def fit_channels(self, img, chan_images, clip_rms, src, beamlist):
"""Fits normalizations of Gaussians in source to multiple channels
If unresolved, the size of the Gaussians are adjusted to match the
channel's beam size (given by beamlist) before fitting.
Returns array of total fluxes (N_channels x N_Gaussians) and array
of errors (N_channels x N_Gaussians).
"""
import functions as func
from const import fwsig
isl = img.islands[src.island_id]
isl_bbox = isl.bbox
nchan = chan_images.shape[0]
x, y = N.mgrid[isl_bbox]
gg = src.gaussians
fitfix = N.ones(len(gg)) # fit only normalization
srcmask = isl.mask_active
total_flux = N.zeros(
(nchan, len(fitfix))) # array of fluxes: N_channels x N_Gaussians
errors = N.zeros(
(nchan, len(fitfix))) # array of fluxes: N_channels x N_Gaussians
for cind in range(nchan):
image = chan_images[cind]
gg_adj = self.adjust_size_by_freq(img.beam, beamlist[cind], gg)
p, ep = func.fit_mulgaus2d(image,
gg_adj,
x,
y,
srcmask,
fitfix,
adj=True)
pbeam = img.beam2pix(beamlist[cind])
bm_pix = (pbeam[0] / fwsig, pbeam[1] / fwsig, pbeam[2]
) # IN SIGMA UNITS
for ig in range(len(fitfix)):
total_flux[cind,
ig] = p[ig * 6] * p[ig * 6 + 3] * p[ig * 6 + 4] / (
bm_pix[0] * bm_pix[1])
p = N.insert(p, N.arange(len(fitfix)) * 6 + 6, total_flux[cind])
rms_isl = N.mean(clip_rms[cind])
errors[cind] = func.get_errors(img,
p,
rms_isl,
bm_pix=(bm_pix[0] * fwsig,
bm_pix[1] * fwsig,
bm_pix[2]))[6]
self.reset_size(gg)
return total_flux, errors
def fit_channels(self, img, chan_images, clip_rms, src, beamlist):
"""Fits normalizations of Gaussians in source to multiple channels
If unresolved, the size of the Gaussians are adjusted to match the
channel's beam size (given by beamlist) before fitting.
Returns array of total fluxes (N_channels x N_Gaussians) and array
of errors (N_channels x N_Gaussians).
"""
import functions as func
from const import fwsig
isl = img.islands[src.island_id]
isl_bbox = isl.bbox
nchan = chan_images.shape[0]
x, y = N.mgrid[isl_bbox]
gg = src.gaussians
fitfix = N.ones(len(gg)) # fit only normalization
srcmask = isl.mask_active
total_flux = N.zeros((nchan, len(fitfix))) # array of fluxes: N_channels x N_Gaussians
errors = N.zeros((nchan, len(fitfix))) # array of fluxes: N_channels x N_Gaussians
for cind in range(nchan):
image = chan_images[cind]
gg_adj = self.adjust_size_by_freq(img.beam, beamlist[cind], gg)
p, ep = func.fit_mulgaus2d(image, gg_adj, x, y, srcmask, fitfix, adj=True)
pbeam = img.beam2pix(beamlist[cind])
bm_pix = (pbeam[0]/fwsig, pbeam[1]/fwsig, pbeam[2]) # IN SIGMA UNITS
for ig in range(len(fitfix)):
total_flux[cind, ig] = p[ig*6]*p[ig*6+3]*p[ig*6+4]/(bm_pix[0]*bm_pix[1])
p = N.insert(p, N.arange(len(fitfix))*6+6, total_flux[cind])
rms_isl = N.mean(clip_rms[cind])
errors[cind] = func.get_errors(img, p, rms_isl, bm_pix=(bm_pix[0]*fwsig, bm_pix[1]*fwsig, bm_pix[2]))[6]
self.reset_size(gg)
return total_flux, errors
def __call__(self, img):
mylog = mylogger.logging.getLogger("PyBDSM." + img.log + "Polarisatn")
if img.opts.polarisation_do:
mylog.info('Extracting polarisation properties for all sources')
pols = ['I', 'Q', 'U', 'V']
# Run gausfit and gual2srl on PI image to look for polarized sources
# undetected in I
fit_PI = img.opts.pi_fit
n_new = 0
ch0_pi = N.sqrt(img.ch0_Q_arr**2 + img.ch0_U_arr**2)
img.ch0_pi_arr = ch0_pi
if fit_PI:
from . import _run_op_list
mylogger.userinfo(mylog, "\nChecking PI image for new sources")
mask = img.mask_arr
minsize = img.opts.minpix_isl
# Set up image object for PI image.
pi_chain, pi_opts = self.setpara_bdsm(img)
pimg = Image(pi_opts)
pimg.beam = img.beam
pimg.pixel_beam = img.pixel_beam
pimg.pixel_beamarea = img.pixel_beamarea
pimg.log = 'PI.'
