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 = 0.01, 100 mcd = ParaxialMICADO(n=3001, m=3001, dpix=50, l=1.49e-3) n = xe.shape[0] no_of_grids = int(n/121) 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[:121])**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=(12,4.8)) gs = GridSpec(ncols=3, nrows=2, width_ratios=[2,1,1], height_ratios=[1,1])#left=0.05, right=0.48, wspace=0.05) ax = fig.add_subplot(gs[:, :1]) ax2 = fig.add_subplot(gs[0, 1]) ax3 = fig.add_subplot(gs[0, 2]) ax4 = fig.add_subplot(gs[1, 1]) ax5 = fig.add_subplot(gs[1, 2]) axilist = [ax, ax2, ax3, ax4, ax5] for axi in axilist: axi.set_aspect('equal') # Add the detector layout if so desired. if add_detectors: add_detector_layout((fig,ax), lw=1, 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)) # Now we iterate over the different iterations of the GD simulations print('Total number of grids: {}'.format(no_of_grids)) for k in range(no_of_grids): # Selecting the different iterations (11x11 grid, but flattened) ind_end = (k+1) * 121 ind_start = k * 121 dpix = np.diff(Y)[ind_start:ind_end][1] # Coordinates of the grid in this iteration 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] # Magnitude of the distortion at coordinates xplot, yplot # Used to color the background Rtemp = np.asarray(R[ind_start:ind_end]).reshape((11,11)) shiftx = X[ind_start:ind_end][61] shifty = Y[ind_start:ind_end][60] if k in [0,1,2,3,7]: # Plot the center part pcolor ax.pcolor(1*shifty-1*yplot.T, 1*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax)) # if k > 3: # Plot the zoom and megazoom ax2.pcolor(2*shifty-1*yplot.T, 2*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax)) ax3.pcolor(2*shifty-1*yplot.T, 2*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax)) if k in [0,4,5,6,8]: # Plot the left bottom part pcolor if k!=0: ax.pcolor(yplot.T, xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax)) ax4.pcolor(2*shifty-1*yplot.T, 2*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax)) ax5.pcolor(2*shifty-1*yplot.T, 2*shiftx-1*xplot.T, Rtemp.T, cmap=cmap, norm=LogNorm(vmin=vmin, vmax=vmax)) # Quiver plots # Full field plot 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=1.2e-5, units='dots') # Center plot zoom if k in [0,1,2]: 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=7e-7, units='dots') # Center plot megazoom elif k in [2,3,7]: ax3.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=1e-6, units='dots') # Left bottom zoom if k in [4,5]: ax4.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=1.2e-5, units='dots') if k in [5,6,8]: ax5.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=7e-6, units='dots') # Adjusting how the axes look in all subfigures for axi in [ax, ax2, ax4]: axi.set_ylabel('y (mm)', fontsize=16) for axi in [ax, ax4, ax5]: axi.set_xlabel('x (mm)', fontsize=16) for axi in axilist: axi.tick_params(axis='both', direction='in', labelsize=14, zorder=-1, top=True, right=True) # Setting some limits to the subplots 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([-1+0.3513,1+0.3513]) ax2.set_ylim([-1+0.3513,1+0.3513]) ax3.set_xlim([-0.01+0.3513, 0.01+0.3513]) ax3.set_ylim([-0.01+0.3513, 0.01+0.3513]) # ax4.set_xlim([-2.01-84,2.75-84]) # ax4.set_ylim([-2.01-84,2.75-84]) ax4.set_xlim([-83.6-1,-83.6+1]) ax4.set_ylim([-83.6-1,-83.6+1]) ax5.set_xlim([-0.01-83.6070, 0.01-83.6070]) ax5.set_ylim([-0.01-83.6070, 0.01-83.6070]) # For the center inset ax.indicate_inset_zoom(ax2, edgecolor='black') ax2.indicate_inset_zoom(ax3, edgecolor='black') # For the off-center inset ax.indicate_inset_zoom(ax4, edgecolor='black') ax4.indicate_inset_zoom(ax5, edgecolor='black') # after plotting the data, format the labels