def print_par(filename, par):
""" Print the maximum likelihood parameters and their
uncertainties.
"""
rec = []
for i in range(len(P.names)):
p = P.ml[i]
m1 = P.p1sig[i]
m2 = P.p2sig[i]
j1 = P.p1sig_joint[i]
j2 = P.p2sig_joint[i]
rec.append((P.names[i], p, p - j1[0], j1[1] - p, p - j2[0], j2[1] - p,
p - m1[0], m1[1] - p, p - m2[0], m2[1] - p))
names = 'name,ml,j1l,j1u,j2l,j2u,m1l,m1u,m2l,m2u'
rec = np.rec.fromrecords(rec, names=names)
hd = """\
# name : parameter name
# ml : maximum likelihood value
# j1l : 1 sigma lower error (joint with all other parameters)
# j1u : 1 sigma upper error (joint)
# j2l : 2 sigma lower error (joint)
# j2u : 2 sigma upper error (joint)
# m1l : 1 sigma lower error (marginalised over all other parameters)
# m1u : 1 sigma upper error (marginalised)
# m2l : 2 sigma lower error (marginalised)
# m2u : 2 sigma upper error (marginalised)
"""
#pdb.set_trace()
writetxt('pars.txt', rec, header=hd, fmt_float='.4g', overwrite=1)
def print_par(par):
""" Print the maximum likelihood parameters and their
uncertainties.
"""
rec = []
for i in range(len(P.names)):
p = P.ml[i]
m1 = P.p1sig[i]
m2 = P.p2sig[i]
j1 = P.p1sig_joint[i]
j2 = P.p2sig_joint[i]
rec.append( (P.names[i], p, p - j1[0], j1[1] - p,
p - j2[0], j2[1] - p, p - m1[0], m1[1] - p,
p - m2[0], m2[1] - p) )
names = 'name,ml,j1l,j1u,j2l,j2u,m1l,m1u,m2l,m2u'
rec = np.rec.fromrecords(rec, names=names)
hd = """\
# name : parameter name
# ml : maximum likelihood value
# j1l : 1 sigma lower error (joint with all other parameters)
# j1u : 1 sigma upper error (joint)
# j2l : 2 sigma lower error (joint)
# j2u : 2 sigma upper error (joint)
# m1l : 1 sigma lower error (marginalised over all other parameters)
# m1u : 1 sigma upper error (marginalised)
# m2l : 2 sigma lower error (marginalised)
# m2u : 2 sigma upper error (marginalised)
"""
#pdb.set_trace()
writetxt('parameters.txt', rec, header=hd, fmt_float='.8g', overwrite=1)
def writeLSF_vpfit(wa, dw):
""" Write a file giving the COS line spread function at wavelength
wa for constant pixel width dw (both in Angstroms), suitable for
input to VPFIT.
"""
outname = 'LSF/LSF_%.1f.txt' % wa
if wa < 1450:
lsf, _ = readLSF('G130M', dw)
else:
lsf, _ = readLSF('G160M', dw)
wavs = [float(n[1:]) for n in lsf.dtype.names[1:]]
lsf1 = np.array([lsf[n] for n in lsf.dtype.names[1:]])
newLSF = []
for ipix in range(lsf1.shape[1]):
newLSF.append(np.interp(wa, wavs, lsf1[:, ipix]))
writetxt(outname, [lsf.relpix, newLSF], overwrite=1)
def writeLSF_vpfit(wa, dw):
""" Write a file giving the COS line spread function at wavelength
wa for constant pixel width dw (both in Angstroms), suitable for
input to VPFIT.
"""
outname = 'LSF/LSF_%.1f.txt' % wa
if wa < 1450:
lsf, _ = readLSF('G130M', dw)
else:
lsf, _ = readLSF('G160M', dw)
wavs = [float(n[1:]) for n in lsf.dtype.names[1:]]
lsf1 = np.array([lsf[n] for n in lsf.dtype.names[1:]])
newLSF = []
for ipix in range(lsf1.shape[1]):
newLSF.append(np.interp(wa, wavs, lsf1[:, ipix]))
writetxt(outname, [lsf.relpix, newLSF], overwrite=1)
def print_par(par):
""" Print the maximum likelihood parameters and their
uncertainties.
