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332 lines
11 KiB
Python
332 lines
11 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import matplotlib.ticker as plticker
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from matplotlib.gridspec import GridSpec
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import pandas as pd
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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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from matplotlib.colors import Normalize, LogNorm
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'''
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Date: 2022-06-20
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Author: Joost van den Born
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Description: Plotting the geometric distortions, that were calculated
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in an earlier simulation.
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Here we also include the detector layout in the plot.
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'''
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def add_detector_layout(figureObj, lw=0.8, alpha=0.5, show_label=True):
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'''
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Adds the detector layout to an existing figure+axes object.
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The detector outlines are based on the MICADO Hawaii 4RG mosaic
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and the dimensions are in mm.
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Parameters
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----------
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figureObj : tuple
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Tuple containing the figure and axes objects (fig,ax)
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lw : float, optional
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Linewidth of the drawn detectors. Default: 0.8
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alpha : float, optional
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Alpha value to draw the rectangles at
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show_label : bool, optional
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If True, shows the detector ID number in the rectangle
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Returns
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-------
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TYPE
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Updated fig, axes objects
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'''
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fig, ax = figureObj
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# Detector layout
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det_hdr = ['id','x_cen', 'y_cen', 'x_size', 'y_size', 'pixel_size', 'angle', 'gain']
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det_props = np.array([[1, -63.94, 63.94, 4096, 4096, 0.015, 0.0, 1.0],
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[2, 0.00, 63.94, 4096, 4096, 0.015, 270.0, 1.0],
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[3, 63.94, 63.94, 4096, 4096, 0.015, 180.0, 1.0],
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[6, -63.94, 0.00, 4096, 4096, 0.015, 0.0, 1.0],
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[5, 0.00, 0.00, 4096, 4096, 0.015, 180.0, 1.0],
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[4, 67.94, 0.00, 4096, 4096, 0.015, 180.0, 1.0],
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[7, -63.94, -63.94, 4096, 4096, 0.015, 0.0, 1.0],
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[8, 0.00, -63.94, 4096, 4096, 0.015, 90.0, 1.0],
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[9, 63.94, -63.94, 4096, 4096, 0.015, 180.0, 1.0]])
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detectors = pd.DataFrame(det_props, columns=det_hdr)
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detectors["id"] = detectors["id"].astype(int)
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detectors["x_size"] = detectors["x_size"].astype(int)
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detectors["y_size"] = detectors["y_size"].astype(int)
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for ind, det in detectors.iterrows():
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x_mm = det['x_cen'] - det['x_size']/2*det['pixel_size']
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y_mm = det['y_cen'] - det['y_size']/2*det['pixel_size']
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width = det['x_size']*det['pixel_size']
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height = det['y_size']*det['pixel_size']
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x_cen, y_cen = det['x_cen'], det['y_cen']
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rect = patches.Rectangle((x_mm, y_mm), width, height,
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linewidth=lw, edgecolor='k', facecolor='none', alpha=alpha, zorder=1)
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ax.add_patch(rect)
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if show_label:
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ax.text(x_cen, y_cen, det['id'])
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return fig, ax
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def convert_detector_size(x, y, old=True):
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old_ps = 100.13151872258219
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new_ps = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3).platescale * 1e3
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conv_fact = old_ps / new_ps
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if old:
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x *= conv_fact
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y *= conv_fact
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elif not old:
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x /= conv_fact
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y /= conv_fact
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return x, y
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def plot_distortions(xe, ye, xp, yp, lims=False, add_detectors=True,
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save=False, figtitle='geom_distortions',
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show=True):
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mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3)
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n = xe.shape[0]
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X = xe.flatten()
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Y = ye.flatten()
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dX = xp - xe
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dY = yp - ye
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dx = dX.flatten()
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dy = dY.flatten()
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# Magnitude of the distortions
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R = np.sqrt(dx**2 + dy**2) * mcd.platescale * 1e6
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print('RMS of the PSF shifts: {:.4f} μas.'.format(np.sqrt(np.mean((R)**2))))
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# Dummy figure, used for the colorbar colors in the quiver plot
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im = plt.imshow(np.reshape(R, (n,n)))
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plt.close()
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# Initiate the figure
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fig = plt.figure(figsize=(6.4,4.8))
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ax = fig.add_subplot(aspect='equal')
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# Add the detector layout if so desired.
