def set_priors(self):
for baseline in self.baselines:
self._lnpriors[baseline] = _function_wrapper(
sp.stats.uniform.logpdf,
[-5, 5],
dict(),
)
def add_prior(self, **lnprior):
"""
Add prior for some parameters.
:param lnprior:
Kwargs with keys - name of the parameter and values - (callable,
args, kwargs,) where args & kwargs - additional arguments to
callable. Each callable is called callable(p, *args, **kwargs).
Example:
{'flux': (scipy.stats.uniform, [0., 10.], dict(),),
'bmaj': (scipy.stats.uniform, [0, 5.], dict(),),
'e': (scipy.stats.beta.logpdf, [alpha, beta], dict(),)}
First key will result in calling: scipy.stats.norm.logpdf(x, mu, s) as
prior for ``flux`` parameter.
"""
for key, value in lnprior.items():
if key in self._parnames:
func, args, kwargs = value
self._lnprior.update(
{key: _function_wrapper(func, args, kwargs)})
else:
raise Exception("Uknown parameter name: " + str(key))
def fit_model_with_nestle(uv_fits, model_file, components_priors, outdir=None,
**nestle_kwargs):
"""
:param uv_fits:
Path to uv-fits file with self-calibrated visibilities.
:param model_file:
Path to file with difmap model.
:param components_priors:
Components prior's ppf. Close to phase center component goes first.
Iterable of dicts with keys - name of the parameter and values -
(callable, args, kwargs,) where args & kwargs - additional arguments to
callable. Each callable is called callable.ppf(p, *args, **kwargs).
Thus callable should has ``ppf`` method.
Example of prior on single component:
{'flux': (scipy.stats.uniform.ppf, [0., 10.], dict(),),
'bmaj': (scipy.stats.uniform.ppf, [0, 5.], dict(),),
'e': (scipy.stats.beta.ppf, [alpha, beta], dict(),)}
First key will result in calling: scipy.stats.uniform.ppf(u, 0, 10) as
value from prior for ``flux`` parameter.
:param outdir: (optional)
Directory to output results. If ``None`` then use cwd. (default:
``None``)
:param nestle_kwargs: (optional)
Any arguments passed to ``nestle.sample`` function.
:return
Results of ``nestle.sample`` work on that model.
"""
if outdir is None:
outdir = os.getcwd()
mdl_file = model_file
uv_data = UVData(uv_fits)
mdl_dir, mdl_fname = os.path.split(mdl_file)
comps = import_difmap_model(mdl_fname, mdl_dir)
# Sort components by distance from phase center
comps = sorted(comps, key=lambda x: np.sqrt(x.p[1]**2 + x.p[2]**2))
ppfs = list()
labels = list()
for component_prior in components_priors:
for comp_name in ('flux', 'x', 'y', 'bmaj', 'e', 'bpa'):
try:
ppfs.append(_function_wrapper(*component_prior[comp_name]))
labels.append(comp_name)
except KeyError:
pass
for ppf in ppfs:
print(ppf.args)
hypercube = hypercube_partial(ppfs)
# Create model
mdl = Model(stokes=stokes)
# Add components to model
mdl.add_components(*comps)
loglike = LnLikelihood(uv_data, mdl)
time0 = time.time()
result = nestle.sample(loglikelihood=loglike, prior_transform=hypercube,
ndim=mdl.size, npoints=50, method='multi',
callback=nestle.print_progress, **nestle_kwargs)
print("Time spent : {}".format(time.time()-time0))
samples = nestle.resample_equal(result.samples, result.weights)
# Save re-weighted samples from posterior to specified ``outdir``
# directory
np.savetxt(os.path.join(outdir, 'samples.txt'), samples)
fig = corner.corner(samples, show_titles=True, labels=labels,
quantiles=[0.16, 0.5, 0.84], title_fmt='.3f')
# Save corner plot os samples from posterior to specified ``outdir``
# directory
fig.savefig(os.path.join(outdir, "corner.png"), bbox_inches='tight',
dpi=200)
return result