diff --git a/data/PictorA_x25d040/README.md b/data/PictorA_x25d040/README.md new file mode 100644 index 0000000..7393031 --- /dev/null +++ b/data/PictorA_x25d040/README.md @@ -0,0 +1,5 @@ +Observation data +Instrument : FOC +Proposal ID : 5156 +Target name : Pictor-A +Dataset : X25D040 diff --git a/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot.png b/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot.png index fab2058..e3df5ea 100644 Binary files a/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot.png and b/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot.png differ diff --git a/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot_IQU.png b/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot_IQU.png index 6202df4..6ac3754 100644 Binary files a/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot_IQU.png and b/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot_IQU.png differ diff --git a/plots/3C109_x3mc010/3C109_FOC_combine_FWHM020_rot_P.png 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a/plots/PictorA_x25d040/PictorA_FOC_errors_background_flux.png b/plots/PictorA_x25d040/PictorA_FOC_errors_background_flux.png new file mode 100644 index 0000000..1c68aef Binary files /dev/null and b/plots/PictorA_x25d040/PictorA_FOC_errors_background_flux.png differ diff --git a/plots/PictorA_x25d040/PictorA_FOC_errors_background_location.png b/plots/PictorA_x25d040/PictorA_FOC_errors_background_location.png new file mode 100644 index 0000000..0cd1ab3 Binary files /dev/null and b/plots/PictorA_x25d040/PictorA_FOC_errors_background_location.png differ diff --git a/src/FOC_reduction.py b/src/FOC_reduction.py index 28f2433..af261d9 100755 --- a/src/FOC_reduction.py +++ b/src/FOC_reduction.py @@ -17,11 +17,11 @@ from lib.convex_hull import image_hull def main(): ##### User inputs ## Input and output locations - globals()['data_folder'] = "../data/NGC1068_x274020/" - 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'] - globals()['plots_folder'] = "../plots/NGC1068_x274020/" +# globals()['data_folder'] = "../data/NGC1068_x274020/" +# 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'] +# globals()['plots_folder'] = "../plots/NGC1068_x274020/" # globals()['data_folder'] = "../data/NGC1068_x14w010/" # infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits', @@ -77,6 +77,10 @@ def main(): # infiles = ['x3nl0201r_c0f.fits','x3nl0202r_c0f.fits','x3nl0203r_c0f.fits'] # globals()['plots_folder'] = "../plots/MKN78_x3nl020/" + globals()['data_folder'] = "../data/PictorA_x25d040/" + infiles = ['x25d0401t_c0f.fits','x25d0402t_c0f.fits','x25d0403t_c0f.fits'] + globals()['plots_folder'] = "../plots/PictorA_x25d040/" + ## Reduction parameters # Deconvolution deconvolve = False @@ -108,10 +112,10 @@ def main(): rotate_stokes = True #rotation to North convention can give erroneous results rotate_data = False #rotation to North convention can give erroneous results # Polarization map output - figname = 'NGC1068_FOC' #target/intrument name + figname = 'PictorA_FOC' #target/intrument name figtype = '_combine_FWHM020_rot' #additionnal informations - SNRp_cut = 3. #P measurments with SNR>3 - SNRi_cut = 30 #I measurments with SNR>30, which implies an uncertainty in P of 4.7%. + SNRp_cut = 20. #P measurments with SNR>3 + SNRi_cut = 120. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%. step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted ##### Pipeline start @@ -193,12 +197,12 @@ def main(): ## Step 5: # Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error). - proj_plots.polarization_map(copy.deepcopy(Stokes_test), 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), 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), 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), 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), 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), 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(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+"_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') return 0 diff --git a/src/lib/plots.py b/src/lib/plots.py index 4dc2bad..ac4ca69 100755 --- a/src/lib/plots.py +++ b/src/lib/plots.py @@ -167,7 +167,7 @@ def plot_Stokes(Stokes, savename=None, plots_folder=""): return 0 -def polarization_map(Stokes, rectangle=None, SNRp_cut=3., SNRi_cut=30., step_vec=1, +def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30., step_vec=1, savename=None, plots_folder="", display=None): """ Plots polarization map from Stokes HDUList. @@ -235,6 +235,7 @@ def polarization_map(Stokes, rectangle=None, SNRp_cut=3., SNRi_cut=30., step_vec SNRi[np.isnan(SNRi)] = 0. pol.data[SNRi < SNRi_cut] = np.nan + data_mask = (1.