for j in range(n_mocks_per_F):
# bookkeeping
file_suffix = '_F'+str(np.int(np.round(1e3*F_cpd[i]))) + \
'uJy_'+str(j).zfill(4)
# inject a mock CPD into the data
vis_cpd = inject_CPD((u, v, vis, wgt), (F_cpd[i], r_cpd[j], az_cpd[j]),
incl=incl, PA=PA, offRA=offRA, offDEC=offDEC)
# frank modeling of the data + CPD injection
sol = FF.fit(u, v, vis_cpd, wgt)
# save the frank results
save_fit(u, v, vis_cpd, wgt, sol,
prefix=target+'_gap'+str(gap_ix)+file_suffix,
save_vis_fit=False, save_solution=False)
# clean up file outputs
os.system('mv '+target+'_gap'+str(gap_ix)+file_suffix + \
'_frank_uv_resid.npz resid_vis/')
os.system('mv '+target+'_gap'+str(gap_ix)+file_suffix + \
'_frank_profile_fit.txt mprofiles/')
os.system('rm '+target+'_gap'+str(gap_ix)+file_suffix+'_frank*')
# record parameter values (F_cpd in uJy, j, r_cpd, az_cpd)
with open(target+'_gap'+str(gap_ix)+'_mpars.'+subsuf+'.txt', 'a') as f:
f.write('%i %s %.3f %i\n' % \
(np.int(np.round(1e3*F_cpd[i])), str(j).zfill(4),
r_cpd[j], az_cpd[j]))
disk.disk[target]['PA'],
dRA=disk.disk[target]['dx'],
dDec=disk.disk[target]['dy'])
# configure the fitting code setup
FF = FrankFitter(Rmax=2 * disk.disk[target]['rout'],
geometry=geom,
N=disk.disk[target]['hyp-Ncoll'],
alpha=disk.disk[target]['hyp-alpha'],
weights_smooth=disk.disk[target]['hyp-wsmth'])
# fit the visibilities
sol = FF.fit(u, v, vis, wgt)
# save the fit
save_fit(u, v, vis, wgt, sol, prefix='fits/' + target)
print('....')
print('Finished visibility modeling')
print('....')
if frank_symm:
print('....')
print('Performing visibility modeling')
print('....')
# load the visibility data
dat = np.load('data/' + target + '_data_symm.vis.npz')
u, v, vis, wgt = dat['u'], dat['v'], dat['Vis'], dat['Wgt']
# set the disk viewing geometry
geom = FixedGeometry(disk.disk[target]['incl'],
CPD_pars = F_cpd[i], r_cpd, az_cpd[j]
dvis_wcpd = inject_CPD(data,
CPD_pars,
incl=incl,
PA=PA,
offRA=offRA,
offDEC=offDEC)
# Frankenstein modeling of the data + CPD injection
sol = FF.fit(u, v, dvis_wcpd, wgt)
# save the results
save_fit(u,
v,
dvis_wcpd,
wgt,
sol,
prefix=dfile + file_suffix,
save_vis_fit=False,
save_solution=False)
# clean up file outputs
os.system('mv ' + dfile + file_suffix +
'_frank_uv_resid.npz resid_vis/')
os.system('mv ' + dfile + file_suffix +
'_frank_profile_fit.txt mprofiles/')
#os.system('mv '+dfile+file_suffix+'_frank_uv_fit.npz model_vis/')
os.system('rm ' + dfile + file_suffix + '_frank*')
# record parameter values (F_cpd in uJy, j, r_cpd, az_cpd)
with open(dfile + '_mpars.txt', 'a') as f:
f.write('%i %s %.3f %i\n' % \
def output_results(u, v, vis, weights, sol, iteration_diagnostics, model):
r"""
Save datafiles of fit results; generate and save figures of fit results (see
frank.io.save_fit, frank.make_figs.make_full_fig,
frank.make_figs.make_quick_fig, frank.make_figs.make_diag_fig)
Parameters
----------
u, v : array, unit = :math:`\lambda`
u and v coordinates of observations
vis : array, unit = Jy
Observed visibilities (complex: real + imag * 1j)
weights : array, unit = Jy^-2
Weights assigned to observed visibilities, of the form
:math:`1 / \sigma^2`
sol : _HankelRegressor object
Reconstructed profile using Maximum a posteriori power spectrum
(see frank.radial_fitters.FrankFitter)
iteration_diagnostics : _HankelRegressor object
Diagnostics of the fit iteration
(see radial_fitters.FrankFitter.fit)
model : dict
Dictionary containing model parameters the fit uses
Returns
-------
figs : Matplotlib `.Figure` instance
All produced figures, including the GridSpecs
axes : Matplotlib `~.axes.Axes` class
Axes for each of the produced figures
"""
logging.info(' Plotting results')
figs, axes = [], []
if model['plotting']['quick_plot']:
quick_fig, quick_axes = make_figs.make_quick_fig(
u, v, vis, weights, sol, model['plotting']['bin_widths'],
model['plotting']['dist'], model['plotting']['force_style'],
model['input_output']['save_prefix'])
figs.append(quick_fig)
axes.append(quick_axes)
if model['plotting']['full_plot']:
full_fig, full_axes = make_figs.make_full_fig(
u, v, vis, weights, sol, model['plotting']['bin_widths'],
model['plotting']['dist'], model['plotting']['force_style'],
model['input_output']['save_prefix'])
figs.append(full_fig)
axes.append(full_axes)
if model['plotting']['diag_plot']:
diag_fig, diag_axes, _ = make_figs.make_diag_fig(
sol.r, sol.q, iteration_diagnostics,
model['plotting']['iter_plot_range'],
model['plotting']['force_style'],
model['input_output']['save_prefix'])
figs.append(diag_fig)
axes.append(diag_axes)