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if add_detectors:
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add_detector_layout((fig,ax), lw=.8, alpha=0.5, show_label=False)
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# Plot the vector plot
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for i in range(len(xe)):
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ax.plot(xe[:,i], ye[:,i], 'k-', alpha=0.2, linewidth=.5)
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ax.plot(xe[i,:], ye[i,:], 'k-', alpha=0.2, linewidth=.5)
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ax.quiver(X,Y,dx,dy, angles='xy', edgecolor='k',
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facecolor='None', linewidth=.8, units='dots', zorder=300)
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ax.quiver(X,Y,dx,dy,R, alpha=1, units='dots', zorder=300)
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ax.set_xlabel('x (mm)', fontsize=16)
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ax.set_ylabel('y (mm)', fontsize=16)
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ax.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
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top=True, right=True)
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cb = fig.colorbar(im, ax=ax)
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cb.set_label(label='PSF centroid shift (μas)', size=14)
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cb.ax.tick_params(labelsize=14)
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if not lims:
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ax.set_xlim([-125, 115])
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ax.set_ylim([-115, 115])
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elif lims:
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ax.set_xlim(lims[0])
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ax.set_ylim(lims[1])
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plt.tight_layout()
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if save:
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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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if show:
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plt.show()
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return fig, ax, R
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def plot_distortions2(xe, ye, xp, yp, lims=False, add_detectors=True,
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save=False, figtitle='geom_distortions',
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show=True, cmap='magma'):
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vmin, vmax = 0.1, 100
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mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3)
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n = xe.shape[0]
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X = xe.flatten()
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Y = ye.flatten()
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dX = xp - xe
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dY = yp - ye
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dx = dX.flatten()
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dy = dY.flatten()
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# Magnitude of the distortions
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R = np.sqrt(dx**2 + dy**2) * mcd.platescale * 1e6
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print('RMS of the PSF shifts: {:.4f} μas.'.format(np.sqrt(np.mean((R)**2))))
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# Dummy figure, used for the colorbar colors in the quiver plot
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# im = plt.imshow(np.reshape(R, (n,n)))
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im = plt.imshow(np.reshape(R[:121], (11,11)))
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im = plt.scatter(X, Y, s=R, c=np.log10(R), cmap=cmap, vmin=np.log10(vmin), vmax=np.log10(vmax))
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# im = plt.scatter(X, Y, s=R, c=R, cmap='jet')
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plt.close()
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# Initiate the figure
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# fig = plt.figure(figsize=(9.6,4.8))
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# ax = fig.add_subplot(121, aspect='equal')
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# ax2 = fig.add_subplot(122, aspect='equal')
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fig, axes = plt.subplots(ncols=2,
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nrows=1,
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gridspec_kw={'width_ratios': [1,1]},
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figsize=(9.6,4.8))
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ax, ax2 = axes
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ax.set_aspect('equal')
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ax2.set_aspect('equal')
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# Add the detector layout if so desired.
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if add_detectors:
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add_detector_layout((fig,ax), lw=.8, alpha=1, show_label=False)
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# Plot the vector plot
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xe = np.asarray(xe[:121]).reshape((11,11))
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ye = np.asarray(ye[:121]).reshape((11,11))
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# for i in range(len(xe)):
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# ax.plot(xe[:,i], ye[:,i], 'k-', alpha=0.2, linewidth=.5)
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# ax.plot(xe[i,:], ye[i,:], 'k-', alpha=0.2, linewidth=.5)
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# ax.quiver(X,Y,dx,dy,R, alpha=1, units='dots', zorder=300)
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for k in range(3):
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print()
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ind_end = (k+1) * 121
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ind_start = k * 121
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dpix = np.diff(Y)[ind_start:ind_end][1]
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print(k, ind_start, ind_end, dpix)
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xplot = np.asarray(X[ind_start:ind_end]).reshape((11,11))
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yplot = np.asarray(Y[ind_start:ind_end]).reshape((11,11))
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yplot, xplot = np.mgrid[yplot.min()+dpix/2:yplot.max()-dpix/2:12j,
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xplot.min()+dpix/2:xplot.max()-dpix/2:12j]
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Rtemp = np.asarray(R[ind_start:ind_end]).reshape((11,11))
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ax.pcolor(-1*yplot.T, -1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
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# ax2.pcolor(-1*xplot.T, -1*yplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=1, vmax=100))
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shiftx = X[ind_start:ind_end][61]#np.abs(dpix)/2
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shifty = Y[ind_start:ind_end][60]
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print(Y[ind_start:ind_end])
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print('shiftx:', shiftx)
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print('shifty:', shifty)
