import os
from hf_only_model import hfonly_66_fixed_fitter, hfonly_fitter, sixsix_movinghf_fitter
from spectral_cube import SpectralCube
from astropy import units as u
import numpy as np
import pyspeckit
pyspeckit.fitters.default_Registry.add_fitter('hfonly', hfonly_fitter(), 7)
pyspeckit.fitters.default_Registry.add_fitter('hfonly66',
hfonly_66_fixed_fitter(), 4)
pyspeckit.fitters.default_Registry.add_fitter('sixsix_movinghf',
sixsix_movinghf_fitter(), 5)
cube = SpectralCube.read(
'/Volumes/passport/W51-GODDI/W51e2_66_baselined-sc-pb.cube.image.fits')
scube = cube[:, 230:307, 205:270]
errmap = scube.spectral_slab(-20 * u.km / u.s, 10 * u.km / u.s).std(axis=0)
mn = scube.min(axis=0).value
guesses = np.empty((7, scube.shape[1], scube.shape[2]))
guesses[0, :, :] = 58
guesses[1, :, :] = 26.9
guesses[2, :, :] = 0.010
guesses[3, :, :] = 1.5
guesses[4, :, :] = 31.4
guesses[5, :, :] = 0.010
guesses[6, :, :] = 1.5
negmask = mn < -0.005
guesses[2, negmask] = -0.1
guesses[5, negmask] = -0.1
def fit_highj_nh3_absorption(j=6, j2=6, object='w51e2-tot',
headerfile='../W51-25GHzcont.map.image.fits',
savepath='.',
fntemplate='spec-{object}-{j}{j2}-pb.txt',
background_tb=4.2e3):
"""
Fit absorption lines of high-J NH3
"""
linename = ammonia_constants.num_to_name[j] * 2
sp, spK, mean_error, mean_error_k = load_spectrum(j=j, object=object, fntemplate=fntemplate)
# create our ammonia fitter from the ammonia model that allows Tau as the
# variable (instead of kinetic temperature)
# the model comes in a few varieties: we've selected the one that assumes
# there *is* a background, but we're fitting the continuum-subtracted
# spectrum, so this one will accept the background as an input parameter
amf = ammonia_hf.nh3_vtau[linename].background_contsub_fitter
spK.specfit.Registry.add_fitter('ammonia_bg', amf, 5)
sp.plotter()
# Fit each component with a gaussian to determine the offsets between components
sp.specfit(fittype='gaussian', guesses=[-0.15, 25, 3,
-0.15, 32, 3,
-0.30, 58, 6,
-0.15, 84, 3,
-0.15, 90, 3], quiet=True)
# Store the fitted velocities and frequencies in convenient variables
center = sp.specfit.parinfo[7].value
voffs = u.Quantity([(((x-center)*u.km/u.s))
for x in sp.specfit.parinfo.values[1::3]])
foffs = u.Quantity([((((x - center)*u.km/u.s) /
constants.c) * sp.xarr.refX).to(u.MHz)
for x in sp.specfit.parinfo.values[1::3]])
print("Velocity Offsets: ",voffs)
print("Frequency Offsets: ",foffs)
models = (pyspeckit.spectrum.models.ammonia_constants.voff_lines_dict[linename][:3]
+
pyspeckit.spectrum.models.ammonia_constants.voff_lines_dict[linename][-2:])
dfoffs = u.Quantity([(((x-center-m)*u.km/u.s))
for x,m in zip(sp.specfit.parinfo.values[1::3], models[::-1])])
print("Difference between observed and modeled offsets: ",dfoffs)
# plot the fits & residuals
sp.specfit.plotresiduals(axis=sp.plotter.axis, clear=False,
yoffset=sp.data.min()*1.2, label=False)
sp.plotter.axis.set_ylim(sp.data.min()*1.2 - 3*mean_error + sp.specfit.residuals.min(),
sp.data.max()*1.2 + 3*mean_error)
sp.plotter.savefig(os.path.join(savepath,
"{object}_nh3_{j}{j2}_eachcomponent.png".format(j=j,
j2=j2,
object=object)))
T = True
F = False
#parnames=['Tbackground','Tex','tau','center','width'],
# input guesses. background_tb is fixed
parinfo = amf.make_parinfo(params=[background_tb, 100, 2.5, 58, 0.5],
fixed=[T,F,F,F,F],)
parinfo.TBACKGROUND0.limited = (False,False)
parinfo.TEX0.mpmaxstep=1000
print("DEBUG CHECK: Is parinfo set correctly? (should have background=fixed)\n",parinfo)
# do the fits on the brightness temperature spectrum now (with ammonia model)
# for the first fit, exclude the main line and fit only the hyperfines
spK.plotter()
spK.specfit.selectregion(exclude=[40,70], highlight=True)
spK.specfit(fittype='ammonia_bg', parinfo=parinfo,
#quiet=False, shh=False, verbose=True,
veryverbose=False,
reset_selection=False)
spK.specfit.plot_fit()
spK.specfit.plotresiduals(axis=spK.plotter.axis, clear=False,
yoffset=spK.data.min()*1.2, label=False)
spK.plotter.axis.set_ylim(spK.data.min()*1.2 - 3*mean_error_k + spK.specfit.residuals.min(),
spK.data.max()*1.2 + 3*mean_error_k)
spK.plotter.savefig("{object}_nh3_{j}{j2}_hyperfineonly.png".format(j=j,
j2=j2,
object=object))
# second try: fit the main line and the hyperfines simultaneously
spK.plotter()
spK.specfit(fittype='ammonia_bg', parinfo=parinfo,
#quiet=False, shh=False, verbose=True,
veryverbose=False,
reset_selection=True)
spK.specfit.plotresiduals(axis=spK.plotter.axis, clear=False,
yoffset=spK.data.min()*1.2, label=False)
spK.plotter.axis.set_ylim(spK.data.min()*1.2 - 3*mean_error_k + spK.specfit.residuals.min(),
spK.data.max()*1.2 + 3*mean_error_k)
spK.plotter.savefig("{object}_nh3_{j}{j2}_fitwhole.png".format(j=j, j2=j2,
object=object))
# third try: all lines, but with two components
parinfo = amf.make_parinfo(params=[background_tb, 200, 2.5, 58, 1.35,
background_tb, 200, 2.5, 59, 0.5,],
fixed=[T,F,F,F,F,]*2,
npeaks=2)
parinfo.TBACKGROUND0.limited = (False,False)
parinfo.TBACKGROUND1.limited = (False,False)
parinfo.TEX0.mpmaxstep=1000
parinfo.TEX1.mpmaxstep=1000
print("DEBUG CHECK: Is parinfo set correctly? (should have background=fixed and two sets of pars)\n",parinfo)
spK.plotter()
spK.specfit(fittype='ammonia_bg', parinfo=parinfo,
#verbose=True, quiet=False, shh=False,
veryverbose=False,
reset_selection=True)
spK.specfit.plotresiduals(axis=spK.plotter.axis, clear=False,
yoffset=spK.data.min()*1.2, label=False)
spK.plotter.axis.set_ylim(spK.data.min()*1.2 - 3*mean_error_k + spK.specfit.residuals.min(),
spK.data.max()*1.2 + 3*mean_error_k)
spK.plotter.axis.set_xlim(-30,200)
spK.plotter.savefig("{object}_nh3_{j}{j2}_fitwhole_twocomp.png".format(j=j,
j2=j2,
object=object))
pl.draw()
pl.show()
# TODO: refit with NH3 hyperfine model with velocity offsets and linewidths free
# ??? optical depth reliable ???
# examine Goddi 2015 on NGC 7538
# -> column density (probably OK using simple LTE)
# -> rotational temperature?
spK.specfit.Registry.add_fitter('sixsix_movinghf', sixsix_movinghf_fitter(), 5)
spK.specfit(fittype='sixsix_movinghf', guesses=[58, 30, 26.9, 31.2, 2])
spK.specfit.plotresiduals(axis=spK.plotter.axis, clear=False,
yoffset=spK.data.min()*1.2, label=False)
spK.plotter.axis.set_ylim(spK.data.min()*1.2 - 3*mean_error_k + spK.specfit.residuals.min(),
spK.data.max()*1.2 + 3*mean_error_k)
spK.plotter.axis.set_xlim(-30,200)
spK.plotter.savefig("{object}_nh3_{j}{j2}_fitwhole_movinghf.png".format(j=j,
j2=j2,
object=object))
return sp, spK, (center, voffs, foffs, dfoffs)
mlpi6e2w = sp6e2w.specfit.parinfo
mlpi7e2w = sp7e2w.specfit.parinfo
mlpi9e2w = sp9e2w.specfit.parinfo
mlpi10e2w = sp9e2w.specfit.parinfo
mlpi13e2w = sp9e2w.specfit.parinfo
print >>outf, "gaussian 6-6 peak={0} +/- {1}, fwhm={2} +/- {3}, integral={4} +/- {5}, center={6} +/- {7}".format(*(tbl_vals_gaussian(sp6e2w.specfit.parinfo)))
print >>outf, "gaussian 7-7 peak={0} +/- {1}, fwhm={2} +/- {3}, integral={4} +/- {5}, center={6} +/- {7}".format(*(tbl_vals_gaussian(sp7e2w.specfit.parinfo)))
print >>outf, "gaussian 9-9 peak={0} +/- {1}, fwhm={2} +/- {3}, integral={4} +/- {5}, center={6} +/- {7}".format(*(tbl_vals_gaussian(sp9e2w.specfit.parinfo)))
print >>outf, "gaussian 10-10 peak={0} +/- {1}, fwhm={2} +/- {3}, integral={4} +/- {5}, center={6} +/- {7}".format(*(tbl_vals_gaussian(sp10e2w.specfit.parinfo)))
