def plot_coverage_maps(cov_array,
cc_fits,
colorbar_label="coverage",
cmap="hsv"):
ccimage = create_clean_image_from_fits_file(cc_fits)
beam = ccimage.beam
rms = rms_image(ccimage)
print(rms)
cov_array[cov_array == 0] = np.nan
blc, trc = find_bbox(ccimage.image, 1.0 * rms, 10)
fig = iplot(ccimage.image,
colors=cov_array,
x=ccimage.x,
y=ccimage.y,
min_abs_level=3.0 * rms,
beam=beam,
show_beam=True,
blc=blc,
trc=trc,
close=False,
colorbar_label=colorbar_label,
show=True,
cmap=cmap)
return fig
def create_boot_pang_errors(self, cred_mass=0.68, n_sigma=None):
"""
Create dictionary with images of PANG errors calculated from bootstrap
PANG maps.
:param cred_mass: (optional)
Credibility mass. (default: ``0.68``)
:param n_sigma: (optional)
Sigma clipping for mask. If ``None`` then use instance's value.
(default: ``None``)
:return:
Dictionary with keys - frequencies & values - instances of ``Image``
class with error maps.
"""
print(
"Calculating maps of PANG errors for each band using bootstrapped"
" PANG maps...")
result = dict()
if n_sigma is not None:
self.set_common_mask(n_sigma)
# Fetch common size `I` map on highest frequency for plotting PANG error
# maps
i_image = self.cc_cs_image_dict[self.freqs[-1]]['I']
rms = rms_image(i_image)
blc, trc = find_bbox(i_image.image,
2. * rms,
delta=int(i_image._beam.beam[0]))
for i, freq in enumerate(self.freqs):
images = self.boot_images.create_pang_images(freq=freq,
mask=self._cs_mask)
pang_images = Images()
pang_images.add_images(images)
error_image = pang_images.create_error_image(cred_mass=cred_mass)
# As this errors are used for linear fit judgement only - add EVPA
# absolute calibration error in quadrature
evpa_error = np.deg2rad(self.sigma_evpa[i]) * np.ones(
error_image.image.shape)
error_image.image = np.sqrt((error_image.image)**2. +
evpa_error**2.)
result[freq] = error_image
fig = iplot(i_image.image,
error_image.image,
x=i_image.x,
y=i_image.y,
min_abs_level=3. * rms,
colors_mask=self._cs_mask,
color_clim=[0, 1],
blc=blc,
trc=trc,
beam=self.common_beam,
colorbar_label='sigma EVPA, [rad]',
slice_points=None,
show_beam=True,
show=False,
cmap='hsv')
self.figures['EVPA_sigma_boot_{}'.format(freq)] = fig
self.evpa_sigma_boot_dict = result
return result
def clean_original_data(uvdata_dict,
data_dir,
beam=None,
plot=False,
mapsize_clean=(512, 0.1),
outfname_postfix=None):
if not isinstance(mapsize_clean, dict):
assert len(mapsize_clean) == 2
mapsize_clean = {band: mapsize_clean for band in bands}
for band in bands:
print("Band - {}".format(band))
for stokes in ('I', 'Q', 'U'):
print("Stokes - {}".format(stokes))
if outfname_postfix is None:
outfname = "cc_{}_{}.fits".format(band, stokes)
else:
outfname = "cc_{}_{}_{}.fits".format(band, stokes,
outfname_postfix)
print("Cleaning {} to {}".format(uvdata_dict[band], outfname))
clean_difmap(fname=uvdata_dict[band],
outfname=outfname,
stokes=stokes.lower(),
path=data_dir,
outpath=data_dir,
mapsize_clean=mapsize_clean[band],
path_to_script=path_to_script,
show_difmap_output=False,
beam_restore=beam)
# Rarely need this one
if plot:
if outfname_postfix is None:
outfname = "cc_{}_{}.fits".format(band, "I")
else:
outfname = "cc_{}_{}_{}.fits".format(band, "I",
outfname_postfix)
