fourierOptics/scripts/SPIE/Thesis/geometric_distortions_fp_wfe_continuous.py

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_fs.pkl')
	ye = load_variable('../data/GD_IFP_obscured_PSF_ycentroid_expected_fs.pkl')

	# Coordinates with WFEs
	xf = load_variable('../data/GD_IFP_obscured_PSF_xcentroid_found_with_wfe_airy_fs.pkl')
	yf = load_variable('../data/GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_fs.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_fs.pdf', bbox_inches='tight', transparent=True)
	plt.savefig('../figures/UPDATED_GD_focal_plane_fs.png', bbox_inches='tight', transparent=True)
	plt.tight_layout()
	plt.show()


if __name__ == '__main__':
	# plt.rc('text', usetex=True)
	plt.rc('font', family='serif')
	plt.rc('axes', labelsize='large')
	
	
	# Setup of simulation
	l = 1.49e-3
	mcd = ParaxialMICADO(n=3001, m=3001, dpix=50)
	# mcd.plot_ray_trace()

	# 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 = 15000e-6
	#PSD Wavefront error

	# Loading the phase generated using a Fourier Series
	fs_array = load_variable('wfe_fp_phase_array_{}_{}.pkl'.format(mcd.n, mcd.m))

	# Retrieving the coefficients for the Fourier series
	np.random.seed(2183)
	coeff = np.random.uniform(-1,1,(101,101))
	wfe_fp.coefficients = coeff
	fs_array = fourier_series(xfp, yfp, coeff, Px=30, Py=30, xs=10, ys=10)

	# Setting the initial full window phase array to generate the full window surface height map.
	wfe_fp.phase = fs_array
	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
	# 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()
	# hxList, hyList = [0.0], [0.0]

	# 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('Data point: i={:.0f} of i_max={:.0f}'.format(i, len(hxList)))
		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=True, 
								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))

		data_tuple = (i, hx, hy, xe, ye, xf, yf)
		data_name = 'gd_point_{:03d}_hx{}_hy{}.pkl'.format(i, hx, hy)
		save_variable('../data/GD_IFP_intermediate_results/{}'.format(data_name), data_tuple)

		# 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_fs.pkl', x_expected)
	save_variable('../data/GD_IFP_obscured_PSF_ycentroid_expected_fs.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_fs.pkl', x_centroid)
	save_variable('../data/GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_fs.pkl', y_centroid)

	plot_distortions()