def ew_teff_lyman(
ilambda,
zem,
fN_model,
NHI_MIN=11.5,
NHI_MAX=22.0,
N_eval=5000,
EW_spline=None,
bval=24.0,
fNz=False,
cosmo=None,
debug=False,
cumul=None,
verbose=False,
):
""" tau effective (follows ew_teff_lyman.pro from XIDL)
teff = ew_teff_lyman(3400., 2.4)
Parameters:
-------------
ilambda: float
Observed wavelength
zem: float
Emission redshift of the source [sets which Lyman lines are included]
bva: float
-- Characteristics Doppler parameter for the Lya forest
-- [Options: 24, 35 km/s]
NHI_MIN: float
-- Minimum log HI column for integration [default = 11.5]
NHI_MAX: float
-- Maximum log HI column for integration [default = 22.0]
fNz: Boolean (False)
-- Inputs f(N,z) instead of f(N,X)
cosmo: astropy.cosmology (None)
-- Cosmological model to adopt (as needed)
cumul: List of cumulative sums
-- Recorded only if cumul is not None
Returns:
teff:
Total effective opacity of all lines contributing
ToDo:
1. Parallelize the Lyman loop
JXP 07 Nov 2014
"""
# Lambda
if not isinstance(ilambda, float):
raise ValueError("igm.tau_eff: ilambda must be a float for now")
Lambda = ilambda
if not isinstance(Lambda, u.quantity.Quantity):
Lambda = Lambda * u.AA # Ang
# Read in EW spline (if needed)
if EW_spline == None:
if int(bval) == 24:
EW_FIL = xa_path + "/igm/EW_SPLINE_b24.p"
elif int(bval) == 35:
EW_FIL = os.environ.get("XIDL_DIR") + "/IGM/EW_SPLINE_b35.fits"
else:
raise ValueError("igm.tau_eff: Not ready for this bvalue %g" % bval)
EW_spline = pickle.load(open(EW_FIL, "rb"))
# Lines
wrest = tau_eff_llist()
# Find the lines
gd_Lyman = wrest[(Lambda / (1 + zem)) < wrest]
nlyman = len(gd_Lyman)
if nlyman == 0:
if verbose:
print("igm.tau_eff: No Lyman lines covered at this wavelength")
return 0
# N_HI grid
lgNval = NHI_MIN + (NHI_MAX - NHI_MIN) * np.arange(N_eval) / (N_eval - 1) # Base 10
dlgN = lgNval[1] - lgNval[0]
Nval = 10.0 ** lgNval
teff_lyman = np.zeros(nlyman)
# For cumulative
if not cumul is None:
cumul.append(lgNval)
# Loop on the lines
for qq, line in enumerate(gd_Lyman): # Would be great to do this in parallel...
# (Can pack together and should)
# Redshift
zeval = ((Lambda / line) - 1).value
if zeval < 0.0:
teff_lyman[qq] = 0.0
continue
# Cosmology
if fNz is False:
if cosmo not in locals():
cosmo = FlatLambdaCDM(H0=70, Om0=0.3) # Vanilla
# dxdz = (np.fabs(xigmu.cosm_xz(zeval-0.1, cosmo=cosmo)-
# xigmu.cosm_xz(zeval+0.1,cosmo=cosmo)) / 0.2 )
# xdb.set_trace()
dxdz = xigmu.cosm_xz(zeval, cosmo=cosmo, flg=1)
else:
dxdz = 1.0 # Code is using f(N,z)
# print('dxdz = %g' % dxdz)
# Get EW values (could pack these all together)
idx = np.where(EW_spline["wrest"] == line)[0]
if len(idx) != 1:
raise ValueError("tau_eff: Line %g not included or over included?!" % line)
restEW = interpolate.splev(lgNval, EW_spline["tck"][idx], der=0)
# dz
dz = ((restEW * u.AA) * (1 + zeval) / line).value
# Evaluate f(N,X) at zeval
log_fnX = fN_model.eval(lgNval, zeval).flatten()
# xdb.set_trace()
# Sum
intgrnd = 10.0 ** (log_fnX) * dxdz * dz * Nval
