Latest data products (.c0f) are already transmition corrected, remove correction by default

src/FOC_reduction.py

@@ -128,25 +128,25 @@ def main():
     # Data binning
     rebin = True
     if rebin:
-        pxsize = 0.10
+        pxsize = 10
-        px_scale = 'arcsec'         #pixel, arcsec or full
+        px_scale = 'pixel'         #pixel, arcsec or full
         rebin_operation = 'sum'     #sum or average
     # Alignement
     align_center = 'image'          #If None will align image to image center
     display_data = False
     # Smoothing
     smoothing_function = 'combine'  #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
-    smoothing_FWHM = 0.20           #If None, no smoothing is done
+    smoothing_FWHM = None           #If None, no smoothing is done
     smoothing_scale = 'arcsec'      #pixel or arcsec
     # Rotation
     rotate_stokes = True            #rotation to North convention can give erroneous results
     rotate_data = False             #rotation to North convention can give erroneous results
     # Final crop
     crop = False                    #Crop to desired ROI
-    final_display = True
+    final_display = False
     # Polarization map output
     figname = 'NGC1068_FOC'         #target/intrument name
-    figtype = '_combine_FWHM020'    #additionnal informations
+    figtype = '_bin10px'    #additionnal informations
     SNRp_cut = 5.    #P measurments with SNR>3
     SNRi_cut = 50.   #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
@@ -197,7 +197,7 @@ def main():
     # FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide
     # see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2
     # Bibcode : 1995chst.conf...10J
-    I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
+    I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function,transmitcorr=False)
 
     ## Step 3:
     # Rotate images to have North up

src/comparison_Kishimoto.py

@@ -65,9 +65,9 @@ ax = fig.add_subplot(111, projection=wcs)
 fig.subplots_adjust(right=0.85)
 cbar_ax = fig.add_axes([0.88, 0.12, 0.01, 0.75])
 
-#im0 = ax.imshow(data_S['I']*convert_flux,norm=LogNorm(data_S['I'][data_S['I']>0].min()*convert_flux,data_S['I'][data_S['I']>0].max()*convert_flux),origin='lower',cmap='gray',label=r"$I_{STOKES}$ through this pipeline")
+im0 = ax.imshow(data_S['I']*convert_flux,norm=LogNorm(data_S['I'][data_S['I']>0].min()*convert_flux,data_S['I'][data_S['I']>0].max()*convert_flux),origin='lower',cmap='gray',label=r"$I_{STOKES}$ through this pipeline")
 #im0 = ax.imshow(data_K['P']*100.,vmin=0.,vmax=100.,origin='lower',cmap='inferno',label=r"$P$ through Kishimoto's pipeline")
-im0 = ax.imshow(data_S['P']*100.,vmin=0.,vmax=100.,origin='lower',cmap='inferno',label=r"$P$ through this pipeline")
+#im0 = ax.imshow(data_S['P']*100.,vmin=0.,vmax=100.,origin='lower',cmap='inferno',label=r"$P$ through this pipeline")
 #im0 = ax.imshow(data_K['PA'],vmin=0.,vmax=360.,origin='lower',cmap='inferno',label=r"$\theta_P$ through Kishimoto's pipeline")
 #im0 = ax.imshow(data_S['PA'],vmin=0.,vmax=360.,origin='lower',cmap='inferno',label=r"$\theta_P$ through this pipeline")
 quiv0 = ax.quiver(data_S['X'],data_S['Y'],data_S['xy_U'],data_S['xy_V'],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='b',alpha=0.75, label="PA through this pipeline")
@@ -83,8 +83,8 @@ ax.coords[1].set_axislabel_position('l')
 ax.coords[1].set_ticklabel_position('l')
 #ax.axis('equal')
 
-#cbar = plt.colorbar(im0, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
+cbar = plt.colorbar(im0, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
-cbar = plt.colorbar(im0, cax=cbar_ax, label=r"$P$ [%]")
+#cbar = plt.colorbar(im0, cax=cbar_ax, label=r"$P$ [%]")
 #cbar = plt.colorbar(im0, cax=cbar_ax, label=r"$\theta_P$ [°]")
 plt.rcParams.update({'font.size': 15})
 ax.legend(loc='upper right')

src/lib/plots.py

@@ -1891,15 +1891,15 @@ class pol_map(object):
                 ax = self.ax
             if hasattr(self, 'an_int'):
                 self.an_int.remove()
-            #self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.93), xycoords='axes fraction')
+            self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.93), xycoords='axes fraction')
-            self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.85), xycoords='axes fraction')
+            #self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.85), xycoords='axes fraction')
             if not self.region is None:
                 self.cont = ax.contour(self.region.astype(float),levels=[0.5], colors='white', linewidths=0.8)
             fig.canvas.draw_idle()
             return self.an_int
         else:
-            #ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.94), xycoords='axes fraction')
+            ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.94), xycoords='axes fraction')
-            ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.90), xycoords='axes fraction')
+            #ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.90), xycoords='axes fraction')
             if not self.region is None:
                 ax.contour(self.region.astype(float),levels=[0.5], colors='white', linewidths=0.8)
             fig.canvas.draw_idle()

