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 lorentzian_fitter():
"""
Generator for lorentzian fitter class
"""
myclass = model.SpectralModel(lorentzian, 3,
parnames=['amplitude','shift','width'],
parlimited=[(False,False),(False,False),(True,False)],
parlimits=[(0,0), (0,0), (0,0)],
shortvarnames=('A',r'\Delta x',r'\sigma'),
)
myclass.__name__ = "lorentzian"
return myclass
def powerlaw_fitter(order=1):
"""
Generator for powerlaw fitter class
"""
myclass = model.SpectralModel(powerlaw,
2,
parnames=['scale', 'power'],
parlimited=[(False, False), (False, False)],
parlimits=[(0, 0), (0, 0)],
shortvarnames=('S', 'P'))
myclass.__name__ = "powerlaw"
return myclass
def poly_fitter(order=1):
"""
Generator for polynomial fitter class
"""
myclass = model.SpectralModel(
polymodel,
order + 1,
parnames=['coeff%i' % ii for ii in xrange(order + 1)],
parlimited=[(False, False) for ii in xrange(order + 1)],
parlimits=[(0, 0) for ii in xrange(order + 1)],
shortvarnames=['C%i' % ii for ii in xrange(order + 1)])
myclass.__name__ = "polymodel"
return myclass
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 gaussian_fitter():
"""
Generator for Gaussian fitter class
"""
myclass = model.SpectralModel(gaussian, 3,
parnames=['amplitude','shift','width'],
parlimited=[(False,False),(False,False),(True,False)],
parlimits=[(0,0), (0,0), (0,0)],
shortvarnames=('A',r'\Delta x',r'\sigma'),
centroid_par='shift',
fwhm_func=gaussian_fwhm,
fwhm_pars=['width'],
integral_func=_integral_modelpars,
)
myclass.__name__ = "gaussian"
return myclass
def voigt_fitter():
"""
Generator for voigt fitter class
"""
myclass = model.SpectralModel(voigt, 4,
parnames=['amplitude','shift','gwidth','lwidth'],
parlimited=[(False,False),(False,False),(True,False),(True,False)],
parlimits=[(0,0), (0,0), (0,0), (0,0)],
shortvarnames=('A',r'\Delta x',r'\sigma_G',r'\sigma_L'),
centroid_par='shift',
fwhm_func=voigt_fwhm,
fwhm_pars=['gwidth','lwidth'],
)
myclass.__name__ = "voigt"
myclass.moments = types.MethodType(voigt_moments, myclass,
myclass.__class__)
return myclass
def voigt_fitter(multisingle='multi'):
"""
Generator for voigt fitter class
"""
myclass = model.SpectralModel(
voigt,
4,
parnames=['amplitude', 'shift', 'gwidth', 'lwidth'],
parlimited=[(False, False), (False, False), (True, False),
(True, False)],
parlimits=[(0, 0), (0, 0), (0, 0), (0, 0)],
shortvarnames=('A', r'\Delta x', r'\sigma_G', r'\sigma_L'),
multisingle=multisingle,
centroid_par='shift',
fwhm_func=voigt_fwhm,
fwhm_pars=['gwidth', 'lwidth'],
)
myclass.__name__ = "voigt"
return myclass
def template_fitter(template_spectrum, xshift_units='km/s'):
"""
Generator for Spectral Template fitter class
Parameters
----------
template_spectrum : pyspeckit.Spectrum
A valid spectrum to be scaled and shifted to match the input
xshift_units : str in pyspeckit.units.unit_type_dict
The units of the shift to fit. If you're using a velocity unit, make
sure there's a reference X-unit for both the template spectrum and the
input spectrum.
Examples
--------
>>> template = pyspeckit.Spectrum("template_spectrum.fits")
>>> dataspec = pyspeckit.Spectrum("DataSpectrum.fits")
>>> template_fitter = pyspeckit.models.template_fitter(template,
... xshift_units='angstroms')
>>> dataspec.Registry.add_fitter('template',template_fitter, 2)
>>> dataspec.specfit(fittype='template',guesses=[1,0])
>>> print dataspec.specfit.parinfo
"""
modelfunc = spectral_template_generator(template_spectrum,
xshift_units=xshift_units)
myclass = model.SpectralModel(
modelfunc,
2,
parnames=['scale', 'shift'],
parlimited=[(True, False), (False, False)],
parlimits=[(0, 0), (0, 0)],
shortvarnames=('A', r'\Delta x'),
centroid_par='shift',
)
myclass.__name__ = "spectral_template"
return myclass
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')
return hill_array
def jfunc(t, nu):
"""
t- kelvin
nu - Hz?
"""
H = 6.6260755e-27
K = 1.380658e-16
NSIG = 2.0
# I think this is only necessary for C
#if(nu<1.0e-6) return t
to = H * nu / K
return to / (np.exp(to / t) - 1.0)
hill5_fitter = model.SpectralModel(
hill5_model,
5,
parnames=['tau', 'v_lsr', 'v_infall', 'sigma', 'tpeak'],
parlimited=[(True, False), (False, False), (True, False), (True, False),
(True, False)],
parlimits=[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
shortvarnames=("\\tau", "v_{lsr}", "v_{infall}", "\\sigma",
"T_{peak}"), # specify the parameter names (TeX is OK)
fitunits='Hz')
