ppol_images.add_images(images_.create_pol_images())
pang_images.add_images(images_.create_pang_images())
ppol_error_image = ppol_images.create_error_image(cred_mass=0.95)
pang_error_image = pang_images.create_error_image(cred_mass=0.68)
ppol_error_images_dict.update({band: ppol_error_image})
pang_error_images_dict.update({band: pang_error_image})
images_ = Images()
for stoke in stokes:
map_path = im_fits_path(source,
band,
epoch,
stoke,
base_path=base_path)
images_.add_from_fits(
wildcard=os.path.join(map_path, 'cc_orig.fits'))
ppol_image = images_.create_pol_images()[0]
ppol_images_dict.update({band: ppol_image})
mask = ppol_image.image < ppol_error_image.image
ppol_masks_dict.update({band: mask})
# Create overall mask for PPOL flux
masks = [np.array(mask, dtype=int) for mask in ppol_masks_dict.values()]
ppol_mask = np.zeros(masks[0].shape, dtype=int)
for mask in masks:
ppol_mask += mask
ppol_mask[ppol_mask != 0] = 1
# Save mask to disk
np.savetxt(os.path.join(base_path, "ppol_mask.txt"), ppol_mask)
ppol_mask = np.loadtxt(os.path.join(base_path, "ppol_mask.txt"))
# Create bootstrap ROTM images with calculated mask
for l in ('l18', 'l20', 'l21', 'l22'):
evpa_error_images_dict[l] = np.sqrt(evpa_error_images_dict[l] ** 2. +
np.deg2rad(sigma_evpa) ** 2.)
s_pang_arrays = list()
# Create list of EVPA error images sorted by frequency
for l in sorted(evpa_error_images_dict.keys(), reverse=True):
s_pang_arrays.append(evpa_error_images_dict[l])
# For all frequencies create masks based on PPOL flux
ppol_images = dict()
ppol_masks = dict()
freqs_dict = {'l18': 1665458750.0, 'l20': 1493458750.0, 'l21': 1430458750.0,
'l22': 1358458750.0}
for l, freq in freqs_dict.items():
ppol_image = images.create_pol_images(freq=freq)[0]
ppol_images.update({l: ppol_image})
ppol_mask = ppol_image.image < 3. * ppol_error_images_dict[l]
ppol_masks.update({l: ppol_mask})
# Create overall mask for PPOL flux
masks = [np.array(mask, dtype=int) for mask in ppol_masks.values()]
ppol_mask = np.zeros(masks[0].shape, dtype=int)
for mask in masks:
ppol_mask += mask
ppol_mask[ppol_mask != 0] = 1
# FIXME: Alter algorithm for resolve n-pi only when fit has bad chi squared
rotm_image, s_rotm_image = images.create_rotm_image(s_pang_arrays=s_pang_arrays,
mask=ppol_mask)
# Plot intensity contours + ROTM map
