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118 lines
4.6 KiB
Python
118 lines
4.6 KiB
Python
import numpy as np
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import os
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import sys
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sys.path.insert(0, '/Users/joost/Dropbox/Werk/PhD/Documents/Published/FourierOptics GitHub release/')
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from fourierOptics import *
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'''
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Author: Joost van den Born, PhD student at University of Groningen
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Contact: born@astro.rug.nl
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Description: Script to plot the results of the analysis where the shift
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of the PSF centroid as a function of the RMS value of the
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WFE was investigated.
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Script made for the SPIE manuscript.
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'''
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if __name__ == '__main__':
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plt.rc('font', family='serif')
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plt.rc('text', usetex=True)
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plt.rc('axes', labelsize='large')
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# Optical system constant
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platescale = 100.13151872258219
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###############################
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### Lowpass filter analysis ###
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###############################
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# Load in the data
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r_airy, r_err_airy = load_variable('./data/WFE frequency analysis/lowpass_frequency_filter_airy_distance.pkl')
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x0_airy, y0_airy, x0_airy_err, y0_airy_err = load_variable('./data/WFE frequency analysis/lowpass_frequency_filter_airy_locations.pkl')
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central_frequencies = np.asarray([0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25,
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30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200,
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220, 250, 280, 310, 340, 370, 400, 450, 500])
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r_airy_mas = r_airy * platescale
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r_err_airy_mas = r_err_airy * platescale
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# Plotting the centroid shift for all bandpasses
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fig = plt.figure(figsize=(6.4,4.8))
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ax1 = fig.add_subplot(111)
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ax1.scatter(central_frequencies, r_airy_mas, edgecolor='k', facecolors='none', alpha=0.7)
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ax1.set_xscale('log')
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ax1.set_xlabel('Cut-off Frequency of the lowpass filter ($\\rho/\\rho_c$)', fontsize=18)
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ax1.set_ylabel('Centroid shift (mas)', fontsize=18)
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ax1.tick_params(axis='both', which='both', direction='in', labelsize=16, zorder=-1,)
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ax1.grid(True, alpha=0.3, which='major')
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ax1.grid(True, alpha=0.1, which='minor')
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ax1.set_xlim([0.3,550])
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plt.tight_layout()
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figtitle = 'new_lowpass_spatial_freq_distance'
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if figtitle:
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plt.savefig('{}.pdf'.format(figtitle),
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bbox_inches='tight',
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transparent=False,
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facecolor=(1, 1, 0, 0.0))
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plt.savefig('{}.png'.format(figtitle),
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bbox_inches='tight',
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transparent=False,
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facecolor=(1, 1, 0, 0.0))
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plt.show()
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# plt.savefig('./figures/wfe_frequency_analysis/lowpass_spatial_freq_distance.png', bbox_inches='tight', transparent=True)
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# plt.savefig('./figures/wfe_frequency_analysis/lowpass_spatial_freq_distance.pdf', bbox_inches='tight', transparent=True)
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################################
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### Bandpass filter analysis ###
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################################
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# Load in the data
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r_airy, r_err_airy = load_variable('./data/WFE frequency analysis/bandpass_frequency_filter_airy_distance.pkl')
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x0_airy, y0_airy, x0_airy_err, y0_airy_err = load_variable('./data/WFE frequency analysis/bandpass_frequency_filter_airy_locations.pkl')
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central_frequencies = load_variable('./data/WFE frequency analysis/bandpass_frequency_filter_central_frequencies.pkl')
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print(central_frequencies, len(central_frequencies), len(r_airy))
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r_airy_mas = r_airy * platescale
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r_err_airy_mas = r_err_airy * platescale
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# Fit a line in Logarithmic space
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log_cf = np.log10(central_frequencies)
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log_r = np.log10(r_airy_mas)
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coefficients = np.polyfit(log_cf[2:], log_r[2:], 1)
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best_fit_fnc = np.poly1d(coefficients)
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best_fit_y = best_fit_fnc(log_cf)
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# Plotting the centroid shift for all bandpasses
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fig = plt.figure(figsize=(6.4,4.8))
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ax1 = fig.add_subplot(111)
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ax1.scatter(central_frequencies, r_airy_mas, edgecolor='k', facecolors='none', alpha=0.7)
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ax1.plot(central_frequencies[2:], 10**best_fit_y[2:], 'b-', alpha=0.5, label='Best fit: $y = 0.030 x^{-0.602}$')
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ax1.set_xscale('log')
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ax1.set_yscale('log')
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ax1.set_xlabel('Central Frequency of the bandpass filter ($\\rho/\\rho_c$)', fontsize=18)
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ax1.set_ylabel('Centroid shift (mas)', fontsize=18)
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ax1.tick_params(axis='both', which='both', direction='in', labelsize=16, zorder=-1,)
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ax1.grid(True, alpha=0.3, which='major')
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ax1.grid(True, alpha=0.1, which='minor')
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ax1.set_xlim([0.3,550])
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ax1.set_ylim([10**-6, 10**0])
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ax1.legend(fontsize=18)
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plt.tight_layout()
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figtitle = 'bandpass_spatial_freq_distance'
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if figtitle:
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plt.savefig('{}.pdf'.format(figtitle),
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bbox_inches='tight',
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transparent=False,
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facecolor=(1, 1, 0, 0.0))
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plt.savefig('{}.png'.format(figtitle),
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bbox_inches='tight',
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transparent=False,
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facecolor=(1, 1, 0, 0.0))
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# plt.savefig('./figures/wfe_frequency_analysis/bandpass_spatial_freq_distance.png', bbox_inches='tight', transparent=True)
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# plt.savefig('./figures/wfe_frequency_analysis/bandpass_spatial_freq_distance.pdf', bbox_inches='tight', transparent=True)
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plt.show() |