add 180° as a convention to PA

src/FOC_reduction.py

@@ -128,7 +128,7 @@ def main():
     # Data binning
     rebin = True
     if rebin:
-        pxsize = 0.05
+        pxsize = 0.10
         px_scale = 'arcsec'         #pixel, arcsec or full
         rebin_operation = 'sum'     #sum or average
     # Alignement
@@ -136,7 +136,7 @@ def main():
     display_data = False
     # Smoothing
     smoothing_function = 'combine'  #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
-    smoothing_FWHM = 0.10           #If None, no smoothing is done
+    smoothing_FWHM = 0.20           #If None, no smoothing is done
     smoothing_scale = 'arcsec'      #pixel or arcsec
     # Rotation
     rotate_stokes = True            #rotation to North convention can give erroneous results
@@ -146,7 +146,7 @@ def main():
     final_display = True
     # Polarization map output
     figname = 'NGC1068_FOC'         #target/intrument name
-    figtype = '_combine_FWHM010'    #additionnal informations
+    figtype = '_combine_FWHM020'    #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

src/comparison_Kishimoto.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-from lib.reduction import align_data, princ_angle
+from lib.reduction import align_data, crop_array, princ_angle
 from lib.deconvolve import zeropad
 from matplotlib.colors import LogNorm
 from os.path import join as path_join
@@ -46,7 +46,7 @@ for d in [data_S, data_K]:
         d[i][np.isnan(d[i])] = 0.
     d['P'] = np.where(d['I']>0.,np.sqrt(d['Q']**2+d['U']**2)/d['I'],0.)
     d['sP'] = np.where(d['I']>0.,np.sqrt((d['Q']**2*d['sQ']**2+d['U']**2*d['sU']**2)/(d['Q']**2+d['U']**2)+((d['Q']/d['I'])**2+(d['U']/d['I'])**2)*d['sI']**2)/d['I'],0.)
-    d['PA'] = (90./np.pi)*np.arctan2(d['U'],d['Q'])
+    d['PA'] = princ_angle((90./np.pi)*np.arctan2(d['U'],d['Q'])+180.)
     d['SNRp'] = np.zeros(d['P'].shape)
     d['SNRp'][d['sP']>0.] = d['P'][d['sP']>0.]/d['sP'][d['sP']>0.]
     d['SNRi'] = np.zeros(d['I'].shape)
@@ -65,7 +65,11 @@ 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_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")
 quiv1 = ax.quiver(data_K['X'],data_K['Y'],data_K['xy_U'],data_K['xy_V'],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='r',alpha=0.75, label="PA through Kishimoto's pipeline")
 
@@ -79,7 +83,9 @@ 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"$\theta_P$ [°]")
 plt.rcParams.update({'font.size': 15})
 ax.legend(loc='upper right')
 

src/lib/plots.py

@@ -450,20 +450,16 @@ class align_maps(object):
         self.other_map = other_map
 
         self.wcs_map = deepcopy(WCS(self.map[0])).celestial
-#        if self.wcs_map.naxis == 4:
+        if len(self.map[0].data.shape) == 4:
-#            self.wcs_map = WCS(self.map[0],naxis=[1,2]).deepcopy()
+            self.map[0].data = self.map[0].data[0,0]
-#            self.map[0].data = self.map[0].data[0,0]
+        elif len(self.map[0].data.shape) == 3:
-#        elif self.wcs_map.naxis == 3:
+            self.map[0].data = self.map[0].data[1]
-#            self.wcs_map = WCS(self.map[0],naxis=[1,2]).deepcopy()
-#            self.map[0].data = self.map[0].data[1]
 
         self.wcs_other = deepcopy(WCS(self.other_map[0])).celestial
-#        if self.wcs_other.naxis == 4:
+        if len(self.other_map[0].data.shape) == 4:
-#            self.wcs_other = WCS(self.other_map[0],naxis=[1,2]).deepcopy()
+            self.other_map[0].data = self.other_map[0].data[0,0]
-#            self.other_map[0].data = self.other_map[0].data[0,0]
+        elif len(self.other_map[0].data.shape) == 3:
-#        elif self.wcs_other.naxis == 3:
+            self.other_map[0].data = self.other_map[0].data[1]
-#            self.wcs_other = WCS(self.other_map[0],naxis=[1,2]).deepcopy()
-#            self.other_map[0].data = self.other_map[0].data[1]
 
         try:
             convert_flux = self.map[0].header['photflam']
@@ -1837,7 +1833,7 @@ class pol_map(object):
             P_cut = np.sqrt(Q_cut**2+U_cut**2)/I_cut
             P_cut_err = np.sqrt((Q_cut**2*Q_cut_err**2 + U_cut**2*U_cut_err**2 + 2.*Q_cut*U_cut*QU_cut_err)/(Q_cut**2 + U_cut**2) + ((Q_cut/I_cut)**2 + (U_cut/I_cut)**2)*I_cut_err**2 - 2.*(Q_cut/I_cut)*IQ_cut_err - 2.*(U_cut/I_cut)*IU_cut_err)/I_cut
 