ccimage = create_clean_image_from_fits_file(
os.path.join(data_dir, outfname))
beam = ccimage.beam
rms = rms_image(ccimage)
blc, trc = find_bbox(ccimage.image, 1.0 * rms, 10)
fig = iplot(ccimage.image,
x=ccimage.x,
y=ccimage.y,
min_abs_level=2.0 * rms,
beam=beam,
show_beam=True,
blc=blc,
trc=trc,
close=False,
colorbar_label="Jy/beam",
show=True)
if outfname_postfix is None:
outfname = "cc_{}.png".format(band)
else:
outfname = "cc_{}_{}.png".format(band, outfname_postfix)
fig.savefig(os.path.join(data_dir, outfname))
cellsize = 0.5
elif freq == 8.1:
cellsize = 0.2
clean_difmap(uv_fits_save_fname,
cc_fits_save_fname,
'I', (1024, cellsize),
path=out_dir,
path_to_script=path_to_script,
show_difmap_output=False,
outpath=out_dir)
ccimage = create_clean_image_from_fits_file(
os.path.join(out_dir, cc_fits_save_fname))
beam = ccimage.beam
rms = rms_image(ccimage)
blc, trc = find_bbox(ccimage.image, rms, 10)
comps = import_difmap_model(out_dfm_model_fn, out_dir)
plot_fitted_model(os.path.join(out_dir, uv_fits_save_fname),
comps,
savefig=os.path.join(
out_dir, "difmap_model_uvplot_{}_{}.png".format(
str(i).zfill(2), freq)))
fig = iplot(ccimage.image,
x=ccimage.x,
y=ccimage.y,
min_abs_level=3 * rms,
beam=beam,
show_beam=True,
# i_image_zeroed[mask] = 0.
# y, x = np.nonzero(i_image_zeroed)
# y -= i_image.pixref[0]
# x -= i_image.pixref[1]
# distances = np.sqrt(x ** 2. + y ** 2.)
# max_dist = int(sorted(distances)[-1])
beam = i_image.beam
pixsize = abs(i_image.pixsize[0]) / mas_to_rad
beam = (beam[0] / pixsize, beam[1] / pixsize, beam[2])
# cc_fits = '/home/ilya/vlbi_errors/examples/X/1226+023/I/boot/68/original_cc.fits'
# cc_fits = '/home/ilya/vlbi_errors/examples/L/1038+064/rms/68/original_cc.fits'
# cc_fits = '/home/ilya/vlbi_errors/examples/L/1633+382/rms/68/original_cc.fits'
image = create_image_from_fits_file(os.path.join(data_dir, cc_fits))
rms = image_ops.rms_image(image)
data = image.image.copy()
from scipy.ndimage.filters import gaussian_filter
data = gaussian_filter(data, 5)
mask = data < 3. * rms
data[mask] = 0
data[~mask] = 1
skel, distance = medial_axis(data, return_distance=True)
dist_on_skel = distance * skel
# Plot area and skeleton
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=(8, 4),
sharex=True,
clean_n(uv_fits_path,
out_fits_fname,
'I', (512, 0.1),
niter=niter,
path_to_script=path_to_script,
outpath=data_dir,
show_difmap_output=True,
shift=(1000, 0))
image = create_image_from_fits_file(out_fits_path)
plt.matshow(image.image)
patch = image.image[250:300, 250:300]
_, _, _, C, A = variogram(patch)
# Image already shifted so don't need rms_image_shifted here
rms = rms_image(image)
from image import plot as iplot
ccimage = create_clean_image_from_fits_file(beam_fits_path)
fig = iplot(image.image,
image.image,
x=image.x,
y=image.y,
show_beam=True,
min_abs_level=rms,
cmap='viridis',
beam=ccimage.beam,
beam_face_color='black',
blc=(250, 250),
trc=(300, 300))
# fig.savefig(os.path.join(data_dir, 'ngc315_niter1000_shifted1000_patch.pdf'),
def analyze_rotm_boot(self, n_sigma=None, cred_mass=0.68, rotm_slices=None,
colors_clim=None, slice_ylim=None,
use_conv_image_in_boot_slice=True):
print("Estimate RM map and it's error using bootstrap...")