io.save_fit(u, v, vis, weights, sol, model['input_output']['save_prefix'],
model['input_output']['save_solution'],
model['input_output']['save_profile_fit'],
model['input_output']['save_vis_fit'],
model['input_output']['save_uvtables'],
model['input_output']['iteration_diag'], iteration_diagnostics,
model['input_output']['format'])
return figs, axes, model
def output_results(u, v, vis, weights, sol, geom, model, iteration_diagnostics=None):
r"""
Save datafiles of fit results; generate and save figures of fit results (see
frank.io.save_fit, frank.make_figs)
Parameters
----------
u, v : array, unit = :math:`\lambda`
u and v coordinates of observations
vis : array, unit = Jy
Observed visibilities (complex: real + imag * 1j)
weights : array, unit = Jy^-2
Weights assigned to observed visibilities, of the form
:math:`1 / \sigma^2`
sol : _HankelRegressor object
Reconstructed profile using Maximum a posteriori power spectrum
(see frank.radial_fitters.FrankFitter)
geom : SourceGeometry object
Fitted geometry (see frank.geometry.SourceGeometry)
model : dict
Dictionary containing model parameters the fit uses
iteration_diagnostics : _HankelRegressor object, optional, default=None
Diagnostics of the fit iteration
(see radial_fitters.FrankFitter.fit)
Returns
-------
figs : Matplotlib `.Figure` instance
All produced figures, including the GridSpecs
axes : Matplotlib `~.axes.Axes` class
Axes for each of the produced figures
"""
io.save_fit(u, v, vis, weights, sol,
model['input_output']['save_prefix'],
model['input_output']['save_solution'],
model['input_output']['save_profile_fit'],
model['input_output']['save_vis_fit'],
model['input_output']['save_uvtables'],
model['input_output']['iteration_diag'],
iteration_diagnostics,
model['input_output']['format']
)
logging.info(' Plotting results')
figs, axes = [], []
if model['plotting']['deprojec_plot']:
deproj_fig, deproj_axes = make_figs.make_deprojection_fig(u, v, vis, weights, geom,
model['plotting']['bin_widths'],
model['plotting']['force_style'],
model['input_output']['save_prefix']
)
figs.append(deproj_fig)
axes.append(deproj_axes)
if model['plotting']['quick_plot']:
quick_fig, quick_axes = make_figs.make_quick_fig(u, v, vis, weights, sol,
model['plotting']['bin_widths'],
model['plotting']['distance'],
model['plotting']['plot_in_logx'],
model['plotting']['force_style'],
model['input_output']['save_prefix'],
model['plotting']['stretch'],
model['plotting']['gamma'],
model['plotting']['asinh_a']
)
figs.append(quick_fig)
axes.append(quick_axes)
if model['plotting']['full_plot']:
full_fig, full_axes = make_figs.make_full_fig(u, v, vis, weights, sol,
model['plotting']['bin_widths'],
model['hyperparameters']['alpha'],
model['hyperparameters']['wsmooth'],
model['plotting']['distance'],
model['plotting']['plot_in_logx'],
model['plotting']['force_style'],
model['input_output']['save_prefix'],
model['plotting']['norm_residuals'],
model['plotting']['stretch'],
model['plotting']['gamma'],
model['plotting']['asinh_a']
)
figs.append(full_fig)
axes.append(full_axes)
if model['plotting']['diag_plot']:
diag_fig, diag_axes, _ = make_figs.make_diag_fig(sol.r, sol.q,
iteration_diagnostics,
model['plotting']['iter_plot_range'],
model['plotting']['force_style'],
model['input_output']['save_prefix']
)
figs.append(diag_fig)
axes.append(diag_axes)
if model['analysis']['compare_profile']:
dat = np.genfromtxt(model['analysis']['compare_profile']).T
if len(dat) not in [2,3,4]:
raise ValueError("The file in your .json's `analysis` --> "
"`compare_profile` must have 2, 3 or 4 "
"columns: r [arcsec], I [Jy / sr], "
"negative uncertainty [Jy / sr] (optional), "
"positive uncertainty [Jy / sr] (optional, "
"assumed equal to negative uncertainty if not "
"provided).")
r_clean, I_clean = dat[0], dat[1]
if len(dat) == 3:
lo_err_clean, hi_err_clean = dat[2], dat[2]
elif len(dat) == 4:
lo_err_clean, hi_err_clean = dat[2], dat[3]
else:
lo_err_clean, hi_err_clean = None, None
clean_profile = {'r': r_clean, 'I': I_clean, 'lo_err': lo_err_clean,
'hi_err': hi_err_clean}
mean_convolved = None
if model['analysis']['clean_beam']['bmaj'] is not None:
mean_convolved = utilities.convolve_profile(sol.r, sol.mean,
geom.inc, geom.PA,
model['analysis']['clean_beam'])
clean_fig, clean_axes = make_figs.make_clean_comparison_fig(u, v, vis,
weights, sol,
clean_profile,
model['plotting']['bin_widths'],
model['plotting']['gamma'],
mean_convolved,
model['plotting']['distance'],
model['plotting']['force_style'],
model['input_output']['save_prefix']
)
figs.append(clean_fig)
axes.append(clean_axes)
return figs, axes, model