revamp imagerie

src/FOC_reduction.py

@@ -20,17 +20,17 @@ from astropy.wcs import WCS
 def main():
     ##### User inputs
     ## Input and output locations
-    globals()['data_folder'] = "../data/NGC1068_x274020/"
-    infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
-            'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
-            'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
-    psf_file = 'NGC1068_f253m00.fits'
-    globals()['plots_folder'] = "../plots/NGC1068_x274020/"
+#    globals()['data_folder'] = "../data/NGC1068_x274020/"
+#    infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
+#            'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
+#            'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
+##    psf_file = 'NGC1068_f253m00.fits'
+#    globals()['plots_folder'] = "../plots/NGC1068_x274020/"
 
-#    globals()['data_folder'] = "../data/IC5063_x3nl030/"
-#    infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
+    globals()['data_folder'] = "../data/IC5063_x3nl030/"
+    infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
 #    psf_file = 'IC5063_f502m00.fits'
-#    globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
+    globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
 
 #    globals()['data_folder'] = "../data/NGC1068_x14w010/"
 #    infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -122,10 +122,10 @@ def main():
     crop = False                    #Crop to desired ROI
     final_display = False
     # Polarization map output
-    figname = 'NGC1068_FOC'         #target/intrument name
+    figname = 'IC5063_FOC'         #target/intrument name
     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%.
+    SNRp_cut = 3.    #P measurments with SNR>3
+    SNRi_cut = 60.   #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

@@ -271,7 +271,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
     if display is None:
         # If no display selected, show intensity map
         vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
-        im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=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)
         print("Total flux contour levels : ", levelsI)
@@ -281,7 +281,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
         # Display polarisation flux
         pf_mask = (stkI.data > 0.) * (pol.data > 0.)
         vmin, vmax = 0., np.max(stkI.data[pf_mask]*convert_flux*pol.data[pf_mask])
-        im = ax.imshow(stkI.data*convert_flux*pol.data, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = ax.imshow(stkI.data*convert_flux*pol.data, vmin=vmin, vmax=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)
         print("Polarized flux contour levels : ", levelsPf)
@@ -290,24 +290,24 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
     elif display.lower() in ['p','pol','pol_deg']:
         # Display polarization degree map
         vmin, vmax = 0., 100.
-        im = ax.imshow(pol.data*100., vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = ax.imshow(pol.data*100., vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$P$ [%]")
     elif display.lower() in ['s_p','pol_err','pol_deg_err']:
         # Display polarization degree error map
         vmin, vmax = 0., 10.
         p_err = deepcopy(pol_err.data)
         p_err[p_err > vmax/100.] = np.nan
-        im = ax.imshow(p_err*100., vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = ax.imshow(p_err*100., vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$\sigma_P$ [%]")
     elif display.lower() in ['s_i','i_err']:
         # Display intensity error map
         vmin, vmax = 0., np.max(np.sqrt(stk_cov.data[0,0][stk_cov.data[0,0] > 0.])*convert_flux)
-        im = ax.imshow(np.sqrt(stk_cov.data[0,0])*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = ax.imshow(np.sqrt(stk_cov.data[0,0])*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$\sigma_I$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
     elif display.lower() in ['snr','snri']:
         # Display I_stokes signal-to-noise map
         vmin, vmax = 0., np.max(SNRi[SNRi > 0.])
-        im = ax.imshow(SNRi, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = ax.imshow(SNRi, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$I_{Stokes}/\sigma_{I}$")
         levelsSNRi = np.linspace(SNRi_cut, vmax*0.99, 10)
         print("SNRi contour levels : ", levelsSNRi)
@@ -316,7 +316,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
     elif display.lower() in ['snrp']:
         # Display polarization degree signal-to-noise map
         vmin, vmax = SNRp_cut, np.max(SNRp[SNRp > 0.])
-        im = ax.imshow(SNRp, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = ax.imshow(SNRp, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$P/\sigma_{P}$")
         levelsSNRp = np.linspace(SNRp_cut, vmax*0.99, 10)
         print("SNRp contour levels : ", levelsSNRp)
@@ -325,7 +325,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
     else:
         # Defaults to intensity map
         vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux*2.)
-        im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA$]")
 
     if (display is None) or not(display.lower() in ['default']):
@@ -427,7 +427,7 @@ class align_maps(object):
         self.ax1.set_facecolor('k')
 
         vmin, vmax = 0., np.max(data[data > 0.]*convert_flux)
-        im1 = self.ax1.imshow(data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im1 = self.ax1.imshow(data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
 
         fontprops = fm.FontProperties(size=16)
         px_size = self.wcs_map.wcs.get_cdelt()[0]*3600.
@@ -455,7 +455,7 @@ class align_maps(object):
             except KeyError:
                 for key_i, val_i in value:
                     kwargs[key_i] = val_i
-        im2 = self.ax2.imshow(other_data*other_convert, aspect='auto', **kwargs)
+        im2 = self.ax2.imshow(other_data*other_convert, aspect='equal', **kwargs)
 
         fontprops = fm.FontProperties(size=16)
         px_size = self.wcs_other.wcs.get_cdelt()[0]*3600.
@@ -575,7 +575,7 @@ class overplot_radio(align_maps):
 