print >>outf, "gaussian 13-13 peak={0} +/- {1}, fwhm={2} +/- {3}, integral={4} +/- {5}, center={6} +/- {7}".format(*(tbl_vals_gaussian(sp13e2w.specfit.parinfo)))
# e2e aka e2east
sp6e2e,spK6e2e,_,_ = goddi_nh3_fits.load_spectrum(6, object='w51e2e', headerfile='/Users/adam/work/w51/goddi/W51-25GHzcont.map.image.fits')
sp6e2e.specfit.Registry.add_fitter('hfonly',hfonly_fitter(),7)
spK6e2e.specfit.Registry.add_fitter('sixsix',sixsix_movinghf_fitter(),7)
sp7e2e,spK7e2e,_,_ = goddi_nh3_fits.load_spectrum(7, object='w51e2e', headerfile='/Users/adam/work/w51/goddi/W51-25GHzcont.map.image.fits')
sp7e2e.specfit.Registry.add_fitter('hfonly',hfonly_fitter(),7)
sp9e2e,spK9e2e,_,_ = goddi_nh3_fits.load_spectrum(9, object='w51e2e', headerfile='/Users/adam/work/w51/goddi/W51-27GHzcont.map.image.fits')
sp9e2e.specfit.Registry.add_fitter('hfonly',hfonly_fitter(),7)
sp10e2e,spK10e2e,_,_ = goddi_nh3_fits.load_spectrum(10, object='w51e2e', headerfile='/Users/adam/work/w51/goddi/W51-27GHzcont.map.image.fits')
sp10e2e.specfit.Registry.add_fitter('hfonly',hfonly_fitter(),7)
sp13e2e,spK13e2e,_,_ = goddi_nh3_fits.load_spectrum(13, object='w51e2e', headerfile='/Users/adam/work/w51/goddi/W51-27GHzcont.map.image.fits')
sp13e2e.specfit.Registry.add_fitter('hfonly',hfonly_fitter(),7)
sp6e2e.plotter()
spK6e2e.plotter()
sp7e2e.plotter()
sp9e2e.plotter()
# sp6e2e might be optically thick (in hyperfines, in emission!)
sp6e2e.specfit(fittype='hfonly', guesses=[58, 26.9, 0.02, 2.0, 31.4, 0.02, 2.0], fixed=[F,T,F,F,T,F,F], tied=tied)
return spectra,tbl
if __name__ == "__main__":
spectra,tbl = fit_all()
print(tbl)
fig = pl.figure(13)
fig.clf()
T=True
F=False
sp = spectra['w51e2-core_66'][1]
mean_error = sp.data[-20:-2].std()
sp.plotter.figure = fig
sp.plotter.axis = pl.subplot(2,1,1)
sp.plotter()
sp.specfit.Registry.add_fitter('sixsix_movinghf', sixsix_movinghf_fitter(), 7)
sp.specfit(fittype='sixsix_movinghf', guesses=[58, 83, 26.9, 31.2, 2, 1800, 8200], fixed=[F,F,F,F,F,T,T], annotate=False)
sp.plotter.axis.set_xticklabels([])
sp.specfit.plotresiduals(axis=sp.plotter.axis, clear=False,
yoffset=sp.data.min()*1.3, label=False)
sp.plotter.axis.set_ylim(sp.data.min()*1.3 - 3*mean_error + sp.specfit.residuals.min(),
sp.data.max()*1.3 + 3*mean_error)
sp.plotter.axis.set_xlim(0, 120)
sp1 = spectra['w51e2-core_77'][1]
mean_error = sp1.data[-20:-2].std()
sp1.plotter.figure = fig
sp1.plotter.axis = pl.subplot(2,1,2)
sp1.plotter()
sp1.specfit.Registry.add_fitter('sevenseven_movinghf', sevenseven_movinghf_fitter(), 7)
sp1.specfit(fittype='sevenseven_movinghf', guesses=[58, 83, 27.3, 31.2, 1.32, 1800, 8200], fixed=[F,F,F,F,F,T,T], annotate=False)
sp1.specfit.plotresiduals(axis=sp1.plotter.axis, clear=False,
import os
from hf_only_model import hfonly_66_fixed_fitter, hfonly_fitter, sixsix_movinghf_fitter
from spectral_cube import SpectralCube
from astropy import units as u
import numpy as np
import pyspeckit
pyspeckit.fitters.default_Registry.add_fitter('hfonly', hfonly_fitter(), 7)
pyspeckit.fitters.default_Registry.add_fitter('hfonly66', hfonly_66_fixed_fitter(), 4)
pyspeckit.fitters.default_Registry.add_fitter('sixsix_movinghf', sixsix_movinghf_fitter(), 5)
cube = SpectralCube.read('/Volumes/passport/W51-GODDI/W51e2_66_baselined-sc-pb.cube.image.fits')
scube = cube[:, 230:307, 205:270]
errmap = scube.spectral_slab(-20*u.km/u.s, 10*u.km/u.s).std(axis=0)
mn = scube.min(axis=0).value
guesses = np.empty((7, scube.shape[1], scube.shape[2]))
guesses[0,:,:] = 58
guesses[1,:,:] = 26.9
guesses[2,:,:] = 0.010
guesses[3,:,:] = 1.5
guesses[4,:,:] = 31.4
guesses[5,:,:] = 0.010
guesses[6,:,:] = 1.5
negmask = mn<-0.005
guesses[2, negmask] = -0.1
guesses[5, negmask] = -0.1