def main():
if not os.path.lexists('grid.cfg'):
print ('./grid.cfg file not found, writing an example grid.cfg to '
'the current directory')
write_example_grid_config()
sys.exit()
cfg = read_config('grid.cfg')
print ''
print 'Input values:'
for k in sorted(cfg):
print ' %s: %s' % (k, cfg[k])
print ''
if cfg.table is None:
fluxname = cfg.prefix + '_temp_uvb.dat'
uvb = calc_uvb(cfg.z, cfg.cuba_name, match_fg=True)
writetable('cloudy_jnu_HM.tbl', [uvb['energy'], uvb['logjnu']],
overwrite=1,
units=['Rydbergs', 'log10(erg/s/cm^2/Hz/ster)'],
names=['energy', 'jnu'])
if cfg.distance_starburst_kpc is not None:
wa, F = read_starburst99('starburst.spectrum1')
nu, logjnu = calc_local_jnu(wa, F, cfg.distance_starburst_kpc,
cfg.fesc)
energy = nu * hplanck / Ryd
# use HM uvb energy limits
cond = between(uvb['energy'], energy[0], energy[-1])
logjnu1 = np.interp(uvb['energy'][cond], energy, logjnu)
uvb['logjnu'][cond] = np.log10(10**uvb['logjnu'][cond] +
10**logjnu1)
writetable('cloudy_jnu_total.tbl', [uvb['energy'], uvb['logjnu']],
overwrite=1,
units=['Rydbergs', 'log10(erg/s/cm^2/Hz/ster)'],
names=['energy', 'jnu'])
write_uvb(fluxname, uvb['energy'], uvb['logjnu'], cfg.overwrite)
# Fnu at 1 Rydberg
k = np.argmin(np.abs(uvb['energy'] - 1.))
logfnu912 = np.log10(10**uvb['logjnu'][k] * 4 * pi)
else:
logfnu912 = cfg.logfnu912
fluxname = None
write_grid_input(cfg, fnu912=logfnu912, fluxfilename=fluxname, table=cfg.table,
abundances=cfg.abundances)
if cfg.run_cloudy:
run_grid(nproc=cfg.nproc, overwrite=cfg.overwrite)
models = parse_grid2(cfg)
filename = cfg.prefix + '_grid.sav.gz'
print 'Writing to', filename
saveobj(filename, models, overwrite=cfg.overwrite)
def save_samples(filename, sampler, pos, state):
saveobj(filename,
dict(chain=sampler.chain,
accept=sampler.acceptance_fraction,
lnprob=sampler.lnprobability,
final_pos=pos,
state=state,
par=P),
overwrite=1)
def x_contifit(specfil, outfil=None, savfil=None, redshift=0., divmult=1, forest_divmult=1):
import os
import barak.fitcont as bf
from barak.spec import read
from barak.io import saveobj, loadobj
import xastropy.spec.readwrite as xsr
reload(xsr)
reload(bf)
# Initialize
if savfil == None:
savfil = 'conti.sav'
if outfil == None:
outfil = 'conti.fits'
# Read spectrum + convert to Barak format
sp = xsr.readspec(specfil)
# Fit spline continuum:
if os.path.lexists(savfil): #'contfit_' + name + '.sav'):
option = raw_input('Adjust old continuum? (y)/n: ')
if option.lower() != 'n':
co_old, knots_old = loadobj(savfil) #'contfit_' + name + '.sav')
co, knots = bf.fitqsocont(sp.wa, sp.fl, sp.er, redshift,
oldco=co_old, knots=knots_old,
divmult=divmult,
forest_divmult=forest_divmult)
else:
co, knots = bf.fitqsocont(sp.wa, sp.fl, sp.er, redshift,
divmult=divmult,
forest_divmult=forest_divmult)
else:
co, knots = bf.fitqsocont(sp.wa, sp.fl, sp.er, redshift,
divmult=divmult,
forest_divmult=forest_divmult)
os.remove('_knots.sav')
# Save continuum:
saveobj(savfil, (co, knots), overwrite=1)
# Check continuum:
print('Plotting new continuum')
plt.clf()
plt.plot(sp.wa, sp.fl, drawstyle='steps-mid')
plt.plot(sp.wa, sp.co, color='r')
plt.show()
# Repeat?
confirm = raw_input('Keep continuum? (y)/n: ')
if confirm == 'y':
fits.writeto(outfil, sp, clobber=True)
else:
print('Writing to tmp.fits anyhow!')