-            PA_cut = princ_angle(np.degrees((1./2.)*np.arctan2(U_cut,Q_cut)))
+            PA_cut = princ_angle(np.degrees((1./2.)*np.arctan2(U_cut,Q_cut))+180.)
             PA_cut_err = princ_angle(np.degrees((1./(2.*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2*Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)))
 
         else:
@@ -1862,7 +1858,7 @@ class pol_map(object):
             P_reg = np.sqrt(Q_reg**2+U_reg**2)/I_reg
             P_reg_err = np.sqrt((Q_reg**2*Q_reg_err**2 + U_reg**2*U_reg_err**2 + 2.*Q_reg*U_reg*QU_reg_err)/(Q_reg**2 + U_reg**2) + ((Q_reg/I_reg)**2 + (U_reg/I_reg)**2)*I_reg_err**2 - 2.*(Q_reg/I_reg)*IQ_reg_err - 2.*(U_reg/I_reg)*IU_reg_err)/I_reg
 
-            PA_reg = princ_angle((90./np.pi)*np.arctan2(U_reg,Q_reg))
+            PA_reg = princ_angle((90./np.pi)*np.arctan2(U_reg,Q_reg)+180.)
             PA_reg_err = (90./(np.pi*(Q_reg**2+U_reg**2)))*np.sqrt(U_reg**2*Q_reg_err**2 + Q_reg**2*U_reg_err**2 - 2.*Q_reg*U_reg*QU_reg_err)
 
             new_cut = np.logical_and(self.region, self.cut)
@@ -1879,7 +1875,7 @@ class pol_map(object):
             P_cut = np.sqrt(Q_cut**2+U_cut**2)/I_cut
             P_cut_err = np.sqrt((Q_cut**2*Q_cut_err**2 + U_cut**2*U_cut_err**2 + 2.*Q_cut*U_cut*QU_cut_err)/(Q_cut**2 + U_cut**2) + ((Q_cut/I_cut)**2 + (U_cut/I_cut)**2)*I_cut_err**2 - 2.*(Q_cut/I_cut)*IQ_cut_err - 2.*(U_cut/I_cut)*IU_cut_err)/I_cut
 
-            PA_cut = princ_angle((90./np.pi)*np.arctan2(U_cut,Q_cut))
+            PA_cut = princ_angle((90./np.pi)*np.arctan2(U_cut,Q_cut)+180.)
             PA_cut_err = (90./(np.pi*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2*Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)
 
         if hasattr(self, 'cont'):

src/lib/reduction.py

@@ -1310,7 +1310,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
         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)
 
-        PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted))
+        PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted)+180.)
         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)
 
         for header in headers:
@@ -1370,7 +1370,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
     P = np.zeros(I_stokes.shape)
     P[mask] = I_pol[mask]/I_stokes[mask]
     PA = np.zeros(I_stokes.shape)
-    PA[mask] = princ_angle((90./np.pi)*np.arctan2(U_stokes[mask],Q_stokes[mask]))
+    PA[mask] = princ_angle((90./np.pi)*np.arctan2(U_stokes[mask],Q_stokes[mask])+180.)
 
     if (P>1).any():
         print("WARNING : found {0:d} pixels for which P > 1".format(P[P>1.].size))
@@ -1569,7 +1569,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
     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)
 
-    PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted))
+    PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted)+180.)
     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)
 
     for header in new_headers:

src/overplot.py

@@ -6,35 +6,35 @@ from copy import deepcopy
 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_UV = fits.open("../data/IC5063_x3nl030/IC5063_FOC_combine_FWHM020.fits")
 #Stokes_18GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063.18GHz.fits")
 #Stokes_24GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063.24GHz.fits")
 #Stokes_103GHz = fits.open("../data/IC5063_x3nl030/radio/I5063_103GHz.fits")
 #Stokes_229GHz = fits.open("../data/IC5063_x3nl030/radio/I5063_229GHz.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")
+Stokes_IR = fits.open("../data/IC5063_x3nl030/IR/u2e65g01t_c0f_rot.fits")
 
 #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 = levelsMorganti*0.28*1e-3
 #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')
-
+#
 ##levels24GHz = np.array([1.,1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_24GHz[0].data.max()
 #levels24GHz = levelsMorganti*0.46*1e-3
 #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')
-
+#
 #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.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.]))
 #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')
-
+#
 #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.plot(levels=levels357GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/357GHz_overplot_forced.png')
@@ -42,8 +42,8 @@ from matplotlib.colors import LogNorm
 #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))
 
-#G = overplot_pol(Stokes_UV, Stokes_IR, cmap='inferno')
+G = overplot_pol(Stokes_UV, Stokes_IR, cmap='inferno')
-#G.plot(SNRp_cut=3.0, SNRi_cut=60.0, savename='../plots/IC5063_x3nl030/IR_overplot_forced.png', norm=LogNorm(vmin=1e-17,vmax=5e-15), cmap='inferno_r')
+G.plot(SNRp_cut=3.0, SNRi_cut=60.0, savename='../plots/IC5063_x3nl030/IR_overplot_forced.png', norm=LogNorm(vmin=1e-17,vmax=5e-15), cmap='inferno_r')
 
 #data_folder1 = "../data/M87/POS1/"
 #plots_folder1 = "../plots/M87/POS1/"