def __init__(self, freq_dict):
"""
Initialize the various parameters defining the redshiftedgroup transitions
freq_dict - frequencies of the indvidual transitions indexed by name
"""
self.freq_dict = freq_dict
self.fitter = model.SpectralModel(
self,
3,
parnames=['amp', 'center', 'width'],
parlimited=[(False, False), (False, False), (True, False)],
parlimits=[(0, 0), (0, 0), (0, 0)],
shortvarnames=(
"A", "v",
"\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
self.vheight_fitter = model.SpectralModel(
fitter.vheightmodel(self),
4,
parnames=['height', 'amp', 'center', 'width'],
parlimited=[(False, False), (False, False), (True, False),
(False, False), (True, False)],
parlimits=[(0, 0), (0, 0), (0, 0), (0, 0)],
shortvarnames=(
"H", "A", "v",
"\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
def __init__(self,
npeaks=1,
npars=6,
parnames=['tkin', 'tex', 'ntot', 'width', 'xoff_v', 'fortho'],
**kwargs):
self.npeaks = npeaks
self.npars = npars
self._default_parnames = parnames
self.parnames = copy.copy(self._default_parnames)
# all fitters must have declared modelfuncs, which should take the fitted pars...
self.modelfunc = ammonia
self.n_modelfunc = self.n_ammonia
# for fitting ammonia simultaneously with a flat background
self.onepeakammonia = fitter.vheightmodel(ammonia)
#self.onepeakammoniafit = self._fourparfitter(self.onepeakammonia)
self.default_parinfo = None
self.default_parinfo, kwargs = self._make_parinfo(**kwargs)
# Remove keywords parsed by parinfo and ignored by the fitter
for kw in ('tied', 'partied'):
if kw in kwargs:
kwargs.pop(kw)
# enforce ammonia-specific parameter limits
for par in self.default_parinfo:
if 'tex' in par.parname.lower():
par.limited = (True, par.limited[1])
par.limits = (max(par.limits[0], 2.73), par.limits[1])
if 'tkin' in par.parname.lower():
par.limited = (True, par.limited[1])
par.limits = (max(par.limits[0], 2.73), par.limits[1])
if 'width' in par.parname.lower():
par.limited = (True, par.limited[1])
par.limits = (max(par.limits[0], 0), par.limits[1])
if 'fortho' in par.parname.lower():
par.limited = (True, True)
if par.limits[1] != 0:
par.limits = (max(par.limits[0], 0), min(par.limits[1], 1))
else:
par.limits = (max(par.limits[0], 0), 1)
if 'ntot' in par.parname.lower():
par.limited = (True, par.limited[1])
par.limits = (max(par.limits[0], 0), par.limits[1])
self.parinfo = copy.copy(self.default_parinfo)
self.modelfunc_kwargs = kwargs
# lower case? self.modelfunc_kwargs.update({'parnames':self.parinfo.parnames})
self.use_lmfit = kwargs.pop(
'use_lmfit') if 'use_lmfit' in kwargs else False
self.fitunits = 'GHz'
def __init__(self,npeaks=1,npars=6,multisingle='multi',**kwargs):
self.npeaks = npeaks
self.npars = npars
self._default_parnames = ['tkin','tex','ntot','width','xoff_v','fortho']
self.parnames = copy.copy(self._default_parnames)
# all fitters must have declared modelfuncs, which should take the fitted pars...
self.modelfunc = ammonia
self.n_modelfunc = self.n_ammonia
# for fitting ammonia simultaneously with a flat background
self.onepeakammonia = fitter.vheightmodel(ammonia)
#self.onepeakammoniafit = self._fourparfitter(self.onepeakammonia)
if multisingle in ('multi','single'):
self.multisingle = multisingle
else:
raise Exception("multisingle must be multi or single")
self.default_parinfo = None
self.default_parinfo, kwargs = self._make_parinfo(**kwargs)
# Remove keywords parsed by parinfo and ignored by the fitter
for kw in ('tied','partied'):
if kw in kwargs:
kwargs.pop(kw)
# enforce ammonia-specific parameter limits
for par in self.default_parinfo:
if 'tex' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],2.73), par.limits[1])
if 'tkin' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],2.73), par.limits[1])
if 'width' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],0), par.limits[1])
if 'fortho' in par.parname.lower():
par.limited = (True,True)
if par.limits[1] != 0:
par.limits = (max(par.limits[0],0), min(par.limits[1],1))
else:
par.limits = (max(par.limits[0],0), 1)
if 'ntot' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],0), par.limits[1])
self.parinfo = copy.copy(self.default_parinfo)
self.modelfunc_kwargs = kwargs
# lower case? self.modelfunc_kwargs.update({'parnames':self.parinfo.parnames})
self.use_lmfit = kwargs.pop('use_lmfit') if 'use_lmfit' in kwargs else False
self.fitunits = 'GHz'
def __init__(self,npeaks=1,npars=6,multisingle='multi',**kwargs):
self.npeaks = npeaks
self.npars = npars
self._default_parnames = ['tkin','tex','ntot','width','xoff_v','fortho']
self.parnames = copy.copy(self._default_parnames)
