add new images for restless AGN abstract

src/FOC_reduction.py

@@ -132,7 +132,7 @@ def main():
     # Error estimation
     error_sub_type = 'freedman-diaconis'   #sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (15,15))
     subtract_error = 1.25
-    display_error = True
+    display_error = False
     # Data binning
     rebin = True
     pxsize = 0.10
@@ -154,8 +154,8 @@ def main():
     # Polarization map output
     figname = 'IC5063_FOC'         #target/intrument name
     figtype = '_c_020'    #additionnal informations
-    SNRp_cut = 3.    #P measurments with SNR>3
+    SNRp_cut = 1.    #P measurments with SNR>3
-    SNRi_cut = 30.   #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
+    SNRi_cut = 10.   #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
                     # if step_vec = 0 then all vectors are displayed at full length
 

src/lib/plots.py

@@ -316,7 +316,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
     if display.lower() in ['intensity']:
         # If no display selected, show intensity map
         display='i'
-        vmin, vmax = 3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
+        vmin, vmax = 1./3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
         im = ax.imshow(stkI.data*convert_flux, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
         levelsI = np.linspace(vmax*0.01, vmax*0.99, 10)
@@ -327,7 +327,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
         # Display polarisation flux
         display='pf'
         pf_mask = (stkI.data > 0.) * (pol.data > 0.)
-        vmin, vmax = 3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
+        vmin, vmax = 1./3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
         im = ax.imshow(stkI.data*convert_flux*pol.data, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
         levelsPf = np.linspace(vmax*0.01, vmax*0.99, 10)