def ch3cn_spw_fitter():
"""
Generator for a multigaussian fitter covering the target spw
"""
myclass = model.SpectralModel(
multigaussian_model,
12,
parnames=[
'shift', 'width', "13CH3CN 13_8", "13CH3CN 13_7", "13CH3CN 13_6",
"13CH3CN 13_5", "13CH3CN 13_4", "13CH3CN 13_3", "13CH3CN 13_2",
"13CH3CN 13_1", "13CH3CN 13_0", 'background'
],
parlimited=[(False, False),
(True, False)] + [(False, False)] * 9 + [(True, False)],
parlimits=[
(0, 0),
] * 12,
shortvarnames=(r'\Delta x', r'\sigma', "^{13}CH_3CN 13_8",
"^{13}CH_3CN 13_7", "^{13}CH_3CN 13_6",
"^{13}CH_3CN 13_5", "^{13}CH_3CN 13_4",
"^{13}CH_3CN 13_3", "^{13}CH_3CN 13_2",
"^{13}CH_3CN 13_1", "^{13}CH_3CN 13_0", 'T_{BG}'),
centroid_par='shift',
)
myclass.__name__ = "multigauss"
return myclass
def ch3cn_spw_fitter():
"""
Generator for a multigaussian fitter covering the target spw
"""
myclass = model.SpectralModel(
multigaussian_model,
17,
parnames=[
'shift', 'width', "HNCO 1019_918", "CH3CN 12_7", "CH3CN 12_6",
"CH13CN 12_3", "CH13CN 12_2", "CH3CN 12_5", "unknown",
"CH13CN 12_1", "CH13CN 12_0", "CH3CN 12_4", "CH3CN 12_3",
"CH3CN 12_2", "CH3CN 12_1", "CH3CN 12_0", 'background'
],
parlimited=[(False, False),
(True, False)] + [(False, False)] * 14 + [(True, False)],
parlimits=[
(0, 0),
] * 17,
shortvarnames=(r'\Delta x', r'\sigma', "HNCO 1019_918", "CH3CN 12_7",
"CH3CN 12_6", "CH_3^{13}CN 12_3", "CH_3^{13}CN 12_2",
"CH3CN 12_5", "unknown", "CH_3^{13}CN 12_1",
"CH_3^{13}CN 12_0", "CH3CN 12_4", "CH3CN 12_3",
"CH3CN 12_2", "CH3CN 12_1", "CH3CN 12_0", 'T_{BG}'),
centroid_par='shift',
)
myclass.__name__ = "multigauss"
return myclass
def nh3_multi_v_model_generator(n_comp, linenames=None):
"""
Works for up to 2 componet fits at the moment
Parameters
----------
n_comp : int
The number of velocity componets to fit
linenames : list
A list of line names from the set ('oneone', ..., 'eighteight'); default is just 'oneone'
Returns
-------
model : `model.SpectralModel`
A SpectralModel class build from N different metastable inversion
hyperfine models
"""
n_para = n_comp * 4
idx_comp = np.arange(n_comp)
if linenames is None:
linenames = ['oneone']
nlines = len(linenames)
if nlines > 1:
print "[ERROR]: modeling more than a line yet to be implemented. Please only use one line for the time being"
return None
def nh3_vtau_multimodel(xarr, *args):
# the parameters are in the order of vel, width, tex, tau for each velocity component
assert len(args) == n_para
return ammonia_multi_v(xarr, *args, line_names=linenames)
mod = model.SpectralModel(
nh3_vtau_multimodel,
n_para,
parnames=[
x for ln in idx_comp
for x in ('vlsr{0}'.format(ln), 'sigma{0}'.format(ln),
'tex{0}'.format(ln), 'tau{0}'.format(ln))
],
parlimited=[(False, False), (True, False), (True, False),
(True, False)] * n_para,
parlimits=[
(0, 0),
] * n_para,
shortvarnames=[
x for ln in idx_comp
for x in ('v_{{VLSR,{0}}}'.format(ln),
'\\sigma_{{{0}}}'.format(ln), 'T_{{ex,{0}}}'.format(ln),
'\\tau_{{{0}}}'.format(ln))
],
fitunit='Hz')
