Add diluted integrated values on display

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

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)')