teff_lyman[qq] = np.sum(intgrnd) * dlgN * np.log(10.0)
if not cumul is None:
cumul.append(np.cumsum(intgrnd) * dlgN * np.log(10.0))
# xdb.set_trace()
# Debug
if debug == True:
xdb.xplot(lgNval, np.log10(10.0 ** (log_fnX) * dxdz * dz * Nval))
# x_splot, lgNval, total(10.d^(log_fnX) * dxdz * dz * Nval,/cumul) * dlgN * alog(10.) / teff_lyman[qq], /bloc
# printcol, lgnval, log_fnx, dz, alog10(10.d^(log_fnX) * dxdz * dz * Nval)
# writecol, 'debug_file'+strtrim(qq,2)+'.dat', lgNval, restEW, log_fnX
xdb.set_trace()
# xdb.set_trace()
return np.sum(teff_lyman)
def eval(self, NHI, z, vel_array=None, cosmo=None):
""" Evaluate the f(N,X) model at a set of NHI values
Parameters:
NHI: array
NHI values
z: float or array
Redshift for evaluation
vel_array: array
Velocities relative to z
Returns:
log_fNX: float, array, or 2D array
f(NHI,X)[z] values
Float if given one NHI,z value each. Otherwise 2D array
If 2D, it is [NHI,z] on the axes
JXP 07 Nov 2014
"""
# Exception checking?
# Imports
from astropy import constants as const
# Tuple?
if isinstance(NHI, tuple): # All values packed into NHI parameter
z = NHI[1]
NHI = NHI[0]
flg_1D = 1
else: # NHI and z separate
flg_1D = 0
# NHI
if isiterable(NHI): NHI = np.array(NHI) # Insist on array
else: NHI = np.array([NHI])
lenNHI = len(NHI)
# Redshift
if vel_array is not None:
z_val = z + (1 + z) * vel_array / (const.c.cgs.value / 1e5)
else:
z_val = z
if isiterable(z_val): z_val = np.array(z_val)
else: z_val = np.array([z_val])
lenz = len(z_val)
# Check on zmnx
bad = np.where((z_val < self.zmnx[0]) | (z_val > self.zmnx[1]))[0]
if len(bad) > 0:
raise ValueError(
'fN.model.eval: z={:g} not within self.zmnx={:g},{:g}'.format(
z_val[bad[0]], *(self.zmnx)))
if self.fN_mtype == 'Hspline':
# Evaluate without z dependence
log_fNX = self.model.__call__(NHI)
# Evaluate
#xdb.set_trace()
if (not isiterable(z_val)) | (flg_1D
== 1): # scalar or 1D array wanted
log_fNX += self.gamma * np.log10(
(1 + z_val) / (1 + self.zpivot))
else:
# Matrix algebra to speed things up
lgNHI_grid = np.outer(log_fNX, np.ones(len(z_val)))
lenfX = len(log_fNX)
#xdb.set_trace()
#
z_grid1 = 10**(np.outer(
np.ones(lenfX) * self.gamma,
np.log10(1 + z_val))) #; (1+z)^gamma
z_grid2 = np.outer(
np.ones(lenfX) * ((1. / (1 + self.zpivot))**self.gamma),
np.ones(len(z_val)))
log_fNX = lgNHI_grid + np.log10(z_grid1 * z_grid2)
# Gamma function (e.g. Inoue+14)
elif self.fN_mtype == 'Gamma':
# Setup the parameters
Nl, Nu, Nc, bval = self.param[0]
# gNHI
Bi = self.param[1]
ncomp = len(Bi)
log_gN = np.zeros((lenNHI, ncomp))
beta = [item[1] for item in self.param[2:]]
for kk in range(ncomp):
#xdb.set_trace()
log_gN[:, kk] += (np.log10(Bi[kk]) + NHI * (-1 * beta[kk]) +
(-1. * 10.**(NHI - Nc) / np.log(10))
) # log10 [ exp(-NHI/Nc) ]
# f(z)
fz = np.zeros((lenz, 2))
# Loop on NHI
for kk in range(ncomp):
if kk == 0: # LyaF
zcuts = self.param[2][2:4]
gamma = self.param[2][4:]
else: # DLA
zcuts = [self.param[3][2]]
gamma = self.param[3][3:]
zcuts = [0] + list(zcuts) + [999.]