src/lib/reduction.py

@@ -1138,7 +1138,7 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
 
 
 def compute_Stokes(data_array, error_array, data_mask, headers,
-        FWHM=None, scale='pixel', smoothing='gaussian_after'):
+        FWHM=None, scale='pixel', smoothing='gaussian_after', transmitcorr=False):
     """
     Compute the Stokes parameters I, Q and U for a given data_set
     ----------
@@ -1170,6 +1170,11 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
         -'gaussian_after' convolve output Stokes I/Q/U with a gaussian of
           standard deviation stdev = FWHM/(2*sqrt(2*log(2))).
         Defaults to 'gaussian_after'. Won't be used if FWHM is None.
+    transmitcorr : bool, optional
+        Weither the images should be transmittance corrected for each filter
+        along the line of sight. Latest calibrated data products (.c0f) does
+        not require such correction.
+        Defaults to False.
     ----------
     Returns:
     I_stokes : numpy.ndarray
@@ -1219,6 +1224,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
             transmit2 = np.min([trans2[header['filtnam2'].lower()] for header in headers])
             transmit3 = np.min([trans3[header['filtnam3'].lower()] for header in headers])
             transmit4 = np.min([trans4[header['filtnam4'].lower()] for header in headers])
+        if transmitcorr:
             transmit *= transmit2*transmit3*transmit4
         pol_eff = np.array([pol_efficiency['pol0'], pol_efficiency['pol60'], pol_efficiency['pol120']])
 

src/overplot.py

@@ -7,37 +7,37 @@ from lib.plots import overplot_radio, overplot_pol, align_pol
 from matplotlib.colors import LogNorm
 
 Stokes_UV = fits.open("../data/IC5063_x3nl030/IC5063_FOC_combine_FWHM020.fits")
-#Stokes_18GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063.18GHz.fits")
+Stokes_18GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063.18GHz.fits")
-#Stokes_24GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063.24GHz.fits")
+Stokes_24GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063.24GHz.fits")
-#Stokes_103GHz = fits.open("../data/IC5063_x3nl030/radio/I5063_103GHz.fits")
+Stokes_103GHz = fits.open("../data/IC5063_x3nl030/radio/I5063_103GHz.fits")
-#Stokes_229GHz = fits.open("../data/IC5063_x3nl030/radio/I5063_229GHz.fits")
+Stokes_229GHz = fits.open("../data/IC5063_x3nl030/radio/I5063_229GHz.fits")
-#Stokes_357GHz = fits.open("../data/IC5063_x3nl030/radio/I5063_357GHz.fits")
+Stokes_357GHz = fits.open("../data/IC5063_x3nl030/radio/I5063_357GHz.fits")
 #Stokes_S2 = fits.open("../data/IC5063_x3nl030/POLARIZATION_COMPARISON/S2_rot_crop.fits")
 Stokes_IR = fits.open("../data/IC5063_x3nl030/IR/u2e65g01t_c0f_rot.fits")
 
-#levelsMorganti = np.array([1.,2.,3.,8.,16.,32.,64.,128.])
+levelsMorganti = np.array([1.,2.,3.,8.,16.,32.,64.,128.])
-#
+
-##levels18GHz = np.array([0.6, 1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_18GHz[0].data.max()
+#levels18GHz = np.array([0.6, 1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_18GHz[0].data.max()
-#levels18GHz = levelsMorganti*0.28*1e-3
+levels18GHz = levelsMorganti*0.28*1e-3
-#A = overplot_radio(Stokes_UV, Stokes_18GHz)
+A = overplot_radio(Stokes_UV, Stokes_18GHz)
-#A.plot(levels=levels18GHz, SNRp_cut=3.0, SNRi_cut=60.0, savename='../plots/IC5063_x3nl030/18GHz_overplot_forced.png')
+A.plot(levels=levels18GHz, SNRp_cut=3.0, SNRi_cut=60.0, savename='../plots/IC5063_x3nl030/18GHz_overplot_forced.png')
-#
+
-##levels24GHz = np.array([1.,1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_24GHz[0].data.max()
+#levels24GHz = np.array([1.,1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_24GHz[0].data.max()
-#levels24GHz = levelsMorganti*0.46*1e-3
+levels24GHz = levelsMorganti*0.46*1e-3
-#B = overplot_radio(Stokes_UV, Stokes_24GHz)
+B = overplot_radio(Stokes_UV, Stokes_24GHz)
-#B.plot(levels=levels24GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/24GHz_overplot_forced.png')
+B.plot(levels=levels24GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/24GHz_overplot_forced.png')
-#
+
-#levels103GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_103GHz[0].data[Stokes_103GHz[0].data > 0.]))
+levels103GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_103GHz[0].data[Stokes_103GHz[0].data > 0.]))
-#C = overplot_radio(Stokes_UV, Stokes_103GHz)
+C = overplot_radio(Stokes_UV, Stokes_103GHz)
-#C.plot(levels=levels103GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/103GHz_overplot_forced.png')
+C.plot(levels=levels103GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/103GHz_overplot_forced.png')
-#
+
-#levels229GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_229GHz[0].data[Stokes_229GHz[0].data > 0.]))
+levels229GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_229GHz[0].data[Stokes_229GHz[0].data > 0.]))
-#D = overplot_radio(Stokes_UV, Stokes_229GHz)
+D = overplot_radio(Stokes_UV, Stokes_229GHz)
-#D.plot(levels=levels229GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/229GHz_overplot_forced.png')
+D.plot(levels=levels229GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/229GHz_overplot_forced.png')
-#
+
-#levels357GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_357GHz[0].data[Stokes_357GHz[0].data > 0.]))
+levels357GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_357GHz[0].data[Stokes_357GHz[0].data > 0.]))
-#E = overplot_radio(Stokes_UV, Stokes_357GHz)
+E = overplot_radio(Stokes_UV, Stokes_357GHz)
-#E.plot(levels=levels357GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/357GHz_overplot_forced.png')
+E.plot(levels=levels357GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/357GHz_overplot_forced.png')
 
 #F = overplot_pol(Stokes_UV, Stokes_S2)
 #F.plot(SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/S2_overplot_forced.png', norm=LogNorm(vmin=5e-20,vmax=5e-18))