"""
===================================================
Ammonia inversion transition: Hyperfine-only fitter
===================================================
.. moduleauthor:: Adam Ginsburg <adam.g.ginsburg@gmail.com>
Module API
^^^^^^^^^^
"""
import numpy as np
import matplotlib.cbook as mpcb
import copy
import collections
from ...mpfit import mpfit
from . import fitter
from . import hyperfine
from . import radex_modelgrid
from . import model
from .ammonia_constants import (line_names, freq_dict, aval_dict, ortho_dict,
TCMB,
voff_lines_dict, tau_wts_dict, line_labels)
from astropy import constants
from astropy import units as u
ckms = constants.c.to(u.km/u.s).value
# sanity check:
for linename in line_names:
assert len(voff_lines_dict[linename]) == len(tau_wts_dict[linename])
# For each individual inversion line, create a Hyperfine model
nh3_vtau = {linename:
hyperfine.hyperfinemodel({lineid:lineid for lineid,name in
enumerate(voff_lines_dict[linename])},
{lineid:voff for lineid,voff in
enumerate(voff_lines_dict[linename])},
{lineid:freq_dict[linename]*(1-voff/ckms)
for lineid,voff in
enumerate(voff_lines_dict[linename])},
{lineid:tauwt for lineid,tauwt in
enumerate(tau_wts_dict[linename])},
{lineid:sum(tau_wts_dict[linename]) for lineid,voff in
enumerate(voff_lines_dict[linename])},
)
for linename in line_names}
[docs]def nh3_vtau_multimodel_generator(linenames):
"""
If you want to use multiple hyperfines for the same spectrum, use this
generator.
It is useful if you want N independent tau/tex values but the same velocity
and linewidth
Parameters
----------
linenames : list
A list of line names from the set ('oneone', ..., 'eighteight')
Returns
-------
model : `model.SpectralModel`
A SpectralModel class build from N different metastable inversion
hyperfine models
"""
nlines = len(linenames)
def nh3_vtau_multimodel(xarr, velocity, width, *args):
assert len(args) == nlines*2
models = [nh3_vtau[linename].hyperfine(xarr, Tex=tex, tau=tau,
xoff_v=velocity, width=width)
for linename,tex,tau in zip(linenames, args[::2], args[1::2])]
return np.array(models).sum(axis=0)
mod = model.SpectralModel(nh3_vtau_multimodel, 2+nlines*2,
parnames=['center','width'] + [x
for ln in linenames
for x in ('tex{0}'.format(ln),
'tau{0}'.format(ln))
],
parlimited=[(False,False), (True,False),] + [(True, False),]*2*nlines,
parlimits=[(0,0), (0,0), ] + [(TCMB, 0), (0, 0)] * nlines,
shortvarnames=["v","\\sigma",] + [x
for ln in linenames
for x in
('T_{{ex}}({0})'.format(line_labels[ln]),
'\\tau({0})'.format(line_labels[ln]))
],
fitunit='Hz')
return mod