pimg.pix2beam = img.pix2beam
pimg.beam2pix = img.beam2pix
pimg.pix2gaus = img.pix2gaus
pimg.gaus2pix = img.gaus2pix
pimg.pix2sky = img.pix2sky
pimg.sky2pix = img.sky2pix
pimg.pix2coord = img.pix2coord
pimg.wcs_obj = img.wcs_obj
pimg.mask_arr = mask
pimg.masked = img.masked
pimg.ch0_arr = ch0_pi
pimg._pi = True
success = _run_op_list(pimg, pi_chain)
if not success:
return
img.pi_islands = pimg.islands
img.pi_gaussians = pimg.gaussians
img.pi_sources = pimg.sources
# Now check for new sources in the PI image that are not
# found in the Stokes I image. If any new sources are found,
# adjust their IDs to follow after those found in I.
new_isl = []
new_src = []
new_gaus = []
n_new_src = 0
isl_id = img.islands[-1].island_id
src_id = img.sources[-1].source_id
gaus_id = img.gaussians[-1].gaus_num
for pi_isl in pimg.islands:
new_sources = []
for pi_src in pi_isl.sources:
if img.pyrank[int(img.sky2pix(pi_src.posn_sky_max)[0]),
int(img.sky2pix(pi_src.posn_sky_max)[1]
)] == -1:
src_id += 1
pi_src._pi = True
pi_src.island_id = isl_id
pi_src.source_id = src_id
pi_src.spec_indx = N.NaN
pi_src.e_spec_indx = N.NaN
pi_src.spec_norm = N.NaN
pi_src.specin_flux = [N.NaN]
pi_src.specin_fluxE = [N.NaN]
pi_src.specin_freq = [N.NaN]
pi_src.specin_freq0 = N.NaN
new_sources.append(pi_src)
new_src.append(pi_src)
n_new_src += 1
for g in pi_src.gaussians:
gaus_id += 1
new_gaus.append(g)
g.gaus_num = gaus_id
if len(new_sources) > 0:
isl_id += 1
pi_isl.sources = new_sources
pi_isl.island_id = isl_id
pi_isl._pi = True
new_isl.append(pi_isl)
n_new = len(new_isl)
mylogger.userinfo(mylog, "New sources found in PI image",
'%i (%i total)' % (n_new, img.nsrc + n_new))
if n_new > 0:
img.islands += new_isl
img.sources += new_src
img.gaussians += new_gaus
img.nsrc += n_new_src
bar = statusbar.StatusBar(
'Calculating polarisation properties .... : ', 0, img.nsrc)
if img.opts.quiet == False:
bar.start()
for isl in img.islands:
isl_bbox = isl.bbox
ch0_I = img.ch0_arr[isl_bbox]
ch0_Q = img.ch0_Q_arr[isl_bbox]
ch0_U = img.ch0_U_arr[isl_bbox]
ch0_V = img.ch0_V_arr[isl_bbox]
ch0_images = [ch0_I, ch0_Q, ch0_U, ch0_V]
for i, src in enumerate(isl.sources):
