if 'RA' in wcs.wcs.ctype[0]:
FF.show_markers(coordinate.ra.deg, coordinate.dec.deg, marker='+', facecolor='r', edgecolor='r')
FF.show_markers(fitcoord.fk5.ra.deg, fitcoord.fk5.dec.deg, marker='x', facecolor='g', edgecolor='g')
elif 'GLON' in wcs.wcs.ctype[0]:
FF.show_markers(coordinate.l.deg, coordinate.b.deg, marker='+', facecolor='r', edgecolor='r')
FF.show_markers(fitcoord.galactic.l.deg, fitcoord.galactic.b.deg, marker='x', facecolor='g', edgecolor='g')
FF.set_tick_labels_format('d.dd')
FF.set_tick_labels_xformat('d.dd')
FF.set_tick_labels_yformat('d.dd')
FF.set_title(sp.specname)
FF.save('../figures/mom0_gfit_{0}.png'.format(cubename))
l_rounded, el_rounded = rounded(fitcoord.galactic.l.deg, dx_deg)
b_rounded, eb_rounded = rounded(fitcoord.galactic.b.deg, dy_deg)
v_rounded, ev_rounded = rounded(sp.specfit.parinfo[1].value, sp.specfit.parinfo[1].error)
if 'v0' not in cubename:
results[cubename] = [l_rounded, b_rounded, el_rounded, eb_rounded, v_rounded, ev_rounded,]
other_masers = Table.read('../data/other_masers.tbl', format='ascii.ecsv')
walshtbl = Vizier.query_region(coordinates.SkyCoord(0.38*u.deg, +0.04*u.deg,
frame='galactic'),
radius=0.5*u.arcmin, catalog='J/MNRAS/442/2240')[0]
h2ocoords = coordinates.SkyCoord(walshtbl['_RAJ2000'], walshtbl['_DEJ2000'],
frame='fk5', unit=(u.deg, u.deg))
tbl = Table([Column([names[name] for name in results], name='Line'),
ss_c = ss.copy()
ss_c.xarr.convert_to_unit(u.GHz)
ss_c.xarr.refX = c77a_freq
ss_c.xarr.convert_to_unit(u.km/u.s)
ss_c.plotter(xmin=-10, xmax=120, axis=pl.subplot(2,1,2), clear=False, color='blue')
ss.plotter.savefig(paths.fpath('spectra/h77/'+ss.specname+"_h_he_c.png"),
bbox_inches='tight')
tbl = table.Table()
names = table.Column(data=[sp.specname for sp in spectra], name='ObjectName')
tbl.add_column(names)
for ii,(parname,unit) in enumerate([('amplitude',u.mJy),
('velocity',u.km/u.s),
('width',u.km/u.s)]):
dataerror = [rounded(sp.specfit.parinfo[ii].value, sp.specfit.parinfo[ii].error)
for sp in spectra]
data,error = zip(*dataerror)
#data = [sp.specfit.parinfo[ii].value
# for sp in spectra]
#error = [sp.specfit.parinfo[ii].error
# for sp in spectra]
column = table.Column(data=data,
name='H77a_'+parname,
unit=unit)
tbl.add_column(column)
column = table.Column(data=error,
name='eH77a_'+parname,
unit=unit)
tbl.add_column(column)
# fitting
for thisspec in sp:
thisspec.plotter(xmin=30,xmax=90)
thisspec.specfit(fittype='gaussian',
guesses=[-0.03,thisspec.xarr[thisspec.data.argmin()].value,3],
limited=[(False,True),(False,False),(True,False)])
log.info(thisspec.specname+" fitting: {0}".format(thisspec.specfit.parinfo))
thisspec.plotter.ymin -= 0.005
thisspec.plotter.ymax += 0.005
thisspec.specfit.annotate(loc='lower left')
thisspec.specfit.plotresiduals(axis=thisspec.plotter.axis,clear=False,yoffset=+0.005,label=False)
thisspec.plotter.savefig(paths.fpath('spectra/hiiregionh2co/'+thisspec.specname+"_h2co22absorption_fit.png"),
bbox_inches='tight')
tbl.add_row([thisspec.specname,]+
list((rounded(thisspec.specfit.parinfo.AMPLITUDE0.value, thisspec.specfit.parinfo.AMPLITUDE0.error)*u.Jy).to(u.mJy))+
list(rounded(thisspec.specfit.parinfo.SHIFT0.value, thisspec.specfit.parinfo.SHIFT0.error)*u.km/u.s)+
list(rounded(thisspec.specfit.parinfo.WIDTH0.value, thisspec.specfit.parinfo.WIDTH0.error)*u.km/u.s)+
[np.round(thisspec.header['APAREA']*(np.pi/180.)**2, int(np.ceil(-np.log10(thisspec.header['APAREA']*(np.pi/180.)**2)))+1)*u.sr])
# sort such that e10 comes after e9
import natsort
tbl = tbl[natsort.index_natsorted(tbl['Object Name'])]
detection_note = ['-' if name in detections else
'ambig' if name in ambiguousdetections else
'none'