Aval = self.param[2 + kk][0]
# Cut on z
for ii in range(1, len(zcuts)):
izcut = np.where((z_val < zcuts[ii])
& (z_val > zcuts[ii - 1]))[0]
liz = len(izcut)
# Evaluate (at last!)
#xdb.set_trace()
if (ii <= 2) & (liz > 0):
fz[izcut, kk] = Aval * ((1 + z_val[izcut]) /
(1 + zcuts[1]))**gamma[ii - 1]
elif (ii == 3) & (liz > 0):
fz[izcut,
kk] = Aval * (((1 + zcuts[2]) /
(1 + zcuts[1]))**gamma[ii - 2] *
((1 + z_val[izcut]) /
(1 + zcuts[2]))**gamma[ii - 1])
# else:
# raise ValueError('fN.model.eval: Should not get here')
# dX/dz
dXdz = igmu.cosm_xz(z_val, cosmo=cosmo, flg=1)
# Final steps
if flg_1D == 1: #
#xdb.set_trace()
fnX = np.sum(fz * 10.**log_gN, 1) / dXdz
log_fNX = np.log10(fnX)
else:
# Generate the matrix
fnz = np.zeros((lenNHI, lenz))
for kk in range(ncomp):
fnz += np.outer(10.**log_gN[:, kk], fz[:, kk])
# Finish up
log_fNX = np.log10(fnz) - np.log10(
np.outer(np.ones(lenNHI), dXdz))
else:
raise ValueError(
'fN.model: Not ready for this model type {:%s}'.format(
self.fN_mtype))
# Return
if (lenNHI + lenz) == 2:
return log_fNX.flatten()[0] # scalar
else:
return log_fNX
def teff_ll(self, z912, zem, N_eval=5000, cosmo=None):
""" Calculate teff_LL
Effective opacity from LL absorption at z912 from zem
Parameters:
z912: float
Redshift for evaluation
zem: float
Redshift of source
cosmo: astropy.cosmology (None)
Cosmological model to adopt (as needed)
N_eval: int (5000)
Discretization parameter
Returns:
zval, teff_LL: array
z values and Effective opacity from LL absorption from z912 to zem
JXP 10 Nov 2014
"""
# Imports
from astropy import constants as const
# NHI array
lgNval = 11.5 + 10.5 * np.arange(N_eval) / (
N_eval - 1.) #; This is base 10 [Max at 22]
dlgN = lgNval[1] - lgNval[0]
Nval = 10.**lgNval
#; z array
zval = z912 + (zem - z912) * np.arange(N_eval) / (N_eval - 1.)
dz = np.fabs(zval[1] - zval[0])
teff_LL = np.zeros(N_eval)
# dXdz
dXdz = igmu.cosm_xz(zval, cosmo=cosmo, flg=1)
#if keyword_set(FNZ) then dXdz = replicate(1.,N_eval)
# Evaluate f(N,X)
velo = (zval - zem) / (1 + zem) * (const.c.cgs.value / 1e5
) # Kludge for eval [km/s]
log_fnX = self.eval(lgNval, zem, vel_array=velo)
log_fnz = log_fnX + np.outer(np.ones(N_eval), np.log10(dXdz))
# Evaluate tau(z,N)
teff_engy = (const.Ryd.to(u.eV, equivalencies=u.spectral()) /
((1 + zval) / (1 + zem)))
sigma_z = xai.photo_cross(1, 1, teff_engy)
#xdb.set_trace()
#sigma_z = teff_cross * ((1+zval)/(1+zem))**(2.75) # Not exact but close
tau_zN = np.outer(Nval, sigma_z)
# Integrand
intg = 10.**(log_fnz) * (1. - np.exp(-1. * tau_zN))
# Sum
sumz_first = False
if sumz_first == False:
#; Sum in N first
N_summed = np.sum(intg * np.outer(Nval, np.ones(N_eval)),
0) * dlgN * np.log(10.)