# For each source, assume the morphology does not change
# across the Stokes cube. This assumption allows us to fit
# the Gaussians of each source to each Stokes image by
# simply fitting only the overall normalizations of the
# individual Gaussians.
#
# First, fit all source Gaussians to each Stokes image:
x, y = N.mgrid[isl_bbox]
gg = src.gaussians
fitfix = N.ones(len(gg)) # fit only normalization
srcmask = isl.mask_active
total_flux = N.zeros(
(4, len(fitfix)), dtype=N.float32
) # array of fluxes: N_Stokes x N_Gaussians
errors = N.zeros(
(4, len(fitfix)), dtype=N.float32
) # array of fluxes: N_Stokes x N_Gaussians
for sind, image in enumerate(ch0_images):
if (sind == 0 and hasattr(src, '_pi')
) or sind > 0: # Fit I only for PI sources
p, ep = func.fit_mulgaus2d(image, gg, x, y,
srcmask, fitfix)
for ig in range(len(fitfix)):
center_pix = (p[ig * 6 + 1], p[ig * 6 + 2])
bm_pix = N.array([
img.pixel_beam()[0],
img.pixel_beam()[1],
img.pixel_beam()[2]
])
total_flux[sind, ig] = p[ig * 6] * p[
ig * 6 + 3] * p[ig * 6 + 4] / (bm_pix[0] *
bm_pix[1])
p = N.insert(p,
N.arange(len(fitfix)) * 6 + 6,
total_flux[sind])
if sind > 0:
rms_img = img.__getattribute__('rms_' +
pols[sind] +
'_arr')
else:
rms_img = img.rms_arr
if len(rms_img.shape) > 1:
rms_isl = rms_img[isl.bbox].mean()
else:
rms_isl = rms_img
errors[sind] = func.get_errors(img, p, rms_isl)[6]
# Now, assign fluxes to each Gaussian.
src_flux_I = 0.0
src_flux_Q = 0.0
src_flux_U = 0.0
src_flux_V = 0.0
src_flux_I_err_sq = 0.0
src_flux_Q_err_sq = 0.0
src_flux_U_err_sq = 0.0
src_flux_V_err_sq = 0.0
for ig, gaussian in enumerate(src.gaussians):
flux_I = total_flux[0, ig]
flux_I_err = abs(errors[0, ig])
flux_Q = total_flux[1, ig]
flux_Q_err = abs(errors[1, ig])
flux_U = total_flux[2, ig]
flux_U_err = abs(errors[2, ig])
flux_V = total_flux[3, ig]
flux_V_err = abs(errors[3, ig])
if hasattr(src, '_pi'):
gaussian.total_flux = flux_I
gaussian.total_fluxE = flux_I_err
gaussian.total_flux_Q = flux_Q
gaussian.total_flux_U = flux_U
gaussian.total_flux_V = flux_V
gaussian.total_fluxE_Q = flux_Q_err
gaussian.total_fluxE_U = flux_U_err
gaussian.total_fluxE_V = flux_V_err
if hasattr(src, '_pi'):
src_flux_I += flux_I
src_flux_I_err_sq += flux_I_err**2
src_flux_Q += flux_Q
src_flux_U += flux_U
src_flux_V += flux_V
src_flux_Q_err_sq += flux_Q_err**2
src_flux_U_err_sq += flux_U_err**2
src_flux_V_err_sq += flux_V_err**2
# Calculate and store polarisation fractions and angle for each Gaussian in the island
# For this we need the I flux, which we can just take from g.total_flux and src.total_flux
flux_I = gaussian.total_flux
flux_I_err = gaussian.total_fluxE
stokes = [flux_I, flux_Q, flux_U, flux_V]
stokes_err = [
flux_I_err, flux_Q_err, flux_U_err, flux_V_err
]
lpol_frac, lpol_frac_loerr, lpol_frac_hierr = self.calc_lpol_fraction(
stokes, stokes_err) # linear pol fraction
lpol_ang, lpol_ang_err = self.calc_lpol_angle(
stokes, stokes_err) # linear pol angle
cpol_frac, cpol_frac_loerr, cpol_frac_hierr = self.calc_cpol_fraction(
stokes, stokes_err) # circular pol fraction
tpol_frac, tpol_frac_loerr, tpol_frac_hierr = self.calc_tpol_fraction(
stokes, stokes_err) # total pol fraction
gaussian.lpol_fraction = lpol_frac
gaussian.lpol_fraction_loerr = lpol_frac_loerr
