show_modelfit(
spectra,
vel,
width,
tem,
col,
figsavename=paths.fpath(
"chemistry/ch3oh_rotdiagram_fits_SelectedPixel{0}.{1}".format(
selreg, filetype)),
fignum=4 + selreg,
ylim=ylim)
speclist = [
pyspeckit.Spectrum(fn) for fn in glob.glob(
paths.merge_spath("*{0}_spw*7m12m_hires*fits".format(
target.format(selreg))))
]
for sp in speclist:
sp.data -= np.nanpercentile(sp.data, 10)
spectra = pyspeckit.Spectra(speclist)
beam = radio_beam.Beam.from_fits_header(spectra.header)
# "baseline"
#spectra.data -= np.nanpercentile(spectra.data, 10)
spectra.data *= beam.jtok(spectra.xarr)
spectra.unit = 'K'
spectra.plotter(figure=selreg + 8)
del spectra.specfit.Registry.multifitters['ch3oh']
spectra.specfit.Registry.add_fitter('ch3oh', ch3oh_fitter(), 4)
spectra.specfit.fitter = spectra.specfit.Registry.multifitters['ch3oh']
pruned_ppcat = Table.read(paths.tpath("dendrogram_continuum_catalog.ipac"),
format='ascii.ipac')
dendromask = fits.getdata(paths.apath('dendrograms_min1mJy_diff1mJy_mask_pruned.fits'))
minvelo = 45*u.km/u.s
maxvelo = 90*u.km/u.s
# array merged
data = {}
for row in pruned_ppcat:
name = row['_idx']
data[name] = {}
fn = paths.merge_spath("dendro{name:03d}_spw{ii}_mean_7m12m.fits")
bgfn = paths.merge_spath("dendro{name:03d}_spw{ii}_background_mean_7m12m.fits")
data[name] = spectral_overlays.spectral_overlays(fn, name=name,
freq_name_mapping=freq_name_mapping,
frequencies=frequencies,
yoffset=yoffset,
minvelo=minvelo,
maxvelo=maxvelo,
suffix="_7m12m",
background_fn=bgfn,
)
firstentry = list(data.keys())[0]
colnames = list(data[firstentry].keys())
coltypes = {k:type(data[firstentry][k]) for k in colnames}
cubefn = paths.dpath(
'merge/fullcube_cutouts/e2cutout_full_W51_7m12m_spw{0}{1}_lines.fits'
.format(spw, suffix))
print(cubefn)
spectra = spectra_from_cubefn(cubefn, reg, bins_arcsec, coordinate)
for bins, (key, spectrum) in zip(bins_arcsec, spectra.items()):
include = np.isfinite(spectrum) & np.array(
[(bm.major < 1 * u.arcsec) & (bm.minor < 1 * u.arcsec)
for bm in spectrum.beams])
avg_beam = spectral_cube.cube_utils.average_beams(
spectrum.beams, includemask=include)
spectrum.meta['beam'] = avg_beam
spectrum.write(paths.merge_spath(
'e2e_radial_bin_{0:0.2f}to{1:0.2f}_7m12m_spw{2}{3}.fits'.