if rotm_slices is None:
rotm_slices = self.rotm_slices
if n_sigma is not None:
self.set_common_mask(n_sigma)
# This accounts for D-terms and EVPA calibration errors.
sigma_pangs_dict = self.create_boot_pang_errors()
sigma_pang_arrays = [sigma_pangs_dict[freq].image for freq in
self.freqs]
# This is RM image made using bootstrap-based PANG errors that
# originally account for D-terms and EVPA calibration errors.
rotm_image, _, chisq_image = \
self.original_cs_images.create_rotm_image(sigma_pang_arrays,
mask=self._cs_mask,
return_chisq=True,
mask_on_chisq=True)
self._rotm_image_boot = rotm_image
self._rotm_chisq_image_boot = chisq_image
i_image = self.cc_cs_image_dict[self.freqs[-1]]['I']
rms = rms_image(i_image)
blc, trc = find_bbox(i_image.image, 2.*rms,
delta=int(i_image._beam.beam[0]/2))
fig = iplot(i_image.image, rotm_image.image, x=i_image.x, y=i_image.y,
min_abs_level=3. * rms, colors_mask=self._cs_mask,
color_clim=colors_clim, blc=blc, trc=trc, beam=self.common_beam,
slice_points=rotm_slices, cmap='viridis',
show_beam=True, show=False, beam_place="ul")
self.figures['rotm_image_boot'] = fig
fig = iplot(i_image.image, chisq_image.image, x=i_image.x, y=i_image.y,
min_abs_level=3. * rms, colors_mask=self._cs_mask,
color_clim=[0., self._chisq_crit], blc=blc, trc=trc, beam=self.common_beam,
colorbar_label='Chi-squared', slice_points=rotm_slices,
show_beam=True, show=False, cmap='viridis')
self.figures['rotm_chisq_image_boot'] = fig
self._boot_rotm_images =\
self.boot_images.create_rotm_images(mask=self._cs_mask,
mask_on_chisq=False,
sigma_evpa=self.sigma_evpa)
sigma_rotm_image =\
self._boot_rotm_images.create_error_image(cred_mass=cred_mass)
self._rotm_image_sigma_boot = sigma_rotm_image
# Now ``self._boot_rotm_images`` doesn't contain contribution from
# EVPA calibration error
self._boot_rotm_images =\
self.boot_images.create_rotm_images(mask=self._cs_mask,
mask_on_chisq=False,
sigma_evpa=None)
sigma_rotm_image_grad =\
self._boot_rotm_images.create_error_image(cred_mass=cred_mass)
self._rotm_image_sigma_boot_grad = sigma_rotm_image_grad
# This sigma take absolute EVPA calibration uncertainty into
# account
fig = iplot(i_image.image, sigma_rotm_image.image, x=i_image.x, y=i_image.y,
min_abs_level=3. * rms, colors_mask=self._cs_mask,
outfile='rotm_image_boot_sigma', outdir=self.data_dir,
color_clim=[0, 200], blc=blc, trc=trc, beam=self.common_beam,
slice_points=rotm_slices, cmap='viridis', beam_place='ul',
show_beam=True, show=False)
self.figures['rotm_image_boot_sigma'] = fig
# This sigma RM doesn't include EVPA
fig = iplot(i_image.image, sigma_rotm_image_grad.image, x=i_image.x, y=i_image.y,
min_abs_level=3. * rms, colors_mask=self._cs_mask,
outfile='rotm_image_boot_sigma', outdir=self.data_dir,
color_clim=[0, 200], blc=blc, trc=trc, beam=self.common_beam,
slice_points=rotm_slices, cmap='viridis', beam_place='ul',
show_beam=True, show=False)
self.figures['rotm_image_boot_sigma_grad'] = fig
if rotm_slices is not None:
self.figures['slices_boot'] = dict()
self.figures['slices_boot_conv'] = dict()
for rotm_slice in rotm_slices:
rotm_slice_ = i_image._convert_coordinates(rotm_slice[0],
rotm_slice[1])
if use_conv_image_in_boot_slice:
rotm_image_ = self._rotm_image_conv
else:
rotm_image_ = rotm_image
# ``self._boot_rotm_images`` doesn't contain contribution from
# EVPA calibration error - so used for RM gradient searches
fig = analyze_rotm_slice(rotm_slice_, rotm_image_,
rotm_images=self._boot_rotm_images,
outdir=self.data_dir,
beam_width=int(i_image._beam.beam[0]),
outfname="ROTM_{}_slice_boot".format(rotm_slice),
ylim=slice_ylim, show_dots_boot=True,
# fig=self.figures['slices_conv'][str(rotm_slice)],
fig=None)
self.figures['slices_boot'][str(rotm_slice)] = fig
fig = analyze_rotm_slice(rotm_slice_, rotm_image_,
rotm_images=self._boot_rotm_images,
outdir=self.data_dir,
beam_width=int(i_image._beam.beam[0]),
outfname="ROTM_{}_slice_boot".format(rotm_slice),
ylim=slice_ylim, show_dots_boot=True,
fig=self.figures['slices_conv'][str(rotm_slice)],
# fig=None,
)
self.figures['slices_boot_conv'][str(rotm_slice)] = fig
return rotm_image, sigma_rotm_image
def analyze_rotm_conv(self, sigma_evpa=None, sigma_d_term=None,
rotm_slices=None, colors_clim=None, n_sigma=None,
pxls_plot=None, plot_points=None, slice_ylim=None):
print("Estimate RM map and it's error using conventional method...")
if sigma_evpa is None:
sigma_evpa = self.sigma_evpa
if sigma_d_term is None:
sigma_d_term = self.sigma_d_term
if rotm_slices is None:
rotm_slices = self.rotm_slices
if n_sigma is not None:
self.set_common_mask(n_sigma)
# Find EVPA error for each frequency
print("Calculating maps of PANG errors for each band using Hovatta's"
" approach...")
# Fetch common size `I` map on highest frequency for plotting PANG error
# maps
i_image = self.cc_cs_image_dict[self.freqs[-1]]['I']
if pxls_plot is not None:
pxls_plot = [i_image._convert_coordinate(pxl) for pxl in pxls_plot]
rms = rms_image(i_image)
blc, trc = find_bbox(i_image.image, 2.*rms,
delta=int(i_image._beam.beam[0]))
for i, freq in enumerate(self.freqs):
n_ant = len(self.uvdata_dict[freq].antennas)
n_if = self.uvdata_dict[freq].nif
d_term = sigma_d_term[i]
s_evpa = sigma_evpa[i]
# Get number of scans
if self.n_scans is not None:
try:
# If `NX` table is present
n_scans = len(self.uvdata_dict[freq].scans)
except TypeError:
scans_dict = self.uvdata_dict[freq].scans_bl
scan_lengths = list()
for scans in scans_dict.values():
if scans is not None:
scan_lengths.append(len(scans))
n_scans = mode(scan_lengths)[0][0]
else:
n_scans = self.n_scans[i]
q = self.cc_cs_image_dict[freq]['Q']
u = self.cc_cs_image_dict[freq]['U']
i = self.cc_cs_image_dict[freq]['I']
# We need ``s_evpa = 0`` for testing RM gradient significance but
# ``s_evpa != 0`` for calculating RM errors
pang_std, ppol_std = hovatta_find_sigma_pang(q, u, i, s_evpa,
d_term, n_ant, n_if,
n_scans)
self.evpa_sigma_dict[freq] = pang_std
fig = iplot(i_image.image, pang_std, x=i_image.x, y=i_image.y,
