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
tmp = [13., 15., 17., 21.5, 22.]
val = sfN_model.eval(tmp, 2.5)
# Not using this! -- JXP 29 Jan 2015
#xdb.set_trace()
elif flg==1:
sfN_model = fNmodel.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
def get_telfer_spec(zqso=0., igm=False, fN_gamma=None, LL_flatten=True):
'''Generate a Telfer QSO composite spectrum
Parameters:
----------
zqso: float, optional
Redshift of the QSO
igm: bool, optional
Include IGM opacity? [False]
fN_gamma: float, optional
Power-law evolution in f(N,X)
LL_flatten: bool, optional
Set Telfer to a constant below the LL?
Returns:
--------
telfer_spec: XSpectrum1D
Spectrum
'''
# Read
telfer = ascii.read(
xa_path+'/data/quasar/telfer_hst_comp01_rq.ascii', comment='#')
scale = telfer['flux'][(telfer['wrest'] == 1450.)]
telfer_spec = XSpectrum1D.from_tuple((telfer['wrest']*(1+zqso),
telfer['flux']/scale[0])) # Observer frame
# IGM?
if igm is True:
'''The following is quite experimental.
Use at your own risk.
'''
import multiprocessing
from xastropy.igm.fN import model as xifm
from xastropy.igm import tau_eff as xit
fN_model = xifm.default_model()
# Expanding range of zmnx (risky)
fN_model.zmnx = (0.,5.)
if fN_gamma is not None:
fN_model.gamma = fN_gamma
# Parallel
igm_wv = np.where(telfer['wrest']<1220.)[0]
adict = []
for wrest in telfer_spec.dispersion[igm_wv].value:
tdict = dict(ilambda=wrest, zem=zqso, fN_model=fN_model)
adict.append(tdict)
# Run
#xdb.set_trace()
pool = multiprocessing.Pool(4) # initialize thread pool N threads
ateff = pool.map(xit.map_etl, adict)
# Apply
telfer_spec.flux[igm_wv] *= np.exp(-1.*np.array(ateff))
# Flatten?
if LL_flatten:
wv_LL = np.where(np.abs(telfer_spec.dispersion/(1+zqso)-914.*u.AA)<3.*u.AA)[0]
f_LL = np.median(telfer_spec.flux[wv_LL])
wv_low = np.where(telfer_spec.dispersion/(1+zqso)<911.7*u.AA)[0]
telfer_spec.flux[wv_low] = f_LL
# Return
return telfer_spec
def test_lya():
# f(N)
fN_model = xifm.default_model()
#fN_model = xifm.default_model(recalc=True)
# tau_eff
lamb = 1215.6701*(1+2.4)
teff = xit.ew_teff_lyman(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
np.testing.assert_allclose(teff, 0.19821448846)
def test_lya():
# f(N)
fN_model = xifm.default_model()
#fN_model = xifm.default_model(recalc=True)
# tau_eff
lamb = 1215.6701 * (1 + 2.4)
teff = xit.ew_teff_lyman(lamb, 2.5, fN_model, NHI_MIN=12., NHI_MAX=17.)
# Test
np.testing.assert_allclose(teff, 0.19821448846)
def get_telfer_spec(zqso=0., igm=False):
'''Generate a Telfer QSO composite spectrum
Paraemters:
----------
zqso: float, optional
Redshift of the QSO
igm: bool, optional
Include IGM opacity? [False]
Returns:
--------
telfer_spec: XSpectrum1D
Spectrum
'''
# Read
telfer = ascii.read(xa_path + '/data/quasar/telfer_hst_comp01_rq.ascii',
comment='#')
scale = telfer['flux'][(telfer['wrest'] == 1450.)]
telfer_spec = XSpectrum1D.from_tuple(
(telfer['wrest'] * (1 + zqso),
telfer['flux'] / scale[0])) # Observer frame
# IGM?
if igm is True:
'''The following is quite experimental.
Use at your own risk.
'''
import multiprocessing
from xastropy.igm.fN import model as xifm
from xastropy.igm import tau_eff as xit
fN_model = xifm.default_model()
# Expanding range of zmnx (risky)
fN_model.zmnx = (0., 5.)
