Save only data in "data_mask"

src/lib/fits.py

@@ -12,6 +12,7 @@ prototypes :
 import numpy as np
 from astropy.io import fits
 from astropy import wcs
+from lib.convex_hull import image_hull
 
 
 def get_obs_data(infiles, data_folder="", compute_flux=False):
@@ -77,6 +78,33 @@ def get_obs_data(infiles, data_folder="", compute_flux=False):
     return data_array, headers
 
 
+def clean_ROI(image):
+    H,J = [],[]
+
+    shape = np.array(image.shape)
+    row, col = np.indices(shape)
+
+    for i in range(0,shape[0]):
+        r = row[i,:][image[i,:]>0.]
+        c = col[i,:][image[i,:]>0.]
+        if len(r)>1 and len(c)>1:
+            H.append((r[0],c[0]))
+            H.append((r[-1],c[-1]))
+    H = np.array(H)
+    for j in range(0,shape[1]):
+        r = row[:,j][image[:,j]>0.]
+        c = col[:,j][image[:,j]>0.]
+        if len(r)>1 and len(c)>1:
+            J.append((r[0],c[0]))
+            J.append((r[-1],c[-1]))
+    J = np.array(J)
+    xmin = np.min([H[:,1].min(),J[:,1].min()])-1
+    xmax = np.max([H[:,1].max(),J[:,1].max()])+1
+    ymin = np.min([H[:,0].min(),J[:,0].min()])-1
+    ymax = np.max([H[:,0].max(),J[:,0].max()])+1
+    return np.array([xmin,xmax,ymin,ymax])
+
+
 def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
         s_P_P, PA, s_PA, s_PA_P, headers, data_mask, filename, data_folder="",
         return_hdul=False):
@@ -116,6 +144,11 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
     ref_header = headers[0]
     exp_tot = np.array([header['exptime'] for header in headers]).sum()
     new_wcs = wcs.WCS(ref_header).deepcopy()
+    
+    vertex = clean_ROI(1.-data_mask)
+    shape = vertex[1::2]-vertex[0::2]
+    new_wcs.array_shape = shape
+    new_wcs.wcs.crpix -= vertex[0::2]
 
     header = new_wcs.to_header()
     header['instrume'] = (ref_header['instrume'], 'Instrument from which data is reduced')
@@ -131,6 +164,27 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
     header['PA_int'] = (ref_header['PA_int'], 'Integrated polarization angle')
     header['PA_int_err'] = (ref_header['PA_int_err'], 'Integrated polarization angle error')
 
+    #Crop Data to mask
+    I_stokes = I_stokes[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    Q_stokes = Q_stokes[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    U_stokes = U_stokes[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    P = P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    debiased_P = debiased_P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    s_P = s_P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    s_P_P = s_P_P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    PA = PA[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    s_PA = s_PA[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    s_PA_P = s_PA_P[vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    
+    new_Stokes_cov = np.zeros((3,3,shape[0],shape[1]))
+    for i in range(3):
+        for j in range(3):
+            Stokes_cov[i,j][data_mask] = 0.
+            new_Stokes_cov[i,j] = Stokes_cov[i,j][vertex[0]:vertex[1],vertex[2]:vertex[3]]
+    Stokes_cov = new_Stokes_cov
+    
+    data_mask = data_mask[vertex[0]:vertex[1],vertex[2]:vertex[3]].astype(float, copy=False)
+
     #Create HDUList object
     hdul = fits.HDUList([])
 
@@ -139,8 +193,6 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
     primary_hdu = fits.PrimaryHDU(data=I_stokes, header=header)
     hdul.append(primary_hdu)
 
-    data_mask = data_mask.astype(float, copy=False)
-
     #Add Q, U, Stokes_cov, P, s_P, PA, s_PA to the HDUList
     for data, name in [[Q_stokes,'Q_stokes'],[U_stokes,'U_stokes'],
             [Stokes_cov,'IQU_cov_matrix'],[P, 'Pol_deg'],