/SPIE/generate_wfe.py

@@ -1,6 +1,7 @@
 import numpy as np
 import sys
-sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+# sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/')
 from fourierOptics import *
 from pdb import set_trace
 

scripts/SPIE/geometric_distortions.py

@@ -1,7 +1,8 @@
 import numpy as np
 import sys
 # sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
-sys.path.insert(0, '/home/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+# sys.path.insert(0, '/home/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/')
 from fourierOptics import *
 
 '''
@@ -51,8 +52,8 @@ def plot_distortions():
 	ax.set_xlabel('x (mm)')
 	ax.set_ylabel('y (mm)')
 	fig.colorbar(im, ax=ax, label='PSF centroid shift ($\\mu$as)')
-	plt.savefig('./figures/GD_full_field_zern_psd.pdf', bbox_inches='tight', transparent=True)
-	plt.savefig('./figures/GD_full_field_zern_psd.png', bbox_inches='tight', transparent=True)
+	plt.savefig('./figures/UPDATED_GD_full_field_zern_psd.pdf', bbox_inches='tight', transparent=True)
+	plt.savefig('./figures/UPDATED_GD_full_field_zern_psd.png', bbox_inches='tight', transparent=True)
 	plt.tight_layout()
 	plt.show()
 
@@ -75,10 +76,10 @@ if __name__ == '__main__':
 	hyList = hyList.flatten()
 
 	# Loading in the WFEs
-	wfe_p1 = load_variable('./data/ADC_tWFE_p1_zern_20nm_psd_15nm.pkl')
-	wfe_p2 = load_variable('./data/ADC_tWFE_p2_zern_20nm_psd_15nm.pkl')
-	wfe_p3 = load_variable('./data/ADC_tWFE_p3_zern_20nm_psd_15nm.pkl')
-	wfe_p4 = load_variable('./data/ADC_tWFE_p4_zern_20nm_psd_15nm.pkl')
+	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')
 	
 	# Propagate through the field positions without any WFEs	
 	for i in range(len(hxList)):

scripts/SPIE/geometric_distortions_incl_rotations.py

@@ -0,0 +1,174 @@
+import numpy as np
+import sys
+# sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Analyses/Python/Analytical/Fourier Optics')
+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()
+	
+
+