def set_fn_model(flg=0):
'''
Load up f(N) data
Parameters
----------
Returns
-------
fN_data :: List of fN_Constraint Classes
JXP on 27 Nov 2014
'''
if flg == 0: # I may choose to pickle a few of these
sfN_model = xifm.default_model(recalc=True,
use_mcmc=True) # Hermite Spline
elif flg == 1:
sfN_model = xifm.fN_Model('Gamma')
else:
raise ValueError(
'mcmc.set_model: Not ready for this type of fN model {:d}'.format(
flg))
#
#print(sfN_model)
return sfN_model
def set_fn_model(flg=0):
'''
Load up f(N) data
Parameters
----------
Returns
-------
fN_data :: List of fN_Constraint Classes
JXP on 27 Nov 2014
'''
if flg==0: # I may choose to pickle a few of these
sfN_model = xifm.default_model(recalc=True,use_mcmc=True) # Hermite Spline
elif flg==1:
sfN_model = xifm.fN_Model('Gamma')
else:
raise ValueError('mcmc.set_model: Not ready for this type of fN model {:d}'.format(flg))
#
#print(sfN_model)
return sfN_model
#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')
hdu = fits.open(p13_file)
p13_data = hdu[1].data
plt.clf()
plt.scatter(p13_data['LGN'], p13_data['FN'])
#plt.plot(p13_data['LGN'],p13_data['FN'], '-')
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
plt.clf()
plt.scatter(p13_data['LGN'],p13_data['FN'])
#plt.plot(p13_data['LGN'],p13_data['FN'], '-')
xplt = np.linspace(12., 22, 10000)
yplt = fN_model.eval(xplt, 2.4)
