def set_fn_data(flg=2, sources=None, extra_fNc=[]):
""" Load up f(N) data
Parameters
----------
flg : int, optional
2 : z~2 constraints
5 : z~5 constraints
sources : list, optional
References for constraints
extra_fNc : list, optional
Returns
-------
fN_data :: List of fN_Constraint Classes
"""
fN_cs = []
if flg == 2:
if sources is None:
sources = ['OPB07', 'OPW12', 'OPW13', 'K05', 'K13R13', 'N12']
fn_file = pyigm_path + '/data/fN/fn_constraints_z2.5_vanilla.fits'
k13r13_file = pyigm_path + '/data/fN/fn_constraints_K13R13_vanilla.fits'
n12_file = pyigm_path + '/data/fN/fn_constraints_N12_vanilla.fits'
all_fN_cs = FNConstraint.from_fitsfile(
[fn_file, k13r13_file, n12_file])
# Add on, e.g. user-supplied
if len(extra_fNc) > 0:
raise IOError("NOT READY FOR THIS YET")
#for src in extra_fNc:
# all_fN_cs.append(xifd.fN_data_from_ascii_file(os.path.abspath(src)))
# Include good data sources
for fN_c in all_fN_cs:
# In list?
if fN_c.ref in sources:
print('Using {:s} as a constraint'.format(fN_c.ref))
# Append
fN_cs.append(fN_c)
# Pop
idx = sources.index(fN_c.ref)
sources.pop(idx)
# Check that all the desired sources were used
if len(sources) > 0:
pdb.set_trace()
elif flg == 5:
if sources is None:
sources = ['Worseck+14', 'Crighton+15', 'Crighton+16', 'Becker+13']
chk_sources = sources[:]
#all_fN_cs = FNConstraint.from_fitsfile([fn_file,k13r13_file,n12_file])
# MFP (Worseck+14)
fN_MFPa = FNConstraint('MFP',
4.56,
ref='Worseck+14',
flavor='\\lmfp',
data=dict(MFP=22.2, SIG_MFP=2.3,
COSM='VANILLA'))
fN_MFPb = FNConstraint('MFP',
4.86,
ref='Worseck+14',
flavor='\\lmfp',
data=dict(MFP=15.1, SIG_MFP=1.8,
COSM='VANILLA'))
fN_MFPc = FNConstraint('MFP',
5.16,
ref='Worseck+14',
flavor='\\lmfp',
data=dict(MFP=10.3, SIG_MFP=1.6,
COSM='VANILLA'))
fN_MFP = [fN_MFPa, fN_MFPb, fN_MFPc]
# LLS (Crighton+16)
fN_LLSa = FNConstraint('LLS',
np.mean([3.75, 4.40]),
ref='Crighton+16',
flavor='\\tlox',
data=dict(LX=0.628,
SIG_LX=0.095,
TAU_LIM=2.,
COSM='VANILLA'))
fN_LLSb = FNConstraint('LLS',
np.mean([4.40, 4.70]),
ref='Crighton+16',
flavor='\\tlox',
data=dict(LX=0.601,
SIG_LX=0.102,
TAU_LIM=2.,
COSM='VANILLA'))
fN_LLSc = FNConstraint('LLS',
np.mean([4.70, 5.40]),
ref='Crighton+16',
flavor='\\tlox',
data=dict(LX=0.928,
SIG_LX=0.151,
TAU_LIM=2.,
COSM='VANILLA'))
fN_LLS = [fN_LLSa, fN_LLSb, fN_LLSc]
# DLA (Crighton+15)
tau_lim = 10.**(20.3 - 17.19)
fN_DLAa = FNConstraint('DLA',
np.mean([3.56, 4.45]),
ref='Crighton+15',
flavor='\\tdlox',
data=dict(LX=0.059,
SIG_LX=0.018,
COSM='VANILLA',
TAU_LIM=tau_lim))
fN_DLAb = FNConstraint('DLA',
np.mean([4.45, 5.31]),
ref='Crighton+15',
flavor='\\tdlox',
data=dict(LX=0.095,
SIG_LX=0.022,
COSM='VANILLA',
TAU_LIM=tau_lim))
fN_DLAc = FNConstraint(
'fN',
np.mean([3.6, 5.2]),
ref='Crighton+15',
flavor='f(N)',
data=dict(COSM='VANILLA',
NPT=5,
FN=np.array([
-22.1247392, -22.12588672, -22.51361414, -22.7732822,
-23.76709909
]),
SIG_FN=np.array([[
0.24127323, 0.17599877, 0.17613792, 0.14095363,
0.30129492
],
[
0.21437162, 0.15275017, 0.12551036,
0.12963855, 0.17654378
]]),
BINS=np.array([[20.175, 20.425, 20.675, 20.925, 21.05],
[20.675, 20.925, 21.175, 21.425,
22.05]])))
fN_DLA = [fN_DLAa, fN_DLAb, fN_DLAc]
# tau_eff (Becker+13)
b13_tab2 = Table.read(pyigm.__path__[0] +
'/data/teff/becker13_tab2.dat',
format='ascii')
fN_teff = []
for row in b13_tab2:
if row['z'] < 4.:
continue
# calculate
teff = -1 * np.log(row['F'])
sigteff = row['s(F)'] / row['F']
# Generate
fN = FNConstraint('teff',
row['z'],
ref='Becker+13',
flavor='\\tlya',
data=dict(Z_TEFF=row['z'],
TEFF=teff,
SIG_TEFF=sigteff,
COSM='N/A',
NHI_MNX=[11., 22.]))
# Append
fN_teff.append(fN)
# Collate
all_fN_cs = fN_MFP + fN_DLA + fN_teff + fN_LLS
# Include good data sources
for fN_c in all_fN_cs:
# In list?
if fN_c.ref in sources:
print('Using {:s} as a constraint'.format(fN_c.ref))
# Append
fN_cs.append(fN_c)
# Pop
try:
idx = chk_sources.index(fN_c.ref)
except ValueError:
pass
else:
chk_sources.pop(idx)
# Check that all the desired sources were used
if len(chk_sources) > 0:
pdb.set_trace()
return fN_cs
def test_init():
tst = FNConstraint('fN')
assert tst.fN_dtype == 'fN'