gaussian.lpol_fraction_hierr = lpol_frac_hierr
gaussian.cpol_fraction = cpol_frac
gaussian.cpol_fraction_loerr = cpol_frac_loerr
gaussian.cpol_fraction_hierr = cpol_frac_hierr
gaussian.tpol_fraction = tpol_frac
gaussian.tpol_fraction_loerr = tpol_frac_loerr
gaussian.tpol_fraction_hierr = tpol_frac_hierr
gaussian.lpol_angle = lpol_ang
gaussian.lpol_angle_err = lpol_ang_err
# Store fluxes for each source in the island
if hasattr(src, '_pi'):
src.total_flux = src_flux_I
src.total_fluxE = N.sqrt(src_flux_I_err_sq)
src.total_flux_Q = src_flux_Q
src.total_flux_U = src_flux_U
src.total_flux_V = src_flux_V
src.total_fluxE_Q = N.sqrt(src_flux_Q_err_sq)
src.total_fluxE_U = N.sqrt(src_flux_U_err_sq)
src.total_fluxE_V = N.sqrt(src_flux_V_err_sq)
# Calculate and store polarisation fractions and angle for each source in the island
# For this we need the I flux, which we can just take from g.total_flux and src.total_flux
src_flux_I = src.total_flux
src_flux_I_err = src.total_fluxE
stokes = [src_flux_I, src_flux_Q, src_flux_U, src_flux_V]
stokes_err = [
src_flux_I_err,
N.sqrt(src_flux_Q_err_sq),
N.sqrt(src_flux_U_err_sq),
N.sqrt(src_flux_V_err_sq)
]
lpol_frac, lpol_frac_loerr, lpol_frac_hierr = self.calc_lpol_fraction(
stokes, stokes_err) # linear pol fraction
lpol_ang, lpol_ang_err = self.calc_lpol_angle(
stokes, stokes_err) # linear pol angle
cpol_frac, cpol_frac_loerr, cpol_frac_hierr = self.calc_cpol_fraction(
stokes, stokes_err) # circular pol fraction
tpol_frac, tpol_frac_loerr, tpol_frac_hierr = self.calc_tpol_fraction(
stokes, stokes_err) # total pol fraction
src.lpol_fraction = lpol_frac
src.lpol_fraction_loerr = lpol_frac_loerr
src.lpol_fraction_hierr = lpol_frac_hierr
src.cpol_fraction = cpol_frac
src.cpol_fraction_loerr = cpol_frac_loerr
src.cpol_fraction_hierr = cpol_frac_hierr
src.tpol_fraction = tpol_frac
src.tpol_fraction_loerr = tpol_frac_loerr
src.tpol_fraction_hierr = tpol_frac_hierr
src.lpol_angle = lpol_ang
src.lpol_angle_err = lpol_ang_err
if bar.started:
bar.increment()
bar.stop()
img.completed_Ops.append('polarisation')
def __call__(self, img):
mylog = mylogger.logging.getLogger("PyBDSM."+img.log+"Polarisatn")
if img.opts.polarisation_do:
mylog.info('Extracting polarisation properties for all sources')
pols = ['I', 'Q', 'U', 'V']
# Run gausfit and gual2srl on PI image to look for polarized sources
# undetected in I
fit_PI = img.opts.pi_fit
n_new = 0
ch0_pi = N.sqrt(img.ch0_Q_arr**2 + img.ch0_U_arr**2)
img.ch0_pi_arr = ch0_pi
if fit_PI:
from . import _run_op_list
mylogger.userinfo(mylog, "\nChecking PI image for new sources")
mask = img.mask_arr
minsize = img.opts.minpix_isl
# Set up image object for PI image.
pi_chain, pi_opts = self.setpara_bdsm(img)
pimg = Image(pi_opts)
pimg.beam = img.beam
pimg.pixel_beam = img.pixel_beam
pimg.pixel_beamarea = img.pixel_beamarea
pimg.log = 'PI.'
pimg.pix2beam = img.pix2beam
pimg.beam2pix = img.beam2pix
pimg.pix2gaus = img.pix2gaus
pimg.gaus2pix = img.gaus2pix
pimg.pix2sky = img.pix2sky
pimg.sky2pix = img.sky2pix
pimg.pix2coord = img.pix2coord
pimg.wcs_obj = img.wcs_obj
pimg.mask_arr = mask
pimg.masked = img.masked
pimg.ch0_arr = ch0_pi
pimg._pi = True
success = _run_op_list(pimg, pi_chain)
if not success:
return
img.pi_islands = pimg.islands
img.pi_gaussians = pimg.gaussians
img.pi_sources = pimg.sources
# Now check for new sources in the PI image that are not
# found in the Stokes I image. If any new sources are found,
# adjust their IDs to follow after those found in I.
new_isl = []
new_src = []
new_gaus = []
n_new_src = 0
isl_id = img.islands[-1].island_id
src_id = img.sources[-1].source_id
gaus_id = img.gaussians[-1].gaus_num
for pi_isl in pimg.islands:
new_sources = []
for pi_src in pi_isl.sources:
if img.pyrank[int(img.sky2pix(pi_src.posn_sky_max)[0]),
int(img.sky2pix(pi_src.posn_sky_max)[1])] == -1:
src_id += 1
pi_src._pi = True
pi_src.island_id = isl_id
pi_src.source_id = src_id
pi_src.spec_indx = N.NaN
pi_src.e_spec_indx = N.NaN
pi_src.spec_norm = N.NaN
pi_src.specin_flux = [N.NaN]
pi_src.specin_fluxE = [N.NaN]
pi_src.specin_freq = [N.NaN]
pi_src.specin_freq0 = N.NaN
new_sources.append(pi_src)
new_src.append(pi_src)
n_new_src += 1
for g in pi_src.gaussians:
gaus_id += 1
new_gaus.append(g)
g.gaus_num = gaus_id
if len(new_sources) > 0:
isl_id += 1
pi_isl.sources = new_sources
pi_isl.island_id = isl_id
pi_isl._pi = True
new_isl.append(pi_isl)
n_new = len(new_isl)
mylogger.userinfo(mylog, "New sources found in PI image", '%i (%i total)' %
(n_new, img.nsrc+n_new))
if n_new > 0:
img.islands += new_isl
img.sources += new_src
img.gaussians += new_gaus
img.nsrc += n_new_src
bar = statusbar.StatusBar('Calculating polarisation properties .... : ', 0, img.nsrc)
if img.opts.quiet == False:
bar.start()
for isl in img.islands:
isl_bbox = isl.bbox
ch0_I = img.ch0_arr[isl_bbox]
ch0_Q = img.ch0_Q_arr[isl_bbox]
ch0_U = img.ch0_U_arr[isl_bbox]
ch0_V = img.ch0_V_arr[isl_bbox]
ch0_images = [ch0_I, ch0_Q, ch0_U, ch0_V]
for i, src in enumerate(isl.sources):
# For each source, assume the morphology does not change
# across the Stokes cube. This assumption allows us to fit
# the Gaussians of each source to each Stokes image by
# simply fitting only the overall normalizations of the
# individual Gaussians.
#
# First, fit all source Gaussians to each Stokes image:
x, y = N.mgrid[isl_bbox]
gg = src.gaussians
fitfix = N.ones(len(gg)) # fit only normalization
srcmask = isl.mask_active
total_flux = N.zeros((4, len(fitfix)), dtype=N.float32) # array of fluxes: N_Stokes x N_Gaussians
errors = N.zeros((4, len(fitfix)), dtype=N.float32) # array of fluxes: N_Stokes x N_Gaussians
for sind, image in enumerate(ch0_images):
if (sind==0 and hasattr(src, '_pi')) or sind > 0: # Fit I only for PI sources
p, ep = func.fit_mulgaus2d(image, gg, x, y, srcmask, fitfix)
for ig in range(len(fitfix)):
center_pix = (p[ig*6 + 1], p[ig*6 + 2])
bm_pix = N.array([img.pixel_beam()[0], img.pixel_beam()[1], img.pixel_beam()[2]])
total_flux[sind, ig] = p[ig*6]*p[ig*6+3]*p[ig*6+4]/(bm_pix[0]*bm_pix[1])
p = N.insert(p, N.arange(len(fitfix))*6+6, total_flux[sind])
if sind > 0:
rms_img = img.__getattribute__('rms_'+pols[sind]+'_arr')
else:
rms_img = img.rms_arr
if len(rms_img.shape) > 1:
rms_isl = rms_img[isl.bbox].mean()
else:
rms_isl = rms_img
errors[sind] = func.get_errors(img, p, rms_isl)[6]
