diff --git a/src/test_Enrique_reduction.py b/src/test_Enrique_reduction.py new file mode 100755 index 0000000..06dd2b2 --- /dev/null +++ b/src/test_Enrique_reduction.py @@ -0,0 +1,256 @@ +from pylab import * +import numpy as np +import matplotlib.pyplot as plt +from astropy.io import fits +from astropy.wcs import WCS +from aplpy import FITSFigure +import scipy.ndimage +import os as os + +plt.close('all') + +def bin_ndarray(ndarray, new_shape, operation='sum'): + """ + Bins an ndarray in all axes based on the target shape, by summing or + averaging. + + Number of output dimensions must match number of input dimensions. + + Example + ------- + >>> m = np.arange(0,100,1).reshape((10,10)) + >>> n = bin_ndarray(m, new_shape=(5,5), operation='sum') + >>> print(n) + + [[ 22 30 38 46 54] + [102 110 118 126 134] + [182 190 198 206 214] + [262 270 278 286 294] + [342 350 358 366 374]] + + """ + if not operation.lower() in ['sum', 'mean', 'average', 'avg']: + raise ValueError("Operation not supported.") + if ndarray.ndim != len(new_shape): + raise ValueError("Shape mismatch: {} -> {}".format(ndarray.shape, + new_shape)) + compression_pairs = [(d, c//d) for d,c in zip(new_shape, + ndarray.shape)] + flattened = [l for p in compression_pairs for l in p] + ndarray = ndarray.reshape(flattened) + for i in range(len(new_shape)): + if operation.lower() == "sum": + ndarray = ndarray.sum(-1*(i+1)) + elif operation.lower() in ["mean", "average", "avg"]: + ndarray = ndarray.mean(-1*(i+1)) + return ndarray + +def plots(ax,data,vmax,vmin): + ax.imshow(data,vmax=vmax,vmin=vmin,origin='lower') + + + +### User input + +infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits','x274020dt.c0f.fits', + 'x274020et.c0f.fits','x274020ft.c0f.fits','x274020gt.c0f.fits','x274020ht.c0f.fits', + 'x274020it.c0f.fits'] + +#Centroids +#The centroids should be estimated by cross-correlating the images. +#Here I used the position of the central source for each file as the reference pixel position. +ycen_0 = [304,306,303,296,295,295,294,305,304] +xcen_0 = [273,274,273,276,274,274,274,272,271] +#size, in pixels, of the FOV centered in x_cen_0,y_cen_0 +Dx = 200 +Dy = 200 + +#set the new image size (Dxy x Dxy pixels binning) +Dxy = 5 +new_shape = (Dx//Dxy,Dy//Dxy) + +#figures +#test alignment +vmin = 0 +vmax = 6 +font_size=40.0 +label_size=20. +lw = 3. + +#pol. map +SNRp_cut = 3 #P measumentes with SNR>3 +SNRi_cut = 30 #I measuremntes with SNR>30, which implies an uncerrtainty in P of 4.7%. +scalevec = 0.05 #length of vectors in pol. map +step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted +vec_legend = 10 #% pol for legend +figname = 'NGC1068_FOC.png' + + +### SCRIPT ### +### Step 1. Check input images before data reduction +#this step is very simplistic. +#Here I used the position of the central source for each file as the +#reference pixel position. +#The centroids should be estimated by cross-correlating the images, +#and compare with the simplistic approach of using the peak pixel of the +#object as the reference pixel. + + +fig,axs = plt.subplots(3,3,figsize=(30,30),dpi=200,sharex=True,sharey=True) + +for jj, a in enumerate(axs.flatten()): + img = fits.open(infiles[jj]) + ima = img[0].data + ima = ima[ycen_0[jj]-Dy:ycen_0[jj]+Dy,xcen_0[jj]-Dx:xcen_0[jj]+Dx] + ima = bin_ndarray(ima,new_shape=new_shape,operation='sum') #binning + exptime = img[0].header['EXPTIME'] + fil = img[0].header['FILTNAM1'] + ima = ima/exptime + globals()['ima_%s' % jj] = ima + #plots + plots(a,ima,vmax=vmax,vmin=vmin) + #position of centroid + a.plot([ima.shape[1]/2,ima.shape[1]/2],[0,ima.shape[0]-1],lw=1,color='black') + a.plot([0,ima.shape[1]-1],[ima.shape[1]/2,ima.shape[1]/2],lw=1,color='black') + a.text(2,2,infiles[jj][0:8],color='white',fontsize=10) + a.text(2,5,fil,color='white',fontsize=30) + a.text(ima.shape[1]-20,1,exptime,color='white',fontsize=20) +fig.subplots_adjust(hspace=0,wspace=0) +fig.savefig('test_alignment.png',dpi=300) +os.system('open test_alignment.png') + + + +### Step 2. average of all images for a single polarizer to have them in the same units e/s. +pol0 = (ima_0 + ima_1 + ima_2)/3. +pol60 = (ima_3 + ima_4 + ima_5 + ima_6)/4. +pol120 = (ima_7 + ima_8)/2. + +fig1,(ax1,ax2,ax3) = plt.subplots(1,3,figsize=(26,8),dpi=200) +CF = ax1.imshow(pol0,vmin=vmin,vmax=vmax,origin='lower') +cbar = plt.colorbar(CF,ax=ax1) +cbar.ax.tick_params(labelsize=20) +ax1.tick_params(axis='both', which='major', labelsize=20) +ax1.text(2,2,'POL0',color='white',fontsize=10) + +CF = ax2.imshow(pol60,vmin=vmin,vmax=vmax,origin='lower') +cbar = plt.colorbar(CF,ax=ax2) +cbar.ax.tick_params(labelsize=20) +ax2.tick_params(axis='both', which='major', labelsize=20) +ax2.text(2,2,'POL60',color='white',fontsize=10) + +CF = ax3.imshow(pol120,vmin=vmin,vmax=vmax,origin='lower') +cbar = plt.colorbar(CF,ax=ax3) +cbar.ax.tick_params(labelsize=20) +ax3.tick_params(axis='both', which='major', labelsize=20) +ax3.text(2,2,'POL120',color='white',fontsize=10) +fig1.savefig('test_combinePol.png',dpi=300) +os.system('open test_combinePol.png') + + +### Step 3. Compute Stokes IQU, P, PA, PI +#Stokes parameters +I_stokes = (2./3.)*(pol0 + pol60 + pol120) +Q_stokes = (2./3.)*(2*pol0 - pol60 - pol120) +U_stokes = (2./np.sqrt(3.))*(pol60 - pol120) + +#Remove nan +I_stokes[np.isnan(I_stokes)]=0. +Q_stokes[np.isnan(Q_stokes)]=0. +U_stokes[np.isnan(U_stokes)]=0. + +#Polarimetry +PI = np.sqrt(Q_stokes*Q_stokes + U_stokes*U_stokes) +P = PI/I_stokes*100 +PA = 0.5*arctan2(U_stokes,Q_stokes)*180./np.pi+90 +s_P = np.sqrt(2.)*(I_stokes)**(-0.5) +s_PA = s_P/(P/100.)*180./np.pi + +fig2,((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3,figsize=(40,20),dpi=200) +CF = ax1.imshow(I_stokes,origin='lower') +cbar = plt.colorbar(CF,ax=ax1) +cbar.ax.tick_params(labelsize=20) +ax1.tick_params(axis='both', which='major', labelsize=20) +ax1.text(2,2,'I',color='white',fontsize=10) + +CF = ax2.imshow(Q_stokes,origin='lower') +cbar = plt.colorbar(CF,ax=ax2) +cbar.ax.tick_params(labelsize=20) +ax2.tick_params(axis='both', which='major', labelsize=20) +ax2.text(2,2,'Q',color='white',fontsize=10) + +CF = ax3.imshow(U_stokes,origin='lower') +cbar = plt.colorbar(CF,ax=ax3) +cbar.ax.tick_params(labelsize=20) +ax3.tick_params(axis='both', which='major', labelsize=20) +ax3.text(2,2,'U',color='white',fontsize=10) + +v = np.linspace(0,40,50) +CF = ax4.imshow(P,origin='lower',vmin=0,vmax=40) +cbar = plt.colorbar(CF,ax=ax4) +cbar.ax.tick_params(labelsize=20) +ax4.tick_params(axis='both', which='major', labelsize=20) +ax4.text(2,2,'P',color='white',fontsize=10) + +CF = ax5.imshow(PA,origin='lower',vmin=0,vmax=180) +cbar = plt.colorbar(CF,ax=ax5) +cbar.ax.tick_params(labelsize=20) +ax5.tick_params(axis='both', which='major', labelsize=20) +ax5.text(2,2,'PA',color='white',fontsize=10) + +CF = ax6.imshow(PI,origin='lower') +cbar = plt.colorbar(CF,ax=ax6) +cbar.ax.tick_params(labelsize=20) +ax6.tick_params(axis='both', which='major', labelsize=20) +ax6.text(2,2,'PI',color='white',fontsize=10) + +fig2.savefig('test_Stokes.png',dpi=300) +os.system('open test_Stokes.png') + +### Step 4. Binning and smoothing +#Images can be binned and smoothed to improve SNR. This step can also be done +#using the PolX images. + + +### Step 5. Roate images to have North up +#Images needs to be reprojected to have North up. +#this procedure implies to rotate the Stokes QU using a rotation matrix + + +### STEP 6. image to FITS with updated WCS +new_wcs = WCS(naxis=2) +new_wcs.wcs.crpix = [I_stokes.shape[0]/2, I_stokes.shape[1]/2] +new_wcs.wcs.crval = [img[0].header['CRVAL1'], img[0].header['CRVAL2']] +new_wcs.wcs.cunit = ["deg", "deg"] +new_wcs.wcs.ctype = ["RA---TAN", "DEC--TAN"] +new_wcs.wcs.cdelt = [img[0].header['CD1_1']*Dxy, img[0].header['CD1_2']*Dxy] + +#hdu_ori = WCS(img[0]) +stkI=fits.PrimaryHDU(data=I_stokes,header=new_wcs.to_header()) +pol=fits.PrimaryHDU(data=P,header=new_wcs.to_header()) +pang=fits.PrimaryHDU(data=PA,header=new_wcs.to_header()) +pol_err=fits.PrimaryHDU(data=s_P,header=new_wcs.to_header()) +pang_err=fits.PrimaryHDU(data=s_PA,header=new_wcs.to_header()) + + +### STEP 7. polarization map +#quality cuts +pxscale = stkI.header['CDELT1'] + +#apply quality cuts +SNRp = pol.data/pol_err.data +pol.data[SNRp < SNRp_cut] = np.nan + +SNRi = stkI.data/np.std(stkI.data[0:10,0:10]) +pol.data[SNRi < SNRi_cut] = np.nan + +fig = plt.figure(figsize=(11,10)) +gc = FITSFigure(stkI,figure=fig) +gc.show_contour(np.log10(SNRi),levels=np.linspace(np.log10(SNRi_cut),np.max(np.log10(SNRi)),20),\ + filled=True,cmap='magma') +gc.show_vectors(pol,pang,scale=scalevec,step=step_vec,color='white',linewidth=1.0) + +fig.savefig(figname,dpi=300) +os.system('open '+figname) + +