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Merge pull request #168 from discsim/log_statements_cleanup
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clean up logging statements
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@rbooth200
rbooth200 authored Sep 9, 2021
2 parents 422f0b0 + 05783bb commit 0f9f073
Showing 1 changed file with 134 additions and 120 deletions.
254 changes: 134 additions & 120 deletions frank/fit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -524,129 +524,143 @@ def output_results(u, v, vis, weights, sol, geom, model, iteration_diagnostics=N
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')
if any([model['input_output']['save_solution'],
model['input_output']['save_profile_fit'],
model['input_output']['save_vis_fit'],
model['input_output']['save_uvtables']
]):

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']
)

figs, axes = [], []

priors = {'alpha': model['hyperparameters']['alpha'],\
'wsmooth': model['hyperparameters']['wsmooth'],\
'Rmax': model['hyperparameters']['rout'],\
'N': model['hyperparameters']['n'],\
'p0': model['hyperparameters']['p0']
}

if model['plotting']['deprojec_plot']:
deproj_fig, deproj_axes = make_figs.make_deprojection_fig(u=u, v=v,
vis=vis, weights=weights,
geom=geom,
bin_widths=model['plotting']['bin_widths'],
force_style=model['plotting']['force_style'],
save_prefix=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=u, v=v, vis=vis,
weights=weights, sol=sol,
bin_widths=model['plotting']['bin_widths'],
priors=priors,
dist=model['plotting']['distance'],
logx=model['plotting']['plot_in_logx'],
force_style=model['plotting']['force_style'],
save_prefix=model['input_output']['save_prefix'],
stretch=model['plotting']['stretch'],
gamma=model['plotting']['gamma'],
asinh_a=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=u, v=v, vis=vis,
weights=weights, sol=sol,
bin_widths=model['plotting']['bin_widths'],
priors=priors,
dist=model['plotting']['distance'],
logx=model['plotting']['plot_in_logx'],
force_style=model['plotting']['force_style'],
save_prefix=model['input_output']['save_prefix'],
norm_residuals=model['plotting']['norm_residuals'],
stretch=model['plotting']['stretch'],
gamma=model['plotting']['gamma'],
asinh_a=model['plotting']['asinh_a']
)

figs.append(full_fig)
axes.append(full_axes)
figs, axes = [], []

if model['plotting']['diag_plot']:
diag_fig, diag_axes, _ = make_figs.make_diag_fig(r=sol.r, q=sol.q,
iteration_diagnostics=iteration_diagnostics,
iter_plot_range=model['plotting']['iter_plot_range'],
force_style=model['plotting']['force_style'],
save_prefix=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=u, v=v, vis=vis,
weights=weights, sol=sol,
clean_profile=clean_profile,
bin_widths=model['plotting']['bin_widths'],
stretch=model['plotting']['stretch'],
gamma=model['plotting']['gamma'],
asinh_a=model['plotting']['asinh_a'],
mean_convolved=mean_convolved,
dist=model['plotting']['distance'],
force_style=model['plotting']['force_style'],
save_prefix=model['input_output']['save_prefix']
)

figs.append(clean_fig)
axes.append(clean_axes)
if any([model['plotting']['deprojec_plot'],
model['plotting']['quick_plot'],
model['plotting']['full_plot'],
model['plotting']['diag_plot'],
model['analysis']['compare_profile']
]):

logging.info(' Plotting results')

priors = {'alpha': model['hyperparameters']['alpha'],\
'wsmooth': model['hyperparameters']['wsmooth'],\
'Rmax': model['hyperparameters']['rout'],\
'N': model['hyperparameters']['n'],\
'p0': model['hyperparameters']['p0']
}

if model['plotting']['deprojec_plot']:
deproj_fig, deproj_axes = make_figs.make_deprojection_fig(u=u, v=v,
vis=vis, weights=weights,
geom=geom,
bin_widths=model['plotting']['bin_widths'],
force_style=model['plotting']['force_style'],
save_prefix=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=u, v=v, vis=vis,
weights=weights, sol=sol,
bin_widths=model['plotting']['bin_widths'],
priors=priors,
dist=model['plotting']['distance'],
logx=model['plotting']['plot_in_logx'],
force_style=model['plotting']['force_style'],
save_prefix=model['input_output']['save_prefix'],
stretch=model['plotting']['stretch'],
gamma=model['plotting']['gamma'],
asinh_a=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=u, v=v, vis=vis,
weights=weights, sol=sol,
bin_widths=model['plotting']['bin_widths'],
priors=priors,
dist=model['plotting']['distance'],
logx=model['plotting']['plot_in_logx'],
force_style=model['plotting']['force_style'],
save_prefix=model['input_output']['save_prefix'],
norm_residuals=model['plotting']['norm_residuals'],
stretch=model['plotting']['stretch'],
gamma=model['plotting']['gamma'],
asinh_a=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(r=sol.r, q=sol.q,
iteration_diagnostics=iteration_diagnostics,
iter_plot_range=model['plotting']['iter_plot_range'],
force_style=model['plotting']['force_style'],
save_prefix=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=u, v=v, vis=vis,
weights=weights, sol=sol,
clean_profile=clean_profile,
bin_widths=model['plotting']['bin_widths'],
stretch=model['plotting']['stretch'],
gamma=model['plotting']['gamma'],
asinh_a=model['plotting']['asinh_a'],
mean_convolved=mean_convolved,
dist=model['plotting']['distance'],
force_style=model['plotting']['force_style'],
save_prefix=model['input_output']['save_prefix']
)

figs.append(clean_fig)
axes.append(clean_axes)

return figs, axes, model

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