-data_mask).astype(bool) mask = (SNRp > SNRp_cut) * (SNRi > SNRi_cut) # Look for pixel of max polarization @@ -340,16 +341,16 @@ def polarization_map(Stokes, rectangle=None, SNRp_cut=3., SNRi_cut=30., step_vec PA_int_err = (90./(np.pi*(Q_int**2 + U_int**2)))*np.sqrt(U_int**2*Q_int_err**2 + Q_int**2*U_int_err**2 - 2.*Q_int*U_int*QU_int_err) # Compute integrated parameters and associated errors for all pixels - n_pix = stkI.data.size - I_diluted = stkI.data.sum() - Q_diluted = stkQ.data.sum() - U_diluted = stkU.data.sum() - I_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,0])) - Q_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,1])) - U_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[2,2])) - IQ_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,1]**2)) - IU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,2]**2)) - QU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,2]**2)) + n_pix = stkI.data[data_mask].size + I_diluted = stkI.data[data_mask].sum() + Q_diluted = stkQ.data[data_mask].sum() + U_diluted = stkU.data[data_mask].sum() + I_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,0][data_mask])) + Q_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,1][data_mask])) + U_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[2,2][data_mask])) + IQ_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,1][data_mask]**2)) + IU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,2][data_mask]**2)) + QU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,2][data_mask]**2)) P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted P_diluted_err = (1./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted**2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err) @@ -359,7 +360,7 @@ def polarization_map(Stokes, rectangle=None, SNRp_cut=3., SNRi_cut=30., step_vec PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err**2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err) #PA_diluted_err = P_diluted_err/(2.*P_diluted)*180./np.pi - ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_int*convert_flux,I_int_err*convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_int*100.,P_int_err*100.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_int,PA_int_err), color='white', fontsize=16, xy=(0.01, 0.92), xycoords='axes fraction') + ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_diluted*convert_flux,I_diluted_err*convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_diluted*100.,P_diluted_err*100.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_diluted,PA_diluted_err), color='white', fontsize=16, xy=(0.01, 0.92), xycoords='axes fraction') ax.coords.grid(True, color='white', ls='dotted', alpha=0.5) ax.coords[0].set_axislabel('Right Ascension (J2000)') diff --git a/src/lib/reduction.py b/src/lib/reduction.py index 751db03..171dffb 100755 --- a/src/lib/reduction.py +++ b/src/lib/reduction.py @@ -1153,9 +1153,9 @@ 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 + A*coeff_stokes[2,1]*(np.sin(2.*theta[1]*Q_stokes) - np.cos(2.*theta[1]*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 + A*coeff_stokes[2,2]*(np.sin(2.*theta[2]*Q_stokes) - np.cos(2.*theta[2]*U_stokes))) - Stokes_cov[0,0] += (dI_dtheta1**2*sigma_theta[0]**2 + dI_dtheta2**2*sigma_theta[1]**2 + dI_dtheta3**2*sigma_theta[2]**2) - Stokes_cov[1,1] += (dQ_dtheta1**2*sigma_theta[0]**2 + dQ_dtheta2**2*sigma_theta[1]**2 + dQ_dtheta3**2*sigma_theta[2]**2) - Stokes_cov[2,2] += (dU_dtheta1**2*sigma_theta[0]**2 + dU_dtheta2**2*sigma_theta[1]**2 + dU_dtheta3**2*sigma_theta[2]**2) + #Stokes_cov[0,0] += (dI_dtheta1**2*sigma_theta[0]**2 + dI_dtheta2**2*sigma_theta[1]**2 + dI_dtheta3**2*sigma_theta[2]**2) + #Stokes_cov[1,1] += (dQ_dtheta1**2*sigma_theta[0]**2 + dQ_dtheta2**2*sigma_theta[1]**2 + dQ_dtheta3**2*sigma_theta[2]**2) + #Stokes_cov[2,2] += (dU_dtheta1**2*sigma_theta[0]**2 + dU_dtheta2**2*sigma_theta[1]**2 + dU_dtheta3**2*sigma_theta[2]**2) plt.imshow(np.abs(Stokes_cov[0,0]/I_stokes)*100., origin='lower') plt.colorbar() plt.show() @@ -1337,7 +1337,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, new_I_stokes = sc_rotate(new_I_stokes, ang, reshape=False, cval=0.) new_Q_stokes = sc_rotate(new_Q_stokes, ang, reshape=False, cval=0.) new_U_stokes = sc_rotate(new_U_stokes, ang, reshape=False, cval=0.) - new_data_mask = sc_rotate(data_mask, ang, reshape=False, cval=True) + new_data_mask = (sc_rotate(data_mask.astype(float)*10., ang, reshape=False, cval=10.).astype(int)).astype(bool) for i in range(3): for j in range(3): new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang,