Change some error handling for better smoothing

src/FOC_reduction.py

@@ -17,11 +17,11 @@ import lib.plots as proj_plots      #Functions for plotting data
 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']
-    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']
+#    globals()['plots_folder'] = "../plots/NGC1068_x274020/"
 
 #    globals()['data_folder'] = "../data/NGC1068_x14w010/"
 #    infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -44,13 +44,13 @@ def main():
 #    infiles = ['x3mc0101m_c0f.fits','x3mc0102m_c0f.fits','x3mc0103m_c0f.fits']
 #    globals()['plots_folder'] = "../plots/3C109_x3mc010/"
 
-#    globals()['data_folder'] = "../data/MKN463_x2rp030/"
-#    infiles = ['x2rp0201t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0203t_c0f.fits',
-#            'x2rp0204t_c0f.fits', 'x2rp0205t_c0f.fits', 'x2rp0206t_c0f.fits',
-#            'x2rp0207t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0302t_c0f.fits',
-#            'x2rp0303t_c0f.fits', 'x2rp0304t_c0f.fits', 'x2rp0305t_c0f.fits',
-#            'x2rp0306t_c0f.fits', 'x2rp0307t_c0f.fits']
-#    globals()['plots_folder'] = "../plots/MKN463_x2rp030/"
+    globals()['data_folder'] = "../data/MKN463_x2rp030/"
+    infiles = ['x2rp0201t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0203t_c0f.fits',
+            'x2rp0204t_c0f.fits', 'x2rp0205t_c0f.fits', 'x2rp0206t_c0f.fits',
+            'x2rp0207t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0302t_c0f.fits',
+            'x2rp0303t_c0f.fits', 'x2rp0304t_c0f.fits', 'x2rp0305t_c0f.fits',
+            'x2rp0306t_c0f.fits', 'x2rp0307t_c0f.fits']
+    globals()['plots_folder'] = "../plots/MKN463_x2rp030/"
 
 #    globals()['data_folder'] = "../data/PG1630+377_x39510/"
 #    infiles = ['x3990201m_c0f.fits', 'x3990205m_c0f.fits', 'x3995101r_c0f.fits',
@@ -108,10 +108,10 @@ def main():
     rotate_stokes = True           #rotation to North convention can give erroneous results
     rotate_data = False              #rotation to North convention can give erroneous results
     # Polarization map output
-    figname = 'NGC1068_FOC'         #target/intrument name
+    figname = 'MKN463_FOC'         #target/intrument name
     figtype = '_combine_FWHM020_rot'    #additionnal informations
-    SNRp_cut = 20    #P measurments with SNR>3
-    SNRi_cut = 130   #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
+    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
 
     ##### Pipeline start

src/lib/reduction.py

@@ -800,7 +800,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
             raise ValueError("Not all images in array have same pixel size")
         FWHM /= pxsize[0].min()
 
-    #Define gaussian stdev
+    # Define gaussian stdev
     stdev = FWHM/(2.*np.sqrt(2.*np.log(2)))
     fmax = np.finfo(np.float64).max
 
@@ -821,15 +821,16 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
                 dist_rc = np.where(data_mask, fmax, np.sqrt((r-xx)**2+(c-yy)**2))
                 g_rc = np.array([np.exp(-0.5*(dist_rc/stdev)**2),]*len(data_array))
                 # Apply weighted combination
-                if data_mask[r,c]:
-                    smoothed[r,c] = 0.
-                    error[r,c] = 1.
-                else:
-                    smoothed[r,c] = np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
-                    error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
+                smoothed[r,c] = (1.-data_mask[r,c])*np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
+                error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
+
+        # Nan handling
+        error[np.isnan(smoothed)] = 0.
+        smoothed[np.isnan(smoothed)] = 0.
+        error[np.isnan(error)] = 0.
 
     elif smoothing.lower() in ['gauss','gaussian']:
-        #Convolution with gaussian function
+        # Convolution with gaussian function
         smoothed = np.zeros(data_array.shape)
         error = np.zeros(error_array.shape)
         for i,image in enumerate(data_array):
@@ -838,12 +839,13 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
                 for c in range(image.shape[1]):
                     dist_rc = np.where(data_mask, fmax, np.sqrt((r-xx)**2+(c-yy)**2))
                     g_rc = np.exp(-0.5*(dist_rc/stdev)**2)/(2.*np.pi*stdev**2)
-                if data_mask[r,c]:
-                    smoothed[r,c] = 0.
-                    error[r,c] = 1.
-                else:
-                    smoothed[r,c] = np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
-                    error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
+                    smoothed[i][r,c] = (1.-data_mask[r,c])*np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
+                    error[i][r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
+
+            # Nan handling
+            error[i][np.isnan(smoothed)] = 0.
+            smoothed[i][np.isnan(smoothed)] = 0.
+            error[i][np.isnan(error)] = 0.
 
     else:
         raise ValueError("{} is not a valid smoothing option".format(smoothing))