diff --git a/FOC_Capetti_test_NGC1068/FOC_Capetti_test_NGC1068.rar b/FOC_Capetti_test_NGC1068/FOC_Capetti_test_NGC1068.rar deleted file mode 100755 index f9b9f44..0000000 Binary files a/FOC_Capetti_test_NGC1068/FOC_Capetti_test_NGC1068.rar and /dev/null differ diff --git a/FOC_Capetti_test_NGC1068/FOC_reduction.py b/FOC_Capetti_test_NGC1068/FOC_reduction.py deleted file mode 100755 index 06dd2b2..0000000 --- a/FOC_Capetti_test_NGC1068/FOC_reduction.py +++ /dev/null @@ -1,256 +0,0 @@ -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) - - diff --git a/FOC_Capetti_test_NGC1068/NGC1068_FOC.png b/FOC_Capetti_test_NGC1068/NGC1068_FOC.png deleted file mode 100755 index 76da21e..0000000 Binary files a/FOC_Capetti_test_NGC1068/NGC1068_FOC.png and /dev/null differ diff --git a/FOC_Capetti_test_NGC1068/test_Stokes.png b/FOC_Capetti_test_NGC1068/test_Stokes.png deleted file mode 100755 index ad51433..0000000 Binary files a/FOC_Capetti_test_NGC1068/test_Stokes.png and /dev/null differ diff --git a/FOC_Capetti_test_NGC1068/test_alignment.png b/FOC_Capetti_test_NGC1068/test_alignment.png deleted file mode 100755 index ef2d020..0000000 Binary files a/FOC_Capetti_test_NGC1068/test_alignment.png and /dev/null differ diff --git a/FOC_Capetti_test_NGC1068/test_combinePol.png b/FOC_Capetti_test_NGC1068/test_combinePol.png deleted file mode 100755 index 2938603..0000000 Binary files a/FOC_Capetti_test_NGC1068/test_combinePol.png and /dev/null differ diff --git a/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_I_err.png b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_I_err.png new file mode 100644 index 0000000..156ac05 Binary files /dev/null and b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_I_err.png differ diff --git a/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_P_err.png b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_P_err.png index 73594d8..117f340 100755 Binary files a/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_P_err.png and b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_P_err.png differ diff --git a/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_SNRi.png b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_SNRi.png index 6d67715..8407d69 100755 Binary files a/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_SNRi.png and b/plots/NGC1068_x274020/NGC1068_FOC_combine_FWHM020_rot_SNRi.png differ diff --git a/src/FOC_reduction.py b/src/FOC_reduction.py index d3e6ac6..c7dcee0 100755 --- a/src/FOC_reduction.py +++ b/src/FOC_reduction.py @@ -7,7 +7,7 @@ Main script where are progressively added the steps for the FOC pipeline reducti #Project libraries import sys import numpy as np -import copy +from copy import deepcopy import lib.fits as proj_fits #Functions to handle fits files import lib.reduction as proj_red #Functions used in reduction pipeline import lib.plots as proj_plots #Functions for plotting data @@ -155,11 +155,10 @@ def main(): rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'w'] # Rotate data to have North up - ref_header = copy.deepcopy(headers[0]) + ref_header = deepcopy(headers[0]) if rotate_data: alpha = ref_header['orientat'] - mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], - [np.sin(-alpha), np.cos(-alpha)]]) + mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]]) rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))+np.array(data_array.shape[1:])/2 rectangle[4] = alpha data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -ref_header['orientat']) @@ -180,7 +179,7 @@ def main(): ## Step 3: # Rotate images to have North up - ref_header = copy.deepcopy(headers[0]) + ref_header = deepcopy(headers[0]) if rotate_stokes: alpha = ref_header['orientat'] mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], @@ -193,7 +192,7 @@ def main(): ## Step 4: # crop to desired region of interest (roi) -# stokescrop = proj_plots.crop_map(copy.deepcopy(stokes_test), copy.deepcopy(data_mask), snrp_cut=snrp_cut, snri_cut=snri_cut) +# stokescrop = proj_plots.crop_map(deepcopy(stokes_test), deepcopy(data_mask), snrp_cut=snrp_cut, snri_cut=snri_cut) # stokescrop.run() # stokes_crop, data_mask = stokescrop.crop() @@ -202,13 +201,13 @@ def main(): Stokes_test = proj_fits.save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, figname+figtype, data_folder=data_folder, return_hdul=True) # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error). - proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None) - proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_flux", plots_folder=plots_folder, display='Pol_Flux') - proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg') - proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_I_err", plots_folder=plots_folder, display='I_err') - proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err') - proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi') - proj_plots.polarization_map(copy.deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp') + proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None) + proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_flux", plots_folder=plots_folder, display='Pol_Flux') + proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg') + proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_I_err", plots_folder=plots_folder, display='I_err') + proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err') + proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi') + proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp') return 0 diff --git a/src/lib/convex_hull.py b/src/lib/convex_hull.py index 0a4f683..08cf5ea 100755 --- a/src/lib/convex_hull.py +++ b/src/lib/convex_hull.py @@ -3,7 +3,7 @@ Library functions for graham algorithm implementation (find the convex hull of a given list of points). """ -import copy +from copy import deepcopy import numpy as np @@ -141,12 +141,12 @@ def partition(s, l, r, order): for j in range(l, r): if order(s[j], s[r]): i = i + 1 - temp = copy.deepcopy(s[i]) - s[i] = copy.deepcopy(s[j]) - s[j] = copy.deepcopy(temp) - temp = copy.deepcopy(s[i+1]) - s[i+1] = copy.deepcopy(s[r]) - s[r] = copy.deepcopy(temp) + temp = deepcopy(s[i]) + s[i] = deepcopy(s[j]) + s[j] = deepcopy(temp) + temp = deepcopy(s[i+1]) + s[i+1] = deepcopy(s[r]) + s[r] = deepcopy(temp) return i + 1 diff --git a/src/lib/reduction.py b/src/lib/reduction.py index a349bb0..35892a5 100755 --- a/src/lib/reduction.py +++ b/src/lib/reduction.py @@ -37,7 +37,7 @@ prototypes : Rotate I, Q, U given an angle in degrees using scipy functions. """ -import copy +from copy import deepcopy import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates @@ -222,15 +222,15 @@ def crop_array(data_array, headers, error_array=None, step=5, null_val=None, null_val = [null_val,]*error_array.shape[0] vertex = np.zeros((data_array.shape[0],4),dtype=int) - for i,image in enumerate(data_array): + for i,image in enumerate(data_array): # Get vertex of the rectangular convex hull of each image vertex[i] = image_hull(image,step=step,null_val=null_val[i],inside=inside) v_array = np.zeros(4,dtype=int) - if inside: + if inside: # Get vertex of the maximum convex hull for all images v_array[0] = np.max(vertex[:,0]).astype(int) v_array[1] = np.min(vertex[:,1]).astype(int) v_array[2] = np.max(vertex[:,2]).astype(int) v_array[3] = np.min(vertex[:,3]).astype(int) - else: + else: # Get vertex of the minimum convex hull for all images v_array[0] = np.min(vertex[:,0]).astype(int) v_array[1] = np.max(vertex[:,1]).astype(int) v_array[2] = np.min(vertex[:,2]).astype(int) @@ -279,7 +279,7 @@ def crop_array(data_array, headers, error_array=None, step=5, null_val=None, crop_array = np.zeros((data_array.shape[0],new_shape[0],new_shape[1])) crop_error_array = np.zeros((data_array.shape[0],new_shape[0],new_shape[1])) - for i,image in enumerate(data_array): + for i,image in enumerate(data_array): #Put the image data in the cropped array crop_array[i] = image[v_array[0]:v_array[1],v_array[2]:v_array[3]] crop_error_array[i] = error_array[i][v_array[0]:v_array[1],v_array[2]:v_array[3]] @@ -732,9 +732,9 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1., center = np.fix(ref_center-shift).astype(int) res_shift = res_center-ref_center rescaled_image[i,res_shift[0]:res_shift[0]+shape[1], - res_shift[1]:res_shift[1]+shape[2]] = copy.deepcopy(image) + res_shift[1]:res_shift[1]+shape[2]] = deepcopy(image) rescaled_error[i,res_shift[0]:res_shift[0]+shape[1], - res_shift[1]:res_shift[1]+shape[2]] = copy.deepcopy(error_array[i]) + res_shift[1]:res_shift[1]+shape[2]] = deepcopy(error_array[i]) rescaled_mask[i,res_shift[0]:res_shift[0]+shape[1], res_shift[1]:res_shift[1]+shape[2]] = False # Shift images to align @@ -1106,14 +1106,19 @@ def compute_Stokes(data_array, error_array, data_mask, headers, pol_eff[1] = pol_efficiency['pol60'] pol_eff[2] = pol_efficiency['pol120'] + # Orientation and error for each polarizer ## THIS IS WHERE WE IMPLEMENT THE ERROR THAT IS GOING WRONG + # POL0 = 0deg, POL60 = 60deg, POL120=120deg theta = np.array([180.