#xdb.set_trace()
# Sum in z
teff_LL = (np.cumsum(N_summed[::-1]))[::-1] * dz
#xdb.set_trace()
# Debug
debug = False
if debug == True:
# x_splot, lgNval, alog10(10.d^(log_fnX) * dxdz * dz * Nval), /bloc
# x_splot, lgNval, total(10.d^(log_fnX) * dxdz * dz * Nval,/cumul) * dlgN * alog(10.) / teff_lyman[qq], /bloc
# printcol, lgnval, log_fnx, dz, alog10(10.d^(log_fnX) * dxdz * dz * Nval)
# writecol, 'debug_file'+strtrim(qq,2)+'.dat', $
# lgNval, restEW, log_fnX
xdb.set_trace()
# Return
return zval, teff_LL
xdb.xplot(zval, teff_LL) #,xlabel='z', ylabel=r'$\tau_{\rm LL}$')
# Check MFP
if (flg_test % 64) >= 32:
fN_model = xifm.default_model()
z = 2.44
mfp = fN_model.mfp(z)
print('MFP at z={:g} is {:g} Mpc'.format(z, mfp.value))
# Check Inoue+14
if (flg_test % 2**7) >= 2**6:
print('Testing Akio Model')
fN_model = fN_Model('Gamma')
NHI = [12., 14., 17., 21.]
z = 2.5
dXdz = igmu.cosm_xz(z, flg=1)
# From Akio
# 12 1.2e-9
# 14 4.9e-13
# 17 4.6e-18
# 21 6.7e-23
# Test 1D
tstNz = (NHI, [z for ii in enumerate(NHI)])
log_fNX = fN_model.eval(tstNz, 0.)
#xdb.set_trace()
for kk, iNHI in enumerate(NHI):
print('I+14 At z={:g} and NHI={:g}, f(N,z) = {:g}'.format(
z, iNHI, 10.**log_fNX[kk] * dXdz))
# Test matrix
def ew_teff_lyman(ilambda,
zem,
fN_model,
NHI_MIN=11.5,
NHI_MAX=22.0,
N_eval=5000,
EW_spline=None,
bval=24.,
fNz=False,
cosmo=None,
debug=False,
cumul=None,
verbose=False):
""" tau effective (follows ew_teff_lyman.pro from XIDL)
teff = ew_teff_lyman(3400., 2.4)
Parameters:
-------------
ilambda: float
Observed wavelength
zem: float
Emission redshift of the source [sets which Lyman lines are included]
bva: float
-- Characteristics Doppler parameter for the Lya forest
-- [Options: 24, 35 km/s]
NHI_MIN: float
-- Minimum log HI column for integration [default = 11.5]
NHI_MAX: float
-- Maximum log HI column for integration [default = 22.0]
fNz: Boolean (False)
-- Inputs f(N,z) instead of f(N,X)
cosmo: astropy.cosmology (None)
-- Cosmological model to adopt (as needed)
cumul: List of cumulative sums
-- Recorded only if cumul is not None
Returns:
teff:
Total effective opacity of all lines contributing
ToDo:
1. Parallelize the Lyman loop
JXP 07 Nov 2014
"""
# Lambda
if not isinstance(ilambda, float):
raise ValueError('igm.tau_eff: ilambda must be a float for now')
Lambda = ilambda
if not isinstance(Lambda, u.quantity.Quantity):
Lambda = Lambda * u.AA # Ang
# Read in EW spline (if needed)
if EW_spline == None:
if int(bval) == 24:
EW_FIL = xa_path + '/igm/EW_SPLINE_b24.p'
elif int(bval) == 35:
EW_FIL = os.environ.get('XIDL_DIR') + '/IGM/EW_SPLINE_b35.fits'
else:
raise ValueError('igm.tau_eff: Not ready for this bvalue %g' %
bval)
EW_spline = pickle.load(open(EW_FIL, "rb"))
# Lines
wrest = tau_eff_llist()
# Find the lines
gd_Lyman = wrest[(Lambda / (1 + zem)) < wrest]
nlyman = len(gd_Lyman)
if nlyman == 0:
if verbose:
print('igm.tau_eff: No Lyman lines covered at this wavelength')
return 0
# N_HI grid
lgNval = NHI_MIN + (NHI_MAX - NHI_MIN) * np.arange(N_eval) / (N_eval - 1
) # Base 10
dlgN = lgNval[1] - lgNval[0]
Nval = 10.**lgNval
teff_lyman = np.zeros(nlyman)
# For cumulative
if not cumul is None:
cumul.append(lgNval)
# Loop on the lines
for qq, line in enumerate(
gd_Lyman): # Would be great to do this in parallel...
