exportfits(imagename=file, fitsimage=file + '.fits')
else:
print(file + '.fits' + ' already exists. Not running exportfits.')
fits_files = glob.glob(data_path + '*.fits')
psf_file = [s for s in fits_files if ".psf.fits" in s][0]
orig_resid = [s for s in fits_files if ".residual.fits" in s][0]
model = [s for s in fits_files if ".model.fits" in s][0]
hdu = fits.open(psf_file)
data = hdu[0].data
header = hdu[0].header
beams = hdu[1]
beams_table = beams.data
beams = Beams.from_fits_bintable(beams) #convert to radio_beam object
hdu.close()
center = np.unravel_index(np.argmax(data[0, plot_channel, :, :]),
data[0, plot_channel, :, :].shape)
cy, cx = center
cutout = data[0, plot_channel, cy - max_npix_peak:cy + max_npix_peak + 1,
cx - max_npix_peak:cx + max_npix_peak + 1]
shape = cutout.shape
sy, sx = shape
Y, X = np.mgrid[0:sy, 0:sx]
center = np.unravel_index(np.argmax(cutout), cutout.shape)
cy, cx = center
def line_ids(fn):
results = []
Ulines = []
sp = pyspeckit.Spectrum(fn)
# this is a bit hackier than I like
# we'll do all our measurements in Kelvin!
beams = Beams.from_fits_bintable(fits.open(fn)[1])
factors = jtok_factors(beams, sp.xarr.to(u.GHz))
sp.data = sp.data * factors
sp.unit = u.K
# want km/s - reference will be ~middle of SPW
sp.xarr.convert_to_unit(u.km / u.s)
med = np.nanmedian(sp.data)
mad = stats.mad_std(sp.data - med)
detections = (sp.data - med) > 5 * mad
labels, ct = label(detections)
for labelid in range(1, ct + 1):
ssp = sp[labels == labelid]
try:
ssp.xarr.convert_to_unit(u.GHz)
ssp.specfit()
ssp.specfit.parinfo
frq = ssp.specfit.parinfo['SHIFT0'].value * ssp.xarr.unit
except Exception as ex:
print(ex)
frq = ssp.xarr.to(u.GHz).mean()
sq = Splatalogue.query_lines(
frq * (1 + 0 / 3e5),
frq * (1 + 75 / 3e5), # 30/3e5 original lower bound
only_astronomically_observed=True)
if len(sq) > 0:
tbl = utils.minimize_table(sq)
try:
total_intensity = ssp.data.sum() * np.abs(
ssp.xarr.to(u.km / u.s).cdelt())
except ValueError:
total_intensity = ssp.data.sum() * np.abs(
sp.xarr.to(u.km / u.s).cdelt())
peak_intensity = ssp.data.max()
tbl.add_column(Column(data=total_intensity, name='TotalIntensity'))
tbl.add_column(Column(data=peak_intensity, name='PeakIntensity'))
tbl.add_column(Column(data=mad, name='RMS'))
tbl.add_column(
Column(data=u.Quantity((-(frq.to(u.GHz).value - tbl['Freq']) /
tbl['Freq'] * constants.c),
u.km / u.s),
name='Velocity'))
# print(tbl.pprint(max_width=200))
results.append(tbl)
else:
log.warning(f"Frequency {frq.to(u.GHz)} had no hits")
Ulines.append(frq)
try:
match_table = table.vstack(results)
except ValueError:
pass
else:
# match_table.remove_column('QNs')
match_table = table.unique(match_table, keys='Species')
match_table.sort('Freq')
print(match_table.pprint(max_width=200))
print(match_table['Species', 'Freq', 'Velocity'])
# match_table.write(f"line_fit_table_{suffix}.ipac", format='ascii.ipac', overwrite=True)
return match_table
factors.append(factor)
factor = np.array(factors)
return factor
for suffix in ('mean', 'max'):
results = []
Ulines = []
for fn in glob.glob(f"spectra/*.{suffix}.fits"):
sp = pyspeckit.Spectrum(fn)
# this is a bit hackier than I like
# we'll do all our measurements in Kelvin!
beams = Beams.from_fits_bintable(fits.open(fn)[1])
factors = jtok_factors(beams, sp.xarr.to(u.GHz))
sp.data = sp.data * factors
sp.unit = u.K
# want km/s - reference will be ~middle of SPW
sp.xarr.convert_to_unit(u.km / u.s)
med = np.nanmedian(sp.data)
mad = stats.mad_std(sp.data - med)
detections = (sp.data - med) > 5 * mad
labels, ct = label(detections)
for labelid in range(1, ct + 1):