# Parallel
igm_wv = np.where(telfer['wrest'] < 1220.)[0]
adict = []
for wrest in telfer_spec.dispersion[igm_wv].value:
tdict = dict(ilambda=wrest, zem=zqso, fN_model=fN_model)
adict.append(tdict)
# Run
#xdb.set_trace()
pool = multiprocessing.Pool(4) # initialize thread pool N threads
ateff = pool.map(xit.map_etl, adict)
# Apply
telfer_spec.flux[igm_wv] *= np.exp(-1. * np.array(ateff))
# Return
return telfer_spec
def test_parallel():
import multiprocessing
# f(N)
fN_model = xifm.default_model()
# Generate dicts
tst_wv = xit.tau_eff_llist()
adict = []
for wrest in tst_wv:
tdict = dict(ilambda=wrest.value*(1+2.4), zem=2.5, fN_model=fN_model)
adict.append(tdict)
pool = multiprocessing.Pool(2) # initialize thread pool N threads
ateff = pool.map(xit.map_etl, adict)
#pdb.set_trace()
np.testing.assert_allclose(ateff[-3:], [0.23437970917295792, 0.20262106481068218, 0.21927058621246398])
def get_telfer_spec(zqso=0., igm=False):
'''Generate a Telfer QSO composite spectrum
Paraemters:
----------
zqso: float, optional
Redshift of the QSO
igm: bool, optional
Include IGM opacity? [False]
Returns:
--------
telfer_spec: XSpectrum1D
Spectrum
'''
# Read
telfer = ascii.read(
xa_path+'/data/quasar/telfer_hst_comp01_rq.ascii', comment='#')
scale = telfer['flux'][(telfer['wrest'] == 1450.)]
telfer_spec = XSpectrum1D.from_tuple((telfer['wrest']*(1+zqso),
telfer['flux']/scale[0])) # Observer frame
# IGM?
if igm is True:
'''The following is quite experimental.
Use at your own risk.
'''
import multiprocessing
from xastropy.igm.fN import model as xifm
from xastropy.igm import tau_eff as xit
fN_model = xifm.default_model()
# Expanding range of zmnx (risky)
fN_model.zmnx = (0.,5.)
# Parallel
igm_wv = np.where(telfer['wrest']<1220.)[0]
adict = []
for wrest in telfer_spec.dispersion[igm_wv].value:
tdict = dict(ilambda=wrest, zem=zqso, fN_model=fN_model)
adict.append(tdict)
# Run
#xdb.set_trace()
pool = multiprocessing.Pool(4) # initialize thread pool N threads
ateff = pool.map(xit.map_etl, adict)
# Apply
telfer_spec.flux[igm_wv] *= np.exp(-1.*np.array(ateff))
# Return
return telfer_spec
def test_parallel():
import multiprocessing
# f(N)
fN_model = xifm.default_model()
# Generate dicts
tst_wv = xit.tau_eff_llist()
adict = []
for wrest in tst_wv:
tdict = dict(ilambda=wrest.value * (1 + 2.4),
zem=2.5,
fN_model=fN_model)
adict.append(tdict)
pool = multiprocessing.Pool(2) # initialize thread pool N threads
ateff = pool.map(xit.map_etl, adict)
#pdb.set_trace()
np.testing.assert_allclose(
ateff[-3:],
[0.23437970917295792, 0.20262106481068218, 0.21927058621246398])
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
## #################################
## TESTING
## #################################
if __name__ == "__main__":
"""
# Make EW spline file
mk_ew_lyman_spline(24.)
"""
from xastropy.igm.fN import model as xifm
import multiprocessing
# xdb.set_trace()
# read f(N)
fN_model = xifm.default_model()
print(fN_model)
# tau_eff
# tst_wv = tau_eff_llist()
tst_wv = np.arange(915.0, 1255, 1.0)
# lamb = 1215.6701*(1+2.4)
adict = []
for wrest in tst_wv:
tdict = dict(ilambda=wrest * (1 + 2.4), zem=2.5, fN_model=fN_model)
adict.append(tdict)
pool = multiprocessing.Pool(4) # initialize thread pool N threads
ateff = pool.map(map_etl, adict)
# Plot
xdb.xplot(tst_wv, np.exp(-np.array(ateff)))
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'], '-')
xplt = np.linspace(12., 22, 10000)
yplt = fN_model.eval(xplt, 2.4)
plt.plot(xplt, yplt, '-', color='red')
#plt.plot(xplt, yplt, '-')
plt.show()
#xdb.set_trace()
## #################################
## #################################
## TESTING
## #################################
if __name__ == '__main__':
'''
# Make EW spline file
mk_ew_lyman_spline(24.)
'''
from xastropy.igm.fN import model as xifm
import multiprocessing
#xdb.set_trace()
# read f(N)
fN_model = xifm.default_model()
print(fN_model)
# tau_eff
#tst_wv = tau_eff_llist()
tst_wv = np.arange(915., 1255, 1.)