fits.writeto('tmp.fits', sp, clobber=True)
def read_redmapper():
d = fits.getdata(prefix + 'clusters/redmapper/'
'dr8_run_redmapper_v5.10_lgt5_catalog.fits')
#d = fits.getdata(prefix + 'clusters/redmapper/DR8/'
# 'dr8_run_redmapper_v5.10_lgt5_catalog.fit')
z = d['Z_LAMBDA']
c0 = d['BCG_SPEC_Z'] != -1
z[c0] = d['BCG_SPEC_Z'][c0]
zer = d['Z_LAMBDA_E']
if CLUS_ZERR == 'erphotz':
zer[c0] = 0.001
elif isinstance(CLUS_ZERR, float):
zer[:] = CLUS_ZERR
else:
raise ValueError
# 0.005 corresponds to a velocity dispersion of 937 km/s at z=0.6
zer = np.where(zer < 0.005, 0.005, zer)
if os.path.exists('dc_redmapper.sav'):
rlos = loadobj('dc_redmapper.sav')
assert len(rlos) == len(d)
else:
# this takes about 5 min to run
print 'calculating comoving distances'
rlos = cosmo.comoving_distance(z)
saveobj('dc_redmapper.sav', rlos)
# in solar masses, conversion from Rykoff 2013 appendix B.
m200 = m200_from_richness(d['LAMBDA_CHISQ'])
d1 = np.rec.fromarrays([
d.RA, d.DEC, z, zer, d.LAMBDA_CHISQ, d.MEM_MATCH_ID, rlos.value, m200
],
names='ra,dec,z,zer,richness,id,rlos,m200')
d2 = d1[d1.z > ZMIN_CLUS]
d3 = d2[between(np.log10(d2['m200']), log10MINMASS, log10MAXMASS)]
iclus_from_id = {idval: i for i, idval in enumerate(d3.id)}
return d3, iclus_from_id
def read_redmapper():
#d = fits.getdata(prefix + 'clusters/redmapper/'
# 'dr8_run_redmapper_v5.10_lgt5_catalog.fits')
d = fits.getdata(prefix + 'clusters/redmapper/DR8/'
'dr8_run_redmapper_v5.10_lgt5_catalog.fit')
z = d['Z_LAMBDA']
c0 = d['BCG_SPEC_Z'] != -1
z[c0] = d['BCG_SPEC_Z'][c0]
zer = d['Z_LAMBDA_E']
if CLUS_ZERR == 'erphotz':
zer[c0] = 0.001
elif isinstance(CLUS_ZERR, float):
zer[:] = CLUS_ZERR
else:
raise ValueError
# 0.005 corresponds to a velocity dispersion of 937 km/s at z=0.6
zer = np.where(zer < 0.005, 0.005, zer)
if os.path.exists('dc_redmapper.sav'):
rlos = loadobj('dc_redmapper.sav')
assert len(rlos) == len(d)
else:
# this takes about 5 min to run
print 'calculating comoving distances'
rlos = cosmo.comoving_distance(z)
saveobj('dc_redmapper.sav', rlos)