ax5.set_xticklabels(['{:.2f}'.format(x) for x in ax5.get_xticks()]) ax5.set_yticklabels(['{:.2f}'.format(x) for x in ax5.get_yticks()]) fig.subplots_adjust(top=1.0, bottom=0.08, left=0.075, right=0.88, hspace=0.145, wspace=0.45) # Setting and modifying the colorbar fig.subplots_adjust(right=0.88) cbar_ax = fig.add_axes([0.9, 0.12, 0.02, 0.85]) cb = fig.colorbar(im, cax=cbar_ax) cb.set_label(label='PSF centroid shift (μas)', size=14) cb.ax.tick_params(labelsize=14) lbls = 10**np.asarray(cb.ax.get_yticks()) newlbls = ['{:.0f}'.format(x) for x in 10**np.asarray(cb.ax.get_yticks())] newlbls[3] = '{:.1f}'.format(lbls[3]) newlbls[2] = '{:.1f}'.format(lbls[2]) newlbls[1] = '{:.2f}'.format(lbls[1]) newlbls[0] = '{:.2f}'.format(lbls[0]) cb.ax.set_yticklabels(newlbls) # 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 plt.rc('font', family='serif') plt.rc('axes', labelsize='large') # convert_detector_size() # Plotting the original distortions, -1/11 :1/11 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: -1/11 :1/11 # 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: -.5/11:.5/11 # 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: -.1/11:.1/11 # 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') # Zoom 97: -1/11: 1/11 xe_og1 = load_variable(data_dir + 'GD_IFP_zoom97_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og1 = load_variable(data_dir + 'GD_IFP_zoom97_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og1 = load_variable(data_dir + 'GD_IFP_zoom97_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og1 = load_variable(data_dir + 'GD_IFP_zoom97_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom 98: -1/121 : 1/121 xe_og2 = load_variable(data_dir + 'GD_IFP_zoom98_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og2 = load_variable(data_dir + 'GD_IFP_zoom98_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og2 = load_variable(data_dir + 'GD_IFP_zoom98_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og2 = load_variable(data_dir + 'GD_IFP_zoom98_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom 99: -1/11**3 : -1/11**3 xe_og3 = load_variable(data_dir + 'GD_IFP_zoom99_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og3 = load_variable(data_dir + 'GD_IFP_zoom99_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og3 = load_variable(data_dir + 'GD_IFP_zoom99_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og3 = load_variable(data_dir + 'GD_IFP_zoom99_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom7: -.01/11:.01/11 xe_og4 = load_variable(data_dir + 'GD_IFP_zoom92_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og4 = load_variable(data_dir + 'GD_IFP_zoom92_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og4 = load_variable(data_dir + 'GD_IFP_zoom92_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og4 = load_variable(data_dir + 'GD_IFP_zoom92_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom8: -.05/11:.05/11 - not plotted # Zoom93: -.004/11:.004/11 xe_og5 = load_variable(data_dir + 'GD_IFP_zoom93_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og5 = load_variable(data_dir + 'GD_IFP_zoom93_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og5 = load_variable(data_dir + 'GD_IFP_zoom93_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og5 = load_variable(data_dir + 'GD_IFP_zoom93_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # # Zoom91: -.01/11:.01/11 but hx and hy shifted to an area near the corner of the image, # # where distortion is supposed to be large # xe_og6 = load_variable(data_dir + 'GD_IFP_zoom91_obscured_PSF_xcentroid_expected_zern_psd.pkl') # ye_og6 = load_variable(data_dir + 'GD_IFP_zoom91_obscured_PSF_ycentroid_expected_zern_psd.pkl') # xp_og6 = load_variable(data_dir + 'GD_IFP_zoom91_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') # yp_og6 = load_variable(data_dir + 'GD_IFP_zoom91_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom94: -1/11:1/11 but hx and hy shifted to an area near the corner of the image, # where distortion is supposed to be large xe_og6 = load_variable(data_dir + 'GD_IFP_zoom94_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og6 = load_variable(data_dir + 'GD_IFP_zoom94_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og6 = load_variable(data_dir + 'GD_IFP_zoom94_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og6 = load_variable(data_dir + 'GD_IFP_zoom94_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom95: -1/121:1/121 but hx and hy shifted to an area near the corner of the image, # where distortion is supposed to be large xe_og7 = load_variable(data_dir + 'GD_IFP_zoom95_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og7 = load_variable(data_dir + 'GD_IFP_zoom95_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og7 = load_variable(data_dir + 'GD_IFP_zoom95_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og7 = load_variable(data_dir + 'GD_IFP_zoom95_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom96: -1/11**3:1/11**3 but hx and hy shifted to an area near the corner of the image, # where distortion is supposed to be large xe_og8 = load_variable(data_dir + 'GD_IFP_zoom96_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og8 = load_variable(data_dir + 'GD_IFP_zoom96_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og8 = load_variable(data_dir + 'GD_IFP_zoom96_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og8 = load_variable(data_dir + 'GD_IFP_zoom96_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom 100: - 1/11**4:1/11**4, phase interpolation, center of the field xe_og9 = load_variable(data_dir + 'GD_IFP_zoom100_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og9 = load_variable(data_dir + 'GD_IFP_zoom100_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og9 = load_variable(data_dir + 'GD_IFP_zoom100_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og9 = load_variable(data_dir + 'GD_IFP_zoom100_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Zoom 101: - 1/11**4:1/11**4, phase interpolation, center of the field, with shift to left bottom corner xe_og10 = load_variable(data_dir + 'GD_IFP_zoom101_obscured_PSF_xcentroid_expected_zern_psd.pkl') ye_og10 = load_variable(data_dir + 'GD_IFP_zoom101_obscured_PSF_ycentroid_expected_zern_psd.pkl') xp_og10 = load_variable(data_dir + 'GD_IFP_zoom101_obscured_PSF_xcentroid_found_with_wfe_airy_zern_psd.pkl') yp_og10 = load_variable(data_dir + 'GD_IFP_zoom101_obscured_PSF_ycentroid_found_with_wfe_airy_zern_psd.pkl') # Combining the data points # xe = np.append(xe_og, [xe_og1, xe_og2, xe_og3, xe_og4, xe_og5, xe_og6, xe_og7, xe_og8]) # ye = np.append(ye_og, [ye_og1, ye_og2, ye_og3, ye_og4, ye_og5, ye_og6, ye_og7, ye_og8]) # xp = np.append(xp_og, [xp_og1, xp_og2, xp_og3, xp_og4, xp_og5, xp_og6, xp_og7, xp_og8]) # yp = np.append(yp_og, [yp_og1, yp_og2, yp_og3, yp_og4, yp_og5, yp_og6, yp_og7, yp_og8]) xe = np.append(xe_og, [xe_og1, xe_og2, xe_og3, xe_og6, xe_og7, xe_og8, xe_og9, xe_og10]) ye = np.append(ye_og, [ye_og1, ye_og2, ye_og3, ye_og6, ye_og7, ye_og8, ye_og9, ye_og10]) xp = np.append(xp_og, [xp_og1, xp_og2, xp_og3, xp_og6, xp_og7, xp_og8, xp_og9, xp_og10]) yp = np.append(yp_og, [yp_og1, yp_og2, yp_og3, yp_og6, yp_og7, yp_og8, yp_og9, yp_og10]) figtitle = '../figures adapted for thesis/Focal Plane/GD_IFP_psd' plot_distortions(xe, ye, xp, yp, show=False, save=True, figtitle=figtitle, lims=[[-120,120],[-120,120]]) plt.close()