min_abs_level=3. * rms, colors_mask=self._cs_mask,
color_clim=[0, 1], blc=blc, trc=trc,
beam=self.common_beam, colorbar_label='sigma EVPA, [rad]',
show_beam=True, show=False)
self.figures['EVPA_sigma_{}'.format(freq)] = fig
sigma_pang_arrays = [self.evpa_sigma_dict[freq] for freq in self.freqs]
sigma_pang_arrays_grad = [np.sqrt(self.evpa_sigma_dict[freq]**2 -
np.deg2rad(self.sigma_evpa[i])**2) for
i, freq in enumerate(self.freqs)]
rotm_image, sigma_rotm_image, chisq_image =\
self.original_cs_images.create_rotm_image(sigma_pang_arrays,
mask=self._cs_mask,
return_chisq=True,
plot_pxls=pxls_plot,
outdir=self.data_dir,
mask_on_chisq=False)
rotm_image_grad, sigma_rotm_image_grad, _ =\
self.original_cs_images.create_rotm_image(sigma_pang_arrays_grad,
mask=self._cs_mask,
return_chisq=True,
plot_pxls=pxls_plot,
outdir=self.data_dir,
mask_on_chisq=False)
self._rotm_image_conv = rotm_image
self._rotm_image_conv_grad = rotm_image_grad
self._rotm_image_sigma_conv = sigma_rotm_image
self._rotm_image_sigma_conv_grad = sigma_rotm_image_grad
self._rotm_chisq_image_conv = chisq_image
i_image = self.cc_cs_image_dict[self.freqs[-1]]['I']
uv_fits_path = self.uvfits_dict[self.freqs[-1]]
image_fits_path = self.cc_cs_fits_dict[self.freqs[-1]]['I']
# RMS using Hovatta-style
rms = rms_image_shifted(uv_fits_path, image_fits=image_fits_path,
path_to_script=self.path_to_script)
blc, trc = find_bbox(i_image.image, 2.*rms,
delta=int(i_image._beam.beam[0]))
fig = iplot(i_image.image, rotm_image.image, x=i_image.x, y=i_image.y,
min_abs_level=3. * rms, colors_mask=self._cs_mask,
color_clim=colors_clim, blc=blc, trc=trc,
beam=self.common_beam, slice_points=rotm_slices,
show_beam=True, show=False, show_points=plot_points,
cmap='viridis')
self.figures['rotm_image_conv'] = fig
fig = iplot(i_image.image, sigma_rotm_image.image, x=i_image.x,
y=i_image.y, min_abs_level=3. * rms,
colors_mask=self._cs_mask, color_clim=[0, 200], blc=blc,
trc=trc, beam=self.common_beam, slice_points=rotm_slices,
show_beam=True, show=False, cmap='viridis', beam_place='ul')
self.figures['rotm_image_conv_sigma'] = fig
fig = iplot(i_image.image, sigma_rotm_image_grad.image, x=i_image.x,
y=i_image.y, min_abs_level=3. * rms,
colors_mask=self._cs_mask, color_clim=[0, 200], blc=blc,
trc=trc, beam=self.common_beam, slice_points=rotm_slices,
show_beam=True, show=False, cmap='viridis', beam_place='ul')
self.figures['rotm_image_conv_sigma_grad'] = fig
fig = iplot(i_image.image, chisq_image.image, x=i_image.x, y=i_image.y,
min_abs_level=3. * rms, colors_mask=self._cs_mask,
outfile='rotm_chisq_image_conv', outdir=self.data_dir,
color_clim=[0., self._chisq_crit], blc=blc, trc=trc,
beam=self.common_beam,
colorbar_label='Chi-squared', slice_points=rotm_slices,
show_beam=True, show=False, cmap='viridis')
self.figures['rotm_chisq_image_conv'] = fig
if rotm_slices is not None:
self.figures['slices_conv'] = dict()
for rotm_slice in rotm_slices:
rotm_slice_ = i_image._convert_coordinates(rotm_slice[0],
rotm_slice[1])