# (Can pack together and should)
# Redshift
zeval = ((Lambda / line) - 1).value
if zeval < 0.:
teff_lyman[qq] = 0.
continue
# Cosmology
if fNz is False:
if cosmo not in locals():
cosmo = FlatLambdaCDM(H0=70, Om0=0.3) # Vanilla
#dxdz = (np.fabs(xigmu.cosm_xz(zeval-0.1, cosmo=cosmo)-
# xigmu.cosm_xz(zeval+0.1,cosmo=cosmo)) / 0.2 )
#xdb.set_trace()
dxdz = xigmu.cosm_xz(zeval, cosmo=cosmo, flg=1)
else:
dxdz = 1. # Code is using f(N,z)
#print('dxdz = %g' % dxdz)
# Get EW values (could pack these all together)
idx = np.where(EW_spline['wrest'] == line)[0]
if len(idx) != 1:
raise ValueError(
'tau_eff: Line %g not included or over included?!' % line)
restEW = interpolate.splev(lgNval, EW_spline['tck'][idx], der=0)
# dz
dz = ((restEW * u.AA) * (1 + zeval) / line).value
# Evaluate f(N,X) at zeval
log_fnX = fN_model.eval(lgNval, zeval).flatten()
#xdb.set_trace()
# Sum
intgrnd = 10.**(log_fnX) * dxdz * dz * Nval
teff_lyman[qq] = np.sum(intgrnd) * dlgN * np.log(10.)
if not cumul is None:
cumul.append(np.cumsum(intgrnd) * dlgN * np.log(10.))
#xdb.set_trace()
# Debug
if debug == True:
xdb.xplot(lgNval, np.log10(10.**(log_fnX) * dxdz * dz * Nval))
#x_splot, lgNval, total(10.d^(log_fnX) * dxdz * dz * Nval,/cumul) * dlgN * alog(10.) / teff_lyman[qq], /bloc
#printcol, lgnval, log_fnx, dz, alog10(10.d^(log_fnX) * dxdz * dz * Nval)
#writecol, 'debug_file'+strtrim(qq,2)+'.dat', lgNval, restEW, log_fnX
xdb.set_trace()
#xdb.set_trace()
return np.sum(teff_lyman)
def teff_ll(self, z912, zem, N_eval=5000, cosmo=None):
""" Calculate teff_LL
Effective opacity from LL absorption at z912 from zem
Parameters:
z912: float
Redshift for evaluation
zem: float
Redshift of source
cosmo: astropy.cosmology (None)
Cosmological model to adopt (as needed)
N_eval: int (5000)
Discretization parameter
Returns:
zval, teff_LL: array
z values and Effective opacity from LL absorption from z912 to zem
JXP 10 Nov 2014
"""
# Imports
from astropy import constants as const
# NHI array
lgNval = 11.5 + 10.5*np.arange(N_eval)/(N_eval-1.) #; This is base 10 [Max at 22]
dlgN = lgNval[1]-lgNval[0]
Nval = 10.**lgNval
#; z array
zval = z912 + (zem-z912)*np.arange(N_eval)/(N_eval-1.)