# Now, assign fluxes to each Gaussian.
src_flux_I = 0.0
src_flux_Q = 0.0
src_flux_U = 0.0
src_flux_V = 0.0
src_flux_I_err_sq = 0.0
src_flux_Q_err_sq = 0.0
src_flux_U_err_sq = 0.0
src_flux_V_err_sq = 0.0
for ig, gaussian in enumerate(src.gaussians):
flux_I = total_flux[0, ig]
flux_I_err = abs(errors[0, ig])
flux_Q = total_flux[1, ig]
flux_Q_err = abs(errors[1, ig])
flux_U = total_flux[2, ig]
flux_U_err = abs(errors[2, ig])
flux_V = total_flux[3, ig]
flux_V_err = abs(errors[3, ig])
if hasattr(src, '_pi'):
gaussian.total_flux = flux_I
gaussian.total_fluxE = flux_I_err
gaussian.total_flux_Q = flux_Q
gaussian.total_flux_U = flux_U
gaussian.total_flux_V = flux_V
gaussian.total_fluxE_Q = flux_Q_err
gaussian.total_fluxE_U = flux_U_err
gaussian.total_fluxE_V = flux_V_err
if hasattr(src, '_pi'):
src_flux_I += flux_I
src_flux_I_err_sq += flux_I_err**2
src_flux_Q += flux_Q
src_flux_U += flux_U
src_flux_V += flux_V
src_flux_Q_err_sq += flux_Q_err**2
src_flux_U_err_sq += flux_U_err**2
src_flux_V_err_sq += flux_V_err**2
# Calculate and store polarisation fractions and angle for each Gaussian in the island
# For this we need the I flux, which we can just take from g.total_flux and src.total_flux
flux_I = gaussian.total_flux
flux_I_err = gaussian.total_fluxE
stokes = [flux_I, flux_Q, flux_U, flux_V]
stokes_err = [flux_I_err, flux_Q_err, flux_U_err, flux_V_err]
lpol_frac, lpol_frac_loerr, lpol_frac_hierr = self.calc_lpol_fraction(stokes, stokes_err) # linear pol fraction
lpol_ang, lpol_ang_err = self.calc_lpol_angle(stokes, stokes_err) # linear pol angle
cpol_frac, cpol_frac_loerr, cpol_frac_hierr = self.calc_cpol_fraction(stokes, stokes_err) # circular pol fraction
tpol_frac, tpol_frac_loerr, tpol_frac_hierr = self.calc_tpol_fraction(stokes, stokes_err) # total pol fraction
gaussian.lpol_fraction = lpol_frac
gaussian.lpol_fraction_loerr = lpol_frac_loerr
gaussian.lpol_fraction_hierr = lpol_frac_hierr
gaussian.cpol_fraction = cpol_frac
gaussian.cpol_fraction_loerr = cpol_frac_loerr
gaussian.cpol_fraction_hierr = cpol_frac_hierr
gaussian.tpol_fraction = tpol_frac
gaussian.tpol_fraction_loerr = tpol_frac_loerr
gaussian.tpol_fraction_hierr = tpol_frac_hierr
gaussian.lpol_angle = lpol_ang
gaussian.lpol_angle_err = lpol_ang_err
# Store fluxes for each source in the island
if hasattr(src, '_pi'):
src.total_flux = src_flux_I
src.total_fluxE = N.sqrt(src_flux_I_err_sq)
src.total_flux_Q = src_flux_Q
src.total_flux_U = src_flux_U
src.total_flux_V = src_flux_V
src.total_fluxE_Q = N.sqrt(src_flux_Q_err_sq)
src.total_fluxE_U = N.sqrt(src_flux_U_err_sq)
src.total_fluxE_V = N.sqrt(src_flux_V_err_sq)
# Calculate and store polarisation fractions and angle for each source in the island
# For this we need the I flux, which we can just take from g.total_flux and src.total_flux
src_flux_I = src.total_flux
src_flux_I_err = src.total_fluxE
stokes = [src_flux_I, src_flux_Q, src_flux_U, src_flux_V]
stokes_err = [src_flux_I_err, N.sqrt(src_flux_Q_err_sq), N.sqrt(src_flux_U_err_sq), N.sqrt(src_flux_V_err_sq)]
lpol_frac, lpol_frac_loerr, lpol_frac_hierr = self.calc_lpol_fraction(stokes, stokes_err) # linear pol fraction
lpol_ang, lpol_ang_err = self.calc_lpol_angle(stokes, stokes_err) # linear pol angle
cpol_frac, cpol_frac_loerr, cpol_frac_hierr = self.calc_cpol_fraction(stokes, stokes_err) # circular pol fraction
tpol_frac, tpol_frac_loerr, tpol_frac_hierr = self.calc_tpol_fraction(stokes, stokes_err) # total pol fraction
src.lpol_fraction = lpol_frac
src.lpol_fraction_loerr = lpol_frac_loerr
src.lpol_fraction_hierr = lpol_frac_hierr
src.cpol_fraction = cpol_frac
src.cpol_fraction_loerr = cpol_frac_loerr
src.cpol_fraction_hierr = cpol_frac_hierr
src.tpol_fraction = tpol_frac
src.tpol_fraction_loerr = tpol_frac_loerr
src.tpol_fraction_hierr = tpol_frac_hierr
src.lpol_angle = lpol_ang
src.lpol_angle_err = lpol_ang_err
if bar.started:
bar.increment()
bar.stop()
img.completed_Ops.append('polarisation')