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# ax2.pcolor(k*shifty-1*yplot.T, k*shiftx-1*xplot.T, Rtemp.T*10, cmap=cmap, norm=LogNorm(vmin=1, vmax=100))
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if k == 1: ax2.pcolor(shifty-1*yplot.T, shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax))
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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))
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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))
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if k == 0:
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ax.quiver(X[ind_start:ind_end],Y[ind_start:ind_end],
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dx[ind_start:ind_end],dy[ind_start:ind_end],
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angles='xy', edgecolor='k', facecolor='k',
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linewidth=.8, zorder=300, alpha=1, scale=1.4e-5,
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units='dots')
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elif k in [2,3]:
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ax2.quiver(X[ind_start:ind_end],Y[ind_start:ind_end],
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dx[ind_start:ind_end],dy[ind_start:ind_end],
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angles='xy', edgecolor='k', facecolor='k',
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linewidth=.8, zorder=300, alpha=1, scale=0.2e-5,
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units='dots')
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# ax.pcolor(xplot * (1 - dpix/105), yplot * (1 - dpix/105), Rtemp, cmap='jet', norm=LogNorm(vmin=1, vmax=100))
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ax.set_xlabel('x (mm)', fontsize=16)
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ax.set_ylabel('y (mm)', fontsize=16)
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ax.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
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top=True, right=True)
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ax2.set_xlabel('x (mm)', fontsize=16)
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ax2.set_ylabel('y (mm)', fontsize=16)
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ax2.tick_params(axis='both', direction='in', labelsize=14, zorder=-1,
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top=True, right=True)
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cb = fig.colorbar(im, ax=axes, fraction=0.025, pad=0.04)
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cb.set_label(label='PSF centroid shift (μas)', size=14)
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cb.ax.tick_params(labelsize=14)
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lbls = 10**np.asarray(cb.ax.get_yticks())
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print(lbls)
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newlbls = ['{:.0f}'.format(x) for x in 10**np.asarray(cb.ax.get_yticks())]
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newlbls[1] = '{:.1f}'.format(lbls[1])
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newlbls[0] = '{:.1f}'.format(lbls[0])
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cb.ax.set_yticklabels(newlbls)
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print(cb.ax.get_yticks())
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if not lims:
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ax.set_xlim([-125, 115])
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ax.set_ylim([-115, 115])
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elif lims:
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ax.set_xlim(lims[0])
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ax.set_ylim(lims[1])
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ax2.set_xlim([-2.01,2.75])
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ax2.set_ylim([-2.01,2.75])
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ax.indicate_inset_zoom(ax2, edgecolor="black")
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fig.subplots_adjust(top=1.0,
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bottom=0.0,
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left=0.1,
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right=0.8,
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hspace=0.135,
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wspace=0.3)
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# plt.tight_layout()
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if save:
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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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if show:
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plt.show()
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return fig, ax, R
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if __name__ == '__main__':
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ps = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3).platescale
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print(ps)
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plt.rc('font', family='serif')
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plt.rc('axes', labelsize='large')
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# convert_detector_size()
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# Plotting the original distortions
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data_dir = '../data/GD Focal Plane WFEs/'
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xe_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_xcentroid_expected_psd.pkl')
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ye_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_ycentroid_expected_psd.pkl')
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xp_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_xcentroid_found_with_wfe_airy_psd.pkl')
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yp_og = load_variable(data_dir + 'GD_IFP_obscured_PSF_ycentroid_found_with_wfe_airy_psd.pkl')
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# # Zoom 1:
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xe_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_xcentroid_expected_psd.pkl')
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ye_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_ycentroid_expected_psd.pkl')
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xp_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_xcentroid_found_with_wfe_airy_psd.pkl')
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yp_og1 = load_variable(data_dir + 'GD_IFP_zoom_obscured_PSF_ycentroid_found_with_wfe_airy_psd.pkl')
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xe = np.append(xe_og, xe_og1)
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ye = np.append(ye_og, ye_og1)
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xp = np.append(xp_og, xp_og1)
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yp = np.append(yp_og, yp_og1)
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xe = np.append(xe_og2, xe_og3)
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ye = np.append(ye_og2, ye_og3)
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xp = np.append(xp_og2, xp_og3)
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yp = np.append(yp_og2, yp_og3)
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figtitle = '../figures adapted for thesis/Focal Plane/GD_IFP_zern2'
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plot_distortions2(xe, ye, xp, yp, show=True, save=False, figtitle=figtitle,
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lims=[[-120,120],[-120,120]])
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