# Here we use RM image error calculated using PANG errors
# without contribution of the EVPA calibration errors.
fig = analyze_rotm_slice(rotm_slice_, rotm_image,
sigma_rotm_image=sigma_rotm_image_grad,
outdir=self.data_dir,
beam_width=int(i_image._beam.beam[0]),
outfname="ROTM_{}_slice".format(rotm_slice),
ylim=slice_ylim)
self.figures['slices_conv'][str(rotm_slice)] = fig
return rotm_image, sigma_rotm_image
def analyze_rotm_boot(self,
n_sigma=None,
cred_mass=0.68,
rotm_slices=None,
colors_clim=None,
slice_ylim=None,
use_conv_image_in_boot_slice=True):
print("Estimate RM map and it's error using bootstrap...")
if rotm_slices is None:
rotm_slices = self.rotm_slices
if n_sigma is not None:
self.set_common_mask(n_sigma)
sigma_pangs_dict = self.create_boot_pang_errors()
sigma_pang_arrays = [
sigma_pangs_dict[freq].image for freq in self.freqs
]
rotm_image, _, chisq_image = \
self.original_cs_images.create_rotm_image(sigma_pang_arrays,
mask=self._cs_mask,
return_chisq=True,
mask_on_chisq=True)
self._rotm_image_boot = rotm_image
self._rotm_chisq_image_boot = chisq_image
i_image = self.cc_cs_image_dict[self.freqs[-1]]['I']
rms = rms_image(i_image)
blc, trc = find_bbox(i_image.image,
2. * rms,
delta=int(i_image._beam.beam[0]))
fig = iplot(i_image.image,
rotm_image.image,
x=i_image.x,
y=i_image.y,
min_abs_level=3. * rms,
colors_mask=self._cs_mask,
color_clim=colors_clim,
blc=blc,
trc=trc,
beam=self.common_beam,
slice_points=rotm_slices,
cmap='hsv',
show_beam=True,
show=False)
self.figures['rotm_image_boot'] = fig
fig = iplot(i_image.image,
chisq_image.image,
x=i_image.x,
y=i_image.y,
min_abs_level=3. * rms,
colors_mask=self._cs_mask,
color_clim=[0., self._chisq_crit],
blc=blc,
trc=trc,
beam=self.common_beam,
colorbar_label='Chi-squared',
slice_points=rotm_slices,
show_beam=True,
show=False)
self.figures['rotm_chisq_image_boot'] = fig
self._boot_rotm_images =\
self.boot_images.create_rotm_images(mask=self._cs_mask,
mask_on_chisq=False)
sigma_rotm_image =\
self._boot_rotm_images.create_error_image(cred_mass=cred_mass)
self._rotm_image_sigma_boot = sigma_rotm_image
# This sigma doesn't take absolute EVPA calibration uncertainty into
# account
fig = iplot(i_image.image,
sigma_rotm_image.image,
x=i_image.x,
y=i_image.y,
min_abs_level=3. * rms,
colors_mask=self._cs_mask,
outfile='rotm_image_boot_sigma',
outdir=self.data_dir,
color_clim=[0, 200],
blc=blc,
trc=trc,
beam=self.common_beam,
slice_points=rotm_slices,
cmap='hsv',
show_beam=True,
show=False,
beam_face_color='black')
self.figures['rotm_image_boot_sigma'] = fig
if rotm_slices is not None:
self.figures['slices_boot'] = dict()
for rotm_slice in rotm_slices:
rotm_slice_ = i_image._convert_coordinates(
rotm_slice[0], rotm_slice[1])
if use_conv_image_in_boot_slice:
rotm_image_ = self._rotm_image_conv
else:
rotm_image_ = rotm_image
fig = analyze_rotm_slice(
rotm_slice_,
rotm_image_,
rotm_images=self._boot_rotm_images,
outdir=self.data_dir,
beam_width=int(i_image._beam.beam[0]),
outfname="ROTM_{}_slice_boot".format(rotm_slice),
ylim=slice_ylim,
show_dots_boot=False,
fig=self.figures['slices_conv'][str(rotm_slice)])
self.figures['slices_boot'][str(rotm_slice)] = fig
return rotm_image, sigma_rotm_image
def abc_simulations(param,
z,
freqs,
uv_fits_templates,
cc_images,
pickle_history,
out_dir=None):
"""
Simulation function for ABC. Simulates data given parameters (``b, n, los``)
and returns the vector of the summary statistics.
