def deblend_cube(region='OrionA', vmin=0., vmax=20.):
tex = fits.getdata(
'{0}/parameterMaps/{0}_Tex_DR1_rebase3_flag.fits'.format(region))
mom0 = fits.getdata(
'{0}/{0}_NH3_11_DR1_rebase3_mom0_QA_trim.fits'.format(region))
vlsr = fits.getdata(
'{0}/parameterMaps/{0}_Vlsr_DR1_rebase3_flag.fits'.format(region))
sigv = fits.getdata(
'{0}/parameterMaps/{0}_Sigma_DR1_rebase3_flag.fits'.format(region))
nnh3 = fits.getdata(
'{0}/parameterMaps/{0}_N_NH3_DR1_rebase3_flag.fits'.format(region))
cube = SpectralCube.read(
'{0}/{0}_NH3_11_DR1_rebase3_trim.fits'.format(region))
cube = cube.with_spectral_unit(u.km / u.s, velocity_convention='radio')
tpeak = mom0 / (np.sqrt(2 * np.pi) * sigv)
vlsr[vlsr == 0] = np.nan
sigv[sigv == 0] = np.nan
deblend = np.zeros(cube.shape)
hdr = cube.wcs.to_header()
spaxis = cube.spectral_axis.value
for plane in np.arange(deblend.shape[0]):
deblend[plane, :, :] = tpeak * np.exp(-(spaxis[plane] - vlsr)**2 /
(2 * sigv**2))
newcube = SpectralCube(deblend, cube.wcs, header=hdr)
slab = newcube.spectral_slab(vmin * u.km / u.s, vmax * u.km / u.s)
slab.write('{0}/{0}_singlecomp.fits'.format(region), overwrite=True)
def deblend_cube(region='OrionA',vmin=0.,vmax=20.):
tex = fits.getdata('{0}/parameterMaps/{0}_Tex_DR1_rebase3_flag.fits'.format(region))
mom0 = fits.getdata('{0}/{0}_NH3_11_DR1_rebase3_mom0_QA_trim.fits'.format(region))
vlsr = fits.getdata('{0}/parameterMaps/{0}_Vlsr_DR1_rebase3_flag.fits'.format(region))
sigv = fits.getdata('{0}/parameterMaps/{0}_Sigma_DR1_rebase3_flag.fits'.format(region))
nnh3 = fits.getdata('{0}/parameterMaps/{0}_N_NH3_DR1_rebase3_flag.fits'.format(region))
cube = SpectralCube.read('{0}/{0}_NH3_11_DR1_rebase3_trim.fits'.format(region))
cube = cube.with_spectral_unit(u.km/u.s,velocity_convention='radio')
tpeak = mom0/(np.sqrt(2*np.pi)*sigv)
vlsr[vlsr==0]=np.nan
sigv[sigv==0]=np.nan
deblend = np.zeros(cube.shape)
hdr = cube.wcs.to_header()
spaxis = cube.spectral_axis.value
for plane in np.arange(deblend.shape[0]):
deblend[plane,:,:] = tpeak*np.exp(-(spaxis[plane]-vlsr)**2/(2*sigv**2))
newcube = SpectralCube(deblend,cube.wcs,header=hdr)
slab = newcube.spectral_slab(vmin*u.km/u.s,vmax*u.km/u.s)
slab.write('{0}/{0}_singlecomp.fits'.format(region),overwrite=True)
strides=(0,)+noise.strides)
noise_spcube = SpectralCube(data=noise_cube, wcs=cube_merge_high.wcs)
cube_merge_high_sm = SpectralCube.read(mpath('APEX_H2CO_merge_high_plait_all_smooth.fits'))
noise_sm = fits.getdata(mpath('APEX_H2CO_merge_high_plait_all_smooth_noise.fits'))
noise_cube_sm = as_strided(noise_sm, shape=cube_merge_high_sm.shape,
strides=(0,)+noise_sm.strides)
noise_spcube_sm = SpectralCube(data=noise_cube_sm, wcs=cube_merge_high_sm.wcs)
# Create a cutout of the cube covering the H2CO lines
# it's barely worth it; cuts off 10% of pixels
f1 = all_lines['H2CO_303_202']*u.GHz
f2 = all_lines['H2CO_321_220']*u.GHz
h2co_cube_merge_high = cube_merge_high.spectral_slab(f1*(1-(150*u.km/u.s/constants.c)),
f2*(1+(100*u.km/u.s/constants.c)))
h2co_noise_cube = noise_spcube.spectral_slab(f1*(1-(150*u.km/u.s/constants.c)),
f2*(1+(100*u.km/u.s/constants.c)))
h2co_cube_merge_high_sm = cube_merge_high_sm.spectral_slab(f1*(1-(150*u.km/u.s/constants.c)),
f2*(1+(100*u.km/u.s/constants.c)))
h2co_noise_cube_sm = noise_spcube_sm.spectral_slab(f1*(1-(150*u.km/u.s/constants.c)),
f2*(1+(100*u.km/u.s/constants.c)))
# Pyspeckit cube made from spectralcube
pcube_merge_high = pyspeckit.Cube(cube=h2co_cube_merge_high._data,
errorcube=h2co_noise_cube._data,
header=h2co_cube_merge_high.header,
xarr=h2co_cube_merge_high.spectral_axis,
)
pcube_merge_high.xarr.refX = 218.22219
pcube_merge_high.xarr.refX_unit = 'GHz'
header=high1e3dens_co_slab3_mom0.hdu.header)
# Purely detection-based thresholding
h2co11 = SpectralCube.read(h2co11subfn)
h2co22 = SpectralCube.read(h2co22subfn)
noisevrange = [-50,0]*u.km/u.s
h2co11noiseslab = h2co11.spectral_slab(*noisevrange)
h2co22noiseslab = h2co22.spectral_slab(*noisevrange)
h2co11noise = h2co11noiseslab.apply_numpy_function(np.std,axis=0)
h2co22noise = h2co22noiseslab.apply_numpy_function(np.std,axis=0)
