def toFits(self, fits):
"""Write to a FITS file
Parameters
----------
fits : `astropy.io.fits.HDUList`
Opened FITS file.
"""
from astropy.io.fits import BinTableHDU, Column
maxLength = max(
len(ff) for ff in self.fiberMags.keys()) if self.fiberMags else 1
header = astropyHeaderFromDict(
{attr.upper(): getattr(self, attr)
for attr in self._attributes})
header.update(TargetType.getFitsHeaders())
hdu = BinTableHDU.from_columns([
Column("filterName",
"%dA" % maxLength,
array=list(self.fiberMags.keys())),
Column("fiberMag",
"E",
array=np.array(list(self.fiberMags.values()))),
],
header=header,
name="TARGET")
fits.append(hdu)
def beams_to_bintable(beams):
"""
Convert a list of beams to a CASA-style BinTableHDU
"""
c1 = Column(name='BMAJ',
format='1E',
array=[bm.major.to(u.arcsec).value for bm in beams],
unit=u.arcsec.to_string('FITS'))
c2 = Column(name='BMIN',
format='1E',
array=[bm.minor.to(u.arcsec).value for bm in beams],
unit=u.arcsec.to_string('FITS'))
c3 = Column(name='BPA',
format='1E',
array=[bm.pa.to(u.deg).value for bm in beams],
unit=u.deg.to_string('FITS'))
c4 = Column(
name='CHAN',
format='1J',
array=[bm.meta['CHAN'] if 'CHAN' in bm.meta else 0 for bm in beams])
c5 = Column(
name='POL',
format='1J',
array=[bm.meta['POL'] if 'POL' in bm.meta else 0 for bm in beams])
bmhdu = BinTableHDU.from_columns([c1, c2, c3, c4, c5])
bmhdu.header['EXTNAME'] = 'BEAMS'
bmhdu.header['EXTVER'] = 1
bmhdu.header['XTENSION'] = 'BINTABLE'
bmhdu.header['NCHAN'] = len(beams)
bmhdu.header['NPOL'] = len(set([bm.meta['POL'] for bm in beams]))
return bmhdu
def append_fits_entry(base_rec, new_entry):
'''
Helper script to append an entry to a FITS_rec object.
Based on astropy documentation here:
https://docs.astropy.org/en/stable/io/fits/usage/table.html
'''
from astropy.io.fits import BinTableHDU
old_rows = base_rec.shape[0]
new_rows = old_rows + 1
new_rec = hdu = BinTableHDU.from_columns(base_rec.columns, nrows=new_rows)
new_rec.data[old_rows:] = new_entry
return new_rec.data
def _writeImpl(self, fits):
"""Implementation for writing to FITS file
Parameters
----------
fits : `astropy.io.fits.HDUList`
List of FITS HDUs. This has a Primary HDU already, the header of
which may be supplemented with additional keywords.
"""
from astropy.io.fits import BinTableHDU, Column
fits.append(BinTableHDU.from_columns([
Column("wavelength", "D", array=self.wavelength),
Column("flux", "D", array=self.flux),
Column("mask", "K", array=self.mask),
], header=astropyHeaderFromDict(self.flags.toFitsHeader()), name="FLUXTBL"))
self.target.toFits(fits)
def _writeImpl(self, fits):
"""Implementation for writing to FITS file
Parameters
----------
fits : `astropy.io.fits.HDUList`
List of FITS HDUs. This has a Primary HDU already, the header of
which may be supplemented with additional keywords.
"""
from astropy.io.fits import BinTableHDU, Column
fits.append(BinTableHDU.from_columns([
Column("wavelength", "D", array=self.wavelength),
Column("flux", "D", array=self.flux),
Column("mask", "K", array=self.mask),
], header=astropyHeaderFromDict(self.flags.toFitsHeader()), name="FLUXTBL"))
self.target.toFits(fits)
def beams_to_bintable(beams):
"""
Convert a list of beams to a CASA-style BinTableHDU
"""
c1 = Column(name='BMAJ', format='1E', array=[bm.major.to(u.arcsec).value for bm in beams], unit=u.arcsec.to_string('FITS'))
c2 = Column(name='BMIN', format='1E', array=[bm.minor.to(u.arcsec).value for bm in beams], unit=u.arcsec.to_string('FITS'))
c3 = Column(name='BPA', format='1E', array=[bm.pa.to(u.deg).value for bm in beams], unit=u.deg.to_string('FITS'))
c4 = Column(name='CHAN', format='1J', array=[bm.meta['CHAN'] if 'CHAN' in bm.meta else 0 for bm in beams])
c5 = Column(name='POL', format='1J', array=[bm.meta['POL'] if 'POL' in bm.meta else 0 for bm in beams])
bmhdu = BinTableHDU.from_columns([c1, c2, c3, c4, c5])
bmhdu.header['EXTNAME'] = 'BEAMS'
bmhdu.header['EXTVER'] = 1
bmhdu.header['XTENSION'] = 'BINTABLE'
bmhdu.header['NCHAN'] = len(beams)
bmhdu.header['NPOL'] = len(set([bm.meta['POL'] for bm in beams]))
return bmhdu
def toFits(self, fits):
"""Write to a FITS file
Parameters
----------
fits : `astropy.io.fits.HDUList`
Opened FITS file.
