def read_deltas(in_dir, nspec=None):
data = {}
ndata = 0
dels = []
fi = glob.glob(in_dir + "/*.fits.gz")
for i, f in enumerate(fi):
sys.stderr.write("\rread {} of {} {}".format(i, len(fi), ndata))
hdus = fitsio.FITS(f)
dels += [delta.from_fitsio(h) for h in hdus[1:]]
ndata += len(hdus[1:])
hdus.close()
if nspec and ndata > nspec:
break
phi = [d.ra for d in dels]
th = [sp.pi / 2. - d.dec for d in dels]
pix = healpy.ang2pix(kappa.nside_data, th, phi)
z_min_pix = 10**dels[0].ll[0] / kappa.lambda_abs - 1
for d, p in zip(dels, pix):
if p not in data:
data[p] = []
data[p].append(d)
z = 10**d.ll / kappa.lambda_abs - 1.
z_min_pix = sp.amin(sp.append([z_min_pix], z))
d.z = z
d.r_comov = kappa.cosmo.r_comoving(z)
d.we *= ((1. + z) / (1. + 2.25))**(2.9 - 1.)
d.project()
kappa.z_min_pix = z_min_pix
kappa.angmax = 2.*\
sp.arcsin(kappa.rt_max/(2.*kappa.cosmo.r_comoving(z_min_pix)))
kappa.ndata = ndata
kappa.data = data
return data
data = {}
ndata = 0
# initialize randoms
sp.random.seed(4)
# loop over input files
for i, f in enumerate(fi):
if i % 1 == 0:
print("\rread {} of {} {}".format(i, len(fi), ndata), end="")
# read fits or ascii file
if (args.in_format == 'fits'):
hdus = fitsio.FITS(f)
dels = [delta.from_fitsio(h, Pk1D_type=True) for h in hdus[1:]]
elif (args.in_format == 'ascii'):
ascii_file = open(f, 'r')
dels = [delta.from_ascii(line) for line in ascii_file]
ndata += len(dels)
print("\n ndata = ", ndata)
out = None
# loop over deltas
for d in dels:
# Selection over the SNR and the resolution
if (d.mean_SNR <= args.SNR_min or d.mean_reso >= args.reso_max):
continue
### Correct for DLAs
if not args.dla_vac is None:
userprint("adding dlas")
dlas = io.read_dlas(args.dla_vac)
for p in data:
for d in data[p]:
if d.thid in dlas:
for dla in dlas[d.thid]:
data.add_dla(dla[0], dla[1], mask)
### Get delta from picca_delta
done_delta = None
f = args.spectrum + "/delta-" + str(pix[0]) + ".fits.gz"
hdus = fitsio.FITS(f)
ds = [delta.from_fitsio(h) for h in hdus[1:]]
for d in ds:
if (d.plate == args.plate) and (d.mjd
== args.mjd) and (d.fid
== args.fiberid):
d.project()
done_delta = d
hdus.close()
break
if done_delta is None:
hdus.close()
userprint("Object not in spectrum")
sys.exit()
### Observed l
plt.errorbar(10**data.ll, data.fl, linewidth=2, color='black')
def read_deltas(indir,
nside,
lambda_abs,
alpha,
zref,
cosmo,
nspec=None,
no_project=False,
from_image=None):
'''
reads deltas from indir
fills the fields delta.z and multiplies the weights by (1+z)^(alpha-1)/(1+zref)^(alpha-1)
returns data,zmin_pix
'''
fi = []
indir = os.path.expandvars(indir)
if from_image is None or len(from_image) == 0:
if len(indir) > 8 and indir[-8:] == '.fits.gz':
fi += glob.glob(indir)
elif len(indir) > 5 and indir[-5:] == '.fits':
fi += glob.glob(indir)
else:
fi += glob.glob(indir + '/*.fits') + glob.glob(indir +
'/*.fits.gz')
else:
for arg in from_image:
if len(arg) > 8 and arg[-8:] == '.fits.gz':
fi += glob.glob(arg)
elif len(arg) > 5 and arg[-5:] == '.fits':
fi += glob.glob(arg)
else:
fi += glob.glob(arg + '/*.fits') + glob.glob(arg +
'/*.fits.gz')
fi = sorted(fi)
dels = []
ndata = 0
for i, f in enumerate(fi):
print("\rread {} of {} {}".format(i, len(fi), ndata))
if from_image is None:
hdus = fitsio.FITS(f)
dels += [delta.from_fitsio(h) for h in hdus[1:]]
hdus.close()
else:
dels += delta.from_image(f)
ndata = len(dels)
if not nspec is None:
if ndata > nspec: break
###
if not nspec is None:
dels = dels[:nspec]
ndata = len(dels)
print("\n")
phi = [d.ra for d in dels]
th = [sp.pi / 2. - d.dec for d in dels]
pix = healpy.ang2pix(nside, th, phi)
if pix.size == 0:
raise AssertionError('ERROR: No data in {}'.format(indir))
data = {}
zmin = 10**dels[0].ll[0] / lambda_abs - 1.
zmax = 0.
for d, p in zip(dels, pix):
if not p in data:
data[p] = []
data[p].append(d)
z = 10**d.ll / lambda_abs - 1.
zmin = min(zmin, z.min())
zmax = max(zmax, z.max())
d.z = z
if not cosmo is None:
d.r_comov = cosmo.r_comoving(z)
d.rdm_comov = cosmo.dm(z)
d.we *= ((1 + z) / (1 + zref))**(alpha - 1)
if not no_project:
d.project()
return data, ndata, zmin, zmax