# in solar masses, conversion from Rykoff 2013 appendix B.
m200 = m200_from_richness(d['LAMBDA_CHISQ'])
d1 = np.rec.fromarrays([d.RA, d.DEC, z, zer,
d.LAMBDA_CHISQ, d.MEM_MATCH_ID, rlos.value, m200],
names='ra,dec,z,zer,richness,id,rlos,m200')
d2 = d1[between(d1.z, ZMIN_CLUS, ZMAX_CLUS)]
d3 = d2[between(np.log10(d2['m200']), log10MINMASS, log10MAXMASS)]
iclus_from_id = {idval:i for i,idval in enumerate(d3.id)}
return d3, iclus_from_id
rho[ibin]['cid'].append(p['cid'])
rho[ibin]['pid'].append(p['pid'])
rho[ibin]['qid'].append(p['qid'])
# count the total redshift path per bin, and the total bumber
for i in range(len(rho)):
zpathlim = np.array(rho[i]['zpathlim'])
if len(zpathlim) == 0:
rho[i]['zpathtot'] = 0
else:
rho[i]['zpathtot'] = (zpathlim[:,1] - zpathlim[:,0]).sum()
# ids of absorbers matching clusters
rho[i]['abid'] = list(flatten(rho[i]['abid']))
print 'Saving to', outname
saveobj(outname, rho, overwrite=1)
if PLOTRES:
outname = run_id + '/rho_dNdz_clus.sav'
fig3 = plt.figure(3, figsize=(7.5,7.5))
#fig3.subplots_adjust(left=0.16)
fig3.clf()
ax = plt.gca()
ax.set_title(run_id)
ewbins = Bins([0.6, 5.0])
labels = ['0.6 < Wr$_{2796}$ < 5']
colors = 'g'
symbols = 'o'
rho[ibin]['cid'].append(p['cid'])
rho[ibin]['pid'].append(p['pid'])
rho[ibin]['qid'].append(p['qid'])
# count the total redshift path per bin, and the total bumber
for i in range(len(rho)):
zpathlim = np.array(rho[i]['zpathlim'])
if len(zpathlim) == 0:
rho[i]['zpathtot'] = 0
else:
rho[i]['zpathtot'] = (zpathlim[:, 1] - zpathlim[:, 0]).sum()
# ids of absorbers matching clusters
rho[i]['abid'] = list(flatten(rho[i]['abid']))
print 'Saving to', outname
saveobj(outname, rho, overwrite=1)
if PLOTRES:
outname = run_id + '/rho_dNdz_clus.sav'
fig3 = plt.figure(3, figsize=(7.5, 7.5))
#fig3.subplots_adjust(left=0.16)
fig3.clf()
ax = plt.gca()
ax.set_title(run_id)
ewbins = Bins([0.6, 5.0])
labels = ['0.6 < Wr$_{2796}$ < 5']
colors = 'g'
symbols = 'o'
offsets = [0]
def x_contifit(specfil,
outfil=None,
savfil=None,
redshift=0.,
divmult=1,
forest_divmult=1):
import os
import barak.fitcont as bf
from barak.spec import read
from barak.io import saveobj, loadobj
import xastropy.spec.readwrite as xsr
reload(xsr)
reload(bf)
# Initialize
if savfil == None:
savfil = 'conti.sav'
if outfil == None:
outfil = 'conti.fits'
# Read spectrum + convert to Barak format
sp = xsr.readspec(specfil)
# Fit spline continuum:
if os.path.lexists(savfil): #'contfit_' + name + '.sav'):
option = raw_input('Adjust old continuum? (y)/n: ')
if option.lower() != 'n':
co_old, knots_old = loadobj(savfil) #'contfit_' + name + '.sav')
co, knots = bf.fitqsocont(sp.wa,
sp.fl,
sp.er,
redshift,
oldco=co_old,
knots=knots_old,
divmult=divmult,
forest_divmult=forest_divmult)
else:
co, knots = bf.fitqsocont(sp.wa,
sp.fl,
sp.er,
redshift,
divmult=divmult,
forest_divmult=forest_divmult)
else:
co, knots = bf.fitqsocont(sp.wa,
sp.fl,
sp.er,
redshift,
divmult=divmult,
forest_divmult=forest_divmult)
os.remove('_knots.sav')
# Save continuum:
saveobj(savfil, (co, knots), overwrite=1)
# Check continuum:
print('Plotting new continuum')
plt.clf()
plt.plot(sp.wa, sp.fl, drawstyle='steps-mid')
plt.plot(sp.wa, sp.co, color='r')
plt.show()
# Repeat?
confirm = raw_input('Keep continuum? (y)/n: ')
if confirm == 'y':
fits.writeto(outfil, sp, clobber=True)
else:
print('Writing to tmp.fits anyhow!')
fits.writeto('tmp.fits', sp, clobber=True)
else:
objects[obj][arm][filter].append(n)
elif imtype == 'FOCUS' or \
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]
for j in range(num_M):
mval = 10**logMvals[j]
print(s + ' {} of {}, M={:g}'.format(j+1, num_M, mval))
res = Phm_integrand(kval, mval)
#print(res)
out[i,j] = res
# save as we go
#with open('temp.npz', 'w') as fh:
# print('Updating temp.npz')
# np.savez(fh, out=out, logk=logkvals, logM=logMvals)
t2 = time.time()
print('Total time elapsed {} min'.format((t2 - t1) / 60))
saveobj('SIGMA_CACHE.sav', SIGMA_CACHE, overwrite=1)
Phm_term = np.trapz(out, x=10**logMvals, axis=-1)
Phm = Phm_term * M['bias'] * power_spectrum(10**logkvals, 0, **COSMO1)