# the keyword fitunits is now fitunit?
return mod
def hnco_absorption_fitter():
"""
Generator for hnco absorption fitter class
"""
myclass = model.SpectralModel(hnco_model, 5,
parnames=['shift','width','tex','column','background'],
parlimited=[(False,False),(True,False),(True,False),(True,False),(True,False)],
parlimits=[(0,0), (0,0), (0,0),(0,0),(0,0)],
shortvarnames=(r'\Delta x',r'\sigma','T_{ex}','N','T_{BG}'),
centroid_par='shift',
)
myclass.__name__ = "hnco_absorption"
return myclass
def ch3cn_fitter():
"""
Generator for CH3CN fitter class
"""
myclass = model.SpectralModel(ch3cn_model, 4,
parnames=['shift','width','tex','column'],
parlimited=[(False,False),(True,False),(True,False),(True,False)],
parlimits=[(0,0), (0,0), (0,0),(0,0)],
shortvarnames=(r'\Delta x',r'\sigma','T_{ex}','N'),
centroid_par='shift',
)
myclass.__name__ = "ch3cn"
return myclass
def hfonly_66_fixed_fitter():
myclass = model.SpectralModel(hfonly_66_fixed, 4,
parnames=['shift', 'hf1amp', 'hf2amp', 'width', ],
parlimited=[(False, False), (False,False), (False,False),
(True,False),
],
parlimits=[(0,0), (0,0), (0,0), (0,0), ],
shortvarnames=(r'\Delta x', r'A_1', r'A_2', r'\sigma',
),
centroid_par='shift',
)
myclass.__name__ = "hfonly_66_fixed"
return myclass
def ninenine_hf_fitter():
myclass = model.SpectralModel(ninenine_hf, 4,
parnames=['shift', 'tex', 'tau', 'width' ],
parlimited=[(False, False), (True,False), (True,False),
(True,False)
],
parlimits=[(0,0), (0,0), (0,0), (0,0),],
shortvarnames=(r'\Delta x', r'T_{ex}', r'\tau',
r'\sigma',
),
centroid_par='shift',
)
myclass.__name__ = "ninenine_hf"
return myclass
def sevenseven_movinghf_fitter():
myclass = model.SpectralModel(sevenseven_movinghf, 7,
parnames=['shift', 'tau', 'hf1off', 'hf2off', 'width',
'tex', 'tbackground', ],
parlimited=[(False, False), (True,False), (True,False),
(True,False), (True,False),
(True,False), (True, False),
],
parlimits=[(0,0), (0,0), (0,0), (0,0), (0,0), (0,0), (0,0)],
shortvarnames=(r'\Delta x', r'\tau', r'\Delta_{hf1}',
r'\Delta_{hf2}', r'\sigma',
r'T_{ex}', r'T_{bg}',
),
centroid_par='shift',
)
myclass.__name__ = "sevenseven_movinghf"
return myclass
v1 = voigt(xarr, amp1, xcen1, gw1, lw1, normalized=normalized)
v2 = voigt(xarr, amp2, xcen2, gw2, lw2, normalized=normalized)
v3 = voigt(xarr, amp3, xcen3, gw3, lw3, normalized=normalized)
v4 = voigt(xarr, amp4, xcen4, gw4, lw4, normalized=normalized)
if return_components:
return np.array([base, v1, v2, v3, v4])
else:
mod = base + v1 + v2 + v3 + v4
return mod
ModelClass = model.SpectralModel(mymodel, 18,
parnames=['b0','b1']+\
[a+str(ii)
for ii in xrange(4)
for a in ['amplitude','shift','gwidth','lwidth']],
parlimited=[(False,False)]*2 + [(False,False),(False,False),(True,False),(True,False)]*4,
parlimits=[(0,0)]*2 + [(0,0), (0,0), (0,0), (0,0)]*4,
shortvarnames=('b0','b1') + ('A',r'\Delta x',r'\sigma_G',r'\sigma_L')*4,
multisingle='multi',
)
ModelClass.components = lambda xarr, pars: mymodel(
xarr, *pars, return_components=True)
def pcygni(xarr,
b0,
b1,
amp1,
xcen1,
gw1,