:param b:
Value of the magnetic field at 1 pc [G].
:param n:
Value of particle density at 1 pc [cm^(-3)].
:param los:
Jet angle to the line of site [rad].
:param z:
Redshift of the source.
:param freqs:
Iterable of frequencies.
:param uv_fits_templates:
Iterable of paths to FITS files with self-calibrated uv-data. In order
of ``freqs``.
:param cc_images:
Iterable of paths to FITS files with CLEAN maps. In order of ``freqs``.
:param out_dir: (optional)
Directory to store files. If ``None`` then use CWD. (default: ``None``)
:param pickle_history:
Path to pickle file with dictionary of simulations history.
:return:
Summary statistics. E.g. "observed" core flux at highest frequency,
"observed" core size at highest frequency, "observed" ellipticity of the
core at highest frequency, core shift between frequencies (distance
between "core shift - frequency" curves).
"""
if out_dir is None:
out_dir = os.getcwd()
else:
# FIXME: Create directory if it doesn't exist
if not os.path.exists(out_dir):
pass
result_dict = dict()
b, n, los = np.exp(param)
p = Parameters(b, n, los)
with open(pickle_history, 'r') as fo:
history = pickle.load(fo)
print("Running simulations with b={}, n={}, los={}".format(b, n, los))
for freq, uv_fits_template, cc_image in zip(freqs, uv_fits_templates,
cc_images):
# Cleaning old results if any
simulated_maps_old = glob.glob(os.path.join(exe_dir, "map*.txt"))
for to_remove in simulated_maps_old:
os.unlink(to_remove)
# Checking if simulations on highest frequency are done. If so then use
# scaled image parameters
if freqs[0] in history[p].keys():
pixel_size_mas = history[p][
freqs[0]]["pixel_size_mas"] * freqs[0] / freq
number_of_pixels = history[p][
freqs[0]]["number_of_pixels"] * freq / freqs[0]
map_size = (number_of_pixels, pixel_size_mas)
print("Using scaled image parameters: {}, {}".format(
number_of_pixels, pixel_size_mas))
else:
pixel_size_mas = 0.01
number_of_pixels = 400
map_size = None
update_dict = {
"jet": {
"bfield": {
"parameters": {
"b_1": b
}
},
"nfield": {
"parameters": {
"n_1": n
}
}
},
"observation": {
"los_angle": los,
"frequency_ghz": freq,
"redshift": z
},
"image": {
"pixel_size_mas": pixel_size_mas,
"number_of_pixels": number_of_pixels
}
}
update_config(cfg_file, update_dict)
# FIXME: Handle ``FailedFindBestImageParamsException`` during ABC run
simulation_params = run_simulations(cfg_file,
path_to_executable,
map_size=map_size)
# Find total flux on simulated image
image = os.path.join(exe_dir, "map_i.txt")
image = np.loadtxt(image)
# Rare case of strange fluxes
image[image < 0] = 0
image[image > 10.0] = 0
# Total model flux at current frequency
total_flux = image.sum()
# Maximum model pixel flux at current frequency
max_flux = image.max()
cc_image = create_clean_image_from_fits_file(cc_image)
noise_factor = 1.0
initial_dfm_model = os.path.join(main_dir, 'initial_cg.mdl')
out_dfm_model_fn = "bk_{}.mdl".format(freq)
uv_fits_save_fname = "bk_{}.fits".format(freq)
modelfit_simulation_result(exe_dir,
initial_dfm_model,
noise_factor=noise_factor,