kvals = 10**logkvals
# now take the fourier transform of Phm to get the correlation function.
# xi(r) = ind dk^3 / (2 * pi)^3 * P(k) * exp(i * k dot r)
# xi(r) = ind dk / (2 * pi) * P(k) * exp(i * k * r)
# Note that exp(i * k * r) = cos(kr) + i * sin(kr). We only want
# the real part.
def main():
if not os.path.lexists('grid.cfg'):
print ('./grid.cfg file not found, writing an example grid.cfg to '
'the current directory')
write_example_grid_config()
sys.exit()
cfg = read_config('grid.cfg')
print ''
print 'Input values:'
for k in sorted(cfg):
print ' %s: %s' % (k, cfg[k])
print ''
if cfg.table is None:
fluxname = cfg.prefix + '_temp_uvb.dat'
if cfg.cuba_name is None:
cfg.cuba_name = get_data_path() + 'UVB.out'
uvb = calc_uvb(cfg.z, cfg.cuba_name, match_fg=False)
writetable('cloudy_jnu_HM.tbl', [uvb['energy'], uvb['logjnu']],
overwrite=1,
units=['Rydbergs', 'erg/s/cm^2/Hz/ster'],
names=['energy', 'log10jnu'])
if cfg.uvb_tilt is not None:
if cfg.distance_starburst_kpc is not None:
raise RuntimeError('Must only specify one of uvb_tilt and\
distance_starburst_kpc!')
# remember which bits had 1e-30
clow = uvb['logjnu'] == -30
# tilt the UV background between 1 and 10 Rydbergs
logjnu = tilt_spec(cfg.uvb_tilt, uvb['energy'], uvb['logjnu'],
emin=1, emax=10)
logjnu[clow] = -30
print('Tilting UVB using parameter {}'.format(cfg.uvb_tilt))
# now re-normalise to match the photoionization rate of the
# default spectrum.
gamma_default = find_gamma(uvb['energy'], 10**uvb['logjnu'])
mult = gamma_default / find_gamma(uvb['energy'], 10**logjnu)
print 'Scaling tilted Jnu by %.3g to match default gamma' % mult
logjnu = logjnu + np.log10(mult)
writetable('cloudy_jnu_tilted.tbl', [uvb['energy'], logjnu],
overwrite=1,
units=['Rydbergs', 'erg/s/cm^2/Hz/ster'],
names=['energy', 'log10jnu'])
uvb['logjnu'] = logjnu
elif cfg.distance_starburst_kpc is not None:
wa, F = read_starburst99(get_data_path() + 'starburst.spectrum1')
nu, logjnu = calc_local_jnu(wa, F, cfg.distance_starburst_kpc,
cfg.fesc)
energy = nu * hplanck / Ryd
# use HM uvb energy limits
cond = between(uvb['energy'], energy[0], energy[-1])
logjnu1 = np.interp(uvb['energy'][cond], energy, logjnu)
uvb['logjnu'][cond] = np.log10(10**uvb['logjnu'][cond] +
10**logjnu1)
writetable('cloudy_jnu_total.tbl', [uvb['energy'], uvb['logjnu']],
overwrite=1,
units=['Rydbergs', 'erg/s/cm^2/Hz/ster'],
names=['energy', 'log10jnu'])
write_uvb(fluxname, uvb['energy'], uvb['logjnu'], cfg.overwrite)
# Fnu at 1 Rydberg
k = np.argmin(np.abs(uvb['energy'] - 1.))
logfnu912 = np.log10(10**uvb['logjnu'][k] * 4 * pi)
else:
logfnu912 = cfg.logfnu912
fluxname = None
write_grid_input(cfg, fnu912=logfnu912, fluxfilename=fluxname, table=cfg.table,
abundances=cfg.abundances)
if cfg.run_cloudy:
run_grid(nproc=cfg.nproc, overwrite=cfg.overwrite)
models = parse_grid(cfg)
filename = cfg.prefix + '_grid.sav.gz'
print 'Writing to', filename
saveobj(filename, models, overwrite=cfg.overwrite)
savehdf5(filename.replace('.sav.gz', '.hdf5'), models,
overwrite=cfg.overwrite)
def save_samples(filename, sampler, pos, state):
saveobj(filename, dict(
chain=sampler.chain, accept=sampler.acceptance_fraction,
lnprob=sampler.lnprobability, final_pos=pos, state=state,
par=P), overwrite=1)
else:
objects[obj][arm][filter].append(n)
elif imtype == 'FOCUS' or \
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]