         #Display UV intensity map with polarization vectors
         vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
-        im = self.ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
+        im = self.ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
         cbar_ax = self.fig2.add_axes([0.95, 0.12, 0.01, 0.75])
         cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
 
@@ -654,8 +654,8 @@ class overplot_pol(align_maps):
 
         #Display Stokes I as contours
         levels_stkI = np.rint(np.linspace(10,99,10))/100.*np.max(stkI.data[stkI.data > 0.]*convert_flux)
-        cont_stkI = self.ax.contour(stkI.data*convert_flux, transform=self.ax.get_transform(self.wcs_UV), levels=levels_stkI, colors='grey')
-        self.ax.clabel(cont_stkI, inline=True, fontsize=8)
+        cont_stkI = self.ax.contour(stkI.data*convert_flux, transform=self.ax.get_transform(self.wcs_UV), levels=levels_stkI, colors='grey', alpha=0.)
+        #self.ax.clabel(cont_stkI, inline=True, fontsize=8)
 
         self.ax.autoscale(False)
 
@@ -680,7 +680,7 @@ class overplot_pol(align_maps):
 
         #Display pixel scale and North direction
         fontprops = fm.FontProperties(size=16)
-        px_size = self.wcs_other.wcs.get_cdelt()[0]*3600.
+        px_size = self.wcs_UV.wcs.get_cdelt()[0]*3600.
         px_sc = AnchoredSizeBar(self.ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
         self.ax.add_artist(px_sc)
         north_dir = AnchoredDirectionArrows(self.ax.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-self.Stokes_UV[0].header['orientat'], color='w', arrow_props={'ec': None, 'fc': 'w', 'alpha': 1,'lw': 2})
@@ -762,7 +762,7 @@ class crop_map(object):
         vmin, vmax = 0., np.max(data[data > 0.]*convert_flux)
         if hasattr(self, 'im'):
             self.im.remove()
-        self.im = self.ax.imshow(data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=self.mask_alpha, origin='lower')
+        self.im = self.ax.imshow(data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=self.mask_alpha, origin='lower')
         if hasattr(self, 'cr'):
             self.cr[0].set_data(*wcs.wcs.crpix)
         else:
@@ -967,7 +967,7 @@ class image_lasso_selector:
             self.ax = ax
             self.mask_alpha = 0.1
             self.embedded = True
-        self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='auto', cmap='inferno',alpha=self.mask_alpha)
+        self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='equal', cmap='inferno',alpha=self.mask_alpha)
         plt.ion()
 
         lineprops = {'color': 'grey', 'linewidth': 1, 'alpha': 0.8}
@@ -996,7 +996,7 @@ class image_lasso_selector:
 
     def update_mask(self):
         self.displayed.remove()
-        self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='auto', cmap='inferno',alpha=self.mask_alpha)
+        self.displayed = self.ax.imshow(self.img, vmin=self.vmin, vmax=self.vmax, aspect='equal', cmap='inferno',alpha=self.mask_alpha)
         array = self.displayed.get_array().data
 
         self.mask = np.zeros(self.img.shape[:2],dtype=bool)
@@ -1390,12 +1390,12 @@ class pol_map(object):
                 ax = self.ax
             if hasattr(self, 'im'):
                 self.im.remove()
-            self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno')
+            self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
             self.cbar = plt.colorbar(self.im, cax=self.cbar_ax, label=label)
             fig.canvas.draw_idle()
             return self.im
         else:
-            im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno')
+            im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
             ax.set_xlim(0,self.data.shape[1])
             ax.set_ylim(0,self.data.shape[0])
             plt.colorbar(im, pad=0.025, aspect=80, label=label)
@@ -1475,4 +1475,3 @@ class pol_map(object):
             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/overplot.py

@@ -16,30 +16,30 @@ 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=80.0, savename='../plots/IC5063_x3nl030/18GHz_overplot_forced.png')
+##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')
-
-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))
+##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')
+#
+#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, norm=LogNorm(vmin=1e-17,vmax=5e-15), cmap='inferno_r')
-G.plot(SNRp_cut=3.0, SNRi_cut=80.0, 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')