# all fitters must have declared modelfuncs, which should take the fitted pars...
self.modelfunc = ammonia
self.n_modelfunc = self.n_ammonia
# for fitting ammonia simultaneously with a flat background
self.onepeakammonia = fitter.vheightmodel(ammonia)
#self.onepeakammoniafit = self._fourparfitter(self.onepeakammonia)
if multisingle in ('multi','single'):
self.multisingle = multisingle
else:
raise Exception("multisingle must be multi or single")
self.default_parinfo = None
self.default_parinfo, kwargs = self._make_parinfo(**kwargs)
# enforce ammonia-specific parameter limits
for par in self.default_parinfo:
if 'tex' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],2.73), par.limits[1])
if 'tkin' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],2.73), par.limits[1])
if 'width' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],0), par.limits[1])
if 'fortho' in par.parname.lower():
par.limited = (True,True)
if par.limits[1] != 0:
par.limits = (max(par.limits[0],0), min(par.limits[1],1))
else:
par.limits = (max(par.limits[0],0), 1)
if 'ntot' in par.parname.lower():
par.limited = (True,par.limited[1])
par.limits = (max(par.limits[0],0), par.limits[1])
self.parinfo = copy.copy(self.default_parinfo)
self.modelfunc_kwargs = kwargs
def gaussian_vheight_fitter():
"""
Generator for Gaussian fitter class
"""
vhg = fitter.vheightmodel(gaussian)
myclass = model.SpectralModel(vhg, 4,
parnames=['height','amplitude','shift','width'],
parlimited=[(False,False),(False,False),(False,False),(True,False)],
parlimits=[(0,0),(0,0), (0,0), (0,0)],
shortvarnames=('B','A',r'\Delta x',r'\sigma'),
centroid_par='shift',
fwhm_func=gaussian_fwhm,
fwhm_pars=['width'],
)
myclass.__name__ = "vheightgaussian"
return myclass
def __init__(self, freq_dict):
"""
Initialize the various parameters defining the redshiftedgroup transitions
freq_dict - frequencies of the indvidual transitions indexed by name
"""
self.freq_dict = freq_dict
self.fitter = model.SpectralModel(self,3,
parnames=['amp','center','width'],
parlimited=[(False,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0)],
shortvarnames=("A","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz' )
self.vheight_fitter = model.SpectralModel(fitter.vheightmodel(self),4,
parnames=['height','amp','center','width'],
parlimited=[(False,False), (False,False), (True,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0), (0,0)],
shortvarnames=("H","A","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz' )
def __init__(self, line_names, voff_lines_dict, freq_dict, line_strength_dict, relative_strength_total_degeneracy):
"""
Initialize the various parameters defining the hyperfine transitions
line_names is a LIST of the line names to be used as indices for the dictionaries
voff_lines_dict is a linename:v_off dictionary of velocity offsets for the hyperfine components. Technically,
this is redundant with freq_dict
freq_dict - frequencies of the indvidual transitions
line_strength_dict - Relative strengths of the hyperfine components, usually determined by their degeneracy and
Einstein A coefficients
"""
self.line_names = line_names
self.voff_lines_dict = voff_lines_dict
self.freq_dict = freq_dict
self.line_strength_dict = line_strength_dict
self.relative_strength_total_degeneracy = relative_strength_total_degeneracy
self.fitter = model.SpectralModel(self,4,
parnames=['Tex','tau','center','width'],
parlimited=[(False,False), (True,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0), (0,0)],
shortvarnames=("T_{ex}","\\tau","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz' )
self.vheight_fitter = model.SpectralModel(fitter.vheightmodel(self),5,
parnames=['height','Tex','tau','center','width'],
parlimited=[(False,False), (False,False), (True,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0), (0,0), (0,0)],
shortvarnames=("H","T_{ex}","\\tau","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz' )
self.ampfitter = model.SpectralModel(self.hyperfine_amp,3,
parnames=['amp','center','width'],
parlimited=[(False,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0)],
shortvarnames=("amp","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz' )