dz = np.fabs(zval[1]-zval[0])
teff_LL = np.zeros(N_eval)
# dXdz
dXdz = igmu.cosm_xz(zval, cosmo=cosmo, flg=1)
#if keyword_set(FNZ) then dXdz = replicate(1.,N_eval)
# Evaluate f(N,X)
velo = (zval-zem)/(1+zem) * (const.c.cgs.value/1e5) # Kludge for eval [km/s]
log_fnX = self.eval(lgNval, zem, vel_array=velo)
log_fnz = log_fnX + np.outer(np.ones(N_eval), np.log10(dXdz))
# Evaluate tau(z,N)
teff_engy = (const.Ryd.to(u.eV,equivalencies=u.spectral()) /
((1+zval)/(1+zem)) )
sigma_z = xai.photo_cross(1,1,teff_engy)
#xdb.set_trace()
#sigma_z = teff_cross * ((1+zval)/(1+zem))**(2.75) # Not exact but close
tau_zN = np.outer(Nval, sigma_z)
# Integrand
intg = 10.**(log_fnz) * (1. - np.exp(-1.*tau_zN))
# Sum
sumz_first = False
if sumz_first == False:
#; Sum in N first
N_summed = np.sum(intg * np.outer(Nval, np.ones(N_eval)), 0) * dlgN * np.log(10.)
#xdb.set_trace()
# Sum in z
teff_LL = (np.cumsum(N_summed[::-1]))[::-1] * dz
#xdb.set_trace()
# Debug
debug=False
if debug == True:
# x_splot, lgNval, alog10(10.d^(log_fnX) * dxdz * dz * Nval), /bloc
# x_splot, lgNval, total(10.d^(log_fnX) * dxdz * dz * Nval,/cumul) * dlgN * alog(10.) / teff_lyman[qq], /bloc
# printcol, lgnval, log_fnx, dz, alog10(10.d^(log_fnX) * dxdz * dz * Nval)
# writecol, 'debug_file'+strtrim(qq,2)+'.dat', $
# lgNval, restEW, log_fnX
xdb.set_trace()
# Return
return zval, teff_LL
def eval(self, NHI, z, vel_array=None, cosmo=None):
""" Evaluate the model at a set of NHI values
Parameters:
NHI: array
NHI values
z: float or array
Redshift for evaluation
vel_array: array
Velocities relative to z
Returns:
log_fNX: float, array, or 2D array
f(NHI,X)[z] values
Float if given one NHI,z value each. Otherwise 2D array
If 2D, it is [NHI,z] on the axes
JXP 07 Nov 2014
"""
# Exception checking?
# Imports
from astropy import constants as const
# Tuple?
if isinstance(NHI,tuple): # All values packed into NHI parameter
z = NHI[1]
NHI = NHI[0]
flg_1D = 1
else: # NHI and z separate
flg_1D = 0
# NHI
if isiterable(NHI): NHI = np.array(NHI) # Insist on array
else: NHI = np.array([NHI])
lenNHI = len(NHI)
# Redshift
if vel_array is not None:
z_val = z + (1+z) * vel_array/(const.c.cgs.value/1e5)
else: z_val = z
if isiterable(z_val): z_val = np.array(z_val)
else: z_val = np.array([z_val])
lenz = len(z_val)
# Check on zmnx
bad = np.where( (z_val < self.zmnx[0]) | (z_val > self.zmnx[1]))[0]
if len(bad) > 0:
raise ValueError(
'fN.model.eval: z={:g} not within self.zmnx={:g},{:g}'.format(z_val[bad[0]],*(self.zmnx)))
if self.fN_mtype == 'Hspline':
# Evaluate without z dependence
log_fNX = self.model.__call__(NHI)
# Evaluate
#xdb.set_trace()
if (not isiterable(z_val)) | (flg_1D == 1): # scalar or 1D array wanted
log_fNX += self.gamma * np.log10((1+z_val)/(1+self.zpivot))
else:
# Matrix algebra to speed things up
lgNHI_grid = np.outer(log_fNX, np.ones(len(z_val)))
lenfX = len(log_fNX)
#xdb.set_trace()
#
z_grid1 = 10**( np.outer(np.ones(lenfX)*self.gamma,
np.log10(1+z_val)) ) #; (1+z)^gamma
z_grid2 = np.outer( np.ones(lenfX)*((1./(1+self.zpivot))**self.gamma),
np.ones(len(z_val)) )
log_fNX = lgNHI_grid + np.log10(z_grid1*z_grid2)
# Gamma function (e.g. Inoue+14)
elif self.fN_mtype == 'Gamma':
# Setup the parameters
Nl, Nu, Nc, bval = self.param[0]
# gNHI
Bi = self.param[1]
ncomp = len(Bi)
log_gN = np.zeros((lenNHI,ncomp))
beta = [item[1] for item in self.param[2:]]
for kk in range(ncomp):
#xdb.set_trace()
log_gN[:,kk] += (np.log10(Bi[kk]) + NHI*(-1 * beta[kk])
+ (-1. * 10.**(NHI-Nc) / np.log(10) ) ) # log10 [ exp(-NHI/Nc) ]
# f(z)
fz = np.zeros((lenz,2))
# Loop on NHI
for kk in range(ncomp):
if kk == 0: # LyaF
zcuts = self.param[2][2:4]
gamma = self.param[2][4:]
else: # DLA
zcuts = [self.param[3][2]]
gamma = self.param[3][3:]
zcuts = [0] + list(zcuts) + [999.]