"""
rec = []
for i in range(len(P['names'])):
p = P['ml'][i]
pmed = P['median'][i]
m1 = P['p1sig'][i]
p0 = 0.5 * (m1[0] + m1[1])
sig1 = 0.5 * (m1[1] - m1[0])
m2 = P['p2sig'][i]
j1 = P['p1sig_joint'][i]
j2 = P['p2sig_joint'][i]
rec.append( (P['names'][i], p0, sig1, m1[0], m1[1],
m2[0], m2[1], j1[0], j1[1],
j2[0], j2[1], pmed, p) )
names = 'name,cen,sig,m1l,m1u,m2l,m2u,j1l,j1u,j2l,j2u,med,ml'
rec = np.rec.fromrecords(rec, names=names)
hd = """\
# name : parameter name
# cen : central value (half way between the marginalised 1 sigma region)
# sig : 1 sigma error around central value
# m1l : 1 sigma lower level (marginalised over all other parameters)
# m1u : 1 sigma upper level (marginalised)
# m2l : 2 sigma lower level (marginalised)
# m2u : 2 sigma upper level (marginalised)
# j1l : 1 sigma lower level (joint with all other parameters)
# j1u : 1 sigma upper level (joint)
# j2l : 2 sigma lower level (joint)
# j2u : 2 sigma upper level (joint)
# ml : maximum likelihood value
# med : median value
"""
from barak.io import writetxt
writetxt('fig/pars.txt', rec, header=hd, fmt_float='.4g', overwrite=1)
def print_par(par):
""" Print the maximum likelihood parameters and their
uncertainties.
"""
rec = []
for i in range(len(P['names'])):
p = P['ml'][i]
pmed = P['median'][i]
m1 = P['p1sig'][i]
p0 = 0.5 * (m1[0] + m1[1])
sig1 = 0.5 * (m1[1] - m1[0])
m2 = P['p2sig'][i]
j1 = P['p1sig_joint'][i]
j2 = P['p2sig_joint'][i]
rec.append( (P['names'][i], p0, sig1, m1[0], m1[1],
m2[0], m2[1], j1[0], j1[1],
j2[0], j2[1], pmed, p) )
names = 'name,cen,sig,m1l,m1u,m2l,m2u,j1l,j1u,j2l,j2u,med,ml'
rec = np.rec.fromrecords(rec, names=names)
hd = """\
# name : parameter name
# cen : central value (half way between the marginalised 1 sigma region)
# sig : 1 sigma error around central value
# m1l : 1 sigma lower level (marginalised over all other parameters)
# m1u : 1 sigma upper level (marginalised)
# m2l : 2 sigma lower level (marginalised)
# m2u : 2 sigma upper level (marginalised)
# j1l : 1 sigma lower level (joint with all other parameters)
# j1u : 1 sigma upper level (joint)
# j2l : 2 sigma lower level (joint)
# j2u : 2 sigma upper level (joint)
# ml : maximum likelihood value
# med : median value
"""
from barak.io import writetxt
writetxt('fig/pars.txt', rec, header=hd, fmt_float='.4g', overwrite=1)
unused.append(n)
else:
unknown.append(n)
print len(biases), 'biases'
print len(objects), 'imaging targets found:'
print ' ', '\n '.join(textwrap.wrap(' '.join(objects)))
print len(flats), 'flats found:'
print ' ', '\n '.join(textwrap.wrap(' '.join(flats)))
print len(unknown), 'unidentified exposures:'
print ' ', '\n '.join(textwrap.wrap(' '.join(unknown)))
saveobj('_sort_LBC.sav',
dict(biases=biases, objects=objects, flats=flats))
# could be a bug writing out an empty file?
writetxt('sort_LBC_unused', [unused], overwrite=1)
if 1:
# make links to the biases
if len(biases) > 0:
makedir('bias')
for arm in biases:
biasdir = 'bias/' + arm_str[arm]
makedir(biasdir)
makedir(biasdir + '/raw', clean=True)
names = []
for filename in sorted(biases[arm]):
n = filename.rsplit('/')[-1]
s = 'ln -s ../../../raw/%s %s/raw/%s' % (n, biasdir, n)
call(s, shell=1)
names.append(n.replace('.fits.gz', '.fits'))
imtype == 'flat' and hd['NEXTEND'] == 1:
unused.append(n)
else:
unknown.append(n)
print len(biases), 'biases'
print len(objects), 'imaging targets found:'
print ' ', '\n '.join(textwrap.wrap(' '.join(objects)))
print len(flats), 'flats found:'
print ' ', '\n '.join(textwrap.wrap(' '.join(flats)))
print len(unknown), 'unidentified exposures:'
print ' ', '\n '.join(textwrap.wrap(' '.join(unknown)))
saveobj('_sort_LBC.sav', dict(biases=biases,objects=objects,flats=flats))
# could be a bug writing out an empty file?
writetxt('sort_LBC_unused', [unused], overwrite=1)
if 1:
# make links to the biases
if len(biases) > 0:
makedir('bias')
for arm in biases:
biasdir = 'bias/' + arm_str[arm]
makedir(biasdir)
makedir(biasdir + '/raw', clean=True)
names = []
for filename in sorted(biases[arm]):
n = filename.rsplit('/')[-1]
s = 'ln -s ../../../raw/%s %s/raw/%s' % (n, biasdir, n)
call(s, shell=1)
names.append(n.replace('.fits.gz', '.fits'))