def __init__(self, line_names, voff_lines_dict, freq_dict,
line_strength_dict, relative_strength_total_degeneracy):
"""
Initialize the various parameters defining the hyperfine transitions
Parameters
----------
line_names: list
list of the line names to be used as indices for the dictionaries
voff_lines_dict: dict
a linename:v_off dictionary of velocity offsets for the hyperfine
components. Technically, this is redundant with freq_dict
freq_dict: dict
frequencies of the indvidual transitions
line_strength_dict: dict
Relative strengths of the hyperfine components, usually determined
by their degeneracy and Einstein A coefficients
"""
self.line_names = line_names
self.voff_lines_dict = voff_lines_dict
self.freq_dict = freq_dict
self.line_strength_dict = line_strength_dict
self.relative_strength_total_degeneracy = relative_strength_total_degeneracy
self.fitter = model.SpectralModel(
self,
4,
parnames=['Tex', 'tau', 'center', 'width'],
parlimited=[(False, False), (True, False), (False, False),
(True, False)],
parlimits=[(0, 0), (0, 0), (0, 0), (0, 0)],
shortvarnames=(
"T_{ex}", "\\tau", "v",
"\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
self.nlines = len(line_names)
self.varyhf_fitter = model.SpectralModel(
self.hyperfine_varyhf,
3 + self.nlines,
parnames=['Tex', 'center', 'width'] +
['tau%s' % k for k in self.line_names],
parlimited=[(False, False), (False, False), (True, False)] + [
(True, False),
] * self.nlines,
parlimits=[(0, 0), (0, 0), (0, 0)] + [
(0, 0),
] * self.nlines,
shortvarnames=("T_{ex}", "v", "\\sigma") + tuple(
("\\tau(\\mathrm{%s})" % k for k in self.line_names)),
# specify the parameter names (TeX is OK)
fitunits='Hz')
self.varyhf_amp_fitter = model.SpectralModel(
self.hyperfine_varyhf_amp,
2 + self.nlines,
parnames=['center', 'width'] +
['amp%s' % k for k in self.line_names],
parlimited=[(False, False), (True, False)] + [
(True, False),
] * self.nlines,
parlimits=[(0, 0), (0, 0)] + [
(0, 0),
] * self.nlines,
shortvarnames=("v", "\\sigma") + tuple(
("amp(\\mathrm{%s})" % k for k in self.line_names)),
# specify the parameter names (TeX is OK)
fitunits='Hz')
self.varyhf_amp_width_fitter = model.SpectralModel(
self.hyperfine_varyhf_amp_width,
1 + self.nlines * 2,
parnames=['center'] + ['amp%s' % k for k in self.line_names] +
['width%s' % k for k in self.line_names],
parlimited=[(False, False)] + [
(True, False),
] * self.nlines + [(True, False)] * self.nlines,
parlimits=[(0, 0)] + [
(0, 0),
] * self.nlines * 2,
shortvarnames=(("v", ) + tuple(
("amp(\\mathrm{%s})" % k for k in self.line_names)) + tuple(
("\\sigma(\\mathrm{%s})" % k for k in self.line_names))),
# specify the parameter names (TeX is OK)
fitunits='Hz')
self.vheight_fitter = model.SpectralModel(
fitter.vheightmodel(self),
5,
parnames=['height', 'Tex', 'tau', 'center', 'width'],
parlimited=[(False, False), (False, False), (True, False),
(False, False), (True, False)],
parlimits=[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
shortvarnames=(
"H", "T_{ex}", "\\tau", "v",
"\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
self.background_fitter = model.SpectralModel(
self.hyperfine_addbackground,
5,
parnames=['Tbackground', 'Tex', 'tau', 'center', 'width'],
parlimited=[(True, False), (False, False), (False, False),
(True, False), (False, False), (True, False)],
parlimits=[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
shortvarnames=(
'T_{BG}', "T_{ex}", "\\tau", "v",
"\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
self.ampfitter = model.SpectralModel(
self.hyperfine_amp,
3,
parnames=['amp', 'center', 'width'],
parlimited=[(False, False), (False, False), (True, False)],
parlimits=[(0, 0), (0, 0), (0, 0)],
shortvarnames=(
"amp", "v",
"\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
self.taufitter = model.SpectralModel(
self.hyperfine_tau,
3,
parnames=['tau', 'center', 'width'],
parlimited=[(True, False), (False, False), (True, False)],
parlimits=[(0, 0), (0, 0), (0, 0)],
shortvarnames=(
r'\tau', "v",
"\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
self.totaltaufitter = model.SpectralModel(
self.hyperfine_tau_total,
3,
parnames=['tau', 'center', 'width'],
parlimited=[(True, False), (False, False), (True, False)],
