def default_model(recalc=False, pckl_fil=None, use_mcmc=False, write=False):
"""
Pass back a default fN_model from Prochaska+13
Tested against XIDL code by JXP on 09 Nov 2014
Parameters:
recalc : boolean (False)
Recalucate the default model
use_mcmc : boolean (False)
Use the MCMC chain to generate the model
write : boolean (False)
Write out the model
"""
if pckl_fil == None:
pckl_fil = xa_path + '/igm/fN/fN_model_P13.p'
if recalc is True:
if use_mcmc == True:
# MCMC Analysis
chain_file = (os.environ.get('DROPBOX_DIR') +
'IGM/fN/MCMC/mcmc_spline_k13r13o13n12_8.fits.gz')
outp = mcmc.chain_stats(chain_file)
# Build a model
NHI_pivots = [12., 15., 17.0, 18.0, 20.0, 21., 21.5, 22.]
fN_model = fN_Model('Hspline',
zmnx=(0.5, 3.0),
pivots=NHI_pivots,
param=outp['best_p'])
else:
# Input the f(N) at z=2.4
fN_file = (os.environ.get('DROPBOX_DIR') +
'IGM/fN/fN_spline_z24.fits.gz')
hdu = fits.open(fN_file)
fN_data = hdu[1].data
#xdb.set_trace()
# Instantiate
fN_model = fN_Model('Hspline',
zmnx=(0.5, 3.0),
pivots=np.array(fN_data['LGN']).flatten(),
param=np.array(fN_data['FN']).flatten())
# Write
if write is True:
print('default_model: Writing %s' % pckl_fil)
pickle.dump(fN_model, open(pckl_fil, "wb"), -1)
else:
fN_model = pickle.load(open(pckl_fil, "rb"))
# Return
return fN_model
def default_model(recalc=False, pckl_fil=None, use_mcmc=False, write=False):
"""
Pass back a default fN_model from Prochaska+13
Tested against XIDL code by JXP on 09 Nov 2014
Parameters:
recalc: boolean (False)
Recalucate the default model
use_mcmc: boolean (False)
Use the MCMC chain to generate the model
write: boolean (False)
Write out the model
"""
if pckl_fil==None:
pckl_fil = xa_path+'/igm/fN/fN_model_P13.p'
if recalc is True:
if use_mcmc == True:
# MCMC Analysis
chain_file = (os.environ.get('DROPBOX_DIR')+
'IGM/fN/MCMC/mcmc_spline_k13r13o13n12_8.fits.gz')
outp = mcmc.chain_stats(chain_file)
# Build a model
NHI_pivots = [12., 15., 17.0, 18.0, 20.0, 21., 21.5, 22.]
fN_model = fN_Model('Hspline', zmnx=(0.5,3.0),
pivots=NHI_pivots, param=outp['best_p'])
else:
# Input the f(N) at z=2.4
fN_file = (os.environ.get('DROPBOX_DIR')+
'IGM/fN/fN_spline_z24.fits.gz')
hdu = fits.open(fN_file)
fN_data = hdu[1].data
#xdb.set_trace()
# Instantiate
fN_model = fN_Model('Hspline', zmnx=(0.5,3.0),
pivots=np.array(fN_data['LGN']).flatten(),
param=np.array(fN_data['FN']).flatten())
# Write
if write is True:
print('default_model: Writing %s' % pckl_fil)
pickle.dump( fN_model, open( pckl_fil, "wb" ), -1)
else: fN_model = pickle.load( open( pckl_fil, "rb" ) )
# Return
return fN_model
from xastropy.igm.fN import model as xifm
flg_test = 0
#flg_test += 2**1 # Compare models
#flg_test += 2**2 # Data
#flg_test += 2**3 # l(X)
#flg_test += 64 # Akio
#flg_test += 2**7 # rho_HI
flg_test += 2**8 # Create Pickle file
#flg_test = 0 + 64
if (flg_test % 2) == 1:
# MCMC Analysis
chain_file = os.environ.get(
'DROPBOX_DIR') + 'IGM/fN/MCMC/mcmc_spline_k13r13o13n12_8.fits.gz'
outp = mcmc.chain_stats(chain_file)
# Build a model
NHI_pivots = [12., 15., 17.0, 18.0, 20.0, 21., 21.5, 22.]
fN_model = xifm.fN_Model('Hspline',
zmnx=(0.5, 3.0),
pivots=NHI_pivots,
param=outp['best_p'])
#xdb.set_trace()
print(fN_model)
# Compare default against P+13
if (flg_test % 2**2) >= 2**1:
fN_model = xifm.default_model()
p13_file = (os.environ.get('DROPBOX_DIR') +
'IGM/fN/fN_spline_z24.fits.gz')
from xastropy.igm.fN import data as fN_data
from xastropy.igm.fN import model as xifm
flg_test = 0
#flg_test += 2**1 # Compare models
#flg_test += 2**2 # Data
#flg_test += 2**3 # l(X)
#flg_test += 64 # Akio
flg_test += 2**7 # rho_HI
#flg_test = 0 + 64
if (flg_test % 2) == 1:
# MCMC Analysis
chain_file = os.environ.get('DROPBOX_DIR')+'IGM/fN/MCMC/mcmc_spline_k13r13o13n12_8.fits.gz'
outp = mcmc.chain_stats(chain_file)
# Build a model
NHI_pivots = [12., 15., 17.0, 18.0, 20.0, 21., 21.5, 22.]
fN_model = xifm.fN_Model('Hspline', zmnx=(0.5,3.0),
pivots=NHI_pivots, param=outp['best_p'])
#xdb.set_trace()
print(fN_model)
# Compare default against P+13
if (flg_test % 4) >= 2:
fN_model = xifm.default_model()
p13_file = (os.environ.get('DROPBOX_DIR')+'IGM/fN/fN_spline_z24.fits.gz')
hdu = fits.open(p13_file)
p13_data = hdu[1].data