#lamb = 1215.6701*(1+2.4)
adict = []
for wrest in tst_wv:
tdict = dict(ilambda=wrest * (1 + 2.4), zem=2.5, fN_model=fN_model)
adict.append(tdict)
pool = multiprocessing.Pool(4) # initialize thread pool N threads
ateff = pool.map(map_etl, adict)
# Plot
xdb.xplot(tst_wv, np.exp(-np.array(ateff)))
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)
plt.plot(xplt, yplt, '-', color='red')
#plt.plot(xplt, yplt, '-')
plt.show()
#xdb.set_trace()
# Reproduce the main figure from P14
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_pivots = ([12.000000, 15.000000, 17.000000, 18.000000,
# 20.000000, 21.000000, 21.500000, 22.000000])
#p13_fN = ([-9.7233782, -14.411575, -17.941498, -19.392904,
# -21.282812, -22.820860, -23.945236, -25.502331])
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(2.4,xplt)
plt.plot(xplt, yplt, '-', color='red')
#plt.plot(xplt, yplt, '-')
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'], '-')
xplt = np.linspace(12., 22, 10000)
yplt = fN_model.eval(xplt, 2.4)
plt.plot(xplt, yplt, '-', color='red')
#plt.plot(xplt, yplt, '-')
plt.show()
#xdb.set_trace()
# Reproduce the main figure from P14
def fig_fn(outfil=None, data_list=None):
from xastropy.igm.fN import model as xifm
from xastropy.igm.fN import data as xifd
# Init
if outfil == None:
outfil = 'fig_fN.pdf'
# Start the plot
if outfil != None:
pp = PdfPages(outfil)
fn_file = xa_path + '/igm/fN/fn_constraints_z2.5_vanilla.fits'
k13r13_file = xa_path + '/igm/fN/fn_constraints_K13R13_vanilla.fits'
n12_file = xa_path + '/igm/fN/fn_constraints_N12_vanilla.fits'
all_fN_cs = xifd.fn_data_from_fits([k13r13_file, fn_file, n12_file])
fN_model = xifm.default_model()
# Remove K12
#data_list = ['K13R13','OPB07', 'N12']
#outfil = 'tmp.png'
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]
# Plot
ymnx = [-26, -10.]
xmnx = [12., 22.]
plt.figure(figsize=(8, 5))
plt.clf()
gs = gridspec.GridSpec(1, 1)
main = plt.subplot(gs[:, :])
# f(N) data
main.set_ylabel(r'$\log \, f(N_{\rm HI})$')
main.set_xlabel(r'$\log \, N_{\rm HI}$')
main.set_ylim(ymnx)
main.set_xlim(xmnx)
syms = ['o', 's', 'D']
isyms = 0
for fN_c in fN_cs:
if fN_c.fN_dtype == 'fN':
# Length
ip = range(fN_c.data['NPT'])
#xdb.set_trace()
val = np.where(fN_c.data['FN'][ip] > -90)[0]
#xdb.set_trace()
if len(val) > 0:
#xdb.set_trace()
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=syms[isyms],
label=fN_c.ref,
capthick=2,
color='gray')
isyms += 1
# Paint on the Media
lsz = 16.
main.fill_between([xmnx[0], 14.5],
ymnx[0],
ymnx[1],
color='red',
alpha=0.3)
main.text(13.1, -18., 'IGM\n(85%)', color='red', size=lsz, ha='center')
main.fill_between([14.5, 19.], ymnx[0], ymnx[1], color='blue', alpha=0.3)
main.text(16.5, -22., 'CGM\n(10%)', color='blue', size=lsz, ha='center')
main.fill_between([19., xmnx[1]],
ymnx[0],
ymnx[1],
color='green',
alpha=0.3)
main.text(20.5, -18., 'ISM\n(5%)', color='green', size=lsz, ha='center')
# Label
main.text(np.sum(xmnx) / 2.,
-12.,
r'$z \approx 2.5$' + '\n(P+14)',
color='black',
size=lsz,
ha='center',
va='top')
# Legend
main.legend(loc='lower left', numpoints=1)
# Model?
xplt = 12.01 + 0.01 * np.arange(1100)
yplt = fN_model.eval(xplt, 2.4)
main.plot(xplt, yplt, '-', color='black')
# Fonts
xputils.set_fontsize(main, 16.)
# Write
plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
pp.savefig()
plt.close()
# Finish
print('tlk_igm.fig_fn: Wrote {:s}'.format(outfil))
pp.close()