format(bins[0], bins[1], spw, suffix)),
overwrite=True)
for spw in (0, 1, 2, 3):
cubefn = paths.dpath(
'12m/fullcube_cutouts/e2cutout_full_W51_spw{0}_lines.fits'.format(
spw))
print(cubefn)
spectra = spectra_from_cubefn(cubefn, reg, bins_arcsec, coordinate)
pl.figure(1).clf()
for bins, (key, spectrum) in zip(bins_arcsec, spectra.items()):
from line_parameters import frequencies, freq_name_mapping, yoffset
import pyregion
cores = pyregion.open(paths.rpath('cores.reg'))
minvelo = 45*u.km/u.s
maxvelo = 90*u.km/u.s
# array merged hi-res
data = {}
for corereg in cores:
name = corereg.attr[1]['text']
data[name] = {}
fn = paths.merge_spath("{name}_spw{ii}_7m12m_hires_mean.fits")
#bgfn = paths.merge_spath("{name}_spw{ii}_background_mean_7m12m.fits")
data[name] = spectral_overlays.spectral_overlays(fn, name=name,
freq_name_mapping=freq_name_mapping,
frequencies=frequencies,
yoffset=yoffset,
minvelo=minvelo,
maxvelo=maxvelo,
suffix="_7m12m_hires",
#background_fn=bgfn,
)
firstentry = list(data.keys())[0]
colnames = list(data[firstentry].keys())
coltypes = {k:type(data[firstentry][k]) for k in colnames}
cubefn = paths.dpath('merge/fullcube_cutouts/e2cutout_full_W51_7m12m_spw{0}{1}_lines.fits'
.format(spw,suffix))
print(cubefn)
spectra = spectra_from_cubefn(cubefn, reg, bins_arcsec, coordinate)
for bins, (key, spectrum) in zip(bins_arcsec, spectra.items()):
include = np.isfinite(spectrum) & np.array([(bm.major < 1*u.arcsec) &
(bm.minor < 1*u.arcsec)
for bm in spectrum.beams])
avg_beam = spectral_cube.cube_utils.average_beams(spectrum.beams,
includemask=include)
spectrum.meta['beam'] = avg_beam
spectrum.write(paths.merge_spath('e2e_radial_bin_{0:0.2f}to{1:0.2f}_7m12m_spw{2}{3}.fits'
.format(bins[0], bins[1], spw,
suffix)),
overwrite=True
)
for spw in (0,1,2,3):
cubefn = paths.dpath('12m/fullcube_cutouts/e2cutout_full_W51_spw{0}_lines.fits'
.format(spw))
print(cubefn)
spectra = spectra_from_cubefn(cubefn, reg, bins_arcsec, coordinate)
pl.figure(1).clf()
for bins, (key, spectrum) in zip(bins_arcsec, spectra.items()):
beam = radio_beam.Beam.from_fits_header(spectra.header)
# "baseline"
#spectra.data -= np.nanpercentile(spectra.data, 10)
spectra.data *= beam.jtok(spectra.xarr)
spectra.unit='K'
pl.figure(selreg, figsize=(24,16)).clf()
spectra.plotter(figure=selreg)
show_modelfit(spectra, vel, width, tem, col,
figsavename=paths.fpath("chemistry/ch3oh_rotdiagram_fits_SelectedPixel{0}.png"
.format(selreg)),
fignum=4+selreg, ylim=ylim)
speclist = [pyspeckit.Spectrum(fn) for fn in
glob.glob(paths.merge_spath("*{0}_spw*7m12m_hires*fits".format(target.format(selreg))))]
for sp in speclist:
sp.data -= np.nanpercentile(sp.data, 10)
spectra = pyspeckit.Spectra(speclist)
beam = radio_beam.Beam.from_fits_header(spectra.header)
# "baseline"
#spectra.data -= np.nanpercentile(spectra.data, 10)
spectra.data *= beam.jtok(spectra.xarr)
spectra.unit='K'
spectra.plotter(figure=selreg+8)
del spectra.specfit.Registry.multifitters['ch3oh']
spectra.specfit.Registry.add_fitter('ch3oh', ch3oh_fitter(), 4)
spectra.specfit.fitter = spectra.specfit.Registry.multifitters['ch3oh']
pruned_ppcat = Table.read(paths.tpath("dendrogram_continuum_catalog.ipac"),
format='ascii.ipac')
dendromask = fits.getdata(
paths.apath('dendrograms_min1mJy_diff1mJy_mask_pruned.fits'))
minvelo = 45 * u.km / u.s
maxvelo = 90 * u.km / u.s
# array merged
data = {}
for row in pruned_ppcat:
name = row['_idx']
data[name] = {}
fn = paths.merge_spath("dendro{name:03d}_spw{ii}_mean_7m12m.fits")
bgfn = paths.merge_spath(
"dendro{name:03d}_spw{ii}_background_mean_7m12m.fits")
data[name] = spectral_overlays.spectral_overlays(
fn,
name=name,
freq_name_mapping=freq_name_mapping,
frequencies=frequencies,
yoffset=yoffset,
minvelo=minvelo,
maxvelo=maxvelo,
suffix="_7m12m",
background_fn=bgfn,
)