parlimits=[(0, 0), (0, 0), (0, 0)],
shortvarnames=(
r'\tau', "v",
"\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
def __init__(self, line_names, voff_lines_dict, freq_dict,
line_strength_dict, relative_strength_total_degeneracy):
"""
Initialize the various parameters defining the hyperfine transitions
Parameters
----------
line_names: list
list of the line names to be used as indices for the dictionaries
voff_lines_dict: dict
a linename:v_off dictionary of velocity offsets for the hyperfine
components. Technically, this is redundant with freq_dict
freq_dict: dict
frequencies of the indvidual transitions
line_strength_dict: dict
Relative strengths of the hyperfine components, usually determined
by their degeneracy and Einstein A coefficients
"""
self.line_names = line_names
self.voff_lines_dict = voff_lines_dict
self.freq_dict = freq_dict
self.line_strength_dict = line_strength_dict
self.relative_strength_total_degeneracy = relative_strength_total_degeneracy
self.fitter = model.SpectralModel(self,4,
parnames=['Tex','tau','center','width'],
parlimited=[(False,False), (True,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0), (0,0)],
shortvarnames=("T_{ex}","\\tau","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz' )
self.nlines = len(line_names)
self.varyhf_fitter = model.SpectralModel(self.hyperfine_varyhf,3+self.nlines,
parnames=['Tex','center','width']+['tau%s' % k for k in self.line_names],
parlimited=[(False,False), (False,False), (True,False)] + [(True,False),]*self.nlines,
parlimits=[(0,0), (0,0), (0,0)]+[(0,0),]*self.nlines,
shortvarnames=("T_{ex}","v","\\sigma") + tuple(("\\tau(\\mathrm{%s})" % k for k in self.line_names)),
# specify the parameter names (TeX is OK)
fitunits='Hz')
self.varyhf_amp_fitter = model.SpectralModel(self.hyperfine_varyhf_amp, 2+self.nlines,
parnames=['center','width']+['amp%s' % k for k in self.line_names],
parlimited=[(False,False), (True,False)] + [(True,False),]*self.nlines,
parlimits=[(0,0), (0,0)]+[(0,0),]*self.nlines,
shortvarnames=("v","\\sigma") + tuple(("amp(\\mathrm{%s})" % k for k in self.line_names)),
# specify the parameter names (TeX is OK)
fitunits='Hz')
self.varyhf_amp_width_fitter = model.SpectralModel(self.hyperfine_varyhf_amp_width,1+self.nlines*2,
parnames=['center']+['amp%s' % k for k in self.line_names]+['width%s' % k for k in self.line_names],
parlimited=[(False,False)] + [(True,False),]*self.nlines + [(True,False)]*self.nlines,
parlimits=[(0,0)]+[(0,0),]*self.nlines*2,
shortvarnames=(("v",) +
tuple(("amp(\\mathrm{%s})" % k for k in self.line_names)) +
tuple(("\\sigma(\\mathrm{%s})" % k for k in self.line_names))),
# specify the parameter names (TeX is OK)
fitunits='Hz' )
self.vheight_fitter = model.SpectralModel(fitter.vheightmodel(self),5,
parnames=['height','Tex','tau','center','width'],
parlimited=[(False,False), (False,False), (True,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0), (0,0), (0,0)],
shortvarnames=("H","T_{ex}","\\tau","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz' )
self.background_fitter = model.SpectralModel(self.hyperfine_addbackground,5,
parnames=['Tbackground','Tex','tau','center','width'],
parlimited=[(True,False), (False,False), (False,False), (True,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0), (0,0), (0,0), (0,0)],
shortvarnames=('T_{BG}',"T_{ex}","\\tau","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
self.ampfitter = model.SpectralModel(self.hyperfine_amp,3,
parnames=['amp','center','width'],
parlimited=[(False,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0)],
shortvarnames=("amp","v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz' )
self.taufitter = model.SpectralModel(self.hyperfine_tau,3,
parnames=['tau','center','width'],
parlimited=[(True,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0)],
shortvarnames=(r'\tau',"v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
self.totaltaufitter = model.SpectralModel(self.hyperfine_tau_total,3,
parnames=['tau','center','width'],
parlimited=[(True,False), (False,False), (True,False)],
parlimits=[(0,0), (0,0), (0,0)],
shortvarnames=(r'\tau',"v","\\sigma"), # specify the parameter names (TeX is OK)
fitunits='Hz')