out_dfm_model_fn=out_dfm_model_fn,
out_dir=out_dir,
params=simulation_params,
uv_fits_save_fname=uv_fits_save_fname,
uv_fits_template=uv_fits_template)
# Find measured and true distance of core to jet component
dr_obs = find_core_separation_from_jet_using_difmap_model(
os.path.join(out_dir, out_dfm_model_fn))
dr_true = find_core_separation_from_center_using_simulations(
os.path.join(exe_dir, "map_i.txt"), simulation_params)
# This means something wrong with jetshow
if total_flux < 0:
print("b = {}, n = {}, los = {}".format(b, n, los))
open(
os.path.join(
out_dir,
"total_disaster_{}_{}_{}_{}.txt".format(b, n, los, freq)),
'a').close()
raise TotalDisasterException
# Plot map with components superimposed
cc_fits_save_fname = "bk_cc_{}.fits".format(freq)
# For 18 cm we need large pixel size
cellsize = 0.1
if freq == 1.665:
cellsize = 0.5
elif freq == 8.1:
cellsize = 0.2
clean_difmap(uv_fits_save_fname,
cc_fits_save_fname,
'I', (1024, cellsize),
path=out_dir,
path_to_script=path_to_script,
show_difmap_output=False,
outpath=out_dir)
ccimage = create_clean_image_from_fits_file(
os.path.join(out_dir, cc_fits_save_fname))
beam = ccimage.beam
rms = rms_image(ccimage)
blc, trc = find_bbox(ccimage.image, rms, 10)
comps = import_difmap_model(out_dfm_model_fn, out_dir)
plot_fitted_model(os.path.join(out_dir, uv_fits_save_fname),
comps,
savefig=os.path.join(
out_dir,
"difmap_model_uvplot_{}.png".format(freq)))
fig = iplot(ccimage.image,
x=ccimage.x,
y=ccimage.y,
min_abs_level=3 * rms,
beam=beam,
show_beam=True,
blc=blc,
trc=trc,
components=comps,
close=True,
colorbar_label="Jy/beam")
fig.savefig(os.path.join(out_dir, "cc_{}.png".format(freq)))
# Move simulated images to data directory
cut_image(os.path.join(exe_dir, "map_i.txt"))
cut_image(os.path.join(exe_dir, "map_l.txt"))
cut_image(os.path.join(exe_dir, "map_q.txt"))
cut_image(os.path.join(exe_dir, "map_u.txt"))
cut_image(os.path.join(exe_dir, "map_v.txt"))
cut_image(os.path.join(exe_dir, "map_tau.txt"))
for name in ('i', 'q', 'u', 'v', 'tau', 'l'):
shutil.move(
os.path.join(exe_dir, "map_{}.txt".format(name)),
os.path.join(out_dir, "map_{}_{}.txt".format(name, freq)))
# Calculate some info
dr_pc = distance_from_SMBH(dr_true, los, z=z)
b_core = b_field(b, dr_pc)
t_syn_years = t_syn(b_core, freq) / (np.pi * 10**7)
result_dict[freq] = dict()
to_results = {
"dr_obs":
dr_obs,
"dr_true":
dr_true,
"flux":
total_flux,
"flux_obs":
comps[0].p[0],
"bmaj_obs":
comps[0].p[3],
"tb_difmap":
np.log10(tb_comp(comps[0].p[0], comps[0].p[3], freq, z=z)),
"tb_pix":
np.log10(
tb(max_flux,
freq,
simulation_params[u'image'][u'pixel_size_mas'],
z=z)),
"b_core":
b_core,
"dr_core_pc":
dr_pc,
"t_syn_core":
t_syn_years,
"pixel_size_mas":
simulation_params[u'image'][u'pixel_size_mas'],
"number_of_pixels":
simulation_params[u'image'][u'number_of_pixels']
}
result_dict[freq] = to_results
history[p] = result_dict
with open(pickle_history, 'w') as fo:
pickle.dump(history, fo)
return create_summary_from_result_dict(result_dict, (0.3, 0.2, 0.1))