from __future__ import print_function
__author__ = 'mcychen'
#=======================================================================================================================
import numpy as np
from pyspeckit.spectrum.models import model
from pyspeckit.spectrum.models.ammonia_constants import (line_names, freq_dict, voff_lines_dict, tau_wts_dict)
from pyspeckit.spectrum.models.ammonia_constants import (ckms, h, kb)
#=======================================================================================================================
# set CMB as a global constant
TCMB = 2.7315 # K
#=======================================================================================================================
[docs]
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:
raise Exception("modeling more than one 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
[docs]
def ammonia_multi_v(xarr, *args, **kwargs):
# the parameters are in the order of vel, width, tex, tau for each velocity component
# note: it may be worth while replacing *args with a single list like keyword called parameters
#args = parameters
if xarr.unit.to_string() != 'GHz':
xarr = xarr.as_unit('GHz')
line_names = ["oneone"]
if kwargs is not None:
if "line_names" in kwargs:
line_names = kwargs["line_names"]
if len(line_names) > 1:
raise Exception("modeling more than one line yet to be implemented. Please only use one line for the time being")
return None
background_ta = T_antenna(TCMB, xarr.value)
tau_dict = {}
# iteratively move through each slabs towards the observer (i.e., radiative transfer)
for vel, width, tex, tau in zip(args[::4], args[1::4], args[2::4], args[3::4]):
for linename in line_names:
tau_dict[linename] = tau
model_spectrum = _ammonia_spectrum(xarr, tex=tex, tau_dict=tau_dict, width=width, xoff_v=vel,
line_names=line_names, background_ta=background_ta)
# Set the spectrum of the current component as the background of the subsequent component
background_ta = model_spectrum
return model_spectrum - T_antenna(TCMB, xarr.value)
def _ammonia_spectrum(xarr, tex, tau_dict, width, xoff_v, line_names, background_ta=0.0, fillingfraction=None,
return_components=False):
"""
Helper function: given a dictionary of ammonia optical depths, an excitation tmeperature... etc, and produce a
spectrum based on a one-slab (i.e., single velocity component model)
Note: this is a modified version of the _ammonia_spectrum found in pyspeckit/spectrum/models/ammonia.py
Note2: the final spectrum returned do not have the background emission subtracted
Parameters
----------
background_ta : float or ndarray
"Antenna temperature" of the background emission
Returns
-------
model : `model.SpectralModel`
A SpectralModel class build from N different metastable inversion
hyperfine models
"""
width_min = 0.01
# fillingfraction is an arbitrary scaling for the data; the model will be (normal model) * fillingfraction
if fillingfraction is None:
fillingfraction = 1.0
# "runspec" means "running spectrum": it is accumulated over a loop
runspec = np.zeros(len(xarr))
if return_components:
components = []
for linename in line_names:
voff_lines = np.array(voff_lines_dict[linename])
tau_wts = np.array(tau_wts_dict[linename])
lines = (1-voff_lines/ckms)*freq_dict[linename]/1e9
tau_wts = tau_wts / (tau_wts).sum()
if width == 0: width = width_min # width shouldn't be 0, saves from divide by 0
nuwidth = np.abs(width/ckms*lines)
nuoff = xoff_v/ckms*lines
# tau array
tauprof = np.zeros(len(xarr))
for kk,nuo in enumerate(nuoff):
tauprof_ = (tau_dict[linename] * tau_wts[kk] *
np.exp(-(xarr.value+nuo-lines[kk])**2 /
(2.0*nuwidth[kk]**2)))
if return_components:
components.append(tauprof_)
tauprof += tauprof_
T0 = (h*xarr.value*1e9/kb) # "temperature" of wavelength
if isinstance(tex, dict):
runspec = ((T0/(np.exp(T0/tex[linename])-1)*(1-np.exp(-tauprof)) +
background_ta*np.exp(-tauprof))*fillingfraction
+ runspec)
else:
if tex < TCMB:
tex = TCMB # tex shouldn't be < TCMB unless we're looking at absorption?, also removes a divide by zero warning
runspec = ((T0/(np.exp(T0/tex)-1)*(1-np.exp(-tauprof)) +
background_ta*np.exp(-tauprof))*fillingfraction
+ runspec)
if return_components:
components = np.array(components)
if isinstance(tex, dict):
term1 = [(T0/(np.exp(T0/tex[linename])-1)*(1-np.exp(-1*components)) + background_ta*np.exp(-1*components))
for linename in line_names]
else:
term1 = (T0/(np.exp(T0/tex)-1)*(1-np.exp(-1*components)) + background_ta*np.exp(-1*components))
# May want to make sure I include fillingfraction here in the future
return term1
else:
return runspec
[docs]
def T_antenna(Tbright, nu):
"""
Calculate antenna temperatures over nu (in GHz)
"""
T0 = (h*nu*1e9/kb)
return T0/(np.exp(T0/Tbright)-1)
