def test_init_from_fits():
""" Load FNConstraint from a set of FITS files
"""
# Load
all_fN_cs = FNConstraint.load_defaults()
# Test
assert len(all_fN_cs) == 8
fN_dtype = [fc.fN_dtype for fc in all_fN_cs]
itau = fN_dtype.index('teff')
np.testing.assert_allclose(all_fN_cs[itau].data['TEFF'], 0.19778812)
def test_init_from_fits():
""" Load FNConstraint from a set of FITS files
"""
# Load
all_fN_cs = FNConstraint.load_defaults()
# Test
assert len(all_fN_cs) == 8
fN_dtype = [fc.fN_dtype for fc in all_fN_cs]
itau = fN_dtype.index('teff')
np.testing.assert_allclose(all_fN_cs[itau].data['TEFF'], 0.19778812)
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'
def tst_fn_data(fN_model=None, model_two=None, data_list=None, outfil=None):
""" Make a matplotlib plot like the final figure from P14
See the Notebook for Bokeh plots
Taken from xastropy without actually trying to run
Parameters
----------
fN_model
model_two
data_list
outfil
"""
import matplotlib as mpl
mpl.rcParams['font.family'] = 'STIXGeneral-Regular' # Not for PDF
mpl.rcParams['lines.linewidth'] = 2
from matplotlib import pyplot as plt
#from matplotlib.backends.backend_pdf import PdfPages
# fN data
all_fN_cs = FNConstraint.load_defaults()
# Remove K12
if data_list is None:
fN_cs = [fN_c for fN_c in all_fN_cs if ((fN_c.ref != 'K02') & (fN_c.ref != 'PW09'))]
else:
fN_cs = [fN_c for fN_c in all_fN_cs if fN_c.ref in data_list]
fN_dtype = [fc.fN_dtype for fc in fN_cs]
fig = plt.figure(figsize=(8, 5))
fig.clf()
main = fig.add_axes( [0.1, 0.1, 0.8, 0.8] ) # xypos, xy-size
# f(N) data
main.set_ylabel(r'$\log f(N_{\rm HI})$')
main.set_xlabel(r'$\log N_{\rm HI}$')
main.set_ylim(-25., -9)
for fN_c in fN_cs:
if fN_c.fN_dtype == 'fN':
# Length
ip = range(fN_c.data['NPT'])
val = np.where(fN_c.data['FN'][ip] > -90)[0]
if len(val) > 0:
ipv = np.array(ip)[val]
xval = np.median(fN_c.data['BINS'][:,ipv],0)
xerror = [ fN_c.data['BINS'][1,ipv]-xval, xval-fN_c.data['BINS'][0,ipv] ]
yerror = [ fN_c.data['SIG_FN'][1,ipv], fN_c.data['SIG_FN'][0,ipv] ] # Inverted!
main.errorbar(xval, fN_c.data['FN'][ipv], xerr=xerror, yerr=yerror, fmt='o',
label=fN_c.ref,capthick=2)
main.legend(loc='lower left', numpoints=1)
# Model?
if fN_model is not None:
xplt = 12.01 + 0.01*np.arange(1100)
yplt = fN_model.evaluate(xplt, 2.4)
main.plot(xplt,yplt,'-',color='black')
print(xplt[0],yplt[0])
if model_two is not None:
xplt = 12.01 + 0.01*np.arange(1100)
yplt = model_two.evaluate(xplt, 2.4)
main.plot(xplt,yplt,'-',color='gray')
# Extras
#mpl.rcParams['lines.capthick'] = 2
inset = fig.add_axes( [0.55, 0.6, 0.25, 0.25] ) # xypos, xy-size
inset.set_ylabel('Value') # LHS
inset.xaxis.set_major_locator(plt.FixedLocator(range(5)))
inset.xaxis.set_major_formatter(plt.FixedFormatter(['',r'$\tau_{\rm eff}^{\rm Ly\alpha}$',
r'$\ell(X)_{\rm LLS}$',
r'$\lambda_{\rm mfp}^{912}$', '']))
inset.set_ylim(0., 0.6)
# tau_eff
flg_teff = 1
try:
itau = fN_dtype.index('teff') # Passes back the first one
except:
flg_teff = 0
if flg_teff:
teff=float(fN_cs[itau].data['TEFF'])
D_A = 1. - np.exp(-1. * teff)
SIGDA_LIMIT = 0.1 # Allows for systemtics and b-value uncertainty
sig_teff = np.max([fN_cs[itau].data['SIG_TEFF'], (SIGDA_LIMIT*teff)])
# Plot
inset.errorbar(1, teff, sig_teff, fmt='_', capthick=2)
# Model
if fN_model is not None:
model_teff = tau_eff.lyman_ew(1215.6701*(1+fN_cs[itau].zeval), fN_cs[itau].zeval+0.1,
fN_model, NHI_MIN=fN_cs[itau].data['NHI_MNX'][0],
NHI_MAX=fN_cs[itau].data['NHI_MNX'][1])
inset.plot(1, model_teff, 'ko')
#xdb.set_trace()
## #######
# LLS constraint
flg_LLS = 1
try:
iLLS = fN_dtype.index('LLS') # Passes back the first one
except:
#raise ValueError('fN.data: Missing LLS type')
flg_LLS = 0
if flg_LLS:
inset.errorbar(2, fN_cs[iLLS].data['LX'], yerr=fN_cs[iLLS].data['SIG_LX'],
fmt='_', capthick=2)
# Model
if fN_model != None:
lX = fN_model.calculate_lox(fN_cs[iLLS].zeval,
17.19+np.log10(fN_cs[iLLS].data['TAU_LIM']), 22.)
inset.plot(2, lX, 'ko')
## #######
# MFP constraint
flg_MFP = 1
try:
iMFP = fN_dtype.index('MFP') # Passes back the first one
except:
#raise ValueError('fN.data: Missing MFP type')
flg_MFP = 0
if flg_MFP:
inset2 = inset.twinx()
inset2.errorbar(3, fN_cs[iMFP].data['MFP'], yerr=fN_cs[iMFP].data['SIG_MFP'],
fmt='_', capthick=2)
inset2.set_xlim(0,4)
inset2.set_ylim(0,350)
inset2.set_ylabel('(Mpc)')
# Model
if fN_model is not None:
#fN_model.zmnx = (0.1, 20.) # Reset for MFP calculation
mfp = fN_model.mfp(fN_cs[iMFP].zeval)
inset2.plot(3, mfp, 'ko')
# Show
if outfil != None:
plt.savefig(outfil,bbox_inches='tight')
else:
plt.show()
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