Aval = self.param[2+kk][0]
# Cut on z
for ii in range(1,len(zcuts)):
izcut = np.where( (z_val < zcuts[ii]) & (z_val > zcuts[ii-1]) )[0]
liz = len(izcut)
# Evaluate (at last!)
#xdb.set_trace()
if (ii <=2) & (liz > 0):
fz[izcut,kk] = Aval * ( (1+z_val[izcut]) / (1+zcuts[1]) )**gamma[ii-1]
elif (ii == 3) & (liz > 0):
fz[izcut,kk] = Aval * ( ( (1+zcuts[2]) / (1+zcuts[1]) )**gamma[ii-2] *
((1+z_val[izcut]) / (1+zcuts[2]) )**gamma[ii-1] )
# else:
# raise ValueError('fN.model.eval: Should not get here')
# dX/dz
dXdz = igmu.cosm_xz(z_val, cosmo=cosmo, flg=1)
# Final steps
if flg_1D == 1: #
#xdb.set_trace()
fnX = np.sum(fz * 10.**log_gN, 1) / dXdz
log_fNX = np.log10(fnX)
else:
# Generate the matrix
fnz = np.zeros((lenNHI,lenz))
for kk in range(ncomp):
fnz += np.outer(10.**log_gN[:,kk],fz[:,kk])
# Finish up
log_fNX = np.log10(fnz) - np.log10( np.outer(np.ones(lenNHI), dXdz) )
else:
raise ValueError('fN.model: Not ready for this model type {:%s}'.format(self.fN_mtype))
# Return
if (lenNHI + lenz) == 2:
return log_fNX.flatten()[0] # scalar
else: return log_fNX
xdb.xplot(zval,teff_LL)#,xlabel='z', ylabel=r'$\tau_{\rm LL}$')
# Check MFP
if (flg_test % 64) >= 32:
fN_model = xifm.default_model()
z = 2.44
mfp = fN_model.mfp(z)
print('MFP at z={:g} is {:g} Mpc'.format(z,mfp.value))
# Check Inoue+14
if (flg_test % 2**7) >= 2**6:
print('Testing Akio Model')
fN_model = fN_Model('Gamma')
NHI = [12.,14.,17.,21.]
z = 2.5
dXdz = igmu.cosm_xz(z, flg=1)
# From Akio
# 12 1.2e-9
# 14 4.9e-13
# 17 4.6e-18
# 21 6.7e-23
# Test 1D
tstNz = ( NHI, [z for ii in enumerate(NHI)] )
log_fNX = fN_model.eval(tstNz,0.)
#xdb.set_trace()
for kk,iNHI in enumerate(NHI):
print('I+14 At z={:g} and NHI={:g}, f(N,z) = {:g}'.format(z,iNHI,10.**log_fNX[kk] * dXdz))
# Test matrix
log_fNX = fN_model.eval(NHI,z)