for name in tbl['Object Name']]
tbl.add_column(table.Column(data=detection_note, name='Detection Status'))
nondetections = tbl['peak_m_background'] < tbl['local_rms_noise']*3
cols_order = ['SourceName', 'Epoch', 'ObservationDate', 'peak_flux', 'peak_m_background', 'local_rms_noise', 'Frequency']
cols = {'SourceName': 'Object',
#'FrequencyName': 'Band',
'ObservationDate': 'Obs. Date',
'peak_flux': 'Peak $S_{\\nu}$',
'peak_m_background': 'Peak - Background',
'local_rms_noise': 'RMS',
}
textbl = tbl.copy()[cols_order]
textbl.sort(['SourceName', 'Frequency'])
textbl[nondetections]['peak_flux'] = np.nan
textbl[nondetections]['peak_m_background'] = np.nan
textbl['peak_flux'] = ((list(map(lambda x,y: rounded(x,y,extra=0)[0],
textbl['peak_flux'].to(u.mJy/u.beam).value,
textbl['local_rms_noise'].to(u.mJy/u.beam).value))))
textbl['peak_m_background'] = ((list(map(lambda x,y: rounded(x,y,extra=0)[0],
textbl['peak_m_background'].to(u.mJy/u.beam).value,
textbl['local_rms_noise'].to(u.mJy/u.beam).value))))
textbl['local_rms_noise'] = ((list(map(lambda x,y: rounded(x,y,extra=0)[0],
textbl['local_rms_noise'].to(u.mJy/u.beam).value,
textbl['local_rms_noise'].to(u.mJy/u.beam).value))))
for name in ('peak_flux', 'peak_m_background', 'local_rms_noise'):
textbl[name].unit = u.mJy/u.beam
textbl['SourceName'] = list(map(lambda x: x.replace("_","-"), textbl['SourceName']))
for old,new in cols.items():
textbl.rename_column(old, new)
]
cols = {
'SourceName': 'Object',
#'FrequencyName': 'Band',
'ObservationDate': 'Obs. Date',
'peak_flux': 'Peak $S_{\\nu}$',
'peak_m_background': 'Peak - Background',
'local_rms_noise': 'RMS',
}
textbl = tbl.copy()[cols_order]
textbl.sort(['SourceName', 'Frequency'])
textbl[nondetections]['peak_flux'] = np.nan
textbl[nondetections]['peak_m_background'] = np.nan
textbl['peak_flux'] = ((list(
map(lambda x, y: rounded(x, y, extra=0)[0],
textbl['peak_flux'].to(u.mJy / u.beam).value,
textbl['local_rms_noise'].to(u.mJy / u.beam).value))))
textbl['peak_m_background'] = ((list(
map(lambda x, y: rounded(x, y, extra=0)[0],
textbl['peak_m_background'].to(u.mJy / u.beam).value,
textbl['local_rms_noise'].to(u.mJy / u.beam).value))))
textbl['local_rms_noise'] = ((list(
map(lambda x, y: rounded(x, y, extra=0)[0],
textbl['local_rms_noise'].to(u.mJy / u.beam).value,
textbl['local_rms_noise'].to(u.mJy / u.beam).value))))
for name in ('peak_flux', 'peak_m_background', 'local_rms_noise'):
textbl[name].unit = u.mJy / u.beam
textbl['SourceName'] = list(
map(lambda x: x.replace("_", "-"), textbl['SourceName']))
ax2 = thisspec.plotter.axis.twinx()
ax2.set_ylim(*(np.array(thisspec.plotter.axis.get_ylim()) *
thisspec.header['JYTOK'] / 1e3))
ax2.set_ylabel("$T_B$ (K)")
thisspec.plotter.savefig(
paths.fpath('spectra/emission/' + thisspec.specname +
"_h2co22emisson_baselined.png"),
bbox_inches='tight')
omega_ap = thisspec.header['APAREA'] * (np.pi / 180.)**2 * u.sr
r_eff = ((omega_ap / np.pi)**0.5).to(u.arcsec).value
tbl.add_row(
[
thisspec.specname,
] + list(
(rounded(thisspec.specfit.parinfo.AMPLITUDE0.value,
thisspec.specfit.parinfo.AMPLITUDE0.error) *
u.Jy).to(u.mJy)) + list(
rounded(thisspec.specfit.parinfo.SHIFT0.value,
thisspec.specfit.parinfo.SHIFT0.error)) + list(
rounded(thisspec.specfit.parinfo.WIDTH0.value,
thisspec.specfit.parinfo.WIDTH0.error)) +
[np.round(r_eff, 1) * u.arcsec]
#[np.round(thisspec.header['APAREA']*(np.pi/180.)**2, int(np.ceil(-np.log10(thisspec.header['APAREA']*(np.pi/180.)**2)))+1)],
)
# sort such that e10 comes after e9
import natsort
tbl = tbl[natsort.index_natsorted(tbl['Object Name'])]
detection_note = [
'-' if name in detections else 'weak' if name in weakdetections else 'none'
