def read_gbt_target(sdfitsfile, objectname, verbose=False):
"""
Give an object name, get all observations of that object as an 'obsblock'
"""
bintable = _get_bintable(sdfitsfile)
whobject = bintable.data['OBJECT'] == objectname
if verbose:
print("Number of individual scans for Object %s: %i" %
(objectname, whobject.sum()))
calON = bintable.data['CAL'] == 'T'
# HACK: apparently bintable.data can sometimes treat itself as scalar...
if np.isscalar(calON):
calON = np.array([(val in ['T', True])
for val in bintable.data['CAL']])
n_nods = np.unique(bintable.data['PROCSIZE'])
blocks = {}
for sampler in np.unique(bintable.data[whobject]['SAMPLER']):
whsampler = bintable.data['SAMPLER'] == sampler
nods = np.unique(bintable.data['PROCSEQN'][whsampler * whobject])
for nod in nods:
whnod = bintable.data['PROCSEQN'] == nod
for onoff in ('ON', 'OFF'):
calOK = (calON - (onoff == 'OFF'))
whOK = (whobject * whsampler * calOK * whnod)
if whOK.sum() == 0:
continue
if verbose:
print(
"Number of spectra for sampler %s, nod %i, cal%s: %i" %
(sampler, nod, onoff, whOK.sum()))
crvals = bintable.data[whOK]['CRVAL1']
if len(crvals) > 1:
maxdiff = np.diff(crvals).max()
else:
maxdiff = 0
freqres = np.max(bintable.data[whOK]['FREQRES'])
if maxdiff < freqres:
splist = [
read_gbt_scan(bintable, ii) for ii in np.where(whOK)[0]
]
blocks[sampler + onoff + str(nod)] = pyspeckit.ObsBlock(
splist, force=True)
blocks[sampler + onoff +
str(nod)]._arithmetic_threshold = np.diff(
blocks[sampler + onoff +
str(nod)].xarr).min() / 5.
else:
print(
"Maximum frequency difference > frequency resolution: %f > %f"
% (maxdiff, freqres))
return blocks
def read_alfalfa_source(savfile, sourcenumber=0):
"""
Create an Observation Block class for a single source in an ALFALFA
'source' IDL save file
"""
if type(savfile) is str and ".src" in savfile:
savfile = idlsave.read(savfile)
src = savfile.src[sourcenumber]
header = pyfits.Header()
splist = []
for spectra in src.spectra:
for par in spectra.dtype.names:
try:
len(spectra[par])
except TypeError:
header[par[:8]] = spectra[par]
# assume ALFALFA spectra in Kelvin
header['BUNIT'] = 'K'
xarr = pyspeckit.spectrum.units.SpectroscopicAxis(
spectra.velarr,
refX=header['RESTFRQ'],
refX_unit='MHz',
unit='km/s')
data = np.ma.masked_where(np.isnan(spectra.spec), spectra.spec)
sp = pyspeckit.Spectrum(xarr=xarr, data=data, header=header)
# the Source has a baseline presubtracted (I think)
sp.baseline.baselinepars = spectra.baseline[::-1]
sp.baseline.subtracted = True
sp.baseline.order = len(spectra.baseline)
sp.baseline.basespec = np.poly1d(sp.baseline.baselinepars)(np.arange(
xarr.shape[0]))
# There are multiple components in each Spectrum, but I think they are not indepedent
sp.specfit.fittype = 'gaussian'
sp.specfit.fitter = sp.specfit.Registry.multifitters['gaussian']
modelpars = zip(spectra['STON'], spectra['VCEN'], spectra['WIDTH'])
modelerrs = zip(spectra['STON'], spectra['VCENERR_STAT'],
spectra['WIDTHERR'])
sp.specfit.modelpars = modelpars[0] # only use the first fit
sp.specfit.fitter.mpp = modelpars[0]
sp.specfit.modelerrs = modelerrs[0]
sp.specfit.fitter.mpperr = modelerrs[0]
sp.specfit._full_model()
splist.append(sp)
return pyspeckit.ObsBlock(splist)
def class_to_obsblocks(filename,
telescope,
line,
datatuple=None,
source=None,
imagfreq=False,
DEBUG=False,
**kwargs):
"""
Load an entire CLASS observing session into a list of ObsBlocks based on
matches to the 'telescope', 'line' and 'source' names
Parameters
----------
filename : string
The Gildas CLASS data file to read the spectra from.
telescope : list
List of telescope names to be matched.
line : list
List of line names to be matched.
source : list (optional)
List of source names to be matched. Defaults to None.
imagfreq : bool
Create a SpectroscopicAxis with the image frequency.
"""
if datatuple is None:
spectra, header, indexes = read_class(filename, DEBUG=DEBUG, **kwargs)
else:
spectra, header, indexes = datatuple
obslist = []
lastscannum = -1
spectrumlist = None
for sp, hdr, ind in zip(spectra, header, indexes):
hdr.update(ind)
# this is slow but necessary...
H = pyfits.Header()
for k, v in hdr.iteritems():
if hasattr(v, "__len__") and not isinstance(v, str):
if len(v) > 1:
for ii, vv in enumerate(v):
H.update(k[:7] + str(ii), vv)
else:
H.update(k, v[0])
elif pyfits.Card._comment_FSC_RE.match(str(v)) is not None:
H.update(k, v)
scannum = hdr['XSCAN']
if 'XTEL' in hdr and hdr['XTEL'].strip() not in telescope:
continue
if hdr['LINE'].strip() not in line:
continue
if (source is not None) and (hdr['SOURC'].strip() not in source):
continue
hdr.update({'RESTFREQ': hdr.get('RESTF')})
H.update('RESTFREQ', hdr.get('RESTF'))
#print "Did not skip %s,%s. Scannum, last: %i,%i" % (hdr['XTEL'],hdr['LINE'],scannum,lastscannum)
if scannum != lastscannum:
lastscannum = scannum
if spectrumlist is not None:
obslist.append(pyspeckit.ObsBlock(spectrumlist))
xarr = make_axis(hdr, imagfreq=imagfreq)
spectrumlist = [(pyspeckit.Spectrum(xarr=xarr, header=H, data=sp))]
else:
spectrumlist.append(
pyspeckit.Spectrum(xarr=xarr, header=H, data=sp))
return obslist