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a/src/FOC_reduction.py +++ b/src/FOC_reduction.py @@ -88,10 +88,10 @@ def main(): smoothing_FWHM = 0.10 #If None, no smoothing is done smoothing_scale = 'arcsec' #pixel or arcsec # Rotation - rotate = False #rotation to North convention can give erroneous results + rotate = True #rotation to North convention can give erroneous results # Polarization map output figname = 'NGC1068_FOC' #target/intrument name - figtype = '_gaussian_after_FWHM010' #additionnal informations + figtype = '_gaussian_after_FWHM010_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%. step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted diff --git a/src/lib/plots.py b/src/lib/plots.py index 43b315a..106ceb4 100755 --- a/src/lib/plots.py +++ b/src/lib/plots.py @@ -209,7 +209,7 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1, pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w') ax.add_artist(pol_sc) - # Compute integrated parameters and associated errors + # Compute integrated parameters and associated errors for pixels in the cut I_int = stkI.data[mask].sum() Q_int = stkQ.data[mask].sum() U_int = stkU.data[mask].sum() @@ -226,7 +226,24 @@ def polarization_map(Stokes, SNRp_cut=3., SNRi_cut=30., step_vec=1, PA_int = (90./np.pi)*np.arctan2(U_int,Q_int)+90. 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) - ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1:.1e} $\pm$ {2:.1e} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,I_int*convert_flux,I_int_err*convert_flux)+"\n"+r"$P^{{int}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_int,P_int_err)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_int,PA_int_err), color='white', fontsize=11, xy=(0.01, 0.94), xycoords='axes fraction') + # Compute integrated parameters and associated errors for all pixels + I_diluted = stkI.data.sum() + Q_diluted = stkQ.data.sum() + U_diluted = stkU.data.sum() + I_diluted_err = np.sqrt(np.sum(stk_cov.data[0,0])) + Q_diluted_err = np.sqrt(np.sum(stk_cov.data[1,1])) + U_diluted_err = np.sqrt(np.sum(stk_cov.data[2,2])) + IQ_diluted_err = np.sqrt(np.sum(stk_cov.data[0,1]**2)) + IU_diluted_err = np.sqrt(np.sum(stk_cov.data[0,2]**2)) + QU_diluted_err = np.sqrt(np.sum(stk_cov.data[1,2]**2)) + + P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted*100. + P_diluted_err = (100./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) + + PA_diluted = (90./np.pi)*np.arctan2(U_diluted,Q_diluted)+90. + 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) + + ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1:.1e} $\pm$ {2:.1e} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,I_int*convert_flux,I_int_err*convert_flux)+"\n"+r"$P^{{int}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_int,P_int_err)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_int,PA_int_err)+"\n"+r"$P^{{diluted}}$ = {0:.2f} $\pm$ {1:.2f} %".format(P_diluted,P_diluted_err)+"\n"+r"$\theta_{{P}}^{{diluted}}$ = {0:.2f} $\pm$ {1:.2f} °".format(PA_diluted,PA_diluted_err), color='white', fontsize=11, xy=(0.01, 0.90), xycoords='axes fraction') ax.coords.grid(True, color='white', ls='dotted', alpha=0.5) ax.coords[0].set_axislabel('Right Ascension (J2000)')