sp1w = cube1.get_spectrum(55,55)
sp1w.data = wtdavg1
sp2w = cube2.get_spectrum(55,55)
sp2w.data = wtdavg2
sp1w.crop(-10,20)
sp2w.crop(-10,20)
both1dW = pyspeckit.Spectra([sp1w,sp2w])
both1dW.plot_special = types.MethodType(fith2co.plotter_override, both1dW, both1dW.__class__)
both1dW.plot_special_kwargs = {'vrange':[-10,20],'fignum':14,'reset_xlimits':True}
both1dW.Registry.add_fitter('formaldehyde_radex_tau',formaldehyde_radex_tau_fitter,4,multisingle='multi')
both1dW.specfit(fittype='formaldehyde_radex_tau',guesses=[4,12,5,1],verbose=True,quiet=False)
both1dW.plot_special(vrange=[-10,20])
# two IRDCs on the west side that have no fits; they are apparently surprisingly low column and density
irdc1 = both.get_apspec((48.921,-0.28267,0.0247*3600),coordsys='galactic')
irdc2 = both.get_apspec((48.99173,-0.304066,0.0247*3600),coordsys='galactic')
for ii,irdc in enumerate((irdc1,irdc2)):
irdc.error[irdc.xarr.as_unit('GHz') > 10] = irdc[irdc.xarr.as_unit('GHz') > 10].stats()['std']
irdc.error[irdc.xarr.as_unit('GHz') < 10] = irdc[irdc.xarr.as_unit('GHz') < 10].stats()['std']
irdc.plot_special = types.MethodType(fith2co.plotter_override, irdc, irdc.__class__)
irdc.plot_special_kwargs = {'vrange':[30,90],'fignum':145+ii,'reset_xlimits':True}
irdc.Registry.add_fitter('formaldehyde_radex_tau',formaldehyde_radex_tau_fitter,4,multisingle='multi')
irdc.specfit(fittype='formaldehyde_radex_tau',guesses=[4.5,13.1,47,2.0,4,13,67,2],verbose=True,quiet=False)
irdc.plot_special(vrange=[30,90],fignum=145+ii,reset_xlimits=True)
print "Something is clearly wrong with how the model is being calculated... the residuals are right, but the models just aren't plotting right."
# tooling around with pymc
from pymodelfit import FunctionModel1DAuto
def do_indiv_fits(regfilename, outpfx, ncomp=1, dobaseline=False, table=None,
tableprefix="", **kwargs):
regions = pyregion.open(regfilename)
spectra = [both.get_apspec(r.coord_list, coordsys=r.coord_format,
wunit='degree')
for r in regions]
cont11hdu = fits.open(cont11filename)[0]
pl.ioff()
for ii,(sp,r) in enumerate(zip(spectra,regions)):
if hasattr(ncomp,'__len__'):
nc = ncomp[ii]
else:
nc = ncomp
chi2, parinfo = {},{}
sp.header['REGION'] = "{shape}({ra},{dec},{radius})".format(shape=r.name,
ra=r.coord_list[0],
dec=r.coord_list[1],
radius=r.coord_list[2])
## this is pretty hacky =(
#if dobaseline:
# spdict = fith2co.BigSpectrum_to_H2COdict(sp)
# for n,s in spdict.items():
# s.baseline(exclude=[2,8,43,75])
# sp = pyspeckit.Spectra(spdict.values())
# spectra[ii] = sp
for cont in ('front','back',):
print("Continuum in the %s" % cont)
fig = pl.figure(ii, figsize=(12,8))
fig.clf()
name = r.attr[1]['text']
if cont == 'back':
M = pyregion.ShapeList([r]).get_mask(cont11hdu)
c11 = cont11[M].mean()
c22 = cont22[M].mean()
sp.header['CONT11'] = c11
sp.header['CONT22'] = c22
elif cont == 'front':
c11 = 2.7315
c22 = 2.7315
dofit(sp, c11, c22, nc, **kwargs)
sp.specname = name
sp.header['OBJECT'] = name
print("ap%i %s: ni=%i, X^2%0.1f, X^2/n=%0.1f, n=%0.1f" % (ii,
sp.specname,
sp.specfit.fitter.mp.niter,
sp.specfit.chi2,
sp.specfit.chi2/sp.specfit.dof,
sp.specfit.parinfo.DENSITY0.value))
#if sp.specfit.chi2 / sp.specfit.dof > 2:
# gg[4] = sp.specfit.parinfo.CENTER0.value
# fixed[4] = True
# sp.specfit(fittype='formaldehyde_radex',guesses=gg,
# fixed=fixed, multifit=True,quiet=False,verbose=True,
# limits=limits,
# limited=limited,
# use_window_limits=False, fit_plotted_area=False)
sp.plotter.autorefresh=False
plotitem(sp, ii)
sp.plotter.savefig(os.path.join(datapath,
outpfx+'_aperture_%s_%s.pdf' %
(name,cont)))
plotitem(sp, ii, dolegend=True)
# seriously, something aint'n't right here
pl.figure(sp.plotter.figure.number)
pl.savefig(os.path.join(datapath,
outpfx+'_aperture_%s_%s_legend.pdf' %
(name,cont)),
bbox_extra_artists=[sp.specfit.fitleg])
# for writing to file, select the best-fit
chi2[cont] = sp.specfit.chi2
parinfo[cont] = sp.specfit.parinfo
if cont == 'back': # second one...
if sp.specfit.chi2 > chi2['front']:
print("Back chi^2 > front chi^2. Replacing parameters with Continuum in the Front")
best = 'front'
else:
best = 'back'
# set the previous, unset to match this...
# since we're guaranteed to be in the 2nd of 2 in a loop here,
# no danger
table[-1]['frontbackbest'] = best
if nc == 2:
# or previous *two* if twocomp
table[-2]['frontbackbest'] = best
# at this stage, 'frontbackbest' should be universally assigned....
if np.any(table['frontbackbest'] == ''):
import ipdb; ipdb.set_trace()
else:
best = ''
if table is not None:
add_parinfo_to_table(table, sp.specfit.parinfo, sp.specfit.chi2,
sp.specfit.dof, sp.specfit.optimal_chi2(reduced=False),
sp.specfit.optimal_chi2(reduced=True),
ra=r.coord_list[0],
dec=r.coord_list[1],
radius=r.coord_list[2],
frontback=cont,
frontbackbest=best,
name=tableprefix+sp.specname)
if best != '':
sp.specfit.parinfo = parinfo[best]
sp.specfit.chi2 = chi2[best]
sp.write(os.path.join(datapath,outpfx+"_aperture_%s.fits" % name))
table.write(os.path.join(datapath_spectra,
tableprefix+"spectralfit_table.ipac"),
format='ascii.ipac')
pl.ion()
return spectra