"""
from astropy.io.fits import BinTableHDU, Column
identityLength = max(len(str(ident)) for ident in self.identity)
hdu = BinTableHDU.from_columns([
Column("identity", "%dA" % identityLength, array=self.identity),
Column("fiberId", "K", array=self.fiberId),
Column("pfiNominal", "2D", array=self.pfiNominal),
Column("pfiCenter", "2D", array=self.pfiCenter),
],
name="OBSERVATIONS")
fits.append(hdu)
def toFits(self, fits):
"""Write to a FITS file
Parameters
----------
fits : `astropy.io.fits.HDUList`
Opened FITS file.
"""
from astropy.io.fits import BinTableHDU, Column
header = self.flags.toFitsHeader()
hdu = BinTableHDU.from_columns([
Column("wavelength", "E", array=self.wavelength),
Column("flux", "E", array=self.flux),
Column("error", "E", array=self.error),
Column("mask", "K", array=self.mask),
],
header=astropyHeaderFromDict(header),
name=self._hduName)
fits.append(hdu)
def toFits(self, fits):
"""Write to a FITS file
Parameters
----------
fits : `astropy.io.fits.HDUList`
Opened FITS file.
"""
armLength = max(len(arm) for arm in self.arm)
columns = [
Column("visit", "J", array=self.visit),
Column("arm", f"{armLength}A", array=self.arm),
Column("spectrograph", "J", array=self.spectrograph),
Column("fiberId", "J", array=self.fiberId),
Column("pfsDesignId", "K", array=self.pfsDesignId),
Column("pfiNominal", "2E", array=self.pfiNominal),
Column("pfiCenter", "2E", array=self.pfiCenter),
]
hdu = BinTableHDU.from_columns(columns, name="OBSERVATIONS")
fits.append(hdu)
def write_to_fits(output, chi2, sampler, nwalkers, thin, params, jfree,
metadata, meta_names, iternum, nwritten,
Nobsbins, array, BinTableHDU, Column, ctime, enumerate,
isfile, izip, transpose, xrange):
nexclude = len(chi2)
lnprior, lnPderived, chi2, lnlike = chi2
if isfile(output):
remove(output)
chain = transpose(sampler.chain, axes=(2,1,0))
columns = [Column(name=param, format='E', array=data[:iternum].flatten())
for param, data in izip(params[jfree], chain)]
columns.append(Column(name='lnprob', format='E',
array=sampler.lnprobability.T[:iternum].flatten()))
if len(meta_names) > 0:
# save only the last chunk (starting from t),
# all others are already in metadata.
# NOTE that this is only implemented for a model with the
# same format as fiducial()
for j, blob in izip(xrange(nwritten, iternum),
sampler.blobs[nwritten:]):
data = [transpose([b[i] for b in blob])
for i in xrange(len(blob[0])-nexclude)]
# re-arrange blobs
if Nobsbins == 1:
for i in xrange(len(data)):
if len(data[i].shape) == 2:
data[i] = array([b[i] for b in blob])
else:
for i in xrange(len(data)):
if len(data[i].shape) == 3:
data[i] = transpose([b[i] for b in blob],
axes=(1,0,2))
# store data
for k in xrange(len(data)):
for i in xrange(len(data[k])):
metadata[k][i][j*nwalkers:(j+1)*nwalkers] = data[k][i]
lnPderived[j*nwalkers:(j+1)*nwalkers] = array([b[-4]
for b in blob])
lnprior[j*nwalkers:(j+1)*nwalkers] = array([b[-3] for b in blob])
chi2[j*nwalkers:(j+1)*nwalkers] = array([b[-2] for b in blob])
lnlike[j*nwalkers:(j+1)*nwalkers] = array([b[-1] for b in blob])
columns.append(Column(name='lnprior', format='E', array=lnprior))
columns.append(Column(name='lnPderived', format='E',
array=lnPderived))
columns.append(Column(name='chi2', format='E', array=chi2))
columns.append(Column(name='lnlike', format='E', array=lnlike))
# this handles exclude_bins properly
for name, val in izip(meta_names, metadata):
for name_i, val_i in izip(name, val):
try:
fmt = '{0}E'.format(val_i.shape[1])
except IndexError:
fmt = 'E'
columns.append(Column(name=name_i, array=val_i, format=fmt))
nwritten = iternum * nwalkers
fitstbl = BinTableHDU.from_columns(columns)
fitstbl.writeto(output)
print 'Saved to {0} with {1} samples'.format(output, iternum*nwalkers),
if thin > 1:
print '(printing every {0}th sample)'.format(thin),
print '- {0}'.format(ctime())
return metadata, nwritten
def time_from_columns_bytes(self):
x = np.repeat(b'a', 2_000_000)
array = np.array(x, dtype=[('col', 'S1')])
BinTableHDU.from_columns(array)