# TODO: implement Cube (lazy?) Arithmetic: avoid filling data!
sn11 = SpectralCube(np.abs(h2co11.filled_data[:])/h2co11noise, wcs=h2co11.wcs)
sn22 = SpectralCube(np.abs(h2co22.filled_data[:])/h2co22noise, wcs=h2co22.wcs)
sn11_slab3 = sn11.spectral_slab(vrange1[0], vrange2[1])
sn22_slab3 = sn22.spectral_slab(vrange1[0], vrange2[1])
h2co11_total = np.array([cube13ss_slab3.with_mask(sn11_slab3>threshold).sum().value
for threshold in np.arange(1,6)])
h2co22_total = np.array([cube13ss_slab3.with_mask(sn22_slab3>threshold).sum().value
for threshold in np.arange(1,6)])
h2co11_fraction = h2co11_total / total_co_slab.value
h2co22_fraction = h2co22_total / total_co_slab.value
both_fraction = cube13ss_slab3.with_mask((sn11_slab3 > 2) &
(sn22_slab3 > 2)).sum().value / total_co_slab.value
log.info("H2CO Fractions: 1-1: {0}".format(h2co11_fraction))
log.info("H2CO Fractions: 2-2: {0}".format(h2co22_fraction))
log.info("Both: {0}".format(both_fraction))
makefig(hdu5, 4, name='stdmasked_weighted_mean_{0}fit_density'.format(meastype), weight_hdu=weight.hdu)
makefig(hdu6, 5, name='stdmasked_min_{0}fit_density'.format(meastype), weight_hdu=weight.hdu)
makefig(hdu7, 6, name='mean_{0}fit_abundance'.format(meastype),
vmin_lin=10**-14, vmax_lin=10**-6, vmin_log=-14, vmax_log=-6,
labeltype='abundance', linscale=1, weight_hdu=weight.hdu)
makefig(hdu8, 7, name='total_{0}fit_column'.format(meastype),
vmin_lin=10**12, vmax_lin=10**16, vmin_log=12, vmax_log=16,
labeltype='column', linscale=1, weight_hdu=weight.hdu)
nextfig = 8
for ii,vrange in enumerate((vrange1,vrange2)):
vrange = vrange[0]*1.01, vrange[1]*0.99
log.info("Integrating over {0}".format(vrange))
wtdmeandens = np.log10(denscol.spectral_slab(*vrange).sum(axis=0) /
colcube_lin.spectral_slab(*vrange).sum(axis=0))
hdu1 = fits.PrimaryHDU(data=wtdmeandens.value,
header=denscube.wcs.dropaxis(2).to_header())
hdu1.writeto(paths.dpath("H2CO_ParameterFits_weighted_mean_{2}_density_v{0}to{1}.fits".format(vrange[0].value,
vrange[1].value,
meastype)),
clobber=True)
masked_wtdmeans = np.log10(denscol.with_mask(goodmask).spectral_slab(*vrange).sum(axis=0) /
colcube_lin.with_mask(goodmask).spectral_slab(*vrange).sum(axis=0))
hdu2 = fits.PrimaryHDU(data=masked_wtdmeans.value,
header=denscube.wcs.dropaxis(2).to_header())
hdu2.writeto(paths.dpath("H2CO_ParameterFits_weighted_mean_{2}_density_chi2masked_v{0}to{1}.fits".format(vrange[0].value,
vrange[1].value,
meastype)),