*np.pi/180., 60.*np.pi/180., 120.*np.pi/180.]) + # Uncertainties on the orientation of the polarizers' axes taken to be 3deg (see Nota et. al 1996, p36; Robinson & Thomson 1995) sigma_theta = np.array([3.*np.pi/180., 3.*np.pi/180., 3.*np.pi/180.]) pol_flux = 2.*np.array([pol0/transmit[0], pol60/transmit[1], pol120/transmit[2]]) + # Normalization parameter for Stokes parameters computation A = pol_eff[1]*pol_eff[2]*np.sin(-2.*theta[1]+2.*theta[2]) \ + pol_eff[2]*pol_eff[0]*np.sin(-2.*theta[2]+2.*theta[0]) \ + pol_eff[0]*pol_eff[1]*np.sin(-2.*theta[0]+2.*theta[1]) coeff_stokes = np.zeros((3,3)) + # Coefficients linking each polarizer flux to each Stokes parameter for i in range(3): coeff_stokes[0,i] = pol_eff[(i+1)%3]*pol_eff[(i+2)%3]*np.sin(-2.*theta[(i+1)%3]+2.*theta[(i+2)%3])/A coeff_stokes[1,i] = (-pol_eff[(i+1)%3]*np.sin(2.*theta[(i+1)%3]) + pol_eff[(i+2)%3]*np.sin(2.*theta[(i+2)%3]))/A @@ -1143,6 +1148,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers, Stokes_cov[0,2] = Stokes_cov[2,0] = coeff_stokes[0,0]*coeff_stokes[2,0]*pol_cov[0,0]+coeff_stokes[0,1]*coeff_stokes[2,1]*pol_cov[1,1]+coeff_stokes[0,2]*coeff_stokes[2,2]*pol_cov[2,2]+(coeff_stokes[0,0]*coeff_stokes[2,1]+coeff_stokes[2,0]*coeff_stokes[0,1])*pol_cov[0,1]+(coeff_stokes[0,0]*coeff_stokes[2,2]+coeff_stokes[2,0]*coeff_stokes[0,2])*pol_cov[0,2]+(coeff_stokes[0,1]*coeff_stokes[2,2]+coeff_stokes[2,1]*coeff_stokes[0,2])*pol_cov[1,2] Stokes_cov[1,2] = Stokes_cov[2,1] = coeff_stokes[1,0]*coeff_stokes[2,0]*pol_cov[0,0]+coeff_stokes[1,1]*coeff_stokes[2,1]*pol_cov[1,1]+coeff_stokes[1,2]*coeff_stokes[2,2]*pol_cov[2,2]+(coeff_stokes[1,0]*coeff_stokes[2,1]+coeff_stokes[2,0]*coeff_stokes[1,1])*pol_cov[0,1]+(coeff_stokes[1,0]*coeff_stokes[2,2]+coeff_stokes[2,0]*coeff_stokes[1,2])*pol_cov[0,2]+(coeff_stokes[1,1]*coeff_stokes[2,2]+coeff_stokes[2,1]*coeff_stokes[1,2])*pol_cov[1,2] + # Compute the derivative of each Stokes parameter with respect to the polarizer orientation dI_dtheta1 = 2.*pol_eff[0]/A*(pol_eff[2]*np.cos(-2.*theta[2]+2.*theta[0])*(pol_flux[1]-I_stokes) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes)) dI_dtheta2 = 2.*pol_eff[1]/A*(pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2])*(pol_flux[0]-I_stokes)) dI_dtheta3 = 2.*pol_eff[2]/A*(pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2])*(pol_flux[0]-I_stokes) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0])*(pol_flux[1]-I_stokes)) @@ -1153,10 +1159,12 @@ def compute_Stokes(data_array, error_array, data_mask, headers, dU_dtheta2 = 2.*pol_eff[1]/A*(np.sin(2.*theta[1])*(pol_flux[2]-pol_flux[0]) - (pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1]) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2]))*U_stokes) dU_dtheta3 = 2.*pol_eff[2]/A*(np.sin(2.*theta[2])*(pol_flux[0]-pol_flux[1]) - (pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2]) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0]))*U_stokes) + # Compute the uncertainty associated with the polarizers' orientation (see Kishimoto 1999) s_I2_axis = (dI_dtheta1**2*sigma_theta[0]**2 + dI_dtheta2**2*sigma_theta[1]**2 + dI_dtheta3**2*sigma_theta[2]**2) s_Q2_axis = (dQ_dtheta1**2*sigma_theta[0]**2 + dQ_dtheta2**2*sigma_theta[1]**2 + dQ_dtheta3**2*sigma_theta[2]**2) s_U2_axis = (dU_dtheta1**2*sigma_theta[0]**2 + dU_dtheta2**2*sigma_theta[1]**2 + dU_dtheta3**2*sigma_theta[2]**2) + # Add quadratically the uncertainty to the Stokes covariance matrix ## THIS IS WHERE THE PROBLEMATIC UNCERTAINTY IS ADDED TO THE PIPELINE Stokes_cov[0,0] += s_I2_axis Stokes_cov[1,1] += s_Q2_axis Stokes_cov[2,2] += s_U2_axis @@ -1361,7 +1369,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, #Compute new covariance matrix on rotated parameters - new_Stokes_cov = copy.deepcopy(Stokes_cov) + new_Stokes_cov = deepcopy(Stokes_cov) new_Stokes_cov[1,1] = np.cos(2.*alpha)**2*Stokes_cov[1,1] + np.sin(2.*alpha)**2*Stokes_cov[2,2] + 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2] new_Stokes_cov[2,2] = np.sin(2.*alpha)**2*Stokes_cov[1,1] + np.cos(2.*alpha)**2*Stokes_cov[2,2] - 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2] new_Stokes_cov[0,1] = new_Stokes_cov[1,0] = np.cos(2.*alpha)*Stokes_cov[0,1] + np.sin(2.*alpha)*Stokes_cov[0,2] @@ -1383,7 +1391,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]]) for header in headers: - new_header = copy.deepcopy(header) + new_header = deepcopy(header) new_header['orientat'] = header['orientat'] + ang new_wcs = WCS(header).deepcopy()