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fourierOptics/scripts/SPIE/Thesis/plotting_detectors.py
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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()
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